From d393546ef27a1ce65db77b25748826fc1d38f773 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Thu, 23 May 2019 16:55:20 -0400 Subject: [PATCH 01/58] fixed broken rates calculation --- proclam/metrics/util.py | 482 +++++++++++++++++++++------------------- 1 file changed, 249 insertions(+), 233 deletions(-) diff --git a/proclam/metrics/util.py b/proclam/metrics/util.py index db2e5e9..94c5eb8 100644 --- a/proclam/metrics/util.py +++ b/proclam/metrics/util.py @@ -4,13 +4,14 @@ from __future__ import absolute_import, division __all__ = ['sanitize_predictions', - 'weight_sum', 'averager', 'check_weights', - 'det_to_prob', 'prob_to_det', 'prob_to_det_threshold', - 'det_to_cm', 'prob_to_cm', - 'cm_to_rate', 'det_to_rate', 'prob_to_rate', 'binary_rates'] + 'weight_sum', 'check_weights', 'averager', + 'cm_to_rate', + 'det_to_prob', 'prob_to_det', + 'det_to_cm', 'prob_to_cm'] import collections import numpy as np +import pycm import sys from scipy.integrate import trapz @@ -41,6 +42,172 @@ def sanitize_predictions(predictions, epsilon=1.e-8): predictions = predictions / np.sum(predictions, axis=1)[:, np.newaxis] return predictions +def weight_sum(per_class_metrics, weight_vector, norm=True): + """ + Calculates the weighted metric + + Parameters + ---------- + per_class_metrics: numpy.float + the scores separated by class (a list of arrays) + weight_vector: numpy.ndarray floar + The array of weights per class + norm: boolean, optional + + Returns + ------- + weight_sum: np.float + The weighted metric + """ + weight_sum = np.dot(weight_vector, per_class_metrics) + + return weight_sum + +def check_weights(avg_info, M, chosen=None, truth=None): + """ + Converts standard weighting schemes to weight vectors for weight_sum + + Parameters + ---------- + avg_info: str or numpy.ndarray, float + keyword about how to calculate weighted average metric + M: int + number of classes + chosen: int, optional + which class is to be singled out for down/up-weighting + truth: numpy.ndarray, int, optional + true class assignments + + Returns + ------- + weights: numpy.ndarray, float + relative weights per class + + Notes + ----- + Assumes a random class + """ + if type(avg_info) != str: + avg_info = np.asarray(avg_info) + weights = avg_info / np.sum(avg_info) + assert(np.isclose(sum(weights), 1.)) + elif avg_info == 'per_class': + weights = np.ones(M) / float(M) + elif avg_info == 'per_item': + classes, counts = np.unique(truth, return_counts=True) + weights = np.zeros(M) + weights[classes] = counts / float(len(truth)) + assert len(weights) == M + elif avg_info == 'flat': + weights = np.ones(M) + elif avg_info == 'up' or avg_info == 'down': + if chosen is None: + chosen = np.random.randint(M) + if avg_info == 'up': + weights = np.ones(M) / np.float(M) + weights[chosen] = 1. + elif avg_info == 'down': + weights = np.ones(M) + weights[chosen] = 1./np.float(M) + else: + print('something has gone wrong with avg_info '+str(avg_info)) + return weights + +def averager(per_object_metrics, truth, M, vb=False): + """ + Creates a list with the metrics per object, separated by class + + Notes + ----- + There is currently a kludge for when there are no true class members, causing an improvement when that class is upweighted due to increasing the weight of 0. + """ + group_metric = per_object_metrics + class_metric = np.empty(M) + for m in range(M): + true_indices = np.where(truth == m)[0] + how_many_in_class = len(true_indices) + try: + assert(how_many_in_class > 0) + per_class_metric = group_metric[true_indices] + # assert(~np.all(np.isnan(per_class_metric))) + class_metric[m] = np.average(per_class_metric) + except AssertionError: + class_metric[m] = 0. + if vb: print('by request '+str((m, how_many_in_class, class_metric[m]))) + return class_metric + +def cm_to_rate(cm, vb=False): + """ + Turns a confusion matrix into true/false positive/negative rates + + Parameters + ---------- + cm: numpy.ndarray, int or float + confusion matrix, first axis is predictions, second axis is truth + vb: boolean, optional + print progress to stdout? + + Returns + ------- + rates: named tuple, float + RateMatrix named tuple + + Notes + ----- + This can be done with a mask to weight the classes differently here. + """ + # if vb: print('by request cm '+str(cm)) + tot = np.sum(cm) + tra = np.trace(cm) + # if vb: print('by request sum, trace '+str((tot, tra))) + + T = np.sum(cm, axis=1) + F = tot[np.newaxis] - T + P = np.sum(cm, axis=0) + N = tot[np.newaxis] - P + # if vb: print('by request T, F, P, N'+str((T, F, P, N))) + + TP = np.diag(cm) + FN = P - TP + FP = T - TP#np.sum(cm - np.diag(cm)[:,np.newaxis], axis=0)# np.sum(np.tril(cm), 1), axis=1) + TN = F - FN#np.sum(cm - np.diag(cm)[np.newaxis], axis=1)# np.sum(np.triu(cm, 1), axis=0) + # if vb: print('by request TP, FP, FN, TN'+str((TP, FP, FN, TN))) + + # P = TP + FP + # N = TN + FN + TPR = TP / P + FPR = FP / N + FNR = FN / P + TNR = TN / N + # if vb: print('by request TPR, FPR, FNR, TNR'+str((TPR, FPR, FNR, TNR))) + + rates = RateMatrix(TPR=TPR, FPR=FPR, FNR=FNR, TNR=TNR) + # if vb: print('by request TPR, FPR, FNR, TNR '+str(rates)) + + return rates + +def auc(x, y): + """ + Computes the area under curve (just a wrapper for trapezoid rule) + + Parameters + ---------- + x: numpy.ndarray, int or float + x-axis + y: numpy.ndarray, int or float + y-axis + + Returns + ------- + auc: float + the area under the curve + """ + x = np.concatenate(([0.], x, [1.]),) + y = np.concatenate(([0.], y, [1.]),) + i = np.argsort(x) + auc = trapz(y[i], x[i]) + return auc + def det_to_prob(dets, prediction=None): """ Reformats vector of class assignments into matrix with 1 at true/assigned class and zero elsewhere @@ -76,28 +243,10 @@ def det_to_prob(dets, prediction=None): return probs -def prob_to_det(probs): +def prob_to_det(probs, m=None, threshold=None): """ Converts probabilistic classifications to deterministic classifications by assigning the class with highest probability - Parameters - ---------- - probs: numpy.ndarray, float - N * M matrix of class probabilities - - Returns - ------- - dets: numpy.ndarray, int - maximum probability classes - """ - dets = np.argmax(probs, axis=1) - - return dets - -def prob_to_det_threshold(probs, m, threshold=0.5): - """ - Converts probabilistic classifications to binary deterministic classifications by assigning the class if probability exceeds threshold - Parameters ---------- probs: numpy.ndarray, float @@ -110,10 +259,18 @@ class relative to binary decision Returns ------- dets: numpy.ndarray, int - deterministic labels, 1 if class m, 0 otherwise + maximum probability classes """ - dets = np.zeros(np.shape(probs)[0]) - dets[probs[:, m] >= threshold] = 1 + if m == None and threshold == None: + dets = np.argmax(probs, axis=1) + else: + try: + assert(type(m) == int) + assert(type(threshold) == float) + except: + raise(AssertionError('type(m) must be int, type(threshold) must be float')) + dets = np.zeros(np.shape(probs)[0]) + dets[probs[:, m] >= threshold] = 1 return dets @@ -143,17 +300,17 @@ def det_to_cm(dets, truth, per_class_norm=True, vb=False): """ pred_classes, pred_counts = np.unique(dets, return_counts=True) true_classes, true_counts = np.unique(truth, return_counts=True) - if vb: print('by request '+str((pred_classes, pred_counts), (true_classes, true_counts))) + if vb: print('by request '+str(((pred_classes, pred_counts), (true_classes, true_counts)))) M = np.int(max(max(pred_classes), max(true_classes)) + 1) - if vb: print('by request '+str((np.shape(dets), np.shape(truth)), M)) + # if vb: print('by request '+str((np.shape(dets), np.shape(truth)), M)) cm = np.zeros((M, M), dtype=float) coords = np.array(list(zip(dets, truth))) indices, index_counts = np.unique(coords, axis=0, return_counts=True) index_counts = index_counts.astype(int) - if vb: print('by request '+str(index_counts)) + # if vb: print('by request '+str(index_counts)) # if vb: print(indices, index_counts) indices = indices.T # if vb: print(np.shape(indices)) @@ -197,207 +354,66 @@ def prob_to_cm(probs, truth, per_class_norm=True, vb=False): return cm -def cm_to_rate(cm, vb=False): - """ - Turns a confusion matrix into true/false positive/negative rates - - Parameters - ---------- - cm: numpy.ndarray, int or float - confusion matrix, first axis is predictions, second axis is truth - vb: boolean, optional - print progress to stdout? - - Returns - ------- - rates: named tuple, float - RateMatrix named tuple - - Notes - ----- - BROKEN! - This can be done with a mask to weight the classes differently here. - """ - if vb: print('by request '+str(cm)) - diag = np.diag(cm) - if vb: print('by request '+str(diag)) - - TP = np.sum(diag) - FN = np.sum(np.sum(cm, axis=0) - diag) - FP = np.sum(np.sum(cm, axis=1) - diag) - TN = np.sum(cm) - TP - if vb: print('by request '+str((TP, FN, FP, TN))) - - T = TP + TN - F = FP + FN - P = TP + FP - N = TN + FN - if vb: print('by request '+str((T, F, P, N))) - - TPR = TP / P - FPR = FP / N - FNR = FN / P - TNR = TN / N - - rates = RateMatrix(TPR=TPR, FPR=FPR, FNR=FNR, TNR=TNR) - if vb: print('by request '+str(rates)) - - return rates - -def det_to_rate(dets, truth, per_class_norm=True, vb=False): - cm = det_to_cm(dets, truth, per_class_norm=per_class_norm, vb=vb) - rates = cm_to_rate(cm, vb=vb) - return rates - -def prob_to_rate(probs, truth, per_class_norm=True, vb=False): - cm = prob_to_cm(probs, truth, per_class_norm=per_class_norm, vb=vb) - rates = cm_to_rate(cm, vb=vb) - return rates - -def weight_sum(per_class_metrics, weight_vector, norm=True): - """ - Calculates the weighted metric - - Parameters - ---------- - per_class_metrics: numpy.float - the scores separated by class (a list of arrays) - weight_vector: numpy.ndarray floar - The array of weights per class - norm: boolean, optional - - Returns - ------- - weight_sum: np.float - The weighted metric - """ - weight_sum = np.dot(weight_vector, per_class_metrics) - - return weight_sum - -def check_weights(avg_info, M, chosen=None, truth=None): - """ - Converts standard weighting schemes to weight vectors for weight_sum - - Parameters - ---------- - avg_info: str or numpy.ndarray, float - keyword about how to calculate weighted average metric - M: int - number of classes - chosen: int, optional - which class is to be singled out for down/up-weighting - truth: numpy.ndarray, int, optional - true class assignments - - Returns - ------- - weights: numpy.ndarray, float - relative weights per class - - Notes - ----- - Assumes a random class - """ - if type(avg_info) != str: - avg_info = np.asarray(avg_info) - weights = avg_info / np.sum(avg_info) - assert(np.isclose(sum(weights), 1.)) - elif avg_info == 'per_class': - weights = np.ones(M) / float(M) - elif avg_info == 'per_item': - classes, counts = np.unique(truth, return_counts=True) - weights = np.zeros(M) - weights[classes] = counts / float(len(truth)) - assert len(weights) == M - elif avg_info == 'flat': - weights = np.ones(M) - elif avg_info == 'up' or avg_info == 'down': - if chosen is None: - chosen = np.random.randint(M) - if avg_info == 'up': - weights = np.ones(M) / np.float(M) - weights[chosen] = 1. - elif avg_info == 'down': - weights = np.ones(M) - weights[chosen] = 1./np.float(M) - else: - print('something has gone wrong with avg_info '+str(avg_info)) - return weights - -def averager(per_object_metrics, truth, M, vb=False): - """ - Creates a list with the metrics per object, separated by class - - Notes - ----- - There is currently a kludge for when there are no true class members, causing an improvement when that class is upweighted due to increasing the weight of 0. - """ - group_metric = per_object_metrics - class_metric = np.empty(M) - for m in range(M): - true_indices = np.where(truth == m)[0] - how_many_in_class = len(true_indices) - try: - assert(how_many_in_class > 0) - per_class_metric = group_metric[true_indices] - # assert(~np.all(np.isnan(per_class_metric))) - class_metric[m] = np.average(per_class_metric) - except AssertionError: - class_metric[m] = 0. - if vb: print('by request '+str((m, how_many_in_class, class_metric[m]))) - return class_metric - -def binary_rates(dets, truth, m): - - tp = np.sum(dets[truth == m]) - fp = np.sum(dets[truth != m]) - tpr = tp/len(dets[truth == m]) - fpr = fp/len(dets[truth != m]) - - return tpr,fpr - -def precision(classifications,truth,class_idx): - - tp = np.sum(classifications[truth == class_idx]) - fp = np.sum(classifications[truth != class_idx]) - - precision = tp/(tp+fp) - if precision != precision: - import pdb; pdb.set_trace() - - return tp/(tp+fp) - -def recall(classifications,truth,class_idx): - - tp = np.sum(classifications[truth == class_idx]) - fp = np.sum(classifications[truth != class_idx]) - fn = len(np.where((classifications == 0) & (truth == class_idx))[0]) - #import pdb; pdb.set_trace() - #print(fn) - - return tp/(tp+fn) - -def auc(x, y): - """ - Computes the area under curve (just a wrapper for trapezoid rule) - - Parameters - ---------- - x: numpy.ndarray, int or float - - y: numpy.ndarray, int or float - - Returns - ------- - rates: named tuple, float - RateMatrix named tuple - """ - - x = np.concatenate(([0.], x, [1.]),) - y = np.concatenate(([0.], y, [1.]),) - - i = np.argsort(x) - auc = trapz(y[i], x[i]) - - return auc +# def det_to_rate(dets, truth, per_class_norm=True, vb=False): +# cm = det_to_cm(dets, truth, per_class_norm=per_class_norm, vb=vb) +# rates = cm_to_rate(cm, vb=vb) +# return rates +# +# def prob_to_rate(probs, truth, per_class_norm=True, vb=False): +# cm = prob_to_cm(probs, truth, per_class_norm=per_class_norm, vb=vb) +# rates = cm_to_rate(cm, vb=vb) +# return rates + +# def binary_rates(dets, truth, m): +# +# tp = np.sum(dets[truth == m]) +# fp = np.sum(dets[truth != m]) +# tpr = tp/len(dets[truth == m]) +# fpr = fp/len(dets[truth != m]) +# +# return tpr,fpr + +def recall(rates): + return 1. - rates.FNR + +# def recall(rates): +# """ +# Calculates recall from rates +# +# Parameters +# ---------- +# rates: namedtuple +# named tuple of 'TPR FPR FNR TNR' +# +# Returns +# ------- +# recall: float +# recall +# """ +# return 1. - rates.FNR + +# def precision(rates): +# """ +# Calculates precision from rates +# +# Parameters +# ---------- +# rates: namedtuple +# named tuple of 'TPR FPR FNR TNR' +# +# Returns +# ------- +# precision: float +# precision +# """ +# return 1. - rates.FNR +# +# def recall(classifications,truth,class_idx): +# +# tp = np.sum(classifications[truth == class_idx]) +# fp = np.sum(classifications[truth != class_idx]) +# fn = len(np.where((classifications == 0) & (truth == class_idx))[0]) +# #import pdb; pdb.set_trace() +# #print(fn) +# +# return tp/(tp+fn) From 97dbe70e6b534c86759095bb090db136a298f6f9 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 28 May 2019 12:57:16 -0400 Subject: [PATCH 02/58] now working rates --- proclam/metrics/util.py | 37 ++++++++++++++++++++++++++++++++++--- 1 file changed, 34 insertions(+), 3 deletions(-) diff --git a/proclam/metrics/util.py b/proclam/metrics/util.py index 94c5eb8..c0b5160 100644 --- a/proclam/metrics/util.py +++ b/proclam/metrics/util.py @@ -11,7 +11,7 @@ import collections import numpy as np -import pycm +# import pycm import sys from scipy.integrate import trapz @@ -208,6 +208,37 @@ def auc(x, y): auc = trapz(y[i], x[i]) return auc +def check_auc_grid(grid): + """ + Checks if a grid for an AUC metric is valid + + Parameters + ---------- + grid: numpy.ndarray, float or float or int + array of values between 0 and 1 at which to evaluate AUC or grid spacing or number of grid points + + Returns + ------- + thresholds_grid: numpy.ndarray, float + grid of thresholds + """ + if type(grid) == list or type(grid) == numpy.ndarray: + thresholds_grid = np.array(grid) + elif type(grid) == float: + if grid > 0. and grid < 1.: + thresholds_grid = np.arange(0., 1., grid) + else: + thresholds_grid = None + elif type(grid) == int: + if grid > 0: + thresholds_grid = np.linspace(0., 1., grid) + else: + thresholds_grid = None + if thresholds_grid == None: + print('Please specify a grid, spacing, or density for this AUC metric.') + return + return thresholds_grid + def det_to_prob(dets, prediction=None): """ Reformats vector of class assignments into matrix with 1 at true/assigned class and zero elsewhere @@ -373,8 +404,8 @@ def prob_to_cm(probs, truth, per_class_norm=True, vb=False): # # return tpr,fpr -def recall(rates): - return 1. - rates.FNR +# def recall(rates): +# return 1. - rates.FNR # def recall(rates): # """ From c0b689396b99ccd8f24d78579a18f70296265a4a Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 28 May 2019 12:57:51 -0400 Subject: [PATCH 03/58] testing working rates --- metrics_evaluation.ipynb | 42 +++++++++++++++++++++++++++++++--------- 1 file changed, 33 insertions(+), 9 deletions(-) diff --git a/metrics_evaluation.ipynb b/metrics_evaluation.ipynb index 7b8163f..61d8c61 100644 --- a/metrics_evaluation.ipynb +++ b/metrics_evaluation.ipynb @@ -28,6 +28,7 @@ "\n", "import numpy as np\n", "import pandas as pd\n", + "from pycm import ConfusionMatrix\n", "\n", "import proclam\n", "from proclam import *" @@ -194,7 +195,7 @@ " data_info_dict['dirname'] = dirname + data_info_dict['label'] + '/'\n", " data_info_dict['classifications'] = ['%s/predicted_prob_%s.csv'%(name, name) for name in names]\n", " data_info_dict['truth_tables'] = ['%s/truth_table_%s.csv'%(name, name) for name in names]\n", - " print(data_info_dict)\n", + "# print(data_info_dict)\n", " return data_info_dict" ] }, @@ -237,6 +238,13 @@ " plt.close()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -336,12 +344,21 @@ " data = np.empty((len(metricslist), len(dataset['names'])))\n", " for cc, pair in enumerate(dataset['class_pairs']):\n", " probm, truthv = read_class_pairs(pair, dataset, cc)#loc=dataset['dirname'], title=dataset['label']+' '+dataset['names'][cc])\n", - " for count, metric in enumerate(metricslist):\n", - " D = getattr(proclam.metrics, metric)()\n", - " hm = D.evaluate(probm, truthv)\n", - " data[count][cc] = hm\n", - " dataset['results'] = data\n", - " metric_plot(dataset, metricslist, markerlist, colors)" + "# plot_cm(probm, truthv, str(cc), loc='./sandbox/')\n", + " det = proclam.metrics.util.prob_to_det(probm)\n", + " cm = proclam.metrics.util.prob_to_cm(probm, truthv, per_class_norm=False, vb=False)\n", + " rates = proclam.metrics.util.cm_to_rate(cm, vb=True)\n", + "# print(rates)\n", + " compare = ConfusionMatrix(truthv, det)\n", + " printout = proclam.metrics.util.RateMatrix(compare.TPR, compare.FPR, compare.FNR, compare.TNR)\n", + " print('for comparison: ' + str(printout))\n", + "# for count, metric in enumerate(metricslist):\n", + "# D = getattr(proclam.metrics, metric)()\n", + "# hm = D.evaluate(probm, truthv)\n", + "# data[count][cc] = hm\n", + "# dataset['results'] = data\n", + " \n", + "# metric_plot(dataset, metricslist, markerlist, colors)" ] }, { @@ -394,6 +411,13 @@ "# metric_plot(names, metricslist, data, markerlist, colors, title='Mystery Dataset', fileloc=dirname+'mysterydata.png')" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -405,9 +429,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "proclam (Python 3)", "language": "python", - "name": "python3" + "name": "proclam_3" }, "language_info": { "codemirror_mode": { From 7718bb45be9cbafbb310c2c3b60166f7fd37fa05 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 28 May 2019 17:26:32 -0400 Subject: [PATCH 04/58] working ROC? --- proclam/metrics/__init__.py | 1 + proclam/metrics/roc.py | 63 +++++++++++++------------------------ proclam/metrics/util.py | 29 ++++++++++------- 3 files changed, 40 insertions(+), 53 deletions(-) diff --git a/proclam/metrics/__init__.py b/proclam/metrics/__init__.py index e0d4471..b70a93e 100644 --- a/proclam/metrics/__init__.py +++ b/proclam/metrics/__init__.py @@ -4,3 +4,4 @@ from .util import * from .brier import * from .logloss import * +from .roc import * diff --git a/proclam/metrics/roc.py b/proclam/metrics/roc.py index 1c1daca..ac510d3 100644 --- a/proclam/metrics/roc.py +++ b/proclam/metrics/roc.py @@ -6,12 +6,14 @@ __all__ = ['ROC'] import numpy as np -from scipy.integrate import trapz from .util import weight_sum from .util import check_weights -from .util import prob_to_det_threshold -from .util import auc, tpr_fpr +from .util import check_auc_grid +from .util import prob_to_det +from .util import det_to_cm +from .util import cm_to_rate +from .util import auc from .metric import Metric class ROC(Metric): @@ -25,11 +27,10 @@ def __init__(self, scheme=None): scheme: string the name of the metric """ - super(ROC, self).__init__(scheme) - self.scheme = scheme + self.scheme = scheme - def evaluate(self, prediction, truth, grid=None, averaging='per_class'): + def evaluate(self, prediction, truth, grid, averaging='per_class'): """ Evaluates the area under the ROC curve for a given class_idx @@ -39,59 +40,39 @@ def evaluate(self, prediction, truth, grid=None, averaging='per_class'): predicted class probabilities truth: numpy.ndarray, int true classes - grid: numpy.ndarray, float, optional + grid: numpy.ndarray, float or float or int array of values between 0 and 1 at which to evaluate ROC averaging: string or numpy.ndarray, float - 'per_class' weights classes equally, other keywords possible - vector assumed to be class weights + 'per_class' weights classes equally, other keywords possible, vector assumed to be class weights Returns ------- metric: float value of the metric """ - if type(grid) == list or type(grid) == numpy.ndarray: - thresholds)grid = np.array(grid) - elif type(grid) == float: - thresholds_grid = np.arange(0., 1., grid) - n_thresholds = len(thresholds_grid) - else: - print('Please specify a grid or spacing for this AUC metric.') - return + thresholds_grid = check_auc_grid(grid) + n_thresholds = len(thresholds_grid) prediction, truth = np.asarray(prediction), np.asarray(truth) - prediction_shape = np.shape(prediction) - (N, M) = prediction_shape - weights = check_weights(averaging, M, truth=truth) + (N, M) = np.shape(prediction) - auc_allclass = 0 + auc_class = np.empty(M) for m in range(M): if not len(np.where(truth == m)[0]): raise RuntimeError('No true values for class %i so ROC is undefined'%m) - thresholds_grid = np.arange(0,1,gridspace) - n_thresholds = len(thresholds_grid) - tpr, fpr = np.zeros(n_thresholds), np.zeros(n_thresholds) - for t,i in zip(thresholds_grid,range(n_thresholds)): - classifications = prob_to_det_threshold(prediction, m, t) - tpr_thresh, fpr_thresh = binary_rates(classifications, truth, m) - - #tp = np.sum(classifications[truth == class_idx]) - #fp = np.sum(classifications[truth != class_idx]) - tpr[i] = tpr_thresh #tp/len(classifications[truth == class_idx]) - fpr[i] = fpr_thresh #fp/len(classifications[truth != class_idx]) - #if tpr[i] != tpr[i]: import pdb; pdb.set_trace() - - auc_class = auc(fpr,tpr) - - #fpr = np.concatenate(([0],fpr,[1]),) - #tpr = np.concatenate(([0],tpr,[1]),) + for i, t in enumerate(thresholds_grid): + dets = prob_to_det(prediction, m, threshold=t) + cm = det_to_cm(dets, truth) + rates = cm_to_rate(cm) + tpr[i] = rates.TPR[m] + fpr[i] = rates.FPR[m] - #ifpr = np.argsort(fpr) - #auc = trapz(tpr[ifpr],fpr[ifpr]) + auc_class[m] = auc(fpr, tpr) - auc_allclass += auc_class*weights[class_idx] + weights = check_weights(averaging, M, truth=truth) + auc_allclass = weight_sum(auc_class, weights) return auc_allclass diff --git a/proclam/metrics/util.py b/proclam/metrics/util.py index c0b5160..0ffd63c 100644 --- a/proclam/metrics/util.py +++ b/proclam/metrics/util.py @@ -6,8 +6,9 @@ __all__ = ['sanitize_predictions', 'weight_sum', 'check_weights', 'averager', 'cm_to_rate', + 'auc', 'check_auc_grid', 'det_to_prob', 'prob_to_det', - 'det_to_cm', 'prob_to_cm'] + 'det_to_cm'] import collections import numpy as np @@ -222,7 +223,7 @@ def check_auc_grid(grid): thresholds_grid: numpy.ndarray, float grid of thresholds """ - if type(grid) == list or type(grid) == numpy.ndarray: + if type(grid) == list or type(grid) == np.ndarray: thresholds_grid = np.array(grid) elif type(grid) == float: if grid > 0. and grid < 1.: @@ -234,10 +235,12 @@ def check_auc_grid(grid): thresholds_grid = np.linspace(0., 1., grid) else: thresholds_grid = None - if thresholds_grid == None: + try: + assert thresholds_grid is not None + return thresholds_grid + except AssertionError: print('Please specify a grid, spacing, or density for this AUC metric.') return - return thresholds_grid def det_to_prob(dets, prediction=None): """ @@ -296,10 +299,9 @@ class relative to binary decision dets = np.argmax(probs, axis=1) else: try: - assert(type(m) == int) - assert(type(threshold) == float) - except: - raise(AssertionError('type(m) must be int, type(threshold) must be float')) + assert(type(m) == int and type(threshold) == np.float64) + except AssertionError: + print(str(m)+' is '+str(type(m))+' and must be int; '+str(threshold)+' is '+str(type(threshold))+' and must be float') dets = np.zeros(np.shape(probs)[0]) dets[probs[:, m] >= threshold] = 1 @@ -331,7 +333,7 @@ def det_to_cm(dets, truth, per_class_norm=True, vb=False): """ pred_classes, pred_counts = np.unique(dets, return_counts=True) true_classes, true_counts = np.unique(truth, return_counts=True) - if vb: print('by request '+str(((pred_classes, pred_counts), (true_classes, true_counts)))) + # if vb: print('by request '+str(((pred_classes, pred_counts), (true_classes, true_counts)))) M = np.int(max(max(pred_classes), max(true_classes)) + 1) @@ -340,10 +342,13 @@ def det_to_cm(dets, truth, per_class_norm=True, vb=False): coords = np.array(list(zip(dets, truth))) indices, index_counts = np.unique(coords, axis=0, return_counts=True) + if vb: print(indices.T, index_counts) index_counts = index_counts.astype(int) + indices = indices.T.astype(int) # if vb: print('by request '+str(index_counts)) # if vb: print(indices, index_counts) - indices = indices.T + # indices = indices.T + # if vb: print(indices) # if vb: print(np.shape(indices)) cm[indices[0], indices[1]] = index_counts # if vb: print(cm) @@ -355,11 +360,11 @@ def det_to_cm(dets, truth, per_class_norm=True, vb=False): # cm /= true_counts[:, np.newaxis] # cm = cm / true_counts[np.newaxis, :] - if vb: print('by request '+str(cm)) + # if vb: print('by request '+str(cm)) return cm -def prob_to_cm(probs, truth, per_class_norm=True, vb=False): +# def prob_to_cm(probs, truth, per_class_norm=True, vb=False): """ Turns probabilistic classifications into confusion matrix by taking maximum probability as deterministic class From 29e959b3e81259fd7a8f3e60bcbb6f55c1106c48 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 28 May 2019 17:39:26 -0400 Subject: [PATCH 05/58] the new ROC does indeed work --- pipeline_sandbox.ipynb | 20 ++++++++++---------- proclam/metrics/metric.py | 4 +--- 2 files changed, 11 insertions(+), 13 deletions(-) diff --git a/pipeline_sandbox.ipynb b/pipeline_sandbox.ipynb index 6d90f2b..6f2dfa8 100644 --- a/pipeline_sandbox.ipynb +++ b/pipeline_sandbox.ipynb @@ -763,20 +763,20 @@ "metadata": {}, "outputs": [], "source": [ - "from proclam.metrics import util as pmu\n", - "from proclam.metrics import roc\n", - "from importlib import reload\n", - "reload(proclam.metrics.roc)\n", - "from proclam.metrics import roc\n", + "# from proclam.metrics import util as pmu\n", + "# from proclam.metrics import roc\n", + "# from importlib import reload\n", + "# reload(proclam.metrics.roc)\n", + "# from proclam.metrics import roc\n", "\n", - "metric = roc.Metric()\n", - "rocB = metric.evaluate(predictionB,truth)\n", - "rocC = metric.evaluate(predictionC,truth)\n", + "ROC_metric = proclam.metrics.ROC()\n", + "rocB = ROC_metric.evaluate(predictionB,truth, 0.1)\n", + "rocC = ROC_metric.evaluate(predictionC,truth, 0.1)\n", "\n", "print('ROC AUC for prediction B = %.3f'%rocB)\n", "print('ROC AUC for prediction C = %.3f'%rocC)\n", "\n", - "rocB = metric.evaluate(predictionB,truth,weights=[0.1,0.3,0.2,0.2,0.2])\n", + "rocB = ROC_metric.evaluate(predictionB,truth,0.1, averaging=[0.1,0.3,0.2,0.2,0.2])\n", "print('ROC AUC for prediction B with weird weights = %.3f'%rocB)" ] }, @@ -982,7 +982,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "ProClaM (Python 3)", + "display_name": "proclam (Python 3)", "language": "python", "name": "proclam_3" }, diff --git a/proclam/metrics/metric.py b/proclam/metrics/metric.py index 9aed8b4..28fa06e 100644 --- a/proclam/metrics/metric.py +++ b/proclam/metrics/metric.py @@ -43,9 +43,7 @@ def evaluate(self, prediction, truth, weights=None, **kwds): value of the metric """ - - - print('No metric specified: returning true positive rate based on maximum value') + print('No metric specified') # # mode = np.argmax(prediction, axis=1) # metric = len(np.where(truth == mode)) From 9f90b78a97dde797c654f93cc090be90343cd74e Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Wed, 29 May 2019 16:17:44 -0400 Subject: [PATCH 06/58] fixed bug caused by int/float mismatch --- .../metrics/{precision_recall.py => prc.py} | 0 proclam/metrics/util.py | 28 ++++++------------- 2 files changed, 8 insertions(+), 20 deletions(-) rename proclam/metrics/{precision_recall.py => prc.py} (100%) diff --git a/proclam/metrics/precision_recall.py b/proclam/metrics/prc.py similarity index 100% rename from proclam/metrics/precision_recall.py rename to proclam/metrics/prc.py diff --git a/proclam/metrics/util.py b/proclam/metrics/util.py index 0ffd63c..e8a82cd 100644 --- a/proclam/metrics/util.py +++ b/proclam/metrics/util.py @@ -159,8 +159,8 @@ def cm_to_rate(cm, vb=False): """ # if vb: print('by request cm '+str(cm)) tot = np.sum(cm) - tra = np.trace(cm) - # if vb: print('by request sum, trace '+str((tot, tra))) + mask = range(len(cm)) + # if vb: print('by request sum '+str(tot)) T = np.sum(cm, axis=1) F = tot[np.newaxis] - T @@ -170,12 +170,10 @@ def cm_to_rate(cm, vb=False): TP = np.diag(cm) FN = P - TP - FP = T - TP#np.sum(cm - np.diag(cm)[:,np.newaxis], axis=0)# np.sum(np.tril(cm), 1), axis=1) - TN = F - FN#np.sum(cm - np.diag(cm)[np.newaxis], axis=1)# np.sum(np.triu(cm, 1), axis=0) + TN = F - FN + FP = T - TP # if vb: print('by request TP, FP, FN, TN'+str((TP, FP, FN, TN))) - # P = TP + FP - # N = TN + FN TPR = TP / P FPR = FP / N FNR = FN / P @@ -307,7 +305,7 @@ class relative to binary decision return dets -def det_to_cm(dets, truth, per_class_norm=True, vb=False): +def det_to_cm(dets, truth, per_class_norm=False, vb=False): """ Converts deterministic classifications and truth into confusion matrix @@ -338,29 +336,19 @@ def det_to_cm(dets, truth, per_class_norm=True, vb=False): M = np.int(max(max(pred_classes), max(true_classes)) + 1) # if vb: print('by request '+str((np.shape(dets), np.shape(truth)), M)) - cm = np.zeros((M, M), dtype=float) + cm = np.zeros((M, M), dtype=int) coords = np.array(list(zip(dets, truth))) indices, index_counts = np.unique(coords, axis=0, return_counts=True) if vb: print(indices.T, index_counts) index_counts = index_counts.astype(int) indices = indices.T.astype(int) - # if vb: print('by request '+str(index_counts)) - # if vb: print(indices, index_counts) - # indices = indices.T - # if vb: print(indices) - # if vb: print(np.shape(indices)) cm[indices[0], indices[1]] = index_counts - # if vb: print(cm) if per_class_norm: - # print(type(cm)) - # print(type(true_counts)) - # cm = cm / true_counts - # cm /= true_counts[:, np.newaxis] # - cm = cm / true_counts[np.newaxis, :] + cm = cm.astype(float) / true_counts[np.newaxis, :].astype(float) - # if vb: print('by request '+str(cm)) + if vb: print('by request '+str(cm)) return cm From db3dc56f781067d3e052edb59ce11da4f27e4489 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Thu, 30 May 2019 12:04:19 -0400 Subject: [PATCH 07/58] fixed rates, again, now troubleshooting discrepant AUC --- proclam/metrics/util.py | 70 ++++++++++++++++++++--------------------- 1 file changed, 34 insertions(+), 36 deletions(-) diff --git a/proclam/metrics/util.py b/proclam/metrics/util.py index e8a82cd..09d7ac0 100644 --- a/proclam/metrics/util.py +++ b/proclam/metrics/util.py @@ -43,17 +43,16 @@ def sanitize_predictions(predictions, epsilon=1.e-8): predictions = predictions / np.sum(predictions, axis=1)[:, np.newaxis] return predictions -def weight_sum(per_class_metrics, weight_vector, norm=True): +def weight_sum(per_class_metrics, weight_vector): """ Calculates the weighted metric Parameters ---------- - per_class_metrics: numpy.float - the scores separated by class (a list of arrays) - weight_vector: numpy.ndarray floar - The array of weights per class - norm: boolean, optional + per_class_metrics: numpy.ndarray, float + vector of per-class scores + weight_vector: numpy.ndarray, float + vector of per-class weights Returns ------- @@ -61,7 +60,6 @@ def weight_sum(per_class_metrics, weight_vector, norm=True): The weighted metric """ weight_sum = np.dot(weight_vector, per_class_metrics) - return weight_sum def check_weights(avg_info, M, chosen=None, truth=None): @@ -191,9 +189,9 @@ def auc(x, y): Parameters ---------- - x: numpy.ndarray, int or float + x: numpy.ndarray, float x-axis - y: numpy.ndarray, int or float + y: numpy.ndarray, float y-axis Returns @@ -201,8 +199,8 @@ def auc(x, y): auc: float the area under the curve """ - x = np.concatenate(([0.], x, [1.]),) - y = np.concatenate(([0.], y, [1.]),) + # x = np.concatenate(([0.], x, [1.]),) + # y = np.concatenate(([0.], y, [1.]),) i = np.argsort(x) auc = trapz(y[i], x[i]) return auc @@ -222,7 +220,7 @@ def check_auc_grid(grid): grid of thresholds """ if type(grid) == list or type(grid) == np.ndarray: - thresholds_grid = np.array(grid) + thresholds_grid = np.concatenate((np.zeros(1), np.array(grid), np.ones(1))) elif type(grid) == float: if grid > 0. and grid < 1.: thresholds_grid = np.arange(0., 1., grid) @@ -353,30 +351,30 @@ def det_to_cm(dets, truth, per_class_norm=False, vb=False): return cm # def prob_to_cm(probs, truth, per_class_norm=True, vb=False): - """ - Turns probabilistic classifications into confusion matrix by taking maximum probability as deterministic class - - Parameters - ---------- - probs: numpy.ndarray, float - N * M matrix of class probabilities - truth: numpy.ndarray, int - N-dimensional vector of true classes - per_class_norm: boolean, optional - equal weight per class if True, equal weight per object if False - vb: boolean, optional - if True, print cm - - Returns - ------- - cm: numpy.ndarray, int - confusion matrix - """ - dets = prob_to_det(probs) - - cm = det_to_cm(dets, truth, per_class_norm=per_class_norm, vb=vb) - - return cm +# """ +# Turns probabilistic classifications into confusion matrix by taking maximum probability as deterministic class +# +# Parameters +# ---------- +# probs: numpy.ndarray, float +# N * M matrix of class probabilities +# truth: numpy.ndarray, int +# N-dimensional vector of true classes +# per_class_norm: boolean, optional +# equal weight per class if True, equal weight per object if False +# vb: boolean, optional +# if True, print cm +# +# Returns +# ------- +# cm: numpy.ndarray, int +# confusion matrix +# """ +# dets = prob_to_det(probs) +# +# cm = det_to_cm(dets, truth, per_class_norm=per_class_norm, vb=vb) +# +# return cm # def det_to_rate(dets, truth, per_class_norm=True, vb=False): # cm = det_to_cm(dets, truth, per_class_norm=per_class_norm, vb=vb) From 783a30dee00a3a74f8fe57f9856faa641bcaee84 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Thu, 30 May 2019 15:47:36 -0400 Subject: [PATCH 08/58] fixed AUC bug due to binarizing deterministic classes --- proclam/metrics/roc.py | 130 ++++++++++++++++++++-------------------- proclam/metrics/util.py | 38 +++++++++--- 2 files changed, 95 insertions(+), 73 deletions(-) diff --git a/proclam/metrics/roc.py b/proclam/metrics/roc.py index ac510d3..c083226 100644 --- a/proclam/metrics/roc.py +++ b/proclam/metrics/roc.py @@ -7,72 +7,72 @@ import numpy as np -from .util import weight_sum -from .util import check_weights -from .util import check_auc_grid -from .util import prob_to_det -from .util import det_to_cm -from .util import cm_to_rate -from .util import auc +from .util import weight_sum, check_weights +from .util import prob_to_det, det_to_cm, cm_to_rate +from .util import auc, check_auc_grid, prep_curve from .metric import Metric class ROC(Metric): - def __init__(self, scheme=None): - """ - An object that evaluates the ROC metric - - Parameters - ---------- - scheme: string - the name of the metric - """ - super(ROC, self).__init__(scheme) - self.scheme = scheme - - def evaluate(self, prediction, truth, grid, averaging='per_class'): - """ - Evaluates the area under the ROC curve for a given class_idx - - Parameters - ---------- - prediction: numpy.ndarray, float - predicted class probabilities - truth: numpy.ndarray, int - true classes - grid: numpy.ndarray, float or float or int - array of values between 0 and 1 at which to evaluate ROC - averaging: string or numpy.ndarray, float - 'per_class' weights classes equally, other keywords possible, vector assumed to be class weights - - Returns - ------- - metric: float - value of the metric - """ - thresholds_grid = check_auc_grid(grid) - n_thresholds = len(thresholds_grid) - - prediction, truth = np.asarray(prediction), np.asarray(truth) - (N, M) = np.shape(prediction) - - auc_class = np.empty(M) - - for m in range(M): - if not len(np.where(truth == m)[0]): - raise RuntimeError('No true values for class %i so ROC is undefined'%m) - - tpr, fpr = np.zeros(n_thresholds), np.zeros(n_thresholds) - for i, t in enumerate(thresholds_grid): - dets = prob_to_det(prediction, m, threshold=t) - cm = det_to_cm(dets, truth) - rates = cm_to_rate(cm) - tpr[i] = rates.TPR[m] - fpr[i] = rates.FPR[m] - - auc_class[m] = auc(fpr, tpr) - - weights = check_weights(averaging, M, truth=truth) - auc_allclass = weight_sum(auc_class, weights) - - return auc_allclass + def __init__(self, scheme=None): + """ + An object that evaluates the ROC metric + + Parameters + ---------- + scheme: string + the name of the metric + """ + super(ROC, self).__init__(scheme) + self.scheme = scheme + + def evaluate(self, prediction, truth, grid, averaging='per_class', vb=False): + """ + Evaluates the area under the ROC for a given class_idx + + Parameters + ---------- + prediction: numpy.ndarray, float + predicted class probabilities + truth: numpy.ndarray, int + true classes + grid: numpy.ndarray, float or float or int + array of values between 0 and 1 at which to evaluate ROC + averaging: string or numpy.ndarray, float + 'per_class' weights classes equally, other keywords possible, vector assumed to be class weights + + Returns + ------- + auc_allclass: float + value of the metric + """ + thresholds_grid = check_auc_grid(grid) + n_thresholds = len(thresholds_grid) + + prediction, truth = np.asarray(prediction), np.asarray(truth) + (N, M) = np.shape(prediction) + + auc_class = np.empty(M) + curve = np.empty((M, 2, n_thresholds)) + + for m in range(M): + m_truth = (truth == m).astype(int) + + if not len(np.where(truth == m)[0]): + raise RuntimeError('No true values for class %i so ROC is undefined'%m) + + tpr, fpr = np.empty(n_thresholds), np.empty(n_thresholds) + for i, t in enumerate(thresholds_grid): + dets = prob_to_det(prediction, m, threshold=t) + cm = det_to_cm(dets, m_truth) + rates = cm_to_rate(cm) + fpr[i], tpr[i] = rates.FPR[-1], rates.TPR[-1] + + (curve[m][0], curve[m][1]) = (fpr, tpr) + auc_class[m] = auc(fpr, tpr) + + weights = check_weights(averaging, M, truth=truth) + auc_allclass = weight_sum(auc_class, weights) + + if vb: return curve + else: return auc_class diff --git a/proclam/metrics/util.py b/proclam/metrics/util.py index 09d7ac0..021243b 100644 --- a/proclam/metrics/util.py +++ b/proclam/metrics/util.py @@ -6,7 +6,7 @@ __all__ = ['sanitize_predictions', 'weight_sum', 'check_weights', 'averager', 'cm_to_rate', - 'auc', 'check_auc_grid', + 'auc', 'check_auc_grid', 'prep_curve', 'det_to_prob', 'prob_to_det', 'det_to_cm'] @@ -155,9 +155,10 @@ def cm_to_rate(cm, vb=False): ----- This can be done with a mask to weight the classes differently here. """ + cm = cm.astype(float) # if vb: print('by request cm '+str(cm)) tot = np.sum(cm) - mask = range(len(cm)) + # mask = range(len(cm)) # if vb: print('by request sum '+str(tot)) T = np.sum(cm, axis=1) @@ -183,6 +184,29 @@ def cm_to_rate(cm, vb=False): return rates +def prep_curve(x, y): + """ + Makes a curve for AUC + + Parameters + ---------- + x: numpy.ndarray, float + x-axis + y: numpy.ndarray, float + y-axis + + Returns + ------- + x: numpy.ndarray, float + x-axis + y: numpy.ndarray, float + y-axis + """ + x = np.concatenate(([0.], x, [1.]),) + y = np.concatenate(([0.], y, [1.]),) + i = np.argsort(x) + return (x[i], y[i]) + def auc(x, y): """ Computes the area under curve (just a wrapper for trapezoid rule) @@ -199,10 +223,8 @@ def auc(x, y): auc: float the area under the curve """ - # x = np.concatenate(([0.], x, [1.]),) - # y = np.concatenate(([0.], y, [1.]),) - i = np.argsort(x) - auc = trapz(y[i], x[i]) + (x, y) = prep_curve(x, y) + auc = trapz(y, x) return auc def check_auc_grid(grid): @@ -233,7 +255,7 @@ def check_auc_grid(grid): thresholds_grid = None try: assert thresholds_grid is not None - return thresholds_grid + return np.sort(thresholds_grid) except AssertionError: print('Please specify a grid, spacing, or density for this AUC metric.') return @@ -298,7 +320,7 @@ class relative to binary decision assert(type(m) == int and type(threshold) == np.float64) except AssertionError: print(str(m)+' is '+str(type(m))+' and must be int; '+str(threshold)+' is '+str(type(threshold))+' and must be float') - dets = np.zeros(np.shape(probs)[0]) + dets = np.zeros(np.shape(probs)[0]).astype(int) dets[probs[:, m] >= threshold] = 1 return dets From 54e0864ae79e37e0756bcc4e018f0fd38d7f8cf5 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Thu, 30 May 2019 18:57:08 -0400 Subject: [PATCH 09/58] tests of rates and ROC AUC --- pipeline_sandbox.ipynb | 88 +++++++++++++++++++++++++++++++++++++----- 1 file changed, 78 insertions(+), 10 deletions(-) diff --git a/pipeline_sandbox.ipynb b/pipeline_sandbox.ipynb index 6f2dfa8..0191598 100644 --- a/pipeline_sandbox.ipynb +++ b/pipeline_sandbox.ipynb @@ -20,8 +20,10 @@ "import random\n", "import numpy as np\n", "import scipy.stats as sct\n", + "import scipy.integrate as spi\n", "import sklearn as skl\n", "from sklearn import metrics\n", + "from pycm import ConfusionMatrix\n", "import pandas as pd\n", "import os\n", "\n", @@ -36,7 +38,7 @@ "outputs": [], "source": [ "import proclam\n", - "from proclam import *" + "# from proclam import *" ] }, { @@ -96,7 +98,7 @@ "M_classes = 5\n", "N_objects = 1000\n", "names = [''.join(random.sample(string.ascii_lowercase, 2)) for i in range(M_classes)]\n", - "truth = A.simulate(M_classes, N_objects)" + "truth = A.simulate(M_classes, N_objects, base=2)" ] }, { @@ -752,34 +754,100 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Deterministic metrics\n", + "## Deterministic metrics\n", "\n", "Let's check that reducing the probabilities to class point estimates actually does what we want; the one based on a good confusion matrix should do better than the random guesser. " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### True/False positive/negative rates\n", + "\n", + "Let's compare `proclam`'s calculation of the standard rates to that of `pycm`." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# from proclam.metrics import util as pmu\n", - "# from proclam.metrics import roc\n", - "# from importlib import reload\n", - "# reload(proclam.metrics.roc)\n", - "# from proclam.metrics import roc\n", + "detC = proclam.metrics.util.prob_to_det(predictionC)\n", + "cmC = proclam.metrics.util.det_to_cm(detC, truth)\n", + "print(proclam.metrics.util.cm_to_rate(cmC, vb=True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "compare = ConfusionMatrix(truth, detC)\n", + "print(proclam.metrics.util.RateMatrix(TPR=compare.TPR, FPR=compare.FPR, FNR=compare.FNR, TNR=compare.TNR))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ROC AUC\n", "\n", + "The ROC is the true positive rate as a function of the false positive rate, where the rates are calculated from a confusion matrix derived by deterministically assigning classes based on a series of threshold values in probability." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "ROC_metric = proclam.metrics.ROC()\n", "rocB = ROC_metric.evaluate(predictionB,truth, 0.1)\n", "rocC = ROC_metric.evaluate(predictionC,truth, 0.1)\n", - "\n", "print('ROC AUC for prediction B = %.3f'%rocB)\n", "print('ROC AUC for prediction C = %.3f'%rocC)\n", - "\n", "rocB = ROC_metric.evaluate(predictionB,truth,0.1, averaging=[0.1,0.3,0.2,0.2,0.2])\n", "print('ROC AUC for prediction B with weird weights = %.3f'%rocB)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's compare the ROC AUC calculated by `proclam` to that of `scikit-learn`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "truth_score = proclam.metrics.util.det_to_prob(truth)\n", + "from_proclam, proclam_auc, from_skl = [],[],[]\n", + "for m in range(M_classes):\n", + " fpr, tpr, thresholds = skl.metrics.roc_curve(truth_score.T[m].T.astype(int), predictionB.T[m].T)\n", + " proclam_says = ROC_metric.evaluate(predictionB, truth, thresholds, vb=True)[m]\n", + " proclam_auc.append(ROC_metric.evaluate(predictionB, truth, thresholds, vb=False)[m])\n", + " i = np.argsort(proclam_says[0])\n", + " new_auc = proclam.metrics.util.auc(proclam_says[0][i], proclam_says[1][i])\n", + " print('proclam: '+str(new_auc))\n", + " from_proclam.append(proclam.metrics.util.auc(proclam_says[0], proclam_says[1]))\n", + " skl_says = proclam.metrics.util.auc(fpr, tpr)\n", + " print('skl: '+str(skl_says))\n", + " from_skl.append(skl_says)\n", + "print('proclam says %.3f'%np.mean(proclam_auc)+' scikit-learn says %.3f'%np.mean(from_skl))\n", + "print('proclam says '+str(proclam_auc)+' scikit-learn says '+str(from_skl))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "code", "execution_count": null, From d2246febac6d190c58667f205d622796d6143768 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Mon, 3 Jun 2019 17:58:53 -0400 Subject: [PATCH 10/58] working PRC AUC --- proclam/metrics/__init__.py | 1 + proclam/metrics/prc.py | 149 ++++++++++++++++++++---------------- proclam/metrics/util.py | 12 +-- 3 files changed, 88 insertions(+), 74 deletions(-) diff --git a/proclam/metrics/__init__.py b/proclam/metrics/__init__.py index b70a93e..e5070b5 100644 --- a/proclam/metrics/__init__.py +++ b/proclam/metrics/__init__.py @@ -5,3 +5,4 @@ from .brier import * from .logloss import * from .roc import * +from .prc import * diff --git a/proclam/metrics/prc.py b/proclam/metrics/prc.py index 9c52dc1..3ad54cb 100644 --- a/proclam/metrics/prc.py +++ b/proclam/metrics/prc.py @@ -1,75 +1,88 @@ """ -A superclass for metrics +A class for the Precision-Recall Curve """ from __future__ import absolute_import -__all__ = ['Metric'] +__all__ = ['PRC'] import numpy as np -from .util import weight_sum -from .util import check_weights -from .util import prob_to_det_threshold -from .util import auc, precision, recall -from scipy.integrate import trapz -from sklearn.metrics import precision_recall_curve - -class Metric(object): - - def __init__(self, scheme=None, **kwargs): - """ - An object that evaluates the F-score - - Parameters - ---------- - scheme: string - the name of the metric - """ - - self.debug = False - self.scheme = scheme - - def evaluate(self, prediction, truth, gridspace=0.01, weights=None, **kwds): - """ - Evaluates the area under the ROC curve for a given class_idx - - Parameters - ---------- - prediction: numpy.ndarray, float - predicted class probabilities - truth: numpy.ndarray, int - true classes - weights: numpy.ndarray, float - per-class weights - - Returns - ------- - metric: float - value of the metric - """ - - auc_allclass = 0 - n_class = np.shape(prediction)[1] - if not weights: - weights = [1./n_class]*n_class - - for class_idx in range(n_class): - if not len(np.where(truth == class_idx)[0]): - raise RuntimeError('No true values for class %i so ROC is undefined'%class_idx) - - truth_bool = np.zeros(len(truth),dtype=bool) - truth_bool[truth == class_idx] = 1 - P,R,thresholds_grid = precision_recall_curve(truth_bool,prediction[:,class_idx]) - - auc_class = auc(R,P) - - if self.debug: - import pylab as plt - plt.clf() - plt.plot(R,P) - plt.show() - import pdb; pdb.set_trace() - - auc_allclass += auc_class*weights[class_idx] - - return auc_allclass +from .util import weight_sum, check_weights +from .util import prob_to_det, det_to_cm, cm_to_rate +from .util import auc, check_auc_grid, prep_curve +from .metric import Metric + +class PRC(Metric): + + def __init__(self, scheme=None): + """ + An object that evaluates the PRC metric + + Parameters + ---------- + scheme: string + the name of the metric + """ + super(PRC, self).__init__(scheme) + self.scheme = scheme + + def evaluate(self, prediction, truth, grid, averaging='per_class', vb=False): + """ + Evaluates the area under the ROC for a given class_idx + + Parameters + ---------- + prediction: numpy.ndarray, float + predicted class probabilities + truth: numpy.ndarray, int + true classes + grid: numpy.ndarray, float or float or int + array of values between 0 and 1 at which to evaluate ROC + averaging: string or numpy.ndarray, float + 'per_class' weights classes equally, other keywords possible, vector assumed to be class weights + + Returns + ------- + auc_allclass: float + value of the metric + """ + thresholds_grid = check_auc_grid(grid) + n_thresholds = len(thresholds_grid) + + prediction, truth = np.asarray(prediction), np.asarray(truth) + (N, M) = np.shape(prediction) + + auc_class = np.empty(M) + curve = np.empty((M, 2, n_thresholds)) + + for m in range(M): + m_truth = (truth == m).astype(int) + + if not len(np.where(truth == m)[0]): + raise RuntimeError('No true values for class %i so PRC is undefined'%m) + + precision, recall = np.empty(n_thresholds), np.empty(n_thresholds) + for i, t in enumerate(thresholds_grid): + dets = prob_to_det(prediction, m, threshold=t) + cm = det_to_cm(dets, m_truth) + rates = cm_to_rate(cm) + recall[i] = rates.TP[-1] / (rates.TP[-1] + rates.FN[-1]) + precision[i] = self._mask_precision(rates.TP[-1] / (rates.TP[-1] + rates.FP[-1])) + + (curve[m][0], curve[m][1]) = (recall, precision) + auc_class[m] = auc(recall, precision) + if np.any(np.isnan(curve)): + print('Where did these NaNs come from?') + return (curve) + + weights = check_weights(averaging, M, truth=truth) + auc_allclass = weight_sum(auc_class, weights) + + if vb: return curve + else: return auc_allclass + + def _mask_precision(self, precision): + if np.isnan(precision): + return 0. + else: + return precision \ No newline at end of file diff --git a/proclam/metrics/util.py b/proclam/metrics/util.py index 021243b..1cd385c 100644 --- a/proclam/metrics/util.py +++ b/proclam/metrics/util.py @@ -16,7 +16,7 @@ import sys from scipy.integrate import trapz -RateMatrix = collections.namedtuple('rates', 'TPR FPR FNR TNR') +RateMatrix = collections.namedtuple('rates', 'TPR FPR FNR TNR TP FP FN TN') def sanitize_predictions(predictions, epsilon=1.e-8): """ @@ -110,6 +110,7 @@ def check_weights(avg_info, M, chosen=None, truth=None): weights[chosen] = 1./np.float(M) else: print('something has gone wrong with avg_info '+str(avg_info)) + weights = None return weights def averager(per_object_metrics, truth, M, vb=False): @@ -179,7 +180,7 @@ def cm_to_rate(cm, vb=False): TNR = TN / N # if vb: print('by request TPR, FPR, FNR, TNR'+str((TPR, FPR, FNR, TNR))) - rates = RateMatrix(TPR=TPR, FPR=FPR, FNR=FNR, TNR=TNR) + rates = RateMatrix(TPR=TPR, FPR=FPR, FNR=FNR, TNR=TNR, TP=TP, FN=FN, TN=TN, FP=FP) # if vb: print('by request TPR, FPR, FNR, TNR '+str(rates)) return rates @@ -204,8 +205,7 @@ def prep_curve(x, y): """ x = np.concatenate(([0.], x, [1.]),) y = np.concatenate(([0.], y, [1.]),) - i = np.argsort(x) - return (x[i], y[i]) + return (x, y) def auc(x, y): """ @@ -223,8 +223,8 @@ def auc(x, y): auc: float the area under the curve """ - (x, y) = prep_curve(x, y) - auc = trapz(y, x) + i = np.argsort(x) + auc = trapz(y[i], x[i]) return auc def check_auc_grid(grid): From d016bd533f712129206d55061a8b9ba80b608642 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 4 Jun 2019 13:57:24 -0400 Subject: [PATCH 11/58] cleaning up and enforcing consistency --- pipeline_sandbox.ipynb | 72 +++++++++++++++++++++++++---------------- proclam/metrics/prc.py | 22 +++++-------- proclam/metrics/roc.py | 3 +- proclam/metrics/util.py | 38 +++++++++++++--------- 4 files changed, 76 insertions(+), 59 deletions(-) diff --git a/pipeline_sandbox.ipynb b/pipeline_sandbox.ipynb index 0191598..d810805 100644 --- a/pipeline_sandbox.ipynb +++ b/pipeline_sandbox.ipynb @@ -438,7 +438,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**3) Cruise classifier CM:** \n", + "**3) Cruise control classifier CM:** \n", "\n", "This is where the confusion matrix has high values on the column of one specific class which means that the classifier constantly classifies all entries as one specific class" ] @@ -786,7 +786,7 @@ "outputs": [], "source": [ "compare = ConfusionMatrix(truth, detC)\n", - "print(proclam.metrics.util.RateMatrix(TPR=compare.TPR, FPR=compare.FPR, FNR=compare.FNR, TNR=compare.TNR))" + "print(proclam.metrics.util.RateMatrix(TPR=compare.TPR, FPR=compare.FPR, FNR=compare.FNR, TNR=compare.TNR, TP=compare.TP, FP=compare.FP, FN=compare.FN, TN=compare.TN))" ] }, { @@ -805,7 +805,7 @@ "outputs": [], "source": [ "ROC_metric = proclam.metrics.ROC()\n", - "rocB = ROC_metric.evaluate(predictionB,truth, 0.1)\n", + "rocB = ROC_metric.evaluate(predictionB,truth, 0.1, averaging='per_class', vb=False)\n", "rocC = ROC_metric.evaluate(predictionC,truth, 0.1)\n", "print('ROC AUC for prediction B = %.3f'%rocB)\n", "print('ROC AUC for prediction C = %.3f'%rocC)\n", @@ -826,27 +826,32 @@ "metadata": {}, "outputs": [], "source": [ - "truth_score = proclam.metrics.util.det_to_prob(truth)\n", - "from_proclam, proclam_auc, from_skl = [],[],[]\n", - "for m in range(M_classes):\n", - " fpr, tpr, thresholds = skl.metrics.roc_curve(truth_score.T[m].T.astype(int), predictionB.T[m].T)\n", - " proclam_says = ROC_metric.evaluate(predictionB, truth, thresholds, vb=True)[m]\n", - " proclam_auc.append(ROC_metric.evaluate(predictionB, truth, thresholds, vb=False)[m])\n", - " i = np.argsort(proclam_says[0])\n", - " new_auc = proclam.metrics.util.auc(proclam_says[0][i], proclam_says[1][i])\n", - " print('proclam: '+str(new_auc))\n", - " from_proclam.append(proclam.metrics.util.auc(proclam_says[0], proclam_says[1]))\n", - " skl_says = proclam.metrics.util.auc(fpr, tpr)\n", - " print('skl: '+str(skl_says))\n", - " from_skl.append(skl_says)\n", - "print('proclam says %.3f'%np.mean(proclam_auc)+' scikit-learn says %.3f'%np.mean(from_skl))\n", - "print('proclam says '+str(proclam_auc)+' scikit-learn says '+str(from_skl))" + "# this runs if you change line 78 in roc.py to return auc_class instead of auc_allclass\n", + "# truth_score = proclam.metrics.util.det_to_prob(truth)\n", + "# from_proclam, proclam_auc, from_skl = [],[],[]\n", + "# for m in range(M_classes):\n", + "# fpr, tpr, thresholds = skl.metrics.roc_curve(truth_score.T[m].T.astype(int), predictionB.T[m].T)\n", + "# proclam_says = ROC_metric.evaluate(predictionB, truth, thresholds, vb=True)[m]\n", + "# proclam_auc.append(ROC_metric.evaluate(predictionB, truth, thresholds, vb=False)[m])\n", + "# i = np.argsort(proclam_says[0])\n", + "# new_auc = proclam.metrics.util.auc(proclam_says[0][i], proclam_says[1][i])\n", + "# print('proclam: '+str(new_auc))\n", + "# from_proclam.append(proclam.metrics.util.auc(proclam_says[0], proclam_says[1]))\n", + "# skl_says = proclam.metrics.util.auc(fpr, tpr)\n", + "# print('skl: '+str(skl_says))\n", + "# from_skl.append(skl_says)\n", + "# print('proclam says %.3f'%np.mean(proclam_auc)+' scikit-learn says %.3f'%np.mean(from_skl))\n", + "# print('proclam says '+str(proclam_auc)+' scikit-learn says '+str(from_skl))" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "### PRC AUC\n", + "\n", + "The precision is the number of correctly classified positives divided by the number all items classified as positive, whereas the recall is the number of correctly classified positives divided by the number of items whose true class was positive. " + ] }, { "cell_type": "code", @@ -854,20 +859,31 @@ "metadata": {}, "outputs": [], "source": [ - "from proclam.metrics import precision_recall\n", - "reload(proclam.metrics.precision_recall)\n", - "reload(proclam.metrics.util)\n", - "from proclam.metrics import precision_recall\n", - "from proclam.metrics import util\n", - "\n", - "metric = precision_recall.Metric()\n", - "prB = metric.evaluate(predictionB,truth)\n", - "prC = metric.evaluate(predictionC,truth)\n", + "metric = proclam.metrics.PRC()\n", + "prB = metric.evaluate(predictionB,truth, 0.01)\n", + "prC = metric.evaluate(predictionC,truth, 0.01)\n", "\n", "print('Precision/Recall AUC for prediction B = %.3f'%prB)\n", "print('Precision/Recall AUC for prediction C = %.3f'%prC)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# compare = ConfusionMatrix(truth, proclam.metrics.util.prob_to_det(predictionC))\n", + "# print((compare.TPR, compare.PPV))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### F-score" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/proclam/metrics/prc.py b/proclam/metrics/prc.py index 3ad54cb..829e976 100644 --- a/proclam/metrics/prc.py +++ b/proclam/metrics/prc.py @@ -9,7 +9,7 @@ from .util import weight_sum, check_weights from .util import prob_to_det, det_to_cm, cm_to_rate -from .util import auc, check_auc_grid, prep_curve +from .util import auc, check_auc_grid, precision from .metric import Metric class PRC(Metric): @@ -61,28 +61,22 @@ def evaluate(self, prediction, truth, grid, averaging='per_class', vb=False): if not len(np.where(truth == m)[0]): raise RuntimeError('No true values for class %i so PRC is undefined'%m) - precision, recall = np.empty(n_thresholds), np.empty(n_thresholds) + precisions, recalls = np.empty(n_thresholds), np.empty(n_thresholds) for i, t in enumerate(thresholds_grid): dets = prob_to_det(prediction, m, threshold=t) cm = det_to_cm(dets, m_truth) rates = cm_to_rate(cm) - recall[i] = rates.TP[-1] / (rates.TP[-1] + rates.FN[-1]) - precision[i] = self._mask_precision(rates.TP[-1] / (rates.TP[-1] + rates.FP[-1])) + recalls[i] = rates.TPR[-1] + precisions[i] = precision(rates.TP[-1], rates.FP[-1]) - (curve[m][0], curve[m][1]) = (recall, precision) - auc_class[m] = auc(recall, precision) + (curve[m][0], curve[m][1]) = (recalls, precisions) + auc_class[m] = auc(recalls, precisions) if np.any(np.isnan(curve)): print('Where did these NaNs come from?') - return (curve) + return curve weights = check_weights(averaging, M, truth=truth) auc_allclass = weight_sum(auc_class, weights) if vb: return curve - else: return auc_allclass - - def _mask_precision(self, precision): - if np.isnan(precision): - return 0. - else: - return precision \ No newline at end of file + else: return auc_allclass \ No newline at end of file diff --git a/proclam/metrics/roc.py b/proclam/metrics/roc.py index c083226..c95a48f 100644 --- a/proclam/metrics/roc.py +++ b/proclam/metrics/roc.py @@ -69,10 +69,11 @@ def evaluate(self, prediction, truth, grid, averaging='per_class', vb=False): fpr[i], tpr[i] = rates.FPR[-1], rates.TPR[-1] (curve[m][0], curve[m][1]) = (fpr, tpr) + (fpr, tpr) = prep_curve(fpr, tpr) auc_class[m] = auc(fpr, tpr) weights = check_weights(averaging, M, truth=truth) auc_allclass = weight_sum(auc_class, weights) if vb: return curve - else: return auc_class + else: return auc_allclass diff --git a/proclam/metrics/util.py b/proclam/metrics/util.py index 1cd385c..0a0b8ba 100644 --- a/proclam/metrics/util.py +++ b/proclam/metrics/util.py @@ -5,7 +5,7 @@ from __future__ import absolute_import, division __all__ = ['sanitize_predictions', 'weight_sum', 'check_weights', 'averager', - 'cm_to_rate', + 'cm_to_rate', 'precision', 'auc', 'check_auc_grid', 'prep_curve', 'det_to_prob', 'prob_to_det', 'det_to_cm'] @@ -436,21 +436,27 @@ def det_to_cm(dets, truth, per_class_norm=False, vb=False): # """ # return 1. - rates.FNR -# def precision(rates): -# """ -# Calculates precision from rates -# -# Parameters -# ---------- -# rates: namedtuple -# named tuple of 'TPR FPR FNR TNR' -# -# Returns -# ------- -# precision: float -# precision -# """ -# return 1. - rates.FNR +def precision(TP, FP): + """ + Calculates precision from rates + + Parameters + ---------- + TP: float + number of true positives + FP: float + number of false positives + + Returns + ------- + p: float + precision + """ + p = TP / (TP + FP) + if np.isnan(p): + return 0. + else: + return p # # def recall(classifications,truth,class_idx): # From 595b1f2068e88de1e7859ad1d374f066236e7b82 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 4 Jun 2019 14:13:24 -0400 Subject: [PATCH 12/58] working F1 score --- proclam/metrics/__init__.py | 1 + proclam/metrics/f1.py | 64 +++++++++++++++++++++++++++++++++++++ proclam/metrics/fscore.py | 55 ------------------------------- proclam/metrics/util.py | 9 +++--- 4 files changed, 69 insertions(+), 60 deletions(-) create mode 100644 proclam/metrics/f1.py delete mode 100644 proclam/metrics/fscore.py diff --git a/proclam/metrics/__init__.py b/proclam/metrics/__init__.py index e5070b5..e27bc98 100644 --- a/proclam/metrics/__init__.py +++ b/proclam/metrics/__init__.py @@ -6,3 +6,4 @@ from .logloss import * from .roc import * from .prc import * +from .f1 import * diff --git a/proclam/metrics/f1.py b/proclam/metrics/f1.py new file mode 100644 index 0000000..1e621ee --- /dev/null +++ b/proclam/metrics/f1.py @@ -0,0 +1,64 @@ +""" +A class for the F1 score +""" + +from __future__ import absolute_import +__all__ = ['F1'] + +import numpy as np + +from .util import weight_sum, check_weights +from .util import prob_to_det, det_to_cm, cm_to_rate +from .util import precision +from .metric import Metric + +class F1(Metric): + + def __init__(self, scheme=None): + """ + An object that evaluates the PRC metric + + Parameters + ---------- + scheme: string + the name of the metric + """ + super(F1, self).__init__(scheme) + self.scheme = scheme + + def evaluate(self, prediction, truth, averaging='per_class'): + """ + Evaluates the area under the ROC for a given class_idx + + Parameters + ---------- + prediction: numpy.ndarray, float + predicted class probabilities + truth: numpy.ndarray, int + true classes + averaging: string or numpy.ndarray, float + 'per_class' weights classes equally, other keywords possible, vector assumed to be class weights + + Returns + ------- + f1: float + value of the metric + """ + prediction, truth = np.asarray(prediction), np.asarray(truth) + (N, M) = np.shape(prediction) + + f1 = np.empty(M) + for m in range(M): + if not len(np.where(truth == m)[0]): + raise RuntimeError('No true values for class %i so F1 is undefined'%m) + dets = prob_to_det(prediction) + cm = det_to_cm(dets, truth) + rates = cm_to_rate(cm) + r = rates.TPR + p = precision(rates.TP, rates.FP) + f1 = 2 * p * r / (p + r) + + weights = check_weights(averaging, M, truth=truth) + f1_all = weight_sum(f1, weights) + + return f1_all \ No newline at end of file diff --git a/proclam/metrics/fscore.py b/proclam/metrics/fscore.py deleted file mode 100644 index d635e0b..0000000 --- a/proclam/metrics/fscore.py +++ /dev/null @@ -1,55 +0,0 @@ -""" -A superclass for metrics -""" - -from __future__ import absolute_import -__all__ = ['Metric'] - -import numpy as np - -from .util import weight_sum -from .util import check_weights -from .util import prob_to_det_threshold -from .util import auc, precision, recall -from scipy.integrate import trapz -from sklearn.metrics import precision_recall_curve -from sklearn.metrics import f1_score - -class Metric(object): - - def __init__(self, scheme=None, **kwargs): - """ - An object that evaluates the F-score - - Parameters - ---------- - scheme: string - the name of the metric - """ - - self.debug = False - self.scheme = scheme - - def evaluate(self, prediction, truth, **kwds): - """ - Evaluates the area under the ROC curve for a given class_idx - - Parameters - ---------- - prediction: numpy.ndarray, float - predicted class probabilities - truth: numpy.ndarray, int - true classes - - Returns - ------- - metric: float - value of the metric - """ - - best_class = np.zeros(len(truth)) - for i in range(len(truth)): - best_class[i] = np.where(prediction[i,:] == np.max(prediction[i,:]))[0] - fscore = f1_score(truth,best_class,average='macro') - - return fscore diff --git a/proclam/metrics/util.py b/proclam/metrics/util.py index 0a0b8ba..98c4671 100644 --- a/proclam/metrics/util.py +++ b/proclam/metrics/util.py @@ -452,11 +452,10 @@ def precision(TP, FP): p: float precision """ - p = TP / (TP + FP) - if np.isnan(p): - return 0. - else: - return p + p = np.asarray(TP / (TP + FP)) + if np.any(np.isnan(p)): + p[np.isnan(p)] = 0. + return p # # def recall(classifications,truth,class_idx): # From 30c91b745e76fcd2e137b920473136ef6c2aa804 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 4 Jun 2019 14:15:17 -0400 Subject: [PATCH 13/58] working F1 score --- pipeline_sandbox.ipynb | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/pipeline_sandbox.ipynb b/pipeline_sandbox.ipynb index d810805..b070cc2 100644 --- a/pipeline_sandbox.ipynb +++ b/pipeline_sandbox.ipynb @@ -890,11 +890,7 @@ "metadata": {}, "outputs": [], "source": [ - "from proclam.metrics import fscore\n", - "reload(proclam.metrics.fscore)\n", - "from proclam.metrics import fscore\n", - "\n", - "metric = fscore.Metric()\n", + "metric = proclam.metrics.F1()\n", "fB = metric.evaluate(predictionB,truth)\n", "fC = metric.evaluate(predictionC,truth)\n", "\n", @@ -902,6 +898,16 @@ "print('f1 score for prediction C = %.3f'%fC)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# compare = ConfusionMatrix(truth, proclam.metrics.util.prob_to_det(predictionB))\n", + "# print((compare.F1))" + ] + }, { "cell_type": "markdown", "metadata": {}, From f2bb4bd03f34dea12f99599cd4f13440607b65d1 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 4 Jun 2019 14:34:06 -0400 Subject: [PATCH 14/58] added Matthews correlation coefficient and fixed typos in docstrings --- proclam/metrics/__init__.py | 1 + proclam/metrics/f1.py | 5 ++- proclam/metrics/mcc.py | 68 +++++++++++++++++++++++++++++++++++++ proclam/metrics/prc.py | 4 +-- proclam/metrics/roc.py | 2 +- 5 files changed, 74 insertions(+), 6 deletions(-) create mode 100644 proclam/metrics/mcc.py diff --git a/proclam/metrics/__init__.py b/proclam/metrics/__init__.py index e27bc98..7d43866 100644 --- a/proclam/metrics/__init__.py +++ b/proclam/metrics/__init__.py @@ -7,3 +7,4 @@ from .roc import * from .prc import * from .f1 import * +from .mcc import * \ No newline at end of file diff --git a/proclam/metrics/f1.py b/proclam/metrics/f1.py index 1e621ee..4822c9f 100644 --- a/proclam/metrics/f1.py +++ b/proclam/metrics/f1.py @@ -16,7 +16,7 @@ class F1(Metric): def __init__(self, scheme=None): """ - An object that evaluates the PRC metric + An object that evaluates the F1 score Parameters ---------- @@ -28,7 +28,7 @@ def __init__(self, scheme=None): def evaluate(self, prediction, truth, averaging='per_class'): """ - Evaluates the area under the ROC for a given class_idx + Evaluates the F1 score Parameters ---------- @@ -47,7 +47,6 @@ def evaluate(self, prediction, truth, averaging='per_class'): prediction, truth = np.asarray(prediction), np.asarray(truth) (N, M) = np.shape(prediction) - f1 = np.empty(M) for m in range(M): if not len(np.where(truth == m)[0]): raise RuntimeError('No true values for class %i so F1 is undefined'%m) diff --git a/proclam/metrics/mcc.py b/proclam/metrics/mcc.py new file mode 100644 index 0000000..7cf28b7 --- /dev/null +++ b/proclam/metrics/mcc.py @@ -0,0 +1,68 @@ +""" +A class for the Matthews correlation coefficient +""" + +from __future__ import absolute_import +__all__ = ['MCC'] + +import numpy as np + +from .util import weight_sum, check_weights +from .util import prob_to_det, det_to_cm, cm_to_rate +from .util import precision +from .metric import Metric + +class MCC(Metric): + + def __init__(self, scheme=None): + """ + An object that evaluates the Matthews correlation coefficient + + Parameters + ---------- + scheme: string + the name of the metric + """ + super(MCC, self).__init__(scheme) + self.scheme = scheme + + def evaluate(self, prediction, truth, averaging='per_class'): + """ + Evaluates the Matthews correlation coefficient + + Parameters + ---------- + prediction: numpy.ndarray, float + predicted class probabilities + truth: numpy.ndarray, int + true classes + averaging: string or numpy.ndarray, float + 'per_class' weights classes equally, other keywords possible, vector assumed to be class weights + + Returns + ------- + f1: float + value of the metric + """ + prediction, truth = np.asarray(prediction), np.asarray(truth) + (N, M) = np.shape(prediction) + + dets = prob_to_det(prediction) + cm = det_to_cm(dets, truth) + rates = cm_to_rate(cm) + + mcc = np.empty(M) + for m in range(M): + if not len(np.where(truth == m)[0]): + raise RuntimeError('No true values for class %i so MCC is undefined'%m) + num = rates.TP[m] * rates.TN[m] - rates.FP[m] * rates.FN[m] + A = rates.TP[m] + rates.FP[m] + B = rates.TP[m] + rates.FN[m] + C = rates.TN[m] + rates.FP[m] + D = rates.TN[m] + rates.FN[m] + mcc[m] = num / np.sqrt(A * B * C * D) + + weights = check_weights(averaging, M, truth=truth) + mcc_all = weight_sum(mcc, weights) + + return mcc_all \ No newline at end of file diff --git a/proclam/metrics/prc.py b/proclam/metrics/prc.py index 829e976..5b4f1de 100644 --- a/proclam/metrics/prc.py +++ b/proclam/metrics/prc.py @@ -16,7 +16,7 @@ class PRC(Metric): def __init__(self, scheme=None): """ - An object that evaluates the PRC metric + An object that evaluates the PRC AUC Parameters ---------- @@ -28,7 +28,7 @@ def __init__(self, scheme=None): def evaluate(self, prediction, truth, grid, averaging='per_class', vb=False): """ - Evaluates the area under the ROC for a given class_idx + Evaluates the area under the PRC Parameters ---------- diff --git a/proclam/metrics/roc.py b/proclam/metrics/roc.py index c95a48f..bd24111 100644 --- a/proclam/metrics/roc.py +++ b/proclam/metrics/roc.py @@ -28,7 +28,7 @@ def __init__(self, scheme=None): def evaluate(self, prediction, truth, grid, averaging='per_class', vb=False): """ - Evaluates the area under the ROC for a given class_idx + Evaluates the ROC AUC Parameters ---------- From 76af326e164ce0913ed1aa38a27f950116b69004 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 4 Jun 2019 14:34:35 -0400 Subject: [PATCH 15/58] added Matthews correlation coefficient and fixed typos in docstrings --- pipeline_sandbox.ipynb | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/pipeline_sandbox.ipynb b/pipeline_sandbox.ipynb index b070cc2..6bb8f7a 100644 --- a/pipeline_sandbox.ipynb +++ b/pipeline_sandbox.ipynb @@ -908,6 +908,37 @@ "# print((compare.F1))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Matthews Correlation Coefficient" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metric = proclam.metrics.MCC()\n", + "mccB = metric.evaluate(predictionB, truth)\n", + "mccC = metric.evaluate(predictionC,truth)\n", + "\n", + "print('MCC for prediction B = %.3f'%mccB)\n", + "print('MCC for prediction C = %.3f'%mccC)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# compare = ConfusionMatrix(truth, proclam.metrics.util.prob_to_det(predictionC))\n", + "# print((compare.MCC))" + ] + }, { "cell_type": "markdown", "metadata": {}, From d3f848176fadea504e7a83f973e7865aa17c6f10 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 4 Jun 2019 14:47:32 -0400 Subject: [PATCH 16/58] added another metric, fixed sloppy typos --- pipeline_sandbox.ipynb | 566 ++++++++++++++++++++++++++++++------ proclam/metrics/__init__.py | 3 +- proclam/metrics/accuracy.py | 57 ++++ proclam/metrics/f1.py | 2 +- proclam/metrics/mcc.py | 3 +- 5 files changed, 544 insertions(+), 87 deletions(-) create mode 100644 proclam/metrics/accuracy.py diff --git a/pipeline_sandbox.ipynb b/pipeline_sandbox.ipynb index 6bb8f7a..f85e160 100644 --- a/pipeline_sandbox.ipynb +++ b/pipeline_sandbox.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -110,9 +110,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'class')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "d = np.diff(np.unique(truth)).min()\n", "left_of_first_bin = truth.min() - float(d)/2\n", @@ -127,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -158,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -178,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -197,9 +220,43 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.77459445 0.11209536 0.06052605 0.00362192 0.07104932]\n", + " [0.11003839 0.80009249 0.01965666 0.02884268 0.01506941]\n", + " [0.09383029 0.07687496 0.65189991 0.07650355 0.12909478]\n", + " [0.12409745 0.0805176 0.00174395 0.75464134 0.06014014]\n", + " [0.06169615 0.08333079 0.04914953 0.02018145 0.77150318]]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'true class')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "cm = np.eye(M_classes) + 0.2 * np.random.uniform(size=(M_classes, M_classes))\n", "cm /= np.sum(cm, axis=1)\n", @@ -221,9 +278,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.11284647 0.80101817 0.03185446 0.03936846 0.01491243]\n", + " [0.09973749 0.07725771 0.60755048 0.08775271 0.12770162]\n", + " [0.08582221 0.08195349 0.05213019 0.02797892 0.75211519]\n", + " ...\n", + " [0.07873401 0.08532457 0.05993838 0.03413578 0.74186726]\n", + " [0.05587737 0.46613713 0.36092296 0.04309275 0.0739698 ]\n", + " [0.09950859 0.07750803 0.61382232 0.07556721 0.13359384]]\n" + ] + } + ], "source": [ "C = proclam.classifiers.from_cm.FromCM(seed=None)\n", "predictionC = C.classify(cm, truth, other=False)\n", @@ -240,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -284,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -295,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -346,9 +417,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.15842418 0.18857113 0.16013153 0.17989965 0.01515237]\n", + " [0.17176994 0.13661988 0.13941695 0.09105078 0.10789923]\n", + " [0.17019976 0.06886277 0.77397623 0.01511753 0.06713813]\n", + " [0.04351717 0.12580389 0.14106496 0.06285216 0.11175521]\n", + " [0.06891721 0.0174588 0.12293988 0.07999228 0.12880625]]\n" + ] + } + ], "source": [ "# N = 3 #len(truth)\n", "# M = len(cm)\n", @@ -374,9 +457,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.matshow(CMtunnel)\n", "plt.xticks(range(max(truth)+1), names)\n", @@ -397,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -422,9 +528,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.matshow(CMbroadbrush)\n", "plt.xticks(range(max(truth)+1), names)\n", @@ -445,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -470,9 +599,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.matshow(CMcruise)\n", "plt.xticks(range(max(truth)+1), names)\n", @@ -493,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -528,9 +680,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 3\n" + ] + } + ], "source": [ "CM = np.zeros((M_classes, M_classes))\n", "class_M = 2 #np.random.randint(0., M_classes, size=1)[0]\n", @@ -564,9 +724,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.matshow(CMsubsumedto2)\n", "plt.xticks(range(max(truth)+1), names)\n", @@ -578,9 +761,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.matshow(CMsubsumedfrom2)\n", "plt.xticks(range(max(truth)+1), names)\n", @@ -606,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -631,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -641,9 +847,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "proclam implementation of log-loss: 1.9467692362663755\n", + "proclam implementation of log-loss: 0.5873053661481069\n", + "proclam implementation of log-loss: 0.4985535342418646\n", + "proclam implementation of log-loss: 3.2716260697307087\n", + "proclam implementation of log-loss: 0.8391912085925096\n", + "proclam implementation of log-loss: 0.5795189847083857\n", + "proclam implementation of log-loss: 4.44547996411847\n" + ] + } + ], "source": [ "for candidate in [predictionB, predictionC, predictionG, predictionH, predictionI, predictionJ, predictionJ1]:\n", " D = proclam.metrics.LogLoss()\n", @@ -660,9 +880,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tunnel logloss = 1.3831099142162573\n", + "broadbrush logloss = 2.147514136377367\n", + "cruise logloss = 1.8925640255539373\n", + "subsumedto2 logloss = 0.8697035708801626\n", + "subsumedfrom logloss = 1.2313487087147357\n" + ] + } + ], "source": [ "test_cases = {'tunnel': CMtunnel, 'broadbrush': CMbroadbrush, 'cruise': CMcruise, 'subsumedto2': CMsubsumedto2, 'subsumedfrom': CMsubsumedfrom2}\n", "LL_metric = proclam.metrics.LogLoss()\n", @@ -675,9 +907,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tunnel logloss = 0.45996840473970635\n", + "broadbrush logloss = 3.3173356388856186\n", + "cruise logloss = 0.7884312373010852\n", + "subsumedto logloss = 0.5772789861655064\n", + "subsumedfrom logloss = 4.401321122622816\n" + ] + } + ], "source": [ "test_cases = {'tunnel': CMtunnel, 'broadbrush': CMbroadbrush, 'cruise': CMcruise, 'subsumedto': CMsubsumedto2,'subsumedfrom': CMsubsumedfrom2}\n", "LL_metric = proclam.metrics.LogLoss()\n", @@ -699,11 +943,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "proclam implementation of Brier score: 0.17481523141779132\n", + "proclam implementation of Brier score: 0.03349662221027182\n", + "proclam implementation of Brier score: 0.13994060524323776\n", + "proclam implementation of Brier score: 0.16442263732364013\n", + "proclam implementation of Brier score: 0.19760600980918508\n", + "proclam implementation of Brier score: 0.08131350128672857\n", + "proclam implementation of Brier score: 0.08047203028278058\n" + ] + } + ], "source": [ "for candidate in [predictionB, predictionC, predictionG, predictionH, predictionI, predictionJ, predictionJ1]:\n", " E = proclam.metrics.Brier()\n", @@ -722,9 +980,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tunnel Brier = 0.13835879922919994\n", + "broadbrush Brier = 0.16647095421593264\n", + "cruise Brier = 0.19722890971661755\n", + "subsumedto Brier = 0.07719957610858942\n", + "subsumedfrom Brier = 0.0838596231599401\n" + ] + } + ], "source": [ "test_cases = {'tunnel': CMtunnel, 'broadbrush': CMbroadbrush, 'cruise': CMcruise, 'subsumedto': CMsubsumedto2, 'subsumedfrom': CMsubsumedfrom2}\n", "B_metric = proclam.metrics.Brier()\n", @@ -737,9 +1007,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tunnel Brier = 0.03829109430194667\n", + "broadbrush Brier = 0.2519580848801754\n", + "cruise Brier = 0.07399778110647401\n", + "subsumedto Brier = 0.0487604318197624\n", + "subsumedfrom Brier = 0.28825253455457156\n" + ] + } + ], "source": [ "test_cases = {'tunnel': CMtunnel, 'broadbrush': CMbroadbrush, 'cruise': CMcruise, 'subsumedto': CMsubsumedto2, 'subsumedfrom': CMsubsumedfrom2}\n", "B_metric = proclam.metrics.Brier()\n", @@ -770,9 +1052,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rates(TPR=array([0.98113208, 1. , 0.94117647, 0.95548961, 0.96296296]), FPR=array([0.00527983, 0.00740741, 0.01256281, 0.01357466, 0.01540832]), FNR=array([0.01886792, 0. , 0.05882353, 0.04451039, 0.03703704]), TNR=array([0.99472017, 0.99259259, 0.98743719, 0.98642534, 0.98459168]), TP=array([ 52., 55., 192., 322., 338.]), FP=array([ 5., 7., 10., 9., 10.]), FN=array([ 1., 0., 12., 15., 13.]), TN=array([942., 938., 786., 654., 639.]))\n" + ] + } + ], "source": [ "detC = proclam.metrics.util.prob_to_det(predictionC)\n", "cmC = proclam.metrics.util.det_to_cm(detC, truth)\n", @@ -781,9 +1071,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rates(TPR={0: 0.9811320754716981, 1: 1.0, 2: 0.9411764705882353, 3: 0.9554896142433235, 4: 0.9629629629629629}, FPR={0: 0.0052798310454065245, 1: 0.007407407407407418, 2: 0.012562814070351758, 3: 0.013574660633484115, 4: 0.015408320493066285}, FNR={0: 0.018867924528301883, 1: 0.0, 2: 0.05882352941176472, 3: 0.04451038575667654, 4: 0.03703703703703709}, TNR={0: 0.9947201689545935, 1: 0.9925925925925926, 2: 0.9874371859296482, 3: 0.9864253393665159, 4: 0.9845916795069337}, TP={0: 52, 1: 55, 2: 192, 3: 322, 4: 338}, FP={0: 5, 1: 7, 2: 10, 3: 9, 4: 10}, FN={0: 1, 1: 0, 2: 12, 3: 15, 4: 13}, TN={0: 942, 1: 938, 2: 786, 3: 654, 4: 639})\n" + ] + } + ], "source": [ "compare = ConfusionMatrix(truth, detC)\n", "print(proclam.metrics.util.RateMatrix(TPR=compare.TPR, FPR=compare.FPR, FNR=compare.FNR, TNR=compare.TNR, TP=compare.TP, FP=compare.FP, FN=compare.FN, TN=compare.TN))" @@ -800,9 +1098,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ROC AUC for prediction B = 0.493\n", + "ROC AUC for prediction C = 0.995\n", + "ROC AUC for prediction B with weird weights = 0.498\n" + ] + } + ], "source": [ "ROC_metric = proclam.metrics.ROC()\n", "rocB = ROC_metric.evaluate(predictionB,truth, 0.1, averaging='per_class', vb=False)\n", @@ -822,7 +1130,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -855,9 +1163,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/aimalz/Code/proclam/proclam/metrics/util.py:455: RuntimeWarning: invalid value encountered in double_scalars\n", + " p = np.asarray(TP / (TP + FP))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Precision/Recall AUC for prediction B = 0.203\n", + "Precision/Recall AUC for prediction C = 0.937\n" + ] + } + ], "source": [ "metric = proclam.metrics.PRC()\n", "prB = metric.evaluate(predictionB,truth, 0.01)\n", @@ -869,7 +1194,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -886,9 +1211,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [], + "source": [ + "prC = metric.evaluate(predictionC,truth, 0.01, vb=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "f1 score for prediction B = 0.184\n", + "f1 score for prediction C = 0.953\n" + ] + } + ], "source": [ "metric = proclam.metrics.F1()\n", "fB = metric.evaluate(predictionB,truth)\n", @@ -900,7 +1243,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -917,9 +1260,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MCC for prediction B = 0.009\n", + "MCC for prediction C = 0.942\n" + ] + } + ], "source": [ "metric = proclam.metrics.MCC()\n", "mccB = metric.evaluate(predictionB, truth)\n", @@ -931,7 +1283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -939,6 +1291,54 @@ "# print((compare.MCC))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for prediction B = 0.207\n", + "Accuracy for prediction C = 0.968\n" + ] + } + ], + "source": [ + "metric = proclam.metrics.Accuracy()\n", + "accB = metric.evaluate(predictionB, truth)\n", + "accC = metric.evaluate(predictionC,truth)\n", + "\n", + "print('Accuracy for prediction B = %.3f'%accB)\n", + "print('Accuracy for prediction C = %.3f'%accC)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0: 0.785, 1: 0.769, 2: 0.675, 3: 0.601, 4: 0.596}\n" + ] + } + ], + "source": [ + "# compare = ConfusionMatrix(truth, proclam.metrics.util.prob_to_det(predictionB))\n", + "# print((compare.ACC))" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/proclam/metrics/__init__.py b/proclam/metrics/__init__.py index 7d43866..97292d2 100644 --- a/proclam/metrics/__init__.py +++ b/proclam/metrics/__init__.py @@ -7,4 +7,5 @@ from .roc import * from .prc import * from .f1 import * -from .mcc import * \ No newline at end of file +from .mcc import * +from .accuracy import * \ No newline at end of file diff --git a/proclam/metrics/accuracy.py b/proclam/metrics/accuracy.py new file mode 100644 index 0000000..00f94c9 --- /dev/null +++ b/proclam/metrics/accuracy.py @@ -0,0 +1,57 @@ +""" +A class for accuracy +""" + +from __future__ import absolute_import +__all__ = ['Accuracy'] + +import numpy as np + +from .util import weight_sum, check_weights +from .util import prob_to_det, det_to_cm, cm_to_rate +from .metric import Metric + +class Accuracy(Metric): + + def __init__(self, scheme=None): + """ + An object that evaluates the accuracy + + Parameters + ---------- + scheme: string + the name of the metric + """ + super(Accuracy, self).__init__(scheme) + self.scheme = scheme + + def evaluate(self, prediction, truth, averaging='per_class'): + """ + Evaluates the accuracy + + Parameters + ---------- + prediction: numpy.ndarray, float + predicted class probabilities + truth: numpy.ndarray, int + true classes + averaging: string or numpy.ndarray, float + 'per_class' weights classes equally, other keywords possible, vector assumed to be class weights + + Returns + ------- + accuracy_all: float + value of the metric + """ + prediction, truth = np.asarray(prediction), np.asarray(truth) + (N, M) = np.shape(prediction) + + dets = prob_to_det(prediction) + cm = det_to_cm(dets, truth) + rates = cm_to_rate(cm) + accuracy = rates.TPR + + weights = check_weights(averaging, M, truth=truth) + accuracy_all = weight_sum(accuracy, weights) + + return accuracy_all \ No newline at end of file diff --git a/proclam/metrics/f1.py b/proclam/metrics/f1.py index 4822c9f..90c204a 100644 --- a/proclam/metrics/f1.py +++ b/proclam/metrics/f1.py @@ -41,7 +41,7 @@ def evaluate(self, prediction, truth, averaging='per_class'): Returns ------- - f1: float + f1_all: float value of the metric """ prediction, truth = np.asarray(prediction), np.asarray(truth) diff --git a/proclam/metrics/mcc.py b/proclam/metrics/mcc.py index 7cf28b7..0a926a6 100644 --- a/proclam/metrics/mcc.py +++ b/proclam/metrics/mcc.py @@ -9,7 +9,6 @@ from .util import weight_sum, check_weights from .util import prob_to_det, det_to_cm, cm_to_rate -from .util import precision from .metric import Metric class MCC(Metric): @@ -41,7 +40,7 @@ def evaluate(self, prediction, truth, averaging='per_class'): Returns ------- - f1: float + mcc_all: float value of the metric """ prediction, truth = np.asarray(prediction), np.asarray(truth) From 6a22e5683e65ff791ffc5e0cc657eab2d0d268cf Mon Sep 17 00:00:00 2001 From: Kara Ponder Date: Fri, 19 Jul 2019 21:35:00 +0200 Subject: [PATCH 17/58] Notebook to calculate plots for Table 3 --- Plot_Table3.ipynb | 220 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 220 insertions(+) create mode 100644 Plot_Table3.ipynb diff --git a/Plot_Table3.ipynb b/Plot_Table3.ipynb new file mode 100644 index 0000000..f9c9fef --- /dev/null +++ b/Plot_Table3.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import pickle\n", + "\n", + "import matplotlib as mpl\n", + "# print(mpl.rcParams.items)\n", + "mpl.use('Agg')\n", + "mpl.rcParams['text.usetex'] = False\n", + "mpl.rcParams['mathtext.rm'] = 'serif'\n", + "mpl.rcParams['font.family'] = 'serif'\n", + "mpl.rcParams['font.serif'] = ['Times New Roman']\n", + "# mpl.rcParams['font.family'] = ['Times New Roman']\n", + "mpl.rcParams['axes.titlesize'] = 25\n", + "mpl.rcParams['axes.labelsize'] = 20\n", + "mpl.rcParams['xtick.labelsize'] = 15\n", + "mpl.rcParams['ytick.labelsize'] = 15\n", + "mpl.rcParams['savefig.dpi'] = 250\n", + "mpl.rcParams['figure.dpi'] = 250\n", + "mpl.rcParams['savefig.format'] = 'pdf'\n", + "mpl.rcParams['savefig.bbox'] = 'tight'\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):\n", + " new_cmap = mpl.colors.LinearSegmentedColormap.from_list(\n", + " 'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),\n", + " cmap(np.linspace(minval, maxval, n)))\n", + " return new_cmap\n", + "\n", + "cmap = plt.get_cmap('hot_r')\n", + "fave_cmap = truncate_colormap(cmap, 0.35, 1.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metric_dictionary = {'TBDT':{'FoM': 1, 'LogLoss': 1, 'Brier': 1},\n", + " 'TKNN':{'FoM': 7, 'LogLoss': 6, 'Brier': 7},\n", + " 'TNB':{'FoM': 8, 'LogLoss': 9, 'Brier': 8},\n", + " 'TNN':{'FoM': 5, 'LogLoss': 3, 'Brier': 3},\n", + " 'TSVM':{'FoM': 3, 'LogLoss': 2, 'Brier': 2},\n", + " 'WBDT':{'FoM': 2, 'LogLoss': 5, 'Brier': 4},\n", + " 'WKNN':{'FoM': 9, 'LogLoss': 8, 'Brier': 9},\n", + " 'WNB':{'FoM': 10, 'LogLoss': 10, 'Brier': 10},\n", + " 'WNN':{'FoM': 6, 'LogLoss': 7, 'Brier': 6},\n", + " 'WSVM':{'FoM': 4, 'LogLoss': 4, 'Brier': 5},\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metric_dictionary['TBDT']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "symbols = {'TBDT':'o',\n", + " 'TKNN':'d',\n", + " 'TNB':'s',\n", + " 'TNN':'*',\n", + " 'TSVM':'^',\n", + " 'WBDT':'o',\n", + " 'WKNN':'d',\n", + " 'WNB':'s',\n", + " 'WNN':'*',\n", + " 'WSVM':'^',\n", + " }\n", + "\n", + "colors = {'TBDT':fave_cmap(0.05),\n", + " 'TKNN':fave_cmap(0.3),\n", + " 'TNB':fave_cmap(0.55),\n", + " 'TNN':fave_cmap(0.8),\n", + " 'TSVM':fave_cmap(1.0),\n", + " 'WBDT':fave_cmap(0.05),\n", + " 'WKNN':fave_cmap(0.3),\n", + " 'WNB':fave_cmap(0.55),\n", + " 'WNN':fave_cmap(0.75),\n", + " 'WSVM':fave_cmap(1.0),\n", + " }\n", + "\n", + "\n", + "plt.figure()\n", + "for key, value in metric_dictionary.items():\n", + " val = []\n", + " for k, v in value.items():\n", + " val.append(v)\n", + " if 'W' in key:\n", + " plt.plot(val, label=key, marker=symbols[key], ls='--', color=colors[key])\n", + " else:\n", + " plt.plot(val, label=key, marker=symbols[key], color=colors[key])\n", + "\n", + "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), prop={'size': 12})\n", + "plt.xticks([0, 1, 2], ['FoM', 'LogLoss', 'Brier'])\n", + "plt.yticks(np.arange(1, 11))\n", + "plt.ylabel('Rank')\n", + "\n", + "#plt.savefig('Tables3_option1.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "colors = {'TBDT':fave_cmap(0.05),\n", + " 'TKNN':fave_cmap(0.2375),\n", + " 'TNB':fave_cmap(0.54),\n", + " 'TNN':fave_cmap(0.712499999),\n", + " 'TSVM':fave_cmap(1.0),\n", + " 'WBDT':fave_cmap(0.05),\n", + " 'WKNN':fave_cmap(0.2375),\n", + " 'WNB':fave_cmap(0.54),\n", + " 'WNN':fave_cmap(0.712499999),\n", + " 'WSVM':fave_cmap(1.0),\n", + " }\n", + "\n", + "plt.figure()\n", + "for key, value in metric_dictionary.items():\n", + " val = []\n", + " for k, v in value.items():\n", + " val.append(v)\n", + " if 'W' in key:\n", + " plt.plot(val, label=key, marker=symbols[key], ls='--', color=colors[key], lw=2, ms=7, alpha=0.3)\n", + " else:\n", + " plt.plot(val, label=key, marker=symbols[key], color=colors[key], lw=2, ms=7)\n", + "\n", + "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), prop={'size': 12})\n", + "plt.xticks([0, 1, 2], ['FoM', 'LogLoss', 'Brier'])\n", + "plt.yticks(np.arange(1, 11))\n", + "plt.ylabel('Rank')\n", + "plt.gca().invert_yaxis()\n", + "\n", + "#plt.savefig('Tables3_option4.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "\n", + "fom = []\n", + "ll = []\n", + "brier = []\n", + "\n", + "for key, value in metric_dictionary.items():\n", + " fom.append(value['FoM'])\n", + " ll.append(value['LogLoss'])\n", + " brier.append(value['Brier'])\n", + "\n", + "plt.plot(fom, label='FoM', marker='o')\n", + "plt.plot(ll, label='LogLoss', marker='D', alpha = 0.5)\n", + "plt.plot(brier, label='Brier', marker='s', alpha=0.23)\n", + "\n", + "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), prop={'size': 12})\n", + "plt.xticks(np.arange(0, 10), list(metric_dictionary.keys()), rotation=45)\n", + "plt.ylabel('Rank')\n", + "plt.savefig('Tables3_option2.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ea4a607529f0b617baabe246ec6937f711103c1a Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 23 Jul 2019 15:37:20 -0400 Subject: [PATCH 18/58] @rbiswas4 fixed a bug --- paper/hacks.ipynb | 33 ++++++++++++++++++++------------- 1 file changed, 20 insertions(+), 13 deletions(-) diff --git a/paper/hacks.ipynb b/paper/hacks.ipynb index 2ec1b65..ed992f6 100644 --- a/paper/hacks.ipynb +++ b/paper/hacks.ipynb @@ -622,7 +622,7 @@ "weight_vecs = np.array([[i] + [(1. - i) / (M_classes - 1.)] * (M_classes - 1) for i in possible_weights])\n", "which_weight_schemes = {str(i): weight_vecs[i] for i in range(len(possible_weights))}\n", "\n", - "alt_mega_test = load_collector('fig/test'+str(M_classes)+'_fromcmdm.pkl')" + "# alt_mega_test = load_collector('fig/test'+str(M_classes)+'_fromcmdm.pkl')" ] }, { @@ -1452,17 +1452,17 @@ "source": [ "# connect lines along systematic, weighting, and affected class\n", "def wt_only_plot(dataset, metric_info, shapes, style='rel'):\n", - " baselines = dataset.keys()\n", + " baselines = list(dataset.keys())\n", " fig = pylab.figure(figsize=(10.2, 10.))\n", " bigAxes = pylab.axes(frameon=False) # hide frame\n", " bigAxes.set_xticks([]) # don't want to see any ticks on this axis\n", " bigAxes.set_yticks([])\n", " bigAxesP = bigAxes.twinx()\n", " bigAxesP.set_yticks([])\n", - " bigAxes.set_ylabel(metric_info.keys()[0], fontsize=20, labelpad=25, color=metric_info.values()[0])\n", + " bigAxes.set_ylabel(list(metric_info.keys())[0], fontsize=20, labelpad=25, color=list(metric_info.values())[0])\n", " bigAxes.set_xlabel(style+r'. weight on class', fontsize=20, labelpad=25)\n", - " bigAxesP.set_ylabel(metric_info.keys()[1], rotation=270, fontsize=20, \n", - " labelpad=50, color=metric_info.values()[1])\n", + " bigAxesP.set_ylabel(list(metric_info.keys())[1], rotation=270, fontsize=20, \n", + " labelpad=50, color=list(metric_info.values())[1])\n", " bigAxes.set_title('tunnel on baselines')\n", " for si in range(len(baselines)):\n", " s = baselines[si]\n", @@ -1493,12 +1493,12 @@ " ax.text(.5, .9, s, \n", " horizontalalignment='center',\n", " transform=ax.transAxes, fontsize=20)\n", - " ax.plot(wts[style].T[0], dataset[s][metric_info.keys()[0]],\n", + " ax.plot(wts[style].T[0], dataset[s][list(metric_info.keys())[0]],\n", " marker=shapes[s],\n", - " alpha=0.5, c=metric_info.values()[0])\n", - " axp.plot(wts[style].T[0], dataset[s][metric_info.keys()[1]],\n", + " alpha=0.5, c=list(metric_info.values())[0])\n", + " axp.plot(wts[style].T[0], dataset[s][list(metric_info.keys())[1]],\n", " marker=shapes[s],\n", - " alpha=0.5, c=metric_info.values()[1])\n", + " alpha=0.5, c=list(metric_info.values())[1])\n", " ax.set_ylim(-0.05, 2.75)\n", " axp.set_ylim(-0.001, 0.081)\n", "# ax.set_xlim(-0.25, 2.25)\n", @@ -2071,15 +2071,22 @@ "# jupyter nbconvert desc_note/main.ipynb --TagRemovePreprocessor.remove_cell_tags='[\"hideme\"]'\n", "# jupyter nbconvert desc_note/main.ipynb --TagRemovePreprocessor.remove_input_tags='[\"hidein\"]'\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Tags", "kernelspec": { - "display_name": "ProClaM (Python 3)", - "language": "python", - "name": "proclam_3" + "display_name": "Python 3", + "language": "python3", + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2091,7 +2098,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.8" } }, "nbformat": 4, From 815aed9d4b918904792a0246b52279cd1cb7acf9 Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Wed, 24 Jul 2019 15:41:15 +0200 Subject: [PATCH 19/58] Added software section with refrerences modified: main.bib modified: main.tex --- paper/main.bib | 67 +++++++++++++++++++++++++++++++++++++++++++++++++- paper/main.tex | 2 ++ 2 files changed, 68 insertions(+), 1 deletion(-) diff --git a/paper/main.bib b/paper/main.bib index 862cd38..4af535b 100644 --- a/paper/main.bib +++ b/paper/main.bib @@ -1,4 +1,3 @@ - @article{kessler_supernova_2010, title = {Supernova {Photometric} {Classification} {Challenge}}, journal = {arXiv:1001.5210 [astro-ph]}, @@ -596,3 +595,69 @@ @article{richards_bayesian_2015 year = {2015}, pages = {39}, } +@ARTICLE{2011CSE....13b..22V, + author = {{van der Walt}, St{\'e}fan and {Colbert}, S. Chris and + {Varoquaux}, Ga{\"e}l}, + title = "{The NumPy Array: A Structure for Efficient Numerical Computation}", + journal = {Computing in Science and Engineering}, + keywords = {Computer Science - Mathematical Software}, + year = "2011", + month = "Mar", + volume = {13}, + number = {2}, + pages = {22-30}, + doi = {10.1109/MCSE.2011.37}, +archivePrefix = {arXiv}, + eprint = {1102.1523}, + primaryClass = {cs.MS}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2011CSE....13b..22V}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} +@iNProceedings{Kluyver2016JupyterN, + title={Jupyter Notebooks - a publishing format for reproducible computational workflows}, + author={Thomas Kluyver and Benjamin Ragan-Kelley and Fernando P{\'e}rez and Brian E. Granger and Matthias Bussonnier and Jonathan Frederic and Kyle Kelley and Jessica B. Hamrick and Jason Grout and Sylvain Corlay and Paul Ivanov and Dami{\'a}n Avila and Safia Abdalla and Carol Willing and et al.}, + booktitle={ELPUB}, + year={2016} +} +@article{scikit-learn, + title={Scikit-learn: Machine Learning in {P}ython}, + author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. + and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. + and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and + Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, + journal={Journal of Machine Learning Research}, + volume={12}, + pages={2825--2830}, + year={2011} +} +@inproceedings{sklearn_api, + author = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and + Fabian Pedregosa and Andreas Mueller and Olivier Grisel and + Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort + and Jaques Grobler and Robert Layton and Jake VanderPlas and + Arnaud Joly and Brian Holt and Ga{\"{e}}l Varoquaux}, + title = {{API} design for machine learning software: experiences from the scikit-learn + project}, + booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning}, + year = {2013}, + pages = {108--122}, +} +@Misc{scipy_ref, author = {Eric Jones and Travis Oliphant and Pearu Peterson and others}, + title = {{SciPy}: Open source scientific tools for {Python}}, + year = {2001}, + url = "http://www.scipy.org/", +} +@Article{Hunter:2007, + Author = {Hunter, J. D.}, + Title = {Matplotlib: A 2D graphics environment}, + Journal = {Computing in Science \& Engineering}, + Volume = {9}, + Number = {3}, + Pages = {90--95}, + abstract = {Matplotlib is a 2D graphics package used for Python for + application development, interactive scripting, and publication-quality + image generation across user interfaces and operating systems.}, + publisher = {IEEE COMPUTER SOC}, + doi = {10.1109/MCSE.2007.55}, + year = 2007 +} diff --git a/paper/main.tex b/paper/main.tex index 69d02b8..5928a19 100644 --- a/paper/main.tex +++ b/paper/main.tex @@ -97,6 +97,8 @@ \subsection*{Acknowledgments} The authors would like to thank Melissa Graham, Weikang Lin, and Chad Schafer for serving as the LSST-DESC publication review committee. The authors further wish to thank Tom Loredo for helpful feedback provided in the preparation of this paper. +\software{Aside from the standard python package, this work used the following software packages: numpy~\citep{2011CSE....13b..22V}, scipy~\citep{scipy_ref}, scikit-learn~\citep{scikit-learn, sklearn_api}. The work also made extensive use of matplotlib~\citep{Hunter:2007} and Jupyter Notebooks~\citep{Kluyver2016JupyterN}.} + AIM is advised by David W. Hogg and was supported by National Science Foundation grant AST-1517237. TA is supported in part by STFC. RB and CS are supported by the Swedish Research Council (VR) through the Oskar Klein Centre. From 872f415ea20c406f71eb74fb942b7bc0d82e549e Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Wed, 24 Jul 2019 15:54:55 +0200 Subject: [PATCH 20/58] Fixed typos in caption of Table 1. - corresponding spelling fixed, te-> the. - The caption is however still unsatisfactory due to repetition and grammar/conventions. --- paper/tex/results.tex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/tex/results.tex b/paper/tex/results.tex index f7e6921..b97dd6d 100644 --- a/paper/tex/results.tex +++ b/paper/tex/results.tex @@ -67,8 +67,8 @@ \subsection{Mock classifier systematics} \end{tabular} \caption{ The value of each metric when the weight is entirely on the class with the indicated characteristic. -Weighting changes the metric performance: the value of each metric when the weight is entirely on the class with the indicated characteristic (correponsding to a $w=1$ case in Figure~\ref{fig:all_combined}). -The log-loss is more sensitive than te Brier score, with larger values of the score (indicating poor classification performance), particularly for the subsuming systematic. +Weighting changes the metric performance: the value of each metric when the weight is entirely on the class with the indicated characteristic (corresponding to a $w=1$ case in Figure~\ref{fig:all_combined}). +The log-loss is more sensitive than the Brier score, with larger values of the score (indicating poor classification performance), particularly for the subsuming systematic. } \label{tab:extents} \end{table} From dc81c753d2587fd26e1d808cd6c10e5d883cc2bb Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Wed, 24 Jul 2019 18:56:02 +0200 Subject: [PATCH 21/58] Changed the color of changed text to blue using changes macro modified: main.tex modified: tex/results.tex --- paper/main.tex | 2 +- paper/tex/results.tex | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/paper/main.tex b/paper/main.tex index 5928a19..6a09e28 100644 --- a/paper/main.tex +++ b/paper/main.tex @@ -97,7 +97,7 @@ \subsection*{Acknowledgments} The authors would like to thank Melissa Graham, Weikang Lin, and Chad Schafer for serving as the LSST-DESC publication review committee. The authors further wish to thank Tom Loredo for helpful feedback provided in the preparation of this paper. -\software{Aside from the standard python package, this work used the following software packages: numpy~\citep{2011CSE....13b..22V}, scipy~\citep{scipy_ref}, scikit-learn~\citep{scikit-learn, sklearn_api}. The work also made extensive use of matplotlib~\citep{Hunter:2007} and Jupyter Notebooks~\citep{Kluyver2016JupyterN}.} +\changes{\software{Aside from the standard python package, this work used the following software packages: numpy~\citep{2011CSE....13b..22V}, scipy~\citep{scipy_ref}, scikit-learn~\citep{scikit-learn, sklearn_api}. The work also made extensive use of matplotlib~\citep{Hunter:2007} and Jupyter Notebooks~\citep{Kluyver2016JupyterN}.}} AIM is advised by David W. Hogg and was supported by National Science Foundation grant AST-1517237. TA is supported in part by STFC. diff --git a/paper/tex/results.tex b/paper/tex/results.tex index b97dd6d..7b1b4bb 100644 --- a/paper/tex/results.tex +++ b/paper/tex/results.tex @@ -67,8 +67,8 @@ \subsection{Mock classifier systematics} \end{tabular} \caption{ The value of each metric when the weight is entirely on the class with the indicated characteristic. -Weighting changes the metric performance: the value of each metric when the weight is entirely on the class with the indicated characteristic (corresponding to a $w=1$ case in Figure~\ref{fig:all_combined}). -The log-loss is more sensitive than the Brier score, with larger values of the score (indicating poor classification performance), particularly for the subsuming systematic. +Weighting changes the metric performance: the value of each metric when the weight is entirely on the class with the indicated characteristic (\changes{corresponding} to a $w=1$ case in Figure~\ref{fig:all_combined}). +The log-loss is more sensitive than \changes{the} Brier score, with larger values of the score (indicating poor classification performance), particularly for the subsuming systematic. } \label{tab:extents} \end{table} From e574b653613d4d7fa2959d2bab3d089c3d3266c7 Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Wed, 24 Jul 2019 19:14:26 +0200 Subject: [PATCH 22/58] changing the text at the start of the discussion section a bit --- paper/tex/discussion.tex | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index 7c51513..7008b70 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -4,8 +4,9 @@ \section{Discussion} The goal of this work is to identify the metric most suited to \plasticc, which seeks classification posteriors of complete light curves similar to those anticipated from \lsst, with an emphasis on classification over all types, rewarding a ``best in show'' classifier rather than focusing on any one class or scientific application.\footnote{At the conclusion of \plasticc, other metrics specific to scientific uses of one or more particular classes will be used to identify ``best in class'' classification procedures that will be useful for more targeted science cases.} The weighted log-loss is thus the metric most suited to the current \plasticc\ release. -Future releases of \plasticc\ will focus on different challenges in transient and variable object classification, with metrics appropriate to identifying methodologies that best enable those goals. -We discuss approaches to identifying optimal metrics for these variations, which may be developed further in future work. +\changes{Classification of transient and variable objects is important for a variety of scientific objectives. This diversity of scientific goals requires different trade-offs, and must be evaluated using multiple metrics. While we leave addressing such challenges for future releases of \plasticc\, and the identification of appropriate metrics to future work, we end by enumerating some of the science goals and approaches for identifying metrics.} +\sout{Future releases of \plasticc\ will focus on different challenges in transient and variable object classification, with metrics appropriate to identifying methodologies that best enable those goals. +We discuss approaches to identifying optimal metrics for these variations, which may be developed further in future work.} \subsection{Early classification} \label{sec:early} @@ -35,6 +36,8 @@ \subsection{Early classification} % balance the value of the information forgone by a false positive and the value of information forgone by a false negative, and the value placed on these is effectively weighted by the value we as researchers place on follow-up resources. % \aim{Ciite some deterministic metrics relating to TP/FP?} +\subsection{Anomaly Detection} +\label{sec:anom} Anomaly detection also gains information only from true positives, but the cost function is different in that the potential gain of information from a true positive, since there is no information about undiscovered classes ahead of time. An example would be the recent detection of a kilonova, flagged initially by the detection of gravitational waves from an object. From 185e6f3fd76cb347f553c29cf6ef9da85676acfb Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Wed, 24 Jul 2019 19:19:51 +0200 Subject: [PATCH 23/58] Have sub-section on anomaly detection, changed color modified: tex/discussion.tex --- paper/tex/discussion.tex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index 7008b70..a818a95 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -34,9 +34,9 @@ \subsection{Early classification} A perfect classifier would lead to a maximum amount of information about the cosmological parameters conditioned on the follow-up resource budget. For this scientific application, the metric must be chosen to not only maximize true positives but also to minimize false positives, and their relative impacts on the cosmological constraints can be quantified in terms of the information one would have about the cosmological parameters under different balances of true and false positives. % balance the value of the information forgone by a false positive and the value of information forgone by a false negative, and the value placed on these is effectively weighted by the value we as researchers place on follow-up resources. -% \aim{Ciite some deterministic metrics relating to TP/FP?} +% \aim{Cite some deterministic metrics relating to TP/FP?} -\subsection{Anomaly Detection} +\changes{\subsection{Anomaly Detection}} \label{sec:anom} Anomaly detection also gains information only from true positives, but the cost function is different in that the potential gain of information from a true positive, since there is no information about undiscovered classes ahead of time. An example would be the recent detection of a kilonova, flagged initially by the detection of gravitational waves from an object. From 1dc698c2286e2a27fe7f33e94958f1fc237cdccf Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Wed, 24 Jul 2019 19:41:39 +0200 Subject: [PATCH 24/58] Made some changes in discussion section not asked for --- paper/tex/discussion.tex | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index a818a95..a299af2 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -4,15 +4,16 @@ \section{Discussion} The goal of this work is to identify the metric most suited to \plasticc, which seeks classification posteriors of complete light curves similar to those anticipated from \lsst, with an emphasis on classification over all types, rewarding a ``best in show'' classifier rather than focusing on any one class or scientific application.\footnote{At the conclusion of \plasticc, other metrics specific to scientific uses of one or more particular classes will be used to identify ``best in class'' classification procedures that will be useful for more targeted science cases.} The weighted log-loss is thus the metric most suited to the current \plasticc\ release. -\changes{Classification of transient and variable objects is important for a variety of scientific objectives. This diversity of scientific goals requires different trade-offs, and must be evaluated using multiple metrics. While we leave addressing such challenges for future releases of \plasticc\, and the identification of appropriate metrics to future work, we end by enumerating some of the science goals and approaches for identifying metrics.} +\changes{Classification of transient and variable objects is important for a variety of scientific objectives. This diversity of scientific goals requires different trade-offs, and must be evaluated using multiple metrics. While we leave addressing such challenges for future releases of \plasticc\, and the identification of corresponding metrics to future work, we give a few examples of such science goals and approaches for identifying metrics for them.} \sout{Future releases of \plasticc\ will focus on different challenges in transient and variable object classification, with metrics appropriate to identifying methodologies that best enable those goals. We discuss approaches to identifying optimal metrics for these variations, which may be developed further in future work.} -\subsection{Early classification} +changes{\subsection{Enabling Transient Followup}} +\subsection{\sout{Early classification}} \label{sec:early} Spectroscopic follow-up is only expected of a small fraction of \lsst's detected transients and variable objects due to limited resources for such observations. -In addition to optical spectroscopic follow-up, photometric observations in other wavelength bands (near infrared and x-ray from space; microwave and radio from the ground) will be key to building a physical understanding of the object, particularly as we enter the era of multi-messenger astronomy with the added possibility of optical gravitational wave signatures. +In addition to optical spectroscopic follow-up, photometric observations in other wavelength bands (near infrared and x-ray from space; microwave and radio from the ground) \changes{or at different times} will be key to building a physical understanding of the object, particularly as we enter the era of multi-messenger astronomy with the added possibility of optical gravitational wave signatures. Prompt follow-up observations are highly informative for fitting models to the light curves of familiar source classes and to characterizing anomalous light curves that could indicate never-before-seen classes that have eluded identification due to rarity or faintness. As such, decisions about follow-up resource allocation must be made quickly and under the constraint that resources wasted on a misclassification consume the budget remaining for future follow-up attempts. A future version of \plasticc\ focused on early light curve classification should have a metric that accounts for these limitations and rewards classifiers that perform better even when fewer observations of the lightcurve are available. @@ -25,6 +26,8 @@ \subsection{Early classification} The critical question for choosing the most appropriate metric for any specific science goal motivating follow-up observations is to maximize information. We provide two examples of the kind of information one must maximize via early light curve classification and the qualities of a deterministic metric that might enable it. +\changes{\subsection{Spectroscopic Supernova Cosmology}} +\label{sec:spec_sncosmo} Supernova cosmology with spectroscopically confirmed light curves benefits from true positives, which contribute to the constraining power of the analysis by including one more data point; when the class in which one is interested is as plentiful as SN Ia and our resources limited a priori, we may not be concerned by a high rate of false negatives. % requires making a decision balancing the improved constraining power of including another SN Ia in the analysis, thereby constraining the cosmological parameters, so only true positives contribute information, and if we had a perfect classifier and standard follow-up spectroscopy resources, there would be a maximum amount of information about the cosmological parameters that could be gained in this way. @@ -36,9 +39,10 @@ \subsection{Early classification} % balance the value of the information forgone by a false positive and the value of information forgone by a false negative, and the value placed on these is effectively weighted by the value we as researchers place on follow-up resources. % \aim{Cite some deterministic metrics relating to TP/FP?} -\changes{\subsection{Anomaly Detection}} +\changes{\subsection{Detecting new classes of transients}} \label{sec:anom} -Anomaly detection also gains information only from true positives, but the cost function is different in that the potential gain of information from a true positive, since there is no information about undiscovered classes ahead of time. +\changes{A particularly exciting science case is the discovery of entirely unknown classes of transient or variable astrophysical sources, or identifying some of the rarely found sources in the sea of abundant sources. Such a goal may be phrased in terms of detecting anomalies.} +\changes{Like the case of spectroscopic supernova cosmology discussed above,} anomaly detection also gains information only from true positives, but the cost function is different in that the potential gain of information from a true positive, since there is no information about undiscovered classes ahead of time. An example would be the recent detection of a kilonova, flagged initially by the detection of gravitational waves from an object. Resource availability for identifying new classes is more flexible, increasing when new predictions or promising preliminary observations attract attention, and decreasing when a discovery is confirmed and the new class is established. From c7a2493cfe74914d30dc0449ff10f13d02f9b087 Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Wed, 24 Jul 2019 19:42:14 +0200 Subject: [PATCH 25/58] Adding emphasis on metric types --- paper/tex/discussion.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index a299af2..3e1551e 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -35,7 +35,7 @@ \subsection{\sout{Early classification}} False positives, on the other hand, may not enter the cosmology analysis, but they consume follow-up resources, thereby depriving the endeavor of the constraining power due to a single SN Ia. A perfect classifier would lead to a maximum amount of information about the cosmological parameters conditioned on the follow-up resource budget. -For this scientific application, the metric must be chosen to not only maximize true positives but also to minimize false positives, and their relative impacts on the cosmological constraints can be quantified in terms of the information one would have about the cosmological parameters under different balances of true and false positives. +For this scientific application, the metric must be chosen to \changes{\emph{not only maximize true positives but also to minimize false positives, and their relative impacts on the cosmological constraints can be quantified in terms of the information one would have about the cosmological parameters under different balances of true and false positives.}} % balance the value of the information forgone by a false positive and the value of information forgone by a false negative, and the value placed on these is effectively weighted by the value we as researchers place on follow-up resources. % \aim{Cite some deterministic metrics relating to TP/FP?} From 2a71c2ef26c81f2483ce580fb26be0875b44899f Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Wed, 24 Jul 2019 19:45:05 +0200 Subject: [PATCH 26/58] MUST BE REMOVED: Added comment modified: tex/discussion.tex --- paper/tex/discussion.tex | 1 + 1 file changed, 1 insertion(+) diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index 3e1551e..b500654 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -43,6 +43,7 @@ \subsection{\sout{Early classification}} \label{sec:anom} \changes{A particularly exciting science case is the discovery of entirely unknown classes of transient or variable astrophysical sources, or identifying some of the rarely found sources in the sea of abundant sources. Such a goal may be phrased in terms of detecting anomalies.} \changes{Like the case of spectroscopic supernova cosmology discussed above,} anomaly detection also gains information only from true positives, but the cost function is different in that the potential gain of information from a true positive, since there is no information about undiscovered classes ahead of time. +\changes{COMMENT RB: not to stay in doc, but I don't understand the prev sentence. I would also object to the recent detection of kilonova as a good example of anomaly detection, I can buy it if I squint very hard} An example would be the recent detection of a kilonova, flagged initially by the detection of gravitational waves from an object. Resource availability for identifying new classes is more flexible, increasing when new predictions or promising preliminary observations attract attention, and decreasing when a discovery is confirmed and the new class is established. From c5590a9960cd608b498bcd0bf6479c78978c72d8 Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Wed, 24 Jul 2019 19:46:17 +0200 Subject: [PATCH 27/58] Made minimal changes to emphasize metric for anomaly detection modified: tex/discussion.tex --- paper/tex/discussion.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index b500654..176948b 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -52,7 +52,7 @@ \subsection{\sout{Early classification}} % For a rare event like a kilonova, a false negative represents an unbounfalse positive does not appreciably reduce the amount of remaining information available to collect, but a false negative represents a large quantity of information forgone. % Furthermore, r % In this case, the information forgone by a false negative is significant compared to the information forgone by a false positive. -Thus, a metric tuned to anomaly detection would aim to minimize the false negative rate and maximize the true positive rate. +Thus, a metric \sout{tuned to} \chnages{evaluating} anomaly detection would aim to \changes{\emph{minimize the false negative rate and maximize the true positive rate.}} % \aim{Cite some deterministic metrics relating to TP/FN?} % \subsection{Hierarchical classes} From 4d389a1b46779bbf6e8d89fd271e696c6aa4f5d3 Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Thu, 25 Jul 2019 15:30:34 +0200 Subject: [PATCH 28/58] Fixed a missing slashback for a changes macro modified: tex/discussion.tex --- paper/tex/discussion.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index 176948b..3448c10 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -8,7 +8,7 @@ \section{Discussion} \sout{Future releases of \plasticc\ will focus on different challenges in transient and variable object classification, with metrics appropriate to identifying methodologies that best enable those goals. We discuss approaches to identifying optimal metrics for these variations, which may be developed further in future work.} -changes{\subsection{Enabling Transient Followup}} +\changes{\subsection{Enabling Transient Followup}} \subsection{\sout{Early classification}} \label{sec:early} From fb0475e44c5dbb1934a72aeff9015d29d4008aed Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Fri, 26 Jul 2019 07:38:40 +0200 Subject: [PATCH 29/58] latex error fixed modified: tex/discussion.tex --- paper/tex/discussion.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index 3448c10..ef49b9f 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -52,7 +52,7 @@ \subsection{\sout{Early classification}} % For a rare event like a kilonova, a false negative represents an unbounfalse positive does not appreciably reduce the amount of remaining information available to collect, but a false negative represents a large quantity of information forgone. % Furthermore, r % In this case, the information forgone by a false negative is significant compared to the information forgone by a false positive. -Thus, a metric \sout{tuned to} \chnages{evaluating} anomaly detection would aim to \changes{\emph{minimize the false negative rate and maximize the true positive rate.}} +Thus, a metric \sout{tuned to} \changes{evaluating} anomaly detection would aim to \changes{\emph{minimize the false negative rate and maximize the true positive rate.}} % \aim{Cite some deterministic metrics relating to TP/FN?} % \subsection{Hierarchical classes} From f6fcf994e3897d3d31a93cfe885465fa789acb93 Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Fri, 26 Jul 2019 08:07:20 +0200 Subject: [PATCH 30/58] Added footnote acknowledging plasticc (version 1) is over modified: tex/introduction.tex --- paper/tex/introduction.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/tex/introduction.tex b/paper/tex/introduction.tex index 950ac13..7f9daf7 100644 --- a/paper/tex/introduction.tex +++ b/paper/tex/introduction.tex @@ -14,7 +14,7 @@ \section{Introduction} As such, there is an acute need for classifiers of photometric light curves that can perform well on datasets that include a wide variety of sources including those that are at the limits of detection. The Photometric \lsst\ Astronomical Time-series Classification Challenge (\plasticc\footnote{\url{http://plasticcblog.wordpress.com/}}) aims to identify and motivate the development of classification techniques that serve astronomical science goals by engaging the broader community outside astronomy. -\plasticc's dataset is comprehensive, including models for well-understood classes, newly observed classes, and classes that have only been proposed to exist, to simulate serendipitous discoveries anticipated of \lsst. +\plasticc's dataset is comprehensive, including models for well-understood classes, newly observed classes, and classes that have only been proposed to exist, to simulate serendipitous discoveries anticipated of \lsst\footnote{\changes{While originally written before the \plasticc\ competition, in the intervening time \plasticc\ was run as a Kaggle challenge (\url{https://www.kaggle.com/c/PLAsTiCC-2018}) starting September 17, 2018 with a submission deadline of December 17, 2018. The leaderboard and eventual winners were announced shortly after this based on evaluation (\url{https://www.kaggle.com/c/PLAsTiCC-2018/overview/evaluation}) using the logloss metric discussed in this paper. We expect future variants of \plasticc\ to run on modified datasets and questions.}} . Additionally, \plasticc\ will join the ranks of a handful of past astronomy classification challenges including \citep[Mapping Dark Matter\footnote{\url{https://www.kaggle.com/c/mdm}}]{kitching_gravitational_2011}, \citep[Observing Dark Worlds\footnote{\url{https://www.kaggle.com/c/DarkWorlds}}]{harvey_observing_2013}, and \citep[the Galaxy Challenge\footnote{\url{https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge}}]{dieleman_rotation-invariant_2015}, all hosted on Kaggle\footnote{\url{https://www.kaggle.com/}}, a platform that hosts data analytics competitions where seasoned professionals and amateurs alike can compete to classify, model, and predict large data sets uploaded by companies or scientific collaborations. Kaggle attracts a broad userbase, and those without domain knowledge may provide novel approaches to the problem at hand. From a3e0466b56449d5c7ddae55c5fbf69ce19565983 Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Fri, 26 Jul 2019 11:51:52 +0200 Subject: [PATCH 31/58] Adding changes to caption of Fig.1 - Needed to add this was simulations in this work. Not plasticc data set/ astronomy data set etc. - Q. What parameter(s) were used in the logarithmic distribution ? Was it log uniform? --- paper/tex/data.tex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/tex/data.tex b/paper/tex/data.tex index 0aaac29..3d97f91 100644 --- a/paper/tex/data.tex +++ b/paper/tex/data.tex @@ -16,8 +16,8 @@ \section{Data} \begin{figure} \begin{center} \includegraphics[width=0.49\textwidth]{./fig/complete_counts.png} - \caption{The number of objects in a given class as a function of class population size. - The true class populations are logarithmically distributed.} + \caption{\sout{The number of objects in a given class as a function of class population size. + The true class populations are logarithmically distributed.} \changes{Class populations used in this work shown as the number of objects in each of the 13 classes: This was simulated by drawing the number of objects in a given class from a logarithmic distribution to emulate the class imbalance frequently observed in typical astronomical samples}} \label{fig:classdist} \end{center} \end{figure} From 323b69c00b2ec57f1e7a87b464c2d8254932c1a0 Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Fri, 26 Jul 2019 12:53:59 +0200 Subject: [PATCH 32/58] Added the abbreviation CPM to the conditional probabibitly matrix in the figure caption, so that the reader does not have to search the definition in the main text --- paper/tex/data.tex | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paper/tex/data.tex b/paper/tex/data.tex index 3d97f91..010855c 100644 --- a/paper/tex/data.tex +++ b/paper/tex/data.tex @@ -75,7 +75,7 @@ \subsection{Mock classification schemes} \begin{figure*} \begin{center} \includegraphics[width=0.8\textwidth]{./fig/all_sim_cm.png} - \caption{Conditional probability matrices for eight mock classifiers. + \caption{Conditional probability matrices \changes{(CPMs)}for eight mock classifiers. Top row: the uncertain classifier's uniform CPM; the perfect classifier's identity CPM; @@ -108,7 +108,7 @@ \subsection{Mock classification schemes} \begin{center} \includegraphics[width=0.49\textwidth]{./fig/combined.png}\\ \includegraphics[width=0.49\textwidth]{./fig/examples.png} - \caption{A realistically complex conditional probability matrix and classification posteriors drawn from it. + \caption{A realistically complex conditional probability matrix \changes{(CPM)} and classification posteriors drawn from it. Top: An example of a realistically complex conditional probability matrix, constructed by selecting a systematic for each individual class. This illustrates (for example), how a classifier may exhibit multiple systematics from Figure~\ref{fig:mock_cm} for each true class. Bottom: Example classification probabilities, drawn from the above CPM, with their true class indicated by a red star and the systematic, characterized by its row in the CPM, affecting that true class described on the right. @@ -191,7 +191,7 @@ \subsection{Realistic classifications} \begin{figure*} \begin{center} \includegraphics[width=\textwidth]{./fig/all_snphotcc_cm.png} - \caption{Conditional probability matrices of the \citet{lochner_photometric_2016} methods applied to the second post-challenge release of the \snphotcc\ dataset. + \caption{Conditional probability matrices \changes{(CPMs)} of the \citet{lochner_photometric_2016} methods applied to the second post-challenge release of the \snphotcc\ dataset. Columns: the five machine learning methods of Boosted Decision Tree (BDT), K-Nearest Neighbors (KNN), Naive Bayes (NB), Neural Network (NN), and Support Vector Machine (SVM). Top row: five machine learning methods applied to template decompositions as features. Bottom row: the same five machine learning methods applied to wavelet decompositions as features. From a5d7721c896c1685addfe30fc30eded644328b93 Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Fri, 26 Jul 2019 13:32:01 +0200 Subject: [PATCH 33/58] Fixing caption for Table 1. modified: tex/results.tex --- paper/tex/results.tex | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/paper/tex/results.tex b/paper/tex/results.tex index 7b1b4bb..dd9485c 100644 --- a/paper/tex/results.tex +++ b/paper/tex/results.tex @@ -65,10 +65,9 @@ \subsection{Mock classifier systematics} Subsumed from Almost & 0.641 & 1.629\\ Subsumed from Perfect & 1.0 & 18.421\footnote{The entry for the log-loss of a classifier that subsumes a class into one that is otherwise perfectly classified should be infinite but is bounded by the numerical precision of our calculations.} \end{tabular} -\caption{ -The value of each metric when the weight is entirely on the class with the indicated characteristic. +\caption{\changes{Metric values computed using Eqn.~\ref{eq:weightavg} with unit weights for the mock data produced by mock classification schemes described in Sec.~\ref{sec:mockdata}. The table shows that log-loss metric is more discerning than the Brier score for poor classification, particularly for the Subsuming from Almost and Subsuming from Perfect schemes. Note, that using different weights changes the values of these metrics as shown in Figure~\ref{fig:all_combined}.} \sout{The value of each metric when the weight is entirely on the class with the indicated characteristic. Weighting changes the metric performance: the value of each metric when the weight is entirely on the class with the indicated characteristic (\changes{corresponding} to a $w=1$ case in Figure~\ref{fig:all_combined}). -The log-loss is more sensitive than \changes{the} Brier score, with larger values of the score (indicating poor classification performance), particularly for the subsuming systematic. +The log-loss is more sensitive than \changes{the} Brier score, with larger values of the score (indicating poor classification performance), particularly for the subsuming systematic.} } \label{tab:extents} \end{table} From 804737220e5acd3f45c63c788f34318644c43a5e Mon Sep 17 00:00:00 2001 From: "R. Biswas" Date: Fri, 26 Jul 2019 19:00:25 +0200 Subject: [PATCH 34/58] Changes to the conclusions section modified: tex/conclusions.tex --- paper/tex/conclusions.tex | 42 ++++++++++++++++++++++++++------------- 1 file changed, 28 insertions(+), 14 deletions(-) diff --git a/paper/tex/conclusions.tex b/paper/tex/conclusions.tex index a25ba76..73e0a02 100644 --- a/paper/tex/conclusions.tex +++ b/paper/tex/conclusions.tex @@ -1,22 +1,36 @@ \section{Conclusion} \label{sec:conclusion} -As part of the preparation for \plasticc\, we investigate the properties of metrics suitable for probabilistic light curve classifications in the absence of a single scientific goal. -To that end, we sought a metric that avoids reducing classification probabilities to deterministic labels and one that rewards a classifier with strong performance across all classes over a classifier that performs well on a small subset of the classes and poorly on all others. +As part of the preparation for \plasticc\, we investigate\changes{d} the properties of metrics suitable for probabilistic light curve classifications in the absence of a single scientific goal. Therefore, we sought a metric that avoids reducing classification probabilities to deterministic labels and \changes{one that works in a multi-class rather than a binary (two class) setting. In line with the goals of the \plasticc\ challenge, an important desideratum was to have a metric that tends to} reward a classifier's performance across all classes over a classifier that performs well on a small subset of the classes and poorly on all others. \changes{Given the potential of large class imbalance in astronomical datasets, we were also interested in the possibility of up-weighting the importance of certain rarer transient classes if need be; consequently we wanted to understand the way the metric would behave with the use of weights.} -We compared two metrics specific to probabilistic classifications: the Brier score and the log-loss. -Even though the Brier score and log-loss metrics take values consistent with one another, they are structurally and conceptually different, with wholly different interpretations. -The Brier score is a sum of square differences between probabilities; the explicit penalty term is an attractive feature, but it treats probabilities as generic scores and is not interpretable in terms of information. -The log-loss on the other hand is readily interpretable, meaning the metric itself could be propagated into forecasting the constraining power of \lsst, affecting the choice of observing strategy. -We discovered that the log-loss is somewhat more sensitive to the systematic errors in classification that we find most concerning for generic scientific applications. -While both metrics could be appropriate for \plasticc, the log-loss is preferable due to its interpretability in terms of information. +\changes{To this end, in Sec.~\ref{sec:data}, we described a set of mock classifiers with characteristics that we expect some transient and variable classifiers to have, and designed methods to simulate submissions from such mock classifiers. This enabled us to test the performance of the metrics on each of these mock submissions.} -Both metrics are susceptible to rewarding a classifier that performs well on the most prevalent class and poorly on all others, which fails to meet the needs of \plasticc's diverse motivations. -We explored a weighted average of the metric values on a per-class basis as a possible mitigation strategy to incentivize classifying uncommon classes, effectively ``leveling the playing field'' in the presence of highly imbalanced class membership. -Although weights do impact the interpretability of the log-loss, we select a per-class weighted log-loss as the optimal choice for \plasticc. +\sout{We compared two metrics specific to probabilistic classifications: the Brier score and the log-loss.} -% We note that in order to map on to the Kaggle evaluation platform, a metric weighted only by class was used for the general challenge, while a log-loss with more complicated weighting procedure will be used for the science competition (which will continue for an additional month after the main Kaggle release). +\changes{To start with, we found two metrics within the literature : the Brier score and the log-loss metric that satisfied the criteria of working with probabilistic classifications in a multi-class problem. We left aside popular metrics (such as accuracy, AUC, the SPCC metrics) which did not satisfy these criteria, even though it might be possible to extend them for these scenarios.} The Brier score and the log-loss metrics are structurally and conceptually different, with wholly different interpretations. The Brier score is a sum of square differences between probabilities; the explicit penalty term is an attractive feature, but it treats probabilities as generic scores and is not interpretable in terms of information. The log-loss on the other hand is readily interpretable, meaning the metric itself could be propagated into forecasting the constraining power of \lsst, affecting the choice of observing strategy. -We conclude by noting that care should be taken in planning future open challenges to ensure alignment between the challenge goals and the performance metric, so that efforts are best directed to achieve the challenge objectives. -We hope that this study of metric performance across a range of systematic effects and weights may serve as a guide to approaching the problem of identifying optimal probabilistic classifiers for general science applications. + +%%%% +\changes{We evaluated these metrics using the mock submissions from the mock classification schemes with unit weights and then by varying the weights in Eqn.~\ref{eq:weightavg}. We found that both the Brier score and the log-loss behaved in an intuitive fashion on all of the mock classification results (see Table.~\ref{tab:extents}). Both metrics reward the best classifiers and penalize the worst (uncertain) classifers, but are susceptible to rewarding a classifier that performs well on the most prevalent class and poorly on all others, which fails to meet the needs of \plasticc's diverse motivations. For these cases, we discovered that the log-loss metric was somewhat more discerning in terms of metric values, but in terms of rankings both could be equivalent. Indeed, upto a monotonic transformation, their performances are quite similar.} +%%%% +%%%% +%%%% Even though the Brier score and log-loss metrics take values consistent with one another, they are structurally and conceptually different, with wholly different interpretations. +%%%% The Brier score is a sum of square differences between probabilities; the explicit penalty term is an attractive feature, but it treats probabilities as generic scores and is not interpretable in terms of information. +%%%% The log-loss on the other hand is readily interpretable, meaning the metric itself could be propagated into forecasting the constraining power of \lsst, affecting the choice of observing strategy. +%%%% +%%%% We discovered that the log-loss is somewhat more sensitive to the systematic errors in classification that we find most concerning for generic scientific applications. +%%%% While both metrics could be appropriate for \plasticc, the log-loss is preferable due to its interpretability in terms of information. +%%%% +%%%% Both metrics are susceptible to rewarding a classifier that performs well on the most prevalent class and poorly on all others, which fails to meet the needs of \plasticc's diverse motivations. + +\changes{Due to our desire to potentially upweight transients,} We explored a weighted average of the metric values on a per-class basis as a possible mitigation strategy to incentivize classifying uncommon classes, effectively ``leveling the playing field'' in the presence of highly imbalanced class membership. %\changes{%Such weights were taken to be the same for all objects in the same class. +\changes{While modifyinging the log-loss metric to handle weights for different classes changes the interpretability, this would likely be appreciably different if the weights were significantly different at the level of orders of magnitude. +As it finally turned out, the weights actually used in \plasticc\ were all of similar orders, rather than different by orders of magnitude but this decision was concealed by design from some of the authors of this paper.} + +\changes{Given that both log-loss and Brier score seemed to work reasonably well for our requirements, we did not further try to define new metrics by extending known concepts. We chose the weighted log-losss metric because it seemed slightly more discerning, and easy to interpret in terms of information theory, at least in the limit of equal weights. It is probable, that the use of the Brier score would be similar as well.} +%%%% Although weights do impact the interpretability of the log-loss, we select a per-class weighted log-loss as the optimal choice for \plasticc. +%%%% +%%%% % We note that in order to map on to the Kaggle evaluation platform, a metric weighted only by class was used for the general challenge, while a log-loss with more complicated weighting procedure will be used for the science competition (which will continue for an additional month after the main Kaggle release). + +We conclude by noting that care should be taken in planning future open challenges to ensure alignment between the challenge goals and the performance metric, so that efforts are best directed to achieve the challenge objectives. We hope that this study of metric performance across a range of systematic effects and weights may serve as a guide to approaching the problem of identifying optimal probabilistic classifiers for general science applications. From a984df99f16332c2d18697a9c50e917942adba03 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Sat, 27 Jul 2019 06:29:39 -0400 Subject: [PATCH 35/58] tweaking the changes of @rbiswas4 --- paper/tex/conclusions.tex | 66 ++++++++++++++++++++------------------ paper/tex/data.tex | 4 ++- paper/tex/discussion.tex | 28 +++++++++------- paper/tex/introduction.tex | 6 ++-- paper/tex/methods.tex | 2 +- paper/tex/results.tex | 4 ++- 6 files changed, 62 insertions(+), 48 deletions(-) diff --git a/paper/tex/conclusions.tex b/paper/tex/conclusions.tex index 73e0a02..5404b6b 100644 --- a/paper/tex/conclusions.tex +++ b/paper/tex/conclusions.tex @@ -1,36 +1,40 @@ \section{Conclusion} \label{sec:conclusion} -As part of the preparation for \plasticc\, we investigate\changes{d} the properties of metrics suitable for probabilistic light curve classifications in the absence of a single scientific goal. Therefore, we sought a metric that avoids reducing classification probabilities to deterministic labels and \changes{one that works in a multi-class rather than a binary (two class) setting. In line with the goals of the \plasticc\ challenge, an important desideratum was to have a metric that tends to} reward a classifier's performance across all classes over a classifier that performs well on a small subset of the classes and poorly on all others. \changes{Given the potential of large class imbalance in astronomical datasets, we were also interested in the possibility of up-weighting the importance of certain rarer transient classes if need be; consequently we wanted to understand the way the metric would behave with the use of weights.} - - -\changes{To this end, in Sec.~\ref{sec:data}, we described a set of mock classifiers with characteristics that we expect some transient and variable classifiers to have, and designed methods to simulate submissions from such mock classifiers. This enabled us to test the performance of the metrics on each of these mock submissions.} +As part of the preparation for \plasticc\, we investigated the properties of metrics suitable for probabilistic light curve classifications in the absence of a single scientific goal. +Therefore, we sought a metric that avoids reducing classification probabilities to deterministic labels \changes{and is compatible with a multi-class, rather than binary (two-class), setting. +In line with the goals of \plasticc, an important desideratum was to have a metric that tends to} reward a classifier's performance across all classes over a classifier that performs well on a small subset of the classes and poorly on others. +\changes{Given the potential of large class imbalance in astronomical datasets, we were also interested in the possibility of up-weighting the importance of certain rarer transient classes if need be; +consequently we wanted to understand the way the metric would behave with the use of per-class weights.} \sout{We compared two metrics specific to probabilistic classifications: the Brier score and the log-loss.} - -\changes{To start with, we found two metrics within the literature : the Brier score and the log-loss metric that satisfied the criteria of working with probabilistic classifications in a multi-class problem. We left aside popular metrics (such as accuracy, AUC, the SPCC metrics) which did not satisfy these criteria, even though it might be possible to extend them for these scenarios.} The Brier score and the log-loss metrics are structurally and conceptually different, with wholly different interpretations. The Brier score is a sum of square differences between probabilities; the explicit penalty term is an attractive feature, but it treats probabilities as generic scores and is not interpretable in terms of information. The log-loss on the other hand is readily interpretable, meaning the metric itself could be propagated into forecasting the constraining power of \lsst, affecting the choice of observing strategy. - - -%%%% -\changes{We evaluated these metrics using the mock submissions from the mock classification schemes with unit weights and then by varying the weights in Eqn.~\ref{eq:weightavg}. We found that both the Brier score and the log-loss behaved in an intuitive fashion on all of the mock classification results (see Table.~\ref{tab:extents}). Both metrics reward the best classifiers and penalize the worst (uncertain) classifers, but are susceptible to rewarding a classifier that performs well on the most prevalent class and poorly on all others, which fails to meet the needs of \plasticc's diverse motivations. For these cases, we discovered that the log-loss metric was somewhat more discerning in terms of metric values, but in terms of rankings both could be equivalent. Indeed, upto a monotonic transformation, their performances are quite similar.} -%%%% -%%%% -%%%% Even though the Brier score and log-loss metrics take values consistent with one another, they are structurally and conceptually different, with wholly different interpretations. -%%%% The Brier score is a sum of square differences between probabilities; the explicit penalty term is an attractive feature, but it treats probabilities as generic scores and is not interpretable in terms of information. -%%%% The log-loss on the other hand is readily interpretable, meaning the metric itself could be propagated into forecasting the constraining power of \lsst, affecting the choice of observing strategy. -%%%% -%%%% We discovered that the log-loss is somewhat more sensitive to the systematic errors in classification that we find most concerning for generic scientific applications. -%%%% While both metrics could be appropriate for \plasticc, the log-loss is preferable due to its interpretability in terms of information. -%%%% -%%%% Both metrics are susceptible to rewarding a classifier that performs well on the most prevalent class and poorly on all others, which fails to meet the needs of \plasticc's diverse motivations. - -\changes{Due to our desire to potentially upweight transients,} We explored a weighted average of the metric values on a per-class basis as a possible mitigation strategy to incentivize classifying uncommon classes, effectively ``leveling the playing field'' in the presence of highly imbalanced class membership. %\changes{%Such weights were taken to be the same for all objects in the same class. -\changes{While modifyinging the log-loss metric to handle weights for different classes changes the interpretability, this would likely be appreciably different if the weights were significantly different at the level of orders of magnitude. -As it finally turned out, the weights actually used in \plasticc\ were all of similar orders, rather than different by orders of magnitude but this decision was concealed by design from some of the authors of this paper.} - -\changes{Given that both log-loss and Brier score seemed to work reasonably well for our requirements, we did not further try to define new metrics by extending known concepts. We chose the weighted log-losss metric because it seemed slightly more discerning, and easy to interpret in terms of information theory, at least in the limit of equal weights. It is probable, that the use of the Brier score would be similar as well.} -%%%% Although weights do impact the interpretability of the log-loss, we select a per-class weighted log-loss as the optimal choice for \plasticc. -%%%% -%%%% % We note that in order to map on to the Kaggle evaluation platform, a metric weighted only by class was used for the general challenge, while a log-loss with more complicated weighting procedure will be used for the science competition (which will continue for an additional month after the main Kaggle release). - -We conclude by noting that care should be taken in planning future open challenges to ensure alignment between the challenge goals and the performance metric, so that efforts are best directed to achieve the challenge objectives. We hope that this study of metric performance across a range of systematic effects and weights may serve as a guide to approaching the problem of identifying optimal probabilistic classifiers for general science applications. +\changes{Our experimental design considers simulated classification submissions from a set of mock classifier archetypes expected of generic transient and variable classifiers.} +\changes{To start with, we identified two metrics of multi-class classification probabilities established in the literature: the Brier score and the log-loss. +We left aside popular metrics (such as accuracy, true/false positive/negative rates, and AUC functions thereof) which did not satisfy these criteria, even though it is in principle possible to extend such metrics for these scenarios.} +The Brier score and the log-loss metrics are structurally and conceptually different, with wholly different interpretations. +The Brier score is a sum of square differences between probabilities; +the explicit penalty term is an attractive feature, but it treats probabilities as generic scores. +The log-loss on the other hand is readily interpretable, meaning the metric itself could be propagated into forecasting the cosmological constraining power of \lsst, affecting the choice of observing strategy. + +\changes{We evaluated these metrics using the simulated classification probability submissions from the classifier archetypes with unit weights and then by varying the weights in Equation~\ref{eq:weightavg}. +In the absence of per-class weights, both the Brier score and the log-loss metrics are susceptible to rewarding a classifier that performs well on the most prevalent class and poorly on all others, which fails to meet the needs of \plasticc's diverse motivations. +On the basis of the mock classifier rankings under equal per-class weights, we found that both metrics reward the classifiers that are better and penalize those that are worse, where better and worse are defined by our common intuition, yielding the same rankings under either metric and demonstrating that both could be appropriate for \plasticc.} + +Even though the Brier score and log-loss metrics take values consistent with one another, they are structurally and conceptually different, with wholly different interpretations. +The Brier score is a sum of square differences between probabilities; the explicit penalty term is an attractive feature, but it treats probabilities as generic scores and is not interpretable in terms of information. +The log-loss on the other hand is readily interpretable, meaning the metric itself could be propagated into forecasting the constraining power of \lsst, affecting the choice of observing strategy. +\sout{We discovered that the log-loss is somewhat more sensitive to the systematic errors in classification that we find most concerning for generic scientific applications.} +While both metrics could be appropriate for \plasticc, the log-loss is preferable due to its interpretability in terms of information. +\sout{Both metrics are susceptible to rewarding a classifier that performs well on the most prevalent class and poorly on all others, which fails to meet the needs of \plasticc's diverse motivations.} + +\changes{Due to our desire to potentially upweight rare classes,} we explored a weighted average of the metric values on a per-class basis as a possible mitigation strategy to incentivize classifying uncommon classes, effectively ``leveling the playing field'' in the presence of highly imbalanced class membership. %\changes{%Such weights were taken to be the same for all objects in the same class. +\changes{While modifyinging the log-loss metric to handle weights for different classes diminishes its interpretability, it can still be understood as information gain subject to the value we as scientists place on knowledge stemming from each class.} + +\changes{Given that both log-loss and Brier score passed the basic sanity tests for \plasticc, there was no need to devise new metrics built upon established metrics of binary or deterministic classification. +Since both were deemed appropriate, we chose the weighted log-losss metric due to its possibility of interpretation in terms of information theory, at least in the limit of equal weights} +\sout{Although weights do impact the interpretability of the log-loss, we select a per-class weighted log-loss as the optimal choice for \plasticc.} +%%%% +%%%% % We note that in order to map on to the Kaggle evaluation platform, a metric weighted only by class was used for the general challenge, while a log-loss with more complicated weighting procedure will be used for the science competition (which will continue for an additional month after the main Kaggle release). + +We conclude by noting that care should be taken in planning future open challenges to ensure alignment between the challenge goals and the performance metric, so that efforts are best directed to achieve the challenge objectives. +It is our hope hope that this study of metric performance across a range of systematic effects and weights may serve as a guide to approaching the problem of identifying optimal probabilistic classifiers for general science applications. diff --git a/paper/tex/data.tex b/paper/tex/data.tex index 010855c..3e590d9 100644 --- a/paper/tex/data.tex +++ b/paper/tex/data.tex @@ -17,7 +17,9 @@ \section{Data} \begin{center} \includegraphics[width=0.49\textwidth]{./fig/complete_counts.png} \caption{\sout{The number of objects in a given class as a function of class population size. - The true class populations are logarithmically distributed.} \changes{Class populations used in this work shown as the number of objects in each of the 13 classes: This was simulated by drawing the number of objects in a given class from a logarithmic distribution to emulate the class imbalance frequently observed in typical astronomical samples}} + The true class populations are logarithmically distributed.} + \changes{The number of members of each of thirteen mock classes considered in this work. + Class populations were simulated by drawing the number of members of a given class from a logarithmic distribution to emulate the extreme class imbalances typical of astronomical samples.}} \label{fig:classdist} \end{center} \end{figure} diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index ef49b9f..6f881ab 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -4,12 +4,13 @@ \section{Discussion} The goal of this work is to identify the metric most suited to \plasticc, which seeks classification posteriors of complete light curves similar to those anticipated from \lsst, with an emphasis on classification over all types, rewarding a ``best in show'' classifier rather than focusing on any one class or scientific application.\footnote{At the conclusion of \plasticc, other metrics specific to scientific uses of one or more particular classes will be used to identify ``best in class'' classification procedures that will be useful for more targeted science cases.} The weighted log-loss is thus the metric most suited to the current \plasticc\ release. -\changes{Classification of transient and variable objects is important for a variety of scientific objectives. This diversity of scientific goals requires different trade-offs, and must be evaluated using multiple metrics. While we leave addressing such challenges for future releases of \plasticc\, and the identification of corresponding metrics to future work, we give a few examples of such science goals and approaches for identifying metrics for them.} \sout{Future releases of \plasticc\ will focus on different challenges in transient and variable object classification, with metrics appropriate to identifying methodologies that best enable those goals. We discuss approaches to identifying optimal metrics for these variations, which may be developed further in future work.} +\changes{Transient and variable object classification is crucial for a variety of scientific objectives. +The impact of a shared performance metric on this diversity of goals leads to complex and covariant trade-offs, which thus must be evaluated using multiple metrics. +While a detailed accounting of these possibilities for future releases of \plasticc\ and the selection of appropriate metrics for individual science cases are outside the scope of this first investigation, we discuss below some issues concerning the identification of metrics for a few example science cases.} -\changes{\subsection{Enabling Transient Followup}} -\subsection{\sout{Early classification}} +\subsection{\sout{Early classification} \changes{Ongoing transient follow-up}} \label{sec:early} Spectroscopic follow-up is only expected of a small fraction of \lsst's detected transients and variable objects due to limited resources for such observations. @@ -26,8 +27,9 @@ \subsection{\sout{Early classification}} The critical question for choosing the most appropriate metric for any specific science goal motivating follow-up observations is to maximize information. We provide two examples of the kind of information one must maximize via early light curve classification and the qualities of a deterministic metric that might enable it. -\changes{\subsection{Spectroscopic Supernova Cosmology}} +\changes{\subsection{Spectroscopic supernova cosmology}} \label{sec:spec_sncosmo} + Supernova cosmology with spectroscopically confirmed light curves benefits from true positives, which contribute to the constraining power of the analysis by including one more data point; when the class in which one is interested is as plentiful as SN Ia and our resources limited a priori, we may not be concerned by a high rate of false negatives. % requires making a decision balancing the improved constraining power of including another SN Ia in the analysis, thereby constraining the cosmological parameters, so only true positives contribute information, and if we had a perfect classifier and standard follow-up spectroscopy resources, there would be a maximum amount of information about the cosmological parameters that could be gained in this way. @@ -35,15 +37,19 @@ \subsection{\sout{Early classification}} False positives, on the other hand, may not enter the cosmology analysis, but they consume follow-up resources, thereby depriving the endeavor of the constraining power due to a single SN Ia. A perfect classifier would lead to a maximum amount of information about the cosmological parameters conditioned on the follow-up resource budget. -For this scientific application, the metric must be chosen to \changes{\emph{not only maximize true positives but also to minimize false positives, and their relative impacts on the cosmological constraints can be quantified in terms of the information one would have about the cosmological parameters under different balances of true and false positives.}} -% balance the value of the information forgone by a false positive and the value of information forgone by a false negative, and the value placed on these is effectively weighted by the value we as researchers place on follow-up resources. +\sout{For this scientific application, the metric must be chosen to balance the value of the information forgone by a false positive and the value of information forgone by a false negative, and the value placed on these is effectively weighted by the value we as researchers place on follow-up resources.} +\changes{In this scientific application, a classifier that maximizes true positives and minimizes false positives boosts the constraining power over cosmological parameters. +However, it does so at a cost of rising false negatives, which represent constraining power forgone. +As this tradeoff is asymmetric, it is insufficient to consider only the true and false positive and negative rates, as the \snphotcc\ FoM does, without propagating their impact on the information gained about the cosmological parameters.} % \aim{Cite some deterministic metrics relating to TP/FP?} -\changes{\subsection{Detecting new classes of transients}} +\changes{\subsection{Anomalous transient and variable detection}} \label{sec:anom} -\changes{A particularly exciting science case is the discovery of entirely unknown classes of transient or variable astrophysical sources, or identifying some of the rarely found sources in the sea of abundant sources. Such a goal may be phrased in terms of detecting anomalies.} -\changes{Like the case of spectroscopic supernova cosmology discussed above,} anomaly detection also gains information only from true positives, but the cost function is different in that the potential gain of information from a true positive, since there is no information about undiscovered classes ahead of time. -\changes{COMMENT RB: not to stay in doc, but I don't understand the prev sentence. I would also object to the recent detection of kilonova as a good example of anomaly detection, I can buy it if I squint very hard} + +\changes{A particularly exciting science case is anomaly detection, the discovery of entirely unknown classes of transient or variable astrophysical sources, or distinguishing some of the rarest types of sources from more abundant types. +Like the case of spectroscopic supernova cosmology discussed above,} anomaly detection also gains information only from true positives, but the cost function is different in that the potential information gain is unbounded when there is no prior information about undiscovered classes. +\aim{COMMENT RB: not to stay in doc, but I don't understand the prev sentence. I would also object to the recent detection of kilonova as a good example of anomaly detection, I can buy it if I squint very hard +COMMENT AIM: Agreed, but I couldn't think of a better one at the time of writing.} An example would be the recent detection of a kilonova, flagged initially by the detection of gravitational waves from an object. Resource availability for identifying new classes is more flexible, increasing when new predictions or promising preliminary observations attract attention, and decreasing when a discovery is confirmed and the new class is established. @@ -52,7 +58,7 @@ \subsection{\sout{Early classification}} % For a rare event like a kilonova, a false negative represents an unbounfalse positive does not appreciably reduce the amount of remaining information available to collect, but a false negative represents a large quantity of information forgone. % Furthermore, r % In this case, the information forgone by a false negative is significant compared to the information forgone by a false positive. -Thus, a metric \sout{tuned to} \changes{evaluating} anomaly detection would aim to \changes{\emph{minimize the false negative rate and maximize the true positive rate.}} +Thus, a metric \sout{tuned to} \changes{for evaluating} anomaly detection would aim to \changes{\emph{minimize the false negative rate and maximize the true positive rate.}} % \aim{Cite some deterministic metrics relating to TP/FN?} % \subsection{Hierarchical classes} diff --git a/paper/tex/introduction.tex b/paper/tex/introduction.tex index 7f9daf7..9c25577 100644 --- a/paper/tex/introduction.tex +++ b/paper/tex/introduction.tex @@ -13,9 +13,9 @@ \section{Introduction} % Thus several science cases (such as SN cosmology) will actively depend om classification of astrophysical sources based on the photometric , and possibly a much smaller training sample/model based on a spectroscopic sub-sample. As such, there is an acute need for classifiers of photometric light curves that can perform well on datasets that include a wide variety of sources including those that are at the limits of detection. -The Photometric \lsst\ Astronomical Time-series Classification Challenge (\plasticc\footnote{\url{http://plasticcblog.wordpress.com/}}) aims to identify and motivate the development of classification techniques that serve astronomical science goals by engaging the broader community outside astronomy. -\plasticc's dataset is comprehensive, including models for well-understood classes, newly observed classes, and classes that have only been proposed to exist, to simulate serendipitous discoveries anticipated of \lsst\footnote{\changes{While originally written before the \plasticc\ competition, in the intervening time \plasticc\ was run as a Kaggle challenge (\url{https://www.kaggle.com/c/PLAsTiCC-2018}) starting September 17, 2018 with a submission deadline of December 17, 2018. The leaderboard and eventual winners were announced shortly after this based on evaluation (\url{https://www.kaggle.com/c/PLAsTiCC-2018/overview/evaluation}) using the logloss metric discussed in this paper. We expect future variants of \plasticc\ to run on modified datasets and questions.}} . -Additionally, \plasticc\ will join the ranks of a handful of past astronomy classification challenges including \citep[Mapping Dark Matter\footnote{\url{https://www.kaggle.com/c/mdm}}]{kitching_gravitational_2011}, \citep[Observing Dark Worlds\footnote{\url{https://www.kaggle.com/c/DarkWorlds}}]{harvey_observing_2013}, and \citep[the Galaxy Challenge\footnote{\url{https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge}}]{dieleman_rotation-invariant_2015}, all hosted on Kaggle\footnote{\url{https://www.kaggle.com/}}, a platform that hosts data analytics competitions where seasoned professionals and amateurs alike can compete to classify, model, and predict large data sets uploaded by companies or scientific collaborations. +The Photometric \lsst\ Astronomical Time-series Classification Challenge (\plasticc\footnote{\url{http://plasticcblog.wordpress.com/}, \url{https://www.kaggle.com/c/PLAsTiCC-2018}}) aims\footnote{\changes{\plasticc\ was run as a Kaggle challenge from 17 September 2018 to 17 December 2018. Though \plasticc\ concluded prior to the final revision of this paper, the study herein was conducted entirely before the commencement of \plasticc, and the draft was submitted to the journal prior to \plasticc's conclusion, hence the use of the present and future tenses throughout this paper.}} to identify and motivate the development of classification techniques that serve astronomical science goals by engaging the broader community outside astronomy. +\plasticc's dataset is comprehensive, including models for well-understood classes, newly observed classes, and classes that have only been proposed to exist, to simulate serendipitous discoveries anticipated of \lsst. +Additionally, \plasticc\ joins the ranks of a handful of past astronomy classification challenges including \citep[Mapping Dark Matter\footnote{\url{https://www.kaggle.com/c/mdm}}]{kitching_gravitational_2011}, \citep[Observing Dark Worlds\footnote{\url{https://www.kaggle.com/c/DarkWorlds}}]{harvey_observing_2013}, and \citep[the Galaxy Challenge\footnote{\url{https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge}}]{dieleman_rotation-invariant_2015}, all hosted on Kaggle\footnote{\url{https://www.kaggle.com/}}, a platform that hosts data analytics competitions where seasoned professionals and amateurs alike can compete to classify, model, and predict large data sets uploaded by companies or scientific collaborations. Kaggle attracts a broad userbase, and those without domain knowledge may provide novel approaches to the problem at hand. Classification in astronomy may proceed through images, as has been done in the contexts of galaxy classification \citep{hoyle_measuring_2016}, supernova classification \citep{cabrera-vives_deep-hits:_2017}, identification of bars in galaxies \citep{abraham_detection_2018}, weak lensing estimation\footnote{\url{http://great3challenge.info/}}\citep{mandelbaum_third_2014}, separation of Near Earth Asteroids from artifacts in images \citep{morii_machine-learning_2016}, as well as time-domain classification \citep{morii_machine-learning_2016, mahabal_deep-learnt_2017, zevin_gravity_2017}, and even noise classification \citep{zevin_gravity_2017, george_classification_2018}. diff --git a/paper/tex/methods.tex b/paper/tex/methods.tex index b6a8911..07d8355 100644 --- a/paper/tex/methods.tex +++ b/paper/tex/methods.tex @@ -141,4 +141,4 @@ \subsection{Weights} The weights for the \plasticc\ metric, however, must be determined before there is knowledge of which systematics affect which classes. Because of this caveat, the choice of weights is isolated to an inherently human problem dictated by the value placed on the scientific merits of knowledge of each class. This paper, on the other hand, can only quantify the impact of weights in relation to the systematics. -We thus agnostically test weighting schemes where classes affected by a particular systematic take a given weight $0 \leq w \leq 1$ and all other classes have a weight $(1 - w) / (M - 1)$. +We thus agnostically test weighting schemes\footnote{\changes{The weights considered in this study are more extreme than those ultimately used for \plasticc\ because the true weights were withheld from some authors prior to the end of the challenge.}} where classes affected by a particular systematic take a given weight $0 \leq w \leq 1$ and all other classes have a weight $(1 - w) / (M - 1)$. diff --git a/paper/tex/results.tex b/paper/tex/results.tex index dd9485c..7042609 100644 --- a/paper/tex/results.tex +++ b/paper/tex/results.tex @@ -65,9 +65,11 @@ \subsection{Mock classifier systematics} Subsumed from Almost & 0.641 & 1.629\\ Subsumed from Perfect & 1.0 & 18.421\footnote{The entry for the log-loss of a classifier that subsumes a class into one that is otherwise perfectly classified should be infinite but is bounded by the numerical precision of our calculations.} \end{tabular} -\caption{\changes{Metric values computed using Eqn.~\ref{eq:weightavg} with unit weights for the mock data produced by mock classification schemes described in Sec.~\ref{sec:mockdata}. The table shows that log-loss metric is more discerning than the Brier score for poor classification, particularly for the Subsuming from Almost and Subsuming from Perfect schemes. Note, that using different weights changes the values of these metrics as shown in Figure~\ref{fig:all_combined}.} \sout{The value of each metric when the weight is entirely on the class with the indicated characteristic. +\caption{\sout{The value of each metric when the weight is entirely on the class with the indicated characteristic. Weighting changes the metric performance: the value of each metric when the weight is entirely on the class with the indicated characteristic (\changes{corresponding} to a $w=1$ case in Figure~\ref{fig:all_combined}). The log-loss is more sensitive than \changes{the} Brier score, with larger values of the score (indicating poor classification performance), particularly for the subsuming systematic.} +\changes{Metric values computed using Equation~\ref{eq:weightavg} with unit weights for the mock data produced by mock classification schemes described in Sec.~\ref{sec:mockdata}. +While the log-loss metric has a larger dynamic range than the Brier score for poor classification, the toy classifiers would be ranked the same way by either metric.} } \label{tab:extents} \end{table} From 9b5fbe7cbcdcfb4645490a13925d6ba637f98bc8 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Sat, 27 Jul 2019 06:40:57 -0400 Subject: [PATCH 36/58] fixed Equation 5 --- paper/tex/methods.tex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/tex/methods.tex b/paper/tex/methods.tex index 07d8355..fb8967b 100644 --- a/paper/tex/methods.tex +++ b/paper/tex/methods.tex @@ -131,9 +131,9 @@ \subsection{Weights} A simpler alternative that we investigate in this paper is to use a weighted average \begin{eqnarray} \label{eq:weightavg} - Q_{m} &=& \frac{1}{\sum_{n} w_{n}} \sum_{n=1}^{N} w_{n} \sum_{m=1}^{M} Q_{n, m} + Q &=& \frac{1}{\sum_{m} w_{m}} \sum_{m} w_{m} Q_{m} \end{eqnarray} -of per-class metrics. +of per-class metrics $Q_{m}$. (While weights could be assigned to each term $Q_{n, m}$, we do not consider this complexity at this time.) Weights that are not proportional to $N^{-1}$ nor $M^{-1}$ may be chosen to encourage challenge participants to direct more attention to classes with less active classification efforts or those that have been historically more difficult to classify due to observational limitations. 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from \snmachine. While the classification methods described in \citet{lochner_photometric_2016} refer to the idealized subset of the \snphotcc\ data, these approaches are the state-of-the-art in classification of extragalactic transients. We present in Table~\ref{tab:snmachineresults} the log-loss and Brier scores assuming an equal weight per object. -%, for classification probabilities derived from running the algorithms of \citet{lochner_photometric_2016} on the \snphotcc\ data of Section~\ref{sec:realdata}. -Table~\ref{tab:snmachineresults} also contains the ranking of classifier performance under each metric. - -\begin{table*}[] - \begin{centering} -\begin{tabular}{lllllll}%ll} -Rank $R$ & $R_\mathrm{FoM}$ & FoM & %$R_\mathrm{AUC}$ & AUC & -$R_\mathrm{LogLoss}$ & Log-loss & $R_\mathrm{Brier}$ & Brier \\ -\hline -1 & TBDT & 0.635 %& TBDT & 0.982 -& TBDT & 0.0907 & TBDT & 0.0486 \\ -2 & WBDT & 0.591 %& WBDT & 0.978 -& TSVM & 0.113 & TSVM & 0.0583 \\ -3 & TSVM & 0.514 %& TSVM & 0.969 -& TNN & 0.125 & TNN & 0.0650 \\ -4 & WSVM & 0.499 %& WSVM & 0.968 -& WSVM & 0.1316 & WBDT & 0.0689 \\ -5 & TNN & 0.496 %& TNN & 0.954 -& WBDT & 0.1321 & WSVM & 0.0730 \\ -6 & WNN & 0.480 %& WNN & 0.946 -& TKNN & 0.146 & WNN & 0.0750 \\ -7 & TKNN & 0.384 %& TKNN & 0.942 -& WNN & 0.152 & TKNN & 0.0787 \\ -8 & TNB & 0.340 %& WKNN & 0.894 -& WKNN & 0.228 & TNB & 0.105 \\ -9 & WKNN & 0.114 %& TNB & 0.879 -& TNB & 0.251 & WKNN & 0.132 \\ -10 & WNB & 0.0365 %& WNB & 0.850 -& WNB & 0.443 & WNB & 0.178 \\ -\end{tabular} - \caption{ - The values of three metrics for each of ten \snmachine\ classifiers with equal weight per object. - The metrics broadly agree on the ranking of the classifiers, confirming consistency between a conventional metric of classification performance and the metrics of probabilistic classifications presented here. - However, there are some differences with pairwise swapping between the log-loss and Brier rankings and some significant reordering of ranks 2 through 5 with the FoM metric relative to the probabilistic metrics. - } - \label{tab:snmachineresults} - \end{centering} -\end{table*} +We apply the log-loss and Brier metrics to the classification output from \snmachine. While the classification methods described in \citet{lochner_photometric_2016} refer to the idealized subset of the \snphotcc\ data, these approaches are the state-of-the-art in classification of extragalactic transients. +We present in \sout{Table~\ref{fig:snmachineresults}}\changes{Figure~\ref{fig:snmachineresults} the rankings under the} log-loss and Brier score metrics assuming an equal weight per object. +%, for classification probabilities derived from running the algorithms of \citet{lochner_photometric_2016} on the \snphotcc\ data of Section~\ref{sec:realdata}. +\sout{Table~\ref{fig:snmachineresults} also contains the ranking of classifier performance under each metric.} + +% \sout{\begin{table*}[] +% \begin{centering} +% \begin{tabular}{lllllll}%ll} +% Rank $R$ & $R_\mathrm{FoM}$ & FoM & %$R_\mathrm{AUC}$ & AUC & +% $R_\mathrm{LogLoss}$ & Log-loss & $R_\mathrm{Brier}$ & Brier \\ +% \hline +% 1 & TBDT & 0.635 %& TBDT & 0.982 +% & TBDT & 0.0907 & TBDT & 0.0486 \\ +% 2 & WBDT & 0.591 %& WBDT & 0.978 +% & TSVM & 0.113 & TSVM & 0.0583 \\ +% 3 & TSVM & 0.514 %& TSVM & 0.969 +% & TNN & 0.125 & TNN & 0.0650 \\ +% 4 & WSVM & 0.499 %& WSVM & 0.968 +% & WSVM & 0.1316 & WBDT & 0.0689 \\ +% 5 & TNN & 0.496 %& TNN & 0.954 +% & WBDT & 0.1321 & WSVM & 0.0730 \\ +% 6 & WNN & 0.480 %& WNN & 0.946 +% & TKNN & 0.146 & WNN & 0.0750 \\ +% 7 & TKNN & 0.384 %& TKNN & 0.942 +% & WNN & 0.152 & TKNN & 0.0787 \\ +% 8 & TNB & 0.340 %& WKNN & 0.894 +% & WKNN & 0.228 & TNB & 0.105 \\ +% 9 & WKNN & 0.114 %& TNB & 0.879 +% & TNB & 0.251 & WKNN & 0.132 \\ +% 10 & WNB & 0.0365 %& WNB & 0.850 +% & WNB & 0.443 & WNB & 0.178 \\ +% \end{tabular} +% \caption{ +% The values of three metrics for each of ten \snmachine\ classifiers with equal weight per object. +% The metrics broadly agree on the ranking of the classifiers, confirming consistency between a conventional metric of classification performance and the metrics of probabilistic classifications presented here. +% However, there are some differences with pairwise swapping between the log-loss and Brier rankings and some significant reordering of ranks 2 through 5 with the FoM metric relative to the probabilistic metrics. +% } +% \label{tab:snmachineresults} +% \end{centering} +% \end{table*}} + +\begin{figure} + \begin{center} + \includegraphics[width=0.49\textwidth]{./fig/Tables3_option4.png} + \caption{ + \changes{The rankings of each of ten \snmachine\ classifiers with equal weight per object under the three metrics. + The metrics broadly agree on the ranking of the classifiers, confirming consistency between a conventional metric of classification performance and the metrics of probabilistic classifications presented here. + However, there are some differences with pairwise swapping between the log-loss and Brier rankings and some significant reordering of ranks 2 through 5 with the FoM metric relative to the probabilistic metrics.} + } + \end{center} + \label{fig:snmachineresults} +\end{figure} We apply our metrics to the classification output from \snmachine\ applied to the \snphotcc\ dataset as an example of representative light curves and representative classifiers used in extragalactic astronomy. -We present in Table~\ref{tab:snmachineresults} the log-loss and Brier scores assuming an equal weight per object, as well as the original \snphotcc\ metric described in Section~\ref{sec:deterministic}. -Table~\ref{tab:snmachineresults} also contains the ranking of classifier performance under each metric. +We present in \sout{Table~\ref{fig:snmachineresults}}\changes{Figure~\ref{fig:snmachineresults}} the rankings of each classifier under the log-loss and Brier scores assuming an equal weight per object, as well as the original \snphotcc\ metric described in Section~\ref{sec:deterministic}. +\sout{Table~\ref{fig:snmachineresults} also contains the ranking of classifier performance under each metric.} The Brier score, log-loss, and \snphotcc\ FoM are in agreement as to the first- and last-ranked classifiers. This consensus indicates that both of the potential \plasticc\ metrics are roughly consistent with our intuition about what makes a good classifier, providing an anchor between accepted notions of an appropriate metric and the metrics of probabilistic classifications under consideration here. From 391977c7372ff7a19b5ce3f74317672b3c9c9338 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Sat, 27 Jul 2019 07:38:30 -0400 Subject: [PATCH 39/58] changed figure colors --- paper/kaggle-run.ipynb | 32 ++++++++----------------------- paper/main.ipynb | 43 ++++++++++++++++++++++++++++++++---------- 2 files changed, 41 insertions(+), 34 deletions(-) diff --git a/paper/kaggle-run.ipynb b/paper/kaggle-run.ipynb index 5f32d29..26948f9 100644 --- a/paper/kaggle-run.ipynb +++ b/paper/kaggle-run.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -169,25 +169,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'KNeighbors': ['KNeighbors/predicted_prob_KNeighbors.csv',\n", - " 'KNeighbors/truth_table_KNeighbors.csv'],\n", - " 'MLPNeuralNet': ['MLPNeuralNet/predicted_prob_MLPNeuralNet.csv',\n", - " 'MLPNeuralNet/truth_table_MLPNeuralNet.csv'],\n", - " 'RandomForest': ['RandomForest/predicted_prob_RandomForest.csv',\n", - " 'RandomForest/truth_table_RandomForest.csv']}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "topdir = '../examples/'\n", "mystery = make_file_locs(mystery)\n", @@ -690,7 +674,7 @@ "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", - "language": "python", + "language": "python3", "name": "python3" }, "language_info": { @@ -703,7 +687,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/paper/main.ipynb b/paper/main.ipynb index 628f498..749dce6 100644 --- a/paper/main.ipynb +++ b/paper/main.ipynb @@ -28,12 +28,19 @@ "source": [ "import matplotlib as mpl\n", "# print(mpl.rcParams.items)\n", - "mpl.use('Agg')\n", + "mpl.use('PS')\n", "mpl.rcParams['text.usetex'] = False\n", "mpl.rcParams['mathtext.rm'] = 'serif'\n", "mpl.rcParams['font.family'] = 'serif'\n", - "mpl.rcParams['font.serif'] = ['Times New Roman']\n", - "# mpl.rcParams['font.family'] = ['Times New Roman']\n", + "mpl.rcParams[\"font.family\"] = \"serif\"\n", + "mpl.rcParams[\"mathtext.fontset\"] = \"dejavuserif\"\n", + "mpl.rcParams['font.serif'] = 'DejaVu Serif'\n", + "# mpl.rcParams['text.usetex'] = False\n", + "# mpl.rcParams['mathtext.rm'] = 'serif'\n", + "# mpl.rcParams['font.weight'] = 'light'\n", + "# mpl.rcParams['font.family'] = 'serif'\n", + "# mpl.rcParams['font.serif'] = ['Times New Roman']\n", + "# # mpl.rcParams['font.family'] = ['Times New Roman']\n", "mpl.rcParams['axes.titlesize'] = 25\n", "mpl.rcParams['axes.labelsize'] = 20\n", "mpl.rcParams['xtick.labelsize'] = 15\n", @@ -177,7 +184,7 @@ "# plt.hist(truth, log=True, alpha=0.5)\n", "ax.set_ylabel('counts', fontsize=20)\n", "ax.set_xlabel('class', fontsize=20)\n", - "plt.savefig('fig/mock_counts.png')\n", + "# plt.savefig('fig/mock_counts.png')\n", "plt.show()\n", "plt.close()" ] @@ -470,6 +477,22 @@ "plasticc = wrap_up_classifier(cm, 'Mutually Subsuming', plasticc, delta=0.1)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):\n", + " new_cmap = mpl.colors.LinearSegmentedColormap.from_list(\n", + " 'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),\n", + " cmap(np.linspace(minval, maxval, n)))\n", + " return new_cmap\n", + "\n", + "cmap = plt.get_cmap('hot_r')\n", + "fave_cmap = truncate_colormap(cmap, 0.3, 0.9)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -515,8 +538,8 @@ "# print(position)\n", " testname = info_dict['names'][i]\n", " \n", - " im = ax.imshow(info_dict['cm'][testname], vmin=0., vmax=1., cmap='winter_r')\n", - " ax.text(.5,.9,testname,horizontalalignment='center',transform=ax.transAxes, fontsize=16)\n", + " im = ax.imshow(info_dict['cm'][testname], vmin=0., vmax=1., cmap=fave_cmap)\n", + " ax.text(.5,.9,testname,horizontalalignment='center',transform=ax.transAxes, fontsize=14)\n", "# ax.tick_params(axis=u'both', which=u'both',length=10)\n", "# pylab.colorbar()\n", "# fig.subplots_adjust(right=0.5)\n", @@ -527,7 +550,7 @@ " bigAxes.set_ylabel(r'true class', fontsize=20, labelpad=-15)\n", " bigAxes.set_xlabel(r'predicted class', fontsize=20, labelpad=15)\n", " pylab.tight_layout()\n", - " pylab.savefig('fig/all_'+fn+'_cm.png', bbox_inches='tight', pad_inches=0)" + " pylab.savefig('fig/all_'+fn+'_cm.png', bbox_inches='tight', pad_inches=0, dpi=200)" ] }, { @@ -1919,9 +1942,9 @@ "anaconda-cloud": {}, "celltoolbar": "Tags", "kernelspec": { - "display_name": "Python 3", - "language": "python3", - "name": "python3" + "display_name": "proclam (Python 3)", + "language": "python", + "name": "proclam_3" }, "language_info": { "codemirror_mode": { From b74699798d52cdf27d8123c915843d582b801b1f Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Sat, 27 Jul 2019 09:27:47 -0400 Subject: [PATCH 40/58] fixing software citation formatting and adding contributions --- paper/authors.csv | 6 +- paper/main.bib | 387 ++++++++++++++++++++++++++++++++++++++-------- paper/main.tex | 11 +- 3 files changed, 335 insertions(+), 69 deletions(-) diff --git a/paper/authors.csv b/paper/authors.csv index 44f07c7..0c841d8 100644 --- a/paper/authors.csv +++ b/paper/authors.csv @@ -1,11 +1,11 @@ Lastname,Firstname,Authorname,AuthorType,Affiliation,Contribution,Email Malz,Alex,A.I.~Malz,Contact,"Center for Cosmology and Particle Physics, New York University, 726 Broadway, New York, NY 10004, USA","conceptualization, data curation, formal analysis, investigation, methodology, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",aimalz@nyu.edu -Malz,Alex,A.I.~Malz,Contact,"Department of Physics, New York University, 726 Broadway, New York, NY 10004, USA","conceptualization, data curation, formal analysis, investigation, methodology, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",aimalz@nyu.edu +Malz,Alex,A.I.~Malz,Contact,"German Centre of Cosmological Lensing, Ruhr-Universitaet Bochum, Universitaetsstra{\ss}e 150, 44801 Bochum, Germany","conceptualization, data curation, formal analysis, investigation, methodology, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",aimalz@nyu.edu Hlo\v{z}ek,Ren\'ee,R.~Hlo\v{z}ek,Contributor,"Department of Astronomy and Astrophysics, University of Toronto, 50 St. George St., Toronto, ON M5S 3H4, Canada","data curation, formal analysis, funding acquisition, investigation, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",hlozek@dunlap.utoronto.ca Hlo\v{z}ek,Ren\'ee,R.~Hlo\v{z}ek,Contributor,"Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George St., Toronto, ON M5S 3H4, Canada","data curation, formal analysis, funding acquisition, investigation, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",hlozek@dunlap.utoronto.ca Allam,Tarek,T.~Allam Jr,Contributor,"Mullard Space Science Laboratory, Department of Space and Climate Physics, University College London, Holmbury Hill Rd, Dorking RH5 6NT, UK","investigation, software, validation, writing - original draft",[email] Bahmanyar,Anita,A.~Bahmanyar,Contributor,"Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George St., Toronto, ON M5S 3H4, Canada","formal analysis, investigation, methodology, software, writing - editing, writing - original draft",[email] -Biswas,Rahul,R.~Biswas,Contributor,"The Oskar Klein Centre for Cosmoparticle Physics, Stockholm University, AlbaNova, Stockholm, SE-106 91, Sweden","conceptualization, methodology, software, writing - original draft",[email] +Biswas,Rahul,R.~Biswas,Contributor,"The Oskar Klein Centre for Cosmoparticle Physics, Stockholm University, AlbaNova, Stockholm, SE-106 91, Sweden","conceptualization, methodology, software, writing - editing, writing - original draft",[email] Dai,Mi,M.~Dai,Contributor,"Rutgers, the State University of New Jersey, 136 Frelinghuysen Road, Piscataway, NJ 08854 USA","writing - editing",[email] Galbany,Llu\'is,L.~Galbany,Contributor,"University of Pittsburgh, 300 Allen Hall, 3941 O'Hara St, Pittsburgh, PA 15260","writing - editing",[email] Ishida,Emille,E.E.O.~Ishida,Contributor,"Universit\'e Clermont Auvergne, CNRS/IN2P3, LPC, F-63000 Clermont-Ferrand, France","conceptualization, project administration, supervision, writing - editing",[email] @@ -25,5 +25,5 @@ Narayan,Gautham,G.~Narayan,Contributor,"Space Telescope Science Institute, 3700 Peiris,Hiranya,H.~Peiris,Contributor,"The Oskar Klein Centre for Cosmoparticle Physics, Stockholm University, AlbaNova, Stockholm, SE-106 91, Sweden","conceptualization, funding acquisition, supervision",[email] Peiris,Hiranya,H.~Peiris,Contributor,"Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT, UK","conceptualization, funding acquisition, supervision",[email] Peters,Christina~M.,C.M.~Peters,Contributor,"Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George St., Toronto, ON M5S 3H4, Canada","writing - editing",[email] -Ponder,Kara,K.~Ponder,Contributor,"Berkeley Center for Cosmological Physics, Campbell Hall 341, University of California Berkeley, Berkeley, CA 94720, USA","writing - editing",[email] +Ponder,Kara,K.~Ponder,Contributor,"Berkeley Center for Cosmological Physics, Campbell Hall 341, University of California Berkeley, Berkeley, CA 94720, USA","visualization, writing - editing",[email] Setzer,Christian,C.N.~Setzer,Contributor,"The Oskar Klein Centre for Cosmoparticle Physics, Stockholm University, AlbaNova, Stockholm, SE-106 91, Sweden","conceptualization, software",christian.setzer@fysik.su.se diff --git a/paper/main.bib b/paper/main.bib index 4af535b..c878d5c 100644 --- a/paper/main.bib +++ b/paper/main.bib @@ -1,23 +1,107 @@ + +@article{pedregosa_scikit-learn:_2011, + title = {Scikit-learn: {Machine} learning in {Python}}, + volume = {12}, + shorttitle = {Scikit-learn}, + url = {http://www.jmlr.org/papers/v12/pedregosa11a.html}, + number = {Oct}, + urldate = {2017-08-30}, + journal = {J Machine Learning Res}, + author = {Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and {others}}, + year = {2011}, + pages = {2825--2830}, + file = {pedregosa11a.pdf:/home/aimalz/Documents/References/storage/FIJ6XGDI/pedregosa11a.pdf:application/pdf} +} + +@book{oliphant_guide_2006, + address = {USA}, + title = {A guide to {NumPy}}, + url = {http://www.numpy.org/}, + publisher = {Trelgol Publishing}, + author = {Oliphant, Travis E.}, + year = {2006} +} + +@misc{jones_scipy:_2001, + title = {{SciPy}: {Open} {Source} {Scientific} {Tools} for {Python}}, + url = {https://www.scipy.org/}, + author = {Jones, Eric and Oliphant, Travis and Peterson, Pearu}, + year = {2001} +} + +@article{hunter_matplotlib:_2007, + title = {Matplotlib: {A} 2D {Graphics} {Environment}}, + volume = {9}, + issn = {1521-9615}, + shorttitle = {Matplotlib}, + doi = {10.1109/MCSE.2007.55}, + abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting,and publication-quality image generation across user interfaces and operating systems}, + number = {3}, + journal = {Computing in Science Engineering}, + author = {Hunter, J. D.}, + month = may, + year = {2007}, + keywords = {2D graphics package, application development, computer graphics, Computer languages, Equations, Graphical user interfaces, Graphics, Image generation, interactive scripting, Interpolation, mathematics computing, Matplotlib, object-oriented programming, operating system, Operating systems, Packaging, Programming profession, publication-quality image generation, Python, scientific programming, scripting languages, software packages, user interface, User interfaces}, + pages = {90--95}, + file = {IEEE Xplore Abstract Record:/home/aimalz/Documents/References/storage/Q3Q8X7A6/4160265.html:text/html;IEEE Xplore Full Text PDF:/home/aimalz/Documents/References/storage/96S8KF8W/Hunter - 2007 - Matplotlib A 2D Graphics Environment.pdf:application/pdf} +} + +@inproceedings{kluyver_jupyter_2016, + title = {Jupyter {Notebooks}-a publishing format for reproducible computational workflows.}, + booktitle = {{ELPUB}}, + author = {Kluyver, Thomas and Ragan-Kelley, Benjamin and P{\'e}rez, Fernando and Granger, Brian E. and Bussonnier, Matthias and Frederic, Jonathan and Kelley, Kyle and Hamrick, Jessica B. and Grout, Jason and Corlay, Sylvain}, + year = {2016}, + pages = {87--90}, + file = {STAL9781614996491-0087.pdf:/home/aimalz/Documents/References/storage/9HSAUBZG/STAL9781614996491-0087.pdf:application/pdf} +} + +@article{walt_numpy_2011, + title = {The {NumPy} {Array}: {A} {Structure} for {Efficient} {Numerical} {Computation}}, + volume = {13}, + issn = {1521-9615}, + shorttitle = {The {NumPy} {Array}}, + url = {doi.ieeecomputersociety.org/10.1109/MCSE.2011.37}, + abstract = {{\textless}p{\textgreater}In the Python world, NumPy arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.{\textless}/p{\textgreater}}, + number = {2}, + urldate = {2018-04-06}, + journal = {Computing in Science \& Engineering}, + author = {Walt, S. v and Colbert, S. C. and Varoquaux, G.}, + year = {2011}, + doi = {10.1109/MCSE.2011.37}, + keywords = {Python, scientific programming, numerical computations, NumPy, programming libraries}, + pages = {22--30}, + file = {Snapshot:/home/aimalz/Documents/References/storage/69Z3K5NW/mcs2011020022-abs.html:text/html} +} + @article{kessler_supernova_2010, title = {Supernova {Photometric} {Classification} {Challenge}}, + url = {http://arxiv.org/abs/1001.5210}, + abstract = {We have publicly released a blinded mix of simulated SNe, with types (Ia, Ib, Ic, II) selected in proportion to their expected rate. The simulation is realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point spread function and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non-Ia type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). We challenge scientists to run their classification algorithms and report a type for each SN. A spectroscopically confirmed subset is provided for training. The goals of this challenge are to (1) learn the relative strengths and weaknesses of the different classification algorithms, (2) use the results to improve classification algorithms, and (3) understand what spectroscopically confirmed sub-sets are needed to properly train these algorithms. The challenge is available at www.hep.anl.gov/SNchallenge, and the due date for classifications is May 1, 2010.}, + urldate = {2018-05-01}, journal = {arXiv:1001.5210 [astro-ph]}, author = {Kessler, Richard and Conley, Alex and Jha, Saurabh and Kuhlmann, Stephen}, month = jan, year = {2010}, + note = {arXiv: 1001.5210}, + keywords = {Astrophysics - Instrumentation and Methods for Astrophysics}, + file = {arXiv\:1001.5210 PDF:/home/aimalz/Documents/References/storage/NV4GQ7JQ/Kessler et al. - 2010 - Supernova Photometric Classification Challenge.pdf:application/pdf;arXiv\:1001.5210 PDF:/home/aimalz/Documents/References/storage/3H2DYRZE/Kessler et al. - 2010 - Supernova Photometric Classification Challenge.pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/9SFKRMMH/1001.html:text/html;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/NVYUBSZJ/1001.html:text/html} } @article{kessler_results_2010, title = {Results from the {Supernova} {Photometric} {Classification} {Challenge}}, volume = {122}, issn = {1538-3873}, + url = {http://iopscience.iop.org/article/10.1086/657607/meta}, doi = {10.1086/657607}, language = {en}, number = {898}, + urldate = {2018-05-01}, journal = {PASP}, author = {Kessler, Richard and Bassett, Bruce and Belov, Pavel and Bhatnagar, Vasudha and Campbell, Heather and Conley, Alex and Frieman, Joshua A. and Glazov, Alexandre and Gonz{\'a}lez-Gait{\'a}n, Santiago and Hlozek, Ren{\'e}e and Jha, Saurabh and Kuhlmann, Stephen and Kunz, Martin and Lampeitl, Hubert and Mahabal, Ashish and Newling, James and Nichol, Robert C. and Parkinson, David and Philip, Ninan Sajeeth and Poznanski, Dovi and Richards, Joseph W. and Rodney, Steven A. and Sako, Masao and Schneider, Donald P. and Smith, Mathew and Stritzinger, Maximilian and Varughese, Melvin}, month = nov, year = {2010}, pages = {1415}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/MBPIA5WS/Kessler et al. - 2010 - Results from the Supernova Photometric Classificat.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/KG8AALJX/657607.html:text/html} } @article{roberts_zbeams:_2017, @@ -25,39 +109,53 @@ @article{roberts_zbeams:_2017 volume = {2017}, issn = {1475-7516}, shorttitle = {{zBEAMS}}, + url = {http://stacks.iop.org/1475-7516/2017/i=10/a=036}, doi = {10.1088/1475-7516/2017/10/036}, + abstract = {Supernova cosmology without spectra will be an important component of future surveys such as LSST. This lack of supernova spectra results in uncertainty in the redshifts which, if ignored, leads to significantly biased estimates of cosmological parameters. Here we present a hierarchical Bayesian formalism{\textemdash} zBEAMS{\textemdash}that addresses this problem by marginalising over the unknown or uncertain supernova redshifts to produce unbiased cosmological estimates that are competitive with supernova data with spectroscopically confirmed redshifts. zBEAMS provides a unified treatment of both photometric redshifts and host galaxy misidentification (occurring due to chance galaxy alignments or faint hosts), effectively correcting the inevitable contamination in the Hubble diagram. Like its predecessor BEAMS, our formalism also takes care of non-Ia supernova contamination by marginalising over the unknown supernova type. We illustrate this technique with simulations of supernovae with photometric redshifts and host galaxy misidentification. A novel feature of the photometric redshift case is the important role played by the redshift distribution of the supernovae.}, language = {en}, number = {10}, + urldate = {2018-05-01}, journal = {J. Cosmol. Astropart. Phys.}, author = {Roberts, Ethan and Lochner, Michelle and Fonseca, Jos{\'e} and Bassett, Bruce A. and Lablanche, Pierre-Yves and Agarwal, Shankar}, year = {2017}, pages = {036}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/HS88HIGJ/Roberts et al. - 2017 - zBEAMS a unified solution for supernova cosmology.pdf:application/pdf} } @article{lochner_photometric_2016, title = {Photometric {Supernova} {Classification} with {Machine} {Learning}}, volume = {225}, issn = {0067-0049}, + url = {http://stacks.iop.org/0067-0049/225/i=2/a=31}, doi = {10.3847/0067-0049/225/2/31}, + abstract = {Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.}, language = {en}, number = {2}, + urldate = {2018-05-01}, journal = {ApJS}, author = {Lochner, Michelle and McEwen, Jason D. and Peiris, Hiranya V. and Lahav, Ofer and Winter, Max K.}, year = {2016}, pages = {31}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/6EPXMN4P/Lochner et al. - 2016 - Photometric Supernova Classification with Machine .pdf:application/pdf} } @article{narayan_machine_2018, title = {Machine {Learning}-based {Brokers} for {Real}-time {Classification} of the {LSST} {Alert} {Stream}}, volume = {236}, issn = {1538-4365}, + url = {http://arxiv.org/abs/1801.07323}, doi = {10.3847/1538-4365/aab781}, + abstract = {The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demands that the astronomical community update its followup paradigm. Alert-brokers -- automated software system to sift through, characterize, annotate and prioritize events for followup -- will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate and retrospective classification of alerts. The first takes the form of variable vs transient categorization, the second, a multi-class typing of the combined variable and transient dataset, and the third, a purity-driven subtyping of a transient class. While several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress towards adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.}, number = {1}, + urldate = {2018-06-05}, journal = {The Astrophysical Journal Supplement Series}, author = {Narayan, Gautham and Zaidi, Tayeb and Soraisam, Monika D. and Wang, Zhe and Lochner, Michelle and Matheson, Thomas and Saha, Abhijit and Yang, Shuo and Zhao, Zhenge and Kececioglu, John and Scheidegger, Carlos and Snodgrass, Richard T. and Axelrod, Tim and Jenness, Tim and Maier, Robert S. and Ridgway, Stephen T. and Seaman, Robert L. and Evans, Eric Michael and Singh, Navdeep and Taylor, Clark and Toeniskoetter, Jackson and Welch, Eric and Zhu, Songzhe}, month = may, year = {2018}, + note = {arXiv: 1801.07323}, + keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena}, pages = {9}, + file = {arXiv\:1801.07323 PDF:/home/aimalz/Documents/References/storage/4R9ZMGGN/Narayan et al. - 2018 - Machine Learning-based Brokers for Real-time Class.pdf:application/pdf;arXiv\:1801.07323 PDF:/home/aimalz/Documents/References/storage/YI7PZ2BW/Narayan et al. - 2018 - Machine Learning-based Brokers for Real-time Class.pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/BAUGHABV/1801.html:text/html;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/TTR8X4NX/1801.html:text/html} } @article{crown_validation_2012, @@ -65,13 +163,18 @@ @article{crown_validation_2012 volume = {10}, copyright = {This paper is not subject to U.S. copyright. Published in 2012 by the American Geophysical Union}, issn = {1542-7390}, + url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2011SW000760}, doi = {10.1029/2011SW000760}, + abstract = {This paper provides an assessment of the operational solar flare look-up table currently in use at the National Oceanic and Atmospheric Administration (NOAA) Space Weather Prediction Center (SWPC) during solar cycle 23 (May 1996 {\textendash} December 2008). To assess the value of human interaction, a validation of subjective flare probability forecasts was conducted and compared to the results obtained from the climatological look-up table used at SWPC. Probabilistic flare forecasts are evaluated using the Brier Skill Score, then discretized and entered into contingency tables from which a variety of verification measures are calculated. The ultimate goal of this report is to provide an operational baseline, whereby the scores and statistics from this paper can be used as the basis for future evaluation of models presented to the operational community.}, language = {en}, number = {6}, + urldate = {2018-07-09}, journal = {Space Weather}, author = {Crown, Misty D.}, month = jun, year = {2012}, + keywords = {flares, forecasting}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/VWZTVEWZ/Crown - Validation of the NOAA Space Weather Prediction Ce.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/E5NGYV7U/2011SW000760.html:text/html} } @article{richards_construction_2012, @@ -79,104 +182,138 @@ @article{richards_construction_2012 volume = {203}, issn = {0067-0049}, shorttitle = {Construction of a {Calibrated} {Probabilistic} {Classification} {Catalog}}, + url = {http://stacks.iop.org/0067-0049/203/i=2/a=32}, doi = {10.1088/0067-0049/203/2/32}, + abstract = {With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20\% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24\% of those sources into one of 12 science classes.}, language = {en}, number = {2}, + urldate = {2018-07-09}, journal = {ApJS}, author = {Richards, Joseph W. and Starr, Dan L. and Miller, Adam A. and Bloom, Joshua S. and Butler, Nathaniel R. and {Henrik Brink} and Crellin-Quick, Arien}, year = {2012}, pages = {32}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/ETX7PM7G/Richards et al. - 2012 - Construction of a Calibrated Probabilistic Classif.pdf:application/pdf} } @article{mays_ensemble_2015, title = {Ensemble {Modeling} of {CMEs} {Using} the {WSA}{\textendash}{ENLIL}+{Cone} {Model}}, volume = {290}, issn = {0038-0938, 1573-093X}, + url = {https://link.springer.com/article/10.1007/s11207-015-0692-1}, doi = {10.1007/s11207-015-0692-1}, + abstract = {Ensemble modeling of coronal mass ejections (CMEs) provides a probabilistic forecast of CME arrival time that includes an estimation of arrival-time uncertainty from the spread and distribution of predictions and forecast confidence in the likelihood of CME arrival. The real-time ensemble modeling of CME propagation uses the Wang{\textendash}Sheeley{\textendash}Arge (WSA){\textendash}ENLIL+Cone model installed at the Community Coordinated Modeling Center (CCMC) and executed in real-time at the CCMC/Space Weather Research Center. The current implementation of this ensemble-modeling method evaluates the sensitivity of WSA{\textendash}ENLIL+Cone model simulations of CME propagation to initial CME parameters. We discuss the results of real-time ensemble simulations for a total of 35 CME events that occurred between January 2013 {\textendash} July 2014. For the 17 events where the CME was predicted to arrive at Earth, the mean absolute arrival-time prediction error was 12.3 hours, which is comparable to the errors reported in other studies. For predictions of CME arrival at Earth, the correct-rejection rate is 62 \%, the false-alarm rate is 38 \%, the correct-alarm ratio is 77 \%, and the false-alarm ratio is 23 \%. The arrival time was within the range of the ensemble arrival predictions for 8 out of 17 events. The Brier Score for CME arrival-predictions is 0.15 (where a score of 0 on a range of 0 to 1 is a perfect forecast), which indicates that on average, the predicted probability, or likelihood, of CME arrival is fairly accurate. The reliability of ensemble CME-arrival predictions is heavily dependent on the initial distribution of CME input parameters (e.g. speed, direction, and width), particularly the median and spread. Preliminary analysis of the probabilistic forecasts suggests undervariability, indicating that these ensembles do not sample a wide-enough spread in CME input parameters. Prediction errors can also arise from ambient-model parameters, the accuracy of the solar-wind background derived from coronal maps, or other model limitations. Finally, predictions of the K P geomagnetic index differ from observed values by less than one for 11 out of 17 of the ensembles and K P prediction errors computed from the mean predicted K P show a mean absolute error of 1.3.}, language = {en}, number = {6}, + urldate = {2018-07-09}, journal = {Sol Phys}, author = {Mays, M. L. and Taktakishvili, A. and Pulkkinen, A. and MacNeice, P. J. and Rast{\"a}tter, L. and Odstrcil, D. and Jian, L. K. and Richardson, I. G. and LaSota, J. A. and Zheng, Y. and Kuznetsova, M. M.}, month = jun, year = {2015}, pages = {1775--1814}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/QEBIDI76/Mays et al. - 2015 - Ensemble Modeling of CMEs Using the WSA{\textendash}ENLIL+Cone.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/BZINT7LE/10.html:text/html} } @article{kim_hybrid_2015, title = {A hybrid ensemble learning approach to star{\textendash}galaxy classification}, volume = {453}, issn = {0035-8711}, + url = {https://academic.oup.com/mnras/article/453/1/507/1749701}, doi = {10.1093/mnras/stv1608}, + abstract = {Abstract. There exist a variety of star{\textendash}galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-}, language = {en}, number = {1}, + urldate = {2018-07-09}, journal = {Mon Not R Astron Soc}, author = {Kim, Edward J. and Brunner, Robert J. and Carrasco Kind, Matias}, month = oct, year = {2015}, pages = {507--521}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/JG8MNGD5/Kim et al. - 2015 - A hybrid ensemble learning approach to star{\textendash}galaxy.pdf:application/pdf} } @article{armstrong_k2_2016, title = {K2 variable catalogue {\textendash} {II}. {Machine} learning classification of variable stars and eclipsing binaries in {K}2 fields 0{\textendash}4}, volume = {456}, issn = {0035-8711}, + url = {https://academic.oup.com/mnras/article/456/2/2260/1071207}, doi = {10.1093/mnras/stv2836}, + abstract = {Abstract. We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximize the potential of such surveys, aut}, language = {en}, number = {2}, + urldate = {2018-07-09}, journal = {Mon Not R Astron Soc}, author = {Armstrong, D. J. and Kirk, J. and Lam, K. W. F. and McCormac, J. and Osborn, H. P. and Spake, J. and Walker, S. and Brown, D. J. A. and Kristiansen, M. H. and Pollacco, D. and West, R. and Wheatley, P. J.}, month = feb, year = {2016}, pages = {2260--2272}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/75I3VKTA/Armstrong et al. - 2016 - K2 variable catalogue {\textendash} II. Machine learning class.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/G2XF6B6R/1071207.html:text/html} } @article{brier_verification_1950, title = {Verification of forecasts expressed in terms of probability}, volume = {78}, issn = {0027-0644}, + url = {https://journals.ametsoc.org/doi/abs/10.1175/1520-0493%281950%29078%3C0001%3AVOFEIT%3E2.0.CO%3B2}, doi = {10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2}, + abstract = {No Abstract Available.}, number = {1}, + urldate = {2018-07-09}, journal = {Mon. Wea. Rev.}, author = {Brier, Glenn W.}, month = jan, year = {1950}, pages = {1--3}, + file = {mwr-078-01-0001.pdf:/home/aimalz/Documents/References/storage/AK89UIM9/mwr-078-01-0001.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/ZEKE9B9J/1520-0493(1950)0780001VOFEIT2.0.html:text/html} } @article{kim_stargalaxy_2017, title = {Star{\textendash}galaxy classification using deep convolutional neural networks}, volume = {464}, issn = {0035-8711}, + url = {https://academic.oup.com/mnras/article/464/4/4463/2417400}, doi = {10.1093/mnras/stw2672}, + abstract = {Abstract. Most existing star{\textendash}galaxy classifiers use the reduced summary information from catalogues, requiring careful feature extraction and selection. The la}, language = {en}, number = {4}, + urldate = {2018-07-09}, journal = {Mon Not R Astron Soc}, author = {Kim, Edward J. and Brunner, Robert J.}, month = feb, year = {2017}, pages = {4463--4475}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/J7SGAR9P/Kim and Brunner - 2017 - Star{\textendash}galaxy classification using deep convolutiona.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/L8XK2G5E/2417400.html:text/html} } @article{florios_forecasting_2018, title = {Forecasting {Solar} {Flares} {Using} {Magnetogram}-based {Predictors} and {Machine} {Learning}}, volume = {293}, issn = {0038-0938, 1573-093X}, + url = {https://link.springer.com/article/10.1007/s11207-018-1250-4}, doi = {10.1007/s11207-018-1250-4}, + abstract = {We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012 {\textendash} 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude {\textgreater}M1{\textgreater}M1\{{\textgreater}\}{\textbackslash},{\textbackslash}mbox\{M1\} and {\textgreater}C1{\textgreater}C1\{{\textgreater}\}{\textbackslash},{\textbackslash}mbox\{C1\} within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy ACC=0.93(0.00)ACC=0.93(0.00){\textbackslash}mathrm\{ACC\}=0.93(0.00), true skill statistic TSS=0.74(0.02)TSS=0.74(0.02){\textbackslash}mathrm\{TSS\}=0.74(0.02), and Heidke skill score HSS=0.49(0.01)HSS=0.49(0.01){\textbackslash}mathrm\{HSS\}=0.49(0.01) for {\textgreater}M1{\textgreater}M1\{{\textgreater}\}{\textbackslash},{\textbackslash}mbox\{M1\} flare prediction with probability threshold 15\% and ACC=0.84(0.00)ACC=0.84(0.00){\textbackslash}mathrm\{ACC\}=0.84(0.00), TSS=0.60(0.01)TSS=0.60(0.01){\textbackslash}mathrm\{TSS\}=0.60(0.01), and HSS=0.59(0.01)HSS=0.59(0.01){\textbackslash}mathrm\{HSS\}=0.59(0.01) for {\textgreater}C1{\textgreater}C1\{{\textgreater}\}{\textbackslash},{\textbackslash}mbox\{C1\} flare prediction with probability threshold 35\%.}, language = {en}, number = {2}, + urldate = {2018-07-09}, journal = {Sol Phys}, author = {Florios, Kostas and Kontogiannis, Ioannis and Park, Sung-Hong and Guerra, Jordan A. and Benvenuto, Federico and Bloomfield, D. Shaun and Georgoulis, Manolis K.}, month = feb, year = {2018}, pages = {28}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/L7WERD2P/Florios et al. - 2018 - Forecasting Solar Flares Using Magnetogram-based P.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/P22P9YMB/10.html:text/html} } @article{mccloskey_flare_2018, title = {Flare {Forecasting} {Using} the {Evolution} of {McIntosh} {Sunspot} {Classifications}}, + url = {http://arxiv.org/abs/1805.00919}, + abstract = {Most solar flares originate in sunspot groups, where magnetic field changes lead to energy build-up and release. However, few flare-forecasting methods use information of sunspot-group evolution, instead focusing on static point-in-time observations. Here, a new forecast method is presented based upon the 24-hr evolution in McIntosh classification of sunspot groups. Evolution-dependent \${\textbackslash}geqslant\$C1.0 and \${\textbackslash}geqslant\$M1.0 flaring rates are found from NOAA-numbered sunspot groups over December 1988 to June 1996 (Solar Cycle 22; SC22) before converting to probabilities assuming Poisson statistics. These flaring probabilities are used to generate operational forecasts for sunspot groups over July 1996 to December 2008 (SC23), with performance studied by verification metrics. Major findings are: i) considering Brier skill score (BSS) for \${\textbackslash}geqslant\$C1.0 flares, the evolution-dependent McIntosh-Poisson method (\${\textbackslash}text\{BSS\}\_\{{\textbackslash}text\{evolution\}\}=0.09\$) performs better than the static McIntosh-Poisson method (\${\textbackslash}text\{BSS\}\_\{{\textbackslash}text\{static\}\} = -0.09\$); ii) low BSS values arise partly from both methods over-forecasting SC23 flares from the SC22 rates, symptomatic of \${\textbackslash}geqslant\$C1.0 rates in SC23 being on average \${\textbackslash}approx\$80\% of those in SC22 (with \${\textbackslash}geqslant\$M1.0 being \${\textbackslash}approx\$50\%); iii) applying a bias-correction factor to reduce the SC22 rates used in forecasting SC23 flares yields modest improvement in skill relative to climatology for both methods (\${\textbackslash}mathrm\{BSS\}{\textasciicircum}\{{\textbackslash}mathrm\{corr\}\}\_\{{\textbackslash}mathrm\{static\}\} = 0.09\$ and \${\textbackslash}mathrm\{BSS\}{\textasciicircum}\{{\textbackslash}mathrm\{corr\}\}\_\{{\textbackslash}mathrm\{evolution\}\} = 0.20\$) and improved forecast reliability diagrams.}, + urldate = {2018-07-09}, journal = {arXiv:1805.00919 [astro-ph]}, author = {McCloskey, Aoife E. and Gallagher, Peter T. and Bloomfield, D. Shaun}, month = may, year = {2018}, + note = {arXiv: 1805.00919}, + keywords = {Astrophysics - Solar and Stellar Astrophysics}, + file = {arXiv\:1805.00919 PDF:/home/aimalz/Documents/References/storage/6TYNDJRV/McCloskey et al. - 2018 - Flare Forecasting Using the Evolution of McIntosh .pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/TQHUPNAT/1805.html:text/html} } @article{hon_deep_2018, @@ -184,105 +321,141 @@ @article{hon_deep_2018 volume = {476}, issn = {0035-8711}, shorttitle = {Deep learning classification in asteroseismology using an improved neural network}, + url = {https://academic.oup.com/mnras/article/476/3/3233/4898088}, doi = {10.1093/mnras/sty483}, + abstract = {Abstract. Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary st}, language = {en}, number = {3}, + urldate = {2018-07-09}, journal = {Mon Not R Astron Soc}, author = {Hon, Marc and Stello, Dennis and Yu, Jie}, month = may, year = {2018}, pages = {3233--3244}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/ISW8CNIF/Hon et al. - 2018 - Deep learning classification in asteroseismology u.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/RY656LKK/4898088.html:text/html} } @article{moller_photometric_2016, title = {Photometric classification of type {Ia} supernovae in the {SuperNova} {Legacy} {Survey} with supervised learning}, volume = {2016}, issn = {1475-7516}, + url = {http://stacks.iop.org/1475-7516/2016/i=12/a=008}, doi = {10.1088/1475-7516/2016/12/008}, + abstract = {In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts(0.2 {\textless} z {\textless} 1.1). Our method consists of two stages: feature extraction (obtaining the SN redshift from photometry and estimating light-curve shape parameters) and machine learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of 0.98.We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than 5\%. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high- z SN survey with application to real SN data.}, language = {en}, number = {12}, + urldate = {2018-07-09}, journal = {J. Cosmol. Astropart. Phys.}, author = {M{\"o}ller, A. and Ruhlmann-Kleider, V. and Leloup, C. and Neveu, J. and Palanque-Delabrouille, N. and Rich, J. and Carlberg, R. and {C. Lidman} and Pritchet, C.}, year = {2016}, pages = {008}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/JNIMCKC5/M{\"o}ller et al. - 2016 - Photometric classification of type Ia supernovae i.pdf:application/pdf} } @article{hon_deep_2017, title = {Deep learning classification in asteroseismology}, volume = {469}, issn = {0035-8711}, + url = {https://academic.oup.com/mnras/article/469/4/4578/3828087}, doi = {10.1093/mnras/stx1174}, + abstract = {Abstract. In the power spectra of oscillating red giants, there are visually distinct features defining stars ascending the red giant branch from those that ha}, language = {en}, number = {4}, + urldate = {2018-07-09}, journal = {Mon Not R Astron Soc}, author = {Hon, Marc and Stello, Dennis and Yu, Jie}, month = aug, year = {2017}, pages = {4578--4583}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/DFNTS5WD/Hon et al. - 2017 - Deep learning classification in asteroseismology.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/Z8K42U5R/3828087.html:text/html} } @article{bethapudi_separation_2018, title = {Separation of pulsar signals from noise using supervised machine learning algorithms}, volume = {23}, issn = {2213-1337}, + url = {http://www.sciencedirect.com/science/article/pii/S2213133717301397}, doi = {10.1016/j.ascom.2018.02.002}, + abstract = {We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP), Adaboost, Gradient Boosting Classifier (GBC), and XGBoost, for the separation of pulsars from radio frequency interference (RFI) and other sources of noise, using a dataset obtained from the post-processing of a pulsar search pipeline. This dataset was previously used for the cross-validation of the SPINN-based machine learning engine, obtained from the reprocessing of the HTRU-S survey data (Morello et~al., 2014). We have used the Synthetic Minority Over-sampling Technique (SMOTE) to deal with high-class imbalance in the dataset. We report a variety of quality scores from all four of these algorithms on both the non-SMOTE and SMOTE datasets. For all the above ML methods, we report high accuracy and G-mean for both the non-SMOTE and SMOTE cases. We study the feature importances using Adaboost, GBC, and XGBoost and also from the minimum Redundancy Maximum Relevance approach to report algorithm-agnostic feature ranking. From these methods, we find that the signal to noise of the folded profile to be the best feature. We find that all the ML algorithms report FPRs about an order of magnitude lower than the corresponding FPRs obtained in Morello et~al. (2014), for the same recall value.}, + urldate = {2018-07-09}, journal = {Astronomy and Computing}, author = {Bethapudi, S. and Desai, S.}, month = apr, year = {2018}, + keywords = {Data analysis stars, Methods, Neutron}, pages = {15--26}, + file = {ScienceDirect Full Text PDF:/home/aimalz/Documents/References/storage/JZHXRFZ7/Bethapudi and Desai - 2018 - Separation of pulsar signals from noise using supe.pdf:application/pdf;ScienceDirect Snapshot:/home/aimalz/Documents/References/storage/7KTDH2FT/S2213133717301397.html:text/html} } @article{hon_detecting_2018, title = {Detecting {Solar}-like {Oscillations} in {Red} {Giants} with {Deep} {Learning}}, volume = {859}, issn = {0004-637X}, + url = {http://stacks.iop.org/0004-637X/859/i=1/a=64}, doi = {10.3847/1538-4357/aabfdb}, + abstract = {Time-resolved photometry of tens of thousands of red giant stars from space missions like Kepler and K 2 has created the need for automated asteroseismic analysis methods. The first and most fundamental step in such analysis is to identify which stars show oscillations. It is critical that this step be performed with no, or little, detection bias, particularly when performing subsequent ensemble analyses that aim to compare the properties of observed stellar populations with those from galactic models. However, an efficient, automated solution to this initial detection step still has not been found, meaning that expert visual inspection of data from each star is required to obtain the highest level of detections. Hence, to mimic how an expert eye analyzes the data, we use supervised deep learning to not only detect oscillations in red giants, but also to predict the location of the frequency at maximum power, $\nu$ max , by observing features in 2D images of power spectra. By training on Kepler data, we benchmark our deep-learning classifier against K 2 data that are given detections by the expert eye, achieving a detection accuracy of 98\% on K 2 Campaign 6 stars and a detection accuracy of 99\% on K 2 Campaign 3 stars. We further find that the estimated uncertainty of our deep-learning-based $\nu$ max predictions is about 5\%. This is comparable to human-level performance using visual inspection. When examining outliers, we find that the deep-learning results are more likely to provide robust $\nu$ max estimates than the classical model-fitting method.}, language = {en}, number = {1}, + urldate = {2018-07-09}, journal = {ApJ}, author = {Hon, Marc and Stello, Dennis and Zinn, Joel C.}, year = {2018}, pages = {64}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/IM3FA62T/Hon et al. - 2018 - Detecting Solar-like Oscillations in Red Giants wi.pdf:application/pdf} } @article{wu_radio_2018, title = {Radio {Galaxy} {Zoo}: {ClaRAN} - {A} {Deep} {Learning} {Classifier} for {Radio} {Morphologies}}, shorttitle = {Radio {Galaxy} {Zoo}}, + url = {http://arxiv.org/abs/1805.12008}, + abstract = {The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification through visual inspection unfeasible. We present ClaRAN - Classifying Radio sources Automatically with Neural networks - a proof-of-concept radio source morphology classifier based upon the Faster Region-based Convolutional Neutral Networks (Faster R-CNN) method. Specifically, we train and test ClaRAN on the FIRST and WISE images from the Radio Galaxy Zoo Data Release 1 catalogue. ClaRAN provides end users with automated identification of radio source morphology classifications from a simple input of a radio image and a counterpart infrared image of the same region. ClaRAN is the first open-source, end-to-end radio source morphology classifier that is capable of locating and associating discrete and extended components of radio sources in a fast ({\textless} 200 milliseconds per image) and accurate ({\textgreater}= 90 \%) fashion. Future work will improve ClaRAN's relatively lower success rates in dealing with multi-source fields and will enable ClaRAN to identify sources on much larger fields without loss in classification accuracy.}, + urldate = {2018-07-09}, journal = {arXiv:1805.12008 [astro-ph]}, author = {Wu, Chen and Wong, O. Ivy and Rudnick, Lawrence and Shabala, Stanislav S. and Alger, Matthew J. and Banfield, Julie K. and Ong, Cheng Soon and White, Sarah V. and Garon, Avery F. and Norris, Ray P. and Andernach, Heinz and Tate, Jean and Lukic, Vesna and Tang, Hongming and Schawinski, Kevin and Diakogiannis, Foivos I.}, month = may, year = {2018}, + note = {arXiv: 1805.12008}, + keywords = {Astrophysics - Instrumentation and Methods for Astrophysics}, + file = {arXiv\:1805.12008 PDF:/home/aimalz/Documents/References/storage/T3JDEB7W/Wu et al. - 2018 - Radio Galaxy Zoo ClaRAN - A Deep Learning Classif.pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/T5V6TXKS/1805.html:text/html} } @article{bloom_automating_2012, title = {Automating {Discovery} and {Classification} of {Transients} and {Variable} {Stars} in the {Synoptic} {Survey} {Era}}, volume = {124}, issn = {1538-3873}, + url = {http://iopscience.iop.org/article/10.1086/668468/meta}, doi = {10.1086/668468}, language = {en}, number = {921}, + urldate = {2018-08-16}, journal = {PASP}, author = {Bloom, J. S. and Richards, J. W. and Nugent, P. E. and Quimby, R. M. and Kasliwal, M. M. and Starr, D. L. and Poznanski, D. and Ofek, E. O. and Cenko, S. B. and Butler, N. R. and Kulkarni, S. R. and Gal-Yam, A. and Law, N.}, month = oct, year = {2012}, pages = {1175}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/BVL9JNXC/Bloom et al. - 2012 - Automating Discovery and Classification of Transie.pdf:application/pdf;Full Text PDF:/home/aimalz/Documents/References/storage/XVVKA5YG/Bloom et al. - 2012 - Automating Discovery and Classification of Transie.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/ZFI2SUZP/668468.html:text/html;Snapshot:/home/aimalz/Documents/References/storage/HFCLT7G2/668468.html:text/html} } @article{hoyle_measuring_2016, title = {Measuring photometric redshifts using galaxy images and {Deep} {Neural} {Networks}}, volume = {16}, issn = {2213-1337}, + url = {http://www.sciencedirect.com/science/article/pii/S221313371630021X}, doi = {10.1016/j.ascom.2016.03.006}, + abstract = {We propose a new method to estimate the photometric redshift of galaxies by using the full galaxy image in each measured band. This method draws from the latest techniques and advances in machine learning, in particular Deep Neural Networks. We pass the entire multi-band galaxy image into the machine learning architecture to obtain a redshift estimate that is competitive, in terms of the measured point prediction metrics, with the best existing standard machine learning techniques. The standard techniques estimate redshifts using post-processed features, such as magnitudes and colours, which are extracted from the galaxy images and are deemed to be salient by the user. This new method removes the user from the photometric redshift estimation pipeline. However we do note that Deep Neural Networks require many orders of magnitude more computing resources than standard machine learning architectures, and as such are only tractable for making predictions on datasets of size <=50k before implementing parallelisation techniques.}, + urldate = {2018-08-20}, journal = {Astronomy and Computing}, author = {Hoyle, B.}, month = jul, year = {2016}, + keywords = {Astronomy, Cosmology, Machine learning}, pages = {34--40}, + file = {ScienceDirect Full Text PDF:/home/aimalz/Documents/References/storage/E3CQ42DJ/Hoyle - 2016 - Measuring photometric redshifts using galaxy image.pdf:application/pdf;ScienceDirect Snapshot:/home/aimalz/Documents/References/storage/MJDVU6JS/S221313371630021X.html:text/html} } @book{murphy_machine_2012, title = {Machine learning: a probabilistic perspective}, + isbn = {0-262-01802-0 978-0-262-01802-9}, publisher = {The MIT Press}, author = {Murphy, Kevin P.}, year = {2012} @@ -292,11 +465,14 @@ @inproceedings{djorgovski_flashes_2012 title = {Flashes in a star stream: {Automated} classification of astronomical transient events}, shorttitle = {Flashes in a star stream}, doi = {10.1109/eScience.2012.6404437}, + abstract = {An automated, rapid classification of transient events detected in the modern synoptic sky surveys is essential for their scientific utility and effective follow-up using scarce resources. This presents some unusual challenges: the data are sparse, heterogeneous and incomplete; evolving in time; and most of the relevant information comes not from the data stream itself, but from a variety of archival data and contextual information (spatial, temporal, and multi-wavelength). We are exploring a variety of novel techniques, mostly Bayesian, to respond to these challenges, using the ongoing CRTS sky survey as a testbed. The current surveys are already overwhelming our ability to effectively follow all of the potentially interesting events, and these challenges will grow by orders of magnitude over the next decade as the more ambitious sky surveys get under way. While we focus on an application in a specific domain (astrophysics), these challenges are more broadly relevant for event or anomaly detection and knowledge discovery in massive data streams.}, booktitle = {2012 {IEEE} 8th {International} {Conference} on {E}-{Science}}, author = {Djorgovski, S. G. and Mahabal, A. A. and Donalek, C. and Graham, M. J. and Drake, A. J. and Moghaddam, B. and Turmon, M.}, month = oct, year = {2012}, + keywords = {data mining, machine learning, astronomy computing, pattern classification, Transient analysis, anomaly detection, archival data, astronomical techniques, astrophysics, automated astronomical transient event classification, automated decision making, Bayes methods, Bayesian methods, Bayesian technique, classification, contextual information, CRTS sky survey, event detection, Extraterrestrial measurements, Histograms, Image color analysis, knowledge discovery, massive data streams, multiwavelength information, Pollution measurement, Real-time systems, sky surveys, spatial information, star stream, stars, synoptic sky surveys, temporal information}, pages = {1--8}, + file = {IEEE Xplore Abstract Record:/home/aimalz/Documents/References/storage/XFJKEV3Q/6404437.html:text/html;IEEE Xplore Full Text PDF:/home/aimalz/Documents/References/storage/FPNA9G7Q/Djorgovski et al. - 2012 - Flashes in a star stream Automated classification.pdf:application/pdf} } @article{conley_sifto:_2008, @@ -304,83 +480,112 @@ @article{conley_sifto:_2008 volume = {681}, issn = {0004-637X}, shorttitle = {{SiFTO}}, + url = {http://stacks.iop.org/0004-637X/681/i=1/a=482}, doi = {10.1086/588518}, + abstract = {We present SiFTO, a new empirical method for modeling Type Ia supernova (SN Ia) light curves by manipulating a spectral template. We make use of high-redshift SN data when training the model, allowing us to extend it bluer than rest-frame U . This increases the utility of our high-redshift SN observations by allowing us to use more of the available data. We find that when the shape of the light curve is described using a stretch prescription, applying the same stretch at all wavelengths is not an adequate description. SiFTO therefore uses a generalization of stretch which applies different stretch factors as a function of both the wavelength of the observed filter and the stretch in the rest-frame B band. We compare SiFTO to other published light-curve models by applying them to the same set of SN photometry, and demonstrate that SiFTO and SALT2 perform better than the alternatives when judged by the scatter around the best-fit luminosity distance relationship. We further demonstrate that when SiFTO and SALT2 are trained on the same data set the cosmological results agree.}, language = {en}, number = {1}, + urldate = {2018-08-20}, journal = {ApJ}, author = {Conley, A. and Sullivan, M. and Hsiao, E. Y. and Guy, J. and Astier, P. and Balam, D. and Balland, C. and Basa, S. and Carlberg, R. G. and {D. Fouchez} and Hardin, D. and Howell, D. A. and Hook, I. M. and Pain, R. and Perrett, K. and Pritchet, C. J. and Regnault, N.}, year = {2008}, pages = {482}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/XZPXXY6A/Conley et al. - 2008 - SiFTO An Empirical Method for Fitting SN Ia Light.pdf:application/pdf} } @article{djorgovski_towards_2011, title = {Towards an {Automated} {Classification} of {Transient} {Events} in {Synoptic} {Sky} {Surveys}}, + url = {http://arxiv.org/abs/1110.4655}, + abstract = {We describe the development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys. The system under development incorporates a number of Machine Learning techniques, mostly using Bayesian approaches, due to the sparse nature, heterogeneity, and variable incompleteness of the available data. The classifications are improved iteratively as the new measurements are obtained. One novel feature is the development of an automated follow-up recommendation engine, that suggest those measurements that would be the most advantageous in terms of resolving classification ambiguities and/or characterization of the astrophysically most interesting objects, given a set of available follow-up assets and their cost functions. This illustrates the symbiotic relationship of astronomy and applied computer science through the emerging discipline of AstroInformatics.}, + urldate = {2018-08-20}, journal = {arXiv:1110.4655 [astro-ph, physics:physics]}, author = {Djorgovski, S. G. and Donalek, C. and Mahabal, A. and Moghaddam, B. and Turmon, M. and Graham, M. and Drake, A. and Sharma, N. and Chen, Y.}, month = oct, year = {2011}, + note = {arXiv: 1110.4655}, + keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Computational Physics}, + file = {arXiv\:1110.4655 PDF:/home/aimalz/Documents/References/storage/KQCNRECI/Djorgovski et al. - 2011 - Towards an Automated Classification of Transient E.pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/CN3C7NU5/1110.html:text/html} } @article{nugent_kcorrections_2002, title = {K-{Corrections} and {Extinction} {Corrections} for {Type} {Ia} {Supernovae}}, volume = {114}, issn = {1538-3873}, + url = {http://iopscience.iop.org/article/10.1086/341707/meta}, doi = {10.1086/341707}, language = {en}, number = {798}, + urldate = {2018-08-20}, journal = {PASP}, author = {Nugent, Peter and Kim, Alex and Perlmutter, Saul}, month = jul, year = {2002}, pages = {803}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/YB84G6IY/Nugent et al. - 2002 - K-Corrections and Extinction Corrections for Type .pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/ZH662SVM/341707.html:text/html} } @article{malz_approximating_2018, title = {Approximating {Photo}- z {PDFs} for {Large} {Surveys}}, volume = {156}, issn = {1538-3881}, + url = {http://stacks.iop.org/1538-3881/156/i=1/a=35}, doi = {10.3847/1538-3881/aac6b5}, + abstract = {Modern galaxy surveys produce redshift probability density functions (PDFs) in addition to traditional photometric redshift (photo- z ) point estimates. However, the storage of photo- z PDFs may present a challenge with increasingly large catalogs, as we face a trade-off between the accuracy of subsequent science measurements and the limitation of finite storage resources. This paper presents qp , a Python package for manipulating parameterizations of one-dimensional PDFs, as suitable for photo- z PDF compression. We use qp to investigate the performance of three simple PDF storage formats (quantiles, samples, and step functions) as a function of the number of stored parameters on two realistic mock data sets, representative of upcoming surveys with different data qualities. We propose some best practices for choosing a photo- z PDF approximation scheme and demonstrate the approach on a science case using performance metrics on both ensembles of individual photo- z PDFs and an estimator of the overall redshift distribution function. We show that both the properties of the set of PDFs we wish to approximate and the fidelity metric(s) chosen affect the optimal parameterization. Additionally, we find that quantiles and samples outperform step functions, and we encourage further consideration of these formats for PDF approximation.}, language = {en}, number = {1}, + urldate = {2018-08-20}, journal = {AJ}, author = {Malz, A. I. and Marshall, P. J. and DeRose, J. and Graham, M. L. and Schmidt, S. J. and Wechsler, R. and Collaboration), (LSST Dark Energy Science}, year = {2018}, + keywords = {Astrophysics - Instrumentation and Methods for Astrophysics}, pages = {35}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/JGQ7SQMS/Malz et al. - 2018 - Approximating Photo- z PDFs for Large Surveys.pdf:application/pdf;IOP Full Text PDF:/home/aimalz/Documents/References/storage/3F6SH9YS/Malz et al. - 2018 - Approximating Photo- z PDFs for Large Surveys.pdf:application/pdf} } @inproceedings{mahabal_deep-learnt_2017, title = {Deep-learnt classification of light curves}, doi = {10.1109/SSCI.2017.8280984}, + abstract = {Astronomy light curves are sparse, gappy, and heteroscedastic. As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and techniques to classify light curves. A common approach is to derive statistical features from the time series and to use machine learning methods, generally supervised, to separate objects into a few of the standard classes. In this work, we transform the time series to two-dimensional light curve representations in order to classify them using modern deep learning techniques. In particular, we show that convolutional neural networks based classifiers work well for broad characterization and classification. We use labeled datasets of periodic variables from CRTS survey and show how this opens doors for a quick classification of diverse classes with several possible exciting extensions.}, booktitle = {2017 {IEEE} {Symposium} {Series} on {Computational} {Intelligence} ({SSCI})}, author = {Mahabal, A. and Sheth, K. and Gieseke, F. and Pai, A. and Djorgovski, S. G. and Drake, A. J. and Graham, M. J.}, month = nov, year = {2017}, + keywords = {astronomers, Astronomy, astronomy computing, astronomy light curves, Cathode ray tubes, convolutional neural networks based classifiers work, data analysis, deep-learnt classification, financial datasets, instruments, Kernel, labeled datasets, learning (artificial intelligence), machine learning methods, modern deep learning techniques, neural nets, pattern classification, periodic variables, standard classes, standard time series methods, Standards, statistical features, time series, Time series analysis, Training, Transient analysis, two-dimensional light curve representations}, pages = {1--8}, + file = {IEEE Xplore Abstract Record:/home/aimalz/Documents/References/storage/QZQDB89Z/8280984.html:text/html;IEEE Xplore Full Text PDF:/home/aimalz/Documents/References/storage/PWM3FB3J/Mahabal et al. - 2017 - Deep-learnt classification of light curves.pdf:application/pdf} } @article{charnock_deep_2017, title = {Deep {Recurrent} {Neural} {Networks} for {Supernovae} {Classification}}, volume = {837}, issn = {2041-8205}, + url = {http://stacks.iop.org/2041-8205/837/i=2/a=L28}, doi = {10.3847/2041-8213/aa603d}, + abstract = {We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae (code available at https://github.com/adammoss/supernovae). The observational time and filter fluxes are used as inputs to the network, but since the inputs are agnostic, additional data such as host galaxy information can also be included. Using the Supernovae Photometric Classification Challenge (SPCC) data, we find that deep networks are capable of learning about light curves, however the performance of the network is highly sensitive to the amount of training data. For a training size of 50\% of the representational SPCC data set (around 10 4 supernovae) we obtain a type-Ia versus non-type-Ia classification accuracy of 94.7\%, an area under the Receiver Operating Characteristic curve AUC of 0.986 and an SPCC figure-of-merit F 1 = 0.64. When using only the data for the early-epoch challenge defined by the SPCC, we achieve a classification accuracy of 93.1\%, AUC of 0.977, and F 1 = 0.58, results almost as good as with the whole light curve. By employing bidirectional neural networks, we can acquire impressive classification results between supernovae types I, II and III at an accuracy of 90.4\% and AUC of 0.974. We also apply a pre-trained model to obtain classification probabilities as a function of time and show that it can give early indications of supernovae type. Our method is competitive with existing algorithms and has applications for future large-scale photometric surveys.}, language = {en}, number = {2}, + urldate = {2018-08-20}, journal = {ApJL}, author = {Charnock, Tom and Moss, Adam}, year = {2017}, pages = {L28}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/2287WY74/Charnock and Moss - 2017 - Deep Recurrent Neural Networks for Supernovae Clas.pdf:application/pdf} } @article{george_classification_2018, title = {Classification and unsupervised clustering of {LIGO} data with {Deep} {Transfer} {Learning}}, volume = {97}, + url = {https://link.aps.org/doi/10.1103/PhysRevD.97.101501}, doi = {10.1103/PhysRevD.97.101501}, + abstract = {Gravitational wave detection requires a detailed understanding of the response of the LIGO and Virgo detectors to true signals in the presence of environmental and instrumental noise. Of particular interest is the study of anomalous non-Gaussian transients, such as glitches, since their occurrence rate in LIGO and Virgo data can obscure or even mimic true gravitational wave signals. Therefore, successfully identifying and excising these anomalies from gravitational wave data is of utmost importance for the detection and characterization of true signals and for the accurate computation of their significance. To facilitate this work, we present the first application of deep learning combined with transfer learning to show that knowledge from pretrained models for real-world object recognition can be transferred for classifying spectrograms of glitches. To showcase this new method, we use a data set of twenty-two classes of glitches, curated and labeled by the Gravity Spy project using data collected during LIGO{\textquoteright}s first discovery campaign. We demonstrate that our Deep Transfer Learning method enables an optimal use of very deep convolutional neural networks for glitch classification given small and unbalanced training data sets, significantly reduces the training time, and achieves state-of-the-art accuracy above 98.8\%, lowering the previous error rate by over 60\%. More importantly, once trained via transfer learning on the known classes, we show that our neural networks can be truncated and used as feature extractors for unsupervised clustering to automatically group together new unknown classes of glitches and anomalous signals. This novel capability is of paramount importance to identify and remove new types of glitches which will occur as the LIGO/Virgo detectors gradually attain design sensitivity.}, number = {10}, + urldate = {2018-08-20}, journal = {Phys. Rev. D}, author = {George, Daniel and Shen, Hongyu and Huerta, E. A.}, month = may, year = {2018}, pages = {101501}, + file = {APS Snapshot:/home/aimalz/Documents/References/storage/9KPPZLWX/PhysRevD.97.html:text/html;PhysRevD.97.101501.pdf:/home/aimalz/Documents/References/storage/AG3P8877/PhysRevD.97.101501.pdf:application/pdf} } @article{zevin_gravity_2017, @@ -388,40 +593,52 @@ @article{zevin_gravity_2017 volume = {34}, issn = {0264-9381}, shorttitle = {Gravity {Spy}}, + url = {http://stacks.iop.org/0264-9381/34/i=6/a=064003}, doi = {10.1088/1361-6382/aa5cea}, + abstract = {With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches , which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO{\textquoteright}s first observing run.}, language = {en}, number = {6}, + urldate = {2018-08-20}, journal = {Class. Quantum Grav.}, author = {Zevin, M. and Coughlin, S. and Bahaadini, S. and Besler, E. and Rohani, N. and Allen, S. and Cabero, M. and Crowston, K. and Katsaggelos, A. K. and Larson, S. L. and Lee, T. K. and Lintott, C. and Littenberg, T. B. and Lundgren, A. and {\O}sterlund, C. and Smith, J. R. and Trouille, L. and Kalogera, V.}, year = {2017}, pages = {064003}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/DNG4D3B7/Zevin et al. - 2017 - Gravity Spy integrating advanced LIGO detector ch.pdf:application/pdf} } @article{morii_machine-learning_2016, title = {Machine-learning selection of optical transients in the {Subaru}/{Hyper} {Suprime}-{Cam} survey}, volume = {68}, issn = {0004-6264}, + url = {https://academic.oup.com/pasj/article/68/6/104/2433400}, doi = {10.1093/pasj/psw096}, + abstract = {Abstract. We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with the Hyper Suprime-Cam (HS}, language = {en}, number = {6}, + urldate = {2018-08-20}, journal = {Publ Astron Soc Jpn Nihon Tenmon Gakkai}, author = {Morii, Mikio and Ikeda, Shiro and Tominaga, Nozomu and Tanaka, Masaomi and Morokuma, Tomoki and Ishiguro, Katsuhiko and Yamato, Junji and Ueda, Naonori and Suzuki, Naotaka and Yasuda, Naoki and Yoshida, Naoki}, month = dec, year = {2016}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/BYILWI7C/Morii et al. - 2016 - Machine-learning selection of optical transients i.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/VI5RGDVC/2433400.html:text/html} } @article{abraham_detection_2018, title = {Detection of bars in galaxies using a deep convolutional neural network}, volume = {477}, issn = {0035-8711}, + url = {https://academic.oup.com/mnras/article/477/1/894/4925012}, doi = {10.1093/mnras/sty627}, + abstract = {Abstract. We present an automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network that is eas}, language = {en}, number = {1}, + urldate = {2018-08-20}, journal = {Mon Not R Astron Soc}, author = {Abraham, Sheelu and Aniyan, A. K. and Kembhavi, Ajit K. and Philip, N. S. and Vaghmare, Kaustubh}, month = jun, year = {2018}, pages = {894--903}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/E2RMQLMR/Abraham et al. - 2018 - Detection of bars in galaxies using a deep convolu.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/NNL43G6E/4925012.html:text/html} } @article{cabrera-vives_deep-hits:_2017, @@ -429,102 +646,136 @@ @article{cabrera-vives_deep-hits:_2017 volume = {836}, issn = {0004-637X}, shorttitle = {Deep-{HiTS}}, + url = {http://stacks.iop.org/0004-637X/836/i=1/a=97}, doi = {10.3847/1538-4357/836/1/97}, + abstract = {We introduce Deep-HiTS, a rotation-invariant convolutional neural network (CNN) model for classifying images of transient candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using random forests (RFs). We show that our CNN significantly outperforms the RF model, reducing the error by almost half. Furthermore, for a fixed number of approximately 2000 allowed false transient candidates per night, we are able to reduce the misclassified real transients by approximately one-fifth. To the best of our knowledge, this is the first time CNNs have been used to detect astronomical transient events. Our approach will be very useful when processing images from next generation instruments such as the Large Synoptic Survey Telescope. We have made all our code and data available to the community for the sake of allowing further developments and comparisons at https://github.com/guille-c/Deep-HiTS [http://https://github.com/guille-c/Deep-HiTS] .}, language = {en}, number = {1}, + urldate = {2018-08-20}, journal = {ApJ}, author = {Cabrera-Vives, Guillermo and Reyes, Ignacio and F{\"o}rster, Francisco and Est{\'e}vez, Pablo A. and Maureira, Juan-Carlos}, year = {2017}, pages = {97}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/B2T9X48G/Cabrera-Vives et al. - 2017 - Deep-HiTS Rotation Invariant Convolutional Neural.pdf:application/pdf} } @article{newling_statistical_2011, title = {Statistical classification techniques for photometric supernova typing}, volume = {414}, issn = {0035-8711}, + url = {https://academic.oup.com/mnras/article/414/3/1987/1035457}, doi = {10.1111/j.1365-2966.2011.18514.x}, + abstract = {Abstract. Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effecti}, language = {en}, number = {3}, + urldate = {2018-08-20}, journal = {Mon Not R Astron Soc}, author = {Newling, J. and Varughese, M. and Bassett, B. and Campbell, H. and Hlozek, R. and Kunz, M. and Lampeitl, H. and Martin, B. and Nichol, R. and Parkinson, D. and Smith, M.}, month = jul, year = {2011}, pages = {1987--2004}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/2QH7KF5N/Newling et al. - 2011 - Statistical classification techniques for photomet.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/M75AJHC2/1035457.html:text/html} } @article{sako_photometric_2011, title = {Photometric {Type} {Ia} {Supernova} {Candidates} from the {Three}-year {SDSS}-{II} {SN} {Survey} {Data}}, volume = {738}, issn = {0004-637X}, + url = {http://stacks.iop.org/0004-637X/738/i=2/a=162}, doi = {10.1088/0004-637X/738/2/162}, + abstract = {We analyze the three-year Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey data and identify a sample of 1070 photometric Type Ia supernova (SN Ia) candidates based on their multiband light curve data. This sample consists of SN candidates with no spectroscopic confirmation, with a subset of 210 candidates having spectroscopic redshifts of their host galaxies measured while the remaining 860 candidates are purely photometric in their identification. We describe a method for estimating the efficiency and purity of photometric SN Ia classification when spectroscopic confirmation of only a limited sample is available, and demonstrate that SN Ia candidates from SDSS-II can be identified photometrically with 91\% efficiency and with a contamination of 6\%. Although this is the largest uniform sample of SN candidates to date for studying photometric identification, we find that a larger spectroscopic sample of contaminating sources is required to obtain a better characterization of the background events. A Hubble diagram using SN candidates with no spectroscopic confirmation, but with host galaxy spectroscopic redshifts, yields a distance modulus dispersion that is only 20\%-40\% larger than that of the spectroscopically confirmed SN Ia sample alone with no significant bias. A Hubble diagram with purely photometric classification and redshift-distance measurements, however, exhibits biases that require further investigation for precision cosmology.}, language = {en}, number = {2}, + urldate = {2018-08-20}, journal = {ApJ}, author = {Sako, Masao and Bassett, Bruce and Connolly, Brian and Dilday, Benjamin and Cambell, Heather and Frieman, Joshua A. and {Larry Gladney} and Kessler, Richard and Lampeitl, Hubert and Marriner, John and Miquel, Ramon and Nichol, Robert C. and Schneider, Donald P. and Smith, Mathew and Sollerman, Jesper}, year = {2011}, pages = {162}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/HGVZPA9D/Sako et al. - 2011 - Photometric Type Ia Supernova Candidates from the .pdf:application/pdf} } @inproceedings{gieseke_detecting_2010, title = {Detecting {Quasars} in {Large}-{Scale} {Astronomical} {Surveys}}, doi = {10.1109/ICMLA.2010.59}, + abstract = {We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies.}, booktitle = {2010 {Ninth} {International} {Conference} on {Machine} {Learning} and {Applications}}, author = {Gieseke, F. and Polsterer, K. L. and Thom, A. and Zinn, P. and Bomanns, D. and Dettmar, R. and Kramer, O. and Vahrenhold, J.}, month = dec, year = {2010}, + keywords = {machine learning, Astronomy, astronomy computing, data analysis, Kernel, learning (artificial intelligence), Training, quasars, classification, astronomical catalogues, astronomical photometry, astronomical surveys, astronomy, classification performance, classification schemes, classification-based approach, Data models, detecting quasars, feature extraction, Feature extraction, large-scale astronomical surveys, manually labeled training set, performance evaluation, photometric data, problem-specific features extraction, quasi-stellar radio sources, sloan digital sky survey, spectroscopic catalogs, spectroscopic data, Spline, Support vector machines}, pages = {352--357}, + file = {IEEE Xplore Abstract Record:/home/aimalz/Documents/References/storage/K8HRKBJ5/5708856.html:text/html;IEEE Xplore Full Text PDF:/home/aimalz/Documents/References/storage/XUN45HCA/Gieseke et al. - 2010 - Detecting Quasars in Large-Scale Astronomical Surv.pdf:application/pdf} } @article{kitching_gravitational_2011, title = {Gravitational {Lensing} {Accuracy} {Testing} 2010 ({GREAT}10) {Challenge} {Handbook}}, volume = {5}, issn = {1932-6157, 1941-7330}, + url = {https://projecteuclid.org/euclid.aoas/1318514302}, doi = {10.1214/11-AOAS484}, + abstract = {GRavitational lEnsing Accuracy Testing 2010 (GREAT10) is a public image analysis challenge aimed at the development of algorithms to analyze astronomical images. Specifically, the challenge is to measure varying image distortions in the presence of a variable convolution kernel, pixelization and noise. This is the second in a series of challenges set to the astronomy, computer science and statistics communities, providing a structured environment in which methods can be improved and tested in preparation for planned astronomical surveys. GREAT10 extends upon previous work by introducing variable fields into the challenge. The {\textquotedblleft}Galaxy Challenge{\textquotedblright} involves the precise measurement of galaxy shape distortions, quantified locally by two parameters called shear, in the presence of a known convolution kernel. Crucially, the convolution kernel and the simulated gravitational lensing shape distortion both now vary as a function of position within the images, as is the case for real data. In addition, we introduce the {\textquotedblleft}Star Challenge{\textquotedblright} that concerns the reconstruction of a variable convolution kernel, similar to that in a typical astronomical observation. This document details the GREAT10 Challenge for potential participants. Continually updated information is also available from www.greatchallenges.info.}, language = {EN}, number = {3}, + urldate = {2018-08-21}, journal = {Ann. Appl. Stat.}, author = {Kitching, Thomas and Amara, Adam and Gill, Mandeep and Harmeling, Stefan and Heymans, Catherine and Massey, Richard and Rowe, Barnaby and Schrabback, Tim and Voigt, Lisa and Balan, Sreekumar and Bernstein, Gary and Bethge, Matthias and Bridle, Sarah and Courbin, Frederic and Gentile, Marc and Heavens, Alan and Hirsch, Michael and Hosseini, Reshad and Kiessling, Alina and Kirk, Donnacha and Kuijken, Konrad and Mandelbaum, Rachel and Moghaddam, Baback and Nurbaeva, Guldariya and Paulin-Henriksson, Stephane and Rassat, Anais and Rhodes, Jason and Sch{\"o}lkopf, Bernhard and Shawe-Taylor, John and Shmakova, Marina and Taylor, Andy and Velander, Malin and Waerbeke, Ludovic van and Witherick, Dugan and Wittman, David}, month = sep, year = {2011}, mrnumber = {MR2884938}, zmnumber = {1228.62164}, + keywords = {cosmology, imaging processing, Statistical inference}, pages = {2231--2263}, + file = {Snapshot:/home/aimalz/Documents/References/storage/IA4I3VDH/1318514302.html:text/html} } @article{harvey_observing_2013, title = {Observing {Dark} {Worlds}: {A} crowdsourcing experiment for dark matter mapping}, shorttitle = {Observing {Dark} {Worlds}}, + url = {http://arxiv.org/abs/1311.0704}, + abstract = {We present the results and conclusions from the citizen science competition `Observing Dark Worlds', where we asked participants to calculate the positions of dark matter halos from 120 catalogues of simulated weak lensing galaxy data, using computational methods. In partnership with Kaggle (http://www.kaggle.com), 357 users participated in the competition which saw 2278 downloads of the data and 3358 submissions. We found that the best algorithms improved on the benchmark code, LENSTOOL by {\textgreater} 30\% and could measure the positions of {\textgreater} 3x10{\textasciicircum}14MSun halos to less than 5'' and {\textless} 10{\textasciicircum}14MSun to within 1'. In this paper, we present a brief overview of the winning algorithms with links to available code. We also discuss the implications of the experiment for future citizen science competitions.}, + urldate = {2018-08-21}, journal = {arXiv:1311.0704 [astro-ph, physics:physics]}, author = {Harvey, David and Kitching, Thomas D. and Noah-Vanhoucke, Joyce and Hamner, Ben and Salimans, Tim}, month = nov, year = {2013}, + note = {arXiv: 1311.0704}, + keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Physics and Society}, + file = {arXiv\:1311.0704 PDF:/home/aimalz/Documents/References/storage/EKMGZ6CP/Harvey et al. - 2013 - Observing Dark Worlds A crowdsourcing experiment .pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/CZF3PAY2/1311.html:text/html} } @article{dieleman_rotation-invariant_2015, title = {Rotation-invariant convolutional neural networks for galaxy morphology prediction}, volume = {450}, issn = {0035-8711}, + url = {https://academic.oup.com/mnras/article/450/2/1441/979677}, doi = {10.1093/mnras/stv632}, + abstract = {Abstract. Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital}, language = {en}, number = {2}, + urldate = {2018-08-21}, journal = {Mon Not R Astron Soc}, author = {Dieleman, Sander and Willett, Kyle W. and Dambre, Joni}, month = jun, year = {2015}, pages = {1441--1459}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/7H75PH6C/Dieleman et al. - 2015 - Rotation-invariant convolutional neural networks f.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/2UELU548/979677.html:text/html} } @article{mandelbaum_third_2014, title = {The {Third} {Gravitational} {Lensing} {Accuracy} {Testing} ({GREAT}3) {Challenge} {Handbook}}, volume = {212}, issn = {0067-0049}, + url = {http://stacks.iop.org/0067-0049/212/i=1/a=5}, doi = {10.1088/0067-0049/212/1/5}, + abstract = {The GRavitational lEnsing Accuracy Testing 3 (GREAT3) challenge is the third in a series of image analysis challenges, with a goal of testing and facilitating the development of methods for analyzing astronomical images that will be used to measure weak gravitational lensing. This measurement requires extremely precise estimation of very small galaxy shape distortions, in the presence of far larger intrinsic galaxy shapes and distortions due to the blurring kernel caused by the atmosphere, telescope optics, and instrumental effects. The GREAT3 challenge is posed to the astronomy, machine learning, and statistics communities, and includes tests of three specific effects that are of immediate relevance to upcoming weak lensing surveys, two of which have never been tested in a community challenge before. These effects include many novel aspects including realistically complex galaxy models based on high-resolution imaging from space; a spatially varying, physically motivated blurring kernel; and a combination of multiple different exposures. To facilitate entry by people new to the field, and for use as a diagnostic tool, the simulation software for the challenge is publicly available, though the exact parameters used for the challenge are blinded. Sample scripts to analyze the challenge data using existing methods will also be provided. See http://great3challenge.info [http://great3challenge.info] and http://great3.projects.phys.ucl.ac.uk/leaderboard/ [http://great3.projects.phys.ucl.ac.uk/leaderboard/] for more information.}, language = {en}, number = {1}, + urldate = {2018-08-21}, journal = {ApJS}, author = {Mandelbaum, Rachel and Rowe, Barnaby and Bosch, James and Chang, Chihway and Courbin, Frederic and Gill, Mandeep and {Mike Jarvis} and Kannawadi, Arun and Kacprzak, Tomasz and Lackner, Claire and Leauthaud, Alexie and Miyatake, Hironao and {Reiko Nakajima} and Rhodes, Jason and Simet, Melanie and Zuntz, Joe and Armstrong, Bob and Bridle, Sarah and Coupon, Jean and Dietrich, J{\"o}rg P. and Gentile, Marc and Heymans, Catherine and Jurling, Alden S. and Kent, Stephen M. and Kirkby, David and {Daniel Margala} and Massey, Richard and Melchior, Peter and Peterson, John and Roodman, Aaron and Schrabback, Tim}, year = {2014}, pages = {5}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/LNU8XNI8/Mandelbaum et al. - 2014 - The Third Gravitational Lensing Accuracy Testing (.pdf:application/pdf} } @article{mahabal_automated_2008, @@ -532,14 +783,19 @@ @article{mahabal_automated_2008 volume = {329}, copyright = {Copyright {\textcopyright} 2008 WILEY-VCH Verlag GmbH \& Co. KGaA, Weinheim}, issn = {1521-3994}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/asna.200710943}, doi = {10.1002/asna.200710943}, + abstract = {There is an increasing number of large, digital, synoptic sky surveys, in which repeated observations are obtained over large areas of the sky in multiple epochs. Likewise, there is a growth in the number of (often automated or robotic) follow-up facilities with varied capabilities in terms of instruments, depth, cadence, wavelengths, etc., most of which are geared toward some specific astrophysical phenomenon. As the number of detected transient events grows, an automated, probabilistic classification of the detected variables and transients becomes increasingly important, so that an optimal use can be made of follow-up facilities, without unnecessary duplication of effort. We describe a methodology now under development for a prototype event classification system; it involves Bayesian and Machine Learning classifiers, automated incorporation of feedback from follow-up observations, and discriminated or directed follow-up requests. This type of methodology may be essential for the massive synoptic sky surveys in the future. ({\textcopyright} 2008 WILEY-VCH Verlag GmbH \& Co. KGaA, Weinheim)}, language = {en}, number = {3}, + urldate = {2018-10-04}, journal = {Astronomische Nachrichten}, author = {Mahabal, A. and Djorgovski, S. G. and Turmon, M. and Jewell, J. and Williams, R. R. and Drake, A. J. and Graham, M. G. and Donalek, C. and Glikman, E. and Team, Palomar-QUEST}, month = mar, year = {2008}, + keywords = {surveys, methods: data analysis, methods: statistical, astronomical databases: miscellaneous}, pages = {288--291}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/E3QFUK24/Mahabal et al. - 2008 - Automated probabilistic classification of transien.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/GBY4QAIM/asna.html:text/html} } @article{rubin_unity:_2015, @@ -547,117 +803,118 @@ @article{rubin_unity:_2015 volume = {813}, issn = {0004-637X}, shorttitle = {{UNITY}}, + url = {http://stacks.iop.org/0004-637X/813/i=2/a=137}, doi = {10.1088/0004-637X/813/2/137}, + abstract = {While recent supernova (SN) cosmology research has benefited from improved measurements, current analysis approaches are not statistically optimal and will prove insufficient for future surveys. This paper discusses the limitations of current SN cosmological analyses in treating outliers, selection effects, shape- and color-standardization relations, unexplained dispersion, and heterogeneous observations. We present a new Bayesian framework, called UNITY (Unified Nonlinear Inference for Type-Ia cosmologY), that incorporates significant improvements in our ability to confront these effects. We apply the framework to real SN observations and demonstrate smaller statistical and systematic uncertainties. We verify earlier results that SNe Ia require nonlinear shape and color standardizations, but we now include these nonlinear relations in a statistically well-justified way. This analysis was primarily performed blinded, in that the basic framework was first validated on simulated data before transitioning to real data. We also discuss possible extensions of the method.}, language = {en}, number = {2}, + urldate = {2018-10-04}, journal = {ApJ}, author = {Rubin, D. and Aldering, G. and Barbary, K. and Boone, K. and Chappell, G. and Currie, M. and Deustua, S. and Fagrelius, P. and {A. Fruchter} and Hayden, B. and Lidman, C. and Nordin, J. and Perlmutter, S. and Saunders, C. and Sofiatti, C. and Project, The Supernova Cosmology}, year = {2015}, pages = {137}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/XCT3AG8Y/Rubin et al. - 2015 - UNITY Confronting Supernova Cosmology's Statistic.pdf:application/pdf} } @article{ishida_kernel_2013, title = {Kernel {PCA} for {Type} {Ia} supernovae photometric classification}, volume = {430}, issn = {0035-8711}, + url = {https://academic.oup.com/mnras/article/430/1/509/985966}, doi = {10.1093/mnras/sts650}, + abstract = {Abstract. The problem of supernova photometric identification will be extremely important for large surveys in the next decade. In this work, we propose the us}, language = {en}, number = {1}, + urldate = {2018-10-04}, journal = {Mon Not R Astron Soc}, author = {Ishida, E. E. O. and Souza, De and S, R.}, month = mar, year = {2013}, pages = {509--532}, + file = {Full Text PDF:/home/aimalz/Documents/References/storage/7HQMT43G/Ishida et al. - 2013 - Kernel PCA for Type Ia supernovae photometric clas.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/6HG5QETD/985966.html:text/html} } @article{jones_measuring_2018, title = {Measuring {Dark} {Energy} {Properties} with {Photometrically} {Classified} {Pan}-{STARRS} {Supernovae}. {II}. {Cosmological} {Parameters}}, volume = {857}, issn = {0004-637X}, + url = {http://stacks.iop.org/0004-637X/857/i=1/a=51}, doi = {10.3847/1538-4357/aab6b1}, + abstract = {We use 1169 Pan-STARRS supernovae (SNe) and 195 low- z ( z {\textless} 0.1) SNe Ia to measure cosmological parameters. Though most Pan-STARRS SNe lack spectroscopic classifications, in a previous paper we demonstrated that photometrically classified SNe can be used to infer unbiased cosmological parameters by using a Bayesian methodology that marginalizes over core-collapse (CC) SN contamination. Our sample contains nearly twice as many SNe as the largest previous SN Ia compilation. Combining SNe with cosmic microwave background (CMB) constraints from Planck, we measure the dark energy equation-of-state parameter w to be -0.989 {\textpm} 0.057 (stat+sys). If w evolves with redshift as w ( a) = w 0 + w a(1 - a ), we find w 0 = -0.912 {\textpm} 0.149 and w a = -0.513 {\textpm} 0.826. These results are consistent with cosmological parameters from the Joint Light-curve Analysis and the Pantheon sample. We try four different photometric classification priors for Pan-STARRS SNe and two alternate ways of modeling CC SN contamination, finding that no variant gives a w differing by more than 2\% from the baseline measurement. The systematic uncertainty on w due to marginalizing over CC SN contamination, \#\#IMG\#\# [http://ej.iop.org/images/0004-637X/857/1/51/apjaab6b1ieqn1.gif] \${\textbackslash}sigma \_w{\textasciicircum}{\textbackslash}mathrmCC=0.012\$ , is the third-smallest source of systematic uncertainty in this work. We find limited (1.6 $\sigma$ ) evidence for evolution of the SN color-luminosity relation with redshift, a possible systematic that could constitute a significant uncertainty in future high- z analyses. Our data provide one of the best current constraints on w , demonstrating that samples with \~{}5\% CC SN contamination can give competitive cosmological constraints when the contaminating distribution is marginalized over in a Bayesian framework.}, language = {en}, number = {1}, + urldate = {2018-10-04}, journal = {ApJ}, author = {Jones, D. O. and Scolnic, D. M. and Riess, A. G. and Rest, A. and Kirshner, R. P. and Berger, E. and Kessler, R. and Pan, Y.-C. and Foley, R. J. and Chornock, R. and Ortega, C. A. and Challis, P. J. and Burgett, W. S. and Chambers, K. C. and Draper, P. W. and {H. Flewelling} and Huber, M. E. and Kaiser, N. and Kudritzki, R.-P. and Metcalfe, N. and Tonry, J. and Wainscoat, R. J. and Waters, C. and Gall, E. E. E. and Kotak, R. and McCrum, M. and Smartt, S. J. and Smith, K. W.}, year = {2018}, pages = {51}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/WDRGIJ4A/Jones et al. - 2018 - Measuring Dark Energy Properties with Photometrica.pdf:application/pdf} } @article{richards_bayesian_2015, title = {Bayesian {High}-redshift {Quasar} {Classification} from {Optical} and {Mid}-{IR} {Photometry}}, volume = {219}, issn = {0067-0049}, + url = {http://stacks.iop.org/0067-0049/219/i=2/a=39}, doi = {10.1088/0067-0049/219/2/39}, + abstract = {We identify 885,503 type 1 quasar candidates to \#\#IMG\#\# [http://ej.iop.org/images/0067-0049/219/2/39/apjs518349ieqn1.gif] \$i{\textbackslash}lesssim 22\$ using the combination of optical and mid-IR photometry. Optical photometry is taken from the Sloan Digital Sky Survey-III: Baryon Oscillation Spectroscopic Survey (SDSS-III/BOSS), while mid-IR photometry comes from a combination of data from the Wide-field Infrared Survey Explorer ( WISE ) {\textquotedblleft}AllWISE{\textquotedblright} data release and several large-area Spitzer Space Telescope fields. Selection is based on a Bayesian kernel density algorithm with a training sample of 157,701 spectroscopically confirmed type 1 quasars with both optical and mid-IR data. Of the quasar candidates, 733,713 lack spectroscopic confirmation (and 305,623 are objects that we have not previously classified as photometric quasar candidates). These candidates include 7874 objects targeted as high-probability potential quasars with \#\#IMG\#\# [http://ej.iop.org/images/0067-0049/219/2/39/apjs518349ieqn2.gif] \$3.5{\textbackslash}lt z{\textbackslash}lt 5\$ (of which 6779 are new photometric candidates). Our algorithm is more complete to \#\#IMG\#\# [http://ej.iop.org/images/0067-0049/219/2/39/apjs518349ieqn3.gif] \$z{\textbackslash}gt 3.5\$ than the traditional mid-IR selection {\textquotedblleft}wedges{\textquotedblright} and to \#\#IMG\#\# [http://ej.iop.org/images/0067-0049/219/2/39/apjs518349ieqn4.gif] \$2.2{\textbackslash}lt z{\textbackslash}lt 3.5\$ quasars than the SDSS-III/BOSS project. Number counts and luminosity function analysis suggest that the resulting catalog is relatively complete to known quasars and is identifying new high- z quasars at \#\#IMG\#\# [http://ej.iop.org/images/0067-0049/219/2/39/apjs518349ieqn5.gif] \$z{\textbackslash}gt 3\$ . This catalog paves the way for luminosity-dependent clustering investigations of large numbers of faint, high-redshift quasars and for further machine-learning quasar selection using Spitzer and WISE data combined with other large-area optical imaging surveys.}, language = {en}, number = {2}, + urldate = {2018-10-04}, journal = {ApJS}, author = {Richards, Gordon T. and Myers, Adam D. and Peters, Christina M. and Krawczyk, Coleman M. and Chase, Greg and Ross, Nicholas P. and Fan, Xiaohui and Jiang, Linhua and Lacy, Mark and McGreer, Ian D. and Trump, Jonathan R. and Riegel, Ryan N.}, year = {2015}, pages = {39}, + file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/UQCKD866/Richards et al. - 2015 - Bayesian High-redshift Quasar Classification from .pdf:application/pdf} +} + +@book{oliphant_python_2007, + title = {Python for {Scientific} {Computing}}, + volume = {9}, + author = {Oliphant, T.}, + month = may, + year = {2007}, + doi = {10.1109/MCSE.2007.58} +} + +@misc{malz_cosmological_2018, + title = {Cosmological {Hierarchical} {Inference} with {Probabilistic} {Photometric} {Redshifts}: aimalz/chippr}, + copyright = {MIT}, + shorttitle = {Cosmological {Hierarchical} {Inference} with {Probabilistic} {Photometric} {Redshifts}}, + url = {https://github.com/aimalz/chippr}, + urldate = {2019-04-12}, + author = {Malz, Alex}, + month = jul, + year = {2018}, + note = {original-date: 2016-12-23T23:41:09Z} +} + +@inproceedings{martin_det_1997, + title = {The {DET} curve in assessment of detection task performance}, + author = {Martin, Alvin F. and Doddington, George R. and Kamm, Terri and Ordowski, Mark and Przybocki, Mark A.}, + year = {1997}, + file = {6f0d7fe2555ed16f405c59ac81eb94a9aec2.pdf:/home/aimalz/Documents/References/storage/GVSSN695/6f0d7fe2555ed16f405c59ac81eb94a9aec2.pdf:application/pdf} } -@ARTICLE{2011CSE....13b..22V, - author = {{van der Walt}, St{\'e}fan and {Colbert}, S. Chris and - {Varoquaux}, Ga{\"e}l}, - title = "{The NumPy Array: A Structure for Efficient Numerical Computation}", - journal = {Computing in Science and Engineering}, - keywords = {Computer Science - Mathematical Software}, - year = "2011", - month = "Mar", - volume = {13}, - number = {2}, - pages = {22-30}, - doi = {10.1109/MCSE.2011.37}, -archivePrefix = {arXiv}, - eprint = {1102.1523}, - primaryClass = {cs.MS}, - adsurl = {https://ui.adsabs.harvard.edu/abs/2011CSE....13b..22V}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} -@iNProceedings{Kluyver2016JupyterN, - title={Jupyter Notebooks - a publishing format for reproducible computational workflows}, - author={Thomas Kluyver and Benjamin Ragan-Kelley and Fernando P{\'e}rez and Brian E. Granger and Matthias Bussonnier and Jonathan Frederic and Kyle Kelley and Jessica B. Hamrick and Jason Grout and Sylvain Corlay and Paul Ivanov and Dami{\'a}n Avila and Safia Abdalla and Carol Willing and et al.}, - booktitle={ELPUB}, - year={2016} -} -@article{scikit-learn, - title={Scikit-learn: Machine Learning in {P}ython}, - author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. - and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. - and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and - Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, - journal={Journal of Machine Learning Research}, - volume={12}, - pages={2825--2830}, - year={2011} -} -@inproceedings{sklearn_api, - author = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and - Fabian Pedregosa and Andreas Mueller and Olivier Grisel and - Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort - and Jaques Grobler and Robert Layton and Jake VanderPlas and - Arnaud Joly and Brian Holt and Ga{\"{e}}l Varoquaux}, - title = {{API} design for machine learning software: experiences from the scikit-learn - project}, - booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning}, - year = {2013}, - pages = {108--122}, -} -@Misc{scipy_ref, author = {Eric Jones and Travis Oliphant and Pearu Peterson and others}, - title = {{SciPy}: Open source scientific tools for {Python}}, - year = {2001}, - url = "http://www.scipy.org/", -} -@Article{Hunter:2007, - Author = {Hunter, J. D.}, - Title = {Matplotlib: A 2D graphics environment}, - Journal = {Computing in Science \& Engineering}, - Volume = {9}, - Number = {3}, - Pages = {90--95}, - abstract = {Matplotlib is a 2D graphics package used for Python for - application development, interactive scripting, and publication-quality - image generation across user interfaces and operating systems.}, - publisher = {IEEE COMPUTER SOC}, - doi = {10.1109/MCSE.2007.55}, - year = 2007 + +@misc{malz_proclam_2018, + title = {{ProClaM}}, + url = {http://www.github.com/aimalz/proclam}, + author = {Malz, Alex}, + year = {2018}, + doi = {10.5281/zenodo.3352639} } + +@article{buitinck_api_2013, + title = {{API} design for machine learning software: experiences from the scikit-learn project}, + shorttitle = {{API} design for machine learning software}, + url = {http://arxiv.org/abs/1309.0238}, + abstract = {Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library.}, + urldate = {2019-07-27}, + journal = {arXiv:1309.0238 [cs]}, + author = {Buitinck, Lars and Louppe, Gilles and Blondel, Mathieu and Pedregosa, Fabian and Mueller, Andreas and Grisel, Olivier and Niculae, Vlad and Prettenhofer, Peter and Gramfort, Alexandre and Grobler, Jaques and Layton, Robert and Vanderplas, Jake and Joly, Arnaud and Holt, Brian and Varoquaux, Ga{\"e}l}, + month = sep, + year = {2013}, + note = {arXiv: 1309.0238}, + keywords = {Computer Science - Machine Learning, Computer Science - Mathematical Software}, + file = {arXiv\:1309.0238 PDF:/home/aimalz/Documents/References/storage/4LSFCM9Z/Buitinck et al. - 2013 - API design for machine learning software experien.pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/HCWMWYR2/1309.html:text/html} +} \ No newline at end of file diff --git a/paper/main.tex b/paper/main.tex index 6a09e28..f885415 100644 --- a/paper/main.tex +++ b/paper/main.tex @@ -97,7 +97,16 @@ \subsection*{Acknowledgments} The authors would like to thank Melissa Graham, Weikang Lin, and Chad Schafer for serving as the LSST-DESC publication review committee. The authors further wish to thank Tom Loredo for helpful feedback provided in the preparation of this paper. -\changes{\software{Aside from the standard python package, this work used the following software packages: numpy~\citep{2011CSE....13b..22V}, scipy~\citep{scipy_ref}, scikit-learn~\citep{scikit-learn, sklearn_api}. The work also made extensive use of matplotlib~\citep{Hunter:2007} and Jupyter Notebooks~\citep{Kluyver2016JupyterN}.}} +\changes{ +\software{ +jupyter \citep{kluyver_jupyter_2016}, +matplotlib \citep{hunter_matplotlib:_2007}, +numpy \citep{walt_numpy_2011}, +proclam \citep{malz_proclam_2018}, +scikit-learn \citep{pedregosa_scikit-learn:_2011}, +scipy \citep{jones_scipy:_2001} +} +} AIM is advised by David W. Hogg and was supported by National Science Foundation grant AST-1517237. TA is supported in part by STFC. From dfd2c4ccef646f656fc519f0be7d08a25166faa9 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Sat, 27 Jul 2019 10:33:26 -0400 Subject: [PATCH 41/58] adding references and footnote about weights --- paper/tex/introduction.tex | 2 +- paper/tex/methods.tex | 5 ++++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/paper/tex/introduction.tex b/paper/tex/introduction.tex index 9c25577..f0e7ccb 100644 --- a/paper/tex/introduction.tex +++ b/paper/tex/introduction.tex @@ -75,7 +75,7 @@ \section{Introduction} %\item The metric must be reliable, giving consistent results for different instantiations of the same test case. %\end{itemize} -We perform a systematic exploration of the sensitivity of metrics of probabilistic classification to anticipated classifier failure modes using the PRObabilistic CLAssification Metric (\proclam) code, which is is publicly available on GitHub\footnote{\url{https://github.com/aimalz/proclam}}. +We perform a systematic exploration of the sensitivity of metrics of probabilistic classification to anticipated classifier failure modes using the PRObabilistic CLAssification Metric (\proclam) code \citep{malz_proclam_2018}, which is publicly available on GitHub\footnote{\url{https://github.com/aimalz/proclam}}. The mock classification submissions that we use for this study are described in Section~\ref{sec:data}. The metrics we consider are presented in Section~\ref{sec:methods}. The behavior of the metrics as a function of mock classification results is presented in Section~\ref{sec:results}. diff --git a/paper/tex/methods.tex b/paper/tex/methods.tex index fb8967b..5ec7e0e 100644 --- a/paper/tex/methods.tex +++ b/paper/tex/methods.tex @@ -141,4 +141,7 @@ \subsection{Weights} The weights for the \plasticc\ metric, however, must be determined before there is knowledge of which systematics affect which classes. Because of this caveat, the choice of weights is isolated to an inherently human problem dictated by the value placed on the scientific merits of knowledge of each class. This paper, on the other hand, can only quantify the impact of weights in relation to the systematics. -We thus agnostically test weighting schemes\footnote{\changes{The weights considered in this study are more extreme than those ultimately used for \plasticc\ because the true weights were withheld from some authors prior to the end of the challenge.}} where classes affected by a particular systematic take a given weight $0 \leq w \leq 1$ and all other classes have a weight $(1 - w) / (M - 1)$. +We thus agnostically test weighting schemes\footnote{\changes{The weights considered in this study are more extreme than those ultimately used for \plasticc\ because the true weights were withheld from some authors prior to the end of the challenge. +However, in the Kaggle framework, it is possible to estimate these values by systematically probing the output of the public leader board with entries from the cruise control classifier archetype targeting each class one at a time. +Some \plasticc\ competitors did, in fact, execute this procedure and publicly announced the weights they had discovered, making the information available to all participants.}} +where classes affected by a particular systematic take a given weight $0 \leq w \leq 1$ and all other classes have a weight $(1 - w) / (M - 1)$. From 7d76850bc2d45d943cf67e25c032448c66052466 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Mon, 29 Jul 2019 04:44:11 -0400 Subject: [PATCH 42/58] adding contributions and fixing typo --- paper/authors.csv | 2 +- paper/tex/data.tex | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/paper/authors.csv b/paper/authors.csv index 0c841d8..ce46c62 100644 --- a/paper/authors.csv +++ b/paper/authors.csv @@ -5,7 +5,7 @@ Hlo\v{z}ek,Ren\'ee,R.~Hlo\v{z}ek,Contributor,"Department of Astronomy and Astrop Hlo\v{z}ek,Ren\'ee,R.~Hlo\v{z}ek,Contributor,"Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George St., Toronto, ON M5S 3H4, Canada","data curation, formal analysis, funding acquisition, investigation, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",hlozek@dunlap.utoronto.ca Allam,Tarek,T.~Allam Jr,Contributor,"Mullard Space Science Laboratory, Department of Space and Climate Physics, University College London, Holmbury Hill Rd, Dorking RH5 6NT, UK","investigation, software, validation, writing - original draft",[email] Bahmanyar,Anita,A.~Bahmanyar,Contributor,"Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George St., Toronto, ON M5S 3H4, Canada","formal analysis, investigation, methodology, software, writing - editing, writing - original draft",[email] -Biswas,Rahul,R.~Biswas,Contributor,"The Oskar Klein Centre for Cosmoparticle Physics, Stockholm University, AlbaNova, Stockholm, SE-106 91, Sweden","conceptualization, methodology, software, writing - editing, writing - original draft",[email] +Biswas,Rahul,R.~Biswas,Contributor,"The Oskar Klein Centre for Cosmoparticle Physics, Stockholm University, AlbaNova, Stockholm, SE-106 91, Sweden","conceptualization, methodology, software, supervision, writing - editing, writing - original draft",[email] Dai,Mi,M.~Dai,Contributor,"Rutgers, the State University of New Jersey, 136 Frelinghuysen Road, Piscataway, NJ 08854 USA","writing - editing",[email] Galbany,Llu\'is,L.~Galbany,Contributor,"University of Pittsburgh, 300 Allen Hall, 3941 O'Hara St, Pittsburgh, PA 15260","writing - editing",[email] Ishida,Emille,E.E.O.~Ishida,Contributor,"Universit\'e Clermont Auvergne, CNRS/IN2P3, LPC, F-63000 Clermont-Ferrand, France","conceptualization, project administration, supervision, writing - editing",[email] diff --git a/paper/tex/data.tex b/paper/tex/data.tex index 3e590d9..f61fd1f 100644 --- a/paper/tex/data.tex +++ b/paper/tex/data.tex @@ -58,7 +58,7 @@ \subsection{Mock classification schemes} % Without loss of generality, we can decompose $\mathbb{C}$ under some basis functions of parameters $\mathcal{C}$, the same as those introduced above in the definition of a classification posterior $p(m \mid d_{n}, D, \mathcal{C})$. % The CPM $\mathbb{C}$ thus defines the behavior of a classifier. -Assuming the light curves contain information about the true class (an assumption that underlies classification as a whole), we can use the appropriate row $\mathbb{C}_{m'_{n}} = p(\hat{m} \mid m', \mathcal{C})$ of the CPM $\mathbb{C}$ as a proxy for $p(m \mid d_{n}, D, \mathcal{C})$, without directly classifying light curves themselves.\footnote{This assumption is key to the generality of this work, which was condicted without any knowledge of the \plasticc\ dataset simulation procedure.} +Assuming the light curves contain information about the true class (an assumption that underlies classification as a whole), we can use the appropriate row $\mathbb{C}_{m'_{n}} = p(\hat{m} \mid m', \mathcal{C})$ of the CPM $\mathbb{C}$ as a proxy for $p(m \mid d_{n}, D, \mathcal{C})$, without directly classifying light curves themselves.\footnote{This assumption is key to the generality of this work, which was conducted without any knowledge of the \plasticc\ dataset simulation procedure.} To emulate the effect of natural variation of information content in different light curves (e.g. a noisy lightcurve has less information to recover than one with a higher signal-to-noise ratio) using the above, we generate a posterior probability vector $\vec{p}(m \mid m', \mathbb{C})$ by taking a Dirichlet-distributed draw \begin{eqnarray} \label{eq:cmtoprob} @@ -77,7 +77,7 @@ \subsection{Mock classification schemes} \begin{figure*} \begin{center} \includegraphics[width=0.8\textwidth]{./fig/all_sim_cm.png} - \caption{Conditional probability matrices \changes{(CPMs)}for eight mock classifiers. + \caption{Conditional probability matrices \changes{(CPMs)} for eight mock classifiers. 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++++--- 2 files changed, 26 insertions(+), 5 deletions(-) diff --git a/paper/main.bib b/paper/main.bib index c878d5c..3c5325b 100644 --- a/paper/main.bib +++ b/paper/main.bib @@ -883,7 +883,7 @@ @misc{malz_cosmological_2018 shorttitle = {Cosmological {Hierarchical} {Inference} with {Probabilistic} {Photometric} {Redshifts}}, url = {https://github.com/aimalz/chippr}, urldate = {2019-04-12}, - author = {Malz, Alex}, + author = {Malz, Alex I.}, month = jul, year = {2018}, note = {original-date: 2016-12-23T23:41:09Z} @@ -899,7 +899,7 @@ @inproceedings{martin_det_1997 @misc{malz_proclam_2018, title = {{ProClaM}}, url = {http://www.github.com/aimalz/proclam}, - author = {Malz, Alex}, + author = {Malz, Alex I.}, year = {2018}, doi = {10.5281/zenodo.3352639} } @@ -917,4 +917,24 @@ @article{buitinck_api_2013 note = {arXiv: 1309.0238}, keywords = {Computer Science - Machine Learning, Computer Science - Mathematical Software}, file = {arXiv\:1309.0238 PDF:/home/aimalz/Documents/References/storage/4LSFCM9Z/Buitinck et al. - 2013 - API design for machine learning software experien.pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/HCWMWYR2/1309.html:text/html} +} + +@phdthesis{bell_burnell_measurement_1969, + type = {Thesis}, + title = {The measurement of radio source diameters using a diffraction method}, + url = {https://www.repository.cam.ac.uk/handle/1810/260694}, + abstract = {This dissertation describes the measurement of angular diameters of compact radio sources by the technique of interplanetary scintillation. The design, construction and testing of a four acre radio aerial +functioning at a frequency of 81.5 MHz is described, and its operation during a survey of the sky between declinations -07{\textdegree} and +46{\textdegree} and right ascensions ten hours and sixteen hours. The calibration of the +apparatus is explained and the method of analysis of the output from the receiving equipment. The theory of interplanetary scintillation has been adapted to +this frequency and extended, especially for the case of radio sources at large solar elongations. More stringent limits have been set on the rate of change with distance from the sun of the size of the +irregularities in the interplanetary medium. Some nine hundred radio sources have been studied in the survey, and one hundred and ninety-four have been found to contain structure of angular dimension less than one second of arc. Limits have been put on all the others. Fifty per cent of sources in the 3C catalogue have been found to show interplanetary scintillations. Angular diameters of eighty-five sources have been measured: these measured +values are in good agreement with other existing measurements, and values are now available for a large number of sources in the 4C catalogue in the area covered by the survey. The radio source 3C 273 +has been found to contain two small diameter components, and the more compact of these to be surprisingly strong. A more rigorous test of the correlation between spectral index of and the presence or absence of fine structure in a source has been carried out. A correlation between an enhancement of scintillation and a reduction in cosmic ray index has been noted. A description of the discovery of pulsed radio sources is given.}, + language = {en}, + urldate = {2019-07-29}, + school = {Department of Radio Astronomy, University of Cambridge}, + author = {Bell Burnell, Jocelyn}, + month = feb, + year = {1969}, + doi = {10.17863/CAM.4926} } \ No newline at end of file diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index 6f881ab..09b4fe0 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -48,9 +48,10 @@ \subsection{\sout{Early classification} \changes{Ongoing transient follow-up}} \changes{A particularly exciting science case is anomaly detection, the discovery of entirely unknown classes of transient or variable astrophysical sources, or distinguishing some of the rarest types of sources from more abundant types. Like the case of spectroscopic supernova cosmology discussed above,} anomaly detection also gains information only from true positives, but the cost function is different in that the potential information gain is unbounded when there is no prior information about undiscovered classes. -\aim{COMMENT RB: not to stay in doc, but I don't understand the prev sentence. I would also object to the recent detection of kilonova as a good example of anomaly detection, I can buy it if I squint very hard -COMMENT AIM: Agreed, but I couldn't think of a better one at the time of writing.} -An example would be the recent detection of a kilonova, flagged initially by the detection of gravitational waves from an object. +% \aim{COMMENT RB: not to stay in doc, but I don't understand the prev sentence. I would also object to the recent detection of kilonova as a good example of anomaly detection, I can buy it if I squint very hard +% COMMENT AIM: Agreed, but I couldn't think of a better one at the time of writing.} +\sout{An example would be the recent detection of a kilonova, flagged initially by the detection of gravitational waves from an object.} +\changes{The discovery of pulsars serves as an example of novelty detection enabled by a human classifier \citep{bell_burnell_measurement_1969}.} Resource availability for identifying new classes is more flexible, increasing when new predictions or promising preliminary observations attract attention, and decreasing when a discovery is confirmed and the new class is established. In this way, a false positive does not necessarily consume a resource that could otherwise be dedicated to a true positive, and the potential information gain is sufficiently great that additional resources would likely be allocated to observe the potential object. From 3633aa3d3ff61092e7ee4b1279b1c6b0b718bfd2 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Mon, 29 Jul 2019 05:50:29 -0400 Subject: [PATCH 45/58] forgot to mark changes --- paper/tex/methods.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/tex/methods.tex b/paper/tex/methods.tex index 5ec7e0e..089a62e 100644 --- a/paper/tex/methods.tex +++ b/paper/tex/methods.tex @@ -131,7 +131,7 @@ \subsection{Weights} A simpler alternative that we investigate in this paper is to use a weighted average \begin{eqnarray} \label{eq:weightavg} - Q &=& \frac{1}{\sum_{m} w_{m}} \sum_{m} w_{m} Q_{m} + \changes{Q &=& \frac{1}{\sum_{m} w_{m}} \sum_{m} w_{m} Q_{m}} \end{eqnarray} of per-class metrics $Q_{m}$. (While weights could be assigned to each term $Q_{n, m}$, we do not consider this complexity at this time.) From bca21685055a1e1e6c4fc2a12795c18c2b915b46 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Mon, 29 Jul 2019 06:01:13 -0400 Subject: [PATCH 46/58] fixed broken color change --- paper/tex/methods.tex | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paper/tex/methods.tex b/paper/tex/methods.tex index 089a62e..7f68812 100644 --- a/paper/tex/methods.tex +++ b/paper/tex/methods.tex @@ -128,12 +128,12 @@ \subsection{Weights} For example, requiring a minimum difference in probability density between the maximum probability class and the next highest probability class would help avert this degeneracy. % (e.g. a newly discovered supernova with a very small number of points may be indistinguishable from a Cataclysmic variable going through a brightening). -A simpler alternative that we investigate in this paper is to use a weighted average +\changes{A simpler alternative that we investigate in this paper is to use a weighted average \begin{eqnarray} \label{eq:weightavg} - \changes{Q &=& \frac{1}{\sum_{m} w_{m}} \sum_{m} w_{m} Q_{m}} + Q &=& \frac{1}{\sum_{m} w_{m}} \sum_{m} w_{m} Q_{m} \end{eqnarray} -of per-class metrics $Q_{m}$. +of per-class metrics $Q_{m}$.} (While weights could be assigned to each term $Q_{n, m}$, we do not consider this complexity at this time.) Weights that are not proportional to $N^{-1}$ nor $M^{-1}$ may be chosen to encourage challenge participants to direct more attention to classes with less active classification efforts or those that have been historically more difficult to classify due to observational limitations. From b26bd2538580f96c6c6f4a6ca84bd29d1590cc90 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Mon, 29 Jul 2019 08:49:36 -0400 Subject: [PATCH 47/58] adding references --- paper/main.bib | 227 +------------------------------------------------ paper/main.tex | 8 +- 2 files changed, 5 insertions(+), 230 deletions(-) diff --git a/paper/main.bib b/paper/main.bib index 3c5325b..c4e772c 100644 --- a/paper/main.bib +++ b/paper/main.bib @@ -3,20 +3,16 @@ @article{pedregosa_scikit-learn:_2011 title = {Scikit-learn: {Machine} learning in {Python}}, volume = {12}, shorttitle = {Scikit-learn}, - url = {http://www.jmlr.org/papers/v12/pedregosa11a.html}, number = {Oct}, - urldate = {2017-08-30}, journal = {J Machine Learning Res}, author = {Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and {others}}, year = {2011}, pages = {2825--2830}, - file = {pedregosa11a.pdf:/home/aimalz/Documents/References/storage/FIJ6XGDI/pedregosa11a.pdf:application/pdf} } @book{oliphant_guide_2006, address = {USA}, title = {A guide to {NumPy}}, - url = {http://www.numpy.org/}, publisher = {Trelgol Publishing}, author = {Oliphant, Travis E.}, year = {2006} @@ -24,7 +20,6 @@ @book{oliphant_guide_2006 @misc{jones_scipy:_2001, title = {{SciPy}: {Open} {Source} {Scientific} {Tools} for {Python}}, - url = {https://www.scipy.org/}, author = {Jones, Eric and Oliphant, Travis and Peterson, Pearu}, year = {2001} } @@ -35,15 +30,12 @@ @article{hunter_matplotlib:_2007 issn = {1521-9615}, shorttitle = {Matplotlib}, doi = {10.1109/MCSE.2007.55}, - abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting,and publication-quality image generation across user interfaces and operating systems}, number = {3}, journal = {Computing in Science Engineering}, author = {Hunter, J. D.}, month = may, year = {2007}, - keywords = {2D graphics package, application development, computer graphics, Computer languages, Equations, Graphical user interfaces, Graphics, Image generation, interactive scripting, Interpolation, mathematics computing, Matplotlib, object-oriented programming, operating system, Operating systems, Packaging, Programming profession, publication-quality image generation, Python, scientific programming, scripting languages, software packages, user interface, User interfaces}, pages = {90--95}, - file = {IEEE Xplore Abstract Record:/home/aimalz/Documents/References/storage/Q3Q8X7A6/4160265.html:text/html;IEEE Xplore Full Text PDF:/home/aimalz/Documents/References/storage/96S8KF8W/Hunter - 2007 - Matplotlib A 2D Graphics Environment.pdf:application/pdf} } @inproceedings{kluyver_jupyter_2016, @@ -52,7 +44,6 @@ @inproceedings{kluyver_jupyter_2016 author = {Kluyver, Thomas and Ragan-Kelley, Benjamin and P{\'e}rez, Fernando and Granger, Brian E. and Bussonnier, Matthias and Frederic, Jonathan and Kelley, Kyle and Hamrick, Jessica B. and Grout, Jason and Corlay, Sylvain}, year = {2016}, pages = {87--90}, - file = {STAL9781614996491-0087.pdf:/home/aimalz/Documents/References/storage/9HSAUBZG/STAL9781614996491-0087.pdf:application/pdf} } @article{walt_numpy_2011, @@ -60,48 +51,34 @@ @article{walt_numpy_2011 volume = {13}, issn = {1521-9615}, shorttitle = {The {NumPy} {Array}}, - url = {doi.ieeecomputersociety.org/10.1109/MCSE.2011.37}, - abstract = {{\textless}p{\textgreater}In the Python world, NumPy arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.{\textless}/p{\textgreater}}, number = {2}, - urldate = {2018-04-06}, journal = {Computing in Science \& Engineering}, author = {Walt, S. v and Colbert, S. C. and Varoquaux, G.}, year = {2011}, doi = {10.1109/MCSE.2011.37}, - keywords = {Python, scientific programming, numerical computations, NumPy, programming libraries}, pages = {22--30}, - file = {Snapshot:/home/aimalz/Documents/References/storage/69Z3K5NW/mcs2011020022-abs.html:text/html} } @article{kessler_supernova_2010, title = {Supernova {Photometric} {Classification} {Challenge}}, - url = {http://arxiv.org/abs/1001.5210}, - abstract = {We have publicly released a blinded mix of simulated SNe, with types (Ia, Ib, Ic, II) selected in proportion to their expected rate. The simulation is realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point spread function and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non-Ia type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). We challenge scientists to run their classification algorithms and report a type for each SN. A spectroscopically confirmed subset is provided for training. The goals of this challenge are to (1) learn the relative strengths and weaknesses of the different classification algorithms, (2) use the results to improve classification algorithms, and (3) understand what spectroscopically confirmed sub-sets are needed to properly train these algorithms. The challenge is available at www.hep.anl.gov/SNchallenge, and the due date for classifications is May 1, 2010.}, - urldate = {2018-05-01}, journal = {arXiv:1001.5210 [astro-ph]}, author = {Kessler, Richard and Conley, Alex and Jha, Saurabh and Kuhlmann, Stephen}, month = jan, year = {2010}, - note = {arXiv: 1001.5210}, - keywords = {Astrophysics - Instrumentation and Methods for Astrophysics}, - file = {arXiv\:1001.5210 PDF:/home/aimalz/Documents/References/storage/NV4GQ7JQ/Kessler et al. - 2010 - Supernova Photometric Classification Challenge.pdf:application/pdf;arXiv\:1001.5210 PDF:/home/aimalz/Documents/References/storage/3H2DYRZE/Kessler et al. - 2010 - Supernova Photometric Classification Challenge.pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/9SFKRMMH/1001.html:text/html;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/NVYUBSZJ/1001.html:text/html} } @article{kessler_results_2010, title = {Results from the {Supernova} {Photometric} {Classification} {Challenge}}, volume = {122}, issn = {1538-3873}, - url = {http://iopscience.iop.org/article/10.1086/657607/meta}, doi = {10.1086/657607}, language = {en}, number = {898}, - urldate = {2018-05-01}, journal = {PASP}, author = {Kessler, Richard and Bassett, Bruce and Belov, Pavel and Bhatnagar, Vasudha and Campbell, Heather and Conley, Alex and Frieman, Joshua A. and Glazov, Alexandre and Gonz{\'a}lez-Gait{\'a}n, Santiago and Hlozek, Ren{\'e}e and Jha, Saurabh and Kuhlmann, Stephen and Kunz, Martin and Lampeitl, Hubert and Mahabal, Ashish and Newling, James and Nichol, Robert C. and Parkinson, David and Philip, Ninan Sajeeth and Poznanski, Dovi and Richards, Joseph W. and Rodney, Steven A. and Sako, Masao and Schneider, Donald P. and Smith, Mathew and Stritzinger, Maximilian and Varughese, Melvin}, month = nov, year = {2010}, pages = {1415}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/MBPIA5WS/Kessler et al. - 2010 - Results from the Supernova Photometric Classificat.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/KG8AALJX/657607.html:text/html} } @article{roberts_zbeams:_2017, @@ -109,53 +86,39 @@ @article{roberts_zbeams:_2017 volume = {2017}, issn = {1475-7516}, shorttitle = {{zBEAMS}}, - url = {http://stacks.iop.org/1475-7516/2017/i=10/a=036}, doi = {10.1088/1475-7516/2017/10/036}, - abstract = {Supernova cosmology without spectra will be an important component of future surveys such as LSST. This lack of supernova spectra results in uncertainty in the redshifts which, if ignored, leads to significantly biased estimates of cosmological parameters. Here we present a hierarchical Bayesian formalism{\textemdash} zBEAMS{\textemdash}that addresses this problem by marginalising over the unknown or uncertain supernova redshifts to produce unbiased cosmological estimates that are competitive with supernova data with spectroscopically confirmed redshifts. zBEAMS provides a unified treatment of both photometric redshifts and host galaxy misidentification (occurring due to chance galaxy alignments or faint hosts), effectively correcting the inevitable contamination in the Hubble diagram. Like its predecessor BEAMS, our formalism also takes care of non-Ia supernova contamination by marginalising over the unknown supernova type. We illustrate this technique with simulations of supernovae with photometric redshifts and host galaxy misidentification. A novel feature of the photometric redshift case is the important role played by the redshift distribution of the supernovae.}, language = {en}, number = {10}, - urldate = {2018-05-01}, journal = {J. Cosmol. Astropart. Phys.}, author = {Roberts, Ethan and Lochner, Michelle and Fonseca, Jos{\'e} and Bassett, Bruce A. and Lablanche, Pierre-Yves and Agarwal, Shankar}, year = {2017}, pages = {036}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/HS88HIGJ/Roberts et al. - 2017 - zBEAMS a unified solution for supernova cosmology.pdf:application/pdf} } @article{lochner_photometric_2016, title = {Photometric {Supernova} {Classification} with {Machine} {Learning}}, volume = {225}, issn = {0067-0049}, - url = {http://stacks.iop.org/0067-0049/225/i=2/a=31}, doi = {10.3847/0067-0049/225/2/31}, - abstract = {Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.}, language = {en}, number = {2}, - urldate = {2018-05-01}, journal = {ApJS}, author = {Lochner, Michelle and McEwen, Jason D. and Peiris, Hiranya V. and Lahav, Ofer and Winter, Max K.}, year = {2016}, pages = {31}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/6EPXMN4P/Lochner et al. - 2016 - Photometric Supernova Classification with Machine .pdf:application/pdf} } @article{narayan_machine_2018, title = {Machine {Learning}-based {Brokers} for {Real}-time {Classification} of the {LSST} {Alert} {Stream}}, volume = {236}, issn = {1538-4365}, - url = {http://arxiv.org/abs/1801.07323}, doi = {10.3847/1538-4365/aab781}, - abstract = {The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demands that the astronomical community update its followup paradigm. Alert-brokers -- automated software system to sift through, characterize, annotate and prioritize events for followup -- will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate and retrospective classification of alerts. The first takes the form of variable vs transient categorization, the second, a multi-class typing of the combined variable and transient dataset, and the third, a purity-driven subtyping of a transient class. While several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress towards adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.}, number = {1}, - urldate = {2018-06-05}, journal = {The Astrophysical Journal Supplement Series}, author = {Narayan, Gautham and Zaidi, Tayeb and Soraisam, Monika D. and Wang, Zhe and Lochner, Michelle and Matheson, Thomas and Saha, Abhijit and Yang, Shuo and Zhao, Zhenge and Kececioglu, John and Scheidegger, Carlos and Snodgrass, Richard T. and Axelrod, Tim and Jenness, Tim and Maier, Robert S. and Ridgway, Stephen T. and Seaman, Robert L. and Evans, Eric Michael and Singh, Navdeep and Taylor, Clark and Toeniskoetter, Jackson and Welch, Eric and Zhu, Songzhe}, month = may, year = {2018}, - note = {arXiv: 1801.07323}, - keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena}, pages = {9}, - file = {arXiv\:1801.07323 PDF:/home/aimalz/Documents/References/storage/4R9ZMGGN/Narayan et al. - 2018 - Machine Learning-based Brokers for Real-time Class.pdf:application/pdf;arXiv\:1801.07323 PDF:/home/aimalz/Documents/References/storage/YI7PZ2BW/Narayan et al. - 2018 - Machine Learning-based Brokers for Real-time Class.pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/BAUGHABV/1801.html:text/html;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/TTR8X4NX/1801.html:text/html} } @article{crown_validation_2012, @@ -163,18 +126,13 @@ @article{crown_validation_2012 volume = {10}, copyright = {This paper is not subject to U.S. copyright. Published in 2012 by the American Geophysical Union}, issn = {1542-7390}, - url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2011SW000760}, doi = {10.1029/2011SW000760}, - abstract = {This paper provides an assessment of the operational solar flare look-up table currently in use at the National Oceanic and Atmospheric Administration (NOAA) Space Weather Prediction Center (SWPC) during solar cycle 23 (May 1996 {\textendash} December 2008). To assess the value of human interaction, a validation of subjective flare probability forecasts was conducted and compared to the results obtained from the climatological look-up table used at SWPC. Probabilistic flare forecasts are evaluated using the Brier Skill Score, then discretized and entered into contingency tables from which a variety of verification measures are calculated. The ultimate goal of this report is to provide an operational baseline, whereby the scores and statistics from this paper can be used as the basis for future evaluation of models presented to the operational community.}, language = {en}, number = {6}, - urldate = {2018-07-09}, journal = {Space Weather}, author = {Crown, Misty D.}, month = jun, year = {2012}, - keywords = {flares, forecasting}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/VWZTVEWZ/Crown - Validation of the NOAA Space Weather Prediction Ce.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/E5NGYV7U/2011SW000760.html:text/html} } @article{richards_construction_2012, @@ -182,138 +140,104 @@ @article{richards_construction_2012 volume = {203}, issn = {0067-0049}, shorttitle = {Construction of a {Calibrated} {Probabilistic} {Classification} {Catalog}}, - url = {http://stacks.iop.org/0067-0049/203/i=2/a=32}, doi = {10.1088/0067-0049/203/2/32}, - abstract = {With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20\% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24\% of those sources into one of 12 science classes.}, language = {en}, number = {2}, - urldate = {2018-07-09}, journal = {ApJS}, author = {Richards, Joseph W. and Starr, Dan L. and Miller, Adam A. and Bloom, Joshua S. and Butler, Nathaniel R. and {Henrik Brink} and Crellin-Quick, Arien}, year = {2012}, pages = {32}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/ETX7PM7G/Richards et al. - 2012 - Construction of a Calibrated Probabilistic Classif.pdf:application/pdf} } @article{mays_ensemble_2015, title = {Ensemble {Modeling} of {CMEs} {Using} the {WSA}{\textendash}{ENLIL}+{Cone} {Model}}, volume = {290}, issn = {0038-0938, 1573-093X}, - url = {https://link.springer.com/article/10.1007/s11207-015-0692-1}, doi = {10.1007/s11207-015-0692-1}, - abstract = {Ensemble modeling of coronal mass ejections (CMEs) provides a probabilistic forecast of CME arrival time that includes an estimation of arrival-time uncertainty from the spread and distribution of predictions and forecast confidence in the likelihood of CME arrival. The real-time ensemble modeling of CME propagation uses the Wang{\textendash}Sheeley{\textendash}Arge (WSA){\textendash}ENLIL+Cone model installed at the Community Coordinated Modeling Center (CCMC) and executed in real-time at the CCMC/Space Weather Research Center. The current implementation of this ensemble-modeling method evaluates the sensitivity of WSA{\textendash}ENLIL+Cone model simulations of CME propagation to initial CME parameters. We discuss the results of real-time ensemble simulations for a total of 35 CME events that occurred between January 2013 {\textendash} July 2014. For the 17 events where the CME was predicted to arrive at Earth, the mean absolute arrival-time prediction error was 12.3 hours, which is comparable to the errors reported in other studies. For predictions of CME arrival at Earth, the correct-rejection rate is 62 \%, the false-alarm rate is 38 \%, the correct-alarm ratio is 77 \%, and the false-alarm ratio is 23 \%. The arrival time was within the range of the ensemble arrival predictions for 8 out of 17 events. The Brier Score for CME arrival-predictions is 0.15 (where a score of 0 on a range of 0 to 1 is a perfect forecast), which indicates that on average, the predicted probability, or likelihood, of CME arrival is fairly accurate. The reliability of ensemble CME-arrival predictions is heavily dependent on the initial distribution of CME input parameters (e.g. speed, direction, and width), particularly the median and spread. Preliminary analysis of the probabilistic forecasts suggests undervariability, indicating that these ensembles do not sample a wide-enough spread in CME input parameters. Prediction errors can also arise from ambient-model parameters, the accuracy of the solar-wind background derived from coronal maps, or other model limitations. Finally, predictions of the K P geomagnetic index differ from observed values by less than one for 11 out of 17 of the ensembles and K P prediction errors computed from the mean predicted K P show a mean absolute error of 1.3.}, language = {en}, number = {6}, - urldate = {2018-07-09}, journal = {Sol Phys}, author = {Mays, M. L. and Taktakishvili, A. and Pulkkinen, A. and MacNeice, P. J. and Rast{\"a}tter, L. and Odstrcil, D. and Jian, L. K. and Richardson, I. G. and LaSota, J. A. and Zheng, Y. and Kuznetsova, M. M.}, month = jun, year = {2015}, pages = {1775--1814}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/QEBIDI76/Mays et al. - 2015 - Ensemble Modeling of CMEs Using the WSA{\textendash}ENLIL+Cone.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/BZINT7LE/10.html:text/html} } @article{kim_hybrid_2015, title = {A hybrid ensemble learning approach to star{\textendash}galaxy classification}, volume = {453}, issn = {0035-8711}, - url = {https://academic.oup.com/mnras/article/453/1/507/1749701}, doi = {10.1093/mnras/stv1608}, - abstract = {Abstract. There exist a variety of star{\textendash}galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-}, language = {en}, number = {1}, - urldate = {2018-07-09}, journal = {Mon Not R Astron Soc}, author = {Kim, Edward J. and Brunner, Robert J. and Carrasco Kind, Matias}, month = oct, year = {2015}, pages = {507--521}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/JG8MNGD5/Kim et al. - 2015 - A hybrid ensemble learning approach to star{\textendash}galaxy.pdf:application/pdf} } @article{armstrong_k2_2016, title = {K2 variable catalogue {\textendash} {II}. {Machine} learning classification of variable stars and eclipsing binaries in {K}2 fields 0{\textendash}4}, volume = {456}, issn = {0035-8711}, - url = {https://academic.oup.com/mnras/article/456/2/2260/1071207}, doi = {10.1093/mnras/stv2836}, - abstract = {Abstract. We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximize the potential of such surveys, aut}, language = {en}, number = {2}, - urldate = {2018-07-09}, journal = {Mon Not R Astron Soc}, author = {Armstrong, D. J. and Kirk, J. and Lam, K. W. F. and McCormac, J. and Osborn, H. P. and Spake, J. and Walker, S. and Brown, D. J. A. and Kristiansen, M. H. and Pollacco, D. and West, R. and Wheatley, P. J.}, month = feb, year = {2016}, pages = {2260--2272}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/75I3VKTA/Armstrong et al. - 2016 - K2 variable catalogue {\textendash} II. Machine learning class.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/G2XF6B6R/1071207.html:text/html} } @article{brier_verification_1950, title = {Verification of forecasts expressed in terms of probability}, volume = {78}, issn = {0027-0644}, - url = {https://journals.ametsoc.org/doi/abs/10.1175/1520-0493%281950%29078%3C0001%3AVOFEIT%3E2.0.CO%3B2}, doi = {10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2}, - abstract = {No Abstract Available.}, number = {1}, - urldate = {2018-07-09}, journal = {Mon. Wea. Rev.}, author = {Brier, Glenn W.}, month = jan, year = {1950}, pages = {1--3}, - file = {mwr-078-01-0001.pdf:/home/aimalz/Documents/References/storage/AK89UIM9/mwr-078-01-0001.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/ZEKE9B9J/1520-0493(1950)0780001VOFEIT2.0.html:text/html} } @article{kim_stargalaxy_2017, title = {Star{\textendash}galaxy classification using deep convolutional neural networks}, volume = {464}, issn = {0035-8711}, - url = {https://academic.oup.com/mnras/article/464/4/4463/2417400}, doi = {10.1093/mnras/stw2672}, - abstract = {Abstract. Most existing star{\textendash}galaxy classifiers use the reduced summary information from catalogues, requiring careful feature extraction and selection. The la}, language = {en}, number = {4}, - urldate = {2018-07-09}, journal = {Mon Not R Astron Soc}, author = {Kim, Edward J. and Brunner, Robert J.}, month = feb, year = {2017}, pages = {4463--4475}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/J7SGAR9P/Kim and Brunner - 2017 - Star{\textendash}galaxy classification using deep convolutiona.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/L8XK2G5E/2417400.html:text/html} } @article{florios_forecasting_2018, title = {Forecasting {Solar} {Flares} {Using} {Magnetogram}-based {Predictors} and {Machine} {Learning}}, volume = {293}, issn = {0038-0938, 1573-093X}, - url = {https://link.springer.com/article/10.1007/s11207-018-1250-4}, doi = {10.1007/s11207-018-1250-4}, - abstract = {We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012 {\textendash} 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude {\textgreater}M1{\textgreater}M1\{{\textgreater}\}{\textbackslash},{\textbackslash}mbox\{M1\} and {\textgreater}C1{\textgreater}C1\{{\textgreater}\}{\textbackslash},{\textbackslash}mbox\{C1\} within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy ACC=0.93(0.00)ACC=0.93(0.00){\textbackslash}mathrm\{ACC\}=0.93(0.00), true skill statistic TSS=0.74(0.02)TSS=0.74(0.02){\textbackslash}mathrm\{TSS\}=0.74(0.02), and Heidke skill score HSS=0.49(0.01)HSS=0.49(0.01){\textbackslash}mathrm\{HSS\}=0.49(0.01) for {\textgreater}M1{\textgreater}M1\{{\textgreater}\}{\textbackslash},{\textbackslash}mbox\{M1\} flare prediction with probability threshold 15\% and ACC=0.84(0.00)ACC=0.84(0.00){\textbackslash}mathrm\{ACC\}=0.84(0.00), TSS=0.60(0.01)TSS=0.60(0.01){\textbackslash}mathrm\{TSS\}=0.60(0.01), and HSS=0.59(0.01)HSS=0.59(0.01){\textbackslash}mathrm\{HSS\}=0.59(0.01) for {\textgreater}C1{\textgreater}C1\{{\textgreater}\}{\textbackslash},{\textbackslash}mbox\{C1\} flare prediction with probability threshold 35\%.}, language = {en}, number = {2}, - urldate = {2018-07-09}, journal = {Sol Phys}, author = {Florios, Kostas and Kontogiannis, Ioannis and Park, Sung-Hong and Guerra, Jordan A. and Benvenuto, Federico and Bloomfield, D. Shaun and Georgoulis, Manolis K.}, month = feb, year = {2018}, pages = {28}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/L7WERD2P/Florios et al. - 2018 - Forecasting Solar Flares Using Magnetogram-based P.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/P22P9YMB/10.html:text/html} } @article{mccloskey_flare_2018, title = {Flare {Forecasting} {Using} the {Evolution} of {McIntosh} {Sunspot} {Classifications}}, - url = {http://arxiv.org/abs/1805.00919}, - abstract = {Most solar flares originate in sunspot groups, where magnetic field changes lead to energy build-up and release. However, few flare-forecasting methods use information of sunspot-group evolution, instead focusing on static point-in-time observations. Here, a new forecast method is presented based upon the 24-hr evolution in McIntosh classification of sunspot groups. Evolution-dependent \${\textbackslash}geqslant\$C1.0 and \${\textbackslash}geqslant\$M1.0 flaring rates are found from NOAA-numbered sunspot groups over December 1988 to June 1996 (Solar Cycle 22; SC22) before converting to probabilities assuming Poisson statistics. These flaring probabilities are used to generate operational forecasts for sunspot groups over July 1996 to December 2008 (SC23), with performance studied by verification metrics. Major findings are: i) considering Brier skill score (BSS) for \${\textbackslash}geqslant\$C1.0 flares, the evolution-dependent McIntosh-Poisson method (\${\textbackslash}text\{BSS\}\_\{{\textbackslash}text\{evolution\}\}=0.09\$) performs better than the static McIntosh-Poisson method (\${\textbackslash}text\{BSS\}\_\{{\textbackslash}text\{static\}\} = -0.09\$); ii) low BSS values arise partly from both methods over-forecasting SC23 flares from the SC22 rates, symptomatic of \${\textbackslash}geqslant\$C1.0 rates in SC23 being on average \${\textbackslash}approx\$80\% of those in SC22 (with \${\textbackslash}geqslant\$M1.0 being \${\textbackslash}approx\$50\%); iii) applying a bias-correction factor to reduce the SC22 rates used in forecasting SC23 flares yields modest improvement in skill relative to climatology for both methods (\${\textbackslash}mathrm\{BSS\}{\textasciicircum}\{{\textbackslash}mathrm\{corr\}\}\_\{{\textbackslash}mathrm\{static\}\} = 0.09\$ and \${\textbackslash}mathrm\{BSS\}{\textasciicircum}\{{\textbackslash}mathrm\{corr\}\}\_\{{\textbackslash}mathrm\{evolution\}\} = 0.20\$) and improved forecast reliability diagrams.}, - urldate = {2018-07-09}, journal = {arXiv:1805.00919 [astro-ph]}, author = {McCloskey, Aoife E. and Gallagher, Peter T. and Bloomfield, D. Shaun}, month = may, year = {2018}, - note = {arXiv: 1805.00919}, - keywords = {Astrophysics - Solar and Stellar Astrophysics}, - file = {arXiv\:1805.00919 PDF:/home/aimalz/Documents/References/storage/6TYNDJRV/McCloskey et al. - 2018 - Flare Forecasting Using the Evolution of McIntosh .pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/TQHUPNAT/1805.html:text/html} } @article{hon_deep_2018, @@ -321,141 +245,105 @@ @article{hon_deep_2018 volume = {476}, issn = {0035-8711}, shorttitle = {Deep learning classification in asteroseismology using an improved neural network}, - url = {https://academic.oup.com/mnras/article/476/3/3233/4898088}, doi = {10.1093/mnras/sty483}, - abstract = {Abstract. Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary st}, language = {en}, number = {3}, - urldate = {2018-07-09}, journal = {Mon Not R Astron Soc}, author = {Hon, Marc and Stello, Dennis and Yu, Jie}, month = may, year = {2018}, pages = {3233--3244}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/ISW8CNIF/Hon et al. - 2018 - Deep learning classification in asteroseismology u.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/RY656LKK/4898088.html:text/html} } @article{moller_photometric_2016, title = {Photometric classification of type {Ia} supernovae in the {SuperNova} {Legacy} {Survey} with supervised learning}, volume = {2016}, issn = {1475-7516}, - url = {http://stacks.iop.org/1475-7516/2016/i=12/a=008}, doi = {10.1088/1475-7516/2016/12/008}, - abstract = {In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts(0.2 {\textless} z {\textless} 1.1). Our method consists of two stages: feature extraction (obtaining the SN redshift from photometry and estimating light-curve shape parameters) and machine learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of 0.98.We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than 5\%. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high- z SN survey with application to real SN data.}, language = {en}, number = {12}, - urldate = {2018-07-09}, journal = {J. Cosmol. Astropart. Phys.}, author = {M{\"o}ller, A. and Ruhlmann-Kleider, V. and Leloup, C. and Neveu, J. and Palanque-Delabrouille, N. and Rich, J. and Carlberg, R. and {C. Lidman} and Pritchet, C.}, year = {2016}, pages = {008}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/JNIMCKC5/M{\"o}ller et al. - 2016 - Photometric classification of type Ia supernovae i.pdf:application/pdf} } @article{hon_deep_2017, title = {Deep learning classification in asteroseismology}, volume = {469}, issn = {0035-8711}, - url = {https://academic.oup.com/mnras/article/469/4/4578/3828087}, doi = {10.1093/mnras/stx1174}, - abstract = {Abstract. In the power spectra of oscillating red giants, there are visually distinct features defining stars ascending the red giant branch from those that ha}, language = {en}, number = {4}, - urldate = {2018-07-09}, journal = {Mon Not R Astron Soc}, author = {Hon, Marc and Stello, Dennis and Yu, Jie}, month = aug, year = {2017}, pages = {4578--4583}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/DFNTS5WD/Hon et al. - 2017 - Deep learning classification in asteroseismology.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/Z8K42U5R/3828087.html:text/html} } @article{bethapudi_separation_2018, title = {Separation of pulsar signals from noise using supervised machine learning algorithms}, volume = {23}, issn = {2213-1337}, - url = {http://www.sciencedirect.com/science/article/pii/S2213133717301397}, doi = {10.1016/j.ascom.2018.02.002}, - abstract = {We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP), Adaboost, Gradient Boosting Classifier (GBC), and XGBoost, for the separation of pulsars from radio frequency interference (RFI) and other sources of noise, using a dataset obtained from the post-processing of a pulsar search pipeline. This dataset was previously used for the cross-validation of the SPINN-based machine learning engine, obtained from the reprocessing of the HTRU-S survey data (Morello et~al., 2014). We have used the Synthetic Minority Over-sampling Technique (SMOTE) to deal with high-class imbalance in the dataset. We report a variety of quality scores from all four of these algorithms on both the non-SMOTE and SMOTE datasets. For all the above ML methods, we report high accuracy and G-mean for both the non-SMOTE and SMOTE cases. We study the feature importances using Adaboost, GBC, and XGBoost and also from the minimum Redundancy Maximum Relevance approach to report algorithm-agnostic feature ranking. From these methods, we find that the signal to noise of the folded profile to be the best feature. We find that all the ML algorithms report FPRs about an order of magnitude lower than the corresponding FPRs obtained in Morello et~al. (2014), for the same recall value.}, - urldate = {2018-07-09}, journal = {Astronomy and Computing}, author = {Bethapudi, S. and Desai, S.}, month = apr, year = {2018}, - keywords = {Data analysis stars, Methods, Neutron}, pages = {15--26}, - file = {ScienceDirect Full Text PDF:/home/aimalz/Documents/References/storage/JZHXRFZ7/Bethapudi and Desai - 2018 - Separation of pulsar signals from noise using supe.pdf:application/pdf;ScienceDirect Snapshot:/home/aimalz/Documents/References/storage/7KTDH2FT/S2213133717301397.html:text/html} } @article{hon_detecting_2018, title = {Detecting {Solar}-like {Oscillations} in {Red} {Giants} with {Deep} {Learning}}, volume = {859}, issn = {0004-637X}, - url = {http://stacks.iop.org/0004-637X/859/i=1/a=64}, doi = {10.3847/1538-4357/aabfdb}, - abstract = {Time-resolved photometry of tens of thousands of red giant stars from space missions like Kepler and K 2 has created the need for automated asteroseismic analysis methods. The first and most fundamental step in such analysis is to identify which stars show oscillations. It is critical that this step be performed with no, or little, detection bias, particularly when performing subsequent ensemble analyses that aim to compare the properties of observed stellar populations with those from galactic models. However, an efficient, automated solution to this initial detection step still has not been found, meaning that expert visual inspection of data from each star is required to obtain the highest level of detections. Hence, to mimic how an expert eye analyzes the data, we use supervised deep learning to not only detect oscillations in red giants, but also to predict the location of the frequency at maximum power, $\nu$ max , by observing features in 2D images of power spectra. By training on Kepler data, we benchmark our deep-learning classifier against K 2 data that are given detections by the expert eye, achieving a detection accuracy of 98\% on K 2 Campaign 6 stars and a detection accuracy of 99\% on K 2 Campaign 3 stars. We further find that the estimated uncertainty of our deep-learning-based $\nu$ max predictions is about 5\%. This is comparable to human-level performance using visual inspection. When examining outliers, we find that the deep-learning results are more likely to provide robust $\nu$ max estimates than the classical model-fitting method.}, language = {en}, number = {1}, - urldate = {2018-07-09}, journal = {ApJ}, author = {Hon, Marc and Stello, Dennis and Zinn, Joel C.}, year = {2018}, pages = {64}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/IM3FA62T/Hon et al. - 2018 - Detecting Solar-like Oscillations in Red Giants wi.pdf:application/pdf} } @article{wu_radio_2018, title = {Radio {Galaxy} {Zoo}: {ClaRAN} - {A} {Deep} {Learning} {Classifier} for {Radio} {Morphologies}}, shorttitle = {Radio {Galaxy} {Zoo}}, - url = {http://arxiv.org/abs/1805.12008}, - abstract = {The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification through visual inspection unfeasible. We present ClaRAN - Classifying Radio sources Automatically with Neural networks - a proof-of-concept radio source morphology classifier based upon the Faster Region-based Convolutional Neutral Networks (Faster R-CNN) method. Specifically, we train and test ClaRAN on the FIRST and WISE images from the Radio Galaxy Zoo Data Release 1 catalogue. ClaRAN provides end users with automated identification of radio source morphology classifications from a simple input of a radio image and a counterpart infrared image of the same region. ClaRAN is the first open-source, end-to-end radio source morphology classifier that is capable of locating and associating discrete and extended components of radio sources in a fast ({\textless} 200 milliseconds per image) and accurate ({\textgreater}= 90 \%) fashion. Future work will improve ClaRAN's relatively lower success rates in dealing with multi-source fields and will enable ClaRAN to identify sources on much larger fields without loss in classification accuracy.}, - urldate = {2018-07-09}, journal = {arXiv:1805.12008 [astro-ph]}, author = {Wu, Chen and Wong, O. Ivy and Rudnick, Lawrence and Shabala, Stanislav S. and Alger, Matthew J. and Banfield, Julie K. and Ong, Cheng Soon and White, Sarah V. and Garon, Avery F. and Norris, Ray P. and Andernach, Heinz and Tate, Jean and Lukic, Vesna and Tang, Hongming and Schawinski, Kevin and Diakogiannis, Foivos I.}, month = may, year = {2018}, - note = {arXiv: 1805.12008}, - keywords = {Astrophysics - Instrumentation and Methods for Astrophysics}, - file = {arXiv\:1805.12008 PDF:/home/aimalz/Documents/References/storage/T3JDEB7W/Wu et al. - 2018 - Radio Galaxy Zoo ClaRAN - A Deep Learning Classif.pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/T5V6TXKS/1805.html:text/html} } @article{bloom_automating_2012, title = {Automating {Discovery} and {Classification} of {Transients} and {Variable} {Stars} in the {Synoptic} {Survey} {Era}}, volume = {124}, issn = {1538-3873}, - url = {http://iopscience.iop.org/article/10.1086/668468/meta}, doi = {10.1086/668468}, language = {en}, number = {921}, - urldate = {2018-08-16}, journal = {PASP}, author = {Bloom, J. S. and Richards, J. W. and Nugent, P. E. and Quimby, R. M. and Kasliwal, M. M. and Starr, D. L. and Poznanski, D. and Ofek, E. O. and Cenko, S. B. and Butler, N. R. and Kulkarni, S. R. and Gal-Yam, A. and Law, N.}, month = oct, year = {2012}, pages = {1175}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/BVL9JNXC/Bloom et al. - 2012 - Automating Discovery and Classification of Transie.pdf:application/pdf;Full Text PDF:/home/aimalz/Documents/References/storage/XVVKA5YG/Bloom et al. - 2012 - Automating Discovery and Classification of Transie.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/ZFI2SUZP/668468.html:text/html;Snapshot:/home/aimalz/Documents/References/storage/HFCLT7G2/668468.html:text/html} } @article{hoyle_measuring_2016, title = {Measuring photometric redshifts using galaxy images and {Deep} {Neural} {Networks}}, volume = {16}, issn = {2213-1337}, - url = {http://www.sciencedirect.com/science/article/pii/S221313371630021X}, doi = {10.1016/j.ascom.2016.03.006}, - abstract = {We propose a new method to estimate the photometric redshift of galaxies by using the full galaxy image in each measured band. This method draws from the latest techniques and advances in machine learning, in particular Deep Neural Networks. We pass the entire multi-band galaxy image into the machine learning architecture to obtain a redshift estimate that is competitive, in terms of the measured point prediction metrics, with the best existing standard machine learning techniques. The standard techniques estimate redshifts using post-processed features, such as magnitudes and colours, which are extracted from the galaxy images and are deemed to be salient by the user. This new method removes the user from the photometric redshift estimation pipeline. However we do note that Deep Neural Networks require many orders of magnitude more computing resources than standard machine learning architectures, and as such are only tractable for making predictions on datasets of size <=50k before implementing parallelisation techniques.}, - urldate = {2018-08-20}, journal = {Astronomy and Computing}, author = {Hoyle, B.}, month = jul, year = {2016}, - keywords = {Astronomy, Cosmology, Machine learning}, pages = {34--40}, - file = {ScienceDirect Full Text PDF:/home/aimalz/Documents/References/storage/E3CQ42DJ/Hoyle - 2016 - Measuring photometric redshifts using galaxy image.pdf:application/pdf;ScienceDirect Snapshot:/home/aimalz/Documents/References/storage/MJDVU6JS/S221313371630021X.html:text/html} } @book{murphy_machine_2012, title = {Machine learning: a probabilistic perspective}, - isbn = {0-262-01802-0 978-0-262-01802-9}, publisher = {The MIT Press}, author = {Murphy, Kevin P.}, year = {2012} @@ -465,14 +353,11 @@ @inproceedings{djorgovski_flashes_2012 title = {Flashes in a star stream: {Automated} classification of astronomical transient events}, shorttitle = {Flashes in a star stream}, doi = {10.1109/eScience.2012.6404437}, - abstract = {An automated, rapid classification of transient events detected in the modern synoptic sky surveys is essential for their scientific utility and effective follow-up using scarce resources. This presents some unusual challenges: the data are sparse, heterogeneous and incomplete; evolving in time; and most of the relevant information comes not from the data stream itself, but from a variety of archival data and contextual information (spatial, temporal, and multi-wavelength). We are exploring a variety of novel techniques, mostly Bayesian, to respond to these challenges, using the ongoing CRTS sky survey as a testbed. The current surveys are already overwhelming our ability to effectively follow all of the potentially interesting events, and these challenges will grow by orders of magnitude over the next decade as the more ambitious sky surveys get under way. While we focus on an application in a specific domain (astrophysics), these challenges are more broadly relevant for event or anomaly detection and knowledge discovery in massive data streams.}, booktitle = {2012 {IEEE} 8th {International} {Conference} on {E}-{Science}}, author = {Djorgovski, S. G. and Mahabal, A. A. and Donalek, C. and Graham, M. J. and Drake, A. J. and Moghaddam, B. and Turmon, M.}, month = oct, year = {2012}, - keywords = {data mining, machine learning, astronomy computing, pattern classification, Transient analysis, anomaly detection, archival data, astronomical techniques, astrophysics, automated astronomical transient event classification, automated decision making, Bayes methods, Bayesian methods, Bayesian technique, classification, contextual information, CRTS sky survey, event detection, Extraterrestrial measurements, Histograms, Image color analysis, knowledge discovery, massive data streams, multiwavelength information, Pollution measurement, Real-time systems, sky surveys, spatial information, star stream, stars, synoptic sky surveys, temporal information}, pages = {1--8}, - file = {IEEE Xplore Abstract Record:/home/aimalz/Documents/References/storage/XFJKEV3Q/6404437.html:text/html;IEEE Xplore Full Text PDF:/home/aimalz/Documents/References/storage/FPNA9G7Q/Djorgovski et al. - 2012 - Flashes in a star stream Automated classification.pdf:application/pdf} } @article{conley_sifto:_2008, @@ -480,112 +365,83 @@ @article{conley_sifto:_2008 volume = {681}, issn = {0004-637X}, shorttitle = {{SiFTO}}, - url = {http://stacks.iop.org/0004-637X/681/i=1/a=482}, doi = {10.1086/588518}, - abstract = {We present SiFTO, a new empirical method for modeling Type Ia supernova (SN Ia) light curves by manipulating a spectral template. We make use of high-redshift SN data when training the model, allowing us to extend it bluer than rest-frame U . This increases the utility of our high-redshift SN observations by allowing us to use more of the available data. We find that when the shape of the light curve is described using a stretch prescription, applying the same stretch at all wavelengths is not an adequate description. SiFTO therefore uses a generalization of stretch which applies different stretch factors as a function of both the wavelength of the observed filter and the stretch in the rest-frame B band. We compare SiFTO to other published light-curve models by applying them to the same set of SN photometry, and demonstrate that SiFTO and SALT2 perform better than the alternatives when judged by the scatter around the best-fit luminosity distance relationship. We further demonstrate that when SiFTO and SALT2 are trained on the same data set the cosmological results agree.}, language = {en}, number = {1}, - urldate = {2018-08-20}, journal = {ApJ}, author = {Conley, A. and Sullivan, M. and Hsiao, E. Y. and Guy, J. and Astier, P. and Balam, D. and Balland, C. and Basa, S. and Carlberg, R. G. and {D. Fouchez} and Hardin, D. and Howell, D. A. and Hook, I. M. and Pain, R. and Perrett, K. and Pritchet, C. J. and Regnault, N.}, year = {2008}, pages = {482}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/XZPXXY6A/Conley et al. - 2008 - SiFTO An Empirical Method for Fitting SN Ia Light.pdf:application/pdf} } @article{djorgovski_towards_2011, title = {Towards an {Automated} {Classification} of {Transient} {Events} in {Synoptic} {Sky} {Surveys}}, - url = {http://arxiv.org/abs/1110.4655}, - abstract = {We describe the development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys. The system under development incorporates a number of Machine Learning techniques, mostly using Bayesian approaches, due to the sparse nature, heterogeneity, and variable incompleteness of the available data. The classifications are improved iteratively as the new measurements are obtained. One novel feature is the development of an automated follow-up recommendation engine, that suggest those measurements that would be the most advantageous in terms of resolving classification ambiguities and/or characterization of the astrophysically most interesting objects, given a set of available follow-up assets and their cost functions. This illustrates the symbiotic relationship of astronomy and applied computer science through the emerging discipline of AstroInformatics.}, - urldate = {2018-08-20}, journal = {arXiv:1110.4655 [astro-ph, physics:physics]}, author = {Djorgovski, S. G. and Donalek, C. and Mahabal, A. and Moghaddam, B. and Turmon, M. and Graham, M. and Drake, A. and Sharma, N. and Chen, Y.}, month = oct, year = {2011}, - note = {arXiv: 1110.4655}, - keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Computational Physics}, - file = {arXiv\:1110.4655 PDF:/home/aimalz/Documents/References/storage/KQCNRECI/Djorgovski et al. - 2011 - Towards an Automated Classification of Transient E.pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/CN3C7NU5/1110.html:text/html} } @article{nugent_kcorrections_2002, title = {K-{Corrections} and {Extinction} {Corrections} for {Type} {Ia} {Supernovae}}, volume = {114}, issn = {1538-3873}, - url = {http://iopscience.iop.org/article/10.1086/341707/meta}, doi = {10.1086/341707}, language = {en}, number = {798}, - urldate = {2018-08-20}, journal = {PASP}, author = {Nugent, Peter and Kim, Alex and Perlmutter, Saul}, month = jul, year = {2002}, pages = {803}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/YB84G6IY/Nugent et al. - 2002 - K-Corrections and Extinction Corrections for Type .pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/ZH662SVM/341707.html:text/html} } @article{malz_approximating_2018, title = {Approximating {Photo}- z {PDFs} for {Large} {Surveys}}, volume = {156}, issn = {1538-3881}, - url = {http://stacks.iop.org/1538-3881/156/i=1/a=35}, doi = {10.3847/1538-3881/aac6b5}, - abstract = {Modern galaxy surveys produce redshift probability density functions (PDFs) in addition to traditional photometric redshift (photo- z ) point estimates. However, the storage of photo- z PDFs may present a challenge with increasingly large catalogs, as we face a trade-off between the accuracy of subsequent science measurements and the limitation of finite storage resources. This paper presents qp , a Python package for manipulating parameterizations of one-dimensional PDFs, as suitable for photo- z PDF compression. We use qp to investigate the performance of three simple PDF storage formats (quantiles, samples, and step functions) as a function of the number of stored parameters on two realistic mock data sets, representative of upcoming surveys with different data qualities. We propose some best practices for choosing a photo- z PDF approximation scheme and demonstrate the approach on a science case using performance metrics on both ensembles of individual photo- z PDFs and an estimator of the overall redshift distribution function. We show that both the properties of the set of PDFs we wish to approximate and the fidelity metric(s) chosen affect the optimal parameterization. Additionally, we find that quantiles and samples outperform step functions, and we encourage further consideration of these formats for PDF approximation.}, language = {en}, number = {1}, - urldate = {2018-08-20}, journal = {AJ}, author = {Malz, A. I. and Marshall, P. J. and DeRose, J. and Graham, M. L. and Schmidt, S. J. and Wechsler, R. and Collaboration), (LSST Dark Energy Science}, year = {2018}, - keywords = {Astrophysics - Instrumentation and Methods for Astrophysics}, pages = {35}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/JGQ7SQMS/Malz et al. - 2018 - Approximating Photo- z PDFs for Large Surveys.pdf:application/pdf;IOP Full Text PDF:/home/aimalz/Documents/References/storage/3F6SH9YS/Malz et al. - 2018 - Approximating Photo- z PDFs for Large Surveys.pdf:application/pdf} } @inproceedings{mahabal_deep-learnt_2017, title = {Deep-learnt classification of light curves}, doi = {10.1109/SSCI.2017.8280984}, - abstract = {Astronomy light curves are sparse, gappy, and heteroscedastic. As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and techniques to classify light curves. A common approach is to derive statistical features from the time series and to use machine learning methods, generally supervised, to separate objects into a few of the standard classes. In this work, we transform the time series to two-dimensional light curve representations in order to classify them using modern deep learning techniques. In particular, we show that convolutional neural networks based classifiers work well for broad characterization and classification. We use labeled datasets of periodic variables from CRTS survey and show how this opens doors for a quick classification of diverse classes with several possible exciting extensions.}, booktitle = {2017 {IEEE} {Symposium} {Series} on {Computational} {Intelligence} ({SSCI})}, author = {Mahabal, A. and Sheth, K. and Gieseke, F. and Pai, A. and Djorgovski, S. G. and Drake, A. J. and Graham, M. J.}, month = nov, year = {2017}, - keywords = {astronomers, Astronomy, astronomy computing, astronomy light curves, Cathode ray tubes, convolutional neural networks based classifiers work, data analysis, deep-learnt classification, financial datasets, instruments, Kernel, labeled datasets, learning (artificial intelligence), machine learning methods, modern deep learning techniques, neural nets, pattern classification, periodic variables, standard classes, standard time series methods, Standards, statistical features, time series, Time series analysis, Training, Transient analysis, two-dimensional light curve representations}, pages = {1--8}, - file = {IEEE Xplore Abstract Record:/home/aimalz/Documents/References/storage/QZQDB89Z/8280984.html:text/html;IEEE Xplore Full Text PDF:/home/aimalz/Documents/References/storage/PWM3FB3J/Mahabal et al. - 2017 - Deep-learnt classification of light curves.pdf:application/pdf} } @article{charnock_deep_2017, title = {Deep {Recurrent} {Neural} {Networks} for {Supernovae} {Classification}}, volume = {837}, issn = {2041-8205}, - url = {http://stacks.iop.org/2041-8205/837/i=2/a=L28}, doi = {10.3847/2041-8213/aa603d}, - abstract = {We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae (code available at https://github.com/adammoss/supernovae). The observational time and filter fluxes are used as inputs to the network, but since the inputs are agnostic, additional data such as host galaxy information can also be included. Using the Supernovae Photometric Classification Challenge (SPCC) data, we find that deep networks are capable of learning about light curves, however the performance of the network is highly sensitive to the amount of training data. For a training size of 50\% of the representational SPCC data set (around 10 4 supernovae) we obtain a type-Ia versus non-type-Ia classification accuracy of 94.7\%, an area under the Receiver Operating Characteristic curve AUC of 0.986 and an SPCC figure-of-merit F 1 = 0.64. When using only the data for the early-epoch challenge defined by the SPCC, we achieve a classification accuracy of 93.1\%, AUC of 0.977, and F 1 = 0.58, results almost as good as with the whole light curve. By employing bidirectional neural networks, we can acquire impressive classification results between supernovae types I, II and III at an accuracy of 90.4\% and AUC of 0.974. We also apply a pre-trained model to obtain classification probabilities as a function of time and show that it can give early indications of supernovae type. Our method is competitive with existing algorithms and has applications for future large-scale photometric surveys.}, language = {en}, number = {2}, - urldate = {2018-08-20}, journal = {ApJL}, author = {Charnock, Tom and Moss, Adam}, year = {2017}, pages = {L28}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/2287WY74/Charnock and Moss - 2017 - Deep Recurrent Neural Networks for Supernovae Clas.pdf:application/pdf} } @article{george_classification_2018, title = {Classification and unsupervised clustering of {LIGO} data with {Deep} {Transfer} {Learning}}, volume = {97}, - url = {https://link.aps.org/doi/10.1103/PhysRevD.97.101501}, doi = {10.1103/PhysRevD.97.101501}, - abstract = {Gravitational wave detection requires a detailed understanding of the response of the LIGO and Virgo detectors to true signals in the presence of environmental and instrumental noise. Of particular interest is the study of anomalous non-Gaussian transients, such as glitches, since their occurrence rate in LIGO and Virgo data can obscure or even mimic true gravitational wave signals. Therefore, successfully identifying and excising these anomalies from gravitational wave data is of utmost importance for the detection and characterization of true signals and for the accurate computation of their significance. To facilitate this work, we present the first application of deep learning combined with transfer learning to show that knowledge from pretrained models for real-world object recognition can be transferred for classifying spectrograms of glitches. To showcase this new method, we use a data set of twenty-two classes of glitches, curated and labeled by the Gravity Spy project using data collected during LIGO{\textquoteright}s first discovery campaign. We demonstrate that our Deep Transfer Learning method enables an optimal use of very deep convolutional neural networks for glitch classification given small and unbalanced training data sets, significantly reduces the training time, and achieves state-of-the-art accuracy above 98.8\%, lowering the previous error rate by over 60\%. More importantly, once trained via transfer learning on the known classes, we show that our neural networks can be truncated and used as feature extractors for unsupervised clustering to automatically group together new unknown classes of glitches and anomalous signals. This novel capability is of paramount importance to identify and remove new types of glitches which will occur as the LIGO/Virgo detectors gradually attain design sensitivity.}, number = {10}, - urldate = {2018-08-20}, journal = {Phys. Rev. D}, author = {George, Daniel and Shen, Hongyu and Huerta, E. A.}, month = may, year = {2018}, pages = {101501}, - file = {APS Snapshot:/home/aimalz/Documents/References/storage/9KPPZLWX/PhysRevD.97.html:text/html;PhysRevD.97.101501.pdf:/home/aimalz/Documents/References/storage/AG3P8877/PhysRevD.97.101501.pdf:application/pdf} } @article{zevin_gravity_2017, @@ -593,52 +449,40 @@ @article{zevin_gravity_2017 volume = {34}, issn = {0264-9381}, shorttitle = {Gravity {Spy}}, - url = {http://stacks.iop.org/0264-9381/34/i=6/a=064003}, doi = {10.1088/1361-6382/aa5cea}, - abstract = {With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches , which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO{\textquoteright}s first observing run.}, language = {en}, number = {6}, - urldate = {2018-08-20}, journal = {Class. Quantum Grav.}, author = {Zevin, M. and Coughlin, S. and Bahaadini, S. and Besler, E. and Rohani, N. and Allen, S. and Cabero, M. and Crowston, K. and Katsaggelos, A. K. and Larson, S. L. and Lee, T. K. and Lintott, C. and Littenberg, T. B. and Lundgren, A. and {\O}sterlund, C. and Smith, J. R. and Trouille, L. and Kalogera, V.}, year = {2017}, pages = {064003}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/DNG4D3B7/Zevin et al. - 2017 - Gravity Spy integrating advanced LIGO detector ch.pdf:application/pdf} } @article{morii_machine-learning_2016, title = {Machine-learning selection of optical transients in the {Subaru}/{Hyper} {Suprime}-{Cam} survey}, volume = {68}, issn = {0004-6264}, - url = {https://academic.oup.com/pasj/article/68/6/104/2433400}, doi = {10.1093/pasj/psw096}, - abstract = {Abstract. We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with the Hyper Suprime-Cam (HS}, language = {en}, number = {6}, - urldate = {2018-08-20}, journal = {Publ Astron Soc Jpn Nihon Tenmon Gakkai}, author = {Morii, Mikio and Ikeda, Shiro and Tominaga, Nozomu and Tanaka, Masaomi and Morokuma, Tomoki and Ishiguro, Katsuhiko and Yamato, Junji and Ueda, Naonori and Suzuki, Naotaka and Yasuda, Naoki and Yoshida, Naoki}, month = dec, year = {2016}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/BYILWI7C/Morii et al. - 2016 - Machine-learning selection of optical transients i.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/VI5RGDVC/2433400.html:text/html} } @article{abraham_detection_2018, title = {Detection of bars in galaxies using a deep convolutional neural network}, volume = {477}, issn = {0035-8711}, - url = {https://academic.oup.com/mnras/article/477/1/894/4925012}, doi = {10.1093/mnras/sty627}, - abstract = {Abstract. We present an automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network that is eas}, language = {en}, number = {1}, - urldate = {2018-08-20}, journal = {Mon Not R Astron Soc}, author = {Abraham, Sheelu and Aniyan, A. K. and Kembhavi, Ajit K. and Philip, N. S. and Vaghmare, Kaustubh}, month = jun, year = {2018}, pages = {894--903}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/E2RMQLMR/Abraham et al. - 2018 - Detection of bars in galaxies using a deep convolu.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/NNL43G6E/4925012.html:text/html} } @article{cabrera-vives_deep-hits:_2017, @@ -646,136 +490,102 @@ @article{cabrera-vives_deep-hits:_2017 volume = {836}, issn = {0004-637X}, shorttitle = {Deep-{HiTS}}, - url = {http://stacks.iop.org/0004-637X/836/i=1/a=97}, doi = {10.3847/1538-4357/836/1/97}, - abstract = {We introduce Deep-HiTS, a rotation-invariant convolutional neural network (CNN) model for classifying images of transient candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using random forests (RFs). We show that our CNN significantly outperforms the RF model, reducing the error by almost half. Furthermore, for a fixed number of approximately 2000 allowed false transient candidates per night, we are able to reduce the misclassified real transients by approximately one-fifth. To the best of our knowledge, this is the first time CNNs have been used to detect astronomical transient events. Our approach will be very useful when processing images from next generation instruments such as the Large Synoptic Survey Telescope. We have made all our code and data available to the community for the sake of allowing further developments and comparisons at https://github.com/guille-c/Deep-HiTS [http://https://github.com/guille-c/Deep-HiTS] .}, language = {en}, number = {1}, - urldate = {2018-08-20}, journal = {ApJ}, author = {Cabrera-Vives, Guillermo and Reyes, Ignacio and F{\"o}rster, Francisco and Est{\'e}vez, Pablo A. and Maureira, Juan-Carlos}, year = {2017}, pages = {97}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/B2T9X48G/Cabrera-Vives et al. - 2017 - Deep-HiTS Rotation Invariant Convolutional Neural.pdf:application/pdf} } @article{newling_statistical_2011, title = {Statistical classification techniques for photometric supernova typing}, volume = {414}, issn = {0035-8711}, - url = {https://academic.oup.com/mnras/article/414/3/1987/1035457}, doi = {10.1111/j.1365-2966.2011.18514.x}, - abstract = {Abstract. Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effecti}, language = {en}, number = {3}, - urldate = {2018-08-20}, journal = {Mon Not R Astron Soc}, author = {Newling, J. and Varughese, M. and Bassett, B. and Campbell, H. and Hlozek, R. and Kunz, M. and Lampeitl, H. and Martin, B. and Nichol, R. and Parkinson, D. and Smith, M.}, month = jul, year = {2011}, pages = {1987--2004}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/2QH7KF5N/Newling et al. - 2011 - Statistical classification techniques for photomet.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/M75AJHC2/1035457.html:text/html} } @article{sako_photometric_2011, title = {Photometric {Type} {Ia} {Supernova} {Candidates} from the {Three}-year {SDSS}-{II} {SN} {Survey} {Data}}, volume = {738}, issn = {0004-637X}, - url = {http://stacks.iop.org/0004-637X/738/i=2/a=162}, doi = {10.1088/0004-637X/738/2/162}, - abstract = {We analyze the three-year Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey data and identify a sample of 1070 photometric Type Ia supernova (SN Ia) candidates based on their multiband light curve data. This sample consists of SN candidates with no spectroscopic confirmation, with a subset of 210 candidates having spectroscopic redshifts of their host galaxies measured while the remaining 860 candidates are purely photometric in their identification. We describe a method for estimating the efficiency and purity of photometric SN Ia classification when spectroscopic confirmation of only a limited sample is available, and demonstrate that SN Ia candidates from SDSS-II can be identified photometrically with 91\% efficiency and with a contamination of 6\%. Although this is the largest uniform sample of SN candidates to date for studying photometric identification, we find that a larger spectroscopic sample of contaminating sources is required to obtain a better characterization of the background events. A Hubble diagram using SN candidates with no spectroscopic confirmation, but with host galaxy spectroscopic redshifts, yields a distance modulus dispersion that is only 20\%-40\% larger than that of the spectroscopically confirmed SN Ia sample alone with no significant bias. A Hubble diagram with purely photometric classification and redshift-distance measurements, however, exhibits biases that require further investigation for precision cosmology.}, language = {en}, number = {2}, - urldate = {2018-08-20}, journal = {ApJ}, author = {Sako, Masao and Bassett, Bruce and Connolly, Brian and Dilday, Benjamin and Cambell, Heather and Frieman, Joshua A. and {Larry Gladney} and Kessler, Richard and Lampeitl, Hubert and Marriner, John and Miquel, Ramon and Nichol, Robert C. and Schneider, Donald P. and Smith, Mathew and Sollerman, Jesper}, year = {2011}, pages = {162}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/HGVZPA9D/Sako et al. - 2011 - Photometric Type Ia Supernova Candidates from the .pdf:application/pdf} } @inproceedings{gieseke_detecting_2010, title = {Detecting {Quasars} in {Large}-{Scale} {Astronomical} {Surveys}}, doi = {10.1109/ICMLA.2010.59}, - abstract = {We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies.}, booktitle = {2010 {Ninth} {International} {Conference} on {Machine} {Learning} and {Applications}}, author = {Gieseke, F. and Polsterer, K. L. and Thom, A. and Zinn, P. and Bomanns, D. and Dettmar, R. and Kramer, O. and Vahrenhold, J.}, month = dec, year = {2010}, - keywords = {machine learning, Astronomy, astronomy computing, data analysis, Kernel, learning (artificial intelligence), Training, quasars, classification, astronomical catalogues, astronomical photometry, astronomical surveys, astronomy, classification performance, classification schemes, classification-based approach, Data models, detecting quasars, feature extraction, Feature extraction, large-scale astronomical surveys, manually labeled training set, performance evaluation, photometric data, problem-specific features extraction, quasi-stellar radio sources, sloan digital sky survey, spectroscopic catalogs, spectroscopic data, Spline, Support vector machines}, pages = {352--357}, - file = {IEEE Xplore Abstract Record:/home/aimalz/Documents/References/storage/K8HRKBJ5/5708856.html:text/html;IEEE Xplore Full Text PDF:/home/aimalz/Documents/References/storage/XUN45HCA/Gieseke et al. - 2010 - Detecting Quasars in Large-Scale Astronomical Surv.pdf:application/pdf} } @article{kitching_gravitational_2011, title = {Gravitational {Lensing} {Accuracy} {Testing} 2010 ({GREAT}10) {Challenge} {Handbook}}, volume = {5}, issn = {1932-6157, 1941-7330}, - url = {https://projecteuclid.org/euclid.aoas/1318514302}, doi = {10.1214/11-AOAS484}, - abstract = {GRavitational lEnsing Accuracy Testing 2010 (GREAT10) is a public image analysis challenge aimed at the development of algorithms to analyze astronomical images. Specifically, the challenge is to measure varying image distortions in the presence of a variable convolution kernel, pixelization and noise. This is the second in a series of challenges set to the astronomy, computer science and statistics communities, providing a structured environment in which methods can be improved and tested in preparation for planned astronomical surveys. GREAT10 extends upon previous work by introducing variable fields into the challenge. The {\textquotedblleft}Galaxy Challenge{\textquotedblright} involves the precise measurement of galaxy shape distortions, quantified locally by two parameters called shear, in the presence of a known convolution kernel. Crucially, the convolution kernel and the simulated gravitational lensing shape distortion both now vary as a function of position within the images, as is the case for real data. In addition, we introduce the {\textquotedblleft}Star Challenge{\textquotedblright} that concerns the reconstruction of a variable convolution kernel, similar to that in a typical astronomical observation. This document details the GREAT10 Challenge for potential participants. Continually updated information is also available from www.greatchallenges.info.}, language = {EN}, number = {3}, - urldate = {2018-08-21}, journal = {Ann. Appl. Stat.}, author = {Kitching, Thomas and Amara, Adam and Gill, Mandeep and Harmeling, Stefan and Heymans, Catherine and Massey, Richard and Rowe, Barnaby and Schrabback, Tim and Voigt, Lisa and Balan, Sreekumar and Bernstein, Gary and Bethge, Matthias and Bridle, Sarah and Courbin, Frederic and Gentile, Marc and Heavens, Alan and Hirsch, Michael and Hosseini, Reshad and Kiessling, Alina and Kirk, Donnacha and Kuijken, Konrad and Mandelbaum, Rachel and Moghaddam, Baback and Nurbaeva, Guldariya and Paulin-Henriksson, Stephane and Rassat, Anais and Rhodes, Jason and Sch{\"o}lkopf, Bernhard and Shawe-Taylor, John and Shmakova, Marina and Taylor, Andy and Velander, Malin and Waerbeke, Ludovic van and Witherick, Dugan and Wittman, David}, month = sep, year = {2011}, mrnumber = {MR2884938}, zmnumber = {1228.62164}, - keywords = {cosmology, imaging processing, Statistical inference}, pages = {2231--2263}, - file = {Snapshot:/home/aimalz/Documents/References/storage/IA4I3VDH/1318514302.html:text/html} } @article{harvey_observing_2013, title = {Observing {Dark} {Worlds}: {A} crowdsourcing experiment for dark matter mapping}, shorttitle = {Observing {Dark} {Worlds}}, - url = {http://arxiv.org/abs/1311.0704}, - abstract = {We present the results and conclusions from the citizen science competition `Observing Dark Worlds', where we asked participants to calculate the positions of dark matter halos from 120 catalogues of simulated weak lensing galaxy data, using computational methods. In partnership with Kaggle (http://www.kaggle.com), 357 users participated in the competition which saw 2278 downloads of the data and 3358 submissions. We found that the best algorithms improved on the benchmark code, LENSTOOL by {\textgreater} 30\% and could measure the positions of {\textgreater} 3x10{\textasciicircum}14MSun halos to less than 5'' and {\textless} 10{\textasciicircum}14MSun to within 1'. In this paper, we present a brief overview of the winning algorithms with links to available code. We also discuss the implications of the experiment for future citizen science competitions.}, - urldate = {2018-08-21}, journal = {arXiv:1311.0704 [astro-ph, physics:physics]}, author = {Harvey, David and Kitching, Thomas D. and Noah-Vanhoucke, Joyce and Hamner, Ben and Salimans, Tim}, month = nov, year = {2013}, - note = {arXiv: 1311.0704}, - keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Physics and Society}, - file = {arXiv\:1311.0704 PDF:/home/aimalz/Documents/References/storage/EKMGZ6CP/Harvey et al. - 2013 - Observing Dark Worlds A crowdsourcing experiment .pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/CZF3PAY2/1311.html:text/html} } @article{dieleman_rotation-invariant_2015, title = {Rotation-invariant convolutional neural networks for galaxy morphology prediction}, volume = {450}, issn = {0035-8711}, - url = {https://academic.oup.com/mnras/article/450/2/1441/979677}, doi = {10.1093/mnras/stv632}, - abstract = {Abstract. Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital}, language = {en}, number = {2}, - urldate = {2018-08-21}, journal = {Mon Not R Astron Soc}, author = {Dieleman, Sander and Willett, Kyle W. and Dambre, Joni}, month = jun, year = {2015}, pages = {1441--1459}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/7H75PH6C/Dieleman et al. - 2015 - Rotation-invariant convolutional neural networks f.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/2UELU548/979677.html:text/html} } @article{mandelbaum_third_2014, title = {The {Third} {Gravitational} {Lensing} {Accuracy} {Testing} ({GREAT}3) {Challenge} {Handbook}}, volume = {212}, issn = {0067-0049}, - url = {http://stacks.iop.org/0067-0049/212/i=1/a=5}, doi = {10.1088/0067-0049/212/1/5}, - abstract = {The GRavitational lEnsing Accuracy Testing 3 (GREAT3) challenge is the third in a series of image analysis challenges, with a goal of testing and facilitating the development of methods for analyzing astronomical images that will be used to measure weak gravitational lensing. This measurement requires extremely precise estimation of very small galaxy shape distortions, in the presence of far larger intrinsic galaxy shapes and distortions due to the blurring kernel caused by the atmosphere, telescope optics, and instrumental effects. The GREAT3 challenge is posed to the astronomy, machine learning, and statistics communities, and includes tests of three specific effects that are of immediate relevance to upcoming weak lensing surveys, two of which have never been tested in a community challenge before. These effects include many novel aspects including realistically complex galaxy models based on high-resolution imaging from space; a spatially varying, physically motivated blurring kernel; and a combination of multiple different exposures. To facilitate entry by people new to the field, and for use as a diagnostic tool, the simulation software for the challenge is publicly available, though the exact parameters used for the challenge are blinded. Sample scripts to analyze the challenge data using existing methods will also be provided. See http://great3challenge.info [http://great3challenge.info] and http://great3.projects.phys.ucl.ac.uk/leaderboard/ [http://great3.projects.phys.ucl.ac.uk/leaderboard/] for more information.}, language = {en}, number = {1}, - urldate = {2018-08-21}, journal = {ApJS}, author = {Mandelbaum, Rachel and Rowe, Barnaby and Bosch, James and Chang, Chihway and Courbin, Frederic and Gill, Mandeep and {Mike Jarvis} and Kannawadi, Arun and Kacprzak, Tomasz and Lackner, Claire and Leauthaud, Alexie and Miyatake, Hironao and {Reiko Nakajima} and Rhodes, Jason and Simet, Melanie and Zuntz, Joe and Armstrong, Bob and Bridle, Sarah and Coupon, Jean and Dietrich, J{\"o}rg P. and Gentile, Marc and Heymans, Catherine and Jurling, Alden S. and Kent, Stephen M. and Kirkby, David and {Daniel Margala} and Massey, Richard and Melchior, Peter and Peterson, John and Roodman, Aaron and Schrabback, Tim}, year = {2014}, pages = {5}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/LNU8XNI8/Mandelbaum et al. - 2014 - The Third Gravitational Lensing Accuracy Testing (.pdf:application/pdf} } @article{mahabal_automated_2008, @@ -783,19 +593,14 @@ @article{mahabal_automated_2008 volume = {329}, copyright = {Copyright {\textcopyright} 2008 WILEY-VCH Verlag GmbH \& Co. KGaA, Weinheim}, issn = {1521-3994}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/asna.200710943}, doi = {10.1002/asna.200710943}, - abstract = {There is an increasing number of large, digital, synoptic sky surveys, in which repeated observations are obtained over large areas of the sky in multiple epochs. Likewise, there is a growth in the number of (often automated or robotic) follow-up facilities with varied capabilities in terms of instruments, depth, cadence, wavelengths, etc., most of which are geared toward some specific astrophysical phenomenon. As the number of detected transient events grows, an automated, probabilistic classification of the detected variables and transients becomes increasingly important, so that an optimal use can be made of follow-up facilities, without unnecessary duplication of effort. We describe a methodology now under development for a prototype event classification system; it involves Bayesian and Machine Learning classifiers, automated incorporation of feedback from follow-up observations, and discriminated or directed follow-up requests. This type of methodology may be essential for the massive synoptic sky surveys in the future. ({\textcopyright} 2008 WILEY-VCH Verlag GmbH \& Co. KGaA, Weinheim)}, language = {en}, number = {3}, - urldate = {2018-10-04}, journal = {Astronomische Nachrichten}, author = {Mahabal, A. and Djorgovski, S. G. and Turmon, M. and Jewell, J. and Williams, R. R. and Drake, A. J. and Graham, M. G. and Donalek, C. and Glikman, E. and Team, Palomar-QUEST}, month = mar, year = {2008}, - keywords = {surveys, methods: data analysis, methods: statistical, astronomical databases: miscellaneous}, pages = {288--291}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/E3QFUK24/Mahabal et al. - 2008 - Automated probabilistic classification of transien.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/GBY4QAIM/asna.html:text/html} } @article{rubin_unity:_2015, @@ -803,69 +608,53 @@ @article{rubin_unity:_2015 volume = {813}, issn = {0004-637X}, shorttitle = {{UNITY}}, - url = {http://stacks.iop.org/0004-637X/813/i=2/a=137}, doi = {10.1088/0004-637X/813/2/137}, - abstract = {While recent supernova (SN) cosmology research has benefited from improved measurements, current analysis approaches are not statistically optimal and will prove insufficient for future surveys. This paper discusses the limitations of current SN cosmological analyses in treating outliers, selection effects, shape- and color-standardization relations, unexplained dispersion, and heterogeneous observations. We present a new Bayesian framework, called UNITY (Unified Nonlinear Inference for Type-Ia cosmologY), that incorporates significant improvements in our ability to confront these effects. We apply the framework to real SN observations and demonstrate smaller statistical and systematic uncertainties. We verify earlier results that SNe Ia require nonlinear shape and color standardizations, but we now include these nonlinear relations in a statistically well-justified way. This analysis was primarily performed blinded, in that the basic framework was first validated on simulated data before transitioning to real data. We also discuss possible extensions of the method.}, language = {en}, number = {2}, - urldate = {2018-10-04}, journal = {ApJ}, author = {Rubin, D. and Aldering, G. and Barbary, K. and Boone, K. and Chappell, G. and Currie, M. and Deustua, S. and Fagrelius, P. and {A. Fruchter} and Hayden, B. and Lidman, C. and Nordin, J. and Perlmutter, S. and Saunders, C. and Sofiatti, C. and Project, The Supernova Cosmology}, year = {2015}, pages = {137}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/XCT3AG8Y/Rubin et al. - 2015 - UNITY Confronting Supernova Cosmology's Statistic.pdf:application/pdf} } @article{ishida_kernel_2013, title = {Kernel {PCA} for {Type} {Ia} supernovae photometric classification}, volume = {430}, issn = {0035-8711}, - url = {https://academic.oup.com/mnras/article/430/1/509/985966}, doi = {10.1093/mnras/sts650}, - abstract = {Abstract. The problem of supernova photometric identification will be extremely important for large surveys in the next decade. In this work, we propose the us}, language = {en}, number = {1}, - urldate = {2018-10-04}, journal = {Mon Not R Astron Soc}, author = {Ishida, E. E. O. and Souza, De and S, R.}, month = mar, year = {2013}, pages = {509--532}, - file = {Full Text PDF:/home/aimalz/Documents/References/storage/7HQMT43G/Ishida et al. - 2013 - Kernel PCA for Type Ia supernovae photometric clas.pdf:application/pdf;Snapshot:/home/aimalz/Documents/References/storage/6HG5QETD/985966.html:text/html} } @article{jones_measuring_2018, title = {Measuring {Dark} {Energy} {Properties} with {Photometrically} {Classified} {Pan}-{STARRS} {Supernovae}. {II}. {Cosmological} {Parameters}}, volume = {857}, issn = {0004-637X}, - url = {http://stacks.iop.org/0004-637X/857/i=1/a=51}, doi = {10.3847/1538-4357/aab6b1}, - abstract = {We use 1169 Pan-STARRS supernovae (SNe) and 195 low- z ( z {\textless} 0.1) SNe Ia to measure cosmological parameters. Though most Pan-STARRS SNe lack spectroscopic classifications, in a previous paper we demonstrated that photometrically classified SNe can be used to infer unbiased cosmological parameters by using a Bayesian methodology that marginalizes over core-collapse (CC) SN contamination. Our sample contains nearly twice as many SNe as the largest previous SN Ia compilation. Combining SNe with cosmic microwave background (CMB) constraints from Planck, we measure the dark energy equation-of-state parameter w to be -0.989 {\textpm} 0.057 (stat+sys). If w evolves with redshift as w ( a) = w 0 + w a(1 - a ), we find w 0 = -0.912 {\textpm} 0.149 and w a = -0.513 {\textpm} 0.826. These results are consistent with cosmological parameters from the Joint Light-curve Analysis and the Pantheon sample. We try four different photometric classification priors for Pan-STARRS SNe and two alternate ways of modeling CC SN contamination, finding that no variant gives a w differing by more than 2\% from the baseline measurement. The systematic uncertainty on w due to marginalizing over CC SN contamination, \#\#IMG\#\# [http://ej.iop.org/images/0004-637X/857/1/51/apjaab6b1ieqn1.gif] \${\textbackslash}sigma \_w{\textasciicircum}{\textbackslash}mathrmCC=0.012\$ , is the third-smallest source of systematic uncertainty in this work. We find limited (1.6 $\sigma$ ) evidence for evolution of the SN color-luminosity relation with redshift, a possible systematic that could constitute a significant uncertainty in future high- z analyses. Our data provide one of the best current constraints on w , demonstrating that samples with \~{}5\% CC SN contamination can give competitive cosmological constraints when the contaminating distribution is marginalized over in a Bayesian framework.}, language = {en}, number = {1}, - urldate = {2018-10-04}, journal = {ApJ}, author = {Jones, D. O. and Scolnic, D. M. and Riess, A. G. and Rest, A. and Kirshner, R. P. and Berger, E. and Kessler, R. and Pan, Y.-C. and Foley, R. J. and Chornock, R. and Ortega, C. A. and Challis, P. J. and Burgett, W. S. and Chambers, K. C. and Draper, P. W. and {H. Flewelling} and Huber, M. E. and Kaiser, N. and Kudritzki, R.-P. and Metcalfe, N. and Tonry, J. and Wainscoat, R. J. and Waters, C. and Gall, E. E. E. and Kotak, R. and McCrum, M. and Smartt, S. J. and Smith, K. W.}, year = {2018}, pages = {51}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/WDRGIJ4A/Jones et al. - 2018 - Measuring Dark Energy Properties with Photometrica.pdf:application/pdf} } @article{richards_bayesian_2015, title = {Bayesian {High}-redshift {Quasar} {Classification} from {Optical} and {Mid}-{IR} {Photometry}}, volume = {219}, issn = {0067-0049}, - url = {http://stacks.iop.org/0067-0049/219/i=2/a=39}, doi = {10.1088/0067-0049/219/2/39}, - abstract = {We identify 885,503 type 1 quasar candidates to \#\#IMG\#\# [http://ej.iop.org/images/0067-0049/219/2/39/apjs518349ieqn1.gif] \$i{\textbackslash}lesssim 22\$ using the combination of optical and mid-IR photometry. Optical photometry is taken from the Sloan Digital Sky Survey-III: Baryon Oscillation Spectroscopic Survey (SDSS-III/BOSS), while mid-IR photometry comes from a combination of data from the Wide-field Infrared Survey Explorer ( WISE ) {\textquotedblleft}AllWISE{\textquotedblright} data release and several large-area Spitzer Space Telescope fields. Selection is based on a Bayesian kernel density algorithm with a training sample of 157,701 spectroscopically confirmed type 1 quasars with both optical and mid-IR data. Of the quasar candidates, 733,713 lack spectroscopic confirmation (and 305,623 are objects that we have not previously classified as photometric quasar candidates). These candidates include 7874 objects targeted as high-probability potential quasars with \#\#IMG\#\# [http://ej.iop.org/images/0067-0049/219/2/39/apjs518349ieqn2.gif] \$3.5{\textbackslash}lt z{\textbackslash}lt 5\$ (of which 6779 are new photometric candidates). Our algorithm is more complete to \#\#IMG\#\# [http://ej.iop.org/images/0067-0049/219/2/39/apjs518349ieqn3.gif] \$z{\textbackslash}gt 3.5\$ than the traditional mid-IR selection {\textquotedblleft}wedges{\textquotedblright} and to \#\#IMG\#\# [http://ej.iop.org/images/0067-0049/219/2/39/apjs518349ieqn4.gif] \$2.2{\textbackslash}lt z{\textbackslash}lt 3.5\$ quasars than the SDSS-III/BOSS project. Number counts and luminosity function analysis suggest that the resulting catalog is relatively complete to known quasars and is identifying new high- z quasars at \#\#IMG\#\# [http://ej.iop.org/images/0067-0049/219/2/39/apjs518349ieqn5.gif] \$z{\textbackslash}gt 3\$ . This catalog paves the way for luminosity-dependent clustering investigations of large numbers of faint, high-redshift quasars and for further machine-learning quasar selection using Spitzer and WISE data combined with other large-area optical imaging surveys.}, language = {en}, number = {2}, - urldate = {2018-10-04}, journal = {ApJS}, author = {Richards, Gordon T. and Myers, Adam D. and Peters, Christina M. and Krawczyk, Coleman M. and Chase, Greg and Ross, Nicholas P. and Fan, Xiaohui and Jiang, Linhua and Lacy, Mark and McGreer, Ian D. and Trump, Jonathan R. and Riegel, Ryan N.}, year = {2015}, pages = {39}, - file = {IOP Full Text PDF:/home/aimalz/Documents/References/storage/UQCKD866/Richards et al. - 2015 - Bayesian High-redshift Quasar Classification from .pdf:application/pdf} } @book{oliphant_python_2007, @@ -881,24 +670,19 @@ @misc{malz_cosmological_2018 title = {Cosmological {Hierarchical} {Inference} with {Probabilistic} {Photometric} {Redshifts}: aimalz/chippr}, copyright = {MIT}, shorttitle = {Cosmological {Hierarchical} {Inference} with {Probabilistic} {Photometric} {Redshifts}}, - url = {https://github.com/aimalz/chippr}, - urldate = {2019-04-12}, author = {Malz, Alex I.}, month = jul, year = {2018}, - note = {original-date: 2016-12-23T23:41:09Z} } @inproceedings{martin_det_1997, title = {The {DET} curve in assessment of detection task performance}, author = {Martin, Alvin F. and Doddington, George R. and Kamm, Terri and Ordowski, Mark and Przybocki, Mark A.}, year = {1997}, - file = {6f0d7fe2555ed16f405c59ac81eb94a9aec2.pdf:/home/aimalz/Documents/References/storage/GVSSN695/6f0d7fe2555ed16f405c59ac81eb94a9aec2.pdf:application/pdf} } @misc{malz_proclam_2018, title = {{ProClaM}}, - url = {http://www.github.com/aimalz/proclam}, author = {Malz, Alex I.}, year = {2018}, doi = {10.5281/zenodo.3352639} @@ -907,23 +691,15 @@ @misc{malz_proclam_2018 @article{buitinck_api_2013, title = {{API} design for machine learning software: experiences from the scikit-learn project}, shorttitle = {{API} design for machine learning software}, - url = {http://arxiv.org/abs/1309.0238}, - abstract = {Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library.}, - urldate = {2019-07-27}, journal = {arXiv:1309.0238 [cs]}, author = {Buitinck, Lars and Louppe, Gilles and Blondel, Mathieu and Pedregosa, Fabian and Mueller, Andreas and Grisel, Olivier and Niculae, Vlad and Prettenhofer, Peter and Gramfort, Alexandre and Grobler, Jaques and Layton, Robert and Vanderplas, Jake and Joly, Arnaud and Holt, Brian and Varoquaux, Ga{\"e}l}, month = sep, year = {2013}, - note = {arXiv: 1309.0238}, - keywords = {Computer Science - Machine Learning, Computer Science - Mathematical Software}, - file = {arXiv\:1309.0238 PDF:/home/aimalz/Documents/References/storage/4LSFCM9Z/Buitinck et al. - 2013 - API design for machine learning software experien.pdf:application/pdf;arXiv.org Snapshot:/home/aimalz/Documents/References/storage/HCWMWYR2/1309.html:text/html} } @phdthesis{bell_burnell_measurement_1969, type = {Thesis}, title = {The measurement of radio source diameters using a diffraction method}, - url = {https://www.repository.cam.ac.uk/handle/1810/260694}, - abstract = {This dissertation describes the measurement of angular diameters of compact radio sources by the technique of interplanetary scintillation. The design, construction and testing of a four acre radio aerial functioning at a frequency of 81.5 MHz is described, and its operation during a survey of the sky between declinations -07{\textdegree} and +46{\textdegree} and right ascensions ten hours and sixteen hours. The calibration of the apparatus is explained and the method of analysis of the output from the receiving equipment. The theory of interplanetary scintillation has been adapted to this frequency and extended, especially for the case of radio sources at large solar elongations. More stringent limits have been set on the rate of change with distance from the sun of the size of the @@ -931,10 +707,9 @@ @phdthesis{bell_burnell_measurement_1969 values are in good agreement with other existing measurements, and values are now available for a large number of sources in the 4C catalogue in the area covered by the survey. The radio source 3C 273 has been found to contain two small diameter components, and the more compact of these to be surprisingly strong. A more rigorous test of the correlation between spectral index of and the presence or absence of fine structure in a source has been carried out. A correlation between an enhancement of scintillation and a reduction in cosmic ray index has been noted. A description of the discovery of pulsed radio sources is given.}, language = {en}, - urldate = {2019-07-29}, school = {Department of Radio Astronomy, University of Cambridge}, author = {Bell Burnell, Jocelyn}, month = feb, year = {1969}, doi = {10.17863/CAM.4926} -} \ No newline at end of file +} diff --git a/paper/main.tex b/paper/main.tex index f885415..f38dd85 100644 --- a/paper/main.tex +++ b/paper/main.tex @@ -94,17 +94,17 @@ \subsection*{Acknowledgments} % Standard papers only: A.B.C. acknowledges support from grant 1234 from ... This paper has undergone internal review in the LSST Dark Energy Science Collaboration. % REQUIRED if true -The authors would like to thank Melissa Graham, Weikang Lin, and Chad Schafer for serving as the LSST-DESC publication review committee. -The authors further wish to thank Tom Loredo for helpful feedback provided in the preparation of this paper. +The authors would like to thank Melissa Graham, Weikang Lin, and Chad Schafer for serving as the LSST-DESC publication review committee, as well as Tom Loredo for other helpful feedback. +The authors also express gratitude to the anonymous referee for substantive suggestions that improved the paper. \changes{ \software{ jupyter \citep{kluyver_jupyter_2016}, matplotlib \citep{hunter_matplotlib:_2007}, -numpy \citep{walt_numpy_2011}, +numpy \citep{oliphant_guide_2006, oliphant_python_2007, walt_numpy_2011}, proclam \citep{malz_proclam_2018}, scikit-learn \citep{pedregosa_scikit-learn:_2011}, -scipy \citep{jones_scipy:_2001} +scipy \citep{jones_scipy:_2001, buitinck_api_2013} } } From 5cac979184f1d7297231eb94edd882307687609f Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Mon, 29 Jul 2019 08:56:06 -0400 Subject: [PATCH 48/58] fixed broken citation --- paper/main.bib | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/paper/main.bib b/paper/main.bib index c4e772c..63ca791 100644 --- a/paper/main.bib +++ b/paper/main.bib @@ -698,18 +698,13 @@ @article{buitinck_api_2013 } @phdthesis{bell_burnell_measurement_1969, + address = {Cambridge, UK}, type = {Thesis}, title = {The measurement of radio source diameters using a diffraction method}, -functioning at a frequency of 81.5 MHz is described, and its operation during a survey of the sky between declinations -07{\textdegree} and +46{\textdegree} and right ascensions ten hours and sixteen hours. The calibration of the -apparatus is explained and the method of analysis of the output from the receiving equipment. The theory of interplanetary scintillation has been adapted to -this frequency and extended, especially for the case of radio sources at large solar elongations. More stringent limits have been set on the rate of change with distance from the sun of the size of the -irregularities in the interplanetary medium. Some nine hundred radio sources have been studied in the survey, and one hundred and ninety-four have been found to contain structure of angular dimension less than one second of arc. Limits have been put on all the others. Fifty per cent of sources in the 3C catalogue have been found to show interplanetary scintillations. Angular diameters of eighty-five sources have been measured: these measured -values are in good agreement with other existing measurements, and values are now available for a large number of sources in the 4C catalogue in the area covered by the survey. The radio source 3C 273 -has been found to contain two small diameter components, and the more compact of these to be surprisingly strong. A more rigorous test of the correlation between spectral index of and the presence or absence of fine structure in a source has been carried out. A correlation between an enhancement of scintillation and a reduction in cosmic ray index has been noted. A description of the discovery of pulsed radio sources is given.}, language = {en}, school = {Department of Radio Astronomy, University of Cambridge}, author = {Bell Burnell, Jocelyn}, month = feb, year = {1969}, - doi = {10.17863/CAM.4926} + doi = {10.17863/CAM.4926}, } From 38bc28036d3321ee50ac58ff2c6c64e79bdd7dda Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Mon, 29 Jul 2019 09:23:44 -0400 Subject: [PATCH 49/58] added reference --- paper/main.bib | 15 +++++++++++++++ paper/tex/discussion.tex | 2 +- 2 files changed, 16 insertions(+), 1 deletion(-) diff --git a/paper/main.bib b/paper/main.bib index 63ca791..5ba64cf 100644 --- a/paper/main.bib +++ b/paper/main.bib @@ -708,3 +708,18 @@ @phdthesis{bell_burnell_measurement_1969 year = {1969}, doi = {10.17863/CAM.4926}, } + +@article{hewish_observation_1968, + title = {Observation of a {Rapidly} {Pulsating} {Radio} {Source}}, + volume = {217}, + copyright = {1968 Nature Publishing Group}, + issn = {1476-4687}, + doi = {10.1038/217709a0}, + language = {En}, + number = {5130}, + journal = {Nature}, + author = {Hewish, A. and Bell, S. J. and Pilkington, J. D. H. and Scott, P. F. and Collins, R. A.}, + month = feb, + year = {1968}, + pages = {709}, +} diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index 09b4fe0..0d6bd20 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -51,7 +51,7 @@ \subsection{\sout{Early classification} \changes{Ongoing transient follow-up}} % \aim{COMMENT RB: not to stay in doc, but I don't understand the prev sentence. I would also object to the recent detection of kilonova as a good example of anomaly detection, I can buy it if I squint very hard % COMMENT AIM: Agreed, but I couldn't think of a better one at the time of writing.} \sout{An example would be the recent detection of a kilonova, flagged initially by the detection of gravitational waves from an object.} -\changes{The discovery of pulsars serves as an example of novelty detection enabled by a human classifier \citep{bell_burnell_measurement_1969}.} +\changes{The discovery of pulsars serves as an example of novelty detection enabled by a human classifier \citep{hewish_observation_1968, bell_burnell_measurement_1969}.} Resource availability for identifying new classes is more flexible, increasing when new predictions or promising preliminary observations attract attention, and decreasing when a discovery is confirmed and the new class is established. In this way, a false positive does not necessarily consume a resource that could otherwise be dedicated to a true positive, and the potential information gain is sufficiently great that additional resources would likely be allocated to observe the potential object. 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zhHyk!VV8rv0JpXperUih1v?8PF?Dr8L<6u-N>Nc!u+m+C#Rg%ONEHb3f#KmRAXWxq z-IceraD(%p86SXkp6Jd1ARS0HdwhIUa&O;W$Fdl3e;GeNKVjX0n~CQ>9GAvW^fWW$ zBh|!crw*n=3f=P_69+Unh~$h~xg2(C;s?Kddn?{(F|=T$82$W@8{I&SV($r|uap2+ zDn2$gJ27>@_(7b3g=i}c2w+k|kj%_VcblK}CXW$A?8z#?4m&qHdx|BHNmo~wm_!k$ z&leS`s;R5L?dnQ7lw@VJS5NN|uu^r&fvs|K)vrB4-V4w?e&t9tmr9NMndD2E9b!)kQ)X0rR((!z}gvD+KEXMAECA6$k^zp3knRRTJmEI z1_N6D9%pA65P>M!^a0`!;Qz5MKj-4%agLbb)z$Ny8%4kJR_Rhs>qQp+DO&iQ3nVLt z_gT%vkI lcs;+}^dH~+|G$Hi{?c(B?rg43UyhI0(cHf~OT#Mge*im|!xsPm diff --git a/paper/tex/data.tex b/paper/tex/data.tex index f61fd1f..49aa124 100644 --- a/paper/tex/data.tex +++ b/paper/tex/data.tex @@ -113,7 +113,7 @@ \subsection{Mock classification schemes} \caption{A realistically complex conditional probability matrix \changes{(CPM)} and classification posteriors drawn from it. Top: An example of a realistically complex conditional probability matrix, constructed by selecting a systematic for each individual class. This illustrates (for example), how a classifier may exhibit multiple systematics from Figure~\ref{fig:mock_cm} for each true class. - Bottom: Example classification probabilities, drawn from the above CPM, with their true class indicated by a red star and the systematic, characterized by its row in the CPM, affecting that true class described on the right. + Bottom: Example classification probabilities, drawn from the above CPM, with their true class indicated by a star and the systematic, characterized by its row in the CPM, affecting that true class described on the right. The Dirichlet process emulates the variation in classification posteriors due to differences between light curves within a given class, leading to different classification posteriors even among rows sharing a true class. } \label{fig:mock_probs} @@ -123,7 +123,7 @@ \subsection{Mock classification schemes} An actual classifier is expected to be more complex than the simplified cases of Figure~\ref{fig:mock_cm}, with different systematic behavior for each class. An example of a combined CPM across different classes and systematics is given in the top panel of Figure~\ref{fig:mock_probs}. The rows of this CPM correspond to rows of the archetypical classifiers of Figure~\ref{fig:mock_cm}. -To demonstrate the procedure by which mock classification posteriors are generated from rows of the CPM, we provide 22 examples of draws of the posterior CPM in the bottom panel of Figure~\ref{fig:mock_probs}. +To demonstrate the procedure by which mock classification posteriors are generated from rows of the CPM, we provide 26 examples of draws of the posterior CPM in the bottom panel of Figure~\ref{fig:mock_probs}. Given a set of true class identities, the mock classification posteriors of the bottom panel are Dirichlet draws from the corresponding row of the CPM of the top panel. \subsubsection{Uncertain classification} From dd53d98fceffd741cc29154f8211d5050be24155 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Tue, 30 Jul 2019 05:20:45 -0400 Subject: [PATCH 51/58] clarifying confusing example --- paper/tex/discussion.tex | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index 0d6bd20..d3e62e5 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -38,8 +38,7 @@ \subsection{\sout{Early classification} \changes{Ongoing transient follow-up}} A perfect classifier would lead to a maximum amount of information about the cosmological parameters conditioned on the follow-up resource budget. \sout{For this scientific application, the metric must be chosen to balance the value of the information forgone by a false positive and the value of information forgone by a false negative, and the value placed on these is effectively weighted by the value we as researchers place on follow-up resources.} -\changes{In this scientific application, a classifier that maximizes true positives and minimizes false positives boosts the constraining power over cosmological parameters. -However, it does so at a cost of rising false negatives, which represent constraining power forgone. +\changes{Consider deterministic labels derived from cutoffs in probabilistic classifications for this scientific application; raising the probability cutoff reduces the number of false positives, boosting the cosmological constraining power, but at a cost of increasing the number of false negatives, which represent constraining power forgone. As this tradeoff is asymmetric, it is insufficient to consider only the true and false positive and negative rates, as the \snphotcc\ FoM does, without propagating their impact on the information gained about the cosmological parameters.} % \aim{Cite some deterministic metrics relating to TP/FP?} From 3b4e92e5168e624c39c121542585e31be2da8c2a Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Wed, 31 Jul 2019 11:26:02 +0200 Subject: [PATCH 52/58] added legend to new caption --- paper/tex/results.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/tex/results.tex b/paper/tex/results.tex index 80be2c4..8c2244b 100644 --- a/paper/tex/results.tex +++ b/paper/tex/results.tex @@ -191,7 +191,7 @@ \subsection{Representative classifications} \begin{center} \includegraphics[width=0.49\textwidth]{./fig/Tables3_option4.png} \caption{ - \changes{The rankings of each of ten \snmachine\ classifiers with equal weight per object under the three metrics. + \changes{The rankings of each of the five \snmachine\ classification algorithms (Boosted Decision Tree (BDT), K-Nearest Neighbors (KNN), Naive Bayes (NB), Neural Network (NN), and Support Vector Machine (SVM)) on template (T*) and wavelet (W*) features with equal weight per object under the three metrics. The metrics broadly agree on the ranking of the classifiers, confirming consistency between a conventional metric of classification performance and the metrics of probabilistic classifications presented here. However, there are some differences with pairwise swapping between the log-loss and Brier rankings and some significant reordering of ranks 2 through 5 with the FoM metric relative to the probabilistic metrics.} } From e5464ae4870663ea027dbb74ba9219d4ad1e4f0a Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Wed, 31 Jul 2019 11:26:17 +0200 Subject: [PATCH 53/58] shortened footnote --- paper/tex/methods.tex | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/paper/tex/methods.tex b/paper/tex/methods.tex index 7f68812..ca66e48 100644 --- a/paper/tex/methods.tex +++ b/paper/tex/methods.tex @@ -141,7 +141,6 @@ \subsection{Weights} The weights for the \plasticc\ metric, however, must be determined before there is knowledge of which systematics affect which classes. Because of this caveat, the choice of weights is isolated to an inherently human problem dictated by the value placed on the scientific merits of knowledge of each class. This paper, on the other hand, can only quantify the impact of weights in relation to the systematics. -We thus agnostically test weighting schemes\footnote{\changes{The weights considered in this study are more extreme than those ultimately used for \plasticc\ because the true weights were withheld from some authors prior to the end of the challenge. -However, in the Kaggle framework, it is possible to estimate these values by systematically probing the output of the public leader board with entries from the cruise control classifier archetype targeting each class one at a time. -Some \plasticc\ competitors did, in fact, execute this procedure and publicly announced the weights they had discovered, making the information available to all participants.}} +We thus agnostically test weighting schemes\footnote{\changes{The weights considered in this study are more extreme than those ultimately used for \plasticc\ because the true weights were blinded from some authors prior to the end of the challenge. +However, we note that the weights could be (and in fact were) discovered by \plasticc\ competitors by systematically probing the output of the public leader board with entries from the cruise control classifier archetype targeting each class one at a time.}} where classes affected by a particular systematic take a given weight $0 \leq w \leq 1$ and all other classes have a weight $(1 - w) / (M - 1)$. From 487ed125f59798a8bcba0d9e85bf46df55724343 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Wed, 31 Jul 2019 11:26:36 +0200 Subject: [PATCH 54/58] reordered affiliations --- paper/authors.csv | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/paper/authors.csv b/paper/authors.csv index ce46c62..d381072 100644 --- a/paper/authors.csv +++ b/paper/authors.csv @@ -1,6 +1,7 @@ Lastname,Firstname,Authorname,AuthorType,Affiliation,Contribution,Email -Malz,Alex,A.I.~Malz,Contact,"Center for Cosmology and Particle Physics, New York University, 726 Broadway, New York, NY 10004, USA","conceptualization, data curation, formal analysis, investigation, methodology, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",aimalz@nyu.edu Malz,Alex,A.I.~Malz,Contact,"German Centre of Cosmological Lensing, Ruhr-Universitaet Bochum, Universitaetsstra{\ss}e 150, 44801 Bochum, Germany","conceptualization, data curation, formal analysis, investigation, methodology, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",aimalz@nyu.edu +Malz,Alex,A.I.~Malz,Contact,"Center for Cosmology and Particle Physics, New York University, 726 Broadway, New York, NY 10004, USA","conceptualization, data curation, formal analysis, investigation, methodology, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",aimalz@nyu.edu +Malz,Alex,A.I.~Malz,Contact,"Department of Physics, New York University, 726 Broadway, New York, NY 10004, USA","conceptualization, data curation, formal analysis, investigation, methodology, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",aimalz@nyu.edu Hlo\v{z}ek,Ren\'ee,R.~Hlo\v{z}ek,Contributor,"Department of Astronomy and Astrophysics, University of Toronto, 50 St. George St., Toronto, ON M5S 3H4, Canada","data curation, formal analysis, funding acquisition, investigation, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",hlozek@dunlap.utoronto.ca Hlo\v{z}ek,Ren\'ee,R.~Hlo\v{z}ek,Contributor,"Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George St., Toronto, ON M5S 3H4, Canada","data curation, formal analysis, funding acquisition, investigation, project administration, software, supervision, validation, visualization, writing - editing, writing - original draft",hlozek@dunlap.utoronto.ca Allam,Tarek,T.~Allam Jr,Contributor,"Mullard Space Science Laboratory, Department of Space and Climate Physics, University College London, Holmbury Hill Rd, Dorking RH5 6NT, UK","investigation, software, validation, writing - original draft",[email] From fd45861ae4475abcaee5c89745c826c00790ebc8 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Wed, 31 Jul 2019 11:27:04 +0200 Subject: [PATCH 55/58] modified acknowledgment --- paper/main.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/main.tex b/paper/main.tex index f38dd85..980f41d 100644 --- a/paper/main.tex +++ b/paper/main.tex @@ -108,7 +108,7 @@ \subsection*{Acknowledgments} } } -AIM is advised by David W. Hogg and was supported by National Science Foundation grant AST-1517237. +AIM was advised by David W. Hogg and was supported by National Science Foundation grant AST-1517237. TA is supported in part by STFC. RB and CS are supported by the Swedish Research Council (VR) through the Oskar Klein Centre. Their work was further supported by the research environment grant ``Gravitational Radiation and Electromagnetic Astrophysical Transients (GREAT)'' funded by the Swedish Research council (VR) under Dnr 2016-06012. From 273e8dff0e34bb721314df49a64545eaef506e2b Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Wed, 31 Jul 2019 11:32:14 +0200 Subject: [PATCH 56/58] added references to other PLAsTiCC papers --- paper/main.bib | 17 +++++++++++++++++ paper/tex/introduction.tex | 8 +++++--- 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/paper/main.bib b/paper/main.bib index 5ba64cf..38b24f7 100644 --- a/paper/main.bib +++ b/paper/main.bib @@ -723,3 +723,20 @@ @article{hewish_observation_1968 year = {1968}, pages = {709}, } + +@article{the_plasticc_team_photometric_2018, + title = {The {Photometric} {LSST} {Astronomical} {Time}-series {Classification} {Challenge} ({PLAsTiCC}): {Data} set}, + shorttitle = {The {Photometric} {LSST} {Astronomical} {Time}-series {Classification} {Challenge} ({PLAsTiCC})}, + journal = {arXiv:1810.00001 [astro-ph]}, + author = {The PLAsTiCC team and Allam Jr., Tarek and Bahmanyar, Anita and Biswas, Rahul and Dai, Mi and Galbany, Llu{\'i}s and Hlo{\v z}ek, Ren{\'e}e and Ishida, Emille E. O. and Jha, Saurabh W. and Jones, David O. and Kessler, Richard and Lochner, Michelle and Mahabal, Ashish A. and Malz, Alex I. and Mandel, Kaisey S. and Mart{\'i}nez-Galarza, Juan Rafael and McEwen, Jason D. and Muthukrishna, Daniel and Narayan, Gautham and Peiris, Hiranya and Peters, Christina M. and Ponder, Kara and Setzer, Christian N. and Collaboration, The LSST Dark Energy Science and Transients, The LSST and Collaboration, Variable Stars Science}, + month = sep, + year = {2018}, +} + +@article{kessler_models_2019, + title = {Models and {Simulations} for the {Photometric} {LSST} {Astronomical} {Time} {Series} {Classification} {Challenge} ({PLAsTiCC})}, + journal = {arXiv:1903.11756 [astro-ph]}, + author = {Kessler, R. and Narayan, G. and Avelino, A. and Bachelet, E. and Biswas, R. and Brown, P. J. and Chernoff, D. F. and Connolly, A. J. and Dai, M. and Daniel, S. and Di Stefano, R. and Drout, M. R. and Galbany, L. and Gonz{\'a}lez-Gait{\'a}n, S. and Graham, M. L. and Hlo{\v z}ek, R. and Ishida, E. E. O. and Guillochon, J. and Jha, S. W. and Jones, D. O. and Mandel, K. S. and Muthukrishna, D. and O'Grady, A. and Peters, C. M. and Pierel, J. R. and Ponder, K. A. and Pr{\v s}a, A. and Rodney, S. and Villar, V. A.}, + month = mar, + year = {2019}, +} diff --git a/paper/tex/introduction.tex b/paper/tex/introduction.tex index f0e7ccb..46bee8a 100644 --- a/paper/tex/introduction.tex +++ b/paper/tex/introduction.tex @@ -13,9 +13,11 @@ \section{Introduction} % Thus several science cases (such as SN cosmology) will actively depend om classification of astrophysical sources based on the photometric , and possibly a much smaller training sample/model based on a spectroscopic sub-sample. As such, there is an acute need for classifiers of photometric light curves that can perform well on datasets that include a wide variety of sources including those that are at the limits of detection. -The Photometric \lsst\ Astronomical Time-series Classification Challenge (\plasticc\footnote{\url{http://plasticcblog.wordpress.com/}, \url{https://www.kaggle.com/c/PLAsTiCC-2018}}) aims\footnote{\changes{\plasticc\ was run as a Kaggle challenge from 17 September 2018 to 17 December 2018. Though \plasticc\ concluded prior to the final revision of this paper, the study herein was conducted entirely before the commencement of \plasticc, and the draft was submitted to the journal prior to \plasticc's conclusion, hence the use of the present and future tenses throughout this paper.}} to identify and motivate the development of classification techniques that serve astronomical science goals by engaging the broader community outside astronomy. -\plasticc's dataset is comprehensive, including models for well-understood classes, newly observed classes, and classes that have only been proposed to exist, to simulate serendipitous discoveries anticipated of \lsst. -Additionally, \plasticc\ joins the ranks of a handful of past astronomy classification challenges including \citep[Mapping Dark Matter\footnote{\url{https://www.kaggle.com/c/mdm}}]{kitching_gravitational_2011}, \citep[Observing Dark Worlds\footnote{\url{https://www.kaggle.com/c/DarkWorlds}}]{harvey_observing_2013}, and \citep[the Galaxy Challenge\footnote{\url{https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge}}]{dieleman_rotation-invariant_2015}, all hosted on Kaggle\footnote{\url{https://www.kaggle.com/}}, a platform that hosts data analytics competitions where seasoned professionals and amateurs alike can compete to classify, model, and predict large data sets uploaded by companies or scientific collaborations. +The Photometric \lsst\ Astronomical Time-series Classification Challenge (\plasticc\footnote{\url{http://plasticcblog.wordpress.com/}, \url{https://www.kaggle.com/c/PLAsTiCC-2018}}) aimed\footnote{\changes{\plasticc\ was run as a Kaggle challenge from 17 September 2018 to 17 December 2018. +Though \plasticc\ concluded prior to the final revision of this paper, the study herein was conducted entirely before the commencement of \plasticc, and the draft was submitted to the journal prior to \plasticc's conclusion, hence the use of the present and future tenses throughout this paper.}} +to identify and motivate the development of classification techniques that serve astronomical science goals by engaging the broader community outside astronomy. +\plasticc's dataset is comprehensive, including models for well-understood classes, newly observed classes, and classes that have only been proposed to exist, to simulate serendipitous discoveries anticipated of \lsst \citep{the_plasticc_team_photometric_2018, kessler_models_2019}. +Additionally, \plasticc\ joins the ranks of a handful of past astronomy classification challenges including \citep[Mapping Dark Matter\footnote{\url{https://www.kaggle.com/c/mdm}}]{kitching_gravitational_2011}, \citep[Observing Dark Worlds\footnote{\url{https://www.kaggle.com/c/DarkWorlds}}]{harvey_observing_2013}, and \citep[the Galaxy Challenge\footnote{\url{https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge}}]{dieleman_rotation-invariant_2015}, all hosted on Kaggle\footnote{\url{https://www.kaggle.com/}}, a platform that hosts data analytics competitions where seasoned professionals and amateurs alike can compete to classify, model, and predict large data sets uploaded by companies or scientific collaborations. Kaggle attracts a broad userbase, and those without domain knowledge may provide novel approaches to the problem at hand. Classification in astronomy may proceed through images, as has been done in the contexts of galaxy classification \citep{hoyle_measuring_2016}, supernova classification \citep{cabrera-vives_deep-hits:_2017}, identification of bars in galaxies \citep{abraham_detection_2018}, weak lensing estimation\footnote{\url{http://great3challenge.info/}}\citep{mandelbaum_third_2014}, separation of Near Earth Asteroids from artifacts in images \citep{morii_machine-learning_2016}, as well as time-domain classification \citep{morii_machine-learning_2016, mahabal_deep-learnt_2017, zevin_gravity_2017}, and even noise classification \citep{zevin_gravity_2017, george_classification_2018}. From dd02413456d1d62d487b192288b7388b71a0d924 Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Wed, 31 Jul 2019 12:38:41 +0200 Subject: [PATCH 57/58] removing strikeout text and rearranging conclusion into coherent paragraphs --- paper/tex/conclusions.tex | 43 ++++++++++++++++----------------------- paper/tex/data.tex | 4 +--- paper/tex/discussion.tex | 21 +++++++++---------- paper/tex/results.tex | 20 ++++++++---------- 4 files changed, 37 insertions(+), 51 deletions(-) diff --git a/paper/tex/conclusions.tex b/paper/tex/conclusions.tex index 5404b6b..551b10f 100644 --- a/paper/tex/conclusions.tex +++ b/paper/tex/conclusions.tex @@ -1,40 +1,33 @@ \section{Conclusion} \label{sec:conclusion} +% intro and data As part of the preparation for \plasticc\, we investigated the properties of metrics suitable for probabilistic light curve classifications in the absence of a single scientific goal. Therefore, we sought a metric that avoids reducing classification probabilities to deterministic labels \changes{and is compatible with a multi-class, rather than binary (two-class), setting. In line with the goals of \plasticc, an important desideratum was to have a metric that tends to} reward a classifier's performance across all classes over a classifier that performs well on a small subset of the classes and poorly on others. -\changes{Given the potential of large class imbalance in astronomical datasets, we were also interested in the possibility of up-weighting the importance of certain rarer transient classes if need be; -consequently we wanted to understand the way the metric would behave with the use of per-class weights.} +\changes{Our experimental design thus explores the response of potential metrics to simulated classification submissions from a set of mock classifier archetypes expected of generic transient and variable classifiers.} -\sout{We compared two metrics specific to probabilistic classifications: the Brier score and the log-loss.} -\changes{Our experimental design considers simulated classification submissions from a set of mock classifier archetypes expected of generic transient and variable classifiers.} -\changes{To start with, we identified two metrics of multi-class classification probabilities established in the literature: the Brier score and the log-loss. -We left aside popular metrics (such as accuracy, true/false positive/negative rates, and AUC functions thereof) which did not satisfy these criteria, even though it is in principle possible to extend such metrics for these scenarios.} +% the metrics +\changes{We identified two metrics of multi-class classification probabilities established in the literature: the Brier score and the log-loss.} The Brier score and the log-loss metrics are structurally and conceptually different, with wholly different interpretations. The Brier score is a sum of square differences between probabilities; the explicit penalty term is an attractive feature, but it treats probabilities as generic scores. -The log-loss on the other hand is readily interpretable, meaning the metric itself could be propagated into forecasting the cosmological constraining power of \lsst, affecting the choice of observing strategy. +The log-loss on the other hand is readily interpretable \changes{as a measure of information}, meaning the metric itself could be propagated into forecasting the cosmological constraining power of \lsst, affecting the choice of observing strategy. -\changes{We evaluated these metrics using the simulated classification probability submissions from the classifier archetypes with unit weights and then by varying the weights in Equation~\ref{eq:weightavg}. -In the absence of per-class weights, both the Brier score and the log-loss metrics are susceptible to rewarding a classifier that performs well on the most prevalent class and poorly on all others, which fails to meet the needs of \plasticc's diverse motivations. -On the basis of the mock classifier rankings under equal per-class weights, we found that both metrics reward the classifiers that are better and penalize those that are worse, where better and worse are defined by our common intuition, yielding the same rankings under either metric and demonstrating that both could be appropriate for \plasticc.} +% weights +\changes{When evaluated with equal weight on each classified object,} both the Brier score and the log-loss metrics are susceptible to rewarding a classifier that performs well on the most prevalent class and poorly on all others, which fails to meet the needs of \plasticc's diverse motivations \changes{under the unavoidable population imbalances of astronomical data}. +\changes{To discourage competitors from neglecting rare classes,} we explored a weighted average of the metric values on a per-class basis as a possible mitigation strategy to incentivize classifying uncommon classes, effectively ``leveling the playing field'' in the presence of highly nonuniform class membership. +%\changes{%Such weights were taken to be the same for all objects in the same class. -Even though the Brier score and log-loss metrics take values consistent with one another, they are structurally and conceptually different, with wholly different interpretations. -The Brier score is a sum of square differences between probabilities; the explicit penalty term is an attractive feature, but it treats probabilities as generic scores and is not interpretable in terms of information. -The log-loss on the other hand is readily interpretable, meaning the metric itself could be propagated into forecasting the constraining power of \lsst, affecting the choice of observing strategy. -\sout{We discovered that the log-loss is somewhat more sensitive to the systematic errors in classification that we find most concerning for generic scientific applications.} -While both metrics could be appropriate for \plasticc, the log-loss is preferable due to its interpretability in terms of information. -\sout{Both metrics are susceptible to rewarding a classifier that performs well on the most prevalent class and poorly on all others, which fails to meet the needs of \plasticc's diverse motivations.} +% findings +On the basis of the mock classifier rankings, we found that both metrics reward the classifiers that are better and penalize those that are worse, where better and worse are defined by our common intuition, yielding the same rankings under either metric and demonstrating that both could be appropriate for \plasticc. +However, since only one could be selected, the log-loss was chosen due to its potential for interpretation after the conclusion of the challenge. +\changes{While modifyinging the log-loss metric to handle weights for different classes diminishes its interpretability, it can still be understood as information gain, subject to the value we as scientists place on knowledge of each class.} -\changes{Due to our desire to potentially upweight rare classes,} we explored a weighted average of the metric values on a per-class basis as a possible mitigation strategy to incentivize classifying uncommon classes, effectively ``leveling the playing field'' in the presence of highly imbalanced class membership. %\changes{%Such weights were taken to be the same for all objects in the same class. -\changes{While modifyinging the log-loss metric to handle weights for different classes diminishes its interpretability, it can still be understood as information gain subject to the value we as scientists place on knowledge stemming from each class.} - -\changes{Given that both log-loss and Brier score passed the basic sanity tests for \plasticc, there was no need to devise new metrics built upon established metrics of binary or deterministic classification. -Since both were deemed appropriate, we chose the weighted log-losss metric due to its possibility of interpretation in terms of information theory, at least in the limit of equal weights} -\sout{Although weights do impact the interpretability of the log-loss, we select a per-class weighted log-loss as the optimal choice for \plasticc.} -%%%% -%%%% % We note that in order to map on to the Kaggle evaluation platform, a metric weighted only by class was used for the general challenge, while a log-loss with more complicated weighting procedure will be used for the science competition (which will continue for an additional month after the main Kaggle release). +% justifying limited choice of metrics to consider +\changes{We did not consider some of the most popular metrics used in astronomy (such as accuracy, combinations of the true and false positive and negative rates, and AUC functions thereof) because they did not satisfy these criteria, even though it is in principle possible to extend such metrics for our situation. +The space of possible metrics we could have considered is truly unbounded, from traditional metrics of deterministic labels to established extensions thereof for probabilistic classifications to novel quantities tuned to any given science case. +Though there was no need to do a more extensive survey of metrics nor to devise new metrics for \plasticc, since both log-loss and Brier score passed the basic sanity tests for this application, further work remains to be done in optimally selecting probabilistic classification metrics in other astronomical contexts.} We conclude by noting that care should be taken in planning future open challenges to ensure alignment between the challenge goals and the performance metric, so that efforts are best directed to achieve the challenge objectives. -It is our hope hope that this study of metric performance across a range of systematic effects and weights may serve as a guide to approaching the problem of identifying optimal probabilistic classifiers for general science applications. +It is our hope hope that this study of metric performance across a range of systematic effects and weights may serve as a guide to approaching the problem of identifying promising probabilistic classifiers for general science applications. diff --git a/paper/tex/data.tex b/paper/tex/data.tex index 49aa124..f119014 100644 --- a/paper/tex/data.tex +++ b/paper/tex/data.tex @@ -16,9 +16,7 @@ \section{Data} \begin{figure} \begin{center} \includegraphics[width=0.49\textwidth]{./fig/complete_counts.png} - \caption{\sout{The number of objects in a given class as a function of class population size. - The true class populations are logarithmically distributed.} - \changes{The number of members of each of thirteen mock classes considered in this work. + \caption{\changes{The number of members of each of thirteen mock classes considered in this work. Class populations were simulated by drawing the number of members of a given class from a logarithmic distribution to emulate the extreme class imbalances typical of astronomical samples.}} \label{fig:classdist} \end{center} diff --git a/paper/tex/discussion.tex b/paper/tex/discussion.tex index d3e62e5..a09e090 100644 --- a/paper/tex/discussion.tex +++ b/paper/tex/discussion.tex @@ -4,13 +4,12 @@ \section{Discussion} The goal of this work is to identify the metric most suited to \plasticc, which seeks classification posteriors of complete light curves similar to those anticipated from \lsst, with an emphasis on classification over all types, rewarding a ``best in show'' classifier rather than focusing on any one class or scientific application.\footnote{At the conclusion of \plasticc, other metrics specific to scientific uses of one or more particular classes will be used to identify ``best in class'' classification procedures that will be useful for more targeted science cases.} The weighted log-loss is thus the metric most suited to the current \plasticc\ release. -\sout{Future releases of \plasticc\ will focus on different challenges in transient and variable object classification, with metrics appropriate to identifying methodologies that best enable those goals. -We discuss approaches to identifying optimal metrics for these variations, which may be developed further in future work.} -\changes{Transient and variable object classification is crucial for a variety of scientific objectives. -The impact of a shared performance metric on this diversity of goals leads to complex and covariant trade-offs, which thus must be evaluated using multiple metrics. -While a detailed accounting of these possibilities for future releases of \plasticc\ and the selection of appropriate metrics for individual science cases are outside the scope of this first investigation, we discuss below some issues concerning the identification of metrics for a few example science cases.} +% \sout{Future releases of \plasticc\ will focus on different challenges in transient and variable object classification, with metrics appropriate to identifying methodologies that best enable those goals. +% We discuss approaches to identifying optimal metrics for these variations, which may be developed further in future work.} +\changes{Because transient and variable object classification is crucial for a variety of scientific objectives, the impact of a shared performance metric on this diversity of goals leads to complex and covariant trade-offs. +Though the selection criteria for metrics specific to each science goal are outside the scope of this work, which concerns only the first instantiation of \plasticc, we discuss below some issues concerning the identification of metrics for a few example science cases.} -\subsection{\sout{Early classification} \changes{Ongoing transient follow-up}} +\subsection{\changes{Ongoing transient follow-up}} \label{sec:early} Spectroscopic follow-up is only expected of a small fraction of \lsst's detected transients and variable objects due to limited resources for such observations. @@ -27,7 +26,7 @@ \subsection{\sout{Early classification} \changes{Ongoing transient follow-up}} The critical question for choosing the most appropriate metric for any specific science goal motivating follow-up observations is to maximize information. We provide two examples of the kind of information one must maximize via early light curve classification and the qualities of a deterministic metric that might enable it. -\changes{\subsection{Spectroscopic supernova cosmology}} +\subsection{\changes{Spectroscopic supernova cosmology}} \label{sec:spec_sncosmo} Supernova cosmology with spectroscopically confirmed light curves benefits from true positives, which contribute to the constraining power of the analysis by including one more data point; @@ -37,19 +36,19 @@ \subsection{\sout{Early classification} \changes{Ongoing transient follow-up}} False positives, on the other hand, may not enter the cosmology analysis, but they consume follow-up resources, thereby depriving the endeavor of the constraining power due to a single SN Ia. A perfect classifier would lead to a maximum amount of information about the cosmological parameters conditioned on the follow-up resource budget. -\sout{For this scientific application, the metric must be chosen to balance the value of the information forgone by a false positive and the value of information forgone by a false negative, and the value placed on these is effectively weighted by the value we as researchers place on follow-up resources.} +% \sout{For this scientific application, the metric must be chosen to balance the value of the information forgone by a false positive and the value of information forgone by a false negative, and the value placed on these is effectively weighted by the value we as researchers place on follow-up resources.} \changes{Consider deterministic labels derived from cutoffs in probabilistic classifications for this scientific application; raising the probability cutoff reduces the number of false positives, boosting the cosmological constraining power, but at a cost of increasing the number of false negatives, which represent constraining power forgone. As this tradeoff is asymmetric, it is insufficient to consider only the true and false positive and negative rates, as the \snphotcc\ FoM does, without propagating their impact on the information gained about the cosmological parameters.} % \aim{Cite some deterministic metrics relating to TP/FP?} -\changes{\subsection{Anomalous transient and variable detection}} +\subsection{\changes{Anomalous transient and variable detection}} \label{sec:anom} \changes{A particularly exciting science case is anomaly detection, the discovery of entirely unknown classes of transient or variable astrophysical sources, or distinguishing some of the rarest types of sources from more abundant types. Like the case of spectroscopic supernova cosmology discussed above,} anomaly detection also gains information only from true positives, but the cost function is different in that the potential information gain is unbounded when there is no prior information about undiscovered classes. % \aim{COMMENT RB: not to stay in doc, but I don't understand the prev sentence. I would also object to the recent detection of kilonova as a good example of anomaly detection, I can buy it if I squint very hard % COMMENT AIM: Agreed, but I couldn't think of a better one at the time of writing.} -\sout{An example would be the recent detection of a kilonova, flagged initially by the detection of gravitational waves from an object.} +% \sout{An example would be the recent detection of a kilonova, flagged initially by the detection of gravitational waves from an object.} \changes{The discovery of pulsars serves as an example of novelty detection enabled by a human classifier \citep{hewish_observation_1968, bell_burnell_measurement_1969}.} Resource availability for identifying new classes is more flexible, increasing when new predictions or promising preliminary observations attract attention, and decreasing when a discovery is confirmed and the new class is established. @@ -58,7 +57,7 @@ \subsection{\sout{Early classification} \changes{Ongoing transient follow-up}} % For a rare event like a kilonova, a false negative represents an unbounfalse positive does not appreciably reduce the amount of remaining information available to collect, but a false negative represents a large quantity of information forgone. % Furthermore, r % In this case, the information forgone by a false negative is significant compared to the information forgone by a false positive. -Thus, a metric \sout{tuned to} \changes{for evaluating} anomaly detection would aim to \changes{\emph{minimize the false negative rate and maximize the true positive rate.}} +Thus, a metric \changes{for evaluating} anomaly detection \changes{effectiveness} would aim to \changes{minimize the false negative rate and maximize the true positive rate.} % \aim{Cite some deterministic metrics relating to TP/FN?} % \subsection{Hierarchical classes} diff --git a/paper/tex/results.tex b/paper/tex/results.tex index 8c2244b..eab81d2 100644 --- a/paper/tex/results.tex +++ b/paper/tex/results.tex @@ -65,11 +65,8 @@ \subsection{Mock classifier systematics} Subsumed from Almost & 0.641 & 1.629\\ Subsumed from Perfect & 1.0 & 18.421\footnote{The entry for the log-loss of a classifier that subsumes a class into one that is otherwise perfectly classified should be infinite but is bounded by the numerical precision of our calculations.} \end{tabular} -\caption{\sout{The value of each metric when the weight is entirely on the class with the indicated characteristic. -Weighting changes the metric performance: the value of each metric when the weight is entirely on the class with the indicated characteristic (\changes{corresponding} to a $w=1$ case in Figure~\ref{fig:all_combined}). -The log-loss is more sensitive than \changes{the} Brier score, with larger values of the score (indicating poor classification performance), particularly for the subsuming systematic.} -\changes{Metric values computed using Equation~\ref{eq:weightavg} with unit weights for the mock data produced by mock classification schemes described in Sec.~\ref{sec:mockdata}. -While the log-loss metric has a larger dynamic range than the Brier score for poor classification, the toy classifiers would be ranked the same way by either metric.} +\caption{\changes{Metric values computed using Equation~\ref{eq:weightavg} with all weight on the mock class affected by the indicated systematic, described in Sec.~\ref{sec:mockdata}, corresponding to the $w=1$ cases in Figure~\ref{fig:all_combined}. +While the log-loss metric has a larger dynamic range than the Brier score for poor classification, the archetypical classifiers would be ranked (lower values are better) the same way by either metric.} } \label{tab:extents} \end{table} @@ -90,8 +87,8 @@ \subsection{Mock classifier systematics} \end{tabular} \caption{ The slopes for each baseline-plus-systematic pair in the space of log-loss versus Brier score. -A higher slope corresponds to increased sensitivity of the log-loss over the Brier score. -The contrast between log-loss and Brier score is highest on a baseline of the perfect classifier, meaning the log-loss may be more appropriate for discriminating between classifiers that are already extremely good. +A higher slope corresponds to increased sensitivity of the log-loss over the Brier score \changes{to the systematic-baseline pair in question}. +The contrast between log-loss and Brier score is highest on a baseline of the perfect classifier, meaning the log-loss may \changes{more strongly discriminate} between classifiers that are already extremely good. } \label{tab:slopes} \end{table} @@ -146,10 +143,10 @@ \subsection{Mock classifier systematics} \subsection{Representative classifications} \label{sec:realresults} -We apply the log-loss and Brier metrics to the classification output from \snmachine. While the classification methods described in \citet{lochner_photometric_2016} refer to the idealized subset of the \snphotcc\ data, these approaches are the state-of-the-art in classification of extragalactic transients. -We present in \sout{Table~\ref{fig:snmachineresults}}\changes{Figure~\ref{fig:snmachineresults} the rankings under the} log-loss and Brier score metrics assuming an equal weight per object. +We apply the log-loss and Brier metrics to the classification output from \snmachine. +While the classification methods described in \citet{lochner_photometric_2016} refer to the idealized subset of the \snphotcc\ data, these approaches are the state-of-the-art in classification of extragalactic transients. +We present in \changes{Figure~\ref{fig:snmachineresults} the rankings under the} log-loss and Brier score metrics assuming an equal weight per object. %, for classification probabilities derived from running the algorithms of \citet{lochner_photometric_2016} on the \snphotcc\ data of Section~\ref{sec:realdata}. -\sout{Table~\ref{fig:snmachineresults} also contains the ranking of classifier performance under each metric.} % \sout{\begin{table*}[] % \begin{centering} @@ -200,8 +197,7 @@ \subsection{Representative classifications} \end{figure} We apply our metrics to the classification output from \snmachine\ applied to the \snphotcc\ dataset as an example of representative light curves and representative classifiers used in extragalactic astronomy. -We present in \sout{Table~\ref{fig:snmachineresults}}\changes{Figure~\ref{fig:snmachineresults}} the rankings of each classifier under the log-loss and Brier scores assuming an equal weight per object, as well as the original \snphotcc\ metric described in Section~\ref{sec:deterministic}. -\sout{Table~\ref{fig:snmachineresults} also contains the ranking of classifier performance under each metric.} +We present in \changes{Figure~\ref{fig:snmachineresults} the rankings of each classifier under the log-loss and Brier scores assuming an equal weight per object, as well as the original \snphotcc\ metric described in Section~\ref{sec:deterministic}.} The Brier score, log-loss, and \snphotcc\ FoM are in agreement as to the first- and last-ranked classifiers. This consensus indicates that both of the potential \plasticc\ metrics are roughly consistent with our intuition about what makes a good classifier, providing an anchor between accepted notions of an appropriate metric and the metrics of probabilistic classifications under consideration here. From cf34dca18c471d0403df190e032dc9730d671e3c Mon Sep 17 00:00:00 2001 From: Alex Malz Date: Wed, 31 Jul 2019 18:36:41 +0200 Subject: [PATCH 58/58] moving an orphaned sentence back where it was supposed to be --- paper/tex/conclusions.tex | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paper/tex/conclusions.tex b/paper/tex/conclusions.tex index 551b10f..97a6090 100644 --- a/paper/tex/conclusions.tex +++ b/paper/tex/conclusions.tex @@ -4,7 +4,8 @@ \section{Conclusion} % intro and data As part of the preparation for \plasticc\, we investigated the properties of metrics suitable for probabilistic light curve classifications in the absence of a single scientific goal. Therefore, we sought a metric that avoids reducing classification probabilities to deterministic labels \changes{and is compatible with a multi-class, rather than binary (two-class), setting. -In line with the goals of \plasticc, an important desideratum was to have a metric that tends to} reward a classifier's performance across all classes over a classifier that performs well on a small subset of the classes and poorly on others. +We did not consider some of the most popular metrics used in astronomy (such as accuracy, combinations of the true and false positive and negative rates, and AUC functions thereof) because they did not satisfy these criteria, even though it is in principle possible to extend such metrics for our situation.} +% In line with the goals of \plasticc, an important desideratum was to have a metric that tends to} reward a classifier's performance across all classes over a classifier that performs well on a small subset of the classes and poorly on others. \changes{Our experimental design thus explores the response of potential metrics to simulated classification submissions from a set of mock classifier archetypes expected of generic transient and variable classifiers.} % the metrics @@ -25,8 +26,7 @@ \section{Conclusion} \changes{While modifyinging the log-loss metric to handle weights for different classes diminishes its interpretability, it can still be understood as information gain, subject to the value we as scientists place on knowledge of each class.} % justifying limited choice of metrics to consider -\changes{We did not consider some of the most popular metrics used in astronomy (such as accuracy, combinations of the true and false positive and negative rates, and AUC functions thereof) because they did not satisfy these criteria, even though it is in principle possible to extend such metrics for our situation. -The space of possible metrics we could have considered is truly unbounded, from traditional metrics of deterministic labels to established extensions thereof for probabilistic classifications to novel quantities tuned to any given science case. +\changes{The space of possible metrics we could have considered is truly unbounded, from traditional metrics of deterministic labels to established extensions thereof for probabilistic classifications to novel quantities tuned to any given science case. Though there was no need to do a more extensive survey of metrics nor to devise new metrics for \plasticc, since both log-loss and Brier score passed the basic sanity tests for this application, further work remains to be done in optimally selecting probabilistic classification metrics in other astronomical contexts.} We conclude by noting that care should be taken in planning future open challenges to ensure alignment between the challenge goals and the performance metric, so that efforts are best directed to achieve the challenge objectives.