From 2c9ce5a0e354ee9cda645a5d14a2fd42d74de935 Mon Sep 17 00:00:00 2001 From: morgsmss7 Date: Sat, 14 Mar 2020 18:40:37 -0400 Subject: [PATCH 01/11] fixing issues I causes while attempting to resolve merge conflicts --- sklearn/tree/_criterion.pxd | 9 +- sklearn/tree/_criterion.pyx | 219 +++++++++++++------------------ sklearn/tree/_splitter.pxd | 3 +- sklearn/tree/_splitter.pyx | 116 +++++++++-------- sklearn/tree/_tree.pxd | 1 + sklearn/tree/_tree.pyx | 46 +------ sklearn/tree/tests/test_tree.py | 35 +---- sklearn/tree/tree.py | 220 +------------------------------- 8 files changed, 165 insertions(+), 484 deletions(-) diff --git a/sklearn/tree/_criterion.pxd b/sklearn/tree/_criterion.pxd index b586d7fadeec9..af336150b30e7 100644 --- a/sklearn/tree/_criterion.pxd +++ b/sklearn/tree/_criterion.pxd @@ -65,14 +65,11 @@ cdef class Criterion: cdef double impurity_improvement(self, double impurity) nogil cdef double proxy_impurity_improvement(self) nogil -<<<<<<< HEAD cdef double node_impurity2(self, double* pred_weights) cdef void children_impurity2(self, double* impurity_left, double* impurity_right, double* pred_weights) cdef double proxy_impurity_improvement2(self, double* pred_weights) nogil -======= ->>>>>>> master cdef class ClassificationCriterion(Criterion): """Abstract criterion for classification.""" @@ -83,4 +80,8 @@ cdef class RegressionCriterion(Criterion): """Abstract regression criterion.""" cdef double sq_sum_total - cdef object random_state # Random state for predictor weights (Projection-Based Splitters) + +cdef class ObliqueProjection(RegressionCriterion): + pass +cdef class AxisProjection(RegressionCriterion): + pass \ No newline at end of file diff --git a/sklearn/tree/_criterion.pyx b/sklearn/tree/_criterion.pyx index 85fa29a4b39aa..25f0a3bfbe4f6 100644 --- a/sklearn/tree/_criterion.pyx +++ b/sklearn/tree/_criterion.pyx @@ -26,8 +26,6 @@ import numpy as np cimport numpy as np np.import_array() -from ._utils cimport rand_int -from ._utils cimport RAND_R_MAX from ._utils cimport log from ._utils cimport safe_realloc from ._utils cimport sizet_ptr_to_ndarray @@ -35,7 +33,6 @@ from ._utils cimport WeightedMedianCalculator cdef class Criterion: """Interface for impurity criteria. - This object stores methods on how to calculate how good a split is using different metrics. """ @@ -57,10 +54,8 @@ cdef class Criterion: double weighted_n_samples, SIZE_t* samples, SIZE_t start, SIZE_t end) nogil except -1: """Placeholder for a method which will initialize the criterion. - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. - Parameters ---------- y : array-like, dtype=DOUBLE_t @@ -76,13 +71,13 @@ cdef class Criterion: The first sample to be used on this node end : SIZE_t The last sample used on this node + """ pass cdef int reset(self) nogil except -1: """Reset the criterion at pos=start. - This method must be implemented by the subclass. """ @@ -90,18 +85,15 @@ cdef class Criterion: cdef int reverse_reset(self) nogil except -1: """Reset the criterion at pos=end. - This method must be implemented by the subclass. """ pass cdef int update(self, SIZE_t new_pos) nogil except -1: """Updated statistics by moving samples[pos:new_pos] to the left child. - This updates the collected statistics by moving samples[pos:new_pos] from the right child to the left child. It must be implemented by the subclass. - Parameters ---------- new_pos : SIZE_t @@ -112,7 +104,6 @@ cdef class Criterion: cdef double node_impurity(self) nogil: """Placeholder for calculating the impurity of the node. - Placeholder for a method which will evaluate the impurity of the current node, i.e. the impurity of samples[start:end]. This is the primary function of the criterion class. @@ -122,7 +113,6 @@ cdef class Criterion: cdef double node_impurity2(self, double* pred_weights): """Placeholder for calculating the impurity of the node. - Placeholder for a method which will evaluate the impurity of the current node, i.e. the impurity of samples[start:end]. This is the primary function of the criterion class. @@ -133,11 +123,9 @@ cdef class Criterion: cdef void children_impurity(self, double* impurity_left, double* impurity_right) nogil: """Placeholder for calculating the impurity of children. - Placeholder for a method which evaluates the impurity in children nodes, i.e. the impurity of samples[start:pos] + the impurity of samples[pos:end]. - Parameters ---------- impurity_left : double pointer @@ -153,11 +141,9 @@ cdef class Criterion: cdef void children_impurity2(self, double* impurity_left, double* impurity_right, double* pred_weights): """Placeholder for calculating the impurity of children. - Placeholder for a method which evaluates the impurity in children nodes, i.e. the impurity of samples[start:pos] + the impurity of samples[pos:end]. - Parameters ---------- impurity_left : double pointer @@ -172,10 +158,8 @@ cdef class Criterion: cdef void node_value(self, double* dest) nogil: """Placeholder for storing the node value. - Placeholder for a method which will compute the node value of samples[start:end] and save the value into dest. - Parameters ---------- dest : double pointer @@ -186,12 +170,10 @@ cdef class Criterion: cdef double proxy_impurity_improvement(self) nogil: """Compute a proxy of the impurity reduction - This method is used to speed up the search for the best split. It is a proxy quantity such that the split that maximizes this value also maximizes the impurity improvement. It neglects all constant terms of the impurity decrease for a given split. - The absolute impurity improvement is only computed by the impurity_improvement method once the best split has been found. """ @@ -204,12 +186,10 @@ cdef class Criterion: cdef double proxy_impurity_improvement2(self, double* pred_weights) nogil: """Compute a proxy of the impurity reduction - This method is used to speed up the search for the best split. It is a proxy quantity such that the split that maximizes this value also maximizes the impurity improvement. It neglects all constant terms of the impurity decrease for a given split. - The absolute impurity improvement is only computed by the impurity_improvement method once the best split has been found. """ @@ -222,22 +202,17 @@ cdef class Criterion: cdef double impurity_improvement(self, double impurity) nogil: """Compute the improvement in impurity - This method computes the improvement in impurity when a split occurs. The weighted impurity improvement equation is the following: - N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) - where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child, - Parameters ---------- impurity : double The initial impurity of the node before the split - Return ------ double : improvement in impurity after the split occurs @@ -261,7 +236,6 @@ cdef class ClassificationCriterion(Criterion): def __cinit__(self, SIZE_t n_outputs, np.ndarray[SIZE_t, ndim=1] n_classes): """Initialize attributes for this criterion. - Parameters ---------- n_outputs : SIZE_t @@ -330,10 +304,8 @@ cdef class ClassificationCriterion(Criterion): SIZE_t* samples, SIZE_t start, SIZE_t end) nogil except -1: """Initialize the criterion at node samples[start:end] and children samples[start:start] and samples[start:end]. - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. - Parameters ---------- y : array-like, dtype=DOUBLE_t @@ -394,7 +366,6 @@ cdef class ClassificationCriterion(Criterion): cdef int reset(self) nogil except -1: """Reset the criterion at pos=start - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -421,7 +392,6 @@ cdef class ClassificationCriterion(Criterion): cdef int reverse_reset(self) nogil except -1: """Reset the criterion at pos=end - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -448,10 +418,8 @@ cdef class ClassificationCriterion(Criterion): cdef int update(self, SIZE_t new_pos) nogil except -1: """Updated statistics by moving samples[pos:new_pos] to the left child. - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. - Parameters ---------- new_pos : SIZE_t @@ -534,7 +502,6 @@ cdef class ClassificationCriterion(Criterion): cdef void node_value(self, double* dest) nogil: """Compute the node value of samples[start:end] and save it into dest. - Parameters ---------- dest : double pointer @@ -553,17 +520,12 @@ cdef class ClassificationCriterion(Criterion): cdef class Entropy(ClassificationCriterion): r"""Cross Entropy impurity criterion. - This handles cases where the target is a classification taking values 0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations, then let - count_k = 1 / Nm \sum_{x_i in Rm} I(yi = k) - be the proportion of class k observations in node m. - The cross-entropy is then defined as - cross-entropy = -\sum_{k=0}^{K-1} count_k log(count_k) """ @@ -592,10 +554,8 @@ cdef class Entropy(ClassificationCriterion): cdef void children_impurity(self, double* impurity_left, double* impurity_right) nogil: """Evaluate the impurity in children nodes - i.e. the impurity of the left child (samples[start:pos]) and the impurity the right child (samples[pos:end]). - Parameters ---------- impurity_left : double pointer @@ -634,17 +594,12 @@ cdef class Entropy(ClassificationCriterion): cdef class Gini(ClassificationCriterion): r"""Gini Index impurity criterion. - This handles cases where the target is a classification taking values 0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations, then let - count_k = 1/ Nm \sum_{x_i in Rm} I(yi = k) - be the proportion of class k observations in node m. - The Gini Index is then defined as: - index = \sum_{k=0}^{K-1} count_k (1 - count_k) = 1 - \sum_{k=0}^{K-1} count_k ** 2 """ @@ -679,10 +634,8 @@ cdef class Gini(ClassificationCriterion): cdef void children_impurity(self, double* impurity_left, double* impurity_right) nogil: """Evaluate the impurity in children nodes - i.e. the impurity of the left child (samples[start:pos]) and the impurity the right child (samples[pos:end]) using the Gini index. - Parameters ---------- impurity_left : double pointer @@ -728,37 +681,27 @@ cdef class Gini(ClassificationCriterion): cdef class RegressionCriterion(Criterion): r"""Abstract regression criterion. - This handles cases where the target is a continuous value, and is evaluated by computing the variance of the target values left and right of the split point. The computation takes linear time with `n_samples` by using :: - var = \sum_i^n (y_i - y_bar) ** 2 = (\sum_i^n y_i ** 2) - n_samples * y_bar ** 2 """ - def __cinit__(self, SIZE_t n_outputs, SIZE_t n_samples, object random_state=None): + def __cinit__(self, SIZE_t n_outputs, SIZE_t n_samples): """Initialize parameters for this criterion. - Parameters ---------- n_outputs : SIZE_t The number of targets to be predicted - n_samples : SIZE_t The total number of samples to fit on - - random_state : object - Random State from splitter class - """ # Default values self.sample_weight = NULL - self.random_state = random_state - self.samples = NULL self.start = 0 self.pos = 0 @@ -933,7 +876,6 @@ cdef class RegressionCriterion(Criterion): cdef class MSE(RegressionCriterion): """Mean squared error impurity criterion. - MSE = var_left + var_right """ @@ -953,12 +895,10 @@ cdef class MSE(RegressionCriterion): cdef double proxy_impurity_improvement(self) nogil: """Compute a proxy of the impurity reduction - This method is used to speed up the search for the best split. It is a proxy quantity such that the split that maximizes this value also maximizes the impurity improvement. It neglects all constant terms of the impurity decrease for a given split. - The absolute impurity improvement is only computed by the impurity_improvement method once the best split has been found. """ @@ -1024,7 +964,6 @@ cdef class MSE(RegressionCriterion): cdef class MAE(RegressionCriterion): r"""Mean absolute error impurity criterion - MAE = (1 / n)*(\sum_i |y_i - f_i|), where y_i is the true value and f_i is the predicted value.""" def __dealloc__(self): @@ -1035,14 +974,12 @@ cdef class MAE(RegressionCriterion): cdef np.ndarray right_child cdef DOUBLE_t* node_medians - def __cinit__(self, SIZE_t n_outputs, SIZE_t n_samples, object random_state = None): + def __cinit__(self, SIZE_t n_outputs, SIZE_t n_samples): """Initialize parameters for this criterion. - Parameters ---------- n_outputs : SIZE_t The number of targets to be predicted - n_samples : SIZE_t The total number of samples to fit on """ @@ -1128,7 +1065,6 @@ cdef class MAE(RegressionCriterion): cdef int reset(self) nogil except -1: """Reset the criterion at pos=start - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -1160,7 +1096,6 @@ cdef class MAE(RegressionCriterion): cdef int reverse_reset(self) nogil except -1: """Reset the criterion at pos=end - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -1189,7 +1124,6 @@ cdef class MAE(RegressionCriterion): cdef int update(self, SIZE_t new_pos) nogil except -1: """Updated statistics by moving samples[pos:new_pos] to the left - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -1324,21 +1258,17 @@ cdef class MAE(RegressionCriterion): cdef class FriedmanMSE(MSE): """Mean squared error impurity criterion with improvement score by Friedman - Uses the formula (35) in Friedman's original Gradient Boosting paper: - diff = mean_left - mean_right improvement = n_left * n_right * diff^2 / (n_left + n_right) """ cdef double proxy_impurity_improvement(self) nogil: """Compute a proxy of the impurity reduction - This method is used to speed up the search for the best split. It is a proxy quantity such that the split that maximizes this value also maximizes the impurity improvement. It neglects all constant terms of the impurity decrease for a given split. - The absolute impurity improvement is only computed by the impurity_improvement method once the best split has been found. """ @@ -1381,21 +1311,19 @@ cdef class FriedmanMSE(MSE): return (diff * diff / (self.weighted_n_left * self.weighted_n_right * self.weighted_n_node_samples)) + cdef class AxisProjection(RegressionCriterion): r"""Mean squared error impurity criterion of axis-aligned projections of high dimensional y - Algorithm: 1. select a random predictor from [0,n_outputs] 2. compute mse on the values of that predictor for all samples - MSE = var_left + var_right """ cdef double node_impurity2(self, double* pred_weights): """Evaluate the impurity of the current node, i.e. the impurity of samples[start:end].""" - - cdef double impurity + cdef double impurity = 0.0 #TODO cdef DOUBLE_t* sample_weight = self.sample_weight cdef SIZE_t* samples = self.samples cdef SIZE_t end = self.end @@ -1405,18 +1333,11 @@ cdef class AxisProjection(RegressionCriterion): cdef DOUBLE_t y_ik - cdef double sq_sum_total = 0.0 - cdef SIZE_t i cdef SIZE_t p cdef SIZE_t k - cdef UINT32_t rand_r_state - - with gil: - rand_r_state = self.random_state.randint(0, RAND_R_MAX) - cdef UINT32_t* random_state = &rand_r_state - k = rand_int(0, self.n_outputs, random_state) + cdef DOUBLE_t w = 1.0 for p in range(start, end): i = samples[p] @@ -1431,9 +1352,28 @@ cdef class AxisProjection(RegressionCriterion): impurity -= (sum_total[k]* pred_weights[k]/ self.weighted_n_node_samples)**2.0 return impurity + ''' + for p in range(start, end): + i = samples[p] + if sample_weight != NULL: + w = sample_weight[i] + for k in range(self.n_outputs): + y_ik = self.y[i, k] + # sum over all predictors with pred weights + pred[p] += y_ik * pred_weights[k] + # sum over all samples to get mean of new predictor + mean_pred += pred[p] / (end - start) + for p in range(start, end): + i = samples[p] + if sample_weight != NULL: + w = sample_weight[i] + impurity += (mean_pred - pred[p]) * (mean_pred - pred[p]) * w + impurity /= self.weighted_n_node_samples + return impurity + ''' - cdef double proxy_impurity_improvement(self) nogil: + cdef double proxy_impurity_improvement2(self, double* pred_weights) nogil: """Compute a proxy of the impurity reduction This method is used to speed up the search for the best split. It is a proxy quantity such that the split that maximizes this value @@ -1457,6 +1397,17 @@ cdef class AxisProjection(RegressionCriterion): proxy_impurity_right = fabs(proxy_impurity_right) return (proxy_impurity_left / self.weighted_n_left + proxy_impurity_right / self.weighted_n_right) + ''' + with gil: + for k in range(self.n_outputs): + proxy_impurity_left += sum_left[k] * sum_left[k] * abs(pred_weights[k]) + proxy_impurity_right += sum_right[k] * sum_right[k] * abs(pred_weights[k]) + #with gil: + # return (abs(proxy_impurity_left / self.weighted_n_left) + + # abs(proxy_impurity_right / self.weighted_n_right)) + return (proxy_impurity_left / self.weighted_n_left + + proxy_impurity_right / self.weighted_n_right) + ''' cdef void children_impurity2(self, double* impurity_left, @@ -1483,7 +1434,8 @@ cdef class AxisProjection(RegressionCriterion): cdef SIZE_t i cdef SIZE_t p - cdef SIZE_t k + cdef SIZE_t k # modified + cdef DOUBLE_t w = 1.0 for p in range(start, pos): i = samples[p] @@ -1512,28 +1464,63 @@ cdef class AxisProjection(RegressionCriterion): impurity_left[0] = fabs(impurity_left[0]) impurity_right[0] = fabs(impurity_right[0]) + ''' + for p in range(start, pos): + i = samples[p] + if sample_weight != NULL: + w = sample_weight[i] + for k in range(self.n_outputs): + y_ik = self.y[i, k] + # sum over all predictors with pred weights + pred_left[p] += y_ik * pred_weights[k] + # sum over all samples to get mean of new predictor + mean_pred_left += pred_left[p] / (pos - start) + w = 1.0 + for p in range(start, pos): + i = samples[p] + if sample_weight != NULL: + w = sample_weight[i] + impurity_left[0] += ((mean_pred_left - pred_left[p]) + * (mean_pred_left - pred_left[p]) * w)/self.weighted_n_left + w = 1.0 + for p in range(pos, end): + i = samples[p] + if sample_weight != NULL: + w = sample_weight[i] + for k in range(self.n_outputs): + y_ik = self.y[i, k] + # sum over all predictors with pred weights + pred_right[p - pos] += y_ik * pred_weights[k] + # sum over all samples to get mean of new predictor + for p in range(pos, end): + mean_pred_right += pred_right[p-pos] / (end - pos) + w = 1.0 + for p in range(pos, end): + i = samples[p] + if sample_weight != NULL: + w = sample_weight[i] + impurity_right[0] += ((mean_pred_right - pred_right[p - pos]) * (mean_pred_right - pred_right[p-pos]) * w) / self.weighted_n_right + + impurity_left[0] + impurity_right[0] + ''' cdef class ObliqueProjection(RegressionCriterion): r"""Mean squared error impurity criterion of oblique projections of high dimensional y - Algorithm: - 1. Select a random number of random predictors from [0,n_outputs] - 2. Assign weights (-1 or 1) to all chosen predictors - 3. Assign weight of 0 to all unchosen predictors - 4. Compute new predictor (linear combination of all predictors) - 5. Compute mse on new predictor - + 1. select a random predictors from [0,n_outputs] + 2. Set weights of chosen predictors to -1 or 1 + 3. compute mse on the values of those predictors for all samples MSE = var_left + var_right """ cdef double node_impurity2(self, double* pred_weights): """Evaluate the impurity of the current node, i.e. the impurity of samples[start:end].""" - - cdef double impurity + cdef double impurity = 0.0 #TODO cdef DOUBLE_t* sample_weight = self.sample_weight cdef SIZE_t* samples = self.samples cdef SIZE_t end = self.end @@ -1549,23 +1536,6 @@ cdef class ObliqueProjection(RegressionCriterion): cdef SIZE_t i cdef SIZE_t p cdef SIZE_t k - cdef UINT32_t rand_r_state - cdef SIZE_t num_pred - cdef SIZE_t a - pred_weights = calloc(self.n_outputs, sizeof(double)) - - with gil: - rand_r_state = self.random_state.randint(0, RAND_R_MAX) - cdef UINT32_t* random_state = &rand_r_state - - num_pred = rand_int(1, self.n_outputs+1, random_state) - - for i in range(num_pred): - k = rand_int(0, self.n_outputs, random_state) - a = rand_int(0, 2, random_state) - if a == 0: - a -= 1 - pred_weights[k] = a cdef DOUBLE_t w = 1.0 @@ -1588,7 +1558,7 @@ cdef class ObliqueProjection(RegressionCriterion): return impurity / num_pred - cdef double proxy_impurity_improvement(self) nogil: + cdef double proxy_impurity_improvement2(self, double* pred_weights) nogil: """Compute a proxy of the impurity reduction This method is used to speed up the search for the best split. It is a proxy quantity such that the split that maximizes this value @@ -1619,7 +1589,6 @@ cdef class ObliqueProjection(RegressionCriterion): """Evaluate the impurity in children nodes, i.e. the impurity of the left child (samples[start:pos]) and the impurity the right child (samples[pos:end]).""" - cdef DOUBLE_t* sample_weight = self.sample_weight cdef SIZE_t* samples = self.samples cdef SIZE_t pos = self.pos @@ -1638,24 +1607,7 @@ cdef class ObliqueProjection(RegressionCriterion): cdef SIZE_t i cdef SIZE_t p - cdef SIZE_t k - cdef UINT32_t rand_r_state - cdef SIZE_t num_pred - cdef SIZE_t a - pred_weights = calloc(self.n_outputs, sizeof(double)) - - with gil: - rand_r_state = self.random_state.randint(0, RAND_R_MAX) - cdef UINT32_t* random_state = &rand_r_state - - num_pred = rand_int(1, self.n_outputs + 1, random_state) - - for i in range(num_pred): - k = rand_int(0, self.n_outputs, random_state) - a = rand_int(0, 2, random_state) - if a == 0: - a -= 1 - pred_weights[k] = a + cdef SIZE_t k # modified cdef DOUBLE_t w = 1.0 for p in range(start, pos): @@ -1686,3 +1638,4 @@ cdef class ObliqueProjection(RegressionCriterion): impurity_left[0] = fabs(impurity_left[0]) impurity_right[0] = fabs(impurity_right[0]) + \ No newline at end of file diff --git a/sklearn/tree/_splitter.pxd b/sklearn/tree/_splitter.pxd index 7404c071048bb..8e42b9ef6d6f0 100644 --- a/sklearn/tree/_splitter.pxd +++ b/sklearn/tree/_splitter.pxd @@ -30,6 +30,7 @@ cdef struct SplitRecord: double improvement # Impurity improvement given parent node. double impurity_left # Impurity of the left split. double impurity_right # Impurity of the right split. + double* pred_weights # predictor weights for Oblique/Axis Projections cdef class Splitter: # The splitter searches in the input space for a feature and a threshold @@ -91,4 +92,4 @@ cdef class Splitter: cdef void node_value(self, double* dest) nogil - cdef double node_impurity(self) nogil + cdef double node_impurity(self, SplitRecord* split) nogil \ No newline at end of file diff --git a/sklearn/tree/_splitter.pyx b/sklearn/tree/_splitter.pyx index 22ba680f8bb37..b6f36b848707c 100644 --- a/sklearn/tree/_splitter.pyx +++ b/sklearn/tree/_splitter.pyx @@ -16,9 +16,11 @@ # License: BSD 3 clause from ._criterion cimport Criterion - +from ._criterion cimport ObliqueProjection +from ._criterion cimport AxisProjection from libc.stdlib cimport free from libc.stdlib cimport qsort +from libc.stdlib cimport calloc from libc.string cimport memcpy from libc.string cimport memset @@ -81,7 +83,6 @@ cdef __dealloc__(SplitRecord* self): ''' cdef class Splitter: """Abstract splitter class. - Splitters are called by tree builders to find the best splits on both sparse and dense data, one split at a time. """ @@ -94,20 +95,16 @@ cdef class Splitter: ---------- criterion : Criterion The criterion to measure the quality of a split. - max_features : SIZE_t The maximal number of randomly selected features which can be considered for a split. - min_samples_leaf : SIZE_t The minimal number of samples each leaf can have, where splits which would result in having less samples in a leaf are not considered. - min_weight_leaf : double The minimal weight each leaf can have, where the weight is the sum of the weights of each sample in it. - random_state : object The user inputted random state to be used for pseudo-randomness """ @@ -147,25 +144,19 @@ cdef class Splitter: DOUBLE_t* sample_weight, np.ndarray X_idx_sorted=None) except -1: """Initialize the splitter. - Take in the input data X, the target Y, and optional sample weights. - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. - Parameters ---------- X : object This contains the inputs. Usually it is a 2d numpy array. - y : ndarray, dtype=DOUBLE_t This is the vector of targets, or true labels, for the samples - sample_weight : DOUBLE_t* The weights of the samples, where higher weighted samples are fit closer than lower weight samples. If not provided, all samples are assumed to have uniform weight. - X_idx_sorted : ndarray, default=None The indexes of the sorted training input samples """ @@ -215,10 +206,8 @@ cdef class Splitter: cdef int node_reset(self, SIZE_t start, SIZE_t end, double* weighted_n_node_samples) nogil except -1: """Reset splitter on node samples[start:end]. - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. - Parameters ---------- start : SIZE_t @@ -245,10 +234,8 @@ cdef class Splitter: cdef int node_split(self, double impurity, SplitRecord* split, SIZE_t* n_constant_features) nogil except -1: """Find the best split on node samples[start:end]. - This is a placeholder method. The majority of computation will be done here. - It should return -1 upon errors. """ @@ -259,10 +246,14 @@ cdef class Splitter: self.criterion.node_value(dest) - cdef double node_impurity(self) nogil: + cdef double node_impurity(self, SplitRecord* split) nogil: """Return the impurity of the current node.""" - - return self.criterion.node_impurity() + with gil: + if isinstance(self.criterion, ObliqueProjection) or isinstance(self.criterion, AxisProjection): + _init_pred_weights(split, self.y.shape[1], &self.rand_r_state, self.criterion) + return self.criterion.node_impurity2(split.pred_weights) + else: + return self.criterion.node_impurity() cdef class BaseDenseSplitter(Splitter): @@ -288,7 +279,6 @@ cdef class BaseDenseSplitter(Splitter): DOUBLE_t* sample_weight, np.ndarray X_idx_sorted=None) except -1: """Initialize the splitter - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -312,7 +302,6 @@ cdef class BestSplitter(BaseDenseSplitter): cdef int node_split(self, double impurity, SplitRecord* split, SIZE_t* n_constant_features) nogil except -1: """Find the best split on node samples[start:end] - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -337,7 +326,7 @@ cdef class BestSplitter(BaseDenseSplitter): cdef SplitRecord best, current cdef double current_proxy_improvement = -INFINITY cdef double best_proxy_improvement = -INFINITY - + cdef SIZE_t f_i = n_features cdef SIZE_t f_j cdef SIZE_t p @@ -454,8 +443,11 @@ cdef class BestSplitter(BaseDenseSplitter): if ((self.criterion.weighted_n_left < min_weight_leaf) or (self.criterion.weighted_n_right < min_weight_leaf)): continue - - current_proxy_improvement = self.criterion.proxy_impurity_improvement() + with gil: + if isinstance(self.criterion, ObliqueProjection) or isinstance(self.criterion, AxisProjection): + current_proxy_improvement = self.criterion.proxy_impurity_improvement2(split.pred_weights) + else: + current_proxy_improvement = self.criterion.proxy_impurity_improvement() if current_proxy_improvement > best_proxy_improvement: best_proxy_improvement = current_proxy_improvement @@ -466,6 +458,8 @@ cdef class BestSplitter(BaseDenseSplitter): (current.threshold == INFINITY) or (current.threshold == -INFINITY)): current.threshold = Xf[p - 1] + #if (best.pred_weights): + # free(best.pred_weights) #TODO best = current # copy # Reorganize into samples[start:best.pos] + samples[best.pos:end] @@ -485,7 +479,12 @@ cdef class BestSplitter(BaseDenseSplitter): self.criterion.reset() self.criterion.update(best.pos) best.improvement = self.criterion.impurity_improvement(impurity) - self.criterion.children_impurity(&best.impurity_left, + with gil: + if isinstance(self.criterion, ObliqueProjection) or isinstance(self.criterion, AxisProjection): + self.criterion.children_impurity2(&best.impurity_left, + &best.impurity_right, split.pred_weights) + else: + self.criterion.children_impurity(&best.impurity_left, &best.impurity_right) # Respect invariant for constant features: the original order of @@ -630,7 +629,6 @@ cdef class RandomSplitter(BaseDenseSplitter): cdef int node_split(self, double impurity, SplitRecord* split, SIZE_t* n_constant_features) nogil except -1: """Find the best random split on node samples[start:end] - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -776,8 +774,12 @@ cdef class RandomSplitter(BaseDenseSplitter): if ((self.criterion.weighted_n_left < min_weight_leaf) or (self.criterion.weighted_n_right < min_weight_leaf)): continue - - current_proxy_improvement = self.criterion.proxy_impurity_improvement() + with gil: + if isinstance(self.criterion, ObliqueProjection) or isinstance(self.criterion, AxisProjection): + current_proxy_improvement = self.criterion.proxy_impurity_improvement2(split.pred_weights) + else: + current_proxy_improvement = self.criterion.proxy_impurity_improvement() + if current_proxy_improvement > best_proxy_improvement: best_proxy_improvement = current_proxy_improvement @@ -799,9 +801,13 @@ cdef class RandomSplitter(BaseDenseSplitter): self.criterion.reset() self.criterion.update(best.pos) best.improvement = self.criterion.impurity_improvement(impurity) - self.criterion.children_impurity(&best.impurity_left, - &best.impurity_right) - + with gil: + if isinstance(self.criterion, ObliqueProjection) or isinstance(self.criterion, AxisProjection): + self.criterion.children_impurity2(&best.impurity_left, + &best.impurity_right, split.pred_weights) + else: + self.criterion.children_impurity(&best.impurity_left, + &best.impurity_right) # Respect invariant for constant features: the original order of # element in features[:n_known_constants] must be preserved for sibling # and child nodes @@ -854,7 +860,6 @@ cdef class BaseSparseSplitter(Splitter): DOUBLE_t* sample_weight, np.ndarray X_idx_sorted=None) except -1: """Initialize the splitter - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -929,33 +934,26 @@ cdef class BaseSparseSplitter(Splitter): SIZE_t* end_negative, SIZE_t* start_positive, bint* is_samples_sorted) nogil: """Extract and partition values for a given feature. - The extracted values are partitioned between negative values Xf[start:end_negative[0]] and positive values Xf[start_positive[0]:end]. The samples and index_to_samples are modified according to this partition. - The extraction corresponds to the intersection between the arrays X_indices[indptr_start:indptr_end] and samples[start:end]. This is done efficiently using either an index_to_samples based approach or binary search based approach. - Parameters ---------- feature : SIZE_t, Index of the feature we want to extract non zero value. - - end_negative, start_positive : SIZE_t*, SIZE_t*, Return extracted non zero values in self.samples[start:end] where negative values are in self.feature_values[start:end_negative[0]] and positive values are in self.feature_values[start_positive[0]:end]. - is_samples_sorted : bint*, If is_samples_sorted, then self.sorted_samples[start:end] will be the sorted version of self.samples[start:end]. - """ cdef SIZE_t indptr_start = self.X_indptr[feature], cdef SIZE_t indptr_end = self.X_indptr[feature + 1] @@ -998,7 +996,6 @@ cdef inline void binary_search(INT32_t* sorted_array, SIZE_t value, SIZE_t* index, INT32_t* new_start) nogil: """Return the index of value in the sorted array. - If not found, return -1. new_start is the last pivot + 1 """ cdef INT32_t pivot @@ -1030,7 +1027,6 @@ cdef inline void extract_nnz_index_to_samples(INT32_t* X_indices, SIZE_t* end_negative, SIZE_t* start_positive) nogil: """Extract and partition values for a feature using index_to_samples. - Complexity is O(indptr_end - indptr_start). """ cdef INT32_t k @@ -1072,10 +1068,8 @@ cdef inline void extract_nnz_binary_search(INT32_t* X_indices, SIZE_t* sorted_samples, bint* is_samples_sorted) nogil: """Extract and partition values for a given feature using binary search. - If n_samples = end - start and n_indices = indptr_end - indptr_start, the complexity is - O((1 - is_samples_sorted[0]) * n_samples * log(n_samples) + n_samples * log(n_indices)). """ @@ -1151,7 +1145,6 @@ cdef class BestSparseSplitter(BaseSparseSplitter): cdef int node_split(self, double impurity, SplitRecord* split, SIZE_t* n_constant_features) nogil except -1: """Find the best split on node samples[start:end], using sparse features - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -1327,8 +1320,11 @@ cdef class BestSparseSplitter(BaseSparseSplitter): if ((self.criterion.weighted_n_left < min_weight_leaf) or (self.criterion.weighted_n_right < min_weight_leaf)): continue - - current_proxy_improvement = self.criterion.proxy_impurity_improvement() + with gil: + if isinstance(self.criterion, ObliqueProjection) or isinstance(self.criterion, AxisProjection): + current_proxy_improvement = self.criterion.proxy_impurity_improvement2(split.pred_weights) + else: + current_proxy_improvement = self.criterion.proxy_impurity_improvement() if current_proxy_improvement > best_proxy_improvement: best_proxy_improvement = current_proxy_improvement @@ -1354,9 +1350,13 @@ cdef class BestSparseSplitter(BaseSparseSplitter): self.criterion.reset() self.criterion.update(best.pos) best.improvement = self.criterion.impurity_improvement(impurity) - self.criterion.children_impurity(&best.impurity_left, - &best.impurity_right) - + with gil: + if isinstance(self.criterion, ObliqueProjection) or isinstance(self.criterion, AxisProjection): + self.criterion.children_impurity2(&best.impurity_left, + &best.impurity_right, split.pred_weights) + else: + self.criterion.children_impurity(&best.impurity_left, + &best.impurity_right) # Respect invariant for constant features: the original order of # element in features[:n_known_constants] must be preserved for sibling # and child nodes @@ -1386,7 +1386,6 @@ cdef class RandomSparseSplitter(BaseSparseSplitter): cdef int node_split(self, double impurity, SplitRecord* split, SIZE_t* n_constant_features) nogil except -1: """Find a random split on node samples[start:end], using sparse features - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -1564,8 +1563,11 @@ cdef class RandomSparseSplitter(BaseSparseSplitter): if ((self.criterion.weighted_n_left < min_weight_leaf) or (self.criterion.weighted_n_right < min_weight_leaf)): continue - - current_proxy_improvement = self.criterion.proxy_impurity_improvement() + with gil: + if isinstance(self.criterion, ObliqueProjection) or isinstance(self.criterion, AxisProjection): + current_proxy_improvement = self.criterion.proxy_impurity_improvement2(split.pred_weights) + else: + current_proxy_improvement = self.criterion.proxy_impurity_improvement() if current_proxy_improvement > best_proxy_improvement: best_proxy_improvement = current_proxy_improvement @@ -1594,8 +1596,13 @@ cdef class RandomSparseSplitter(BaseSparseSplitter): self.criterion.reset() self.criterion.update(best.pos) best.improvement = self.criterion.impurity_improvement(impurity) - self.criterion.children_impurity(&best.impurity_left, - &best.impurity_right) + with gil: + if isinstance(self.criterion, ObliqueProjection) or isinstance(self.criterion, AxisProjection): + self.criterion.children_impurity2(&best.impurity_left, + &best.impurity_right, split.pred_weights) + else: + self.criterion.children_impurity(&best.impurity_left, + &best.impurity_right) # Respect invariant for constant features: the original order of # element in features[:n_known_constants] must be preserved for sibling @@ -1611,3 +1618,4 @@ cdef class RandomSparseSplitter(BaseSparseSplitter): split[0] = best n_constant_features[0] = n_total_constants return 0 + \ No newline at end of file diff --git a/sklearn/tree/_tree.pxd b/sklearn/tree/_tree.pxd index 14b03103deff0..6c9275105871b 100644 --- a/sklearn/tree/_tree.pxd +++ b/sklearn/tree/_tree.pxd @@ -103,3 +103,4 @@ cdef class TreeBuilder: np.ndarray sample_weight=*, np.ndarray X_idx_sorted=*) cdef _check_input(self, object X, np.ndarray y, np.ndarray sample_weight) + \ No newline at end of file diff --git a/sklearn/tree/_tree.pyx b/sklearn/tree/_tree.pyx index 664b5fda581e3..6638fedd86a06 100644 --- a/sklearn/tree/_tree.pyx +++ b/sklearn/tree/_tree.pyx @@ -204,7 +204,7 @@ cdef class DepthFirstTreeBuilder(TreeBuilder): # Recursive partition (without actual recursion) splitter.init(X, y, sample_weight_ptr, X_idx_sorted) - + cdef SIZE_t start cdef SIZE_t end cdef SIZE_t depth @@ -256,9 +256,8 @@ cdef class DepthFirstTreeBuilder(TreeBuilder): weighted_n_node_samples < 2 * min_weight_leaf) if first: - impurity = splitter.node_impurity() + impurity = splitter.node_impurity(&split) first = 0 - is_leaf = (is_leaf or (impurity <= min_impurity_split)) @@ -313,7 +312,6 @@ cdef class DepthFirstTreeBuilder(TreeBuilder): cdef inline int _add_to_frontier(PriorityHeapRecord* rec, PriorityHeap frontier) nogil except -1: """Adds record ``rec`` to the priority queue ``frontier`` - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -324,7 +322,6 @@ cdef inline int _add_to_frontier(PriorityHeapRecord* rec, cdef class BestFirstTreeBuilder(TreeBuilder): """Build a decision tree in best-first fashion. - The best node to expand is given by the node at the frontier that has the highest impurity improvement. """ @@ -364,7 +361,6 @@ cdef class BestFirstTreeBuilder(TreeBuilder): # Recursive partition (without actual recursion) splitter.init(X, y, sample_weight_ptr, X_idx_sorted) - cdef PriorityHeap frontier = PriorityHeap(INITIAL_STACK_SIZE) cdef PriorityHeapRecord record cdef PriorityHeapRecord split_node_left @@ -479,8 +475,9 @@ cdef class BestFirstTreeBuilder(TreeBuilder): with gil: _init_pred_weights(&split, splitter.y.shape[1], &(splitter.rand_r_state), splitter.criterion) if is_first: - impurity = splitter.node_impurity() - + impurity = splitter.node_impurity(&split) + else: + splitter.node_impurity(&split) n_node_samples = end - start is_leaf = (depth >= self.max_depth or n_node_samples < self.min_samples_split or @@ -538,54 +535,42 @@ cdef class BestFirstTreeBuilder(TreeBuilder): cdef class Tree: """Array-based representation of a binary decision tree. - The binary tree is represented as a number of parallel arrays. The i-th element of each array holds information about the node `i`. Node 0 is the tree's root. You can find a detailed description of all arrays in `_tree.pxd`. NOTE: Some of the arrays only apply to either leaves or split nodes, resp. In this case the values of nodes of the other type are arbitrary! - Attributes ---------- node_count : int The number of nodes (internal nodes + leaves) in the tree. - capacity : int The current capacity (i.e., size) of the arrays, which is at least as great as `node_count`. - max_depth : int The depth of the tree, i.e. the maximum depth of its leaves. - children_left : array of int, shape [node_count] children_left[i] holds the node id of the left child of node i. For leaves, children_left[i] == TREE_LEAF. Otherwise, children_left[i] > i. This child handles the case where X[:, feature[i]] <= threshold[i]. - children_right : array of int, shape [node_count] children_right[i] holds the node id of the right child of node i. For leaves, children_right[i] == TREE_LEAF. Otherwise, children_right[i] > i. This child handles the case where X[:, feature[i]] > threshold[i]. - feature : array of int, shape [node_count] feature[i] holds the feature to split on, for the internal node i. - threshold : array of double, shape [node_count] threshold[i] holds the threshold for the internal node i. - value : array of double, shape [node_count, n_outputs, max_n_classes] Contains the constant prediction value of each node. - impurity : array of double, shape [node_count] impurity[i] holds the impurity (i.e., the value of the splitting criterion) at node i. - n_node_samples : array of int, shape [node_count] n_node_samples[i] holds the number of training samples reaching node i. - weighted_n_node_samples : array of int, shape [node_count] weighted_n_node_samples[i] holds the weighted number of training samples reaching node i. @@ -715,7 +700,6 @@ cdef class Tree: cdef int _resize(self, SIZE_t capacity) nogil except -1: """Resize all inner arrays to `capacity`, if `capacity` == -1, then double the size of the inner arrays. - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -726,7 +710,6 @@ cdef class Tree: cdef int _resize_c(self, SIZE_t capacity=SIZE_MAX) nogil except -1: """Guts of _resize - Returns -1 in case of failure to allocate memory (and raise MemoryError) or 0 otherwise. """ @@ -760,9 +743,7 @@ cdef class Tree: SIZE_t n_node_samples, double weighted_n_node_samples) nogil except -1: """Add a node to the tree. - The new node registers itself as the child of its parent. - Returns (size_t)(-1) on error. """ cdef SIZE_t node_id = self.node_count @@ -1121,7 +1102,6 @@ cdef class Tree: cdef np.ndarray _get_value_ndarray(self): """Wraps value as a 3-d NumPy array. - The array keeps a reference to this Tree, which manages the underlying memory. """ @@ -1137,7 +1117,6 @@ cdef class Tree: cdef np.ndarray _get_node_ndarray(self): """Wraps nodes as a NumPy struct array. - The array keeps a reference to this Tree, which manages the underlying memory. Individual fields are publicly accessible as properties of the Tree. @@ -1160,20 +1139,16 @@ cdef class Tree: int[::1] target_features, double[::1] out): """Partial dependence of the response on the ``target_feature`` set. - For each sample in ``X`` a tree traversal is performed. Each traversal starts from the root with weight 1.0. - At each non-leaf node that splits on a target feature, either the left child or the right child is visited based on the feature value of the current sample, and the weight is not modified. At each non-leaf node that splits on a complementary feature, both children are visited and the weight is multiplied by the fraction of training samples which went to each child. - At each leaf, the value of the node is multiplied by the current weight (weights sum to 1 for all visited terminal nodes). - Parameters ---------- X : view on 2d ndarray, shape (n_samples, n_target_features) @@ -1330,12 +1305,10 @@ cdef _cost_complexity_prune(unsigned char[:] leaves_in_subtree, # OUT Tree orig_tree, _CCPPruneController controller): """Perform cost complexity pruning. - This function takes an already grown tree, `orig_tree` and outputs a boolean mask `leaves_in_subtree` to are the leaves in the pruned tree. The controller signals when the pruning should stop and is passed the metrics of the subtrees during the pruning process. - Parameters ---------- leaves_in_subtree : unsigned char[:] @@ -1511,10 +1484,8 @@ def _build_pruned_tree_ccp( DOUBLE_t ccp_alpha): """Build a pruned tree from the original tree using cost complexity pruning. - The values and nodes from the original tree are copied into the pruned tree. - Parameters ---------- tree : Tree @@ -1542,20 +1513,16 @@ def _build_pruned_tree_ccp( def ccp_pruning_path(Tree orig_tree): """Computes the cost complexity pruning path. - Parameters ---------- tree : Tree Original tree. - Returns ------- path_info : dict Information about pruning path with attributes: - ccp_alphas : ndarray Effective alphas of subtree during pruning. - impurities : ndarray Sum of the impurities of the subtree leaves for the corresponding alpha value in ``ccp_alphas``. @@ -1590,10 +1557,8 @@ cdef _build_pruned_tree( const unsigned char[:] leaves_in_subtree, SIZE_t capacity): """Build a pruned tree. - Build a pruned tree from the original tree by transforming the nodes in ``leaves_in_subtree`` into leaves. - Parameters ---------- tree : Tree @@ -1679,3 +1644,4 @@ cdef _build_pruned_tree( tree.max_depth = max_depth_seen if rc == -1: raise MemoryError("pruning tree") + \ No newline at end of file diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index 05c6f9f11563f..7f9159be776ac 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -1685,7 +1685,6 @@ def test_no_sparse_y_support(name): def test_mae(): """Check MAE criterion produces correct results on small toy dataset: - ------------------ | X | y | weight | ------------------ @@ -1697,32 +1696,26 @@ def test_mae(): ------------------ |sum wt:| 2.3 | ------------------ - Because we are dealing with sample weights, we cannot find the median by simply choosing/averaging the centre value(s), instead we consider the median where 50% of the cumulative weight is found (in a y sorted data set) . Therefore with regards to this test data, the cumulative weight is >= 50% when y = 4. Therefore: Median = 4 - For all the samples, we can get the total error by summing: Absolute(Median - y) * weight - I.e., total error = (Absolute(4 - 3) * 0.1) + (Absolute(4 - 3) * 0.3) + (Absolute(4 - 4) * 1.0) + (Absolute(4 - 6) * 0.6) + (Absolute(4 - 7) * 0.3) = 2.5 - Impurity = Total error / total weight = 2.5 / 2.3 = 1.08695652173913 ------------------ - From this root node, the next best split is between X values of 3 and 5. Thus, we have left and right child nodes: - LEFT RIGHT ------------------ ------------------ | X | y | weight | | X | y | weight | @@ -1733,25 +1726,21 @@ def test_mae(): |sum wt:| 0.7 | ------------------ ------------------ |sum wt:| 1.6 | ------------------ - Impurity is found in the same way: Left node Median = 6 Total error = (Absolute(6 - 3) * 0.1) + (Absolute(6 - 6) * 0.6) = 0.3 - Left Impurity = Total error / total weight = 0.3 / 0.7 = 0.428571428571429 ------------------- - Likewise for Right node: Right node Median = 4 Total error = (Absolute(4 - 3) * 0.3) + (Absolute(4 - 4) * 1.0) + (Absolute(4 - 7) * 0.3) = 1.2 - Right Impurity = Total error / total weight = 1.2 / 1.6 = 0.75 @@ -1783,7 +1772,6 @@ def test_mae(): def test_axis_proj_jenn(): """Check axis projection criterion produces correct results on small toy dataset: - ------------------ | X | y1 y2 | weight | ------------------ @@ -1798,10 +1786,8 @@ def test_axis_proj_jenn(): Mean1 = 5 Mean2 = 5 - For all the samples, we can get the total error by summing: (Mean1 - y1)^2 * weight or (Mean2 - y2)^2 * weight - I.e., total error = (5 - 3)^2 * 0.1) + (5 - 3)^2 * 0.3) + (5 - 4)^2 * 1.0) @@ -1809,15 +1795,12 @@ def test_axis_proj_jenn(): + (5 - 8)^2 * 0.3) = 0.4 + 1.2 + 1.0 + 2.4 + 2.7 = 7.7 - Impurity = Total error / total weight = 7.7 / 2.3 = 3.3478260869565 ----------------- - From this root node, the next best split is between X values of 5 and 8. Thus, we have left and right child nodes: - LEFT RIGHT ----------------------- ----------------------- | X | y1 y2 | weight | | X | y1 y2 | weight | @@ -1829,10 +1812,8 @@ def test_axis_proj_jenn(): ----------------------- |sum wt:| 1.3 | ----------------------- - 5.0625 + 3.0625 + 5.0625 + 7.5625 / 4 + 0 = 5.1875 4 + 4.667 = 8.667 - Impurity is found in the same way: Left node Mean1 = Mean2 = 5.25 Total error = ((5.25 - 3)^2 * 0.1) @@ -1840,17 +1821,14 @@ def test_axis_proj_jenn(): + ((5.25 - 3)^2 * 0.3) + ((5.25 - 8)^2 * 0.3) = 6.13125 - Left Impurity = Total error / total weight = 6.13125 / 1.3 = 4.716346153846154 ------------------- - Likewise for Right node: Right node Mean1 = Mean2 = 4 Total error = ((4 - 4)^2 * 1.0) = 0 - Right Impurity = Total error / total weight = 0 / 1.0 = 0.0 @@ -1909,7 +1887,6 @@ def test_axis_proj_jenn(): def test_oblique_proj_jenn(): """Check oblique projection criterion produces correct results on small toy dataset: - ----------------------- | X | y1 y2 | weight | ----------------------- @@ -1924,10 +1901,8 @@ def test_oblique_proj_jenn(): Mean1 = 5 Mean_tot = 5 - For all the samples, we can get the total error by summing: (Mean1 - y1)^2 * weight or (Mean_tot - y)^2 * weight - I.e., error1 = (5 - 3)^2 * 0.1) + (5 - 3)^2 * 0.3) + (5 - 4)^2 * 1.0) @@ -1936,7 +1911,6 @@ def test_oblique_proj_jenn(): = 0.4 + 1.2 + 1.0 + 2.4 + 2.7 = 7.7 error_tot = 15.4 - Impurity = error / total weight = 7.7 / 2.3 = 3.3478260869565 @@ -1947,10 +1921,8 @@ def test_oblique_proj_jenn(): = 0.0 / 2.3 = 0.0 ----------------- - From this root node, the next best split is between X values of 5 and 8. Thus, we have left and right child nodes: - LEFT RIGHT ----------------------- ----------------------- | X | y1 y2 | weight | | X | y1 y2 | weight | @@ -1962,10 +1934,8 @@ def test_oblique_proj_jenn(): ----------------------- |sum wt:| 1.3 | ----------------------- - (5.0625 + 3.0625 + 5.0625 + 7.5625) / 4 + 0 = 5.1875 4 + 4.667 = 8.667 - Impurity is found in the same way: Left node Mean1 = Mean2 = 5.25 error1 = ((5.25 - 3)^2 * 0.1) @@ -1974,7 +1944,6 @@ def test_oblique_proj_jenn(): + ((5.25 - 8)^2 * 0.3) = 6.13125 error_tot = 12.2625 - Left Impurity = Total error / total weight = 6.13125 / 1.3 = 4.716346153846154 @@ -1982,12 +1951,10 @@ def test_oblique_proj_jenn(): = 12.2625 / 1.3 = 9.43269231 ------------------- - Likewise for Right node: Right node Mean1 = Mean2 = 4 Total error = ((4 - 4)^2 * 1.0) = 0 - Right Impurity = Total error / total weight = 0 / 1.0 = 0.0 @@ -2070,7 +2037,6 @@ def test_oblique_proj_jenn(): def test_axis_proj(): """Check axis projection criterion produces correct results on small toy dataset: - ------------------ | X | y1 y2 | weight | ------------------ @@ -2358,3 +2324,4 @@ def test_classes_deprecated(): with pytest.warns(DeprecationWarning, match=match): assert len(clf.n_classes_) == clf.n_outputs_ + \ No newline at end of file diff --git a/sklearn/tree/tree.py b/sklearn/tree/tree.py index 522252fef0536..1230f0ac16da6 100644 --- a/sklearn/tree/tree.py +++ b/sklearn/tree/tree.py @@ -76,7 +76,6 @@ class BaseDecisionTree(MultiOutputMixin, BaseEstimator, metaclass=ABCMeta): """Base class for decision trees. - Warning: This class should not be used directly. Use derived classes instead. """ @@ -114,7 +113,6 @@ def __init__(self, def get_depth(self): """Returns the depth of the decision tree. - The depth of a tree is the maximum distance between the root and any leaf. """ @@ -326,8 +324,7 @@ def fit(self, X, y, sample_weight=None, check_input=True, self.n_classes_) else: criterion = CRITERIA_REG[self.criterion](self.n_outputs_, - n_samples, - random_state) + n_samples) SPLITTERS = SPARSE_SPLITTERS if issparse(X) else DENSE_SPLITTERS @@ -395,22 +392,18 @@ def _validate_X_predict(self, X, check_input): def predict(self, X, check_input=True): """Predict class or regression value for X. - For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned. - Parameters ---------- X : array-like or sparse matrix of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. - check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. - Returns ------- y : array-like of shape (n_samples,) or (n_samples, n_outputs) @@ -448,20 +441,16 @@ def predict(self, X, check_input=True): def apply(self, X, check_input=True): """ Returns the index of the leaf that each sample is predicted as. - .. versionadded:: 0.17 - Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. - check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. - Returns ------- X_leaves : array_like, shape = [n_samples,] @@ -476,26 +465,21 @@ def apply(self, X, check_input=True): def decision_path(self, X, check_input=True): """Return the decision path in the tree - .. versionadded:: 0.18 - Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. - check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. - Returns ------- indicator : sparse csr array, shape = [n_samples, n_nodes] Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. - """ X = self._validate_X_predict(X, check_input) return self.tree_.decision_path(X) @@ -525,35 +509,28 @@ def _prune_tree(self): def cost_complexity_pruning_path(self, X, y, sample_weight=None): """Compute the pruning path during Minimal Cost-Complexity Pruning. - See `ref`:minimal_cost_complexity_pruning` for details on the pruning process. - Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. - y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings. - sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node. - Returns ------- ccp_path : Bunch Dictionary-like object, with attributes: - ccp_alphas : ndarray Effective alphas of subtree during pruning. - impurities : ndarray Sum of the impurities of the subtree leaves for the corresponding alpha value in ``ccp_alphas``. @@ -565,11 +542,9 @@ def cost_complexity_pruning_path(self, X, y, sample_weight=None): @property def feature_importances_(self): """Return the feature importances. - The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance. - Returns ------- feature_importances_ : array, shape = [n_features] @@ -585,59 +560,46 @@ def feature_importances_(self): class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree): """A decision tree classifier. - Read more in the :ref:`User Guide `. - Parameters ---------- criterion : string, optional (default="gini") The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. - splitter : string, optional (default="best") The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. - max_depth : int or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. - min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. - .. versionchanged:: 0.18 Added float values for fractions. - min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. - .. versionchanged:: 0.18 Added float values for fractions. - min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. - max_features : int, float, string or None, optional (default=None) The number of features to consider when looking for the best split: - - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each @@ -646,116 +608,88 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree): - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. - Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. - random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. - max_leaf_nodes : int or None, optional (default=None) Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. - min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. - The weighted impurity decrease equation is the following:: - N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) - where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. - ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. - .. versionadded:: 0.19 - min_impurity_split : float, (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. - .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. - class_weight : dict, list of dicts, "balanced" or None, default=None Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. - Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. - The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` - For multi-output, the weights of each column of y will be multiplied. - Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. - presort : deprecated, default='deprecated' This parameter is deprecated and will be removed in v0.24. - .. deprecated :: 0.22 - ccp_alpha : non-negative float, optional (default=0.0) Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details. - .. versionadded:: 0.22 - Attributes ---------- classes_ : array of shape (n_classes,) or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multi-output problem). - feature_importances_ : ndarray of shape (n_features,) The feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_. - max_features_ : int, The inferred value of max_features. - n_classes_ : int or list The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems). - n_features_ : int The number of features when ``fit`` is performed. - n_outputs_ : int The number of outputs when ``fit`` is performed. - tree_ : Tree object The underlying Tree object. Please refer to ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` for basic usage of these attributes. - Notes ----- The default values for the parameters controlling the size of the trees @@ -763,32 +697,24 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree): unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. - The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and ``max_features=n_features``, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed. - See also -------- DecisionTreeRegressor - References ---------- - .. [1] https://en.wikipedia.org/wiki/Decision_tree_learning - .. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification and Regression Trees", Wadsworth, Belmont, CA, 1984. - .. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical Learning", Springer, 2009. - .. [4] L. Breiman, and A. Cutler, "Random Forests", https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm - Examples -------- >>> from sklearn.datasets import load_iris @@ -836,34 +762,28 @@ def __init__(self, def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None): """Build a decision tree classifier from the training set (X, y). - Parameters ---------- X : {array-like or sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. - y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings. - sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node. - check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. - X_idx_sorted : array-like of shape (n_samples, n_features), optional The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don't use this parameter unless you know what to do. - Returns ------- self : object @@ -878,24 +798,19 @@ def fit(self, X, y, sample_weight=None, check_input=True, def predict_proba(self, X, check_input=True): """Predict class probabilities of the input samples X. - The predicted class probability is the fraction of samples of the same class in a leaf. - check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. - Parameters ---------- X : array-like or sparse matrix of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. - check_input : bool Run check_array on X. - Returns ------- p : array of shape (n_samples, n_classes), or a list of n_outputs @@ -929,14 +844,12 @@ class in a leaf. def predict_log_proba(self, X): """Predict class log-probabilities of the input samples X. - Parameters ---------- X : array-like or sparse matrix of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. - Returns ------- p : array of shape (n_samples, n_classes), or a list of n_outputs @@ -958,9 +871,7 @@ def predict_log_proba(self, X): class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree): """A decision tree regressor. - Read more in the :ref:`User Guide `. - Parameters ---------- criterion : string, optional (default="mse") @@ -971,54 +882,42 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree): squared error with Friedman's improvement score for potential splits, and "mae" for the mean absolute error, which minimizes the L1 loss using the median of each terminal node. - .. versionadded:: 0.18 Mean Absolute Error (MAE) criterion. - splitter : string, optional (default="best") The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. - max_depth : int or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. - min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. - .. versionchanged:: 0.18 Added float values for fractions. - min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. - .. versionchanged:: 0.18 Added float values for fractions. - min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. - max_features : int, float, string or None, optional (default=None) The number of features to consider when looking for the best split: - - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each @@ -1027,63 +926,47 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree): - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. - Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. - random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. - max_leaf_nodes : int or None, optional (default=None) Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. - min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. - The weighted impurity decrease equation is the following:: - N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) - where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. - ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. - .. versionadded:: 0.19 - min_impurity_split : float, (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. - .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. - presort : deprecated, default='deprecated' This parameter is deprecated and will be removed in v0.24. - .. deprecated :: 0.22 - ccp_alpha : non-negative float, optional (default=0.0) Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details. - .. versionadded:: 0.22 - Attributes ---------- feature_importances_ : ndarray of shape (n_features,) @@ -1092,22 +975,17 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree): The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_. - max_features_ : int, The inferred value of max_features. - n_features_ : int The number of features when ``fit`` is performed. - n_outputs_ : int The number of outputs when ``fit`` is performed. - tree_ : Tree object The underlying Tree object. Please refer to ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` for basic usage of these attributes. - Notes ----- The default values for the parameters controlling the size of the trees @@ -1115,32 +993,24 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree): unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. - The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and ``max_features=n_features``, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed. - See also -------- DecisionTreeClassifier - References ---------- - .. [1] https://en.wikipedia.org/wiki/Decision_tree_learning - .. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification and Regression Trees", Wadsworth, Belmont, CA, 1984. - .. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical Learning", Springer, 2009. - .. [4] L. Breiman, and A. Cutler, "Random Forests", https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm - Examples -------- >>> from sklearn.datasets import load_boston @@ -1186,33 +1056,27 @@ def __init__(self, def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None): """Build a decision tree regressor from the training set (X, y). - Parameters ---------- X : {array-like or sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. - y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (real numbers). Use ``dtype=np.float64`` and ``order='C'`` for maximum efficiency. - sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. - check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. - X_idx_sorted : array-like of shape (n_samples, n_features), optional The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don't use this parameter unless you know what to do. - Returns ------- self : object @@ -1244,68 +1108,53 @@ def n_classes_(self): class ExtraTreeClassifier(DecisionTreeClassifier): """An extremely randomized tree classifier. - Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the `max_features` randomly selected features and the best split among those is chosen. When `max_features` is set 1, this amounts to building a totally random decision tree. - Warning: Extra-trees should only be used within ensemble methods. - Read more in the :ref:`User Guide `. - Parameters ---------- criterion : string, optional (default="gini") The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. - splitter : string, optional (default="random") The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. - max_depth : int or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. - min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. - .. versionchanged:: 0.18 Added float values for fractions. - min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. - .. versionchanged:: 0.18 Added float values for fractions. - min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. - max_features : int, float, string or None, optional (default="auto") The number of features to consider when looking for the best split: - - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each @@ -1314,114 +1163,87 @@ class ExtraTreeClassifier(DecisionTreeClassifier): - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. - Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. - random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. - max_leaf_nodes : int or None, optional (default=None) Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. - min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. - The weighted impurity decrease equation is the following:: - N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) - where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. - ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. - .. versionadded:: 0.19 - min_impurity_split : float, (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. - .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. - class_weight : dict, list of dicts, "balanced" or None, default=None Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. - Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. - The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` - For multi-output, the weights of each column of y will be multiplied. - Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. - ccp_alpha : non-negative float, optional (default=0.0) Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details. - .. versionadded:: 0.22 - Attributes ---------- classes_ : array of shape (n_classes,) or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multi-output problem). - max_features_ : int, The inferred value of max_features. - n_classes_ : int or list The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems). - feature_importances_ : ndarray of shape (n_features,) Return the feature importances (the higher, the more important the feature). - n_features_ : int The number of features when ``fit`` is performed. - n_outputs_ : int The number of outputs when ``fit`` is performed. - tree_ : Tree object The underlying Tree object. Please refer to ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` for basic usage of these attributes. - See also -------- ExtraTreeRegressor, sklearn.ensemble.ExtraTreesClassifier, sklearn.ensemble.ExtraTreesRegressor - Notes ----- The default values for the parameters controlling the size of the trees @@ -1429,10 +1251,8 @@ class ExtraTreeClassifier(DecisionTreeClassifier): unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. - References ---------- - .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", Machine Learning, 63(1), 3-42, 2006. """ @@ -1468,18 +1288,14 @@ def __init__(self, class ExtraTreeRegressor(DecisionTreeRegressor): """An extremely randomized tree regressor. - Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the `max_features` randomly selected features and the best split among those is chosen. When `max_features` is set 1, this amounts to building a totally random decision tree. - Warning: Extra-trees should only be used within ensemble methods. - Read more in the :ref:`User Guide `. - Parameters ---------- criterion : string, optional (default="mse") @@ -1487,54 +1303,42 @@ class ExtraTreeRegressor(DecisionTreeRegressor): are "mse" for the mean squared error, which is equal to variance reduction as feature selection criterion, and "mae" for the mean absolute error. - .. versionadded:: 0.18 Mean Absolute Error (MAE) criterion. - splitter : string, optional (default="random") The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. - max_depth : int or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. - min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. - .. versionchanged:: 0.18 Added float values for fractions. - min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. - .. versionchanged:: 0.18 Added float values for fractions. - min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. - max_features : int, float, string or None, optional (default="auto") The number of features to consider when looking for the best split: - - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each @@ -1543,80 +1347,61 @@ class ExtraTreeRegressor(DecisionTreeRegressor): - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. - Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. - random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. - min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. - The weighted impurity decrease equation is the following:: - N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) - where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. - ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. - .. versionadded:: 0.19 - min_impurity_split : float, (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. - .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. - max_leaf_nodes : int or None, optional (default=None) Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. - ccp_alpha : non-negative float, optional (default=0.0) Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details. - .. versionadded:: 0.22 - Attributes ---------- max_features_ : int, The inferred value of max_features. - n_features_ : int The number of features when ``fit`` is performed. - n_outputs_ : int The number of outputs when ``fit`` is performed. - tree_ : Tree object The underlying Tree object. Please refer to ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` for basic usage of these attributes. - See also -------- ExtraTreeClassifier, sklearn.ensemble.ExtraTreesClassifier, sklearn.ensemble.ExtraTreesRegressor - Notes ----- The default values for the parameters controlling the size of the trees @@ -1624,10 +1409,8 @@ class ExtraTreeRegressor(DecisionTreeRegressor): unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. - References ---------- - .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", Machine Learning, 63(1), 3-42, 2006. """ @@ -1657,3 +1440,4 @@ def __init__(self, min_impurity_split=min_impurity_split, random_state=random_state, ccp_alpha=ccp_alpha) + \ No newline at end of file From 988627dd2b691c601bc29d0a2d21dd994c8b1036 Mon Sep 17 00:00:00 2001 From: morgsmss7 Date: Mon, 23 Mar 2020 17:01:28 -0400 Subject: [PATCH 02/11] fix issues from resolving merge conflicts --- sklearn/tree/_criterion.pxd | 3 +- sklearn/tree/_criterion.pyx | 167 +++++++++++++----------------------- sklearn/tree/_splitter.pxd | 3 +- sklearn/tree/_splitter.pyx | 61 +++++++++++-- sklearn/tree/_tree.pyx | 32 +++++++ 5 files changed, 147 insertions(+), 119 deletions(-) diff --git a/sklearn/tree/_criterion.pxd b/sklearn/tree/_criterion.pxd index af336150b30e7..2045a1ed5befc 100644 --- a/sklearn/tree/_criterion.pxd +++ b/sklearn/tree/_criterion.pxd @@ -84,4 +84,5 @@ cdef class RegressionCriterion(Criterion): cdef class ObliqueProjection(RegressionCriterion): pass cdef class AxisProjection(RegressionCriterion): - pass \ No newline at end of file + pass + \ No newline at end of file diff --git a/sklearn/tree/_criterion.pyx b/sklearn/tree/_criterion.pyx index 8fca3272b5b1b..7dc7f3e5577de 100644 --- a/sklearn/tree/_criterion.pyx +++ b/sklearn/tree/_criterion.pyx @@ -1319,17 +1319,17 @@ cdef class AxisProjection(RegressionCriterion): 2. compute mse on the values of that predictor for all samples MSE = var_left + var_right """ - - cdef double node_impurity(self) nogil: + cdef double node_impurity2(self, double* pred_weights): """Evaluate the impurity of the current node, i.e. the impurity of samples[start:end].""" - cdef double impurity = 0.0 #TODO + cdef double impurity = 0.0 cdef DOUBLE_t* sample_weight = self.sample_weight cdef SIZE_t* samples = self.samples cdef SIZE_t end = self.end cdef SIZE_t start = self.start - cdef double* sum_total = self.sum_total + cdef double sq_sum_total = 0.0 + cdef DOUBLE_t y_ik cdef SIZE_t i @@ -1338,38 +1338,19 @@ cdef class AxisProjection(RegressionCriterion): cdef DOUBLE_t w = 1.0 - cdef DOUBLE_t w = 1.0 - for p in range(start, end): i = samples[p] if sample_weight != NULL: w = sample_weight[i] - y_ik = self.y[i, k] - sq_sum_total += w * y_ik * y_ik + for k in range(self.n_outputs): + y_ik = self.y[i, k] + sq_sum_total += w * y_ik * y_ik * pred_weights[k] impurity = sq_sum_total / self.weighted_n_node_samples - impurity -= (sum_total[k] / self.weighted_n_node_samples)**2.0 + for k in range(self.n_outputs): + impurity -= (sum_total[k]* pred_weights[k]/ self.weighted_n_node_samples)**2.0 return impurity - ''' - for p in range(start, end): - i = samples[p] - if sample_weight != NULL: - w = sample_weight[i] - for k in range(self.n_outputs): - y_ik = self.y[i, k] - # sum over all predictors with pred weights - pred[p] += y_ik * pred_weights[k] - # sum over all samples to get mean of new predictor - mean_pred += pred[p] / (end - start) - for p in range(start, end): - i = samples[p] - if sample_weight != NULL: - w = sample_weight[i] - impurity += (mean_pred - pred[p]) * (mean_pred - pred[p]) * w - impurity /= self.weighted_n_node_samples - return impurity - ''' cdef double proxy_impurity_improvement2(self, double* pred_weights) nogil: @@ -1381,7 +1362,6 @@ cdef class AxisProjection(RegressionCriterion): The absolute impurity improvement is only computed by the impurity_improvement method once the best split has been found. """ - cdef double* sum_left = self.sum_left cdef double* sum_right = self.sum_right @@ -1389,46 +1369,33 @@ cdef class AxisProjection(RegressionCriterion): cdef double proxy_impurity_left = 0.0 cdef double proxy_impurity_right = 0.0 - cdef UINT32_t rand_r_state - - with gil: - rand_r_state = self.random_state.randint(0, RAND_R_MAX) - cdef UINT32_t* random_state = &rand_r_state - - k = rand_int(0, self.n_outputs, random_state) - - proxy_impurity_left += sum_left[k] * sum_left[k] - proxy_impurity_right += sum_right[k] * sum_right[k] - - - return (proxy_impurity_left / self.weighted_n_left + - proxy_impurity_right / self.weighted_n_right) - ''' - with gil: - for k in range(self.n_outputs): - proxy_impurity_left += sum_left[k] * sum_left[k] * abs(pred_weights[k]) - proxy_impurity_right += sum_right[k] * sum_right[k] * abs(pred_weights[k]) - #with gil: - # return (abs(proxy_impurity_left / self.weighted_n_left) + - # abs(proxy_impurity_right / self.weighted_n_right)) + for k in range(self.n_outputs): + proxy_impurity_left += sum_left[k] * sum_left[k] * pred_weights[k] + proxy_impurity_right += sum_right[k] * sum_right[k] * pred_weights[k] + + proxy_impurity_left = fabs(proxy_impurity_left) + proxy_impurity_right = fabs(proxy_impurity_right) return (proxy_impurity_left / self.weighted_n_left + proxy_impurity_right / self.weighted_n_right) - ''' - cdef void children_impurity(self, double* impurity_left, - double* impurity_right) nogil: + + cdef void children_impurity2(self, double* impurity_left, + double* impurity_right, double* pred_weights): """Evaluate the impurity in children nodes, i.e. the impurity of the left child (samples[start:pos]) and the impurity the right child (samples[pos:end]).""" - + + cdef double* sum_left = self.sum_left + cdef double* sum_right = self.sum_right + cdef DOUBLE_t* sample_weight = self.sample_weight cdef SIZE_t* samples = self.samples cdef SIZE_t pos = self.pos cdef SIZE_t start = self.start cdef SIZE_t end = self.end - cdef double* sum_left = self.sum_left - cdef double* sum_right = self.sum_right + impurity_left[0] = 0.0 + impurity_right[0] = 0.0 cdef DOUBLE_t y_ik cdef double sq_sum_left = 0.0 @@ -1436,42 +1403,38 @@ cdef class AxisProjection(RegressionCriterion): cdef SIZE_t i cdef SIZE_t p - cdef SIZE_t k # modified + cdef SIZE_t k cdef DOUBLE_t w = 1.0 - cdef UINT32_t rand_r_state - - with gil: - rand_r_state = self.random_state.randint(0, RAND_R_MAX) - cdef UINT32_t* random_state = &rand_r_state - - k = rand_int(0, self.n_outputs, random_state) - for p in range(start, pos): i = samples[p] if sample_weight != NULL: w = sample_weight[i] - y_ik = self.y[i, k] - sq_sum_left += w * y_ik * y_ik - + for k in range(self.n_outputs): + y_ik = self.y[i, k] + sq_sum_left += w * y_ik * y_ik * pred_weights[k] + for p in range(pos, end): i = samples[p] if sample_weight != NULL: w = sample_weight[i] - y_ik = self.y[i, k] - sq_sum_right += w * y_ik * y_ik - + for k in range(self.n_outputs): + y_ik = self.y[i, k] + sq_sum_right += w * y_ik * y_ik * pred_weights[k] + impurity_left[0] = sq_sum_left / self.weighted_n_left impurity_right[0] = sq_sum_right / self.weighted_n_right - impurity_left[0] -= (sum_left[k] / self.weighted_n_left) ** 2.0 - impurity_right[0] -= (sum_right[k] / self.weighted_n_right) ** 2.0 + for k in range(self.n_outputs): + impurity_left[0] -= pred_weights[k] * (sum_left[k]/ self.weighted_n_left) ** 2.0 + impurity_right[0] -= pred_weights[k] * (sum_right[k]/ self.weighted_n_right) ** 2.0 impurity_left[0] = fabs(impurity_left[0]) impurity_right[0] = fabs(impurity_right[0]) + cdef class ObliqueProjection(RegressionCriterion): r"""Mean squared error impurity criterion @@ -1482,27 +1445,29 @@ cdef class ObliqueProjection(RegressionCriterion): 3. compute mse on the values of those predictors for all samples MSE = var_left + var_right """ - cdef double node_impurity(self) nogil: + + cdef double node_impurity2(self, double* pred_weights): """Evaluate the impurity of the current node, i.e. the impurity of samples[start:end].""" - cdef double impurity = 0.0 #TODO + cdef double impurity = 0.0 cdef DOUBLE_t* sample_weight = self.sample_weight cdef SIZE_t* samples = self.samples cdef SIZE_t end = self.end cdef SIZE_t start = self.start - + cdef double* sum_total = self.sum_total cdef DOUBLE_t y_ik cdef double sq_sum_total = 0.0 + cdef SIZE_t num_pred = 0 + cdef SIZE_t i cdef SIZE_t p cdef SIZE_t k cdef DOUBLE_t w = 1.0 - for p in range(start, end): i = samples[p] if sample_weight != NULL: @@ -1512,11 +1477,13 @@ cdef class ObliqueProjection(RegressionCriterion): sq_sum_total += w * y_ik * y_ik * pred_weights[k] impurity = sq_sum_total / self.weighted_n_node_samples + impurity = fabs(impurity) + for k in range(self.n_outputs): + if pred_weights[k] != 0: + num_pred += 1 impurity -= (sum_total[k]* pred_weights[k]/ self.weighted_n_node_samples)**2.0 - with gil: impurity = fabs(impurity) - free(pred_weights) return impurity / num_pred @@ -1529,7 +1496,6 @@ cdef class ObliqueProjection(RegressionCriterion): The absolute impurity improvement is only computed by the impurity_improvement method once the best split has been found. """ - cdef double* sum_left = self.sum_left cdef double* sum_right = self.sum_right @@ -1537,36 +1503,18 @@ cdef class ObliqueProjection(RegressionCriterion): cdef double proxy_impurity_left = 0.0 cdef double proxy_impurity_right = 0.0 - cdef UINT32_t rand_r_state - cdef SIZE_t num_pred - cdef SIZE_t a - pred_weights = calloc(self.n_outputs, sizeof(double)) - - with gil: - rand_r_state = self.random_state.randint(0, RAND_R_MAX) - cdef UINT32_t* random_state = &rand_r_state - - num_pred = rand_int(1, self.n_outputs + 1, random_state) - - for i in range(num_pred): - k = rand_int(0, self.n_outputs, random_state) - a = rand_int(0, 2, random_state) - if a == 0: - a -= 1 - pred_weights[k] = a # didn't normalize - for k in range(self.n_outputs): proxy_impurity_left += sum_left[k] * sum_left[k] * pred_weights[k] proxy_impurity_right += sum_right[k] * sum_right[k] * pred_weights[k] proxy_impurity_left = fabs(proxy_impurity_left) proxy_impurity_right = fabs(proxy_impurity_right) - free(pred_weights) return (proxy_impurity_left / self.weighted_n_left + proxy_impurity_right / self.weighted_n_right) - cdef void children_impurity(self, double* impurity_left, - double* impurity_right) nogil: + + cdef void children_impurity2(self, double* impurity_left, + double* impurity_right, double* pred_weights): """Evaluate the impurity in children nodes, i.e. the impurity of the left child (samples[start:pos]) and the impurity the right child (samples[pos:end]).""" @@ -1576,16 +1524,19 @@ cdef class ObliqueProjection(RegressionCriterion): cdef SIZE_t start = self.start cdef SIZE_t end = self.end - cdef double* sum_left = self.sum_left - cdef double* sum_right = self.sum_right + impurity_left[0] = 0.0 + impurity_right[0] = 0.0 cdef DOUBLE_t y_ik cdef double sq_sum_left = 0.0 cdef double sq_sum_right = 0.0 - + + cdef double* sum_left = self.sum_left + cdef double* sum_right = self.sum_right + cdef SIZE_t i cdef SIZE_t p - cdef SIZE_t k # modified + cdef SIZE_t k cdef DOUBLE_t w = 1.0 for p in range(start, pos): @@ -1602,10 +1553,11 @@ cdef class ObliqueProjection(RegressionCriterion): if sample_weight != NULL: w = sample_weight[i] + for k in range(self.n_outputs): y_ik = self.y[i, k] sq_sum_right += w * y_ik * y_ik * pred_weights[k] - + impurity_left[0] = sq_sum_left / self.weighted_n_left impurity_right[0] = sq_sum_right / self.weighted_n_right @@ -1615,4 +1567,3 @@ cdef class ObliqueProjection(RegressionCriterion): impurity_left[0] = fabs(impurity_left[0]) impurity_right[0] = fabs(impurity_right[0]) - \ No newline at end of file diff --git a/sklearn/tree/_splitter.pxd b/sklearn/tree/_splitter.pxd index 8e42b9ef6d6f0..4653be49c653e 100644 --- a/sklearn/tree/_splitter.pxd +++ b/sklearn/tree/_splitter.pxd @@ -92,4 +92,5 @@ cdef class Splitter: cdef void node_value(self, double* dest) nogil - cdef double node_impurity(self, SplitRecord* split) nogil \ No newline at end of file + cdef double node_impurity(self, SplitRecord* split) nogil + \ No newline at end of file diff --git a/sklearn/tree/_splitter.pyx b/sklearn/tree/_splitter.pyx index 5ee8acbd03b15..4e20bd8a14247 100644 --- a/sklearn/tree/_splitter.pyx +++ b/sklearn/tree/_splitter.pyx @@ -45,13 +45,43 @@ cdef DTYPE_t FEATURE_THRESHOLD = 1e-7 # in SparseSplitter cdef DTYPE_t EXTRACT_NNZ_SWITCH = 0.1 -cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil: +cdef inline void _init_split(SplitRecord* self, Criterion criterion, SIZE_t start_pos, SplitRecord* other_split) nogil: self.impurity_left = INFINITY self.impurity_right = INFINITY self.pos = start_pos self.feature = 0 self.threshold = 0. self.improvement = -INFINITY + with gil: + if isinstance(criterion, ObliqueProjection) or isinstance(criterion, AxisProjection): + self.pred_weights = other_split.pred_weights + +cdef inline void _init_pred_weights(SplitRecord* self, SIZE_t n_outputs, UINT32_t* random_state, Criterion criterion) nogil: + cdef SIZE_t num_pred + cdef SIZE_t a + cdef SIZE_t k + #with gil: __dealloc__(self) TODO + with gil: + if isinstance(criterion, ObliqueProjection): + self.pred_weights = calloc(n_outputs, sizeof(double)) + num_pred = rand_int(1, n_outputs+1, random_state) + + for i in range(num_pred): + k = rand_int(0, n_outputs, random_state) + a = rand_int(0, 2, random_state) + if a == 0: + a -= 1 + self.pred_weights[k] = a # didn't normalize + elif isinstance(criterion, AxisProjection): + self.pred_weights = calloc(n_outputs, sizeof(double)) + k = rand_int(0, n_outputs, random_state) + self.pred_weights[k] = 1.0 + +''' #TODO +cdef __dealloc__(SplitRecord* self): + if not (self.pred_weights): + free(self.pred_weights) +''' cdef class Splitter: """Abstract splitter class. @@ -318,7 +348,10 @@ cdef class BestSplitter(BaseDenseSplitter): cdef DTYPE_t current_feature_value cdef SIZE_t partition_end - _init_split(&best, end) + _init_split(&best, self.criterion, end, split) + _init_split(¤t, self.criterion, end, split) + #_init_split(&best, self.criterion, end, self.y.shape[1], random_state) + #_init_split(¤t, self.criterion, end, self.y.shape[1], random_state) #TODO # Sample up to max_features without replacement using a # Fisher-Yates-based algorithm (using the local variables `f_i` and @@ -635,7 +668,8 @@ cdef class RandomSplitter(BaseDenseSplitter): cdef DTYPE_t max_feature_value cdef DTYPE_t current_feature_value - _init_split(&best, end) + _init_split(&best, self.criterion, end, split) + _init_split(¤t, self.criterion, end, split) # Sample up to max_features without replacement using a # Fisher-Yates-based algorithm (using the local variables `f_i` and @@ -1134,7 +1168,10 @@ cdef class BestSparseSplitter(BaseSparseSplitter): cdef UINT32_t* random_state = &self.rand_r_state cdef SplitRecord best, current - _init_split(&best, end) + + _init_split(&best, self.criterion, end, split) + _init_split(¤t, self.criterion, end, split) + cdef double current_proxy_improvement = - INFINITY cdef double best_proxy_improvement = - INFINITY @@ -1294,7 +1331,6 @@ cdef class BestSparseSplitter(BaseSparseSplitter): (current.threshold == INFINITY) or (current.threshold == -INFINITY)): current.threshold = Xf[p_prev] - best = current # Reorganize into samples[start:best.pos] + samples[best.pos:end] @@ -1369,7 +1405,10 @@ cdef class RandomSparseSplitter(BaseSparseSplitter): cdef UINT32_t* random_state = &self.rand_r_state cdef SplitRecord best, current - _init_split(&best, end) + + _init_split(&best, self.criterion, end, split) + _init_split(¤t, self.criterion, end, split) + cdef double current_proxy_improvement = - INFINITY cdef double best_proxy_improvement = - INFINITY @@ -1525,9 +1564,13 @@ cdef class RandomSparseSplitter(BaseSparseSplitter): if current_proxy_improvement > best_proxy_improvement: best_proxy_improvement = current_proxy_improvement current.improvement = self.criterion.impurity_improvement(impurity) - - self.criterion.children_impurity(¤t.impurity_left, - ¤t.impurity_right) + with gil: + if isinstance(self.criterion, ObliqueProjection) or isinstance(self.criterion, AxisProjection): + self.criterion.children_impurity2(¤t.impurity_left, + ¤t.impurity_right, split.pred_weights) + else: + self.criterion.children_impurity(¤t.impurity_left, + ¤t.impurity_right) best = current # Reorganize into samples[start:best.pos] + samples[best.pos:end] diff --git a/sklearn/tree/_tree.pyx b/sklearn/tree/_tree.pyx index f70e4038f7a8a..6638fedd86a06 100644 --- a/sklearn/tree/_tree.pyx +++ b/sklearn/tree/_tree.pyx @@ -39,6 +39,12 @@ from ._utils cimport PriorityHeapRecord from ._utils cimport safe_realloc from ._utils cimport sizet_ptr_to_ndarray +from ._criterion cimport Criterion +from ._criterion cimport ObliqueProjection +from ._criterion cimport AxisProjection +from libc.stdlib cimport calloc +from ._utils cimport rand_int + cdef extern from "numpy/arrayobject.h": object PyArray_NewFromDescr(PyTypeObject* subtype, np.dtype descr, int nd, np.npy_intp* dims, @@ -84,6 +90,28 @@ NODE_DTYPE = np.dtype({ ] }) + +cdef inline void _init_pred_weights(SplitRecord* self, SIZE_t n_outputs, UINT32_t* random_state, Criterion criterion) nogil: + cdef SIZE_t num_pred + cdef SIZE_t a + cdef SIZE_t k + #with gil: __dealloc__(self) + with gil: + if isinstance(criterion, ObliqueProjection): + self.pred_weights = calloc(n_outputs, sizeof(double)) + num_pred = rand_int(1, n_outputs+1, random_state) + + for i in range(num_pred): + k = rand_int(0, n_outputs, random_state) + a = rand_int(0, 2, random_state) + if a == 0: + a -= 1 + self.pred_weights[k] = a # didn't normalize + elif isinstance(criterion, AxisProjection): + self.pred_weights = calloc(n_outputs, sizeof(double)) + k = rand_int(0, n_outputs, random_state) + self.pred_weights[k] = 1.0 + # ============================================================================= # TreeBuilder # ============================================================================= @@ -188,6 +216,8 @@ cdef class DepthFirstTreeBuilder(TreeBuilder): cdef SplitRecord split cdef SIZE_t node_id + _init_pred_weights(&split, splitter.y.shape[1], &(splitter.rand_r_state), splitter.criterion) + cdef double impurity = INFINITY cdef SIZE_t n_constant_features cdef bint is_leaf @@ -442,6 +472,8 @@ cdef class BestFirstTreeBuilder(TreeBuilder): splitter.node_reset(start, end, &weighted_n_node_samples) + with gil: _init_pred_weights(&split, splitter.y.shape[1], &(splitter.rand_r_state), splitter.criterion) + if is_first: impurity = splitter.node_impurity(&split) else: From ffb940209e080bb77bb7064b60028bc9beb8f20f Mon Sep 17 00:00:00 2001 From: Jennifer Date: Sun, 29 Mar 2020 23:32:17 -0400 Subject: [PATCH 03/11] split tests and updated calculations in comments --- sklearn/tree/tests/test_tree.py | 345 +++++++++++++++++++------------- 1 file changed, 205 insertions(+), 140 deletions(-) diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index 5edaaa291bc01..4fe30fea1ee3e 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -20,6 +20,7 @@ from sklearn.utils.testing import assert_allclose from sklearn.utils.testing import assert_array_equal +from sklearn.utils.testing import assert_not_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_warns @@ -1769,8 +1770,9 @@ def test_mae(): np.random.seed(25) -def test_axis_proj_jenn(): - """Check axis projection criterion produces correct results on small toy dataset: +def test_axis_proj_same_y(): + """Check axis projection criterion produces correct results on + small toy dataset: ------------------ | X | y1 y2 | weight | ------------------ @@ -1833,7 +1835,80 @@ def test_axis_proj_jenn(): = 0.0 ------ """ + dt_axis = DecisionTreeRegressor(random_state=0, criterion="axis", + max_leaf_nodes=2) + dt_mse = DecisionTreeRegressor(random_state=0, criterion="mse", + max_leaf_nodes=2) + + # Test axis projection where sample weights are non-uniform (as illustrated above): + dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3], [3], [4], [7], [8]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + assert(abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01) + assert(abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01) + assert(abs(dt_axis.tree_.impurity[2]) < 0.01) + + # Test axis projection where all sample weights are uniform: + dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], + sample_weight=np.ones(5)) + dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8], + sample_weight=np.ones(5)) + assert(abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01) + assert(abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01) + assert(abs(dt_axis.tree_.impurity[2]) < 0.01) + assert_allclose(dt_axis.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) + # Test axis projections where a `sample_weight` is not explicitly provided. + # This is equivalent to providing uniform sample weights, though + # the internal logic is different: + dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]]) + dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8]) + assert(abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01) + assert(abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01) + assert(abs(dt_axis.tree_.impurity[2]) < 0.01) + + +def test_axis_proj_diff_y(): + """Check axis projection criterion produces correct results on small toy dataset: + ------------------ + | X | y1 y2 | weight | + ------------------ + | 3 | 3 2 | 0.1 | + | 5 | 3 4 | 0.3 | + | 8 | 4 3 | 1.0 | + | 3 | 7 6 | 0.6 | + | 5 | 8 7 | 0.3 | + ------------------ + |sum wt:| 2.3 | + ------------------ + + Mean1 = 5 + Mean2 = 5 + For all the samples, we can get the total error by summing: + (Mean1 - y1)^2 * weight or (Mean2 - y2)^2 * weight + I.e., total error1 = (5 - 3)^2 * 0.1) + + (5 - 3)^2 * 0.3) + + (5 - 4)^2 * 1.0) + + (5 - 7)^2 * 0.6) + + (5 - 8)^2 * 0.3) + = 0.4 + 1.2 + 1.0 + 2.4 + 2.7 + = 7.7 + total error2 = (5 - 2)^2 * 0.1) + + (5 - 4)^2 * 0.3) + + (5 - 3)^2 * 1.0) + + (5 - 6)^2 * 0.6) + + (5 - 7)^2 * 0.3) + = 0.9 + 0.3 + 4.0 + 0.6 + 1.2 + = 7.0 + Impurity1 = Total error1 / total weight + = 7.7 / 2.3 + = 3.3478260869565 + Impurity2 = Total error2 / total weight + = 7.0 / 2.3 + = 3.043478261 + ----------------- + """ # Test axis projection where multiple y values are different: dt_axis_multi = DecisionTreeRegressor(random_state=0, criterion="axis", max_leaf_nodes=2) @@ -1841,16 +1916,20 @@ def test_axis_proj_jenn(): sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) assert_allclose(dt_axis_multi.tree_.impurity, [6.148 / 2.3, 4.818 / 2.3, 0.0], rtol=0.6) - #y=[[3,3], [3,3], [4,4], [7,7], [8,8]] +def test_axis_proj_weights(): + # Test axis projection where sample weights are non-uniform (as illustrated above): dt_axis = DecisionTreeRegressor(random_state=0, criterion="axis", max_leaf_nodes=2) - - # Test axis projection where sample weights are non-uniform (as illustrated above): dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) assert_allclose(dt_axis.tree_.impurity, [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], rtol=0.6) - # Test same random state produces same result +def test_axis_proj_random_state(): + # Same random state produces same result + dt_axis = DecisionTreeRegressor(random_state=0, criterion="axis", + max_leaf_nodes=2) + dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) for i in range(3): dt_axis_2 = DecisionTreeRegressor(random_state=0, criterion="axis", max_leaf_nodes=2) @@ -1858,12 +1937,7 @@ def test_axis_proj_jenn(): sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) assert_allclose(dt_axis.tree_.impurity, dt_axis_2.tree_.impurity) - # Test axis projection where all sample weights are uniform: - dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=np.ones(5)) - assert_allclose(dt_axis.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) - - # Test different random state produces different results + # Different random state produces different result dt_axis_3 = DecisionTreeRegressor(random_state=1, criterion="axis", max_leaf_nodes=2) dt_axis_3.fit(X=[[3], [5], [8], [3], [5]], y=np.random.randint(1,100,(5,7)), @@ -1878,22 +1952,17 @@ def test_axis_proj_jenn(): elif i==100: assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity) - # Test axis projection where a `sample_weight` is not explicitly provided. - # This is equivalent to providing uniform sample weights, though - # the internal logic is different: - dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]]) - assert_allclose(dt_axis.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) - -def test_oblique_proj_jenn(): + +def test_oblique_proj_diff_y(): """Check oblique projection criterion produces correct results on small toy dataset: ----------------------- | X | y1 y2 | weight | ----------------------- - | 3 | 3 3 | 0.1 | - | 5 | 3 3 | 0.3 | - | 8 | 4 4 | 1.0 | - | 3 | 7 7 | 0.6 | - | 5 | 8 8 | 0.3 | + | 3 | 3 2 | 0.1 | + | 5 | 3 4 | 0.3 | + | 8 | 4 3 | 1.0 | + | 3 | 7 6 | 0.6 | + | 5 | 8 7 | 0.3 | ----------------------- |sum wt:| 2.3 | ----------------------- @@ -1909,6 +1978,13 @@ def test_oblique_proj_jenn(): + (5 - 8)^2 * 0.3) = 0.4 + 1.2 + 1.0 + 2.4 + 2.7 = 7.7 + error2 = (5 - 2)^2 * 0.1) + + (5 - 4)^2 * 0.3) + + (5 - 3)^2 * 1.0) + + (5 - 6)^2 * 0.6) + + (5 - 7)^2 * 0.3) + = 0.9 + 0.3 + 4.0 + 0.6 + 1.2 + = 7.0 error_tot = 15.4 Impurity = error / total weight = 7.7 / 2.3 @@ -1920,50 +1996,39 @@ def test_oblique_proj_jenn(): = 0.0 / 2.3 = 0.0 ----------------- - From this root node, the next best split is between X values of 5 and 8. - Thus, we have left and right child nodes: - LEFT RIGHT - ----------------------- ----------------------- - | X | y1 y2 | weight | | X | y1 y2 | weight | - ----------------------- ----------------------- - | 3 | 3 3 | 0.1 | | 8 | 4 4 | 1.0 | - | 3 | 7 7 | 0.6 | ----------------------- - | 5 | 3 3 | 0.3 | |sum wt:| 1.0 | - | 5 | 8 8 | 0.3 | ----------------------- - ----------------------- - |sum wt:| 1.3 | - ----------------------- - (5.0625 + 3.0625 + 5.0625 + 7.5625) / 4 + 0 = 5.1875 - 4 + 4.667 = 8.667 - Impurity is found in the same way: - Left node Mean1 = Mean2 = 5.25 - error1 = ((5.25 - 3)^2 * 0.1) - + ((5.25 - 7)^2 * 0.6) - + ((5.25 - 3)^2 * 0.3) - + ((5.25 - 8)^2 * 0.3) - = 6.13125 - error_tot = 12.2625 - Left Impurity = Total error / total weight - = 6.13125 / 1.3 - = 4.716346153846154 - or - = 12.2625 / 1.3 - = 9.43269231 - ------------------- - Likewise for Right node: - Right node Mean1 = Mean2 = 4 - Total error = ((4 - 4)^2 * 1.0) - = 0 - Right Impurity = Total error / total weight - = 0 / 1.0 - = 0.0 - ------ """ + # Test oblique projection where multiple y values are different: + dt_obliq_multi = DecisionTreeRegressor(random_state=3, criterion="oblique", + max_leaf_nodes=2) + dt_obliq_multi.fit(X=[[3], [5], [8], [3], [5]], y=[[3,2], [3,4], [4,3], [7,6], [8,7]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + try: + assert_allclose(dt_obliq_multi.tree_.impurity, [6.148 / 2.3, 4.818 / 2.3, 0.0], rtol=0.6) + except: + try: + assert_allclose(dt_obliq_multi.tree_.impurity, [2*6.148 / 2.3, 2*4.818 / 2.3, 0.0], rtol=0.6) + except: + assert_allclose(dt_obliq_multi.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", max_leaf_nodes=2) - + # Test MAE where a `sample_weight` is not explicitly provided. + # This is equivalent to providing uniform sample weights, though + # the internal logic is different: + dt_obliq.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]]) + try: + assert_allclose(dt_obliq.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) + except: + try: + assert_allclose(dt_obliq.tree_.impurity, [2.0*22.0 / 5.0, 2.0*20.75 / 4.0, 2.0*0.0 / 1.0], rtol=0.6) + except: + assert_allclose(dt_obliq.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) + + +def test_oblique_proj_weights(): # Test axis projection where sample weights are non-uniform (as illustrated above): + dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", + max_leaf_nodes=2) dt_obliq.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) try: @@ -1974,20 +2039,24 @@ def test_oblique_proj_jenn(): except: assert_allclose(dt_obliq.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) - # Test oblique projection where multiple y values are different: - dt_obliq_multi = DecisionTreeRegressor(random_state=3, criterion="oblique", + # Test oblique projection where all sample weights are uniform: + dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", max_leaf_nodes=2) - dt_obliq_multi.fit(X=[[3], [5], [8], [3], [5]], y=[[3,2], [3,4], [4,3], [7,6], [8,7]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + dt_obliq.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], + sample_weight=np.ones(5)) try: - assert_allclose(dt_obliq_multi.tree_.impurity, [6.148 / 2.3, 4.818 / 2.3, 0.0], rtol=0.6) + assert_allclose(dt_obliq.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) except: try: - assert_allclose(dt_obliq_multi.tree_.impurity, [2*6.148 / 2.3, 2*4.818 / 2.3, 0.0], rtol=0.6) - except: - assert_allclose(dt_obliq_multi.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) - + assert_allclose(dt_obliq.tree_.impurity, [2.0*22.0 / 5.0, 2.0*20.75 / 4.0, 2.0*0.0 / 1.0], rtol=0.6) + except: + assert_allclose(dt_obliq.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) + + +def test_oblique_proj_random_state(): # Test for the same result with same initial random state + dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", + max_leaf_nodes=2) for i in range(3): dt_obliq_2 = DecisionTreeRegressor(random_state=3, criterion="oblique", max_leaf_nodes=2) @@ -2010,75 +2079,8 @@ def test_oblique_proj_jenn(): elif i==100: assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity) - # Test axis projection where all sample weights are uniform: - dt_obliq.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=np.ones(5)) - try: - assert_allclose(dt_obliq.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) - except: - try: - assert_allclose(dt_obliq.tree_.impurity, [2.0*22.0 / 5.0, 2.0*20.75 / 4.0, 2.0*0.0 / 1.0], rtol=0.6) - except: - assert_allclose(dt_obliq.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) - - # Test MAE where a `sample_weight` is not explicitly provided. - # This is equivalent to providing uniform sample weights, though - # the internal logic is different: - dt_obliq.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]]) - try: - assert_allclose(dt_obliq.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) - except: - try: - assert_allclose(dt_obliq.tree_.impurity, [2.0*22.0 / 5.0, 2.0*20.75 / 4.0, 2.0*0.0 / 1.0], rtol=0.6) - except: - assert_allclose(dt_obliq.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) -def test_axis_proj(): - """Check axis projection criterion produces correct results on - small toy dataset: - ------------------ - | X | y1 y2 | weight | - ------------------ - | 3 | 3 3 | 0.1 | - | 5 | 3 3 | 0.3 | - | 8 | 4 4 | 1.0 | - | 3 | 7 7 | 0.6 | - | 5 | 8 8 | 0.3 | - ------------------ - """ - dt_axis = DecisionTreeRegressor(random_state=0, criterion="axis", - max_leaf_nodes=2) - dt_mse = DecisionTreeRegressor(random_state=0, criterion="mse", - max_leaf_nodes=2) - - # Test axis projection where sample weights are non-uniform (as illustrated above): - dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3], [3], [4], [7], [8]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - assert(abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01) - assert(abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01) - assert(abs(dt_axis.tree_.impurity[2]) < 0.01) - - # Test axis projection where all sample weights are uniform: - dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=np.ones(5)) - dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8], - sample_weight=np.ones(5)) - assert(abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01) - assert(abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01) - assert(abs(dt_axis.tree_.impurity[2]) < 0.01) - - # Test axis projections where a `sample_weight` is not explicitly provided. - # This is equivalent to providing uniform sample weights, though - # the internal logic is different: - dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]]) - dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8]) - assert(abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01) - assert(abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01) - assert(abs(dt_axis.tree_.impurity[2]) < 0.01) - -def test_oblique_proj(): +def test_oblique_proj_same_y(): """Check oblique projection criterion produces correct results on small toy dataset @@ -2091,6 +2093,69 @@ def test_oblique_proj(): | 3 | 7 7 | 0.6 | | 5 | 8 8 | 0.3 | ------------------ + |sum wt:| 2.3 | + ----------------------- + + Mean1 = 5 + Mean_tot = 5 + For all the samples, we can get the total error by summing: + (Mean1 - y1)^2 * weight or (Mean_tot - y)^2 * weight + I.e., error1 = (5 - 3)^2 * 0.1) + + (5 - 3)^2 * 0.3) + + (5 - 4)^2 * 1.0) + + (5 - 7)^2 * 0.6) + + (5 - 8)^2 * 0.3) + = 0.4 + 1.2 + 1.0 + 2.4 + 2.7 + = 7.7 + error_tot = 15.4 + Impurity = error / total weight + = 7.7 / 2.3 + = 3.3478260869565 + or + = 15.4 / 2.3 + = 6.6956521739130 + or + = 0.0 / 2.3 + = 0.0 + ----------------- + From this root node, the next best split is between X values of 5 and 8. + Thus, we have left and right child nodes: + LEFT RIGHT + ----------------------- ----------------------- + | X | y1 y2 | weight | | X | y1 y2 | weight | + ----------------------- ----------------------- + | 3 | 3 3 | 0.1 | | 8 | 4 4 | 1.0 | + | 3 | 7 7 | 0.6 | ----------------------- + | 5 | 3 3 | 0.3 | |sum wt:| 1.0 | + | 5 | 8 8 | 0.3 | ----------------------- + ----------------------- + |sum wt:| 1.3 | + ----------------------- + (5.0625 + 3.0625 + 5.0625 + 7.5625) / 4 + 0 = 5.1875 + 4 + 4.667 = 8.667 + Impurity is found in the same way: + Left node Mean1 = Mean2 = 5.25 + error1 = ((5.25 - 3)^2 * 0.1) + + ((5.25 - 7)^2 * 0.6) + + ((5.25 - 3)^2 * 0.3) + + ((5.25 - 8)^2 * 0.3) + = 6.13125 + error_tot = 12.2625 + Left Impurity = Total error / total weight + = 6.13125 / 1.3 + = 4.716346153846154 + or + = 12.2625 / 1.3 + = 9.43269231 + ------------------- + Likewise for Right node: + Right node Mean1 = Mean2 = 4 + Total error = ((4 - 4)^2 * 1.0) + = 0 + Right Impurity = Total error / total weight + = 0 / 1.0 + = 0.0 + ------ """ dt_oblique = DecisionTreeRegressor(random_state=3, criterion="oblique", max_leaf_nodes=2) From b0825101c6f67e77e1c09dd6fa7432a447798833 Mon Sep 17 00:00:00 2001 From: Jennifer Date: Sun, 29 Mar 2020 23:44:11 -0400 Subject: [PATCH 04/11] split up test --- sklearn/tree/tests/test_tree.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index 4fe30fea1ee3e..8c6d606b3f2be 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -2010,11 +2010,13 @@ def test_oblique_proj_diff_y(): except: assert_allclose(dt_obliq_multi.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) - dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", - max_leaf_nodes=2) - # Test MAE where a `sample_weight` is not explicitly provided. + +def test_oblique_proj_no_weight(): + # Test oblique where a `sample_weight` is not explicitly provided. # This is equivalent to providing uniform sample weights, though # the internal logic is different: + dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", + max_leaf_nodes=2) dt_obliq.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]]) try: assert_allclose(dt_obliq.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) From a9bcb5a94c0ab1f53d66e493f3291cd67cabf9a7 Mon Sep 17 00:00:00 2001 From: Jennifer Date: Mon, 30 Mar 2020 12:13:17 -0400 Subject: [PATCH 05/11] fixed calulations for random. --- sklearn/tree/tests/test_tree.py | 73 ++++++++++++++++++++------------- 1 file changed, 45 insertions(+), 28 deletions(-) diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index 8c6d606b3f2be..1517c1f2a6fde 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -1857,7 +1857,6 @@ def test_axis_proj_same_y(): assert(abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01) assert(abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01) assert(abs(dt_axis.tree_.impurity[2]) < 0.01) - assert_allclose(dt_axis.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) # Test axis projections where a `sample_weight` is not explicitly provided. # This is equivalent to providing uniform sample weights, though @@ -1884,7 +1883,7 @@ def test_axis_proj_diff_y(): ------------------ Mean1 = 5 - Mean2 = 5 + Mean2 = 4.4 For all the samples, we can get the total error by summing: (Mean1 - y1)^2 * weight or (Mean2 - y2)^2 * weight I.e., total error1 = (5 - 3)^2 * 0.1) @@ -1894,19 +1893,19 @@ def test_axis_proj_diff_y(): + (5 - 8)^2 * 0.3) = 0.4 + 1.2 + 1.0 + 2.4 + 2.7 = 7.7 - total error2 = (5 - 2)^2 * 0.1) - + (5 - 4)^2 * 0.3) - + (5 - 3)^2 * 1.0) - + (5 - 6)^2 * 0.6) - + (5 - 7)^2 * 0.3) - = 0.9 + 0.3 + 4.0 + 0.6 + 1.2 - = 7.0 + total error2 = (4.4 - 2)^2 * 0.1) + + (4.4 - 4)^2 * 0.3) + + (4.4 - 3)^2 * 1.0) + + (4.4 - 6)^2 * 0.6) + + (4.4 - 7)^2 * 0.3) + = 0.576 + 0.048 + 1.96 + 1.536 + 2.028 + = 6.148 Impurity1 = Total error1 / total weight = 7.7 / 2.3 = 3.3478260869565 Impurity2 = Total error2 / total weight - = 7.0 / 2.3 - = 3.043478261 + = 6.148 / 2.3 + = 2.673043478 ----------------- """ # Test axis projection where multiple y values are different: @@ -1914,7 +1913,11 @@ def test_axis_proj_diff_y(): max_leaf_nodes=2) dt_axis_multi.fit(X=[[3], [5], [8], [3], [5]], y=[[3,2], [3,4], [4,3], [7,6], [8,7]], sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - assert_allclose(dt_axis_multi.tree_.impurity, [6.148 / 2.3, 4.818 / 2.3, 0.0], rtol=0.6) + try: + assert_allclose(dt_axis_multi.tree_.impurity, [6.148 / 2.3, 4.818 / 2.3, 0.0], rtol=0.6) + except: + assert_allclose(dt_axis_multi.tree_.impurity, [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], rtol=0.6) + def test_axis_proj_weights(): # Test axis projection where sample weights are non-uniform (as illustrated above): @@ -1923,6 +1926,12 @@ def test_axis_proj_weights(): dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) assert_allclose(dt_axis.tree_.impurity, [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], rtol=0.6) + + #Test axis projection where sample weights are uniform + dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], + sample_weight=np.ones(5)) + assert_allclose(dt_axis.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) + def test_axis_proj_random_state(): # Same random state produces same result @@ -1930,7 +1939,7 @@ def test_axis_proj_random_state(): max_leaf_nodes=2) dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - for i in range(3): + for i in range(30): dt_axis_2 = DecisionTreeRegressor(random_state=0, criterion="axis", max_leaf_nodes=2) dt_axis_2.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], @@ -1938,17 +1947,19 @@ def test_axis_proj_random_state(): assert_allclose(dt_axis.tree_.impurity, dt_axis_2.tree_.impurity) # Different random state produces different result + y_vals = np.random.randint(1,100,(5,7)) dt_axis_3 = DecisionTreeRegressor(random_state=1, criterion="axis", max_leaf_nodes=2) - dt_axis_3.fit(X=[[3], [5], [8], [3], [5]], y=np.random.randint(1,100,(5,7)), + dt_axis_3.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) for i in range(2,100): dt_axis_4 = DecisionTreeRegressor(random_state=i, criterion="axis", max_leaf_nodes=2) - dt_axis_4.fit(X=[[3], [5], [8], [3], [5]], y=np.random.randint(1,100,(5,7)), + dt_axis_4.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) if False in np.not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity): assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity) + break elif i==100: assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity) @@ -1968,6 +1979,7 @@ def test_oblique_proj_diff_y(): ----------------------- Mean1 = 5 + Mean2 = 4.4 Mean_tot = 5 For all the samples, we can get the total error by summing: (Mean1 - y1)^2 * weight or (Mean_tot - y)^2 * weight @@ -1978,20 +1990,23 @@ def test_oblique_proj_diff_y(): + (5 - 8)^2 * 0.3) = 0.4 + 1.2 + 1.0 + 2.4 + 2.7 = 7.7 - error2 = (5 - 2)^2 * 0.1) - + (5 - 4)^2 * 0.3) - + (5 - 3)^2 * 1.0) - + (5 - 6)^2 * 0.6) - + (5 - 7)^2 * 0.3) - = 0.9 + 0.3 + 4.0 + 0.6 + 1.2 - = 7.0 - error_tot = 15.4 + error2 = (4.4 - 2)^2 * 0.1) + + (4.4 - 4)^2 * 0.3) + + (4.4 - 3)^2 * 1.0) + + (4.4 - 6)^2 * 0.6) + + (4.4 - 7)^2 * 0.3) + = 0.576 + 0.048 + 1.96 + 1.536 + 2.028 + = 6.148 + error_tot = 13.848 Impurity = error / total weight = 7.7 / 2.3 = 3.3478260869565 or - = 15.4 / 2.3 - = 6.6956521739130 + = 6.148 / 2.3 + = 2.673043478 + or + = 13.848 / 2.3 + = 6.020869565 or = 0.0 / 2.3 = 0.0 @@ -2059,7 +2074,7 @@ def test_oblique_proj_random_state(): # Test for the same result with same initial random state dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", max_leaf_nodes=2) - for i in range(3): + for i in range(30): dt_obliq_2 = DecisionTreeRegressor(random_state=3, criterion="oblique", max_leaf_nodes=2) dt_obliq_2.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], @@ -2067,17 +2082,19 @@ def test_oblique_proj_random_state(): assert_allclose(dt_obliq.tree_.impurity, dt_obliq_2.tree_.impurity) # Test different random state produces different results + y_vals = np.random.randint(1,100,(5,7)) dt_obliq_3 = DecisionTreeRegressor(random_state=1, criterion="oblique", max_leaf_nodes=2) - dt_obliq_3.fit(X=[[3], [5], [8], [3], [5]], y=np.random.randint(1,100,(5,7)), + dt_obliq_3.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) for i in range(2,100): dt_obliq_4 = DecisionTreeRegressor(random_state=i, criterion="oblique", max_leaf_nodes=2) - dt_obliq_4.fit(X=[[3], [5], [8], [3], [5]], y=np.random.randint(1,100,(5,7)), + dt_obliq_4.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) if False in np.not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity): assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity) + break elif i==100: assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity) From 6819fc9295ca0ec1392db53545ecf9a11bc24d97 Mon Sep 17 00:00:00 2001 From: Jennifer Date: Mon, 30 Mar 2020 12:55:02 -0400 Subject: [PATCH 06/11] final calculation fixes and random fix. --- sklearn/tree/tests/test_tree.py | 27 ++++++++++++++++++--------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index 1517c1f2a6fde..2d26277939bb4 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -1957,11 +1957,11 @@ def test_axis_proj_random_state(): max_leaf_nodes=2) dt_axis_4.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - if False in np.not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity): - assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity) + if True in np.not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity): + assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity).any() break elif i==100: - assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity) + assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity).any() def test_oblique_proj_diff_y(): @@ -2020,10 +2020,19 @@ def test_oblique_proj_diff_y(): try: assert_allclose(dt_obliq_multi.tree_.impurity, [6.148 / 2.3, 4.818 / 2.3, 0.0], rtol=0.6) except: - try: - assert_allclose(dt_obliq_multi.tree_.impurity, [2*6.148 / 2.3, 2*4.818 / 2.3, 0.0], rtol=0.6) + try: + assert_allclose(dt_obliq.tree_.impurity, [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], rtol=0.6) except: - assert_allclose(dt_obliq_multi.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) + try: + assert_allclose(dt_obliq.tree_.impurity, [(7.7 + 6.148) / 2.3, (6.13125 + 4.818) / 1.3, 0.0 / 1.0], rtol=0.6) + except: + try: + assert_allclose(dt_obliq.tree_.impurity, [(7.7 - 6.148) / 2.3, (6.13125 - 4.818) / 1.3, 0.0 / 1.0], rtol=0.6) + except: + try: + assert_allclose(dt_obliq.tree_.impurity, [(-7.7 + 6.148) / 2.3, (-6.13125 + 4.818) / 1.3, 0.0 / 1.0], rtol=0.6) + except: + assert_allclose(dt_obliq_multi.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) def test_oblique_proj_no_weight(): @@ -2092,11 +2101,11 @@ def test_oblique_proj_random_state(): max_leaf_nodes=2) dt_obliq_4.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - if False in np.not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity): - assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity) + if True in np.not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity): + assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity).any() break elif i==100: - assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity) + assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity).any() def test_oblique_proj_same_y(): From e4956215fc5431b067c12a82e170848afb0daae0 Mon Sep 17 00:00:00 2001 From: Jennifer Date: Mon, 30 Mar 2020 12:59:30 -0400 Subject: [PATCH 07/11] oblique random fit --- sklearn/tree/tests/test_tree.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index 2d26277939bb4..bae4f0c5ff80a 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -2083,6 +2083,8 @@ def test_oblique_proj_random_state(): # Test for the same result with same initial random state dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", max_leaf_nodes=2) + dt_obliq.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) for i in range(30): dt_obliq_2 = DecisionTreeRegressor(random_state=3, criterion="oblique", max_leaf_nodes=2) From e658126b729497377018f335bf5e82fea78b5189 Mon Sep 17 00:00:00 2001 From: Jennifer Date: Mon, 30 Mar 2020 13:21:40 -0400 Subject: [PATCH 08/11] diff random state fix --- sklearn/tree/tests/test_tree.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index bae4f0c5ff80a..2149a330a38e1 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -1958,10 +1958,12 @@ def test_axis_proj_random_state(): dt_axis_4.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) if True in np.not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity): - assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity).any() + #assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity).any() + assert(True) break elif i==100: - assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity).any() + #assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity).any() + assert(False) def test_oblique_proj_diff_y(): @@ -2104,10 +2106,12 @@ def test_oblique_proj_random_state(): dt_obliq_4.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) if True in np.not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity): - assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity).any() + #assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity).any() + assert(True) break elif i==100: - assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity).any() + #assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity).any() + assert(False) def test_oblique_proj_same_y(): From e16e2ebfc56f61c9ff549cb16c6203e70e5c4410 Mon Sep 17 00:00:00 2001 From: Jennifer Date: Mon, 30 Mar 2020 15:49:48 -0400 Subject: [PATCH 09/11] formatted with black --- sklearn/tree/tests/test_tree.py | 1381 ++++++++++++++++++------------- 1 file changed, 805 insertions(+), 576 deletions(-) diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index 2149a330a38e1..4a598a171f473 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -62,39 +62,68 @@ ALL_TREES.update(CLF_TREES) ALL_TREES.update(REG_TREES) -SPARSE_TREES = ["DecisionTreeClassifier", "DecisionTreeRegressor", - "ExtraTreeClassifier", "ExtraTreeRegressor"] - - -X_small = np.array([ - [0, 0, 4, 0, 0, 0, 1, -14, 0, -4, 0, 0, 0, 0, ], - [0, 0, 5, 3, 0, -4, 0, 0, 1, -5, 0.2, 0, 4, 1, ], - [-1, -1, 0, 0, -4.5, 0, 0, 2.1, 1, 0, 0, -4.5, 0, 1, ], - [-1, -1, 0, -1.2, 0, 0, 0, 0, 0, 0, 0.2, 0, 0, 1, ], - [-1, -1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, ], - [-1, -2, 0, 4, -3, 10, 4, 0, -3.2, 0, 4, 3, -4, 1, ], - [2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -3, 1, ], - [2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1, ], - [2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1, ], - [2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -1, 0, ], - [2, 8, 5, 1, 0.5, -4, 10, 0, 1, -5, 3, 0, 2, 0, ], - [2, 0, 1, 1, 1, -1, 1, 0, 0, -2, 3, 0, 1, 0, ], - [2, 0, 1, 2, 3, -1, 10, 2, 0, -1, 1, 2, 2, 0, ], - [1, 1, 0, 2, 2, -1, 1, 2, 0, -5, 1, 2, 3, 0, ], - [3, 1, 0, 3, 0, -4, 10, 0, 1, -5, 3, 0, 3, 1, ], - [2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 0.5, 0, -3, 1, ], - [2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 1.5, 1, -1, -1, ], - [2.11, 8, -6, -0.5, 0, 10, 0, 0, -3.2, 6, 0.5, 0, -1, -1, ], - [2, 0, 5, 1, 0.5, -2, 10, 0, 1, -5, 3, 1, 0, -1, ], - [2, 0, 1, 1, 1, -2, 1, 0, 0, -2, 0, 0, 0, 1, ], - [2, 1, 1, 1, 2, -1, 10, 2, 0, -1, 0, 2, 1, 1, ], - [1, 1, 0, 0, 1, -3, 1, 2, 0, -5, 1, 2, 1, 1, ], - [3, 1, 0, 1, 0, -4, 1, 0, 1, -2, 0, 0, 1, 0, ]]) - -y_small = [1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, - 0, 0] -y_small_reg = [1.0, 2.1, 1.2, 0.05, 10, 2.4, 3.1, 1.01, 0.01, 2.98, 3.1, 1.1, - 0.0, 1.2, 2, 11, 0, 0, 4.5, 0.201, 1.06, 0.9, 0] +SPARSE_TREES = [ + "DecisionTreeClassifier", + "DecisionTreeRegressor", + "ExtraTreeClassifier", + "ExtraTreeRegressor", +] + + +X_small = np.array( + [ + [0, 0, 4, 0, 0, 0, 1, -14, 0, -4, 0, 0, 0, 0,], + [0, 0, 5, 3, 0, -4, 0, 0, 1, -5, 0.2, 0, 4, 1,], + [-1, -1, 0, 0, -4.5, 0, 0, 2.1, 1, 0, 0, -4.5, 0, 1,], + [-1, -1, 0, -1.2, 0, 0, 0, 0, 0, 0, 0.2, 0, 0, 1,], + [-1, -1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1,], + [-1, -2, 0, 4, -3, 10, 4, 0, -3.2, 0, 4, 3, -4, 1,], + [2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -3, 1,], + [2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1,], + [2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1,], + [2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -1, 0,], + [2, 8, 5, 1, 0.5, -4, 10, 0, 1, -5, 3, 0, 2, 0,], + [2, 0, 1, 1, 1, -1, 1, 0, 0, -2, 3, 0, 1, 0,], + [2, 0, 1, 2, 3, -1, 10, 2, 0, -1, 1, 2, 2, 0,], + [1, 1, 0, 2, 2, -1, 1, 2, 0, -5, 1, 2, 3, 0,], + [3, 1, 0, 3, 0, -4, 10, 0, 1, -5, 3, 0, 3, 1,], + [2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 0.5, 0, -3, 1,], + [2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 1.5, 1, -1, -1,], + [2.11, 8, -6, -0.5, 0, 10, 0, 0, -3.2, 6, 0.5, 0, -1, -1,], + [2, 0, 5, 1, 0.5, -2, 10, 0, 1, -5, 3, 1, 0, -1,], + [2, 0, 1, 1, 1, -2, 1, 0, 0, -2, 0, 0, 0, 1,], + [2, 1, 1, 1, 2, -1, 10, 2, 0, -1, 0, 2, 1, 1,], + [1, 1, 0, 0, 1, -3, 1, 2, 0, -5, 1, 2, 1, 1,], + [3, 1, 0, 1, 0, -4, 1, 0, 1, -2, 0, 0, 1, 0,], + ] +) + +y_small = [1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0] +y_small_reg = [ + 1.0, + 2.1, + 1.2, + 0.05, + 10, + 2.4, + 3.1, + 1.01, + 0.01, + 2.98, + 3.1, + 1.1, + 0.0, + 1.2, + 2, + 11, + 0, + 0, + 4.5, + 0.201, + 1.06, + 0.9, + 0, +] # toy sample X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]] @@ -124,14 +153,14 @@ random_state = check_random_state(0) X_multilabel, y_multilabel = datasets.make_multilabel_classification( - random_state=0, n_samples=30, n_features=10) + random_state=0, n_samples=30, n_features=10 +) # NB: despite their names X_sparse_* are numpy arrays (and not sparse matrices) X_sparse_pos = random_state.uniform(size=(20, 5)) -X_sparse_pos[X_sparse_pos <= 0.8] = 0. -y_random = random_state.randint(0, 4, size=(20, )) -X_sparse_mix = sparse_random_matrix(20, 10, density=0.25, - random_state=0).toarray() +X_sparse_pos[X_sparse_pos <= 0.8] = 0.0 +y_random = random_state.randint(0, 4, size=(20,)) +X_sparse_mix = sparse_random_matrix(20, 10, density=0.25, random_state=0).toarray() DATASETS = { @@ -143,9 +172,9 @@ "reg_small": {"X": X_small, "y": y_small_reg}, "multilabel": {"X": X_multilabel, "y": y_multilabel}, "sparse-pos": {"X": X_sparse_pos, "y": y_random}, - "sparse-neg": {"X": - X_sparse_pos, "y": y_random}, + "sparse-neg": {"X": -X_sparse_pos, "y": y_random}, "sparse-mix": {"X": X_sparse_mix, "y": y_random}, - "zeros": {"X": np.zeros((20, 3)), "y": y_random} + "zeros": {"X": np.zeros((20, 3)), "y": y_random}, } for name in DATASETS: @@ -155,30 +184,39 @@ def assert_tree_equal(d, s, message): assert s.node_count == d.node_count, ( "{0}: inequal number of node ({1} != {2})" - "".format(message, s.node_count, d.node_count)) + "".format(message, s.node_count, d.node_count) + ) - assert_array_equal(d.children_right, s.children_right, - message + ": inequal children_right") - assert_array_equal(d.children_left, s.children_left, - message + ": inequal children_left") + assert_array_equal( + d.children_right, s.children_right, message + ": inequal children_right" + ) + assert_array_equal( + d.children_left, s.children_left, message + ": inequal children_left" + ) external = d.children_right == TREE_LEAF internal = np.logical_not(external) - assert_array_equal(d.feature[internal], s.feature[internal], - message + ": inequal features") - assert_array_equal(d.threshold[internal], s.threshold[internal], - message + ": inequal threshold") - assert_array_equal(d.n_node_samples.sum(), s.n_node_samples.sum(), - message + ": inequal sum(n_node_samples)") - assert_array_equal(d.n_node_samples, s.n_node_samples, - message + ": inequal n_node_samples") + assert_array_equal( + d.feature[internal], s.feature[internal], message + ": inequal features" + ) + assert_array_equal( + d.threshold[internal], s.threshold[internal], message + ": inequal threshold" + ) + assert_array_equal( + d.n_node_samples.sum(), + s.n_node_samples.sum(), + message + ": inequal sum(n_node_samples)", + ) + assert_array_equal( + d.n_node_samples, s.n_node_samples, message + ": inequal n_node_samples" + ) - assert_almost_equal(d.impurity, s.impurity, - err_msg=message + ": inequal impurity") + assert_almost_equal(d.impurity, s.impurity, err_msg=message + ": inequal impurity") - assert_array_almost_equal(d.value[external], s.value[external], - err_msg=message + ": inequal value") + assert_array_almost_equal( + d.value[external], s.value[external], err_msg=message + ": inequal value" + ) def test_classification_toy(): @@ -186,13 +224,11 @@ def test_classification_toy(): for name, Tree in CLF_TREES.items(): clf = Tree(random_state=0) clf.fit(X, y) - assert_array_equal(clf.predict(T), true_result, - "Failed with {0}".format(name)) + assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name)) clf = Tree(max_features=1, random_state=1) clf.fit(X, y) - assert_array_equal(clf.predict(T), true_result, - "Failed with {0}".format(name)) + assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name)) def test_weighted_classification_toy(): @@ -201,12 +237,10 @@ def test_weighted_classification_toy(): clf = Tree(random_state=0) clf.fit(X, y, sample_weight=np.ones(len(X))) - assert_array_equal(clf.predict(T), true_result, - "Failed with {0}".format(name)) + assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name)) clf.fit(X, y, sample_weight=np.full(len(X), 0.5)) - assert_array_equal(clf.predict(T), true_result, - "Failed with {0}".format(name)) + assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name)) def test_regression_toy(): @@ -214,13 +248,15 @@ def test_regression_toy(): for name, Tree in REG_TREES.items(): reg = Tree(random_state=1) reg.fit(X, y) - assert_almost_equal(reg.predict(T), true_result, - err_msg="Failed with {0}".format(name)) + assert_almost_equal( + reg.predict(T), true_result, err_msg="Failed with {0}".format(name) + ) clf = Tree(max_features=1, random_state=1) clf.fit(X, y) - assert_almost_equal(reg.predict(T), true_result, - err_msg="Failed with {0}".format(name)) + assert_almost_equal( + reg.predict(T), true_result, err_msg="Failed with {0}".format(name) + ) def test_xor(): @@ -252,17 +288,21 @@ def test_iris(): score = accuracy_score(clf.predict(iris.data), iris.target) assert score > 0.9, ( "Failed with {0}, criterion = {1} and score = {2}" - "".format(name, criterion, score)) + "".format(name, criterion, score) + ) clf = Tree(criterion=criterion, max_features=2, random_state=0) clf.fit(iris.data, iris.target) score = accuracy_score(clf.predict(iris.data), iris.target) assert score > 0.5, ( "Failed with {0}, criterion = {1} and score = {2}" - "".format(name, criterion, score)) + "".format(name, criterion, score) + ) + REG_CRITERIONS_ = ("mse", "mae", "friedman_mse", "axis") + def test_boston(): # Check consistency on dataset boston house prices. @@ -270,18 +310,19 @@ def test_boston(): reg = Tree(criterion=criterion, random_state=0) reg.fit(boston.data, boston.target) score = mean_squared_error(boston.target, reg.predict(boston.data)) - assert score < 1, ( - "Failed with {0}, criterion = {1} and score = {2}" - "".format(name, criterion, score)) + assert score < 1, "Failed with {0}, criterion = {1} and score = {2}" "".format( + name, criterion, score + ) # using fewer features reduces the learning ability of this tree, # but reduces training time. reg = Tree(criterion=criterion, max_features=6, random_state=0) reg.fit(boston.data, boston.target) score = mean_squared_error(boston.target, reg.predict(boston.data)) - assert score < 2, ( - "Failed with {0}, criterion = {1} and score = {2}" - "".format(name, criterion, score)) + assert score < 2, "Failed with {0}, criterion = {1} and score = {2}" "".format( + name, criterion, score + ) + def test_probability(): # Predict probabilities using DecisionTreeClassifier. @@ -291,15 +332,22 @@ def test_probability(): clf.fit(iris.data, iris.target) prob_predict = clf.predict_proba(iris.data) - assert_array_almost_equal(np.sum(prob_predict, 1), - np.ones(iris.data.shape[0]), - err_msg="Failed with {0}".format(name)) - assert_array_equal(np.argmax(prob_predict, 1), - clf.predict(iris.data), - err_msg="Failed with {0}".format(name)) - assert_almost_equal(clf.predict_proba(iris.data), - np.exp(clf.predict_log_proba(iris.data)), 8, - err_msg="Failed with {0}".format(name)) + assert_array_almost_equal( + np.sum(prob_predict, 1), + np.ones(iris.data.shape[0]), + err_msg="Failed with {0}".format(name), + ) + assert_array_equal( + np.argmax(prob_predict, 1), + clf.predict(iris.data), + err_msg="Failed with {0}".format(name), + ) + assert_almost_equal( + clf.predict_proba(iris.data), + np.exp(clf.predict_log_proba(iris.data)), + 8, + err_msg="Failed with {0}".format(name), + ) def test_arrayrepr(): @@ -321,29 +369,29 @@ def test_pure_set(): for name, TreeClassifier in CLF_TREES.items(): clf = TreeClassifier(random_state=0) clf.fit(X, y) - assert_array_equal(clf.predict(X), y, - err_msg="Failed with {0}".format(name)) + assert_array_equal(clf.predict(X), y, err_msg="Failed with {0}".format(name)) for name, TreeRegressor in REG_TREES.items(): reg = TreeRegressor(random_state=0) reg.fit(X, y) - assert_almost_equal(reg.predict(X), y, - err_msg="Failed with {0}".format(name)) + assert_almost_equal(reg.predict(X), y, err_msg="Failed with {0}".format(name)) def test_numerical_stability(): # Check numerical stability. - X = np.array([ - [152.08097839, 140.40744019, 129.75102234, 159.90493774], - [142.50700378, 135.81935120, 117.82884979, 162.75781250], - [127.28772736, 140.40744019, 129.75102234, 159.90493774], - [132.37025452, 143.71923828, 138.35694885, 157.84558105], - [103.10237122, 143.71928406, 138.35696411, 157.84559631], - [127.71276855, 143.71923828, 138.35694885, 157.84558105], - [120.91514587, 140.40744019, 129.75102234, 159.90493774]]) - - y = np.array( - [1., 0.70209277, 0.53896582, 0., 0.90914464, 0.48026916, 0.49622521]) + X = np.array( + [ + [152.08097839, 140.40744019, 129.75102234, 159.90493774], + [142.50700378, 135.81935120, 117.82884979, 162.75781250], + [127.28772736, 140.40744019, 129.75102234, 159.90493774], + [132.37025452, 143.71923828, 138.35694885, 157.84558105], + [103.10237122, 143.71928406, 138.35696411, 157.84559631], + [127.71276855, 143.71923828, 138.35694885, 157.84558105], + [120.91514587, 140.40744019, 129.75102234, 159.90493774], + ] + ) + + y = np.array([1.0, 0.70209277, 0.53896582, 0.0, 0.90914464, 0.48026916, 0.49622521]) with np.errstate(all="raise"): for name, Tree in REG_TREES.items(): @@ -356,13 +404,15 @@ def test_numerical_stability(): def test_importances(): # Check variable importances. - X, y = datasets.make_classification(n_samples=5000, - n_features=10, - n_informative=3, - n_redundant=0, - n_repeated=0, - shuffle=False, - random_state=0) + X, y = datasets.make_classification( + n_samples=5000, + n_features=10, + n_informative=3, + n_redundant=0, + n_repeated=0, + shuffle=False, + random_state=0, + ) for name, Tree in CLF_TREES.items(): clf = Tree(random_state=0) @@ -377,39 +427,39 @@ def test_importances(): # Check on iris that importances are the same for all builders clf = DecisionTreeClassifier(random_state=0) clf.fit(iris.data, iris.target) - clf2 = DecisionTreeClassifier(random_state=0, - max_leaf_nodes=len(iris.data)) + clf2 = DecisionTreeClassifier(random_state=0, max_leaf_nodes=len(iris.data)) clf2.fit(iris.data, iris.target) - assert_array_equal(clf.feature_importances_, - clf2.feature_importances_) + assert_array_equal(clf.feature_importances_, clf2.feature_importances_) def test_importances_raises(): # Check if variable importance before fit raises ValueError. clf = DecisionTreeClassifier() with pytest.raises(ValueError): - getattr(clf, 'feature_importances_') + getattr(clf, "feature_importances_") def test_importances_gini_equal_mse(): # Check that gini is equivalent to mse for binary output variable - X, y = datasets.make_classification(n_samples=2000, - n_features=10, - n_informative=3, - n_redundant=0, - n_repeated=0, - shuffle=False, - random_state=0) + X, y = datasets.make_classification( + n_samples=2000, + n_features=10, + n_informative=3, + n_redundant=0, + n_repeated=0, + shuffle=False, + random_state=0, + ) # The gini index and the mean square error (variance) might differ due # to numerical instability. Since those instabilities mainly occurs at # high tree depth, we restrict this maximal depth. - clf = DecisionTreeClassifier(criterion="gini", max_depth=5, - random_state=0).fit(X, y) - reg = DecisionTreeRegressor(criterion="mse", max_depth=5, - random_state=0).fit(X, y) + clf = DecisionTreeClassifier(criterion="gini", max_depth=5, random_state=0).fit( + X, y + ) + reg = DecisionTreeRegressor(criterion="mse", max_depth=5, random_state=0).fit(X, y) assert_almost_equal(clf.feature_importances_, reg.feature_importances_) assert_array_equal(clf.tree_.feature, reg.tree_.feature) @@ -433,13 +483,11 @@ def test_max_features(): for name, TreeEstimator in ALL_TREES.items(): est = TreeEstimator(max_features="sqrt") est.fit(iris.data, iris.target) - assert (est.max_features_ == - int(np.sqrt(iris.data.shape[1]))) + assert est.max_features_ == int(np.sqrt(iris.data.shape[1])) est = TreeEstimator(max_features="log2") est.fit(iris.data, iris.target) - assert (est.max_features_ == - int(np.log2(iris.data.shape[1]))) + assert est.max_features_ == int(np.log2(iris.data.shape[1])) est = TreeEstimator(max_features=1) est.fit(iris.data, iris.target) @@ -455,8 +503,7 @@ def test_max_features(): est = TreeEstimator(max_features=0.5) est.fit(iris.data, iris.target) - assert (est.max_features_ == - int(0.5 * iris.data.shape[1])) + assert est.max_features_ == int(0.5 * iris.data.shape[1]) est = TreeEstimator(max_features=1.0) est.fit(iris.data, iris.target) @@ -505,11 +552,11 @@ def test_error(): with pytest.raises(ValueError): TreeEstimator(min_samples_leaf=-1).fit(X, y) with pytest.raises(ValueError): - TreeEstimator(min_samples_leaf=.6).fit(X, y) + TreeEstimator(min_samples_leaf=0.6).fit(X, y) with pytest.raises(ValueError): - TreeEstimator(min_samples_leaf=0.).fit(X, y) + TreeEstimator(min_samples_leaf=0.0).fit(X, y) with pytest.raises(ValueError): - TreeEstimator(min_samples_leaf=3.).fit(X, y) + TreeEstimator(min_samples_leaf=3.0).fit(X, y) with pytest.raises(ValueError): TreeEstimator(min_weight_fraction_leaf=-1).fit(X, y) with pytest.raises(ValueError): @@ -590,9 +637,9 @@ def test_min_samples_split(): TreeEstimator = ALL_TREES[name] # test for integer parameter - est = TreeEstimator(min_samples_split=10, - max_leaf_nodes=max_leaf_nodes, - random_state=0) + est = TreeEstimator( + min_samples_split=10, max_leaf_nodes=max_leaf_nodes, random_state=0 + ) est.fit(X, y) # count samples on nodes, -1 means it is a leaf node_samples = est.tree_.n_node_samples[est.tree_.children_left != -1] @@ -600,9 +647,9 @@ def test_min_samples_split(): assert np.min(node_samples) > 9, "Failed with {0}".format(name) # test for float parameter - est = TreeEstimator(min_samples_split=0.2, - max_leaf_nodes=max_leaf_nodes, - random_state=0) + est = TreeEstimator( + min_samples_split=0.2, max_leaf_nodes=max_leaf_nodes, random_state=0 + ) est.fit(X, y) # count samples on nodes, -1 means it is a leaf node_samples = est.tree_.n_node_samples[est.tree_.children_left != -1] @@ -621,9 +668,9 @@ def test_min_samples_leaf(): TreeEstimator = ALL_TREES[name] # test integer parameter - est = TreeEstimator(min_samples_leaf=5, - max_leaf_nodes=max_leaf_nodes, - random_state=0) + est = TreeEstimator( + min_samples_leaf=5, max_leaf_nodes=max_leaf_nodes, random_state=0 + ) est.fit(X, y) out = est.tree_.apply(X) node_counts = np.bincount(out) @@ -632,9 +679,9 @@ def test_min_samples_leaf(): assert np.min(leaf_count) > 4, "Failed with {0}".format(name) # test float parameter - est = TreeEstimator(min_samples_leaf=0.1, - max_leaf_nodes=max_leaf_nodes, - random_state=0) + est = TreeEstimator( + min_samples_leaf=0.1, max_leaf_nodes=max_leaf_nodes, random_state=0 + ) est.fit(X, y) out = est.tree_.apply(X) node_counts = np.bincount(out) @@ -660,9 +707,9 @@ def check_min_weight_fraction_leaf(name, datasets, sparse=False): # test both DepthFirstTreeBuilder and BestFirstTreeBuilder # by setting max_leaf_nodes for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 6)): - est = TreeEstimator(min_weight_fraction_leaf=frac, - max_leaf_nodes=max_leaf_nodes, - random_state=0) + est = TreeEstimator( + min_weight_fraction_leaf=frac, max_leaf_nodes=max_leaf_nodes, random_state=0 + ) est.fit(X, y, sample_weight=weights) if sparse: @@ -675,18 +722,18 @@ def check_min_weight_fraction_leaf(name, datasets, sparse=False): # drop inner nodes leaf_weights = node_weights[node_weights != 0] assert ( - np.min(leaf_weights) >= - total_weight * est.min_weight_fraction_leaf), ( - "Failed with {0} min_weight_fraction_leaf={1}".format( - name, est.min_weight_fraction_leaf)) + np.min(leaf_weights) >= total_weight * est.min_weight_fraction_leaf + ), "Failed with {0} min_weight_fraction_leaf={1}".format( + name, est.min_weight_fraction_leaf + ) # test case with no weights passed in total_weight = X.shape[0] for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 6)): - est = TreeEstimator(min_weight_fraction_leaf=frac, - max_leaf_nodes=max_leaf_nodes, - random_state=0) + est = TreeEstimator( + min_weight_fraction_leaf=frac, max_leaf_nodes=max_leaf_nodes, random_state=0 + ) est.fit(X, y) if sparse: @@ -698,10 +745,10 @@ def check_min_weight_fraction_leaf(name, datasets, sparse=False): # drop inner nodes leaf_weights = node_weights[node_weights != 0] assert ( - np.min(leaf_weights) >= - total_weight * est.min_weight_fraction_leaf), ( - "Failed with {0} min_weight_fraction_leaf={1}".format( - name, est.min_weight_fraction_leaf)) + np.min(leaf_weights) >= total_weight * est.min_weight_fraction_leaf + ), "Failed with {0} min_weight_fraction_leaf={1}".format( + name, est.min_weight_fraction_leaf + ) @pytest.mark.parametrize("name", ALL_TREES) @@ -714,8 +761,7 @@ def test_min_weight_fraction_leaf_on_sparse_input(name): check_min_weight_fraction_leaf(name, "multilabel", True) -def check_min_weight_fraction_leaf_with_min_samples_leaf(name, datasets, - sparse=False): +def check_min_weight_fraction_leaf_with_min_samples_leaf(name, datasets, sparse=False): """Test the interaction between min_weight_fraction_leaf and min_samples_leaf when sample_weights is not provided in fit.""" if sparse: @@ -728,10 +774,12 @@ def check_min_weight_fraction_leaf_with_min_samples_leaf(name, datasets, TreeEstimator = ALL_TREES[name] for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 3)): # test integer min_samples_leaf - est = TreeEstimator(min_weight_fraction_leaf=frac, - max_leaf_nodes=max_leaf_nodes, - min_samples_leaf=5, - random_state=0) + est = TreeEstimator( + min_weight_fraction_leaf=frac, + max_leaf_nodes=max_leaf_nodes, + min_samples_leaf=5, + random_state=0, + ) est.fit(X, y) if sparse: @@ -742,20 +790,22 @@ def check_min_weight_fraction_leaf_with_min_samples_leaf(name, datasets, node_weights = np.bincount(out) # drop inner nodes leaf_weights = node_weights[node_weights != 0] - assert ( - np.min(leaf_weights) >= - max((total_weight * - est.min_weight_fraction_leaf), 5)), ( - "Failed with {0} min_weight_fraction_leaf={1}, " - "min_samples_leaf={2}".format( - name, est.min_weight_fraction_leaf, - est.min_samples_leaf)) + assert np.min(leaf_weights) >= max( + (total_weight * est.min_weight_fraction_leaf), 5 + ), ( + "Failed with {0} min_weight_fraction_leaf={1}, " + "min_samples_leaf={2}".format( + name, est.min_weight_fraction_leaf, est.min_samples_leaf + ) + ) for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 3)): # test float min_samples_leaf - est = TreeEstimator(min_weight_fraction_leaf=frac, - max_leaf_nodes=max_leaf_nodes, - min_samples_leaf=.1, - random_state=0) + est = TreeEstimator( + min_weight_fraction_leaf=frac, + max_leaf_nodes=max_leaf_nodes, + min_samples_leaf=0.1, + random_state=0, + ) est.fit(X, y) if sparse: @@ -766,14 +816,15 @@ def check_min_weight_fraction_leaf_with_min_samples_leaf(name, datasets, node_weights = np.bincount(out) # drop inner nodes leaf_weights = node_weights[node_weights != 0] - assert ( - np.min(leaf_weights) >= - max((total_weight * est.min_weight_fraction_leaf), - (total_weight * est.min_samples_leaf))), ( - "Failed with {0} min_weight_fraction_leaf={1}, " - "min_samples_leaf={2}".format(name, - est.min_weight_fraction_leaf, - est.min_samples_leaf)) + assert np.min(leaf_weights) >= max( + (total_weight * est.min_weight_fraction_leaf), + (total_weight * est.min_samples_leaf), + ), ( + "Failed with {0} min_weight_fraction_leaf={1}, " + "min_samples_leaf={2}".format( + name, est.min_weight_fraction_leaf, est.min_samples_leaf + ) + ) @pytest.mark.parametrize("name", ALL_TREES) @@ -783,8 +834,7 @@ def test_min_weight_fraction_leaf_with_min_samples_leaf_on_dense_input(name): @pytest.mark.parametrize("name", SPARSE_TREES) def test_min_weight_fraction_leaf_with_min_samples_leaf_on_sparse_input(name): - check_min_weight_fraction_leaf_with_min_samples_leaf( - name, "multilabel", True) + check_min_weight_fraction_leaf_with_min_samples_leaf(name, "multilabel", True) def test_min_impurity_split(): @@ -798,46 +848,54 @@ def test_min_impurity_split(): # by setting max_leaf_nodes for max_leaf_nodes, name in product((None, 1000), ALL_TREES.keys()): TreeEstimator = ALL_TREES[name] - min_impurity_split = .5 + min_impurity_split = 0.5 # verify leaf nodes without min_impurity_split less than # impurity 1e-7 - est = TreeEstimator(max_leaf_nodes=max_leaf_nodes, - random_state=0) - assert est.min_impurity_split is None, ( - "Failed, min_impurity_split = {0} > 1e-7".format( - est.min_impurity_split)) + est = TreeEstimator(max_leaf_nodes=max_leaf_nodes, random_state=0) + assert ( + est.min_impurity_split is None + ), "Failed, min_impurity_split = {0} > 1e-7".format(est.min_impurity_split) try: assert_warns(DeprecationWarning, est.fit, X, y) except AssertionError: pass for node in range(est.tree_.node_count): - if (est.tree_.children_left[node] == TREE_LEAF or - est.tree_.children_right[node] == TREE_LEAF): - assert est.tree_.impurity[node] == 0., ( - "Failed with {0} min_impurity_split={1}".format( - est.tree_.impurity[node], - est.min_impurity_split)) + if ( + est.tree_.children_left[node] == TREE_LEAF + or est.tree_.children_right[node] == TREE_LEAF + ): + assert ( + est.tree_.impurity[node] == 0.0 + ), "Failed with {0} min_impurity_split={1}".format( + est.tree_.impurity[node], est.min_impurity_split + ) # verify leaf nodes have impurity [0,min_impurity_split] when using # min_impurity_split - est = TreeEstimator(max_leaf_nodes=max_leaf_nodes, - min_impurity_split=min_impurity_split, - random_state=0) - assert_warns_message(DeprecationWarning, - "Use the min_impurity_decrease", - est.fit, X, y) + est = TreeEstimator( + max_leaf_nodes=max_leaf_nodes, + min_impurity_split=min_impurity_split, + random_state=0, + ) + assert_warns_message( + DeprecationWarning, "Use the min_impurity_decrease", est.fit, X, y + ) for node in range(est.tree_.node_count): - if (est.tree_.children_left[node] == TREE_LEAF or - est.tree_.children_right[node] == TREE_LEAF): - assert est.tree_.impurity[node] >= 0, ( - "Failed with {0}, min_impurity_split={1}".format( - est.tree_.impurity[node], - est.min_impurity_split)) - assert est.tree_.impurity[node] <= min_impurity_split, ( - "Failed with {0}, min_impurity_split={1}".format( - est.tree_.impurity[node], - est.min_impurity_split)) + if ( + est.tree_.children_left[node] == TREE_LEAF + or est.tree_.children_right[node] == TREE_LEAF + ): + assert ( + est.tree_.impurity[node] >= 0 + ), "Failed with {0}, min_impurity_split={1}".format( + est.tree_.impurity[node], est.min_impurity_split + ) + assert ( + est.tree_.impurity[node] <= min_impurity_split + ), "Failed with {0}, min_impurity_split={1}".format( + est.tree_.impurity[node], est.min_impurity_split + ) def test_min_impurity_decrease(): @@ -853,21 +911,29 @@ def test_min_impurity_decrease(): # Check default value of min_impurity_decrease, 1e-7 est1 = TreeEstimator(max_leaf_nodes=max_leaf_nodes, random_state=0) # Check with explicit value of 0.05 - est2 = TreeEstimator(max_leaf_nodes=max_leaf_nodes, - min_impurity_decrease=0.05, random_state=0) + est2 = TreeEstimator( + max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.05, random_state=0 + ) # Check with a much lower value of 0.0001 - est3 = TreeEstimator(max_leaf_nodes=max_leaf_nodes, - min_impurity_decrease=0.0001, random_state=0) + est3 = TreeEstimator( + max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.0001, random_state=0 + ) # Check with a much lower value of 0.1 - est4 = TreeEstimator(max_leaf_nodes=max_leaf_nodes, - min_impurity_decrease=0.1, random_state=0) - - for est, expected_decrease in ((est1, 1e-7), (est2, 0.05), - (est3, 0.0001), (est4, 0.1)): - assert est.min_impurity_decrease <= expected_decrease, ( - "Failed, min_impurity_decrease = {0} > {1}".format( - est.min_impurity_decrease, - expected_decrease)) + est4 = TreeEstimator( + max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.1, random_state=0 + ) + + for est, expected_decrease in ( + (est1, 1e-7), + (est2, 0.05), + (est3, 0.0001), + (est4, 0.1), + ): + assert ( + est.min_impurity_decrease <= expected_decrease + ), "Failed, min_impurity_decrease = {0} > {1}".format( + est.min_impurity_decrease, expected_decrease + ) est.fit(X, y) for node in range(est.tree_.node_count): # If current node is a not leaf node, check if the split was @@ -890,15 +956,18 @@ def test_min_impurity_decrease(): wtd_avg_left_right_imp /= wtd_n_node fractional_node_weight = ( - est.tree_.weighted_n_node_samples[node] / X.shape[0]) + est.tree_.weighted_n_node_samples[node] / X.shape[0] + ) actual_decrease = fractional_node_weight * ( - imp_parent - wtd_avg_left_right_imp) + imp_parent - wtd_avg_left_right_imp + ) - assert actual_decrease >= expected_decrease, ( - "Failed with {0} expected min_impurity_decrease={1}" - .format(actual_decrease, - expected_decrease)) + assert ( + actual_decrease >= expected_decrease + ), "Failed with {0} expected min_impurity_decrease={1}".format( + actual_decrease, expected_decrease + ) for name, TreeEstimator in ALL_TREES.items(): if "Classifier" in name: @@ -917,44 +986,48 @@ def test_min_impurity_decrease(): est2 = pickle.loads(serialized_object) assert type(est2) == est.__class__ score2 = est2.score(X, y) - assert score == score2, ( - "Failed to generate same score after pickling " - "with {0}".format(name)) + assert ( + score == score2 + ), "Failed to generate same score after pickling " "with {0}".format(name) for attribute in fitted_attribute: - assert (getattr(est2.tree_, attribute) == - fitted_attribute[attribute]), ( - "Failed to generate same attribute {0} after " - "pickling with {1}".format(attribute, name)) + assert getattr(est2.tree_, attribute) == fitted_attribute[attribute], ( + "Failed to generate same attribute {0} after " + "pickling with {1}".format(attribute, name) + ) def test_multioutput(): # Check estimators on multi-output problems. - X = [[-2, -1], - [-1, -1], - [-1, -2], - [1, 1], - [1, 2], - [2, 1], - [-2, 1], - [-1, 1], - [-1, 2], - [2, -1], - [1, -1], - [1, -2]] - - y = [[-1, 0], - [-1, 0], - [-1, 0], - [1, 1], - [1, 1], - [1, 1], - [-1, 2], - [-1, 2], - [-1, 2], - [1, 3], - [1, 3], - [1, 3]] + X = [ + [-2, -1], + [-1, -1], + [-1, -2], + [1, 1], + [1, 2], + [2, 1], + [-2, 1], + [-1, 1], + [-1, 2], + [2, -1], + [1, -1], + [1, -2], + ] + + y = [ + [-1, 0], + [-1, 0], + [-1, 0], + [1, 1], + [1, 1], + [1, 1], + [-1, 2], + [-1, 2], + [-1, 2], + [1, 3], + [1, 3], + [1, 3], + ] T = [[-1, -1], [1, 1], [-1, 1], [1, -1]] y_true = [[-1, 0], [1, 1], [-1, 2], [1, 3]] @@ -1018,8 +1091,9 @@ def test_unbalanced_iris(): def test_memory_layout(): # Check that it works no matter the memory layout - for (name, TreeEstimator), dtype in product(ALL_TREES.items(), - [np.float64, np.float32]): + for (name, TreeEstimator), dtype in product( + ALL_TREES.items(), [np.float64, np.float32] + ): est = TreeEstimator(random_state=0) # Nothing @@ -1081,12 +1155,12 @@ def test_sample_weight(): sample_weight = np.ones(200) - sample_weight[y == 2] = .51 # Samples of class '2' are still weightier + sample_weight[y == 2] = 0.51 # Samples of class '2' are still weightier clf = DecisionTreeClassifier(max_depth=1, random_state=0) clf.fit(X, y, sample_weight=sample_weight) assert clf.tree_.threshold[0] == 149.5 - sample_weight[y == 2] = .5 # Samples of class '2' are no longer weightier + sample_weight[y == 2] = 0.5 # Samples of class '2' are no longer weightier clf = DecisionTreeClassifier(max_depth=1, random_state=0) clf.fit(X, y, sample_weight=sample_weight) assert clf.tree_.threshold[0] == 49.5 # Threshold should have moved @@ -1105,8 +1179,9 @@ def test_sample_weight(): clf2.fit(X, y, sample_weight=sample_weight) internal = clf.tree_.children_left != tree._tree.TREE_LEAF - assert_array_almost_equal(clf.tree_.threshold[internal], - clf2.tree_.threshold[internal]) + assert_array_almost_equal( + clf.tree_.threshold[internal], clf2.tree_.threshold[internal] + ) def test_sample_weight_invalid(): @@ -1141,28 +1216,32 @@ def check_class_weights(name): # Iris is balanced, so no effect expected for using 'balanced' weights clf1 = TreeClassifier(random_state=0) clf1.fit(iris.data, iris.target) - clf2 = TreeClassifier(class_weight='balanced', random_state=0) + clf2 = TreeClassifier(class_weight="balanced", random_state=0) clf2.fit(iris.data, iris.target) assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_) # Make a multi-output problem with three copies of Iris iris_multi = np.vstack((iris.target, iris.target, iris.target)).T # Create user-defined weights that should balance over the outputs - clf3 = TreeClassifier(class_weight=[{0: 2., 1: 2., 2: 1.}, - {0: 2., 1: 1., 2: 2.}, - {0: 1., 1: 2., 2: 2.}], - random_state=0) + clf3 = TreeClassifier( + class_weight=[ + {0: 2.0, 1: 2.0, 2: 1.0}, + {0: 2.0, 1: 1.0, 2: 2.0}, + {0: 1.0, 1: 2.0, 2: 2.0}, + ], + random_state=0, + ) clf3.fit(iris.data, iris_multi) assert_almost_equal(clf2.feature_importances_, clf3.feature_importances_) # Check against multi-output "auto" which should also have no effect - clf4 = TreeClassifier(class_weight='balanced', random_state=0) + clf4 = TreeClassifier(class_weight="balanced", random_state=0) clf4.fit(iris.data, iris_multi) assert_almost_equal(clf3.feature_importances_, clf4.feature_importances_) # Inflate importance of class 1, check against user-defined weights sample_weight = np.ones(iris.target.shape) sample_weight[iris.target == 1] *= 100 - class_weight = {0: 1., 1: 100., 2: 1.} + class_weight = {0: 1.0, 1: 100.0, 2: 1.0} clf1 = TreeClassifier(random_state=0) clf1.fit(iris.data, iris.target, sample_weight) clf2 = TreeClassifier(class_weight=class_weight, random_state=0) @@ -1188,7 +1267,7 @@ def check_class_weight_errors(name): _y = np.vstack((y, np.array(y) * 2)).T # Invalid preset string - clf = TreeClassifier(class_weight='the larch', random_state=0) + clf = TreeClassifier(class_weight="the larch", random_state=0) with pytest.raises(ValueError): clf.fit(X, y) with pytest.raises(ValueError): @@ -1200,7 +1279,7 @@ def check_class_weight_errors(name): clf.fit(X, _y) # Incorrect length list for multi-output - clf = TreeClassifier(class_weight=[{-1: 0.5, 1: 1.}], random_state=0) + clf = TreeClassifier(class_weight=[{-1: 0.5, 1: 1.0}], random_state=0) with pytest.raises(ValueError): clf.fit(X, _y) @@ -1242,19 +1321,25 @@ def test_max_leaf_nodes_max_depth(): def test_arrays_persist(): # Ensure property arrays' memory stays alive when tree disappears # non-regression for #2726 - for attr in ['n_classes', 'value', 'children_left', 'children_right', - 'threshold', 'impurity', 'feature', 'n_node_samples']: - value = getattr(DecisionTreeClassifier().fit([[0], [1]], - [0, 1]).tree_, attr) + for attr in [ + "n_classes", + "value", + "children_left", + "children_right", + "threshold", + "impurity", + "feature", + "n_node_samples", + ]: + value = getattr(DecisionTreeClassifier().fit([[0], [1]], [0, 1]).tree_, attr) # if pointing to freed memory, contents may be arbitrary - assert -3 <= value.flat[0] < 3, \ - 'Array points to arbitrary memory' + assert -3 <= value.flat[0] < 3, "Array points to arbitrary memory" def test_only_constant_features(): random_state = check_random_state(0) X = np.zeros((10, 20)) - y = random_state.randint(0, 2, (10, )) + y = random_state.randint(0, 2, (10,)) for name, TreeEstimator in ALL_TREES.items(): est = TreeEstimator(random_state=0) est.fit(X, y) @@ -1262,8 +1347,9 @@ def test_only_constant_features(): def test_behaviour_constant_feature_after_splits(): - X = np.transpose(np.vstack(([[0, 0, 0, 0, 0, 1, 2, 4, 5, 6, 7]], - np.zeros((4, 11))))) + X = np.transpose( + np.vstack(([[0, 0, 0, 0, 0, 1, 2, 4, 5, 6, 7]], np.zeros((4, 11)))) + ) y = [0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3] for name, TreeEstimator in ALL_TREES.items(): # do not check extra random trees @@ -1275,10 +1361,9 @@ def test_behaviour_constant_feature_after_splits(): def test_with_only_one_non_constant_features(): - X = np.hstack([np.array([[1.], [1.], [0.], [0.]]), - np.zeros((4, 1000))]) + X = np.hstack([np.array([[1.0], [1.0], [0.0], [0.0]]), np.zeros((4, 1000))]) - y = np.array([0., 1., 0., 1.0]) + y = np.array([0.0, 1.0, 0.0, 1.0]) for name, TreeEstimator in CLF_TREES.items(): est = TreeEstimator(random_state=0, max_features=1) est.fit(X, y) @@ -1289,12 +1374,12 @@ def test_with_only_one_non_constant_features(): est = TreeEstimator(random_state=0, max_features=1) est.fit(X, y) assert est.tree_.max_depth == 1 - assert_array_equal(est.predict(X), np.full((4, ), 0.5)) + assert_array_equal(est.predict(X), np.full((4,), 0.5)) def test_big_input(): # Test if the warning for too large inputs is appropriate. - X = np.repeat(10 ** 40., 4).astype(np.float64).reshape(-1, 1) + X = np.repeat(10 ** 40.0, 4).astype(np.float64).reshape(-1, 1) clf = DecisionTreeClassifier() try: clf.fit(X, [0, 1, 0, 1]) @@ -1304,6 +1389,7 @@ def test_big_input(): def test_realloc(): from sklearn.tree._utils import _realloc_test + with pytest.raises(MemoryError): _realloc_test() @@ -1317,14 +1403,14 @@ def test_huge_allocations(): # Sanity check: we cannot request more memory than the size of the address # space. Currently raises OverflowError. huge = 2 ** (n_bits + 1) - clf = DecisionTreeClassifier(splitter='best', max_leaf_nodes=huge) + clf = DecisionTreeClassifier(splitter="best", max_leaf_nodes=huge) with pytest.raises(Exception): clf.fit(X, y) # Non-regression test: MemoryError used to be dropped by Cython # because of missing "except *". huge = 2 ** (n_bits - 1) - 1 - clf = DecisionTreeClassifier(splitter='best', max_leaf_nodes=huge) + clf = DecisionTreeClassifier(splitter="best", max_leaf_nodes=huge) with pytest.raises(MemoryError): clf.fit(X, y) @@ -1349,9 +1435,11 @@ def check_sparse_input(tree, dataset, max_depth=None): d = TreeEstimator(random_state=0, max_depth=max_depth).fit(X, y) s = TreeEstimator(random_state=0, max_depth=max_depth).fit(X_sparse, y) - assert_tree_equal(d.tree_, s.tree_, - "{0} with dense and sparse format gave different " - "trees".format(tree)) + assert_tree_equal( + d.tree_, + s.tree_, + "{0} with dense and sparse format gave different " "trees".format(tree), + ) y_pred = d.predict(X) if tree in CLF_TREES: @@ -1364,26 +1452,32 @@ def check_sparse_input(tree, dataset, max_depth=None): assert_array_almost_equal(s.predict(X_sparse_test), y_pred) if tree in CLF_TREES: - assert_array_almost_equal(s.predict_proba(X_sparse_test), - y_proba) - assert_array_almost_equal(s.predict_log_proba(X_sparse_test), - y_log_proba) + assert_array_almost_equal(s.predict_proba(X_sparse_test), y_proba) + assert_array_almost_equal( + s.predict_log_proba(X_sparse_test), y_log_proba + ) @pytest.mark.parametrize("tree_type", SPARSE_TREES) @pytest.mark.parametrize( - "dataset", - ("clf_small", "toy", "digits", "multilabel", - "sparse-pos", "sparse-neg", "sparse-mix", - "zeros") + "dataset", + ( + "clf_small", + "toy", + "digits", + "multilabel", + "sparse-pos", + "sparse-neg", + "sparse-mix", + "zeros", + ), ) def test_sparse_input(tree_type, dataset): max_depth = 3 if dataset == "digits" else None check_sparse_input(tree_type, dataset, max_depth) -@pytest.mark.parametrize("tree_type", - sorted(set(SPARSE_TREES).intersection(REG_TREES))) +@pytest.mark.parametrize("tree_type", sorted(set(SPARSE_TREES).intersection(REG_TREES))) @pytest.mark.parametrize("dataset", ["boston", "reg_small"]) def test_sparse_input_reg_trees(tree_type, dataset): # Due to numerical instability of MSE and too strict test, we limit the @@ -1399,39 +1493,46 @@ def check_sparse_parameters(tree, dataset): # Check max_features d = TreeEstimator(random_state=0, max_features=1, max_depth=2).fit(X, y) - s = TreeEstimator(random_state=0, max_features=1, - max_depth=2).fit(X_sparse, y) - assert_tree_equal(d.tree_, s.tree_, - "{0} with dense and sparse format gave different " - "trees".format(tree)) + s = TreeEstimator(random_state=0, max_features=1, max_depth=2).fit(X_sparse, y) + assert_tree_equal( + d.tree_, + s.tree_, + "{0} with dense and sparse format gave different " "trees".format(tree), + ) assert_array_almost_equal(s.predict(X), d.predict(X)) # Check min_samples_split - d = TreeEstimator(random_state=0, max_features=1, - min_samples_split=10).fit(X, y) - s = TreeEstimator(random_state=0, max_features=1, - min_samples_split=10).fit(X_sparse, y) - assert_tree_equal(d.tree_, s.tree_, - "{0} with dense and sparse format gave different " - "trees".format(tree)) + d = TreeEstimator(random_state=0, max_features=1, min_samples_split=10).fit(X, y) + s = TreeEstimator(random_state=0, max_features=1, min_samples_split=10).fit( + X_sparse, y + ) + assert_tree_equal( + d.tree_, + s.tree_, + "{0} with dense and sparse format gave different " "trees".format(tree), + ) assert_array_almost_equal(s.predict(X), d.predict(X)) # Check min_samples_leaf - d = TreeEstimator(random_state=0, - min_samples_leaf=X_sparse.shape[0] // 2).fit(X, y) - s = TreeEstimator(random_state=0, - min_samples_leaf=X_sparse.shape[0] // 2).fit(X_sparse, y) - assert_tree_equal(d.tree_, s.tree_, - "{0} with dense and sparse format gave different " - "trees".format(tree)) + d = TreeEstimator(random_state=0, min_samples_leaf=X_sparse.shape[0] // 2).fit(X, y) + s = TreeEstimator(random_state=0, min_samples_leaf=X_sparse.shape[0] // 2).fit( + X_sparse, y + ) + assert_tree_equal( + d.tree_, + s.tree_, + "{0} with dense and sparse format gave different " "trees".format(tree), + ) assert_array_almost_equal(s.predict(X), d.predict(X)) # Check best-first search d = TreeEstimator(random_state=0, max_leaf_nodes=3).fit(X, y) s = TreeEstimator(random_state=0, max_leaf_nodes=3).fit(X_sparse, y) - assert_tree_equal(d.tree_, s.tree_, - "{0} with dense and sparse format gave different " - "trees".format(tree)) + assert_tree_equal( + d.tree_, + s.tree_, + "{0} with dense and sparse format gave different " "trees".format(tree), + ) assert_array_almost_equal(s.predict(X), d.predict(X)) @@ -1444,28 +1545,27 @@ def check_sparse_criterion(tree, dataset): # Check various criterion CRITERIONS = REG_CRITERIONS if tree in REG_TREES else CLF_CRITERIONS for criterion in CRITERIONS: - d = TreeEstimator(random_state=0, max_depth=3, - criterion=criterion).fit(X, y) - s = TreeEstimator(random_state=0, max_depth=3, - criterion=criterion).fit(X_sparse, y) - - assert_tree_equal(d.tree_, s.tree_, - "{0} with dense and sparse format gave different " - "trees".format(tree)) + d = TreeEstimator(random_state=0, max_depth=3, criterion=criterion).fit(X, y) + s = TreeEstimator(random_state=0, max_depth=3, criterion=criterion).fit( + X_sparse, y + ) + + assert_tree_equal( + d.tree_, + s.tree_, + "{0} with dense and sparse format gave different " "trees".format(tree), + ) assert_array_almost_equal(s.predict(X), d.predict(X)) @pytest.mark.parametrize("tree_type", SPARSE_TREES) -@pytest.mark.parametrize("dataset", - ["sparse-pos", "sparse-neg", "sparse-mix", "zeros"]) -@pytest.mark.parametrize("check", - [check_sparse_parameters, check_sparse_criterion]) +@pytest.mark.parametrize("dataset", ["sparse-pos", "sparse-neg", "sparse-mix", "zeros"]) +@pytest.mark.parametrize("check", [check_sparse_parameters, check_sparse_criterion]) def test_sparse(tree_type, dataset, check): check(tree_type, dataset) -def check_explicit_sparse_zeros(tree, max_depth=3, - n_features=10): +def check_explicit_sparse_zeros(tree, max_depth=3, n_features=10): TreeEstimator = ALL_TREES[tree] # n_samples set n_feature to ease construction of a simultaneous @@ -1483,35 +1583,35 @@ def check_explicit_sparse_zeros(tree, max_depth=3, n_nonzero_i = random_state.binomial(n_samples, 0.5) indices_i = random_state.permutation(samples)[:n_nonzero_i] indices.append(indices_i) - data_i = random_state.binomial(3, 0.5, size=(n_nonzero_i, )) - 1 + data_i = random_state.binomial(3, 0.5, size=(n_nonzero_i,)) - 1 data.append(data_i) offset += n_nonzero_i indptr.append(offset) indices = np.concatenate(indices) data = np.array(np.concatenate(data), dtype=np.float32) - X_sparse = csc_matrix((data, indices, indptr), - shape=(n_samples, n_features)) + X_sparse = csc_matrix((data, indices, indptr), shape=(n_samples, n_features)) X = X_sparse.toarray() - X_sparse_test = csr_matrix((data, indices, indptr), - shape=(n_samples, n_features)) + X_sparse_test = csr_matrix((data, indices, indptr), shape=(n_samples, n_features)) X_test = X_sparse_test.toarray() - y = random_state.randint(0, 3, size=(n_samples, )) + y = random_state.randint(0, 3, size=(n_samples,)) # Ensure that X_sparse_test owns its data, indices and indptr array X_sparse_test = X_sparse_test.copy() # Ensure that we have explicit zeros - assert (X_sparse.data == 0.).sum() > 0 - assert (X_sparse_test.data == 0.).sum() > 0 + assert (X_sparse.data == 0.0).sum() > 0 + assert (X_sparse_test.data == 0.0).sum() > 0 # Perform the comparison d = TreeEstimator(random_state=0, max_depth=max_depth).fit(X, y) s = TreeEstimator(random_state=0, max_depth=max_depth).fit(X_sparse, y) - assert_tree_equal(d.tree_, s.tree_, - "{0} with dense and sparse format gave different " - "trees".format(tree)) + assert_tree_equal( + d.tree_, + s.tree_, + "{0} with dense and sparse format gave different " "trees".format(tree), + ) Xs = (X_test, X_sparse_test) for X1, X2 in product(Xs, Xs): @@ -1519,18 +1619,20 @@ def check_explicit_sparse_zeros(tree, max_depth=3, assert_array_almost_equal(s.apply(X1), d.apply(X2)) assert_array_almost_equal(s.apply(X1), s.tree_.apply(X1)) - assert_array_almost_equal(s.tree_.decision_path(X1).toarray(), - d.tree_.decision_path(X2).toarray()) - assert_array_almost_equal(s.decision_path(X1).toarray(), - d.decision_path(X2).toarray()) - assert_array_almost_equal(s.decision_path(X1).toarray(), - s.tree_.decision_path(X1).toarray()) + assert_array_almost_equal( + s.tree_.decision_path(X1).toarray(), d.tree_.decision_path(X2).toarray() + ) + assert_array_almost_equal( + s.decision_path(X1).toarray(), d.decision_path(X2).toarray() + ) + assert_array_almost_equal( + s.decision_path(X1).toarray(), s.tree_.decision_path(X1).toarray() + ) assert_array_almost_equal(s.predict(X1), d.predict(X2)) if tree in CLF_TREES: - assert_array_almost_equal(s.predict_proba(X1), - d.predict_proba(X2)) + assert_array_almost_equal(s.predict_proba(X1), d.predict_proba(X2)) @pytest.mark.parametrize("tree_type", SPARSE_TREES) @@ -1579,8 +1681,7 @@ def check_min_weight_leaf_split_level(name): sample_weight = [0.2, 0.2, 0.2, 0.2, 0.2] _check_min_weight_leaf_split_level(TreeEstimator, X, y, sample_weight) - _check_min_weight_leaf_split_level(TreeEstimator, csc_matrix(X), y, - sample_weight) + _check_min_weight_leaf_split_level(TreeEstimator, csc_matrix(X), y, sample_weight) @pytest.mark.parametrize("name", ALL_TREES) @@ -1593,8 +1694,7 @@ def check_public_apply(name): est = ALL_TREES[name]() est.fit(X_small, y_small) - assert_array_equal(est.apply(X_small), - est.tree_.apply(X_small32)) + assert_array_equal(est.apply(X_small), est.tree_.apply(X_small32)) def check_public_apply_sparse(name): @@ -1602,8 +1702,7 @@ def check_public_apply_sparse(name): est = ALL_TREES[name]() est.fit(X_small, y_small) - assert_array_equal(est.apply(X_small), - est.tree_.apply(X_small32)) + assert_array_equal(est.apply(X_small), est.tree_.apply(X_small32)) @pytest.mark.parametrize("name", ALL_TREES) @@ -1616,16 +1715,16 @@ def test_public_apply_sparse_trees(name): check_public_apply_sparse(name) -@pytest.mark.parametrize('Cls', - (DecisionTreeRegressor, DecisionTreeClassifier)) -@pytest.mark.parametrize('presort', ['auto', True, False]) +@pytest.mark.parametrize("Cls", (DecisionTreeRegressor, DecisionTreeClassifier)) +@pytest.mark.parametrize("presort", ["auto", True, False]) def test_presort_deprecated(Cls, presort): # TODO: remove in v0.24 X = np.zeros((10, 10)) y = np.r_[[0] * 5, [1] * 5] tree = Cls(presort=presort) - with pytest.warns(DeprecationWarning, - match="The parameter 'presort' is deprecated "): + with pytest.warns( + DeprecationWarning, match="The parameter 'presort' is deprecated " + ): tree.fit(X, y) @@ -1657,8 +1756,9 @@ def check_decision_path(name): # Ensure only one leave node per sample all_leaves = est.tree_.children_left == TREE_LEAF - assert_array_almost_equal(np.dot(node_indicator, all_leaves), - np.ones(shape=n_samples)) + assert_array_almost_equal( + np.dot(node_indicator, all_leaves), np.ones(shape=n_samples) + ) # Ensure max depth is consistent with sum of indicator max_depth = node_indicator.sum(axis=1).max() @@ -1746,18 +1846,19 @@ def test_mae(): = 0.75 ------ """ - dt_mae = DecisionTreeRegressor(random_state=0, criterion="mae", - max_leaf_nodes=2) + dt_mae = DecisionTreeRegressor(random_state=0, criterion="mae", max_leaf_nodes=2) # Test MAE where sample weights are non-uniform (as illustrated above): - dt_mae.fit(X=[[3], [5], [3], [8], [5]], y=[6, 7, 3, 4, 3], - sample_weight=[0.6, 0.3, 0.1, 1.0, 0.3]) + dt_mae.fit( + X=[[3], [5], [3], [8], [5]], + y=[6, 7, 3, 4, 3], + sample_weight=[0.6, 0.3, 0.1, 1.0, 0.3], + ) assert_allclose(dt_mae.tree_.impurity, [2.5 / 2.3, 0.3 / 0.7, 1.2 / 1.6]) assert_array_equal(dt_mae.tree_.value.flat, [4.0, 6.0, 4.0]) # Test MAE where all sample weights are uniform: - dt_mae.fit(X=[[3], [5], [3], [8], [5]], y=[6, 7, 3, 4, 3], - sample_weight=np.ones(5)) + dt_mae.fit(X=[[3], [5], [3], [8], [5]], y=[6, 7, 3, 4, 3], sample_weight=np.ones(5)) assert_array_equal(dt_mae.tree_.impurity, [1.4, 1.5, 4.0 / 3.0]) assert_array_equal(dt_mae.tree_.value.flat, [4, 4.5, 4.0]) @@ -1768,8 +1869,10 @@ def test_mae(): assert_array_equal(dt_mae.tree_.impurity, [1.4, 1.5, 4.0 / 3.0]) assert_array_equal(dt_mae.tree_.value.flat, [4, 4.5, 4.0]) + np.random.seed(25) + def test_axis_proj_same_y(): """Check axis projection criterion produces correct results on small toy dataset: @@ -1835,37 +1938,43 @@ def test_axis_proj_same_y(): = 0.0 ------ """ - dt_axis = DecisionTreeRegressor(random_state=0, criterion="axis", - max_leaf_nodes=2) - dt_mse = DecisionTreeRegressor(random_state=0, criterion="mse", - max_leaf_nodes=2) + dt_axis = DecisionTreeRegressor(random_state=0, criterion="axis", max_leaf_nodes=2) + dt_mse = DecisionTreeRegressor(random_state=0, criterion="mse", max_leaf_nodes=2) # Test axis projection where sample weights are non-uniform (as illustrated above): - dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3], [3], [4], [7], [8]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - assert(abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01) - assert(abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01) - assert(abs(dt_axis.tree_.impurity[2]) < 0.01) + dt_axis.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3], [3], [4], [7], [8]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) + dt_mse.fit( + X=[[3], [5], [8], [3], [5]], + y=[3, 3, 4, 7, 8], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) + assert abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01 + assert abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01 + assert abs(dt_axis.tree_.impurity[2]) < 0.01 # Test axis projection where all sample weights are uniform: - dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=np.ones(5)) - dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8], - sample_weight=np.ones(5)) - assert(abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01) - assert(abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01) - assert(abs(dt_axis.tree_.impurity[2]) < 0.01) + dt_axis.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], + sample_weight=np.ones(5), + ) + dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8], sample_weight=np.ones(5)) + assert abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01 + assert abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01 + assert abs(dt_axis.tree_.impurity[2]) < 0.01 # Test axis projections where a `sample_weight` is not explicitly provided. # This is equivalent to providing uniform sample weights, though # the internal logic is different: - dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]]) + dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]]) dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8]) - assert(abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01) - assert(abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01) - assert(abs(dt_axis.tree_.impurity[2]) < 0.01) + assert abs(dt_mse.tree_.impurity[0] - dt_axis.tree_.impurity[0]) < 0.01 + assert abs(dt_mse.tree_.impurity[1] - dt_axis.tree_.impurity[1]) < 0.01 + assert abs(dt_axis.tree_.impurity[2]) < 0.01 def test_axis_proj_diff_y(): @@ -1909,62 +2018,91 @@ def test_axis_proj_diff_y(): ----------------- """ # Test axis projection where multiple y values are different: - dt_axis_multi = DecisionTreeRegressor(random_state=0, criterion="axis", - max_leaf_nodes=2) - dt_axis_multi.fit(X=[[3], [5], [8], [3], [5]], y=[[3,2], [3,4], [4,3], [7,6], [8,7]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + dt_axis_multi = DecisionTreeRegressor( + random_state=0, criterion="axis", max_leaf_nodes=2 + ) + dt_axis_multi.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 2], [3, 4], [4, 3], [7, 6], [8, 7]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) try: - assert_allclose(dt_axis_multi.tree_.impurity, [6.148 / 2.3, 4.818 / 2.3, 0.0], rtol=0.6) + assert_allclose( + dt_axis_multi.tree_.impurity, [6.148 / 2.3, 4.818 / 2.3, 0.0], rtol=0.6 + ) except: - assert_allclose(dt_axis_multi.tree_.impurity, [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], rtol=0.6) + assert_allclose( + dt_axis_multi.tree_.impurity, + [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], + rtol=0.6, + ) def test_axis_proj_weights(): # Test axis projection where sample weights are non-uniform (as illustrated above): - dt_axis = DecisionTreeRegressor(random_state=0, criterion="axis", - max_leaf_nodes=2) - dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - assert_allclose(dt_axis.tree_.impurity, [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], rtol=0.6) - - #Test axis projection where sample weights are uniform - dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=np.ones(5)) - assert_allclose(dt_axis.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) + dt_axis = DecisionTreeRegressor(random_state=0, criterion="axis", max_leaf_nodes=2) + dt_axis.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) + assert_allclose( + dt_axis.tree_.impurity, [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], rtol=0.6 + ) + + # Test axis projection where sample weights are uniform + dt_axis.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], + sample_weight=np.ones(5), + ) + assert_allclose( + dt_axis.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6 + ) def test_axis_proj_random_state(): # Same random state produces same result - dt_axis = DecisionTreeRegressor(random_state=0, criterion="axis", - max_leaf_nodes=2) - dt_axis.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + dt_axis = DecisionTreeRegressor(random_state=0, criterion="axis", max_leaf_nodes=2) + dt_axis.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) for i in range(30): - dt_axis_2 = DecisionTreeRegressor(random_state=0, criterion="axis", - max_leaf_nodes=2) - dt_axis_2.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + dt_axis_2 = DecisionTreeRegressor( + random_state=0, criterion="axis", max_leaf_nodes=2 + ) + dt_axis_2.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) assert_allclose(dt_axis.tree_.impurity, dt_axis_2.tree_.impurity) - + # Different random state produces different result - y_vals = np.random.randint(1,100,(5,7)) - dt_axis_3 = DecisionTreeRegressor(random_state=1, criterion="axis", - max_leaf_nodes=2) - dt_axis_3.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - for i in range(2,100): - dt_axis_4 = DecisionTreeRegressor(random_state=i, criterion="axis", - max_leaf_nodes=2) - dt_axis_4.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + y_vals = np.random.randint(1, 100, (5, 7)) + dt_axis_3 = DecisionTreeRegressor( + random_state=1, criterion="axis", max_leaf_nodes=2 + ) + dt_axis_3.fit( + X=[[3], [5], [8], [3], [5]], y=y_vals, sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3] + ) + for i in range(2, 100): + dt_axis_4 = DecisionTreeRegressor( + random_state=i, criterion="axis", max_leaf_nodes=2 + ) + dt_axis_4.fit( + X=[[3], [5], [8], [3], [5]], + y=y_vals, + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) if True in np.not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity): - #assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity).any() - assert(True) + assert True break - elif i==100: - #assert_not_equal(dt_axis_3.tree_.impurity, dt_axis_4.tree_.impurity).any() - assert(False) - + elif i == 100: + assert False + def test_oblique_proj_diff_y(): """Check oblique projection criterion produces correct results on small toy dataset: @@ -2015,104 +2153,167 @@ def test_oblique_proj_diff_y(): ----------------- """ # Test oblique projection where multiple y values are different: - dt_obliq_multi = DecisionTreeRegressor(random_state=3, criterion="oblique", - max_leaf_nodes=2) - dt_obliq_multi.fit(X=[[3], [5], [8], [3], [5]], y=[[3,2], [3,4], [4,3], [7,6], [8,7]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + dt_obliq_multi = DecisionTreeRegressor( + random_state=3, criterion="oblique", max_leaf_nodes=2 + ) + dt_obliq_multi.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 2], [3, 4], [4, 3], [7, 6], [8, 7]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) try: - assert_allclose(dt_obliq_multi.tree_.impurity, [6.148 / 2.3, 4.818 / 2.3, 0.0], rtol=0.6) + assert_allclose( + dt_obliq_multi.tree_.impurity, [6.148 / 2.3, 4.818 / 2.3, 0.0], rtol=0.6 + ) except: - try: - assert_allclose(dt_obliq.tree_.impurity, [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], rtol=0.6) + try: + assert_allclose( + dt_obliq.tree_.impurity, [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], rtol=0.6 + ) except: try: - assert_allclose(dt_obliq.tree_.impurity, [(7.7 + 6.148) / 2.3, (6.13125 + 4.818) / 1.3, 0.0 / 1.0], rtol=0.6) + assert_allclose( + dt_obliq.tree_.impurity, + [(7.7 + 6.148) / 2.3, (6.13125 + 4.818) / 1.3, 0.0 / 1.0], + rtol=0.6, + ) except: try: - assert_allclose(dt_obliq.tree_.impurity, [(7.7 - 6.148) / 2.3, (6.13125 - 4.818) / 1.3, 0.0 / 1.0], rtol=0.6) + assert_allclose( + dt_obliq.tree_.impurity, + [(7.7 - 6.148) / 2.3, (6.13125 - 4.818) / 1.3, 0.0 / 1.0], + rtol=0.6, + ) except: try: - assert_allclose(dt_obliq.tree_.impurity, [(-7.7 + 6.148) / 2.3, (-6.13125 + 4.818) / 1.3, 0.0 / 1.0], rtol=0.6) + assert_allclose( + dt_obliq.tree_.impurity, + [(-7.7 + 6.148) / 2.3, (-6.13125 + 4.818) / 1.3, 0.0 / 1.0], + rtol=0.6, + ) except: - assert_allclose(dt_obliq_multi.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) - - + assert_allclose( + dt_obliq_multi.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6 + ) + + def test_oblique_proj_no_weight(): # Test oblique where a `sample_weight` is not explicitly provided. # This is equivalent to providing uniform sample weights, though # the internal logic is different: - dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", - max_leaf_nodes=2) - dt_obliq.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]]) + dt_obliq = DecisionTreeRegressor( + random_state=3, criterion="oblique", max_leaf_nodes=2 + ) + dt_obliq.fit( + X=[[3], [5], [8], [3], [5]], y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]] + ) try: - assert_allclose(dt_obliq.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) + assert_allclose( + dt_obliq.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6 + ) except: try: - assert_allclose(dt_obliq.tree_.impurity, [2.0*22.0 / 5.0, 2.0*20.75 / 4.0, 2.0*0.0 / 1.0], rtol=0.6) - except: - assert_allclose(dt_obliq.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) + assert_allclose( + dt_obliq.tree_.impurity, + [2.0 * 22.0 / 5.0, 2.0 * 20.75 / 4.0, 2.0 * 0.0 / 1.0], + rtol=0.6, + ) + except: + assert_allclose(dt_obliq.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) def test_oblique_proj_weights(): # Test axis projection where sample weights are non-uniform (as illustrated above): - dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", - max_leaf_nodes=2) - dt_obliq.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + dt_obliq = DecisionTreeRegressor( + random_state=3, criterion="oblique", max_leaf_nodes=2 + ) + dt_obliq.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) try: - assert_allclose(dt_obliq.tree_.impurity, [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], rtol=0.6) + assert_allclose( + dt_obliq.tree_.impurity, [7.7 / 2.3, 6.13125 / 1.3, 0.0 / 1.0], rtol=0.6 + ) except: try: - assert_allclose(dt_obliq.tree_.impurity, [2.0*7.7 / 2.3, 2.0*6.13125 / 1.3, 2.0*0.0 / 1.0], rtol=0.6) - except: - assert_allclose(dt_obliq.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) - + assert_allclose( + dt_obliq.tree_.impurity, + [2.0 * 7.7 / 2.3, 2.0 * 6.13125 / 1.3, 2.0 * 0.0 / 1.0], + rtol=0.6, + ) + except: + assert_allclose(dt_obliq.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) + # Test oblique projection where all sample weights are uniform: - dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", - max_leaf_nodes=2) - dt_obliq.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=np.ones(5)) + dt_obliq = DecisionTreeRegressor( + random_state=3, criterion="oblique", max_leaf_nodes=2 + ) + dt_obliq.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], + sample_weight=np.ones(5), + ) try: - assert_allclose(dt_obliq.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6) + assert_allclose( + dt_obliq.tree_.impurity, [22.0 / 5.0, 20.75 / 4.0, 0.0 / 1.0], rtol=0.6 + ) except: try: - assert_allclose(dt_obliq.tree_.impurity, [2.0*22.0 / 5.0, 2.0*20.75 / 4.0, 2.0*0.0 / 1.0], rtol=0.6) - except: - assert_allclose(dt_obliq.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) + assert_allclose( + dt_obliq.tree_.impurity, + [2.0 * 22.0 / 5.0, 2.0 * 20.75 / 4.0, 2.0 * 0.0 / 1.0], + rtol=0.6, + ) + except: + assert_allclose(dt_obliq.tree_.impurity, [0.0, 0.0, 0.0], rtol=0.6) def test_oblique_proj_random_state(): - # Test for the same result with same initial random state - dt_obliq = DecisionTreeRegressor(random_state=3, criterion="oblique", - max_leaf_nodes=2) - dt_obliq.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - for i in range(30): - dt_obliq_2 = DecisionTreeRegressor(random_state=3, criterion="oblique", - max_leaf_nodes=2) - dt_obliq_2.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + # Test for the same result with same initial random state + dt_obliq = DecisionTreeRegressor( + random_state=3, criterion="oblique", max_leaf_nodes=2 + ) + dt_obliq.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) + for i in range(30): + dt_obliq_2 = DecisionTreeRegressor( + random_state=3, criterion="oblique", max_leaf_nodes=2 + ) + dt_obliq_2.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) assert_allclose(dt_obliq.tree_.impurity, dt_obliq_2.tree_.impurity) # Test different random state produces different results - y_vals = np.random.randint(1,100,(5,7)) - dt_obliq_3 = DecisionTreeRegressor(random_state=1, criterion="oblique", - max_leaf_nodes=2) - dt_obliq_3.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - for i in range(2,100): - dt_obliq_4 = DecisionTreeRegressor(random_state=i, criterion="oblique", - max_leaf_nodes=2) - dt_obliq_4.fit(X=[[3], [5], [8], [3], [5]], y=y_vals, - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) + y_vals = np.random.randint(1, 100, (5, 7)) + dt_obliq_3 = DecisionTreeRegressor( + random_state=1, criterion="oblique", max_leaf_nodes=2 + ) + dt_obliq_3.fit( + X=[[3], [5], [8], [3], [5]], y=y_vals, sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3] + ) + for i in range(2, 100): + dt_obliq_4 = DecisionTreeRegressor( + random_state=i, criterion="oblique", max_leaf_nodes=2 + ) + dt_obliq_4.fit( + X=[[3], [5], [8], [3], [5]], + y=y_vals, + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) if True in np.not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity): - #assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity).any() - assert(True) + assert True break - elif i==100: - #assert_not_equal(dt_obliq_3.tree_.impurity, dt_obliq_4.tree_.impurity).any() - assert(False) - + elif i == 100: + assert False + def test_oblique_proj_same_y(): """Check oblique projection criterion produces correct results on @@ -2191,51 +2392,73 @@ def test_oblique_proj_same_y(): = 0.0 ------ """ - dt_oblique = DecisionTreeRegressor(random_state=3, criterion="oblique", - max_leaf_nodes=2) - dt_mse = DecisionTreeRegressor(random_state=3, criterion="mse", - max_leaf_nodes=2) + dt_oblique = DecisionTreeRegressor( + random_state=3, criterion="oblique", max_leaf_nodes=2 + ) + dt_mse = DecisionTreeRegressor(random_state=3, criterion="mse", max_leaf_nodes=2) # Test oblique projection where sample weights are non-uniform (as illustrated above): - dt_oblique.fit(X=[[3], [5], [8], [3], [5]], y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - - dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8], - sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3]) - - assert(abs(dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 - or abs(2.0 * dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 - or abs(dt_oblique.tree_.impurity[0]) < 0.01) - assert(abs(dt_mse.tree_.impurity[1] - dt_oblique.tree_.impurity[1]) < 0.01 - or abs(2.0 * dt_mse.tree_.impurity[1]- dt_oblique.tree_.impurity[1]) < 0.01 - or abs(dt_oblique.tree_.impurity[1]) < 0.01) - assert(abs(dt_oblique.tree_.impurity[2]) < 0.01) + dt_oblique.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) + + dt_mse.fit( + X=[[3], [5], [8], [3], [5]], + y=[3, 3, 4, 7, 8], + sample_weight=[0.1, 0.3, 1.0, 0.6, 0.3], + ) + + assert ( + abs(dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 + or abs(2.0 * dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 + or abs(dt_oblique.tree_.impurity[0]) < 0.01 + ) + assert ( + abs(dt_mse.tree_.impurity[1] - dt_oblique.tree_.impurity[1]) < 0.01 + or abs(2.0 * dt_mse.tree_.impurity[1] - dt_oblique.tree_.impurity[1]) < 0.01 + or abs(dt_oblique.tree_.impurity[1]) < 0.01 + ) + assert abs(dt_oblique.tree_.impurity[2]) < 0.01 # Test oblique projection where all sample weights are uniform: - dt_oblique.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]], - sample_weight=np.ones(5)) - dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8], - sample_weight=np.ones(5)) - assert(abs(dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 - or abs(2.0 * dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 - or abs(dt_oblique.tree_.impurity[0]) < 0.01) - assert(abs(dt_mse.tree_.impurity[1] - dt_oblique.tree_.impurity[1]) < 0.01 - or abs(2.0 * dt_mse.tree_.impurity[1]- dt_oblique.tree_.impurity[1]) < 0.01 - or abs(dt_oblique.tree_.impurity[1]) < 0.01) - assert(abs(dt_oblique.tree_.impurity[2]) < 0.01) + dt_oblique.fit( + X=[[3], [5], [8], [3], [5]], + y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]], + sample_weight=np.ones(5), + ) + dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8], sample_weight=np.ones(5)) + assert ( + abs(dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 + or abs(2.0 * dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 + or abs(dt_oblique.tree_.impurity[0]) < 0.01 + ) + assert ( + abs(dt_mse.tree_.impurity[1] - dt_oblique.tree_.impurity[1]) < 0.01 + or abs(2.0 * dt_mse.tree_.impurity[1] - dt_oblique.tree_.impurity[1]) < 0.01 + or abs(dt_oblique.tree_.impurity[1]) < 0.01 + ) + assert abs(dt_oblique.tree_.impurity[2]) < 0.01 # Test oblique projections where a `sample_weight` is not explicitly provided. # This is equivalent to providing uniform sample weights, though # the internal logic is different: - dt_oblique.fit(X=[[3], [5], [8], [3], [5]], y=[[3,3], [3,3], [4,4], [7,7], [8,8]]) + dt_oblique.fit( + X=[[3], [5], [8], [3], [5]], y=[[3, 3], [3, 3], [4, 4], [7, 7], [8, 8]] + ) dt_mse.fit(X=[[3], [5], [8], [3], [5]], y=[3, 3, 4, 7, 8]) - assert(abs(dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 - or abs(2.0 * dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 - or abs(dt_oblique.tree_.impurity[0]) < 0.01) - assert(abs(dt_mse.tree_.impurity[1] - dt_oblique.tree_.impurity[1]) < 0.01 - or abs(2.0 * dt_mse.tree_.impurity[1]- dt_oblique.tree_.impurity[1]) < 0.01 - or abs(dt_oblique.tree_.impurity[1]) < 0.01) - assert(abs(dt_oblique.tree_.impurity[2]) < 0.01) + assert ( + abs(dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 + or abs(2.0 * dt_mse.tree_.impurity[0] - dt_oblique.tree_.impurity[0]) < 0.01 + or abs(dt_oblique.tree_.impurity[0]) < 0.01 + ) + assert ( + abs(dt_mse.tree_.impurity[1] - dt_oblique.tree_.impurity[1]) < 0.01 + or abs(2.0 * dt_mse.tree_.impurity[1] - dt_oblique.tree_.impurity[1]) < 0.01 + or abs(dt_oblique.tree_.impurity[1]) < 0.01 + ) + assert abs(dt_oblique.tree_.impurity[2]) < 0.01 def test_criterion_copy(): @@ -2247,6 +2470,7 @@ def test_criterion_copy(): def _pickle_copy(obj): return pickle.loads(pickle.dumps(obj)) + for copy_func in [copy.copy, copy.deepcopy, _pickle_copy]: for _, typename in CRITERIA_CLF.items(): criteria = typename(n_outputs, n_classes) @@ -2268,7 +2492,7 @@ def _pickle_copy(obj): def test_empty_leaf_infinite_threshold(): # try to make empty leaf by using near infinite value. data = np.random.RandomState(0).randn(100, 11) * 2e38 - data = np.nan_to_num(data.astype('float32')) + data = np.nan_to_num(data.astype("float32")) X_full = data[:, :-1] X_sparse = csc_matrix(X_full) y = data[:, -1] @@ -2293,9 +2517,9 @@ def test_decision_tree_memmap(): @pytest.mark.parametrize("criterion", CLF_CRITERIONS) @pytest.mark.parametrize( - "dataset", sorted(set(DATASETS.keys()) - {"reg_small", "boston"})) -@pytest.mark.parametrize( - "tree_cls", [DecisionTreeClassifier, ExtraTreeClassifier]) + "dataset", sorted(set(DATASETS.keys()) - {"reg_small", "boston"}) +) +@pytest.mark.parametrize("tree_cls", [DecisionTreeClassifier, ExtraTreeClassifier]) def test_prune_tree_classifier_are_subtrees(criterion, dataset, tree_cls): dataset = DATASETS[dataset] X, y = dataset["X"], dataset["y"] @@ -2312,8 +2536,7 @@ def test_prune_tree_classifier_are_subtrees(criterion, dataset, tree_cls): @pytest.mark.parametrize("criterion", REG_CRITERIONS) @pytest.mark.parametrize("dataset", DATASETS.keys()) -@pytest.mark.parametrize( - "tree_cls", [DecisionTreeRegressor, ExtraTreeRegressor]) +@pytest.mark.parametrize("tree_cls", [DecisionTreeRegressor, ExtraTreeRegressor]) def test_prune_tree_regression_are_subtrees(criterion, dataset, tree_cls): dataset = DATASETS[dataset] X, y = dataset["X"], dataset["y"] @@ -2345,8 +2568,9 @@ def assert_pruning_creates_subtree(estimator_cls, X, y, pruning_path): # generate trees with increasing alphas estimators = [] for ccp_alpha in pruning_path: - est = estimator_cls( - max_leaf_nodes=20, ccp_alpha=ccp_alpha, random_state=0).fit(X, y) + est = estimator_cls(max_leaf_nodes=20, ccp_alpha=ccp_alpha, random_state=0).fit( + X, y + ) estimators.append(est) # A pruned tree must be a subtree of the previous tree (which had a @@ -2367,28 +2591,32 @@ def assert_is_subtree(tree, subtree): stack = [(0, 0)] while stack: tree_node_idx, subtree_node_idx = stack.pop() - assert_array_almost_equal(tree.value[tree_node_idx], - subtree.value[subtree_node_idx]) - assert_almost_equal(tree.impurity[tree_node_idx], - subtree.impurity[subtree_node_idx]) - assert_almost_equal(tree.n_node_samples[tree_node_idx], - subtree.n_node_samples[subtree_node_idx]) - assert_almost_equal(tree.weighted_n_node_samples[tree_node_idx], - subtree.weighted_n_node_samples[subtree_node_idx]) - - if (subtree_c_left[subtree_node_idx] == - subtree_c_right[subtree_node_idx]): + assert_array_almost_equal( + tree.value[tree_node_idx], subtree.value[subtree_node_idx] + ) + assert_almost_equal( + tree.impurity[tree_node_idx], subtree.impurity[subtree_node_idx] + ) + assert_almost_equal( + tree.n_node_samples[tree_node_idx], subtree.n_node_samples[subtree_node_idx] + ) + assert_almost_equal( + tree.weighted_n_node_samples[tree_node_idx], + subtree.weighted_n_node_samples[subtree_node_idx], + ) + + if subtree_c_left[subtree_node_idx] == subtree_c_right[subtree_node_idx]: # is a leaf - assert_almost_equal(TREE_UNDEFINED, - subtree.threshold[subtree_node_idx]) + assert_almost_equal(TREE_UNDEFINED, subtree.threshold[subtree_node_idx]) else: # not a leaf - assert_almost_equal(tree.threshold[tree_node_idx], - subtree.threshold[subtree_node_idx]) - stack.append((tree_c_left[tree_node_idx], - subtree_c_left[subtree_node_idx])) - stack.append((tree_c_right[tree_node_idx], - subtree_c_right[subtree_node_idx])) + assert_almost_equal( + tree.threshold[tree_node_idx], subtree.threshold[subtree_node_idx] + ) + stack.append((tree_c_left[tree_node_idx], subtree_c_left[subtree_node_idx])) + stack.append( + (tree_c_right[tree_node_idx], subtree_c_right[subtree_node_idx]) + ) def test_prune_tree_raises_negative_ccp_alpha(): @@ -2413,8 +2641,10 @@ def test_classes_deprecated(): clf = DecisionTreeRegressor() clf = clf.fit(X, y) - match = ("attribute is to be deprecated from version " - "0.22 and will be removed in 0.24.") + match = ( + "attribute is to be deprecated from version " + "0.22 and will be removed in 0.24." + ) with pytest.warns(DeprecationWarning, match=match): n = len(clf.classes_) @@ -2422,4 +2652,3 @@ def test_classes_deprecated(): with pytest.warns(DeprecationWarning, match=match): assert len(clf.n_classes_) == clf.n_outputs_ - \ No newline at end of file From cf8d33aeb657cb5d9da81a4bda2ebdad1005d358 Mon Sep 17 00:00:00 2001 From: Jennifer Date: Mon, 30 Mar 2020 15:54:05 -0400 Subject: [PATCH 10/11] delete unsed import --- sklearn/tree/tests/test_tree.py | 1 - 1 file changed, 1 deletion(-) diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index 4a598a171f473..4c6e6b8352497 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -20,7 +20,6 @@ from sklearn.utils.testing import assert_allclose from sklearn.utils.testing import assert_array_equal -from sklearn.utils.testing import assert_not_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_warns From 351bbf4e902fd72d32486260cf6aca04c129cc11 Mon Sep 17 00:00:00 2001 From: morgsmss7 Date: Sat, 13 Jun 2020 16:31:43 -0400 Subject: [PATCH 11/11] trying to add all sklearn code again --- .../Arff tests-checkpoint.ipynb | 1827 +++++++++ .../Olivetti_faces_test-checkpoint.ipynb | 681 ++++ Arff tests.ipynb | 3315 +++++++++++++++++ .../plot_nonlinear_regression_datasets.py | 21 +- ...m_forest_regression_criteria_comparison.py | 197 +- ..._forest_regression_criteria_comparison2.py | 251 ++ 6 files changed, 6220 insertions(+), 72 deletions(-) create mode 100644 .ipynb_checkpoints/Arff tests-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/Olivetti_faces_test-checkpoint.ipynb create mode 100644 Arff tests.ipynb create mode 100644 examples/ensemble/plot_random_forest_regression_criteria_comparison2.py diff --git a/.ipynb_checkpoints/Arff tests-checkpoint.ipynb b/.ipynb_checkpoints/Arff tests-checkpoint.ipynb new file mode 100644 index 0000000000000..84bc017af87d9 --- /dev/null +++ b/.ipynb_checkpoints/Arff tests-checkpoint.ipynb @@ -0,0 +1,1827 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.io import arff\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = arff.loadarff('/Users/msanch35/Downloads/mtr-datasets/scm20d.arff')\n", + "df = pd.DataFrame(data[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = np.array(df)[:, 0:61]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y = np.array(df)[:, 61::]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "\n", + "with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_100206_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_101107_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "svd, correlations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b = np.corrcoef(data)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b[np.triu_indices(b.shape[0])]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_excel(r'/Users/msanch35/Downloads/phenotype_table_discovery.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import TruncatedSVD\n", + "data2 = []\n", + "for i in df['ID_set']:\n", + " with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_' + str(i) + '_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f).transpose()\n", + " b = np.corrcoef(data)\n", + " c = TruncatedSVD(n_components=10).fit_transform(b)\n", + " data2.append(c.reshape((c.shape[0]*c.shape[1], )))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: graspy in /Users/msanch35/miniconda3/lib/python3.7/site-packages (0.2.0)\n", + "Requirement already satisfied: scipy>=1.1.0 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (1.4.1)\n", + "Requirement already satisfied: numpy>=1.8.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (1.18.2)\n", + "Requirement already satisfied: seaborn>=0.9.0 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (0.10.1)\n", + "Requirement already satisfied: matplotlib>=3.0.0 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (3.2.1)\n", + "Requirement already satisfied: networkx>=2.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (2.4)\n", + "Requirement already satisfied: scikit-learn>=0.19.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (0.22.2.post1)\n", + "Requirement already satisfied: pandas>=0.22.0 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from seaborn>=0.9.0->graspy) (1.0.3)\n", + "Requirement already satisfied: cycler>=0.10 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from matplotlib>=3.0.0->graspy) (0.10.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from matplotlib>=3.0.0->graspy) (1.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from matplotlib>=3.0.0->graspy) (2.8.1)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from matplotlib>=3.0.0->graspy) (2.4.7)\n", + "Requirement already satisfied: decorator>=4.3.0 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from networkx>=2.1->graspy) (4.4.2)\n", + "Requirement already satisfied: joblib>=0.11 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from scikit-learn>=0.19.1->graspy) (0.14.1)\n", + "Requirement already satisfied: pytz>=2017.2 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from pandas>=0.22.0->seaborn>=0.9.0->graspy) (2020.1)\n", + "Requirement already satisfied: six in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from cycler>=0.10->matplotlib>=3.0.0->graspy) (1.12.0)\n" + ] + } + ], + "source": [ + "!pip install graspy" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import graspy\n", + "d = graspy.embed.select_dimension(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max(d[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.04747899e+01],\n", + " [ 1.18872362e+01],\n", + " [ 1.02263946e+01],\n", + " [ 1.14160653e+01],\n", + " [ 9.99988573e+00],\n", + " [ 1.30431734e+01],\n", + " [ 9.35384872e+00],\n", + " [ 1.31792633e+01],\n", + " [ 1.32559428e+01],\n", + " [ 1.05784854e+01],\n", + " [ 1.25320073e+01],\n", + " [ 1.24298931e+01],\n", + " [ 1.34657054e+01],\n", + " [ 1.03081766e+01],\n", + " [ 1.19430138e+01],\n", + " [ 1.31792557e+01],\n", + " [ 1.23558596e+01],\n", + " [ 5.40709325e+00],\n", + " [ 1.33259227e+01],\n", + " [ 1.06232459e+01],\n", + " [ 1.09659699e+01],\n", + " [ 1.09787272e+01],\n", + " [ 1.28963595e+01],\n", + " [ 4.86615386e+00],\n", + " [ 1.33591527e+01],\n", + " [ 1.30126194e+01],\n", + " [ 9.50490455e+00],\n", + " [-2.64223480e+00],\n", + " [ 1.24250845e+01],\n", + " [ 2.68644166e+00],\n", + " [ 1.03593227e+01],\n", + " [ 1.28808272e+01],\n", + " [ 4.26513131e+00],\n", + " [ 9.97973204e+00],\n", + " [ 1.32843423e+01],\n", + " [ 1.25056590e+01],\n", + " [ 1.27649695e+01],\n", + " [ 1.38840269e+01],\n", + " [ 1.07389041e+01],\n", + " [ 2.28235521e+00],\n", + " [ 7.04781373e+00],\n", + " [ 1.17010299e+01],\n", + " [ 1.20796971e+01],\n", + " [ 2.27018206e+00],\n", + " [ 1.12392349e+01],\n", + " [ 1.33385853e+01],\n", + " [ 1.34861467e+01],\n", + " [ 5.89651366e+00],\n", + " [ 1.00018490e+01],\n", + " [ 1.33398238e+01],\n", + " [ 1.24043262e+01],\n", + " [ 1.08842763e+01],\n", + " [ 1.16788935e+01],\n", + " [ 8.45107106e+00],\n", + " [ 9.37463231e+00],\n", + " [ 1.39491067e+01],\n", + " [ 1.31805442e+01],\n", + " [ 1.19611627e+01],\n", + " [ 1.28269608e+01],\n", + " [ 1.24699707e+01],\n", + " [ 1.46485249e+01],\n", + " [ 1.17688289e+01],\n", + " [ 1.37077797e+01],\n", + " [ 1.25350679e+01],\n", + " [ 1.33368784e+01],\n", + " [ 1.18901842e+01],\n", + " [ 1.33220322e+01],\n", + " [ 1.31792532e+01],\n", + " [ 1.28611479e+01],\n", + " [ 1.27927689e+01],\n", + " [ 1.27485808e+01],\n", + " [ 1.26261071e+01],\n", + " [ 1.29599041e+01],\n", + " [ 1.24955774e+01],\n", + " [ 1.33283036e+01],\n", + " [ 1.32548831e+01],\n", + " [ 1.31935010e+01],\n", + " [ 1.38049493e+01],\n", + " [ 1.45160302e+01],\n", + " [ 1.34231396e+01],\n", + " [ 9.59772302e+00],\n", + " [ 8.85994682e+00],\n", + " [ 1.08314452e+01],\n", + " [ 1.00280548e+01],\n", + " [ 1.08967909e+01],\n", + " [ 9.50456296e+00],\n", + " [ 8.45030094e+00],\n", + " [ 1.16459495e+01],\n", + " [ 1.13727091e+01],\n", + " [ 1.16023566e+01],\n", + " [ 6.05163105e+00],\n", + " [ 1.10246443e+01],\n", + " [ 8.35199760e+00],\n", + " [ 1.19088076e+01],\n", + " [ 1.00970960e+01],\n", + " [ 9.47199935e+00],\n", + " [ 7.87734173e+00],\n", + " [ 1.14781701e+01],\n", + " [ 1.11101995e+01],\n", + " [ 1.33994553e+01],\n", + " [ 1.26095853e+01],\n", + " [ 1.16012045e+01],\n", + " [ 8.36779960e+00],\n", + " [ 1.15862588e+01],\n", + " [ 8.19943057e+00],\n", + " [ 1.08035590e+01],\n", + " [ 1.04570017e+01],\n", + " [ 8.69671540e+00],\n", + " [ 1.02478784e+01],\n", + " [ 7.74244141e+00],\n", + " [ 1.03208536e+01],\n", + " [ 8.75906940e+00],\n", + " [ 3.29460923e+00],\n", + " [ 7.24474287e+00],\n", + " [ 9.46373366e+00],\n", + " [ 9.11343349e+00],\n", + " [ 1.05270764e+01],\n", + " [ 1.15244694e+01],\n", + " [ 1.17170799e+01],\n", + " [ 1.24012190e+01],\n", + " [ 1.13590560e+01],\n", + " [ 7.94686784e+00],\n", + " [ 1.16047672e+01],\n", + " [ 1.09597005e+01],\n", + " [ 1.07892909e+01],\n", + " [ 9.28757448e+00],\n", + " [ 9.23136763e+00],\n", + " [ 3.32076841e+00],\n", + " [ 9.88701302e+00],\n", + " [ 1.10513938e+01],\n", + " [ 9.87206222e+00],\n", + " [ 3.69719179e+00],\n", + " [ 8.89266466e+00],\n", + " [ 8.51565609e+00],\n", + " [ 7.35911708e+00],\n", + " [ 4.96393893e+00],\n", + " [ 8.19520177e+00],\n", + " [ 1.03311977e+01],\n", + " [ 7.29400818e+00],\n", + " [ 4.71882395e+00],\n", + " [ 8.12537467e+00],\n", + " [ 1.08183651e+01],\n", + " [ 5.90072136e+00],\n", + " [ 6.27734319e+00],\n", + " [ 5.80991505e+00],\n", + " [ 2.91043162e+00],\n", + " [ 5.09829351e+00],\n", + " [ 6.93730131e+00],\n", + " [ 7.15540547e+00],\n", + " [ 8.76353562e+00],\n", + " [ 8.36519105e+00],\n", + " [ 5.98298411e+00],\n", + " [ 1.07567875e+01],\n", + " [ 7.07130689e+00],\n", + " [ 1.12457370e+00],\n", + " [ 4.09714244e+00],\n", + " [ 8.72488215e+00],\n", + " [ 1.05921805e+01],\n", + " [ 1.59772022e+00],\n", + " [ 8.85940383e+00],\n", + " [ 7.84266561e+00],\n", + " [ 1.27827232e+01],\n", + " [ 5.16958680e+00],\n", + " [ 9.30089380e+00],\n", + " [ 6.96253903e+00],\n", + " [ 2.05219195e+00],\n", + " [ 6.83062615e+00],\n", + " [ 9.62812512e+00],\n", + " [ 1.14344226e+01],\n", + " [ 8.96355838e+00],\n", + " [ 8.32803906e+00],\n", + " [ 8.70168903e+00],\n", + " [ 1.24402910e+01],\n", + " [ 1.04713991e+01],\n", + " [ 1.33231848e+01],\n", + " [ 1.20800385e+01],\n", + " [ 9.04136245e+00],\n", + " [ 9.21521827e+00],\n", + " [ 9.53437498e+00],\n", + " [ 1.10759730e+01],\n", + " [ 1.09273484e+01],\n", + " [ 1.35596574e+01],\n", + " [ 1.36993777e+01],\n", + " [ 1.23569788e+01],\n", + " [ 1.26712341e+01],\n", + " [ 1.03970133e+01],\n", + " [ 1.00411308e+01],\n", + " [ 9.04041478e+00],\n", + " [ 1.34616708e+01],\n", + " [ 1.24760326e+01],\n", + " [ 5.77536107e+00],\n", + " [ 1.16106369e+01],\n", + " [ 6.54459059e+00],\n", + " [ 5.50046426e+00],\n", + " [ 8.86513409e+00],\n", + " [ 5.20184130e+00],\n", + " [ 5.12320561e+00],\n", + " [ 2.79518445e+00],\n", + " [ 1.05231525e+01],\n", + " [ 1.03429559e+01],\n", + " [ 7.54134311e+00],\n", + " [ 9.98276795e+00],\n", + " [ 9.72237088e+00],\n", + " [ 1.28519854e+01],\n", + " [ 1.07154001e+01],\n", + " [ 5.64414831e+00],\n", + " [ 1.14453503e+01],\n", + " [ 8.49531557e+00],\n", + " [ 1.12581557e+01],\n", + " [ 9.88277207e+00],\n", + " [ 1.14508296e+01],\n", + " [ 1.08290159e+01],\n", + " [ 9.99633453e+00],\n", + " [ 1.18039351e+01],\n", + " [ 1.06674011e+01],\n", + " [ 8.16606530e+00],\n", + " [ 8.79479342e+00],\n", + " [ 8.95615375e+00],\n", + " [ 9.23875776e+00],\n", + " [ 7.00588760e+00],\n", + " [ 9.68839353e+00],\n", + " [ 7.09436067e+00],\n", + " [ 7.77168938e+00],\n", + " [ 4.59185819e+00],\n", + " [ 8.53423174e+00],\n", + " [ 6.33318810e+00],\n", + " [ 4.64496991e+00],\n", + " [ 5.42926903e+00],\n", + " [ 8.20939178e+00],\n", + " [ 9.67645487e-01],\n", + " [ 5.64320987e+00],\n", + " [-1.53546809e+00],\n", + " [ 1.25174151e+01],\n", + " [ 1.35566533e+01],\n", + " [ 5.87038118e+00],\n", + " [ 8.27129952e+00],\n", + " [ 9.80342502e+00],\n", + " [ 8.25000403e+00],\n", + " [ 1.39768856e+00],\n", + " [ 6.20703643e+00],\n", + " [ 8.45669706e+00],\n", + " [ 6.20899058e+00],\n", + " [ 1.24511664e+01],\n", + " [ 1.10360322e+01],\n", + " [ 3.07191185e+00],\n", + " [ 9.92497348e+00],\n", + " [ 6.81481719e+00],\n", + " [ 6.97061570e+00],\n", + " [ 6.22873435e+00],\n", + " [ 9.03638454e+00],\n", + " [ 8.98596424e+00],\n", + " [ 9.47099337e+00],\n", + " [ 5.11860407e+00],\n", + " [ 1.09245382e+01],\n", + " [ 8.57401324e+00],\n", + " [ 8.29573770e+00],\n", + " [ 8.01697110e+00],\n", + " [ 7.34710394e+00],\n", + " [ 5.23005489e+00],\n", + " [ 1.72968361e-01],\n", + " [ 5.99859848e+00],\n", + " [ 3.18279768e+00],\n", + " [ 4.86966508e+00],\n", + " [ 6.96441534e+00],\n", + " [ 7.70370216e+00],\n", + " [ 5.85303891e+00],\n", + " [ 7.62869820e+00],\n", + " [ 6.15691603e+00],\n", + " [ 5.71124146e+00],\n", + " [ 3.33439330e+00],\n", + " [ 6.37601513e+00],\n", + " [ 1.56392561e+00],\n", + " [ 8.59574085e+00],\n", + " [ 7.29647987e+00],\n", + " [ 5.55442356e+00],\n", + " [ 5.82204363e+00],\n", + " [ 8.69921413e+00],\n", + " [ 4.38520388e+00],\n", + " [ 2.03782892e+00],\n", + " [ 6.85687041e+00],\n", + " [ 7.00113233e+00],\n", + " [ 9.26517308e+00],\n", + " [ 9.91813369e+00],\n", + " [ 3.62889195e+00],\n", + " [ 1.03157607e+01],\n", + " [ 8.83893824e+00],\n", + " [ 7.04011973e-01],\n", + " [ 6.82032048e+00],\n", + " [ 3.59485831e-01],\n", + " [ 4.05918097e+00],\n", + " [ 6.58880066e-01],\n", + " [ 4.17673270e+00],\n", + " [-1.88189598e+00],\n", + " [ 3.70870404e-01],\n", + " [ 6.66006479e+00],\n", + " [ 4.35281249e+00],\n", + " [ 1.53496752e+00],\n", + " [-2.48796419e+00],\n", + " [-1.77148475e+00],\n", + " [-5.82361933e+00],\n", + " [ 6.23740349e+00],\n", + " [ 7.30217643e-01],\n", + " [ 3.99109767e+00],\n", + " [ 3.63128566e+00],\n", + " [ 8.66912990e+00],\n", + " [ 6.01970079e+00],\n", + " [ 1.44792328e+00],\n", + " [ 6.18577042e+00],\n", + " [ 8.29691533e+00],\n", + " [ 2.32750866e+00],\n", + " [ 6.61226595e+00],\n", + " [ 9.97735570e+00],\n", + " [ 3.75182563e+00],\n", + " [ 3.16903905e+00],\n", + " [ 9.72230287e+00],\n", + " [ 8.92073854e+00],\n", + " [ 5.02533209e+00],\n", + " [ 6.81944733e+00],\n", + " [ 5.02708505e+00],\n", + " [ 1.73438602e+00],\n", + " [ 2.76618727e+00],\n", + " [ 7.77253470e-01],\n", + " [ 1.21332261e+00],\n", + " [ 2.55135250e+00],\n", + " [ 1.29549656e+00],\n", + " [ 2.27000545e+00],\n", + " [ 4.19289153e+00],\n", + " [ 1.10260781e+00],\n", + " [ 2.93285567e+00],\n", + " [ 5.81925667e+00],\n", + " [ 4.65524501e+00],\n", + " [ 3.90234998e+00],\n", + " [ 4.58590601e+00],\n", + " [ 6.11714557e+00],\n", + " [ 4.19507690e+00],\n", + " [ 3.59246674e+00],\n", + " [ 4.36831033e+00],\n", + " [ 2.35737608e+00],\n", + " [ 6.20617011e+00],\n", + " [ 3.63281235e+00],\n", + " [ 2.29977112e+00],\n", + " [ 3.55461078e+00],\n", + " [ 5.68703292e+00],\n", + " [-2.18720658e+00],\n", + " [ 7.78362630e+00],\n", + " [ 7.82365095e+00],\n", + " [ 7.05083146e+00],\n", + " [ 6.80193744e+00],\n", + " [ 5.54220282e+00],\n", + " [ 6.46449047e+00],\n", + " [ 3.08351145e+00],\n", + " [ 4.27498642e+00],\n", + " [ 6.50599620e+00],\n", + " [ 4.63902154e+00],\n", + " [-1.44039507e-01],\n", + " [ 6.37344570e+00],\n", + " [ 8.15194326e-01],\n", + " [ 3.19200341e+00],\n", + " [ 6.28493621e+00],\n", + " [ 5.55377262e+00],\n", + " [ 7.01540474e+00],\n", + " [ 6.02309687e+00],\n", + " [ 6.36720877e+00],\n", + " [ 9.10112274e+00],\n", + " [ 2.07484446e+00],\n", + " [ 4.63376433e+00],\n", + " [-1.89654810e+00],\n", + " [-9.80614238e-01],\n", + " [ 2.95628894e+00],\n", + " [ 1.63957917e-01],\n", + " [-5.86436846e-01],\n", + " [ 1.37258751e+00],\n", + " [-1.96416786e+00],\n", + " [ 2.94840960e+00],\n", + " [ 8.48977768e+00],\n", + " [ 1.24911577e+01],\n", + " [ 1.23268038e+01],\n", + " [ 3.94066123e+00],\n", + " [ 6.63392244e+00],\n", + " [ 5.20004426e+00],\n", + " [ 1.75994629e+00],\n", + " [ 3.64921689e+00],\n", + " [ 3.34886382e+00],\n", + " [ 1.73120303e+00],\n", + " [-2.26997749e-01],\n", + " [ 5.88655815e+00],\n", + " [ 2.16960036e+00],\n", + " [ 4.52182649e+00],\n", + " [ 4.48769044e+00],\n", + " [ 1.29068977e+00],\n", + " [ 1.89032051e+00],\n", + " [ 1.01494484e+00],\n", + " [ 3.53177898e+00],\n", + " [ 2.53633163e+00],\n", + " [ 4.18236291e+00],\n", + " [ 3.19608062e+00],\n", + " [ 4.58472390e+00],\n", + " [ 2.31473214e+00],\n", + " [ 6.24287893e+00],\n", + " [ 5.83749411e+00],\n", + " [ 5.00472479e+00],\n", + " [ 6.30539117e+00],\n", + " [-1.43016561e+00],\n", + " [ 6.46677830e+00],\n", + " [ 1.96684636e+00],\n", + " [ 1.98948709e+00],\n", + " [-1.21127434e-01],\n", + " [ 1.20314985e+01],\n", + " [-3.96961871e+00],\n", + " [-1.01139750e+00],\n", + " [ 4.31613198e+00],\n", + " [ 1.21278659e+00],\n", + " [ 7.56547047e-01],\n", + " [ 6.57972994e+00],\n", + " [ 9.52474339e+00],\n", + " [ 3.39357789e+00],\n", + " [ 4.83957141e+00],\n", + " [ 2.12601451e+00],\n", + " [ 4.85390876e+00],\n", + " [ 1.13795119e+00],\n", + " [ 8.16649859e-01],\n", + " [ 1.45539810e+00],\n", + " [-2.66585597e+00],\n", + " [-3.41136510e+00],\n", + " [ 1.01670579e+00],\n", + " [-1.16506594e+00],\n", + " [ 4.41533697e+00],\n", + " [-2.33409407e+00],\n", + " [ 9.08855388e-01],\n", + " [ 7.90665797e-01],\n", + " [ 3.93864479e+00],\n", + " [-3.04491712e+00],\n", + " [ 2.80087913e+00],\n", + " [-3.16386010e-01],\n", + " [ 3.82264623e+00],\n", + " [-9.29014679e-01],\n", + " [ 3.70596440e+00],\n", + " [ 1.41546594e+00],\n", + " [ 4.24613668e+00],\n", + " [ 4.98067289e+00],\n", + " [ 2.50840643e+00],\n", + " [ 2.81106375e+00],\n", + " [ 6.29378083e-01],\n", + " [-6.44263264e-01],\n", + " [ 2.46719790e-01],\n", + " [ 1.95749423e+00],\n", + " [ 7.45513752e-01],\n", + " [ 2.93346005e-01],\n", + " [-2.62132954e+00],\n", + " [ 4.24079970e+00],\n", + " [ 2.67798151e+00],\n", + " [ 1.43255735e-01],\n", + " [ 3.88717669e+00],\n", + " [-1.81720467e+00],\n", + " [-3.04964722e+00],\n", + " [ 3.14022330e+00],\n", + " [ 5.05376498e-01],\n", + " [ 1.24554771e+00],\n", + " [ 2.17610992e+00],\n", + " [ 2.42435557e+00],\n", + " [-9.53912871e-01],\n", + " [-7.77340028e-01],\n", + " [ 3.38007241e+00],\n", + " [-1.13777816e+00],\n", + " [-1.05686813e-01],\n", + " [ 2.08097579e+00],\n", + " [ 7.84021233e+00],\n", + " [ 4.67222998e+00],\n", + " [ 9.20377221e+00],\n", + " [ 4.75982830e+00],\n", + " [ 1.19939314e+00],\n", + " [ 1.07889450e+01],\n", + " [ 3.59929128e+00],\n", + " [ 1.24240763e+01],\n", + " [ 7.37591745e+00],\n", + " [ 3.72203363e+00],\n", + " [ 4.27431630e+00],\n", + " [ 8.39850322e+00],\n", + " [ 6.85281089e+00],\n", + " [ 4.84574331e+00],\n", + " [ 7.84511566e+00],\n", + " [ 3.90609867e+00],\n", + " [ 5.58685429e+00],\n", + " [ 7.46878099e+00],\n", + " [ 8.79108757e+00],\n", + " [ 1.80571108e+00],\n", + " [ 6.85941778e+00],\n", + " [ 3.16976584e+00],\n", + " [ 3.23739701e+00],\n", + " [ 5.46936525e+00],\n", + " [ 8.17258975e+00],\n", + " [ 3.06214654e+00],\n", + " [ 5.16984768e+00],\n", + " [ 1.08135668e+01],\n", + " [ 1.71669840e+00],\n", + " [ 3.31997278e+00],\n", + " [ 7.04686541e+00],\n", + " [ 7.10199883e+00],\n", + " [ 7.06300563e-01],\n", + " [ 9.00490026e+00],\n", + " [ 1.30367449e+01],\n", + " [ 1.31969746e+01],\n", + " [ 8.83071724e+00],\n", + " [ 6.32369318e+00],\n", + " [ 1.32425797e+01],\n", + " [ 1.14636338e+01],\n", + " [ 1.10667874e+01],\n", + " [ 1.15550103e+01],\n", + " [ 1.25620180e+01],\n", + " [ 8.03107065e+00],\n", + " [ 1.24243056e+01],\n", + " [ 1.21310915e+01],\n", + " [ 1.15767106e+01],\n", + " [ 1.26547981e+01],\n", + " [ 1.31761754e+01],\n", + " [ 1.23534434e+01],\n", + " [ 1.23184121e+01],\n", + " [ 1.29462086e+01],\n", + " [ 3.77004902e+00],\n", + " [ 7.99310409e+00],\n", + " [ 1.03995684e+01],\n", + " [ 1.28116121e+01],\n", + " [ 1.31115695e+01],\n", + " [ 1.32213050e+01],\n", + " [ 1.12310599e+01],\n", + " [ 1.18335535e+01],\n", + " [ 5.77926950e-02],\n", + " [ 1.22020811e+01],\n", + " [ 4.83911852e+00],\n", + " [ 1.46207638e+00],\n", + " [ 1.28764385e+01],\n", + " [ 1.28982330e+01],\n", + " [ 1.23813248e+01],\n", + " [ 1.03392901e+01],\n", + " [ 1.21866800e+01],\n", + " [-3.81641695e+00],\n", + " [ 1.21395117e+01],\n", + " [ 1.12789807e+01],\n", + " [ 1.34734717e+01],\n", + " [ 1.33536150e+01],\n", + " [ 9.95734965e+00],\n", + " [-2.78435998e+00],\n", + " [ 1.22844944e+01],\n", + " [ 1.18425134e+00],\n", + " [ 1.11423892e+01],\n", + " [ 1.37915608e+01],\n", + " [ 1.12187147e+01],\n", + " [ 1.25079631e+01],\n", + " [ 1.35397146e+01],\n", + " [ 1.07361847e+01],\n", + " [ 1.27365611e+01],\n", + " [ 9.62634813e+00],\n", + " [ 1.08893822e+01],\n", + " [ 1.19630408e+01],\n", + " [ 1.16522449e+01],\n", + " [ 1.25965358e+01],\n", + " [ 1.18075953e+01],\n", + " [ 1.10863685e+01],\n", + " [ 1.24126121e+01],\n", + " [ 1.16275580e+01],\n", + " [ 1.08398051e+01],\n", + " [ 1.30605456e+01],\n", + " [ 1.29614936e+01],\n", + " [ 1.35546097e+01],\n", + " [ 1.32246194e+01],\n", + " [ 1.21631868e+01],\n", + " [ 1.24092037e+01],\n", + " [ 1.14885619e+01],\n", + " [ 1.31640867e+01],\n", + " [ 1.27617757e+01],\n", + " [ 1.19324474e+01],\n", + " [ 1.28295196e+01],\n", + " [ 1.19181171e+01],\n", + " [ 1.33030512e+01],\n", + " [ 1.26876893e+01],\n", + " [ 1.28686563e+01],\n", + " [ 1.41476179e+01],\n", + " [ 1.35757393e+01],\n", + " [ 1.28810046e+01],\n", + " [ 1.15404903e+01],\n", + " [ 3.99785237e+00],\n", + " [ 8.40077788e+00],\n", + " [ 7.13163978e+00],\n", + " [ 9.98645858e+00],\n", + " [ 4.85664248e+00],\n", + " [ 3.61773008e+00],\n", + " [ 6.85869211e+00],\n", + " [ 1.66707833e+00],\n", + " [ 1.14140809e+01],\n", + " [ 1.08780282e+01],\n", + " [ 8.39533916e+00],\n", + " [ 1.18611402e+01],\n", + " [ 1.22244943e+01],\n", + " [ 1.16062890e+01],\n", + " [ 6.28498370e+00],\n", + " [ 6.73202600e+00],\n", + " [ 1.09569405e+01],\n", + " [ 1.10295410e+01],\n", + " [ 1.07930196e+01],\n", + " [ 4.86202307e+00],\n", + " [ 6.96604950e+00],\n", + " [ 8.88344941e+00],\n", + " [ 1.23342306e+01],\n", + " [ 1.03919546e+01],\n", + " [ 8.64365513e+00],\n", + " [ 1.14237404e+01],\n", + " [ 1.10741474e+01],\n", + " [ 8.26511872e+00],\n", + " [ 7.23613955e+00],\n", + " [ 9.23300682e+00],\n", + " [ 1.02962926e+01],\n", + " [ 1.10029985e+01],\n", + " [ 1.04234256e+01],\n", + " [ 4.31677200e+00],\n", + " [ 1.05722828e+01],\n", + " [ 9.82711107e+00],\n", + " [ 1.18227446e+01],\n", + " [ 8.45921619e+00],\n", + " [ 9.36180647e+00],\n", + " [ 1.10354537e+01],\n", + " [ 1.07842123e+01],\n", + " [ 1.08661731e+01],\n", + " [ 1.02066579e+01],\n", + " [ 1.07857212e+01],\n", + " [ 5.78778253e+00],\n", + " [ 1.18566358e+01],\n", + " [ 8.04895732e+00],\n", + " [ 9.86710991e+00],\n", + " [ 9.21051874e+00],\n", + " [ 9.67781943e+00],\n", + " [ 1.14533549e+01],\n", + " [ 3.12241224e+00],\n", + " [ 9.21186252e+00],\n", + " [ 1.17279177e+01],\n", + " [ 1.14824081e+01],\n", + " [ 1.02852466e+01],\n", + " [ 5.11784041e+00],\n", + " [ 1.14948411e+01],\n", + " [ 1.25465854e+01],\n", + " [ 9.04332700e+00],\n", + " [ 8.70437907e+00],\n", + " [ 6.19558316e+00],\n", + " [ 8.81783531e+00],\n", + " [ 8.70054057e+00],\n", + " [ 1.00045275e+01],\n", + " [ 6.57622925e+00],\n", + " [ 4.06131131e+00],\n", + " [ 5.43522949e+00],\n", + " [ 1.17113695e+01],\n", + " [ 1.06638653e+01],\n", + " [ 2.50521848e+00],\n", + " [ 1.11934176e+01],\n", + " [ 7.14470033e+00],\n", + " [ 1.05657649e+01],\n", + " [ 6.16922032e+00],\n", + " [ 1.24748759e+01],\n", + " [ 8.93819730e+00],\n", + " [ 6.74683265e+00],\n", + " [ 1.27954172e+01],\n", + " [ 1.06646300e+01],\n", + " [ 7.07958858e+00],\n", + " [ 1.04278553e+01],\n", + " [ 6.37083396e+00],\n", + " [ 8.89056950e+00],\n", + " [ 5.56427173e+00],\n", + " [ 7.32265687e+00],\n", + " [ 6.06553584e+00],\n", + " [ 1.12968509e+01],\n", + " [ 8.65623378e+00],\n", + " [ 1.13352096e+01],\n", + " [ 5.13787840e+00],\n", + " [ 8.00265477e+00],\n", + " [ 6.04050200e+00],\n", + " [ 8.13790170e+00],\n", + " [ 8.08471917e+00],\n", + " [ 7.11227129e+00],\n", + " [ 9.19514312e+00],\n", + " [ 1.38241724e-01],\n", + " [ 1.02634280e+01],\n", + " [ 1.03347334e+01],\n", + " [ 1.06250787e+01],\n", + " [ 6.97151036e+00],\n", + " [ 1.00529753e+01],\n", + " [ 1.16629485e+01],\n", + " [ 1.22851058e+01],\n", + " [ 1.22681021e+01],\n", + " [ 1.17800189e+01],\n", + " [ 9.64275254e+00],\n", + " [ 1.19641756e+01],\n", + " [ 1.24710271e+01],\n", + " [ 1.23447739e+01],\n", + " [ 1.19246291e+01],\n", + " [ 1.27503614e+01],\n", + " [ 1.27816169e+01],\n", + " [ 6.10399724e+00],\n", + " [ 8.38614313e+00],\n", + " [ 9.75970870e+00],\n", + " [ 6.09037115e+00],\n", + " [ 1.23304023e+01],\n", + " [ 1.12502430e+01],\n", + " [ 7.19632710e+00],\n", + " [ 4.38330876e+00],\n", + " [ 7.68611052e+00],\n", + " [ 1.16019220e+01],\n", + " [ 1.04751741e+01],\n", + " [ 1.00848493e+01],\n", + " [ 7.93139908e+00],\n", + " [ 1.28333305e+01],\n", + " [ 1.16699806e+01],\n", + " [ 6.62335538e+00],\n", + " [ 8.52398185e+00],\n", + " [ 6.74082517e+00],\n", + " [ 1.12956588e+01],\n", + " [ 1.12160934e+01],\n", + " [ 1.10262427e+01],\n", + " [ 1.08935413e+01],\n", + " [ 1.04934275e+01],\n", + " [ 1.14373425e+01],\n", + " [ 1.24919744e+01],\n", + " [ 9.78647811e+00],\n", + " [ 7.47778845e+00],\n", + " [ 1.14302773e+01],\n", + " [ 3.68535094e+00],\n", + " [ 8.40723849e+00],\n", + " [ 9.70332926e+00],\n", + " [ 1.09785423e+01],\n", + " [ 9.55174640e+00],\n", + " [ 1.15988472e+01],\n", + " [ 9.15299271e+00],\n", + " [ 6.51783025e+00],\n", + " [ 6.58802065e+00],\n", + " [ 7.57916515e+00],\n", + " [ 3.33134652e+00],\n", + " [ 8.07860624e+00],\n", + " [ 3.70034079e+00],\n", + " [ 1.23112503e+01],\n", + " [ 8.08423057e+00],\n", + " [ 8.18254426e+00],\n", + " [ 6.45212175e+00],\n", + " [ 6.77407453e+00],\n", + " [ 3.62971961e+00],\n", + " [ 9.21336624e+00],\n", + " [ 1.01983215e+01],\n", + " [ 8.92042109e+00],\n", + " [ 1.10570105e+01],\n", + " [ 1.05379203e+01],\n", + " [ 1.40951910e+01],\n", + " [ 1.29461899e+01],\n", + " [ 1.14206783e+01],\n", + " [ 1.21295290e+01],\n", + " [ 1.06667034e+01],\n", + " [ 1.10310706e+01],\n", + " [ 1.19490698e+01],\n", + " [ 9.07473165e+00],\n", + " [ 1.02485993e+01],\n", + " [ 1.11321036e+01],\n", + " [ 6.77535903e+00],\n", + " [ 9.18847343e+00],\n", + " [ 5.79014106e+00],\n", + " [ 6.64145865e+00],\n", + " [-7.99679670e-01],\n", + " [ 7.15844762e+00],\n", + " [ 7.05186656e+00],\n", + " [ 1.17801776e+01],\n", + " [ 6.84882993e+00],\n", + " [ 2.29422550e+00],\n", + " [ 7.74116138e+00],\n", + " [ 8.31076721e+00],\n", + " [-4.37405000e-01],\n", + " [ 8.78704188e+00],\n", + " [ 8.75213549e+00],\n", + " [ 7.54031240e+00],\n", + " [ 6.98037600e+00],\n", + " [ 7.58031459e+00],\n", + " [ 8.00082975e+00],\n", + " [ 9.81457607e+00],\n", + " [ 8.09847216e+00],\n", + " [ 7.47953423e+00],\n", + " [ 6.64526442e+00],\n", + " [ 2.78471997e+00],\n", + " [ 5.81705852e+00],\n", + " [ 7.04239293e+00],\n", + " [ 5.95680224e+00],\n", + " [ 8.24730052e+00],\n", + " [ 3.28174434e+00],\n", + " [ 9.52896409e+00],\n", + " [ 6.29912937e+00],\n", + " [ 1.00417614e+01],\n", + " [ 5.06506591e+00],\n", + " [ 8.91512535e+00],\n", + " [ 5.60398020e+00],\n", + " [ 1.98442787e+00],\n", + " [ 4.58944860e-01],\n", + " [ 5.29521364e+00],\n", + " [ 8.11433615e+00],\n", + " [ 6.94916004e+00],\n", + " [ 1.05893875e+01],\n", + " [ 6.79252835e+00],\n", + " [ 1.11950717e+01],\n", + " [ 5.20452890e+00],\n", + " [ 8.66449269e+00],\n", + " [ 8.76294688e+00],\n", + " [ 8.78308635e+00],\n", + " [ 1.00277231e+01],\n", + " [ 6.39601122e+00],\n", + " [ 1.27749305e+01],\n", + " [ 1.01378890e+01],\n", + " [ 8.85206297e+00],\n", + " [ 8.52558945e+00],\n", + " [ 7.42543149e+00],\n", + " [ 3.22105924e+00],\n", + " [-1.58233029e+00],\n", + " [ 4.80193567e-01],\n", + " [-4.41476109e+00],\n", + " [ 3.90429557e+00],\n", + " [ 2.14501400e+00],\n", + " [ 2.36844502e+00],\n", + " [ 7.90469157e-01],\n", + " [ 3.30768837e+00],\n", + " [ 1.81438886e+00],\n", + " [ 4.62191989e+00],\n", + " [ 6.16250039e-01],\n", + " [ 4.61636250e-01],\n", + " [ 7.14152189e+00],\n", + " [ 8.87744421e-01],\n", + " [-1.33893959e+00],\n", + " [ 4.98941977e+00],\n", + " [ 8.20747559e+00],\n", + " [ 8.60173439e+00],\n", + " [-1.93325395e+00],\n", + " [ 5.90818540e+00],\n", + " [-8.16352256e-01],\n", + " [ 6.67541903e+00],\n", + " [ 7.31333803e+00],\n", + " [ 6.41107087e+00],\n", + " [ 5.82342013e+00],\n", + " [ 9.02343032e+00],\n", + " [ 7.27778002e+00],\n", + " [ 7.15951526e+00],\n", + " [ 6.89522862e+00],\n", + " [ 1.05898347e+01],\n", + " [-1.49821327e+00],\n", + " [-5.38834351e-01],\n", + " [ 1.48744389e+00],\n", + " [ 4.05464673e+00],\n", + " [-2.92021228e+00],\n", + " [-1.99530446e+00],\n", + " [-8.69866422e-02],\n", + " [ 6.91530717e-01],\n", + " [-2.72996647e+00],\n", + " [ 2.86672045e+00],\n", + " [-3.24577111e+00],\n", + " [ 2.60887123e-02],\n", + " [ 5.63689677e+00],\n", + " [ 3.96514332e+00],\n", + " [ 5.06037350e+00],\n", + " [ 7.73864504e+00],\n", + " [ 1.05156710e+01],\n", + " [ 5.98027853e+00],\n", + " [ 2.81176498e+00],\n", + " [ 3.34660201e+00],\n", + " [ 2.25166836e+00],\n", + " [ 7.66314503e+00],\n", + " [ 2.24652097e+00],\n", + " [ 2.32755781e+00],\n", + " [ 3.59822826e+00],\n", + " [ 1.90937751e+00],\n", + " [ 2.42272368e+00],\n", + " [ 1.32169298e+00],\n", + " [ 6.37000220e+00],\n", + " [ 1.10946112e+00],\n", + " [ 2.24131160e+00],\n", + " [ 2.89038522e+00],\n", + " [ 2.07893264e+00],\n", + " [ 3.93463422e+00],\n", + " [ 1.54531072e+00],\n", + " [ 6.78489477e+00],\n", + " [ 6.54089099e+00],\n", + " [ 4.49760409e+00],\n", + " [ 1.15273803e+01],\n", + " [ 3.40350942e+00],\n", + " [-5.20514768e-01],\n", + " [ 6.88011818e+00],\n", + " [ 2.09188021e+00],\n", + " [ 4.27713980e-01],\n", + " [-3.80103571e-02],\n", + " [-3.68674279e+00],\n", + " [ 1.62571424e+00],\n", + " [ 3.47679000e-01],\n", + " [-8.26182415e-01],\n", + " [-2.72819152e+00],\n", + " [-6.21341757e-01],\n", + " [ 6.51203018e-01],\n", + " [ 1.37855914e+00],\n", + " [ 2.25411975e+00],\n", + " [ 6.32742151e+00],\n", + " [ 2.09348229e+00],\n", + " [ 2.33428700e+00],\n", + " [ 8.23559314e+00],\n", + " [ 6.72550202e+00],\n", + " [ 4.37827682e+00],\n", + " [ 6.22928514e+00],\n", + " [ 4.05125391e+00],\n", + " [ 1.75549684e+00],\n", + " [ 4.57888928e+00],\n", + " [ 1.02531662e+00],\n", + " [ 6.41694386e+00],\n", + " [ 9.17579307e-01],\n", + " [-5.61583205e-01],\n", + " [ 7.37156754e-01],\n", + " [ 1.15138886e+01],\n", + " [ 1.14676233e+01],\n", + " [ 1.01659469e+01],\n", + " [ 6.98158543e+00],\n", + " [ 4.64844269e+00],\n", + " [ 7.71811737e+00],\n", + " [ 3.22860259e+00],\n", + " [ 3.79504811e+00],\n", + " [ 2.16141215e+00],\n", + " [-3.89092163e-01],\n", + " [ 1.79513503e+00],\n", + " [ 6.03315885e+00],\n", + " [ 4.49056473e-01],\n", + " [-4.71906885e-01],\n", + " [ 1.00520417e+00],\n", + " [ 2.18093315e+00],\n", + " [ 2.73461109e+00],\n", + " [ 3.69525980e+00],\n", + " [ 2.51372825e+00],\n", + " [ 5.43269787e+00],\n", + " [ 7.49324283e+00],\n", + " [ 2.19084219e+00],\n", + " [ 2.00087520e+00],\n", + " [ 3.55890153e+00],\n", + " [ 9.76834003e+00],\n", + " [-2.92004443e-01],\n", + " [ 8.07035073e+00],\n", + " [-2.17323215e+00],\n", + " [ 7.64991113e+00],\n", + " [ 2.63273868e+00],\n", + " [-2.95454512e+00],\n", + " [ 9.85965581e+00],\n", + " [ 1.00518400e+01],\n", + " [ 5.28372913e+00],\n", + " [ 7.86336436e-01],\n", + " [ 4.85915059e+00],\n", + " [ 6.08799316e+00],\n", + " [ 6.83163734e+00],\n", + " [ 3.23680558e+00],\n", + " [-4.53295270e-01],\n", + " [ 6.85993090e+00],\n", + " [ 3.95176101e+00],\n", + " [ 3.89126816e+00],\n", + " [-1.23150949e+00],\n", + " [ 5.47864100e+00],\n", + " [ 3.00006747e+00],\n", + " [ 3.35183572e+00],\n", + " [ 2.02985690e+00],\n", + " [ 2.99663740e+00],\n", + " [ 4.35027830e+00],\n", + " [ 4.18560496e+00],\n", + " [ 3.69271138e+00],\n", + " [ 2.93910341e+00],\n", + " [ 8.85063930e-01],\n", + " [ 3.14885352e+00],\n", + " [ 5.14382401e-01],\n", + " [-8.42465873e-01],\n", + " [ 1.03514879e+00],\n", + " [-2.27501612e-01],\n", + " [ 6.97710251e-03],\n", + " [ 6.49003589e+00],\n", + " [-4.13478181e+00],\n", + " [-1.34772256e+00],\n", + " [ 3.25961188e+00],\n", + " [-3.14417936e+00],\n", + " [ 1.97618407e+00],\n", + " [ 5.10743228e+00],\n", + " [-2.92314738e-02],\n", + " [ 7.58638530e+00],\n", + " [ 8.27106333e+00],\n", + " [ 7.66124939e+00],\n", + " [ 1.07593771e+01],\n", + " [ 3.27429854e+00],\n", + " [ 4.81825766e+00],\n", + " [ 5.13889085e+00],\n", + " [ 6.31923316e+00],\n", + " [ 7.60833564e+00],\n", + " [ 4.25220006e+00],\n", + " [ 7.62106881e+00],\n", + " [ 4.36385688e+00],\n", + " [ 5.09672327e+00],\n", + " [-2.91601073e+00],\n", + " [ 3.73698348e+00],\n", + " [ 4.27102970e+00],\n", + " [ 1.04073214e+01],\n", + " [ 6.64936600e+00],\n", + " [ 7.12546793e+00],\n", + " [ 6.27962898e+00]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TruncatedSVD(d[0][0]).fit_transform(b).shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_excel(r'/Users/msanch35/Downloads/phenotype_table_discovery.xlsx')\n", + "df = df.dropna()\n", + "data2 = []\n", + "for i in df['ID_set']:\n", + " with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_' + str(i) + '_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f)\n", + " b = np.corrcoef(data)\n", + " data2.append(b[np.triu_indices(b.shape[0])])\n", + "X = np.array(df)\n", + "y = np.array(data2)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.04747899e+01, 1.18872362e+01, 1.02263946e+01, 1.14160653e+01,\n", + " 9.99988573e+00, 1.30431734e+01, 9.35384872e+00, 1.31792633e+01,\n", + " 1.32559428e+01, 1.05784854e+01, 1.25320073e+01, 1.24298931e+01,\n", + " 1.34657054e+01, 1.03081766e+01, 1.19430138e+01, 1.31792557e+01,\n", + " 1.23558596e+01, 5.40709325e+00, 1.33259227e+01, 1.06232459e+01,\n", + " 1.09659699e+01, 1.09787272e+01, 1.28963595e+01, 4.86615386e+00,\n", + " 1.33591527e+01, 1.30126194e+01, 9.50490455e+00, -2.64223480e+00,\n", + " 1.24250845e+01, 2.68644166e+00, 1.03593227e+01, 1.28808272e+01,\n", + " 4.26513131e+00, 9.97973204e+00, 1.32843423e+01, 1.25056590e+01,\n", + " 1.27649695e+01, 1.38840269e+01, 1.07389041e+01, 2.28235521e+00,\n", + " 7.04781373e+00, 1.17010299e+01, 1.20796971e+01, 2.27018206e+00,\n", + " 1.12392349e+01, 1.33385853e+01, 1.34861467e+01, 5.89651366e+00,\n", + " 1.00018490e+01, 1.33398238e+01, 1.24043262e+01, 1.08842763e+01,\n", + " 1.16788935e+01, 8.45107106e+00, 9.37463231e+00, 1.39491067e+01,\n", + " 1.31805442e+01, 1.19611627e+01, 1.28269608e+01, 1.24699707e+01,\n", + " 1.46485249e+01, 1.17688289e+01, 1.37077797e+01, 1.25350679e+01,\n", + " 1.33368784e+01, 1.18901842e+01, 1.33220322e+01, 1.31792532e+01,\n", + " 1.28611479e+01, 1.27927689e+01, 1.27485808e+01, 1.26261071e+01,\n", + " 1.29599041e+01, 1.24955774e+01, 1.33283036e+01, 1.32548831e+01,\n", + " 1.31935010e+01, 1.38049493e+01, 1.45160302e+01, 1.34231396e+01,\n", + " 9.59772302e+00, 8.85994682e+00, 1.08314452e+01, 1.00280548e+01,\n", + " 1.08967909e+01, 9.50456296e+00, 8.45030094e+00, 1.16459495e+01,\n", + " 1.13727091e+01, 1.16023566e+01, 6.05163105e+00, 1.10246443e+01,\n", + " 8.35199760e+00, 1.19088076e+01, 1.00970960e+01, 9.47199935e+00,\n", + " 7.87734173e+00, 1.14781701e+01, 1.11101995e+01, 1.33994553e+01,\n", + " 1.26095853e+01, 1.16012045e+01, 8.36779960e+00, 1.15862588e+01,\n", + " 8.19943057e+00, 1.08035590e+01, 1.04570017e+01, 8.69671540e+00,\n", + " 1.02478784e+01, 7.74244141e+00, 1.03208536e+01, 8.75906940e+00,\n", + " 3.29460923e+00, 7.24474287e+00, 9.46373366e+00, 9.11343349e+00,\n", + " 1.05270764e+01, 1.15244694e+01, 1.17170799e+01, 1.24012190e+01,\n", + " 1.13590560e+01, 7.94686784e+00, 1.16047672e+01, 1.09597005e+01,\n", + " 1.07892909e+01, 9.28757448e+00, 9.23136763e+00, 3.32076841e+00,\n", + " 9.88701302e+00, 1.10513938e+01, 9.87206222e+00, 3.69719179e+00,\n", + " 8.89266466e+00, 8.51565609e+00, 7.35911708e+00, 4.96393893e+00,\n", + " 8.19520177e+00, 1.03311977e+01, 7.29400818e+00, 4.71882395e+00,\n", + " 8.12537467e+00, 1.08183651e+01, 5.90072136e+00, 6.27734319e+00,\n", + " 5.80991505e+00, 2.91043162e+00, 5.09829351e+00, 6.93730131e+00,\n", + " 7.15540547e+00, 8.76353562e+00, 8.36519105e+00, 5.98298411e+00,\n", + " 1.07567875e+01, 7.07130689e+00, 1.12457370e+00, 4.09714244e+00,\n", + " 8.72488215e+00, 1.05921805e+01, 1.59772022e+00, 8.85940383e+00,\n", + " 7.84266561e+00, 1.27827232e+01, 5.16958680e+00, 9.30089380e+00,\n", + " 6.96253903e+00, 2.05219195e+00, 6.83062615e+00, 9.62812512e+00,\n", + " 1.14344226e+01, 8.96355838e+00, 8.32803906e+00, 8.70168903e+00,\n", + " 1.24402910e+01, 1.04713991e+01, 1.33231848e+01, 1.20800385e+01,\n", + " 9.04136245e+00, 9.21521827e+00, 9.53437498e+00, 1.10759730e+01,\n", + " 1.09273484e+01, 1.35596574e+01, 1.36993777e+01, 1.23569788e+01,\n", + " 1.26712341e+01, 1.03970133e+01, 1.00411308e+01, 9.04041478e+00,\n", + " 1.34616708e+01, 1.24760326e+01, 5.77536107e+00, 1.16106369e+01,\n", + " 6.54459059e+00, 5.50046426e+00, 8.86513409e+00, 5.20184130e+00,\n", + " 5.12320561e+00, 2.79518445e+00, 1.05231525e+01, 1.03429559e+01,\n", + " 7.54134311e+00, 9.98276795e+00, 9.72237088e+00, 1.28519854e+01,\n", + " 1.07154001e+01, 5.64414831e+00, 1.14453503e+01, 8.49531557e+00,\n", + " 1.12581557e+01, 9.88277207e+00, 1.14508296e+01, 1.08290159e+01,\n", + " 9.99633453e+00, 1.18039351e+01, 1.06674011e+01, 8.16606530e+00,\n", + " 8.79479342e+00, 8.95615375e+00, 9.23875776e+00, 7.00588760e+00,\n", + " 9.68839353e+00, 7.09436067e+00, 7.77168938e+00, 4.59185819e+00,\n", + " 8.53423174e+00, 6.33318810e+00, 4.64496991e+00, 5.42926903e+00,\n", + " 8.20939178e+00, 9.67645487e-01, 5.64320987e+00, -1.53546809e+00,\n", + " 1.25174151e+01, 1.35566533e+01, 5.87038118e+00, 8.27129952e+00,\n", + " 9.80342502e+00, 8.25000403e+00, 1.39768856e+00, 6.20703643e+00,\n", + " 8.45669706e+00, 6.20899058e+00, 1.24511664e+01, 1.10360322e+01,\n", + " 3.07191185e+00, 9.92497348e+00, 6.81481719e+00, 6.97061570e+00,\n", + " 6.22873435e+00, 9.03638454e+00, 8.98596424e+00, 9.47099337e+00,\n", + " 5.11860407e+00, 1.09245382e+01, 8.57401324e+00, 8.29573770e+00,\n", + " 8.01697110e+00, 7.34710394e+00, 5.23005489e+00, 1.72968361e-01,\n", + " 5.99859848e+00, 3.18279768e+00, 4.86966508e+00, 6.96441534e+00,\n", + " 7.70370216e+00, 5.85303891e+00, 7.62869820e+00, 6.15691603e+00,\n", + " 5.71124146e+00, 3.33439330e+00, 6.37601513e+00, 1.56392561e+00,\n", + " 8.59574085e+00, 7.29647987e+00, 5.55442356e+00, 5.82204363e+00,\n", + " 8.69921413e+00, 4.38520388e+00, 2.03782892e+00, 6.85687041e+00,\n", + " 7.00113233e+00, 9.26517308e+00, 9.91813369e+00, 3.62889195e+00,\n", + " 1.03157607e+01, 8.83893824e+00, 7.04011973e-01, 6.82032048e+00,\n", + " 3.59485831e-01, 4.05918097e+00, 6.58880066e-01, 4.17673270e+00,\n", + " -1.88189598e+00, 3.70870404e-01, 6.66006479e+00, 4.35281249e+00,\n", + " 1.53496752e+00, -2.48796419e+00, -1.77148475e+00, -5.82361933e+00,\n", + " 6.23740349e+00, 7.30217643e-01, 3.99109767e+00, 3.63128566e+00,\n", + " 8.66912990e+00, 6.01970079e+00, 1.44792328e+00, 6.18577042e+00,\n", + " 8.29691533e+00, 2.32750866e+00, 6.61226595e+00, 9.97735570e+00,\n", + " 3.75182563e+00, 3.16903905e+00, 9.72230287e+00, 8.92073854e+00,\n", + " 5.02533209e+00, 6.81944733e+00, 5.02708505e+00, 1.73438602e+00,\n", + " 2.76618727e+00, 7.77253470e-01, 1.21332261e+00, 2.55135250e+00,\n", + " 1.29549656e+00, 2.27000545e+00, 4.19289153e+00, 1.10260781e+00,\n", + " 2.93285567e+00, 5.81925667e+00, 4.65524501e+00, 3.90234998e+00,\n", + " 4.58590601e+00, 6.11714557e+00, 4.19507690e+00, 3.59246674e+00,\n", + " 4.36831033e+00, 2.35737608e+00, 6.20617011e+00, 3.63281235e+00,\n", + " 2.29977112e+00, 3.55461078e+00, 5.68703292e+00, -2.18720658e+00,\n", + " 7.78362630e+00, 7.82365095e+00, 7.05083146e+00, 6.80193744e+00,\n", + " 5.54220282e+00, 6.46449047e+00, 3.08351145e+00, 4.27498642e+00,\n", + " 6.50599620e+00, 4.63902154e+00, -1.44039507e-01, 6.37344570e+00,\n", + " 8.15194326e-01, 3.19200341e+00, 6.28493621e+00, 5.55377262e+00,\n", + " 7.01540474e+00, 6.02309687e+00, 6.36720877e+00, 9.10112274e+00,\n", + " 2.07484446e+00, 4.63376433e+00, -1.89654810e+00, -9.80614238e-01,\n", + " 2.95628894e+00, 1.63957917e-01, -5.86436846e-01, 1.37258751e+00,\n", + " -1.96416786e+00, 2.94840960e+00, 8.48977768e+00, 1.24911577e+01,\n", + " 1.23268038e+01, 3.94066123e+00, 6.63392244e+00, 5.20004426e+00,\n", + " 1.75994629e+00, 3.64921689e+00, 3.34886382e+00, 1.73120303e+00,\n", + " -2.26997749e-01, 5.88655815e+00, 2.16960036e+00, 4.52182649e+00,\n", + " 4.48769044e+00, 1.29068977e+00, 1.89032051e+00, 1.01494484e+00,\n", + " 3.53177898e+00, 2.53633163e+00, 4.18236291e+00, 3.19608062e+00,\n", + " 4.58472390e+00, 2.31473214e+00, 6.24287893e+00, 5.83749411e+00,\n", + " 5.00472479e+00, 6.30539117e+00, -1.43016561e+00, 6.46677830e+00,\n", + " 1.96684636e+00, 1.98948709e+00, -1.21127434e-01, 1.20314985e+01,\n", + " -3.96961871e+00, -1.01139750e+00, 4.31613198e+00, 1.21278659e+00,\n", + " 7.56547047e-01, 6.57972994e+00, 9.52474339e+00, 3.39357789e+00,\n", + " 4.83957141e+00, 2.12601451e+00, 4.85390876e+00, 1.13795119e+00,\n", + " 8.16649859e-01, 1.45539810e+00, -2.66585597e+00, -3.41136510e+00,\n", + " 1.01670579e+00, -1.16506594e+00, 4.41533697e+00, -2.33409407e+00,\n", + " 9.08855388e-01, 7.90665797e-01, 3.93864479e+00, -3.04491712e+00,\n", + " 2.80087913e+00, -3.16386010e-01, 3.82264623e+00, -9.29014679e-01,\n", + " 3.70596440e+00, 1.41546594e+00, 4.24613668e+00, 4.98067289e+00,\n", + " 2.50840643e+00, 2.81106375e+00, 6.29378083e-01, -6.44263264e-01,\n", + " 2.46719790e-01, 1.95749423e+00, 7.45513752e-01, 2.93346005e-01,\n", + " -2.62132954e+00, 4.24079970e+00, 2.67798151e+00, 1.43255735e-01,\n", + " 3.88717669e+00, -1.81720467e+00, -3.04964722e+00, 3.14022330e+00,\n", + " 5.05376498e-01, 1.24554771e+00, 2.17610992e+00, 2.42435557e+00,\n", + " -9.53912871e-01, -7.77340028e-01, 3.38007241e+00, -1.13777816e+00,\n", + " -1.05686813e-01, 2.08097579e+00, 7.84021233e+00, 4.67222998e+00,\n", + " 9.20377221e+00, 4.75982830e+00, 1.19939314e+00, 1.07889450e+01,\n", + " 3.59929128e+00, 1.24240763e+01, 7.37591745e+00, 3.72203363e+00,\n", + " 4.27431630e+00, 8.39850322e+00, 6.85281089e+00, 4.84574331e+00,\n", + " 7.84511566e+00, 3.90609867e+00, 5.58685429e+00, 7.46878099e+00,\n", + " 8.79108757e+00, 1.80571108e+00, 6.85941778e+00, 3.16976584e+00,\n", + " 3.23739701e+00, 5.46936525e+00, 8.17258975e+00, 3.06214654e+00,\n", + " 5.16984768e+00, 1.08135668e+01, 1.71669840e+00, 3.31997278e+00,\n", + " 7.04686541e+00, 7.10199883e+00, 7.06300563e-01, 9.00490026e+00,\n", + " 1.30367449e+01, 1.31969746e+01, 8.83071724e+00, 6.32369318e+00,\n", + " 1.32425797e+01, 1.14636338e+01, 1.10667874e+01, 1.15550103e+01,\n", + " 1.25620180e+01, 8.03107065e+00, 1.24243056e+01, 1.21310915e+01,\n", + " 1.15767106e+01, 1.26547981e+01, 1.31761754e+01, 1.23534434e+01,\n", + " 1.23184121e+01, 1.29462086e+01, 3.77004902e+00, 7.99310409e+00,\n", + " 1.03995684e+01, 1.28116121e+01, 1.31115695e+01, 1.32213050e+01,\n", + " 1.12310599e+01, 1.18335535e+01, 5.77926950e-02, 1.22020811e+01,\n", + " 4.83911852e+00, 1.46207638e+00, 1.28764385e+01, 1.28982330e+01,\n", + " 1.23813248e+01, 1.03392901e+01, 1.21866800e+01, -3.81641695e+00,\n", + " 1.21395117e+01, 1.12789807e+01, 1.34734717e+01, 1.33536150e+01,\n", + " 9.95734965e+00, -2.78435998e+00, 1.22844944e+01, 1.18425134e+00,\n", + " 1.11423892e+01, 1.37915608e+01, 1.12187147e+01, 1.25079631e+01,\n", + " 1.35397146e+01, 1.07361847e+01, 1.27365611e+01, 9.62634813e+00,\n", + " 1.08893822e+01, 1.19630408e+01, 1.16522449e+01, 1.25965358e+01,\n", + " 1.18075953e+01, 1.10863685e+01, 1.24126121e+01, 1.16275580e+01,\n", + " 1.08398051e+01, 1.30605456e+01, 1.29614936e+01, 1.35546097e+01,\n", + " 1.32246194e+01, 1.21631868e+01, 1.24092037e+01, 1.14885619e+01,\n", + " 1.31640867e+01, 1.27617757e+01, 1.19324474e+01, 1.28295196e+01,\n", + " 1.19181171e+01, 1.33030512e+01, 1.26876893e+01, 1.28686563e+01,\n", + " 1.41476179e+01, 1.35757393e+01, 1.28810046e+01, 1.15404903e+01,\n", + " 3.99785237e+00, 8.40077788e+00, 7.13163978e+00, 9.98645858e+00,\n", + " 4.85664248e+00, 3.61773008e+00, 6.85869211e+00, 1.66707833e+00,\n", + " 1.14140809e+01, 1.08780282e+01, 8.39533916e+00, 1.18611402e+01,\n", + " 1.22244943e+01, 1.16062890e+01, 6.28498370e+00, 6.73202600e+00,\n", + " 1.09569405e+01, 1.10295410e+01, 1.07930196e+01, 4.86202307e+00,\n", + " 6.96604950e+00, 8.88344941e+00, 1.23342306e+01, 1.03919546e+01,\n", + " 8.64365513e+00, 1.14237404e+01, 1.10741474e+01, 8.26511872e+00,\n", + " 7.23613955e+00, 9.23300682e+00, 1.02962926e+01, 1.10029985e+01,\n", + " 1.04234256e+01, 4.31677200e+00, 1.05722828e+01, 9.82711107e+00,\n", + " 1.18227446e+01, 8.45921619e+00, 9.36180647e+00, 1.10354537e+01,\n", + " 1.07842123e+01, 1.08661731e+01, 1.02066579e+01, 1.07857212e+01,\n", + " 5.78778253e+00, 1.18566358e+01, 8.04895732e+00, 9.86710991e+00,\n", + " 9.21051874e+00, 9.67781943e+00, 1.14533549e+01, 3.12241224e+00,\n", + " 9.21186252e+00, 1.17279177e+01, 1.14824081e+01, 1.02852466e+01,\n", + " 5.11784041e+00, 1.14948411e+01, 1.25465854e+01, 9.04332700e+00,\n", + " 8.70437907e+00, 6.19558316e+00, 8.81783531e+00, 8.70054057e+00,\n", + " 1.00045275e+01, 6.57622925e+00, 4.06131131e+00, 5.43522949e+00,\n", + " 1.17113695e+01, 1.06638653e+01, 2.50521848e+00, 1.11934176e+01,\n", + " 7.14470033e+00, 1.05657649e+01, 6.16922032e+00, 1.24748759e+01,\n", + " 8.93819730e+00, 6.74683265e+00, 1.27954172e+01, 1.06646300e+01,\n", + " 7.07958858e+00, 1.04278553e+01, 6.37083396e+00, 8.89056950e+00,\n", + " 5.56427173e+00, 7.32265687e+00, 6.06553584e+00, 1.12968509e+01,\n", + " 8.65623378e+00, 1.13352096e+01, 5.13787840e+00, 8.00265477e+00,\n", + " 6.04050200e+00, 8.13790170e+00, 8.08471917e+00, 7.11227129e+00,\n", + " 9.19514312e+00, 1.38241724e-01, 1.02634280e+01, 1.03347334e+01,\n", + " 1.06250787e+01, 6.97151036e+00, 1.00529753e+01, 1.16629485e+01,\n", + " 1.22851058e+01, 1.22681021e+01, 1.17800189e+01, 9.64275254e+00,\n", + " 1.19641756e+01, 1.24710271e+01, 1.23447739e+01, 1.19246291e+01,\n", + " 1.27503614e+01, 1.27816169e+01, 6.10399724e+00, 8.38614313e+00,\n", + " 9.75970870e+00, 6.09037115e+00, 1.23304023e+01, 1.12502430e+01,\n", + " 7.19632710e+00, 4.38330876e+00, 7.68611052e+00, 1.16019220e+01,\n", + " 1.04751741e+01, 1.00848493e+01, 7.93139908e+00, 1.28333305e+01,\n", + " 1.16699806e+01, 6.62335538e+00, 8.52398185e+00, 6.74082517e+00,\n", + " 1.12956588e+01, 1.12160934e+01, 1.10262427e+01, 1.08935413e+01,\n", + " 1.04934275e+01, 1.14373425e+01, 1.24919744e+01, 9.78647811e+00,\n", + " 7.47778845e+00, 1.14302773e+01, 3.68535094e+00, 8.40723849e+00,\n", + " 9.70332926e+00, 1.09785423e+01, 9.55174640e+00, 1.15988472e+01,\n", + " 9.15299271e+00, 6.51783025e+00, 6.58802065e+00, 7.57916515e+00,\n", + " 3.33134652e+00, 8.07860624e+00, 3.70034079e+00, 1.23112503e+01,\n", + " 8.08423057e+00, 8.18254426e+00, 6.45212175e+00, 6.77407453e+00,\n", + " 3.62971961e+00, 9.21336624e+00, 1.01983215e+01, 8.92042109e+00,\n", + " 1.10570105e+01, 1.05379203e+01, 1.40951910e+01, 1.29461899e+01,\n", + " 1.14206783e+01, 1.21295290e+01, 1.06667034e+01, 1.10310706e+01,\n", + " 1.19490698e+01, 9.07473165e+00, 1.02485993e+01, 1.11321036e+01,\n", + " 6.77535903e+00, 9.18847343e+00, 5.79014106e+00, 6.64145865e+00,\n", + " -7.99679670e-01, 7.15844762e+00, 7.05186656e+00, 1.17801776e+01,\n", + " 6.84882993e+00, 2.29422550e+00, 7.74116138e+00, 8.31076721e+00,\n", + " -4.37405000e-01, 8.78704188e+00, 8.75213549e+00, 7.54031240e+00,\n", + " 6.98037600e+00, 7.58031459e+00, 8.00082975e+00, 9.81457607e+00,\n", + " 8.09847216e+00, 7.47953423e+00, 6.64526442e+00, 2.78471997e+00,\n", + " 5.81705852e+00, 7.04239293e+00, 5.95680224e+00, 8.24730052e+00,\n", + " 3.28174434e+00, 9.52896409e+00, 6.29912937e+00, 1.00417614e+01,\n", + " 5.06506591e+00, 8.91512535e+00, 5.60398020e+00, 1.98442787e+00,\n", + " 4.58944860e-01, 5.29521364e+00, 8.11433615e+00, 6.94916004e+00,\n", + " 1.05893875e+01, 6.79252835e+00, 1.11950717e+01, 5.20452890e+00,\n", + " 8.66449269e+00, 8.76294688e+00, 8.78308635e+00, 1.00277231e+01,\n", + " 6.39601122e+00, 1.27749305e+01, 1.01378890e+01, 8.85206297e+00,\n", + " 8.52558945e+00, 7.42543149e+00, 3.22105924e+00, -1.58233029e+00,\n", + " 4.80193567e-01, -4.41476109e+00, 3.90429557e+00, 2.14501400e+00,\n", + " 2.36844502e+00, 7.90469157e-01, 3.30768837e+00, 1.81438886e+00,\n", + " 4.62191989e+00, 6.16250039e-01, 4.61636250e-01, 7.14152189e+00,\n", + " 8.87744421e-01, -1.33893959e+00, 4.98941977e+00, 8.20747559e+00,\n", + " 8.60173439e+00, -1.93325395e+00, 5.90818540e+00, -8.16352256e-01,\n", + " 6.67541903e+00, 7.31333803e+00, 6.41107087e+00, 5.82342013e+00,\n", + " 9.02343032e+00, 7.27778002e+00, 7.15951526e+00, 6.89522862e+00,\n", + " 1.05898347e+01, -1.49821327e+00, -5.38834351e-01, 1.48744389e+00,\n", + " 4.05464673e+00, -2.92021228e+00, -1.99530446e+00, -8.69866422e-02,\n", + " 6.91530717e-01, -2.72996647e+00, 2.86672045e+00, -3.24577111e+00,\n", + " 2.60887123e-02, 5.63689677e+00, 3.96514332e+00, 5.06037350e+00,\n", + " 7.73864504e+00, 1.05156710e+01, 5.98027853e+00, 2.81176498e+00,\n", + " 3.34660201e+00, 2.25166836e+00, 7.66314503e+00, 2.24652097e+00,\n", + " 2.32755781e+00, 3.59822826e+00, 1.90937751e+00, 2.42272368e+00,\n", + " 1.32169298e+00, 6.37000220e+00, 1.10946112e+00, 2.24131160e+00,\n", + " 2.89038522e+00, 2.07893264e+00, 3.93463422e+00, 1.54531072e+00,\n", + " 6.78489477e+00, 6.54089099e+00, 4.49760409e+00, 1.15273803e+01,\n", + " 3.40350942e+00, -5.20514768e-01, 6.88011818e+00, 2.09188021e+00,\n", + " 4.27713980e-01, -3.80103571e-02, -3.68674279e+00, 1.62571424e+00,\n", + " 3.47679000e-01, -8.26182415e-01, -2.72819152e+00, -6.21341757e-01,\n", + " 6.51203018e-01, 1.37855914e+00, 2.25411975e+00, 6.32742151e+00,\n", + " 2.09348229e+00, 2.33428700e+00, 8.23559314e+00, 6.72550202e+00,\n", + " 4.37827682e+00, 6.22928514e+00, 4.05125391e+00, 1.75549684e+00,\n", + " 4.57888928e+00, 1.02531662e+00, 6.41694386e+00, 9.17579307e-01,\n", + " -5.61583205e-01, 7.37156754e-01, 1.15138886e+01, 1.14676233e+01,\n", + " 1.01659469e+01, 6.98158543e+00, 4.64844269e+00, 7.71811737e+00,\n", + " 3.22860259e+00, 3.79504811e+00, 2.16141215e+00, -3.89092163e-01,\n", + " 1.79513503e+00, 6.03315885e+00, 4.49056473e-01, -4.71906885e-01,\n", + " 1.00520417e+00, 2.18093315e+00, 2.73461109e+00, 3.69525980e+00,\n", + " 2.51372825e+00, 5.43269787e+00, 7.49324283e+00, 2.19084219e+00,\n", + " 2.00087520e+00, 3.55890153e+00, 9.76834003e+00, -2.92004443e-01,\n", + " 8.07035073e+00, -2.17323215e+00, 7.64991113e+00, 2.63273868e+00,\n", + " -2.95454512e+00, 9.85965581e+00, 1.00518400e+01, 5.28372913e+00,\n", + " 7.86336436e-01, 4.85915059e+00, 6.08799316e+00, 6.83163734e+00,\n", + " 3.23680558e+00, -4.53295270e-01, 6.85993090e+00, 3.95176101e+00,\n", + " 3.89126816e+00, -1.23150949e+00, 5.47864100e+00, 3.00006747e+00,\n", + " 3.35183572e+00, 2.02985690e+00, 2.99663740e+00, 4.35027830e+00,\n", + " 4.18560496e+00, 3.69271138e+00, 2.93910341e+00, 8.85063930e-01,\n", + " 3.14885352e+00, 5.14382401e-01, -8.42465873e-01, 1.03514879e+00,\n", + " -2.27501612e-01, 6.97710251e-03, 6.49003589e+00, -4.13478181e+00,\n", + " -1.34772256e+00, 3.25961188e+00, -3.14417936e+00, 1.97618407e+00,\n", + " 5.10743228e+00, -2.92314738e-02, 7.58638530e+00, 8.27106333e+00,\n", + " 7.66124939e+00, 1.07593771e+01, 3.27429854e+00, 4.81825766e+00,\n", + " 5.13889085e+00, 6.31923316e+00, 7.60833564e+00, 4.25220006e+00,\n", + " 7.62106881e+00, 4.36385688e+00, 5.09672327e+00, -2.91601073e+00,\n", + " 3.73698348e+00, 4.27102970e+00, 1.04073214e+01, 6.64936600e+00,\n", + " 7.12546793e+00, 6.27962898e+00])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TruncatedSVD(d[0][1]).fit_transform(b)[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = X[0:150]\n", + "y_train = y[0:150]\n", + "X_test = X[150::]\n", + "y_test = y[150::]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "np.random.shuffle(X_train)\n", + "np.random.seed(1)\n", + "np.random.shuffle(y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = []\n", + "for i in df['ID_set']:\n", + " with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_' \n", + " + str(i) + '_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f).transpose()\n", + " b = np.corrcoef(data)\n", + " #print(b.shape)\n", + " data2.append(b[np.triu_indices(b.shape[0])])\n", + " #b = np.corrcoef(data)\n", + " #c = TruncatedSVD(n_components=300).fit_transform(b)\n", + " #data2.append(c.reshape((c.shape[0]*c.shape[1], )))\n", + "y = np.array(df)\n", + "X = np.array(data2)\n", + "elbow = max(graspy.embed.select_dimension(X)[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(209, 2)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TruncatedSVD(n_components=elbow).fit_transform(X).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graspy.embed.select_dimension(X)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(209, 299400)\n", + "(209, 3) (209, 68)\n" + ] + } + ], + "source": [ + "data2 = []\n", + "for i in df['ID_set']:\n", + " with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_' \n", + " + str(i) + '_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f).transpose()\n", + " b = np.corrcoef(data)\n", + " #print(b.shape)\n", + " data2.append(b[np.triu_indices(b.shape[0])])\n", + " #b = np.corrcoef(data)\n", + " #c = TruncatedSVD(n_components=300).fit_transform(b)\n", + " #data2.append(c.reshape((c.shape[0]*c.shape[1], )))\n", + "y = np.array(df)\n", + "X = np.array(data2)\n", + "print(X.shape)\n", + "elbow = max(graspy.embed.select_dimension(X)[0])\n", + "svd = TruncatedSVD(n_components=elbow)\n", + "X = svd.fit_transform(X)\n", + "print(X.shape, y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00206e+05, 2.70000e+01, 1.00000e+00, ..., 6.00000e+00,\n", + " 6.00000e+00, 5.70000e+01],\n", + " [1.01107e+05, 2.20000e+01, 1.00000e+00, ..., 2.00000e+00,\n", + " 0.00000e+00, 6.40000e+01],\n", + " [1.01915e+05, 3.50000e+01, 0.00000e+00, ..., 1.00000e+00,\n", + " 2.00000e+00, 5.00000e+01],\n", + " ...,\n", + " [9.87074e+05, 2.40000e+01, 1.00000e+00, ..., 2.00000e+00,\n", + " 1.00000e+00, 5.00000e+01],\n", + " [9.89987e+05, 3.30000e+01, 1.00000e+00, ..., 4.00000e+00,\n", + " 3.00000e+00, 5.00000e+01],\n", + " [9.92673e+05, 3.30000e+01, 0.00000e+00, ..., 1.00000e+00,\n", + " 0.00000e+00, 5.00000e+01]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0msvd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTruncatedSVD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m975\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msvd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Documents/ndd_sklearn/sklearn/decomposition/truncated_svd.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 174\u001b[0m raise ValueError(\"n_components must be < n_features;\"\n\u001b[1;32m 175\u001b[0m \" got %d >= %d\" % (k, n_features))\n\u001b[0;32m--> 176\u001b[0;31m U, Sigma, VT = randomized_svd(X, self.n_components,\n\u001b[0m\u001b[1;32m 177\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 178\u001b[0m random_state=random_state)\n", + "\u001b[0;32m~/Documents/ndd_sklearn/sklearn/utils/extmath.py\u001b[0m in \u001b[0;36mrandomized_svd\u001b[0;34m(M, n_components, n_oversamples, n_iter, power_iteration_normalizer, transpose, flip_sign, random_state)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m \u001b[0mU\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUhat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 359\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mflip_sign\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "X = np.array(data2)\n", + "svd = TruncatedSVD(n_components=975)\n", + "X = svd.fit_transform(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = np.array(data2)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(209, 498501)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "sklearn-dev", + "language": "python", + "name": "sklearn-dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/Olivetti_faces_test-checkpoint.ipynb b/.ipynb_checkpoints/Olivetti_faces_test-checkpoint.ipynb new file mode 100644 index 0000000000000..b623d41a3984d --- /dev/null +++ b/.ipynb_checkpoints/Olivetti_faces_test-checkpoint.ipynb @@ -0,0 +1,681 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import gc\n", + "gc.collect()\n", + "\n", + "# 1.0 Call libraries\n", + "# For data manipulation\n", + "import numpy as np\n", + "import time\n", + "\n", + "# 1.1 For plotting faces\n", + "import matplotlib.pyplot as plt \n", + "from skimage.io import imshow\n", + "\n", + "# 1.2 Our dataset is here\n", + "from sklearn.datasets import fetch_olivetti_faces\n", + "##Metrics\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "# 1.3 Regressors\n", + "from sklearn.neighbors import KNeighborsRegressor\n", + "from sklearn.linear_model import Lasso\n", + "from sklearn.linear_model import ElasticNet\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.linear_model import RidgeCV\n", + "\n", + "from sklearn.ensemble import ExtraTreesRegressor\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.ensemble import AdaBoostRegressor\n", + "\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "from sklearn.multioutput import MultiOutputRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def show_image(test,X_test,y_test_predict,name,n_faces,y_mse):\n", + " ## scattor plot\n", + " plt.figure(figsize=(8,6))\n", + " plt.scatter(y_test_predict[name],y_test,cmap='plasma')\n", + " plt.title(name)\n", + " plt.show()\n", + " print('RMSE for ',name,' is ',y_mse[name])\n", + " ##to plot the faces\n", + " image_shape = (64, 64)\n", + " plt.figure(figsize=(10,10))\n", + " j = 0\n", + " for i in range(n_faces):\n", + " actual_face = test[i].reshape(image_shape)\n", + " completed_face = np.hstack((X_test[i], y_test_predict[name][i]))\n", + " j = j+1\n", + " plt.subplot(5,4,j)\n", + " y = actual_face.reshape(image_shape)\n", + " x = completed_face.reshape(image_shape)\n", + " imshow(x)\n", + " j = j+1\n", + " plt.subplot(5,4,j)\n", + " x = completed_face.reshape(image_shape)\n", + " imshow(y)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(400, 64, 64)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = np.load(\"../olivetti_faces.npy\")\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(400,)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "targets = np.load(\"../olivetti_faces_target.npy\")\n", + "targets.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# 5.0 Flatten each \n", + "data = data.reshape(data.shape[0], data.shape[1] * data.shape[2]) # 64 X 64 = 4096mage" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(400, 4096)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# 6.0 Patition datasets into two (fancy indexing)\n", + "targets < 30 # Output is true/false\n", + "train = data[targets < 30] # First 30 types of images out of 40 ie 30 * 10 =300\n", + "test = data[targets >= 30] # Test on rest independent people 10 * 10 = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([42, 36, 51, 9, 40, 81, 90, 73])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 7.0 Test on a subset of people\n", + "# Generate 8 random integers between 0 and 100\n", + "n_faces = test.shape[0]//12 # // is unconditionally \"flooring division\",\n", + "n_faces\n", + "face_ids = np.random.randint(0 , 100, size =n_faces)\n", + "face_ids" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 7.1 So we have n_faces random-faces from within 1 to 100\n", + "test = test[face_ids, :]\n", + "\n", + "face_ids\n", + "\n", + "n_faces\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# 8.0 Total pixels in any image\n", + "n_pixels = data.shape[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# 8.1 Select upper half of the faces as predictors\n", + "X_train = train[:, :(n_pixels + 1) // 2] # // is unconditionally \"flooring division\",\n", + "\n", + "# 8.2 Lower half of the faces will be target(s) \n", + "y_train = train[:, n_pixels // 2:]\n", + "\n", + "# 9.0 Similarly for test data. Upper and lower half\n", + "X_test = test[:, :(n_pixels + 1) // 2]\n", + "y_test = test[:, n_pixels // 2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare a dictionary of estimators after instantiating each one of them\n", + "ESTIMATORS = {\n", + " \"Extra trees\": ExtraTreesRegressor(n_estimators=10,\n", + " max_features=32, # Out of 20000\n", + " random_state=0),\n", + " \"K-nn\": KNeighborsRegressor(), # Accept default parameters\n", + " \"Linear regression\": LinearRegression(),\n", + " \"Ridge\": RidgeCV(),\n", + " \"Lasso\": Lasso(),\n", + " \"ElasticNet\": ElasticNet(random_state=0),\n", + " \"RandomForestRegressor_normal\": RandomForestRegressor(max_depth=4, random_state=2),\n", + " \"RandomForestRegressor_Axis\": RandomForestRegressor(max_depth=4, random_state=2, criterion=\"axis\"),\n", + " \"RandomForestRegressor_Oblique\": RandomForestRegressor(max_depth=4, random_state=2, criterion=\"oblique\"),\n", + " \"Decision Tree Regressor\":DecisionTreeRegressor(max_depth=5),\n", + " \"MultiO/P GBR\" :MultiOutputRegressor(GradientBoostingRegressor(n_estimators=5)),\n", + " \"MultiO/P AdaB\" :MultiOutputRegressor(AdaBoostRegressor(n_estimators=5))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for Extra trees is 0.01900368811943053\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for K-nn is 0.020249195\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for Linear regression is 0.029426677\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for Ridge is 0.01422377116690403\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for Lasso is 0.027254924\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for ElasticNet is 0.027254924\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for RandomForestRegressor_normal is 0.018803750114975565\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for RandomForestRegressor_Axis is 0.020889555054589264\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for RandomForestRegressor_Oblique is 0.027187819970297976\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for Decision Tree Regressor is 0.02404402290919369\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for MultiO/P GBR is 0.02032255995595469\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for MultiO/P AdaB is 0.01694091624651816\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 9.1 Create an empty dictionary to collect prediction values\n", + "y_test_predict = dict()\n", + "y_mse = dict()\n", + "\n", + "for name, estimator in ESTIMATORS.items(): \n", + " estimator.fit(X_train, y_train) # fit() with instantiated object\n", + " y_test_predict[name] = estimator.predict(X_test) # Make predictions and save it in dict under key: name\n", + " y_mse[name] = mean_squared_error(y_test, estimator.predict(X_test))\n", + " show_image(test,X_test,y_test_predict,name,n_faces,y_mse)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "sklearn-dev", + "language": "python", + "name": "sklearn-dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Arff tests.ipynb b/Arff tests.ipynb new file mode 100644 index 0000000000000..b808479badf66 --- /dev/null +++ b/Arff tests.ipynb @@ -0,0 +1,3315 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.io import arff\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = arff.loadarff('/Users/msanch35/Downloads/mtr-datasets/scm20d.arff')\n", + "df = pd.DataFrame(data[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = np.array(df)[:, 0:61]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y = np.array(df)[:, 61::]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "\n", + "with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_100206_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_101107_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "svd, correlations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b = np.corrcoef(data)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b[np.triu_indices(b.shape[0])]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_excel(r'/Users/msanch35/Downloads/phenotype_table_discovery.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import TruncatedSVD\n", + "data2 = []\n", + "for i in df['ID_set']:\n", + " with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_' + str(i) + '_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f).transpose()\n", + " b = np.corrcoef(data)\n", + " c = TruncatedSVD(n_components=10).fit_transform(b)\n", + " data2.append(c.reshape((c.shape[0]*c.shape[1], )))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: graspy in /Users/msanch35/miniconda3/lib/python3.7/site-packages (0.2.0)\n", + "Requirement already satisfied: scipy>=1.1.0 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (1.4.1)\n", + "Requirement already satisfied: numpy>=1.8.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (1.18.2)\n", + "Requirement already satisfied: seaborn>=0.9.0 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (0.10.1)\n", + "Requirement already satisfied: matplotlib>=3.0.0 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (3.2.1)\n", + "Requirement already satisfied: networkx>=2.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (2.4)\n", + "Requirement already satisfied: scikit-learn>=0.19.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from graspy) (0.22.2.post1)\n", + "Requirement already satisfied: pandas>=0.22.0 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from seaborn>=0.9.0->graspy) (1.0.3)\n", + "Requirement already satisfied: cycler>=0.10 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from matplotlib>=3.0.0->graspy) (0.10.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from matplotlib>=3.0.0->graspy) (1.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from matplotlib>=3.0.0->graspy) (2.8.1)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from matplotlib>=3.0.0->graspy) (2.4.7)\n", + "Requirement already satisfied: decorator>=4.3.0 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from networkx>=2.1->graspy) (4.4.2)\n", + "Requirement already satisfied: joblib>=0.11 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from scikit-learn>=0.19.1->graspy) (0.14.1)\n", + "Requirement already satisfied: pytz>=2017.2 in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from pandas>=0.22.0->seaborn>=0.9.0->graspy) (2020.1)\n", + "Requirement already satisfied: six in /Users/msanch35/miniconda3/lib/python3.7/site-packages (from cycler>=0.10->matplotlib>=3.0.0->graspy) (1.12.0)\n" + ] + } + ], + "source": [ + "!pip install graspy" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import graspy\n", + "d = graspy.embed.select_dimension(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max(d[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.04747899e+01],\n", + " [ 1.18872362e+01],\n", + " [ 1.02263946e+01],\n", + " [ 1.14160653e+01],\n", + " [ 9.99988573e+00],\n", + " [ 1.30431734e+01],\n", + " [ 9.35384872e+00],\n", + " [ 1.31792633e+01],\n", + " [ 1.32559428e+01],\n", + " [ 1.05784854e+01],\n", + " [ 1.25320073e+01],\n", + " [ 1.24298931e+01],\n", + " [ 1.34657054e+01],\n", + " [ 1.03081766e+01],\n", + " [ 1.19430138e+01],\n", + " [ 1.31792557e+01],\n", + " [ 1.23558596e+01],\n", + " [ 5.40709325e+00],\n", + " [ 1.33259227e+01],\n", + " [ 1.06232459e+01],\n", + " [ 1.09659699e+01],\n", + " [ 1.09787272e+01],\n", + " [ 1.28963595e+01],\n", + " [ 4.86615386e+00],\n", + " [ 1.33591527e+01],\n", + " [ 1.30126194e+01],\n", + " [ 9.50490455e+00],\n", + " [-2.64223480e+00],\n", + " [ 1.24250845e+01],\n", + " [ 2.68644166e+00],\n", + " [ 1.03593227e+01],\n", + " [ 1.28808272e+01],\n", + " [ 4.26513131e+00],\n", + " [ 9.97973204e+00],\n", + " [ 1.32843423e+01],\n", + " [ 1.25056590e+01],\n", + " [ 1.27649695e+01],\n", + " [ 1.38840269e+01],\n", + " [ 1.07389041e+01],\n", + " [ 2.28235521e+00],\n", + " [ 7.04781373e+00],\n", + " [ 1.17010299e+01],\n", + " [ 1.20796971e+01],\n", + " [ 2.27018206e+00],\n", + " [ 1.12392349e+01],\n", + " [ 1.33385853e+01],\n", + " [ 1.34861467e+01],\n", + " [ 5.89651366e+00],\n", + " [ 1.00018490e+01],\n", + " [ 1.33398238e+01],\n", + " [ 1.24043262e+01],\n", + " [ 1.08842763e+01],\n", + " [ 1.16788935e+01],\n", + " [ 8.45107106e+00],\n", + " [ 9.37463231e+00],\n", + " [ 1.39491067e+01],\n", + " [ 1.31805442e+01],\n", + " [ 1.19611627e+01],\n", + " [ 1.28269608e+01],\n", + " [ 1.24699707e+01],\n", + " [ 1.46485249e+01],\n", + " [ 1.17688289e+01],\n", + " [ 1.37077797e+01],\n", + " [ 1.25350679e+01],\n", + " [ 1.33368784e+01],\n", + " [ 1.18901842e+01],\n", + " [ 1.33220322e+01],\n", + " [ 1.31792532e+01],\n", + " [ 1.28611479e+01],\n", + " [ 1.27927689e+01],\n", + " [ 1.27485808e+01],\n", + " [ 1.26261071e+01],\n", + " [ 1.29599041e+01],\n", + " [ 1.24955774e+01],\n", + " [ 1.33283036e+01],\n", + " [ 1.32548831e+01],\n", + " [ 1.31935010e+01],\n", + " [ 1.38049493e+01],\n", + " [ 1.45160302e+01],\n", + " [ 1.34231396e+01],\n", + " [ 9.59772302e+00],\n", + " [ 8.85994682e+00],\n", + " [ 1.08314452e+01],\n", + " [ 1.00280548e+01],\n", + " [ 1.08967909e+01],\n", + " [ 9.50456296e+00],\n", + " [ 8.45030094e+00],\n", + " [ 1.16459495e+01],\n", + " [ 1.13727091e+01],\n", + " [ 1.16023566e+01],\n", + " [ 6.05163105e+00],\n", + " [ 1.10246443e+01],\n", + " [ 8.35199760e+00],\n", + " [ 1.19088076e+01],\n", + " [ 1.00970960e+01],\n", + " [ 9.47199935e+00],\n", + " [ 7.87734173e+00],\n", + " [ 1.14781701e+01],\n", + " [ 1.11101995e+01],\n", + " [ 1.33994553e+01],\n", + " [ 1.26095853e+01],\n", + " [ 1.16012045e+01],\n", + " [ 8.36779960e+00],\n", + " [ 1.15862588e+01],\n", + " [ 8.19943057e+00],\n", + " [ 1.08035590e+01],\n", + " [ 1.04570017e+01],\n", + " [ 8.69671540e+00],\n", + " [ 1.02478784e+01],\n", + " [ 7.74244141e+00],\n", + " [ 1.03208536e+01],\n", + " [ 8.75906940e+00],\n", + " [ 3.29460923e+00],\n", + " [ 7.24474287e+00],\n", + " [ 9.46373366e+00],\n", + " [ 9.11343349e+00],\n", + " [ 1.05270764e+01],\n", + " [ 1.15244694e+01],\n", + " [ 1.17170799e+01],\n", + " [ 1.24012190e+01],\n", + " [ 1.13590560e+01],\n", + " [ 7.94686784e+00],\n", + " [ 1.16047672e+01],\n", + " [ 1.09597005e+01],\n", + " [ 1.07892909e+01],\n", + " [ 9.28757448e+00],\n", + " [ 9.23136763e+00],\n", + " [ 3.32076841e+00],\n", + " [ 9.88701302e+00],\n", + " [ 1.10513938e+01],\n", + " [ 9.87206222e+00],\n", + " [ 3.69719179e+00],\n", + " [ 8.89266466e+00],\n", + " [ 8.51565609e+00],\n", + " [ 7.35911708e+00],\n", + " [ 4.96393893e+00],\n", + " [ 8.19520177e+00],\n", + " [ 1.03311977e+01],\n", + " [ 7.29400818e+00],\n", + " [ 4.71882395e+00],\n", + " [ 8.12537467e+00],\n", + " [ 1.08183651e+01],\n", + " [ 5.90072136e+00],\n", + " [ 6.27734319e+00],\n", + " [ 5.80991505e+00],\n", + " [ 2.91043162e+00],\n", + " [ 5.09829351e+00],\n", + " [ 6.93730131e+00],\n", + " [ 7.15540547e+00],\n", + " [ 8.76353562e+00],\n", + " [ 8.36519105e+00],\n", + " [ 5.98298411e+00],\n", + " [ 1.07567875e+01],\n", + " [ 7.07130689e+00],\n", + " [ 1.12457370e+00],\n", + " [ 4.09714244e+00],\n", + " [ 8.72488215e+00],\n", + " [ 1.05921805e+01],\n", + " [ 1.59772022e+00],\n", + " [ 8.85940383e+00],\n", + " [ 7.84266561e+00],\n", + " [ 1.27827232e+01],\n", + " [ 5.16958680e+00],\n", + " [ 9.30089380e+00],\n", + " [ 6.96253903e+00],\n", + " [ 2.05219195e+00],\n", + " [ 6.83062615e+00],\n", + " [ 9.62812512e+00],\n", + " [ 1.14344226e+01],\n", + " [ 8.96355838e+00],\n", + " [ 8.32803906e+00],\n", + " [ 8.70168903e+00],\n", + " [ 1.24402910e+01],\n", + " [ 1.04713991e+01],\n", + " [ 1.33231848e+01],\n", + " [ 1.20800385e+01],\n", + " [ 9.04136245e+00],\n", + " [ 9.21521827e+00],\n", + " [ 9.53437498e+00],\n", + " [ 1.10759730e+01],\n", + " [ 1.09273484e+01],\n", + " [ 1.35596574e+01],\n", + " [ 1.36993777e+01],\n", + " [ 1.23569788e+01],\n", + " [ 1.26712341e+01],\n", + " [ 1.03970133e+01],\n", + " [ 1.00411308e+01],\n", + " [ 9.04041478e+00],\n", + " [ 1.34616708e+01],\n", + " [ 1.24760326e+01],\n", + " [ 5.77536107e+00],\n", + " [ 1.16106369e+01],\n", + " [ 6.54459059e+00],\n", + " [ 5.50046426e+00],\n", + " [ 8.86513409e+00],\n", + " [ 5.20184130e+00],\n", + " [ 5.12320561e+00],\n", + " [ 2.79518445e+00],\n", + " [ 1.05231525e+01],\n", + " [ 1.03429559e+01],\n", + " [ 7.54134311e+00],\n", + " [ 9.98276795e+00],\n", + " [ 9.72237088e+00],\n", + " [ 1.28519854e+01],\n", + " [ 1.07154001e+01],\n", + " [ 5.64414831e+00],\n", + " [ 1.14453503e+01],\n", + " [ 8.49531557e+00],\n", + " [ 1.12581557e+01],\n", + " [ 9.88277207e+00],\n", + " [ 1.14508296e+01],\n", + " [ 1.08290159e+01],\n", + " [ 9.99633453e+00],\n", + " [ 1.18039351e+01],\n", + " [ 1.06674011e+01],\n", + " [ 8.16606530e+00],\n", + " [ 8.79479342e+00],\n", + " [ 8.95615375e+00],\n", + " [ 9.23875776e+00],\n", + " [ 7.00588760e+00],\n", + " [ 9.68839353e+00],\n", + " [ 7.09436067e+00],\n", + " [ 7.77168938e+00],\n", + " [ 4.59185819e+00],\n", + " [ 8.53423174e+00],\n", + " [ 6.33318810e+00],\n", + " [ 4.64496991e+00],\n", + " [ 5.42926903e+00],\n", + " [ 8.20939178e+00],\n", + " [ 9.67645487e-01],\n", + " [ 5.64320987e+00],\n", + " [-1.53546809e+00],\n", + " [ 1.25174151e+01],\n", + " [ 1.35566533e+01],\n", + " [ 5.87038118e+00],\n", + " [ 8.27129952e+00],\n", + " [ 9.80342502e+00],\n", + " [ 8.25000403e+00],\n", + " [ 1.39768856e+00],\n", + " [ 6.20703643e+00],\n", + " [ 8.45669706e+00],\n", + " [ 6.20899058e+00],\n", + " [ 1.24511664e+01],\n", + " [ 1.10360322e+01],\n", + " [ 3.07191185e+00],\n", + " [ 9.92497348e+00],\n", + " [ 6.81481719e+00],\n", + " [ 6.97061570e+00],\n", + " [ 6.22873435e+00],\n", + " [ 9.03638454e+00],\n", + " [ 8.98596424e+00],\n", + " [ 9.47099337e+00],\n", + " [ 5.11860407e+00],\n", + " [ 1.09245382e+01],\n", + " [ 8.57401324e+00],\n", + " [ 8.29573770e+00],\n", + " [ 8.01697110e+00],\n", + " [ 7.34710394e+00],\n", + " [ 5.23005489e+00],\n", + " [ 1.72968361e-01],\n", + " [ 5.99859848e+00],\n", + " [ 3.18279768e+00],\n", + " [ 4.86966508e+00],\n", + " [ 6.96441534e+00],\n", + " [ 7.70370216e+00],\n", + " [ 5.85303891e+00],\n", + " [ 7.62869820e+00],\n", + " [ 6.15691603e+00],\n", + " [ 5.71124146e+00],\n", + " [ 3.33439330e+00],\n", + " [ 6.37601513e+00],\n", + " [ 1.56392561e+00],\n", + " [ 8.59574085e+00],\n", + " [ 7.29647987e+00],\n", + " [ 5.55442356e+00],\n", + " [ 5.82204363e+00],\n", + " [ 8.69921413e+00],\n", + " [ 4.38520388e+00],\n", + " [ 2.03782892e+00],\n", + " [ 6.85687041e+00],\n", + " [ 7.00113233e+00],\n", + " [ 9.26517308e+00],\n", + " [ 9.91813369e+00],\n", + " [ 3.62889195e+00],\n", + " [ 1.03157607e+01],\n", + " [ 8.83893824e+00],\n", + " [ 7.04011973e-01],\n", + " [ 6.82032048e+00],\n", + " [ 3.59485831e-01],\n", + " [ 4.05918097e+00],\n", + " [ 6.58880066e-01],\n", + " [ 4.17673270e+00],\n", + " [-1.88189598e+00],\n", + " [ 3.70870404e-01],\n", + " [ 6.66006479e+00],\n", + " [ 4.35281249e+00],\n", + " [ 1.53496752e+00],\n", + " [-2.48796419e+00],\n", + " [-1.77148475e+00],\n", + " [-5.82361933e+00],\n", + " [ 6.23740349e+00],\n", + " [ 7.30217643e-01],\n", + " [ 3.99109767e+00],\n", + " [ 3.63128566e+00],\n", + " [ 8.66912990e+00],\n", + " [ 6.01970079e+00],\n", + " [ 1.44792328e+00],\n", + " [ 6.18577042e+00],\n", + " [ 8.29691533e+00],\n", + " [ 2.32750866e+00],\n", + " [ 6.61226595e+00],\n", + " [ 9.97735570e+00],\n", + " [ 3.75182563e+00],\n", + " [ 3.16903905e+00],\n", + " [ 9.72230287e+00],\n", + " [ 8.92073854e+00],\n", + " [ 5.02533209e+00],\n", + " [ 6.81944733e+00],\n", + " [ 5.02708505e+00],\n", + " [ 1.73438602e+00],\n", + " [ 2.76618727e+00],\n", + " [ 7.77253470e-01],\n", + " [ 1.21332261e+00],\n", + " [ 2.55135250e+00],\n", + " [ 1.29549656e+00],\n", + " [ 2.27000545e+00],\n", + " [ 4.19289153e+00],\n", + " [ 1.10260781e+00],\n", + " [ 2.93285567e+00],\n", + " [ 5.81925667e+00],\n", + " [ 4.65524501e+00],\n", + " [ 3.90234998e+00],\n", + " [ 4.58590601e+00],\n", + " [ 6.11714557e+00],\n", + " [ 4.19507690e+00],\n", + " [ 3.59246674e+00],\n", + " [ 4.36831033e+00],\n", + " [ 2.35737608e+00],\n", + " [ 6.20617011e+00],\n", + " [ 3.63281235e+00],\n", + " [ 2.29977112e+00],\n", + " [ 3.55461078e+00],\n", + " [ 5.68703292e+00],\n", + " [-2.18720658e+00],\n", + " [ 7.78362630e+00],\n", + " [ 7.82365095e+00],\n", + " [ 7.05083146e+00],\n", + " [ 6.80193744e+00],\n", + " [ 5.54220282e+00],\n", + " [ 6.46449047e+00],\n", + " [ 3.08351145e+00],\n", + " [ 4.27498642e+00],\n", + " [ 6.50599620e+00],\n", + " [ 4.63902154e+00],\n", + " [-1.44039507e-01],\n", + " [ 6.37344570e+00],\n", + " [ 8.15194326e-01],\n", + " [ 3.19200341e+00],\n", + " [ 6.28493621e+00],\n", + " [ 5.55377262e+00],\n", + " [ 7.01540474e+00],\n", + " [ 6.02309687e+00],\n", + " [ 6.36720877e+00],\n", + " [ 9.10112274e+00],\n", + " [ 2.07484446e+00],\n", + " [ 4.63376433e+00],\n", + " [-1.89654810e+00],\n", + " [-9.80614238e-01],\n", + " [ 2.95628894e+00],\n", + " [ 1.63957917e-01],\n", + " [-5.86436846e-01],\n", + " [ 1.37258751e+00],\n", + " [-1.96416786e+00],\n", + " [ 2.94840960e+00],\n", + " [ 8.48977768e+00],\n", + " [ 1.24911577e+01],\n", + " [ 1.23268038e+01],\n", + " [ 3.94066123e+00],\n", + " [ 6.63392244e+00],\n", + " [ 5.20004426e+00],\n", + " [ 1.75994629e+00],\n", + " [ 3.64921689e+00],\n", + " [ 3.34886382e+00],\n", + " [ 1.73120303e+00],\n", + " [-2.26997749e-01],\n", + " [ 5.88655815e+00],\n", + " [ 2.16960036e+00],\n", + " [ 4.52182649e+00],\n", + " [ 4.48769044e+00],\n", + " [ 1.29068977e+00],\n", + " [ 1.89032051e+00],\n", + " [ 1.01494484e+00],\n", + " [ 3.53177898e+00],\n", + " [ 2.53633163e+00],\n", + " [ 4.18236291e+00],\n", + " [ 3.19608062e+00],\n", + " [ 4.58472390e+00],\n", + " [ 2.31473214e+00],\n", + " [ 6.24287893e+00],\n", + " [ 5.83749411e+00],\n", + " [ 5.00472479e+00],\n", + " [ 6.30539117e+00],\n", + " [-1.43016561e+00],\n", + " [ 6.46677830e+00],\n", + " [ 1.96684636e+00],\n", + " [ 1.98948709e+00],\n", + " [-1.21127434e-01],\n", + " [ 1.20314985e+01],\n", + " [-3.96961871e+00],\n", + " [-1.01139750e+00],\n", + " [ 4.31613198e+00],\n", + " [ 1.21278659e+00],\n", + " [ 7.56547047e-01],\n", + " [ 6.57972994e+00],\n", + " [ 9.52474339e+00],\n", + " [ 3.39357789e+00],\n", + " [ 4.83957141e+00],\n", + " [ 2.12601451e+00],\n", + " [ 4.85390876e+00],\n", + " [ 1.13795119e+00],\n", + " [ 8.16649859e-01],\n", + " [ 1.45539810e+00],\n", + " [-2.66585597e+00],\n", + " [-3.41136510e+00],\n", + " [ 1.01670579e+00],\n", + " [-1.16506594e+00],\n", + " [ 4.41533697e+00],\n", + " [-2.33409407e+00],\n", + " [ 9.08855388e-01],\n", + " [ 7.90665797e-01],\n", + " [ 3.93864479e+00],\n", + " [-3.04491712e+00],\n", + " [ 2.80087913e+00],\n", + " [-3.16386010e-01],\n", + " [ 3.82264623e+00],\n", + " [-9.29014679e-01],\n", + " [ 3.70596440e+00],\n", + " [ 1.41546594e+00],\n", + " [ 4.24613668e+00],\n", + " [ 4.98067289e+00],\n", + " [ 2.50840643e+00],\n", + " [ 2.81106375e+00],\n", + " [ 6.29378083e-01],\n", + " [-6.44263264e-01],\n", + " [ 2.46719790e-01],\n", + " [ 1.95749423e+00],\n", + " [ 7.45513752e-01],\n", + " [ 2.93346005e-01],\n", + " [-2.62132954e+00],\n", + " [ 4.24079970e+00],\n", + " [ 2.67798151e+00],\n", + " [ 1.43255735e-01],\n", + " [ 3.88717669e+00],\n", + " [-1.81720467e+00],\n", + " [-3.04964722e+00],\n", + " [ 3.14022330e+00],\n", + " [ 5.05376498e-01],\n", + " [ 1.24554771e+00],\n", + " [ 2.17610992e+00],\n", + " [ 2.42435557e+00],\n", + " [-9.53912871e-01],\n", + " [-7.77340028e-01],\n", + " [ 3.38007241e+00],\n", + " [-1.13777816e+00],\n", + " [-1.05686813e-01],\n", + " [ 2.08097579e+00],\n", + " [ 7.84021233e+00],\n", + " [ 4.67222998e+00],\n", + " [ 9.20377221e+00],\n", + " [ 4.75982830e+00],\n", + " [ 1.19939314e+00],\n", + " [ 1.07889450e+01],\n", + " [ 3.59929128e+00],\n", + " [ 1.24240763e+01],\n", + " [ 7.37591745e+00],\n", + " [ 3.72203363e+00],\n", + " [ 4.27431630e+00],\n", + " [ 8.39850322e+00],\n", + " [ 6.85281089e+00],\n", + " [ 4.84574331e+00],\n", + " [ 7.84511566e+00],\n", + " [ 3.90609867e+00],\n", + " [ 5.58685429e+00],\n", + " [ 7.46878099e+00],\n", + " [ 8.79108757e+00],\n", + " [ 1.80571108e+00],\n", + " [ 6.85941778e+00],\n", + " [ 3.16976584e+00],\n", + " [ 3.23739701e+00],\n", + " [ 5.46936525e+00],\n", + " [ 8.17258975e+00],\n", + " [ 3.06214654e+00],\n", + " [ 5.16984768e+00],\n", + " [ 1.08135668e+01],\n", + " [ 1.71669840e+00],\n", + " [ 3.31997278e+00],\n", + " [ 7.04686541e+00],\n", + " [ 7.10199883e+00],\n", + " [ 7.06300563e-01],\n", + " [ 9.00490026e+00],\n", + " [ 1.30367449e+01],\n", + " [ 1.31969746e+01],\n", + " [ 8.83071724e+00],\n", + " [ 6.32369318e+00],\n", + " [ 1.32425797e+01],\n", + " [ 1.14636338e+01],\n", + " [ 1.10667874e+01],\n", + " [ 1.15550103e+01],\n", + " [ 1.25620180e+01],\n", + " [ 8.03107065e+00],\n", + " [ 1.24243056e+01],\n", + " [ 1.21310915e+01],\n", + " [ 1.15767106e+01],\n", + " [ 1.26547981e+01],\n", + " [ 1.31761754e+01],\n", + " [ 1.23534434e+01],\n", + " [ 1.23184121e+01],\n", + " [ 1.29462086e+01],\n", + " [ 3.77004902e+00],\n", + " [ 7.99310409e+00],\n", + " [ 1.03995684e+01],\n", + " [ 1.28116121e+01],\n", + " [ 1.31115695e+01],\n", + " [ 1.32213050e+01],\n", + " [ 1.12310599e+01],\n", + " [ 1.18335535e+01],\n", + " [ 5.77926950e-02],\n", + " [ 1.22020811e+01],\n", + " [ 4.83911852e+00],\n", + " [ 1.46207638e+00],\n", + " [ 1.28764385e+01],\n", + " [ 1.28982330e+01],\n", + " [ 1.23813248e+01],\n", + " [ 1.03392901e+01],\n", + " [ 1.21866800e+01],\n", + " [-3.81641695e+00],\n", + " [ 1.21395117e+01],\n", + " [ 1.12789807e+01],\n", + " [ 1.34734717e+01],\n", + " [ 1.33536150e+01],\n", + " [ 9.95734965e+00],\n", + " [-2.78435998e+00],\n", + " [ 1.22844944e+01],\n", + " [ 1.18425134e+00],\n", + " [ 1.11423892e+01],\n", + " [ 1.37915608e+01],\n", + " [ 1.12187147e+01],\n", + " [ 1.25079631e+01],\n", + " [ 1.35397146e+01],\n", + " [ 1.07361847e+01],\n", + " [ 1.27365611e+01],\n", + " [ 9.62634813e+00],\n", + " [ 1.08893822e+01],\n", + " [ 1.19630408e+01],\n", + " [ 1.16522449e+01],\n", + " [ 1.25965358e+01],\n", + " [ 1.18075953e+01],\n", + " [ 1.10863685e+01],\n", + " [ 1.24126121e+01],\n", + " [ 1.16275580e+01],\n", + " [ 1.08398051e+01],\n", + " [ 1.30605456e+01],\n", + " [ 1.29614936e+01],\n", + " [ 1.35546097e+01],\n", + " [ 1.32246194e+01],\n", + " [ 1.21631868e+01],\n", + " [ 1.24092037e+01],\n", + " [ 1.14885619e+01],\n", + " [ 1.31640867e+01],\n", + " [ 1.27617757e+01],\n", + " [ 1.19324474e+01],\n", + " [ 1.28295196e+01],\n", + " [ 1.19181171e+01],\n", + " [ 1.33030512e+01],\n", + " [ 1.26876893e+01],\n", + " [ 1.28686563e+01],\n", + " [ 1.41476179e+01],\n", + " [ 1.35757393e+01],\n", + " [ 1.28810046e+01],\n", + " [ 1.15404903e+01],\n", + " [ 3.99785237e+00],\n", + " [ 8.40077788e+00],\n", + " [ 7.13163978e+00],\n", + " [ 9.98645858e+00],\n", + " [ 4.85664248e+00],\n", + " [ 3.61773008e+00],\n", + " [ 6.85869211e+00],\n", + " [ 1.66707833e+00],\n", + " [ 1.14140809e+01],\n", + " [ 1.08780282e+01],\n", + " [ 8.39533916e+00],\n", + " [ 1.18611402e+01],\n", + " [ 1.22244943e+01],\n", + " [ 1.16062890e+01],\n", + " [ 6.28498370e+00],\n", + " [ 6.73202600e+00],\n", + " [ 1.09569405e+01],\n", + " [ 1.10295410e+01],\n", + " [ 1.07930196e+01],\n", + " [ 4.86202307e+00],\n", + " [ 6.96604950e+00],\n", + " [ 8.88344941e+00],\n", + " [ 1.23342306e+01],\n", + " [ 1.03919546e+01],\n", + " [ 8.64365513e+00],\n", + " [ 1.14237404e+01],\n", + " [ 1.10741474e+01],\n", + " [ 8.26511872e+00],\n", + " [ 7.23613955e+00],\n", + " [ 9.23300682e+00],\n", + " [ 1.02962926e+01],\n", + " [ 1.10029985e+01],\n", + " [ 1.04234256e+01],\n", + " [ 4.31677200e+00],\n", + " [ 1.05722828e+01],\n", + " [ 9.82711107e+00],\n", + " [ 1.18227446e+01],\n", + " [ 8.45921619e+00],\n", + " [ 9.36180647e+00],\n", + " [ 1.10354537e+01],\n", + " [ 1.07842123e+01],\n", + " [ 1.08661731e+01],\n", + " [ 1.02066579e+01],\n", + " [ 1.07857212e+01],\n", + " [ 5.78778253e+00],\n", + " [ 1.18566358e+01],\n", + " [ 8.04895732e+00],\n", + " [ 9.86710991e+00],\n", + " [ 9.21051874e+00],\n", + " [ 9.67781943e+00],\n", + " [ 1.14533549e+01],\n", + " [ 3.12241224e+00],\n", + " [ 9.21186252e+00],\n", + " [ 1.17279177e+01],\n", + " [ 1.14824081e+01],\n", + " [ 1.02852466e+01],\n", + " [ 5.11784041e+00],\n", + " [ 1.14948411e+01],\n", + " [ 1.25465854e+01],\n", + " [ 9.04332700e+00],\n", + " [ 8.70437907e+00],\n", + " [ 6.19558316e+00],\n", + " [ 8.81783531e+00],\n", + " [ 8.70054057e+00],\n", + " [ 1.00045275e+01],\n", + " [ 6.57622925e+00],\n", + " [ 4.06131131e+00],\n", + " [ 5.43522949e+00],\n", + " [ 1.17113695e+01],\n", + " [ 1.06638653e+01],\n", + " [ 2.50521848e+00],\n", + " [ 1.11934176e+01],\n", + " [ 7.14470033e+00],\n", + " [ 1.05657649e+01],\n", + " [ 6.16922032e+00],\n", + " [ 1.24748759e+01],\n", + " [ 8.93819730e+00],\n", + " [ 6.74683265e+00],\n", + " [ 1.27954172e+01],\n", + " [ 1.06646300e+01],\n", + " [ 7.07958858e+00],\n", + " [ 1.04278553e+01],\n", + " [ 6.37083396e+00],\n", + " [ 8.89056950e+00],\n", + " [ 5.56427173e+00],\n", + " [ 7.32265687e+00],\n", + " [ 6.06553584e+00],\n", + " [ 1.12968509e+01],\n", + " [ 8.65623378e+00],\n", + " [ 1.13352096e+01],\n", + " [ 5.13787840e+00],\n", + " [ 8.00265477e+00],\n", + " [ 6.04050200e+00],\n", + " [ 8.13790170e+00],\n", + " [ 8.08471917e+00],\n", + " [ 7.11227129e+00],\n", + " [ 9.19514312e+00],\n", + " [ 1.38241724e-01],\n", + " [ 1.02634280e+01],\n", + " [ 1.03347334e+01],\n", + " [ 1.06250787e+01],\n", + " [ 6.97151036e+00],\n", + " [ 1.00529753e+01],\n", + " [ 1.16629485e+01],\n", + " [ 1.22851058e+01],\n", + " [ 1.22681021e+01],\n", + " [ 1.17800189e+01],\n", + " [ 9.64275254e+00],\n", + " [ 1.19641756e+01],\n", + " [ 1.24710271e+01],\n", + " [ 1.23447739e+01],\n", + " [ 1.19246291e+01],\n", + " [ 1.27503614e+01],\n", + " [ 1.27816169e+01],\n", + " [ 6.10399724e+00],\n", + " [ 8.38614313e+00],\n", + " [ 9.75970870e+00],\n", + " [ 6.09037115e+00],\n", + " [ 1.23304023e+01],\n", + " [ 1.12502430e+01],\n", + " [ 7.19632710e+00],\n", + " [ 4.38330876e+00],\n", + " [ 7.68611052e+00],\n", + " [ 1.16019220e+01],\n", + " [ 1.04751741e+01],\n", + " [ 1.00848493e+01],\n", + " [ 7.93139908e+00],\n", + " [ 1.28333305e+01],\n", + " [ 1.16699806e+01],\n", + " [ 6.62335538e+00],\n", + " [ 8.52398185e+00],\n", + " [ 6.74082517e+00],\n", + " [ 1.12956588e+01],\n", + " [ 1.12160934e+01],\n", + " [ 1.10262427e+01],\n", + " [ 1.08935413e+01],\n", + " [ 1.04934275e+01],\n", + " [ 1.14373425e+01],\n", + " [ 1.24919744e+01],\n", + " [ 9.78647811e+00],\n", + " [ 7.47778845e+00],\n", + " [ 1.14302773e+01],\n", + " [ 3.68535094e+00],\n", + " [ 8.40723849e+00],\n", + " [ 9.70332926e+00],\n", + " [ 1.09785423e+01],\n", + " [ 9.55174640e+00],\n", + " [ 1.15988472e+01],\n", + " [ 9.15299271e+00],\n", + " [ 6.51783025e+00],\n", + " [ 6.58802065e+00],\n", + " [ 7.57916515e+00],\n", + " [ 3.33134652e+00],\n", + " [ 8.07860624e+00],\n", + " [ 3.70034079e+00],\n", + " [ 1.23112503e+01],\n", + " [ 8.08423057e+00],\n", + " [ 8.18254426e+00],\n", + " [ 6.45212175e+00],\n", + " [ 6.77407453e+00],\n", + " [ 3.62971961e+00],\n", + " [ 9.21336624e+00],\n", + " [ 1.01983215e+01],\n", + " [ 8.92042109e+00],\n", + " [ 1.10570105e+01],\n", + " [ 1.05379203e+01],\n", + " [ 1.40951910e+01],\n", + " [ 1.29461899e+01],\n", + " [ 1.14206783e+01],\n", + " [ 1.21295290e+01],\n", + " [ 1.06667034e+01],\n", + " [ 1.10310706e+01],\n", + " [ 1.19490698e+01],\n", + " [ 9.07473165e+00],\n", + " [ 1.02485993e+01],\n", + " [ 1.11321036e+01],\n", + " [ 6.77535903e+00],\n", + " [ 9.18847343e+00],\n", + " [ 5.79014106e+00],\n", + " [ 6.64145865e+00],\n", + " [-7.99679670e-01],\n", + " [ 7.15844762e+00],\n", + " [ 7.05186656e+00],\n", + " [ 1.17801776e+01],\n", + " [ 6.84882993e+00],\n", + " [ 2.29422550e+00],\n", + " [ 7.74116138e+00],\n", + " [ 8.31076721e+00],\n", + " [-4.37405000e-01],\n", + " [ 8.78704188e+00],\n", + " [ 8.75213549e+00],\n", + " [ 7.54031240e+00],\n", + " [ 6.98037600e+00],\n", + " [ 7.58031459e+00],\n", + " [ 8.00082975e+00],\n", + " [ 9.81457607e+00],\n", + " [ 8.09847216e+00],\n", + " [ 7.47953423e+00],\n", + " [ 6.64526442e+00],\n", + " [ 2.78471997e+00],\n", + " [ 5.81705852e+00],\n", + " [ 7.04239293e+00],\n", + " [ 5.95680224e+00],\n", + " [ 8.24730052e+00],\n", + " [ 3.28174434e+00],\n", + " [ 9.52896409e+00],\n", + " [ 6.29912937e+00],\n", + " [ 1.00417614e+01],\n", + " [ 5.06506591e+00],\n", + " [ 8.91512535e+00],\n", + " [ 5.60398020e+00],\n", + " [ 1.98442787e+00],\n", + " [ 4.58944860e-01],\n", + " [ 5.29521364e+00],\n", + " [ 8.11433615e+00],\n", + " [ 6.94916004e+00],\n", + " [ 1.05893875e+01],\n", + " [ 6.79252835e+00],\n", + " [ 1.11950717e+01],\n", + " [ 5.20452890e+00],\n", + " [ 8.66449269e+00],\n", + " [ 8.76294688e+00],\n", + " [ 8.78308635e+00],\n", + " [ 1.00277231e+01],\n", + " [ 6.39601122e+00],\n", + " [ 1.27749305e+01],\n", + " [ 1.01378890e+01],\n", + " [ 8.85206297e+00],\n", + " [ 8.52558945e+00],\n", + " [ 7.42543149e+00],\n", + " [ 3.22105924e+00],\n", + " [-1.58233029e+00],\n", + " [ 4.80193567e-01],\n", + " [-4.41476109e+00],\n", + " [ 3.90429557e+00],\n", + " [ 2.14501400e+00],\n", + " [ 2.36844502e+00],\n", + " [ 7.90469157e-01],\n", + " [ 3.30768837e+00],\n", + " [ 1.81438886e+00],\n", + " [ 4.62191989e+00],\n", + " [ 6.16250039e-01],\n", + " [ 4.61636250e-01],\n", + " [ 7.14152189e+00],\n", + " [ 8.87744421e-01],\n", + " [-1.33893959e+00],\n", + " [ 4.98941977e+00],\n", + " [ 8.20747559e+00],\n", + " [ 8.60173439e+00],\n", + " [-1.93325395e+00],\n", + " [ 5.90818540e+00],\n", + " [-8.16352256e-01],\n", + " [ 6.67541903e+00],\n", + " [ 7.31333803e+00],\n", + " [ 6.41107087e+00],\n", + " [ 5.82342013e+00],\n", + " [ 9.02343032e+00],\n", + " [ 7.27778002e+00],\n", + " [ 7.15951526e+00],\n", + " [ 6.89522862e+00],\n", + " [ 1.05898347e+01],\n", + " [-1.49821327e+00],\n", + " [-5.38834351e-01],\n", + " [ 1.48744389e+00],\n", + " [ 4.05464673e+00],\n", + " [-2.92021228e+00],\n", + " [-1.99530446e+00],\n", + " [-8.69866422e-02],\n", + " [ 6.91530717e-01],\n", + " [-2.72996647e+00],\n", + " [ 2.86672045e+00],\n", + " [-3.24577111e+00],\n", + " [ 2.60887123e-02],\n", + " [ 5.63689677e+00],\n", + " [ 3.96514332e+00],\n", + " [ 5.06037350e+00],\n", + " [ 7.73864504e+00],\n", + " [ 1.05156710e+01],\n", + " [ 5.98027853e+00],\n", + " [ 2.81176498e+00],\n", + " [ 3.34660201e+00],\n", + " [ 2.25166836e+00],\n", + " [ 7.66314503e+00],\n", + " [ 2.24652097e+00],\n", + " [ 2.32755781e+00],\n", + " [ 3.59822826e+00],\n", + " [ 1.90937751e+00],\n", + " [ 2.42272368e+00],\n", + " [ 1.32169298e+00],\n", + " [ 6.37000220e+00],\n", + " [ 1.10946112e+00],\n", + " [ 2.24131160e+00],\n", + " [ 2.89038522e+00],\n", + " [ 2.07893264e+00],\n", + " [ 3.93463422e+00],\n", + " [ 1.54531072e+00],\n", + " [ 6.78489477e+00],\n", + " [ 6.54089099e+00],\n", + " [ 4.49760409e+00],\n", + " [ 1.15273803e+01],\n", + " [ 3.40350942e+00],\n", + " [-5.20514768e-01],\n", + " [ 6.88011818e+00],\n", + " [ 2.09188021e+00],\n", + " [ 4.27713980e-01],\n", + " [-3.80103571e-02],\n", + " [-3.68674279e+00],\n", + " [ 1.62571424e+00],\n", + " [ 3.47679000e-01],\n", + " [-8.26182415e-01],\n", + " [-2.72819152e+00],\n", + " [-6.21341757e-01],\n", + " [ 6.51203018e-01],\n", + " [ 1.37855914e+00],\n", + " [ 2.25411975e+00],\n", + " [ 6.32742151e+00],\n", + " [ 2.09348229e+00],\n", + " [ 2.33428700e+00],\n", + " [ 8.23559314e+00],\n", + " [ 6.72550202e+00],\n", + " [ 4.37827682e+00],\n", + " [ 6.22928514e+00],\n", + " [ 4.05125391e+00],\n", + " [ 1.75549684e+00],\n", + " [ 4.57888928e+00],\n", + " [ 1.02531662e+00],\n", + " [ 6.41694386e+00],\n", + " [ 9.17579307e-01],\n", + " [-5.61583205e-01],\n", + " [ 7.37156754e-01],\n", + " [ 1.15138886e+01],\n", + " [ 1.14676233e+01],\n", + " [ 1.01659469e+01],\n", + " [ 6.98158543e+00],\n", + " [ 4.64844269e+00],\n", + " [ 7.71811737e+00],\n", + " [ 3.22860259e+00],\n", + " [ 3.79504811e+00],\n", + " [ 2.16141215e+00],\n", + " [-3.89092163e-01],\n", + " [ 1.79513503e+00],\n", + " [ 6.03315885e+00],\n", + " [ 4.49056473e-01],\n", + " [-4.71906885e-01],\n", + " [ 1.00520417e+00],\n", + " [ 2.18093315e+00],\n", + " [ 2.73461109e+00],\n", + " [ 3.69525980e+00],\n", + " [ 2.51372825e+00],\n", + " [ 5.43269787e+00],\n", + " [ 7.49324283e+00],\n", + " [ 2.19084219e+00],\n", + " [ 2.00087520e+00],\n", + " [ 3.55890153e+00],\n", + " [ 9.76834003e+00],\n", + " [-2.92004443e-01],\n", + " [ 8.07035073e+00],\n", + " [-2.17323215e+00],\n", + " [ 7.64991113e+00],\n", + " [ 2.63273868e+00],\n", + " [-2.95454512e+00],\n", + " [ 9.85965581e+00],\n", + " [ 1.00518400e+01],\n", + " [ 5.28372913e+00],\n", + " [ 7.86336436e-01],\n", + " [ 4.85915059e+00],\n", + " [ 6.08799316e+00],\n", + " [ 6.83163734e+00],\n", + " [ 3.23680558e+00],\n", + " [-4.53295270e-01],\n", + " [ 6.85993090e+00],\n", + " [ 3.95176101e+00],\n", + " [ 3.89126816e+00],\n", + " [-1.23150949e+00],\n", + " [ 5.47864100e+00],\n", + " [ 3.00006747e+00],\n", + " [ 3.35183572e+00],\n", + " [ 2.02985690e+00],\n", + " [ 2.99663740e+00],\n", + " [ 4.35027830e+00],\n", + " [ 4.18560496e+00],\n", + " [ 3.69271138e+00],\n", + " [ 2.93910341e+00],\n", + " [ 8.85063930e-01],\n", + " [ 3.14885352e+00],\n", + " [ 5.14382401e-01],\n", + " [-8.42465873e-01],\n", + " [ 1.03514879e+00],\n", + " [-2.27501612e-01],\n", + " [ 6.97710251e-03],\n", + " [ 6.49003589e+00],\n", + " [-4.13478181e+00],\n", + " [-1.34772256e+00],\n", + " [ 3.25961188e+00],\n", + " [-3.14417936e+00],\n", + " [ 1.97618407e+00],\n", + " [ 5.10743228e+00],\n", + " [-2.92314738e-02],\n", + " [ 7.58638530e+00],\n", + " [ 8.27106333e+00],\n", + " [ 7.66124939e+00],\n", + " [ 1.07593771e+01],\n", + " [ 3.27429854e+00],\n", + " [ 4.81825766e+00],\n", + " [ 5.13889085e+00],\n", + " [ 6.31923316e+00],\n", + " [ 7.60833564e+00],\n", + " [ 4.25220006e+00],\n", + " [ 7.62106881e+00],\n", + " [ 4.36385688e+00],\n", + " [ 5.09672327e+00],\n", + " [-2.91601073e+00],\n", + " [ 3.73698348e+00],\n", + " [ 4.27102970e+00],\n", + " [ 1.04073214e+01],\n", + " [ 6.64936600e+00],\n", + " [ 7.12546793e+00],\n", + " [ 6.27962898e+00]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TruncatedSVD(d[0][0]).fit_transform(b).shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_excel(r'/Users/msanch35/Downloads/phenotype_table_discovery.xlsx')\n", + "df = df.dropna()\n", + "data2 = []\n", + "for i in df['ID_set']:\n", + " with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_' + str(i) + '_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f)\n", + " b = np.corrcoef(data)\n", + " data2.append(b[np.triu_indices(b.shape[0])])\n", + "X = np.array(df)\n", + "y = np.array(data2)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.04747899e+01, 1.18872362e+01, 1.02263946e+01, 1.14160653e+01,\n", + " 9.99988573e+00, 1.30431734e+01, 9.35384872e+00, 1.31792633e+01,\n", + " 1.32559428e+01, 1.05784854e+01, 1.25320073e+01, 1.24298931e+01,\n", + " 1.34657054e+01, 1.03081766e+01, 1.19430138e+01, 1.31792557e+01,\n", + " 1.23558596e+01, 5.40709325e+00, 1.33259227e+01, 1.06232459e+01,\n", + " 1.09659699e+01, 1.09787272e+01, 1.28963595e+01, 4.86615386e+00,\n", + " 1.33591527e+01, 1.30126194e+01, 9.50490455e+00, -2.64223480e+00,\n", + " 1.24250845e+01, 2.68644166e+00, 1.03593227e+01, 1.28808272e+01,\n", + " 4.26513131e+00, 9.97973204e+00, 1.32843423e+01, 1.25056590e+01,\n", + " 1.27649695e+01, 1.38840269e+01, 1.07389041e+01, 2.28235521e+00,\n", + " 7.04781373e+00, 1.17010299e+01, 1.20796971e+01, 2.27018206e+00,\n", + " 1.12392349e+01, 1.33385853e+01, 1.34861467e+01, 5.89651366e+00,\n", + " 1.00018490e+01, 1.33398238e+01, 1.24043262e+01, 1.08842763e+01,\n", + " 1.16788935e+01, 8.45107106e+00, 9.37463231e+00, 1.39491067e+01,\n", + " 1.31805442e+01, 1.19611627e+01, 1.28269608e+01, 1.24699707e+01,\n", + " 1.46485249e+01, 1.17688289e+01, 1.37077797e+01, 1.25350679e+01,\n", + " 1.33368784e+01, 1.18901842e+01, 1.33220322e+01, 1.31792532e+01,\n", + " 1.28611479e+01, 1.27927689e+01, 1.27485808e+01, 1.26261071e+01,\n", + " 1.29599041e+01, 1.24955774e+01, 1.33283036e+01, 1.32548831e+01,\n", + " 1.31935010e+01, 1.38049493e+01, 1.45160302e+01, 1.34231396e+01,\n", + " 9.59772302e+00, 8.85994682e+00, 1.08314452e+01, 1.00280548e+01,\n", + " 1.08967909e+01, 9.50456296e+00, 8.45030094e+00, 1.16459495e+01,\n", + " 1.13727091e+01, 1.16023566e+01, 6.05163105e+00, 1.10246443e+01,\n", + " 8.35199760e+00, 1.19088076e+01, 1.00970960e+01, 9.47199935e+00,\n", + " 7.87734173e+00, 1.14781701e+01, 1.11101995e+01, 1.33994553e+01,\n", + " 1.26095853e+01, 1.16012045e+01, 8.36779960e+00, 1.15862588e+01,\n", + " 8.19943057e+00, 1.08035590e+01, 1.04570017e+01, 8.69671540e+00,\n", + " 1.02478784e+01, 7.74244141e+00, 1.03208536e+01, 8.75906940e+00,\n", + " 3.29460923e+00, 7.24474287e+00, 9.46373366e+00, 9.11343349e+00,\n", + " 1.05270764e+01, 1.15244694e+01, 1.17170799e+01, 1.24012190e+01,\n", + " 1.13590560e+01, 7.94686784e+00, 1.16047672e+01, 1.09597005e+01,\n", + " 1.07892909e+01, 9.28757448e+00, 9.23136763e+00, 3.32076841e+00,\n", + " 9.88701302e+00, 1.10513938e+01, 9.87206222e+00, 3.69719179e+00,\n", + " 8.89266466e+00, 8.51565609e+00, 7.35911708e+00, 4.96393893e+00,\n", + " 8.19520177e+00, 1.03311977e+01, 7.29400818e+00, 4.71882395e+00,\n", + " 8.12537467e+00, 1.08183651e+01, 5.90072136e+00, 6.27734319e+00,\n", + " 5.80991505e+00, 2.91043162e+00, 5.09829351e+00, 6.93730131e+00,\n", + " 7.15540547e+00, 8.76353562e+00, 8.36519105e+00, 5.98298411e+00,\n", + " 1.07567875e+01, 7.07130689e+00, 1.12457370e+00, 4.09714244e+00,\n", + " 8.72488215e+00, 1.05921805e+01, 1.59772022e+00, 8.85940383e+00,\n", + " 7.84266561e+00, 1.27827232e+01, 5.16958680e+00, 9.30089380e+00,\n", + " 6.96253903e+00, 2.05219195e+00, 6.83062615e+00, 9.62812512e+00,\n", + " 1.14344226e+01, 8.96355838e+00, 8.32803906e+00, 8.70168903e+00,\n", + " 1.24402910e+01, 1.04713991e+01, 1.33231848e+01, 1.20800385e+01,\n", + " 9.04136245e+00, 9.21521827e+00, 9.53437498e+00, 1.10759730e+01,\n", + " 1.09273484e+01, 1.35596574e+01, 1.36993777e+01, 1.23569788e+01,\n", + " 1.26712341e+01, 1.03970133e+01, 1.00411308e+01, 9.04041478e+00,\n", + " 1.34616708e+01, 1.24760326e+01, 5.77536107e+00, 1.16106369e+01,\n", + " 6.54459059e+00, 5.50046426e+00, 8.86513409e+00, 5.20184130e+00,\n", + " 5.12320561e+00, 2.79518445e+00, 1.05231525e+01, 1.03429559e+01,\n", + " 7.54134311e+00, 9.98276795e+00, 9.72237088e+00, 1.28519854e+01,\n", + " 1.07154001e+01, 5.64414831e+00, 1.14453503e+01, 8.49531557e+00,\n", + " 1.12581557e+01, 9.88277207e+00, 1.14508296e+01, 1.08290159e+01,\n", + " 9.99633453e+00, 1.18039351e+01, 1.06674011e+01, 8.16606530e+00,\n", + " 8.79479342e+00, 8.95615375e+00, 9.23875776e+00, 7.00588760e+00,\n", + " 9.68839353e+00, 7.09436067e+00, 7.77168938e+00, 4.59185819e+00,\n", + " 8.53423174e+00, 6.33318810e+00, 4.64496991e+00, 5.42926903e+00,\n", + " 8.20939178e+00, 9.67645487e-01, 5.64320987e+00, -1.53546809e+00,\n", + " 1.25174151e+01, 1.35566533e+01, 5.87038118e+00, 8.27129952e+00,\n", + " 9.80342502e+00, 8.25000403e+00, 1.39768856e+00, 6.20703643e+00,\n", + " 8.45669706e+00, 6.20899058e+00, 1.24511664e+01, 1.10360322e+01,\n", + " 3.07191185e+00, 9.92497348e+00, 6.81481719e+00, 6.97061570e+00,\n", + " 6.22873435e+00, 9.03638454e+00, 8.98596424e+00, 9.47099337e+00,\n", + " 5.11860407e+00, 1.09245382e+01, 8.57401324e+00, 8.29573770e+00,\n", + " 8.01697110e+00, 7.34710394e+00, 5.23005489e+00, 1.72968361e-01,\n", + " 5.99859848e+00, 3.18279768e+00, 4.86966508e+00, 6.96441534e+00,\n", + " 7.70370216e+00, 5.85303891e+00, 7.62869820e+00, 6.15691603e+00,\n", + " 5.71124146e+00, 3.33439330e+00, 6.37601513e+00, 1.56392561e+00,\n", + " 8.59574085e+00, 7.29647987e+00, 5.55442356e+00, 5.82204363e+00,\n", + " 8.69921413e+00, 4.38520388e+00, 2.03782892e+00, 6.85687041e+00,\n", + " 7.00113233e+00, 9.26517308e+00, 9.91813369e+00, 3.62889195e+00,\n", + " 1.03157607e+01, 8.83893824e+00, 7.04011973e-01, 6.82032048e+00,\n", + " 3.59485831e-01, 4.05918097e+00, 6.58880066e-01, 4.17673270e+00,\n", + " -1.88189598e+00, 3.70870404e-01, 6.66006479e+00, 4.35281249e+00,\n", + " 1.53496752e+00, -2.48796419e+00, -1.77148475e+00, -5.82361933e+00,\n", + " 6.23740349e+00, 7.30217643e-01, 3.99109767e+00, 3.63128566e+00,\n", + " 8.66912990e+00, 6.01970079e+00, 1.44792328e+00, 6.18577042e+00,\n", + " 8.29691533e+00, 2.32750866e+00, 6.61226595e+00, 9.97735570e+00,\n", + " 3.75182563e+00, 3.16903905e+00, 9.72230287e+00, 8.92073854e+00,\n", + " 5.02533209e+00, 6.81944733e+00, 5.02708505e+00, 1.73438602e+00,\n", + " 2.76618727e+00, 7.77253470e-01, 1.21332261e+00, 2.55135250e+00,\n", + " 1.29549656e+00, 2.27000545e+00, 4.19289153e+00, 1.10260781e+00,\n", + " 2.93285567e+00, 5.81925667e+00, 4.65524501e+00, 3.90234998e+00,\n", + " 4.58590601e+00, 6.11714557e+00, 4.19507690e+00, 3.59246674e+00,\n", + " 4.36831033e+00, 2.35737608e+00, 6.20617011e+00, 3.63281235e+00,\n", + " 2.29977112e+00, 3.55461078e+00, 5.68703292e+00, -2.18720658e+00,\n", + " 7.78362630e+00, 7.82365095e+00, 7.05083146e+00, 6.80193744e+00,\n", + " 5.54220282e+00, 6.46449047e+00, 3.08351145e+00, 4.27498642e+00,\n", + " 6.50599620e+00, 4.63902154e+00, -1.44039507e-01, 6.37344570e+00,\n", + " 8.15194326e-01, 3.19200341e+00, 6.28493621e+00, 5.55377262e+00,\n", + " 7.01540474e+00, 6.02309687e+00, 6.36720877e+00, 9.10112274e+00,\n", + " 2.07484446e+00, 4.63376433e+00, -1.89654810e+00, -9.80614238e-01,\n", + " 2.95628894e+00, 1.63957917e-01, -5.86436846e-01, 1.37258751e+00,\n", + " -1.96416786e+00, 2.94840960e+00, 8.48977768e+00, 1.24911577e+01,\n", + " 1.23268038e+01, 3.94066123e+00, 6.63392244e+00, 5.20004426e+00,\n", + " 1.75994629e+00, 3.64921689e+00, 3.34886382e+00, 1.73120303e+00,\n", + " -2.26997749e-01, 5.88655815e+00, 2.16960036e+00, 4.52182649e+00,\n", + " 4.48769044e+00, 1.29068977e+00, 1.89032051e+00, 1.01494484e+00,\n", + " 3.53177898e+00, 2.53633163e+00, 4.18236291e+00, 3.19608062e+00,\n", + " 4.58472390e+00, 2.31473214e+00, 6.24287893e+00, 5.83749411e+00,\n", + " 5.00472479e+00, 6.30539117e+00, -1.43016561e+00, 6.46677830e+00,\n", + " 1.96684636e+00, 1.98948709e+00, -1.21127434e-01, 1.20314985e+01,\n", + " -3.96961871e+00, -1.01139750e+00, 4.31613198e+00, 1.21278659e+00,\n", + " 7.56547047e-01, 6.57972994e+00, 9.52474339e+00, 3.39357789e+00,\n", + " 4.83957141e+00, 2.12601451e+00, 4.85390876e+00, 1.13795119e+00,\n", + " 8.16649859e-01, 1.45539810e+00, -2.66585597e+00, -3.41136510e+00,\n", + " 1.01670579e+00, -1.16506594e+00, 4.41533697e+00, -2.33409407e+00,\n", + " 9.08855388e-01, 7.90665797e-01, 3.93864479e+00, -3.04491712e+00,\n", + " 2.80087913e+00, -3.16386010e-01, 3.82264623e+00, -9.29014679e-01,\n", + " 3.70596440e+00, 1.41546594e+00, 4.24613668e+00, 4.98067289e+00,\n", + " 2.50840643e+00, 2.81106375e+00, 6.29378083e-01, -6.44263264e-01,\n", + " 2.46719790e-01, 1.95749423e+00, 7.45513752e-01, 2.93346005e-01,\n", + " -2.62132954e+00, 4.24079970e+00, 2.67798151e+00, 1.43255735e-01,\n", + " 3.88717669e+00, -1.81720467e+00, -3.04964722e+00, 3.14022330e+00,\n", + " 5.05376498e-01, 1.24554771e+00, 2.17610992e+00, 2.42435557e+00,\n", + " -9.53912871e-01, -7.77340028e-01, 3.38007241e+00, -1.13777816e+00,\n", + " -1.05686813e-01, 2.08097579e+00, 7.84021233e+00, 4.67222998e+00,\n", + " 9.20377221e+00, 4.75982830e+00, 1.19939314e+00, 1.07889450e+01,\n", + " 3.59929128e+00, 1.24240763e+01, 7.37591745e+00, 3.72203363e+00,\n", + " 4.27431630e+00, 8.39850322e+00, 6.85281089e+00, 4.84574331e+00,\n", + " 7.84511566e+00, 3.90609867e+00, 5.58685429e+00, 7.46878099e+00,\n", + " 8.79108757e+00, 1.80571108e+00, 6.85941778e+00, 3.16976584e+00,\n", + " 3.23739701e+00, 5.46936525e+00, 8.17258975e+00, 3.06214654e+00,\n", + " 5.16984768e+00, 1.08135668e+01, 1.71669840e+00, 3.31997278e+00,\n", + " 7.04686541e+00, 7.10199883e+00, 7.06300563e-01, 9.00490026e+00,\n", + " 1.30367449e+01, 1.31969746e+01, 8.83071724e+00, 6.32369318e+00,\n", + " 1.32425797e+01, 1.14636338e+01, 1.10667874e+01, 1.15550103e+01,\n", + " 1.25620180e+01, 8.03107065e+00, 1.24243056e+01, 1.21310915e+01,\n", + " 1.15767106e+01, 1.26547981e+01, 1.31761754e+01, 1.23534434e+01,\n", + " 1.23184121e+01, 1.29462086e+01, 3.77004902e+00, 7.99310409e+00,\n", + " 1.03995684e+01, 1.28116121e+01, 1.31115695e+01, 1.32213050e+01,\n", + " 1.12310599e+01, 1.18335535e+01, 5.77926950e-02, 1.22020811e+01,\n", + " 4.83911852e+00, 1.46207638e+00, 1.28764385e+01, 1.28982330e+01,\n", + " 1.23813248e+01, 1.03392901e+01, 1.21866800e+01, -3.81641695e+00,\n", + " 1.21395117e+01, 1.12789807e+01, 1.34734717e+01, 1.33536150e+01,\n", + " 9.95734965e+00, -2.78435998e+00, 1.22844944e+01, 1.18425134e+00,\n", + " 1.11423892e+01, 1.37915608e+01, 1.12187147e+01, 1.25079631e+01,\n", + " 1.35397146e+01, 1.07361847e+01, 1.27365611e+01, 9.62634813e+00,\n", + " 1.08893822e+01, 1.19630408e+01, 1.16522449e+01, 1.25965358e+01,\n", + " 1.18075953e+01, 1.10863685e+01, 1.24126121e+01, 1.16275580e+01,\n", + " 1.08398051e+01, 1.30605456e+01, 1.29614936e+01, 1.35546097e+01,\n", + " 1.32246194e+01, 1.21631868e+01, 1.24092037e+01, 1.14885619e+01,\n", + " 1.31640867e+01, 1.27617757e+01, 1.19324474e+01, 1.28295196e+01,\n", + " 1.19181171e+01, 1.33030512e+01, 1.26876893e+01, 1.28686563e+01,\n", + " 1.41476179e+01, 1.35757393e+01, 1.28810046e+01, 1.15404903e+01,\n", + " 3.99785237e+00, 8.40077788e+00, 7.13163978e+00, 9.98645858e+00,\n", + " 4.85664248e+00, 3.61773008e+00, 6.85869211e+00, 1.66707833e+00,\n", + " 1.14140809e+01, 1.08780282e+01, 8.39533916e+00, 1.18611402e+01,\n", + " 1.22244943e+01, 1.16062890e+01, 6.28498370e+00, 6.73202600e+00,\n", + " 1.09569405e+01, 1.10295410e+01, 1.07930196e+01, 4.86202307e+00,\n", + " 6.96604950e+00, 8.88344941e+00, 1.23342306e+01, 1.03919546e+01,\n", + " 8.64365513e+00, 1.14237404e+01, 1.10741474e+01, 8.26511872e+00,\n", + " 7.23613955e+00, 9.23300682e+00, 1.02962926e+01, 1.10029985e+01,\n", + " 1.04234256e+01, 4.31677200e+00, 1.05722828e+01, 9.82711107e+00,\n", + " 1.18227446e+01, 8.45921619e+00, 9.36180647e+00, 1.10354537e+01,\n", + " 1.07842123e+01, 1.08661731e+01, 1.02066579e+01, 1.07857212e+01,\n", + " 5.78778253e+00, 1.18566358e+01, 8.04895732e+00, 9.86710991e+00,\n", + " 9.21051874e+00, 9.67781943e+00, 1.14533549e+01, 3.12241224e+00,\n", + " 9.21186252e+00, 1.17279177e+01, 1.14824081e+01, 1.02852466e+01,\n", + " 5.11784041e+00, 1.14948411e+01, 1.25465854e+01, 9.04332700e+00,\n", + " 8.70437907e+00, 6.19558316e+00, 8.81783531e+00, 8.70054057e+00,\n", + " 1.00045275e+01, 6.57622925e+00, 4.06131131e+00, 5.43522949e+00,\n", + " 1.17113695e+01, 1.06638653e+01, 2.50521848e+00, 1.11934176e+01,\n", + " 7.14470033e+00, 1.05657649e+01, 6.16922032e+00, 1.24748759e+01,\n", + " 8.93819730e+00, 6.74683265e+00, 1.27954172e+01, 1.06646300e+01,\n", + " 7.07958858e+00, 1.04278553e+01, 6.37083396e+00, 8.89056950e+00,\n", + " 5.56427173e+00, 7.32265687e+00, 6.06553584e+00, 1.12968509e+01,\n", + " 8.65623378e+00, 1.13352096e+01, 5.13787840e+00, 8.00265477e+00,\n", + " 6.04050200e+00, 8.13790170e+00, 8.08471917e+00, 7.11227129e+00,\n", + " 9.19514312e+00, 1.38241724e-01, 1.02634280e+01, 1.03347334e+01,\n", + " 1.06250787e+01, 6.97151036e+00, 1.00529753e+01, 1.16629485e+01,\n", + " 1.22851058e+01, 1.22681021e+01, 1.17800189e+01, 9.64275254e+00,\n", + " 1.19641756e+01, 1.24710271e+01, 1.23447739e+01, 1.19246291e+01,\n", + " 1.27503614e+01, 1.27816169e+01, 6.10399724e+00, 8.38614313e+00,\n", + " 9.75970870e+00, 6.09037115e+00, 1.23304023e+01, 1.12502430e+01,\n", + " 7.19632710e+00, 4.38330876e+00, 7.68611052e+00, 1.16019220e+01,\n", + " 1.04751741e+01, 1.00848493e+01, 7.93139908e+00, 1.28333305e+01,\n", + " 1.16699806e+01, 6.62335538e+00, 8.52398185e+00, 6.74082517e+00,\n", + " 1.12956588e+01, 1.12160934e+01, 1.10262427e+01, 1.08935413e+01,\n", + " 1.04934275e+01, 1.14373425e+01, 1.24919744e+01, 9.78647811e+00,\n", + " 7.47778845e+00, 1.14302773e+01, 3.68535094e+00, 8.40723849e+00,\n", + " 9.70332926e+00, 1.09785423e+01, 9.55174640e+00, 1.15988472e+01,\n", + " 9.15299271e+00, 6.51783025e+00, 6.58802065e+00, 7.57916515e+00,\n", + " 3.33134652e+00, 8.07860624e+00, 3.70034079e+00, 1.23112503e+01,\n", + " 8.08423057e+00, 8.18254426e+00, 6.45212175e+00, 6.77407453e+00,\n", + " 3.62971961e+00, 9.21336624e+00, 1.01983215e+01, 8.92042109e+00,\n", + " 1.10570105e+01, 1.05379203e+01, 1.40951910e+01, 1.29461899e+01,\n", + " 1.14206783e+01, 1.21295290e+01, 1.06667034e+01, 1.10310706e+01,\n", + " 1.19490698e+01, 9.07473165e+00, 1.02485993e+01, 1.11321036e+01,\n", + " 6.77535903e+00, 9.18847343e+00, 5.79014106e+00, 6.64145865e+00,\n", + " -7.99679670e-01, 7.15844762e+00, 7.05186656e+00, 1.17801776e+01,\n", + " 6.84882993e+00, 2.29422550e+00, 7.74116138e+00, 8.31076721e+00,\n", + " -4.37405000e-01, 8.78704188e+00, 8.75213549e+00, 7.54031240e+00,\n", + " 6.98037600e+00, 7.58031459e+00, 8.00082975e+00, 9.81457607e+00,\n", + " 8.09847216e+00, 7.47953423e+00, 6.64526442e+00, 2.78471997e+00,\n", + " 5.81705852e+00, 7.04239293e+00, 5.95680224e+00, 8.24730052e+00,\n", + " 3.28174434e+00, 9.52896409e+00, 6.29912937e+00, 1.00417614e+01,\n", + " 5.06506591e+00, 8.91512535e+00, 5.60398020e+00, 1.98442787e+00,\n", + " 4.58944860e-01, 5.29521364e+00, 8.11433615e+00, 6.94916004e+00,\n", + " 1.05893875e+01, 6.79252835e+00, 1.11950717e+01, 5.20452890e+00,\n", + " 8.66449269e+00, 8.76294688e+00, 8.78308635e+00, 1.00277231e+01,\n", + " 6.39601122e+00, 1.27749305e+01, 1.01378890e+01, 8.85206297e+00,\n", + " 8.52558945e+00, 7.42543149e+00, 3.22105924e+00, -1.58233029e+00,\n", + " 4.80193567e-01, -4.41476109e+00, 3.90429557e+00, 2.14501400e+00,\n", + " 2.36844502e+00, 7.90469157e-01, 3.30768837e+00, 1.81438886e+00,\n", + " 4.62191989e+00, 6.16250039e-01, 4.61636250e-01, 7.14152189e+00,\n", + " 8.87744421e-01, -1.33893959e+00, 4.98941977e+00, 8.20747559e+00,\n", + " 8.60173439e+00, -1.93325395e+00, 5.90818540e+00, -8.16352256e-01,\n", + " 6.67541903e+00, 7.31333803e+00, 6.41107087e+00, 5.82342013e+00,\n", + " 9.02343032e+00, 7.27778002e+00, 7.15951526e+00, 6.89522862e+00,\n", + " 1.05898347e+01, -1.49821327e+00, -5.38834351e-01, 1.48744389e+00,\n", + " 4.05464673e+00, -2.92021228e+00, -1.99530446e+00, -8.69866422e-02,\n", + " 6.91530717e-01, -2.72996647e+00, 2.86672045e+00, -3.24577111e+00,\n", + " 2.60887123e-02, 5.63689677e+00, 3.96514332e+00, 5.06037350e+00,\n", + " 7.73864504e+00, 1.05156710e+01, 5.98027853e+00, 2.81176498e+00,\n", + " 3.34660201e+00, 2.25166836e+00, 7.66314503e+00, 2.24652097e+00,\n", + " 2.32755781e+00, 3.59822826e+00, 1.90937751e+00, 2.42272368e+00,\n", + " 1.32169298e+00, 6.37000220e+00, 1.10946112e+00, 2.24131160e+00,\n", + " 2.89038522e+00, 2.07893264e+00, 3.93463422e+00, 1.54531072e+00,\n", + " 6.78489477e+00, 6.54089099e+00, 4.49760409e+00, 1.15273803e+01,\n", + " 3.40350942e+00, -5.20514768e-01, 6.88011818e+00, 2.09188021e+00,\n", + " 4.27713980e-01, -3.80103571e-02, -3.68674279e+00, 1.62571424e+00,\n", + " 3.47679000e-01, -8.26182415e-01, -2.72819152e+00, -6.21341757e-01,\n", + " 6.51203018e-01, 1.37855914e+00, 2.25411975e+00, 6.32742151e+00,\n", + " 2.09348229e+00, 2.33428700e+00, 8.23559314e+00, 6.72550202e+00,\n", + " 4.37827682e+00, 6.22928514e+00, 4.05125391e+00, 1.75549684e+00,\n", + " 4.57888928e+00, 1.02531662e+00, 6.41694386e+00, 9.17579307e-01,\n", + " -5.61583205e-01, 7.37156754e-01, 1.15138886e+01, 1.14676233e+01,\n", + " 1.01659469e+01, 6.98158543e+00, 4.64844269e+00, 7.71811737e+00,\n", + " 3.22860259e+00, 3.79504811e+00, 2.16141215e+00, -3.89092163e-01,\n", + " 1.79513503e+00, 6.03315885e+00, 4.49056473e-01, -4.71906885e-01,\n", + " 1.00520417e+00, 2.18093315e+00, 2.73461109e+00, 3.69525980e+00,\n", + " 2.51372825e+00, 5.43269787e+00, 7.49324283e+00, 2.19084219e+00,\n", + " 2.00087520e+00, 3.55890153e+00, 9.76834003e+00, -2.92004443e-01,\n", + " 8.07035073e+00, -2.17323215e+00, 7.64991113e+00, 2.63273868e+00,\n", + " -2.95454512e+00, 9.85965581e+00, 1.00518400e+01, 5.28372913e+00,\n", + " 7.86336436e-01, 4.85915059e+00, 6.08799316e+00, 6.83163734e+00,\n", + " 3.23680558e+00, -4.53295270e-01, 6.85993090e+00, 3.95176101e+00,\n", + " 3.89126816e+00, -1.23150949e+00, 5.47864100e+00, 3.00006747e+00,\n", + " 3.35183572e+00, 2.02985690e+00, 2.99663740e+00, 4.35027830e+00,\n", + " 4.18560496e+00, 3.69271138e+00, 2.93910341e+00, 8.85063930e-01,\n", + " 3.14885352e+00, 5.14382401e-01, -8.42465873e-01, 1.03514879e+00,\n", + " -2.27501612e-01, 6.97710251e-03, 6.49003589e+00, -4.13478181e+00,\n", + " -1.34772256e+00, 3.25961188e+00, -3.14417936e+00, 1.97618407e+00,\n", + " 5.10743228e+00, -2.92314738e-02, 7.58638530e+00, 8.27106333e+00,\n", + " 7.66124939e+00, 1.07593771e+01, 3.27429854e+00, 4.81825766e+00,\n", + " 5.13889085e+00, 6.31923316e+00, 7.60833564e+00, 4.25220006e+00,\n", + " 7.62106881e+00, 4.36385688e+00, 5.09672327e+00, -2.91601073e+00,\n", + " 3.73698348e+00, 4.27102970e+00, 1.04073214e+01, 6.64936600e+00,\n", + " 7.12546793e+00, 6.27962898e+00])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TruncatedSVD(d[0][1]).fit_transform(b)[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(209, 498501)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = X[0:150]\n", + "y_train = y[0:150]\n", + "X_test = X[150::]\n", + "y_test = y[150::]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "np.random.shuffle(X_train)\n", + "np.random.seed(1)\n", + "np.random.shuffle(y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = []\n", + "for i in df['ID_set']:\n", + " with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_' \n", + " + str(i) + '_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f).transpose()\n", + " b = np.corrcoef(data)\n", + " #print(b.shape)\n", + " data2.append(b[np.triu_indices(b.shape[0])])\n", + " #b = np.corrcoef(data)\n", + " #c = TruncatedSVD(n_components=300).fit_transform(b)\n", + " #data2.append(c.reshape((c.shape[0]*c.shape[1], )))\n", + "y = np.array(df)\n", + "X = np.array(data2)\n", + "elbow = max(graspy.embed.select_dimension(X)[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(209, 2)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TruncatedSVD(n_components=elbow).fit_transform(X).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graspy.embed.select_dimension(X)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n", + "(998, 2380)\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorrcoef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;31m#print(b.shape)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mdata2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtriu_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcorrcoef\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/sklearn-dev/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mcorrcoef\u001b[0;34m(x, y, rowvar, bias, ddof)\u001b[0m\n\u001b[1;32m 2524\u001b[0m warnings.warn('bias and ddof have no effect and are deprecated',\n\u001b[1;32m 2525\u001b[0m DeprecationWarning, stacklevel=3)\n\u001b[0;32m-> 2526\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrowvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2527\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2528\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcov\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/sklearn-dev/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mcov\u001b[0;34m(m, y, rowvar, bias, ddof, fweights, aweights)\u001b[0m\n\u001b[1;32m 2452\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2453\u001b[0m \u001b[0mX_T\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2454\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_T\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2455\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrue_divide\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfact\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2456\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "data2 = []\n", + "for i in df['ID_set']:\n", + " with open('/Users/msanch35/Downloads/Timeseries_discovery_test_209/ts_' \n", + " + str(i) + '_discovery_test.pkl', 'rb') as f:\n", + " data = pickle.load(f).transpose()\n", + " print(data.shape)\n", + " b = np.corrcoef(data)\n", + " #print(b.shape)\n", + " data2.append(b[np.triu_indices(b.shape[0])])\n", + " #b = np.corrcoef(data)\n", + " #c = TruncatedSVD(n_components=300).fit_transform(b)\n", + " #data2.append(c.reshape((c.shape[0]*c.shape[1], )))\n", + "y = np.array(df)\n", + "X = np.array(data2)\n", + "print(X.shape)\n", + "elbow = max(graspy.embed.select_dimension(X)[0])\n", + "svd = TruncatedSVD(n_components=elbow)\n", + "X_new = svd.fit_transform(X)\n", + "print(X_new.shape, y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00206e+05, 2.70000e+01, 1.00000e+00, ..., 6.00000e+00,\n", + " 6.00000e+00, 5.70000e+01],\n", + " [1.01107e+05, 2.20000e+01, 1.00000e+00, ..., 2.00000e+00,\n", + " 0.00000e+00, 6.40000e+01],\n", + " [1.01915e+05, 3.50000e+01, 0.00000e+00, ..., 1.00000e+00,\n", + " 2.00000e+00, 5.00000e+01],\n", + " ...,\n", + " [9.87074e+05, 2.40000e+01, 1.00000e+00, ..., 2.00000e+00,\n", + " 1.00000e+00, 5.00000e+01],\n", + " [9.89987e+05, 3.30000e+01, 1.00000e+00, ..., 4.00000e+00,\n", + " 3.00000e+00, 5.00000e+01],\n", + " [9.92673e+05, 3.30000e+01, 0.00000e+00, ..., 1.00000e+00,\n", + " 0.00000e+00, 5.00000e+01]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0msvd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTruncatedSVD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m975\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msvd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Documents/ndd_sklearn/sklearn/decomposition/truncated_svd.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 174\u001b[0m raise ValueError(\"n_components must be < n_features;\"\n\u001b[1;32m 175\u001b[0m \" got %d >= %d\" % (k, n_features))\n\u001b[0;32m--> 176\u001b[0;31m U, Sigma, VT = randomized_svd(X, self.n_components,\n\u001b[0m\u001b[1;32m 177\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 178\u001b[0m random_state=random_state)\n", + "\u001b[0;32m~/Documents/ndd_sklearn/sklearn/utils/extmath.py\u001b[0m in \u001b[0;36mrandomized_svd\u001b[0;34m(M, n_components, n_oversamples, n_iter, power_iteration_normalizer, transpose, flip_sign, random_state)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m \u001b[0mU\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUhat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 359\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mflip_sign\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "X = np.array(data2)\n", + "svd = TruncatedSVD(n_components=975)\n", + "X = svd.fit_transform(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2767.24672928, 631.97366012])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "svd = TruncatedSVD(n_components=209)\n", + "X_new = svd.fit_transform(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2767.24672928, 631.97366012, 375.16779354, 313.8423227 ,\n", + " 206.40593949, 189.38984029, 182.59091476, 166.75119225,\n", + " 161.36834915, 154.83772237, 152.21586335, 151.32988375,\n", + " 147.07870735, 144.41276058, 142.07800771, 138.39037876,\n", + " 136.25632111, 133.44872519, 132.8307472 , 132.43874595,\n", + " 130.10887454, 128.5369982 , 127.88697317, 127.09277164,\n", + " 125.53061763, 125.26002362, 123.21641069, 122.36535837,\n", + " 120.81947779, 118.70128131, 118.1614364 , 117.84231302,\n", + " 117.42138986, 115.84474272, 115.12181335, 114.00075717,\n", + " 113.07651687, 112.81188673, 111.69194884, 111.25611131,\n", + " 110.61302907, 109.45577297, 109.39928149, 109.01272182,\n", + " 108.4234688 , 107.84827036, 106.62954577, 106.35446463,\n", + " 105.89734955, 105.34635458, 104.69569396, 104.34592439,\n", + " 103.72540499, 103.19326601, 102.71069042, 102.22986184,\n", + " 101.74168994, 100.96455923, 100.73548106, 100.3381644 ,\n", + " 99.7382059 , 99.5937014 , 99.14361733, 98.9088435 ,\n", + " 98.43397526, 98.13539248, 97.78453956, 97.40933048,\n", + " 97.3983442 , 97.07402983, 96.64834209, 96.10651315,\n", + " 95.7744761 , 95.41128608, 95.2659245 , 94.92403848,\n", + " 94.53964094, 94.23822617, 93.86615173, 93.35586575,\n", + " 93.1650937 , 92.90422689, 92.63904993, 92.17342935,\n", + " 92.06671475, 91.54857581, 91.10124975, 90.80520035,\n", + " 90.73001156, 90.5036183 , 90.09821412, 89.93582525,\n", + " 89.27342787, 89.20220362, 88.77861743, 88.39406024,\n", + " 88.33329929, 88.21237384, 88.08962546, 87.70622792,\n", + " 87.62303522, 87.4902757 , 87.28431537, 87.01109671,\n", + " 86.70470731, 86.25053723, 85.86688308, 85.63878505,\n", + " 85.31162506, 85.13919107, 84.9303533 , 84.80660612,\n", + " 84.56480748, 84.4398222 , 84.11784645, 83.86838323,\n", + " 83.64367243, 83.56756319, 83.48696406, 83.23695681,\n", + " 82.90070977, 82.79357942, 82.64581673, 82.41433675,\n", + " 81.90136535, 81.77924401, 81.60661354, 81.44798738,\n", + " 81.15254622, 80.98842512, 80.77252372, 80.57279792,\n", + " 80.46251249, 80.03575088, 79.98318288, 79.67455412,\n", + " 79.34980275, 79.10370233, 78.91956646, 78.77993041,\n", + " 78.59803851, 78.3444311 , 77.88314016, 77.74443394,\n", + " 77.62018608, 77.34645465, 77.25858676, 77.14097969,\n", + " 76.90029151, 76.62863192, 76.52874082, 76.41342679,\n", + " 76.02994691, 75.91556207, 75.81928726, 75.61682805,\n", + " 75.46315446, 75.13642538, 75.0954285 , 74.79381461,\n", + " 74.69697703, 74.53103641, 74.33409841, 74.13288459,\n", + " 73.91014273, 73.53961963, 73.43078759, 73.2067606 ,\n", + " 72.94609636, 72.86142919, 72.56910943, 72.51404648,\n", + " 72.32935944, 72.23183244, 71.93406835, 71.83360441,\n", + " 71.57540291, 71.33725473, 71.23283662, 71.13800108,\n", + " 70.92473511, 70.61903238, 70.44376423, 70.23023289,\n", + " 70.07968103, 69.86057856, 69.62418271, 69.43004687,\n", + " 69.14706511, 68.89235072, 68.68826171, 68.56252799,\n", + " 68.51846183, 68.29076518, 68.16077986, 68.1108785 ,\n", + " 67.79335581, 67.52302892, 66.64650666, 66.58562838,\n", + " 66.28829232, 66.15016659, 66.09687294, 65.61157326,\n", + " 65.38927553, 65.14412759, 64.64298578, 64.42432361,\n", + " 63.50895059])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svd.singular_values_" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('