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runner_PVCCA.py
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runner_PVCCA.py
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from __future__ import division, print_function
# # TODO:
import sys, os, time, copy
from datetime import datetime
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import util
from util.misc import myprint_file, struct
from util.misc import HParams_new as HParams
from util.downstream_tasks import linear_svm_classify, linear_svm_classify2, spectral_clustering, spectral_clustering2, spectral_clustering3
from util.downstream_tasks import orth_measure, orth_measure2, orth_measure3
from util.graphics import plot_tile_image
try:
from urllib.request import urlretrieve # Python 3
except ImportError:
from urllib import urlretrieve # Python 2
homepath = os.getcwd() #+ "/.."
sys.path.append(homepath)
sys.path.append(homepath+'/util')
# Hyper-parameters default
def get_default_arch(args):
"""Get the default hyperparameters for conv flow."""
return HParams(
hidden_dim=args.hidden_dim, # hidden_dim1=60,hidden_dim2=60,
hidden_dim_shrd = args.hidden_dim_shrd,
var_v1=1., var_v2=1.,
reg_var_wgt=1e-4, # Weight decay parameter
p_drop=0., # dropout probability in NN (keep_prob= 1. - p_drop)
reg_bias=1, # to regularize the bias with the same weight or not
gate='relu',
# Multi-view hyperparameters
var_prior0=1., # variance of prior Guassina on Phi
var_prior1=1., var_prior2_ratio=1., # variance of prior Guassina on eps1, eps2
postenc1='sp', postenc2='sp',
postdec2='sp', #{'exp', 'sp'}
shrd_on_all='none', # 'none', 'early', 'late'
shrd_est='z1', # 'nn', 'z1', 'z12', 'z2'
predec='convT', #'fc' or 'convT'
clstr_mode='advanced', #'simple' only used for mnist, usps and digits
# n_basis=args.hidden_dim_shrd,
)
class Runner(object):
def __init__(self, args):
self.args = args
args.dtype = tf.float32
# Initialization
tf.reset_default_graph()
args.ckpt_best_filename = "model_best_loss"
args.ckpt_filename = "model_last.ckpt"
args.data_dir = homepath + "/data/"
# sys.path.append(data_dir)
logdir_name = args.logdir
if args.mode == "cross-validate":
time.sleep(47 * args.fold)
args.logdir = homepath + "/logs/{}/CV/".format(args.dataset) + logdir_name
args.logdir += '/'
args.log_file = args.logdir + 'resultsLogFold%d.txt' % args.fold
self.npy_result_file = 'fold%d_output.npy' % args.fold
self.npy_result_file_running = self.npy_result_file
else:
args.logdir = homepath + "/logs/{}/".format(args.dataset) + logdir_name \
if (args.logdir[0] != '/') and (args.logdir[0:2] != './') else logdir_name
args.logdir += '/'
args.log_file = args.logdir + \
('results_log.txt' if args.mode == "train" else 'test_log.txt')
self.npy_result_file = 'final_output.npy'
self.npy_result_file_running = 'running_output.npy'
if not os.path.exists(args.logdir): os.makedirs(args.logdir) # , exist_ok=True)
hp = get_default_arch(self.args).update_config(args.hpconfig)
args.hp = hp
args._print = self._print = lambda str_in, verbose: myprint_file(args.log_file, verbose, str_in)
self._print("\n\n ------------------------------------------------- \n\n", verbose='log')
self._print("Results will be saved in sub_directory: %s \n" % logdir_name, verbose='log')
self._print("\n MODEL ARGUMENTS ARE:", verbose='log')
self._print(args.__dict__, verbose='log')
self._print("\n NN CONFIG HYPER-PARAMETERS ARE:", verbose='log')
self._print(args.hp.__dict__['dict_'], verbose='log')
for key in hp.dict_.keys():
args.__setattr__(key, hp[key])
args.SEED_gra = args.seed if args.seed >= 0 else np.random.randint(1, 100000) # None for random seed
args.SEED_op = args.SEED_gra
tf.set_random_seed(args.SEED_gra)
np.random.seed(args.SEED_gra)
self.data = self.load_data()
args.eval_methods = args.eval_methods.split('_')
args.eval_mtd_init ={
'class': 1.1,
'NMI': 0., 'ACC': 0., 'ARI': 0.,
'NMI0': 0., 'ACC0': 0., 'ARI0': 0., 'NMI00': 0., 'ACC00': 0., 'ARI00': 0.,
'NMI2': 0., 'ACC2': 0., 'ARI2': 0., 'NMI3': 0., 'ACC3': 0., 'ARI3': 0.,
'classv1': 1.1, 'NMIv1': 0., 'ACCv1': 0., 'classCCA': 1.1, 'classEYB':1.1,
'nllrec': np.inf}
args.eval_mtd_init = {key: args.eval_mtd_init[key] for key in args.eval_methods}
args.evalCCA = True if 'classCCA' in args.eval_methods else False
if args.mode == "cross-validate":
self.data.validation_set_fold(args.fold, args.n_folds)
self._print("FOLD number=%d" % args.fold, verbose='log')
self.load_model()
def load_data(self):
#########################
# initialize data loaders for train/test splits
#########################
hp = self.args.hp
self.is_nmnist = False
if self.args.dataset == 'n-mnist':
self.is_nmnist = True
from util.load_data import mv_data_mnist as mv_data
# args.score1, args.score2 = 'NMI', 'ACC'
self.args.inp_dtype = tf.float32
data = mv_data(self.args.data_dir + 'MNIST.mat', self.args, self.args.SEED_gra)
self.args.modalities = 2
self.args.ae1, self.args.ae2 = struct(), struct()
self.args.ae1.shape_enc = np.array([784, 1024, 1024, 1024, hp.hidden_dim])
self.args.ae1.gate_enc = [hp.gate, hp.gate, hp.gate, 'linear']
self.args.ae1.shape_dec = np.array([hp.hidden_dim, 1024, 1024, 1024, 784])
self.args.ae1.gate_dec = [hp.gate, hp.gate, hp.gate, 'linear']
self.args.ae1.postenc_gate = ['linear', hp.postenc1]
self.args.ae1.postdec_gate = ['sigmoid', None]
self.args.ae1.loss_rec = 'CE'
self.args.ae1.var_rec, self.args.ae1.var_prior = hp.var_v1, hp.var_prior1
self.args.ae2.shape_enc, self.args.ae2.gate_enc = self.args.ae1.shape_enc, self.args.ae1.gate_enc
self.args.ae2.shape_dec = self.args.ae1.shape_dec
self.args.ae2.gate_dec = [hp.gate, hp.gate, hp.gate, 'linear']
self.args.ae2.postenc_gate = ['linear', hp.postenc2]
self.args.ae2.postdec_gate = ['sigmoid', hp.postdec2]
self.args.ae2.loss_rec = 'WLS'
self.args.ae2.var_rec, self.args.ae2.var_prior = hp.var_v2, hp.var_prior1 * hp.var_prior2_ratio
self.args.ae_shrd = struct()
self.args.ae_shrd.shape_enc = np.array([784 if not self.args.hp.shrd_on_all else 2 * 784,
1024, 1024, 1024, self.args.hidden_dim_shrd])
self.args.ae_shrd.gate_enc = [hp.gate, hp.gate, hp.gate, 'linear'] # 'sigmoid']
self.args.ae_shrd.var_prior = hp.var_prior0
self.args.ae_shrd.loss_rec, self.args.ae_shrd.var_rec, = None, None
self.args.ae_shrd.postenc_gate = ['linear', 'sigmoid'] if hp.shrd_est == 'nn' else [None, 'sigmoid']
self.args.ae_shrd.postdec_gate = [None, None]
self.args.ae_shrd.shape_dec = [-1]
self.args.ae_shrd.gate_dec = [None]
self.args.inp_shape1 = [None] + list(data.d1_in)
self.args.inp_shape2 = [None] + list(data.d2_in)
elif self.args.dataset == 'yaleb':
from util.load_data_MultiModal import mv_data_yaleb as mv_data
self.args.modalities = 5
self.args.inp_dtype = tf.float32
data = mv_data(self.args.data_dir, self.args, self.args.SEED_gra)
self.args.inp_shapes = [[None] + list(d_in_) for d_in_ in data.d_in]
data.cluster = struct()
data.cluster.k_knn = [5] # [5, 10, 20, 30]
data.cluster.assign_labels = 'discretize' # 'kmeans'
data.cluster.affinity = 'pre_nearest_neighbors'
self.args.cluster = data.cluster
self.args.ae_list = []
# ---------- 1st view -------------
ae_ = struct()
ae_.enc_spec = [
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 20, 'filter_size': [3, 3], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
# the order othe decoder layer is reveresed, last spec is first one the decoder network
ae_.dec_spec = [
{'gate': 'linear', 'dim_out': 1, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [3, 3], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 20, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
ae_.postenc_gate = ['linear', hp.postenc1]
ae_.postenc_dims = [hp.hidden_dim, hp.hidden_dim]
ae_.postdec_gate = ['sigmoid', None]
ae_.loss_rec = 'CE'
ae_.var_rec, ae_.var_prior = hp.var_v1, hp.var_prior1
self.args.ae_list += [ae_]
# ---------- 2nd view -------------
ae_.var_prior = hp.var_prior1 * hp.var_prior2_ratio # todo: to fix, using deEp copy and on all views
self.args.ae_list += [copy.deepcopy(ae_)]
# ---------- 3rd view -------------
self.args.ae_list += [copy.deepcopy(ae_)]
# ---------- 4rd view -------------
self.args.ae_list += [copy.deepcopy(ae_)]
# ---------- 5rd view -------------
self.args.ae_list += [copy.deepcopy(ae_)]
# ---------- shared factor --------
self.args.ae_shrd = struct()
self.args.ae_shrd.enc_spec = self.args.ae_list[0].enc_spec
if self.args.hp.shrd_on_all == 'late':
self.args.ae_shrd.enc_spec = [
{'gate': hp.gate, 'dim_out': 5 * hp.hidden_dim_shrd, 'filter_size': [1, 1], 'stride': [1, 1],
'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 5 * hp.hidden_dim_shrd, 'filter_size': [1, 1], 'stride': [1, 1],
'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 5 * hp.hidden_dim_shrd, 'filter_size': [1, 1], 'stride': [1, 1],
'pad': 'SAME'},
]
self.args.ae_shrd.dec_spec = self.args.ae_shrd.enc_spec # dummy, it is not used
self.args.ae_shrd.postenc_gate = ['linear', 'sigmoid'] if hp.shrd_est == 'nn' else [None, 'sigmoid']
self.args.ae_shrd.postenc_dims = [hp.hidden_dim_shrd, hp.hidden_dim_shrd] if hp.shrd_est == 'nn' \
else [0, hp.hidden_dim_shrd]
self.args.ae_shrd.var_prior = hp.var_prior0
self.args.ae_shrd.loss_rec, self.args.ae_shrd.var_rec, = None, None
self.args.ae_shrd.postdec_gate = [None, None]
if self.args.reconst in ['all2one', '345to12']:
for jj in self.args.reconst_views[0]:
self.args.ae_list[jj].enc_spec = [
{'gate': hp.gate, 'dim_out': 5 * hp.hidden_dim, 'filter_size': [1, 1], 'stride': [1, 1],
'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 5 * hp.hidden_dim, 'filter_size': [1, 1], 'stride': [1, 1],
'pad': 'SAME'},
]
elif self.args.dataset == 'digits':
from util.load_data_MultiModal import mv_data_digits as mv_data
self.args.modalities = 2
self.args.inp_dtype = tf.float32
data = mv_data(self.args.data_dir, self.args, self.args.SEED_gra)
self.args.modalities = 2
self.args.inp_shapes = [[None] + list(d_in_) for d_in_ in data.d_in]
data.cluster = struct()
if hp.clstr_mode == 'simple':
data.cluster.k_knn = [5] # [5, 10, 20, 30]
data.cluster.assign_labels = 'discretize' # 'kmeans'
else:
data.cluster.k_knn = [5, 10, 20, 30] # [5] #
data.cluster.assign_labels = 'kmeans' # 'discretize' #
data.cluster.affinity = 'pre_nearest_neighbors'
self.args.cluster = data.cluster
self.args.ae_list = []
# ---------- 1st view -------------
ae_ = struct()
ae_.enc_spec = [
{'gate': hp.gate, 'dim_out': 7, 'filter_size': [7, 7], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
# the order othe decoder layer is reveresed, last spec is first one the decoder network
ae_.dec_spec = [
{'gate': 'linear', 'dim_out': 1, 'filter_size': [7, 7], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 7, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
ae_.postenc_gate = ['linear', hp.postenc1]
ae_.postenc_dims = [hp.hidden_dim, hp.hidden_dim]
ae_.postdec_gate = ['sigmoid', None]
ae_.loss_rec = 'CE'
ae_.var_rec, ae_.var_prior = hp.var_v1, hp.var_prior1
self.args.ae_list += [ae_]
# ---------- 2nd view -------------
ae_ = struct()
ae_.enc_spec = [
{'gate': hp.gate, 'dim_out': 7, 'filter_size': [7, 7], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 15, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 15, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
ae_.dec_spec = [
{'gate': 'linear', 'dim_out': 1, 'filter_size': [7, 7], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 7, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 15, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
ae_.postenc_gate = ['linear', hp.postenc2]
ae_.postenc_dims = [hp.hidden_dim, hp.hidden_dim]
ae_.postdec_gate = ['sigmoid', None]
ae_.loss_rec = 'CE'
ae_.var_rec, ae_.var_prior = hp.var_v2, hp.var_prior1 * hp.var_prior2_ratio
self.args.ae_list += [ae_]
# ---------- shared factor --------
self.args.ae_shrd = struct()
self.args.ae_shrd.enc_spec = [
{'gate': hp.gate, 'dim_out': 7, 'filter_size': [7, 7], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
if self.args.hp.shrd_on_all == 'late':
self.args.ae_shrd.enc_spec = [
{'gate': hp.gate, 'dim_out': 5 * hp.hidden_dim_shrd, 'filter_size': [1, 1], 'stride': [1, 1],
'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 5 * hp.hidden_dim_shrd, 'filter_size': [1, 1], 'stride': [1, 1],
'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 5 * hp.hidden_dim_shrd, 'filter_size': [1, 1], 'stride': [1, 1],
'pad': 'SAME'},
]
self.args.ae_shrd.dec_spec = self.args.ae_shrd.enc_spec # dummy, it is not used
self.args.ae_shrd.postenc_gate = ['linear', 'sigmoid'] if hp.shrd_est == 'nn' else [None, 'sigmoid']
self.args.ae_shrd.postenc_dims = [hp.hidden_dim_shrd, hp.hidden_dim_shrd] if hp.shrd_est == 'nn' \
else [0, hp.hidden_dim_shrd]
self.args.ae_shrd.var_prior = hp.var_prior0
self.args.ae_shrd.loss_rec, self.args.ae_shrd.var_rec, = None, None
self.args.ae_shrd.postdec_gate = [None, None]
elif self.args.dataset == 'digits_fc':
# args.score1, args.score2 = 'NMI', 'ACC'
from util.load_data_MultiModal import mv_data_digits as mv_data
self.args.modalities = 1
self.args.inp_dtype = tf.float32
data = mv_data(self.args.data_dir, self.args, self.args.SEED_gra)
self.args.inp_shape1 = [None] + list(data.d1_in)
self.args.inp_shape2 = [None] + list(data.d2_in)
self.args.ae1 = struct()
self.args.ae1.shape_enc = np.array([784, 1024, 1024, 1024, hp.hidden_dim])
self.args.ae1.gate_enc = [hp.gate, hp.gate, hp.gate, 'linear']
self.args.ae1.shape_dec = np.array([hp.hidden_dim, 1024, 1024, 1024, 784])
self.args.ae1.gate_dec = [hp.gate, hp.gate, hp.gate, 'linear']
self.args.ae1.postenc_gate = ['linear', hp.postenc1]
self.args.ae1.postdec_gate = ['sigmoid', None]
self.args.ae1.loss_rec = 'CE'
self.args.ae1.var_rec, self.args.ae1.var_prior = hp.var_v1, hp.var_prior1
self.args.ae2 = struct()
self.args.ae2.shape_enc = np.array([256, 1024, 1024, 1024, hp.hidden_dim])
self.args.ae2.gate_enc = [hp.gate, hp.gate, hp.gate, 'linear']
self.args.ae2.shape_dec = np.array([hp.hidden_dim, 1024, 1024, 1024, 256])
self.args.ae2.gate_dec = [hp.gate, hp.gate, hp.gate, 'linear']
self.args.ae2.postenc_gate = ['linear', hp.postenc2]
self.args.ae2.postdec_gate = ['sigmoid', None]
self.args.ae2.loss_rec = 'CE'
self.args.ae2.var_rec, self.args.ae2.var_prior = hp.var_v2, hp.var_prior1 * hp.var_prior2_ratio
self.args.ae_shrd = struct()
self.args.ae_shrd.shape_enc = np.array([784 if not self.args.hp.shrd_on_all else (784 + 256),
1024, 1024, 1024, self.args.hidden_dim_shrd])
self.args.ae_shrd.gate_enc = [hp.gate, hp.gate, hp.gate, 'linear'] # 'sigmoid']
self.args.ae_shrd.var_prior = hp.var_prior0
self.args.ae_shrd.loss_rec, self.args.ae_shrd.var_rec, = None, None
self.args.ae_shrd.postenc_gate = ['linear', 'sigmoid'] if hp.shrd_est == 'nn' else [None, 'sigmoid']
self.args.ae_shrd.postdec_gate = [None, None]
self.args.ae_shrd.shape_dec = [-1]
self.args.ae_shrd.gate_dec = [None]
elif self.args.dataset == 'mnist':
from util.load_data_MultiModal import mv_data_mnist as mv_data
self.args.modalities = 1
self.args.inp_dtype = tf.float32
data = mv_data(self.args.data_dir, self.args, self.args.SEED_gra)
self.args.modalities = 1
self.args.inp_shapes = [[None] + list(d_in_) for d_in_ in data.d_in]
data.cluster = struct()
if hp.clstr_mode == 'simple':
data.cluster.k_knn = [5] # [5, 10, 20, 30]
data.cluster.assign_labels = 'discretize' # 'kmeans'
else:
data.cluster.k_knn = [5, 10, 20, 30] # [5] #
data.cluster.assign_labels = 'kmeans' # 'discretize' #
data.cluster.affinity = 'pre_nearest_neighbors'
self.args.cluster = data.cluster
self.args.ae_list = []
# ---------- 1st view -------------
ae_ = struct()
ae_.enc_spec = [
{'gate': hp.gate, 'dim_out': 7, 'filter_size': [7, 7], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
# the order othe decoder layer is reveresed, last spec is first one the decoder network
ae_.dec_spec = [
{'gate': 'linear', 'dim_out': 1, 'filter_size': [7, 7], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 7, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
ae_.postenc_gate = ['linear', hp.postenc1]
ae_.postenc_dims = [hp.hidden_dim, hp.hidden_dim]
ae_.postdec_gate = ['sigmoid', None]
ae_.loss_rec = 'CE'
ae_.var_rec, ae_.var_prior = hp.var_v1, hp.var_prior1
self.args.ae_list += [ae_]
# ---------- shared factor --------
self.args.ae_shrd = struct()
self.args.ae_shrd.enc_spec = [
{'gate': hp.gate, 'dim_out': 7, 'filter_size': [7, 7], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 30, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
self.args.ae_shrd.dec_spec = self.args.ae_shrd.enc_spec # dummy, it is not used
self.args.ae_shrd.postenc_gate = ['linear', 'sigmoid'] if hp.shrd_est == 'nn' else [None, 'sigmoid']
self.args.ae_shrd.postenc_dims = [hp.hidden_dim_shrd, hp.hidden_dim_shrd] if hp.shrd_est == 'nn' \
else [0, hp.hidden_dim_shrd]
self.args.ae_shrd.var_prior = hp.var_prior0
self.args.ae_shrd.loss_rec, self.args.ae_shrd.var_rec, = None, None
self.args.ae_shrd.postdec_gate = [None, None]
elif self.args.dataset == 'usps':
from util.load_data_MultiModal import mv_data_usps as mv_data
self.args.modalities = 1
self.args.inp_dtype = tf.float32
data = mv_data(self.args.data_dir, self.args, self.args.SEED_gra)
self.args.modalities = 1
self.args.inp_shapes = [[None] + list(d_in_) for d_in_ in data.d_in]
data.cluster = struct()
if hp.clstr_mode == 'simple':
data.cluster.k_knn = [5] # [5, 10, 20, 30]
data.cluster.assign_labels = 'discretize' # 'kmeans'
else:
data.cluster.k_knn = [5, 10, 20, 30] # [5] #
data.cluster.assign_labels = 'kmeans' # 'discretize' #
data.cluster.affinity = 'pre_nearest_neighbors'
self.args.cluster = data.cluster
self.args.ae_list = []
# ---------- 1st view -------------
ae_ = struct()
ae_.enc_spec = [
{'gate': hp.gate, 'dim_out': 7, 'filter_size': [7, 7], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 15, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 15, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
ae_.dec_spec = [
{'gate': 'linear', 'dim_out': 1, 'filter_size': [7, 7], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 7, 'filter_size': [5, 5], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 15, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
ae_.postenc_gate = ['linear', hp.postenc1]
ae_.postenc_dims = [hp.hidden_dim, hp.hidden_dim]
ae_.postdec_gate = ['sigmoid', None]
ae_.loss_rec = 'CE'
ae_.var_rec, ae_.var_prior = hp.var_v1, hp.var_prior1
self.args.ae_list += [ae_]
# ---------- shared factor --------
self.args.ae_shrd = struct()
self.args.ae_shrd.enc_spec = [
{'gate': hp.gate, 'dim_out': 7, 'filter_size': [7, 7], 'stride': [2, 2], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 10, 'filter_size': [5, 5], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 15, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
{'gate': hp.gate, 'dim_out': 15, 'filter_size': [3, 3], 'stride': [1, 1], 'pad': 'SAME'},
]
self.args.ae_shrd.dec_spec = self.args.ae_shrd.enc_spec # dummy, it is not used
self.args.ae_shrd.postenc_gate = ['linear', 'sigmoid'] if hp.shrd_est == 'nn' else [None, 'sigmoid']
self.args.ae_shrd.postenc_dims = [hp.hidden_dim_shrd, hp.hidden_dim_shrd] if hp.shrd_est == 'nn' \
else [0, hp.hidden_dim_shrd]
self.args.ae_shrd.var_prior = hp.var_prior0
self.args.ae_shrd.loss_rec, self.args.ae_shrd.var_rec, = None, None
self.args.ae_shrd.postdec_gate = [None, None]
else:
raise ValueError('not a valid dataset')
return data
def load_model(self):
#########################
# load the model
#########################
if self.args.dataset == 'n-mnist':
import model_VPCCA as mdl
elif self.args.dataset == 'yaleb':
import model_MVCCA_CNN as mdl
if self.args.reconst == 'one2all':
import model_MVCCA_Recons1 as mdl
if self.args.reconst in ['all2one', '345to1', '345to12']:
import model_MVCCA_Recons_all2one as mdl
if self.args.reconst == 'all2one':
self.args.reconst_views = [[0], [1, 2, 3, 4]]
elif self.args.reconst == '345to12':
self.args.reconst_views = [[0, 1], [2, 3, 4]]
elif self.args.dataset == 'mnist': # for uni-modal clustering
import model_MVCCA_CNN as mdl
elif self.args.dataset == 'usps': # for uni-modal clustering
import model_MVCCA_CNN as mdl
elif self.args.dataset == 'digits_fc':
import model_VCCA2 as mdl
elif self.args.dataset == 'digits':
import model_MVCCA_CNN as mdl
self.model = mdl.model_VCCA(self.args, data=self.data)
def train(self):
# Train
# sess.graph.finalize()
valid_loss_best = np.inf
self.model.init_global_step0()
self.model.init_epoch = self.model.sess.run(self.model.epoch) + 1
results_list = []
final_output = []
sim_measure_list = []
# best_misclass_err_tu, best_misclass_err_te, misclass_err_tu, misclass_err_te = 2., 2., 1., 1.
# revoke
if self.model.init_epoch > 0:
_old_eval_ = 'OLD BEST '
for key_ in self.model.best_score_tu:
_old_eval_ += ': ' + key_ + ' _tu =' + str(100 * self.model.val(self.model.best_score_tu[key_])) \
+ ', ' + key_ + '_te =' + str(100 * self.model.val(self.model.best_score_te[key_]))
self._print(_old_eval_, 'log')
self.model._t0 = time.time()
while self.model.val(self.model.epoch) < self.args.n_epoch:
self.model.epoch_inc()
epoch = self.model.val(self.model.epoch)
self.args.to_eval_now = True if ((epoch % self.args.eval_interval == 0 or epoch == self.args.n_epoch - 1)
and self.model.completed_warmup and epoch >= self.args.warmupDelay - 1) \
else False
results_dict = self.model.train_epoch(epoch, self.data, )
results_list.append(results_dict)
if self.args.compute_corr:
n_samples_ = 1000
# add some tensors for computing similarities
if not hasattr(self.model, 'Phi_tensor_samples'):
self.model.Z1_tensor_samples, self.model.Z2_tensor_samples, self.model.EPS1_tensor_samples, self.model.EPS2_tensor_samples, self.model.Phi_tensor_samples = \
self.model.CA.draw_2v_samples(
Phi_mean=self.model.Phi_mean, Phi_sigmasq=self.model.Phi_sigmasq,
Eps1_mean=self.model.Eps1_mean, Eps1_sigmasq=self.model.Eps1_sigmasq,
Eps2_mean=self.model.Eps2_mean, Eps2_sigmasq=self.model.Eps2_sigmasq, n_samples=n_samples_)
latent_tensors_ = [self.model.Phi_mean, self.model.Eps1_mean, self.model.Eps2_mean, self.model.Phi_sigmasq,
self.model.Eps1_sigmasq,
self.model.Eps2_sigmasq] + \
[self.model.Phi_tensor_samples, self.model.EPS1_tensor_samples, self.model.EPS2_tensor_samples] + \
[self.model.Z1_tensor_samples, self.model.Z2_tensor_samples]
latent_dictKeys = ['Phi_mean', 'Eps1_mean', 'Eps2_mean', 'Phi_sigmasq', 'Eps1_sigmasq',
'Eps2_sigmasq'] + \
['Phi', 'Eps1', 'Eps2'] + ['Z1', 'Z2']
sim_measure_ = self._compute_correlation(
x_value=[self.data.x1_tu[::5], self.data.x2_tu[::5]],
n_samples=n_samples_, model=self.model, epoch=epoch,
latent_tensors=latent_tensors_, latent_dictKeys=latent_dictKeys
)
sim_measure_list.append(sim_measure_)
np.save(self.args.logdir + 'similarity_measures.npy', sim_measure_list)
if epoch < self.args.warmupDelay - 1: # or (not completed_warmup and args.mode == "cross-validate"):
continue # to skip the remaining statements in the current iteration of the loop
if (epoch % self.args.save_interval == 0) or (epoch == 1) or (
epoch == self.args.warmupDelay + 1): # and args.tf_save != 'ignore'
if np.isnan(results_dict['obj_tr']):
self._print("Loss is nan. Reverting to last saved checkpoint ...", 'log')
self.model.restore(self.args.logdir + self.args.ckpt_filename)
self.model.load_var(self.model.lr0, self.model.val(self.model.lr0) * 0.9)
continue
else:
self.model.save(self.args.logdir + self.args.ckpt_filename)
print("Model saved in file: %s" % self.args.logdir + self.args.ckpt_filename)
if self.args.to_eval_now:
# ------- inference 2v self.model-------
self.model.latent_mean_list = [self.model.Phi_mean]
self.model.latent_dict2ind = {'phi': 0}
if self.args.reconst in ['one2all', 'all2one', '345to12']:
self.model.latent_mean_list = [self.model.NLL_rec]
self.model.latent_dict2ind = {'nllrec': 0}
if self.is_nmnist:
if set(self.args.eval_methods).intersection(['classv1', 'NMIv1', 'ACCv1']):
self.model.latent_dict2ind.update({'phi_v1': len(self.model.latent_mean_list)})
self.model.latent_mean_list.append(self.model.Phi_mean_v1)
if set(self.args.eval_methods).intersection(['classv2', 'NMIv2', 'ACCv2']):
self.model.latent_dict2ind.update({'phi_v2': len(self.model.latent_mean_list)})
self.model.latent_mean_list.append(self.model.Phi_mean_v2)
self.model.CA.latent_tr_list = self.model.encode_batched(self.model.latent_mean_list,
x1_value=self.data.x1_tr, x2_value=self.data.x2_tr,
bs=self.args.batch_size_valid)
self.model.CA.latent_tu_list = self.model.encode_batched(self.model.latent_mean_list,
x1_value=self.data.x1_tu, x2_value=self.data.x2_tu,
bs=self.args.batch_size_valid)
self.model.CA.latent_te_list = self.model.encode_batched(self.model.latent_mean_list,
x1_value=self.data.x1_te, x2_value=self.data.x2_te,
bs=self.args.batch_size_valid)
# compute OBJective values for tune dataset
elbo_tu, NLL1_tu, NLL2_tu = self.model.test(x1_value=self.data.x1_tu,
x2_value=self.data.x2_tu,
bs=self.args.batch_size_valid)
# compute OBJective values for test dataset
elbo_te, NLL1_te, NLL2_te = self.model.test(x1_value=self.data.x1_te,
x2_value=self.data.x2_te,
bs=self.args.batch_size_valid)
self._print(">>>>>Epoch| TUNE: obj, NLL1, NLL2 | TEST: obj, NLL1, NLL2", 'log')
self._print(">>>>>%05d| %.4g %.4g %.4g| %.4g %.4g %.4g "
% (epoch, elbo_tu, NLL1_tu, NLL2_tu, elbo_te, NLL1_te, NLL2_te), 'log')
dict_out = {'CV_config': self.args.hpconfig, 'epoch': epoch, 'msg': '',
'obj_tu': elbo_tu, 'NLL1_tu': NLL1_tu, 'NLL2_tu': NLL2_tu,
'obj_te': elbo_te, 'NLL1_te': NLL1_te, 'NLL2_te': NLL2_te}
else:
self.model.CA.latent_tr_list = self.model.run_batched(self.model.latent_mean_list,
x_values=self.data.x_tr,
bs=self.args.batch_size_valid)
self.model.CA.latent_tu_list = self.model.run_batched(self.model.latent_mean_list,
x_values=self.data.x_tu,
bs=self.args.batch_size_valid)
self.model.CA.latent_te_list = self.model.run_batched(self.model.latent_mean_list,
x_values=self.data.x_te,
bs=self.args.batch_size_valid)
# compute OBJective values for tune dataset
out_ = self.model.test(x_values=self.data.x_tu, bs=self.args.batch_size_valid)
elbo_tu, NLL_tu = out_[0], out_[1:]
# compute OBJective values for test dataset
out_ = self.model.test(x_values=self.data.x_te, bs=self.args.batch_size_valid)
elbo_te, NLL_te = out_[0], out_[1:]
str_NLL = ""
str_NLL = str_NLL.join([', NLL%d' % i for i in range(1, self.args.modalities + 1)])
self._print(">>>>> EPOCH| TUNE: obj%s || TEST: obj%s " % (str_NLL, str_NLL), 'log')
str_ = ">>>>> %05d| %.4g" + " %.4g" * self.args.modalities + "|| %.4g" + " %.4g" * self.args.modalities
self._print(str_ % ((epoch, elbo_tu) + tuple(NLL_tu) + (elbo_te,) + tuple(NLL_te)), 'log')
dict_out = {'CV_config': self.args.hpconfig, 'epoch': epoch, 'msg': '',
'obj_tu': elbo_tu, 'NLL_tu': NLL_tu,
'obj_te': elbo_te, 'NLL_te': NLL_te}
dict_out = self._eval_method_fun(self.args.eval_methods, model=self.model,
data=self.data, dict_out=dict_out, epoch=epoch)
final_output.append(dict_out)
np.save(self.args.logdir + self.npy_result_file_running, final_output)
if epoch == self.args.n_epoch - 1:
np.save(self.args.logdir + self.npy_result_file, final_output)
results_dict.update(dict_out)
def infer(self, data=None):
data = self.data if data is None else data
if self.is_nmnist:
return self._infer_NoisyMNIST(data=data)
else:
return self._infer_cluster(data=data)
def _infer_NoisyMNIST(self, data=None):
data = self.data if data is None else data
epoch = self.model.val(self.model.epoch)
self.model.latent_mean_list = [self.model.Phi_mean, self.model.Eps1_mean, self.model.Eps2_mean]
self.model.latent_dict2ind = {'phi_mean': 0, 'eps1_mean': 1, 'eps2_mean': 2}
if set(self.args.eval_methods).intersection(['classv1', 'NMIv1', 'ACCv1']):
self.model.latent_dict2ind.update({'phi_v1': len(self.model.latent_mean_list)})
self.model.latent_mean_list.append(self.model.Phi_mean_v1)
if set(self.args.eval_methods).intersection(['classv2', 'NMIv2', 'ACCv2']):
self.model.latent_dict2ind.update({'phi_v2': len(self.model.latent_mean_list)})
self.model.latent_mean_list.append(self.model.Phi_mean_v2)
_, _, EPS1_tensor_sample, EPS2_tensor_sample, Phi_tensor_sample = \
self.model.CA.draw_2v_samples(
Phi_mean=self.model.Phi_mean, Phi_sigmasq=self.model.Phi_sigmasq,
Eps1_mean=self.model.Eps1_mean, Eps1_sigmasq=self.model.Eps1_sigmasq,
Eps2_mean=self.model.Eps2_mean, Eps2_sigmasq=self.model.Eps2_sigmasq, n_samples=1)
tensors_ = self.model.latent_mean_list + [Phi_tensor_sample, EPS1_tensor_sample, EPS2_tensor_sample]
self.model.latent_dict2ind.update({'Phi': len(self.model.latent_mean_list),
'Eps1': len(self.model.latent_mean_list) + 1,
'Eps2': len(self.model.latent_mean_list) + 2})
self.model.CA.latent_tr_list = self.model.encode_batched(tensors_,
x1_value=data.x1_tr, x2_value=data.x2_tr,
bs=self.args.batch_size_valid)
self.model.CA.latent_tu_list = self.model.encode_batched(tensors_,
x1_value=data.x1_tu, x2_value=data.x2_tu,
bs=self.args.batch_size_valid)
self.model.CA.latent_te_list = self.model.encode_batched(tensors_,
x1_value=data.x1_te, x2_value=data.x2_te,
bs=self.args.batch_size_valid)
# compute OBJective values for tune dataset
elbo_tu, NLL1_tu, NLL2_tu = self.model.test(x1_value=data.x1_tu,
x2_value=data.x2_tu,
bs=self.args.batch_size_valid)
# compute OBJective values for test dataset
elbo_te, NLL1_te, NLL2_te = self.model.test(x1_value=data.x1_te,
x2_value=data.x2_te,
bs=self.args.batch_size_valid)
self._print(">>>>>Epoch| TUNE: obj, NLL1, NLL2 | TEST: obj, NLL1, NLL2", 'log')
self._print(">>>>>%05d| %.4g %.4g %.4g| %.4g %.4g %.4g "
% (epoch, elbo_tu, NLL1_tu, NLL2_tu, elbo_te, NLL1_te, NLL2_te), 'log')
dict_out = {'CV_config': self.args.hpconfig, 'epoch': epoch, 'msg': '',
'obj_tu': elbo_tu, 'NLL1_tu': NLL1_tu, 'NLL2_tu': NLL2_tu,
'obj_te': elbo_te, 'NLL1_te': NLL1_te, 'NLL2_te': NLL2_te}
from util.downstream_tasks import tsne_embeding
downsample_tsne = 2
# mean of the latent factors
# Phi_tr_ = self.self.model.CA.latent_tr_list[self.self.model.latent_dict2ind['phi']]
# Phi_tu_ = self.self.model.CA.latent_tu_list[self.self.model.latent_dict2ind['phi']]
Phi_te_ = self.model.CA.latent_te_list[self.model.latent_dict2ind['phi_mean']]
z_tsne_mess = tsne_embeding(Phi_te_[::downsample_tsne], data.label_te[::downsample_tsne],
self.args.logdir + "Phi_mean_te_tsne.eps")
if set(self.args.eval_methods).intersection(['classv1', 'NMIv1', 'ACCv1']):
Phi_te_ = self.model.CA.latent_te_list[self.model.latent_dict2ind['phi_v1']]
z_tsne_mess = tsne_embeding(Phi_te_[::downsample_tsne], data.label_te[::downsample_tsne],
self.args.logdir + "Phi_mean_tev1_tsne.eps")
Eps_te_ = self.model.CA.latent_te_list[self.model.latent_dict2ind['eps1_mean']]
z_tsne_mess = tsne_embeding(Eps_te_[::downsample_tsne], data.label_te[::downsample_tsne],
self.args.logdir + "Eps1_mean_te_tsne.eps")
Eps_te_ = self.model.CA.latent_te_list[self.model.latent_dict2ind['eps2_mean']]
z_tsne_mess = tsne_embeding(Eps_te_[::downsample_tsne], data.label_te[::downsample_tsne],
self.args.logdir + "Eps2_mean_te_tsne.eps")
# samples from latent factors
Phi_te_ = self.model.CA.latent_te_list[self.model.latent_dict2ind['Phi']]
z_tsne_mess = tsne_embeding(Phi_te_[::downsample_tsne], data.label_te[::downsample_tsne],
self.args.logdir + "Phi_te_tsne.eps")
Eps_te_ = self.model.CA.latent_te_list[self.model.latent_dict2ind['Eps1']]
z_tsne_mess = tsne_embeding(Eps_te_[::downsample_tsne], data.label_te[::downsample_tsne],
self.args.logdir + "Eps1_te_tsne.eps")
Eps_te_ = self.model.CA.latent_te_list[self.model.latent_dict2ind['Eps2']]
z_tsne_mess = tsne_embeding(Eps_te_[::downsample_tsne], data.label_te[::downsample_tsne],
self.args.logdir + "Eps2_te_tsne.eps")
dict_out = self._eval_method_fun(self.args.eval_methods, model=self.model,
data=data, dict_out=dict_out, epoch=epoch)
def _infer_cluster(self, data=None):
data = self.data if data is None else data
epoch = self.model.val(self.model.epoch)
self.model.Z_tensor_samples, self.model.EPS_tensor_samples, self.model.Phi_tensor_samples = \
self.model.CA.draw_samples(
Phi_mean=self.model.Phi_mean, Phi_sigmasq=self.model.Phi_sigmasq,
Eps_mean=self.model.Eps_mean, Eps_sigmasq=self.model.Eps_sigmasq, n_samples=1)
tensors_ = [self.model.Phi_mean,
self.model.Phi_tensor_samples] + self.model.Eps_mean + self.model.EPS_tensor_samples + self.model.Z_tensor_samples # [self.self.model.Phi_sigmasq] + self.self.model.Eps_sigmasq + \
latent_dictKeys = ['Phi_mean', 'Phi'] + \
['Eps%d_mean' % d_ for d_ in range(1, self.args.modalities + 1)] + \
['Eps%d' % d_ for d_ in range(1, self.args.modalities + 1)] + \
['Z%d' % d_ for d_ in range(1, self.args.modalities + 1)]
out_np_ = self.model.run_batched(tensors_,
x_values=data.x_tr,
bs=self.args.batch_size_valid)
self.model.CA.latent_tr_list = dict(zip(latent_dictKeys, out_np_))
out_np_ = self.model.run_batched(tensors_,
x_values=data.x_tu,
bs=self.args.batch_size_valid)
self.model.CA.latent_tu_list = dict(zip(latent_dictKeys, out_np_))
out_np_ = self.model.run_batched(tensors_,
x_values=data.x_te,
bs=self.args.batch_size_valid)
self.model.CA.latent_te_list = dict(zip(latent_dictKeys, out_np_))
# compute OBJective values for tune dataset
out_ = self.model.test(x_values=data.x_tu, bs=self.args.batch_size_valid)
elbo_tu, NLL_tu = out_[0], out_[1:]
out_ = self.model.test(x_values=data.x_te, bs=self.args.batch_size_valid)
elbo_te, NLL_te = out_[0], out_[1:]
str_NLL = ""
str_NLL = str_NLL.join([', NLL%d' % i for i in range(1, self.args.modalities + 1)])
self.args._print(">>>>> EPOCH| TUNE: obj%s || TEST: obj%s " % (str_NLL, str_NLL), 'log')
str_ = ">>>>> %05d| %.4g" + " %.4g" * self.args.modalities + "|| %.4g" + " %.4g" * self.args.modalities
self.args._print(str_ % ((epoch, elbo_tu) + tuple(NLL_tu) + (elbo_te,) + tuple(NLL_te)), 'log')
from util.downstream_tasks import tsne_embeding
downsample_tsne = 2
# mean of the latent factors
# Phi_tr_ = self.self.model.CA.latent_tr_list[self.self.model.latent_dict2ind['phi']]
# Phi_tu_ = self.self.model.CA.latent_tu_list[self.self.model.latent_dict2ind['phi']]
Phi_te_ = self.model.CA.latent_te_list['Phi_mean']
z_tsne_mess = tsne_embeding(Phi_te_[::downsample_tsne], data.label_te[::downsample_tsne],
self.args.logdir + "Phi_mean_te_tsne.eps")
# samples from latent shared
Phi_te_ = self.model.CA.latent_te_list['Phi']
z_tsne_mess = tsne_embeding(Phi_te_[::downsample_tsne], data.label_te[::downsample_tsne],
self.args.logdir + "Phi_te_tsne.eps")
for d_ in range(1, self.args.modalities + 1):
Eps_te_ = self.model.CA.latent_te_list['Eps%d_mean' % d_]
z_tsne_mess = tsne_embeding(Eps_te_[::downsample_tsne], data.label_te[::downsample_tsne],
self.args.logdir + "Eps%d_mean_te_tsne.eps" % d_)
Eps_te_ = self.model.CA.latent_te_list['Eps%d' % d_]
z_tsne_mess = tsne_embeding(Eps_te_[::downsample_tsne], data.label_te[::downsample_tsne],
self.args.logdir + "Eps%d_te_tsne.eps" % d_)
def _eval_method_fun(self, eval_methods, model, data, dict_out, epoch):
if 'class' in eval_methods :
key = 'class'
ckpt_file_name_ = self.args.ckpt_best_filename + key + ".ckpt"
Phi_tr_= model.CA.latent_tr_list[model.latent_dict2ind['phi']]
Phi_tu_= model.CA.latent_tu_list[model.latent_dict2ind['phi']]
Phi_te_= model.CA.latent_te_list[model.latent_dict2ind['phi']]
class_err_tu, class_err_te = \
linear_svm_classify(Phi_tr_, data.label_tr,
Phi_tu_, data.label_tu,
Phi_te_, data.label_te,
cache_size=1000,
alg='LinearSVC' if self.args.fast_SVM else 'SVC')
model.load_var(model.score_tu[key], class_err_tu)
model.load_var(model.score_te[key], class_err_te)
if class_err_tu < model.val(model.best_score_tu[key]):
model.load_var(model.best_score_tu[key], class_err_tu)
model.load_var(model.best_score_te[key], class_err_te)
dict_out['msg'] += ' *'
if self.args.tf_save != 'ignore':
model.save(self.args.logdir + ckpt_file_name_)
dtime = time.time() - model._t0
self._print("EPOCH: %d, time:%ds, tune_err = %f, test_err=%f %s"
% (epoch, dtime, 100 * class_err_tu, 100 * class_err_te, dict_out['msg']), 'log')
dict_out.update({'class_err_tu': class_err_tu, 'class_err_te': class_err_te,
'best_class_err_tu': model.val(model.best_score_tu[key]),
'best_class_err_te': model.val(model.best_score_te[key])})
if 'classv1' in eval_methods :
# im = self.self.args.eval_mtd_ind['class']
key = 'classv1'
ckpt_file_name_ = None #self.self.args.ckpt_best_filename+ key + ".ckpt"
Phi_tr_= model.CA.latent_tr_list[model.latent_dict2ind['phi_v1']]
Phi_tu_= model.CA.latent_tu_list[model.latent_dict2ind['phi_v1']]
Phi_te_= model.CA.latent_te_list[model.latent_dict2ind['phi_v1']]
class_err_tu, class_err_te = \
linear_svm_classify(Phi_tr_, data.label_tr,
Phi_tu_, data.label_tu,
Phi_te_, data.label_te,
cache_size=1000,
alg='LinearSVC' if self.args.fast_SVM else 'SVC')
model.load_var(model.score_tu[key], class_err_tu)
model.load_var(model.score_te[key], class_err_te)
if class_err_tu < model.val(model.best_score_tu[key]):
model.load_var(model.best_score_tu[key], class_err_tu)
model.load_var(model.best_score_te[key], class_err_te)
dict_out['msg'] += ' *'
if self.args.tf_save != 'ignore' and ckpt_file_name_:
model.save(self.args.logdir + ckpt_file_name_)
dtime = time.time() - model._t0
self._print("EPOCH: %d, time:%ds, tune_err_v1 = %f, test_err_v1 =%f %s"
% (epoch, dtime, 100 * class_err_tu, 100 * class_err_te, dict_out['msg']), 'log')
dict_out.update({'class_err_tu_v1': class_err_tu, 'class_err_te_v1': class_err_te,
'best_class_err_tu_v1': model.val(model.best_score_tu[key]),
'best_class_err_te_v1': model.val(model.best_score_te[key])})
if 'classEYB' in eval_methods: # this is for extended yale-b multi-modal
# im = self.self.args.eval_mtd_ind['class']
key = 'classEYB'
C_list_ = [1.0, 10.0]
ckpt_file_name_ = self.args.ckpt_best_filename + key + ".ckpt"
Phi_tr_ = model.CA.latent_tr_list[model.latent_dict2ind['phi']]
Phi_tu_ = model.CA.latent_tu_list[model.latent_dict2ind['phi']]
Phi_te_ = model.CA.latent_te_list[model.latent_dict2ind['phi']]
class_err_tu, class_err_te = \
linear_svm_classify2(Phi_tr_, data.label_tr,
Phi_tu_[0::2], data.label_tu[0::2],
Phi_tu_[1::2], data.label_tu[1::2],
cache_size=1000, C_list=C_list_,
alg='LinearSVC' if self.args.fast_SVM else 'SVC')
# self._print("classification with alg: %s, tune_err = %f, test_err=%f " %('LinearSVC' if self.self.args.fast_SVM else 'SVC',
# class_err_tu, class_err_te), 'log')
#
# class_err_tu_, class_err_te_ = \
# linear_svm_classify(Phi_tr_, data.label_tr,
# Phi_tu_[0::2], data.label_tu[0::2],
# Phi_tu_[1::2], data.label_tu[1::2],
# cache_size=1000, C_list=C_list_,
# alg='LinearSVC' if not self.self.args.fast_SVM else 'SVC')
# self._print("classification with alg: %s, tune_err = %f, test_err=%f " %('LinearSVC' if not self.self.args.fast_SVM else 'SVC',
# class_err_tu_, class_err_te_), 'log')
model.load_var(model.score_tu[key], class_err_tu)
model.load_var(model.score_te[key], class_err_te)
if class_err_tu < model.val(model.best_score_tu[key]):
model.load_var(model.best_score_tu[key], class_err_tu)
model.load_var(model.best_score_te[key], class_err_te)
dict_out['msg'] += ' *'
if self.args.tf_save != 'ignore':
model.save(self.args.logdir + ckpt_file_name_)
dtime = time.time() - model._t0
self._print("EPOCH: %d, time:%ds, tune_err = %f, test_err=%f %s"
% (epoch, dtime, 100 * class_err_tu, 100 * class_err_te, dict_out['msg']), 'log')
dict_out.update({'class_err_tu': class_err_tu, 'class_err_te': class_err_te,
'best_class_err_tu': model.val(model.best_score_tu[key]),
'best_class_err_te': model.val(model.best_score_te[key])})
if 'classCCA' in eval_methods :
# im = self.self.args.eval_mtd_ind['class']
key = 'classCCA'
ckpt_file_name_ = None #self.self.args.ckpt_best_filename+ key + ".ckpt"
class_err_tu, class_err_te = \
linear_svm_classify(model.CA.Phi_tr_cca1, data.label_tr,
model.CA.Phi_tu_cca1, data.label_tu,
model.CA.Phi_te_cca1, data.label_te,
cache_size=1000,
alg='LinearSVC' if self.args.fast_SVM else 'SVC')
model.load_var(model.score_tu[key], class_err_tu)
model.load_var(model.score_te[key], class_err_te)
if class_err_tu < model.val(model.best_score_tu[key]):
model.load_var(model.best_score_tu[key], class_err_tu)
model.load_var(model.best_score_te[key], class_err_te)
dict_out['msg'] += ' *'
if self.args.tf_save != 'ignore' and ckpt_file_name_:
model.save(self.args.logdir + ckpt_file_name_)
dtime = time.time() - model._t0
self._print("CCA class error, time:%ds, tune_err = %f, test_err=%f %s"
% (dtime, 100 * class_err_tu, 100 * class_err_te, dict_out['msg']), 'log')
dict_out.update({'class_errCCA_tu': class_err_tu, 'class_errCCA_te': class_err_te,
'best_class_errCCA_tu': model.val(model.best_score_tu[key]),
'best_class_errCCA_te': model.val(model.best_score_te[key])})
if ('NMI' in eval_methods): # and ('ACC' in eval_methods):
key1 = 'NMI'
key2 = 'ACC'
ckpt_file_name_ = self.args.ckpt_best_filename + key1 + ".ckpt"
# spectral clustering
Phi_tr_ = model.CA.latent_tr_list[model.latent_dict2ind['phi']]
Phi_tu_ = model.CA.latent_tu_list[model.latent_dict2ind['phi']]
Phi_te_ = model.CA.latent_te_list[model.latent_dict2ind['phi']]
NMI_tu, NMI_te, ACC_tu, ACC_te = \
spectral_clustering(Phi_tr_, data.label_tr,
Phi_tu_, data.label_tu,
Phi_te_, data.label_te,
compute_knn_graph=True, n_clusters=10) # todo: is this NMI used in any clustering exp?
model.load_var(model.score_tu[key1], NMI_tu)
model.load_var(model.score_te[key1], NMI_te)