def evaluate_classifier(featxs, datasets):
posfeats, negfeats = feature_extraction(featxs, datasets)
print '\ncross validation MLP'
print cross_validation(posfeats,
negfeats,
folds=5,
classifier='decision_tree')
def evaluate_classifier(featxs, datasets):
posfeats, negfeats = feature_extraction(featxs, datasets)
print '\ncross validation KNN'
print cross_validation(posfeats,
negfeats,
folds=5,
classifier='k_neighbors')
def evaluate_classifier(featxs, datasets):
posfeats, negfeats = feature_extraction(featxs,
datasets,
punctuation=False)
print '\ncross validation NB'
print cross_validation(posfeats,
negfeats,
folds=5,
classifier='naive_bayes')
def evaluation_function(y, x):
"""Evaluation function accepts data samples and is expected to return a number as an output.
Parameters
----------
y : np.ndarray
Labels
x : np.ndarray
Features
"""
accs, f_scores = validation.cross_validation(y, x, train_model,
number_of_folds)
return cross_validation_score_reducer(accs, f_scores)
reg = 0.0001
def single_train(data, label):
clf = SGDClassifier(loss='hinge', penalty='l2', alpha=reg)
return clf.fit(data, label)
def train_both(data, label):
clf1 = SGDClassifier(loss='hinge', penalty='l2', alpha=reg)
clf2 = SGDClassifier(loss='hinge', penalty='l2', alpha=reg)
return clf1.fit(data, label[:, 0]), clf2.fit(data, label[:, 1])
if __name__ == '__main__':
total_data = loadFile.file2mat_bag_of_wordvec('./data/final_review_set.csv')
shuffled_data = vld.data_reshuffle(total_data)
# rbf_feature = RBFSampler(gamma=10)
# train_mat = rbf_feature.fit_transform(shuffled_data[0])
train_mat = shuffled_data[0]
aspect_label = shuffled_data[1]
rating_label = shuffled_data[2]
label_mat = np.vstack((aspect_label, rating_label)).T
single_label = aspect_label * len(loadFile.aspect_dic) + rating_label
print vld.cross_validation(train_mat, aspect_label, single_train, vld.test_single)
print vld.cross_validation(train_mat, rating_label, single_train, vld.test_single)
print vld.cross_validation(train_mat, single_label, single_train, vld.test_single)
print vld.cross_validation(train_mat, single_label, single_train, vld.test_aspect)
print vld.cross_validation(train_mat, single_label, single_train, vld.test_rating)
print vld.cross_validation(train_mat, label_mat, train_both, vld.test_mat)
def main():
######################
# Prepare filesystem #
######################
directory_exists(models_path)
mkdir(results_path)
###################
# Load embeddings #
###################
embeddings_file = glob.glob(os.path.join(models_path, '*.mat'))[0]
model_name = os.path.splitext(os.path.basename(embeddings_file))[0]
print(model_name)
stdout('Loading embeddings', embeddings_file)
embeddings = load_embeddings(embeddings_file)
embeddings = minmax_scale(embeddings)
#######################
# Load GO annotations #
#######################
annotation_dir = os.path.join(data_path, 'annotations')
if validation == 'cerevisiae':
annotation_file = os.path.join(annotation_dir,
'cerevisiae_annotations.mat')
else:
annotation_file = os.path.join(annotation_dir, 'yeast_annotations.mat')
stdout('Loading GO annotations', annotation_file)
GO = sio.loadmat(annotation_file)
####################
# Train classifier #
####################
stdout('Running cross-validation for', level)
annotations = GO[level]
# Silence certain warning messages during cross-validation
for w in (sklearn.exceptions.UndefinedMetricWarning, UserWarning,
RuntimeWarning):
warnings.filterwarnings("ignore", category=w)
# Only use a subset of the data for testing purposes
embeddings = embeddings[:test]
annotations = annotations[:test]
# performance = cross_validation(
# embeddings,
# annotations,
# n_trials=n_trials,
# n_jobs=n_jobs,
# n_threads=n_threads,
# random_state=random_state,
# clf_type=clf_type,
# max_depth=max_depth[level])
performance = cross_validation(embeddings, annotations, n_trials=n_trials)
performance['my_level'] = level
pprint(performance)
fout = f'{model_name}_{level}_{clf_type}_performance.json'
with open(os.path.join(results_path, fout), 'w') as f:
json.dump(performance, f)
"""
# Author: Jael Zela <*****@*****.**>
from feature_extraction import feature_extraction, bag_of_words, bigram_feats, tf_idf, part_of_speech
from validation import cross_validation
from datasets import g2crowd
if __name__ == "__main__":
posfeats, negfeats = feature_extraction([tf_idf], [g2crowd],
stopwords=False,
punctuation=False)
print '\ncross validation NB'
print cross_validation(posfeats,
negfeats,
folds=5,
classifier='naive_bayes')
print '\ncross validation SVM'
print cross_validation(posfeats, negfeats, folds=5, classifier='svm')
print '\ncross validation ME'
print cross_validation(posfeats,
negfeats,
folds=5,
classifier='maximum_entropy')
#print '\ncross validation DT'
#print cross_validation(posfeats, negfeats, folds=5, classifier='decision_tree')
#print '\ncross validation RF'
#print cross_validation(posfeats, negfeats, folds=5, classifier='random_forest')
#print '\ncross validation MLP'
#print cross_validation(posfeats, negfeats, folds=5, classifier='mlp_nn')
#print '\ncross validation KNN'
def evaluate_classifier(featxs, datasets):
posfeats, negfeats = feature_extraction(featxs, datasets)
print '\ncross validation SVM'
print cross_validation(posfeats, negfeats, folds=5, classifier='svm')
X_stem = stemmed_df["cleaned"]
y_stem = stemmed_df["label"]
X_lemma = lemmatized_df["cleaned"]
y_lemma = lemmatized_df["label"]
# Model
log_reg = LogisticRegression(C=4.5,
penalty="l2",
multi_class='ovr',
solver='liblinear',
max_iter=300,
dual=False,
warm_start=True,
fit_intercept=0.4)
# Model parameters
params_log_reg = {
'clf__max_iter': (150, 250, 350),
'clf__intercept_scaling': (0.3, 0.4, 0.5)
# 'clf__multi_class': ('ovr', 'multinomial'), # one vs all or multinomial, hence one vs all is better for logistic regression
# 'clf__solver': ('newton-cg', 'sag', 'lbfgs','saga'),
}
# Number of cross validation folds
folds = 5
# Perform cross validation
print(cross_validation(model=log_reg, X=X_stem, y=y_stem, folds=folds))
# Perform Grid Search CV
# print(grid_search_cv(model=log_reg,X=X_stem, y=y_stem,params=params_log_reg, folds=folds))
def evaluate_classifier(featxs, datasets):
posfeats, negfeats = feature_extraction(featxs, datasets)
print '\ncross validation RF'
print cross_validation(posfeats, negfeats, folds=5, classifier='random_forest')
# Read data
df = pd.read_csv("preprocessed_reddit_train_SnowballStemmer.csv")
y_train = df["label"]
X_train = df["cleaned"]
# Create Ada Boosting classifier
clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=4),
n_estimators=1000,
algorithm="SAMME.R",
learning_rate=0.1)
# Model parameters
params = {
'clf__n_estimators': (50, 100, 200),
'clf__learning_rate': (0.5, 1.0, 1.5),
'clf__algorithm': ("SAMME", "SAMME.R"),
}
# Number of cross validation folds
folds = 2
"""
Results
AdaBoost(base_estimator=DecisionTreeClassifier(max_depth=4), n_estimators=1000, algorithm="SAMME.R", learning_rate=0.1)
"""
# Perform Cross-Validation to validate model
print(cross_validation(model=clf, X=X_train, y=y_train, folds=folds))
# Perform Grid Search CV to find the best parameters
# best_scores, best_params, best_estimator_params = grid_search_cv(model=clf, X=X_train, y=y_train, params=params, folds=folds)
def main(annot_fname, ont, model_name, data_folder, tax_ids, alpha, test_goid_fname, test_annot_fname=None,
results_path='./results/test_results', block_matrix_folder='block_matrix_files/',
network_folder='network_files/', use_orig_feats=False, use_nn=False,
num_hyperparam_sets=None, arch_set=None, n_trials=5, save_only=False, load_fname=None,
isorank_diag=False, subsample=False, lm_feat_path=None, lm_only=False):
if load_fname is None:
if test_annot_fname is None:
X, Y, aligned_net_prots, test_goids = process_and_align_matrices(annot_fname,
ont, model_name, data_folder, tax_ids, alpha, test_goid_fname,
results_path=results_path, block_matrix_folder=block_matrix_folder,
network_folder=network_folder, use_orig_feats=use_orig_feats,
use_nn=use_nn, test_annot_fname=test_annot_fname, isorank_diag=isorank_diag, lm_feat_path=lm_feat_path, lm_only=lm_only)
else:
(X_rest, Y_rest, rest_prot_names, test_goids, X_test_species, Y_test_species,
test_species_aligned_net_prots, all_string_prots) = process_and_align_matrices(annot_fname,
ont, model_name, data_folder, tax_ids, alpha, test_goid_fname,
results_path=results_path, block_matrix_folder=block_matrix_folder,
network_folder=network_folder, use_orig_feats=use_orig_feats,
use_nn=use_nn, test_annot_fname=test_annot_fname, isorank_diag=isorank_diag, lm_feat_path=lm_feat_path, lm_only=lm_only)
else:
load_file = pickle.load(open(load_fname, 'rb'))
if test_annot_fname is None:
X = load_file['X']
Y = load_file['y']
aligned_net_prots = load_file['prot_names']
test_goids = load_file['test_goids']
else:
X_rest = load_file['X_rest']
X_test_species = load_file['X_test_species']
Y_rest = load_file['y_rest']
Y_test_species = load_file['y_test_species']
rest_prot_names = load_file['rest_prot_names']
test_species_aligned_net_prots = load_file['test_species_prots']
test_goids = load_file['test_goids']
#print("Saving X and Y matrices") # TODO so I can use a DataGenerator in order to train the maxout nns without loading whole dataset in memory
# But honestly, not that bad for now
'''
trial_file = {}
if test_annot_fname is None:
trial_file['X'] = X
trial_file['Y'] = Y
trial_file['aligned_net_prots'] = aligned_net_prots
trial_file['test_goids'] = test_goids
pickle.dump(trial_file, open('./train_test_data/' + model_name + '_' + ont + '_train_test_data_file.pckl', 'wb'), protocol=4)
else:
trial_file['X_rest'] = X_rest
trial_file['Y_rest'] = Y_rest
trial_file['rest_prot_names'] = rest_prot_names
trial_file['test_goids'] = test_goids
trial_file['X_test_species'] = X_test_species
trial_file['Y_test_species'] = Y_test_species
trial_file['test_species_aligned_net_prots'] = test_species_aligned_net_prots
pickle.dump(trial_file, open('./train_test_data/' + model_name + '_' + ont + '_one_spec_train_test_data_file.pckl', 'wb'), protocol=4)
print(test_goids)
exit()
'''
#output_projection_files(X, Y, model_name, ont, list(test_goids))
# 5 fold cross val
if use_nn:
if test_annot_fname is not None:
perf, y_score_trials, pred_file = one_spec_cross_val(X_test_species,
Y_test_species, test_species_aligned_net_prots, X_rest, Y_rest,
rest_prot_names, test_goids, model_name, ont, n_trials=n_trials,
num_hyperparam_sets=num_hyperparam_sets, arch_set=arch_set, save_only=save_only,
subsample=subsample)
pickle.dump(pred_file, open(results_path
+ model_name + '_one_spec_cv_use_nn_'
+ ont + '_pred_file.pckl', 'wb'))
else:
perf, y_score_trials, pred_file = cross_validation_nn(X, Y,
aligned_net_prots, test_goids, model_name, ont, n_trials=n_trials,
num_hyperparam_sets=num_hyperparam_sets, arch_set=arch_set)
pickle.dump(pred_file, open(results_path
+ model_name + '_cv_use_nn_'
+ ont + '_pred_file.pckl', 'wb'))
else:
perf, y_score_trials, y_score_pred = cross_validation(X, Y,
n_trials=5, X_pred=None)
print('aupr[micro], aupr[macro], F_max, accuracy\n')
avg_micro = 0.0
for ii in range(0, len(perf['F1'])):
print('%0.5f %0.5f %0.5f %0.5f'
% (perf['pr_micro'][ii], perf['pr_macro'][ii], perf['F1'][ii], perf['acc'][ii]))
avg_micro += perf['pr_micro'][ii]
avg_micro /= len(perf['F1'])
print ("### Average (over trials): m-AUPR = %0.3f" % (avg_micro))
print
if use_nn:
val_type = 'nn'
else:
val_type = 'svm'
pickle.dump(y_score_trials,
open(results_path + model_name
+ "_goterm_" + ont + '_' + val_type + "_perf.pckl", "wb"))
constraints_ml[idx] = np.floor(temp_mustlink /
(FLAGS.net_nums - 1))
constraints_cl[idx] = np.floor(temp_cannotlink /
(FLAGS.net_nums - 1))
print(
len(constraints_ml[idx].nonzero()[0]) / 2,
len(constraints_cl[idx].nonzero()[0]) / 2)
input_dim = FLAGS.hidden_dim[idx_layer]
yeast_fusions = emb
# output embedding
str_nets = [
'coexpression', 'cooccurence', 'database', 'experimental', 'fusion',
'neighborhood'
]
for idxx in range(FLAGS.net_nums):
temp_path = './emb/' + FLAGS.org + '_' + str_nets[
idxx] + '_' + FLAGS.optimizer + '_' + str(
FLAGS.learning_rate[0]) + '_new.txt'
write_encoded_file(emb[idxx], temp_path)
perf = cross_validation(emb, labels)
print(
"Average (over trials) of DeepMNE: m-AUPR = %0.3f, M-AUPR = %0.3f, F1 = %0.3f, Acc = %0.3f"
% (np.mean(perf['pr_micro']), np.mean(
perf['pr_macro']), np.mean(perf['fmax']), np.mean(perf['acc'])))
print
print(FLAGS.layers_num, FLAGS.optimizer, FLAGS.learning_rate, FLAGS.batch_size)
def train_both(data, label):
clf1 = MultinomialNB()
clf2 = MultinomialNB()
return clf1.fit(data, label[:, 0]), clf2.fit(data, label[:, 1])
if __name__ == '__main__':
total_data = loadFile.file2mat('./data/final_review_set.csv')
shuffled_data = vld.data_reshuffle(total_data)
train_mat = shuffled_data[0]
aspect_label = shuffled_data[1]
rating_label = shuffled_data[2]
label_mat = np.vstack((aspect_label, rating_label)).T
single_label = aspect_label * len(loadFile.aspect_dic) + rating_label
print "SAS, aspect:\t", vld.cross_validation(train_mat, aspect_label,
single_train, vld.test_single)
print "SAS, rating:\t", vld.cross_validation(train_mat, rating_label,
single_train, vld.test_single)
print "SAS, both:\t", vld.cross_validation(train_mat, single_label,
single_train, vld.test_single)
print "JMAS, aspect:\t", vld.cross_validation(train_mat, single_label,
single_train,
vld.test_aspect)
print "JMAS, rating:\t", vld.cross_validation(train_mat, single_label,
single_train,
vld.test_rating)
print "JMAS, both:\t", vld.cross_validation(train_mat, label_mat,
train_both, vld.test_mat)
max_iter=2000,
C=1.1,
tol=0.00005)
svc_clf = SVC(probability=True,
kernel="linear",
decision_function_shape="ovr",
max_iter=2000,
C=1.1,
tol=0.00005)
# Voting Classifier
clf = VotingClassifier(estimators=[('lr', log_reg), ("nb", multi_NB),
("svc", svc_clf)],
voting="soft")
clf2 = VotingClassifier(estimators=[('lr', log_reg), ("nb", multi_NB),
("svc", linear_svc)],
voting="hard")
clf3 = VotingClassifier(estimators=[('lr', log_reg), ("nb", multi_NB)],
voting="soft")
# Number of cross validation folds
folds = 5
# Perform cross validation
print(cross_validation(model=clf, X=X_stem, y=y_stem, folds=folds))
# Perform Grid Search CV
# print(grid_search_cv(model=log_reg,X=X_stem, y=y_stem,params=params_log_reg, folds=folds))
# Predict on test set
# classify(clf)
Stemmed
Lemmatized
"""
# Read DataFrame
stemmed_df = pd.read_csv("preprocessed_reddit_train_SnowballStemmer.csv")
# lemmatized_df = pd.read_csv("preprocessed_reddit_train_WordNetLemmatizer.csv")
# Separate X and Y
X_stem = stemmed_df["cleaned"]
y_stem = stemmed_df["label"]
# X_lemma = lemmatized_df["cleaned"]
# y_lemma = lemmatized_df["label"]
# Estimators
multi_NB = MultinomialNB(alpha=0.225)
# Model parameters
params = {
'clf__alpha': (0.225, 0.25, 0.275),
}
# Number of folds for Cross Validation
# Perform Cross-Validation to validate model
print(cross_validation(model=multi_NB, X=X_stem, y=y_stem, folds=folds))
# Perform Grid Search CV to find the best parameters
# best_scores, best_params, best_estimator_params = grid_search_cv(model=multi_NB, X=X_stem, y=y_stem, params=params, folds=5)
if Path.isfile(models_path + model_name):
mid_model = load_model(models_path + model_name)
else:
print(
"### Model % s does not exist. Check the 'models_path' directory.\n"
% (model_name))
break
mid_model = load_model(models_path + model_name)
features = mid_model.predict(Nets)
features = minmax_scale(features)
sio.savemat(models_path + model_name.split('.')[0] + '_features.mat',
{'features': features})
for level in annot:
print("### Running for level: %s" % (level))
if valid_type == 'cv':
perf = cross_validation(features, GO[level], n_trials=n_trials)
else:
perf = temporal_holdout(features, Annot['GO'][level].tolist(),
Annot['indx'][level].tolist(),
Annot['labels'][level].tolist())
fout.write('### %s goterms:\n' % (level))
fout.write('GO_id, AUPRs\n')
for goid in perf['pr_goterms']:
fout.write('%s' % (goid))
for pr in perf['pr_goterms'][goid]:
fout.write(' %0.5f' % (pr))
fout.write('\n')
fout.write('\n')
fout.write('### %s trials:\n' % (level))
fout.write('aupr[micro], aupr[macro], F_max, accuracy\n')
avg_micro = 0.0
import loadFile
import numpy as np
reg = 0.0001
def single_train(data, label):
clf = SGDClassifier(loss='hinge', penalty='l2', alpha=reg)
return clf.fit(data, label)
def train_both(data, label):
clf1 = SGDClassifier(loss='hinge', penalty='l2', alpha=reg)
clf2 = SGDClassifier(loss='hinge', penalty='l2', alpha=reg)
return clf1.fit(data, label[:, 0]), clf2.fit(data, label[:, 1])
if __name__ == '__main__':
total_data = loadFile.file2mat('./data/final_review_set.csv')
shuffled_data = vld.data_reshuffle(total_data)
train_mat = shuffled_data[0]
aspect_label = shuffled_data[1]
rating_label = shuffled_data[2]
label_mat = np.vstack((aspect_label, rating_label)).T
single_label = aspect_label * len(loadFile.aspect_dic) + rating_label
print "SAS, aspect:\t", vld.cross_validation(train_mat, aspect_label, single_train, vld.test_single)
print "SAS, rating:\t", vld.cross_validation(train_mat, rating_label, single_train, vld.test_single)
print "SAS, both:\t", vld.cross_validation(train_mat, single_label, single_train, vld.test_single)
print "JMAS, aspect:\t", vld.cross_validation(train_mat, single_label, single_train, vld.test_aspect)
print "JMAS, rating:\t", vld.cross_validation(train_mat, single_label, single_train, vld.test_rating)
print "JMAS, both:\t", vld.cross_validation(train_mat, label_mat, train_both, vld.test_mat)
def train_both(data, label):
clf1 = SGDClassifier(loss='hinge', penalty='l2', alpha=reg)
clf2 = SGDClassifier(loss='hinge', penalty='l2', alpha=reg)
return clf1.fit(data, label[:, 0]), clf2.fit(data, label[:, 1])
if __name__ == '__main__':
total_data = loadFile.file2mat_bag_of_wordvec(
'./data/final_review_set.csv')
shuffled_data = vld.data_reshuffle(total_data)
# rbf_feature = RBFSampler(gamma=10)
# train_mat = rbf_feature.fit_transform(shuffled_data[0])
train_mat = shuffled_data[0]
aspect_label = shuffled_data[1]
rating_label = shuffled_data[2]
label_mat = np.vstack((aspect_label, rating_label)).T
single_label = aspect_label * len(loadFile.aspect_dic) + rating_label
print vld.cross_validation(train_mat, aspect_label, single_train,
vld.test_single)
print vld.cross_validation(train_mat, rating_label, single_train,
vld.test_single)
print vld.cross_validation(train_mat, single_label, single_train,
vld.test_single)
print vld.cross_validation(train_mat, single_label, single_train,
vld.test_aspect)
print vld.cross_validation(train_mat, single_label, single_train,
vld.test_rating)
print vld.cross_validation(train_mat, label_mat, train_both, vld.test_mat)
