def test_testing_convolution(text_vectorizer, class_vectorizer, classif_level, classif_type, dataset_location):
load_standard_sets = True
root_location = fh.get_root_location('data/convolutional_outcome/')
sets_location = fh.join_paths(root_location, "model_sets")
checkpoint_path = fh.join_paths(root_location, "model_checkpoints")
model_path = fh.link_paths(checkpoint_path, 'convolution_model')
weights_path = fh.link_paths(checkpoint_path, 'convolution_weights')
# load sets and settings
model_name = text_vectorizer+'/'+class_vectorizer+'/NN'
# train_data, test_data, val_data, train_labels, test_labels, val_labels, settings = ch.load_sets(sets_location, '2020-01-08T22:28:44.757410')
# classes, n_classes, vocab_processor, len_vocabulary = settings
# only for test
train_data, test_data, val_data, train_labels, test_labels, val_labels, _ = ch.load_sets(sets_location)
# it could be that a label is only in the test/data data, might be a problem
sequence_length = train_data.shape[1]
# calculates metrics with testing data
model, predictions = pmh.run_cnn_test(_,
train_data, train_labels, test_data, test_labels, val_data, val_labels,
model_path, weights_path, False)
binary_predictions = mh.get_binary_0_5(predictions)
# display testing metrics
metrics = mh.get_sequential_metrics(test_labels, predictions, binary_predictions)
mh.display_sequential_metrics('testing convolution sequence', metrics)
classifier_name, layers = ch.get_sequential_classifier_information(model)
ch.save_results(classifier_name+' Test', metrics, layers, model_name, classif_level, classif_type, dataset_location)
def second_attempt_from_web(data_frame, text_vectorizer, class_vectorizer, classif_level, classif_type, dataset_location):
root_location = fh.get_root_location('data/convolutional_outcome/')
# imdb = keras.datasets.imdb
# (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
# explore data
# print("Training entries: {}, labels: {}".format(len(train_data), len(train_labels)))
# print("how data looks like: ", train_data[0]) # [1, 14, 34...]
# print("how labels looks like: ", train_labels[0]) # 0
# preprocess data
# A dictionary mapping words to an integer index
# word_index = imdb.get_word_index()
# The first indices are reserved, thus it increases all the indices by 3
# word_index = {k : (v + 3) for k, v in word_index.items()}
# word_index["<PAD>"] = 0
# word_index["<START>"] = 1
# word_index["<UNK>"] = 2 # unknown
# word_index["<UNUSED>"] = 3
# make both the train and the test dataset the same length
# train_data = keras.preprocessing.sequence.pad_sequences(train_data,
# value=word_index["<PAD>"],
# padding='post',
# maxlen=256)
# test_data = keras.preprocessing.sequence.pad_sequences(test_data,
# value=word_index["<PAD>"],
# padding='post',
# maxlen=256)
# print(train_data[0]) # [1 14 34 0 0 0] - with little difference: PAD,START,UNK,UNUSED
model_name = text_vectorizer+'/'+class_vectorizer+'/NN'
standard_results = ch.apply_df_vectorizer(data_frame, text_vectorizer, class_vectorizer, model_name)
train_data, test_data, train_labels, test_labels, classes, n_classes, vocab_processor, len_vocabulary = standard_results
train_data, val_data, train_labels, val_labels = ch.get_train_test_from_data(train_data, train_labels)
# print("Training entries: {}, labels: {}".format(len(train_data), len(train_labels)))
# print("how data looks like: ", train_data[0]) # [1 14 34 0 0 0]
# print("how labels looks like: ", train_labels[0:5]) # list of lists [[0 1 0 0 0 1 0 1], ...]
model = pmh.get_text_convolutional_from_web(len_vocabulary, n_classes)
metrics, predictions = pmh.run_text_cnn_model(model, train_data, train_labels, test_data, test_labels)
classifier_name, layers = ch.get_sequential_classifier_information(model)
mh.display_convolutional_metrics(classifier_name, metrics[0], metrics[1], metrics[2], test_labels, predictions)
ch.save_results(classifier_name, metrics, layers, model_name, classif_level, classif_type, dataset_location)
def apply_word2vec_extratrees(data_frame, classif_level, classif_type,
source_path):
data_frame['text'] = data_frame.apply(
lambda row: th.tokenize_complex_text(row['text']), axis=1)
data_frame['classification'] = data_frame.apply(
lambda row: th.tokenize_complex_text(row['classification']), axis=1)
df_single_classification = ch.get_list_each_text_a_different_classification(
data_frame)
x = df_single_classification['text']
y = df_single_classification['classification']
X_train, X_test, y_train, y_test = ch.get_train_test_from_data(x, y)
model_w2v = wmh.get_word2vec_model(X_train)
etree_w2v = Pipeline([("word2vec vectorizer",
wmh.MeanEmbeddingVectorizer(model_w2v)),
("extra trees", pmh.get_extra_tree())])
etree_w2v_tfidf = Pipeline([("word2vec vectorizer",
wmh.TfidfEmbeddingVectorizer(model_w2v)),
("extra trees", pmh.get_extra_tree())])
# NB!!!: the model does not support multi targets, so i duplicate the sources and give them different targets
y_pred = pmh.fit_predict_functions(etree_w2v_tfidf, X_train, y_train,
X_test)
classifier_name_0 = 'Word2Vec/MeanEmbeddingVectorizer'
classifier_name_1, parameters_1 = ch.get_extratree_classifier_information(
str(etree_w2v))
model_name = '[all classes predictions]' + classifier_name_0 + '/' + classifier_name_1
# this should be changed by comparing all the possibilities for specified text (i can use the original dataframe!)
list_metrics = mh.calculate_metrics(model_name, y_test, y_pred)
none_average, binary_average, micro_average, macro_average = list_metrics
ch.save_results(classifier_name_1, list_metrics, parameters_1, model_name,
classif_level, classif_type, source_path)
# NB!!!: the model does not support multi targets, so i duplicate the sources and give them different targets
y_pred = pmh.fit_predict_functions(etree_w2v, X_train, y_train, X_test)
classifier_name_2 = 'Word2Vec/TfidfEmbeddingVectorizer'
model_name = '[all classes predictions]' + classifier_name_2 + '/' + classifier_name_1
# this should be changed by comparing all the possibilities for specified text (i can use the original dataframe!)
list_metrics = mh.calculate_metrics(model_name, y_test, y_pred)
none_average, binary_average, micro_average, macro_average = list_metrics
ch.save_results(classifier_name_1, list_metrics, parameters_1, model_name,
classif_level, classif_type, source_path)
def test_basic_LSTM(data_frame, text_vectorizer, classif_level, classif_type, dataset_location):
print('### LSTM Doing Testing ###')
root_location = fh.get_root_location('data/lstm_outcome/')
doc2vec_model_location = fh.link_paths(fh.join_paths(root_location, "doc2vec_model/vocab_model/"), "doc2vec_model")
model_location = link_paths(join_paths(root_location, "lstm_model"), "lstm_model")
sets_location = join_paths(root_location, "model_sets")
save_results = True
load_model = True
# date problem
X_train, X_test, X_val, y_train, y_test, y_val, settings = load_sets(sets_location)
classes, n_classes, vocab_processor, len_vocabulary = settings
training_docs_list = X_train['patent_id']
test_docs_list = X_test['patent_id']
val_docs_list = X_val['patent_id']
sequence_size, embedding_size = 1, 150
X_data, Xv_data, Xt_data = ch.get_df_data(3, training_docs_list, val_docs_list, test_docs_list, sequence_size, embedding_size, doc2vec_model_location)
#####
print(X_data.shape)
input_size, output_size = X_data.shape[2], n_classes
if load_model:
model = tf.keras.models.load_model(model_location)
min_delta, patience = 0.00001, 15
early_stopper = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=min_delta, patience=patience, verbose=1, mode='auto')
metrics_callback = MetricsCNNCallback(Xv_data, y_val, patience)
metrics, predictions, val_metrics = run_lstm(model,
X_data, y_train, Xt_data, y_test, Xv_data, y_val,
batch_size, [early_stopper, metrics_callback], queue_size,
training_docs_list, val_docs_list)
binary_predictions = mh.get_binary_0_5(predictions)
print('\nGenerating Testing Metrics')
metrics = mh.get_sequential_metrics(y_val, predictions, binary_predictions)
mh.display_sequential_metrics(metrics)
if save_results:
classifier_name, parameters = ch.get_sequential_classifier_information(model)
model_name = text_vectorizer+'/'+class_vectorizer+'/'+classifier_name
ch.save_results(classifier_name+'_LSTM', metrics, parameters, model_name, classif_level, classif_type, dataset_location)
print('end testing step')
def apply_svm(X_train_tfidf, y_train, X_test_tfidf, y_test, classif_level,
classif_type, source_path):
svm = pmh.get_SVC()
classifier_name, parameters = ch.get_classifier_information(str(svm))
y_pred = pmh.fit_predict_functions(svm, X_train_tfidf, y_train,
X_test_tfidf)
model_name = '[all classes predictions]label_encoder/tfidf/' + classifier_name
list_metrics = mh.calculate_metrics(model_name, y_test, y_pred)
none_average, binary_average, micro_average, macro_average = list_metrics
ch.save_results(classifier_name, list_metrics, parameters, model_name,
classif_level, classif_type, source_path)
def apply_label_powerset(X_train, y_train, X_test, y_test, baseline_name,
source_path):
classifier = ch.get_label_powerset(pmh.get_logistic())
classifier_name, parameters = ch.get_complex_classifier_information(
str(classifier), 1, 1, 2, 0)
y_pred = pmh.fit_predict_functions(classifier, X_train, y_train, X_test)
model_name = '[all classes predictions]' + baseline_name + classifier_name
list_metrics = mh.calculate_metrics(model_name, y_test, y_pred)
none_average, binary_average, micro_average, macro_average = list_metrics
ch.save_results(classifier_name, list_metrics, parameters, model_name,
classif_level, classif_type, source_path)
def apply_onevsrest(X_train, y_train, X_test, y_test, classes, baseline_name,
source_path):
# X_train.sort_indices() # SVC needs this line in addition
custom_pipeline = Pipeline([
('clf', OneVsRestClassifier(pmh.get_logistic(), n_jobs=-1)),
# ('clf', OneVsRestClassifier(pmh.get_SVC(), n_jobs=-1)),
# ('clf', OneVsRestClassifier(pmh.get_multinomialNB(), n_jobs=-1)),
# ('clf', OneVsRestClassifier(pmh.get_decision_tree(), n_jobs=-1)),
# ('clf', OneVsRestClassifier(pmh.get_kneighbors(), n_jobs=-1)),
# ('clf', OneVsRestClassifier(pmh.get_linear_SVC(), n_jobs=-1)),
# ('clf', OneVsRestClassifier(pmh.get_random_forest_classifier(), n_jobs=-1)),
# ('clf', OneVsRestClassifier(pmh.get_SGD_classifier(), n_jobs=-1)),
])
classifier_name, parameters = ch.get_complex_classifier_information(
str(custom_pipeline), 3, 1, 4, 0)
model_name = '[each class predictions]' + baseline_name + classifier_name
accuracies = []
for _class in classes:
print('**Processing {} texts...**'.format(_class))
y_pred = pmh.fit_predict_functions(custom_pipeline, X_train,
y_train[_class], X_test)
accuracies.append(mh.get_accuracy_score(y_test[_class], y_pred))
list_metrics = mh.calculate_metrics(model_name, y_test[_class], y_pred)
none_average, binary_average, micro_average, macro_average = list_metrics
ch.save_results(classifier_name, list_metrics, parameters, model_name,
classif_level, classif_type, source_path)
print('Pipeline score on training {}'.format(
custom_pipeline.score(X_train, y_train[_class])))
true_positives, false_positives, tpfn = mh.get_predictions_distribution(
y_test[_class], y_pred)
model_name = '[all classes predictions]' + baseline_name + classifier_name
if tpfn == 0 or (true_positives + false_positives) == 0:
mh.display_directly_metrics(model_name, 0, 0, 0, -1)
else:
precision = true_positives / (true_positives + false_positives)
recall = true_positives / tpfn
mh.display_directly_metrics(
model_name, precision, recall,
2 * (precision * recall) / (precision + recall), -1)
def apply_adapted_algorithm(X_train, y_train, X_test, y_test, baseline_name,
source_path):
classifier = pmh.get_MLkNN()
classifier_name, parameters = ch.get_complex_classifier_information(
str(classifier), 0, 0, 1, 0)
X_train, y_train, X_test = th.get_lil_matrices(X_train, y_train, X_test)
y_pred = pmh.fit_predict_functions(classifier, X_train, y_train, X_test)
model_name = '[all classes predictions]' + baseline_name + classifier_name
list_metrics = mh.calculate_metrics(model_name, y_test, y_pred)
none_average, binary_average, micro_average, macro_average = list_metrics
ch.save_results(classifier_name, list_metrics, parameters, model_name,
classif_level, classif_type, source_path)
def fourth_attemp_from_web(data_frame, text_vectorizer, class_vectorizer, classif_level, classif_type, dataset_location):
model_name = text_vectorizer+'/'+class_vectorizer+'/CNN'
standard_results = ch.apply_df_vectorizer(data_frame, text_vectorizer, class_vectorizer, model_name)
train_data, test_data, train_labels, test_labels, classes, n_classes, vocab_processor, len_vocabulary = standard_results
# TODO: is it useful?
# test_labels = np_utils.to_categorical(test_labels, n_classes)
model = pmh.get_fourth_attempt_model_from_web(train_data, n_classes)
y_train_predclass, y_test_predclass, train_metrics, test_metrics, train_predictions, test_predictions = pmh.run_fourth_attempt_model(model, train_data, train_labels, test_data, test_labels)
# mh.display_convolution_metrics_fourth_attempt(train_labels, test_labels, y_train_predclass, y_test_predclass)
# mh.display_convolutional_metrics(classifier_name, train_metrics[0], train_metrics[1], train_metrics[2], train_labels, train_predictions)
mh.display_convolutional_metrics(classifier_name, test_metrics[0], test_metrics[1], test_metrics[2], test_labels, test_predictions)
classifier_name, layers = ch.get_sequential_classifier_information(model)
ch.save_results(classifier_name, test_metrics, layers, model_name, classif_level, classif_type, dataset_location)
def apply_doc2vec_logistic_regression(data_frame, text_vectorizer,
class_vectorizer, classif_level,
classif_type, source_path):
baseline_name = '[all classes predictions]' + text_vectorizer + '/' + class_vectorizer
results_model_dbow = ch.apply_doc2vec_separated_train_test(
data_frame, baseline_name)
X_train, y_train, X_test, y_test, model_dbow, train_tagged, test_tagged = results_model_dbow
logreg = pmh.get_logistic()
classifier_name_0, parameters_0 = ch.get_classifier_information(
str(model_dbow))
classifier_name_1, parameters_1 = ch.get_classifier_information(
str(logreg))
y_pred = pmh.fit_predict_functions(logreg, X_train, y_train, X_test)
model_name = baseline_name + classifier_name_0 + '/' + classifier_name_1
list_metrics = mh.calculate_metrics(model_name, y_test, y_pred)
none_average, binary_average, micro_average, macro_average = list_metrics
ch.save_results(classifier_name_1, list_metrics, parameters_1, model_name,
classif_level, classif_type, source_path)
# baseline_name = '[each class predictions]'+text_vectorizer+'/'+class_vectorizer
# vectorizer_results = ch.apply_df_vectorizer(data_frame, 'doc2vec', 'multi_label', baseline_name)
# X_train, X_test, y_train, y_test, classes, n_classes, vocab_processor, len_vocabulary = vectorizer_results
# model_name = baseline_name+'/'+classifier_name_0+'/'+classifier_name_1
#
# for i in range(n_classes):
# unique, counts = np.unique(y_train[:, i], return_counts=True)
# if len(counts) > 1 and counts[1] > 1:
# y_pred = pmh.fit_predict_functions(custom_pipeline, X_train, y_train[:, i], X_test)
# print('### ', classes[i] ,' ###')
# list_metrics = mh.calculate_metrics(model_name, y_test[:, i], y_pred)
# none_average, binary_average, micro_average, macro_average = list_metrics
#
# ch.save_results(classifier_name_1, list_metrics, parameters_1, model_name, classif_level, classif_type, source_path)
# improvement
improved_logistic_regression(train_tagged, test_tagged, model_dbow, logreg,
text_vectorizer, class_vectorizer,
classif_level, classif_type, source_path)
def improved_logistic_regression(train_tagged, test_tagged, model_dbow, logreg,
text_vectorizer, class_vectorizer,
classif_level, classif_type, source_path):
classifier_name_0, parameters_0 = ch.get_classifier_information(
str(model_dbow))
classifier_name_1, parameters_1 = ch.get_classifier_information(
str(logreg))
model_dmm = wmh.train_doc2vec_with_tagged_data(train_tagged.values)
classifier_name_2, parameters_2 = ch.get_classifier_information(
str(model_dmm))
y_train, X_train = wmh.vec_for_learning(model_dmm, train_tagged)
y_test, X_test = wmh.vec_for_learning(model_dmm, test_tagged)
y_pred = pmh.fit_predict_functions(logreg, X_train, y_train, X_test)
model_dbow.delete_temporary_training_data(keep_doctags_vectors=True,
keep_inference=True)
model_dmm.delete_temporary_training_data(keep_doctags_vectors=True,
keep_inference=True)
merged_model = wmh.get_concatenated_doc2vec(model_dbow, model_dmm)
y_train, X_train = wmh.vec_for_learning(merged_model, train_tagged)
y_test, X_test = wmh.vec_for_learning(merged_model, test_tagged)
y_pred = pmh.fit_predict_functions(logreg, X_train, y_train, X_test)
model_name = '[all classes predictions]' + classifier_name_0 + '/' + classifier_name_2 + '/' + classifier_name_1
list_metrics = mh.calculate_metrics(model_name, y_test, y_pred)
none_average, binary_average, micro_average, macro_average = list_metrics
ch.save_results(classifier_name_1, list_metrics, parameters_1, model_name,
classif_level, classif_type, source_path)
def test_LSTM_from_web(data_frame, text_vectorizer, class_vectorizer, classif_level, classif_type, dataset_location):
print('### LSTM Doing Testing ###')
root_location = fh.get_root_location('data/lstm_outcome/')
nn_parameter_search_location = fh.join_paths(root_location, "nn_fhv_parameter_search")
doc2vec_model_location = fh.link_paths(fh.join_paths(root_location, "doc2vec_model/vocab_model/"), "doc2vec_model")
save_results = True
sequence_size = 1
EMBEDDING_SIZE = 150
model_name = text_vectorizer+'/'+class_vectorizer+'/LSTM'
results = ch.apply_df_vectorizer(data_frame, text_vectorizer, class_vectorizer, model_name)
X_train, X_test, y_train, y_test, classes, n_classes, vocab_processor, len_vocabulary = results
X_train, X_val, y_train, y_val = ch.get_train_test_from_data(X_train, y_train)
training_docs_list = X_train['patent_id']
test_docs_list = X_test['patent_id']
val_docs_list = X_val['patent_id']
X_data, Xv_data, Xt_data = ch.get_df_data(3, training_docs_list, val_docs_list, test_docs_list, sequence_size, EMBEDDING_SIZE, doc2vec_model_location)
GLOBAL_VARS.DOC2VEC_MODEL_NAME, GLOBAL_VARS.MODEL_NAME = wmh.set_parameters_lstm_doc2vec(nn_parameter_search_location, classif_level, classif_type)
NN_INPUT_NEURONS, NN_SEQUENCE_SIZE, NN_OUTPUT_NEURONS = pmh.get_lstm_shapes(X_data, n_classes)
NN_BATCH_SIZE, PARTS_LEVEL, NN_MAX_EPOCHS, QUEUE_SIZE = pmh.get_lstm_basic_parameters()
params = pmh.get_lstm_testing_parameters()
lstm_output_size,w_dropout_do,u_dropout_do, stack_layers, conv_size, conv_filter_length, conv_max_pooling_length = params
EARLY_STOPPER_MIN_DELTA, EARLY_STOPPER_PATIENCE = pmh.get_early_stopping_parameters()
TEST_METRICS_FILENAME = '{}_level_{}_standard_nn_test_metrics_dict.pkl'
test_metrics_dict = dict()
test_metrics_path = fh.link_paths(fh.link_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME), TEST_METRICS_FILENAME.format(classif_type, PARTS_LEVEL))
param_results_path = fh.link_paths(fh.link_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME), NN_PARAMETER_SEARCH_PREFIX.format(classif_type, classif_level, NN_BATCH_SIZE))
param_results_dict = pickle.load(open(param_results_path, 'rb'))
GLOBAL_VARS.NN_MODEL_NAME = 'lstm_size_{}_w-drop_{}_u-drop_{}_stack_{}_conv_{}'.format(lstm_output_size,
w_dropout_do,
u_dropout_do,
stack_layers,
str(conv_size)
)
if conv_size:
GLOBAL_VARS.NN_MODEL_NAME += '_conv-filter-length_{}_max-pooling-size_{}'.format(conv_filter_length,
conv_max_pooling_length)
if GLOBAL_VARS.NN_MODEL_NAME not in param_results_dict.keys():
print("Can't find model: {}".format(GLOBAL_VARS.NN_MODEL_NAME))
raise Exception()
if fh.ensure_exists_path_location(test_metrics_path):
test_metrics_dict = pickle.load(open(fh.link_paths(fh.link_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME), TEST_METRICS_FILENAME.format(classif_type,PARTS_LEVEL)), 'rb'))
if GLOBAL_VARS.NN_MODEL_NAME in test_metrics_dict.keys():
print("Test metrics already exist for: {}".format(GLOBAL_VARS.NN_MODEL_NAME))
test_metrics = test_metrics_dict[GLOBAL_VARS.NN_MODEL_NAME]
print("** Test Metrics: Cov Err: {:.3f}, Avg Labels: {:.3f}, \n\t\t Top 1: {:.3f}, Top 3: {:.3f}, Top 5: {:.3f}, \n\t\t F1 Micro: {:.3f}, F1 Macro: {:.3f}".format(
test_metrics['coverage_error'], test_metrics['average_num_of_labels'],
test_metrics['top_1'], test_metrics['top_3'], test_metrics['top_5'],
test_metrics['f1_micro'], test_metrics['f1_macro']))
raise Exception()
print('***************************************************************************************')
print(GLOBAL_VARS.NN_MODEL_NAME)
model = pmh.get_keras_rnn_model(NN_INPUT_NEURONS, NN_SEQUENCE_SIZE, NN_OUTPUT_NEURONS,
lstm_output_size, w_dropout_do, u_dropout_do, stack_layers, conv_size,
conv_filter_length, conv_max_pooling_length)
# get model best weights
weights = param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['best_weights']
model.set_weights(weights)
print('Evaluating on Test Data using best weights')
_, ytp, ytp_binary = pmh.predict_generator(None, model, Xt_data, y_test, NN_BATCH_SIZE, QUEUE_SIZE, test_docs_list)
print('Generating Test Metrics')
test_metrics = mh.get_sequential_metrics(y_test, ytp, ytp_binary)
mh.display_sequential_metrics(test_metrics)
if save_results:
classifier_name, parameters = ch.get_sequential_classifier_information(model)
ch.save_results(classifier_name+'_LSTM', test_metrics, parameters, model_name, classif_level, classif_type, dataset_location)
test_metrics_dict[GLOBAL_VARS.NN_MODEL_NAME] = test_metrics
pickle.dump(test_metrics_dict, open(test_metrics_path, 'wb'))
def train_LSTM_from_web(data_frame, text_vectorizer, class_vectorizer, classif_level, classif_type, dataset_location):
print('### LSTM Doing Training ###')
root_location = fh.get_root_location('data/lstm_outcome/')
exports_location = fh.join_paths(root_location, "exported_data/")
matrices_save_location = fh.join_paths(root_location, "fhv_matrices/")
nn_parameter_search_location = fh.join_paths(root_location, "nn_fhv_parameter_search")
doc2vec_model_location = fh.link_paths(fh.join_paths(root_location, "doc2vec_model/vocab_model/"), "doc2vec_model")
load_existing_results = True # it was True
save_results = True
sequence_size = 1
EMBEDDING_SIZE = 150
model_name = text_vectorizer+'/'+class_vectorizer+'/LSTM'
results = ch.apply_df_vectorizer(data_frame, text_vectorizer, class_vectorizer, model_name)
X_train, X_val, y_train, y_val, classes, n_classes, vocab_processor, len_vocabulary = results
training_docs_list = X_train['patent_id']
val_docs_list = X_val['patent_id']
X_data, Xv_data, _ = ch.get_df_data(2, training_docs_list, val_docs_list, None, sequence_size, EMBEDDING_SIZE, doc2vec_model_location)
GLOBAL_VARS.DOC2VEC_MODEL_NAME, GLOBAL_VARS.MODEL_NAME = wmh.set_parameters_lstm_doc2vec(nn_parameter_search_location, classif_level, classif_type)
# print(X_data.shape) # 64, 1, 200
# print(Xt_data.shape) # 20, 1, 200
# print(Xv_data.shape) # 16, 1, 200
NN_INPUT_NEURONS, NN_SEQUENCE_SIZE, NN_OUTPUT_NEURONS = pmh.get_lstm_shapes(X_data, n_classes)
NN_BATCH_SIZE, PARTS_LEVEL, NN_MAX_EPOCHS, QUEUE_SIZE = pmh.get_lstm_basic_parameters()
param_sampler = pmh.get_lstm_training_parameters()
EARLY_STOPPER_MIN_DELTA, EARLY_STOPPER_PATIENCE = pmh.get_early_stopping_parameters()
param_results_dict = dict()
param_results_path = fh.link_paths(fh.join_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME), NN_PARAMETER_SEARCH_PREFIX.format(classif_type, classif_level, NN_BATCH_SIZE))
index = param_results_path.rfind('/')
fh.create_folder(fh.link_paths(fh.join_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME), NN_PARAMETER_SEARCH_PREFIX.format(classif_type, classif_level, NN_BATCH_SIZE))[:index])
###########
# print(X_data.shape)
# input_size, sequence_size, output_size = X_data.shape[2], X_data.shape[0], n_classes
# NN_BATCH_SIZE, PARTS_LEVEL, NN_MAX_EPOCHS, QUEUE_SIZE = 1, 2, 200, 100
# lstm_output_sizes = [500, 1000]
# w_dropout_options = [0., 0.5]
# u_dropout_options = [0., 0.5]
# stack_layers_options = [1, 2, 3]
# lstm_output_size, w_dropout_do, u_dropout_do, stack_layers = 500, 0.0, 0.0, 1
# conv_size, conv_filter_length, max_pooling_length = 128, 2, 2
# EARLY_STOPPER_MIN_DELTA, EARLY_STOPPER_PATIENCE = 0.00001, 15
# import tensorflow as tf
# from tensorflow import keras
# from keras.layers import Input, Dense, Dropout, Activation
# from keras.models import Model, Sequential
# from keras.layers.convolutional import MaxPooling1D, Convolution1D
# from keras.layers.recurrent import LSTM
# model = Sequential()
# # model.add(Convolution1D(nb_filter=conv_size, input_shape=(sequence_size, input_size),
# # filter_length=conv_filter_length, border_mode='same', activation='relu'))
# # model.add(MaxPooling1D(pool_length=max_pooling_length))
# model.add(LSTM(lstm_output_size, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=False,
# input_dim=input_size, dropout_W=w_dropout_do, dropout_U=u_dropout_do,
# implementation=1,
# return_sequences=False if 1 == stack_layers else True,
# go_backwards=False, stateful=False, unroll=False,
# name='lstm_{}_w-drop_{}_u-drop_{}_layer_{}'.format(lstm_output_size, str(u_dropout_do),
# str(w_dropout_do), str(1))))
# # model.add(LSTM(lstm_output_size, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=False,
# # input_dim=input_size, dropout_W=w_dropout_do, dropout_U=u_dropout_do,
# # implementation=1,
# # return_sequences=False if 2 == stack_layers else True,
# # go_backwards=False, stateful=False, unroll=False,
# # name='lstm_{}_w-drop_{}_u-drop_{}_layer_{}'.format(lstm_output_size, str(u_dropout_do),
# # str(w_dropout_do), str(2))))
# # model.add(LSTM(lstm_output_size, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=False,
# # input_dim=input_size, dropout_W=w_dropout_do, dropout_U=u_dropout_do,
# # implementation=1,
# # return_sequences=False if 3 == stack_layers else True,
# # go_backwards=False, stateful=False, unroll=False,
# # name='lstm_{}_w-drop_{}_u-drop_{}_layer_{}'.format(lstm_output_size, str(u_dropout_do),
# # str(w_dropout_do), str(3))))
# model.add(Dense(output_size, activation='sigmoid', name='sigmoid_output'))
# model.compile(optimizer='rmsprop', loss='binary_crossentropy')
# input_matrix = fh.join_paths(matrices_save_location, GLOBAL_VARS.MODEL_NAME)
# early_stopper = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=EARLY_STOPPER_MIN_DELTA,
# patience=EARLY_STOPPER_PATIENCE, verbose=1, mode='auto')
# metrics_callback = mh.MetricsCallback(input_matrix, classif_type, PARTS_LEVEL, NN_BATCH_SIZE, is_mlp=False)
# history, yvp, yvp_binary = pmh.fit_predict_generator(model, X_data, y_train, Xv_data, y_val, training_docs_list, val_docs_list, early_stopper, metrics_callback, NN_BATCH_SIZE, NN_MAX_EPOCHS, QUEUE_SIZE)
# validation_metrics = mh.get_sequential_metrics(y_val, yvp, yvp_binary)
# mh.display_sequential_metrics(validation_metrics)
# ###########
# useful to skip all the already tested models
if load_existing_results:
param_results_path = fh.link_paths(fh.join_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME),
NN_PARAMETER_SEARCH_PREFIX.format(classif_type, classif_level, NN_BATCH_SIZE))
if fh.ensure_exists_path_location(param_results_path):
print('Loading Previous results in {}'.format(param_results_path))
param_results_dict = pickle.load(open(param_results_path, 'rb'))
else:
print('No Previous results exist in {}'.format(param_results_path))
for params in param_sampler:
start_time = time.time()
lstm_output_size = params['lstm_output_size']
w_dropout_do = params['w_dropout']
u_dropout_do = params['u_dropout']
stack_layers = params['stack_layers']
conv_size = params['conv_size']
conv_filter_length = params['conv_filter_length']
conv_max_pooling_length = params['conv_max_pooling_length']
GLOBAL_VARS.NN_MODEL_NAME = 'lstm_size_{}_w-drop_{}_u-drop_{}_stack_{}_conv_{}'.format(
lstm_output_size, w_dropout_do, u_dropout_do, stack_layers, str(conv_size))
if conv_size:
GLOBAL_VARS.NN_MODEL_NAME += '_conv-filter-length_{}_max-pooling-size_{}'.format(conv_filter_length,
conv_max_pooling_length)
if GLOBAL_VARS.NN_MODEL_NAME in param_results_dict.keys():
print("skipping: {}".format(GLOBAL_VARS.NN_MODEL_NAME))
continue
# creating the actual keras model
model = pmh.get_keras_rnn_model(NN_INPUT_NEURONS, NN_SEQUENCE_SIZE, NN_OUTPUT_NEURONS,
lstm_output_size, w_dropout_do, u_dropout_do, stack_layers, conv_size,
conv_filter_length, conv_max_pooling_length)
classifier_name, parameters = ch.get_sequential_classifier_information(model)
model_name = text_vectorizer+'/'+class_vectorizer+'/'+classifier_name
input_matrix = fh.join_paths(matrices_save_location, GLOBAL_VARS.MODEL_NAME)
early_stopper = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=EARLY_STOPPER_MIN_DELTA,
patience=EARLY_STOPPER_PATIENCE, verbose=1, mode='auto')
metrics_callback = mh.MetricsCallback(input_matrix, classif_type, PARTS_LEVEL, NN_BATCH_SIZE, is_mlp=False)
history, yvp, yvp_binary = pmh.fit_predict_generator(model, X_data, y_train, Xv_data, y_val, training_docs_list, val_docs_list, early_stopper, metrics_callback, NN_BATCH_SIZE, NN_MAX_EPOCHS, QUEUE_SIZE)
print('\nGenerating Validation Metrics')
validation_metrics = mh.get_sequential_metrics(y_val, yvp, yvp_binary)
mh.display_sequential_metrics(classifier_name, validation_metrics)
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME] = dict()
# param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['best_validation_metrics'] = best_validation_metrics
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['epochs'] = len(history.history['val_loss'])
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['best_weights'] = metrics_callback.best_weights
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['best_val_loss'] = metrics_callback.best_val_loss
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['training_loss'] = metrics_callback.losses
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['validation_loss'] = metrics_callback.val_losses
duration = time.time() - start_time
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['duration'] = duration
ch.delete_variables(history, metrics_callback)
ch.save_results(classifier_name+'_LSTM', validation_metrics, parameters, model_name, classif_level, classif_type, dataset_location)
if save_results:
file = open(param_results_path, 'wb')
pickle.dump(param_results_dict, file)
def third_attempt_from_web(data_frame, text_vectorizer, class_vectorizer, classif_level, classif_type, dataset_location):
# fashion_mnist = keras.datasets.fashion_mnist
# (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
# class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
# 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
# print("### images ###")
# print(test_images)
# print(type(test_images))
# print(test_images.shape)
# print(test_images[0])
# print(test_labels)
# print(type(test_labels))
# print(test_labels.shape)
# print(test_labels[0])
model_name = text_vectorizer+'/'+class_vectorizer+'/CNN'
standard_results = ch.apply_df_vectorizer(data_frame, text_vectorizer, class_vectorizer, model_name)
train_data, test_data, train_labels, test_labels, classes, n_classes, vocab_processor, len_vocabulary = standard_results
# print("### texts ###")
# print(test_data)
# print(type(test_data))
# print(test_data.shape)
# print(test_data[0])
# print(test_labels)
# print(type(test_labels))
# print(test_labels.shape)
# print(test_labels[0])
# preprocess data
# train_images = train_images / 255.0
# test_images = test_images / 255.0
# it reduced all in the range 0--1
# normalize the values
train_data = np.reshape(train_data, [train_data.shape[0], 1, train_data.shape[1]])
test_data = np.reshape(test_data, [test_data.shape[0], 1, test_data.shape[1]])
print(train_labels)
print(train_labels.shape)
for index in range(train_labels.shape[1]):
temp_train_labels = train_labels[:, index] # estimating the first class
temp_test_labels = test_labels[:, index] # estimating the first class
print(temp_train_labels)
model = pmh.get_image_convolutional_from_web(train_data, n_classes)
metrics, predictions = pmh.run_image_cnn_model(model, train_data, temp_train_labels, test_data, temp_test_labels)
print("predictions: ", predictions)
classifier_name, layers = ch.get_sequential_classifier_information(model)
mh.display_convolutional_metrics(classifier_name, metrics[0], metrics[1], metrics[2], temp_test_labels, predictions)
ch.save_results(classifier_name, metrics, layers, model_name, classif_level, classif_type, dataset_location)
def train_basic_LSTM(data_frame, text_vectorizer, classif_level, classif_type, dataset_location):
print('### LSTM - Training ###')
root_location = get_root_location('data/lstm_outcome/')
doc2vec_model_location = link_paths(join_paths(root_location, "doc2vec_model/vocab_model/"), "doc2vec_model")
model_location = link_paths(join_paths(root_location, "lstm_model"), "lstm_model")
sets_location = join_paths(root_location, "model_sets")
save_results = False
save_model = True
model_name = text_vectorizer+'/'+class_vectorizer+'/LSTM'
results = apply_df_vectorizer(data_frame, text_vectorizer, class_vectorizer, model_name)
X_train, X_val, y_train, y_val, classes, n_classes, vocab_processor, len_vocabulary = results
save_sets(sets_location, X_train, None, X_val, y_train, None, y_val, [classes, n_classes, vocab_processor, len_vocabulary])
# val_path problem
training_docs_list = X_train['patent_id']
val_docs_list = X_val['patent_id']
sequence_size, embedding_size = 1, 150
X_data, Xv_data, _ = ch.get_df_data(2, training_docs_list, val_docs_list, None, sequence_size, embedding_size, doc2vec_model_location)
# sequence size is the (, number, ) in the tuple data
print(X_data.shape)
input_size, output_size = X_data.shape[2], n_classes
parameters = {
"estimator__epochs": 200,
"estimator__batch_size": 64,
"estimator__optimizer_1": 'Adam',
"estimator__optimizer_2": 'Adam',
"estimator__optimizer_3": 'Adam',
"estimator__optimizer_4": 'Adam',
"estimator__optimizer_5": 'Adam',
"estimator__optimizer_6": 'Adam',
"estimator__init_mode_1": 'uniform',
"estimator__init_mode_2": 'uniform',
"estimator__init_mode_3": 'uniform',
"estimator__init_mode_4": 'uniform',
"estimator__init_mode_5": 'uniform',
"estimator__init_mode_6": 'uniform',
"estimator__activation_1": 'softmax',
"estimator__activation_2": 'softmax',
"estimator__activation_3": 'softmax',
"estimator__activation_4": 'softmax',
"estimator__activation_5": 'softmax',
"estimator__activation_6": 'softmax',
"estimator__dropout_rate_1": .0,
"estimator__dropout_rate_2": .0,
"estimator__dropout_rate_3": .0,
"estimator__dropout_rate_4": .0,
"estimator__weight_constraint_1": 1,
"estimator__weight_constraint_2": 1,
"estimator__weight_constraint_3": 1,
"estimator__weight_constraint_4": 1,
"estimator__weight_constraint_5": 1,
"estimator__weight_constraint_6": 1,
"estimator__neurons_1": n_classes*20,
"estimator__neurons_2": n_classes*5,
"estimator__neurons_3": n_classes*2,
"estimator__neurons_4": n_classes*1.5,
"estimator__filters": 16,
"estimator__filters_2": 16,
"estimator__filters_3": 16,
"estimator__kernel_size_1": 8,
"estimator__kernel_size_2": 8, "estimator__kernel_size_3": 8,
"estimator__strides_1": 8,
"estimator__strides_2": 8,
"estimator__strides_3": 8,
"estimator__activation_lstm_1": 'tanh',
"estimator__activation_lstm_2": 'tanh',
"estimator__activation_lstm_3": 'tanh',
"estimator__recurrent_activation_1": 'hard_sigmoid',
"estimator__recurrent_activation_2": 'hard_sigmoid',
"estimator__recurrent_activation_3": 'hard_sigmoid',
"estimator__w_dropout_do_1": .2,
"estimator__w_dropout_do_2": .2,
"estimator__w_dropout_do_3": .2,
"estimator__u_dropout_do_1": .2,
"estimator__u_dropout_do_2": .2,
"estimator__u_dropout_do_3": .2,
"estimator__backwards_1": False,
"estimator__backwards_2": False,
"estimator__backwards_3": False,
"estimator__unroll_1": False,
"estimator__unroll_2": False,
"estimator__unroll_3": False
}
model = get_lstm(optimizer,
init_mode_1, activation_1,
init_mode_2, activation_2,
init_mode_3, activation_3,
init_mode_4, activation_4,
init_mode_5, activation_5,
init_mode_6, activation_6,
weight_constraint_1,
weight_constraint_2,
weight_constraint_3,
weight_constraint_4,
weight_constraint_5,
weight_constraint_6,
dropout_rate_1,
dropout_rate_2,
dropout_rate_3,
dropout_rate_4,
neurons_1, neurons_2, neurons_3,
filters_1, filters_2, filters_3,
kernel_size_1, kernel_size_2, kernel_size_3,
strides_1, strides_2, strides_3,
activation_lstm_1, activation_lstm_2, activation_lstm_3,
recurrent_activation_1, recurrent_activation_2, recurrent_activation_3,
w_dropout_do_1, w_dropout_do_2, w_dropout_do_3,
u_dropout_do_1, u_dropout_do_2, u_dropout_do_3,
backwards_1, backwards_2, backwards_3,
unroll_1, unroll_2, unroll_3,
lstm_output_size_1, lstm_output_size_2, lstm_output_size_3,
input_size) # input size
# check the X_data shape and vocabulary size
# check the X_data shape and vocabulary size
if save_model:
model.save(model_location)
min_delta, patience = 0.00001, 15
early_stopper = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=min_delta, patience=patience, verbose=1, mode='auto')
metrics_callback = MetricsCNNCallback(Xv_data, y_val, patience)
metrics, val_predictions, val_metrics = run_lstm(model,
X_data, y_train, Xt_data, y_test, Xv_data, y_val,
batch_size, [early_stopper, metrics_callback], queue_size,
training_docs_list, val_docs_list)
binary_val_predictions = mh.get_binary_0_5(val_predictions)
print('\nGenerating Validation Metrics')
validation_metrics = mh.get_sequential_metrics(y_val, val_predictions, binary_val_predictions)
mh.display_sequential_metrics(validation_metrics)
if save_results:
classifier_name, parameters = ch.get_sequential_classifier_information(model)
model_name = text_vectorizer+'/'+class_vectorizer+'/'+classifier_name
ch.save_results(classifier_name+'_LSTM', validation_metrics, parameters, model_name, classif_level, classif_type, dataset_location)
print('end training step')
def apply_fasttext(data_frame, text_vectorizer, class_vectorizer,
classif_level, classif_type, dataset_location):
root_location = fh.get_root_location('data/fasttext_outcome/')
fasttext_location = fh.join_paths(root_location, 'fasttext_models/')
word2vec_location = fh.join_paths(root_location, 'word2vec_models/')
timestamp = datetime.datetime.now().isoformat()
#############################
# unsupervised model
# model = fasttext.train_unsupervised(input=fh.link_paths(root_location, 'training set.csv'),
# autotuneValidationFile=fh.link_paths(root_location, 'training set.csv'))
#############################
# supervised model
# model = fasttext.train_supervised(input=fh.link_paths(root_location, 'training set.csv'),
# autotuneValidationFile=fh.link_paths(root_location, 'validating set.csv'), verbose=3, autotuneDuration=5000)
#############################
model, parameters = pmh.get_fasttext(
fh.link_paths(root_location, 'training set.csv'))
pmh.save_fasttext_model(
model, fh.link_paths(fasttext_location, 'model ' + timestamp + '.bin'))
# pmh.load_fasttext_model(fh.link_paths(fasttext_location, 'model '+timestamp+'.bin'))
test_labels, predictions, results = pmh.predict_test_fasttext(
model, fh.link_paths(root_location, 'testing set.csv'))
# print('predicted labels {}, probabilities of the labels {}'.format(predictions[0], predictions[1]))
result_top_15 = model.test(fh.link_paths(root_location, 'testing set.csv'),
k=15)
result_top_8 = model.test(fh.link_paths(root_location, 'testing set.csv'),
k=8)
result_top_5 = model.test(fh.link_paths(root_location, 'testing set.csv'),
k=5)
result_top_3 = model.test(fh.link_paths(root_location, 'testing set.csv'),
k=3)
result_top_1 = model.test(fh.link_paths(root_location, 'testing set.csv'),
k=1)
classifier_name = ch.get_fasttext_classifier_information(str(model))
model_name = text_vectorizer + '/' + class_vectorizer + '/' + classifier_name
mh.display_directly_metrics(
'k=-1 ' + classifier_name, -1, results[1], results[2],
2 * (results[1] * results[2]) / (results[1] + results[2]))
mh.display_directly_metrics(
'k= 15 ' + classifier_name, -1, result_top_15[1], result_top_15[2],
2 * (result_top_15[1] * result_top_15[2]) /
(result_top_15[1] + result_top_15[2]))
mh.display_directly_metrics(
'k= 8 ' + classifier_name, -1, result_top_8[1], result_top_8[2],
2 * (result_top_8[1] * result_top_8[2]) /
(result_top_8[1] + result_top_8[2]))
mh.display_directly_metrics(
'k= 5 ' + classifier_name, -1, result_top_5[1], result_top_5[2],
2 * (result_top_5[1] * result_top_5[2]) /
(result_top_5[1] + result_top_5[2]))
mh.display_directly_metrics(
'k= 3 ' + classifier_name, -1, result_top_3[1], result_top_3[2],
2 * (result_top_3[1] * result_top_3[2]) /
(result_top_3[1] + result_top_3[2]))
mh.display_directly_metrics(
'k= 1 ' + classifier_name, -1, result_top_1[1], result_top_1[2],
2 * (result_top_1[1] * result_top_1[2]) /
(result_top_1[1] + result_top_1[2]))
ch.save_results(classifier_name, results, parameters, model_name,
classif_level, classif_type, dataset_location)
manual_metrics = mh.calculate_manual_metrics(model_name, test_labels,
predictions)
none_average, binary_average, micro_average, macro_average = manual_metrics
# print(model.test_label()) # path is missing
# list_ = model.words
# new_list = []
# for token in list_:
# if len(token) in [0,1,2]:
# new_list.append(token)
# elif len(token) > 29:
# new_list.append(token)
# # print(new_list)
# print(len(new_list))
print(model.labels)
def apply_multi_label_classification_without_pipeline(
data_frame, text_vectorizer, class_vectorizer, classif_level,
classif_type, source_path):
baseline_name = text_vectorizer + '/' + class_vectorizer + '/onevsrest/'
vectorizer_results = ch.apply_df_vectorizer(data_frame, text_vectorizer,
class_vectorizer,
'[both]' + baseline_name)
X_train, X_test, y_train, y_test, classes, n_classes, vocab_processor, len_vocabulary = vectorizer_results
# len_vocabulary = 57335
# len_vocabulary = 34736
print(X_train[0])
print('len_vocabulary: ', len_vocabulary, ' num_classes: ', n_classes)
# Run classifier
classifier = OneVsRestClassifier(pmh.get_logistic()) # here
# classifier = OneVsRestClassifier(pmh.get_SVC()) # here
# classifier = OneVsRestClassifier(pmh.get_multinomialNB()) # ERROR WORD2VEC/DOC2VEC (X has negative value)
# classifier = OneVsRestClassifier(pmh.get_decision_tree()) # here
# classifier = OneVsRestClassifier(pmh.get_kneighbors()) # here
# classifier = OneVsRestClassifier(pmh.get_linear_SVC())
# classifier = OneVsRestClassifier(pmh.get_random_forest_classifier()) # here
# classifier = OneVsRestClassifier(pmh.get_SGD_classifier())
train_predictions = np.ndarray(shape=(n_classes, y_train.shape[0]),
dtype=int)
predictions = np.ndarray(shape=(n_classes, y_test.shape[0]), dtype=int)
###
precision = dict()
recall = dict()
average_precision = dict()
classifier_name, parameters = ch.get_complex_classifier_information(
str(classifier), 1, 1, 2, 0)
second_training = False
just_once = True
another_try = False # single train and estimation instead of multi-train-estimation steps (it performs better with svm and logistic)
if not another_try:
for _ in range(1):
for i in range(n_classes):
if second_training:
if classifier_name in [
'DecisionTreeClassifier', 'KNeighborsClassifier',
'MultinomialNB', 'RandomForestClassifier'
]:
# do not provide the second metrics
break
elif just_once:
# fit again with the whole set of classes - should be better
classifier.fit(X_train, y_train)
y_score = classifier.decision_function(X_test)
precision[i], recall[i], average_precision[
i] = mh.calculate_recall_curve_precision_score(
y_test[:, i], y_score[:, i], None,
y_test[:, i], y_score[:, i])
just_once = False
else:
predictions[i] = pmh.fit_predict_functions(
classifier, X_train, y_train[:, i], X_test)
train_predictions[i] = classifier.predict(X_train)
print('**Processing classes {0:0.2f} % ...**'.format(
((i + 1) / n_classes) * 100))
second_training = True
break
predictions = predictions.transpose()
print('transposed')
else:
predictions = pmh.fit_predict_functions(classifier, X_train, y_train,
X_test)
train_predictions = classifier.predict(X_train)
# metrics
model_name = '[each class predictions]' + baseline_name + classifier_name
manual_metrics = mh.calculate_manual_metrics(model_name, y_test,
predictions)
none_average, binary_average, micro_average, macro_average = manual_metrics
# metrics
list_metrics = mh.calculate_metrics(model_name, y_test, predictions)
none_average, binary_average, micro_average, macro_average = list_metrics
ch.save_results(classifier_name, list_metrics, parameters, model_name,
classif_level, classif_type, source_path)
if not just_once:
print('not just once')
train_predictions = classifier.predict(X_train)
predictions = classifier.predict(X_test)
# metrics
mh.calculate_metrics_with_recall_curve(y_score, y_train, y_test,
train_predictions, predictions)
# metrics
model_name = '[all classes predictions]' + baseline_name + classifier_name
list_metrics = mh.calculate_metrics(model_name, y_test, predictions)
none_average, binary_average, micro_average, macro_average = list_metrics
ch.save_results(classifier_name, list_metrics, parameters, model_name,
classif_level, classif_type, source_path)