def extract_features(train_dir, test_dir=""):
print_message("Extracting Features")
train_data, train_labels, test_data, test_labels = get_data(
test_dir, train_dir)
glbs.LABELS = train_labels + test_labels
glbs.TRAIN_DATA = train_data
feature_lst = []
# add all the N-Grams feature to the list
for feature in glbs.FEATURES:
if is_ngrams(feature):
vectorizer = get_vectorizer(feature)
feature_lst = add_feature(feature_lst, feature, vectorizer)
# add all the stylistic features to the list
for feature in glbs.STYLISTIC_FEATURES:
vectorizers = get_stylistic_features_vectorizer(feature)
for i in range(len(vectorizers)):
feature_lst = add_feature(feature_lst, feature + str(i),
vectorizers[i])
# convert the list to one vectoriazer using FeatureUnion
all_features = FeatureUnion(feature_lst)
train_features = all_features.fit_transform(train_data)
test_features = all_features.transform(test_data)
return train_features, train_labels, test_features, test_labels, all_features
def classify(train,
tr_labels,
test,
ts_labels,
all_features,
num_iteration=1,
model_number=0):
results = {}
result = []
le = LabelEncoder()
le.fit(tr_labels)
ts_labels = le.transform(ts_labels)
tr_labels = le.transform(tr_labels)
print_message("Classifying")
# if os.path.exists(temp_file_path):
# results = load_backup_file(temp_file_path)
for classifier in glbs.METHODS:
print_message("running " + str(classifier), num_tabs=1)
if classifier in results.keys():
continue
if classifier == "rnn":
clf = get_rnn_model(train)
clf.fit(train, tr_labels, epochs=3, batch_size=64)
# scores = clf.evaluate(test, ts_labels, verbose=0)
# acc_score += scores[1]
else:
clf = methods[classifier]
clf.fit(train, tr_labels)
prediction = clf.predict(test)
decision = []
try:
decision = clf.decision_function(test)
except:
decision = clf.predict_proba(test)
decision = decision[:, 1]
result = get_results(ts_labels, prediction, decision)
model_path = os.path.join(glbs.RESULTS_PATH,
"Model " + classifier + str(model_number))
save_model(clf, model_path)
del clf
results[classifier] = result
# save_backup_file(results, temp_file_path)
# print(results)
return results
def extract_features(dataset_dir):
print_message("Extracting Features")
X, y = get_data(dataset_dir)
glbs.LABELS = y
glbs.DATASET_DATA = X
########################################
# X, y = zip(*list(zip(X, y))[:160])
# from help_functions import get_fetuer_by_DF
# get_fetuer_by_DF(X)
########################################
feature_lst = []
# add all the N-Grams feature to the list
for feature in glbs.FEATURES:
if is_ngrams(feature):
vectorizer = get_vectorizer(feature)
feature_lst = add_feature(feature_lst, feature, vectorizer)
# add all the stylistic features to the list
for feature in glbs.STYLISTIC_FEATURES:
vectorizers = get_stylistic_features_vectorizer(feature)
for i in range(len(vectorizers)):
feature_lst = add_feature(feature_lst, feature + str(i),
vectorizers[i])
# convert the list to one vectoriazer using FeatureUnion
if glbs.MULTIPROCESSING:
n_jobs = -1
else:
n_jobs = None
all_features = FeatureUnion(feature_lst, n_jobs=n_jobs)
glbs.FEATURE_MODEL.append(all_features)
all_features.fit(X, y)
glbs.NUM_OF_FEATURE = len(all_features.get_feature_names())
if glbs.SELECTION:
from feature_selction import get_selected_features
get_selected_features(X, y, all_features)
return X, y
def write_results(results):
glbs = GlobalParameters()
print_message("Writing results...")
pickle_path = glbs.RESULTS_PATH + "\\Pickle files"
if path.exists(pickle_path):
shutil.rmtree(pickle_path, ignore_errors=True)
os.makedirs(pickle_path)
xlsx_path = glbs.RESULTS_PATH + "\\Xlsx files"
if path.exists(xlsx_path):
shutil.rmtree(xlsx_path, ignore_errors=True)
time.sleep(0.5)
os.makedirs(xlsx_path)
for key in results.keys():
with open(pickle_path + "\\" + key + ".pickle", "wb+") as file:
pickle.dump(results[key], file)
new_write_file_content(pickle_path + "\\" + key + ".pickle", key, xlsx_path)
def write_results(results):
glbs = GlobalParameters()
print_message("Writing results...")
# add_to_csv(results, glbs.RESULTS_PATH)
pickle_path = os.path.join(glbs.RESULTS_PATH, "Pickle files")
if path.exists(pickle_path):
shutil.rmtree(pickle_path, ignore_errors=True)
os.makedirs(pickle_path)
xlsx_path = os.path.join(glbs.RESULTS_PATH, "Xlsx files")
if path.exists(xlsx_path):
shutil.rmtree(xlsx_path, ignore_errors=True)
time.sleep(0.5)
os.makedirs(xlsx_path)
for key in results.keys():
with open(os.path.join(pickle_path, key) + ".pickle", "wb+") as file:
pickle.dump(results[key], file)
new_write_file_content(
os.path.join(pickle_path, key) + ".pickle", key, xlsx_path)
def main(cfg):
try:
# nltk.download("vader_lexicon")
# nltk.download('wordnet')
glbs = GlobalParameters()
configs = get_cfg_files(cfg)
total_files = len(configs)
results = {}
for i, config in enumerate(configs):
print_message("Running config {}/{}".format(i + 1, total_files))
set_global_parameters(config)
print_run_details()
dataset_dir = normalize()
X, y = extract_features(dataset_dir)
config_result = classify(X, y, glbs.K_FOLDS, glbs.ITERATIONS)
glbs.RESULTS[glbs.FILE_NAME] = config_result
glbs.RESULTS = add_results(glbs.RESULTS, glbs)
if glbs.EXPORT_AS_BASELINE:
export_as_baseline(config_result, config[1])
if glbs.WORDCLOUD:
print_message("Generating word clouds (long processes)")
generate_word_clouds()
add_results_glbs(results, glbs)
write_results(divide_results(glbs.RESULTS))
send_work_done(glbs.DATASET_DIR)
print_message("Done!")
except Exception as e:
traceback.print_exc()
send_work_done(glbs.DATASET_DIR,
"",
error=str(e),
traceback=str(traceback.format_exc()))
def normalize(test=False):
init_tools()
n = glbs.NORMALIZATION
if not test:
i = glbs.TRAIN_DIR
print_message("Normalizing")
else:
i = glbs.TEST_DIR
print_message("Normalizing test dataset")
if not is_nargs_empty(n):
# the normalized folder
parent_dir = i + "@" + n
# create the dir if does not exist
if not os.path.exists(parent_dir):
os.mkdir(parent_dir)
for category in os.listdir(i):
with open(os.path.join(i, category),
'r',
encoding='utf8',
errors='ignore') as read:
n_lines = []
for line in read:
line = line.rstrip('\n')
n_lines.append(normal(line, n))
n_file = '\n'.join(n_lines)
del n_lines
# write normalized_file
with open(os.path.join(parent_dir, category),
'w+',
encoding='utf8') as write:
write.write(n_file)
del n_file
else: # if it does exist
print_message("found normalized dataset")
# check if both dirs have the same number of files
if not len(os.listdir(parent_dir)) == len(os.listdir(i)):
# delete normalized folder and create a new one.
print_message(
"Corrupted normalization found, deleting and starting over..."
)
shutil.rmtree(parent_dir, ignore_errors=True)
normalize()
glbsNORM_PATH = parent_dir
else:
glbsNORM_PATH = i
return glbsNORM_PATH
def selectionHalfMethod(X, y, all_features):
glbs = GlobalParameters()
filename = glbs.FILE_NAME
results = {}
# nxt = (glbs.SELECTION[0][0], int(glbs.SELECTION[0][1]))
nxt = (glbs.SELECTION[0][0], int(glbs.SELECTION[0][1]))
max_last_result = 0
bottom = (0, 0)
top = nxt
while top != bottom:
max_nxt_result = 0
print_message(nxt[0])
print_message(nxt[1])
glbs.FILE_NAME = glbs.FILE_NAME + str(nxt[1])
select = select_k_best(nxt[0], int(nxt[1]))
glbs.FEATURE_MODEL[1] = select
results[glbs.FILE_NAME] = classify(X, y, glbs.K_FOLDS, glbs.ITERATIONS)
for method in results[glbs.FILE_NAME].items():
if mean(method[1]["accuracy"]) > max_nxt_result:
max_nxt_result = mean(method[1]["accuracy"])
results = add_results(results, glbs, nxt)
if max_nxt_result >= max_last_result:
top = nxt
if bottom[1] == 0:
nxt = (nxt[0], int(int(nxt[1]) / 2))
if bottom[1] != 0:
nxt = (nxt[0], int((int(nxt[1]) + bottom[1]) / 2))
max_last_result = max_nxt_result
elif max_nxt_result < max_last_result:
bottom = nxt
nxt = (nxt[0], int((top[1] + bottom[1]) / 2))
glbs.SELECTION[0] = nxt
if bottom[1] - top[1] == -1 and bottom == nxt:
break
glbs.FILE_NAME = filename
add_results_glbs(results, glbs)
def main(cfg):
try:
glbs = GlobalParameters()
configs = get_cfg_files(cfg)
results = {}
n_test_dir = ""
total_files = len(configs)
for i, config in enumerate(configs):
print_message("Running config {}/{}".format(i + 1, total_files))
set_global_parameters(config)
print_run_details()
n_train_dir = normalize()
if glbs.TEST_DIR != "":
n_test_dir = normalize(test=True)
train, tr_labels, test, ts_labels, all_features = extract_features(
n_train_dir, n_test_dir)
for selection in glbs.SELECTION:
try:
train, test = get_selected_features(
selection, train, tr_labels, test, ts_labels,
all_features)
except:
pass
results[glbs.FILE_NAME] = classify(train,
tr_labels,
test,
ts_labels,
all_features,
model_number=i)
results = add_results(results)
if glbs.WORDCLOUD:
print_message("Generating word clouds (long processes)")
generate_word_clouds()
write_results(divide_results(results))
send_work_done(glbs.TRAIN_DIR)
print_message("Done!")
# clean_backup_files()
except Exception as e:
traceback.print_exc()
send_work_done(glbs.TRAIN_DIR,
"",
error=str(e),
traceback=str(traceback.format_exc()))
def classify(X, y, k_fold, num_iteration=1):
results = {}
le = LabelEncoder()
le.fit(y)
y = le.transform(y)
print_message("Classifying")
"""def cross_validation(X, classifier, clf, i, k_fold, num_iteration, y):
print_message("iteration " + str(i + 1) + "/" + str(num_iteration), 2)
scores = cross_validate(clf, X, y, cv=k_fold, scoring=glbs.MEASURE)
for measure in glbs.MEASURE:
if measure in results[classifier].keys():
results[classifier][measure] += list(scores['test_' + measure])
else:
results[classifier][measure] = list(scores['test_' + measure])"""
for classifier in glbs.METHODS:
print_message("running " + str(classifier), num_tabs=1)
if classifier not in results.keys():
results[classifier] = {}
if classifier == "rnn":
# clf = get_rnn_model(X)
continue
else:
clf = methods[classifier]
lst = []
if glbs.SELECTION:
lst.append(("feture", glbs.FEATURE_MODEL[0]))
lst.append(("select", glbs.FEATURE_MODEL[1]))
lst.append(("classifier", clf))
clf = Pipeline(lst)
else:
clf = make_pipeline(glbs.FEATURE_MODEL[0], clf)
if glbs.MULTIPROCESSING:
n_jobs = -1
else:
n_jobs = None
####################################################################
# Used for parameters tuning
from sklearn.model_selection import RandomizedSearchCV
# Number of trees in random forest
n_estimators = [int(x) for x in np.linspace(start=1000, stop=2000, num=10)]
# Number of features to consider at every split
max_features = ['auto', 'sqrt']
# Maximum number of levels in tree
max_depth = [int(x) for x in np.linspace(10, 110, num=11)]
max_depth.append(None)
# Minimum number of samples required to split a node
min_samples_split = [2, 5, 10, 15]
# Minimum number of samples required at each leaf node
min_samples_leaf = [1, 2, 4, 6]
# Method of selecting samples for training each tree
bootstrap = [True, False]
# Create the random grid
random_grid = {'classifier__n_estimators': n_estimators,
'classifier__max_features': max_features,
'classifier__max_depth': max_depth,
'classifier__min_samples_split': min_samples_split,
'classifier__min_samples_leaf': min_samples_leaf,
'classifier__bootstrap': bootstrap}
print(random_grid)
# Use the random grid to search for best hyperparameters
# Random search of parameters, using 3 fold cross validation,
# search across 100 different combinations, and use all available cores
rf_random = RandomizedSearchCV(estimator=clf, param_distributions=random_grid, n_iter=100, cv=k_fold, verbose=2,
random_state=42, n_jobs=-1)
print(clf.get_params().keys())
# Fit the random search model
rf_random.fit(X, y)
print(rf_random.best_params_)
clf = rf_random.best_estimator_
with open("best_estimator.json", "w") as file:
json.dump(rf_random.best_params_, file, indent=6)
"""best_random = rf_random.best_estimator_
scores = cross_validate(best_random, X, y, cv=k_fold, scoring=glbs.MEASURE, n_jobs=n_jobs)
random_accuracy = numpy.mean(list(scores["test_accuracy"]))
base_model = clf
scores = cross_validate(base_model, X, y, cv=k_fold, scoring=glbs.MEASURE, n_jobs=n_jobs)
base_accuracy = numpy.mean(list(scores["test_accuracy"]))
print('Base accuracy: {:0.2f}%.'.format(100 * base_accuracy))
print('Random accuracy: {:0.2f}%.'.format(100 * random_accuracy))
print('Improvement of {:0.2f}%.'.format(100 * (random_accuracy - base_accuracy) / base_accuracy))
stop = "Put breakpoint here"""""
####################################################################
for i in range(num_iteration):
# Shuffle the inner order of the posts within each fold
Xy = list(zip(X, y))
splited = []
len_l = len(Xy)
for j in range(k_fold):
start = int(j * len_l / k_fold)
end = int((j + 1) * len_l / k_fold)
splited.append(Xy[start:end])
Xy = []
for fold in splited:
random.Random(num_iteration).shuffle(fold)
Xy += fold
X, y = zip(*Xy)
print_message("iteration " + str(i + 1) + "/" + str(num_iteration), 2)
scores = cross_validate(
clf, X, y, cv=k_fold, scoring=glbs.MEASURE, n_jobs=n_jobs
)
for measure in glbs.MEASURE:
if measure in results[classifier].keys():
results[classifier][measure] += list(scores["test_" + measure])
else:
results[classifier][measure] = list(scores["test_" + measure])
del clf
return results
def clean_backup_files():
glbs = GlobalParameters()
print_message("removing temp files...")
folder_path = os.sep.join(glbs.RESULTS_PATH.split(
os.sep)[:-1]) + os.sep + "temp_backups"
shutil.rmtree(folder_path, ignore_errors=True)
def classify(X, y, k_fold, num_iteration=1):
results = {}
le = LabelEncoder()
le.fit(y)
y = le.transform(y)
print_message("Classifying")
"""def cross_validation(X, classifier, clf, i, k_fold, num_iteration, y):
print_message("iteration " + str(i + 1) + "/" + str(num_iteration), 2)
scores = cross_validate(clf, X, y, cv=k_fold, scoring=glbs.MEASURE)
for measure in glbs.MEASURE:
if measure in results[classifier].keys():
results[classifier][measure] += list(scores['test_' + measure])
else:
results[classifier][measure] = list(scores['test_' + measure])"""
for classifier in glbs.METHODS:
print_message("running " + str(classifier), num_tabs=1)
if classifier not in results.keys():
results[classifier] = {}
if classifier == "rnn":
# clf = get_rnn_model(X)
continue
else:
clf = methods[classifier]
lst = []
if glbs.SELECTION:
lst.append(("feture", glbs.FEATURE_MODEL[0]))
lst.append(("select", glbs.FEATURE_MODEL[1]))
lst.append(("classifier", clf))
clf = Pipeline(lst)
A = clf.fit(X, y)
print(A)
else:
clf = make_pipeline(glbs.FEATURE_MODEL[0], clf)
if glbs.MULTIPROCESSING:
n_jobs = -1
else:
n_jobs = None
for i in range(num_iteration):
# Shuffle the inner order of the posts within each fold
Xy = list(zip(X, y))
splited = []
len_l = len(Xy)
for j in range(k_fold):
start = int(j * len_l / k_fold)
end = int((j + 1) * len_l / k_fold)
splited.append(Xy[start:end])
Xy = []
for fold in splited:
random.Random(num_iteration).shuffle(fold)
Xy += fold
X, y = zip(*Xy)
print_message("iteration " + str(i + 1) + "/" + str(num_iteration),
2)
scores = cross_validate(clf,
X,
y,
cv=k_fold,
scoring=glbs.MEASURE,
n_jobs=n_jobs)
for measure in glbs.MEASURE:
if measure in results[classifier].keys():
results[classifier][measure] += list(scores["test_" +
measure])
else:
results[classifier][measure] = list(scores["test_" +
measure])
del clf
return results
