def main():
dfTrain = readdata.read_clean_data(readdata.TRAINFILEPATH)
X = dfTrain['text'].to_numpy()
y = dfTrain['label'].to_numpy()
if sys.argv[1] == "cv":
X = CV(X) # train shape: (17973, 10000)
X, y = getRemovedVals(X=X, Y=y, Ftype="CV_Train")
elif sys.argv[1] == 'tfidf':
X = TFIDF(X) # shape: (17973, 10000)
X, y = getRemovedVals(X=X, Y=y, Ftype="TFIDF_Train")
elif sys.argv[1] == 'word2vec':
X = word2vec(X)
X, y = getRemovedVals(X=X, Y=y, Ftype="W2V_Train")
else:
print("Error")
return
if int(sys.argv[2]) == 0: # actual run
# after k-fold and run support vector machine
kf = KFold(n_splits=3, random_state=1)
svm = SVC(C=0.25, kernel='linear')
acc_list = []
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
svm.fit(X_train, y_train)
print("----Start Evaluating----")
acc = svm.score(X_test, y_test)
acc_list.append(acc)
print("Testing Accuracy:", acc)
print("Mean testing accuracy:", sum(acc_list) / len(acc_list))
y_pred = svm.predict(X_test)
# Store y_pred vector
save_y(sys.argv[1], "svm_y_pred", y_pred)
else: # grid search
print("Performing Grid Search on SVM...")
svm = SVC()
parameters = {'kernel': ('linear', 'rbf'), 'C': (0.25, 0.5, 0.75)}
grid = GridSearchCV(estimator=svm,
param_grid=parameters,
n_jobs=-1,
cv=3,
verbose=1)
grid_result = grid.fit(X, y)
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
def main():
dfTrain = readdata.read_clean_data(readdata.TRAINFILEPATH)
X = dfTrain['text'].to_numpy()
y = dfTrain['label'].to_numpy()
if sys.argv[1] == "cv":
X = CV(X)
X, y = getRemovedVals(X=X, Y=y, Ftype="CV_Train")
look_back = 1
elif sys.argv[1] == 'tfidf':
X = TFIDF(X)
X, y = getRemovedVals(X=X, Y=y, Ftype="TFIDF_Train")
look_back = 1
elif sys.argv[1] == 'word2vec':
# if you were to run real recurrence, use lstm=True
# And un-comment the below lookback
X = word2vec(X, lstm=False)
X, y = getRemovedVals(X=X, Y=y, Ftype="W2V_Train")
look_back = 1
# look_back = X.shape[1] # un-comment to be used for real recurrence
else:
print("Error")
return
num_samples = X.shape[0]
if look_back == 1:
# reshape input to be [samples, time steps, features]
num_features = X.shape[1]
X = np.reshape(np.array(X), (num_samples, look_back, num_features))
else:
num_features = X.shape[2]
batch_size = 256
if int(sys.argv[2]) == 0: # actual run
epochs = 25 # can change this
kf = KFold(n_splits=3, random_state=1)
acc_list = []
X_train = None # init
X_test = None # init
y_test = None #init
# Doing cross validation testing
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
model = create_model(look_back=look_back, input_nodes=num_features)
history = model.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=epochs,
batch_size=batch_size)
print("----Start Evaluating----")
_, acc = model.evaluate(X_test, y_test, verbose=1)
acc_list.append(acc)
print("Testing Accuracy:", acc)
print("Mean testing accuracy:", sum(acc_list) / len(acc_list))
loss = history.history['loss']
val_loss = history.history['val_loss']
accuracy = history.history['accuracy']
val_accuracy = history.history['val_accuracy']
graphs_nn(loss, val_loss, accuracy, val_accuracy)
y_pred = model.predict(X_test)
# Store y_pred vector
save_y(sys.argv[1], "lstm_y_pred", y_pred)
# Store y_true vector (Only one script needs this)
y_true_file = Path("./model_Ys/true/y_true_" + sys.argv[1] + ".npy")
if not y_true_file.is_file():
save_y("true", "y_true_" + sys.argv[1] + ".npy", y_test)
else: # doing grid search
epochs = 20
model = KerasClassifier(build_fn=create_model,
look_back=look_back,
input_nodes=num_features,
epochs=epochs,
batch_size=batch_size,
verbose=1)
param_grid = get_param_grid()
grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)
grid_result = grid.fit(X, y)
evaluate(grid_result)
def main():
dfTrain = readdata.read_clean_data(readdata.TRAINFILEPATH)
X = dfTrain['text'].to_numpy()
y = dfTrain['label'].to_numpy()
if sys.argv[1] == "cv":
X = CV(X) # train shape: (17973, 10000)
X, y = getRemovedVals(X=X, Y=y, Ftype="CV_Train")
elif sys.argv[1] == 'tfidf':
X = TFIDF(X) # train shape: (17973, 10000)
X, y = getRemovedVals(X=X, Y=y, Ftype="TFIDF_Train")
elif sys.argv[1] == 'word2vec':
X = word2vec(X)
X, y = getRemovedVals(X=X, Y=y, Ftype="W2V_Train")
else:
print("Error")
return
if int(sys.argv[2]) == 0: # actual run
X_train, X_test, y_train, y_test = train_test_split(X,
y,
random_state=1,
test_size=0.34)
# These are the best hyper-para from the results of grid search
max_depth = 9
min_samples_leaf = 4
min_samples_split = 5
n_estimators = 400
acc_list = []
X_train = None # init
X_test = None # init
kf = KFold(n_splits=3, random_state=1)
for train_index, test_index in kf.split(X):
# Doing cross validation testing
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
model = RandomForestClassifier(max_depth=max_depth,
min_samples_leaf=min_samples_leaf,
min_samples_split=min_samples_split,
n_estimators=n_estimators,
n_jobs=-1)
model.fit(X_train, y_train)
print("----Start Evaluating----")
acc = model.score(X_test, y_test)
acc_list.append(acc)
print("Testing Accuracy:", acc)
print("Mean testing accuracy:", sum(acc_list) / len(acc_list))
y_pred = model.predict(X_test)
# Store y_pred vector
save_y(sys.argv[1], "random_forest_y_pred", y_pred)
elif int(sys.argv[2]) == 1: # grid search
# below are the hyperparameters to be grid-searched on
n_estimators = [200, 400, 800]
max_depth = [1, 5, 9]
min_samples_leaf = [2, 4]
min_samples_split = [5, 10]
param_grid = dict(n_estimators=n_estimators,
max_depth=max_depth,
min_samples_leaf=min_samples_leaf,
min_samples_split=min_samples_split)
model = RandomForestClassifier()
grid = GridSearchCV(estimator=model,
param_grid=param_grid,
n_jobs=-1,
cv=3)
grid_result = grid.fit(X, y)
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
else:
print("Error")
return
def main():
dfTrain = readdata.read_clean_data(readdata.TRAINFILEPATH)
X = dfTrain['text'].to_numpy()
y = dfTrain['label'].to_numpy()
if sys.argv[1] == "cv":
X = CV(X) # train shape: (17973, 10000)
X, y = getRemovedVals(X=X, Y=y, Ftype="CV_Train")
elif sys.argv[1] == 'tfidf':
X = TFIDF(X) # train shape: (17973, 10000)
X, y = getRemovedVals(X=X, Y=y, Ftype="TFIDF_Train")
elif sys.argv[1] == 'word2vec':
X = word2vec(X)
X, y = getRemovedVals(X=X, Y=y, Ftype="W2V_Train")
else:
print("Error")
return
num_samples = X.shape[0]
num_features = X.shape[1]
if int(sys.argv[2]) == 0:
kf = KFold(n_splits=3, random_state=1)
model = ANN(
) #need to populate this with best hyperparameters after all Grid search
acc_list = []
X_train = None # init
X_test = None # init
for train_index, test_index in kf.split(X):
# Doing cross validation testing
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
model = ANN(input_dim=num_features)
history = model.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=EPOCHS,
batch_size=BATCH_SIZE)
print("----Start Evaluating----")
_, acc = model.evaluate(X_test, y_test, verbose=1)
acc_list.append(acc)
print("Testing Accuracy:", acc)
print("Mean testing accuracy:", sum(acc_list) / len(acc_list))
loss = history.history['loss']
val_loss = history.history['val_loss']
accuracy = history.history['acc']
val_accuracy = history.history['val_acc']
graphs_nn(loss, val_loss, accuracy, val_accuracy)
y_pred = model.predict(X_test)
# Store y_pred vector
save_y(sys.argv[1], "ann_y_pred", y_pred)
else:
model = KerasClassifier(build_fn=ANN,
input_dim=num_features,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
verbose=1,
activation="relu",
optimizer="Adam")
# grid search on ann hyperparameters
param_grid = get_param_grid()
grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)
grid_result = grid.fit(X, y)
print("Best: %f using %s" %
(grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
def main():
dfTrain = readdata.read_clean_data(readdata.TRAINFILEPATH)
X = dfTrain['text'].to_numpy()
y = dfTrain['label'].to_numpy()
if sys.argv[1] == "cv":
X = CV(X) # train shape: (17973, 10000)
X, y = getRemovedVals(X=X, Y=y, Ftype="CV_Train")
elif sys.argv[1] == 'tfidf':
X = TFIDF(X) # train shape: (17973, 10000)
X, y = getRemovedVals(X=X, Y=y, Ftype="TFIDF_Train")
elif sys.argv[1] == 'word2vec':
X = word2vec(X)
X, y = getRemovedVals(X=X, Y=y, Ftype="W2V_Train")
else:
print("Error")
return
# reshape input to be [samples, features]
num_samples = X.shape[0]
num_features = X.shape[1]
X = np.reshape(np.array(X), (num_samples, num_features))
if int(sys.argv[2]) == 0: # actual run
C = None # to be set to the best hyperpara
solver = None # to be set to the best hyperpara
kf = KFold(n_splits=3, random_state=1)
logistic = LogisticRegression(max_iter=500, C=C, solver=solver)
acc_list = []
# Doing cross validation testing
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
logistic.fit(X_train, y_train)
print("----Start Evaluating----")
acc = logistic.score(X_test, y_test)
acc_list.append(acc)
print("Testing Accuracy:", acc)
print("Mean testing accuracy:", sum(acc_list) / len(acc_list))
y_pred = logistic.predict(X_test)
# Store y_pred vector
save_y(sys.argv[1], "logreg_y_pred", y_pred)
else: # grid search
# creating space for constant C
c = np.logspace(0, 4, 10)
# various solvers - only used solvers that supported L2
solver = ['newton-cg', 'sag', 'lbfgs', 'liblinear']
param_grid = dict(C=c, solver=solver)
logistic = LogisticRegression(max_iter=500)
grid = GridSearchCV(logistic, param_grid=param_grid, cv=3, verbose=1)
grid_result = grid.fit(X, y)
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))