def main():
args = get_args()
check_args(args)
if args.mode.lower() == "train":
# Load the training data.
X, y = load_data(args.data)
# Create the model.
# TODO: Add other algorithms as necessary.
if args.algorithm.lower() == 'useless':
model = models.Useless()
elif args.algorithm.lower() == 'sumoffeatures':
model = models.SumOfFeatures()
elif args.algorithm.lower() == 'perceptron':
model = models.Perceptron()
else:
raise Exception('The model given by --model is not yet supported.')
#########################################QUESTION!
I = args.online_training_iterations
n = args.online_learning_rate
# Train the model.
if args.algorithm.lower() == 'perceptron':
model.fit(X, y,n,I)
else:
model.fit(X, y)
# Save the model.
try:
with open(args.model_file, 'wb') as f:
pickle.dump(model, f)
except IOError:
raise Exception("Exception while writing to the model file.")
except pickle.PickleError:
raise Exception("Exception while dumping model pickle.")
elif args.mode.lower() == "test":
# Load the test data.
X, y = load_data(args.data)
# Load the model.
try:
with open(args.model_file, 'rb') as f:
model = pickle.load(f)
except IOError:
raise Exception("Exception while reading the model file.")
except pickle.PickleError:
raise Exception("Exception while loading model pickle.")
# Compute and save the predictions.
y_hat = model.predict(X)
invalid_label_mask = (y_hat != 0) & (y_hat != 1)
if any(invalid_label_mask):
raise Exception('All predictions must be 0 or 1, but found other predictions.')
np.savetxt(args.predictions_file, y_hat, fmt='%d')
else:
raise Exception("Mode given by --mode is unrecognized.")
def main():
args = get_args()
check_args(args)
if args.mode.lower() == "train":
# Load the training data.
X, y = load_data(args.data)
# Create and train the model.
if args.algorithm.lower() == 'useless':
model = models.Useless()
model.fit(X, y)
elif args.algorithm.lower() == 'lambda_means':
model = models.LambdaMeans()
model.fit(X,
y,
lambda0=args.cluster_lambda,
iterations=args.clustering_training_iterations)
elif args.algorithm.lower() == 'stochastic_k_means':
model = models.StochasticKMeans()
model.fit(X,
y,
num_clusters=args.number_of_clusters,
iterations=args.clustering_training_iterations)
else:
raise Exception('The model given by --model is not yet supported.')
# Save the model.
try:
with open(args.model_file, 'wb') as f:
pickle.dump(model, f)
except IOError:
raise Exception("Exception while writing to the model file.")
except pickle.PickleError:
raise Exception("Exception while dumping model pickle.")
elif args.mode.lower() == "test":
# Load the test data.
X, y = load_data(args.data)
# Load the model.
try:
with open(args.model_file, 'rb') as f:
model = pickle.load(f)
except IOError:
raise Exception("Exception while reading the model file.")
except pickle.PickleError:
raise Exception("Exception while loading model pickle.")
# Compute and save the predictions.
y_hat = model.predict(X)
np.savetxt(args.predictions_file, y_hat, fmt='%d')
else:
raise Exception("Mode given by --mode is unrecognized.")
def main():
args = get_args()
check_args(args)
if args.mode.lower() == "train":
# Load the training data.
X, y = load_data(args.data)
if args.algorithm.lower() == 'useless':
model = models.Useless()
elif args.algorithm.lower() == 'logisticregression':
model = models.LogisticRegression(args.online_learning_rate,
args.gd_iterations, args.num_features_to_select)
else:
raise Exception('The model given by --model is not yet supported.')
# Train the model.
model.fit(X, y)
# Save the model.
try:
with open(args.model_file, 'wb') as f:
pickle.dump(model, f)
except IOError:
raise Exception("Exception while writing to the model file.")
except pickle.PickleError:
raise Exception("Exception while dumping model pickle.")
elif args.mode.lower() == "test":
# Load the test data.
X, y = load_data(args.data)
# Load the model.
try:
with open(args.model_file, 'rb') as f:
model = pickle.load(f)
except IOError:
raise Exception("Exception while reading the model file.")
except pickle.PickleError:
raise Exception("Exception while loading model pickle.")
# Compute and save the predictions.
y_hat = model.predict(X)
invalid_label_mask = (y_hat != 0) & (y_hat != 1)
if any(invalid_label_mask):
raise Exception('All predictions must be 0 or 1, but found other predictions.')
np.savetxt(args.predictions_file, y_hat, fmt='%d')
else:
raise Exception("Mode given by --mode is unrecognized.")
def main():
args = get_args()
check_args(args)
if args.mode.lower() == "train":
# Load the training data.
X, y = load_data(args.data)
# Create the model.
# TODO: Add other algorithms as necessary.
if args.algorithm.lower() == 'sumoffeatures':
model = models.SumOfFeatures()
elif args.algorithm.lower() == 'perceptron':
model = models.Perceptron()
elif args.algorithm.lower() == 'useless':
model = models.Useless()
elif args.algorithm.lower() == 'logisticregression':
model = models.LogisticRegression()
else:
raise Exception('The model given by --model is not yet supported.')
# Select features.
num_orig_features = X.shape[1]
index_array = np.empty(1)
if args.num_features_to_select > 0:
index_array = select_features(X, y, args.num_features_to_select)
index_array = np.sort(index_array)
X_selected = X[:, index_array[0]]
for i in range(index_array.shape[0]):
if i != 0:
X_selected = hstack([X_selected, X[:,index_array[i]]])
X = X_selected
# Train the model.
if args.algorithm.lower() == 'perceptron':
model.fit(X ,y, args.online_learning_rate, args.online_training_iterations)
elif args.algorithm.lower() == 'logisticregression':
model.fit(X, y, args.online_learning_rate, args.gd_iterations, num_orig_features, index_array)
else:
model.fit(X, y)
# Save the model.
try:
with open(args.model_file, 'wb') as f:
pickle.dump(model, f)
except IOError:
raise Exception("Exception while writing to the model file.")
except pickle.PickleError:
raise Exception("Exception while dumping model pickle.")
elif args.mode.lower() == "test":
# Load the test data.
X, y = load_data(args.data)
# Load the model.
try:
with open(args.model_file, 'rb') as f:
model = pickle.load(f)
except IOError:
raise Exception("Exception while reading the model file.")
except pickle.PickleError:
raise Exception("Exception while loading model pickle.")
# Compute and save the predictions.
y_hat = model.predict(X)
invalid_label_mask = (y_hat != 0) & (y_hat != 1)
if any(invalid_label_mask):
raise Exception('All predictions must be 0 or 1, but found other predictions.')
np.savetxt(args.predictions_file, y_hat, fmt='%d')
else:
raise Exception("Mode given by --mode is unrecognized.")
def main():
args = get_args()
check_args(args)
if args.mode.lower() == "train":
# Load the training data.
X, y = load_data(args.data)
# print(type(args.data))
# Create the model.
# TODO: Add other algorithms as necessary.
models.Perceptron(args.online_learning_rate, args.online_training_iterations)
models.Logistic(args.online_learning_rate, args.online_training_iterations)
models.nb(args.independent_mode, args.training_iterations, args.latent_states)
# Create model for each algorithm
if args.algorithm.lower() == 'useless':
model = models.Useless()
elif args.algorithm.lower() == 'perceptron':
model = models.Perceptron(args.online_learning_rate,args.online_training_iterations)
elif args.algorithm.lower() == 'logistic':
model = models.Logistic(args.online_learning_rate,args.online_training_iterations)
elif args.algorithm.lower() == 'pegasos':
model = models.Pegasos(args.online_learning_rate, args.online_training_iterations, args.pegasos_lambda)
elif args.algorithm.lower() == 'nb':
model = models.nb(args.independent_mode, args.training_iterations, args.latent_states)
else:
raise Exception('The model given by --model is not yet supported.')
# Train the model.
model.fit(X, y)
# Save the model.
try:
with open(args.model_file, 'wb') as f:
pickle.dump(model, f)
except IOError:
raise Exception("Exception while writing to the model file.")
except pickle.PickleError:
raise Exception("Exception while dumping model pickle.")
elif args.mode.lower() == "test":
# Load the test data.
X, y = load_data(args.data)
# Load the model.
try:
with open(args.model_file, 'rb') as f:
model = pickle.load(f)
except IOError:
raise Exception("Exception while reading the model file.")
except pickle.PickleError:
raise Exception("Exception while loading model pickle.")
# Compute and save the predictions.
y_hat = model.predict(X)
# invalid_label_mask = (y_hat != 0) & (y_hat != 1)
# if any(invalid_label_mask):
# raise Exception('All predictions must be 0 or 1, but found other predictions.')
if np.issubdtype(type(y[0]), np.dtype(int)):
np.savetxt(args.predictions_file, y_hat, fmt='%d')
else:
np.savetxt(args.predictions_file, y_hat, fmt='%s')
else:
raise Exception("Mode given by --mode is unrecognized.")
def main():
args = get_args()
check_args(args)
if args.mode.lower() == "train":
# Load the training data.
X, y = load_data(args.data)
# Create the model.
# TODO: Add other algorithms as necessary.
if args.algorithm.lower() == 'adaboost':
model = models.Adaboost(args.num_boosting_iterations)
model.fit(X, y)
elif args.algorithm.lower() == 'logisticregression':
model = models.LogisticRegression(args.online_learning_rate,
args.num_features_to_select,
args.gd_iterations)
model.fit(X, y)
elif args.algorithm.lower() == 'sumoffeatures':
model = models.SumOfFeatures()
model.fit(X, y)
elif args.algorithm.lower() == 'perceptron':
model = models.Perceptron(args.online_learning_rate,
args.online_training_iterations)
model.fit(X, y)
elif args.algorithm.lower() == 'lambda_means':
model = models.LambdaMeans()
model.fit(X,
y,
lambda0=args.cluster_lambda,
iterations=args.clustering_training_iterations)
elif args.algorithm.lower() == 'stochastic_k_means':
model = models.StochasticKMeans()
model.fit(X,
y,
num_clusters=args.number_of_clusters,
iterations=args.clustering_training_iterations)
elif args.algorithm.lower() == 'useless':
model = models.Useless()
model.fit(X, y)
else:
raise Exception('The model given by --model is not yet supported.')
# Save the model.
try:
with open(args.model_file, 'wb') as f:
pickle.dump(model, f)
except IOError:
raise Exception("Exception while writing to the model file.")
except pickle.PickleError:
raise Exception("Exception while dumping model pickle.")
elif args.mode.lower() == "test":
# Load the test data.
X, y = load_data(args.data)
# Load the model.
try:
with open(args.model_file, 'rb') as f:
model = pickle.load(f)
except IOError:
raise Exception("Exception while reading the model file.")
except pickle.PickleError:
raise Exception("Exception while loading model pickle.")
# Compute and save the predictions.
y_hat = model.predict(X)
invalid_label_mask = (y_hat != 0) & (y_hat != 1)
if any(invalid_label_mask):
raise Exception(
'All predictions must be 0 or 1, but found other predictions.')
np.savetxt(args.predictions_file, y_hat, fmt='%d')
else:
raise Exception("Mode given by --mode is unrecognized.")