def wrapper_kaggle(epochs=40, validate_ratio=0.1, save_prob=True):
train_x_1, test_x_1 = read_all_predict_score()
train_x_2, train_y, test_x_2 = read_aggregated_vectors()
train_x_3, train_y, test_x_3 = read_aggregated_vectors(google=False)
old_train_x = train_x_1 # np.concatenate((train_x_1, train_x_2, train_x_3), axis=1)
test_x = test_x_1 # np.concatenate((test_x_1, test_x_2, test_x_3), axis=1)
old_train_y = np.asarray(train_y)
# split train validate data
sss_indices = StratifiedShuffleSplit(y=old_train_y, n_iter=1, test_size=validate_ratio)
for indices in sss_indices:
train_index, test_index = indices
train_x = old_train_x[train_index]
validate_x = old_train_x[test_index]
train_y = old_train_y[train_index]
validate_y = old_train_y[test_index]
# add validation set for training
# train_x = np.concatenate((train_x, validate_x))
# train_y = np.concatenate((train_y, validate_y))
# get dataset info
dim = train_x[0].shape[0]
n_out = len(np.unique(validate_y))
datasets = (train_x, train_y, validate_x, validate_y, test_x)
n_layers = 1
print "input dimension is %d, output dimension is %d" % (dim, n_out)
return_val = train_dropout_net(
datasets=datasets,
use_bias=True,
n_epochs=epochs,
dim=dim,
lr_rate=0.02,
n_out=n_out,
dropout=True,
dropout_rates=[0.7],
n_hidden=[100],
activations=[tanh] * n_layers,
batch_size=50,
update_rule='adagrad',
no_test_y=True,
save_prob=save_prob
)
if not save_prob:
save_csv(return_val)
else:
train_prob, validate_prob, test_prob = return_val
saved_train_prob = np.zeros((old_train_x.shape[0], n_out))
saved_train_prob[train_index] = train_prob
saved_train_prob[test_index] = validate_prob
save_path = "D:/data/nlpdata/pickled_data/" + SST_KAGGLE + "_prob.pkl"
print "saving probability feature to %s" % save_path
f = open(Path(save_path), "wb")
pkl.dump((saved_train_prob, test_prob), f, -1)
f.close()
def wrapper_kaggle(valid_portion=0.1):
train_x, test_x = read_all_predict_score()
_, train_y, _ = read_sst_kaggle_pickle()
train_y = np.asarray(train_y)
# train_x = train_x.reshape(train_x.shape[0], 18, 5)
# test_x = test_x.reshape(test_x.shape[0], 18, 5)
train_x, validate_x, train_y, validate_y = train_test_split(
train_x, train_y, test_size=valid_portion, stratify=train_y
)
dim = train_x[0].shape
print "input dimension is", dim
img_size = (18, 5)
n_out = len(np.unique(validate_y))
datasets = (train_x, train_y, validate_x, validate_y, test_x)
best_prediction = train_lecun_net(
img_size=img_size,
datasets=datasets,
filter_size=(7, 2),
pool_size=(2, 1),
n_epochs=10,
lr_rate=0.05,
n_out=n_out,
dropout_rate=0.5,
n_hidden=500,
nkerns=10,
activation=leaky_relu,
batch_size=100,
update_rule="adagrad",
user_bias=True,
no_test_y=True,
)
import csv
save_path = Path("C:/Users/Song/Course/571/hw3/kaggle_result.csv")
with open(save_path, "wb") as f:
writer = csv.writer(f, delimiter=",")
writer.writerow(["PhraseId", "Sentiment"])
phrase_ids = np.arange(156061, 222353)
for phrase_id, sentiment in zip(phrase_ids, best_prediction):
writer.writerow([phrase_id, sentiment])
