def build_submission(model, test_data_path, baseline_path):
print("> Building kaggle submission...")
df_test = load_data(test_data_path)
df_test = transform_data(df_test)
X_test, _ = extract_features(df_test)
df_baseline = load_data(baseline_path)
# print(df_baseline)
# TODO: for NaNs, we just use the "baseline" predictor?
y_predicted = model.predict(X_test)
print(y_predicted)
print("----------------")
def load_and_save():
qq_data = load_data("QQ", "pre_data", "cn", "data/QQ/NewsQQ.txt",
"data/QQ/LabelQQ.txt")
qq_data.load_dataset()
qq_data.save_dataset()
sina_data = load_data("Sina", "pre_data", "cn", "data/Sina/NewsSina.txt",
"data/Sina/LabelSina.txt")
sina_data.load_dataset()
sina_data.save_dataset()
reddit_data = load_data("Reddit", "pre_data", "en", "data/Reddit", "")
reddit_data.load_dataset()
reddit_data.save_dataset()
def predict_for_csv(model, test_data_path):
"""
:return:
"""
print("> Predicting for csv file...")
df_test = load_data(test_data_path)
df_test = preprocess_data(df_test)
X_test, _ = extract_features(df_test)
print("Predicting set: %s" % str(X_test.shape))
print("----------------")
return predict_for_items(model, X_test)
def main():
paths = setup_paths()
raw_data_path = paths.get("raw_data_path") + "train.csv"
# get raw data, from /datasets/
df = load_data(raw_data_path)
# deal with cleaning/transforming raw data into the correct types
df = preprocess_data(df)
# building the relevant features set
X, y, features = extract_features(df)
X_train, X_eval, y_train, y_eval = split_train_test(X, y)
model = train_model(X_train, y_train, features)
evaluate_model(model, X_eval, y_eval)
def main():
paths = setup_paths()
raw_data_path = paths.get("raw_data_path") + "train.csv"
df = load_data(raw_data_path)
explore_data(df)
