def main():
df, df_store = load_data(debug=False)
feat_matrix, features_x, feature_y = extract_features(df, df_store)
quick_score(feat_matrix.loc[~(feat_matrix['Type'] == 'test')], features_x,
feature_y)
ts = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y-%m-%d %H:%M:%S')
print(ts)
def main():
df, df_store = load_data(debug=False)
feat_matrix, features_x, feature_y = extract_features(df, df_store)
df_test = feat_matrix.loc[feat_matrix['Type'] == 'test'].copy()
results = []
for top in TRIALS:
predict = forecast(df_test, features_x, ENSEMBLE, top)
save_result(df_test, predict)
summit(sub_msg(top))
result = get_kaggle_score(sub_msg(top))
results.append((top, result))
print(results)
def main():
df, df_store = load_data(debug=False)
feat_matrix, features_x, feature_y = extract_features(df, df_store)
tune_random_forest(feat_matrix.loc[~(feat_matrix['Type'] == 'test')],
features_x, feature_y)
def main():
df, df_store = load_data(debug=False)
feat_matrix, features_x, feature_y = extract_features(df, df_store)
train_ensemble(feat_matrix.loc[~(feat_matrix['Type'] == 'test')],
features_x, feature_y)