def modif_submission(path_sub="best_sub.csv"):
"""
"""
arr = np.loadtxt(path_sub, skiprows=1, delimiter=",", usecols=range(1, 10))
pdb.set_trace()
sub = np.where(arr > 0.5, arr + 0.0001, arr - 0.0001)
sub = np.where(sub < 0, 0, sub)
sub = np.where(sub > 1, 1, sub)
make_submission(sub)
def main(data_folder="./data", path_submission="sub.csv"):
"""
"""
# pdb.set_trace()
# Load train, test and sample submission
# print 'Loading data...'
# X_train, y_train, X_valid, y_valid, X_test = load_train_valid_test(folder=data_folder)
# y_train = y_train.astype(np.int32)
# y_valid = y_valid.astype(np.int32)
#
# #Data normalization
# print 'Normalizing data...'
# X_train, X_valid, X_test = normalize(X_train, X_valid, X_test,
# normalizer='StandardScaler')
# #Feature engineering
# print 'Feature engineering...'
## X_train, X_test = feat_eng(X_train, X_test)
##
## #Applying classifiers...
# print 'Classifying...'
# num_features = X_train.shape[1]
# num_classes = len(np.unique(y_train))
# clf = Clf_nolearn_simple(num_features, num_classes)
# clf = Clf_nolearn_2_levels(num_features, num_classes)
# clf = Clf_nolearn_simple_play(num_features, num_classes)
# clf = Clf_xgboost_simple(num_classes)
# clf = Clf_xgboost_2_levels(num_classes)
# clf = Clf_xgboost_split(num_classes)
# clf = Clf_rf_simple()
# clf = Clf_clust_simple()
###
# clf.process(X_train, y_train, X_valid, y_valid, X_test,
# validating=True, testing=True, file_name=None, verbose=1)
####
# print 'Ensembling...'
ens = Ens_log_reg()
#### ens = Ens_opt_cal()
y_pred = ens.process()
######
make_submission(y_pred)
