"""
Code shared between all models for Otto competition.
"""
import gc
import numpy as np
import os
import pandas as pd
from sklearn.calibration import CalibratedClassifierCV
from sklearn.cross_validation import StratifiedKFold, StratifiedShuffleSplit
from sklearn.metrics import log_loss
import consts
def load_data(path_train=consts.DATA_TRAIN_PATH, path_test=consts.DATA_TEST_PATH):
train = pd.read_csv(path_train)
train_labels = [int(v[-1])-1 for v in train.target.values]
train_ids = train.id.values
train = train.drop('id', axis=1)
train = train.drop('target', axis=1)
test = pd.read_csv(path_test)
test_ids = test.id.values
test = test.drop('id', axis=1)
return np.array(train, dtype=float), np.array(train_labels), np.array(test, dtype=float),\
np.array(train_ids), np.array(test_ids)
def make_blender_cv(classifier, x, y, calibrate=False):
skf = StratifiedKFold(y, n_folds=5, random_state=23)
scores, predictions = [], None
for train_index, test_index in skf:
if calibrate:
# Make training and calibration
calibrated_classifier = CalibratedClassifierCV(classifier, method='isotonic', cv=get_cv(y[train_index]))
fitted_classifier = calibrated_classifier.fit(x[train_index, :], y[train_index])
else:
fitted_classifier = classifier.fit(x[train_index, :], y[train_index])
preds = fitted_classifier.predict_proba(x[test_index, :])
# Free memory
calibrated_classifier, fitted_classifier = None, None
gc.collect()
scores.append(log_loss(y[test_index], preds))
predictions = np.append(predictions, preds, axis=0) if predictions is not None else preds
return scores, predictions
def write_blender_data(path, file_name, predictions):
file_path = os.path.join(path, file_name)
np.savetxt(file_path, predictions, delimiter=',', fmt='%.5f')
def save_submission(path_sample_submission, output_file_path, predictions):
sample = pd.read_csv(path_sample_submission)
submission = pd.DataFrame(predictions, index=sample.id.values, columns=sample.columns[1:])
submission.to_csv(output_file_path, index_label='id')
def stratified_split(x, y, test_size=0.2):
strat_shuffled_split = StratifiedShuffleSplit(y, n_iter=1, test_size=test_size, random_state=23)
train_index, valid_index = [s for s in strat_shuffled_split][0]
x_train, y_train, x_valid, y_valid = x[train_index, :], y[train_index], x[valid_index, :], y[valid_index]
return x_train, y_train, x_valid, y_valid
def hold_out_evaluation(classifier, x, y, test_size=0.2, calibrate=False):
x_train, y_train, x_valid, y_valid = stratified_split(x, y, test_size)
# Train
if calibrate:
# Make training and calibration
calibrated_classifier = CalibratedClassifierCV(classifier, method='isotonic', cv=get_cv(y_train))
fitted_classifier = calibrated_classifier.fit(x_train, y_train)
else:
fitted_classifier = classifier.fit(x_train, y_train)
# Evaluate
score = log_loss(y_valid, fitted_classifier.predict_proba(x_valid))
return score
def get_prediction_files():
return ['model_%s.csv' % f for f in consts.PREDICTION_FILES]
def get_cv(y, n_folds=5):
return StratifiedKFold(y, n_folds=n_folds, random_state=23)
