from extraction import prepare_data, load_extra_features

import xgboost as xgb

import numpy as np

import pandas as pd

from time import time

from sklearn import cross_validation

if __name__ == "__main__":

X, y, X_holdout, ids = prepare_data("./data/", drop_categorical=False)

X_extra, X_holdout_extra = load_extra_features()

params = {

"objective" : "binary:logistic",

"eval_metric" : "logloss",

"eta" : 0.005, # 0.01

"subsample" : 0.8,

"colsample_bytree" : 0.8,

"min_child_weight" : 1,

"max_depth" : 10

}

xg_train = xgb.DMatrix(np.hstack((X, X_extra)), label=y)

xg_test = xgb.DMatrix(np.hstack((X_holdout, X_holdout_extra)))

#xg_train = xgb.DMatrix(X, label=y)

xgb_clf = xgb.train(params, xg_train, num_boost_round=2500, verbose_eval=True, maximize=False)

y_pred = xgb_clf.predict(xg_test)# ,ntree_limit=xgb_clf.best_iteration)

#cv_scores = xgb.cv(params, xg_train, num_boost_round=100, nfold=5, metrics="logloss", seed=42, early_stopping_rounds=5)

#print cv_scores

df = pd.DataFrame({'ID': ids, 'PredictedProb': y_pred})

df.to_csv("submission_xgb_ensemble_{}.csv".format(time()), index=False)