def build_rf_features():
X, y, X_holdout, ids = prepare_data("./data/", drop_categorical=False)
X1, X2 = load_extra_features()
X = np.hstack((X, X1))
X_holdout = np.hstack((X_holdout, X2))
X, X_test, y, y_test = train_test_split(X, y, test_size=0.2)
t0 = time()
for max_depth in range(1, 15):
print "max depth {}".format(max_depth)
rf_clf = RandomForestClassifier(n_estimators=200, max_depth=max_depth, criterion="entropy", n_jobs=-1)
rf_clf.fit(X, y)
print "Done in %0.3fs" % (time() - t0)
print log_loss(y_test, rf_clf.predict_proba(X_test))
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
