# Note: Kaggle only runs Python 3, not Python 2

# We'll use the pandas library to read CSV files into dataframes
import pandas as pd
from sklearn import ensemble, tree

# The competition datafiles are in the directory ../input
# List the files we have available to work with
#print("> ls ../input")
#from subprocess import check_output
#print(check_output(["ls", "../input"]).decode("utf8"))

# Read competition data files:
train = pd.read_csv("../input/training.csv")
test  = pd.read_csv("../input/test.csv")
sub = pd.read_csv("../input/sample_submission.csv")
#variables = list(set([i for i in test]) - set(['SPDhits']))
variables = list(set([i for i in test]) - set([]))

print(variables)

base = tree.DecisionTreeClassifier(criterion='gini',
                                   #max_depth=maxdpt(),
                                   max_features=.48,
                                   #max_leaf_nodes=None, 
                                   min_samples_leaf=82,
                                   splitter= 'best')

clf = ensemble.AdaBoostClassifier(n_estimators=13, learning_rate=0.098643,base_estimator=base)
clf.fit(train[variables], train['signal'])

sub['prediction'] = clf.predict_proba(test[variables])[:, 1]
sub.to_csv('AdaBoost.csv', index=False, sep=',')

# Write summaries of the train and test sets to the log
#print('\nSummary of train dataset:\n')
#print(train.describe())
#print('\nSummary of test dataset:\n')
#print(test.describe())