import pandas as pd

import numpy as np

from datetime import datetime

from sklearn import ensemble

if __name__ == '__main__':

loc_train = "kaggle_hermes\\train.csv"

loc_test = "kaggle_hermes\\evaluate.csv"

loc_submission = "kaggle_hermes\\kaggle.hermes.submission.csv"

#start counter and set random seed

start = datetime.now()

np.random.seed(10)

#fetch dataframes

df_train = pd.read_csv(loc_train)

df_test = pd.read_csv(loc_test)

#shuffle train df to prevent malordered samples

df_train = df_train.reindex(np.random.permutation(df_train.index))

#get the feature columns

feature_cols = [col for col in df_train.columns if col not in ['class']]

#create a train and test set

X_train = df_train[feature_cols]

X_test = df_test[feature_cols]

#fetch the labels into 'y'

y = df_train['class']

#classifier config and fitting

clf_base = ensemble.RandomForestClassifier(n_estimators=1050,criterion="entropy",max_features=None,random_state=777,n_jobs=-1)

clf = ensemble.AdaBoostClassifier(clf_base, n_estimators=4, random_state=93)

print "\nFitting:\n", clf, "\non train set shaped:\n", X_train.shape

clf.fit(X_train,y)

#predicting and storing results

with open(loc_submission, "wb") as outfile:

print "\nPredicting on test set shaped:\n", X_test.shape, "\nWriting to:", outfile

outfile.write("Id,Class\n")

for e, val in enumerate(clf.predict_proba(X_test)):

outfile.write( "%s,%f\n" % (float(e+1),float(val[1])))

print "\nScript running time:", datetime.now()-start