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from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn import cross_validation
import pandas as pd
import logging
import time
import datetime
import numpy as np
def log_info(message):
ts = time.time() + " " + datetime.datetime
.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S'))
def init_logging(log_file_path):
logging.basicConfig(format='%(message)s', level=logging.INFO, filename=log_file_path)
def xval(classifier, train_instances, judgements):
log_info('Crossvalidation started... ')
cv = cross_validation.StratifiedKFold(np.array(judgements), n_folds=5)
avg_quality = 0.0
for train_index, test_index in cv:
train_cv, test_cv = train_instances[train_index], train_instances[test_index]
train_judgements_cv, test_judgements_cv = judgements[train_index], judgements[test_index]
decisions_cv =, train_judgements_cv).predict(test_cv)
quality = accuracy_score(decisions_cv, test_judgements_cv)
avg_quality += quality
log_info('Quality of split... ' + str(quality))
quality = avg_quality/len(cv)
log_info('Estimated quality of model... ' + str(quality))
def main():
log_info('============== \nReading training data... ')
train_data = pd.read_csv('data/train.csv', header=0).values
#the first column of the training set will be the judgements
judgements = np.array([str(int (x[0])) for x in train_data])
train_instances = np.array([x[1:] for x in train_data])
classifier = RandomForestClassifier(n_estimators=100)
log_info('Cross-validation... ')
quality = xval(classifier, train_instances, judgements)
log_info('Building model... '), judgements)
log_info('Reading testing data... ')
test_data = pd.read_csv('data/test.csv', header=0).values
decisions = classifier.predict(test_data)
log_info('Output results... ')
decisions_formatted = np.append(np.array('Label'), decisions)
ids = ['ImageId'] + list(range(1, len(decisions_formatted)))
output = np.column_stack((ids, decisions_formatted))
pd.DataFrame(output).to_csv('data/results.csv', header=False, index=False)
if __name__=='__main__':
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