#read in the training file

header, csv = csv_io.read_csv("transactions.csv")

header = np.array(header[2:])

weight = np.array([x[0] for x in csv])

target = [x[1] for x in csv]

days_ago = [x[2] for x in csv]

#balance = [x[9] for x in csv]

train = [x[2:] for x in csv]

print('fitting the model')

rf = RandomForestRegressor(oob_score=True, n_estimators=100, max_features="auto")

#rf = RandomForestRegressor(n_estimators=100)

scores = cross_validation.cross_val_score(rf, train, target, cv=3)

print scores

print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

#rf = AdaBoostRegressor()

#scores = cross_validation.cross_val_score(rf, train, target, cv=3)

#print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

#rf = GradientBoostingRegressor()

#scores = cross_validation.cross_val_score(rf, train, target, cv=3)

#print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

#rf = BaggingRegressor()

#scores = cross_validation.cross_val_score(rf, train, target, cv=3)

#print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

#return

#rf.fit(train, target, weight)

rf.fit(train, target)

print "oob_score: %f" % rf.oob_score_

#print target

#print list(rf.oob_prediction_)

order = np.argsort(rf.feature_importances_)[::-1]

print order[0:19]

print header[order[0:19]]

print rf.feature_importances_[order[0:19]]

print np.sum(rf.feature_importances_[order[0:19]])

print np.sum(rf.feature_importances_)

return

print np.array(np.array(range(len(train))),rf.feature_importances_)

print list(np.sort(rf.feature_importances_))

#x = np.array([target, list(rf.oob_prediction_)]).T

#print x

print rf.oob_score_

#plt.plot(days_ago, balance, 'g-')

#plt.show()

plt.plot(days_ago, target, 'g-', days_ago, list(rf.oob_prediction_), 'r-')

plt.show()

#plt.plot(target, list(rf.oob_prediction_), 'r.')