import ReadFiles
import pandas as pd
import utils
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
# read the data that we need from the original file
trainSet, trainSetFeatures, trainSetLabels, testSet, testSetFeatures, testSetLabels = ReadFiles.readFiles(
)
#train the model
# random_forest_regressor = RandomForestRegressor(n_estimators=15, max_depth=6, random_state=0)
clf = GradientBoostingRegressor(warm_start=True,
n_estimators=45,
max_depth=6,
random_state=25)
clf.fit(trainSetFeatures, trainSetLabels)
#make the prediction on the test set
predictedLabels = clf.predict(testSetFeatures)
#output the prediction
id_test = testSet['id']
pd.DataFrame({
"id": id_test,
"relevance": predictedLabels
}).to_csv('IOFolder/XGBoosting_Results.csv', index=False)
print "RMSE :\t", utils.getRMSE(testSetLabels, predictedLabels)
print "MAE :\t", utils.getMAE(testSetLabels, predictedLabels)
import ReadFiles
import numpy as np
import pandas as pd
import utils
# read the data that we need from the original file
trainSet, trainSetFeatures, trainSetLabels, testSet, testSetFeatures, testSetLabels = ReadFiles.readFiles()
# make the prediction on the test set by random guess
predictedLabels = []
for index in range(testSet.shape[0]):
predictedLabels.append(utils.getRandFloat(1, 3))
predictedLabels = np.array(predictedLabels)
#output the prediction
id_test = testSet['id']
pd.DataFrame({"id": id_test, "relevance": predictedLabels}).to_csv('IOFolder/random_guess_results.csv', index=False)
print "RMSE :\t", utils.getRMSE(testSetLabels, predictedLabels)
print "MAE :\t", utils.getMAE(testSetLabels, predictedLabels)
