def estimate():
from loadData import loadSets
from helper import splitDataset, separateTargetFromTrain
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import math
best_rmsle = 2
best_i = 0
trainingSet, testingSet = loadSets()
testingSet = None
trainingData, testingData = splitDataset(trainingSet, 0.6)
testingData, validationData = splitDataset(testingData, 0.5)
trainingSet = None
trainingTarget, trainingFeatures = separateTargetFromTrain(trainingData)
testingTarget, testingFeatures = separateTargetFromTrain(testingData)
validationTarget, validationFeatures = separateTargetFromTrain(validationData)
testingTarget = testingTarget.values
validationTarget = validationTarget.values
trainingData = None
testingData = None
validationData = None
for i in range(20, 50, 1):
rf = RandomForestClassifier(n_estimators = i, n_jobs = -1)
rf.fit(trainingFeatures, trainingTarget)
predictions = rf.predict(testingFeatures)
rf = None
cost = pow(np.log(predictions + 1) - np.log(testingTarget + 1), 2)
rmsle = math.sqrt(np.mean(cost))
print i, "components: ", rmsle
if rmsle < best_rmsle:
best_rmsle = rmsle
best_i = i
print "Best: ", best_i, " components with rmsle: ", best_rmsle
rf = RandomForestClassifier(n_estimators = best_i, n_jobs = -1)
rf.fit(trainingFeatures, trainingTarget)
predictions = rf.predict(validationFeatures)
rf = None
cost = pow(np.log(predictions + 1) - np.log(validationTarget + 1), 2)
rmsle = math.sqrt(np.mean(cost))
print "Model cost: ", rmsle
def estimate():
from loadData import loadSets
from helper import splitDataset, separateTargetFromTrain
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import math
best_rmsle = 2
best_i = 0
trainingSet, testingSet = loadSets()
testingSet = None
trainingData, testingData = splitDataset(trainingSet, 0.6)
testingData, validationData = splitDataset(testingData, 0.5)
trainingSet = None
trainingTarget, trainingFeatures = separateTargetFromTrain(trainingData)
testingTarget, testingFeatures = separateTargetFromTrain(testingData)
validationTarget, validationFeatures = separateTargetFromTrain(validationData)
testingTarget = testingTarget.values
validationTarget = validationTarget.values
trainingData = None
testingData = None
validationData = None
for i in range(2000, 3001, 1000):
model = ExtraTreesRegressor(n_estimators = i, n_jobs = -1)
model.fit(trainingFeatures, trainingTarget)
predictions = model.predict(testingFeatures)
cost = pow(np.log(predictions + 1) - np.log(testingTarget + 1), 2)
rmsle = math.sqrt(np.mean(cost))
print i, " estimators: ", rmsle
if rmsle < best_rmsle:
best_rmsle = rmsle
best_i = i
print "Best: ", best_i, " estimators with rmsle: ", best_rmsle
model = ExtraTreesRegressor(n_estimators = best_i, n_jobs = -1)
model.fit(trainingFeatures, trainingTarget)
predictions = model.predict(validationFeatures)
cost = pow(np.log(predictions + 1) - np.log(validationTarget + 1), 2)
rmsle = math.sqrt(np.mean(cost))
print "Final model cost: ", rmsle
