random_forest.py

from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy
from scipy import stats
from sklearn.grid_search import RandomizedSearchCV
from sklearn.grid_search import GridSearchCV
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.ensemble import RandomForestClassifier

data = numpy.loadtxt("Data/data.csv", delimiter=",")

X = data[:,0:8]
Y = data[:,8]

print X

random_state = numpy.random.RandomState(0)
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=.2,random_state=42)

n_feat = X_train.shape[1]
n_targets = y_train.max() + 1

forest = RandomForestClassifier(n_estimators=100)

rs = GridSearchCV(forest, param_grid={
    'n_estimators': [100,200],
    'criterion':["gini","entropy"],
    'max_features':[None,"auto","log2"],
    'max_depth':[20,None]},verbose=2,n_jobs=2)

rs.fit(X_train, y_train)

from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

expected = y_test
predicted = rs.predict(X_test)

print("Classification report for classifier %s:\n%s\n" % (
    forest, classification_report(expected, predicted)))
print("Confusion matrix:\n%s" % confusion_matrix(expected, predicted))

print rs.best_params_