try.py

print(__doc__)

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesClassifier

datos = np.loadtxt('datasetnewtest.txt', delimiter=',')
X, y = datos[:,:10], datos[:,10].astype(np.int)
# Build a classification task using 3 informative features


# Build a forest and compute the feature importances
forest = ExtraTreesClassifier(n_estimators=250,
                              random_state=0)

forest.fit(X, y)
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_],
             axis=0)
indices = np.argsort(importances)[::-1]

# Print the feature ranking
print("Feature ranking:")

for f in range(10):
    print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))

# Plot the feature importances of the forest
plt.figure()
plt.title("Feature importances")
plt.bar(range(10), importances[indices],
       color="r", yerr=std[indices], align="center")
plt.xticks(range(10), indices)
plt.xlim([-1, 10])
plt.show()