def learn_by_one_feature(data, targets, estimator):
for columnNumber in range(data.shape[1]):
mean, standart_deviation, time = cross_validation(estimator,
data[:, columnNumber:columnNumber + 1], targets)
print("Column number: %d" % columnNumber)
print("Accuracy: %0.2f (+/- %0.2f)" % (mean, standart_deviation))
print("Time: %0.2f" % time)
from SVM.EvaluatingEstimator import cross_validation_for_grid
from sklearn.decomposition import RandomizedPCA
dt = DataHandler()
training_data, targets = dt.get_training_data(samples_size=5000)
# training_data, targets, test_data = dt.get_pretreated_data(training_samples_size=5000,
# test_samples_size=5000)
# pca = RandomizedPCA(n_components=5, whiten=False).fit(training_data)
# training_data = pca.transform(training_data)
estimator = tree.DecisionTreeClassifier(max_depth=6, min_samples_leaf=9)
mean, standart_deviation, time = cross_validation(estimator, training_data, targets)
# param_grid = [{'max_depth': list(range(3, 20)), 'min_samples_leaf': list(range(5, 10)),
# 'min_samples_split': list(range(1, 5))}]
#
# estimator = tree.DecisionTreeClassifier()
# estimator = GridSearchCV(estimator, param_grid)
# mean, standart_deviation, time = cross_validation_for_grid(estimator, training_data, targets)
print("Accuracy: %0.2f (+/- %0.2f)" % (mean, standart_deviation))
print("Time: %0.2f" % time)
# from sklearn.externals.six import StringIO
# with open("boson.dot", 'w') as f:
# f = tree.export_graphviz(estimator, out_file=f)
