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import bz2
import urllib.request
import numpy as np

from sklearn.datasets import load_svmlight_file
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression

from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel

from nni.feature_engineering.gradient_selector import FeatureGradientSelector

url_zip_train = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/rcv1_train.binary.bz2'
urllib.request.urlretrieve(url_zip_train, filename='train.bz2')

f_svm = open('train.svm', 'wt')
with bz2.open('train.bz2', 'rb') as f_zip:
    data = f_zip.read()
    f_svm.write(data.decode('utf-8'))
f_svm.close()


X, y = load_svmlight_file('train.svm')
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

fgs = FeatureGradientSelector(n_features=10)
fgs.fit(X_train, y_train)

print("selected features\t", fgs.get_selected_features())

pipeline = make_pipeline(FeatureGradientSelector(n_epochs=1, n_features=10), LogisticRegression())
# pipeline = make_pipeline(SelectFromModel(ExtraTreesClassifier(n_estimators=50)), LogisticRegression())
pipeline.fit(X_train, y_train)

print("Pipeline Score: ", pipeline.score(X_train, y_train))