import numpy as np
from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline

#X=np.loadtxt("Density_Xray_exist.dat")
X=np.load("Density_Xray_exist.npy")
y=np.array(X[:,2],dtype='int')
X = X[:,0:2]
X[:,1] = np.log10(X[:,1])

#lr = LogisticRegression()
model = make_pipeline(PolynomialFeatures(2), LogisticRegression())
model.fit(X,y)

joblib.dump(model, 'X_ray_sources_poly.pkl') 

clf = joblib.load('X_ray_sources_poly.pkl')