import numpy as np
import time
import warnings

import numpy as np
import matplotlib.pyplot as plt

from sklearn import cluster, datasets, mixture
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from itertools import cycle, islice
from sklearn.metrics import fbeta_score, make_scorer

n_samples = 1500
X, y = datasets.make_circles(n_samples=n_samples, factor=.5,
                             noise=.05)

import numpy as np
from sklearn.linear_model import Ridge
from sklearn.grid_search import GridSearchCV

# prepare a range of alpha values to test
alphas = np.array([10, 1, 0.1, 0.01, 0.001, 0.0001, 0])
# create and fit a ridge regression model, testing each alpha
model = Ridge()
ftwo_scorer = make_scorer(fbeta_score, beta=2)
grid = GridSearchCV(estimator=model, param_grid=dict(alpha=alphas), scoring=Ridge.score)
grid.fit(X, y)
print(grid)
print("asdasdasdsada")
# summarize the results of the grid search
print(grid.best_score_)
print(grid.best_estimator_.alpha)