import pylab as pl
from sklearn import datasets, linear_model

vectors = [
    [0, 0], 
    [1, 1],
    [2, 2],
]
targets = [
    0,
    1,
    2,
]
iris = datasets.load_iris()
boston = datasets.load_boston()
diabetes = datasets.load_diabetes()

model = linear_model.LinearRegression()
model.fit(vectors, targets)
print model.coef_
print model.intercept_
print model.predict([3, 3])

# Exercise:
# Fit a linear regression on the Boston House Prices dataset.
# Look at the coefficients.

model = linear_model.Ridge(alpha=0.5)
model.fit(vectors, targets)
print model.coef_
print model.intercept_

model = linear_model.RidgeCV(alphas=[0.1, 1.0, 10.0])
model.fit(vectors, targets)
model.alpha_

# Exercise:
# Find an optimal value of alpha for
# Ridge Regression on the Boston House Prices dataset.

model = linear_model.Lasso(alpha=0.1)
model.fit(vectors, targets)
print model.coef_
print model.intercept_

# Adapted from
# http://scikit-learn.org/dev/auto_examples/linear_model/plot_lasso_model_selection.html
model = linear_model.LassoCV(cv=20)
model.fit(diabetes.data, diabetes.target)

def plot_model_selection(model):
    logAlphas = -np.log10(model.alphas_)
    pl.figure()
    pl.plot(logAlphas, model.mse_path_, ':')
    pl.plot(logAlphas, model.mse_path_.mean(axis=-1), 'k',
        label='Average across folds', linewidth=2)
    pl.axvline(-np.log10(model.alpha_), linestyle='--', color='k', 
        label='alpha: CV estimate')
    pl.legend()
    pl.title('Mean square error on each fold: Coordinate descent')
    pl.xlabel('-log(alpha)')
    pl.ylabel('Mean square error')
    pl.axis('tight')
    pl.ylim(2300, 3800)
    
plot_model_selection(model)

model = linear_model.LassoLars(alpha=0.1)
model.fit(vectors, targets)
print model.coef_
print model.intercept_

# Adapted from
# http://scikit-learn.org/dev/auto_examples/linear_model/plot_lasso_model_selection.html
model = linear_model.LassoLarsIC(criterion='bic')
model.fit(diabetes.data, diabetes.target)

pl.figure()
pl.plot(-np.log10(model.alphas_), model.criterion_, '--', linewidth=3)
pl.axvline(-np.log10(model.alpha_), linewidth=3)
pl.xlabel('-log(alpha)')
pl.ylabel('Information criterion')
pl.title('Model selection by information criterion');

# Exercise:
# Modify the above example to use the Akaike information criterion
# instead of the Bayes Information criterion.

# Exercise:
# Use cross validation instead of information criterion
# to select alpha.

model = linear_model.BayesianRidge()
model.fit(vectors, targets)

model = linear_model.LogisticRegression()
model.fit(iris.data[:-1], iris.target[:-1])
print model.predict(iris.data[-1])
print iris.target[-1]