In [2]:

    

from sklearn.datasets import load_diabetes
from sklearn.linear_model import Lasso
from sklearn.cross_validation import cross_val_score

data = load_diabetes()
X = data.data[:200]
y = data.target[:200]

model = Lasso()
alphas = np.logspace(-4, -.5, 50)

scores = list()

for alpha in alphas:
    model.alpha = alpha
    this_scores = cross_val_score(model, X, y, "mean_squared_error", cv=5)
    scores.append(np.mean(this_scores))

plt.semilogx(alphas, scores)
plt.ylabel('CV score')
plt.xlabel('alpha')
plt.axhline(np.max(scores), linestyle=':')
plt.show()


    

In [3]:

    

from sklearn.linear_model import LassoCV

alphas = np.logspace(-4, -.5, 50)
lasso_cv = LassoCV(alphas=alphas, cv=5)
lasso_cv.fit(X, y)

print(lasso_cv.alpha_ )

scores = -lasso_cv.mse_path_.mean(axis=1)
plt.semilogx(lasso_cv.alphas_, scores)
plt.axhline(np.max(scores), linestyle=':')    
plt.axvline(lasso_cv.alpha_ , linestyle=':')   
plt.ylabel('CV score')
plt.xlabel('alpha')
plt.show()


    

    




0.0439397056076






    

In [4]:

    

from sklearn.datasets import make_classification
X0, y = make_classification(n_features=1, n_redundant = 0, n_informative = 1, n_clusters_per_class = 1, random_state=4)
X = sm.add_constant(X0)


    

In [5]:

    

from sklearn.linear_model import LogisticRegression
model = LogisticRegression().fit(X0,y)


    

In [9]:

    

import matplotlib as mpl
xx = np.linspace(-3,3,100)
sigm = 1.0/(1 + np.exp(-model.coef_[0][0] * xx - model.intercept_[0]))
plt.plot(xx, sigm)
plt.scatter(X0, y, marker='o', c = y, s = 100)
plt.scatter(X0, model.predict(X0), marker='x', c = y, s = 200, lw = 2, alpha = 0.5, cmap = mpl.cm.jet)
plt.xlim(-3,3)
plt.show()


    

In [11]:

    

logit_mod = sm.Logit(y,X)
logit_res = logit_mod.fit(disp=0)
print(logit_res.summary())


    

    




                           Logit Regression Results                           
==============================================================================
Dep. Variable:                      y   No. Observations:                  100
Model:                          Logit   Df Residuals:                       98
Method:                           MLE   Df Model:                            1
Date:                Mon, 13 Jun 2016   Pseudo R-squ.:                  0.7679
Time:                        08:48:35   Log-Likelihood:                -16.084
converged:                       True   LL-Null:                       -69.295
                                        LLR p-value:                 5.963e-25
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
const          0.2515      0.477      0.527      0.598      -0.683       1.186
x1             4.2382      0.902      4.699      0.000       2.470       6.006
==============================================================================

In [10]:

    

logit_mod = sm.Logit(y,X)
logit_res = logit_mod.fit()
print(logit_res.summary())


    

    




Optimization terminated successfully.
         Current function value: 0.160844
         Iterations 8
                           Logit Regression Results                           
==============================================================================
Dep. Variable:                      y   No. Observations:                  100
Model:                          Logit   Df Residuals:                       98
Method:                           MLE   Df Model:                            1
Date:                Mon, 13 Jun 2016   Pseudo R-squ.:                  0.7679
Time:                        08:47:48   Log-Likelihood:                -16.084
converged:                       True   LL-Null:                       -69.295
                                        LLR p-value:                 5.963e-25
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
const          0.2515      0.477      0.527      0.598      -0.683       1.186
x1             4.2382      0.902      4.699      0.000       2.470       6.006
==============================================================================

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