How to get odds-ratios and other related features with scikit-learn

Question

I'm going through this odds ratios in logistic regression tutorial, and trying to get the exactly the same results with the logistic regression module of scikit-learn. With the code below, I am able to get the coefficient and intercept but I could not find a way to find other properties of the model listed in the tutorial such as log-likelyhood, Odds Ratio, Std. Err., z, P>|z|, [95% Conf. Interval]. If someone could show me how to have them calculated with sklearn package, I would appreciate it.

import pandas as pd
from sklearn.linear_model import LogisticRegression

url = 'https://stats.idre.ucla.edu/wp-content/uploads/2016/02/sample.csv'
df = pd.read_csv(url, na_values=[''])
y = df.hon.values
X = df.math.values
y = y.reshape(200,1)
X = X.reshape(200,1)
clf = LogisticRegression(C=1e5)
clf.fit(X,y)
clf.coef_
clf.intercept_

Answer 1

You can get the odds ratios by taking the exponent of the coeffecients:

import numpy as np
X = df.female.values.reshape(200,1)
clf.fit(X,y)
np.exp(clf.coef_)

# array([[ 1.80891307]])

As for the other statistics, these are not easy to get from scikit-learn (where model evaluation is mostly done using cross-validation), if you need them you're better off using a different library such as statsmodels.

Answer 2

In addition to @maxymoo's answer, to get other statistics, statsmodel can be used. Assuming that you have your data in a DataFrame called df, the code below should show a good summary:

import pandas as pd
from patsy import dmatrices
import statsmodels.api as sm 

y, X = dmatrices( 'label ~ age + gender', data=df, return_type='dataframe')
mod = sm.Logit(y, X)
res = mod.fit()
print res.summary()