RandomizedLogisticRegression.py

from sklearn.datasets import make_classification
from sklearn.linear_model import RandomizedLogisticRegression
import pandas as pd

clf = RandomizedLogisticRegression(sample_fraction = .75, n_resampling = 3, normalize = False)
 
X, y = make_classification(n_samples=10,
                           n_features=3,
                           n_informative=2,
                           n_redundant=0,
                           n_repeated=0,
                           n_classes=2,
                           random_state=123,
                           shuffle=False)
clf.fit(X,y)  

data = pd.read_csv("~/dev/tmp/demo_model_data.csv")
y = data['dep_var']
X = data.ix[:, 2:]
clf.fit(X,y)