dssg/catwalk

# coding: utf-8

from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression

from catwalk.estimators.transformers import CutOff

class ScaledLogisticRegression(BaseEstimator, ClassifierMixin):
    """
    An in-place replacement for the scikit-learn's LogisticRegression.

    It incorporates the MaxMinScaler, and the CutOff as preparations
    for the  logistic regression.
    """
    def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0,
                 fit_intercept=True, intercept_scaling=1, class_weight=None,
                 random_state=None, solver='liblinear', max_iter=100,
                 multi_class='ovr', verbose=0, warm_start=False, n_jobs=1):


        self.penalty = penalty
        self.dual = dual
        self.tol = tol
        self.C = C
        self.fit_intercept = fit_intercept
        self.intercept_scaling = intercept_scaling
        self.class_weight = class_weight
        self.random_state = random_state
        self.solver = solver
        self.max_iter = max_iter
        self.multi_class = multi_class
        self.verbose = verbose
        self.warm_start = warm_start
        self.n_jobs = n_jobs

        self.minmax_scaler = MinMaxScaler()
        self.dsapp_cutoff = CutOff()
        self.lr = LogisticRegression(penalty=penalty, dual=dual, tol=tol, C=C,
                                     fit_intercept=fit_intercept, intercept_scaling=intercept_scaling, class_weight=class_weight,
                                     random_state=random_state, solver=solver, max_iter=max_iter,
                                     multi_class=multi_class, verbose=verbose, warm_start=warm_start, n_jobs=n_jobs)

        self.pipeline =Pipeline([
            ('minmax_scaler', self.minmax_scaler),
            ('dsapp_cutoff', self.dsapp_cutoff),
            ('lr', self.lr)
        ])


    def fit(self, X, y = None):
        self.pipeline.fit(X, y)

        self.min_ = self.pipeline.named_steps['minmax_scaler'].min_
        self.scale_ = self.pipeline.named_steps['minmax_scaler'].scale_
        self.data_min_ = self.pipeline.named_steps['minmax_scaler'].data_min_
        self.data_max_ = self.pipeline.named_steps['minmax_scaler'].data_max_
        self.data_range_ = self.pipeline.named_steps['minmax_scaler'].data_range_

        self.coef_ = self.pipeline.named_steps['lr'].coef_
        self.intercept_ = self.pipeline.named_steps['lr'].intercept_

        self.classes_ = self.pipeline.named_steps['lr'].classes_

        return self

    def predict_proba(self, X):
        return self.pipeline.predict_proba(X)

    def predict_log_proba(self, X):
        return self.pipeline.predict_log_proba(X)

    def predict(self, X):
        return self.pipeline.predict(X)

    def score(self, X, y):
        return self.pipeline.score(X,y)