Helper.py

# -*- coding: utf-8 -*-
"""
Created on Sun May 07 21:34:16 2017

@author: raghavendra harish
"""
import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.base import TransformerMixin

class Preprocessor:

    @staticmethod
    def fill_nans(dat):
        '''
        Fills in NaNs with either the mean or the most common value.
        '''

        return DataFrameImputer().fit_transform(dat)
    
class DataFrameImputer(TransformerMixin):
    '''
    This class came from http://stackoverflow.com/questions/25239958/
    impute-categorical-missing-values-in-scikit-learn
    '''

    def __init__(self):
        '''
        Impute missing values.
        Columns of dtype object are imputed with the most frequent value in col.
        Columns of other types are imputed with mean of column.
        '''

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

        self.fill = pd.Series([X[c].value_counts().index[0]
            if X[c].dtype == np.dtype('O') else X[c].mean() for c in X],
            index = X.columns)

        return self

    def transform(self, X, y = None):
        return X.fillna(self.fill)
    
class Performance:

    @staticmethod
    def get_perf(y, y_pred):
        '''
        This method outputs several performance metrics for classification.
        '''

        # Gets Confusion Matrix
        #conf_matrix = metrics.confusion_matrix(y_true = y, y_pred = y_pred)

        # Gets Accuracy
        accuracy = metrics.accuracy_score(y_true = y, y_pred = y_pred)

        # Gets Recall
        recall = metrics.recall_score(y_true = y, y_pred = y_pred)

        # Gets Precision
        precision = metrics.precision_score(y_true = y, y_pred = y_pred)

        # F1
        f1 = metrics.f1_score(y_true = y, y_pred = y_pred)

        return {'accuracy': accuracy, 'recall': recall,
                'precision': precision, 'F1': f1}