utils.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

SEED = 1

# L1.Classification
house_votes_columns = ['party', 'infants', 'water', 'budget', 'physician', 'salvador', 'religious', 'satellite', 'aid',
                       'missile', 'immigration', 'synfuels', 'education', 'superfund', 'crime', 'duty_free_exports',
                       'eaa_rsa']
house_votes = pd.read_csv('data/house-votes-84.csv', true_values=['y'], false_values=['n'], na_values=['?'],
                          names=house_votes_columns)
house_votes.fillna(False, inplace=True)
house_votes.iloc[:, 1:] = house_votes.iloc[:, 1:].astype('int8')

# L3.Model Validation
nba_y_test = np.array(
    [53, 51, 51, 49, 43, 42, 42, 41, 41, 37, 36, 31, 29, 28, 20, 67, 61, 55, 51, 51, 47, 43, 41, 40, 34, 33, 32, 31, 26,
     24])
nba_predictions = np.array(
    [60, 62, 42, 42, 30, 50, 52, 42, 44, 35, 30, 30, 35, 40, 15, 72, 58, 60, 40, 42, 45, 46, 40, 35, 25, 40, 20, 34, 25,
     24])
nba_labels = np.array(
    ['E', 'E', 'E', 'E', 'E', 'E', 'E', 'E', 'E', 'E', 'E', 'E', 'E', 'E', 'E', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W',
     'W', 'W', 'W', 'W', 'W', 'W', 'W'])

tic_tac_toe = pd.read_csv('data/tic-tac-toe.csv')
X = pd.get_dummies(tic_tac_toe.drop('Class', axis=1))
y = tic_tac_toe['Class'].astype('category').cat.codes
X_train, X_test, y_train, y_test = train_test_split(
    X, y, train_size=0.1, random_state=SEED)
classifier = RandomForestClassifier(n_estimators=500, random_state=42)
classifier.fit(X_train, y_train)

# L4.Tree-Based Models
diabetes = pd.read_csv('data/diabetes.csv')
wbc = pd.read_csv('data/wbc.csv')
wbc.drop('Unnamed: 32', axis=1, inplace=True)

auto = pd.read_csv('data/auto.csv')
# Hot encode origin column
auto = pd.concat([auto, pd.get_dummies(
    auto['origin'], prefix='origin')], axis=1)
auto.drop('origin', axis=1, inplace=True)

liver_patients = pd.read_csv('data/indian_liver_patient_preprocessed.csv')
liver_patients.drop('Unnamed: 0', axis=1, inplace=True)

liver_disease = pd.read_csv('data/indian_liver_patient.csv')
liver_disease['Is_male'] = (liver_disease['Gender'] == 'Male').astype('int64')
liver_disease['Liver_disease'] = (
    liver_disease['Dataset'] == 1).astype('int64')
liver_disease.drop(['Gender', 'Dataset'], inplace=True, axis=1)
liver_disease.fillna(liver_disease.mean(), inplace=True)