q6_Bagging.py

"""
The current code given is for the Assignment 2.
> Classification
> Regression
"""

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from metrics import *

from ensemble.bagging import BaggingClassifier
from tree.base import DecisionTree
# Or use sklearn decision tree
# from linearRegression.linearRegression import LinearRegression
from sklearn import tree
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, plot_tree

########### BaggingClassifier ###################

N = 30
P = 2
NUM_OP_CLASSES = 2
n_estimators = 3
X = pd.DataFrame(np.abs(np.random.randn(N, P)))
y = pd.Series(np.random.randint(NUM_OP_CLASSES, size = N), dtype="category")

criteria = 'information_gain'
Dtree =  tree.DecisionTreeClassifier()
Classifier_B = BaggingClassifier(base_estimator=Dtree, n_estimators=n_estimators )
Classifier_B.fit(X, y)
y_hat = Classifier_B.predict(X)
[fig1, fig2] = Classifier_B.plot()
print('Criteria :', criteria)
print('Accuracy: ', accuracy(y_hat, y))
for cls in y.unique():
    print('Precision: ', precision(y_hat, y, cls))
    print('Recall: ', recall(y_hat, y, cls))