usage.py

"""
The current code given is for the Assignment 1.
You will be expected to use this to make trees for:
> discrete input, discrete output
> real input, real output
> real input, discrete output
> discrete input, real output
"""

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tree.base import DecisionTree
from metrics import *

np.random.seed(42)
# Test case 1
# Real Input and Real Output
print("################### RIRO ###################")
N = 30
P = 5
X = pd.DataFrame(np.random.randn(N, P))
y = pd.Series(np.random.randn(N))


for criteria in ['information_gain', 'gini_index']:
    tree = DecisionTree(criterion=criteria) #Split based on Inf. Gain
    tree.fit(X, y)
    y_hat = tree.predict(X)
    tree.plot()
    print('Criteria :', criteria)
    print('RMSE: ', rmse(y_hat, y))
    print('MAE: ', mae(y_hat, y))

# Test case 2
# Real Input and Discrete Output
print("################### RIDO ###################")
N = 30
P = 5
X = pd.DataFrame(np.random.randn(N, P))
y = pd.Series(np.random.randint(P, size = N), dtype="category")

for criteria in ['information_gain', 'gini_index']:
    X = pd.DataFrame(np.random.randn(N, P))
    y = pd.Series(np.random.randint(P, size = N), dtype="category")
    tree = DecisionTree(criterion=criteria) #Split based on Inf. Gain
    tree.fit(X, y)
    
    y_hat = tree.predict(X)
    tree.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))


# Test case 3
# Discrete Input and Discrete Output
print("################### DIDO ###################")
N = 30
P = 5
X = pd.DataFrame({i:pd.Series(np.random.randint(P, size = N), dtype="category") for i in range(5)})
y = pd.Series(np.random.randint(P, size = N) , dtype="category")

for criteria in ['information_gain', 'gini_index']:
    tree = DecisionTree(criterion=criteria) #Split based on Inf. Gain
    tree.fit(X, y)
    y_hat = tree.predict(X)
    tree.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))

# Test case 4
# Discrete Input and Real Output
print("################### DIRO ###################")
N = 30
P = 5
X = pd.DataFrame({i:pd.Series(np.random.randint(P, size = N), dtype="category") for i in range(5)})
y = pd.Series(np.random.randn(N))

for criteria in ['information_gain', 'gini_index']:
    tree = DecisionTree(criterion=criteria) #Split based on Inf. Gain
    tree.fit(X, y)
    y_hat = tree.predict(X)
    tree.plot()
    print('Criteria :', criteria)
    print('RMSE: ', rmse(y_hat, y))
    print('MAE: ', mae(y_hat, y))