my_DT.py

import pandas as pd
import numpy as np
from collections import Counter

class my_DT:

    def __init__(self, criterion="gini", max_depth=8, min_impurity_decrease=0, min_samples_split=2):
        # criterion = {"gini", "entropy"},
        # Stop training if depth = max_depth. Depth of a binary tree: the max number of edges from the root node to a leaf node
        # Only split node if impurity decrease >= min_impurity_decrease after the split
        #   Weighted impurity decrease: impurity - (N_t_R / N * right_impurity + N_t_L / N * left_impurity)
        # Only split node with >= min_samples_split samples
        self.criterion = criterion
        self.max_depth = int(max_depth)
        self.min_impurity_decrease = min_impurity_decrease
        self.min_samples_split = int(min_samples_split)

    def fit(self, X, y):
        # X: pd.DataFrame, independent variables, float
        # y: list, np.array or pd.Series, dependent variables, int or str
        self.classes_ = list(set(list(y)))
        # write your code below
        return

    def predict(self, X):
        # X: pd.DataFrame, independent variables, float
        # return predictions: list
        # write your code below
        return predictions

    def predict_proba(self, X):
        # X: pd.DataFrame, independent variables, float
        # Eample:
        # self.classes_ = {"2", "1"}
        # the reached node for the test data point has {"1":2, "2":1}
        # then the prob for that data point is {"2": 1/3, "1": 2/3}
        # return probs = pd.DataFrame(list of prob, columns = self.classes_)
        # write your code below

        ##################
        return probs