# coding: utf-8

import numpy as np
import pandas as pd
import random

from helper_functions import determine_type_of_feature


# 1. Decision Tree helper functions 
# (see "decision tree algorithm flow chart.png")

# 1.1 Data pure?
def check_purity(data):
    
    label_column = data[:, -1]
    unique_classes = np.unique(label_column)

    if len(unique_classes) == 1:
        return True
    else:
        return False

    
# 1.2 Classify
def classify_data(data):
    
    label_column = data[:, -1]
    unique_classes, counts_unique_classes = np.unique(label_column, return_counts=True)

    index = counts_unique_classes.argmax()
    classification = unique_classes[index]
    
    return classification


# 1.3 Potential splits?
def get_potential_splits(data, random_subspace):
    
    potential_splits = {}
    _, n_columns = data.shape
    column_indices = list(range(n_columns - 1))    # excluding the last column which is the label
    
    if random_subspace and random_subspace <= len(column_indices):
        column_indices = random.sample(population=column_indices, k=random_subspace)
    
    for column_index in column_indices:          
        values = data[:, column_index]
        unique_values = np.unique(values)
        
        potential_splits[column_index] = unique_values
    
    return potential_splits


# 1.4 Lowest Overall Entropy?
def calculate_entropy(data):
    
    label_column = data[:, -1]
    _, counts = np.unique(label_column, return_counts=True)

    probabilities = counts / counts.sum()
    entropy = sum(probabilities * -np.log2(probabilities))
     
    return entropy


def calculate_overall_entropy(data_below, data_above):
    
    n = len(data_below) + len(data_above)
    p_data_below = len(data_below) / n
    p_data_above = len(data_above) / n

    overall_entropy =  (p_data_below * calculate_entropy(data_below) 
                      + p_data_above * calculate_entropy(data_above))
    
    return overall_entropy


def determine_best_split(data, potential_splits):
    
    overall_entropy = 9999
    for column_index in potential_splits:
        for value in potential_splits[column_index]:
            data_below, data_above = split_data(data, split_column=column_index, split_value=value)
            current_overall_entropy = calculate_overall_entropy(data_below, data_above)
            
            if current_overall_entropy <= overall_entropy:
                overall_entropy = current_overall_entropy
                best_split_column = column_index
                best_split_value = value
    
    return best_split_column, best_split_value