Create DTree-Classifier.py
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# import streamlit as st
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import pandas as pd
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def unique_values(rows, col):
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return set([row[col] for row in rows])
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def class_counts(rows):
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counts = {}
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for row in rows:
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label = row[-1]
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if label not in counts:
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counts[label] = 0
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counts[label]+=1
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return counts
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def is_numeric(value):
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return isinstance(value, int) or isinstance(value, float)
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class Question:
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def __init__(self, column, value):
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self.column = column
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self.value = value
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def match(self, example):
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val = example[self.column]
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if is_numeric(val):
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return val >= self.value
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else:
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return val == self.value
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def __repr__(self):
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condition = "=="
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if is_numeric(self.value):
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condition=">="
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return f'Is {header[self.column]} {condition} {str(self.value)} ?'
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def partition(rows, question):
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true_rows, false_rows = [], []
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for row in rows:
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if question.match(row):
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true_rows.append(row)
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else:
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false_rows.append(row)
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return true_rows, false_rows
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def gini(rows):
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counts = class_counts(rows)
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impurity = 1
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for lbl in counts:
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prob_of_lbl = counts[lbl] / float(len(rows))
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impurity -=prob_of_lbl**2
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return impurity
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def info_gain(left, right, current_uncertainty):
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p = float(len(left)) / (len(left) + len(right))
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return current_uncertainty - p * gini(left) - (1 - p) * gini(right)
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def find_best_split(rows):
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best_gain = 0
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best_question = None
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current_uncertainty = gini(rows)
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n_features = len(rows[0]) - 1
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for col in range(n_features):
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values = set([row[col] for row in rows])
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for val in values:
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question = Question(col, val)
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true_rows, false_rows = partition(rows, question)
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if len(true_rows) == 0 or len(false_rows) == 0:
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continue
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gain = info_gain(true_rows, false_rows, current_uncertainty)
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if gain>= best_gain:
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best_gain, best_question = gain, question
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return best_gain, best_question
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class Leaf:
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def __init__(self, rows):
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self.predictions = class_counts(rows)
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class Decision_Node:
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def __init__(self, question, true_branch, false_branch):
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self.question = question
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self.true_branch = true_branch
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self.false_branch = false_branch
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def build_tree(rows):
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gain, question = find_best_split(rows)
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if gain == 0:
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return Leaf(rows)
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true_rows, false_rows = partition(rows, question)
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true_branch = build_tree(true_rows)
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false_branch = build_tree(false_rows)
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return Decision_Node(question, true_branch, false_branch)
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def print_tree(node, spacing=''):
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if isinstance(node, Leaf):
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print(spacing + 'Predict', node.predictions)
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return
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print(spacing + str(node.question))
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print(spacing + '--> True:')
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print_tree(node.true_branch, spacing + ' ')
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print(spacing + '--> False:')
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print_tree(node.false_branch, spacing + ' ')
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def classify(row, node):
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if isinstance(node, Leaf):
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return node.predictions
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if node.question.match(row):
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return classify(row, node.true_branch)
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else:
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return classify(row, node.false_branch)
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def print_leaf(counts):
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total = sum(counts.values()) * 1.0
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probs = {}
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for lbl in counts.keys():
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probs[lbl] = str(int(counts[lbl] / total * 100))+'%'
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return probs
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def predict(data, header):
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header = header
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tree = build_tree(data)
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results = {}
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for row in data:
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# print(f'Actual: {row[-1]}. Predicted: {print_leaf(classify(row, tree))}')
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results[row[-1]] = print_leaf(classify(row, tree))
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return results, tree
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# results, tree = predict(data, header)
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# print(results)
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# print_tree(tree)
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