From 1af747769dee3a5414da57ca3b66ea915d0fd06a Mon Sep 17 00:00:00 2001
From: op07n <39182105+op07n@users.noreply.github.com>
Date: Tue, 21 Jan 2020 16:32:46 +0100
Subject: [PATCH] Create DTree-Classifier.py

---
 apps/DTree-Classifier.py | 141 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 141 insertions(+)
 create mode 100644 apps/DTree-Classifier.py

diff --git a/apps/DTree-Classifier.py b/apps/DTree-Classifier.py
new file mode 100644
index 0000000..502ae51
--- /dev/null
+++ b/apps/DTree-Classifier.py
@@ -0,0 +1,141 @@
+# import streamlit as st
+import pandas as pd
+
+
+def unique_values(rows, col):
+    return set([row[col] for row in rows])
+
+def class_counts(rows):
+    counts = {}
+    for row in rows:
+        label = row[-1]
+        if label not in counts:
+            counts[label] = 0
+        counts[label]+=1
+    return counts
+
+
+def is_numeric(value):
+    return isinstance(value, int) or isinstance(value, float)
+
+
+class Question:
+    def __init__(self, column, value):
+        self.column = column
+        self.value = value
+
+    def match(self, example):
+        val = example[self.column]
+        if is_numeric(val):
+            return val >= self.value
+        else:
+            return val == self.value
+
+    def __repr__(self):
+        condition = "=="
+        if is_numeric(self.value):
+            condition=">="
+        return f'Is {header[self.column]} {condition} {str(self.value)} ?'
+
+
+def partition(rows, question):
+    true_rows, false_rows = [], []
+    for row in rows:
+        if question.match(row):
+            true_rows.append(row)
+        else:
+            false_rows.append(row)
+    return true_rows, false_rows
+
+def gini(rows):
+    counts = class_counts(rows)
+    impurity = 1
+    for lbl in counts:
+        prob_of_lbl = counts[lbl] / float(len(rows))
+        impurity -=prob_of_lbl**2
+    return impurity
+
+
+def info_gain(left, right, current_uncertainty):
+    p = float(len(left)) / (len(left) + len(right))
+    return current_uncertainty - p * gini(left) - (1 - p) * gini(right)
+
+def find_best_split(rows):
+    best_gain = 0
+    best_question = None
+    current_uncertainty = gini(rows)
+    n_features = len(rows[0]) - 1
+
+    for col in range(n_features):
+        values = set([row[col] for row in rows])
+        for val in values:
+            question = Question(col, val)
+            true_rows, false_rows = partition(rows, question)
+            if len(true_rows) == 0 or len(false_rows) == 0:
+                continue
+            gain = info_gain(true_rows, false_rows, current_uncertainty)
+            if gain>= best_gain:
+                best_gain, best_question = gain, question
+    return best_gain, best_question
+
+class Leaf:
+    def __init__(self, rows):
+        self.predictions = class_counts(rows)
+
+class Decision_Node:
+    def __init__(self, question, true_branch, false_branch):
+        self.question = question
+        self.true_branch = true_branch
+        self.false_branch = false_branch
+
+def build_tree(rows):
+    gain, question = find_best_split(rows)
+    if gain == 0:
+        return Leaf(rows)
+
+    true_rows, false_rows = partition(rows, question)
+    true_branch = build_tree(true_rows)
+    false_branch = build_tree(false_rows)
+
+    return Decision_Node(question, true_branch, false_branch)
+
+def print_tree(node, spacing=''):
+    if isinstance(node, Leaf):
+        print(spacing + 'Predict', node.predictions)
+        return
+    print(spacing + str(node.question))
+    print(spacing + '--> True:')
+    print_tree(node.true_branch, spacing + ' ')
+    print(spacing + '--> False:')
+    print_tree(node.false_branch, spacing + ' ')
+
+
+def classify(row, node):
+    if isinstance(node, Leaf):
+        return node.predictions
+    if node.question.match(row):
+        return classify(row, node.true_branch)
+    else:
+        return classify(row, node.false_branch)
+
+
+def print_leaf(counts):
+    total = sum(counts.values()) * 1.0
+    probs = {}
+    for lbl in counts.keys():
+        probs[lbl] = str(int(counts[lbl] / total * 100))+'%'
+    return probs
+
+
+def predict(data, header):
+    header = header
+    tree = build_tree(data)
+    results = {}
+    for row in data:
+        # print(f'Actual: {row[-1]}. Predicted: {print_leaf(classify(row, tree))}')
+        results[row[-1]] = print_leaf(classify(row, tree))
+    return results, tree
+
+# results, tree = predict(data, header)
+# print(results)
+# print_tree(tree)