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import pandas as pd
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import math
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import numpy as np
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from math import log2
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import os
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# Hitung entropy dari target
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target_column = 'TARGET'
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model_cache = {} # Menyimpan tree dan atribut input per file CSV
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def calculate_entropy(target_series):
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values = target_series.value_counts(normalize=True)
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return -sum(p * math.log2(p) for p in values if p > 0)
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def calculate_entropy(subset):
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if len(subset) == 0:
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return 0
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counts = subset[target_column].value_counts()
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probabilities = counts / len(subset)
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entropy = -sum(p * log2(p) for p in probabilities)
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return entropy
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# Cari split terbaik berdasarkan gain tertinggi dari mean/median
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def count_classes(subset):
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return subset[target_column].value_counts().to_dict()
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def find_best_split(data, target):
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total_data = len(data)
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entropy_total = calculate_entropy(data[target])
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all_columns = data.columns.drop(target)
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numeric_columns = data[all_columns].select_dtypes(include=['number']).columns
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best = None
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def get_majority_class(counts):
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return max(counts.items(), key=lambda x: x[1])[0]
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for column in numeric_columns:
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for method in ['mean', 'median']:
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threshold = data[column].mean() if method == 'mean' else data[column].median()
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gt_split = data[data[column] > threshold]
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le_split = data[data[column] <= threshold]
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def build_tree_structure(data):
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entropy = calculate_entropy(data)
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counts = count_classes(data)
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if len(gt_split) == 0 or len(le_split) == 0:
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continue
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if entropy == 0 or len(data) < 2:
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return {
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"type": "leaf",
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"prediction": get_majority_class(counts),
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"class_counts": counts
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}
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entropy_gt = calculate_entropy(gt_split[target])
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entropy_le = calculate_entropy(le_split[target])
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gain = entropy_total - (len(gt_split) / total_data) * entropy_gt - (len(le_split) / total_data) * entropy_le
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gains = {}
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thresholds = {}
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for column in data.columns:
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if column == target_column or not np.issubdtype(data[column].dtype, np.number):
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continue
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threshold = data[column].mean()
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le_subset = data[data[column] <= threshold]
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gt_subset = data[data[column] > threshold]
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if best is None or gain > best['gain']:
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best = {
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'column': column,
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'method': method,
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'threshold': threshold,
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'gain': gain,
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'gt_split': gt_split,
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'le_split': le_split
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}
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return best
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le_entropy = calculate_entropy(le_subset)
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gt_entropy = calculate_entropy(gt_subset)
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# Bangun decision tree secara rekursif
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le_weight = len(le_subset) / len(data)
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gt_weight = len(gt_subset) / len(data)
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def build_tree(data, target, depth=0, max_depth=12):
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counts = data[target].value_counts().to_dict()
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samples = len(data)
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is_pure_leaf = len(counts) == 1
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is_single_sample = samples == 1
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weighted_entropy = le_weight * le_entropy + gt_weight * gt_entropy
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gain = entropy - weighted_entropy
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if samples == 0 or is_pure_leaf or is_single_sample or depth >= max_depth:
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if samples == 0:
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return {'type': 'leaf', 'class': 'Unknown'}
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chosen = max(counts, key=counts.get)
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return {'type': 'leaf', 'class': chosen}
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gains[column] = gain
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thresholds[column] = threshold
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split = find_best_split(data, target)
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if split is None or split['gain'] <= 0:
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chosen = max(counts, key=counts.get)
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return {'type': 'leaf', 'class': chosen}
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if not gains:
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return {
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"type": "leaf",
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"prediction": get_majority_class(counts),
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"class_counts": counts
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}
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best_attribute = max(gains, key=gains.get)
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threshold = thresholds[best_attribute]
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le_data = data[data[best_attribute] <= threshold]
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gt_data = data[data[best_attribute] > threshold]
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return {
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'type': 'node',
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'column': split['column'],
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'method': split['method'],
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'threshold': split['threshold'],
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'left': build_tree(split['le_split'], target, depth + 1, max_depth),
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'right': build_tree(split['gt_split'], target, depth + 1, max_depth)
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"type": "node",
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"attribute": best_attribute,
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"threshold": threshold,
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"class_counts": counts,
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"left": build_tree_structure(le_data),
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"right": build_tree_structure(gt_data)
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}
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# Prediksi menggunakan pohon keputusan
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def predict_tree(tree, input_data):
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if tree['type'] == 'leaf':
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return tree['class']
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value = input_data[tree['column']]
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if value <= tree['threshold']:
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return predict_tree(tree['left'], input_data)
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def predict(tree, input_data):
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if tree["type"] == "leaf":
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return tree["prediction"]
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attr = tree["attribute"]
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threshold = tree["threshold"]
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if input_data[attr] <= threshold:
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return predict(tree["left"], input_data)
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else:
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return predict_tree(tree['right'], input_data)
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return predict(tree["right"], input_data)
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def tree_input_attributes(tree, attributes=None):
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if attributes is None:
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attributes = set()
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if tree["type"] == "node":
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attributes.add(tree["attribute"])
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tree_input_attributes(tree["left"], attributes)
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tree_input_attributes(tree["right"], attributes)
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return sorted(attributes)
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def load_model_from_csv(csv_filename):
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if csv_filename in model_cache:
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return model_cache[csv_filename]
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full_path = os.path.join("data", csv_filename)
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if not os.path.exists(full_path):
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raise FileNotFoundError(f"CSV file '{csv_filename}' tidak ditemukan.")
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df = pd.read_csv(full_path)
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tree = build_tree_structure(df)
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input_fields = tree_input_attributes(tree)
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model_cache[csv_filename] = {
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"tree": tree,
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"input_fields": input_fields
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}
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return model_cache[csv_filename]
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def get_required_fields(csv_filename):
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model = load_model_from_csv(csv_filename)
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return model["input_fields"]
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def predict_from_input_dict(csv_filename, input_dict):
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model = load_model_from_csv(csv_filename)
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return predict(model["tree"], input_dict)
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def get_available_csv_files():
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return [f for f in os.listdir("data") if f.endswith(".csv")]
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def get_feature_columns_from_csv(csv_filename):
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full_path = os.path.join("data", csv_filename)
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df = pd.read_csv(full_path)
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return [
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col for col in df.columns
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if col != target_column and np.issubdtype(df[col].dtype, np.number)
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]
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@ -24,8 +24,8 @@
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<div class="sidebar-wrapper active">
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<div class="sidebar-header">
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<div class="d-flex justify-content-between">
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<div class="logo flex justify-center items-center"">
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<img src="{{ asset('assets/images/logo/logo_aplikasi.png') }}" alt="Logo" style="width: 100px; height: auto;" />
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<div class="d-flex justify-content-center w-100">
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<img src="{{ asset('assets/images/logo/logo_aplikasi.png') }}" alt="Logo" style="width: 125px; height: auto; " />
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</div>
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<div class="toggler">
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