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revisi algoritma

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zulfikar yuniar 2025-06-04 23:11:40 +07:00
parent 05d5c9ca93
commit f38403f0d6
2 changed files with 23 additions and 4 deletions
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Fastapi/__pycache__/decision_tree_model.cpython-310.pyc
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Fastapi/decision_tree_model.py
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@ -20,7 +20,14 @@ def count_classes(subset):
def get_majority_class(counts):
return max(counts.items(), key=lambda x: x[1])[0]
def build_tree_structure(data):
def is_one_per_class(subset):
counts = subset[target_column].value_counts()
return len(subset) == 2 and set(counts.values) == {1} and len(counts) == 2
def build_tree_structure(data, used_attributes=None):
if used_attributes is None:
used_attributes = set()
entropy = calculate_entropy(data)
counts = count_classes(data)
@ -34,7 +41,9 @@ def build_tree_structure(data):
gains = {}
thresholds = {}
for column in data.columns:
if column == target_column or not np.issubdtype(data[column].dtype, np.number):
if (column == target_column or
column in used_attributes or
not np.issubdtype(data[column].dtype, np.number)):
continue
threshold = data[column].mean()
le_subset = data[data[column] <= threshold]
@ -51,6 +60,7 @@ def build_tree_structure(data):
gains[column] = gain
thresholds[column] = threshold
if not gains:
return {
@ -61,17 +71,26 @@ def build_tree_structure(data):
best_attribute = max(gains, key=gains.get)
threshold = thresholds[best_attribute]
new_used_attributes = used_attributes | {best_attribute}
le_data = data[data[best_attribute] <= threshold]
gt_data = data[data[best_attribute] > threshold]
#pengecekan jika node diteruskan akan menghasilkan leaf node kelas 1=1 dan 0=1
if is_one_per_class(le_data) or is_one_per_class(gt_data):
return {
"type": "leaf",
"prediction": get_majority_class(counts),
"class_counts": counts
}
return {
"type": "node",
"attribute": best_attribute,
"threshold": threshold,
"class_counts": counts,
"left": build_tree_structure(le_data),
"right": build_tree_structure(gt_data)
"left": build_tree_structure(le_data, new_used_attributes),
"right": build_tree_structure(gt_data, new_used_attributes)
}
def predict(tree, input_data):