I want to create predict and predict_proba methods in my DecisionTreeClassifier implementation, but it gives the error Traceback (most recent call last): File "c:\Users\Nijat\project.py", line 136, in <module> print(model.predict(X)) ^^^^^^^^^^^^^^^^ File "c:\Users\Nijat\project.py", line 128, in predict return [1 if p[0] > 0.5 else 0 for p in self.predict_proba(X)] ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\Nijat\project.py", line 121, in predict_proba class1_proba = self.bypass_tree(self.tree, sample) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\Nijat\project.py", line 105, in bypass_tree while node['type'] == 'node': ~~~~^^^^^^^^ KeyError: 'type' Here's my code: import numpy as np import pandas as pd class MyTreeClf: def __init__(self, max_depth=5, min_samples_split=2, max_leafs=20): self.max_depth = max_depth self.min_samples_split = min_samples_split self.max_leafs = max_leafs self.tree = None self.leafs_cnt = 0 def node_entropy(self, probs): return -np.sum([p * np.log2(p) for p in probs if p > 0]) def node_ig(self, x_col, y, split_value): left_mask = x_col <= split_value right_mask = x_col > split_value if len(x_col[left_mask]) == 0 or len(x_col[right_mask]) == 0: return 0 left_counts = np.bincount(y[left_mask]) right_counts = np.bincount(y[right_mask]) left_probs = left_counts / len(y[left_mask]) if len(y[left_mask]) > 0 else np.zeros_like(left_counts) right_probs = right_counts / len(y[right_mask]) if len(y[right_mask]) > 0 else np.zeros_like(right_counts) entropy_after = (len(y[left_mask]) / len(y) * self.node_entropy(left_probs) + len(y[right_mask]) / len(y) * self.node_entropy(right_probs)) entropy_before = self.node_entropy(np.bincount(y) / len(y)) return entropy_before - entropy_after def get_best_split(self, X: pd.DataFrame, y: pd.Series): best_col, best_split_value, best_ig = None, None, -np.inf for col in X.columns: sorted_unique_values = np.sort(X[col].unique()) for i in range(1, len(sorted_unique_values)): split_value = (sorted_unique_values[i - 1] + sorted_unique_values) / 2 ig = self.node_ig(X[col], y, split_value) if ig > best_ig: best_ig = ig best_col = col best_split_value = split_value return best_col, best_split_value def fit(self, X: pd.DataFrame, y: pd.Series, depth=1, node=None): if self.max_leafs < 2: self.leafs_cnt = 2 return if node is None: node = {} self.tree = node best_col, best_split_value = self.get_best_split(X, y) node['type'] = None node['feature'] = best_col node['threshold'] = best_split_value if len(y.unique()) == 1: self.leafs_cnt += 1 node['type'] = 'leaf' node['class_counts'] = {y.unique()[0]: len(y)} return if len(y) == 1: self.leafs_cnt += 1 node['type'] = 'leaf' node['class_counts'] = {y.values[0]: 1} return if depth >= self.max_depth or len(y) < self.min_samples_split or (self.leafs_cnt + 2 > self.max_leafs): self.leafs_cnt += 1 node['type'] = 'leaf' node['class_counts'] = y.value_counts().to_dict() return if best_col is None: node['type'] = 'leaf' node['class_counts'] = y.value_counts().to_dict() self.leafs_cnt += 1 return node['type'] = 'node' node['feature'] = best_col node['threshold'] = best_split_value left_mask = X[best_col] <= best_split_value right_mask = X[best_col] > best_split_value node['left'] = {} node['right'] = {} self.fit(X[left_mask], y[left_mask], depth + 1, node['left']) self.fit(X[right_mask], y[right_mask], depth + 1, node['right']) def bypass_tree(self, node, sample): while node['type'] == 'node': feature_value = sample[node['feature']] if feature_value <= node['threshold']: node = node['left'] else: node = node['right'] total_count = sum(node['class_counts'].values()) class_1_count = node['class_counts'].get(1, 0) class1_proba = class_1_count / total_count if total_count > 0 else 0 return class1_proba def predict_proba(self, X: pd.DataFrame): proba = [] for _, sample in X.iterrows(): class1_proba = self.bypass_tree(self.tree, sample) proba.append(class1_proba) return np.array(proba) def predict(self, X: pd.DataFrame): return [1 if p[0] > 0.5 else 0 for p in self.predict_proba(X)] df = pd.read_csv('c:\\Users\\Nijat\\Downloads\\banknote+authentication.zip', header=None) df.columns = ['variance', 'skewness', 'curtosis', 'entropy', 'target'] X, y = df.iloc[:, :4], df['target'] model = MyTreeClf(max_depth=3, min_samples_split=2, max_leafs=1) model.fit(X, y) print(model.predict(X)) The predict and predict_proba methods take a matrix of features in the form of a pandas dataframe. For each row from the dataframe: Traverse the nodes of the tree up to one of the leaves. Write the probability for the first class. Return a list of predictions - as many as there are rows in the dataframe: predict_proba - returns probabilities (for the first class). predict - translates probabilities into binary classes by threshold > 0.5 Validation: Input data: two sets of parameters for the decision tree Output: returned predictions (their sum) of probabilities and labels I exactly don't know about datasets that used for checking the code but I think that it's "Banknote authentication" Sample input: {"max_depth": 3, "min_samples_split": 2, "max_leafs": 1} Sample output: 11.2443438914 Continue reading...
