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- from copy import deepcopy
- import numpy as np
- from sklearn.base import BaseEstimator
- from sklearn.tree import DecisionTreeRegressor
- from sklearn.utils import check_array
- from sklearn.utils.multiclass import check_classification_targets
- from sklearn.utils.validation import check_X_y, check_is_fitted, _check_sample_weight
- from sklearn.model_selection import train_test_split
- from sklearn.base import RegressorMixin, ClassifierMixin
- from sklearn.metrics import accuracy_score, roc_auc_score
- import imodels
- class TreeGAMMinimal(BaseEstimator):
- """Tree-based GAM classifier.
- Uses cyclical boosting to fit a GAM with small trees.
- Simplified version of the explainable boosting machine described in https://github.com/interpretml/interpret
- Only works for binary classification.
- Fits a scalar bias to the mean.
- """
- def __init__(
- self,
- n_boosting_rounds=100,
- max_leaf_nodes=3,
- learning_rate: float = 0.01,
- boosting_strategy="cyclic",
- validation_frac=0.15,
- random_state=None,
- ):
- """
- Params
- ------
- n_boosting_rounds : int
- Number of boosting rounds for the cyclic boosting.
- max_leaf_nodes : int
- Maximum number of leaf nodes for the trees in the cyclic boosting.
- learning_rate: float
- Learning rate for the cyclic boosting.
- boosting_strategy : str ["cyclic", "greedy"]
- Whether to use cyclic boosting (cycle over features) or greedy boosting (select best feature at each step)
- validation_frac: float
- Fraction of data to use for early stopping.
- random_state : int
- Random seed.
- """
- self.n_boosting_rounds = n_boosting_rounds
- self.max_leaf_nodes = max_leaf_nodes
- self.learning_rate = learning_rate
- self.boosting_strategy = boosting_strategy
- self.validation_frac = validation_frac
- self.random_state = random_state
- def fit(self, X, y, sample_weight=None):
- X, y = check_X_y(X, y, accept_sparse=False, multi_output=False)
- if isinstance(self, ClassifierMixin):
- check_classification_targets(y)
- self.classes_, y = np.unique(y, return_inverse=True)
- sample_weight = _check_sample_weight(sample_weight, X, dtype=None)
- # split into train and validation for early stopping
- (
- X_train,
- X_val,
- y_train,
- y_val,
- sample_weight_train,
- sample_weight_val,
- ) = train_test_split(
- X,
- y,
- sample_weight,
- test_size=self.validation_frac,
- random_state=self.random_state,
- stratify=y if isinstance(self, ClassifierMixin) else None,
- )
- self.estimators_ = []
- self.bias_ = np.mean(y)
- self._cyclic_boost(
- X_train,
- y_train,
- sample_weight_train,
- X_val,
- y_val,
- sample_weight_val,
- )
- self.mse_val_ = self._calc_mse(X_val, y_val, sample_weight_val)
- return self
- def _cyclic_boost(
- self, X_train, y_train, sample_weight_train, X_val, y_val, sample_weight_val
- ):
- """Apply cyclic boosting, storing trees in self.estimators_"""
- residuals_train = y_train - self.predict_proba(X_train)[:, 1]
- mse_val = self._calc_mse(X_val, y_val, sample_weight_val)
- for _ in range(self.n_boosting_rounds):
- boosting_round_ests = []
- boosting_round_mses = []
- feature_nums = np.arange(X_train.shape[1])
- for feature_num in feature_nums:
- X_ = np.zeros_like(X_train)
- X_[:, feature_num] = X_train[:, feature_num]
- est = DecisionTreeRegressor(
- max_leaf_nodes=self.max_leaf_nodes,
- random_state=self.random_state,
- )
- est.fit(X_, residuals_train, sample_weight=sample_weight_train)
- succesfully_split_on_feature = np.all(
- (est.tree_.feature[0] == feature_num) | (
- est.tree_.feature[0] == -2)
- )
- if not succesfully_split_on_feature:
- continue
- self.estimators_.append(est)
- residuals_train_new = (
- residuals_train - self.learning_rate * est.predict(X_train)
- )
- if self.boosting_strategy == "cyclic":
- residuals_train = residuals_train_new
- elif self.boosting_strategy == "greedy":
- mse_train_new = self._calc_mse(
- X_train, y_train, sample_weight_train
- )
- # don't add each estimator for greedy
- boosting_round_ests.append(
- deepcopy(self.estimators_.pop()))
- boosting_round_mses.append(mse_train_new)
- if self.boosting_strategy == "greedy":
- best_est = boosting_round_ests[np.argmin(boosting_round_mses)]
- self.estimators_.append(best_est)
- residuals_train = (
- residuals_train - self.learning_rate *
- best_est.predict(X_train)
- )
- # early stopping if validation error does not decrease
- mse_val_new = self._calc_mse(X_val, y_val, sample_weight_val)
- if mse_val_new >= mse_val:
- # print("early stop!")
- return
- else:
- mse_val = mse_val_new
- def predict_proba(self, X):
- X = check_array(X, accept_sparse=False, dtype=None)
- check_is_fitted(self)
- probs1 = np.ones(X.shape[0]) * self.bias_
- for i, est in enumerate(self.estimators_):
- probs1 += self.learning_rate * est.predict(X)
- probs1 = np.clip(probs1, a_min=0, a_max=1)
- return np.array([1 - probs1, probs1]).T
- def predict(self, X):
- if isinstance(self, RegressorMixin):
- return self.predict_proba(X)[:, 1]
- elif isinstance(self, ClassifierMixin):
- return np.argmax(self.predict_proba(X), axis=1)
- def _calc_mse(self, X, y, sample_weight=None):
- return np.average(
- np.square(y - self.predict_proba(X)[:, 1]),
- weights=sample_weight,
- )
- class TreeGAMMinimalRegressor(TreeGAMMinimal, RegressorMixin):
- ...
- class TreeGAMMinimalClassifier(TreeGAMMinimal, ClassifierMixin):
- ...
- if __name__ == "__main__":
- X, y, feature_names = imodels.get_clean_dataset("heart")
- X, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
- gam = TreeGAMMinimalClassifier(
- boosting_strategy="cyclic",
- random_state=42,
- learning_rate=0.1,
- max_leaf_nodes=3,
- n_boosting_rounds=100,
- )
- gam.fit(X, y_train)
- # check roc auc score
- y_pred = gam.predict_proba(X_test)[:, 1]
- # print(
- # "train roc:",
- # roc_auc_score(y_train, gam.predict_proba(X)[:, 1]).round(3),
- # )
- print(f"test roc: {roc_auc_score(y_test, y_pred):.3f}")
- print(f"test acc {accuracy_score(y_test, gam.predict(X_test)):.3f}")
- print('\t(imb:', np.mean(y_test).round(3), ')')
- # print(
- # "accs",
- # accuracy_score(y_train, gam.predict(X)).round(3),
- # accuracy_score(y_test, gam.predict(X_test)).round(3),
- # "imb",
- # np.mean(y_train).round(3),
- # np.mean(y_test).round(3),
- # )
- # # print(gam.estimators_)
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