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|
- from copy import deepcopy
- import numpy as np
- import pandas as pd
- from sklearn.base import BaseEstimator
- from sklearn.linear_model import ElasticNetCV, LinearRegression, RidgeCV, LassoCV, LogisticRegressionCV
- from sklearn.tree import DecisionTreeRegressor
- from sklearn.utils.validation import check_is_fitted
- from sklearn.utils import check_array
- from sklearn.utils.multiclass import check_classification_targets
- from sklearn.utils.validation import check_X_y
- from sklearn.utils.validation import _check_sample_weight
- from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, AdaBoostClassifier, AdaBoostRegressor
- from sklearn.model_selection import train_test_split
- from sklearn.metrics import accuracy_score, roc_auc_score
- from tqdm import tqdm
- from sklearn.multioutput import MultiOutputRegressor, MultiOutputClassifier
- from collections import defaultdict
- import pandas as pd
- import json
- from sklearn.preprocessing import StandardScaler
- from imodels.util.transforms import CorrelationScreenTransformer
- import imodels
- from interpret.glassbox import ExplainableBoostingClassifier, ExplainableBoostingRegressor
- from sklearn.base import RegressorMixin, ClassifierMixin
- # See notes on EBM in the docs
- # main file: https://github.com/interpretml/interpret/blob/develop/python/interpret-core/interpret/glassbox/_ebm/_ebm.py
- # merge ebms: https://github.com/interpretml/interpret/blob/develop/python/interpret-core/interpret/glassbox/_ebm/_merge_ebms.py#L280
- # eval_terms: https://interpret.ml/docs/python/api/ExplainableBoostingRegressor.html#interpret.glassbox.ExplainableBoostingRegressor.eval_terms
- class MultiTaskGAM(BaseEstimator):
- """EBM-based GAM that shares curves for predicting different outputs.
- - If only one target is given, we fit an EBM to predict each covariate
- - If multiple targets are given, we fit a an EBM to predict each target
- - If only one target is given and use_single_task_with_reweighting, we fit an EBM to predict the single target, then apply reweighting
- """
- def __init__(
- self,
- ebm_kwargs={'n_jobs': 1, 'max_rounds': 5000, },
- multitask=True,
- interactions=0.95,
- linear_penalty='ridge',
- onehot_prior=False,
- renormalize_features=False,
- use_normalize_feature_targets=False,
- use_internal_classifiers=False,
- fit_target_curves=True,
- use_correlation_screening_for_features=False,
- use_single_task_with_reweighting=False,
- fit_linear_frac: float = None,
- random_state=42,
- ):
- """
- Params
- ------
- Note: args override ebm_kwargs if there are duplicates
- one_hot_prior: bool
- If True and multitask, the linear model will be fit with a prior that the ebm
- features predicting the target should have coef 1
- renormalize_features: bool
- If True, renormalize the features before fitting the linear model
- use_normalize_feature_targets: bool
- whether to normalize the features used as targets for internal EBMs
- (does not apply to target columns)
- If input features are normalized already, this has no effect
- use_internal_classifiers: bool
- whether to use internal classifiers (as opposed to regressors)
- fit_target_curves: bool
- whether to fit an EBM to predict the target
- use_single_task_with_reweighting: bool
- fit an EBM to predict the single target, then apply linear reweighting
- use_correlation_screening_for_features: bool
- whether to use correlation screening for features
- fit_linear_frac: float
- If not None, the fraction of features to use for the linear model (the rest are used for the EBM)
- """
- self.ebm_kwargs = ebm_kwargs
- self.multitask = multitask
- self.linear_penalty = linear_penalty
- self.random_state = random_state
- self.interactions = interactions
- self.onehot_prior = onehot_prior
- self.use_normalize_feature_targets = use_normalize_feature_targets
- self.renormalize_features = renormalize_features
- self.use_internal_classifiers = use_internal_classifiers
- self.fit_target_curves = fit_target_curves
- self.use_single_task_with_reweighting = use_single_task_with_reweighting
- self.use_correlation_screening_for_features = use_correlation_screening_for_features
- self.fit_linear_frac = fit_linear_frac
- # override ebm_kwargs
- ebm_kwargs['random_state'] = random_state
- ebm_kwargs['interactions'] = interactions
- def fit(self, X, y, sample_weight=None):
- X, y = check_X_y(X, y, accept_sparse=False, multi_output=True)
- self.n_outputs_ = 1 if len(y.shape) == 1 else y.shape[1]
- if self.n_outputs_ > 1 and not self.fit_target_curves:
- raise ValueError(
- "fit_target_curves must be True when n_outputs > 1")
- if isinstance(self, ClassifierMixin):
- check_classification_targets(y)
- if self.n_outputs_ == 1:
- self.classes_, y = np.unique(y, return_inverse=True)
- if len(self.classes_) > 2:
- raise ValueError(
- "MultiTaskGAMClassifier currently only supports binary classification")
- elif self.n_outputs_ > 1:
- self.classes_ = [np.unique(y[:, i])
- for i in range(self.n_outputs_)]
- if any(len(c) > 2 for c in self.classes_):
- raise ValueError(
- "MultiTaskGAMClassifier currently only supports binary classification")
- sample_weight = _check_sample_weight(sample_weight, X, dtype=None)
- if self.use_single_task_with_reweighting:
- assert self.n_outputs_ == 1, "use_single_task_with_reweighting only works with one output"
- assert self.multitask, "use_single_task_with_reweighting only works with multitask"
- # just fit ebm normally
- if not self.multitask:
- if isinstance(self, ClassifierMixin):
- self.ebm_ = ExplainableBoostingClassifier(**self.ebm_kwargs)
- else:
- self.ebm_ = ExplainableBoostingRegressor(**self.ebm_kwargs)
- # fit
- if self.n_outputs_ > 1:
- if isinstance(self, ClassifierMixin):
- self.ebm_multioutput_ = MultiOutputClassifier(self.ebm_)
- else:
- self.ebm_multioutput_ = MultiOutputRegressor(self.ebm_)
- self.ebm_multioutput_.fit(X, y, sample_weight=sample_weight)
- else:
- self.ebm_.fit(X, y, sample_weight=sample_weight)
- return self
- # fit EBM(s)
- self.ebms_ = []
- num_samples, num_features = X.shape
- idxs_ebm, idxs_lin = self._split_data(num_samples)
- # fit EBM
- if self.use_single_task_with_reweighting:
- # fit an EBM to predict the single output
- self.ebms_.append(self._initialize_ebm_internal(y[idxs_ebm]))
- self.ebms_[-1].fit(X[idxs_ebm], y[idxs_ebm],
- sample_weight=sample_weight[idxs_ebm])
- elif self.n_outputs_ == 1:
- # with 1 output, we fit an EBM to each feature
- for task_num in tqdm(range(num_features)):
- y_ = np.ascontiguousarray(X[idxs_ebm][:, task_num])
- X_ = deepcopy(X[idxs_ebm])
- X_[:, task_num] = 0
- self.ebms_.append(self._initialize_ebm_internal(y_))
- if isinstance(self, ClassifierMixin):
- _, y_ = np.unique(y_, return_inverse=True)
- elif self.use_normalize_feature_targets:
- y_ = StandardScaler().fit_transform(y_.reshape(-1, 1)).ravel()
- self.ebms_[task_num].fit(
- X_, y_, sample_weight=sample_weight[idxs_ebm])
- # also fit an EBM to the target
- if self.fit_target_curves:
- self.ebms_.append(self._initialize_ebm_internal(y[idxs_ebm]))
- self.ebms_[num_features].fit(
- X[idxs_ebm], y[idxs_ebm], sample_weight=sample_weight[idxs_ebm])
- elif self.n_outputs_ > 1:
- # with multiple outputs, we fit an EBM to each output
- for task_num in tqdm(range(self.n_outputs_)):
- self.ebms_.append(self._initialize_ebm_internal(y[idxs_ebm]))
- y_ = np.ascontiguousarray(y[idxs_ebm][:, task_num])
- self.ebms_[task_num].fit(
- X[idxs_ebm], y_, sample_weight=sample_weight[idxs_ebm])
- # extract features from EBMs
- self.term_names_list_ = [
- ebm_.term_names_ for ebm_ in self.ebms_]
- self.term_names_ = sum(self.term_names_list_, [])
- feats = self._extract_ebm_features(X)
- if self.renormalize_features:
- self.scaler_ = StandardScaler()
- feats = self.scaler_.fit_transform(feats)
- if self.use_correlation_screening_for_features:
- self.correlation_screener_ = CorrelationScreenTransformer()
- feats = self.correlation_screener_.fit_transform(feats, y)
- feats[np.isinf(feats)] = 0
- # fit linear model
- self.lin_model = self._fit_linear_model(
- feats[idxs_lin], y[idxs_lin], sample_weight[idxs_lin])
- return self
- def _initialize_ebm_internal(self, y):
- if self.use_internal_classifiers and len(np.unique(y)) == 2:
- return ExplainableBoostingClassifier(**self.ebm_kwargs)
- else:
- return ExplainableBoostingRegressor(**self.ebm_kwargs)
- def _split_data(self, num_samples):
- '''Split data into EBM and linear model data
- '''
- if self.fit_linear_frac is not None:
- rng = np.random.RandomState(self.random_state)
- idxs_ebm = rng.choice(num_samples, int(
- num_samples * self.fit_linear_frac), replace=False)
- idxs_lin = np.array(
- [i for i in range(num_samples) if i not in idxs_ebm])
- else:
- idxs_ebm = np.arange(num_samples)
- idxs_lin = idxs_ebm
- assert len(idxs_ebm) > 0, f"No data for EBM! {self.fit_linear_frac=}"
- assert len(
- idxs_lin) > 0, f"No data for linear model! {self.fit_linear_frac=}"
- return idxs_ebm, idxs_lin
- def _fit_linear_model(self, feats, y, sample_weight):
- # fit a linear model to the features
- if isinstance(self, ClassifierMixin):
- lin_model = {
- 'ridge': LogisticRegressionCV(penalty='l2'),
- 'elasticnet': LogisticRegressionCV(penalty='elasticnet'),
- 'lasso': LogisticRegressionCV(penalty='l1'),
- }[self.linear_penalty]
- if self.n_outputs_ > 1:
- lin_model = MultiOutputClassifier(lin_model)
- else:
- lin_model = {
- 'ridge': RidgeCV(alphas=np.logspace(-2, 3, 7)),
- 'elasticnet': ElasticNetCV(n_alphas=7),
- 'lasso': LassoCV(n_alphas=7),
- }[self.linear_penalty]
- # onehot prior is a prior (for regression only) that
- # the ebm features predicting the target should have coef 1
- if not self.onehot_prior or isinstance(self, ClassifierMixin):
- lin_model.fit(feats, y, sample_weight=sample_weight)
- else:
- coef_prior_ = np.zeros((feats.shape[1], ))
- coef_prior_[:-len(self.term_names_list_[-1])] = 1
- preds_prior = feats @ coef_prior_
- residuals = y - preds_prior
- lin_model.fit(feats, residuals, sample_weight=sample_weight)
- lin_model.coef_ = lin_model.coef_ + coef_prior_
- return lin_model
- def _extract_ebm_features(self, X):
- '''
- Extract features by extracting all terms with EBM
- '''
- feats = np.empty((X.shape[0], len(self.term_names_)))
- offset = 0
- for ebm_num in range(len(self.ebms_)):
- n_features_ebm_num = len(self.term_names_list_[ebm_num])
- feats[:, offset: offset + n_features_ebm_num] = \
- self.ebms_[ebm_num].eval_terms(X)
- offset += n_features_ebm_num
- return feats
- def predict(self, X):
- check_is_fitted(self)
- X = check_array(X, accept_sparse=False)
- if hasattr(self, 'ebms_'):
- feats = self._extract_ebm_features(X)
- if hasattr(self, 'scaler_'):
- feats = self.scaler_.transform(feats)
- if hasattr(self, 'correlation_screener_'):
- feats = self.correlation_screener_.transform(feats)
- feats[np.isinf(feats)] = 0
- return self.lin_model.predict(feats)
- # multi-output without multitask learning
- elif hasattr(self, 'ebm_multioutput_'):
- return self.ebm_multioutput_.predict(X)
- # single-task standard
- elif hasattr(self, 'ebm_'):
- return self.ebm_.predict(X)
- def predict_proba(self, X):
- check_is_fitted(self)
- if hasattr(self, 'ebms_'):
- feats = self._extract_ebm_features(X)
- if hasattr(self, 'scaler_'):
- feats = self.scaler_.transform(feats)
- if hasattr(self, 'correlation_screener_'):
- feats = self.correlation_screener_.transform(feats)
- return self.lin_model.predict_proba(feats)
- # multi-output without multitask learning
- elif hasattr(self, 'ebm_multioutput_'):
- return self.ebm_multioutput_.predict_proba(X)
- # single-task standard
- elif hasattr(self, 'ebm_'):
- return self.ebm_.predict_proba(X)
- class MultiTaskGAMRegressor(MultiTaskGAM, RegressorMixin):
- ...
- class MultiTaskGAMClassifier(MultiTaskGAM, ClassifierMixin):
- ...
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