Loading deepchem/models/__init__.py +7 −1 Original line number Diff line number Diff line """ Gathers all models in one place for convenient imports """ # flake8: noqa from deepchem.models.models import Model from deepchem.models.keras_model import KerasModel from deepchem.models.xgboost_models import XGBoostModel from deepchem.models.multitask import SingletaskToMultitask from deepchem.models.callbacks import ValidationCallback Loading @@ -27,6 +27,12 @@ from deepchem.models.chemnet_models import Smiles2Vec, ChemCeption # scikit-learn model from deepchem.models.sklearn_models import SklearnModel # XGBoost model try: from deepchem.models.xgboost_models import XGBoostModel except ModuleNotFoundError: pass try: from deepchem.models.torch_model import TorchModel except ModuleNotFoundError: Loading deepchem/models/xgboost_models/__init__.py +2 −129 Original line number Diff line number Diff line """ Scikit-learn wrapper interface of xgboost """ import numpy as np import os import logging from deepchem.models import Model from deepchem.models.sklearn_models import SklearnModel from deepchem.utils.save import load_from_disk from deepchem.utils.save import save_to_disk from sklearn.model_selection import train_test_split, GridSearchCV import tempfile logger = logging.getLogger(__name__) class XGBoostModel(SklearnModel): """ Abstract base class for XGBoost model. """ def __init__(self, model_instance=None, model_dir=None, **kwargs): """Abstract class for XGBoost models. Parameters ---------- model_instance: object Scikit-learn wrapper interface of xgboost model_dir: str Path to directory where model will be stored. """ if model_dir is not None: if not os.path.exists(model_dir): os.makedirs(model_dir) else: model_dir = tempfile.mkdtemp() self.model_dir = model_dir self.model_instance = model_instance self.model_class = model_instance.__class__ if 'early_stopping_rounds' in kwargs: self.early_stopping_rounds = kwargs['early_stopping_rounds'] else: self.early_stopping_rounds = 50 def fit(self, dataset, **kwargs): """ Fits XGBoost model to data. """ X = dataset.X y = np.squeeze(dataset.y) w = np.squeeze(dataset.w) seed = self.model_instance.random_state import xgboost as xgb if isinstance(self.model_instance, xgb.XGBClassifier): xgb_metric = "auc" sklearn_metric = "roc_auc" stratify = y elif isinstance(self.model_instance, xgb.XGBRegressor): xgb_metric = "mae" sklearn_metric = "neg_mean_absolute_error" stratify = None best_param = self._search_param(sklearn_metric, X, y) # update model with best param self.model_instance = self.model_class(**best_param) # Find optimal n_estimators based on original learning_rate # and early_stopping_rounds X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=seed, stratify=stratify) self.model_instance.fit( X_train, y_train, early_stopping_rounds=self.early_stopping_rounds, eval_metric=xgb_metric, eval_set=[(X_train, y_train), (X_test, y_test)]) # Since test size is 20%, when retrain model to whole data, expect # n_estimator increased to 1/0.8 = 1.25 time. estimated_best_round = np.round(self.model_instance.best_ntree_limit * 1.25) self.model_instance.n_estimators = np.int64(estimated_best_round) self.model_instance.fit(X, y, eval_metric=xgb_metric) def _search_param(self, metric, X, y): ''' Find best potential parameters set using few n_estimators ''' # Make sure user specified params are in the grid. def unique_not_none(values): return list(np.unique([x for x in values if x is not None])) max_depth_grid = unique_not_none([self.model_instance.max_depth, 5, 7]) colsample_bytree_grid = unique_not_none( [self.model_instance.colsample_bytree, 0.66, 0.9]) reg_lambda_grid = unique_not_none([self.model_instance.reg_lambda, 1, 5]) learning_rate = 0.3 if self.model_instance.learning_rate is not None: learning_rate = max(learning_rate, self.model_instance.learning_rate) n_estimators = 60 if self.model_instance.n_estimators is not None: n_estimators = min(n_estimators, self.model_instance.n_estimators) param_grid = { 'max_depth': max_depth_grid, 'learning_rate': [learning_rate], 'n_estimators': [n_estimators], 'gamma': [self.model_instance.gamma], 'min_child_weight': [self.model_instance.min_child_weight], 'max_delta_step': [self.model_instance.max_delta_step], 'subsample': [self.model_instance.subsample], 'colsample_bytree': colsample_bytree_grid, 'colsample_bylevel': [self.model_instance.colsample_bylevel], 'reg_alpha': [self.model_instance.reg_alpha], 'reg_lambda': reg_lambda_grid, 'scale_pos_weight': [self.model_instance.scale_pos_weight], 'base_score': [self.model_instance.base_score], 'seed': [self.model_instance.random_state] } grid_search = GridSearchCV( self.model_instance, param_grid, cv=2, refit=False, scoring=metric) grid_search.fit(X, y) best_params = grid_search.best_params_ # Change params back original params best_params['learning_rate'] = self.model_instance.learning_rate best_params['n_estimators'] = self.model_instance.n_estimators return best_params # flake8: noqa from deepchem.models.sklean_models.xgboost_models.xgboost_model import XGBoostModel deepchem/models/xgboost_models/xgboost_model.py 0 → 100644 +136 −0 Original line number Diff line number Diff line """ Scikit-learn wrapper interface of xgboost """ import os import logging import tempfile from typing import Any, Dict, Optional, Union import numpy as np import xgboost as xgb from sklearn.model_selection import train_test_split, GridSearchCV from deepchem.data import Dataset from deepchem.models.sklearn_models import SklearnModel logger = logging.getLogger(__name__) class XGBoostModel(SklearnModel): """ Scikit-learn wrapper class for XGBoost model. Notes ----- This class require XGBoost to be installed. """ def __init__(self, model_instance: Union[xgb.XGBClassifier, xgb.XGBRegressor], model_dir: Optional[str] = None, **kwargs): """ Parameters ---------- model_instance: Union[xgb.XGBClassifier, xgb.XGBRegressor] Scikit-learn wrapper interface of XGBoost models. model_dir: str, optional (default None) Path to directory where model will be stored. """ if model_dir is not None: if not os.path.exists(model_dir): os.makedirs(model_dir) else: model_dir = tempfile.mkdtemp() self.model_dir = model_dir self.model_instance = model_instance self.model_class = model_instance.__class__ if 'early_stopping_rounds' in kwargs: self.early_stopping_rounds = kwargs['early_stopping_rounds'] else: self.early_stopping_rounds = 50 def fit(self, dataset: Dataset, **kwargs) -> None: """Fits XGBoost model to data. dataset: Dataset The `Dataset` to train this model on. """ X = dataset.X y = np.squeeze(dataset.y) seed = self.model_instance.random_state if isinstance(self.model_instance, xgb.XGBClassifier): xgb_metric = "auc" sklearn_metric = "roc_auc" stratify = y elif isinstance(self.model_instance, xgb.XGBRegressor): xgb_metric = "mae" sklearn_metric = "neg_mean_absolute_error" stratify = None best_param = self._search_param(sklearn_metric, X, y) # update model with best param self.model_instance = self.model_class(**best_param) # Find optimal n_estimators based on original learning_rate # and early_stopping_rounds X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=seed, stratify=stratify) self.model_instance.fit( X_train, y_train, early_stopping_rounds=self.early_stopping_rounds, eval_metric=xgb_metric, eval_set=[(X_train, y_train), (X_test, y_test)]) # Since test size is 20%, when retrain model to whole data, expect # n_estimator increased to 1/0.8 = 1.25 time. estimated_best_round = np.round(self.model_instance.best_ntree_limit * 1.25) self.model_instance.n_estimators = np.int64(estimated_best_round) self.model_instance.fit(X, y, eval_metric=xgb_metric) def _search_param(self, metric: str, X: np.ndarray, y: np.ndarray) -> Dict[str, Any]: """Find best potential parameters set using few n_estimators""" # Make sure user specified params are in the grid. def unique_not_none(values): return list(np.unique([x for x in values if x is not None])) max_depth_grid = unique_not_none([self.model_instance.max_depth, 5, 7]) colsample_bytree_grid = unique_not_none( [self.model_instance.colsample_bytree, 0.66, 0.9]) reg_lambda_grid = unique_not_none([self.model_instance.reg_lambda, 1, 5]) learning_rate = 0.3 if self.model_instance.learning_rate is not None: learning_rate = max(learning_rate, self.model_instance.learning_rate) n_estimators = 60 if self.model_instance.n_estimators is not None: n_estimators = min(n_estimators, self.model_instance.n_estimators) param_grid = { 'max_depth': max_depth_grid, 'learning_rate': [learning_rate], 'n_estimators': [n_estimators], 'gamma': [self.model_instance.gamma], 'min_child_weight': [self.model_instance.min_child_weight], 'max_delta_step': [self.model_instance.max_delta_step], 'subsample': [self.model_instance.subsample], 'colsample_bytree': colsample_bytree_grid, 'colsample_bylevel': [self.model_instance.colsample_bylevel], 'reg_alpha': [self.model_instance.reg_alpha], 'reg_lambda': reg_lambda_grid, 'scale_pos_weight': [self.model_instance.scale_pos_weight], 'base_score': [self.model_instance.base_score], 'seed': [self.model_instance.random_state] } grid_search = GridSearchCV( self.model_instance, param_grid, cv=2, refit=False, scoring=metric) grid_search.fit(X, y) best_params = grid_search.best_params_ # Change params back original params best_params['learning_rate'] = self.model_instance.learning_rate best_params['n_estimators'] = self.model_instance.n_estimators return best_params Loading
deepchem/models/__init__.py +7 −1 Original line number Diff line number Diff line """ Gathers all models in one place for convenient imports """ # flake8: noqa from deepchem.models.models import Model from deepchem.models.keras_model import KerasModel from deepchem.models.xgboost_models import XGBoostModel from deepchem.models.multitask import SingletaskToMultitask from deepchem.models.callbacks import ValidationCallback Loading @@ -27,6 +27,12 @@ from deepchem.models.chemnet_models import Smiles2Vec, ChemCeption # scikit-learn model from deepchem.models.sklearn_models import SklearnModel # XGBoost model try: from deepchem.models.xgboost_models import XGBoostModel except ModuleNotFoundError: pass try: from deepchem.models.torch_model import TorchModel except ModuleNotFoundError: Loading
deepchem/models/xgboost_models/__init__.py +2 −129 Original line number Diff line number Diff line """ Scikit-learn wrapper interface of xgboost """ import numpy as np import os import logging from deepchem.models import Model from deepchem.models.sklearn_models import SklearnModel from deepchem.utils.save import load_from_disk from deepchem.utils.save import save_to_disk from sklearn.model_selection import train_test_split, GridSearchCV import tempfile logger = logging.getLogger(__name__) class XGBoostModel(SklearnModel): """ Abstract base class for XGBoost model. """ def __init__(self, model_instance=None, model_dir=None, **kwargs): """Abstract class for XGBoost models. Parameters ---------- model_instance: object Scikit-learn wrapper interface of xgboost model_dir: str Path to directory where model will be stored. """ if model_dir is not None: if not os.path.exists(model_dir): os.makedirs(model_dir) else: model_dir = tempfile.mkdtemp() self.model_dir = model_dir self.model_instance = model_instance self.model_class = model_instance.__class__ if 'early_stopping_rounds' in kwargs: self.early_stopping_rounds = kwargs['early_stopping_rounds'] else: self.early_stopping_rounds = 50 def fit(self, dataset, **kwargs): """ Fits XGBoost model to data. """ X = dataset.X y = np.squeeze(dataset.y) w = np.squeeze(dataset.w) seed = self.model_instance.random_state import xgboost as xgb if isinstance(self.model_instance, xgb.XGBClassifier): xgb_metric = "auc" sklearn_metric = "roc_auc" stratify = y elif isinstance(self.model_instance, xgb.XGBRegressor): xgb_metric = "mae" sklearn_metric = "neg_mean_absolute_error" stratify = None best_param = self._search_param(sklearn_metric, X, y) # update model with best param self.model_instance = self.model_class(**best_param) # Find optimal n_estimators based on original learning_rate # and early_stopping_rounds X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=seed, stratify=stratify) self.model_instance.fit( X_train, y_train, early_stopping_rounds=self.early_stopping_rounds, eval_metric=xgb_metric, eval_set=[(X_train, y_train), (X_test, y_test)]) # Since test size is 20%, when retrain model to whole data, expect # n_estimator increased to 1/0.8 = 1.25 time. estimated_best_round = np.round(self.model_instance.best_ntree_limit * 1.25) self.model_instance.n_estimators = np.int64(estimated_best_round) self.model_instance.fit(X, y, eval_metric=xgb_metric) def _search_param(self, metric, X, y): ''' Find best potential parameters set using few n_estimators ''' # Make sure user specified params are in the grid. def unique_not_none(values): return list(np.unique([x for x in values if x is not None])) max_depth_grid = unique_not_none([self.model_instance.max_depth, 5, 7]) colsample_bytree_grid = unique_not_none( [self.model_instance.colsample_bytree, 0.66, 0.9]) reg_lambda_grid = unique_not_none([self.model_instance.reg_lambda, 1, 5]) learning_rate = 0.3 if self.model_instance.learning_rate is not None: learning_rate = max(learning_rate, self.model_instance.learning_rate) n_estimators = 60 if self.model_instance.n_estimators is not None: n_estimators = min(n_estimators, self.model_instance.n_estimators) param_grid = { 'max_depth': max_depth_grid, 'learning_rate': [learning_rate], 'n_estimators': [n_estimators], 'gamma': [self.model_instance.gamma], 'min_child_weight': [self.model_instance.min_child_weight], 'max_delta_step': [self.model_instance.max_delta_step], 'subsample': [self.model_instance.subsample], 'colsample_bytree': colsample_bytree_grid, 'colsample_bylevel': [self.model_instance.colsample_bylevel], 'reg_alpha': [self.model_instance.reg_alpha], 'reg_lambda': reg_lambda_grid, 'scale_pos_weight': [self.model_instance.scale_pos_weight], 'base_score': [self.model_instance.base_score], 'seed': [self.model_instance.random_state] } grid_search = GridSearchCV( self.model_instance, param_grid, cv=2, refit=False, scoring=metric) grid_search.fit(X, y) best_params = grid_search.best_params_ # Change params back original params best_params['learning_rate'] = self.model_instance.learning_rate best_params['n_estimators'] = self.model_instance.n_estimators return best_params # flake8: noqa from deepchem.models.sklean_models.xgboost_models.xgboost_model import XGBoostModel
deepchem/models/xgboost_models/xgboost_model.py 0 → 100644 +136 −0 Original line number Diff line number Diff line """ Scikit-learn wrapper interface of xgboost """ import os import logging import tempfile from typing import Any, Dict, Optional, Union import numpy as np import xgboost as xgb from sklearn.model_selection import train_test_split, GridSearchCV from deepchem.data import Dataset from deepchem.models.sklearn_models import SklearnModel logger = logging.getLogger(__name__) class XGBoostModel(SklearnModel): """ Scikit-learn wrapper class for XGBoost model. Notes ----- This class require XGBoost to be installed. """ def __init__(self, model_instance: Union[xgb.XGBClassifier, xgb.XGBRegressor], model_dir: Optional[str] = None, **kwargs): """ Parameters ---------- model_instance: Union[xgb.XGBClassifier, xgb.XGBRegressor] Scikit-learn wrapper interface of XGBoost models. model_dir: str, optional (default None) Path to directory where model will be stored. """ if model_dir is not None: if not os.path.exists(model_dir): os.makedirs(model_dir) else: model_dir = tempfile.mkdtemp() self.model_dir = model_dir self.model_instance = model_instance self.model_class = model_instance.__class__ if 'early_stopping_rounds' in kwargs: self.early_stopping_rounds = kwargs['early_stopping_rounds'] else: self.early_stopping_rounds = 50 def fit(self, dataset: Dataset, **kwargs) -> None: """Fits XGBoost model to data. dataset: Dataset The `Dataset` to train this model on. """ X = dataset.X y = np.squeeze(dataset.y) seed = self.model_instance.random_state if isinstance(self.model_instance, xgb.XGBClassifier): xgb_metric = "auc" sklearn_metric = "roc_auc" stratify = y elif isinstance(self.model_instance, xgb.XGBRegressor): xgb_metric = "mae" sklearn_metric = "neg_mean_absolute_error" stratify = None best_param = self._search_param(sklearn_metric, X, y) # update model with best param self.model_instance = self.model_class(**best_param) # Find optimal n_estimators based on original learning_rate # and early_stopping_rounds X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=seed, stratify=stratify) self.model_instance.fit( X_train, y_train, early_stopping_rounds=self.early_stopping_rounds, eval_metric=xgb_metric, eval_set=[(X_train, y_train), (X_test, y_test)]) # Since test size is 20%, when retrain model to whole data, expect # n_estimator increased to 1/0.8 = 1.25 time. estimated_best_round = np.round(self.model_instance.best_ntree_limit * 1.25) self.model_instance.n_estimators = np.int64(estimated_best_round) self.model_instance.fit(X, y, eval_metric=xgb_metric) def _search_param(self, metric: str, X: np.ndarray, y: np.ndarray) -> Dict[str, Any]: """Find best potential parameters set using few n_estimators""" # Make sure user specified params are in the grid. def unique_not_none(values): return list(np.unique([x for x in values if x is not None])) max_depth_grid = unique_not_none([self.model_instance.max_depth, 5, 7]) colsample_bytree_grid = unique_not_none( [self.model_instance.colsample_bytree, 0.66, 0.9]) reg_lambda_grid = unique_not_none([self.model_instance.reg_lambda, 1, 5]) learning_rate = 0.3 if self.model_instance.learning_rate is not None: learning_rate = max(learning_rate, self.model_instance.learning_rate) n_estimators = 60 if self.model_instance.n_estimators is not None: n_estimators = min(n_estimators, self.model_instance.n_estimators) param_grid = { 'max_depth': max_depth_grid, 'learning_rate': [learning_rate], 'n_estimators': [n_estimators], 'gamma': [self.model_instance.gamma], 'min_child_weight': [self.model_instance.min_child_weight], 'max_delta_step': [self.model_instance.max_delta_step], 'subsample': [self.model_instance.subsample], 'colsample_bytree': colsample_bytree_grid, 'colsample_bylevel': [self.model_instance.colsample_bylevel], 'reg_alpha': [self.model_instance.reg_alpha], 'reg_lambda': reg_lambda_grid, 'scale_pos_weight': [self.model_instance.scale_pos_weight], 'base_score': [self.model_instance.base_score], 'seed': [self.model_instance.random_state] } grid_search = GridSearchCV( self.model_instance, param_grid, cv=2, refit=False, scoring=metric) grid_search.fit(X, y) best_params = grid_search.best_params_ # Change params back original params best_params['learning_rate'] = self.model_instance.learning_rate best_params['n_estimators'] = self.model_instance.n_estimators return best_params
