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model.py
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model.py
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'''!
* Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
'''
import numpy as np
import xgboost as xgb
import time
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.ensemble import ExtraTreesRegressor, ExtraTreesClassifier
from sklearn.linear_model import LogisticRegression
from lightgbm import LGBMClassifier, LGBMRegressor
from scipy.sparse import issparse
import pandas as pd
from . import tune
import logging
logger = logging.getLogger(__name__)
class BaseEstimator:
'''The abstract class for all learners
Typical example:
XGBoostEstimator: for regression
XGBoostSklearnEstimator: for classification
LGBMEstimator, RandomForestEstimator, LRL1Classifier, LRL2Classifier:
for both regression and classification
'''
def __init__(self, task='binary:logistic', **params):
'''Constructor
Args:
task: A string of the task type, one of
'binary:logistic', 'multi:softmax', 'regression'
n_jobs: An integer of the number of parallel threads
params: A dictionary of the hyperparameter names and values
'''
self.params = params
self.estimator_class = self._model = None
self._task = task
if '_estimator_type' in params:
self._estimator_type = params['_estimator_type']
del self.params['_estimator_type']
else:
self._estimator_type = "regressor" if task == 'regression' \
else "classifier"
def get_params(self, deep=False):
params = self.params.copy()
params["task"] = self._task
if hasattr(self, '_estimator_type'):
params['_estimator_type'] = self._estimator_type
return params
@property
def classes_(self):
return self._model.classes_
@property
def n_features_in_(self):
return self.model.n_features_in_
@property
def model(self):
'''Trained model after fit() is called, or None before fit() is called
'''
return self._model
@property
def estimator(self):
'''Trained model after fit() is called, or None before fit() is called
'''
return self._model
def _preprocess(self, X):
return X
def _fit(self, X_train, y_train, **kwargs):
current_time = time.time()
X_train = self._preprocess(X_train)
model = self.estimator_class(**self.params)
model.fit(X_train, y_train, **kwargs)
train_time = time.time() - current_time
self._model = model
return train_time
def fit(self, X_train, y_train, budget=None, **kwargs):
'''Train the model from given training data
Args:
X_train: A numpy array of training data in shape n*m
y_train: A numpy array of labels in shape n*1
budget: A float of the time budget in seconds
Returns:
train_time: A float of the training time in seconds
'''
return self._fit(X_train, y_train, **kwargs)
def predict(self, X_test):
'''Predict label from features
Args:
X_test: A numpy array of featurized instances, shape n*m
Returns:
A numpy array of shape n*1.
Each element is the label for a instance
'''
if self._model is not None:
X_test = self._preprocess(X_test)
return self._model.predict(X_test)
else:
return np.ones(X_test.shape[0])
def predict_proba(self, X_test):
'''Predict the probability of each class from features
Only works for classification problems
Args:
model: An object of trained model with method predict_proba()
X_test: A numpy array of featurized instances, shape n*m
Returns:
A numpy array of shape n*c. c is the # classes
Each element at (i,j) is the probability for instance i to be in
class j
'''
if 'regression' in self._task:
raise ValueError('Regression tasks do not support predict_prob')
else:
X_test = self._preprocess(X_test)
return self._model.predict_proba(X_test)
def cleanup(self):
pass
@classmethod
def search_space(cls, **params):
'''[required method] search space
Returns:
A dictionary of the search space.
Each key is the name of a hyperparameter, and value is a dict with
its domain and init_value (optional), cat_hp_cost (optional)
e.g.,
{'domain': tune.randint(lower=1, upper=10), 'init_value': 1}
'''
return {}
@classmethod
def size(cls, config: dict) -> float:
'''[optional method] memory size of the estimator in bytes
Args:
config - the dict of the hyperparameter config
Returns:
A float of the memory size required by the estimator to train the
given config
'''
return 1.0
@classmethod
def cost_relative2lgbm(cls) -> float:
'''[optional method] relative cost compared to lightgbm'''
return 1.0
@classmethod
def init(cls):
'''[optional method] initialize the class'''
pass
class SKLearnEstimator(BaseEstimator):
def __init__(self, task='binary:logistic', **params):
super().__init__(task, **params)
def _preprocess(self, X):
if isinstance(X, pd.DataFrame):
cat_columns = X.select_dtypes(include=['category']).columns
if not cat_columns.empty:
X = X.copy()
X[cat_columns] = X[cat_columns].apply(lambda x: x.cat.codes)
elif isinstance(X, np.ndarray) and X.dtype.kind not in 'buif':
# numpy array is not of numeric dtype
X = pd.DataFrame(X)
for col in X.columns:
if isinstance(X[col][0], str):
X[col] = X[col].astype('category').cat.codes
X = X.to_numpy()
return X
class LGBMEstimator(BaseEstimator):
@classmethod
def search_space(cls, data_size, **params):
upper = min(32768, int(data_size))
return {
'n_estimators': {
'domain': tune.lograndint(lower=4, upper=upper),
'init_value': 4,
'low_cost_init_value': 4,
},
'num_leaves': {
'domain': tune.lograndint(lower=4, upper=upper),
'init_value': 4,
'low_cost_init_value': 4,
},
'min_child_samples': {
'domain': tune.lograndint(lower=2, upper=2**7 + 1),
'init_value': 20,
},
'learning_rate': {
'domain': tune.loguniform(lower=1 / 1024, upper=1.0),
'init_value': 0.1,
},
'subsample': {
'domain': tune.uniform(lower=0.1, upper=1.0),
'init_value': 1.0,
},
'log_max_bin': {
'domain': tune.lograndint(lower=3, upper=11),
'init_value': 8,
},
'colsample_bytree': {
'domain': tune.uniform(lower=0.01, upper=1.0),
'init_value': 1.0,
},
'reg_alpha': {
'domain': tune.loguniform(lower=1 / 1024, upper=1024),
'init_value': 1 / 1024,
},
'reg_lambda': {
'domain': tune.loguniform(lower=1 / 1024, upper=1024),
'init_value': 1.0,
},
}
@classmethod
def size(cls, config):
num_leaves = int(round(config.get('num_leaves') or config['max_leaves']))
n_estimators = int(round(config['n_estimators']))
return (num_leaves * 3 + (num_leaves - 1) * 4 + 1.0) * n_estimators * 8
def __init__(self, task='binary:logistic', log_max_bin=8, **params):
super().__init__(task, **params)
# Default: ‘regression’ for LGBMRegressor,
# ‘binary’ or ‘multiclass’ for LGBMClassifier
if 'regression' in task:
objective = 'regression'
elif 'binary' in task:
objective = 'binary'
elif 'multi' in task:
objective = 'multiclass'
else:
objective = 'regression'
if "n_estimators" in self.params:
self.params["n_estimators"] = int(round(self.params["n_estimators"]))
if "num_leaves" in self.params:
self.params["num_leaves"] = int(round(self.params["num_leaves"]))
if "min_child_samples" in self.params:
self.params["min_child_samples"] = int(round(self.params["min_child_samples"]))
if "objective" not in self.params:
self.params["objective"] = objective
if "max_bin" not in self.params:
self.params['max_bin'] = 1 << int(round(log_max_bin)) - 1
if "verbose" not in self.params:
self.params['verbose'] = -1
if 'regression' in task:
self.estimator_class = LGBMRegressor
else:
self.estimator_class = LGBMClassifier
self._time_per_iter = None
self._train_size = 0
def _preprocess(self, X):
if not isinstance(X, pd.DataFrame) and issparse(X) and np.issubdtype(
X.dtype, np.integer):
X = X.astype(float)
elif isinstance(X, np.ndarray) and X.dtype.kind not in 'buif':
# numpy array is not of numeric dtype
X = pd.DataFrame(X)
for col in X.columns:
if isinstance(X[col][0], str):
X[col] = X[col].astype('category').cat.codes
X = X.to_numpy()
return X
def fit(self, X_train, y_train, budget=None, **kwargs):
start_time = time.time()
n_iter = self.params["n_estimators"]
if (not self._time_per_iter or abs(
self._train_size - X_train.shape[0]) > 4) and budget is not None:
self.params["n_estimators"] = 1
self._t1 = self._fit(X_train, y_train, **kwargs)
if self._t1 >= budget:
self.params["n_estimators"] = n_iter
return self._t1
self.params["n_estimators"] = 4
self._t2 = self._fit(X_train, y_train, **kwargs)
self._time_per_iter = (self._t2 - self._t1) / (
self.params["n_estimators"] - 1) if self._t2 > self._t1 \
else self._t1 if self._t1 else 0.001
self._train_size = X_train.shape[0]
if self._t1 + self._t2 >= budget or n_iter == self.params[
"n_estimators"]:
self.params["n_estimators"] = n_iter
return time.time() - start_time
if budget is not None:
self.params["n_estimators"] = min(n_iter, int(
(budget - time.time() + start_time - self._t1)
/ self._time_per_iter + 1))
if self.params["n_estimators"] > 0:
self._fit(X_train, y_train, **kwargs)
self.params["n_estimators"] = n_iter
train_time = time.time() - start_time
return train_time
class XGBoostEstimator(SKLearnEstimator):
''' not using sklearn API, used for regression '''
@classmethod
def search_space(cls, data_size, **params):
upper = min(32768, int(data_size))
return {
'n_estimators': {
'domain': tune.lograndint(lower=4, upper=upper),
'init_value': 4,
'low_cost_init_value': 4,
},
'max_leaves': {
'domain': tune.lograndint(lower=4, upper=upper),
'init_value': 4,
'low_cost_init_value': 4,
},
'min_child_weight': {
'domain': tune.loguniform(lower=0.001, upper=128),
'init_value': 1,
},
'learning_rate': {
'domain': tune.loguniform(lower=1 / 1024, upper=1.0),
'init_value': 0.1,
},
'subsample': {
'domain': tune.uniform(lower=0.1, upper=1.0),
'init_value': 1.0,
},
'colsample_bylevel': {
'domain': tune.uniform(lower=0.01, upper=1.0),
'init_value': 1.0,
},
'colsample_bytree': {
'domain': tune.uniform(lower=0.01, upper=1.0),
'init_value': 1.0,
},
'reg_alpha': {
'domain': tune.loguniform(lower=1 / 1024, upper=1024),
'init_value': 1 / 1024,
},
'reg_lambda': {
'domain': tune.loguniform(lower=1 / 1024, upper=1024),
'init_value': 1.0,
},
}
@classmethod
def size(cls, config):
return LGBMEstimator.size(config)
@classmethod
def cost_relative2lgbm(cls):
return 1.6
def __init__(
self, task='regression', all_thread=False, n_jobs=1,
n_estimators=4, max_leaves=4, subsample=1.0, min_child_weight=1,
learning_rate=0.1, reg_lambda=1.0, reg_alpha=0.0, colsample_bylevel=1.0,
colsample_bytree=1.0, tree_method='auto', **params
):
super().__init__(task, **params)
self._n_estimators = int(round(n_estimators))
self.params.update({
'max_leaves': int(round(max_leaves)),
'max_depth': params.get('max_depth', 0),
'grow_policy': params.get("grow_policy", 'lossguide'),
'tree_method': tree_method,
'verbosity': params.get('verbosity', 0),
'nthread': n_jobs,
'learning_rate': float(learning_rate),
'subsample': float(subsample),
'reg_alpha': float(reg_alpha),
'reg_lambda': float(reg_lambda),
'min_child_weight': float(min_child_weight),
'booster': params.get('booster', 'gbtree'),
'colsample_bylevel': float(colsample_bylevel),
'colsample_bytree': float(colsample_bytree),
'objective': params.get("objective")
})
if all_thread:
del self.params['nthread']
def get_params(self, deep=False):
params = super().get_params()
params["n_jobs"] = params['nthread']
return params
def fit(self, X_train, y_train, budget=None, **kwargs):
start_time = time.time()
if not issparse(X_train):
self.params['tree_method'] = 'hist'
X_train = self._preprocess(X_train)
if 'sample_weight' in kwargs:
dtrain = xgb.DMatrix(X_train, label=y_train, weight=kwargs[
'sample_weight'])
else:
dtrain = xgb.DMatrix(X_train, label=y_train)
objective = self.params.get('objective')
if isinstance(objective, str):
obj = None
else:
obj = objective
if 'objective' in self.params:
del self.params['objective']
self._model = xgb.train(self.params, dtrain, self._n_estimators,
obj=obj)
self.params['objective'] = objective
del dtrain
train_time = time.time() - start_time
return train_time
def predict(self, X_test):
if not issparse(X_test):
X_test = self._preprocess(X_test)
dtest = xgb.DMatrix(X_test)
return super().predict(dtest)
class XGBoostSklearnEstimator(SKLearnEstimator, LGBMEstimator):
''' using sklearn API, used for classification '''
@classmethod
def search_space(cls, data_size, **params):
return XGBoostEstimator.search_space(data_size)
@classmethod
def cost_relative2lgbm(cls):
return XGBoostEstimator.cost_relative2lgbm()
def __init__(
self, task='binary:logistic', n_jobs=1,
n_estimators=4, max_leaves=4, subsample=1.0,
min_child_weight=1, learning_rate=0.1, reg_lambda=1.0, reg_alpha=0.0,
colsample_bylevel=1.0, colsample_bytree=1.0, tree_method='hist',
**params
):
super().__init__(task, **params)
del self.params['objective']
del self.params['max_bin']
del self.params['verbose']
self.params.update({
"n_estimators": int(round(n_estimators)),
'max_leaves': int(round(max_leaves)),
'max_depth': 0,
'grow_policy': params.get("grow_policy", 'lossguide'),
'tree_method': tree_method,
'n_jobs': n_jobs,
'verbosity': 0,
'learning_rate': float(learning_rate),
'subsample': float(subsample),
'reg_alpha': float(reg_alpha),
'reg_lambda': float(reg_lambda),
'min_child_weight': float(min_child_weight),
'booster': params.get('booster', 'gbtree'),
'colsample_bylevel': float(colsample_bylevel),
'colsample_bytree': float(colsample_bytree),
'use_label_encoder': params.get('use_label_encoder', False),
})
if 'regression' in task:
self.estimator_class = xgb.XGBRegressor
else:
self.estimator_class = xgb.XGBClassifier
self._time_per_iter = None
self._train_size = 0
def fit(self, X_train, y_train, budget=None, **kwargs):
if issparse(X_train):
self.params['tree_method'] = 'auto'
return super().fit(X_train, y_train, budget, **kwargs)
class RandomForestEstimator(SKLearnEstimator, LGBMEstimator):
@classmethod
def search_space(cls, data_size, task, **params):
data_size = int(data_size)
upper = min(2048, data_size)
space = {
'n_estimators': {
'domain': tune.lograndint(lower=4, upper=upper),
'init_value': 4,
'low_cost_init_value': 4,
},
'max_features': {
'domain': tune.loguniform(lower=0.1, upper=1.0),
'init_value': 1.0,
},
'max_leaves': {
'domain': tune.lograndint(lower=4, upper=min(32768, data_size)),
'init_value': 4,
'low_cost_init_value': 4,
},
}
if task != 'regression':
space['criterion'] = {
'domain': tune.choice(['gini', 'entropy']),
# 'init_value': 'gini',
}
return space
@classmethod
def cost_relative2lgbm(cls):
return 2.0
def __init__(
self, task='binary:logistic', n_jobs=1,
n_estimators=4, max_features=1.0, criterion='gini', max_leaves=4,
**params
):
super().__init__(task, **params)
del self.params['objective']
del self.params['max_bin']
self.params.update({
"n_estimators": int(round(n_estimators)),
"n_jobs": n_jobs,
"verbose": 0,
'max_features': float(max_features),
"max_leaf_nodes": params.get('max_leaf_nodes', int(round(max_leaves))),
})
if 'regression' in task:
self.estimator_class = RandomForestRegressor
else:
self.estimator_class = RandomForestClassifier
self.params['criterion'] = criterion
def get_params(self, deep=False):
params = super().get_params()
return params
class ExtraTreeEstimator(RandomForestEstimator):
@classmethod
def cost_relative2lgbm(cls):
return 1.9
def __init__(self, task='binary:logistic', **params):
super().__init__(task, **params)
if 'regression' in task:
self.estimator_class = ExtraTreesRegressor
else:
self.estimator_class = ExtraTreesClassifier
class LRL1Classifier(SKLearnEstimator):
@classmethod
def search_space(cls, **params):
return {
'C': {
'domain': tune.loguniform(lower=0.03125, upper=32768.0),
'init_value': 1.0,
},
}
@classmethod
def cost_relative2lgbm(cls):
return 160
def __init__(
self, task='binary:logistic', n_jobs=1, tol=0.0001, C=1.0,
**params
):
super().__init__(task, **params)
self.params.update({
'penalty': params.get("penalty", 'l1'),
'tol': float(tol),
'C': float(C),
'solver': params.get("solver", 'saga'),
'n_jobs': n_jobs,
})
if 'regression' in task:
self.estimator_class = None
raise NotImplementedError('LR does not support regression task')
else:
self.estimator_class = LogisticRegression
class LRL2Classifier(SKLearnEstimator):
@classmethod
def search_space(cls, **params):
return LRL1Classifier.search_space(**params)
@classmethod
def cost_relative2lgbm(cls):
return 25
def __init__(
self, task='binary:logistic', n_jobs=1, tol=0.0001, C=1.0,
**params
):
super().__init__(task, **params)
self.params.update({
'penalty': params.get("penalty", 'l2'),
'tol': float(tol),
'C': float(C),
'solver': params.get("solver", 'lbfgs'),
'n_jobs': n_jobs,
})
if 'regression' in task:
self.estimator_class = None
raise NotImplementedError('LR does not support regression task')
else:
self.estimator_class = LogisticRegression
class CatBoostEstimator(BaseEstimator):
_time_per_iter = None
_train_size = 0
@classmethod
def search_space(cls, data_size, **params):
upper = max(min(round(1500000 / data_size), 150), 12)
return {
'early_stopping_rounds': {
'domain': tune.lograndint(lower=10, upper=upper),
'init_value': 10,
'low_cost_init_value': 10,
},
'learning_rate': {
'domain': tune.loguniform(lower=.005, upper=.2),
'init_value': 0.1,
},
}
@classmethod
def size(cls, config):
n_estimators = 8192
max_leaves = 64
return (max_leaves * 3 + (max_leaves - 1) * 4 + 1.0) * n_estimators * 8
@classmethod
def cost_relative2lgbm(cls):
return 15
@classmethod
def init(cls):
CatBoostEstimator._time_per_iter = None
CatBoostEstimator._train_size = 0
def _preprocess(self, X):
if isinstance(X, pd.DataFrame):
cat_columns = X.select_dtypes(include=['category']).columns
if not cat_columns.empty:
X = X.copy()
X[cat_columns] = X[cat_columns].apply(
lambda x:
x.cat.rename_categories(
[str(c) if isinstance(c, float) else c
for c in x.cat.categories]))
elif isinstance(X, np.ndarray) and X.dtype.kind not in 'buif':
# numpy array is not of numeric dtype
X = pd.DataFrame(X)
for col in X.columns:
if isinstance(X[col][0], str):
X[col] = X[col].astype('category').cat.codes
X = X.to_numpy()
return X
def __init__(
self, task='binary:logistic', n_jobs=1,
n_estimators=8192, learning_rate=0.1, early_stopping_rounds=4, **params
):
super().__init__(task, **params)
self.params.update({
"early_stopping_rounds": int(round(early_stopping_rounds)),
"n_estimators": n_estimators,
'learning_rate': learning_rate,
'thread_count': n_jobs,
'verbose': params.get('verbose', False),
'random_seed': params.get("random_seed", 10242048),
})
if 'regression' in task:
from catboost import CatBoostRegressor
self.estimator_class = CatBoostRegressor
else:
from catboost import CatBoostClassifier
self.estimator_class = CatBoostClassifier
def get_params(self, deep=False):
params = super().get_params()
params['n_jobs'] = params['thread_count']
return params
def fit(self, X_train, y_train, budget=None, **kwargs):
start_time = time.time()
n_iter = self.params["n_estimators"]
X_train = self._preprocess(X_train)
if isinstance(X_train, pd.DataFrame):
cat_features = list(X_train.select_dtypes(
include='category').columns)
else:
cat_features = []
# from catboost import CatBoostError
# try:
if (not CatBoostEstimator._time_per_iter or abs(
CatBoostEstimator._train_size - len(y_train)) > 4) and budget:
# measure the time per iteration
self.params["n_estimators"] = 1
CatBoostEstimator._smallmodel = self.estimator_class(**self.params)
CatBoostEstimator._smallmodel.fit(
X_train, y_train, cat_features=cat_features, **kwargs)
CatBoostEstimator._t1 = time.time() - start_time
if CatBoostEstimator._t1 >= budget:
self.params["n_estimators"] = n_iter
self._model = CatBoostEstimator._smallmodel
return CatBoostEstimator._t1
self.params["n_estimators"] = 4
CatBoostEstimator._smallmodel = self.estimator_class(**self.params)
CatBoostEstimator._smallmodel.fit(
X_train, y_train, cat_features=cat_features, **kwargs)
CatBoostEstimator._time_per_iter = (
time.time() - start_time - CatBoostEstimator._t1) / (
self.params["n_estimators"] - 1)
if CatBoostEstimator._time_per_iter <= 0:
CatBoostEstimator._time_per_iter = CatBoostEstimator._t1
CatBoostEstimator._train_size = len(y_train)
if time.time() - start_time >= budget or n_iter == self.params[
"n_estimators"]:
self.params["n_estimators"] = n_iter
self._model = CatBoostEstimator._smallmodel
return time.time() - start_time
if budget:
train_times = 1
self.params["n_estimators"] = min(n_iter, int(
(budget - time.time() + start_time - CatBoostEstimator._t1)
/ train_times / CatBoostEstimator._time_per_iter + 1))
self._model = CatBoostEstimator._smallmodel
if self.params["n_estimators"] > 0:
n = max(int(len(y_train) * 0.9), len(y_train) - 1000)
X_tr, y_tr = X_train[:n], y_train[:n]
if 'sample_weight' in kwargs:
weight = kwargs['sample_weight']
if weight is not None:
kwargs['sample_weight'] = weight[:n]
else:
weight = None
from catboost import Pool
model = self.estimator_class(**self.params)
model.fit(
X_tr, y_tr, cat_features=cat_features,
eval_set=Pool(
data=X_train[n:], label=y_train[n:],
cat_features=cat_features),
**kwargs) # model.get_best_iteration()
if weight is not None:
kwargs['sample_weight'] = weight
self._model = model
# except CatBoostError:
# self._model = None
self.params["n_estimators"] = n_iter
train_time = time.time() - start_time
return train_time
class KNeighborsEstimator(BaseEstimator):
@classmethod
def search_space(cls, data_size, **params):
upper = min(512, int(data_size / 2))
return {
'n_neighbors': {
'domain': tune.lograndint(lower=1, upper=upper),
'init_value': 5,
'low_cost_init_value': 1,
},
}
@classmethod
def cost_relative2lgbm(cls):
return 30
def __init__(
self, task='binary:logistic', n_jobs=1, n_neighbors=5, **params
):
super().__init__(task, **params)
self.params.update({
'n_neighbors': int(round(n_neighbors)),
'weights': params.get('weights', 'distance'),
'n_jobs': n_jobs,
})
if 'regression' in task:
from sklearn.neighbors import KNeighborsRegressor
self.estimator_class = KNeighborsRegressor
else:
from sklearn.neighbors import KNeighborsClassifier
self.estimator_class = KNeighborsClassifier
def _preprocess(self, X):
if isinstance(X, pd.DataFrame):
cat_columns = X.select_dtypes(['category']).columns
if X.shape[1] == len(cat_columns):
raise ValueError(
"kneighbor requires at least one numeric feature")
X = X.drop(cat_columns, axis=1)
elif isinstance(X, np.ndarray) and X.dtype.kind not in 'buif':
# drop categocial columns if any
X = pd.DataFrame(X)
cat_columns = []
for col in X.columns:
if isinstance(X[col][0], str):
cat_columns.append(col)
X = X.drop(cat_columns, axis=1)
X = X.to_numpy()
return X