def __init__(self,
estimator=None,
max_evals=50,
algo='tpe',
cv=5,
handle_cv_failure=False,
scoring='accuracy',
best_score=0.0,
max_opt_time=None,
max_eval_time=None,
pgo: Optional[PGO] = None,
show_progressbar=True,
args_to_scorer=None,
verbose=False):
self.max_evals = max_evals
if estimator is None:
self.estimator = LogisticRegression()
else:
self.estimator = estimator
self.search_space = hyperopt.hp.choice(
'meta_model', [hyperopt_search_space(self.estimator, pgo=pgo)])
self.algo = algo
self.scoring = scoring
self.best_score = best_score
self.handle_cv_failure = handle_cv_failure
self.cv = cv
self._trials = hyperopt.Trials()
self.max_opt_time = max_opt_time
self.max_eval_time = max_eval_time
self.show_progressbar = show_progressbar
if args_to_scorer is not None:
self.args_to_scorer = args_to_scorer
else:
self.args_to_scorer = {}
self.verbose = verbose
def __init__(self,
model=None,
max_evals=50,
handle_cv_failure=False,
pgo: Optional[PGO] = None):
self.max_evals = max_evals
if model is None:
self.model = RandomForestRegressor
else:
self.model = model
self.search_space = hp.choice(
'meta_model', [hyperopt_search_space(self.model, pgo=pgo)])
self.handle_cv_failure = handle_cv_failure
self.trials = Trials()
def __init__(self,
model=None,
max_evals=50,
cv=5,
handle_cv_failure=False,
pgo: Optional[PGO] = None):
""" Instantiate the HyperoptClassifier that will use the given model and other parameters to select the
best performing trainable instantiation of the model. This optimizer uses negation of accuracy_score
as the performance metric to be minimized by Hyperopt.
Parameters
----------
model : lale.operators.IndividualOp or lale.operators.Pipeline, optional
A valid Lale individual operator or pipeline, by default None
max_evals : int, optional
Number of trials of Hyperopt search, by default 50
cv : an integer or an object that has a split function as a generator yielding (train, test) splits as arrays of indices.
Integer value is used as number of folds in sklearn.model_selection.StratifiedKFold, default is 5.
Note that any of the iterators from https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators can be used here.
The fit method performs cross validation on the input dataset for per trial,
and uses the mean cross validation performance for optimization. This behavior is also impacted by handle_cv_failure flag,
by default 5
handle_cv_failure : bool, optional
A boolean flag to indicating how to deal with cross validation failure for a trial.
If True, the trial is continued by doing a 80-20 percent train-validation split of the dataset input to fit
and reporting the accuracy on the validation part.
If False, the trial is terminated by assigning accuracy to zero.
, by default False
pgo : Optional[PGO], optional
[description], by default None
Raises
------
e
[description]
"""
self.max_evals = max_evals
if model is None:
self.model = LogisticRegression
else:
self.model = model
self.search_space = hp.choice(
'meta_model', [hyperopt_search_space(self.model, pgo=pgo)])
self.handle_cv_failure = handle_cv_failure
self.cv = cv
self.trials = Trials()
def __init__(self,
estimator=None,
max_evals=50,
cv=5,
handle_cv_failure=False,
scoring='r2',
best_score=1.0,
max_opt_time=None,
pgo: Optional[PGO] = None):
self.max_evals = max_evals
if estimator is None:
self.estimator = RandomForestRegressor
else:
self.estimator = estimator
self.search_space = hp.choice(
'meta_model', [hyperopt_search_space(self.estimator, pgo=pgo)])
self.scoring = scoring
self.best_score = best_score
self.handle_cv_failure = handle_cv_failure
self.cv = cv
self.trials = Trials()
self.max_opt_time = max_opt_time
def __init__(self,
estimator=None,
max_evals=50,
cv=5,
handle_cv_failure=False,
scoring='accuracy',
best_score=0.0,
max_opt_time=None,
pgo: Optional[PGO] = None):
""" Instantiate the HyperoptClassifier that will use the given estimator and other parameters to select the
best performing trainable instantiation of the estimator. This optimizer uses negation of accuracy_score
as the performance metric to be minimized by Hyperopt.
Parameters
----------
estimator : lale.operators.IndividualOp or lale.operators.Pipeline, optional
A valid Lale individual operator or pipeline, by default None
max_evals : int, optional
Number of trials of Hyperopt search, by default 50
cv : an integer or an object that has a split function as a generator yielding (train, test) splits as arrays of indices.
Integer value is used as number of folds in sklearn.model_selection.StratifiedKFold, default is 5.
Note that any of the iterators from https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators can be used here.
The fit method performs cross validation on the input dataset for per trial,
and uses the mean cross validation performance for optimization. This behavior is also impacted by handle_cv_failure flag,
by default 5
handle_cv_failure : bool, optional
A boolean flag to indicating how to deal with cross validation failure for a trial.
If True, the trial is continued by doing a 80-20 percent train-validation split of the dataset input to fit
and reporting the score on the validation part.
If False, the trial is terminated by assigning accuracy to zero.
, by default False
scoring: string or a scorer object created using
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer.
A string from sklearn.metrics.SCORERS.keys() can be used or a scorer created from one of
sklearn.metrics (https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics).
A completely custom scorer object can be created from a python function following the example at
https://scikit-learn.org/stable/modules/model_evaluation.html
The metric has to return a scalar value, and note that scikit-learns's scorer object always returns values such that
higher score is better. Since Hyperopt solves a minimization problem, we negate the score value to pass to Hyperopt.
by default 'accuracy'.
best_score : float, optional
The best score for the specified scorer. This allows us to return a loss to hyperopt that is
greater than equal to zero, where zero is the best loss. By default, this is set to zero to
follow current behavior.
max_opt_time : float, optional
Maximum amout of time in seconds for the optimization. By default, None, implying no runtime
bound.
pgo : Optional[PGO], optional
[description], by default None
Raises
------
e
[description]
Examples
--------
>>> from sklearn.metrics import make_scorer, f1_score, accuracy_score
>>> lr = LogisticRegression()
>>> clf = HyperoptClassifier(estimator=lr, scoring='accuracy', cv=5, max_evals=2)
>>> from sklearn import datasets
>>> diabetes = datasets.load_diabetes()
>>> X = diabetes.data[:150]
>>> y = diabetes.target[:150]
>>> trained = clf.fit(X, y)
>>> predictions = trained.predict(X)
Other scoring metrics:
>>> clf = HyperoptClassifier(estimator=lr, scoring=make_scorer(f1_score, average='macro'), cv=3, max_evals=2)
"""
self.max_evals = max_evals
if estimator is None:
self.estimator = LogisticRegression
else:
self.estimator = estimator
self.search_space = hp.choice(
'meta_model', [hyperopt_search_space(self.estimator, pgo=pgo)])
self.scoring = scoring
self.best_score = best_score
self.handle_cv_failure = handle_cv_failure
self.cv = cv
self.trials = Trials()
self.max_opt_time = max_opt_time
def test_hyperparam_overriding_with_hyperopt(self):
pca1 = PCA(n_components=3)
pca2 = PCA()
search_space1 = hyperopt_search_space(pca1)
search_space2 = hyperopt_search_space(pca2)
self.assertNotEqual(search_space1, search_space2)
def test_lr_parameters(self):
pgo = PGO.load_pgo_file(example_pgo_fp)
lr = LogisticRegression()
parameters: SearchSpace = hyperopt_search_space(lr, pgo=pgo)
def fit(self, X_train, y_train):
opt_start_time = time.time()
is_clf = self.estimator.is_classifier()
self.cv = check_cv(self.cv, y = y_train, classifier=is_clf)
data_schema = lale.helpers.fold_schema(
X_train, y_train, self.cv, is_clf)
self.search_space = hyperopt.hp.choice(
'meta_model', [hyperopt_search_space(self.estimator, pgo=self.pgo,
data_schema=data_schema)])
#Create a search space with default hyperparameters for all trainable parts of the pipeline.
#This search space is used for `frac_evals_with_defaults` fraction of the total trials.
try:
self.search_space_with_defaults = hyperopt.hp.choice('meta_model',
[hyperopt_search_space(self.estimator.freeze_trainable(),
pgo=self.pgo, data_schema=data_schema)])
except:
logger.warning(f"Exception caught during generation of default search space, setting frac_evals_with_defaults to zero.")
self.evals_with_defaults = 0
def hyperopt_train_test(params, X_train, y_train):
warnings.filterwarnings("ignore")
trainable = create_instance_from_hyperopt_search_space(self.estimator, params)
try:
cv_score, logloss, execution_time = cross_val_score_track_trials(trainable, X_train, y_train, cv=self.cv, scoring=self.scoring, args_to_scorer=self.args_to_scorer)
logger.debug("Successful trial of hyperopt with hyperparameters:{}".format(params))
except BaseException as e:
#If there is any error in cross validation, use the score based on a random train-test split as the evaluation criterion
if self.handle_cv_failure:
X_train_part, X_validation, y_train_part, y_validation = train_test_split(X_train, y_train, test_size=0.20)
start = time.time()
trained = trainable.fit(X_train_part, y_train_part)
scorer = check_scoring(trainable, scoring=self.scoring)
cv_score = scorer(trained, X_validation, y_validation, **self.args_to_scorer)
execution_time = time.time() - start
y_pred_proba = trained.predict_proba(X_validation)
try:
logloss = log_loss(y_true=y_validation, y_pred=y_pred_proba)
except BaseException:
logloss = 0
logger.debug("Warning, log loss cannot be computed")
else:
logger.debug(e)
logger.debug("Error {} with pipeline:{}".format(e, trainable.to_json()))
raise e
return cv_score, logloss, execution_time
def merge_trials(trials1, trials2):
max_tid = max([trial['tid'] for trial in trials1.trials])
for trial in trials2:
tid = trial['tid'] + max_tid + 1
hyperopt_trial = hyperopt.Trials().new_trial_docs(
tids=[None],
specs=[None],
results=[None],
miscs=[None])
hyperopt_trial[0] = trial
hyperopt_trial[0]['tid'] = tid
hyperopt_trial[0]['misc']['tid'] = tid
for key in hyperopt_trial[0]['misc']['idxs'].keys():
hyperopt_trial[0]['misc']['idxs'][key] = [tid]
trials1.insert_trial_docs(hyperopt_trial)
trials1.refresh()
return trials1
def proc_train_test(params, X_train, y_train, return_dict):
return_dict['params'] = copy.deepcopy(params)
try:
score, logloss, execution_time = hyperopt_train_test(params, X_train=X_train, y_train=y_train)
return_dict['loss'] = self.best_score - score
return_dict['time'] = execution_time
return_dict['log_loss'] = logloss
return_dict['status'] = hyperopt.STATUS_OK
except BaseException as e:
logger.warning(f"Exception caught in Hyperopt:{type(e)}, {traceback.format_exc()} with hyperparams: {params}, setting status to FAIL")
return_dict['status'] = hyperopt.STATUS_FAIL
return_dict['error_msg'] = f"Exception caught in Hyperopt:{type(e)}, {traceback.format_exc()} with hyperparams: {params}"
if self.verbose:
print(return_dict['error_msg'])
def get_final_trained_estimator(params, X_train, y_train):
warnings.filterwarnings("ignore")
trainable = create_instance_from_hyperopt_search_space(self.estimator, params)
trained = trainable.fit(X_train, y_train)
return trained
def f(params):
current_time = time.time()
if (self.max_opt_time is not None) and ((current_time - opt_start_time) > self.max_opt_time) :
# if max optimization time set, and we have crossed it, exit optimization completely
sys.exit(0)
if self.max_eval_time:
# Run hyperopt in a subprocess that can be interupted
manager = multiprocessing.Manager()
proc_dict = manager.dict()
p = multiprocessing.Process(
target=proc_train_test,
args=(params, X_train, y_train, proc_dict))
p.start()
p.join(self.max_eval_time)
if p.is_alive():
p.terminate()
p.join()
logger.warning(f"Maximum alloted evaluation time exceeded. with hyperparams: {params}, setting status to FAIL")
proc_dict['status'] = hyperopt.STATUS_FAIL
if 'status' not in proc_dict:
logger.warning(f"Corrupted results, setting status to FAIL")
proc_dict['status'] = hyperopt.STATUS_FAIL
else:
proc_dict = {}
proc_train_test(params, X_train, y_train, proc_dict)
return proc_dict
algo = getattr(hyperopt, self.algo)
#Search in the search space with defaults
if self.evals_with_defaults > 0:
try:
hyperopt.fmin(f, self.search_space_with_defaults, algo=algo.suggest, max_evals=self.evals_with_defaults, trials=self._default_trials, rstate=np.random.RandomState(SEED),
show_progressbar=self.show_progressbar)
except SystemExit :
logger.warning('Maximum alloted optimization time exceeded. Optimization exited prematurely')
except AllTrialsFailed:
self._best_estimator = None
if hyperopt.STATUS_OK not in self._trials.statuses():
raise ValueError('Error from hyperopt, none of the trials succeeded.')
try :
hyperopt.fmin(f, self.search_space, algo=algo.suggest, max_evals=self.max_evals-self.evals_with_defaults, trials=self._trials, rstate=np.random.RandomState(SEED),
show_progressbar=self.show_progressbar)
except SystemExit :
logger.warning('Maximum alloted optimization time exceeded. Optimization exited prematurely')
except AllTrialsFailed:
self._best_estimator = None
if hyperopt.STATUS_OK not in self._trials.statuses():
raise ValueError('Error from hyperopt, none of the trials succeeded.')
self._trials = merge_trials(self._trials, self._default_trials)
try :
best_trial = self._trials.best_trial
val_loss = self._trials.best_trial['result']['loss']
if len(self._default_trials) > 0:
default_val_loss = self._default_trials.best_trial['result']['loss']
if default_val_loss < val_loss:
best_trial = self._default_trials.best_trial
best_params = best_trial['result']['params']
logger.info(
'best score: {:.1%}\nbest hyperparams found using {} hyperopt trials: {}'.format(
self.best_score - self._trials.average_best_error(), self.max_evals, best_params
)
)
trained = get_final_trained_estimator(best_params, X_train, y_train)
self._best_estimator = trained
except BaseException as e :
logger.warning('Unable to extract the best parameters from optimization, the error: {}'.format(e))
self._best_estimator = None
return self
def test_hyperparam_defaults(self):
trainable = J48()
hyperopt_search_space(trainable)
del params['name']
clf = get_classifier(t, params)
clf_trained = clf.fit(X_train, y_train)
predictions = clf_trained.predict(X_test)
accuracy = accuracy_score(y_test, [round(pred) for pred in predictions])
return accuracy
def get_classifier(t, param_dict):
if 'LogisticRegression' in t:
clf = LogisticRegression(**param_dict)
else:
return 0
return clf
search_space = hp.choice('classifier', [hyperopt_search_space(LogisticRegression)])
count = 0
best = 0
def f(params):
global best, count
count += 1
acc = hyperopt_train_test(params.copy())
if acc > best:
print('new best:', acc, 'using', params['name'])
best = acc
if count % 1 == 0:
print('iters:', count, ', acc:', acc, 'using', params)
return {'loss': -acc, 'status': STATUS_OK}
trials = Trials()
