def save_model(self, folder_path, file_name=None):
if file_name is None:
file_name = self.RECOMMENDER_NAME
self._print("Saving model in file '{}'".format(folder_path +
file_name))
data_dict_to_save = {
"item_embeddings": self.lightFM_model.item_embeddings,
"item_embedding_gradients":
self.lightFM_model.item_embedding_gradients,
"item_embedding_momentum":
self.lightFM_model.item_embedding_momentum,
"item_biases": self.lightFM_model.item_biases,
"item_bias_gradients": self.lightFM_model.item_bias_gradients,
"item_bias_momentum": self.lightFM_model.item_bias_momentum,
"user_embeddings": self.lightFM_model.user_embeddings,
"user_embedding_gradients":
self.lightFM_model.user_embedding_gradients,
"user_embedding_momentum":
self.lightFM_model.user_embedding_momentum,
"user_biases": self.lightFM_model.user_biases,
"user_bias_gradients": self.lightFM_model.user_bias_gradients,
"user_bias_momentum": self.lightFM_model.user_bias_momentum,
}
dataIO = DataIO(folder_path=folder_path)
dataIO.save_data(file_name=file_name,
data_dict_to_save=data_dict_to_save)
self._print("Saving complete")
def save_model(self, folder_path, file_name = None, is_earlystopping_format = False):
#https://cv-tricks.com/tensorflow-tutorial/save-restore-tensorflow-models-quick-complete-tutorial/
if file_name is None:
file_name = self.RECOMMENDER_NAME
self._print("Saving model in file '{}'".format(folder_path + file_name))
# Save session within a temp folder called as the desired file_name
saver = tf.compat.v1.train.Saver()
saver.save(self.sess, folder_path + file_name + "/session")
data_dict_to_save = {
"batch_size": self.batch_size,
"dropout": self.dropout,
"learning_rate": self.learning_rate,
"l2_reg": self.l2_reg,
"total_anneal_steps": self.total_anneal_steps,
"anneal_cap": self.anneal_cap,
"update_count": self.update_count,
"p_dims": self.p_dims,
"batches_per_epoch": self.batches_per_epoch,
# "log_dir": self.log_dir,
# "chkpt_dir": self.chkpt_dir,
}
dataIO = DataIO(folder_path=folder_path + file_name + "/")
dataIO.save_data(file_name="fit_attributes", data_dict_to_save = data_dict_to_save)
# Create a zip folder containing fit_attributes and saved session
if not is_earlystopping_format:
# Unfortunately I cannot avoid compression so it is too slow for earlystopping
shutil.make_archive(
folder_path + file_name, # name of the file to create
'zip', # archive format - or tar, bztar, gztar
root_dir = folder_path + file_name + "/", # root for archive
base_dir = None) # start archiving from the root_dir
shutil.rmtree(folder_path + file_name + "/", ignore_errors=True)
#
# else:
#
# with zipfile.ZipFile(folder_path + file_name + ".zip", 'w', compression=zipfile.ZIP_STORED) as myzip:
# for file_to_compress in os.listdir(folder_path + file_name + "/"):
# myzip.write(folder_path + file_name + "/" + file_to_compress, arcname = file_to_compress)
#
self._print("Saving complete")
def save_model(self, folder_path, file_name=None):
if file_name is None:
file_name = self.RECOMMENDER_NAME
self._print("Saving model in file '{}'".format(folder_path +
file_name))
data_dict_to_save = {"item_pop": self.item_pop}
dataIO = DataIO(folder_path=folder_path)
dataIO.save_data(file_name=file_name,
data_dict_to_save=data_dict_to_save)
self._print("Saving complete")
def save_model(self, folder_path, file_name=None):
if file_name is None:
file_name = self.RECOMMENDER_NAME
print("{}: Saving model in file '{}'".format(self.RECOMMENDER_NAME,
folder_path + file_name))
data_dict_to_save = {
"D_best": self.D_best,
"topK": self.topK,
"W_sparse": self.W_sparse
}
dataIO = DataIO(folder_path=folder_path)
dataIO.save_data(file_name=file_name,
data_dict_to_save=data_dict_to_save)
print("{}: Saving complete".format(self.RECOMMENDER_NAME))
def save_model(self, folder_path, file_name = None):
if file_name is None:
file_name = self.RECOMMENDER_NAME
self._print("Saving model in file '{}'".format(folder_path + file_name))
data_dict_to_save = {"USER_factors": self.USER_factors,
"ITEM_factors": self.ITEM_factors,
"use_bias": self.use_bias,
}
if self.use_bias:
data_dict_to_save["ITEM_bias"] = self.ITEM_bias
data_dict_to_save["USER_bias"] = self.USER_bias
data_dict_to_save["GLOBAL_bias"] = self.GLOBAL_bias
dataIO = DataIO(folder_path=folder_path)
dataIO.save_data(file_name=file_name, data_dict_to_save = data_dict_to_save)
self._print("Saving complete")
def save_data(self, save_folder_path):
dataIO = DataIO(folder_path = save_folder_path)
global_attributes_dict = {
"item_original_ID_to_index": self.item_original_ID_to_index,
"user_original_ID_to_index": self.user_original_ID_to_index,
"DATASET_NAME": self.DATASET_NAME,
"_IS_IMPLICIT": self._IS_IMPLICIT,
"_HAS_ICM": self._HAS_ICM,
"_HAS_UCM": self._HAS_UCM,
"_HAS_additional_mapper": self._HAS_additional_mapper
}
dataIO.save_data(data_dict_to_save = global_attributes_dict,
file_name = "dataset_global_attributes")
dataIO.save_data(data_dict_to_save = self.AVAILABLE_URM,
file_name = "dataset_URM")
if self._HAS_ICM:
dataIO.save_data(data_dict_to_save = self.AVAILABLE_ICM,
file_name = "dataset_ICM")
dataIO.save_data(data_dict_to_save = self.AVAILABLE_ICM_feature_mapper,
file_name = "dataset_ICM_mappers")
if self._HAS_UCM:
dataIO.save_data(data_dict_to_save = self.AVAILABLE_UCM,
file_name = "dataset_UCM")
dataIO.save_data(data_dict_to_save = self.AVAILABLE_UCM_feature_mapper,
file_name = "dataset_UCM_mappers")
if self._HAS_additional_mapper:
dataIO.save_data(data_dict_to_save = self.additional_data_mapper,
file_name = "dataset_additional_mappers")
def _save_split(self, save_folder_path):
if save_folder_path:
if self.allow_cold_users:
allow_cold_users_suffix = "allow_cold_users"
else:
allow_cold_users_suffix = "only_warm_users"
if self.user_wise:
user_wise_string = "user_wise"
else:
user_wise_string = "global_sample"
name_suffix = "_{}_{}".format(allow_cold_users_suffix,
user_wise_string)
split_parameters_dict = {
"input_split_interaction_quota_list":
self.input_split_interaction_quota_list,
"actual_split_interaction_quota_list":
self.actual_split_interaction_quota_list,
"allow_cold_users": self.allow_cold_users
}
dataIO = DataIO(folder_path=save_folder_path)
dataIO.save_data(data_dict_to_save=split_parameters_dict,
file_name="split_parameters" + name_suffix)
dataIO.save_data(data_dict_to_save=self.SPLIT_GLOBAL_MAPPER_DICT,
file_name="split_mappers" + name_suffix)
dataIO.save_data(data_dict_to_save=self.SPLIT_URM_DICT,
file_name="split_URM" + name_suffix)
if len(self.SPLIT_ICM_DICT) > 0:
dataIO.save_data(data_dict_to_save=self.SPLIT_ICM_DICT,
file_name="split_ICM" + name_suffix)
dataIO.save_data(data_dict_to_save=self.SPLIT_ICM_MAPPER_DICT,
file_name="split_ICM_mappers" + name_suffix)
if len(self.SPLIT_UCM_DICT) > 0:
dataIO.save_data(data_dict_to_save=self.SPLIT_UCM_DICT,
file_name="split_UCM" + name_suffix)
dataIO.save_data(data_dict_to_save=self.SPLIT_UCM_MAPPER_DICT,
file_name="split_UCM_mappers" + name_suffix)
def _save_split(self, save_folder_path):
if save_folder_path:
if self.allow_cold_users:
allow_cold_users_suffix = "allow_cold_users"
else:
allow_cold_users_suffix = "only_warm_users"
if self.use_validation_set:
validation_set_suffix = "use_validation_set"
else:
validation_set_suffix = "no_validation_set"
name_suffix = "_{}_{}".format(allow_cold_users_suffix,
validation_set_suffix)
split_parameters_dict = {
"k_out_value": self.k_out_value,
"allow_cold_users": self.allow_cold_users,
"removed_cold_users": self.removed_cold_users,
}
dataIO = DataIO(folder_path=save_folder_path)
dataIO.save_data(data_dict_to_save=split_parameters_dict,
file_name="split_parameters" + name_suffix)
dataIO.save_data(data_dict_to_save=self.SPLIT_GLOBAL_MAPPER_DICT,
file_name="split_mappers" + name_suffix)
dataIO.save_data(data_dict_to_save=self.SPLIT_URM_DICT,
file_name="split_URM" + name_suffix)
if len(self.SPLIT_ICM_DICT) > 0:
dataIO.save_data(data_dict_to_save=self.SPLIT_ICM_DICT,
file_name="split_ICM" + name_suffix)
dataIO.save_data(data_dict_to_save=self.SPLIT_ICM_MAPPER_DICT,
file_name="split_ICM_mappers" + name_suffix)
if len(self.SPLIT_UCM_DICT) > 0:
dataIO.save_data(data_dict_to_save=self.SPLIT_UCM_DICT,
file_name="split_UCM" + name_suffix)
dataIO.save_data(data_dict_to_save=self.SPLIT_UCM_MAPPER_DICT,
file_name="split_UCM_mappers" + name_suffix)
class SearchAbstractClass(object):
ALGORITHM_NAME = "SearchAbstractClass"
# Available values for the save_model attribute
_SAVE_MODEL_VALUES = ["all", "best", "last", "no"]
# Available values for the evaluate_on_test attribute
_EVALUATE_ON_TEST_VALUES = ["all", "best", "last", "no"]
# Value to be assigned to invalid configuration or if an Exception is raised
INVALID_CONFIG_VALUE = np.finfo(np.float16).max
def __init__(self,
recommender_class,
evaluator_validation=None,
evaluator_test=None,
verbose=True):
super(SearchAbstractClass, self).__init__()
self.recommender_class = recommender_class
self.verbose = verbose
self.log_file = None
self.evaluator_validation = evaluator_validation
if evaluator_test is None:
self.evaluator_test = None
else:
self.evaluator_test = evaluator_test
def search(
self,
recommender_input_args,
hyperparameter_search_space,
metric_to_optimize="MAP",
cutoff_to_optimize=None,
n_cases=None,
output_folder_path=None,
output_file_name_root=None,
parallelize=False,
save_model="best",
evaluate_on_test="best",
save_metadata=True,
terminate_on_memory_error=True,
):
raise NotImplementedError(
"Function search not implemented for this class")
def _was_already_evaluated_check(self, current_fit_hyperparameters_dict):
"""
Check if the current hyperparameter configuration was already evaluated
:param current_fit_hyperparameters_dict:
:return:
"""
raise NotImplementedError(
"Function search not implemented for this class")
def _set_search_attributes(self, recommender_input_args,
recommender_input_args_last_test,
hyperparameter_names, metric_to_optimize,
cutoff_to_optimize, output_folder_path,
output_file_name_root, resume_from_saved,
save_metadata, save_model, evaluate_on_test,
n_cases, terminate_on_memory_error):
if save_model not in self._SAVE_MODEL_VALUES:
raise ValueError(
"{}: argument save_model must be in '{}', provided was '{}'.".
format(self.ALGORITHM_NAME, self._SAVE_MODEL_VALUES,
save_model))
if evaluate_on_test not in self._EVALUATE_ON_TEST_VALUES:
raise ValueError(
"{}: argument evaluate_on_test must be in '{}', provided was '{}'."
.format(self.ALGORITHM_NAME, self._EVALUATE_ON_TEST_VALUES,
evaluate_on_test))
self.output_folder_path = output_folder_path
self.output_file_name_root = output_file_name_root
# If directory does not exist, create
if not os.path.exists(self.output_folder_path):
os.makedirs(self.output_folder_path)
self.log_file = open(
self.output_folder_path + self.output_file_name_root +
"_{}.txt".format(self.ALGORITHM_NAME), "a")
if save_model == "last" and recommender_input_args_last_test is None:
self._write_log(
"{}: argument save_model is 'last' but no recommender_input_args_last_test provided, saving best model on train data alone."
.format(self.ALGORITHM_NAME))
save_model = "best"
self.recommender_input_args = recommender_input_args
self.recommender_input_args_last_test = recommender_input_args_last_test
self.metric_to_optimize = metric_to_optimize
self.cutoff_to_optimize = cutoff_to_optimize
self.resume_from_saved = resume_from_saved
self.terminate_on_memory_error = terminate_on_memory_error
self.save_metadata = save_metadata
self.save_model = save_model
self.evaluate_on_test = "no" if self.evaluator_test is None else evaluate_on_test
self.model_counter = 0
self.n_cases = n_cases
self._init_metadata_dict(n_cases=n_cases,
hyperparameter_names=hyperparameter_names)
if self.save_metadata:
self.dataIO = DataIO(folder_path=self.output_folder_path)
def _init_metadata_dict(self, n_cases, hyperparameter_names):
self.metadata_dict = {
"algorithm_name_search":
self.ALGORITHM_NAME,
"algorithm_name_recommender":
self.recommender_class.RECOMMENDER_NAME,
"metric_to_optimize":
self.metric_to_optimize,
"cutoff_to_optimize":
self.cutoff_to_optimize,
"exception_list": [None] * n_cases,
"hyperparameters_df":
pd.DataFrame(columns=hyperparameter_names,
index=np.arange(n_cases),
dtype=object),
"hyperparameters_best":
None,
"hyperparameters_best_index":
None,
"result_on_validation_df":
None,
"result_on_validation_best":
None,
"result_on_test_df":
None,
"result_on_test_best":
None,
"time_df":
pd.DataFrame(columns=["train", "validation", "test"],
index=np.arange(n_cases)),
"time_on_train_total":
0.0,
"time_on_train_avg":
0.0,
"time_on_validation_total":
0.0,
"time_on_validation_avg":
0.0,
"time_on_test_total":
0.0,
"time_on_test_avg":
0.0,
"result_on_last":
None,
"time_on_last_df":
pd.DataFrame(columns=["train", "test"], index=[0]),
}
def _print(self, string):
if self.verbose:
print(string)
def _write_log(self, string):
self._print(string)
if self.log_file is not None:
self.log_file.write(string)
self.log_file.flush()
def _fit_model(self, current_fit_hyperparameters):
start_time = time.time()
# Construct a new recommender instance
recommender_instance = self.recommender_class(
*self.recommender_input_args.CONSTRUCTOR_POSITIONAL_ARGS,
**self.recommender_input_args.CONSTRUCTOR_KEYWORD_ARGS)
recommender_instance.fit(
*self.recommender_input_args.FIT_POSITIONAL_ARGS,
**self.recommender_input_args.FIT_KEYWORD_ARGS,
**self.recommender_input_args.EARLYSTOPPING_KEYWORD_ARGS,
**current_fit_hyperparameters)
train_time = time.time() - start_time
return recommender_instance, train_time
def _evaluate_on_validation(self, current_fit_hyperparameters,
was_already_evaluated_flag,
was_already_evaluated_index):
"""
Fit and evaluate model with the given hyperparameter configuration on the validation set, or
load previously explored configuration
:param current_fit_hyperparameters:
:param was_already_evaluated_flag:
:param was_already_evaluated_index:
:return:
"""
if not was_already_evaluated_flag:
# Add hyperparameter values into dataframe iteratively because the simple solution
# hyperparameters_df.loc[self.model_counter] = current_fit_hyperparameters
# would (sometimes?) automatically convert integers to floats, which is undesirable (e.g., for the topK value)
# This occurs when the dictionary contains only numerical data (int, floats) but not when it contains also strings or booleans
for key in current_fit_hyperparameters.keys():
self.metadata_dict["hyperparameters_df"].loc[
self.model_counter, key] = current_fit_hyperparameters[key]
recommender_instance, train_time = self._fit_model(
current_fit_hyperparameters)
start_time = time.time()
# Evaluate recommender and get results for the first cutoff
result_df, _ = self.evaluator_validation.evaluateRecommender(
recommender_instance)
evaluation_time = time.time() - start_time
# If the recommender uses Earlystopping, get the selected number of epochs instead of the maximum
if isinstance(recommender_instance,
Incremental_Training_Early_Stopping):
for epoch_key, epoch_value in recommender_instance.get_early_stopping_final_epochs_dict(
).items():
self.metadata_dict["hyperparameters_df"].loc[
self.model_counter, epoch_key] = int(epoch_value)
else:
# If it was already evaluated load the data
recommender_instance = None
self.metadata_dict["hyperparameters_df"].loc[
self.
model_counter] = self.metadata_dict["hyperparameters_df"].loc[
was_already_evaluated_index].copy()
result_df = self.metadata_dict["result_on_validation_df"].loc[
was_already_evaluated_index].copy()
train_time = self.metadata_dict["time_df"].loc[
was_already_evaluated_index, "train"]
evaluation_time = self.metadata_dict["time_df"].loc[
was_already_evaluated_index, "validation"]
if self.metadata_dict["result_on_validation_df"] is None:
# The dataframe will have the case number and cutoff as index, the metric name as column
self.metadata_dict[
"result_on_validation_df"] = create_result_multiindex_dataframe(
self.n_cases, result_df)
add_result_to_multiindex_dataframe(
self.metadata_dict["result_on_validation_df"], result_df,
self.model_counter)
self.metadata_dict["time_df"].loc[self.model_counter,
"train"] = train_time
self.metadata_dict["time_df"].loc[self.model_counter,
"validation"] = evaluation_time
self.metadata_dict["time_on_train_avg"] = self.metadata_dict[
"time_df"]["train"].mean(axis=0, skipna=True)
self.metadata_dict["time_on_train_total"] = self.metadata_dict[
"time_df"]["train"].sum(axis=0, skipna=True)
self.metadata_dict["time_on_validation_avg"] = self.metadata_dict[
"time_df"]["validation"].mean(axis=0, skipna=True)
self.metadata_dict["time_on_validation_total"] = self.metadata_dict[
"time_df"]["validation"].sum(axis=0, skipna=True)
return result_df, recommender_instance
def _evaluate_on_test(self,
recommender_instance,
current_fit_hyperparameters_dict,
was_already_evaluated_flag,
was_already_evaluated_index,
print_log=True):
if was_already_evaluated_flag:
result_df_test = self.metadata_dict['result_on_test_df'].loc[
was_already_evaluated_index].copy()
evaluation_test_time = self.metadata_dict["time_df"].loc[
was_already_evaluated_index, "test"]
else:
start_time = time.time()
result_df_test, _ = self.evaluator_test.evaluateRecommender(
recommender_instance)
evaluation_test_time = time.time() - start_time
result_string = get_result_string_df(result_df_test)
if print_log:
self._write_log(
"{}: Config evaluated with evaluator_test. Config: {} - results:\n{}\n"
.format(self.ALGORITHM_NAME, current_fit_hyperparameters_dict,
result_string))
if self.metadata_dict["result_on_test_df"] is None:
# The dataframe will have the case number and cutoff as index, the metric name as column
self.metadata_dict[
"result_on_test_df"] = create_result_multiindex_dataframe(
self.n_cases, result_df_test)
add_result_to_multiindex_dataframe(
self.metadata_dict["result_on_test_df"], result_df_test,
self.model_counter)
self.metadata_dict["time_df"].loc[self.model_counter,
"test"] = evaluation_test_time
self.metadata_dict["time_on_test_avg"] = self.metadata_dict["time_df"][
"test"].mean(axis=0, skipna=True)
self.metadata_dict["time_on_test_total"] = self.metadata_dict[
"time_df"]["test"].sum(axis=0, skipna=True)
return result_df_test
def _evaluate_on_test_with_data_last(self):
start_time = time.time()
# Construct a new recommender instance
recommender_instance = self.recommender_class(
*self.recommender_input_args_last_test.CONSTRUCTOR_POSITIONAL_ARGS,
**self.recommender_input_args_last_test.CONSTRUCTOR_KEYWORD_ARGS)
# Check if last was already evaluated
if self.resume_from_saved and self.metadata_dict[
"result_on_last"] is not None:
self._print(
"{}: Resuming '{}'... Result on last already available.".
format(self.ALGORITHM_NAME, self.output_file_name_root))
return
self._print(
"{}: Evaluation with constructor data for final test. Using best config: {}"
.format(self.ALGORITHM_NAME,
self.metadata_dict["hyperparameters_best"]))
# Use the hyperparameters that have been saved
assert self.metadata_dict[
"hyperparameters_best"] is not None, "{}: Best hyperparameters not available, the search might have failed.".format(
self.ALGORITHM_NAME)
hyperparameters_best_args = self.metadata_dict[
"hyperparameters_best"].copy()
recommender_instance.fit(
*self.recommender_input_args_last_test.FIT_POSITIONAL_ARGS,
**self.recommender_input_args.FIT_KEYWORD_ARGS,
**hyperparameters_best_args)
train_time = time.time() - start_time
self.metadata_dict["time_on_last_df"].loc[0, "train"] = train_time
if self.evaluate_on_test in ["all", "best", "last"]:
start_time = time.time()
result_df_test, _ = self.evaluator_test.evaluateRecommender(
recommender_instance)
evaluation_test_time = time.time() - start_time
self._write_log(
"{}: Best config evaluated with evaluator_test with constructor data for final test. Config: {} - results:\n{}\n"
.format(self.ALGORITHM_NAME,
self.metadata_dict["hyperparameters_best"],
get_result_string_df(result_df_test)))
self.metadata_dict["result_on_last"] = result_df_test
self.metadata_dict["time_on_last_df"].loc[
0, "test"] = evaluation_test_time
if self.save_metadata:
self.dataIO.save_data(data_dict_to_save=self.metadata_dict.copy(),
file_name=self.output_file_name_root +
"_metadata")
if self.save_model in ["all", "best", "last"]:
self._print("{}: Saving model in {}\n".format(
self.ALGORITHM_NAME,
self.output_folder_path + self.output_file_name_root))
recommender_instance.save_model(
self.output_folder_path,
file_name=self.output_file_name_root + "_best_model_last")
def _objective_function(self, current_fit_hyperparameters_dict):
try:
self._print("{}: Testing config: {}".format(
self.ALGORITHM_NAME, current_fit_hyperparameters_dict))
was_already_evaluated_flag, was_already_evaluated_index = self._was_already_evaluated_check(
current_fit_hyperparameters_dict)
result_df, recommender_instance = self._evaluate_on_validation(
current_fit_hyperparameters_dict, was_already_evaluated_flag,
was_already_evaluated_index)
result_series = result_df.loc[
self.metadata_dict["cutoff_to_optimize"]]
current_result = -result_series[self.metric_to_optimize]
current_fit_hyperparameters_dict = self.metadata_dict[
"hyperparameters_df"].loc[self.model_counter].to_dict()
# Save current model if "all" is chosen
if self.save_model in ["all"] and not was_already_evaluated_flag:
self._print("{}: Saving model in {}\n".format(
self.ALGORITHM_NAME,
self.output_folder_path + self.output_file_name_root))
recommender_instance.save_model(
self.output_folder_path,
file_name=self.output_file_name_root +
"_model_{}".format(self.model_counter))
# Check if this is a new best hyperparameter configuration
if self.metadata_dict["result_on_validation_best"] is None:
new_best_config_found = True
else:
best_solution_val = self.metadata_dict[
"result_on_validation_best"][self.metric_to_optimize]
new_best_config_found = best_solution_val < result_series[
self.metric_to_optimize]
if new_best_config_found:
self._write_log(
"{}: New best config found. Config {}: {} - results: {}\n".
format(
self.ALGORITHM_NAME, self.model_counter,
current_fit_hyperparameters_dict,
get_result_string_prettyprint(result_series,
n_decimals=7)))
if self.evaluate_on_test in ["all", "best"]:
result_df_test = self._evaluate_on_test(
recommender_instance,
current_fit_hyperparameters_dict,
was_already_evaluated_flag,
was_already_evaluated_index,
print_log=True)
else:
# Config is either suboptimal or was already explored previously
self._write_log(
"{}: Config {} {}. Config: {} - results: {}\n".format(
self.ALGORITHM_NAME, self.model_counter,
"is suboptimal" if not was_already_evaluated_flag else
"was already explored at index {}".format(
was_already_evaluated_index),
current_fit_hyperparameters_dict,
get_result_string_prettyprint(result_series,
n_decimals=7)))
if self.evaluate_on_test in ["all"]:
result_df_test = self._evaluate_on_test(
recommender_instance,
current_fit_hyperparameters_dict,
was_already_evaluated_flag,
was_already_evaluated_index,
print_log=True)
if current_result >= self.INVALID_CONFIG_VALUE:
self._write_log(
"{}: WARNING! Config {} returned a value equal or worse than the default value to be assigned to invalid configurations."
" If no better valid configuration is found, this hyperparameter search may produce an invalid result.\n"
)
if new_best_config_found:
self.metadata_dict[
"hyperparameters_best"] = current_fit_hyperparameters_dict.copy(
)
self.metadata_dict[
"hyperparameters_best_index"] = self.model_counter
self.metadata_dict[
"result_on_validation_best"] = result_series.to_dict()
if self.evaluate_on_test in ["all", "best"]:
self.metadata_dict[
"result_on_test_best"] = result_df_test.copy()
# Clean any previous data about the "last"
# If the search has been extended then the "last" is recomputed only if a better solution is found
self.metadata_dict["result_on_last"] = None
self.metadata_dict["time_on_last_df"] = pd.DataFrame(
columns=["train", "test"], index=[0])
# Save best model if "all" and "best" are chosen
if self.save_model in ["all", "best"]:
self._print("{}: Saving model in {}\n".format(
self.ALGORITHM_NAME,
self.output_folder_path + self.output_file_name_root))
recommender_instance.save_model(
self.output_folder_path,
file_name=self.output_file_name_root + "_best_model")
except (KeyboardInterrupt, SystemExit) as e:
# If getting a interrupt, terminate without saving the exception
raise e
# Catch exception only if terminate_on_memory_error is True
except (_ArrayMemoryError,
MemoryError) if self.terminate_on_memory_error else (
NeverMatch) as e:
self._print(
"{}: Search for '{}' interrupted due to MemoryError.".format(
self.ALGORITHM_NAME,
self.metadata_dict["algorithm_name_recommender"]))
return
except:
# Catch any error: Exception, Tensorflow errors etc...
traceback_string = traceback.format_exc()
self._write_log(
"{}: Config {} Exception. Config: {} - Exception: {}\n".format(
self.ALGORITHM_NAME, self.model_counter,
current_fit_hyperparameters_dict, traceback_string))
self.metadata_dict["exception_list"][
self.model_counter] = traceback_string
# Assign to this configuration the worst possible score
# Being a minimization problem, set it to the max value of a float
current_result = +self.INVALID_CONFIG_VALUE
traceback.print_exc()
if self.save_metadata:
self.dataIO.save_data(data_dict_to_save=self.metadata_dict.copy(),
file_name=self.output_file_name_root +
"_metadata")
self.model_counter += 1
return current_result