def make_feature_factory_manager(split_num, size, window, model_id=None):
logger = get_logger()
feature_factory_dict = {}
feature_factory_dict["tags"] = {
"TagsSeparator": TagsSeparator(is_partial_fit=True)
}
for column in ["user_id", "content_id"]:
is_partial_fit = (column == "content_id" or column == "user_id")
if type(column) == str:
feature_factory_dict[column] = {
"TargetEncoder":
TargetEncoder(column=column, is_partial_fit=is_partial_fit)
}
else:
feature_factory_dict[column] = {
"TargetEncoder":
TargetEncoder(column=list(column),
is_partial_fit=is_partial_fit)
}
feature_factory_dict["user_id"][
"ShiftDiffEncoderTimestamp"] = ShiftDiffEncoder(groupby="user_id",
column="timestamp",
is_partial_fit=True)
feature_factory_dict["user_id"]["StudyTermEncoder"] = StudyTermEncoder(
is_partial_fit=True)
# feature_factory_dict["user_id"]["UserLevelEncoder2ContentId"] = UserLevelEncoder2(vs_column="content_id")
# feature_factory_dict["content_id"]["ContentLevelEncoder2UserId"] = ContentLevelEncoder(vs_column="user_id", is_partial_fit=True)
# feature_factory_dict["user_id"]["MeanAggregatorContentLevel"] = MeanAggregator(column="user_id",
# agg_column="content_level_user_id",
# remove_now=False)
feature_factory_dict["user_id"]["CountEncoder"] = CountEncoder(
column="user_id", is_partial_fit=True)
feature_factory_dict["user_id"][
"UserCountBinningEncoder"] = UserCountBinningEncoder(
is_partial_fit=True)
feature_factory_dict["user_count_bin"] = {}
feature_factory_dict["user_count_bin"]["TargetEncoder"] = TargetEncoder(
column="user_count_bin")
feature_factory_dict[("user_id", "user_count_bin")] = {
"TargetEncoder": TargetEncoder(column=["user_id", "user_count_bin"])
}
feature_factory_dict[("content_id", "user_count_bin")] = {
"TargetEncoder": TargetEncoder(column=["content_id", "user_count_bin"])
}
feature_factory_dict[("user_id", "part")] = {
"UserContentRateEncoder":
UserContentRateEncoder(column=["user_id", "part"], rate_func="elo")
}
for column in ["user_id", "content_id", "part", ("user_id", "part")]:
if column not in feature_factory_dict:
feature_factory_dict[column] = {}
if type(column) == str:
feature_factory_dict[column][
f"MeanAggregatorPriorQuestionElapsedTimeby{column}"] = MeanAggregator(
column=column,
agg_column="prior_question_elapsed_time",
remove_now=True)
feature_factory_dict[column][
f"MeanAggregatorStudyTimeby{column}"] = MeanAggregator(
column=column, agg_column="study_time", remove_now=True)
else:
feature_factory_dict[column][
f"MeanAggregatorPriorQuestionElapsedTimeby{column}"] = MeanAggregator(
column=list(column),
agg_column="prior_question_elapsed_time",
remove_now=True)
feature_factory_dict[column][
f"MeanAggregatorStudyTimeby{column}"] = MeanAggregator(
column=list(column),
agg_column="study_time",
remove_now=True)
feature_factory_dict["user_id"][
"CategoryLevelEncoderPart"] = CategoryLevelEncoder(
groupby_column="user_id", agg_column="part", categories=[2, 5])
feature_factory_dict["prior_question_elapsed_time"] = {
"PriorQuestionElapsedTimeBinningEncoder":
PriorQuestionElapsedTimeBinningEncoder(is_partial_fit=True)
}
feature_factory_dict[("part", "prior_question_elapsed_time_bin")] = {
"TargetEncoder":
TargetEncoder(column=["part", "prior_question_elapsed_time_bin"])
}
feature_factory_dict["user_id"]["PreviousAnswer2"] = PreviousAnswer2(
groupby="user_id",
column="content_id",
is_debug=is_debug,
model_id=model_id,
n=500)
feature_factory_dict["user_id"][
"PreviousNAnsweredCorrectly"] = PreviousNAnsweredCorrectly(
n=3, is_partial_fit=True)
feature_factory_dict[f"previous_3_ans"] = {
"TargetEncoder": TargetEncoder(column="previous_3_ans")
}
feature_factory_dict["user_id"][
"QuestionLectureTableEncoder2"] = QuestionLectureTableEncoder2(
model_id=model_id, is_debug=is_debug, past_n=100, min_size=100)
feature_factory_dict["user_id"][
"QuestionQuestionTableEncoder"] = QuestionQuestionTableEncoder(
model_id=model_id, is_debug=is_debug, past_n=100, min_size=300)
feature_factory_dict["user_id"][
"UserContentRateEncoder"] = UserContentRateEncoder(column="user_id",
rate_func="elo")
feature_factory_dict["post"] = {
"ContentIdTargetEncoderAggregator": TargetEncoderAggregator()
}
feature_factory_manager = FeatureFactoryManager(
feature_factory_dict=feature_factory_dict,
logger=logger,
split_num=split_num,
model_id=model_id,
load_feature=not is_debug,
save_feature=not is_debug)
return feature_factory_manager
def make_feature_factory_manager(split_num, model_id=None):
logger = get_logger()
feature_factory_dict = {}
for column in ["user_id", "content_id", ("user_id", "part"),
("content_id", "prior_question_had_explanation")]:
is_partial_fit = (column == "content_id" or column == "user_id")
if type(column) == str:
feature_factory_dict[column] = {
"TargetEncoder": TargetEncoder(column=column, is_partial_fit=is_partial_fit)
}
else:
feature_factory_dict[column] = {
"TargetEncoder": TargetEncoder(column=list(column), is_partial_fit=is_partial_fit)
}
feature_factory_dict["user_id"]["ShiftDiffEncoderTimestamp"] = ShiftDiffEncoder(groupby="user_id",
column="timestamp")
for column in ["user_id", "content_id"]:
feature_factory_dict[column][f"MeanAggregatorPriorQuestionElapsedTimeby{column}"] = MeanAggregator(column=column,
agg_column="prior_question_elapsed_time",
remove_now=True)
feature_factory_dict["user_id"]["UserLevelEncoder2ContentId"] = UserLevelEncoder2(vs_column="content_id")
feature_factory_dict["content_id"]["ContentLevelEncoder2UserId"] = ContentLevelEncoder(vs_column="user_id", is_partial_fit=True)
feature_factory_dict["user_id"]["MeanAggregatorContentLevel"] = MeanAggregator(column="user_id",
agg_column="content_level_user_id",
remove_now=False)
feature_factory_dict["user_id"]["CountEncoder"] = CountEncoder(column="user_id", is_partial_fit=True)
feature_factory_dict["user_id"]["UserCountBinningEncoder"] = UserCountBinningEncoder(is_partial_fit=True)
feature_factory_dict["user_count_bin"] = {}
feature_factory_dict["user_count_bin"]["TargetEncoder"] = TargetEncoder(column="user_count_bin")
feature_factory_dict[("user_id", "user_count_bin")] = {
"TargetEncoder": TargetEncoder(column=["user_id", "user_count_bin"])
}
feature_factory_dict[("content_id", "user_count_bin")] = {
"TargetEncoder": TargetEncoder(column=["content_id", "user_count_bin"])
}
feature_factory_dict[("prior_question_had_explanation", "user_count_bin")] = {
"TargetEncoder": TargetEncoder(column=["prior_question_had_explanation", "user_count_bin"])
}
feature_factory_dict["user_id"]["CategoryLevelEncoderPart"] = CategoryLevelEncoder(groupby_column="user_id",
agg_column="part",
categories=[2, 5])
feature_factory_dict["user_id"]["FirstColumnEncoderContentId"] = FirstColumnEncoder(agg_column="content_id",
astype="int16",
is_partial_fit=True)
feature_factory_dict["user_id"]["FirstColumnEncoderPart"] = FirstColumnEncoder(agg_column="part",
astype="int8",
is_partial_fit=True)
for column in ["user_id", "user_count_bin", "first_column_content_id", "first_column_part", ("user_id", "part")]:
if column not in feature_factory_dict:
feature_factory_dict[column] = {}
if type(column) == str:
feature_factory_dict[column][f"MeanAggregatorTargetEncContentIdBy{column}"] = MeanAggregator(
column=column,
agg_column="target_enc_content_id",
remove_now=False
)
else:
feature_factory_dict[column][f"MeanAggregatorTargetEncContentIdBy{column}"] = MeanAggregator(
column=list(column),
agg_column="target_enc_content_id",
remove_now=False
)
for column in [("content_id", "prior_question_had_explanation")]:
if column not in feature_factory_dict:
feature_factory_dict[column] = {}
if type(column) == str:
feature_factory_dict[column][f"MeanAggregatorTargetEncContentIdBy{column}"] = MeanAggregator(
column=column,
agg_column="target_enc_user_id",
remove_now=False
)
else:
feature_factory_dict[column][f"MeanAggregatorTargetEncContentIdBy{column}"] = MeanAggregator(
column=list(column),
agg_column="target_enc_user_id",
remove_now=False
)
feature_factory_dict["prior_question_elapsed_time"] = {
"PriorQuestionElapsedTimeBinningEncoder": PriorQuestionElapsedTimeBinningEncoder(is_partial_fit=True)
}
feature_factory_dict[("part", "prior_question_elapsed_time_bin")] = {
"TargetEncoder": TargetEncoder(column=["part", "prior_question_elapsed_time_bin"])
}
feature_factory_dict["user_id"]["PreviousAnswer2"] = PreviousAnswer2(groupby="user_id",
column="content_id",
is_debug=is_debug,
model_id=model_id,
n=1000)
feature_factory_dict["user_id"]["PreviousNAnsweredCorrectly"] = PreviousNAnsweredCorrectly(n=3,
is_partial_fit=True)
feature_factory_dict[f"previous_3_ans"] = {
"TargetEncoder": TargetEncoder(column="previous_3_ans")
}
feature_factory_dict["user_id"]["QuestionLectureTableEncoder2"] = QuestionLectureTableEncoder2(model_id=model_id,
is_debug=is_debug,
past_n=100)
feature_factory_dict["user_id"]["QuestionQuestionTableEncoder"] = QuestionQuestionTableEncoder(model_id=model_id,
is_debug=is_debug,
past_n=100)
feature_factory_dict["user_id"]["UserAnswerLevelEncoder"] = UserAnswerLevelEncoder(model_id=model_id,
is_debug=is_debug,
past_n=100)
feature_factory_dict["user_id"]["WeightDecayTargetEncoder"] = WeightDecayTargetEncoder(column="user_id")
feature_factory_dict["post"] = {
"ContentIdTargetEncoderAggregator": TargetEncoderAggregator()
}
feature_factory_manager = FeatureFactoryManager(feature_factory_dict=feature_factory_dict,
logger=logger,
split_num=split_num,
model_id=model_id,
load_feature=not is_debug,
save_feature=not is_debug)
return feature_factory_manager
