def make_feature_factory_manager(split_num, model_id=None):
logger = get_logger()
feature_factory_dict = {}
for column in ["user_id", "content_id", ("last_lecture", "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"][
"PastNTimestampEncoder"] = PastNFeatureEncoder(
column="timestamp",
past_ns=[2, 3, 4, 5, 6, 7, 8, 9, 10],
agg_funcs=["vslast"],
remove_now=False)
feature_factory_dict["user_id"][
"Past1ContentTypeId"] = PastNFeatureEncoder(column="content_type_id",
past_ns=[5, 15],
agg_funcs=["mean"],
remove_now=False)
feature_factory_dict["user_id"]["StudyTermEncoder"] = StudyTermEncoder(
is_partial_fit=True)
feature_factory_dict["user_id"][
"ElapsedTimeVsShiftDiffEncoder"] = ElapsedTimeVsShiftDiffEncoder()
feature_factory_dict["user_id"]["CountEncoder"] = CountEncoder(
column="user_id", is_partial_fit=True)
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"MeanAggregatorShiftDiffTimeElapsedTimeby{column}"] = MeanAggregator(
column=column,
agg_column="shiftdiff_timestamp_by_user_id_cap200k",
remove_now=False)
feature_factory_dict[column][
f"MeanAggregatorStudyTimeby{column}"] = MeanAggregator(
column=column, agg_column="study_time", remove_now=False)
else:
feature_factory_dict[column][
f"MeanAggregatorShiftDiffTimeElapsedTimeby{column}"] = MeanAggregator(
column=list(column),
agg_column="shiftdiff_timestamp_by_user_id_cap200k",
remove_now=False)
feature_factory_dict[column][
f"MeanAggregatorStudyTimeby{column}"] = MeanAggregator(
column=list(column),
agg_column="study_time",
remove_now=False)
feature_factory_dict["user_id"][
"CategoryLevelEncoderPart"] = CategoryLevelEncoder(
groupby_column="user_id", agg_column="part", categories=[2, 5])
feature_factory_dict["user_id"]["PreviousAnswer2"] = PreviousAnswer2(
groupby="user_id",
column="content_id",
is_debug=is_debug,
model_id=model_id,
n=300)
feature_factory_dict["user_id"][
"PreviousNAnsweredCorrectly"] = PreviousNAnsweredCorrectly(
n=5, is_partial_fit=True)
feature_factory_dict[f"previous_5_ans"] = {
"TargetEncoder": TargetEncoder(column="previous_5_ans")
}
feature_factory_dict["user_id"][
"QuestionLectureTableEncoder2"] = QuestionLectureTableEncoder2(
model_id=model_id, is_debug=is_debug, past_n=100, min_size=300)
feature_factory_dict["user_id"][
"QuestionQuestionTableEncoder2"] = QuestionQuestionTableEncoder2(
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"][
"QuestionLectureTableEncoder"] = QuestionLectureTableEncoder2(
model_id=model_id, is_debug=is_debug, past_n=10)
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, 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"]:
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"][
"DurationPreviousContent"] = DurationPreviousContent(
is_partial_fit=True)
feature_factory_dict["user_id"][
"PastNTimestampEncoder"] = PastNFeatureEncoder(
column="timestamp",
past_ns=[2, 3, 4, 5, 6, 7, 8, 9, 10],
agg_funcs=["vslast"],
remove_now=False)
feature_factory_dict["user_id"]["StudyTermEncoder2"] = StudyTermEncoder2(
is_partial_fit=True)
feature_factory_dict["user_id"][
"ElapsedTimeMeanByContentIdEncoder"] = ElapsedTimeMeanByContentIdEncoder(
)
feature_factory_dict["user_id"]["CountEncoder"] = CountEncoder(
column="user_id", is_partial_fit=True)
feature_factory_dict[("user_id", "part")] = {
"UserContentRateEncoder":
UserContentRateEncoder(column=["user_id", "part"], rate_func="elo")
}
feature_factory_dict["user_id"][
"PastNUserAnswerHistory"] = PastNUserAnswerHistory(past_n=2,
min_size=300)
for column in [("user_id", "prior_question_had_explanation"),
("content_id", "prior_question_had_explanation"),
("part", "prior_question_had_explanation"),
("user_id", "part", "prior_question_had_explanation")]:
if column not in feature_factory_dict:
feature_factory_dict[column] = {}
if type(column) == str:
feature_factory_dict[column][
f"MeanAggregatorShiftDiffTimeElapsedTimeby{column}"] = MeanAggregator(
column=column,
agg_column="duration_previous_content_cap100k",
remove_now=False)
feature_factory_dict[column][
f"MeanAggregatorStudyTimeby{column}"] = MeanAggregator(
column=column, agg_column="study_time", remove_now=False)
else:
feature_factory_dict[column][
f"MeanAggregatorShiftDiffTimeElapsedTimeby{column}"] = MeanAggregator(
column=list(column),
agg_column="duration_previous_content_cap100k",
remove_now=False)
feature_factory_dict[column][
f"MeanAggregatorStudyTimeby{column}"] = MeanAggregator(
column=list(column),
agg_column="study_time",
remove_now=False)
feature_factory_dict["user_id"][
"CategoryLevelEncoderPart"] = CategoryLevelEncoder(
groupby_column="user_id", agg_column="part", categories=[2, 5])
feature_factory_dict["user_id"][
"UserContentNowRateEncoder"] = UserContentNowRateEncoder(
column="part", target=[1, 2, 3, 4, 5, 6, 7], rate_func="elo")
feature_factory_dict["user_id"]["PreviousAnswer2"] = PreviousAnswer2(
groupby="user_id",
column="content_id",
is_debug=is_debug,
model_id=model_id,
n=300)
feature_factory_dict["user_id"][
"PreviousNAnsweredCorrectly"] = PreviousNAnsweredCorrectly(
n=5, is_partial_fit=True)
feature_factory_dict[f"previous_5_ans"] = {
"TargetEncoder": TargetEncoder(column="previous_5_ans")
}
feature_factory_dict["user_id"][
"QuestionLectureTableEncoder2"] = QuestionLectureTableEncoder2(
model_id=model_id, is_debug=is_debug, past_n=100, min_size=300)
feature_factory_dict["user_id"][
"QuestionQuestionTableEncoder2"] = QuestionQuestionTableEncoder2(
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["content_id"]["CorrectVsIncorrectMeanEncoderContent-Duration100k"] = \
CorrectVsIncorrectMeanEncoder(groupby="content_id",
column="duration_previous_content_cap100k",
min_size=300)
feature_factory_dict["content_id"]["CorrectVsIncorrectMeanEncoderContent-UserIdTargetEnc"] = \
CorrectVsIncorrectMeanEncoder(groupby="part",
column="target_enc_user_id",
min_size=300)
feature_factory_dict["user_id"][
"PreviousContentAnswerTargetEncoder"] = PreviousContentAnswerTargetEncoder(
min_size=300)
feature_factory_dict["post"] = {
"DurationFeaturePostProcess": DurationFeaturePostProcess()
}
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
