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
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 main(params: dict, output_dir: str): import mlflow print("start params={}".format(params)) model_id = "train_0" logger = get_logger() # df = pd.read_pickle("../input/riiid-test-answer-prediction/train_merged.pickle") df = pd.read_pickle( "../input/riiid-test-answer-prediction/split10/train_0.pickle" ).sort_values(["user_id", "timestamp"]).reset_index(drop=True) if is_debug: df = df.head(30000) df["prior_question_had_explanation"] = df[ "prior_question_had_explanation"].fillna(-1) column_config = { ("content_id", "content_type_id"): { "type": "category" }, "user_answer": { "type": "leakage_feature" }, "answered_correctly": { "type": "leakage_feature" }, "part": { "type": "category" }, "prior_question_elapsed_time_bin300": { "type": "category" }, "duration_previous_content_bin300": { "type": "category" }, "prior_question_had_explanation": { "type": "category" }, "rating_diff_content_user_id": { "type": "numeric" }, "task_container_id_bin300": { "type": "category" }, "study_time_bin300": { "type": "category" }, "diff_mean_study_time_by_user_id_bin300": { "type": "category" }, "past2_timestamp_vslast_bin300": { "type": "category" }, "past3_timestamp_vslast_bin300": { "type": "category" }, "past4_timestamp_vslast_bin300": { "type": "category" }, "past5_timestamp_vslast_bin300": { "type": "category" }, "rating_diff_content_user_id_bin500": { "type": "category" } } if not load_pickle or is_debug: feature_factory_dict = {"user_id": {}} feature_factory_dict["user_id"][ "DurationPreviousContent"] = DurationPreviousContent() feature_factory_dict["user_id"][ "ElapsedTimeBinningEncoder"] = ElapsedTimeBinningEncoder() feature_factory_dict["user_id"][ "UserContentRateEncoder"] = UserContentRateEncoder( rate_func="elo", column="user_id") feature_factory_dict["user_id"][ "StudyTermEncoder"] = StudyTermEncoder2() feature_factory_dict["user_id"][ "MeanAggregatorStudyTimebyUserId"] = MeanAggregator( column="user_id", agg_column="study_time", remove_now=False) feature_factory_dict["user_id"][ "PastNTimestampEncoder"] = PastNFeatureEncoder( column="timestamp", past_ns=[2, 3, 4, 5], agg_funcs=["vslast"], remove_now=False) feature_factory_manager = FeatureFactoryManager( feature_factory_dict=feature_factory_dict, logger=logger, split_num=1, model_id="train_0", load_feature=not is_debug, save_feature=not is_debug) print("all_predict") df = feature_factory_manager.all_predict(df) df["task_container_id_bin300"] = [ x if x < 300 else 300 for x in df["task_container_id"].values ] def f(x): x = x // 1000 if x > 150: return 150 if x < -150: return -150 return x df["study_time_bin300"] = [f(x) for x in df["study_time"].values] df["diff_mean_study_time_by_user_id_bin300"] = [ f(x) for x in df["diff_mean_study_time_by_user_id"].values ] df["past2_timestamp_vslast_bin300"] = [ f(x) for x in df["past2_timestamp_vslast"].values ] df["past3_timestamp_vslast_bin300"] = [ f(x) for x in df["past3_timestamp_vslast"].values ] df["past4_timestamp_vslast_bin300"] = [ f(x) for x in df["past4_timestamp_vslast"].values ] df["past5_timestamp_vslast_bin300"] = [ f(x) for x in df["past5_timestamp_vslast"].values ] df["rating_diff_content_user_id_bin500"] = [ f(x) for x in df["rating_diff_content_user_id"].values ] df = df[[ "user_id", "content_id", "content_type_id", "part", "user_answer", "answered_correctly", "prior_question_elapsed_time_bin300", "duration_previous_content_bin300", "study_time_bin300", "prior_question_had_explanation", "rating_diff_content_user_id", "task_container_id_bin300", "past2_timestamp_vslast_bin300", "past3_timestamp_vslast_bin300", "past4_timestamp_vslast_bin300", "past5_timestamp_vslast_bin300", "rating_diff_content_user_id_bin500", "diff_mean_study_time_by_user_id_bin300" ]].fillna(0) print(df.head(10)) print("data preprocess") train_idx = [] val_idx = [] np.random.seed(0) for _, w_df in df[df["content_type_id"] == 0].groupby("user_id"): if np.random.random() < 0.01: # all val val_idx.extend(w_df.index.tolist()) else: train_num = int(len(w_df) * 0.95) train_idx.extend(w_df[:train_num].index.tolist()) val_idx.extend(w_df[train_num:].index.tolist()) ff_for_transformer = FeatureFactoryForTransformer( column_config=column_config, dict_path="../feature_engineering/", sequence_length=params["max_seq"], logger=logger) ff_for_transformer.make_dict(df=df) n_skill = len(ff_for_transformer.embbed_dict[("content_id", "content_type_id")]) if not load_pickle or is_debug: df["is_val"] = 0 df["is_val"].loc[val_idx] = 1 w_df = df[df["is_val"] == 0] w_df["group"] = ( w_df.groupby("user_id")["user_id"].transform("count") - w_df.groupby("user_id").cumcount()) // params["max_seq"] w_df["user_id"] = w_df["user_id"].astype( str) + "_" + w_df["group"].astype(str) group = ff_for_transformer.all_predict(w_df) dataset_train = SAKTDataset(group, n_skill=n_skill, max_seq=params["max_seq"]) del w_df gc.collect() ff_for_transformer = FeatureFactoryForTransformer( column_config=column_config, dict_path="../feature_engineering/", sequence_length=params["max_seq"], logger=logger) if not load_pickle or is_debug: group = ff_for_transformer.all_predict(df[df["content_type_id"] == 0]) dataset_val = SAKTDataset(group, is_test=True, n_skill=n_skill, max_seq=params["max_seq"]) os.makedirs("../input/feature_engineering/model139", exist_ok=True) if not is_debug and not load_pickle: with open(f"../input/feature_engineering/model139/train.pickle", "wb") as f: pickle.dump(dataset_train, f) with open(f"../input/feature_engineering/model139/val.pickle", "wb") as f: pickle.dump(dataset_val, f) if not is_debug and load_pickle: with open(f"../input/feature_engineering/model139/train.pickle", "rb") as f: dataset_train = pickle.load(f) with open(f"../input/feature_engineering/model139/val.pickle", "rb") as f: dataset_val = pickle.load(f) print("loaded!") dataloader_train = DataLoader(dataset_train, batch_size=params["batch_size"], shuffle=True, num_workers=1) dataloader_val = DataLoader(dataset_val, batch_size=params["batch_size"], shuffle=False, num_workers=1) model = SAKTModel(n_skill, embed_dim=params["embed_dim"], max_seq=params["max_seq"], dropout=dropout) param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [{ 'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01 }, { 'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0 }] optimizer = AdamW( optimizer_grouped_parameters, lr=params["lr"], weight_decay=0.01, ) num_train_optimization_steps = int(len(dataloader_train) * epochs) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=params["num_warmup_steps"], num_training_steps=num_train_optimization_steps) criterion = nn.BCEWithLogitsLoss() model.to(device) criterion.to(device) for epoch in range(epochs): loss, acc, auc, auc_val = train_epoch(model, dataloader_train, dataloader_val, optimizer, criterion, scheduler, device) print("epoch - {} train_loss - {:.3f} auc - {:.4f} auc-val: {:.4f}". format(epoch, loss, auc, auc_val)) preds = [] labels = [] with torch.no_grad(): for item in tqdm(dataloader_val): label = item["label"].to(device).float() output = model(item, device) preds.extend(torch.nn.Sigmoid()( output[:, -1]).view(-1).data.cpu().numpy().tolist()) labels.extend(label[:, -1].view(-1).data.cpu().numpy().tolist()) auc_transformer = roc_auc_score(labels, preds) print("single transformer: {:.4f}".format(auc_transformer)) df_oof = pd.DataFrame() # df_oof["row_id"] = df.loc[val_idx].index print(len(dataloader_val)) print(len(preds)) df_oof["predict"] = preds df_oof["target"] = labels df_oof.to_csv(f"{output_dir}/transformers1.csv", index=False) """ df_oof2 = pd.read_csv("../output/ex_237/20201213110353/oof_train_0_lgbm.csv") df_oof2.columns = ["row_id", "predict_lgbm", "target"] df_oof2 = pd.merge(df_oof, df_oof2, how="inner") auc_lgbm = roc_auc_score(df_oof2["target"].values, df_oof2["predict_lgbm"].values) print("lgbm: {:.4f}".format(auc_lgbm)) print("ensemble") max_auc = 0 max_nn_ratio = 0 for r in np.arange(0, 1.05, 0.05): auc = roc_auc_score(df_oof2["target"].values, df_oof2["predict_lgbm"].values*(1-r) + df_oof2["predict"].values*r) print("[nn_ratio: {:.2f}] AUC: {:.4f}".format(r, auc)) if max_auc < auc: max_auc = auc max_nn_ratio = r print(len(df_oof2)) """ if not is_debug: mlflow.start_run(experiment_id=10, run_name=os.path.basename(__file__)) for key, value in params.items(): mlflow.log_param(key, value) mlflow.log_metric("auc_val", auc_transformer) mlflow.end_run() torch.save(model.state_dict(), f"{output_dir}/transformers.pth") del model torch.cuda.empty_cache() with open(f"{output_dir}/transformer_param.json", "w") as f: json.dump(params, f) if is_make_feature_factory: # feature factory feature_factory_dict = {"user_id": {}} feature_factory_dict["user_id"][ "DurationPreviousContent"] = DurationPreviousContent( is_partial_fit=True) feature_factory_dict["user_id"][ "ElapsedTimeBinningEncoder"] = ElapsedTimeBinningEncoder() feature_factory_manager = FeatureFactoryManager( feature_factory_dict=feature_factory_dict, logger=logger, split_num=1, model_id="all", load_feature=not is_debug, save_feature=not is_debug) ff_for_transformer = FeatureFactoryForTransformer( column_config=column_config, dict_path="../feature_engineering/", sequence_length=params["max_seq"], logger=logger) df = pd.read_pickle( "../input/riiid-test-answer-prediction/train_merged.pickle") if is_debug: df = df.head(10000) df = df.sort_values(["user_id", "timestamp"]).reset_index(drop=True) feature_factory_manager.fit(df) df = feature_factory_manager.all_predict(df) for dicts in feature_factory_manager.feature_factory_dict.values(): for factory in dicts.values(): factory.logger = None feature_factory_manager.logger = None with open(f"{output_dir}/feature_factory_manager.pickle", "wb") as f: pickle.dump(feature_factory_manager, f) ff_for_transformer.fit(df) ff_for_transformer.logger = None with open( f"{output_dir}/feature_factory_manager_for_transformer.pickle", "wb") as f: pickle.dump(ff_for_transformer, f)