def main(params: dict,
output_dir: str):
import mlflow
print("start params={}".format(params))
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"}
}
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_dict["user_id"]["UserContentRateEncoder"] = UserContentRateEncoder(rate_func="elo",
column="user_id")
feature_factory_manager = FeatureFactoryManager(feature_factory_dict=feature_factory_dict,
logger=logger,
split_num=1,
model_id=model_id,
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)
ff_for_transformer.make_dict(df=df)
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)
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)
df["answered_correctly"] = df["answered_correctly"].replace(-1, np.nan)
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"
},
"previous_answer_index_content_id": {
"type": "category"
},
"previous_answer_content_id": {
"type": "category"
},
"timediff-elapsedtime_bin500": {
"type": "category"
},
"timedelta_log10": {
"type": "category"
}
}
if not load_pickle or is_debug:
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_dict["user_id"][
"UserContentRateEncoder"] = UserContentRateEncoder(
rate_func="elo", column="user_id")
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"][
"StudyTermEncoder2"] = StudyTermEncoder2(is_partial_fit=True)
feature_factory_dict["user_id"][
f"MeanAggregatorStudyTimebyUserId"] = MeanAggregator(
column="user_id", agg_column="study_time", remove_now=False)
feature_factory_dict["user_id"][
"ElapsedTimeMeanByContentIdEncoder"] = ElapsedTimeMeanByContentIdEncoder(
)
feature_factory_dict["post"] = {
"DurationFeaturePostProcess": DurationFeaturePostProcess()
}
feature_factory_manager = FeatureFactoryManager(
feature_factory_dict=feature_factory_dict,
logger=logger,
split_num=1,
model_id=model_id,
load_feature=not is_debug,
save_feature=not is_debug)
print("all_predict")
df = feature_factory_manager.all_predict(df)
def f(x):
x = x // 1000
if x < -100:
return -100
if x > 400:
return 400
return x
df["task_container_id_bin300"] = [
x if x < 300 else 300 for x in df["task_container_id"]
]
df["timediff-elapsedtime_bin500"] = [
f(x) for x in df["timediff-elapsedtime"].values
]
df["timedelta_log10"] = np.log10(
df["duration_previous_content"].values)
df["timedelta_log10"] = df["timedelta_log10"].replace(
-np.inf, -1).replace(np.inf, -1).fillna(-1).astype("int8")
df = df[[
"user_id", "content_id", "content_type_id", "part", "user_answer",
"answered_correctly", "prior_question_elapsed_time_bin300",
"duration_previous_content_bin300",
"prior_question_had_explanation", "rating_diff_content_user_id",
"task_container_id_bin300", "previous_answer_index_content_id",
"previous_answer_content_id", "row_id",
"timediff-elapsedtime_bin500", "timedelta_log10"
]]
print(df.head(10))
print("data preprocess")
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_val_row = pd.read_feather(
"../../riiid_takoi/notebook/fe/validation_row_id.feather").head(
len(df))
if is_debug:
df_val_row = df_val_row.head(3000)
df_val_row["is_val"] = 1
df = pd.merge(df, df_val_row, how="left", on="row_id")
df["is_val"] = df["is_val"].fillna(0)
print(df["is_val"].value_counts())
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/model256", exist_ok=True)
if not is_debug and not load_pickle:
with open(f"../input/feature_engineering/model256/train.pickle",
"wb") as f:
pickle.dump(dataset_train, f)
with open(f"../input/feature_engineering/model256/val.pickle",
"wb") as f:
pickle.dump(dataset_val, f)
if not is_debug and load_pickle:
with open(f"../input/feature_engineering/model256/train.pickle",
"rb") as f:
dataset_train = pickle.load(f)
with open(f"../input/feature_engineering/model256/val.pickle",
"rb") as f:
dataset_val = pickle.load(f)
print("loaded!")
dataloader_train = DataLoader(dataset_train,
batch_size=params["batch_size"],
shuffle=True)
dataloader_val = DataLoader(dataset_val,
batch_size=params["batch_size"],
shuffle=False)
model = SAKTModel(n_skill,
embed_dim=params["embed_dim"],
max_seq=params["max_seq"],
dropout=dropout,
cont_emb=params["cont_emb"])
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) * 20)
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, epoch,
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)
def main(params: dict,
output_dir: str):
import mlflow
print("start params={}".format(params))
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": "category"},
"part": {"type": "category"},
"prior_question_elapsed_time_bin300": {"type": "category"},
"duration_previous_content_bin300": {"type": "category"},
"prior_question_had_explanation": {"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_manager = FeatureFactoryManager(feature_factory_dict=feature_factory_dict,
logger=logger,
split_num=1,
model_id=model_id,
load_feature=not is_debug,
save_feature=not is_debug)
print("all_predict")
df = feature_factory_manager.all_predict(df)
df = df[["user_id", "content_id", "content_type_id", "part", "user_answer", "answered_correctly",
"prior_question_elapsed_time_bin300", "duration_previous_content_bin300",
"prior_question_had_explanation"]].replace(-99, -1)
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/model106_all", exist_ok=True)
if not is_debug and not load_pickle:
with open(f"../input/feature_engineering/model106_all/train.pickle", "wb") as f:
pickle.dump(dataset_train, f)
with open(f"../input/feature_engineering/model106_all/val.pickle", "wb") as f:
pickle.dump(dataset_val, f)
if not is_debug and load_pickle:
with open(f"../input/feature_engineering/model106_all/train.pickle", "rb") as f:
dataset_train = pickle.load(f)
with open(f"../input/feature_engineering/model106_all/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):
x = item["x"].to(device).long()
target_id = item["target_id"].to(device).long()
part = item["part"].to(device).long()
label = item["label"].to(device).float()
elapsed_time = item["elapsed_time"].to(device).long()
duration_previous_content = item["duration_previous_content"].to(device).long()
prior_question_had_explanation = item["prior_q"].to(device).long()
user_answer = item["user_answer"].to(device).long()
output = model(x, target_id, part, elapsed_time,
duration_previous_content, prior_question_had_explanation, user_answer)
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)
def main(params: dict,
output_dir: str):
import mlflow
print("start params={}".format(params))
model_id = "all"
logger = get_logger()
column_config = {
("content_id", "content_type_id"): {"type": "category", "dtype": np.int16},
"user_answer": {"type": "leakage_feature", "dtype": np.int8},
"answered_correctly": {"type": "leakage_feature", "dtype": np.int8},
"part": {"type": "category", "dtype": np.int8},
"prior_question_elapsed_time_bin300": {"type": "category", "dtype": np.int16},
"duration_previous_content_bin300": {"type": "category", "dtype": np.int16},
"prior_question_had_explanation": {"type": "category", "dtype": np.int8},
"rating_diff_content_user_id": {"type": "numeric", "dtype": np.float16},
"task_container_id_bin300": {"type": "category", "dtype": np.int16},
"previous_answer_index_content_id": {"type": "category", "dtype": np.int16},
"previous_answer_content_id": {"type": "category", "dtype": np.int8},
"timediff-elapsedtime_bin500": {"type": "category", "dtype": np.int16}
}
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_dict["user_id"]["UserContentRateEncoder"] = UserContentRateEncoder(rate_func="elo",
column="user_id")
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"]["StudyTermEncoder2"] = StudyTermEncoder2(is_partial_fit=True)
feature_factory_dict["user_id"][f"MeanAggregatorStudyTimebyUserId"] = MeanAggregator(column="user_id",
agg_column="study_time",
remove_now=False)
feature_factory_dict["user_id"]["ElapsedTimeMeanByContentIdEncoder"] = ElapsedTimeMeanByContentIdEncoder()
feature_factory_dict["post"] = {
"DurationFeaturePostProcess": DurationFeaturePostProcess()
}
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")
df["prior_question_had_explanation"] = df["prior_question_had_explanation"].fillna(-1)
df["answered_correctly"] = df["answered_correctly"].replace(-1, np.nan)
if is_debug:
df = df.head(10000)
df = df.sort_values(["user_id", "timestamp"]).reset_index(drop=True)
ff_for_transformer.make_dict(df=df)
feature_factory_manager.fit(df)
df = feature_factory_manager.all_predict(df)
def f(x):
x = x // 1000
if x < -90:
return -90
if x > 90:
return 90
return x
df["task_container_id_bin300"] = [x if x < 300 else 300 for x in df["task_container_id"].values]
df["timediff-elapsedtime_bin500"] = [f(x) for x in df["timediff-elapsedtime"].values]
df = df[["user_id", "content_id", "content_type_id", "part", "user_answer", "answered_correctly",
"prior_question_elapsed_time_bin300", "duration_previous_content_bin300",
"prior_question_had_explanation", "rating_diff_content_user_id", "task_container_id_bin300",
"previous_answer_index_content_id", "previous_answer_content_id", "row_id",
"timediff-elapsedtime_bin500"]]
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)
def run(debug, model_dir, kaggle=False):
if kaggle:
files_dir = "/kaggle/input/riiid-split10/*.pickle"
else:
files_dir = "../input/riiid-test-answer-prediction/split10_base/*.pickle"
logger = get_logger()
# environment
env = riiideducation.make_env()
df_question = pd.read_csv(
"../input/riiid-test-answer-prediction/questions.csv",
dtype={
"bundle_id": "int32",
"question_id": "int32",
"correct_answer": "int8",
"part": "int8"
})
df_lecture = pd.read_csv(
"../input/riiid-test-answer-prediction/lectures.csv",
dtype={
"lecture_id": "int32",
"tag": "int16",
"part": "int8"
})
# model loading
models = []
for model_path in glob.glob(f"{model_dir}/*model*.pickle"):
with open(model_path, "rb") as f:
models.append(pickle.load(f))
# data preprocessing
logger = get_logger()
feature_factory_dict = {}
feature_factory_dict["tags"] = {"TagsSeparator": TagsSeparator()}
for column in [
"content_id", "user_id", "part", "prior_question_had_explanation",
"tags1", "tags2", ("user_id", "prior_question_had_explanation"),
("user_id", "part")
]:
is_partial_fit = (column == "content_id" or column == "user_id")
if type(column) == str:
feature_factory_dict[column] = {
"CountEncoder":
CountEncoder(column=column, is_partial_fit=is_partial_fit),
"TargetEncoder":
TargetEncoder(column=column, is_partial_fit=is_partial_fit)
}
else:
feature_factory_dict[column] = {
"CountEncoder":
CountEncoder(column=list(column),
is_partial_fit=is_partial_fit),
"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"][
"ShiftDiffEncoderContentId"] = ShiftDiffEncoder(groupby="user_id",
column="content_id")
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["user_id"][
"UserCountBinningEncoder"] = UserCountBinningEncoder(
is_partial_fit=True)
feature_factory_dict["user_count_bin"] = {}
feature_factory_dict["user_count_bin"]["CountEncoder"] = CountEncoder(
column="user_count_bin")
feature_factory_dict["user_count_bin"]["TargetEncoder"] = TargetEncoder(
column="user_count_bin")
feature_factory_dict[("user_id", "user_count_bin")] = {
"CountEncoder": CountEncoder(column=["user_id", "user_count_bin"]),
"TargetEncoder": TargetEncoder(column=["user_id", "user_count_bin"])
}
feature_factory_dict[("content_id", "user_count_bin")] = {
"CountEncoder": CountEncoder(column=["content_id", "user_count_bin"]),
"TargetEncoder": TargetEncoder(column=["content_id", "user_count_bin"])
}
feature_factory_dict["user_id"][
"CategoryLevelEncoderPart"] = CategoryLevelEncoder(
groupby_column="user_id",
agg_column="part",
categories=[1, 2, 3, 4, 5, 6, 7])
feature_factory_dict["user_count_bin"]["CategoryLevelEncoderUserCountBin"] = \
CategoryLevelEncoder(groupby_column="user_id",
agg_column="user_count_bin",
categories=[0, 1, 2, 3, 4, 5])
feature_factory_manager = FeatureFactoryManager(
feature_factory_dict=feature_factory_dict, logger=logger)
for model_id, fname in enumerate(glob.glob(files_dir)):
logger.info(f"loading... {fname}")
df = pd.read_pickle(fname)
df = df[df["answered_correctly"] != -1]
df["prior_question_had_explanation"] = df[
"prior_question_had_explanation"].fillna(-1).astype("int8")
if debug:
df = df.head(1000)
df = pd.concat([
pd.merge(df[df["content_type_id"] == 0],
df_question,
how="left",
left_on="content_id",
right_on="question_id"),
pd.merge(df[df["content_type_id"] == 1],
df_lecture,
how="left",
left_on="content_id",
right_on="lecture_id")
]).sort_values(["user_id", "timestamp"])
# df = feature_factory_manager.feature_factory_dict["content_id"]["TargetEncoder"].all_predict(df)
feature_factory_manager.fit(df, is_first_fit=True)
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"feature_factory_manager.pickle", "wb") as f:
pickle.dump(feature_factory_manager, f)
return
def run(debug, model_dir, kaggle=False):
if kaggle:
files_dir = "/kaggle/input/riiid-split10/*.pickle"
else:
files_dir = "../input/riiid-test-answer-prediction/split10_base/*.pickle"
logger = get_logger()
# environment
env = riiideducation.make_env()
df_question = pd.read_csv(
"../input/riiid-test-answer-prediction/questions.csv",
dtype={
"bundle_id": "int32",
"question_id": "int32",
"correct_answer": "int8",
"part": "int8"
})
df_lecture = pd.read_csv(
"../input/riiid-test-answer-prediction/lectures.csv",
dtype={
"lecture_id": "int32",
"tag": "int16",
"part": "int8"
})
# model loading
models = []
for model_path in glob.glob(f"{model_dir}/*model*.pickle"):
with open(model_path, "rb") as f:
models.append(pickle.load(f))
# data preprocessing
logger = get_logger()
feature_factory_dict = {}
feature_factory_dict["tags"] = {"TagsSeparator": TagsSeparator()}
for column in [
"content_id", "user_id", "part", "prior_question_had_explanation",
"tags1", "tags2", ("user_id", "prior_question_had_explanation"),
("user_id", "part")
]:
is_partial_fit = (column == "content_id" or column == "user_id")
if type(column) == str:
feature_factory_dict[column] = {
"CountEncoder":
CountEncoder(column=column, is_partial_fit=is_partial_fit),
"TargetEncoder":
TargetEncoder(column=column, is_partial_fit=is_partial_fit)
}
else:
feature_factory_dict[column] = {
"CountEncoder":
CountEncoder(column=list(column),
is_partial_fit=is_partial_fit),
"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"][
"ShiftDiffEncoderContentId"] = ShiftDiffEncoder(groupby="user_id",
column="content_id")
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["user_id"][
"UserCountBinningEncoder"] = UserCountBinningEncoder(
is_partial_fit=True)
feature_factory_dict["user_count_bin"] = {}
feature_factory_dict["user_count_bin"]["CountEncoder"] = CountEncoder(
column="user_count_bin")
feature_factory_dict["user_count_bin"]["TargetEncoder"] = TargetEncoder(
column="user_count_bin")
feature_factory_dict[("user_id", "user_count_bin")] = {
"CountEncoder": CountEncoder(column=["user_id", "user_count_bin"]),
"TargetEncoder": TargetEncoder(column=["user_id", "user_count_bin"])
}
feature_factory_dict[("content_id", "user_count_bin")] = {
"CountEncoder": CountEncoder(column=["content_id", "user_count_bin"]),
"TargetEncoder": TargetEncoder(column=["content_id", "user_count_bin"])
}
feature_factory_dict["user_id"][
"CategoryLevelEncoderPart"] = CategoryLevelEncoder(
groupby_column="user_id",
agg_column="part",
categories=[1, 2, 3, 4, 5, 6, 7])
feature_factory_dict["user_count_bin"]["CategoryLevelEncoderUserCountBin"] = \
CategoryLevelEncoder(groupby_column="user_id",
agg_column="user_count_bin",
categories=[0, 1, 2, 3, 4, 5])
feature_factory_manager = FeatureFactoryManager(
feature_factory_dict=feature_factory_dict, logger=logger)
"""
for model_id, fname in enumerate(glob.glob(files_dir)):
logger.info(f"loading... {fname}")
df = pd.read_pickle(fname)
df = df[df["answered_correctly"] != -1]
df["prior_question_had_explanation"] = df["prior_question_had_explanation"].fillna(-1).astype("int8")
if debug:
df = df.head(1000)
df = pd.concat([pd.merge(df[df["content_type_id"] == 0], df_question,
how="left", left_on="content_id", right_on="question_id"),
pd.merge(df[df["content_type_id"] == 1], df_lecture,
how="left", left_on="content_id", right_on="lecture_id")]).sort_values(["user_id", "timestamp"])
# df = feature_factory_manager.feature_factory_dict["content_id"]["TargetEncoder"].all_predict(df)
feature_factory_manager.fit(df, is_first_fit=True)
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"feature_factory_manager.pickle", "wb") as f:
pickle.dump(feature_factory_manager, f)
return
"""
with open(f"feature_factory_manager.pickle", "rb") as f:
feature_factory_manager = pickle.load(f)
for dicts in feature_factory_manager.feature_factory_dict.values():
for factory in dicts.values():
factory.logger = logger
feature_factory_manager.logger = logger
iter_test = env.iter_test()
df_test_prev = pd.DataFrame()
answered_correctlies = []
user_answers = []
i = 0
t = time.time()
for (df_test, df_sample_prediction) in iter_test:
i += 1
logger.info(
f"[time: {int(time.time() - t)}iteration {i}: data_length: {len(df_test)}"
)
# 前回のデータ更新
if len(df_test_prev) > 0: # 初回のみパスするためのif
answered_correctly = df_test.iloc[0]["prior_group_answers_correct"]
user_answer = df_test.iloc[0]["prior_group_responses"]
answered_correctlies.extend([
int(x) for x in answered_correctly.replace("[", "").replace(
"'", "").replace("]", "").replace(" ", "").split(",")
])
user_answers.extend([
int(x)
for x in user_answer.replace("[", "").replace("'", "").replace(
"]", "").replace(" ", "").split(",")
])
if debug:
update_record = 1
else:
update_record = 75
if len(df_test_prev) > update_record:
df_test_prev["answered_correctly"] = answered_correctlies
df_test_prev["user_answer"] = user_answers
# df_test_prev = df_test_prev.drop(prior_columns, axis=1)
df_test_prev = df_test_prev[
df_test_prev["answered_correctly"] != -1]
df_test_prev["answered_correctly"] = df_test_prev[
"answered_correctly"].replace(-1, np.nan)
df_test_prev["prior_question_had_explanation"] = df_test_prev[
"prior_question_had_explanation"].fillna(-1).astype("int8")
feature_factory_manager.fit(df_test_prev)
df_test_prev = pd.DataFrame()
answered_correctlies = []
user_answers = []
# 今回のデータ取得&計算
# logger.info(f"[time: {int(time.time() - t)}dataload")
logger.info(f"merge... ")
w_df1 = pd.merge(df_test[df_test["content_type_id"] == 0],
df_question,
how="left",
left_on="content_id",
right_on="question_id")
w_df2 = pd.merge(df_test[df_test["content_type_id"] == 1],
df_lecture,
how="left",
left_on="content_id",
right_on="lecture_id")
df_test = pd.concat([w_df1,
w_df2]).sort_values(["user_id",
"timestamp"]).sort_index()
df_test["tag"] = df_test["tag"].fillna(-1)
df_test["correct_answer"] = df_test["correct_answer"].fillna(-1)
df_test["bundle_id"] = df_test["bundle_id"].fillna(-1)
logger.info(f"transform... ")
df_test["prior_question_had_explanation"] = df_test[
"prior_question_had_explanation"].astype("float16").fillna(
-1).astype("int8")
df = feature_factory_manager.partial_predict(df_test)
df.columns = [x.replace(" ", "_") for x in df.columns]
logger.info(f"other... ")
# predict
predicts = []
cols = models[0].feature_name()
for model in models:
predicts.append(model.predict(df[cols]))
df["answered_correctly"] = np.array(predicts).transpose().mean(axis=1)
df_sample_prediction = pd.merge(df_sample_prediction[["row_id"]],
df[["row_id", "answered_correctly"]],
how="inner")
env.predict(df_sample_prediction)
df_test_prev = df_test_prev.append(df[cols + ["user_id", "tags"]])
if i < 5:
df_test_prev.to_csv(f"{i}.csv")
