def __getitem__(self, idx):
id_ = self.df.loc[idx, "Id"]
fnc_feature = self.df.loc[idx, self.fnc_cols].values
loading_feature = self.df.loc[idx, self.loading_cols].values
voxel_feature = self.df.loc[idx, self.voxel_stat_cols].values
if self.mode != "test":
labels = self.df.loc[idx, self.label_cols].values
if self.mode == "train":
labels[0] = randomize_age(labels[0])
return {
"Id": id_,
"fnc": fnc_feature,
"loading": loading_feature,
"voxel": voxel_feature,
"label": labels,
}
else:
return {
"Id": id_,
"fnc": fnc_feature,
"loading": loading_feature,
"voxel": voxel_feature,
}
def main(config: DictConfig) -> None:
prepair_dir(config)
train_df, test_df = load_data(config)
label_cols = [
"age", "domain1_var1", "domain1_var2", "domain2_var1", "domain2_var2"
]
feature_cols = [
col for col in train_df.columns if col not in label_cols + ["Id"]
]
# From adversarial valiadtion
# feature_cols.remove("IC_20")
train_df["age_rank"] = train_df["age"] // 10 * 10
age_rank = train_df["age_rank"].to_array()
if config.randomize_age:
set_seed(100)
train_df["age"] += [randomize_age(age) for age in train_df["age"]]
skf = StratifiedKFold(n_splits=5,
random_state=config.data.seed,
shuffle=True)
for label_col, k in zip(label_cols, [20, 160, 180, 120, 120]):
y_oof = np.zeros(train_df.shape[0])
for n_fold, (train_index,
val_index) in enumerate(skf.split(age_rank, age_rank)):
train_df_fold = train_df.iloc[train_index]
valid_df_fold = train_df.iloc[val_index]
train_df_fold = train_df_fold[train_df_fold[label_col].notnull()]
model = KNeighborsRegressor(n_neighbors=k)
model.fit(train_df_fold[feature_cols], train_df_fold[label_col])
y_oof[val_index] = model.predict(
valid_df_fold[feature_cols]).to_array()
test_df[f"{label_col}_pred_fold{n_fold}"] = model.predict(
test_df[feature_cols])
train_df[f"{label_col}_pred"] = y_oof
notnull_idx = train_df[label_col].notnull()
score = normalized_absolute_errors(
train_df[notnull_idx][label_col].values,
train_df[notnull_idx][f"{label_col}_pred"].values,
)
logger.info(f"{label_col}, score: {score}")
test_df[label_col] = test_df[[
f"{label_col}_pred_fold{i}" for i in range(5)
]].mean(1)
score = 0
for label_col, weight in zip(label_cols,
[0.3, 0.175, 0.175, 0.175, 0.175]):
notnull_idx = train_df[label_col].notnull()
score += (normalized_absolute_errors(
train_df[notnull_idx][label_col].to_array(),
train_df[notnull_idx][f"{label_col}_pred"].to_array(),
) * weight)
logger.info(f"all score: {score}")
train_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_train.csv"),
index=False,
)
test_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_test.csv"),
index=False,
)
if config.store.gcs_project is not None:
upload_directory(config.store)
def main(config: DictConfig) -> None:
prepair_dir(config)
set_seed(777777)
train_df, test_df = load_data(config)
label_cols = [
"age", "domain1_var1", "domain1_var2", "domain2_var1", "domain2_var2"
]
feature_cols = [
col for col in train_df.columns if col not in label_cols + ["Id"]
]
# From adversarial valiadtion
feature_cols.remove("IC_20")
train_df["age_rank"] = train_df["age"] // 10 * 10
skf = StratifiedKFold(n_splits=5,
random_state=config.data.seed,
shuffle=True)
for i, (_,
val_index) in enumerate(skf.split(train_df, train_df["age_rank"])):
train_df.loc[val_index, "fold"] = i
if config.randomize_age:
train_df["age"] += np.array(
[randomize_age(age) for age in train_df["age"].values])
params = {}
params["age"] = {
"alpha": 0.8,
"reg_lambda": 0.8,
"max_leaves": 3,
"colsample_bytree": 0.6,
"subsample": 0.8,
"min_child_weight": 50,
"booster": "gbtree",
"objective": "reg:squarederror",
"eval_metric": "mae",
"max_depth": 10,
"learning_rate": 0.01,
"nthread": -1,
"max_bin": 256,
"tree_method": "gpu_hist",
"disable_default_eval_metric": 1,
}
params["domain1_var1"] = {
"alpha": 0.8,
"reg_lambda": 0.8,
"max_leaves": 4,
"colsample_bytree": 0.5,
"subsample": 0.8,
"min_child_weight": 5,
"booster": "gbtree",
"objective": "reg:squarederror",
"eval_metric": "mae",
"max_depth": 7,
"learning_rate": 0.01,
"nthread": -1,
"max_bin": 256,
"tree_method": "gpu_hist",
"disable_default_eval_metric": 1,
}
params["domain1_var2"] = {
"alpha": 0.8,
"reg_lambda": 0.8,
"max_leaves": 4,
"colsample_bytree": 0.8,
"subsample": 0.6,
"min_child_weight": 5,
"booster": "gbtree",
"objective": "reg:squarederror",
"eval_metric": "mae",
"max_depth": 7,
"learning_rate": 0.01,
"nthread": -1,
"max_bin": 256,
"tree_method": "gpu_hist",
"disable_default_eval_metric": 1,
}
params["domain2_var1"] = {
"alpha": 0.8,
"reg_lambda": 0.8,
"max_leaves": 4,
"colsample_bytree": 0.7,
"subsample": 0.4,
"min_child_weight": 5,
"booster": "gbtree",
"objective": "reg:squarederror",
"eval_metric": "mae",
"max_depth": 7,
"learning_rate": 0.01,
"nthread": -1,
"max_bin": 256,
"tree_method": "gpu_hist",
"disable_default_eval_metric": 1,
}
params["domain2_var2"] = {
"alpha": 0.8,
"reg_lambda": 0.8,
"max_leaves": 4,
"colsample_bytree": 0.7,
"subsample": 0.4,
"min_child_weight": 5,
"booster": "gbtree",
"objective": "reg:squarederror",
"eval_metric": "mae",
"max_depth": 7,
"learning_rate": 0.01,
"nthread": -1,
"max_bin": 256,
"tree_method": "gpu_hist",
"disable_default_eval_metric": 1,
}
for label_col in label_cols:
model = XGBModel(feature_cols, label_col, params[label_col])
train_df, test_df = model.cv(train_df, test_df)
score = normalized_absolute_errors(
train_df[label_col].values, train_df[f"{label_col}_pred"].values)
logger.info(f"{label_col} score: {score}")
# test_df[label_col] = test_df[
# [f"{label_col}_pred_fold{i}" for i in range(5)]
# ].mean(1)
save_importance(model.importance_df, label_col, config.store)
score = weighted_normalized_absolute_errors(
train_df[label_cols].values,
train_df[[f"{label_col}_pred" for label_col in label_cols]].values,
)
logger.info(f"all score: {score}")
train_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_train.csv"),
index=False,
)
test_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_test.csv"),
index=False,
)
# with pseudo label
for label_col in label_cols:
feature_cols = list(
pd.read_csv(
f"{config.store.workdir}/output/{config.store.model_name}/result/importance_{label_col}.csv"
)["index"].values[:1024])
model = XGBModel(feature_cols, label_col, params[label_col])
train_df, test_df = model.cv(train_df, test_df)
score = normalized_absolute_errors(
train_df[label_col].values, train_df[f"{label_col}_pred"].values)
logger.info(f"{label_col} score: {score}")
test_df[label_col] = test_df[[
f"{label_col}_pred_fold{i}" for i in range(5)
]].mean(1)
save_importance(
model.importance_df,
label_col,
config.store,
suffix=f"_feature_section{config.n_feature}",
)
score = weighted_normalized_absolute_errors(
train_df[label_cols].values,
train_df[[f"{label_col}_pred"
for label_col in label_cols]].values.copy(),
)
logger.info(f"all score: {score}")
train_df.to_csv(
os.path.join(
config.store.result_path,
f"{config.store.model_name}_feature_section{config.n_feature}_train.csv",
),
index=False,
)
test_df.to_csv(
os.path.join(
config.store.result_path,
f"{config.store.model_name}_feature_section{config.n_feature}_test.csv",
),
index=False,
)
sub_df = make_submission(test_df)
sub_df.to_csv(
os.path.join(
config.store.result_path,
f"{config.store.model_name}_feature_section{config.n_feature}_submission.csv",
),
index=False,
)
if config.store.gcs_project is not None:
upload_directory(config.store)
def main(config: DictConfig) -> None:
prepair_dir(config)
train_df, test_df = load_data(config)
label_cols = [
"age", "domain1_var1", "domain1_var2", "domain2_var1", "domain2_var2"
]
feature_cols = [
col for col in train_df.columns if col not in label_cols + ["Id"]
]
train_df["age_rank"] = train_df["age"] // 10 * 10
skf = StratifiedKFold(n_splits=5,
random_state=config.data.seed,
shuffle=True)
for i, (_,
val_index) in enumerate(skf.split(train_df, train_df["age_rank"])):
train_df.loc[val_index, "fold"] = i
if config.randomize_age:
set_seed(100)
train_df["age"] += [randomize_age(age) for age in train_df["age"]]
for label_col in label_cols:
best_score = np.inf
best_alpha = 0.0
best_pred = np.zeros([train_df.shape[0]])
for alpha in [0.01, 0.001, 0.0003, 0.0001]:
for n_fold in range(5):
if not config.use_bagging:
model = Ridge(alpha=alpha)
else:
model = BaggingRegressor(
Ridge(alpha=alpha),
n_estimators=30,
random_state=42,
max_samples=0.3,
max_features=0.3,
)
X_train = train_df.query("fold!=@n_fold")[feature_cols]
y_train = train_df.query("fold!=@n_fold")[label_col]
X_train = X_train[y_train.notnull()]
y_train = y_train[y_train.notnull()]
model.fit(X_train, y_train)
train_df.loc[train_df.query("fold==@n_fold").index,
f"{label_col}_pred"] = model.predict(
train_df.query("fold==@n_fold")[feature_cols])
score = normalized_absolute_errors(
train_df[label_col].values,
train_df[f"{label_col}_pred"].values)
logger.info(f"{label_col} alpha: {alpha}, score: {score}")
if score <= best_score:
best_score = score
best_alpha = alpha
best_pred[:] = train_df[f"{label_col}_pred"].values
train_df[f"{label_col}_pred"] = best_pred
for n_fold in range(5):
if not config.use_bagging:
model = Ridge(alpha=best_alpha)
else:
model = BaggingRegressor(
Ridge(alpha=best_alpha),
n_estimators=30,
random_state=42,
max_samples=0.3,
max_features=0.3,
)
X_train = train_df.query("fold!=@n_fold")[feature_cols]
y_train = train_df.query("fold!=@n_fold")[label_col]
X_train = X_train[y_train.notnull()]
y_train = y_train[y_train.notnull()]
model.fit(X_train, y_train)
test_df[f"{label_col}_pred_fold{n_fold}"] = model.predict(
test_df[feature_cols])
score = normalized_absolute_errors(
train_df[label_col].values, train_df[f"{label_col}_pred"].values)
logger.info(f"{label_col} alpha: {best_alpha}, score: {score}")
test_df[label_col] = test_df[[
f"{label_col}_pred_fold{i}" for i in range(5)
]].mean(1)
score = weighted_normalized_absolute_errors(
train_df[label_cols].values,
train_df[[f"{label_col}_pred" for label_col in label_cols]].values,
)
logger.info(f"all score: {score}")
train_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_train.csv"),
index=False,
)
test_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_test.csv"),
index=False,
)
if config.store.gcs_project is not None:
upload_directory(config.store)
def main(config: DictConfig) -> None:
prepair_dir(config)
set_seed(777)
train_df, test_df = load_data(config)
label_cols = [
"age",
"domain1_var1",
"domain1_var2",
"domain2_var1",
"domain2_var2",
]
feature_cols = [
col for col in train_df.columns if col not in label_cols + ["Id"]
]
# From adversarial valiadtion
train_df["age_rank"] = train_df["age"] // 10 * 10
skf = StratifiedKFold(n_splits=5,
random_state=config.data.seed,
shuffle=True)
for i, (_,
val_index) in enumerate(skf.split(train_df, train_df["age_rank"])):
train_df.loc[val_index, "fold"] = i
if config.randomize_age:
set_seed(777)
train_df["age"] += np.array(
[randomize_age(age) for age in train_df["age"].values])
params = {}
params["age"] = {
"lambda_l1": 0.8,
"lambda_l2": 0.8,
"num_leaves": 2,
"feature_fraction": 0.4,
"bagging_fraction": 0.6,
"bagging_freq": 1,
"min_child_samples": 20,
"task": "train",
"boosting_type": "gbdt",
"objective": "mae",
"metric": "normalized_mae",
"max_depth": 7,
"learning_rate": 0.01,
"num_thread": 4,
"max_bin": 256,
"verbose": -1,
"device": "cpu",
}
params["domain1_var1"] = {
"lambda_l1": 0.8,
"lambda_l2": 0.8,
"num_leaves": 4,
"feature_fraction": 0.5,
"bagging_fraction": 0.8,
"bagging_freq": 1,
"min_child_samples": 5,
"task": "train",
"boosting_type": "gbdt",
"objective": "mae",
"metric": "normalized_mae",
"max_depth": 7,
"learning_rate": 0.01,
"num_thread": 4,
"max_bin": 256,
"verbose": -1,
"device": "cpu",
}
params["domain1_var2"] = {
"lambda_l1": 0.8,
"lambda_l2": 0.8,
"num_leaves": 4,
"feature_fraction": 0.8,
"bagging_fraction": 0.6,
"bagging_freq": 6,
"min_child_samples": 5,
"task": "train",
"boosting_type": "gbdt",
"objective": "mae",
"metric": "normalized_mae",
"max_depth": 7,
"learning_rate": 0.01,
"num_thread": 4,
"max_bin": 256,
"verbose": -1,
"device": "cpu",
}
params["domain2_var1"] = {
"lambda_l1": 0.8,
"lambda_l2": 0.8,
"num_leaves": 4,
"feature_fraction": 0.7,
"bagging_fraction": 0.4,
"bagging_freq": 1,
"min_child_samples": 5,
"task": "train",
"boosting_type": "gbdt",
"objective": "mae",
"metric": "normalized_mae",
"max_depth": 7,
"learning_rate": 0.01,
"num_thread": 4,
"max_bin": 256,
"verbose": -1,
"device": "cpu",
}
params["domain2_var2"] = {
"lambda_l1": 0.8,
"lambda_l2": 0.8,
"num_leaves": 4,
"feature_fraction": 0.4,
"bagging_fraction": 0.8,
"bagging_freq": 1,
"min_child_samples": 5,
"task": "train",
"boosting_type": "gbdt",
"objective": "mae",
"metric": "normalized_mae",
"max_depth": 7,
"learning_rate": 0.01,
"num_thread": 4,
"max_bin": 256,
"verbose": -1,
"device": "cpu",
}
if not config.is_null_importance and not config.is_adversarial_validation:
for label_col in label_cols:
model = LGBMModel(feature_cols, label_col, params[label_col])
train_df, test_df = model.cv(train_df, test_df)
score = normalized_absolute_errors(
train_df[label_col].values,
train_df[f"{label_col}_pred"].values)
logger.info(f"{label_col} score: {score}")
# test_df[label_col] = test_df[
# [f"{label_col}_pred_fold{i}" for i in range(5)]
# ].mean(1)
save_importance(model.importance_df, label_col, config.store)
score = weighted_normalized_absolute_errors(
train_df[label_cols].values,
train_df[[f"{label_col}_pred"
for label_col in label_cols]].values.copy(),
)
logger.info(f"all score: {score}")
train_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_train.csv"),
index=False,
)
test_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_test.csv"),
index=False,
)
if config.store.gcs_project is not None:
upload_directory(config.store)
# Feature Selection
for label_col in label_cols:
feature_cols = list(
pd.read_csv(
f"{config.store.workdir}/output/{config.store.model_name}/result/importance_{label_col}.csv"
)["index"].values[:config.n_feature])
model = LGBMModel(feature_cols, label_col, params[label_col])
train_df, test_df = model.cv(train_df, test_df)
score = normalized_absolute_errors(
train_df[label_col].values,
train_df[f"{label_col}_pred"].values)
logger.info(f"{label_col} score: {score}")
test_df[label_col] = test_df[[
f"{label_col}_pred_fold{i}" for i in range(5)
]].mean(1)
save_importance(
model.importance_df,
label_col,
config.store,
suffix=f"_feature_section{config.n_feature}",
)
score = weighted_normalized_absolute_errors(
train_df[label_cols].values,
train_df[[f"{label_col}_pred"
for label_col in label_cols]].values.copy(),
)
logger.info(f"all score: {score}")
train_df.to_csv(
os.path.join(
config.store.result_path,
f"{config.store.model_name}_feature_section{config.n_feature}_train.csv",
),
index=False,
)
test_df.to_csv(
os.path.join(
config.store.result_path,
f"{config.store.model_name}_feature_section{config.n_feature}_test.csv",
),
index=False,
)
sub_df = make_submission(test_df)
sub_df.to_csv(
os.path.join(
config.store.result_path,
f"{config.store.model_name}_feature_section{config.n_feature}_submission.csv",
),
index=False,
)
if config.store.gcs_project is not None:
upload_directory(config.store)
elif config.is_adversarial_validation:
skf = StratifiedKFold(n_splits=5,
random_state=config.data.seed,
shuffle=True)
if True:
train_df["is_train"] = 1
test_df["is_train"] = 0
label_col = "is_train"
train_df = pd.concat([train_df, test_df],
axis=0).reset_index(drop=True)
if False:
site2_ids = pd.read_csv(
f"{config.store.workdir}/input/reveal_ID_site2.csv"
)["Id"].values
test_df.loc[test_df.query("Id in @site2_ids").index,
"is_site2"] = 1
train_df = pd.concat(
[train_df, test_df.query("is_site2==1")],
axis=0).reset_index(drop=True)
train_df["is_site2"].fillna(0, inplace=True)
label_col = "is_site2"
for i, (_, val_index) in enumerate(
skf.split(train_df, train_df[label_col])):
train_df.loc[val_index, "fold"] = i
print(i, train_df.loc[val_index, label_col].sum())
param = {
"lambda_l1": 0.1,
"lambda_l2": 0.1,
"num_leaves": 32,
"feature_fraction": 0.4,
"bagging_fraction": 0.8,
"bagging_freq": 1,
"min_child_samples": 20,
"task": "train",
"boosting_type": "gbdt",
"objective": "binary",
"metric": "auc",
"max_depth": 10,
"learning_rate": 0.01,
"num_thread": 4,
"max_bin": 256,
"verbose": -1,
"device": "cpu",
}
model = LGBMModel(feature_cols, label_col, param)
train_df, test_df = model.cv(train_df, test_df)
test_df[label_col] = test_df[[
f"{label_col}_pred_fold{i}" for i in range(5)
]].mean(1)
score = roc_auc_score(train_df[label_col].values,
train_df[f"{label_col}_pred"].values)
logger.info(f"{label_col} score: {score}")
save_importance(model.importance_df, label_col, config.store)
train_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_adv_val.csv"),
index=False,
)
test_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_adv_val_test.csv"),
index=False,
)
else:
for label_col in label_cols:
null_imp_df = pd.DataFrame()
for i in range(config.n_run):
train_df[label_col] = np.random.permutation(
train_df[label_col])
model = LGBMModel(feature_cols, label_col, params[label_col])
train_df, test_df = model.cv(train_df, test_df)
score = normalized_absolute_errors(
train_df[label_col].values,
train_df[f"{label_col}_pred"].values)
logger.info(f"{label_col} score: {score}")
test_df[label_col] = test_df[[
f"{label_col}_pred_fold{i}" for i in range(5)
]].mean(1)
importance_df = model.importance_df
importance_df["run"] = i + 1
null_imp_df = pd.concat([null_imp_df, importance_df])
save_importance(null_imp_df, label_col, config.store)
def main(config: DictConfig) -> None:
prepair_dir(config)
set_seed(config.data.seed)
label_cols = [
"age", "domain1_var1", "domain1_var2", "domain2_var1", "domain2_var2"
]
train_dfs, names = load_train_data(config.store.workdir)
test_dfs = load_test_data(config.store.workdir)
remove_cols = [
"knn_age_pred",
"knn_domain1_var1",
"densenet121_age_pred",
"densenet121_domain1_var1_pred",
"densenet121_domain1_var2_pred",
"densenet121_domain2_var2_pred",
"3dcnn_resnet18_domain1_var2_pred",
"3dcnn_resnet18_domain2_var1_pred",
"3dcnn_resnet18_domain2_var2_pred",
"1dresnet18_domain1_var1_pred",
"1dresnet18_domain1_var2_pred",
"1dresnet18_domain2_var2_pred",
"simple_3dcnn_domain1_var1_pred",
"simple_3dcnn_domain1_var2_pred",
"simple_3dcnn_domain2_var2_pred",
"transformer_domain2_var1_pred",
"transformer_domain2_var2_pred",
"transformer_domain1_var1_pred",
"transformer_domain1_var2_pred",
"lgbm_gnn_feature_domain1_var2_pred",
"lgbm_gnn_feature_domain2_var2_pred",
"lgbm_gnn_featured_domain1_var2_pred",
"lgbm_gnn_featured_domain2_var2_pred",
"lgbm_cnn_feature_domain1_var2_pred",
"lgbm_cnn_feature_domain2_var2_pred",
"lgbm_2plus1dcnn_feature_domain1_var2_pred",
"lgbm_2plus1dcnn_feature_domain2_var2_pred",
"xgb_2plus1dcnn_feature_age_pred",
"xgb_2plus1dcnn_feature_domain1_var2_pred",
"xgb_2plus1dcnn_feature_domain2_var2_pred",
"simple_3dcnn_domain2_var1_pred",
"simple_3dcnn_3label_domain1_var2_pred",
"gin_domain1_var1_pred",
"gin_domain2_var1_pred",
"2plus1dcnn_resnet10_domain1_var2_pred",
"resnest14d_domain1_var1_pred",
"resnest14d_domain1_var2_pred",
"resnest14d_domain2_var2_pred",
]
train_ft_dict = {}
test_ft_dict = {}
feature_cols = []
train_ft_dict["Id"] = train_dfs[0]["Id"]
test_ft_dict["Id"] = test_dfs[0]["Id"]
for label_col in label_cols:
train_ft_dict[label_col] = train_dfs[0][label_col]
for name, df in zip(names, train_dfs):
for label_col in label_cols:
if (f"{label_col}_pred" in df.columns
and f"{name}_{label_col}_pred" not in remove_cols):
train_ft_dict[f"{name}_{label_col}_pred"] = df[
f"{label_col}_pred"]
feature_cols += [f"{name}_{label_col}_pred"]
elif f"{name}_{label_col}_pred" in remove_cols:
df.drop(f"{label_col}_pred", axis=1, inplace=True)
feat_dict = make_domain_feature(df, mode="train", name=name)
train_ft_dict.update(feat_dict)
feature_cols += list(feat_dict.keys())
for name, df in zip(names, test_dfs):
for label_col in label_cols:
for i in range(5):
if (f"{label_col}_pred_fold{i}" in df.columns
and f"{name}_{label_col}_pred" not in remove_cols):
test_ft_dict[f"{name}_{label_col}_pred_fold{i}"] = df[
f"{label_col}_pred_fold{i}"]
elif (f"{name}_{label_col}_pred" in remove_cols
and f"{label_col}_pred_fold{i}" in df.columns):
df.drop(f"{label_col}_pred_fold{i}", axis=1, inplace=True)
feat_dict = make_domain_feature(df, mode="test", name=name)
test_ft_dict.update(feat_dict)
train_df = pd.DataFrame(train_ft_dict)
test_df = pd.DataFrame(test_ft_dict)
train_df["age"] = (
pd.read_csv(f"{config.store.workdir}/input/train_scores.csv"
).sort_values("Id").reset_index(drop=True)["age"])
age_rank = train_df["age"].values // 10 * 10
skf = StratifiedKFold(n_splits=5, random_state=777, shuffle=True)
train_df, test_df = preprocess(train_df, test_df, feature_cols)
for feature_col in feature_cols:
train_df[feature_col].fillna(0, inplace=True)
test_df[feature_col].fillna(0, inplace=True)
train_df = cudf.from_pandas(train_df)
test_df = cudf.from_pandas(test_df)
if config.randomize_age:
set_seed(777_777_777)
train_df["age"] += np.array(
[randomize_age(age) for age in train_df["age"].values])
skf = StratifiedKFold(n_splits=5, random_state=777, shuffle=True)
train_df = train_df.reset_index(drop=True)
logger.info("=" * 10 + "parameter search" + "=" * 10)
best_c = {}
for label_col in label_cols:
best = np.inf
if label_col == "age":
feature_cols_ = [
col for col in feature_cols if f"{label_col}" in col
]
else:
feature_cols_ = feature_cols
for c in [2**(i) for i in range(-14, 1)]:
y_oof = np.zeros(train_df.shape[0])
for n_fold, (train_index,
val_index) in enumerate(skf.split(age_rank,
age_rank)):
train_df_fold = train_df.iloc[train_index]
valid_df_fold = train_df.iloc[val_index]
train_df_fold = train_df_fold[
train_df_fold[label_col].notnull()]
model = SVR(kernel="linear", C=c, cache_size=3000.0)
model.fit(train_df_fold[feature_cols_],
train_df_fold[label_col])
y_oof[val_index] = model.predict(
valid_df_fold[feature_cols_]).to_array()
test_df[f"{label_col}_pred_fold{n_fold}"] = model.predict(
test_df[feature_cols_])
train_df[f"{label_col}_pred"] = y_oof
notnull_idx = train_df[label_col].notnull()
score = normalized_absolute_errors(
train_df[notnull_idx][label_col].values,
train_df[notnull_idx][f"{label_col}_pred"].values,
)
logger.info(f"c={c}, {label_col}: {score}")
if score <= best:
best = score
best_c[label_col] = c
logger.info("=" * 10 + "prediction" + "=" * 10)
for label_col in label_cols:
y_oof = np.zeros(train_df.shape[0])
if label_col == "age":
feature_cols_ = [
col for col in feature_cols if f"{label_col}" in col
]
else:
feature_cols_ = feature_cols
for n_fold, (train_index,
val_index) in enumerate(skf.split(age_rank, age_rank)):
train_df_fold = train_df.iloc[train_index]
valid_df_fold = train_df.iloc[val_index]
train_df_fold = train_df_fold[train_df_fold[label_col].notnull()]
model = SVR(kernel="linear",
C=best_c[label_col],
cache_size=3000.0)
model.fit(train_df_fold[feature_cols_], train_df_fold[label_col])
y_oof[val_index] = model.predict(
valid_df_fold[feature_cols_]).to_array()
test_df[f"{label_col}_pred_fold{n_fold}"] = model.predict(
test_df[feature_cols_])
train_df[f"{label_col}_pred"] = y_oof
notnull_idx = train_df[label_col].notnull()
score = normalized_absolute_errors(
train_df[notnull_idx][label_col].values,
train_df[notnull_idx][f"{label_col}_pred"].values,
)
logger.info(f"c={c}, {label_col}: {score}")
score = 0
for label_col, weight in zip(label_cols,
[0.3, 0.175, 0.175, 0.175, 0.175]):
notnull_idx = train_df[label_col].notnull()
score += (normalized_absolute_errors(
train_df[notnull_idx][label_col].to_array(),
train_df[notnull_idx][f"{label_col}_pred"].to_array(),
) * weight)
logger.info(f"all: {score}")
train_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_train.csv"),
index=False,
)
test_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_test.csv"),
index=False,
)
if config.store.gcs_project is not None:
upload_directory(config.store)
sub_df = make_submission(test_df)
sub_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_submission.csv"),
index=False,
)
def main(config: DictConfig) -> None:
prepair_dir(config)
set_seed(config.data.seed)
label_cols = [
"age", "domain1_var1", "domain1_var2", "domain2_var1", "domain2_var2"
]
train_dfs, names = load_train_data(config.store.workdir)
test_dfs = load_test_data(config.store.workdir)
params = {
"lambda_l1": 0.1,
"lambda_l2": 0.1,
"num_leaves": 2,
"feature_fraction": 0.6,
"bagging_fraction": 0.6,
"bagging_freq": 1,
"min_child_samples": 10,
"task": "train",
"boosting_type": "gbdt",
"objective": "mae",
"metric": "normalized_mae",
"max_depth": 7,
"learning_rate": 0.01,
"num_thread": 4,
"max_bin": 256,
"verbose": -1,
"device": "cpu",
}
remove_cols = [
"knn_age_pred",
"knn_domain1_var1",
"densenet121_age_pred",
"densenet121_domain1_var1_pred",
"densenet121_domain1_var2_pred",
"densenet121_domain2_var2_pred",
"3dcnn_resnet18_domain1_var2_pred",
"3dcnn_resnet18_domain2_var1_pred",
"3dcnn_resnet18_domain2_var2_pred",
"1dresnet18_domain1_var1_pred",
"1dresnet18_domain1_var2_pred",
"1dresnet18_domain2_var2_pred",
"simple_3dcnn_domain1_var1_pred",
"simple_3dcnn_domain1_var2_pred",
"simple_3dcnn_domain2_var2_pred",
"transformer_domain2_var1_pred",
"transformer_domain2_var2_pred",
"transformer_domain1_var1_pred",
"transformer_domain1_var2_pred",
"lgbm_gnn_feature_domain1_var2_pred",
"lgbm_gnn_feature_domain2_var2_pred",
"lgbm_gnn_featured_domain1_var2_pred",
"lgbm_gnn_featured_domain2_var2_pred",
"lgbm_cnn_feature_domain1_var2_pred",
"lgbm_cnn_feature_domain2_var2_pred",
"lgbm_2plus1dcnn_feature_domain1_var2_pred",
"lgbm_2plus1dcnn_feature_domain2_var2_pred",
"xgb_2plus1dcnn_feature_age_pred",
"xgb_2plus1dcnn_feature_domain1_var2_pred",
"xgb_2plus1dcnn_feature_domain2_var2_pred",
"simple_3dcnn_domain2_var1_pred",
"simple_3dcnn_3label_domain1_var2_pred",
"gin_domain1_var1_pred",
"gin_domain2_var1_pred",
"2plus1dcnn_resnet10_domain1_var2_pred",
"resnest14d_domain1_var1_pred",
"resnest14d_domain1_var2_pred",
"resnest14d_domain2_var2_pred",
]
train_ft_dict = {}
test_ft_dict = {}
feature_cols = []
train_ft_dict["Id"] = train_dfs[0]["Id"]
test_ft_dict["Id"] = test_dfs[0]["Id"]
for label_col in label_cols:
train_ft_dict[label_col] = train_dfs[0][label_col]
for name, df in zip(names, train_dfs):
for label_col in label_cols:
if (f"{label_col}_pred" in df.columns
and f"{name}_{label_col}_pred" not in remove_cols):
train_ft_dict[f"{name}_{label_col}_pred"] = df[
f"{label_col}_pred"]
feature_cols += [f"{name}_{label_col}_pred"]
elif f"{name}_{label_col}_pred" in remove_cols:
df.drop(f"{label_col}_pred", axis=1, inplace=True)
feat_dict = make_domain_feature(df, mode="train", name=name)
train_ft_dict.update(feat_dict)
feature_cols += list(feat_dict.keys())
for name, df in zip(names, test_dfs):
for label_col in label_cols:
for i in range(5):
if (f"{label_col}_pred_fold{i}" in df.columns
and f"{name}_{label_col}_pred" not in remove_cols):
test_ft_dict[f"{name}_{label_col}_pred_fold{i}"] = df[
f"{label_col}_pred_fold{i}"]
elif f"{name}_{label_col}_pred" in remove_cols:
df.drop(f"{label_col}_pred_fold{i}", axis=1, inplace=True)
feat_dict = make_domain_feature(df, mode="test", name=name)
test_ft_dict.update(feat_dict)
train_df = pd.DataFrame(train_ft_dict)
test_df = pd.DataFrame(test_ft_dict)
train_df["age"] = (
pd.read_csv(f"{config.store.workdir}/input/train_scores.csv"
).sort_values("Id").reset_index(drop=True)["age"])
train_df["age_rank"] = train_df["age"] // 10 * 10
skf = StratifiedKFold(n_splits=5, random_state=777, shuffle=True)
for i, (train_index,
val_index) in enumerate(skf.split(train_df, train_df["age_rank"])):
train_df.loc[val_index, "fold"] = i
if config.randomize_age:
set_seed(777777777)
train_df["age"] += np.array(
[randomize_age(age) for age in train_df["age"].values])
if (not config.is_null_importance and not config.is_adversarial_validation
and not config.is_quantile):
for label_col in label_cols:
if not config.is_split_label:
model = LGBMModel(feature_cols, label_col, params)
else:
model = LGBMModel(
[col for col in feature_cols if f"{label_col}" in col],
label_col,
params,
)
train_df, test_df = model.cv(train_df, test_df)
score = normalized_absolute_errors(
train_df[label_col].values,
train_df[f"{label_col}_pred"].values)
logger.info(f"{label_col} score: {score}")
test_df[label_col] = test_df[[
f"{label_col}_pred_fold{i}" for i in range(5)
]].mean(1)
save_importance(model.importance_df, label_col, config.store)
score = weighted_normalized_absolute_errors(
train_df[label_cols].values,
train_df[[f"{label_col}_pred"
for label_col in label_cols]].values.copy(),
)
logger.info(f"{names} all score: {score}")
train_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_train.csv"),
index=False,
)
test_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_test.csv"),
index=False,
)
if config.store.gcs_project is not None:
upload_directory(config.store)
sub_df = make_submission(test_df)
sub_df.to_csv(
os.path.join(
config.store.result_path,
f"{config.store.model_name}_submission.csv",
),
index=False,
)
elif config.is_quantile:
params = {
"lambda_l1": 0.1,
"lambda_l2": 0.1,
"num_leaves": 13,
"feature_fraction": 1.0,
"bagging_fraction": 0.6,
"bagging_freq": 1,
"min_child_samples": 10,
"task": "train",
"boosting_type": "gbdt",
"objective": "quantile",
"alpha": 0.75,
"metric": None,
"max_depth": 7,
"learning_rate": 0.01,
"num_thread": 4,
"max_bin": 256,
"verbose": -1,
"device": "cpu",
}
for label_col in label_cols:
model = LGBMModel(feature_cols, label_col, params)
train_df, test_df = model.cv(train_df, test_df)
train_df = train_df.rename(
columns={f"{label_col}_pred": f"{label_col}_pred_upper"})
for i in range(5):
test_df = test_df.rename(columns={
f"{label_col}_pred_fold{i}":
f"{label_col}_pred_fold{i}_upper"
})
test_df[f"{label_col}_pred_upper"] = test_df[[
f"{label_col}_pred_fold{i}_upper" for i in range(5)
]].mean(1)
params["alpha"] = 0.25
for label_col in label_cols:
model = LGBMModel(feature_cols, label_col, params)
train_df, test_df = model.cv(train_df, test_df)
train_df = train_df.rename(
columns={f"{label_col}_pred": f"{label_col}_pred_lower"})
for i in range(5):
test_df = test_df.rename(columns={
f"{label_col}_pred_fold{i}":
f"{label_col}_pred_fold{i}_lower"
})
test_df[f"{label_col}_pred_lower"] = test_df[[
f"{label_col}_pred_fold{i}_lower" for i in range(5)
]].mean(1)
params["alpha"] = 0.5
for label_col in label_cols:
model = LGBMModel(feature_cols, label_col, params)
train_df, test_df = model.cv(train_df, test_df)
score = normalized_absolute_errors(
train_df[label_col].values,
train_df[f"{label_col}_pred"].values)
logger.info(f"{label_col} score: {score}")
test_df[label_col] = test_df[[
f"{label_col}_pred_fold{i}" for i in range(5)
]].mean(1)
save_importance(model.importance_df,
label_col,
config.store,
suffix="_quantile")
test_df[f"{label_col}_pred"] = test_df[[
f"{label_col}_pred_fold{i}" for i in range(5)
]].mean(1)
score = weighted_normalized_absolute_errors(
train_df[label_cols].values,
train_df[[f"{label_col}_pred"
for label_col in label_cols]].values.copy(),
)
logger.info(f"{names} all score: {score}")
train_df.to_csv(
os.path.join(
config.store.result_path,
f"{config.store.model_name}_quantile_train.csv",
),
index=False,
)
test_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_quantile_test.csv"),
index=False,
)
elif config.is_adversarial_validation:
skf = StratifiedKFold(n_splits=5,
random_state=config.data.seed,
shuffle=True)
if True:
train_df["is_train"] = 1
test_df["is_train"] = 0
label_col = "is_train"
train_df = pd.concat([train_df, test_df],
axis=0).reset_index(drop=True)
if False:
site2_ids = pd.read_csv(
f"{config.store.workdir}/input/reveal_ID_site2.csv"
)["Id"].values
train_df.loc[train_df.query("Id in @site2_ids").index,
"is_site2"] = 1
train_df["is_site2"].fillna(0, inplace=True)
label_col = "is_site2"
for i, (_, val_index) in enumerate(
skf.split(train_df, train_df[label_col])):
train_df.loc[val_index, "fold"] = i
param = {
"lambda_l1": 0.1,
"lambda_l2": 0.1,
"num_leaves": 32,
"feature_fraction": 0.4,
"bagging_fraction": 0.8,
"bagging_freq": 1,
"min_child_samples": 20,
"task": "train",
"boosting_type": "gbdt",
"objective": "binary",
"metric": "auc",
"max_depth": 10,
"learning_rate": 0.01,
"num_thread": -1,
"max_bin": 256,
"verbose": -1,
"device": "cpu",
}
model = LGBMModel(feature_cols, label_col, param)
train_df, test_df = model.cv(train_df, test_df)
test_df[label_col] = test_df[[
f"{label_col}_pred_fold{i}" for i in range(5)
]].mean(1)
score = roc_auc_score(train_df[label_col].values,
train_df[f"{label_col}_pred"].values)
logger.info(f"{label_col} score: {score}")
save_importance(model.importance_df, label_col, config.store)
train_df.to_csv(
os.path.join(config.store.result_path,
f"{config.store.model_name}_adv_val.csv"),
index=False,
)