]
metrics = [Accuracy, Precision]
trainer = Trainer(
model,
objective="binary",
optimizers=optimizers,
lr_schedulers=schedulers,
initializers=initializers,
callbacks=callbacks,
metrics=metrics,
)
trainer.fit(
X_wide=X_wide,
X_tab=X_tab,
target=target,
n_epochs=4,
batch_size=64,
val_split=0.2,
)
# # to save/load the model
# trainer.save_model("model_weights/model.t")
# # ... days after
# model = Trainer.load_model("model_weights/model.t")
# # or via state dictionaries
# trainer.save_model_state_dict("model_weights/model_dict.t")
# # ... days after, with an instantiated class of Trainer
# trainer.load_model_state_dict("model_weights/model_dict.t")
)
trainer = Trainer(
model,
objective="regression",
optimizers=optimizers,
lr_schedulers=lr_schedulers,
reducelronplateau_criterion=args.monitor.split("_")[-1],
callbacks=[early_stopping, LRHistory(n_epochs=args.n_epochs)],
)
start = time()
trainer.fit(
X_train={"X_tab": X_train, "target": y_train},
X_val={"X_tab": X_valid, "target": y_valid},
n_epochs=args.n_epochs,
batch_size=args.batch_size,
validation_freq=args.eval_every,
)
runtime = time() - start
if args.save_results:
suffix = str(datetime.now()).replace(" ", "_").split(".")[:-1][0]
filename = "_".join(["fb_comments_tabresnet", suffix]) + ".p"
results_d = {}
results_d["args"] = args.__dict__
results_d["early_stopping"] = early_stopping
results_d["trainer_history"] = trainer.history
results_d["trainer_lr_history"] = trainer.lr_history
results_d["runtime"] = runtime
with open(RESULTS_DIR / filename, "wb") as f:
def run_experiment_and_save(
model,
model_name,
results_dir,
models_dir,
args,
X_train,
X_test,
y_train,
y_test,
):
optimizers = set_optimizer(model, args)
steps_per_epoch = (X_train.shape[0] // args.batch_size) + 1
lr_schedulers = set_lr_scheduler(optimizers, steps_per_epoch, args)
early_stopping = EarlyStopping(
monitor=args.monitor,
min_delta=args.early_stop_delta,
patience=args.early_stop_patience,
)
model_checkpoint = ModelCheckpoint(
filepath=str(models_dir / "best_model"),
monitor=args.monitor,
save_best_only=True,
max_save=1,
)
trainer = Trainer(
model,
objective="regression",
optimizers=optimizers,
lr_schedulers=lr_schedulers,
reducelronplateau_criterion=args.monitor.split("_")[-1],
callbacks=[
early_stopping, model_checkpoint,
LRHistory(n_epochs=args.n_epochs)
],
)
start = time()
trainer.fit(
X_train={
"X_tab": X_train,
"target": y_train
},
X_val={
"X_tab": X_test,
"target": y_test
},
n_epochs=args.n_epochs,
batch_size=args.batch_size,
validation_freq=args.eval_every,
)
runtime = time() - start
y_pred = trainer.predict(X_tab=X_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
r2 = r2_score(y_test, y_pred)
print(f"rmse with the best model: {rmse}")
if args.save_results:
suffix = str(datetime.now()).replace(" ", "_").split(".")[:-1][0]
filename = "_".join(["fb_comments", model_name, "best", suffix]) + ".p"
results_d = {}
results_d["args"] = args
results_d["rmse"] = rmse
results_d["r2"] = r2
results_d["early_stopping"] = early_stopping
results_d["trainer_history"] = trainer.history
results_d["trainer_lr_history"] = trainer.lr_history
results_d["runtime"] = runtime
with open(results_dir / filename, "wb") as f:
pickle.dump(results_d, f)
trainer = Trainer(
model,
objective="regression",
optimizers=optimizers,
lr_schedulers=lr_schedulers,
reducelronplateau_criterion=args.monitor.split("_")[-1],
callbacks=[early_stopping,
LRHistory(n_epochs=args.n_epochs)],
)
start = time()
trainer.fit(
X_train=X_train,
X_val=X_val,
n_epochs=args.n_epochs,
batch_size=args.batch_size,
validation_freq=args.eval_every,
)
runtime = time() - start
if args.save_results:
suffix = str(datetime.now()).replace(" ", "_").split(".")[:-1][0]
filename = "_".join(["nyc_taxi_tabtransformer", suffix]) + ".p"
results_d = {}
results_d["args"] = args.__dict__
results_d["early_stopping"] = early_stopping
results_d["trainer_history"] = trainer.history
results_d["runtime"] = runtime
with open(RESULTS_DIR / filename, "wb") as f:
pickle.dump(results_d, f)
def run_experiment_and_save(
model,
model_name,
results_dir,
models_dir,
args,
X_train,
X_test,
y_train,
y_test,
fl_exp_indx: int = 0,
):
try:
if args.focal_loss:
alpha, gamma = load_focal_loss_params(results_dir, fl_exp_indx)
focal_loss = True
else:
alpha = 0.25
gamma = 2
focal_loss = False
except AttributeError:
alpha = 0.25
gamma = 2
focal_loss = False
optimizers = set_optimizer(model, args)
steps_per_epoch = (X_train.shape[0] // args.batch_size) + 1
lr_schedulers = set_lr_scheduler(optimizers, steps_per_epoch, args)
early_stopping = EarlyStopping(
monitor=args.monitor,
min_delta=args.early_stop_delta,
patience=args.early_stop_patience,
)
model_checkpoint = ModelCheckpoint(
filepath=str(models_dir / "best_model"),
monitor=args.monitor,
save_best_only=True,
max_save=1,
)
trainer = Trainer(
model,
objective="binary_focal_loss" if focal_loss else "binary",
optimizers=optimizers,
lr_schedulers=lr_schedulers,
reducelronplateau_criterion=args.monitor.split("_")[-1],
callbacks=[early_stopping, model_checkpoint, LRHistory(n_epochs=args.n_epochs)],
metrics=[Accuracy, F1Score],
alpha=alpha,
gamma=gamma,
)
start = time()
trainer.fit(
X_train={"X_tab": X_train, "target": y_train},
X_val={"X_tab": X_test, "target": y_test},
n_epochs=args.n_epochs,
batch_size=args.batch_size,
validation_freq=args.eval_every,
)
runtime = time() - start
y_pred = trainer.predict(X_tab=X_test)
acc = accuracy_score(y_test, y_pred)
auc = roc_auc_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print(f"Accuracy: {acc}. F1: {f1}. ROC_AUC: {auc}")
print(confusion_matrix(y_test, y_pred))
if args.save_results:
suffix = str(datetime.now()).replace(" ", "_").split(".")[:-1][0]
filename = "_".join(["bankm", model_name, "best", suffix]) + ".p"
results_d = {}
results_d["args"] = args
results_d["acc"] = acc
results_d["auc"] = auc
results_d["f1"] = f1
results_d["early_stopping"] = early_stopping
results_d["trainer_history"] = trainer.history
results_d["trainer_lr_history"] = trainer.lr_history
results_d["runtime"] = runtime
with open(results_dir / filename, "wb") as f:
pickle.dump(results_d, f)
def objective(params):
deeptabular = TabMlp(
column_idx=prepare_tab.column_idx,
mlp_hidden_dims=mlp_hidden_dims,
mlp_activation=args.mlp_activation,
mlp_dropout=args.mlp_dropout,
mlp_batchnorm=args.mlp_batchnorm,
mlp_batchnorm_last=args.mlp_batchnorm_last,
mlp_linear_first=args.mlp_linear_first,
embed_input=prepare_tab.embeddings_input,
embed_dropout=args.embed_dropout,
)
model = WideDeep(deeptabular=deeptabular)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
mode=args.rop_mode,
factor=args.rop_factor,
patience=args.rop_patience,
threshold=args.rop_threshold,
threshold_mode=args.rop_threshold_mode,
)
early_stopping = EarlyStopping(
monitor=args.monitor,
min_delta=args.early_stop_delta,
patience=args.early_stop_patience,
)
trainer = Trainer(
model,
objective="binary_focal_loss",
optimizers=optimizer,
lr_schedulers=lr_scheduler,
reducelronplateau_criterion=args.monitor.split("_")[-1],
callbacks=[early_stopping,
LRHistory(n_epochs=args.n_epochs)],
metrics=[Accuracy, F1Score],
alpha=params["alpha"],
gamma=params["gamma"],
verbose=0,
)
trainer.fit(
X_train={
"X_tab": X_train,
"target": y_train
},
X_val={
"X_tab": X_valid,
"target": y_valid
},
n_epochs=args.n_epochs,
batch_size=args.batch_size,
validation_freq=args.eval_every,
)
score = early_stopping.best
return score
x_wide = wide_preprocessor.fit_transform(train_df)
# Deep
tab_preprocessor = TabPreprocessor(
embed_cols=deep_embedding_columns_list,
continuous_cols=deep_continuous_column_list)
x_deep = tab_preprocessor.fit_transform(train_df)
"""
Model 구조 정의
"""
# Model
wide = Wide(wide_dim=np.unique(x_wide).shape[0], pred_dim=1)
deeptabular = TabMlp(mlp_hidden_dims=[64, 32],
column_idx=tab_preprocessor.column_idx,
embed_input=tab_preprocessor.embeddings_input,
continuous_cols=deep_continuous_column_list)
model = WideDeep(wide=wide, deeptabular=deeptabular)
"""
학습
"""
trainer = Trainer(model, objective="binary", metrics=[Accuracy])
trainer.fit(X_wide=x_wide,
X_tab=x_deep,
target=target,
n_epochs=5,
batch_size=256,
val_split=0.1)
trainer.save_model(MODEL_PATH)
save_pickle(WIDE_PROC_PATH, wide_preprocessor)
save_pickle(DEEP_PROC_PATH, tab_preprocessor)