def test_history_callback(optimizers, schedulers, len_loss_output,
len_lr_output, init_lr, schedulers_type):
model.compile(
method="binary",
optimizers=optimizers,
lr_schedulers=schedulers,
callbacks=[LRHistory(n_epochs=5)],
verbose=0,
)
model.fit(
X_wide=X_wide,
X_deep=X_deep,
X_text=X_text,
target=target,
n_epochs=5,
batch_size=16,
)
out = []
out.append(len(model.history._history["train_loss"]) == len_loss_output)
try:
lr_list = list(chain.from_iterable(model.lr_history["lr_deepdense_0"]))
except TypeError:
lr_list = model.lr_history["lr_deepdense_0"]
except Exception:
lr_list = model.lr_history["lr_0"]
out.append(len(lr_list) == len_lr_output)
if init_lr is not None and schedulers_type == "step":
out.append(lr_list[-1] == init_lr / 10)
elif init_lr is not None and schedulers_type == "cyclic":
out.append(lr_list[-1] == init_lr)
assert all(out)
def test_history_callback_w_tabtransformer(
optimizers, schedulers, len_loss_output, len_lr_output, init_lr, schedulers_type
):
trainer_tt = Trainer(
model_tt,
objective="binary",
optimizers=optimizers,
lr_schedulers=schedulers,
callbacks=[LRHistory(n_epochs=5)],
verbose=0,
)
trainer_tt.fit(
X_wide=X_wide,
X_tab=X_tab,
target=target,
n_epochs=5,
batch_size=16,
)
out = []
out.append(len(trainer_tt.history["train_loss"]) == len_loss_output)
try:
lr_list = list(chain.from_iterable(trainer_tt.lr_history["lr_deeptabular_0"]))
except TypeError:
lr_list = trainer_tt.lr_history["lr_deeptabular_0"]
except Exception:
lr_list = trainer_tt.lr_history["lr_0"]
out.append(len(lr_list) == len_lr_output)
if init_lr is not None and schedulers_type == "step":
out.append(lr_list[-1] == init_lr / 10)
elif init_lr is not None and schedulers_type == "cyclic":
out.append(lr_list[-1] == init_lr)
assert all(out)
def test_history_callback(optimizers, schedulers, len_loss_output, len_lr_output): model.compile(method='binary', optimizers=optimizers, lr_schedulers=schedulers, callbacks=[LRHistory(n_epochs=5)], verbose=0) model.fit(X_wide=X_wide, X_deep=X_deep, X_text=X_text, target=target, n_epochs=5) out = [] out.append(len(model.history._history['train_loss'])==len_loss_output) try: lr_list = list(chain.from_iterable(model.lr_history['lr_deepdense_0'])) except TypeError: lr_list = model.lr_history['lr_deepdense_0'] out.append(len(lr_list)==len_lr_output) assert all(out)
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,
)
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"
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,
)
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,
deep_column_idx=prepare_deep.deep_column_idx,
embed_input=prepare_deep.embeddings_input,
continuous_cols=continuous_cols,
)
model = WideDeep(wide=wide, deepdense=deepdense)
wide_opt = torch.optim.Adam(model.wide.parameters())
deep_opt = RAdam(model.deepdense.parameters())
wide_sch = torch.optim.lr_scheduler.StepLR(wide_opt, step_size=3)
deep_sch = torch.optim.lr_scheduler.StepLR(deep_opt, step_size=5)
optimizers = {"wide": wide_opt, "deepdense": deep_opt}
schedulers = {"wide": wide_sch, "deepdense": deep_sch}
initializers = {"wide": KaimingNormal, "deepdense": XavierNormal}
callbacks = [
LRHistory(n_epochs=10),
EarlyStopping,
ModelCheckpoint(filepath="model_weights/wd_out"),
]
metrics = [BinaryAccuracy]
model.compile(
method="binary",
optimizers=optimizers,
lr_schedulers=schedulers,
initializers=initializers,
callbacks=callbacks,
metrics=metrics,
)
model.fit(
)
model_checkpoint = ModelCheckpoint(
filepath=str(MODELS_DIR / "best_model"),
monitor=args.monitor,
save_best_only=True,
max_save=1,
)
trainer = Trainer(
model,
objective="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],
)
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)
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)
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
