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_basic_run_with_metrics_multiclass():
wide = Wide(np.unique(X_wide).shape[0], 3)
deeptabular = TabMlp(
mlp_hidden_dims=[32, 16],
mlp_dropout=[0.5, 0.5],
column_idx={k: v
for v, k in enumerate(colnames)},
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deeptabular=deeptabular, pred_dim=3)
trainer = Trainer(model,
objective="multiclass",
metrics=[Accuracy],
verbose=False)
trainer.fit(
X_wide=X_wide,
X_tab=X_tab,
target=target_multi,
n_epochs=1,
batch_size=16,
val_split=0.2,
)
assert ("train_loss" in trainer.history.keys()
and "train_acc" in trainer.history.keys())
def test_fit_objectives_tab_transformer(
X_wide,
X_tab,
target,
objective,
X_wide_test,
X_tab_test,
X_test,
pred_dim,
probs_dim,
):
wide = Wide(np.unique(X_wide).shape[0], pred_dim)
tab_transformer = TabTransformer(
column_idx={k: v
for v, k in enumerate(colnames)},
embed_input=embed_input_tt,
continuous_cols=colnames[5:],
)
model = WideDeep(wide=wide, deeptabular=tab_transformer, pred_dim=pred_dim)
trainer = Trainer(model, objective=objective, verbose=0)
trainer.fit(X_wide=X_wide, X_tab=X_tab, target=target, batch_size=16)
preds = trainer.predict(X_wide=X_wide, X_tab=X_tab, X_test=X_test)
if objective == "binary":
pass
else:
probs = trainer.predict_proba(X_wide=X_wide,
X_tab=X_tab,
X_test=X_test)
assert preds.shape[0] == 32, probs.shape[1] == probs_dim
def test_model_checkpoint(save_best_only, max_save, n_files):
wide = Wide(np.unique(X_wide).shape[0], 1)
deeptabular = TabMlp(
mlp_hidden_dims=[32, 16],
mlp_dropout=[0.5, 0.5],
column_idx=column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deeptabular=deeptabular)
trainer = Trainer(
model=model,
objective="binary",
callbacks=[
ModelCheckpoint(
"tests/test_model_functioning/weights/test_weights",
save_best_only=save_best_only,
max_save=max_save,
)
],
verbose=0,
)
trainer.fit(X_wide=X_wide, X_tab=X_tab, target=target, n_epochs=5, val_split=0.2)
n_saved = len(os.listdir("tests/test_model_functioning/weights/"))
shutil.rmtree("tests/test_model_functioning/weights/")
assert n_saved <= n_files
def test_save_and_load_dict():
wide = Wide(np.unique(X_wide).shape[0], 1)
tabmlp = TabMlp(
mlp_hidden_dims=[32, 16],
column_idx={k: v
for v, k in enumerate(colnames)},
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model1 = WideDeep(wide=deepcopy(wide), deeptabular=deepcopy(tabmlp))
trainer1 = Trainer(model1, objective="binary", verbose=0)
trainer1.fit(
X_wide=X_wide,
X_tab=X_tab,
X_text=X_text,
X_img=X_img,
target=target,
batch_size=16,
)
wide_weights = model1.wide.wide_linear.weight.data
trainer1.save_model_state_dict(
"tests/test_model_functioning/model_dir/model_d.t")
model2 = WideDeep(wide=wide, deeptabular=tabmlp)
trainer2 = Trainer(model2, objective="binary", verbose=0)
trainer2.load_model_state_dict(
"tests/test_model_functioning/model_dir/model_d.t")
n_wide_weights = trainer2.model.wide.wide_linear.weight.data
shutil.rmtree("tests/test_model_functioning/model_dir/")
assert torch.allclose(wide_weights, n_wide_weights)
def test_fit_objectives(
X_wide,
X_tab,
target,
objective,
X_wide_test,
X_tab_test,
X_test,
pred_dim,
probs_dim,
):
wide = Wide(np.unique(X_wide).shape[0], pred_dim)
deeptabular = TabMlp(
mlp_hidden_dims=[32, 16],
mlp_dropout=[0.5, 0.5],
column_idx=column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deeptabular=deeptabular, pred_dim=pred_dim)
trainer = Trainer(model, objective=objective, verbose=0)
trainer.fit(X_wide=X_wide, X_tab=X_tab, target=target, batch_size=16)
preds = trainer.predict(X_wide=X_wide, X_tab=X_tab, X_test=X_test)
if objective == "binary":
pass
else:
probs = trainer.predict_proba(X_wide=X_wide,
X_tab=X_tab,
X_test=X_test)
assert preds.shape[0] == 32, probs.shape[1] == probs_dim
def test_xtrain_xval_assertion(
X_wide,
X_tab,
X_text,
X_img,
X_train,
X_val,
target,
):
model = WideDeep(wide=wide,
deeptabular=deeptabular,
deeptext=deeptext,
deepimage=deepimage)
trainer = Trainer(model, objective="binary", verbose=0)
with pytest.raises(AssertionError):
trainer.fit(
X_wide=X_wide,
X_tab=X_tab,
X_text=X_text,
X_img=X_img,
X_train=X_train,
X_val=X_val,
target=target,
batch_size=16,
)
def test_widedeep_inputs(
X_wide,
X_tab,
X_text,
X_img,
X_train,
X_val,
target,
val_split,
transforms,
):
model = WideDeep(wide=wide,
deeptabular=deeptabular,
deeptext=deeptext,
deepimage=deepimage)
trainer = Trainer(model,
objective="binary",
transforms=transforms,
verbose=0)
trainer.fit(
X_wide=X_wide,
X_tab=X_tab,
X_text=X_text,
X_img=X_img,
X_train=X_train,
X_val=X_val,
target=target,
val_split=val_split,
batch_size=16,
)
assert trainer.history["train_loss"] is not None
def test_save_and_load():
model = WideDeep(wide=wide, deeptabular=tabmlp)
trainer = Trainer(model, objective="binary", verbose=0)
trainer.fit(X_wide=X_wide, X_tab=X_tab, target=target, batch_size=16)
wide_weights = model.wide.wide_linear.weight.data
trainer.save_model("tests/test_model_functioning/model_dir/model.t")
n_model = Trainer.load_model(
"tests/test_model_functioning/model_dir/model.t")
n_wide_weights = n_model.wide.wide_linear.weight.data
assert torch.allclose(wide_weights, n_wide_weights)
def test_basic_run_with_metrics_binary(wide, deeptabular):
model = WideDeep(wide=wide, deeptabular=deeptabular)
trainer = Trainer(model,
objective="binary",
metrics=[Accuracy],
verbose=False)
trainer.fit(
X_wide=X_wide,
X_tab=X_tab,
target=target,
n_epochs=1,
batch_size=16,
val_split=0.2,
)
assert ("train_loss" in trainer.history.keys()
and "train_acc" in trainer.history.keys())
def test_individual_inputs(wide, deeptabular, deeptext, deepimage, X_wide,
X_tab, X_text, X_img, target):
model = WideDeep(wide=wide,
deeptabular=deeptabular,
deeptext=deeptext,
deepimage=deepimage)
trainer = Trainer(model, objective="binary", verbose=0)
trainer.fit(
X_wide=X_wide,
X_tab=X_tab,
X_text=X_text,
X_img=X_img,
target=target,
batch_size=16,
)
# check it has run succesfully
assert len(trainer.history) == 1
def test_aliases():
wide = Wide(np.unique(X_wide).shape[0], 1)
deeptabular = TabMlp(
mlp_hidden_dims=[32, 16],
mlp_dropout=[0.5, 0.5],
column_idx=column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deeptabular=deeptabular, pred_dim=1)
trainer = Trainer(model, loss="regression", verbose=0)
trainer.fit(X_wide=X_wide,
X_tab=X_tab,
target=target_regres,
batch_size=16,
warmup=True)
assert ("train_loss" in trainer.history.keys()
and trainer.__wd_aliases_used["objective"] == "loss"
and trainer.__wd_aliases_used["finetune"] == "warmup")
def test_fit_with_regression_and_metric():
wide = Wide(np.unique(X_wide).shape[0], 1)
deeptabular = TabMlp(
mlp_hidden_dims=[32, 16],
mlp_dropout=[0.5, 0.5],
column_idx=column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deeptabular=deeptabular, pred_dim=1)
trainer = Trainer(model,
objective="regression",
metrics=[R2Score],
verbose=0)
trainer.fit(X_wide=X_wide,
X_tab=X_tab,
target=target_regres,
batch_size=16)
assert "train_r2" in trainer.history.keys()
def test_head_layers_individual_components(deeptabular, deeptext, deepimage,
X_tab, X_text, X_img, target):
model = WideDeep(
deeptabular=deeptabular,
deeptext=deeptext,
deepimage=deepimage,
head_hidden_dims=[8, 4],
) # noqa: F841
trainer = Trainer(model, objective="binary", verbose=0)
trainer.fit(
X_wide=X_wide,
X_tab=X_tab,
X_text=X_text,
X_img=X_img,
target=target,
batch_size=16,
)
# check it has run succesfully
assert len(trainer.history) == 1
def test_fit_with_deephead():
wide = Wide(np.unique(X_wide).shape[0], 1)
deeptabular = TabMlp(
mlp_hidden_dims=[32, 16],
column_idx=column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
deephead = nn.Sequential(nn.Linear(16, 8), nn.Linear(8, 4))
model = WideDeep(wide=wide,
deeptabular=deeptabular,
pred_dim=1,
deephead=deephead)
trainer = Trainer(model, objective="binary", verbose=0)
trainer.fit(X_wide=X_wide,
X_tab=X_tab,
target=target_binary,
batch_size=16)
preds = trainer.predict(X_wide=X_wide, X_tab=X_tab, X_test=X_test)
probs = trainer.predict_proba(X_wide=X_wide, X_tab=X_tab, X_test=X_test)
assert preds.shape[0] == 32, probs.shape[1] == 2
def test_early_stop():
wide = Wide(np.unique(X_wide).shape[0], 1)
deeptabular = TabMlp(
mlp_hidden_dims=[32, 16],
mlp_dropout=[0.5, 0.5],
column_idx=column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deeptabular=deeptabular)
trainer = Trainer(
model=model,
objective="binary",
callbacks=[
EarlyStopping(
min_delta=5.0, patience=3, restore_best_weights=True, verbose=1
)
],
verbose=1,
)
trainer.fit(X_wide=X_wide, X_tab=X_tab, target=target, val_split=0.2, n_epochs=5)
# length of history = patience+1
assert len(trainer.history["train_loss"]) == 3 + 1
def test_predict_with_individual_component(wide, deeptabular, deeptext,
deepimage, X_wide, X_tab, X_text,
X_img, target):
model = WideDeep(wide=wide,
deeptabular=deeptabular,
deeptext=deeptext,
deepimage=deepimage)
trainer = Trainer(model, objective="binary", verbose=0)
trainer.fit(
X_wide=X_wide,
X_tab=X_tab,
X_text=X_text,
X_img=X_img,
target=target,
batch_size=16,
)
# simply checking that runs and produces outputs
preds = trainer.predict(X_wide=X_wide,
X_tab=X_tab,
X_text=X_text,
X_img=X_img)
assert preds.shape[0] == 32 and "train_loss" in trainer.history