def test_initializers_with_pattern():
wide = Wide(100, 1)
deepdense = DeepDense(
hidden_layers=[32, 16],
dropout=[0.5, 0.5],
deep_column_idx=deep_column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
deeptext = DeepText(vocab_size=vocab_size, embed_dim=32, padding_idx=0)
model = WideDeep(wide=wide,
deepdense=deepdense,
deeptext=deeptext,
pred_dim=1)
cmodel = c(model)
org_word_embed = []
for n, p in cmodel.named_parameters():
if "word_embed" in n:
org_word_embed.append(p)
model.compile(method="binary", verbose=0, initializers=initializers_2)
init_word_embed = []
for n, p in model.named_parameters():
if "word_embed" in n:
init_word_embed.append(p)
assert torch.all(org_word_embed[0] == init_word_embed[0].cpu())
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_no_deephead_and_head_layers():
out = []
model = WideDeep(wide=wide, deepdense=deepdense,
head_layers=[8, 4]) # noqa: F841
for n, p in model.named_parameters():
if n == "deephead.head_layer_0.0.weight":
out.append(p.size(0) == 8 and p.size(1) == 8)
if n == "deephead.head_layer_1.0.weight":
out.append(p.size(0) == 4 and p.size(1) == 8)
assert all(out)
def test_optimizer_scheduler_format():
model = WideDeep(deepdense=deepdense)
optimizers = {
"deepdense": torch.optim.Adam(model.deepdense.parameters(), lr=0.01)
}
schedulers = torch.optim.lr_scheduler.StepLR(optimizers["deepdense"],
step_size=3)
with pytest.raises(ValueError):
model.compile(
method="binary",
optimizers=optimizers,
lr_schedulers=schedulers,
)
def test_history_callback(deepcomponent, component_name):
if deepcomponent is None:
deepcomponent = deepcopy(deepdense)
deepcomponent.__dict__.pop("output_dim")
with pytest.raises(AttributeError):
if component_name == "dense":
model = WideDeep(wide, deepdense=deepcomponent)
elif component_name == "text":
model = WideDeep(wide, deepdense=deepdense, deeptext=deepcomponent)
elif component_name == "image":
model = WideDeep( # noqa: F841
wide,
deepdense=deepdense,
deepimage=deepcomponent)
def test_fit_methods(
X_wide,
X_deep,
target,
method,
X_wide_test,
X_deep_test,
X_test,
pred_dim,
probs_dim,
):
wide = Wide(np.unique(X_wide).shape[0], pred_dim)
deepdense = DeepDense(
hidden_layers=[32, 16],
dropout=[0.5, 0.5],
deep_column_idx=deep_column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deepdense=deepdense, pred_dim=pred_dim)
model.compile(method=method, verbose=0)
model.fit(X_wide=X_wide, X_deep=X_deep, target=target)
preds = model.predict(X_wide=X_wide, X_deep=X_deep, X_test=X_test)
if method == "binary":
pass
else:
probs = model.predict_proba(X_wide=X_wide,
X_deep=X_deep,
X_test=X_test)
assert preds.shape[0] == 100, probs.shape[1] == probs_dim
def test_filepath_error():
wide = Wide(np.unique(X_wide).shape[0], 1)
deepdense = DeepDense(
hidden_layers=[16, 4],
deep_column_idx=deep_column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deepdense=deepdense)
with pytest.raises(ValueError):
model.compile(
method="binary",
callbacks=[ModelCheckpoint(filepath="wrong_file_path")],
verbose=0,
)
def test_initializers_with_pattern():
wide = Wide(100, 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:],
)
deeptext = DeepText(vocab_size=vocab_size, embed_dim=32, padding_idx=0)
model = WideDeep(wide=wide,
deeptabular=deeptabular,
deeptext=deeptext,
pred_dim=1)
cmodel = c(model)
org_word_embed = []
for n, p in cmodel.named_parameters():
if "word_embed" in n:
org_word_embed.append(p)
trainer = Trainer(model,
objective="binary",
verbose=0,
initializers=initializers_2)
init_word_embed = []
for n, p in trainer.model.named_parameters():
if "word_embed" in n:
init_word_embed.append(p)
assert torch.all(org_word_embed[0] == init_word_embed[0].cpu())
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_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_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 set_model(args, prepare_tab):
if args.blocks_dims == "same":
n_inp_dim = sum([e[2] for e in prepare_tab.embeddings_input])
blocks_dims = [n_inp_dim, n_inp_dim, n_inp_dim]
else:
blocks_dims = eval(args.blocks_dims)
if args.mlp_hidden_dims == "auto":
n_inp_dim = blocks_dims[-1]
mlp_hidden_dims = [4 * n_inp_dim, 2 * n_inp_dim]
else:
mlp_hidden_dims = eval(args.mlp_hidden_dims)
deeptabular = TabResnet(
embed_input=prepare_tab.embeddings_input,
column_idx=prepare_tab.column_idx,
blocks_dims=blocks_dims,
blocks_dropout=args.blocks_dropout,
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_dropout=args.embed_dropout,
)
model = WideDeep(deeptabular=deeptabular)
return model
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_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_deephead_and_head_layers():
deephead = nn.Sequential(nn.Linear(32, 16), nn.Linear(16, 8))
with pytest.warns(UserWarning):
model = WideDeep( # noqa: F841
wide=wide,
deepdense=deepdense,
head_layers=[16, 8],
deephead=deephead)
def test_deephead_and_head_layers():
deephead = nn.Sequential(nn.Linear(32, 16), nn.Linear(16, 8))
with pytest.raises(ValueError):
model = WideDeep( # noqa: F841
wide=wide,
deepdense=deepdense,
head_layers=[16, 8],
deephead=deephead)
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_initializers_1(): wide = Wide(100, 1) deepdense = DeepDense(hidden_layers=[32,16], dropout=[0.5, 0.5], deep_column_idx=deep_column_idx, embed_input=embed_input, continuous_cols=colnames[-5:]) deeptext = DeepText( vocab_size=vocab_size, embed_dim=32, padding_idx=0) deepimage=DeepImage(pretrained=True) model = WideDeep(wide=wide, deepdense=deepdense, deeptext=deeptext, deepimage=deepimage, output_dim=1) cmodel = c(model) org_weights = [] for n,p in cmodel.named_parameters(): if n in test_layers_1: org_weights.append(p) model.compile(method='binary', verbose=0, initializers=initializers_1) init_weights = [] for n,p in model.named_parameters(): if n in test_layers_1: init_weights.append(p) res = all([torch.all((1-(a==b).int()).bool()) for a,b in zip(org_weights, init_weights)]) assert res
def test_xtrain_xval_assertion(
X_wide,
X_deep,
X_text,
X_img,
X_train,
X_val,
target,
):
model = WideDeep(wide=wide,
deepdense=deepdense,
deeptext=deeptext,
deepimage=deepimage)
model.compile(method="binary", verbose=0)
with pytest.raises(AssertionError):
model.fit(
X_wide=X_wide,
X_deep=X_deep,
X_text=X_text,
X_img=X_img,
X_train=X_train,
X_val=X_val,
target=target,
batch_size=16,
)
def test_early_stop():
wide = Wide(100, 1)
deepdense = DeepDense(
hidden_layers=[32, 16],
dropout=[0.5, 0.5],
deep_column_idx=deep_column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deepdense=deepdense)
model.compile(
method="binary",
callbacks=[
EarlyStopping(min_delta=0.1,
patience=3,
restore_best_weights=True,
verbose=1)
],
verbose=1,
)
model.fit(X_wide=X_wide,
X_deep=X_deep,
target=target,
val_split=0.2,
n_epochs=5)
# length of history = patience+1
assert len(model.history._history["train_loss"]) == 3 + 1
def test_widedeep_inputs(
X_wide,
X_deep,
X_text,
X_img,
X_train,
X_val,
target,
val_split,
transforms,
nepoch,
null,
):
model = WideDeep(wide=wide,
deepdense=deepdense,
deeptext=deeptext,
deepimage=deepimage)
model.compile(method="binary", transforms=transforms, verbose=0)
model.fit(
X_wide=X_wide,
X_deep=X_deep,
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 (model.history.epoch[0] == nepoch
and model.history._history["train_loss"] is not null)
def test_model_checkpoint(save_best_only, max_save, n_files):
wide = Wide(100, 1)
deepdense = DeepDense(
hidden_layers=[32, 16],
dropout=[0.5, 0.5],
deep_column_idx=deep_column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deepdense=deepdense)
model.compile(
method="binary",
callbacks=[
ModelCheckpoint("weights/test_weights",
save_best_only=save_best_only,
max_save=max_save)
],
verbose=0,
)
model.fit(X_wide=X_wide,
X_deep=X_deep,
target=target,
n_epochs=5,
val_split=0.2)
n_saved = len(os.listdir("weights/"))
for f in os.listdir("weights/"):
os.remove("weights/" + f)
assert n_saved <= n_files
def test_focal_loss(X_wide, X_deep, target, method, output_dim, probs_dim):
wide = Wide(100, output_dim)
deepdense = model3 = DeepDense(hidden_layers=[32, 16],
dropout=[0.5, 0.5],
deep_column_idx=deep_column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:])
model = WideDeep(wide=wide, deepdense=deepdense, output_dim=output_dim)
model.compile(method=method, verbose=0, with_focal_loss=True)
model.fit(X_wide=X_wide, X_deep=X_deep, target=target)
probs = model.predict_proba(X_wide=X_wide, X_deep=X_deep)
assert probs.shape[1] == probs_dim
def test_optimizer_scheduler_format():
model = WideDeep(deeptabular=tabmlp)
optimizers = {
"deeptabular": torch.optim.Adam(model.deeptabular.parameters(),
lr=0.01)
}
schedulers = torch.optim.lr_scheduler.StepLR(optimizers["deeptabular"],
step_size=3)
with pytest.raises(ValueError):
trainer = Trainer( # noqa: F841
model,
objective="binary",
optimizers=optimizers,
lr_schedulers=schedulers,
)
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_filepath_error():
wide = Wide(np.unique(X_wide).shape[0], 1)
deeptabular = TabMlp(
mlp_hidden_dims=[16, 4],
column_idx=column_idx,
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deeptabular=deeptabular)
with pytest.raises(ValueError):
trainer = Trainer( # noqa: F841
model=model,
objective="binary",
callbacks=[ModelCheckpoint(filepath="wrong_file_path")],
verbose=0,
)
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_basic_run_with_metrics_binary():
model = WideDeep(wide=wide, deepdense=deepdense)
model.compile(method="binary", metrics=[Accuracy], verbose=False)
model.fit(
X_wide=X_wide,
X_deep=X_deep,
target=target,
n_epochs=1,
batch_size=16,
val_split=0.2,
)
assert ("train_loss" in model.history._history.keys()
and "train_acc" in model.history._history.keys())
def test_fit_with_deephead():
wide = Wide(np.unique(X_wide).shape[0], 1)
deepdense = DeepDense(
hidden_layers=[32, 16],
deep_column_idx=deep_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,
deepdense=deepdense,
pred_dim=1,
deephead=deephead)
model.compile(method="binary", verbose=0)
model.fit(X_wide=X_wide,
X_deep=X_deep,
target=target_binary,
batch_size=16)
preds = model.predict(X_wide=X_wide, X_deep=X_deep, X_test=X_test)
probs = model.predict_proba(X_wide=X_wide, X_deep=X_deep, X_test=X_test)
assert preds.shape[0] == 32, probs.shape[1] == 2
