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_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_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_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_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_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_head_layers_individual_components(deepdense, deeptext, deepimage,
X_deep, X_text, X_img, target):
model = WideDeep(deepdense=deepdense,
deeptext=deeptext,
deepimage=deepimage,
head_layers=[8, 4]) # noqa: F841
model.compile(method="binary", verbose=0)
model.fit(
X_wide=X_wide,
X_deep=X_deep,
X_text=X_text,
X_img=X_img,
target=target,
batch_size=16,
)
# check it has run succesfully
assert len(model.history._history) == 1
def test_individual_inputs(wide, deepdense, deeptext, deepimage, X_wide,
X_deep, X_text, X_img, target):
model = WideDeep(wide=wide,
deepdense=deepdense,
deeptext=deeptext,
deepimage=deepimage)
model.compile(method="binary", verbose=0)
model.fit(
X_wide=X_wide,
X_deep=X_deep,
X_text=X_text,
X_img=X_img,
target=target,
batch_size=16,
)
# check it has run succesfully
assert len(model.history._history) == 1
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
def test_basic_run_with_metrics_multiclass():
wide = Wide(np.unique(X_wide).shape[0], 3)
deepdense = DeepDense(
hidden_layers=[32, 16],
dropout=[0.5, 0.5],
deep_column_idx={k: v
for v, k in enumerate(colnames)},
embed_input=embed_input,
continuous_cols=colnames[-5:],
)
model = WideDeep(wide=wide, deepdense=deepdense, pred_dim=3)
model.compile(method="multiclass", metrics=[Accuracy], verbose=False)
model.fit(
X_wide=X_wide,
X_deep=X_deep,
target=target_multi,
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_predict_with_individual_component(wide, deepdense, deeptext,
deepimage, X_wide, X_deep, X_text,
X_img, target):
model = WideDeep(wide=wide,
deepdense=deepdense,
deeptext=deeptext,
deepimage=deepimage)
model.compile(method="binary", verbose=0)
model.fit(
X_wide=X_wide,
X_deep=X_deep,
X_text=X_text,
X_img=X_img,
target=target,
batch_size=16,
)
# simply checking that runs and produces outputs
preds = model.predict(X_wide=X_wide,
X_deep=X_deep,
X_text=X_text,
X_img=X_img)
assert preds.shape[0] == 32 and "train_loss" in model.history._history
df.drop("yield", axis=1, inplace=True)
target = "yield_cat"
target = np.array(df[target].values)
prepare_wide = WidePreprocessor(wide_cols=wide_cols,
crossed_cols=crossed_cols)
X_wide = prepare_wide.fit_transform(df)
prepare_deep = DensePreprocessor(embed_cols=cat_embed_cols,
continuous_cols=continuous_cols)
X_deep = prepare_deep.fit_transform(df)
wide = Wide(wide_dim=X_wide.shape[1], pred_dim=3)
deepdense = DeepDense(
hidden_layers=[64, 32],
dropout=[0.2, 0.2],
deep_column_idx=prepare_deep.deep_column_idx,
embed_input=prepare_deep.embeddings_input,
continuous_cols=continuous_cols,
)
model = WideDeep(wide=wide, deepdense=deepdense, pred_dim=3)
model.compile(method="multiclass", metrics=[Accuracy, F1Score])
model.fit(
X_wide=X_wide,
X_deep=X_deep,
target=target,
n_epochs=1,
batch_size=32,
val_split=0.2,
)
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(X_wide=X_wide,
X_deep=X_deep,
target=target,
n_epochs=10,
batch_size=256,
val_split=0.2)
]
model.compile(
method="regression",
initializers=initializers,
optimizers=optimizers,
lr_schedulers=schedulers,
callbacks=callbacks,
transforms=transforms,
)
model.fit(
X_wide=X_wide,
X_deep=X_deep,
X_text=X_text,
X_img=X_images,
target=target,
n_epochs=1,
batch_size=32,
val_split=0.2,
)
# # With warm_up
# child = list(model.deepimage.children())[0]
# img_layers = list(child.backbone.children())[4:8] + [list(model.deepimage.children())[1]]
# img_layers = img_layers[::-1]
# model.fit(X_wide=X_wide, X_deep=X_deep, X_text=X_text, X_img=X_images,
# target=target, n_epochs=1, batch_size=32, val_split=0.2, warm_up=True,
# warm_epochs=1, warm_deepimage_gradual=True, warm_deepimage_layers=img_layers,
# warm_deepimage_max_lr=0.01, warm_routine='howard')
model.compile(
method="binary",
optimizers=optimizers,
lr_schedulers=schedulers,
initializers=initializers,
callbacks=callbacks,
metrics=metrics,
)
X_wide_train, X_wide_test, X_deep_train, X_deep_test, y_train, y_test = train_test_split(
X_wide, X_deep, target, test_size=0.2)
model.fit(
X_wide=X_wide_train,
X_deep=X_deep_train,
target=y_train,
n_epochs=4,
batch_size=64,
val_split=0.2,
)
# # to save/load the model
# torch.save(model, "model_weights/model.t")
# model = torch.load("model_weights/model.t")
preds = model.predict(X_wide_test, X_deep_test)
accuracy = accuracy_score(y_test, preds)
print(accuracy)
# # to save/load the model
# torch.save(model, "model_weights/model.t")
# model = torch.load("model_weights/model.t")
# # or via state dictionaries
