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_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_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
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
# torch.save(model.state_dict(), "model_weights/model_dict.t")
# model = WideDeep(wide=wide, deepdense=deepdense)
# model.load_state_dict(torch.load("model_weights/model_dict.t"))
# # <All keys matched successfully>
