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_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_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
