From 45d1f776484ab8dda9e5d8e3e655d8177a1f8d51 Mon Sep 17 00:00:00 2001
From: Paul Andrey <paul.andrey@inria.fr>
Date: Mon, 27 May 2024 11:06:46 +0200
Subject: [PATCH] Fix pylint-identified variable (non-)assignment issues.
---
declearn/dataset/_split_data.py | 2 ++
declearn/model/sklearn/_sgd.py | 6 ++++++
declearn/optimizer/_base.py | 1 +
test/model/test_haiku_model.py | 4 ++++
test/model/test_tflow_model.py | 4 ++++
test/model/test_torch_model.py | 2 ++
6 files changed, 19 insertions(+)
diff --git a/declearn/dataset/_split_data.py b/declearn/dataset/_split_data.py
index 1729c2dd..aa4829f7 100644
--- a/declearn/dataset/_split_data.py
+++ b/declearn/dataset/_split_data.py
@@ -130,6 +130,8 @@ def _extract_column_by_index(
csc = inputs.tocsc() # sparse matrix with efficient column slicing
idx = [i for i in range(inputs.shape[1]) if i != target]
inputs = type(inputs)(csc[:, idx])
+ else: # pragma: no cover
+ raise TypeError("Invalid type for 'inputs'.")
return inputs, labels
diff --git a/declearn/model/sklearn/_sgd.py b/declearn/model/sklearn/_sgd.py
index 17263da5..c0c8aba0 100644
--- a/declearn/model/sklearn/_sgd.py
+++ b/declearn/model/sklearn/_sgd.py
@@ -302,6 +302,12 @@ class SklearnSGDModel(Model):
if loss not in REG_LOSSES:
raise ValueError(f"Invalid loss '{loss}' for SGDRegressor.")
sk_cls = SGDRegressor
+ # Invalid input case.
+ else: # pragma: no cover
+ raise ValueError(
+ "Invalid value for SklearnSGDModel 'kind': must be one of "
+ f"{'classifier', 'regressor'}, received '{kind}'."
+ )
# Instantiate the sklearn model, wrap it up and return.
model = sk_cls(
loss=loss,
diff --git a/declearn/optimizer/_base.py b/declearn/optimizer/_base.py
index 4894e1e4..1b637693 100644
--- a/declearn/optimizer/_base.py
+++ b/declearn/optimizer/_base.py
@@ -295,6 +295,7 @@ class Optimizer:
weights = model.get_weights(trainable=True)
# Run input gradients and weights through plug-in regularizers.
if self.regularizers:
+ # false-positive; pylint: disable=possibly-used-before-assignment
for regularizer in self.regularizers:
gradients = regularizer.run(gradients, weights)
# Run input gradients through plug-in modules.
diff --git a/test/model/test_haiku_model.py b/test/model/test_haiku_model.py
index 7e31f048..8d0d9aec 100644
--- a/test/model/test_haiku_model.py
+++ b/test/model/test_haiku_model.py
@@ -144,6 +144,8 @@ class HaikuTestCase(ModelTestCase):
inputs = rng.choice(100, size=(2, 32, 128))
elif self.kind == "CNN":
inputs = rng.normal(size=(2, 32, 64, 64, 3)).astype("float32")
+ else:
+ raise ValueError("Invalid model 'kind'.")
labels = rng.choice(2, size=(2, 32))
# Convert that data to jax-numpy and return it.
with warnings.catch_warnings(): # jax.jit(device=...) is deprecated
@@ -164,6 +166,8 @@ class HaikuTestCase(ModelTestCase):
elif self.kind == "RNN":
shape = [128]
model_fn = rnn_fn
+ else:
+ raise ValueError("Invalid model 'kind'.")
model = HaikuModel(model_fn, loss_fn)
model.initialize(
{
diff --git a/test/model/test_tflow_model.py b/test/model/test_tflow_model.py
index 65805b5f..d9d45896 100644
--- a/test/model/test_tflow_model.py
+++ b/test/model/test_tflow_model.py
@@ -97,6 +97,8 @@ class TensorflowTestCase(ModelTestCase):
inputs = rng.uniform((2, 32, 128), 0, 100, tf.int32)
elif self.kind == "CNN":
inputs = rng.normal((2, 32, 64, 64, 3))
+ else:
+ raise ValueError("Invalid model 'kind'.")
labels = rng.uniform((2, 32), 0, 2, tf.int32)
dataset = tf.data.Dataset.from_tensor_slices((inputs, labels, None))
return list(iter(dataset))
@@ -133,6 +135,8 @@ class TensorflowTestCase(ModelTestCase):
tf_keras.layers.Dense(1, activation="sigmoid"),
]
shape = [None, 64, 64, 3]
+ else:
+ raise ValueError("Invalid model 'kind'.")
tfmod = tf_keras.Sequential(stack)
tfmod.build(shape) # as model is built, no data_info is required
return TensorflowModel(tfmod, loss="binary_crossentropy", metrics=None)
diff --git a/test/model/test_torch_model.py b/test/model/test_torch_model.py
index 725e81c8..e442e075 100644
--- a/test/model/test_torch_model.py
+++ b/test/model/test_torch_model.py
@@ -127,6 +127,8 @@ class TorchTestCase(ModelTestCase):
inputs = torch.randint(0, 100, (2, 32, 128), generator=rng)
elif self.kind == "CNN":
inputs = torch.randn((2, 32, 3, 64, 64), generator=rng)
+ else:
+ raise ValueError("Invalid model 'kind'.")
labels = torch.randint(0, 2, (2, 32, 1), generator=rng)
labels = labels.type(torch.float)
dataset = torch.utils.data.TensorDataset(inputs, labels)
--
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