From 6a799511037230e98865b65c4d3e37e818d148ec Mon Sep 17 00:00:00 2001
From: Yannick DAYER <yannick.dayer@idiap.ch>
Date: Sat, 11 Feb 2023 22:34:29 +0100
Subject: [PATCH] [qa] Autoupdate pre-commit and apply on all files.
---
.pre-commit-config.yaml | 8 ++++----
src/bob/pipelines/dataset/database.py | 1 -
src/bob/pipelines/distributed/sge.py | 2 --
src/bob/pipelines/wrappers.py | 5 -----
src/bob/pipelines/xarray.py | 1 -
tests/test_samples.py | 3 ---
tests/test_utils.py | 2 --
tests/test_wrappers.py | 18 ------------------
tests/test_xarray.py | 2 --
9 files changed, 4 insertions(+), 38 deletions(-)
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index b4d1d1c..4933719 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,20 +2,20 @@
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/timothycrosley/isort
- rev: 5.10.1
+ rev: 5.12.0
hooks:
- id: isort
args: [--settings-path, "pyproject.toml"]
- repo: https://github.com/psf/black
- rev: 22.3.0
+ rev: 23.1.0
hooks:
- id: black
- repo: https://github.com/pycqa/flake8
- rev: 3.9.2
+ rev: 6.0.0
hooks:
- id: flake8
- repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v4.2.0
+ rev: v4.4.0
hooks:
- id: check-ast
- id: check-case-conflict
diff --git a/src/bob/pipelines/dataset/database.py b/src/bob/pipelines/dataset/database.py
index 2ea4653..fe43dee 100644
--- a/src/bob/pipelines/dataset/database.py
+++ b/src/bob/pipelines/dataset/database.py
@@ -299,7 +299,6 @@ class FileListDatabase:
)
all_samples = []
for grp in groups:
-
for sample in self.get_reader(grp):
all_samples.append(sample)
diff --git a/src/bob/pipelines/distributed/sge.py b/src/bob/pipelines/distributed/sge.py
index 83c678d..048ee02 100644
--- a/src/bob/pipelines/distributed/sge.py
+++ b/src/bob/pipelines/distributed/sge.py
@@ -42,7 +42,6 @@ class SGEIdiapJob(Job):
config_name="sge",
**kwargs,
):
-
if queue is None:
queue = dask.config.get("jobqueue.%s.queue" % config_name)
if project is None:
@@ -244,7 +243,6 @@ class SGEMultipleQueuesCluster(JobQueueCluster):
project=rc.get("sge.project"),
**kwargs,
):
-
# Defining the job launcher
self.job_cls = SGEIdiapJob
self.sge_job_spec = sge_job_spec
diff --git a/src/bob/pipelines/wrappers.py b/src/bob/pipelines/wrappers.py
index 182dec2..fe3326f 100644
--- a/src/bob/pipelines/wrappers.py
+++ b/src/bob/pipelines/wrappers.py
@@ -566,7 +566,6 @@ class CheckpointWrapper(BaseWrapper, TransformerMixin):
return self.estimator.score(samples)
def fit(self, samples, y=None, **kwargs):
-
if not estimator_requires_fit(self.estimator):
return self
@@ -582,7 +581,6 @@ class CheckpointWrapper(BaseWrapper, TransformerMixin):
return self.save_model()
def make_path(self, sample):
-
if self.features_dir is None:
return None
@@ -605,7 +603,6 @@ class CheckpointWrapper(BaseWrapper, TransformerMixin):
to_save = getattr(sample, self.sample_attribute)
for _ in range(self.attempts):
try:
-
dirname = os.path.dirname(path)
os.makedirs(dirname, exist_ok=True)
@@ -697,7 +694,6 @@ def _shape_samples(samples):
def _array_from_sample_bags(X: dask.bag.Bag, attribute: str, ndim: int = 2):
-
if ndim not in (1, 2):
raise NotImplementedError(f"ndim must be 1 or 2. Got: {ndim}")
@@ -1028,7 +1024,6 @@ def wrap(bases, estimator=None, **kwargs):
if isinstance(estimator, Pipeline):
# wrap inner steps
for idx, name, trans in estimator._iter():
-
# when checkpointing a pipeline, checkpoint each transformer in its own folder
new_kwargs = dict(kwargs)
features_dir, model_path = (
diff --git a/src/bob/pipelines/xarray.py b/src/bob/pipelines/xarray.py
index 6b35d1c..1a13b83 100644
--- a/src/bob/pipelines/xarray.py
+++ b/src/bob/pipelines/xarray.py
@@ -313,7 +313,6 @@ def _get_dask_args_from_ds(ds, columns):
def _blockwise_with_block_args(args, block, method_name=None):
-
meta = []
for _ in range(1, block.output_ndim):
meta = [meta]
diff --git a/tests/test_samples.py b/tests/test_samples.py
index 4b246a3..84b79f7 100644
--- a/tests/test_samples.py
+++ b/tests/test_samples.py
@@ -17,7 +17,6 @@ from bob.pipelines import (
def test_sampleset_collection():
-
n_samples = 10
X = np.ones(shape=(n_samples, 2), dtype=int)
sampleset = SampleSet(
@@ -46,7 +45,6 @@ def test_sampleset_collection():
# Testing delayed sampleset
with tempfile.TemporaryDirectory() as dir_name:
-
samples = [Sample(data, key=str(i)) for i, data in enumerate(X)]
filename = os.path.join(dir_name, "samples.pkl")
with open(filename, "wb") as f:
@@ -59,7 +57,6 @@ def test_sampleset_collection():
# Testing delayed sampleset cached
with tempfile.TemporaryDirectory() as dir_name:
-
samples = [Sample(data, key=str(i)) for i, data in enumerate(X)]
filename = os.path.join(dir_name, "samples.pkl")
with open(filename, "wb") as f:
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 488c796..8a335be 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -101,7 +101,6 @@ def test_is_instance_nested():
def test_break_sample_set():
-
samplesets = []
n_samples = 10
X = np.ones(shape=(n_samples, 2), dtype=int)
@@ -109,7 +108,6 @@ def test_break_sample_set():
# Creating a face list of samplesets
for i in range(n_samples):
-
samplesets.append(
SampleSet(
[
diff --git a/tests/test_wrappers.py b/tests/test_wrappers.py
index a90090d..83c4c76 100644
--- a/tests/test_wrappers.py
+++ b/tests/test_wrappers.py
@@ -65,7 +65,6 @@ class DummyTransformer(TransformerMixin, BaseEstimator):
return self
def transform(self, X):
-
# Input validation
X = check_array(X)
# Check that the input is of the same shape as the one passed
@@ -181,7 +180,6 @@ def test_sklearn_compatible_estimator():
def test_function_sample_transfomer():
-
X = np.zeros(shape=(10, 2), dtype=int)
samples = [bob.pipelines.Sample(data) for data in X]
@@ -200,7 +198,6 @@ def test_function_sample_transfomer():
def test_fittable_sample_transformer():
-
X = np.ones(shape=(10, 2), dtype=int)
samples = [bob.pipelines.Sample(data) for data in X]
@@ -214,7 +211,6 @@ def test_fittable_sample_transformer():
def test_tagged_sample_transformer():
-
X = np.ones(shape=(10, 2), dtype=int)
samples = [bob.pipelines.Sample(data) for data in X]
@@ -227,7 +223,6 @@ def test_tagged_sample_transformer():
def test_tagged_input_sample_transformer():
-
X = np.ones(shape=(10, 2), dtype=int)
samples = [bob.pipelines.Sample(data) for data in X]
@@ -242,7 +237,6 @@ def test_tagged_input_sample_transformer():
def test_dask_tag_transformer():
-
X = np.ones(shape=(10, 2), dtype=int)
samples = [bob.pipelines.Sample(data) for data in X]
sample_bags = bob.pipelines.ToDaskBag().transform(samples)
@@ -255,7 +249,6 @@ def test_dask_tag_transformer():
def test_dask_tag_checkpoint_transformer():
-
X = np.ones(shape=(10, 2), dtype=int)
samples = [bob.pipelines.Sample(data) for data in X]
sample_bags = bob.pipelines.ToDaskBag().transform(samples)
@@ -279,7 +272,6 @@ def test_dask_tag_checkpoint_transformer():
def test_dask_tag_daskml_estimator():
-
X, labels = make_blobs(
n_samples=1000,
n_features=2,
@@ -328,7 +320,6 @@ def test_dask_tag_daskml_estimator():
def test_failing_sample_transformer():
-
X = np.zeros(shape=(10, 2))
samples = [bob.pipelines.Sample(data) for i, data in enumerate(X)]
expected = np.full_like(X, 2, dtype=object)
@@ -371,7 +362,6 @@ def test_failing_sample_transformer():
def test_failing_checkpoint_transformer():
-
X = np.zeros(shape=(10, 2))
samples = [bob.pipelines.Sample(data, key=i) for i, data in enumerate(X)]
expected = np.full_like(X, 2)
@@ -470,7 +460,6 @@ def _assert_delayed_samples(samples):
def test_checkpoint_function_sample_transfomer():
-
X = np.arange(20, dtype=int).reshape(10, 2)
samples = [
bob.pipelines.Sample(data, key=str(i)) for i, data in enumerate(X)
@@ -576,7 +565,6 @@ def _build_estimator(path, i):
def _build_transformer(path, i, force=False):
-
features_dir = os.path.join(path, f"transformer{i}")
estimator = bob.pipelines.wrap(
[DummyTransformer, "sample", "checkpoint"],
@@ -588,7 +576,6 @@ def _build_transformer(path, i, force=False):
def test_checkpoint_fittable_pipeline():
-
X = np.ones(shape=(10, 2), dtype=int)
samples = [
bob.pipelines.Sample(data, key=str(i)) for i, data in enumerate(X)
@@ -613,7 +600,6 @@ def test_checkpoint_fittable_pipeline():
def test_checkpoint_transform_pipeline():
def _run(dask_enabled):
-
X = np.ones(shape=(10, 2), dtype=int)
samples_transform = [
bob.pipelines.Sample(data, key=str(i)) for i, data in enumerate(X)
@@ -642,11 +628,9 @@ def test_checkpoint_transform_pipeline():
def test_checkpoint_transform_pipeline_force():
-
with tempfile.TemporaryDirectory() as d:
def _run():
-
X = np.ones(shape=(10, 2), dtype=int)
samples_transform = [
bob.pipelines.Sample(data, key=str(i))
@@ -782,7 +766,6 @@ def test_dask_checkpoint_transform_pipeline():
def test_checkpoint_transform_pipeline_with_sampleset():
def _run(dask_enabled):
-
X = np.ones(shape=(10, 2), dtype=int)
samples_transform = bob.pipelines.SampleSet(
[
@@ -821,7 +804,6 @@ def test_checkpoint_transform_pipeline_with_sampleset():
def test_estimator_requires_fit():
-
all_wraps = [
["sample"],
["sample", "checkpoint"],
diff --git a/tests/test_xarray.py b/tests/test_xarray.py
index 6bdab97..b126467 100644
--- a/tests/test_xarray.py
+++ b/tests/test_xarray.py
@@ -67,7 +67,6 @@ def test_delayed_samples_to_dataset():
def _build_iris_dataset(shuffle=False, delayed=False):
-
iris = datasets.load_iris()
X = iris.data
@@ -228,7 +227,6 @@ def test_dataset_pipeline_with_failures():
def test_dataset_pipeline_with_dask_ml():
-
scaler = dask_ml.preprocessing.StandardScaler()
pca = dask_ml.decomposition.PCA(n_components=3, random_state=0)
clf = SGDClassifier(random_state=0, loss="log_loss", penalty="l2", tol=1e-3)
--
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