Activating session on fixture to try to speed up tests

Giskard-AI · Sep 26, 2023 · f3e4b21 · f3e4b21
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commit f3e4b21
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Showing 12 changed files with 22 additions and 22 deletions.
diff --git a/python-client/tests/fixtures/amazon_review__binary_classification.py b/python-client/tests/fixtures/amazon_review__binary_classification.py
@@ -56,7 +56,7 @@ def preprocess_data(df: pd.DataFrame) -> pd.DataFrame:
     return df
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def amazon_review_data() -> Dataset:
     raw_data = preprocess_data(download_data(nrows=5000))
     wrapped_data = Dataset(
@@ -88,7 +88,7 @@ def tokenizer(x):
     return stems
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def amazon_review_model(amazon_review_data: Dataset) -> SKLearnModel:
     x = amazon_review_data.df[[FEATURE_COLUMN_NAME]]
     y = amazon_review_data.df[TARGET_COLUMN_NAME]

diff --git a/python-client/tests/fixtures/diabetes__regression.py b/python-client/tests/fixtures/diabetes__regression.py
@@ -8,7 +8,7 @@
 from giskard.ml_worker.utils.logging import Timer
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def linear_regression_diabetes_raw():
     timer = Timer()
     diabetes = datasets.load_diabetes()

diff --git a/python-client/tests/fixtures/drug_classification__multiclass_classification.py b/python-client/tests/fixtures/drug_classification__multiclass_classification.py
@@ -47,7 +47,7 @@ def _bin_na_to_k(_df: pd.DataFrame) -> pd.DataFrame:
     return df
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def drug_classification_data() -> Dataset:
     # Download data.
     fetch_from_ftp(DATA_URL, DATA_PATH)
@@ -63,7 +63,7 @@ def drug_classification_data() -> Dataset:
     return wrapped_dataset
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def drug_classification_model(drug_classification_data) -> SKLearnModel:
     x = drug_classification_data.df.drop(TARGET_NAME, axis=1)
     y = drug_classification_data.df.Drug

diff --git a/python-client/tests/fixtures/enron_multilabel_classification.py b/python-client/tests/fixtures/enron_multilabel_classification.py
@@ -65,7 +65,7 @@ def get_labels(filename):
     return dict(labels)
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def enron_data() -> Dataset:
     logger.info("Fetching Enron Data")
     df = pd.read_csv(path("test_data/enron_data.csv"), keep_default_na=False, na_values=["_GSK_NA_"])
@@ -127,7 +127,7 @@ def enron_test_data(enron_data):
     return Dataset(df=pd.DataFrame(enron_data.df).drop(columns=["Target"]), target=None, column_types=input_types)
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def enron_model(enron_data) -> SKLearnModel:
     timer = Timer()
 

diff --git a/python-client/tests/fixtures/fraud_detection__binary_classification.py b/python-client/tests/fixtures/fraud_detection__binary_classification.py
@@ -148,7 +148,7 @@ def fraud_detection_data() -> Dataset:
     return wrapped_dataset
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def fraud_detection_train_data() -> Dataset:
     train_set, _ = preprocess_dataset(*read_dataset())
     wrapped_dataset = Dataset(
@@ -157,7 +157,7 @@ def fraud_detection_train_data() -> Dataset:
     return wrapped_dataset
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def fraud_detection_model(fraud_detection_train_data: Dataset) -> Model:
     from lightgbm import LGBMClassifier
 

diff --git a/python-client/tests/fixtures/german_credit_scoring.py b/python-client/tests/fixtures/german_credit_scoring.py
@@ -43,7 +43,7 @@
 }
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def german_credit_data() -> Dataset:
     logger.info("Reading german_credit_prepared.csv")
     df = pd.read_csv(path("test_data/german_credit_prepared.csv"), keep_default_na=False, na_values=["_GSK_NA_"])
@@ -92,7 +92,7 @@ def german_credit_test_data(german_credit_data):
     return Dataset(df=df, target=None, column_types=input_types)
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def german_credit_raw_model(german_credit_data):
     timer = Timer()
 

diff --git a/python-client/tests/fixtures/hotel_text__regression.py b/python-client/tests/fixtures/hotel_text__regression.py
@@ -31,7 +31,7 @@ def load_data(**kwargs) -> pd.DataFrame:
     return df
 
 
-@pytest.fixture
+@pytest.fixture(scope="session")
 def hotel_text_data() -> Dataset:
     fetch_from_ftp(DATA_URL, DATA_PATH)
 
@@ -56,7 +56,7 @@ def adapt_vectorizer_input(df: pd.DataFrame) -> Iterable:
     return df
 
 
-@pytest.fixture
+@pytest.fixture(scope="session")
 def hotel_text_model(hotel_text_data) -> SKLearnModel:
     x = hotel_text_data.df[[FEATURE_COLUMN_NAME]]
     y = hotel_text_data.df[TARGET_COLUMN_NAME]

diff --git a/python-client/tests/fixtures/medical_transcript_multiclass_classification.py b/python-client/tests/fixtures/medical_transcript_multiclass_classification.py
@@ -56,7 +56,7 @@ def load_data() -> pd.DataFrame:
     return df
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def medical_transcript_data() -> Dataset:
     raw_data = load_data()
     wrapped_data = Dataset(
@@ -85,7 +85,7 @@ def adapt_vectorizer_input(df: pd.DataFrame) -> Iterable:
     return df
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def medical_transcript_model(medical_transcript_data: Dataset) -> SKLearnModel:
     # Define final pipeline.
     pipeline = Pipeline(

diff --git a/python-client/tests/fixtures/pytorch_sst2.py b/python-client/tests/fixtures/pytorch_sst2.py
@@ -13,7 +13,7 @@
 from tests.utils import resource_dir
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def sst2_dev_data():
     dev_datapipe = SST2(split="dev")
     return pd.DataFrame(dev_datapipe, columns=["text", "label"])
@@ -24,7 +24,7 @@ def sst2_data(sst2_dev_data):
     return Dataset(sst2_dev_data.head(), name="test dataset", target="label")
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def sst2_model(sst2_dev_data):
     torch_softmax = nn.Softmax(dim=1)
     device = "cuda" if torch.cuda.is_available() else "cpu"

diff --git a/python-client/tests/fixtures/titanic.py b/python-client/tests/fixtures/titanic.py
@@ -4,7 +4,7 @@
 from giskard.demo import titanic
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def titanic_model_data_raw():
     return titanic()
 

diff --git a/python-client/tests/fixtures/tripadvisor_text_classification_torch.py b/python-client/tests/fixtures/tripadvisor_text_classification_torch.py
@@ -201,7 +201,7 @@ def model_predict(self, df: pd.DataFrame) -> np.ndarray:
         return predicted_probabilities
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def tripadvisor_data() -> Dataset:
     # Download dataset
     df = load_dataset()
@@ -210,7 +210,7 @@ def tripadvisor_data() -> Dataset:
     )
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def tripadvisor_model(tripadvisor_data: Dataset) -> Model:
     # Load model.
     model = DistilBertForSequenceClassification.from_pretrained(

diff --git a/python-client/tests/fixtures/xboost_classification.py b/python-client/tests/fixtures/xboost_classification.py
@@ -16,7 +16,7 @@
 TARGET_COLUMN_NAME = "target"
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def breast_cancer_data() -> Dataset:
     logger.info("Fetching Breast Cancer Data")
     raw_data = load_breast_cancer(as_frame=True)
@@ -25,7 +25,7 @@ def breast_cancer_data() -> Dataset:
     return Dataset(df, name="breast_cancer", target="target", column_types=column_types)
 
 
-@pytest.fixture()
+@pytest.fixture(scope="session")
 def breast_cancer_model(breast_cancer_data: Dataset) -> Model:
     X_train, X_test, y_train, y_test = train_test_split(
         breast_cancer_data.df.loc[:, breast_cancer_data.df.columns != TARGET_COLUMN_NAME],