added linting checks at push

.github/CI/check_latest_commit_for_nightly.py

@@ -1,6 +1,7 @@
-from datetime import datetime, timedelta
 import sys
 import traceback
+from datetime import datetime, timedelta
+
 import requests
 
 # Author: Antoni Baum (Yard1)

.github/workflows/test.yml

@@ -13,6 +13,30 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
+
+  isort:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out source repository
+        uses: actions/checkout@v2
+      - name: Set up Python environment
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.9
+          architecture: x64
+      - name: Check imports
+        uses: jamescurtin/isort-action@master
+        with:
+          sortPaths: pycaret
+
+  black:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out source repository
+        uses: actions/checkout@v2
+      - name: Run black
+        uses: psf/black@stable
+
   # JOBS MUST START WITH test !!!!
   test:
     runs-on: ubuntu-latest

pycaret/classification/functional.py

@@ -1,15 +1,20 @@
 import logging
 import warnings
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
 import numpy as np
 import pandas as pd
 from joblib.memory import Memory
-from typing import List, Tuple, Any, Union, Optional, Dict, Callable
 
-# from pycaret.parallel import ParallelBackend # unused
-from pycaret.loggers.base_logger import BaseLogger
 from pycaret.classification import ClassificationExperiment
-from pycaret.internal.utils import DATAFRAME_LIKE, TARGET_LIKE, check_if_global_is_not_none
+from pycaret.internal.utils import (
+    DATAFRAME_LIKE,
+    TARGET_LIKE,
+    check_if_global_is_not_none,
+)
 
+# from pycaret.parallel import ParallelBackend # unused
+from pycaret.loggers.base_logger import BaseLogger
 
 warnings.filterwarnings("ignore")
 

pycaret/classification/oop.py

@@ -1,40 +1,44 @@
+import datetime
 import gc
-import time
 import logging
-import datetime
-import warnings
+import time
 import traceback
+import warnings
+from typing import Any, Dict, List, Optional, Tuple, Union
+
 import numpy as np  # type: ignore
 import pandas as pd
-from typing import Tuple, Union, Optional, Dict, Any, List
-from joblib.memory import Memory
 import plotly.express as px  # type: ignore
 import plotly.graph_objects as go  # type: ignore
+from joblib.memory import Memory
 
-# Own modules
+import pycaret.containers.metrics.classification
+import pycaret.containers.models.classification
+import pycaret.internal.patches.sklearn
+import pycaret.internal.patches.yellowbrick
+import pycaret.internal.persistence
+import pycaret.internal.preprocess
+from pycaret.internal.Display import Display
+from pycaret.internal.logging import get_logger
+from pycaret.internal.meta_estimators import (
+    CustomProbabilityThresholdClassifier,
+    get_estimator_from_meta_estimator,
+)
 from pycaret.internal.pipeline import Pipeline as InternalPipeline
-from pycaret.internal.pycaret_experiment.utils import highlight_setup, MLUsecase
+from pycaret.internal.preprocess.preprocessor import Preprocessor
 from pycaret.internal.pycaret_experiment.supervised_experiment import (
     _SupervisedExperiment,
 )
-from pycaret.internal.preprocess.preprocessor import Preprocessor
-from pycaret.internal.meta_estimators import (
-    CustomProbabilityThresholdClassifier,
-    get_estimator_from_meta_estimator,
+from pycaret.internal.pycaret_experiment.utils import MLUsecase, highlight_setup
+from pycaret.internal.utils import (
+    DATAFRAME_LIKE,
+    TARGET_LIKE,
+    get_classification_task,
+    get_label_encoder,
 )
-from pycaret.internal.utils import DATAFRAME_LIKE, TARGET_LIKE, get_classification_task, get_label_encoder
-import pycaret.internal.patches.sklearn
-import pycaret.internal.patches.yellowbrick
-from pycaret.internal.logging import get_logger
 from pycaret.internal.validation import is_sklearn_cv_generator
-import pycaret.containers.metrics.classification
-import pycaret.containers.models.classification
-import pycaret.internal.preprocess
-import pycaret.internal.persistence
-from pycaret.internal.Display import Display
 from pycaret.loggers.base_logger import BaseLogger
 
-
 warnings.filterwarnings("ignore")
 LOGGER = get_logger()
 
@@ -2822,7 +2826,7 @@ def dashboard(
         dashboard_kwargs = dashboard_kwargs or {}
         run_kwargs = run_kwargs or {}
 
-        from explainerdashboard import ExplainerDashboard, ClassifierExplainer
+        from explainerdashboard import ClassifierExplainer, ExplainerDashboard
 
         le = get_label_encoder(self.pipeline)
         if le:

pycaret/regression/functional.py

@@ -1,16 +1,20 @@
 import logging
+import warnings
+from typing import Any, Dict, List, Optional, Union
+
 import numpy as np
 import pandas as pd
 from joblib.memory import Memory
 
+from pycaret.internal.utils import (
+    DATAFRAME_LIKE,
+    TARGET_LIKE,
+    check_if_global_is_not_none,
+)
+
 # from pycaret.parallel import ParallelBackend # Unused
 from pycaret.loggers.base_logger import BaseLogger
-
 from pycaret.regression import RegressionExperiment
-from pycaret.internal.utils import DATAFRAME_LIKE, TARGET_LIKE, check_if_global_is_not_none
-
-from typing import List, Any, Union, Optional, Dict
-import warnings
 
 warnings.filterwarnings("ignore")
 
@@ -2948,6 +2952,7 @@ def deep_check(estimator, check_kwargs: Optional[dict] = None) -> None:
         estimator=estimator, check_kwargs=check_kwargs
     )
 
+
 def set_current_experiment(experiment: RegressionExperiment):
     global _CURRENT_EXPERIMENT
 

pycaret/regression/oop.py

@@ -1,31 +1,32 @@
-import time
 import logging
+import time
 import warnings
+from typing import Any, Dict, List, Optional, Tuple, Union
+
 import numpy as np  # type: ignore
 import pandas as pd
-from joblib.memory import Memory
-from typing import List, Tuple, Dict, Union, Optional, Any
 import plotly.express as px  # type: ignore
 import plotly.graph_objects as go  # type: ignore
+from joblib.memory import Memory
+
+import pycaret.containers.metrics.regression
+import pycaret.containers.models.regression
+import pycaret.internal.patches.sklearn
+import pycaret.internal.patches.yellowbrick
+import pycaret.internal.persistence
+import pycaret.internal.preprocess
+from pycaret.internal.Display import Display
+from pycaret.internal.logging import get_logger
 
 # Own module
 from pycaret.internal.pipeline import Pipeline as InternalPipeline
 from pycaret.internal.preprocess.preprocessor import Preprocessor
-from pycaret.internal.pycaret_experiment.utils import MLUsecase, highlight_setup
 from pycaret.internal.pycaret_experiment.supervised_experiment import (
     _SupervisedExperiment,
 )
-import pycaret.internal.patches.sklearn
-import pycaret.internal.patches.yellowbrick
-from pycaret.internal.logging import get_logger
-import pycaret.containers.metrics.regression
-import pycaret.containers.models.regression
-import pycaret.internal.preprocess
-import pycaret.internal.persistence
-from pycaret.internal.Display import Display
-from pycaret.loggers.base_logger import BaseLogger
+from pycaret.internal.pycaret_experiment.utils import MLUsecase, highlight_setup
 from pycaret.internal.utils import DATAFRAME_LIKE, TARGET_LIKE
-
+from pycaret.loggers.base_logger import BaseLogger
 
 warnings.filterwarnings("ignore")
 LOGGER = get_logger()

pycaret/tests/test_time_series_base.py

@@ -4,9 +4,9 @@
 import pandas as pd
 import pytest
 from pandas.testing import assert_frame_equal
+from sktime.forecasting.compose import ForecastingPipeline
 
 from pycaret.time_series import TSForecastingExperiment
-from sktime.forecasting.compose import ForecastingPipeline
 
 from .time_series_test_utils import _return_compare_model_args, _return_model_parameters
 

pycaret/tests/test_time_series_preprocess.py

@@ -1,21 +1,19 @@
 """Module to test time_series functionality
 """
-import pytest
 import numpy as np
-
+import pytest
 from sktime.forecasting.compose import ForecastingPipeline, TransformedTargetForecaster
 
+from pycaret.time_series import TSForecastingExperiment
+
 from .time_series_test_utils import (
-    _return_model_names_for_missing_data,
     _IMPUTE_METHODS_STR,
+    _SCALE_METHODS,
     _TRANSFORMATION_METHODS,
     _TRANSFORMATION_METHODS_NO_NEG,
-    _SCALE_METHODS,
+    _return_model_names_for_missing_data,
 )
 
-from pycaret.time_series import TSForecastingExperiment
-
-
 pytestmark = pytest.mark.filterwarnings("ignore::UserWarning")
 
 ########################################################

pycaret/tests/test_time_series_tune_base.py

@@ -4,10 +4,9 @@
 import numpy as np
 import pandas as pd
 import pytest
-
-from pycaret.time_series import TSForecastingExperiment
 from sktime.forecasting.compose import ForecastingPipeline
 
+from pycaret.time_series import TSForecastingExperiment
 
 from .time_series_test_utils import _ALL_METRICS
 

pycaret/tests/test_time_series_utils_forecasting_pipeline.py

@@ -8,10 +8,10 @@
 from pycaret.utils.time_series.forecasting.models import DummyForecaster
 from pycaret.utils.time_series.forecasting.pipeline import (
     _add_model_to_pipeline,
-    _transformations_present_X,
-    _transformations_present_y,
     _are_pipeline_tansformations_empty,
     _get_imputed_data,
+    _transformations_present_X,
+    _transformations_present_y,
 )
 
 pytestmark = pytest.mark.filterwarnings("ignore::UserWarning")