[ENH] Introducing test file for polars support for estimators (#370)

#### Reference Issues/PRs

#342 is the linked issue that overlays the entire workflow for polars
support. Opening this thread mainly for the test file I am planning to
implement for polars support in skpro

#### What does this implement/fix? Explain your changes.

Implements a test file for testing various fit/predict functions using
polars dataframes. Ideas for any tests regarding testing polars support
in skpro is much appreciated!

skpro/regression/base/_base.py

@@ -6,15 +6,22 @@
 
 from skpro.base import BaseEstimator
 from skpro.datatypes import check_is_error_msg, check_is_mtype, convert
-from skpro.utils.validation._dependencies import _check_estimator_deps
+from skpro.utils.validation._dependencies import (
+    _check_estimator_deps,
+    _check_soft_dependencies,
+)
 
 # allowed input mtypes
+# include mtypes that are core dependencies
 ALLOWED_MTYPES = [
     "pd_DataFrame_Table",
     "pd_Series_Table",
     "numpy1D",
     "numpy2D",
 ]
+# include polars eager table if the soft dependency is installed
+if _check_soft_dependencies(["polars", "pyarrow"], severity="none"):
+    ALLOWED_MTYPES.append("polars_eager_table")
 
 
 class BaseProbaRegressor(BaseEstimator):

skpro/tests/test_polars.py

@@ -0,0 +1,146 @@
+"""Test file for polars dataframes"""
+
+import pandas as pd
+import pytest
+from sklearn.datasets import load_diabetes
+from sklearn.model_selection import train_test_split
+
+from skpro.utils.validation._dependencies import _check_soft_dependencies
+
+if _check_soft_dependencies(["polars", "pyarrow"], severity="none"):
+    import polars as pl
+
+    from skpro.datatypes._table._check import check_polars_table
+    from skpro.datatypes._table._convert import convert_pandas_to_polars_eager
+
+TEST_ALPHAS = [0.05, 0.1, 0.25]
+
+
+@pytest.fixture
+def polars_load_diabetes_pandas():
+    X, y = load_diabetes(return_X_y=True, as_frame=True)
+    X = X.iloc[:75]
+    y = y.iloc[:75]
+
+    # typically y is returned as a pd.Series to we call y as a Dataframe here
+    y = pd.DataFrame(y)
+
+    X_train, X_test, y_train, _ = train_test_split(
+        X, y, test_size=0.33, random_state=42
+    )
+    return [X_train, X_test, y_train]
+
+
+@pytest.fixture
+def estimator():
+    from sklearn.linear_model import LinearRegression
+
+    from skpro.regression.residual import ResidualDouble
+
+    # refactor to use ResidualDouble with Linear Regression
+    _estimator = ResidualDouble(LinearRegression())
+    return _estimator
+
+
+@pytest.fixture
+def polars_load_diabetes_polars(polars_load_diabetes_pandas):
+    X_train, X_test, y_train = polars_load_diabetes_pandas
+    X_train_pl = convert_pandas_to_polars_eager(X_train)
+    X_test_pl = convert_pandas_to_polars_eager(X_test)
+    y_train_pl = convert_pandas_to_polars_eager(y_train)
+
+    return [X_train_pl, X_test_pl, y_train_pl]
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["polars", "pyarrow"], severity="none"),
+    reason="skip test if polars/pyarrow is not installed in environment",
+)
+def test_polars_eager_conversion_methods(
+    polars_load_diabetes_pandas, polars_load_diabetes_polars
+):
+    """
+    Tests to ensure that given a pandas dataframe, the conversion methods
+    convert properly to polars dataframe
+    """
+
+    X_train, X_test, y_train = polars_load_diabetes_pandas
+    X_train_pl, X_test_pl, y_train_pl = polars_load_diabetes_polars
+
+    assert check_polars_table(X_train_pl)
+    assert check_polars_table(X_test_pl)
+    assert check_polars_table(y_train_pl)
+    assert (X_train.values == X_train_pl.to_numpy()).all()
+    assert (X_test.values == X_test_pl.to_numpy()).all()
+    assert (y_train.values == y_train_pl.to_numpy()).all()
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["polars", "pyarrow"], severity="none"),
+    reason="skip test if polars/pyarrow is not installed in environment",
+)
+def test_polars_eager_regressor_in_fit_predict(
+    estimator, polars_load_diabetes_pandas, polars_load_diabetes_polars
+):
+    """
+    Tests to ensure that given a polars dataframe, the regression estimator
+    can fit and predict and return the correct set of outputs
+
+    Parameters
+    ----------
+
+    estimator: a given regression estimator
+
+    """
+    # TODO - expand estimator to include a list of regression models to test
+    # create a copy of estimator to run further checks
+    estimator_copy = estimator
+    X_train, X_test, y_train = polars_load_diabetes_pandas
+    X_train_pl, X_test_pl, y_train_pl = polars_load_diabetes_polars
+
+    estimator.fit(X_train_pl, y_train_pl)
+    y_pred = estimator.predict(X_test_pl)
+
+    assert isinstance(y_pred, pl.DataFrame)
+    assert y_pred.columns == y_train_pl.columns
+    assert y_pred.shape[0] == X_test_pl.shape[0]
+
+    # code to ensure prediction values match up correctly
+    estimator_copy.fit(X_train, y_train)
+    y_pred_pd = estimator_copy.predict(X_test)
+    assert (y_pred_pd.values == y_pred.to_numpy()).all()
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["polars", "pyarrow"], severity="none"),
+    reason="skip test if polars/pyarrow is not installed in environment",
+)
+def test_polars_eager_regressor_in_predict_interval(
+    estimator, polars_load_diabetes_polars
+):
+    X_train_pl, X_test_pl, y_train_pl = polars_load_diabetes_polars
+    # TODO - expand estimator to include a list of regression models to test
+    estimator.fit(X_train_pl, y_train_pl)
+    y_pred_interval = estimator.predict_interval(X_test_pl)
+
+    assert isinstance(y_pred_interval, pd.DataFrame)
+    assert y_pred_interval.columns[0] == ("target", 0.9, "lower")
+    assert y_pred_interval.columns[1] == ("target", 0.9, "upper")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["polars", "pyarrow"], severity="none"),
+    reason="skip test if polars/pyarrow is not installed in environment",
+)
+def test_polars_eager_regressor_in_predict_quantiles(
+    estimator, polars_load_diabetes_polars
+):
+    X_train_pl, X_test_pl, y_train_pl = polars_load_diabetes_polars
+
+    estimator.fit(X_train_pl, y_train_pl)
+    y_pred_quantile = estimator.predict_quantiles(X_test_pl, alpha=TEST_ALPHAS)
+
+    assert isinstance(y_pred_quantile, pd.DataFrame)
+    assert y_pred_quantile.columns[0] == ("target", 0.05)
+    assert y_pred_quantile.columns[1] == ("target", 0.1)
+    assert y_pred_quantile.columns[2] == ("target", 0.25)