Add pytest conftest

tests/conftest.py

@@ -0,0 +1,354 @@
+import importlib.resources as pkg_resources
+import json
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import pandas as pd
+import pytest
+from pytest import CaptureFixture, FixtureRequest
+from scipy.sparse import csr_matrix
+from sklearn.model_selection import train_test_split
+
+from tests import resources
+
+
+@pytest.fixture(scope="session")
+def clf_train_test_x_y(
+ request: FixtureRequest,
+) -> Tuple[
+ Union[pd.DataFrame, np.ndarray],
+ Union[pd.DataFrame, np.ndarray],
+ Union[np.ndarray, List],
+ Union[np.ndarray, List],
+]:
+ """Returns stratified train/test features/targets sets as a `pytest.fixture` for binary classification problems.
+
+ Parameters
+ ----------
+ request : FixtureRequest
+ Fixture request for params
+
+ Returns
+ -------
+ Tuple[Union[pd.DataFrame, np.ndarray],
+ Union[pd.DataFrame, np.ndarray],
+ Union[np.ndarray, List],
+ Union[np.ndarray, List],
+ ]
+ """
+ df = _load_test_data_from_csv(
+ filename="clf_test_data.csv",
+ )
+ y = df["CLASS"].values
+ X = df.drop(
+ ["CLASS"],
+ axis=1,
+ )
+ X_train, X_test, y_train, y_test = train_test_split(
+ X,
+ y,
+ test_size=0.2,
+ shuffle=True,
+ stratify=y,
+ random_state=1367,
+ )
+ if request.param == "dataframe":
+ return (X_train, X_test, y_train, y_test)
+ elif request.param == "array":
+ return (X_train.values, X_test.values, y_train, y_test)
+ elif request.param == "list":
+ return (X_train, X_test, y_train.tolist(), y_test.tolist())
+ else:
+ return None
+
+
+@pytest.fixture(scope="session")
+def clf_x_y(
+ request: FixtureRequest,
+) -> Tuple[Union[pd.DataFrame, np.ndarray], Union[np.ndarray, List]]:
+ """Returns features/targets sets a `pytest.fixture` for binary classification problems.
+
+ Parameters
+ ----------
+ request : FixtureRequest
+ Fixture request for params
+
+ Returns
+ -------
+ Tuple[Union[pd.DataFrame, np.ndarray], Union[np.ndarray, List]]
+ """
+ df = _load_test_data_from_csv(
+ filename="clf_test_data.csv",
+ )
+ y = df["CLASS"].values
+ X = df.drop(
+ ["CLASS"],
+ axis=1,
+ )
+ if request.param == "dataframe":
+ return (X, y)
+ elif request.param == "array":
+ return (X.values, y)
+ elif request.param == "list":
+ return (X, y.tolist())
+ else:
+ return None
+
+
+@pytest.fixture(scope="session")
+def reg_train_test_x_y(
+ request: FixtureRequest,
+) -> Tuple[
+ Union[pd.DataFrame, np.ndarray],
+ Union[pd.DataFrame, np.ndarray],
+ Union[np.ndarray, List],
+ Union[np.ndarray, List],
+]:
+ """Returns train/test features/targets sets as a `pytest.fixture` for regression problems.
+
+ Parameters
+ ----------
+ request : FixtureRequest
+ Fixture request for params
+
+ Returns
+ -------
+ Tuple[Union[pd.DataFrame, np.ndarray],
+ Union[pd.DataFrame, np.ndarray],
+ Union[np.ndarray, List],
+ Union[np.ndarray, List],
+ ]
+ """
+ df = _load_test_data_from_csv(
+ filename="reg_test_data.csv",
+ )
+ # TODO(amir): try to pull-out multi target regression as well here
+ y = df["TARGET1"].values
+ X = df.drop(
+ ["TARGET1", "TARGET2"],
+ axis=1,
+ )
+ X_train, X_test, y_train, y_test = train_test_split(
+ X,
+ y,
+ test_size=0.2,
+ shuffle=True,
+ random_state=1367,
+ )
+ if request.param == "dataframe":
+ return (X_train, X_test, y_train, y_test)
+ elif request.param == "array":
+ return (X_train.values, X_test.values, y_train, y_test)
+ elif request.param == "list":
+ return (X_train, X_test, y_train.tolist(), y_test.tolist())
+ else:
+ return None
+
+
+@pytest.fixture(scope="session")
+def reg_x_y(
+ request: FixtureRequest,
+) -> Tuple[Union[pd.DataFrame, np.ndarray], Union[np.ndarray, List]]:
+ """Returns features/targets sets a `pytest.fixture` for regression problems.
+
+ Parameters
+ ----------
+ request : FixtureRequest
+ Fixture request for params
+
+ Returns
+ -------
+ Tuple[Union[pd.DataFrame, np.ndarray], Union[np.ndarray, List]]
+ """
+ df = _load_test_data_from_csv(
+ filename="reg_test_data.csv",
+ )
+ # TODO(amir): try to pull-out multi target regression as well here
+ y = df["TARGET1"].values
+ X = df.drop(
+ ["TARGET1", "TARGET2"],
+ axis=1,
+ )
+ if request.param == "dataframe":
+ return (X, y)
+ elif request.param == "array":
+ return (X.values, y)
+ elif request.param == "list":
+ return (X, y.tolist())
+ else:
+ return None
+
+
+@pytest.fixture(scope="session")
+def datafarame_for_testing() -> pd.DataFrame:
+ """Returns a `pandas.DataFrame` as `pytest.fixture`.
+
+ Returns
+ -------
+ pd.DataFrame
+ """
+ return _dummy_pandas_dataframe(
+ size=100,
+ random_state=1367,
+ )
+
+
+@pytest.fixture(scope="session")
+def sparse_matrix_for_testing() -> csr_matrix:
+ """Returns a `scipy.csr_matrix` as `pytest.fixture`.
+
+ Returns
+ -------
+ csr_matrix
+ """
+ return _dummy_sparse_matrix()
+
+
+# TODO(amir): what if values is list ?
+def _ids(values: Any) -> str:
+ """Returns a user-friendly test case ID from the parametrized values.
+
+ Parameters
+ ----------
+ values : Any
+ Test resource values
+
+ Returns
+ -------
+ str
+ """
+ if isinstance(values, dict):
+ return ", ".join(f"{k} : {v}" for (k, v) in values.items())
+ else:
+ return str(values)
+
+
+def _load_test_scenarios_from_json(filename: str) -> Dict[str, Any]:
+ """Returns a json file contains valid and invalid test cases that can be used for `pytest.fixtures`.
+
+ Parameters
+ ----------
+ filename : str
+ Json filename
+
+ Returns
+ -------
+ Dict[str, Any]
+ """
+ return json.loads(
+ pkg_resources.read_text(
+ resources,
+ filename,
+ ),
+ )
+
+
+def _load_test_data_from_csv(filename: str) -> pd.DataFrame:
+ """Returns a `pandas.DataFrame` data loaded from a csv file that can be used for `pytest.fixtures`.
+
+ Parameters
+ ----------
+ filename : str
+ Data filename
+
+ Returns
+ -------
+ pd.DataFrame
+ """
+ with pkg_resources.path(resources, filename) as path:
+ return pd.read_csv(path)
+
+
+def _captured_log(capsys: CaptureFixture) -> Tuple[str, str]:
+ """Returns the captured standard output/error via `pytest.capsys` [1]_.
+
+ Parameters
+ ----------
+ capsys : CaptureFixture
+ Pytest capture fixture to read output and error
+
+ References
+ ----------
+ .. [1] https://docs.pytest.org/en/7.1.x/how-to/capture-stdout-stderr.html
+
+ Returns
+ -------
+ Tuple[str]
+ Captured output and caputred error
+ """
+ captured = capsys.readouterr()
+ return (captured.out, captured.err)
+
+
+def _dummy_pandas_dataframe(
+ size: Optional[int] = 100,
+ random_state: Optional[int] = 1367,
+) -> pd.DataFrame:
+ """Returns a dummy pandas DataFrame that can be used for `pytest.fixtures`.
+
+ Notes
+ -----
+ The DataFrame shape is (size, 4), two features ("feature_1", "feature_2"), and two targets
+ ("binary_target", "multi_target").
+
+ Parameters
+ ----------
+ size : int, optional
+ Number of samples, by default 100
+
+ random_state : int, optional
+ Random seed, by default 1367
+
+ Returns
+ -------
+ pd.DataFrame
+ """
+ np.random.seed(
+ seed=random_state,
+ )
+ return pd.DataFrame(
+ {
+ "feature_1": np.random.random_sample(
+ size=size,
+ ),
+ "feature_2": np.random.random_sample(
+ size=size,
+ ),
+ "binary_target": np.random.randint(
+ low=0,
+ high=2,
+ size=size,
+ dtype=int,
+ ),
+ "multi_target": np.random.randint(
+ low=0,
+ high=3,
+ size=size,
+ dtype=int,
+ ),
+ },
+ )
+
+
+def _dummy_sparse_matrix() -> csr_matrix:
+ """Returns a sparse matrix in CSR format with a shape of (3,3) with float entries.
+
+ Notes
+ -----
+ The numpy representation `_dummy_sparse_matrix().toarray()` is as follows:
+ array([[1., 0., 2.],
+ [0., 0., 3.],
+ [4., 5., 6.]])
+
+ Returns
+ -------
+ csr_matrix
+ """
+ row = np.array([0, 0, 1, 2, 2, 2])
+ col = np.array([0, 2, 2, 0, 1, 2])
+ data = np.array([1, 2, 3, 4, 5, 6])
+ return csr_matrix(
+ (data, (row, col)),
+ shape=(3, 3),
+ dtype=np.float64,
+ )