cleanlab--cleanlab
Elías Snorrason
8b60a381e4
* 🏷️ annotate function args and return values Starting with the validation module: - I think (X, y) might need some custom Union type to handle both numpy arrays and pandas dataframe, etc. - All of the "assert" functions return None. Ref #307 * refactor: 🏷️ swap npt.NDArray -> np.ndarray np.ndarray seems more consistent with the rest of the repo. Maybe it's necessary to go back to npt.NDArray when disallowing generics? See numpy docs: https://numpy.org/devdocs/reference/typing.html#numpy.typing.NDArray * refactor: 🔥 remove unused import * 🏷️ unconstrain X input types * 🐛 handle label type issues - Have to restrict the output of labels_to_array to pass mypy checks. - Returning the values of pd.Series isn't type-stable. * 🏷️ include np.generic in arg-type union * test: ✅ test labels_to_array * 🏷️ add type aliases for X and y * 🚨 ignore type-checks for pandas indexing assertions CI typechecker runs on Python 3.10 which gives this error: 'cleanlab/internal/validation.py:125: error: No overload variant of "__getitem__" of "_iLocIndexerSeries" matches argument type "List[int]"' It should be fine to let mypy ignore these expressions as they don't return anything. * 🥅 specify errors to ignore "type: ignore" doesn't pass strict mypy type-checks unless the specific errors are provided * 🏷️ annotate label series to array
30 行
925 B
Python
30 行
925 B
Python
# coding: utf-8
|
|
|
|
from cleanlab.internal import validation
|
|
import numpy as np
|
|
import pandas as pd
|
|
import pytest
|
|
|
|
|
|
@pytest.mark.parametrize("y_list", [["a", "b", "a"], [0, 1, 2]])
|
|
@pytest.mark.parametrize("format", [list, np.array, pd.Series, pd.DataFrame])
|
|
def test_labels_to_array_return_types(y_list, format):
|
|
y = format(y_list)
|
|
labels = validation.labels_to_array(y)
|
|
assert isinstance(labels, np.ndarray)
|
|
|
|
|
|
@pytest.mark.parametrize("y_list", [["a", "b", "a"], [0, 1, 2]])
|
|
@pytest.mark.parametrize("format", [list, np.array, pd.Series])
|
|
def test_labels_to_array_return_values(y_list, format):
|
|
y = format(y_list)
|
|
labels = validation.labels_to_array(y)
|
|
assert np.array_equal(y, labels)
|
|
|
|
|
|
def test_label_to_array_raises_error():
|
|
# Pandas DataFrame should have only one column
|
|
y = pd.DataFrame({"a": [0, 1], "b": [2, 3]})
|
|
with pytest.raises(ValueError):
|
|
validation.labels_to_array(y)
|