wehub-resource-sync
114 lines
3.4 KiB
Python
114 lines
3.4 KiB
Python
"""
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Helper functions internally used in cleanlab.regression.
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"""
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import numpy as np
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import pandas as pd
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from numpy.typing import ArrayLike
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from typing import Tuple, Union
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def assert_valid_prediction_inputs(
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labels: ArrayLike,
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predictions: ArrayLike,
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method: str,
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) -> Tuple[np.ndarray, np.ndarray]:
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"""Checks that ``labels``, ``predictions``, ``method`` are correctly formatted."""
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# Load array_like input as numpy array. If not raise error.
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try:
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labels = np.asarray(labels)
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except:
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raise ValueError(f"labels must be array_like.")
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try:
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predictions = np.asarray(predictions)
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except:
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raise ValueError(f"predictions must be array_like.")
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# Check if labels and predictions are 1-D and numeric
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valid_labels = check_dimension_and_datatype(check_input=labels, text="labels")
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valid_predictions = check_dimension_and_datatype(check_input=predictions, text="predictions")
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# Check if number of examples are same.
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assert (
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valid_labels.shape == valid_predictions.shape
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), f"Number of examples in labels {labels.shape} and predictions {predictions.shape} are not same."
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# Check if inputs have missing values
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check_missing_values(valid_labels, text="labels")
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check_missing_values(valid_predictions, text="predictions")
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# Check if method is among allowed scoring method
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scoring_methods = ["residual", "outre"]
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if method not in scoring_methods:
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raise ValueError(f"Specified method '{method}' must be one of: {scoring_methods}.")
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# return 1-D numpy array
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return valid_labels, valid_predictions
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def assert_valid_regression_inputs(
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X: Union[np.ndarray, pd.DataFrame],
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y: ArrayLike,
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) -> Tuple[np.ndarray, np.ndarray]:
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"""
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Checks that regression inputs are properly formatted and returns the inputs in numpy array format.
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"""
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try:
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X = np.asarray(X)
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except:
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raise ValueError(f"X must be array_like.")
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y = check_dimension_and_datatype(y, "y")
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check_missing_values(y, text="y")
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if len(X) != len(y):
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raise ValueError("X and y must have same length.")
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return X, y
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def check_dimension_and_datatype(check_input: ArrayLike, text: str) -> np.ndarray:
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"""
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Raises errors related to:
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1. If input is empty
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2. If input is not 1-D
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3. If input is not numeric
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If all the checks are passed, it returns the squeezed 1-D array required by the main algorithm.
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"""
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try:
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check_input = np.asarray(check_input)
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except:
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raise ValueError(f"{text} could not be converted to numpy array, check input.")
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# Check if input is empty
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if not check_input.size:
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raise ValueError(f"{text} cannot be empty array.")
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# Remove axis with length one
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check_input = np.squeeze(check_input)
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# Check if input is 1-D
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if check_input.ndim != 1:
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raise ValueError(
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f"Expected 1-Dimensional inputs for {text}, got {check_input.ndim} dimensions."
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)
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# Check if datatype is numeric
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if not np.issubdtype(check_input.dtype, np.number):
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raise ValueError(
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f"Expected {text} to contain numeric values, got values of type {check_input.dtype}."
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)
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return check_input
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def check_missing_values(check_input: np.ndarray, text: str):
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"""Raise error if there are any missing values in Numpy array."""
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if np.isnan(check_input).any():
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raise ValueError(f"{text} cannot contain missing values.")
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