module

Contains functions responsible for validating data types.

Module: aisp.utils.validation
Import: from aisp.utils import validation

Functions

detect_vector_data_type

def detect_vector_data_type(vector: npt.NDArray) -> FeatureType:
    ...

Detect the type of data in a vector.

The function detects if the vector contains data of type:

• Binary features: boolean values or integers restricted to 0 and 1.
• Continuous features: floating-point values in the normalized range [0.0, 1.0].
• Ranged features: floating-point values outside the normalized range.

Parameters

Name	Type	Default	Description
vector	npt.NDArray	-	An array containing the data to be classified.

Returns

Type	Description
FeatureType	The data type of the vector: "binary-features", "continuous-features", or " ranged-features".

Raises

Exception	Description
UnsupportedTypeError	If the data type of the vector is not supported by the function.

---

check_array_type

def check_array_type(x, name: str = "X") -> npt.NDArray:
    ...

Ensure X is a numpy array. Convert from list if needed.

Parameters

Name	Type	Default	Description
x	Any	-	Array, containing the samples and their characteristics, Shape: (n_samples, n_features).
name	str	'X'	Variable name used in error messages.

Returns

Type	Description
npt.NDArray	The converted or validated array

Raises

Exception	Description
TypeError	If X is not ndarray or a list.

---

check_shape_match

def check_shape_match(x: npt.NDArray, y: npt.NDArray):
    ...

Ensure X and y have compatible first dimensions.

Parameters

Name	Type	Default	Description
x	npt.NDArray	-	Array, containing the samples and their characteristics, Shape: (n_samples, n_features).
y	npt.NDArray	-	Array of target classes of x with (n_samples).

Raises

Exception	Description
TypeError	If x or y have incompatible shapes.

---

check_feature_dimension

def check_feature_dimension(x: npt.NDArray, expected: int):
    ...

Ensure X has the expected number of features.

Parameters

Name	Type	Default	Description
x	npt.NDArray	-	Input array for prediction, containing the samples and their characteristics, Shape: (n_samples, n_features).
expected	int	-	Expected number of features per sample (columns in X).

Raises

Exception	Description
FeatureDimensionMismatch	If the number of features in X does not match the expected number.

---

check_binary_array

def check_binary_array(x: npt.NDArray):
    ...

Ensure X contains only 0 and 1.

Parameters

Name	Type	Default	Description
x	npt.NDArray	-	Array, containing the samples.

Raises

Exception	Description
ValueError	If the array contains values other than 0 and 1.

---

check_value_range

def check_value_range(
    x: npt.NDArray,
    name: str = 'X',
    min_value: float = 0.0,
    max_value: float = 1.0
) -> None:
    ...

Ensure all values in the x array fall within a range.

Parameters

Name	Type	Default	Description
x	npt.NDArray	-	Array, containing the samples.
name	str	'X'	Name used in the error message.
min_value	float	0.0	Minimum allowed value.
max_value	float	1.0	Maximum allowed value.

Raises

Exception	Description
ValueError	If the array fall outside the interval (min_value, max_value).