Validation

def detect_vector_data_type(...)

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

Detects the type of data in a given vector.

This function analyzes the input vector and classifies its data as one of the supported types:

• binary: Boolean values (True/False) or integer 0/1.
• continuous: Float values within the normalized range [0.0, 1.0].
• ranged: Float values outside the normalized range.

Parameters

• vector (npt.NDArray): An array containing the data to be classified.

Returns

• FeatureType (Literal["binary-features", "continuous-features", "ranged-features"]): The detected type of data in the vector.

Raises

• UnsupportedDataTypeError: Raised if the vector contains an unsupported data type.

---

def 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

• x (Any): Array, containing the samples and their characteristics, [N samples (rows)][N features (columns)].
• name (str, default='X'): Variable name used in error messages.

Returns

• npt.NDArray: The converted or validated array.

Raises

• TypeError: If X or y are not ndarrays or have incompatible shapes.

---

def check_shape_match(...)

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

Ensure X and y have compatible first dimensions.

Parameters

• x (npt.NDArray): Array, containing the samples and their characteristics, [N samples (rows)][N features (columns)].
• y (npt.NDArray): Array of target classes of x with [N samples (lines)].

Raises

• TypeError: If x or y are not ndarrays or have incompatible shapes.

---

def check_feature_dimension(...)

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

Ensure X has the expected number of features.

Parameters

• x (npt.NDArray): Input array for prediction, containing the samples and their characteristics, [N samples (rows)][N features (columns)].
• expected (int): Expected number of features per sample (columns in X).

Raises

• FeatureDimensionMismatch: If the number of features in X does not match the expected number.

---

def check_binary_array(...)

def check_binary_array(x: npt.NDArray):

Ensure X contains only 0 and 1.

Raises

• ValueError: If feature_type is binary-features and X contains values that are not composed only of 0 and 1.