model.noisecut_model

NoiseCut class for the usage of user.

Module Contents

Classes

NoiseCut

NoiseCut class for implementing NoiseCut method.

class model.noisecut_model.NoiseCut(n_input_each_box: list[int] | numpy.typing.NDArray[numpy.int_], threshold: float = 0.5)

NoiseCut class for implementing NoiseCut method.

Parameters:

• n_input_each_box ({list, ndarray} of shape (n_box,)) – An array of size n_box (number of first-layer black boxes) which keeps number of input features to each box. For instance, when n_input_each_box=[2, 4, 1], it means there are three first-layer black boxes and number of input features to box1, box2 and box3 is 2, 4, and 1, respectively.

• threshold (float in range (0,1), default=0.5≤) – Calculation of the function of the 2nd layer black box is done based on the threshold value. It is more reasonable to be set to 0.5 (default value).

fit(x: list[bool] | numpy.typing.NDArray[numpy.bool_], y: list[bool] | numpy.typing.NDArray[numpy.bool_], with_more_data: bool = False, print_result: bool = False, print_weights: bool = False) → None

Fit the model based on the input dataset.

Parameters:

• x ({array-like, ndarray, dataframe} of shape (n_data, dimension)) – Training data to fit the NoiseCut model. Each row of the array x is a binary input.

• y ({array-like, ndarray, dataframe} of shape (n_data,)) – Training data output in one_to_one mapping of binary training data input x.

• with_more_data (bool, default=False) – Whether to fit the model with additional data if the model has been fitted once. Indeed, the x, y will be added to the existing data from the previous fitting and then the model will fit again if with_more_data=True.

• print_result (bool, default=False) – Whether to print the result of fitting.

• print_weights (bool, default=False) – Whether to print the set weights of the 1st-layer black boxes (for debugging purpose).

predict(x: list[bool] | numpy.typing.NDArray[numpy.bool_]) → numpy.typing.NDArray[numpy.bool_]

Return predicted output of the NoiseCut model to the binary inout x.

Parameters:

x ({array-like, ndarray, dataframe} of shape (n_test_data, dimension)) – Data input to predict the binary output for each row of it, which is a binary input.

Returns:

Predicted output in one_to_one mapping of binary input x.

Return type:

ndarray of bool of shape (n_test_data,)

predict_all() → numpy.typing.NDArray[numpy.bool_]

Return all predicted output of the structured system by NoiseCut.

Predict output of the structured system for any possible binary input based on the fitted NoiseCut model. Decimal value of binary input can be seen as an index for the returned array to get the predicted value for that binary input.

For instance, consider a structured system with ‘dimension=5’:

returned_array[ 1] is the predicted output of the fitted NoiseCut model to binary input [1, 0, 0, 0, 0].

returned_array[22] is the predicted output of the fitted NoiseCut model to binary input [1, 0, 1, 1, 0].

Returns:

Predicted Output.

Return type:

ndarray of shape (2**dimension,)

set_uncertainty_measure(file_path_result: str | None = None) → None

Set uncertainty.

Parameters:

file_path_result (str, default=None) – Path of a file to save the uncertainty result to it.

predict_probability_of_being_1(x: list[bool] | numpy.typing.NDArray[numpy.bool_]) → numpy.typing.NDArray[numpy.float_]

Return probability of the predicted target output to be 1.

Parameters:

x ({array-like, ndarray, dataframe} of shape (n_samples, dimension)) – Data input to predict probability of being 1 for each row of it, which is a binary input.

Returns:

Probability of the predicted target output to be 1 in one_to_one mapping of binary input x.

Return type:

ndarray of float of shape (n_samples,)

predict_pseudo_boolean_func_coef() → numpy.typing.NDArray[numpy.float_]

Return the Pseudo boolean coefficients of 2nd-layer black box.

Return the Pseudo boolean coefficients of the Pseudo boolean function of the 2nd-layer black box based on the values of the probability_of_being_1.

Returns:

Pseudo boolean coefficients in array type.

Return type:

ndarray of float of shape (2**n_box,)

predict_score(x: list[bool] | numpy.typing.NDArray[numpy.bool_], vector_n_score: list[float] | numpy.typing.NDArray[numpy.float_]) → numpy.typing.NDArray[numpy.int_]

Return predicted score for each binary input of x.

Parameters:

• x ({array-like, ndarray, dataframe} of shape (n_samples, dimension)) – Input data to predict score of output for it. Each row of the array x is a binary input.

• vector_n_score ({array-like, ndarray}) – An indicator how the score is given to a range of probabilities. vector_n_score array Should have 0 as the first element of the array and 1 as the last element. The other elements in between should be unique and in range 0 to 1. The array should be also sorted from lowest, which is zero, to highest, which is one. For instance, consider vector_n_score is [0, 0.2, 0.4, 0.6, 0.8, 1]. Highest score is set to binary inputs of the 2nd-layer black box, which has a probability in range 0.8 to 1. Lowest score is always one and highest score depends on the length of vector_n_score. In this example, highest score is 5.

Returns:

Score array is set in one_to_one mapping of binary input x.

Return type:

ndarray of int of shape (n_samples,)

predict_mortality_of_each_score(x_test: list[bool] | numpy.typing.NDArray[numpy.bool_], y_test: list[bool] | numpy.typing.NDArray[numpy.bool_], vector_n_score: list[float] | numpy.typing.NDArray[numpy.float_], print_mortality: bool = False) → tuple[numpy.typing.NDArray[numpy.float_], numpy.typing.NDArray[numpy.int_], numpy.typing.NDArray[numpy.int_]]

Return percentage of output to be 1 for test data with specific score.

Parameters:

• x_test ({array-like, ndarray, dataframe} of shape (n_data,) – dimension) Test data input against the fitted NoiseCut model. Each row of the array x is a binary input.

• y_test ({array-like, ndarray, dataframe} of shape (n_data,)) – Predicted output of test data in one_to_one mapping of test data x.

• vector_n_score ({array-like, ndarray}) – An indicator how the score is given to a range of probabilities. vector_n_score array Should have 0 as the first element of the array and 1 as the last element. The other elements in between should be unique and in range 0 to 1. The array should be also sorted from lowest, which is zero, to highest, which is one. For instance, consider vector_n_score is [0, 0.2, 0.4, 0.6, 0.8, 1]. Highest score is set to binary inputs of the 2nd-layer black box, which has a probability in range 0.8 to 1. Lowest score is always one and highest score depends on the length of vector_n_score. In this example, highest score is 5.

• print_mortality (bool, default=False) – Whether print result of the method.

Returns:

Percentage for each score. For example: returned_array[0] -> for score 1.

Return type:

ndarray of shape (n_score,)

Raises:

RuntimeError – Riase error if model is not fitted.

get_binary_function_of_box(id_box: int) → numpy.typing.NDArray[numpy.bool_]

Return the binary function of the requested box.

Parameters:

id_box (int) – Index of the box, it starts from zero.

Returns:

Binary function of the id_box.

Return type:

ndarray of bool of shape (2**`self.n_input_each_box[id_box]`,)

get_binary_function_black_box() → numpy.typing.NDArray[numpy.bool_]

Return binary function of the 2nd-layer black box.

Returns:

Binary function of the output box.

Return type:

ndarray of bool of shape (2**n_box,)

print_model() → None

Print the model data when the model has been fitted.