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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -22,29 +22,34 @@ | |
| """ | ||
| import importlib | ||
| import logging | ||
| from collections.abc import Callable | ||
| from collections.abc import Iterable | ||
| from typing import Union | ||
| import numpy as np | ||
| from . import utils | ||
|
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| logging.getLogger(__name__).addHandler(logging.NullHandler()) | ||
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| __author__ = 'DIANNA Team' | ||
| __email__ = '[email protected]' | ||
| __version__ = '1.4.0' | ||
| __version__ = '1.5.0' | ||
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| def explain_timeseries(model_or_function, input_timeseries, method, labels, | ||
| **kwargs): | ||
| def explain_timeseries(model_or_function: Union[Callable, str], | ||
| input_timeseries: np.ndarray, method: str, | ||
| labels: Iterable[int], **kwargs) -> np.ndarray: | ||
| """Explain timeseries data given a model and a chosen method. | ||
| Args: | ||
| model_or_function (callable or str): The function that runs the model to be explained _or_ | ||
| the path to a ONNX model on disk. | ||
| input_timeseries (np.ndarray): Timeseries data to be explained | ||
| method (string): One of the supported methods: RISE, LIME or KernelSHAP | ||
| method (str): One of the supported methods: RISE, LIME or KernelSHAP | ||
| labels (Iterable(int)): Labels to be explained | ||
| kwargs: key word arguments | ||
| Returns: | ||
| One heatmap per class. | ||
| np.ndarray: One heatmap per class. | ||
| """ | ||
| explainer = _get_explainer(method, kwargs, modality='Timeseries') | ||
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@@ -58,19 +63,21 @@ def explain_timeseries(model_or_function, input_timeseries, method, labels, | |
| **explain_timeseries_kwargs) | ||
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| def explain_image(model_or_function, input_image, method, labels, **kwargs): | ||
| def explain_image(model_or_function: Union[Callable, | ||
| str], input_image: np.ndarray, | ||
| method: str, labels: Iterable[int], **kwargs) -> np.ndarray: | ||
| """Explain an image (input_data) given a model and a chosen method. | ||
| Args: | ||
| model_or_function (callable or str): The function that runs the model to be explained _or_ | ||
| the path to a ONNX model on disk. | ||
| input_image (np.ndarray): Image data to be explained | ||
| method (string): One of the supported methods: RISE, LIME or KernelSHAP | ||
| method (str): One of the supported methods: RISE, LIME or KernelSHAP | ||
| labels (Iterable(int)): Labels to be explained | ||
| kwargs: These keyword parameters are passed on | ||
| Returns: | ||
| One heatmap (2D array) per class. | ||
| np.ndarray: An array containing the heat maps for each class. | ||
| """ | ||
| if method.upper() == 'KERNELSHAP': | ||
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@@ -87,21 +94,22 @@ def explain_image(model_or_function, input_image, method, labels, **kwargs): | |
| **explain_image_kwargs) | ||
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| def explain_text(model_or_function, input_text, tokenizer, method, labels, | ||
| **kwargs): | ||
| def explain_text(model_or_function: Union[Callable, | ||
| str], input_text: str, tokenizer, | ||
| method: str, labels: Iterable[int], **kwargs) -> list: | ||
| """Explain text (input_text) given a model and a chosen method. | ||
| Args: | ||
| model_or_function (callable or str): The function that runs the model to be explained _or_ | ||
| the path to a ONNX model on disk. | ||
| input_text (string): Text to be explained | ||
| tokenizer : Tokenizer class with tokenize and convert_tokens_to_string methods, and mask_token attribute | ||
| method (string): One of the supported methods: RISE or LIME | ||
| input_text (str): Text to be explained | ||
| tokenizer: Tokenizer class with tokenize and convert_tokens_to_string methods, and mask_token attribute | ||
| method (str): One of the supported methods: RISE or LIME | ||
| labels (Iterable(int)): Labels to be explained | ||
| kwargs: These keyword parameters are passed on | ||
| Returns: | ||
| List of (word, index of word in raw text, importance for target class) tuples. | ||
| list: List of tuples (word, index of word in raw text, importance for target class) for each class. | ||
| """ | ||
| explainer = _get_explainer(method, kwargs, modality='Text') | ||
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@@ -120,23 +128,23 @@ def explain_text(model_or_function, input_text, tokenizer, method, labels, | |
| ) | ||
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| def explain_tabular(model_or_function, | ||
| input_tabular, | ||
| method, | ||
| def explain_tabular(model_or_function: Union[Callable, str], | ||
| input_tabular: np.ndarray, | ||
| method: str, | ||
| labels=None, | ||
| **kwargs): | ||
| **kwargs) -> np.ndarray: | ||
| """Explain tabular (input_text) given a model and a chosen method. | ||
| Args: | ||
| model_or_function (callable or str): The function that runs the model to be explained _or_ | ||
| the path to a ONNX model on disk. | ||
| input_tabular (np.ndarray): Tabular data to be explained | ||
| method (string): One of the supported methods: RISE, LIME or KernelSHAP | ||
| method (str): One of the supported methods: RISE, LIME or KernelSHAP | ||
| labels (Iterable(int), optional): Labels to be explained | ||
| kwargs: These keyword parameters are passed on | ||
| Returns: | ||
| One heatmap (2D array) per class. | ||
| np.ndarray: An array containing the heat maps for each class. | ||
| """ | ||
| explainer = _get_explainer(method, kwargs, modality='Tabular') | ||
| explain_tabular_kwargs = utils.get_kwargs_applicable_to_function( | ||
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