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Adds components.core library w/ functions for default interpreters + …
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| # Copyright 2022 Google LLC | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================== | ||
| """Helpers for getting default values for LitApp configurations.""" | ||
| from typing import Union | ||
| from lit_nlp.api import components as lit_components | ||
| from lit_nlp.api import model as lit_model | ||
| from lit_nlp.components import ablation_flip | ||
| from lit_nlp.components import classification_results | ||
| from lit_nlp.components import curves | ||
| from lit_nlp.components import gradient_maps | ||
| from lit_nlp.components import hotflip | ||
| from lit_nlp.components import lemon_explainer | ||
| from lit_nlp.components import lime_explainer | ||
| from lit_nlp.components import metrics | ||
| from lit_nlp.components import model_salience | ||
| from lit_nlp.components import nearest_neighbors | ||
| from lit_nlp.components import pca | ||
| from lit_nlp.components import pdp | ||
| from lit_nlp.components import projection | ||
| from lit_nlp.components import regression_results | ||
| from lit_nlp.components import salience_clustering | ||
| from lit_nlp.components import scrambler | ||
| from lit_nlp.components import shap_explainer | ||
| from lit_nlp.components import tcav | ||
| from lit_nlp.components import thresholder | ||
| from lit_nlp.components import umap | ||
| from lit_nlp.components import word_replacer | ||
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| ComponentGroup = lit_components.ComponentGroup | ||
| Generator = lit_components.Generator | ||
| Interpreter = lit_components.Interpreter | ||
| Model = lit_model.Model | ||
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| def default_generators() -> dict[str, Generator]: | ||
| """Returns a dict of the default generators used in a LitApp.""" | ||
| return { | ||
| 'Ablation Flip': ablation_flip.AblationFlip(), | ||
| 'Hotflip': hotflip.HotFlip(), | ||
| 'Scrambler': scrambler.Scrambler(), | ||
| 'Word Replacer': word_replacer.WordReplacer(), | ||
| } | ||
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| def default_interpreters(models: dict[str, Model]) -> dict[str, Interpreter]: | ||
| """Returns a dict of the default interpreters (and metrics) used in a LitApp. | ||
| Args: | ||
| models: A dictionary of models that included in the LitApp that may provide | ||
| thier own salience information. | ||
| """ | ||
| # Ensure the embedding-based interpreters are included. | ||
| embedding_based_interpreters: dict[str, Interpreter] = { | ||
| 'nearest neighbors': nearest_neighbors.NearestNeighbors(), | ||
| # Embedding projectors expose a standard interface, but get special | ||
| # handling so we can precompute the projections if requested. | ||
| 'pca': projection.ProjectionManager(pca.PCAModel), | ||
| 'umap': projection.ProjectionManager(umap.UmapModel), | ||
| } | ||
| gradient_map_interpreters: dict[str, Interpreter] = { | ||
| 'Grad L2 Norm': gradient_maps.GradientNorm(), | ||
| 'Grad ⋅ Input': gradient_maps.GradientDotInput(), | ||
| 'Integrated Gradients': gradient_maps.IntegratedGradients(), | ||
| 'LIME': lime_explainer.LIME(), | ||
| } | ||
| metrics_group: ComponentGroup = ComponentGroup({ | ||
| 'regression': metrics.RegressionMetrics(), | ||
| 'multiclass': metrics.MulticlassMetrics(), | ||
| 'paired': metrics.MulticlassPairedMetrics(), | ||
| 'bleu': metrics.CorpusBLEU(), | ||
| 'rouge': metrics.RougeL(), | ||
| }) | ||
| # Ensure the prediction analysis interpreters are included. | ||
| prediction_analysis_interpreters: dict[str, Interpreter] = { | ||
| 'classification': classification_results.ClassificationInterpreter(), | ||
| 'regression': regression_results.RegressionInterpreter(), | ||
| } | ||
| # pyformat: disable | ||
| interpreters: dict[str, Union[ComponentGroup, Interpreter]] = { | ||
| 'Model-provided salience': model_salience.ModelSalience(models), | ||
| 'counterfactual explainer': lemon_explainer.LEMON(), | ||
| 'tcav': tcav.TCAV(), | ||
| 'curves': curves.CurvesInterpreter(), | ||
| 'thresholder': thresholder.Thresholder(), | ||
| 'metrics': metrics_group, | ||
| 'pdp': pdp.PdpInterpreter(), | ||
| 'Salience Clustering': salience_clustering.SalienceClustering( | ||
| gradient_map_interpreters), | ||
| 'Tabular SHAP': shap_explainer.TabularShapExplainer(), | ||
| } | ||
| # pyformat: enable | ||
| interpreters.update(**gradient_map_interpreters, | ||
| **prediction_analysis_interpreters, | ||
| **embedding_based_interpreters) | ||
| return interpreters |