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lionelkuschjpaillardbthirionantoinebakerAngelReyero
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Create a base for managing groups of features. (#357)
* separate the management of the group from base permutation * fix some error * fix tests * fix examples * fix declaration in the API * change name of groups * update docstring * fix doc * rename class * Apply suggestions from code review Co-authored-by: Joseph Paillard <[email protected]> * chaneg features to feature * rename features to feature * groups in the init * fix test * fix spelling * fix return * fix docstring * fix format * fix examples [skip tests] * add fit method * Improve docstring * fix bug * Update src/hidimstat/base_variable_importance.py Co-authored-by: bthirion <[email protected]> * fix suggestion * Apply suggestion from @bthirion Co-authored-by: bthirion <[email protected]> * Update src/hidimstat/base_variable_importance.py Co-authored-by: bthirion <[email protected]> * Update src/hidimstat/conditional_feature_importance.py Co-authored-by: bthirion <[email protected]> * fix docstring * fix docstring * error * Update condition of the string * sort features * fix a bug * fix test * fix docstring * fix tests * fix function plot * fix example * create a new check * move check_fetaure type in CFI * Update src/hidimstat/leave_one_covariate_out.py Co-authored-by: Joseph Paillard <[email protected]> * Update src/hidimstat/leave_one_covariate_out.py Co-authored-by: Joseph Paillard <[email protected]> * Update src/hidimstat/leave_one_covariate_out.py Co-authored-by: Joseph Paillard <[email protected]> * Update src/hidimstat/leave_one_covariate_out.py Co-authored-by: Joseph Paillard <[email protected]> * Update src/hidimstat/leave_one_covariate_out.py Co-authored-by: Joseph Paillard <[email protected]> * Update src/hidimstat/leave_one_covariate_out.py Co-authored-by: Joseph Paillard <[email protected]> * add option for parameters * homogeneis name for features * update * fix minimal version for test * [DOC] User guide section 3. model-agnostic methods: CFI (#402) * first commit * add section structure * [doc quick] [skip tests] skip * missing .rst? [doc quick] [skip tests] * fix link [doc quick] [skip tests] * add CFI fiirst draft and TSI [doc quick] [skip tests] * missing space? [doc quick] [skip tests] * replace space * [doc quick] [skip tests] * add ref and note section [doc quick] [skip tests] * add code snippets * typo cfi [doc quick] [skip tests] * add total sobol index ref [doc quick] [skip tests] * add copy button [doc quick] [skip tests] * missing sphinx requirements [quick doc] [skip tests] * add copybutton config * [doc quick] [skip tests] * solve example test * clarify "sub-model" for classif and regression * trry add figure + update note * add intro * try fix image path * trigger CI * try not to scale * definition * try image * [skip tests] trigger CI * [tests skip] another one * trigger CI * back to figure * add inference section * add reff * skip tests * [skip tests] * Update docs/src/model_agnostic_methods/conditional_feature_importance.rst Co-authored-by: lionel kusch <[email protected]> * [skip tests] format bullet * rephrase not * [skip tests] * [skip tests] * [skip tests] linkcheck generated ignore images * [skip tests] linkcheck generated ignore * review * trigger CI * Update docs/src/model_agnostic_methods/conditional_feature_importance.rst Co-authored-by: Ángel Reyero Lobo <[email protected]> * Update docs/src/model_agnostic_methods/conditional_feature_importance.rst Co-authored-by: Ángel Reyero Lobo <[email protected]> * Update docs/src/model_agnostic_methods/conditional_feature_importance.rst Co-authored-by: Ángel Reyero Lobo <[email protected]> * Update docs/src/model_agnostic_methods/conditional_feature_importance.rst Co-authored-by: Ángel Reyero Lobo <[email protected]> * genetic example * Update docs/src/model_agnostic_methods/conditional_feature_importance.rst Co-authored-by: Ángel Reyero Lobo <[email protected]> * Update docs/src/model_agnostic_methods/conditional_feature_importance.rst Co-authored-by: Ángel Reyero Lobo <[email protected]> * Update docs/src/model_agnostic_methods/conditional_feature_importance.rst Co-authored-by: Ángel Reyero Lobo <[email protected]> * Update docs/src/model_agnostic_methods/total_sobol_index.rst Co-authored-by: Ángel Reyero Lobo <[email protected]> * Update docs/src/model_agnostic_methods/conditional_feature_importance.rst Co-authored-by: bthirion <[email protected]> * Update docs/src/model_agnostic_methods/conditional_feature_importance.rst Co-authored-by: bthirion <[email protected]> * Update docs/src/model_agnostic_methods/total_sobol_index.rst Co-authored-by: Ángel Reyero Lobo <[email protected]> --------- Co-authored-by: jpaillard <[email protected]> Co-authored-by: lionel kusch <[email protected]> Co-authored-by: Ángel Reyero Lobo <[email protected]> Co-authored-by: bthirion <[email protected]> * add an exception * Add a black line at this of the file of documentation [skip tests] --------- Co-authored-by: Joseph Paillard <[email protected]> Co-authored-by: bthirion <[email protected]> Co-authored-by: antoinebaker <[email protected]> Co-authored-by: Ángel Reyero Lobo <[email protected]>
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‎docs/src/api.rst‎

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base_variable_importance.BaseVariableImportance
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base_perturbation.BasePerturbation
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base_variable_importance.GroupVariableImportanceMixin
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Feature Importance Classes
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==========================

‎docs/src/concepts.rst‎

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======================
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Definition of concepts
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======================

‎docs/src/glm_methods.rst‎

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:maxdepth: 2
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glm_methods/desparsified_lasso.rst
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glm_methods/knockoffs.rst
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glm_methods/knockoffs.rst

‎docs/src/grouping.rst‎

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==========================================
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Measuring the importance of feature groups
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==========================================

‎docs/src/high_dimension.rst‎

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===========================
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Inference in high dimension
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===========================
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‎docs/src/marginal_methods.rst‎

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.. toctree::
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:maxdepth: 2
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marginal_methods/leave_one_covariate_in.rst
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marginal_methods/leave_one_covariate_in.rst

‎docs/src/model_agnostic_methods/leave_one_covariate_out.rst‎

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Leave-One-Covariate-Out
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========================
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TODO: Write this section.
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TODO: Write this section.

‎docs/src/model_agnostic_methods/total_sobol_index.rst‎

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:math:`\mu_{-j}(X^{-j})` is the same predictive model as :math:`\mu(X)` but retrained
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on the reduced feature set :math:`X^{-j}`. When :math:`\mathcal{L}` is the squared loss,
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for a regression task, :math:`\mu_{-j}(X^{-j}) = \mathbb{E}[y | X^{-j}]` and when
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:math:`\mathcal{L}` is the log-loss, for a classification task, :math:`\mu_{-j}(X^{-j}) = P(y | X^{-j})`.
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:math:`\mathcal{L}` is the log-loss, for a classification task, :math:`\mu_{-j}(X^{-j}) = P(y | X^{-j})`.

‎docs/src/visualization.rst‎

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Tools for visualization
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‎examples/plot_cfi.py‎

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loss=log_loss,
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method="predict_proba",
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imputation_model_continuous=RidgeCV(),
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features_groups={
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feat_name: [i] for i, feat_name in enumerate(load_wine().feature_names)
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},
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random_state=0,
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)
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cfi.fit(
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X_train,
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y_train,
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groups={feat_name: [i] for i, feat_name in enumerate(load_wine().feature_names)},
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)
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importances = cfi.importance(X_test, y_test)
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