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+
  • Examples using piml.Experiment
    • @@ -264,7 +265,7 @@

    -data_loader(data: Optional[Union[str, DataFrame]] = None, silent: bool = False)
    +data_loader(data: Optional[Union[str, DataFrame]] = None, silent: bool = False)

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zk?-*~T9`wrvXs&{9?5Gd)p#TpQ<}CDFqN@0df)8?a3}Mt+X(~!9%b5yLYtzX()V

    Load data for experimentation.

    If no parameter is passed into the method, the program will run in low code mode, otherwise high code mode.

    @@ -295,7 +296,7 @@

    -data_prepare(target: unicode = None, sample_weight: unicode = None, test_ratio: Union[float, list] = None, random_state: int = None, task_type: unicode = None, silent: bool = None, split_method: unicode = None, train_idx=None, test_idx=None)
    +data_prepare(target: unicode = None, sample_weight: unicode = None, test_ratio: Union[float, list] = None, random_state: int = None, task_type: unicode = None, silent: bool = None, split_method: unicode = None, train_idx=None, test_idx=None)

    Prepare data for model fitting.

    This step will set the target response, task type (classification or regression), and train / test split. If no parameter is passed into the method, the program will run in low code mode, otherwise high code mode.

    @@ -334,13 +335,12 @@

    -data_quality_check(method: object = None, show: unicode = None, threshold: float = None, return_data: bool = None, figsize: tuple = None)
    +data_quality_check(method: object = None, show: unicode = None, threshold: float = None, return_data: bool = None, figsize: tuple = None)

    Check the data quality

    Parameters
    -
    methodobject, optional

    The outlier detection method to use, by default None -Four types of outlier detection method object is supported.

    +
    methodobject, optional

    Four types of outlier detection method object is supported, by default None.

    • ‘IsolationForest’: piml.data.outlier_detection.IsolationForest

      • @@ -372,7 +372,7 @@

      -data_summary(feature_type: Optional[Dict] = None, feature_exclude: Optional[List] = None, silent: Optional[bool] = None)
      +data_summary(feature_type: Optional[Dict] = None, feature_exclude: Optional[List] = None, silent: Optional[bool] = None)

      Summarize basic data statistics.

      If no parameter is passed into the method, the program will run in low code mode, otherwise high code mode.

      @@ -393,7 +393,7 @@

      -eda(show: unicode = None, uni_feature: unicode = None, bi_features: List = None, multi_type: unicode = None, return_data: bool = None, figsize: Tuple = None)
      +eda(show: unicode = None, uni_feature: unicode = None, bi_features: List = None, multi_type: unicode = None, return_data: bool = None, figsize: Tuple = None)

      Run exploratory data analysis.

      If no parameter is passed into the method, the program will run in low code mode, otherwise high code mode.

      @@ -421,7 +421,7 @@

      -feature_select(method: unicode = None, threshold: float = None, corr_algorithm: unicode = None, preset: list = None, kernel_size: int = None, n_forward_phase: int = None, return_data: bool = None, figsize: Tuple = None)
      +feature_select(method: unicode = None, threshold: float = None, corr_algorithm: unicode = None, preset: list = None, kernel_size: int = None, n_forward_phase: int = None, return_data: bool = None, figsize: Tuple = None)

      Select features that are important for modeling.

      If no parameter is passed into the method, the program will run in low code mode, otherwise high code mode.

      @@ -655,7 +655,7 @@

      -model_compare(models: List[str] = None, show: unicode = None, metric: unicode = None, immu_feature: unicode = None, perturb_features: Union[str, List[str]] = None, perturb_method: unicode = None, resilience_method: unicode = None, perturb_size: float = None, psi_buckets: unicode = None, distance_metric: unicode = None, min_samples: int = None, alpha: float = None, n_clusters: int = None, bins: int = None, slice_feature: unicode = None, slice_method: unicode = None, threshold: int = None, original_scale: bool = None, return_data: bool = None, figsize: Tuple[int, int] = None)
      +model_compare(models: List[str] = None, show: unicode = None, metric: unicode = None, immu_feature: unicode = None, perturb_features: Union[str, List[str]] = None, perturb_method: unicode = None, resilience_method: unicode = None, perturb_size: float = None, psi_buckets: unicode = None, distance_metric: unicode = None, min_samples: int = None, alpha: float = None, n_clusters: int = None, bins: int = None, slice_feature: unicode = None, slice_method: unicode = None, threshold: int = None, original_scale: bool = None, return_data: bool = None, figsize: Tuple[int, int] = None)

      Compare the diagnostic results of multiple models.

      If no parameter is passed into the method, the program will run in low code mode, otherwise high code mode.

      @@ -803,7 +803,7 @@

      -model_diagnose(model: unicode = None, show: unicode = None, metric: unicode = None, perturb_size: float = None, perturb_features: Union[str, List[str]] = None, perturb_method: unicode = None, bins: int = None, resilience_method: unicode = None, alpha: float = None, n_clusters: int = None, slice_features: Union[str, List[str]] = None, slice_method: unicode = None, threshold: float = None, min_samples: int = None, use_test: bool = None, psi_buckets: unicode = None, immu_feature: unicode = None, target_feature: unicode = None, distance_metric: unicode = None, original_scale: bool = None, return_data: bool = None, figsize: Tuple[int, int] = None)
      +model_diagnose(model: unicode = None, show: unicode = None, metric: unicode = None, perturb_size: float = None, perturb_features: Union[str, List[str]] = None, perturb_method: unicode = None, bins: int = None, resilience_method: unicode = None, alpha: float = None, n_clusters: int = None, slice_features: Union[str, List[str]] = None, slice_method: unicode = None, threshold: float = None, min_samples: int = None, use_test: bool = None, psi_buckets: unicode = None, immu_feature: unicode = None, target_feature: unicode = None, distance_metric: unicode = None, original_scale: bool = None, return_data: bool = None, figsize: Tuple[int, int] = None)

      Test model performance using various diagnostic tools.

      If no parameter is passed into the method, the program will run in low code mode, otherwise high code mode.

      @@ -985,7 +985,7 @@

      -model_explain(model: unicode = None, show: unicode = None, uni_feature: unicode = None, bi_features: List[str] = None, sample_id: int = None, sample_size: int = None, n_repeats: int = None, return_data: bool = None, pdp_size: int = None, centered: bool = None, original_scale: bool = None, figsize: Tuple[int, int] = None)
      +model_explain(model: unicode = None, show: unicode = None, uni_feature: unicode = None, bi_features: List[str] = None, sample_id: int = None, sample_size: int = None, n_repeats: int = None, return_data: bool = None, pdp_size: int = None, centered: bool = None, original_scale: bool = None, figsize: Tuple[int, int] = None)

      Explain an arbitrary fitted model using post-hoc explanation tools.

      If no parameter is passed into the method, the program will run in low code mode, otherwise high code mode.

      @@ -1049,7 +1049,7 @@

      -model_fairness(model: unicode = None, show: unicode = None, metric: unicode = None, metric_threshold: float = None, favorable_class: int = None, favorable_threshold: float = None, group_category: List = None, reference_group: List = None, protected_group: List = None, thresholding_bins: int = None, segmented_feature: unicode = None, segmented_bins: int = None, performance_metric: unicode = None, distance_metric: unicode = None, binning_dict: Dict = None, by_weights: List = None, return_data: bool = None, figsize: Tuple[int, int] = None)
      +model_fairness(model: unicode = None, show: unicode = None, metric: unicode = None, metric_threshold: float = None, favorable_class: int = None, favorable_threshold: float = None, group_category: List = None, reference_group: List = None, protected_group: List = None, thresholding_bins: int = None, segmented_feature: unicode = None, segmented_bins: int = None, performance_metric: unicode = None, distance_metric: unicode = None, binning_dict: Dict = None, by_weights: List = None, return_data: bool = None, figsize: Tuple[int, int] = None)

      Test model fairness.

      Parameters
      @@ -1122,7 +1122,7 @@

      -model_fairness_compare(models: list = None, show: unicode = None, metric: unicode = None, favorable_class: int = None, favorable_threshold: float = None, group_category: List = None, reference_group: List = None, protected_group: List = None, metric_threshold: float = None, segmented_feature: unicode = None, segmented_bins: int = None, by_weights: List = None, return_data: bool = None, figsize: Tuple[int, int] = None)
      +model_fairness_compare(models: list = None, show: unicode = None, metric: unicode = None, favorable_class: int = None, favorable_threshold: float = None, group_category: List = None, reference_group: List = None, protected_group: List = None, metric_threshold: float = None, segmented_feature: unicode = None, segmented_bins: int = None, by_weights: List = None, return_data: bool = None, figsize: Tuple[int, int] = None)

      Compare the fairness results of multiple models.

      Parameters
      @@ -1174,7 +1174,7 @@

      -model_fairness_solas(model: unicode = None, show: unicode = None, metric: unicode = None, favorable_class: int = None, favorable_threshold: float = None, group_category: List = None, reference_group: List = None, protected_group: List = None, metric_threshold: float = None, segmented_feature: unicode = None, segmented_bins: int = None, by_weights: List = None, return_data: bool = None, figsize: tuple = None)
      +model_fairness_solas(model: unicode = None, show: unicode = None, metric: unicode = None, favorable_class: int = None, favorable_threshold: float = None, group_category: List = None, reference_group: List = None, protected_group: List = None, metric_threshold: float = None, segmented_feature: unicode = None, segmented_bins: int = None, by_weights: List = None, return_data: bool = None, figsize: tuple = None)

      Test model fairness based on solas-ai.

      Parameters
      @@ -1225,7 +1225,7 @@

      -model_interpret(model: unicode = None, show: unicode = None, uni_feature: unicode = None, bi_features: List[str] = None, sample_id: int = None, tree_idx: int = None, return_data: bool = None, root: int = None, depth: int = None, centered: bool = None, original_scale: bool = None, figsize: Tuple[int, int] = None)
      +model_interpret(model: unicode = None, show: unicode = None, uni_feature: unicode = None, bi_features: List[str] = None, sample_id: int = None, tree_idx: int = None, return_data: bool = None, root: int = None, depth: int = None, centered: bool = None, original_scale: bool = None, figsize: Tuple[int, int] = None)

      Interpret inherently interpretable models.

      If no parameter is passed into the method, the program will run in low code mode, otherwise high code mode.

      @@ -1315,7 +1315,7 @@

      -model_train(model=None, name: unicode = None, train_x: ndarray = None, train_y: ndarray = None, test_x: ndarray = None, test_y: ndarray = None, train_sample_weight: ndarray = None, test_sample_weight: ndarray = None)
      +model_train(model=None, name: unicode = None, train_x: ndarray = None, train_y: ndarray = None, test_x: ndarray = None, test_y: ndarray = None, train_sample_weight: ndarray = None, test_sample_weight: ndarray = None)

      Fit interpretable models.

      If no parameter is passed into the method, the program will run in low code mode, otherwise high code mode.

      @@ -1386,7 +1386,7 @@

      -twosample_test(metric: unicode = None, psi_buckets: unicode = None, feature: Union[str, int] = None, return_data: bool = None, figsize: Tuple = None)
      +twosample_test(metric: unicode = None, psi_buckets: unicode = None, feature: Union[str, int] = None, return_data: bool = None, figsize: Tuple = None)

      Test the distributional difference of train_x and test_x.

      Parameters
      @@ -1425,7 +1425,13 @@

      -
      +
      +

      Examples using piml.Experiment

      +
      +

      EDA

      +
      EDA
      +
      +

    diff --git a/docs/_build/html/modules/generated/piml.models.GAMClassifier.html b/docs/_build/html/modules/generated/piml.models.GAMClassifier.html index 88fd597f..d9caaff8 100644 --- a/docs/_build/html/modules/generated/piml.models.GAMClassifier.html +++ b/docs/_build/html/modules/generated/piml.models.GAMClassifier.html @@ -325,7 +325,7 @@

    -global_interpret(x, truncate_dict: Optional[dict] = None)
    +global_interpret(x, truncate_dict: Optional[dict] = None)

    Global interpretation.

    Parameters
    @@ -355,7 +355,7 @@

    -interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)
    +interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)

    Interpret local feature importance

    Parameters
    @@ -421,7 +421,7 @@

    -plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))
    +plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))

    The plot function for display local feature importance result.

    Parameters
    diff --git a/docs/_build/html/modules/generated/piml.models.GAMINetClassifier.html b/docs/_build/html/modules/generated/piml.models.GAMINetClassifier.html index e07db2e0..eda180ec 100644 --- a/docs/_build/html/modules/generated/piml.models.GAMINetClassifier.html +++ b/docs/_build/html/modules/generated/piml.models.GAMINetClassifier.html @@ -773,7 +773,7 @@

    -interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)
    +interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)

    Interpret local feature importance

    Parameters
    @@ -887,7 +887,7 @@

    -plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))
    +plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))

    The plot function for display local feature importance result.

    Parameters
    diff --git a/docs/_build/html/modules/generated/piml.models.GAMINetRegressor.html b/docs/_build/html/modules/generated/piml.models.GAMINetRegressor.html index 26dbea4c..45765013 100644 --- a/docs/_build/html/modules/generated/piml.models.GAMINetRegressor.html +++ b/docs/_build/html/modules/generated/piml.models.GAMINetRegressor.html @@ -743,7 +743,7 @@

    -interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)
    +interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)

    Interpret local feature importance

    Parameters
    @@ -857,7 +857,7 @@

    -plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))
    +plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))

    The plot function for display local feature importance result.

    Parameters
    diff --git a/docs/_build/html/modules/generated/piml.models.GAMRegressor.html b/docs/_build/html/modules/generated/piml.models.GAMRegressor.html index 9f368f4e..3804b20f 100644 --- a/docs/_build/html/modules/generated/piml.models.GAMRegressor.html +++ b/docs/_build/html/modules/generated/piml.models.GAMRegressor.html @@ -316,7 +316,7 @@

    -global_interpret(x, truncate_dict: Optional[dict] = None)
    +global_interpret(x, truncate_dict: Optional[dict] = None)

    Global interpretation.

    Parameters
    @@ -346,7 +346,7 @@

    -interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)
    +interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)

    Interpret local feature importance

    Parameters
    @@ -412,7 +412,7 @@

    -plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))
    +plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))

    The plot function for display local feature importance result.

    Parameters
    diff --git a/docs/_build/html/modules/generated/piml.models.TreeClassifier.html b/docs/_build/html/modules/generated/piml.models.TreeClassifier.html index f4ea6494..53e637af 100644 --- a/docs/_build/html/modules/generated/piml.models.TreeClassifier.html +++ b/docs/_build/html/modules/generated/piml.models.TreeClassifier.html @@ -554,7 +554,7 @@

    -get_tree_diag(depth: int, root: int, tree_idx: int, truncate_dict: Optional[dict] = None)
    +get_tree_diag(depth: int, root: int, tree_idx: int, truncate_dict: Optional[dict] = None)

    Get tree diagram data.

    Parameters
    @@ -580,7 +580,7 @@

    -global_interpret(x: Optional[ndarray] = None, truncate_dict: Optional[dict] = None)
    +global_interpret(x: Optional[ndarray] = None, truncate_dict: Optional[dict] = None)

    Global interpret calculating.

    Parameters
    @@ -598,7 +598,7 @@

    -interpret_local_tree(x: array, tree_idx: Optional[int] = None, truncate_dict: Optional[dict] = None)
    +interpret_local_tree(x: array, tree_idx: Optional[int] = None, truncate_dict: Optional[dict] = None)

    Interpret local tree for specific sample.

    Parameters
    @@ -628,7 +628,7 @@

    -plot_local_tree(interpret_result: TestDataResult, pipeline=None, original_scale: bool = False, return_fig: bool = False, figsize: tuple = (20, 10))
    +plot_local_tree(interpret_result: TestDataResult, pipeline=None, original_scale: bool = False, return_fig: bool = False, figsize: tuple = (20, 10))

    The plot function for local tree diagram

    Parameters
    @@ -648,7 +648,7 @@

    -plot_tree_diag(interpret_result: TestDataResult, pipeline=None, return_fig=False, original_scale: bool = False, figsize: tuple = (20, 10))
    +plot_tree_diag(interpret_result: TestDataResult, pipeline=None, return_fig=False, original_scale: bool = False, figsize: tuple = (20, 10))

    The plot function for global tree diagram

    Parameters
    diff --git a/docs/_build/html/modules/generated/piml.models.TreeRegressor.html b/docs/_build/html/modules/generated/piml.models.TreeRegressor.html index 094216e8..f7e89da7 100644 --- a/docs/_build/html/modules/generated/piml.models.TreeRegressor.html +++ b/docs/_build/html/modules/generated/piml.models.TreeRegressor.html @@ -524,7 +524,7 @@

    -get_tree_diag(depth: int, root: int, tree_idx: int, truncate_dict: Optional[dict] = None)
    +get_tree_diag(depth: int, root: int, tree_idx: int, truncate_dict: Optional[dict] = None)

    Get tree diagram data.

    Parameters
    @@ -550,7 +550,7 @@

    -global_interpret(x: Optional[ndarray] = None, truncate_dict: Optional[dict] = None)
    +global_interpret(x: Optional[ndarray] = None, truncate_dict: Optional[dict] = None)

    Global interpret calculating.

    Parameters
    @@ -568,7 +568,7 @@

    -interpret_local_tree(x: array, tree_idx: Optional[int] = None, truncate_dict: Optional[dict] = None)
    +interpret_local_tree(x: array, tree_idx: Optional[int] = None, truncate_dict: Optional[dict] = None)

    Interpret local tree for specific sample.

    Parameters
    @@ -598,7 +598,7 @@

    -plot_local_tree(interpret_result: TestDataResult, pipeline=None, original_scale: bool = False, return_fig: bool = False, figsize: tuple = (20, 10))
    +plot_local_tree(interpret_result: TestDataResult, pipeline=None, original_scale: bool = False, return_fig: bool = False, figsize: tuple = (20, 10))

    The plot function for local tree diagram

    Parameters
    @@ -618,7 +618,7 @@

    -plot_tree_diag(interpret_result: TestDataResult, pipeline=None, return_fig=False, original_scale: bool = False, figsize: tuple = (20, 10))
    +plot_tree_diag(interpret_result: TestDataResult, pipeline=None, return_fig=False, original_scale: bool = False, figsize: tuple = (20, 10))

    The plot function for global tree diagram

    Parameters
    diff --git a/docs/_build/html/modules/generated/piml.models.XGB1Classifier.html b/docs/_build/html/modules/generated/piml.models.XGB1Classifier.html index e426e719..d24e9175 100644 --- a/docs/_build/html/modules/generated/piml.models.XGB1Classifier.html +++ b/docs/_build/html/modules/generated/piml.models.XGB1Classifier.html @@ -444,7 +444,7 @@

    -interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)
    +interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)

    Interpret local feature importance

    Parameters
    @@ -538,7 +538,7 @@

    -plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))
    +plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))

    The plot function for display local feature importance result.

    Parameters
    diff --git a/docs/_build/html/modules/generated/piml.models.XGB1Regressor.html b/docs/_build/html/modules/generated/piml.models.XGB1Regressor.html index ec27be42..0ff0bded 100644 --- a/docs/_build/html/modules/generated/piml.models.XGB1Regressor.html +++ b/docs/_build/html/modules/generated/piml.models.XGB1Regressor.html @@ -418,7 +418,7 @@

    -interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)
    +interpret_local_fi(x_: array, y_: array, centered: Optional[bool] = None)

    Interpret local feature importance

    Parameters
    @@ -512,7 +512,7 @@

    -plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))
    +plot_local_fi(interpret_result: TestDataResult, max_show: int = 10, return_fig: bool = False, figsize: tuple = (8, 6))

    The plot function for display local feature importance result.

    Parameters
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Case Studies", "8.1. BikeSharing Data", "8.2. CaliforniaHousing Data", "8.4. Fairness Simulation Study 1", "8.5. Fairness Simulation Study 2", "8.3. TaiwanCredit Data", "7. Model Comparison", "7.2. Comparison for Classification", "7.3. Fairness Comparison", "7.1. Comparison for Regression", "2. Data Pipeline", "2.4. Exploratory Analysis", "2.1. Data Load", "2.3. Data Preparation", "2.5. Data Quality", "2.2. Data Summary", "2.6. Feature Selection", "2.7. Two Sample Test", "4.1.4. ALE (Accumulated Local Effects)", "4.1.3. ICE (Individual Conditional Expectation)", "4.2.1. LIME (Local Interpretable Model-Agnostic Explanation)", "4.1.2. PDP (Partial Dependence Plot)", "4.1.1. PFI (Permutation Feature Importance)", "4.2.2. SHAP (SHapley Additive exPlanations)", "4. Post-hoc Explainability", "3. Black-box Models", "1. Introduction", "5. Interpretable Models", "5.7. Explainable Boosting Machines", "5.4. Fast Interpretable Greedy-tree Sums", "5.2. Generalized Additive Model", "5.8. GAMI-Net", "5.1. Generalized Linear Models", "5.9. ReLU Neural Network", "5.3. Decision Tree", "5.5. XGBoost Depth 1", "5.6. XGBoost Depth 2", "6. Diagnostic Suite", "6.1. Accuracy", "6.7. Fairness", "6.3. Overfit", "6.4. Reliability", "6.6. Resilience", "6.5. Robustness", "6.2. WeakSpot", "Installation", "API Reference", "piml.Experiment", "piml.data.outlier_detection.CBLOF", "piml.data.outlier_detection.IsolationForest", "piml.data.outlier_detection.KMeansTree", "piml.data.outlier_detection.PCA", "piml.models.ExplainableBoostingClassifier", "piml.models.ExplainableBoostingRegressor", "piml.models.FIGSClassifier", "piml.models.FIGSRegressor", "piml.models.GAMClassifier", "piml.models.GAMINetClassifier", "piml.models.GAMINetRegressor", "piml.models.GAMRegressor", "piml.models.GLMClassifier", "piml.models.GLMRegressor", "piml.models.ReluDNNClassifier", "piml.models.ReluDNNRegressor", "piml.models.TreeClassifier", "piml.models.TreeRegressor", "piml.models.XGB1Classifier", "piml.models.XGB1Regressor", "piml.models.XGB2Classifier", "piml.models.XGB2Regressor", "Welcome to scikit-learn", "<no title>", "User guide: contents"], "terms": {"load": [0, 9, 18, 44, 61, 68, 71, 87, 89, 93, 97, 99, 101, 102, 104, 105, 108, 118, 119, 133], "summari": [0, 9, 16, 18, 36, 37, 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{"Data Pipeline": [[0, "data-pipeline"], [18, "data-pipeline"], [71, "data-pipeline"], [107, "data-pipeline"]], "Data Load (Built-in Dataset)": [[1, "data-load-built-in-dataset"]], "Data Load (Pandas DataFrame)": [[2, "data-load-pandas-dataframe"]], "Data Summary": [[3, "data-summary"], [76, "data-summary"]], "EDA": [[4, "eda"]], "Data Preparation": [[5, "data-preparation"], [74, "data-preparation"]], "Data Quality Check": [[6, "data-quality-check"]], "Feature Selection": [[7, "feature-selection"], [77, "feature-selection"]], "Two Sample Test": [[8, "two-sample-test"], [78, "two-sample-test"]], "Computation times": [[9, "computation-times"], [17, "computation-times"], [38, "computation-times"], [53, "computation-times"], [58, "computation-times"]], "Post hoc Explainability": [[10, "post-hoc-explainability"], [18, "post-hoc-explainability"]], "Permutation Feature Importance": [[11, "permutation-feature-importance"]], "Partial Dependence Plot": [[12, "partial-dependence-plot"]], "Individual Conditional Expectation": [[13, "individual-conditional-expectation"]], "Accumulated Local Effects": [[14, "accumulated-local-effects"]], "Local Interpretable Model-Agnostic Explanation": [[15, "local-interpretable-model-agnostic-explanation"]], "SHapley Additive exPlanations": [[16, "shapley-additive-explanations"]], "Examples": [[18, "examples"], [72, "examples"], [73, "examples"], [74, "examples"], [76, "examples"], [77, "examples"], [78, "examples"], [79, "examples"], [80, "examples"], [81, "examples"], [82, "examples"], [84, "examples"], [90, "examples"], [92, "examples"], [99, "examples"], [101, "examples"], [102, "examples"], [103, "examples"], [104, "examples"], [105, "examples"], [75, "examples"], [69, "examples"], [89, "examples"], [93, "examples"], [94, "examples"], [95, "examples"], [96, "examples"], [97, "examples"], [100, "examples"], [68, "examples"], [70, "examples"]], "Interpretable Models": [[18, "interpretable-models"], [19, "interpretable-models"], [87, "interpretable-models"], [88, "interpretable-models"], [107, "interpretable-models"]], "Outcome Testing": [[18, "outcome-testing"], [39, "outcome-testing"], [107, "outcome-testing"]], "Model Comparison": [[18, "model-comparison"], [54, "model-comparison"], [67, "model-comparison"]], "GLM Logistic Regression (Taiwan Credit)": [[20, "glm-logistic-regression-taiwan-credit"]], "GLM Linear Regression (Bike Sharing)": [[21, "glm-linear-regression-bike-sharing"]], "GAM Classification (CoCircles)": [[22, "gam-classification-cocircles"]], "GAM Regression (California Housing)": [[23, "gam-regression-california-housing"]], "Tree Classification (TaiwanCredit)": [[24, "tree-classification-taiwancredit"]], "Tree Regression (California Housing)": [[25, "tree-regression-california-housing"]], "FIGS Classification (Taiwan Credit)": [[26, "figs-classification-taiwan-credit"]], "FIGS Regression (California Housing)": [[27, "figs-regression-california-housing"]], "XGB-1 Classification (CoCircles)": [[28, "xgb-1-classification-cocircles"]], "XGB-1 Regression (California Housing)": [[29, "xgb-1-regression-california-housing"]], "XGB-2 Classification (Taiwan Credit)": [[30, "xgb-2-classification-taiwan-credit"]], "XGB-2 Regression (Bike Sharing)": [[31, "xgb-2-regression-bike-sharing"]], "EBM Classification (Taiwan Credit)": [[32, "ebm-classification-taiwan-credit"]], "EBM Regression (Bike Sharing)": [[33, "ebm-regression-bike-sharing"]], "GAMI-Net Classification (Taiwan Credit)": [[34, "gami-net-classification-taiwan-credit"]], "GAMI-Net Regression (Bike Sharing)": [[35, "gami-net-regression-bike-sharing"]], "ReLU DNN Classification (Taiwan Credit)": [[36, "relu-dnn-classification-taiwan-credit"]], "ReLU DNN Regression (Friedman)": [[37, "relu-dnn-regression-friedman"]], "Accuracy: Classification": [[40, "accuracy-classification"]], "Accuracy: Regression": [[41, "accuracy-regression"]], "WeakSpot: Classification": [[42, "weakspot-classification"]], "WeakSpot: Regression": [[43, "weakspot-regression"]], "Overfit: Classification": [[44, "overfit-classification"]], "Overfit: Regression": [[45, "overfit-regression"]], "Reliability: Classification": [[46, "reliability-classification"]], "Reliability: Regression": [[47, "reliability-regression"]], "Robustness: Classification": [[48, "robustness-classification"]], "Robustness: Regression": [[49, "robustness-regression"]], "Resilience: Classification": [[50, "resilience-classification"]], "Resilience - Regression": [[51, "resilience-regression"]], "Fairness Test: XGB2": [[52, "fairness-test-xgb2"]], "Model Comparison: Classification": [[55, "model-comparison-classification"]], "Model Comparison: Regression": [[56, "model-comparison-regression"]], "Fairness Comparison": [[57, "fairness-comparison"], [69, "fairness-comparison"]], "Table Of Contents": [[59, "table-of-contents"]], "Frequently Asked Questions": [[60, "frequently-asked-questions"]], "Case Studies": [[61, "case-studies"]], "BikeSharing Data": [[62, "BikeSharing-Data"]], "Load and Prepare Data": [[62, "Load-and-Prepare-Data"], [63, "Load-and-Prepare-Data"], [64, "Load-and-Prepare-Data"], [66, "Load-and-Prepare-Data"]], "Train Intepretable Models": [[62, "Train-Intepretable-Models"], [63, "Train-Intepretable-Models"], [66, "Train-Intepretable-Models"]], "Interpretability and Explainability": [[62, "Interpretability-and-Explainability"], [63, "Interpretability-and-Explainability"], [66, "Interpretability-and-Explainability"]], "Model Diagnostics and Outcome Testing": [[62, "Model-Diagnostics-and-Outcome-Testing"], [63, "Model-Diagnostics-and-Outcome-Testing"], [66, "Model-Diagnostics-and-Outcome-Testing"]], "Model Comparison and Benchmarking": [[62, "Model-Comparison-and-Benchmarking"], [63, "Model-Comparison-and-Benchmarking"], [66, "Model-Comparison-and-Benchmarking"]], "CaliforniaHousing Data": [[63, "CaliforniaHousing-Data"]], "Fairness Simulation Study 1": [[64, "Fairness-Simulation-Study-1"]], "Train ML Model(s)": [[64, "Train-ML-Model(s)"], [65, "Train-ML-Model(s)"]], "Fairness Testing": [[64, "Fairness-Testing"], [65, "Fairness-Testing"]], "Fairness Simulation Study 2": [[65, "Fairness-Simulation-Study-2"]], "Data Description": [[65, "Data-Description"]], "Load and Prepare data": [[65, "Load-and-Prepare-data"]], "Fairness Testing Comparison": [[65, "Fairness-Testing-Comparison"]], "TaiwanCredit Data": [[66, "TaiwanCredit-Data"]], "Example": [[72, null], [74, null], [76, null], [77, null], [78, null], [83, "example"], [75, null], [69, null], [100, null], [91, "example"]], "Exploratory Analysis": [[72, "exploratory-analysis"]], "Univariate Plots": [[72, "univariate-plots"]], "Bivariate Plots": [[72, "bivariate-plots"]], "Multivariate Plots": [[72, "multivariate-plots"]], "Data Load": [[73, "data-load"]], "Built-in Dataset": [[73, "built-in-dataset"]], "External Dataset": [[73, "external-dataset"]], "Example 1: Load built-in datasets": [[73, null]], "Example 2: Load data from pandas DataFrame": [[73, null]], "Setting": [[74, "setting"]], "Train-Test Distance": [[74, "train-test-distance"]], "Summary Statistics": [[76, "summary-statistics"]], "References": [[77, null], [80, null], [83, null], [84, null], [87, null], [75, null], [93, null]], "Correlations": [[77, "correlations"]], "Distance Correlation": [[77, "distance-correlation"]], "Use of Feature Importance": [[77, "use-of-feature-importance"]], "Randomized Conditional Independence Test": [[77, "randomized-conditional-independence-test"]], "RCIT Test": [[77, "rcit-test"]], "Forward-Backward selection with Early Dropping (FBEDk):": [[77, "forward-backward-selection-with-early-dropping-fbedk"]], "Distance Metrics": [[78, "distance-metrics"]], "Usage": [[78, "usage"], [79, "usage"], [80, "usage"], [81, "usage"], [82, "usage"], [83, "usage"], [84, "usage"], [101, "usage"], [103, "usage"], [104, "usage"], [105, "usage"]], "Distance Metric Plot": [[78, "distance-metric-plot"]], "Marginal Density Comparison": [[78, "marginal-density-comparison"], [103, "marginal-density-comparison"]], "ALE (Accumulated Local Effects)": [[79, "ale-accumulated-local-effects"]], "Algorithm Details": [[79, "algorithm-details"], [80, "algorithm-details"], [81, "algorithm-details"], [82, "algorithm-details"], [83, "algorithm-details"], [84, "algorithm-details"], [101, "algorithm-details"], [103, "algorithm-details"], [104, "algorithm-details"], [105, "algorithm-details"]], "One-way ALE": [[79, "one-way-ale"]], "Two-way ALE": [[79, "two-way-ale"]], "Example 1: Bike Sharing": [[79, null], [80, null], [81, null], [82, null], [83, null], [84, null], [93, null]], "ICE (Individual Conditional Expectation)": [[80, "ice-individual-conditional-expectation"]], "LIME (Local Interpretable Model-Agnostic Explanation)": [[81, "lime-local-interpretable-model-agnostic-explanation"]], "PDP (Partial Dependence Plot)": [[82, "pdp-partial-dependence-plot"]], "One-way PDPs": [[82, "one-way-pdps"]], "Two-way PDPs": [[82, "two-way-pdps"]], "PFI (Permutation Feature Importance)": [[83, "pfi-permutation-feature-importance"]], "SHAP (SHapley Additive exPlanations)": [[84, "shap-shapley-additive-explanations"]], "Exact Solution": [[84, "exact-solution"]], "KernelSHAP": [[84, "kernelshap"]], "Algorithms for specific models": [[84, "algorithms-for-specific-models"]], "The Waterfall plot": [[84, "the-waterfall-plot"]], "SHAP Feature importance": [[84, "shap-feature-importance"]], "SHAP Summary plot": [[84, "shap-summary-plot"]], "SHAP Dependence Plot": [[84, "shap-dependence-plot"]], "Post-hoc Explainability": [[85, "post-hoc-explainability"], [107, "post-hoc-explainability"]], "Global Explainability": [[85, "global-explainability"]], "Local Explainability": [[85, "local-explainability"]], "Black-box Models": [[86, "black-box-models"]], "Train and Register Models": [[86, "train-and-register-models"]], "Save Fitted Models": [[86, "save-fitted-models"]], "Load and Register Fitted Models": [[86, "load-and-register-fitted-models"]], "Register Arbitrary Models and Data": [[86, "register-arbitrary-models-and-data"]], "Introduction": [[87, "introduction"], [87, "id1"]], "Toolbox Design": [[87, "toolbox-design"]], "Diagnostic Suite": [[87, "diagnostic-suite"], [98, "diagnostic-suite"]], "Future Plan": [[87, "future-plan"]], "Model Training": [[90, "model-training"], [92, "model-training"], [89, "model-training"], [93, "model-training"], [94, "model-training"], [95, "model-training"], [96, "model-training"], [97, "model-training"], [91, "model-training"]], "Global Interpretation": [[90, "global-interpretation"], [92, "global-interpretation"], [89, "global-interpretation"], [93, "global-interpretation"], [94, "global-interpretation"], [95, "global-interpretation"], [96, "global-interpretation"], [97, "global-interpretation"], [91, "global-interpretation"]], "Local Interpretation": [[90, "local-interpretation"], [92, "local-interpretation"], [89, "local-interpretation"], [93, "local-interpretation"], [94, "local-interpretation"], [95, "local-interpretation"], [96, "local-interpretation"], [97, "local-interpretation"], [91, "local-interpretation"]], "Examples 2: Taiwan Credit": [[90, null], [92, null], [99, null], [101, null], [102, null], [103, null], [104, null], [105, null], [89, null], [94, null], [95, null], [97, null]], "Fast Interpretable Greedy-tree Sums": [[90, "fast-interpretable-greedy-tree-sums"]], "Feature Importance Heatmap": [[90, "feature-importance-heatmap"]], "Tree Diagram": [[90, "tree-diagram"]], "Example 1: CaliforniaHousing": [[90, null], [103, null], [95, null]], "Example 1: BikeSharing": [[92, null], [99, null], [101, null], [102, null], [104, null], [105, null], [89, null], [96, null], [97, null], [70, null]], "Main Effect Plot": [[92, "main-effect-plot"], [89, "main-effect-plot"], [96, "main-effect-plot"], [97, "main-effect-plot"], [91, "main-effect-plot"]], "Interaction Plot": [[92, "interaction-plot"], [89, "interaction-plot"], 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"data-pipeline"], [18, "data-pipeline"], [71, "data-pipeline"], [107, "data-pipeline"]], "Data Load (Built-in Dataset)": [[1, "data-load-built-in-dataset"]], "Data Load (Pandas DataFrame)": [[2, "data-load-pandas-dataframe"]], "Data Summary": [[3, "data-summary"], [76, "data-summary"]], "EDA": [[4, "eda"]], "Data Preparation": [[5, "data-preparation"], [74, "data-preparation"]], "Data Quality Check": [[6, "data-quality-check"]], "Feature Selection": [[7, "feature-selection"], [77, "feature-selection"]], "Two Sample Test": [[8, "two-sample-test"], [78, "two-sample-test"]], "Computation times": [[9, "computation-times"], [17, "computation-times"], [38, "computation-times"], [53, "computation-times"], [58, "computation-times"]], "Post hoc Explainability": [[10, "post-hoc-explainability"], [18, "post-hoc-explainability"]], "Permutation Feature Importance": [[11, "permutation-feature-importance"]], "Partial Dependence Plot": [[12, "partial-dependence-plot"]], "Individual Conditional Expectation": [[13, "individual-conditional-expectation"]], "Accumulated Local Effects": [[14, "accumulated-local-effects"]], "Local Interpretable Model-Agnostic Explanation": [[15, "local-interpretable-model-agnostic-explanation"]], "SHapley Additive exPlanations": [[16, "shapley-additive-explanations"]], "Examples": [[18, "examples"], [68, "examples"], [69, "examples"], [70, "examples"], [72, "examples"], [73, "examples"], [74, "examples"], [75, "examples"], [76, "examples"], [77, "examples"], [78, "examples"], [79, "examples"], [80, "examples"], [81, "examples"], [82, "examples"], [84, "examples"], [89, "examples"], [90, "examples"], [92, "examples"], [93, "examples"], [94, "examples"], [95, "examples"], [96, "examples"], [97, "examples"], [99, "examples"], [100, "examples"], [101, "examples"], [102, "examples"], [103, "examples"], [104, "examples"], [105, "examples"]], "Interpretable Models": [[18, "interpretable-models"], [19, "interpretable-models"], [87, "interpretable-models"], [88, "interpretable-models"], [107, "interpretable-models"]], "Outcome Testing": [[18, "outcome-testing"], [39, "outcome-testing"], [107, "outcome-testing"]], "Model Comparison": [[18, "model-comparison"], [54, "model-comparison"], [67, "model-comparison"]], "GLM Logistic Regression (Taiwan Credit)": [[20, "glm-logistic-regression-taiwan-credit"]], "GLM Linear Regression (Bike Sharing)": [[21, "glm-linear-regression-bike-sharing"]], "GAM Classification (CoCircles)": [[22, "gam-classification-cocircles"]], "GAM Regression (California Housing)": [[23, "gam-regression-california-housing"]], "Tree Classification (TaiwanCredit)": [[24, "tree-classification-taiwancredit"]], "Tree Regression (California Housing)": [[25, "tree-regression-california-housing"]], "FIGS Classification (Taiwan Credit)": [[26, "figs-classification-taiwan-credit"]], "FIGS Regression (California Housing)": [[27, "figs-regression-california-housing"]], "XGB-1 Classification (CoCircles)": [[28, "xgb-1-classification-cocircles"]], "XGB-1 Regression (California Housing)": [[29, "xgb-1-regression-california-housing"]], "XGB-2 Classification (Taiwan Credit)": [[30, "xgb-2-classification-taiwan-credit"]], "XGB-2 Regression (Bike Sharing)": [[31, "xgb-2-regression-bike-sharing"]], "EBM Classification (Taiwan Credit)": [[32, "ebm-classification-taiwan-credit"]], "EBM Regression (Bike Sharing)": [[33, "ebm-regression-bike-sharing"]], "GAMI-Net Classification (Taiwan Credit)": [[34, "gami-net-classification-taiwan-credit"]], "GAMI-Net Regression (Bike Sharing)": [[35, "gami-net-regression-bike-sharing"]], "ReLU DNN Classification (Taiwan Credit)": [[36, "relu-dnn-classification-taiwan-credit"]], "ReLU DNN Regression (Friedman)": [[37, "relu-dnn-regression-friedman"]], "Accuracy: Classification": [[40, "accuracy-classification"]], "Accuracy: Regression": [[41, "accuracy-regression"]], "WeakSpot: Classification": [[42, "weakspot-classification"]], "WeakSpot: Regression": [[43, "weakspot-regression"]], "Overfit: Classification": [[44, "overfit-classification"]], "Overfit: Regression": [[45, "overfit-regression"]], "Reliability: Classification": [[46, "reliability-classification"]], "Reliability: Regression": [[47, "reliability-regression"]], "Robustness: Classification": [[48, "robustness-classification"]], "Robustness: Regression": [[49, "robustness-regression"]], "Resilience: Classification": [[50, "resilience-classification"]], "Resilience - Regression": [[51, "resilience-regression"]], "Fairness Test: XGB2": [[52, "fairness-test-xgb2"]], "Model Comparison: Classification": [[55, "model-comparison-classification"]], "Model Comparison: Regression": [[56, "model-comparison-regression"]], "Fairness Comparison": [[57, "fairness-comparison"], [69, "fairness-comparison"]], "Table Of Contents": [[59, "table-of-contents"]], "Frequently Asked Questions": [[60, "frequently-asked-questions"]], "Case Studies": [[61, "case-studies"]], "BikeSharing Data": [[62, "BikeSharing-Data"]], "Load and Prepare Data": [[62, "Load-and-Prepare-Data"], [63, "Load-and-Prepare-Data"], [64, "Load-and-Prepare-Data"], [66, "Load-and-Prepare-Data"]], "Train Intepretable Models": [[62, "Train-Intepretable-Models"], [63, "Train-Intepretable-Models"], [66, "Train-Intepretable-Models"]], "Interpretability and Explainability": [[62, "Interpretability-and-Explainability"], [63, "Interpretability-and-Explainability"], [66, "Interpretability-and-Explainability"]], "Model Diagnostics and Outcome Testing": [[62, "Model-Diagnostics-and-Outcome-Testing"], [63, "Model-Diagnostics-and-Outcome-Testing"], [66, "Model-Diagnostics-and-Outcome-Testing"]], "Model Comparison and Benchmarking": [[62, "Model-Comparison-and-Benchmarking"], [63, "Model-Comparison-and-Benchmarking"], [66, "Model-Comparison-and-Benchmarking"]], "CaliforniaHousing Data": [[63, "CaliforniaHousing-Data"]], "Fairness Simulation Study 1": [[64, "Fairness-Simulation-Study-1"]], "Train ML Model(s)": [[64, "Train-ML-Model(s)"], [65, "Train-ML-Model(s)"]], "Fairness Testing": [[64, "Fairness-Testing"], [65, "Fairness-Testing"]], "Fairness Simulation Study 2": [[65, "Fairness-Simulation-Study-2"]], "Data Description": [[65, "Data-Description"]], "Load and Prepare data": [[65, "Load-and-Prepare-data"]], "Fairness Testing Comparison": [[65, "Fairness-Testing-Comparison"]], "TaiwanCredit Data": [[66, "TaiwanCredit-Data"]], "Comparison for Classification": [[68, "comparison-for-classification"]], "Accuracy Comparison": [[68, "accuracy-comparison"], [70, "accuracy-comparison"]], "Accuracy Score": [[68, "accuracy-score"]], "AUC Score": [[68, "auc-score"]], "F1 Score": [[68, "f1-score"]], "Overfit Comparison": [[68, "overfit-comparison"], [70, "overfit-comparison"]], "Reliability Comparison": [[68, "reliability-comparison"], [70, "reliability-comparison"]], "Bandwidth Comparison": [[68, "bandwidth-comparison"], [70, "bandwidth-comparison"]], "Reliability Diagram Comparison": [[68, "reliability-diagram-comparison"]], "Robustness Comparison": [[68, "robustness-comparison"], [70, "robustness-comparison"]], "Robustness Performance": [[68, "robustness-performance"], [70, "robustness-performance"]], "Robustness Performance on Worst Samples": [[68, "robustness-performance-on-worst-samples"], [70, "robustness-performance-on-worst-samples"]], "Resilience Comparison": [[68, "resilience-comparison"], [70, "resilience-comparison"]], "Resilience Performance": [[68, "resilience-performance"], [70, "resilience-performance"], [103, "resilience-performance"]], "Resilience Performance on Worst Samples": [[68, "resilience-performance-on-worst-samples"], [70, "resilience-performance-on-worst-samples"]], "Examples 1: Taiwan Credit": [[68, null]], "Fairness Metrics": [[69, "fairness-metrics"], [100, "fairness-metrics"]], "Segmented": [[69, "segmented"]], "Example": [[69, null], [72, null], [74, null], [75, null], [76, null], [77, null], [78, null], [83, "example"], [91, "example"], [100, null]], "Comparison for Regression": [[70, "comparison-for-regression"]], "Mean Squared Error": [[70, "mean-squared-error"]], "Mean Absolute Error": [[70, "mean-absolute-error"]], "R-squared Score": [[70, "r-squared-score"]], "Coverage Comparison": [[70, "coverage-comparison"]], "Example 1: BikeSharing": [[70, null], [89, null], [92, null], [96, null], [97, null], [99, null], [101, null], [102, null], [104, null], [105, null]], "Exploratory Analysis": [[72, "exploratory-analysis"]], "Univariate Plots": [[72, "univariate-plots"]], "Bivariate Plots": [[72, "bivariate-plots"]], "Multivariate Plots": [[72, "multivariate-plots"]], "Data Load": [[73, "data-load"]], "Built-in Dataset": [[73, "built-in-dataset"]], "External Dataset": [[73, "external-dataset"]], "Example 1: Load built-in datasets": [[73, null]], "Example 2: Load data from pandas DataFrame": [[73, null]], "Basic Settings": [[74, "basic-settings"]], "Train-test Splits": [[74, "train-test-splits"]], "Random Split": [[74, 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(Accumulated Local Effects)": [[79, "ale-accumulated-local-effects"]], "Algorithm Details": [[79, "algorithm-details"], [80, "algorithm-details"], [81, "algorithm-details"], [82, "algorithm-details"], [83, "algorithm-details"], [84, "algorithm-details"], [101, "algorithm-details"], [103, "algorithm-details"], [104, "algorithm-details"], [105, "algorithm-details"]], "One-way ALE": [[79, "one-way-ale"]], "Two-way ALE": [[79, "two-way-ale"]], "Example 1: Bike Sharing": [[79, null], [80, null], [81, null], [82, null], [83, null], [84, null], [93, null]], "ICE (Individual Conditional Expectation)": [[80, "ice-individual-conditional-expectation"]], "LIME (Local Interpretable Model-Agnostic Explanation)": [[81, "lime-local-interpretable-model-agnostic-explanation"]], "PDP (Partial Dependence Plot)": [[82, "pdp-partial-dependence-plot"]], "One-way PDPs": [[82, "one-way-pdps"]], "Two-way PDPs": [[82, "two-way-pdps"]], "PFI (Permutation Feature Importance)": [[83, 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"main-effect-plot"]], "Interaction Plot": [[89, "interaction-plot"], [92, "interaction-plot"], [97, "interaction-plot"]], "Effect Importance": [[89, "effect-importance"], [92, "effect-importance"], [97, "effect-importance"]], "Feature Importance": [[89, "feature-importance"], [91, "feature-importance"], [92, "feature-importance"], [93, "feature-importance"], [96, "feature-importance"], [97, "feature-importance"]], "Local Interpretation": [[89, "local-interpretation"], [90, "local-interpretation"], [91, "local-interpretation"], [92, "local-interpretation"], [93, "local-interpretation"], [94, "local-interpretation"], [95, "local-interpretation"], [96, "local-interpretation"], [97, "local-interpretation"]], "Local Effect Contribution": [[89, "local-effect-contribution"], [92, "local-effect-contribution"], [97, "local-effect-contribution"]], "Local Feature Contribution": [[89, "local-feature-contribution"], [92, "local-feature-contribution"], [97, "local-feature-contribution"]], "Examples 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"id": "62eefff2", "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,7 @@ { "cell_type": "code", "execution_count": null, - "id": "10650873", + "id": "34561e39", "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3ef2de2b", + "id": "b44e6d14", "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2971dff4", + "id": "2ab9d9b2", "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4bbe51bc", + "id": "5d6c11cf", "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ { "cell_type": "code", "execution_count": null, - "id": "41e11d73", + "id": "8badc322", "metadata": {}, "outputs": [], "source": [ @@ -78,7 +78,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ce937ada", + "id": "37a24661", "metadata": {}, "outputs": [], "source": [ @@ -89,7 +89,7 @@ { "cell_type": "code", "execution_count": null, - "id": "81d263d9", + "id": "796ff18d", "metadata": {}, "outputs": [], "source": [ @@ -100,7 +100,7 @@ { "cell_type": "code", "execution_count": null, - "id": "97cf68e1", + "id": "cd34995e", "metadata": {}, "outputs": [], "source": [ @@ -111,7 +111,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8a7308da", + "id": "3caea22d", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/comparison/compare_fairness.ipynb b/docs/_build/jupyter_execute/guides/comparison/compare_fairness.ipynb index 2bfc636a..5ff09ca7 100644 --- a/docs/_build/jupyter_execute/guides/comparison/compare_fairness.ipynb +++ b/docs/_build/jupyter_execute/guides/comparison/compare_fairness.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d6fcfa29", + "id": "a9c7e826", "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7df002f3", + "id": "54463a28", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/comparison/compare_regression.ipynb b/docs/_build/jupyter_execute/guides/comparison/compare_regression.ipynb index 56f189e9..f411f44a 100644 --- a/docs/_build/jupyter_execute/guides/comparison/compare_regression.ipynb +++ b/docs/_build/jupyter_execute/guides/comparison/compare_regression.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2628d04e", + "id": "54345f4d", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "379a3163", + "id": "93368e59", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "aa0eb18e", + "id": "a968a108", "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e98b6f04", + "id": "773975c1", "metadata": {}, "outputs": [], "source": [ @@ -48,7 +48,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e0d115f4", + "id": "c7595ddc", "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a00672a4", + "id": "19413830", "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ { "cell_type": "code", "execution_count": null, - "id": "47db233b", + "id": "bd0a457b", "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4dc17fa0", + "id": "39be8dbf", "metadata": {}, "outputs": [], "source": [ @@ -93,7 +93,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e27f8bed", + "id": "fc20243c", "metadata": {}, "outputs": [], "source": [ @@ -104,7 +104,7 @@ { "cell_type": "code", "execution_count": null, - "id": "fc6aef06", + "id": "bf8dec5c", "metadata": {}, "outputs": [], "source": [ @@ -115,7 +115,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1f680c85", + "id": "73fe966c", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/data/data_eda.ipynb b/docs/_build/jupyter_execute/guides/data/data_eda.ipynb index 3a8bafbf..4bb612fd 100644 --- a/docs/_build/jupyter_execute/guides/data/data_eda.ipynb +++ b/docs/_build/jupyter_execute/guides/data/data_eda.ipynb @@ -3,71 +3,71 @@ { "cell_type": "code", "execution_count": null, - "id": "c1b7bde0", + "id": "d869d96f", "metadata": {}, "outputs": [], "source": [ - "exp.eda(show='univariate', uni_feature='cnt')" + "exp.eda(show='univariate', uni_feature='cnt', figsize=(5, 4))" ] }, { "cell_type": "code", "execution_count": null, - "id": "6683b1d1", + "id": "041da081", "metadata": {}, "outputs": [], "source": [ - "exp.eda(show='univariate', uni_feature='yr')" + "exp.eda(show='univariate', uni_feature='yr', figsize=(5, 4))" ] }, { "cell_type": "code", "execution_count": null, - "id": "bea6c4e7", + "id": "3308f0c8", "metadata": {}, "outputs": [], "source": [ - "exp.eda(show='bivariate', bi_features=['temp', 'cnt'])" + "exp.eda(show='bivariate', bi_features=['temp', 'cnt'], figsize=(5, 4))" ] }, { "cell_type": "code", "execution_count": null, - "id": "7022e036", + "id": "bf732c88", "metadata": {}, "outputs": [], "source": [ - "exp.eda(show='bivariate', bi_features=['hr', 'season'])" + "exp.eda(show='bivariate', bi_features=['hr', 'season'], figsize=(5, 4))" ] }, { "cell_type": "code", "execution_count": null, - "id": "e67ccd32", + "id": "20443336", "metadata": {}, "outputs": [], "source": [ - "exp.eda(show='bivariate', bi_features=['yr', 'season'])" + "exp.eda(show='bivariate', bi_features=['yr', 'season'], figsize=(5, 4))" ] }, { "cell_type": "code", "execution_count": null, - "id": "84658da4", + "id": "793ca3a8", "metadata": {}, "outputs": [], "source": [ - "exp.eda(show='multivariate', multi_type='correlation_heatmap')" + "exp.eda(show='multivariate', multi_type='correlation_heatmap', figsize=(6, 5))" ] }, { "cell_type": "code", "execution_count": null, - "id": "2124fe2c", + "id": "883ba029", "metadata": {}, "outputs": [], "source": [ - "exp.eda(show='multivariate', multi_type='correlation_graph')" + "exp.eda(show='multivariate', multi_type='correlation_graph', figsize=(6, 5))" ] } ], diff --git a/docs/_build/jupyter_execute/guides/data/data_eda.py b/docs/_build/jupyter_execute/guides/data/data_eda.py index f9f4152a..468e6b77 100644 --- a/docs/_build/jupyter_execute/guides/data/data_eda.py +++ b/docs/_build/jupyter_execute/guides/data/data_eda.py @@ -4,41 +4,41 @@ # In[ ]: -exp.eda(show='univariate', uni_feature='cnt') +exp.eda(show='univariate', uni_feature='cnt', figsize=(5, 4)) # In[ ]: -exp.eda(show='univariate', uni_feature='yr') +exp.eda(show='univariate', uni_feature='yr', figsize=(5, 4)) # In[ ]: -exp.eda(show='bivariate', bi_features=['temp', 'cnt']) +exp.eda(show='bivariate', bi_features=['temp', 'cnt'], figsize=(5, 4)) # In[ ]: -exp.eda(show='bivariate', bi_features=['hr', 'season']) +exp.eda(show='bivariate', bi_features=['hr', 'season'], figsize=(5, 4)) # In[ ]: -exp.eda(show='bivariate', bi_features=['yr', 'season']) +exp.eda(show='bivariate', bi_features=['yr', 'season'], figsize=(5, 4)) # In[ ]: -exp.eda(show='multivariate', multi_type='correlation_heatmap') +exp.eda(show='multivariate', multi_type='correlation_heatmap', figsize=(6, 5)) # In[ ]: -exp.eda(show='multivariate', multi_type='correlation_graph') +exp.eda(show='multivariate', multi_type='correlation_graph', figsize=(6, 5)) diff --git a/docs/_build/jupyter_execute/guides/data/data_load.ipynb b/docs/_build/jupyter_execute/guides/data/data_load.ipynb index 8df62b45..fc374c17 100644 --- a/docs/_build/jupyter_execute/guides/data/data_load.ipynb +++ b/docs/_build/jupyter_execute/guides/data/data_load.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0c704fde", + "id": "6c9af2d3", "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ { "cell_type": "code", "execution_count": null, - "id": "43e5fa5d", + "id": 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"IPY_MODEL_ef737ab42f0247409ebdea97d37658cc", + "placeholder": "​", + "style": "IPY_MODEL_f62e032b6a8847c1b614ff82c845f987", + "tabbable": null, + "tooltip": null, + "value": "\n \n " + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/docs/_build/jupyter_execute/guides/data/data_prepare.py b/docs/_build/jupyter_execute/guides/data/data_prepare.py index e57c8ade..158c4b45 100644 --- a/docs/_build/jupyter_execute/guides/data/data_prepare.py +++ b/docs/_build/jupyter_execute/guides/data/data_prepare.py @@ -1,17 +1,48 @@ #!/usr/bin/env python # coding: utf-8 -# In[ ]: +# In[1]: -custom_train_idx = np.arange(0,16000) -custom_test_idx = np.arange(16000, 17379) -exp.data_prepare(train_idx=custom_train_idx, test_idx=custom_test_idx) +import numpy as np +from piml import Experiment + +exp = Experiment() +exp.data_loader(data="BikeSharing", silent=True) + + +# In[2]: + + +exp.data_prepare(target='cnt', task_type='regression', sample_weight=None) + + +# In[3]: + + +exp.data_prepare(target='cnt', task_type='regression', sample_weight=None, + split_method='random', test_ratio=0.2, random_state=0) -# In[ ]: +# In[4]: -exp.data_prepare(target='cnt', task_type='Regression', sample_weight=None, - split_method='random', test_ratio=0.2, random_state=0) +exp.data_prepare(target='cnt', task_type='regression', sample_weight=None, + split_method='outer-sample', test_ratio=0.2, random_state=0) + + +# In[5]: + + +exp.data_prepare(target='cnt', task_type='regression', sample_weight=None, + split_method='kmeans', test_ratio=[0.0, 1.0, 0.0], random_state=0) + + +# In[6]: + + +custom_train_idx = np.arange(0, 16000) +custom_test_idx = np.arange(16000, 17379) +exp.data_prepare(target='cnt', task_type='regression', sample_weight=None, + train_idx=custom_train_idx, test_idx=custom_test_idx) diff --git a/docs/_build/jupyter_execute/guides/data/data_quality.ipynb b/docs/_build/jupyter_execute/guides/data/data_quality.ipynb index 3c057fd9..33a56a28 100644 --- a/docs/_build/jupyter_execute/guides/data/data_quality.ipynb +++ b/docs/_build/jupyter_execute/guides/data/data_quality.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9e0f0a52", + "id": "67c3efef", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "42f890f4", + "id": "b41339c4", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "81c736a5", + "id": "d453db09", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/data/data_summary.ipynb b/docs/_build/jupyter_execute/guides/data/data_summary.ipynb index 71462e15..9b420f08 100644 --- a/docs/_build/jupyter_execute/guides/data/data_summary.ipynb +++ b/docs/_build/jupyter_execute/guides/data/data_summary.ipynb @@ -2,12 +2,202 @@ "cells": [ { "cell_type": "code", - "execution_count": null, - "id": "d100f2f8", - "metadata": {}, + "execution_count": 1, + "id": "275516b7", + "metadata": { + "execution": { + "iopub.execute_input": "2023-05-18T09:45:48.998665Z", + "iopub.status.busy": "2023-05-18T09:45:48.998665Z", + "iopub.status.idle": "2023-05-18T09:45:52.118293Z", + "shell.execute_reply": "2023-05-18T09:45:52.118293Z" + } + }, "outputs": [], "source": [ - "exp.data_summary(feature_type={},feature_exclude=[])" + "from piml import Experiment\n", + "\n", + "exp = Experiment()\n", + "exp.data_loader(data=\"BikeSharing\", silent=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e1679b38", + "metadata": { + "execution": { + "iopub.execute_input": "2023-05-18T09:45:52.118293Z", + "iopub.status.busy": "2023-05-18T09:45:52.118293Z", + "iopub.status.idle": "2023-05-18T09:45:52.228366Z", + "shell.execute_reply": "2023-05-18T09:45:52.228366Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "464d3ead19114351a1f039e027837f96", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='\\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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    1hr0.011.5467526.9144050.06.000012.000018.000023.0000
    2weekday0.03.0036832.0057710.01.00003.00005.00006.0000
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"padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/docs/_build/jupyter_execute/guides/data/data_summary.py b/docs/_build/jupyter_execute/guides/data/data_summary.py index b9b530eb..2c0a144e 100644 --- a/docs/_build/jupyter_execute/guides/data/data_summary.py +++ b/docs/_build/jupyter_execute/guides/data/data_summary.py @@ -1,8 +1,29 @@ #!/usr/bin/env python # coding: utf-8 -# In[ ]: +# In[1]: -exp.data_summary(feature_type={},feature_exclude=[]) +from piml import Experiment + +exp = Experiment() +exp.data_loader(data="BikeSharing", silent=True) + + +# In[2]: + + +exp.data_summary(feature_exclude=[], feature_type={}) + + +# In[3]: + + +exp.data_summary(feature_exclude=["yr", "mnth", "temp"]) + + +# In[4]: + + +exp.data_summary(feature_type={"weekday": "categorical"}) diff --git a/docs/_build/jupyter_execute/guides/data/feature_select.ipynb b/docs/_build/jupyter_execute/guides/data/feature_select.ipynb index d32f9d20..1f04012d 100644 --- a/docs/_build/jupyter_execute/guides/data/feature_select.ipynb +++ b/docs/_build/jupyter_execute/guides/data/feature_select.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d1c18b83", + "id": "094ad7e2", "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7b04f600", + "id": "8355c7e7", "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b206adfa", + "id": "032f3ac5", "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ { "cell_type": "code", "execution_count": null, - "id": "20936108", + "id": "f4423d09", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ea6f3653", + "id": "efbed167", "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c4a04262", + "id": "915d7ae1", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/data/twosample_test.ipynb b/docs/_build/jupyter_execute/guides/data/twosample_test.ipynb index d0e99643..d77bb56e 100644 --- a/docs/_build/jupyter_execute/guides/data/twosample_test.ipynb +++ b/docs/_build/jupyter_execute/guides/data/twosample_test.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2e496b05", + "id": "abc4e42a", "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ { "cell_type": "code", "execution_count": null, - "id": "17470bf0", + "id": "6c8e05b0", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/explain/PDP.ipynb b/docs/_build/jupyter_execute/guides/explain/PDP.ipynb index 087137eb..cc0925f9 100644 --- a/docs/_build/jupyter_execute/guides/explain/PDP.ipynb +++ b/docs/_build/jupyter_execute/guides/explain/PDP.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d21d9904", + "id": "c075f714", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "263f8d84", + "id": "7ad6a18f", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "88dcc5d7", + "id": "c75cbdb3", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/explain/ale.ipynb b/docs/_build/jupyter_execute/guides/explain/ale.ipynb index 050cf30f..d58451e2 100644 --- a/docs/_build/jupyter_execute/guides/explain/ale.ipynb +++ b/docs/_build/jupyter_execute/guides/explain/ale.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "03cad665", + "id": "36c2dbb2", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3e7a3e11", + "id": "2751327a", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5e96eaee", + "id": "98fa7bab", "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ad2d0054", + "id": "2d543603", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/explain/ice.ipynb b/docs/_build/jupyter_execute/guides/explain/ice.ipynb index 07ab667f..0c9e820a 100644 --- a/docs/_build/jupyter_execute/guides/explain/ice.ipynb +++ b/docs/_build/jupyter_execute/guides/explain/ice.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c6f10263", + "id": "1ff85b1c", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/explain/lime.ipynb b/docs/_build/jupyter_execute/guides/explain/lime.ipynb index 5c03db68..ade55c98 100644 --- a/docs/_build/jupyter_execute/guides/explain/lime.ipynb +++ b/docs/_build/jupyter_execute/guides/explain/lime.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d66a11ba", + "id": "ce4b88ff", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "17b5d7c4", + "id": "d7c24686", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/explain/pfi.ipynb b/docs/_build/jupyter_execute/guides/explain/pfi.ipynb index 986d0a17..f2d3f76c 100644 --- a/docs/_build/jupyter_execute/guides/explain/pfi.ipynb +++ b/docs/_build/jupyter_execute/guides/explain/pfi.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3c2aac0f", + "id": "a71af280", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/explain/shap.ipynb b/docs/_build/jupyter_execute/guides/explain/shap.ipynb index 318e8f2a..e5c61b12 100644 --- a/docs/_build/jupyter_execute/guides/explain/shap.ipynb +++ b/docs/_build/jupyter_execute/guides/explain/shap.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "876898a9", + "id": "f9325f57", "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c75dddac", + "id": "5944247c", "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b53936cb", + "id": "4a796c5f", "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ { "cell_type": "code", "execution_count": null, - "id": "af264d9b", + "id": "693a8d06", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/extmodels.ipynb b/docs/_build/jupyter_execute/guides/extmodels.ipynb index 3bec9ed5..45be9216 100644 --- a/docs/_build/jupyter_execute/guides/extmodels.ipynb +++ b/docs/_build/jupyter_execute/guides/extmodels.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0dea14bf", + "id": "680aa0c6", "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8d5946f7", + "id": "4c5d209a", "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ { "cell_type": "code", "execution_count": null, - "id": "df4b636d", + "id": "99ea627c", "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4fa6cc66", + "id": "a5355f65", "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4e2f811a", + "id": "d94084ae", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/models/ebm.ipynb b/docs/_build/jupyter_execute/guides/models/ebm.ipynb index 179529ae..ef2fe3e6 100644 --- a/docs/_build/jupyter_execute/guides/models/ebm.ipynb +++ b/docs/_build/jupyter_execute/guides/models/ebm.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "afc55ff1", + "id": "58af4e08", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "980774eb", + "id": "387a164e", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "63f37861", + "id": "4c2a77a0", "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ { "cell_type": "code", "execution_count": null, - "id": "de2e4105", + "id": "645aaaa1", "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d47d1f8f", + "id": "fc816d56", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f156958c", + "id": "ab3ff216", "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6f4a778b", + "id": "3c0f4ef8", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/models/figs.ipynb b/docs/_build/jupyter_execute/guides/models/figs.ipynb index 32a2e797..675c27ce 100644 --- a/docs/_build/jupyter_execute/guides/models/figs.ipynb +++ b/docs/_build/jupyter_execute/guides/models/figs.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "26613e7f", + "id": "024b6fe3", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c64b3245", + "id": "f4877c55", "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0c788405", + "id": "bcf81534", "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3309b202", + "id": "a5aeb1ee", "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b7aba38a", + "id": "1ea8b7a0", "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ { "cell_type": "code", "execution_count": null, - "id": "66270fe5", + "id": "f4bac4f3", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/models/gam.ipynb b/docs/_build/jupyter_execute/guides/models/gam.ipynb index 5b16a06f..ec9ca553 100644 --- a/docs/_build/jupyter_execute/guides/models/gam.ipynb +++ b/docs/_build/jupyter_execute/guides/models/gam.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3a8c41aa", + "id": "d0c2a977", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c2a2a12e", + "id": "9c650178", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "567ac96c", + "id": "762dfab6", "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cee5d216", + "id": "d2382634", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/models/gaminet.ipynb b/docs/_build/jupyter_execute/guides/models/gaminet.ipynb index bacd9296..34ca39c8 100644 --- a/docs/_build/jupyter_execute/guides/models/gaminet.ipynb +++ b/docs/_build/jupyter_execute/guides/models/gaminet.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "519c96ae", + "id": "c9e6e9aa", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8cc3c989", + "id": "d285c4fb", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "deef1646", + "id": "353ee503", "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dfd809a6", + "id": "bc33720f", "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ { "cell_type": "code", "execution_count": null, - "id": "83131155", + "id": "12a9169a", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1c664fc7", + "id": "1b82eecf", "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c564236d", + "id": "2b314b05", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/models/glm.ipynb b/docs/_build/jupyter_execute/guides/models/glm.ipynb index cc3a68d7..97518fce 100644 --- a/docs/_build/jupyter_execute/guides/models/glm.ipynb +++ b/docs/_build/jupyter_execute/guides/models/glm.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e49c7645", + "id": "5dde7181", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9634e695", + "id": "80a99080", "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3eda131e", + "id": "f6bd20a8", "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": null, - "id": "26f8687a", + "id": "ce00b4ee", "metadata": {}, "outputs": [], "source": [ @@ -44,7 +44,7 @@ { "cell_type": "code", "execution_count": null, - "id": "aba8cf0c", + "id": "1ea0be65", "metadata": {}, "outputs": [], "source": [ @@ -54,7 +54,7 @@ { "cell_type": "code", "execution_count": null, - "id": "edfbd893", + "id": "473480ff", "metadata": {}, "outputs": [], "source": [ @@ -64,7 +64,7 @@ { "cell_type": "code", "execution_count": null, - "id": "fe7c8029", + "id": "67431660", "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ { "cell_type": "code", "execution_count": null, - "id": "30c661fe", + "id": "2d35ba9c", "metadata": {}, "outputs": [], "source": [ @@ -84,7 +84,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bc5198e0", + "id": "c1750b59", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/models/reludnn.ipynb b/docs/_build/jupyter_execute/guides/models/reludnn.ipynb index ecf553bc..67e0320e 100644 --- a/docs/_build/jupyter_execute/guides/models/reludnn.ipynb +++ b/docs/_build/jupyter_execute/guides/models/reludnn.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f1a3d220", + "id": "0c28964f", "metadata": {}, "outputs": [], "source": [ @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e3130919", + "id": "0c55c46b", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6f3fc26c", + "id": "288494c3", "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dc4e645e", + "id": "5e6a3593", "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5997e7f9", + "id": "0f3f3b7a", "metadata": {}, "outputs": [], "source": [ @@ -55,7 +55,7 @@ { "cell_type": "code", "execution_count": null, - "id": "78353c3c", + "id": "bc16b2c8", "metadata": {}, "outputs": [], "source": [ @@ -65,7 +65,7 @@ { "cell_type": "code", "execution_count": null, - "id": "188f2582", + "id": "39b62600", "metadata": {}, "outputs": [], "source": [ @@ -75,7 +75,7 @@ { "cell_type": "code", "execution_count": null, - "id": "aed37fff", + "id": "e2546b81", "metadata": {}, "outputs": [], "source": [ @@ -85,7 +85,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9753d45f", + "id": "a1bd0abc", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/models/tree.ipynb b/docs/_build/jupyter_execute/guides/models/tree.ipynb index 813cbcdb..bc691cab 100644 --- a/docs/_build/jupyter_execute/guides/models/tree.ipynb +++ b/docs/_build/jupyter_execute/guides/models/tree.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "362108b2", + "id": "2e71f106", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5c2a4093", + "id": "2d145329", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "33c1db67", + "id": "2496a163", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/models/xgb1.ipynb b/docs/_build/jupyter_execute/guides/models/xgb1.ipynb index 6d9e9fce..e48b4ce4 100644 --- a/docs/_build/jupyter_execute/guides/models/xgb1.ipynb +++ b/docs/_build/jupyter_execute/guides/models/xgb1.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6c5bc6d4", + "id": "72966977", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ad2e5940", + "id": "0750cbba", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "353e7efa", + "id": "0f0721b7", "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d1accc4b", + "id": "1a520251", "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b9e6d0c3", + "id": "1bde30f0", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cbc79c26", + "id": "a4ca3c60", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/models/xgb2.ipynb b/docs/_build/jupyter_execute/guides/models/xgb2.ipynb index 57fd27bd..2e6e4240 100644 --- a/docs/_build/jupyter_execute/guides/models/xgb2.ipynb +++ b/docs/_build/jupyter_execute/guides/models/xgb2.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3e4988cf", + "id": "af67f11b", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2e7b3cde", + "id": "6c436d1c", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f0d63488", + "id": "2131e64c", "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ { "cell_type": "code", "execution_count": null, - "id": "46cd352b", + "id": "2bfa3fe6", "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4f35ee2d", + "id": "f3d97340", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b733ef36", + "id": "e32f6daf", "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5734278e", + "id": "696fbe80", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/testing/accuracy.ipynb b/docs/_build/jupyter_execute/guides/testing/accuracy.ipynb index 1093763e..fd20983a 100644 --- a/docs/_build/jupyter_execute/guides/testing/accuracy.ipynb +++ b/docs/_build/jupyter_execute/guides/testing/accuracy.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0aa87ccd", + "id": "a3ebbfdc", "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ { "cell_type": "code", "execution_count": null, - "id": "26e947da", + "id": "33d40315", "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ { "cell_type": "code", "execution_count": null, - "id": "292fc6da", + "id": "09036187", "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a8dae2af", + "id": "51cfd397", "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4aabb9d9", + "id": "23a611b5", "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a796be3d", + "id": "fab75a34", "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9b91e261", + "id": "d7b6fcf7", "metadata": {}, "outputs": [], "source": [ @@ -78,7 +78,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6ce21031", + "id": "38d30fd8", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/testing/fairness.ipynb b/docs/_build/jupyter_execute/guides/testing/fairness.ipynb index 9d39d7bb..3ed0d192 100644 --- a/docs/_build/jupyter_execute/guides/testing/fairness.ipynb +++ b/docs/_build/jupyter_execute/guides/testing/fairness.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0a38ef00", + "id": "95f05dc9", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4d209e5a", + "id": "b7036562", "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7b03f101", + "id": "ea4385f8", "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ { "cell_type": "code", "execution_count": null, - "id": "165cd8bf", + "id": "15f8baed", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/testing/overfit.ipynb b/docs/_build/jupyter_execute/guides/testing/overfit.ipynb index 98ad0fa9..19f93f66 100644 --- a/docs/_build/jupyter_execute/guides/testing/overfit.ipynb +++ b/docs/_build/jupyter_execute/guides/testing/overfit.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b23d45e6", + "id": "63fe1f32", "metadata": {}, "outputs": [], "source": [ @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7b57ea39", + "id": "e5518637", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "95a973b6", + "id": "43e41d19", "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0330530e", + "id": "af9943e2", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/testing/reliability.ipynb b/docs/_build/jupyter_execute/guides/testing/reliability.ipynb index 23e3a42d..3117c7b2 100644 --- a/docs/_build/jupyter_execute/guides/testing/reliability.ipynb +++ b/docs/_build/jupyter_execute/guides/testing/reliability.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8c7cdad3", + "id": "fdf0b8ef", "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f94e3b57", + "id": "8e887f93", "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9ba1fb60", + "id": "d7d88e63", "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ { "cell_type": "code", "execution_count": null, - "id": "681ec7ae", + "id": "b78b4226", "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f9bdc3c8", + "id": "70eb372e", "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a216c453", + "id": "f39b464b", "metadata": {}, "outputs": [], "source": [ @@ -68,7 +68,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ee7d9eeb", + "id": "e6398585", "metadata": {}, "outputs": [], "source": [ @@ -78,7 +78,7 @@ { "cell_type": "code", "execution_count": null, - "id": "583e70cf", + "id": "d424663a", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/testing/resilience.ipynb b/docs/_build/jupyter_execute/guides/testing/resilience.ipynb index 19a7c90c..373fced3 100644 --- a/docs/_build/jupyter_execute/guides/testing/resilience.ipynb +++ b/docs/_build/jupyter_execute/guides/testing/resilience.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "51358ef8", + "id": "1777e53b", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "009c151d", + "id": "81c162e5", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4f27d89c", + "id": "06ff40a0", "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c9e462fe", + "id": "288c7510", "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ { "cell_type": "code", "execution_count": null, - "id": "71a51c7a", + "id": "6995cf67", "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ { "cell_type": "code", "execution_count": null, - "id": "70dff7be", + "id": "bf6888e9", "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8c7b875b", + "id": "1a114555", "metadata": {}, "outputs": [], "source": [ @@ -80,7 +80,7 @@ { "cell_type": "code", "execution_count": null, - "id": "986b67c5", + "id": "2803d086", "metadata": {}, "outputs": [], "source": [ @@ -91,7 +91,7 @@ { "cell_type": "code", "execution_count": null, - "id": "95c28432", + "id": "c7704594", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/testing/robustness.ipynb b/docs/_build/jupyter_execute/guides/testing/robustness.ipynb index 6a28caea..7a6a58a1 100644 --- a/docs/_build/jupyter_execute/guides/testing/robustness.ipynb +++ b/docs/_build/jupyter_execute/guides/testing/robustness.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "183e30a7", + "id": "ade451a5", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f8bfdc3b", + "id": "a1a48e5d", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "44c5dadf", + "id": "8807cd91", "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_build/jupyter_execute/guides/testing/weakspot.ipynb b/docs/_build/jupyter_execute/guides/testing/weakspot.ipynb index 6bec998a..4c94a52e 100644 --- a/docs/_build/jupyter_execute/guides/testing/weakspot.ipynb +++ b/docs/_build/jupyter_execute/guides/testing/weakspot.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6f591b5e", + "id": "57082442", "metadata": {}, "outputs": [], "source": [ @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b07aac39", + "id": "a95e6ad7", "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9f6fccf9", + "id": "37d55024", "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2c23f889", + "id": "685f476d", "metadata": {}, "outputs": [], "source": [