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/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.proxies.retrofit;
import water.bindings.pojos.*;
import retrofit2.*;
import retrofit2.http.*;
public interface ModelMetrics {
/**
* Return the saved scoring metrics for the specified Model and Frame.
* @param model Key of Model of interest (optional)
* @param frame Key of Frame of interest (optional)
* @param predictions_frame Key of predictions frame, if predictions are requested (optional)
* @param deviances_frame Key for the frame containing per-observation deviances (optional)
* @param reconstruction_error Compute reconstruction error (optional, only for Deep Learning AutoEncoder models)
* @param reconstruction_error_per_feature Compute reconstruction error per feature (optional, only for Deep
* Learning AutoEncoder models)
* @param deep_features_hidden_layer Extract Deep Features for given hidden layer (optional, only for Deep Learning
* models)
* @param deep_features_hidden_layer_name Extract Deep Features for given hidden layer by name (optional, only for
* Deep Water models)
* @param reconstruct_train Reconstruct original training frame (optional, only for GLRM models)
* @param project_archetypes Project GLRM archetypes back into original feature space (optional, only for GLRM
* models)
* @param reverse_transform Reverse transformation applied during training to model output (optional, only for GLRM
* models)
* @param leaf_node_assignment Return the leaf node assignment (optional, only for DRF/GBM models)
* @param leaf_node_assignment_type Type of the leaf node assignment (optional, only for DRF/GBM models)
* @param predict_staged_proba Predict the class probabilities at each stage (optional, only for GBM models)
* @param predict_contributions Predict the feature contributions - Shapley values (optional, only for DRF, GBM and
* XGBoost models)
* @param row_to_tree_assignment Return which row is used in which tree (optional, only for GBM models)
* @param predict_contributions_output_format Specify how to output feature contributions in XGBoost - XGBoost by
* default outputs contributions for 1-hot encoded features, specifying a
* Compact output format will produce a per-feature contribution
* @param top_n Only for predict_contributions function - sort Shapley values and return top_n highest (optional)
* @param bottom_n Only for predict_contributions function - sort Shapley values and return bottom_n lowest
* (optional)
* @param compare_abs Only for predict_contributions function - sort absolute Shapley values (optional)
* @param feature_frequencies Retrieve the feature frequencies on paths in trees in tree-based models (optional,
* only for GBM, DRF and Isolation Forest)
* @param exemplar_index Retrieve all members for a given exemplar (optional, only for Aggregator models)
* @param deviances Compute the deviances per row (optional, only for classification or regression models)
* @param custom_metric_func Reference to custom evaluation function, format: `language:keyName=funcName`
* @param auc_type Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR",
* "MACRO_OVO", "WEIGHTED_OVO". Default is "NONE" (optional, only for multinomial classification).
* @param auuc_type Set default AUUC type for uplift binomial classification. Must be one of: "AUTO", "qini",
* "lift", "gain". Default is "AUTO" (optional, only for uplift binomial classification).
* @param custom_auuc_thresholds Custom AUUC thresholds (for uplift binomial classification).
* @param auuc_nbins Set number of bins to calculate AUUC. Must be -1 or higher than 0. Default is -1 which means
* 1000 (optional, only for uplift binomial classification).
* @param background_frame Specify background frame used as a reference for calculating SHAP.
* @param output_space If true, transform contributions so that they sum up to the difference in the output space
* (applicable iff contributions are in link space). Note that this transformation is an
* approximation and the contributions won't be exact SHAP values.
* @param output_per_reference If true, return contributions against each background sample (aka reference), i.e.
* phi(feature, x, bg), otherwise return contributions averaged over the background
* sample (phi(feature, x) = E_{bg} phi(feature, x, bg))
* @param _exclude_fields Comma-separated list of JSON field paths to exclude from the result, used like:
* "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
*/
@GET("/3/ModelMetrics/models/{model}/frames/{frame}")
Call fetch(
@Path("model") String model,
@Path("frame") String frame,
@Query("predictions_frame") String predictions_frame,
@Query("deviances_frame") String deviances_frame,
@Query("reconstruction_error") boolean reconstruction_error,
@Query("reconstruction_error_per_feature") boolean reconstruction_error_per_feature,
@Query("deep_features_hidden_layer") int deep_features_hidden_layer,
@Query("deep_features_hidden_layer_name") String deep_features_hidden_layer_name,
@Query("reconstruct_train") boolean reconstruct_train,
@Query("project_archetypes") boolean project_archetypes,
@Query("reverse_transform") boolean reverse_transform,
@Query("leaf_node_assignment") boolean leaf_node_assignment,
@Query("leaf_node_assignment_type") ModelLeafNodeAssignmentLeafNodeAssignmentType leaf_node_assignment_type,
@Query("predict_staged_proba") boolean predict_staged_proba,
@Query("predict_contributions") boolean predict_contributions,
@Query("row_to_tree_assignment") boolean row_to_tree_assignment,
@Query("predict_contributions_output_format") ModelContributionsContributionsOutputFormat predict_contributions_output_format,
@Query("top_n") int top_n,
@Query("bottom_n") int bottom_n,
@Query("compare_abs") boolean compare_abs,
@Query("feature_frequencies") boolean feature_frequencies,
@Query("exemplar_index") int exemplar_index,
@Query("deviances") boolean deviances,
@Query("custom_metric_func") String custom_metric_func,
@Query("auc_type") String auc_type,
@Query("auuc_type") String auuc_type,
@Query("custom_auuc_thresholds") double[] custom_auuc_thresholds,
@Query("auuc_nbins") int auuc_nbins,
@Query("background_frame") String background_frame,
@Query("output_space") boolean output_space,
@Query("output_per_reference") boolean output_per_reference,
@Query("_exclude_fields") String _exclude_fields
);
@GET("/3/ModelMetrics/models/{model}/frames/{frame}")
Call fetch(
@Path("model") String model,
@Path("frame") String frame
);
/**
* Return the saved scoring metrics for the specified Model and Frame.
* @param model Key of Model of interest (optional)
* @param frame Key of Frame of interest (optional)
* @param predictions_frame Key of predictions frame, if predictions are requested (optional)
* @param deviances_frame Key for the frame containing per-observation deviances (optional)
* @param reconstruction_error Compute reconstruction error (optional, only for Deep Learning AutoEncoder models)
* @param reconstruction_error_per_feature Compute reconstruction error per feature (optional, only for Deep
* Learning AutoEncoder models)
* @param deep_features_hidden_layer Extract Deep Features for given hidden layer (optional, only for Deep Learning
* models)
* @param deep_features_hidden_layer_name Extract Deep Features for given hidden layer by name (optional, only for
* Deep Water models)
* @param reconstruct_train Reconstruct original training frame (optional, only for GLRM models)
* @param project_archetypes Project GLRM archetypes back into original feature space (optional, only for GLRM
* models)
* @param reverse_transform Reverse transformation applied during training to model output (optional, only for GLRM
* models)
* @param leaf_node_assignment Return the leaf node assignment (optional, only for DRF/GBM models)
* @param leaf_node_assignment_type Type of the leaf node assignment (optional, only for DRF/GBM models)
* @param predict_staged_proba Predict the class probabilities at each stage (optional, only for GBM models)
* @param predict_contributions Predict the feature contributions - Shapley values (optional, only for DRF, GBM and
* XGBoost models)
* @param row_to_tree_assignment Return which row is used in which tree (optional, only for GBM models)
* @param predict_contributions_output_format Specify how to output feature contributions in XGBoost - XGBoost by
* default outputs contributions for 1-hot encoded features, specifying a
* Compact output format will produce a per-feature contribution
* @param top_n Only for predict_contributions function - sort Shapley values and return top_n highest (optional)
* @param bottom_n Only for predict_contributions function - sort Shapley values and return bottom_n lowest
* (optional)
* @param compare_abs Only for predict_contributions function - sort absolute Shapley values (optional)
* @param feature_frequencies Retrieve the feature frequencies on paths in trees in tree-based models (optional,
* only for GBM, DRF and Isolation Forest)
* @param exemplar_index Retrieve all members for a given exemplar (optional, only for Aggregator models)
* @param deviances Compute the deviances per row (optional, only for classification or regression models)
* @param custom_metric_func Reference to custom evaluation function, format: `language:keyName=funcName`
* @param auc_type Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR",
* "MACRO_OVO", "WEIGHTED_OVO". Default is "NONE" (optional, only for multinomial classification).
* @param auuc_type Set default AUUC type for uplift binomial classification. Must be one of: "AUTO", "qini",
* "lift", "gain". Default is "AUTO" (optional, only for uplift binomial classification).
* @param custom_auuc_thresholds Custom AUUC thresholds (for uplift binomial classification).
* @param auuc_nbins Set number of bins to calculate AUUC. Must be -1 or higher than 0. Default is -1 which means
* 1000 (optional, only for uplift binomial classification).
* @param background_frame Specify background frame used as a reference for calculating SHAP.
* @param output_space If true, transform contributions so that they sum up to the difference in the output space
* (applicable iff contributions are in link space). Note that this transformation is an
* approximation and the contributions won't be exact SHAP values.
* @param output_per_reference If true, return contributions against each background sample (aka reference), i.e.
* phi(feature, x, bg), otherwise return contributions averaged over the background
* sample (phi(feature, x) = E_{bg} phi(feature, x, bg))
* @param _exclude_fields Comma-separated list of JSON field paths to exclude from the result, used like:
* "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
*/
@DELETE("/3/ModelMetrics/models/{model}/frames/{frame}")
Call delete(
@Path("model") String model,
@Path("frame") String frame,
@Field("predictions_frame") String predictions_frame,
@Field("deviances_frame") String deviances_frame,
@Field("reconstruction_error") boolean reconstruction_error,
@Field("reconstruction_error_per_feature") boolean reconstruction_error_per_feature,
@Field("deep_features_hidden_layer") int deep_features_hidden_layer,
@Field("deep_features_hidden_layer_name") String deep_features_hidden_layer_name,
@Field("reconstruct_train") boolean reconstruct_train,
@Field("project_archetypes") boolean project_archetypes,
@Field("reverse_transform") boolean reverse_transform,
@Field("leaf_node_assignment") boolean leaf_node_assignment,
@Field("leaf_node_assignment_type") ModelLeafNodeAssignmentLeafNodeAssignmentType leaf_node_assignment_type,
@Field("predict_staged_proba") boolean predict_staged_proba,
@Field("predict_contributions") boolean predict_contributions,
@Field("row_to_tree_assignment") boolean row_to_tree_assignment,
@Field("predict_contributions_output_format") ModelContributionsContributionsOutputFormat predict_contributions_output_format,
@Field("top_n") int top_n,
@Field("bottom_n") int bottom_n,
@Field("compare_abs") boolean compare_abs,
@Field("feature_frequencies") boolean feature_frequencies,
@Field("exemplar_index") int exemplar_index,
@Field("deviances") boolean deviances,
@Field("custom_metric_func") String custom_metric_func,
@Field("auc_type") String auc_type,
@Field("auuc_type") String auuc_type,
@Field("custom_auuc_thresholds") double[] custom_auuc_thresholds,
@Field("auuc_nbins") int auuc_nbins,
@Field("background_frame") String background_frame,
@Field("output_space") boolean output_space,
@Field("output_per_reference") boolean output_per_reference,
@Field("_exclude_fields") String _exclude_fields
);
@DELETE("/3/ModelMetrics/models/{model}/frames/{frame}")
Call delete(
@Path("model") String model,
@Path("frame") String frame
);
/**
* Return the scoring metrics for the specified Frame with the specified Model. If the Frame has already been scored
* with the Model then cached results will be returned; otherwise predictions for all rows in the Frame will be
* generated and the metrics will be returned.
* @param model Key of Model of interest (optional)
* @param frame Key of Frame of interest (optional)
* @param predictions_frame Key of predictions frame, if predictions are requested (optional)
* @param deviances_frame Key for the frame containing per-observation deviances (optional)
* @param reconstruction_error Compute reconstruction error (optional, only for Deep Learning AutoEncoder models)
* @param reconstruction_error_per_feature Compute reconstruction error per feature (optional, only for Deep
* Learning AutoEncoder models)
* @param deep_features_hidden_layer Extract Deep Features for given hidden layer (optional, only for Deep Learning
* models)
* @param deep_features_hidden_layer_name Extract Deep Features for given hidden layer by name (optional, only for
* Deep Water models)
* @param reconstruct_train Reconstruct original training frame (optional, only for GLRM models)
* @param project_archetypes Project GLRM archetypes back into original feature space (optional, only for GLRM
* models)
* @param reverse_transform Reverse transformation applied during training to model output (optional, only for GLRM
* models)
* @param leaf_node_assignment Return the leaf node assignment (optional, only for DRF/GBM models)
* @param leaf_node_assignment_type Type of the leaf node assignment (optional, only for DRF/GBM models)
* @param predict_staged_proba Predict the class probabilities at each stage (optional, only for GBM models)
* @param predict_contributions Predict the feature contributions - Shapley values (optional, only for DRF, GBM and
* XGBoost models)
* @param row_to_tree_assignment Return which row is used in which tree (optional, only for GBM models)
* @param predict_contributions_output_format Specify how to output feature contributions in XGBoost - XGBoost by
* default outputs contributions for 1-hot encoded features, specifying a
* Compact output format will produce a per-feature contribution
* @param top_n Only for predict_contributions function - sort Shapley values and return top_n highest (optional)
* @param bottom_n Only for predict_contributions function - sort Shapley values and return bottom_n lowest
* (optional)
* @param compare_abs Only for predict_contributions function - sort absolute Shapley values (optional)
* @param feature_frequencies Retrieve the feature frequencies on paths in trees in tree-based models (optional,
* only for GBM, DRF and Isolation Forest)
* @param exemplar_index Retrieve all members for a given exemplar (optional, only for Aggregator models)
* @param deviances Compute the deviances per row (optional, only for classification or regression models)
* @param custom_metric_func Reference to custom evaluation function, format: `language:keyName=funcName`
* @param auc_type Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR",
* "MACRO_OVO", "WEIGHTED_OVO". Default is "NONE" (optional, only for multinomial classification).
* @param auuc_type Set default AUUC type for uplift binomial classification. Must be one of: "AUTO", "qini",
* "lift", "gain". Default is "AUTO" (optional, only for uplift binomial classification).
* @param custom_auuc_thresholds Custom AUUC thresholds (for uplift binomial classification).
* @param auuc_nbins Set number of bins to calculate AUUC. Must be -1 or higher than 0. Default is -1 which means
* 1000 (optional, only for uplift binomial classification).
* @param background_frame Specify background frame used as a reference for calculating SHAP.
* @param output_space If true, transform contributions so that they sum up to the difference in the output space
* (applicable iff contributions are in link space). Note that this transformation is an
* approximation and the contributions won't be exact SHAP values.
* @param output_per_reference If true, return contributions against each background sample (aka reference), i.e.
* phi(feature, x, bg), otherwise return contributions averaged over the background
* sample (phi(feature, x) = E_{bg} phi(feature, x, bg))
* @param _exclude_fields Comma-separated list of JSON field paths to exclude from the result, used like:
* "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
*/
@FormUrlEncoded
@POST("/3/ModelMetrics/models/{model}/frames/{frame}")
Call score(
@Path("model") String model,
@Path("frame") String frame,
@Field("predictions_frame") String predictions_frame,
@Field("deviances_frame") String deviances_frame,
@Field("reconstruction_error") boolean reconstruction_error,
@Field("reconstruction_error_per_feature") boolean reconstruction_error_per_feature,
@Field("deep_features_hidden_layer") int deep_features_hidden_layer,
@Field("deep_features_hidden_layer_name") String deep_features_hidden_layer_name,
@Field("reconstruct_train") boolean reconstruct_train,
@Field("project_archetypes") boolean project_archetypes,
@Field("reverse_transform") boolean reverse_transform,
@Field("leaf_node_assignment") boolean leaf_node_assignment,
@Field("leaf_node_assignment_type") ModelLeafNodeAssignmentLeafNodeAssignmentType leaf_node_assignment_type,
@Field("predict_staged_proba") boolean predict_staged_proba,
@Field("predict_contributions") boolean predict_contributions,
@Field("row_to_tree_assignment") boolean row_to_tree_assignment,
@Field("predict_contributions_output_format") ModelContributionsContributionsOutputFormat predict_contributions_output_format,
@Field("top_n") int top_n,
@Field("bottom_n") int bottom_n,
@Field("compare_abs") boolean compare_abs,
@Field("feature_frequencies") boolean feature_frequencies,
@Field("exemplar_index") int exemplar_index,
@Field("deviances") boolean deviances,
@Field("custom_metric_func") String custom_metric_func,
@Field("auc_type") String auc_type,
@Field("auuc_type") String auuc_type,
@Field("custom_auuc_thresholds") double[] custom_auuc_thresholds,
@Field("auuc_nbins") int auuc_nbins,
@Field("background_frame") String background_frame,
@Field("output_space") boolean output_space,
@Field("output_per_reference") boolean output_per_reference,
@Field("_exclude_fields") String _exclude_fields
);
@FormUrlEncoded
@POST("/3/ModelMetrics/models/{model}/frames/{frame}")
Call score(
@Path("model") String model,
@Path("frame") String frame
);
/**
* Create a ModelMetrics object from the predicted and actual values, and a domain for classification problems or a
* distribution family for regression problems.
* @param predictions_frame Predictions Frame.
* @param actuals_frame Actuals Frame.
* @param weights_frame Weights Frame.
* @param treatment_frame Treatment Frame.
* @param domain Domain (for classification).
* @param distribution Distribution (for regression).
* @param auc_type Default AUC type (for multinomial classification).
* @param auuc_type Default AUUC type (for uplift binomial classification).
* @param auuc_nbins Number of bins to calculate AUUC (for uplift binomial classification).
* @param custom_auuc_thresholds Custom AUUC thresholds (for uplift binomial classification).
*/
@FormUrlEncoded
@POST("/3/ModelMetrics/predictions_frame/{predictions_frame}/actuals_frame/{actuals_frame}")
Call make(
@Path("predictions_frame") String predictions_frame,
@Path("actuals_frame") String actuals_frame,
@Field("weights_frame") String weights_frame,
@Field("treatment_frame") String treatment_frame,
@Field("domain") String[] domain,
@Field("distribution") GenmodelutilsDistributionFamily distribution,
@Field("auc_type") MultinomialAucType auc_type,
@Field("auuc_type") AUUCType auuc_type,
@Field("auuc_nbins") int auuc_nbins,
@Field("custom_auuc_thresholds") double[] custom_auuc_thresholds
);
@FormUrlEncoded
@POST("/3/ModelMetrics/predictions_frame/{predictions_frame}/actuals_frame/{actuals_frame}")
Call make(
@Path("predictions_frame") String predictions_frame,
@Path("actuals_frame") String actuals_frame
);
}
