public interface ModelMetrics
| Modifier and Type | Method and Description |
|---|---|
retrofit2.Call<ModelMetricsListSchemaV3> |
delete(java.lang.String model,
java.lang.String frame) |
retrofit2.Call<ModelMetricsListSchemaV3> |
delete(java.lang.String model,
java.lang.String frame,
java.lang.String predictions_frame,
java.lang.String deviances_frame,
boolean reconstruction_error,
boolean reconstruction_error_per_feature,
int deep_features_hidden_layer,
java.lang.String deep_features_hidden_layer_name,
boolean reconstruct_train,
boolean project_archetypes,
boolean reverse_transform,
boolean leaf_node_assignment,
ModelLeafNodeAssignmentLeafNodeAssignmentType leaf_node_assignment_type,
boolean predict_staged_proba,
boolean predict_contributions,
ModelContributionsContributionsOutputFormat predict_contributions_output_format,
int top_n,
int bottom_n,
boolean compare_abs,
boolean feature_frequencies,
int exemplar_index,
boolean deviances,
java.lang.String custom_metric_func,
java.lang.String auc_type,
java.lang.String auuc_type,
int auuc_nbins,
java.lang.String _exclude_fields)
Return the saved scoring metrics for the specified Model and Frame.
|
retrofit2.Call<ModelMetricsListSchemaV3> |
fetch(java.lang.String model,
java.lang.String frame) |
retrofit2.Call<ModelMetricsListSchemaV3> |
fetch(java.lang.String model,
java.lang.String frame,
java.lang.String predictions_frame,
java.lang.String deviances_frame,
boolean reconstruction_error,
boolean reconstruction_error_per_feature,
int deep_features_hidden_layer,
java.lang.String deep_features_hidden_layer_name,
boolean reconstruct_train,
boolean project_archetypes,
boolean reverse_transform,
boolean leaf_node_assignment,
ModelLeafNodeAssignmentLeafNodeAssignmentType leaf_node_assignment_type,
boolean predict_staged_proba,
boolean predict_contributions,
ModelContributionsContributionsOutputFormat predict_contributions_output_format,
int top_n,
int bottom_n,
boolean compare_abs,
boolean feature_frequencies,
int exemplar_index,
boolean deviances,
java.lang.String custom_metric_func,
java.lang.String auc_type,
java.lang.String auuc_type,
int auuc_nbins,
java.lang.String _exclude_fields)
Return the saved scoring metrics for the specified Model and Frame.
|
retrofit2.Call<ModelMetricsMakerSchemaV3> |
make(java.lang.String predictions_frame,
java.lang.String actuals_frame) |
retrofit2.Call<ModelMetricsMakerSchemaV3> |
make(java.lang.String predictions_frame,
java.lang.String actuals_frame,
java.lang.String weights_frame,
java.lang.String treatment_frame,
java.lang.String[] domain,
GenmodelutilsDistributionFamily distribution,
MultinomialAucType auc_type,
AUUCType auuc_type,
int auuc_nbins)
Create a ModelMetrics object from the predicted and actual values, and a domain for classification problems or a
distribution family for regression problems.
|
retrofit2.Call<ModelMetricsListSchemaV3> |
score(java.lang.String model,
java.lang.String frame) |
retrofit2.Call<ModelMetricsListSchemaV3> |
score(java.lang.String model,
java.lang.String frame,
java.lang.String predictions_frame,
java.lang.String deviances_frame,
boolean reconstruction_error,
boolean reconstruction_error_per_feature,
int deep_features_hidden_layer,
java.lang.String deep_features_hidden_layer_name,
boolean reconstruct_train,
boolean project_archetypes,
boolean reverse_transform,
boolean leaf_node_assignment,
ModelLeafNodeAssignmentLeafNodeAssignmentType leaf_node_assignment_type,
boolean predict_staged_proba,
boolean predict_contributions,
ModelContributionsContributionsOutputFormat predict_contributions_output_format,
int top_n,
int bottom_n,
boolean compare_abs,
boolean feature_frequencies,
int exemplar_index,
boolean deviances,
java.lang.String custom_metric_func,
java.lang.String auc_type,
java.lang.String auuc_type,
int auuc_nbins,
java.lang.String _exclude_fields)
Return the scoring metrics for the specified Frame with the specified Model.
|
@GET(value="/3/ModelMetrics/models/{model}/frames/{frame}")
retrofit2.Call<ModelMetricsListSchemaV3> fetch(@Path(value="model")
java.lang.String model,
@Path(value="frame")
java.lang.String frame,
@Query(value="predictions_frame")
java.lang.String predictions_frame,
@Query(value="deviances_frame")
java.lang.String deviances_frame,
@Query(value="reconstruction_error")
boolean reconstruction_error,
@Query(value="reconstruction_error_per_feature")
boolean reconstruction_error_per_feature,
@Query(value="deep_features_hidden_layer")
int deep_features_hidden_layer,
@Query(value="deep_features_hidden_layer_name")
java.lang.String deep_features_hidden_layer_name,
@Query(value="reconstruct_train")
boolean reconstruct_train,
@Query(value="project_archetypes")
boolean project_archetypes,
@Query(value="reverse_transform")
boolean reverse_transform,
@Query(value="leaf_node_assignment")
boolean leaf_node_assignment,
@Query(value="leaf_node_assignment_type")
ModelLeafNodeAssignmentLeafNodeAssignmentType leaf_node_assignment_type,
@Query(value="predict_staged_proba")
boolean predict_staged_proba,
@Query(value="predict_contributions")
boolean predict_contributions,
@Query(value="predict_contributions_output_format")
ModelContributionsContributionsOutputFormat predict_contributions_output_format,
@Query(value="top_n")
int top_n,
@Query(value="bottom_n")
int bottom_n,
@Query(value="compare_abs")
boolean compare_abs,
@Query(value="feature_frequencies")
boolean feature_frequencies,
@Query(value="exemplar_index")
int exemplar_index,
@Query(value="deviances")
boolean deviances,
@Query(value="custom_metric_func")
java.lang.String custom_metric_func,
@Query(value="auc_type")
java.lang.String auc_type,
@Query(value="auuc_type")
java.lang.String auuc_type,
@Query(value="auuc_nbins")
int auuc_nbins,
@Query(value="_exclude_fields")
java.lang.String _exclude_fields)
model - Key of Model of interest (optional)frame - Key of Frame of interest (optional)predictions_frame - Key of predictions frame, if predictions are requested (optional)deviances_frame - Key for the frame containing per-observation deviances (optional)reconstruction_error - Compute reconstruction error (optional, only for Deep Learning AutoEncoder models)reconstruction_error_per_feature - Compute reconstruction error per feature (optional, only for Deep
Learning AutoEncoder models)deep_features_hidden_layer - Extract Deep Features for given hidden layer (optional, only for Deep Learning
models)deep_features_hidden_layer_name - Extract Deep Features for given hidden layer by name (optional, only for
Deep Water models)reconstruct_train - Reconstruct original training frame (optional, only for GLRM models)project_archetypes - Project GLRM archetypes back into original feature space (optional, only for GLRM
models)reverse_transform - Reverse transformation applied during training to model output (optional, only for GLRM
models)leaf_node_assignment - Return the leaf node assignment (optional, only for DRF/GBM models)leaf_node_assignment_type - Type of the leaf node assignment (optional, only for DRF/GBM models)predict_staged_proba - Predict the class probabilities at each stage (optional, only for GBM models)predict_contributions - Predict the feature contributions - Shapley values (optional, only for DRF, GBM and
XGBoost models)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 contributiontop_n - Only for predict_contributions function - sort Shapley values and return top_n highest (optional)bottom_n - Only for predict_contributions function - sort Shapley values and return bottom_n lowest
(optional)compare_abs - Only for predict_contributions function - sort absolute Shapley values (optional)feature_frequencies - Retrieve the feature frequencies on paths in trees in tree-based models (optional,
only for GBM, DRF and Isolation Forest)exemplar_index - Retrieve all members for a given exemplar (optional, only for Aggregator models)deviances - Compute the deviances per row (optional, only for classification or regression models)custom_metric_func - Reference to custom evaluation function, format: `language:keyName=funcName`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).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).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)._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(value="/3/ModelMetrics/models/{model}/frames/{frame}")
retrofit2.Call<ModelMetricsListSchemaV3> fetch(@Path(value="model")
java.lang.String model,
@Path(value="frame")
java.lang.String frame)
@DELETE(value="/3/ModelMetrics/models/{model}/frames/{frame}")
retrofit2.Call<ModelMetricsListSchemaV3> delete(@Path(value="model")
java.lang.String model,
@Path(value="frame")
java.lang.String frame,
@Field(value="predictions_frame")
java.lang.String predictions_frame,
@Field(value="deviances_frame")
java.lang.String deviances_frame,
@Field(value="reconstruction_error")
boolean reconstruction_error,
@Field(value="reconstruction_error_per_feature")
boolean reconstruction_error_per_feature,
@Field(value="deep_features_hidden_layer")
int deep_features_hidden_layer,
@Field(value="deep_features_hidden_layer_name")
java.lang.String deep_features_hidden_layer_name,
@Field(value="reconstruct_train")
boolean reconstruct_train,
@Field(value="project_archetypes")
boolean project_archetypes,
@Field(value="reverse_transform")
boolean reverse_transform,
@Field(value="leaf_node_assignment")
boolean leaf_node_assignment,
@Field(value="leaf_node_assignment_type")
ModelLeafNodeAssignmentLeafNodeAssignmentType leaf_node_assignment_type,
@Field(value="predict_staged_proba")
boolean predict_staged_proba,
@Field(value="predict_contributions")
boolean predict_contributions,
@Field(value="predict_contributions_output_format")
ModelContributionsContributionsOutputFormat predict_contributions_output_format,
@Field(value="top_n")
int top_n,
@Field(value="bottom_n")
int bottom_n,
@Field(value="compare_abs")
boolean compare_abs,
@Field(value="feature_frequencies")
boolean feature_frequencies,
@Field(value="exemplar_index")
int exemplar_index,
@Field(value="deviances")
boolean deviances,
@Field(value="custom_metric_func")
java.lang.String custom_metric_func,
@Field(value="auc_type")
java.lang.String auc_type,
@Field(value="auuc_type")
java.lang.String auuc_type,
@Field(value="auuc_nbins")
int auuc_nbins,
@Field(value="_exclude_fields")
java.lang.String _exclude_fields)
model - Key of Model of interest (optional)frame - Key of Frame of interest (optional)predictions_frame - Key of predictions frame, if predictions are requested (optional)deviances_frame - Key for the frame containing per-observation deviances (optional)reconstruction_error - Compute reconstruction error (optional, only for Deep Learning AutoEncoder models)reconstruction_error_per_feature - Compute reconstruction error per feature (optional, only for Deep
Learning AutoEncoder models)deep_features_hidden_layer - Extract Deep Features for given hidden layer (optional, only for Deep Learning
models)deep_features_hidden_layer_name - Extract Deep Features for given hidden layer by name (optional, only for
Deep Water models)reconstruct_train - Reconstruct original training frame (optional, only for GLRM models)project_archetypes - Project GLRM archetypes back into original feature space (optional, only for GLRM
models)reverse_transform - Reverse transformation applied during training to model output (optional, only for GLRM
models)leaf_node_assignment - Return the leaf node assignment (optional, only for DRF/GBM models)leaf_node_assignment_type - Type of the leaf node assignment (optional, only for DRF/GBM models)predict_staged_proba - Predict the class probabilities at each stage (optional, only for GBM models)predict_contributions - Predict the feature contributions - Shapley values (optional, only for DRF, GBM and
XGBoost models)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 contributiontop_n - Only for predict_contributions function - sort Shapley values and return top_n highest (optional)bottom_n - Only for predict_contributions function - sort Shapley values and return bottom_n lowest
(optional)compare_abs - Only for predict_contributions function - sort absolute Shapley values (optional)feature_frequencies - Retrieve the feature frequencies on paths in trees in tree-based models (optional,
only for GBM, DRF and Isolation Forest)exemplar_index - Retrieve all members for a given exemplar (optional, only for Aggregator models)deviances - Compute the deviances per row (optional, only for classification or regression models)custom_metric_func - Reference to custom evaluation function, format: `language:keyName=funcName`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).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).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)._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(value="/3/ModelMetrics/models/{model}/frames/{frame}")
retrofit2.Call<ModelMetricsListSchemaV3> delete(@Path(value="model")
java.lang.String model,
@Path(value="frame")
java.lang.String frame)
@FormUrlEncoded
@POST(value="/3/ModelMetrics/models/{model}/frames/{frame}")
retrofit2.Call<ModelMetricsListSchemaV3> score(@Path(value="model")
java.lang.String model,
@Path(value="frame")
java.lang.String frame,
@Field(value="predictions_frame")
java.lang.String predictions_frame,
@Field(value="deviances_frame")
java.lang.String deviances_frame,
@Field(value="reconstruction_error")
boolean reconstruction_error,
@Field(value="reconstruction_error_per_feature")
boolean reconstruction_error_per_feature,
@Field(value="deep_features_hidden_layer")
int deep_features_hidden_layer,
@Field(value="deep_features_hidden_layer_name")
java.lang.String deep_features_hidden_layer_name,
@Field(value="reconstruct_train")
boolean reconstruct_train,
@Field(value="project_archetypes")
boolean project_archetypes,
@Field(value="reverse_transform")
boolean reverse_transform,
@Field(value="leaf_node_assignment")
boolean leaf_node_assignment,
@Field(value="leaf_node_assignment_type")
ModelLeafNodeAssignmentLeafNodeAssignmentType leaf_node_assignment_type,
@Field(value="predict_staged_proba")
boolean predict_staged_proba,
@Field(value="predict_contributions")
boolean predict_contributions,
@Field(value="predict_contributions_output_format")
ModelContributionsContributionsOutputFormat predict_contributions_output_format,
@Field(value="top_n")
int top_n,
@Field(value="bottom_n")
int bottom_n,
@Field(value="compare_abs")
boolean compare_abs,
@Field(value="feature_frequencies")
boolean feature_frequencies,
@Field(value="exemplar_index")
int exemplar_index,
@Field(value="deviances")
boolean deviances,
@Field(value="custom_metric_func")
java.lang.String custom_metric_func,
@Field(value="auc_type")
java.lang.String auc_type,
@Field(value="auuc_type")
java.lang.String auuc_type,
@Field(value="auuc_nbins")
int auuc_nbins,
@Field(value="_exclude_fields")
java.lang.String _exclude_fields)
model - Key of Model of interest (optional)frame - Key of Frame of interest (optional)predictions_frame - Key of predictions frame, if predictions are requested (optional)deviances_frame - Key for the frame containing per-observation deviances (optional)reconstruction_error - Compute reconstruction error (optional, only for Deep Learning AutoEncoder models)reconstruction_error_per_feature - Compute reconstruction error per feature (optional, only for Deep
Learning AutoEncoder models)deep_features_hidden_layer - Extract Deep Features for given hidden layer (optional, only for Deep Learning
models)deep_features_hidden_layer_name - Extract Deep Features for given hidden layer by name (optional, only for
Deep Water models)reconstruct_train - Reconstruct original training frame (optional, only for GLRM models)project_archetypes - Project GLRM archetypes back into original feature space (optional, only for GLRM
models)reverse_transform - Reverse transformation applied during training to model output (optional, only for GLRM
models)leaf_node_assignment - Return the leaf node assignment (optional, only for DRF/GBM models)leaf_node_assignment_type - Type of the leaf node assignment (optional, only for DRF/GBM models)predict_staged_proba - Predict the class probabilities at each stage (optional, only for GBM models)predict_contributions - Predict the feature contributions - Shapley values (optional, only for DRF, GBM and
XGBoost models)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 contributiontop_n - Only for predict_contributions function - sort Shapley values and return top_n highest (optional)bottom_n - Only for predict_contributions function - sort Shapley values and return bottom_n lowest
(optional)compare_abs - Only for predict_contributions function - sort absolute Shapley values (optional)feature_frequencies - Retrieve the feature frequencies on paths in trees in tree-based models (optional,
only for GBM, DRF and Isolation Forest)exemplar_index - Retrieve all members for a given exemplar (optional, only for Aggregator models)deviances - Compute the deviances per row (optional, only for classification or regression models)custom_metric_func - Reference to custom evaluation function, format: `language:keyName=funcName`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).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).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)._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(value="/3/ModelMetrics/models/{model}/frames/{frame}")
retrofit2.Call<ModelMetricsListSchemaV3> score(@Path(value="model")
java.lang.String model,
@Path(value="frame")
java.lang.String frame)
@FormUrlEncoded
@POST(value="/3/ModelMetrics/predictions_frame/{predictions_frame}/actuals_frame/{actuals_frame}")
retrofit2.Call<ModelMetricsMakerSchemaV3> make(@Path(value="predictions_frame")
java.lang.String predictions_frame,
@Path(value="actuals_frame")
java.lang.String actuals_frame,
@Field(value="weights_frame")
java.lang.String weights_frame,
@Field(value="treatment_frame")
java.lang.String treatment_frame,
@Field(value="domain")
java.lang.String[] domain,
@Field(value="distribution")
GenmodelutilsDistributionFamily distribution,
@Field(value="auc_type")
MultinomialAucType auc_type,
@Field(value="auuc_type")
AUUCType auuc_type,
@Field(value="auuc_nbins")
int auuc_nbins)
predictions_frame - Predictions Frame.actuals_frame - Actuals Frame.weights_frame - Weights Frame.treatment_frame - Treatment Frame.domain - Domain (for classification).distribution - Distribution (for regression).auc_type - Default AUC type (for multinomial classification).auuc_type - Default AUUC type (for uplift binomial classification).auuc_nbins - Number of bins to calculate AUUC (for uplift binomial classification).@FormUrlEncoded
@POST(value="/3/ModelMetrics/predictions_frame/{predictions_frame}/actuals_frame/{actuals_frame}")
retrofit2.Call<ModelMetricsMakerSchemaV3> make(@Path(value="predictions_frame")
java.lang.String predictions_frame,
@Path(value="actuals_frame")
java.lang.String actuals_frame)