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,
boolean reconstruct_train,
boolean project_archetypes,
boolean reverse_transform,
boolean leaf_node_assignment,
int exemplar_index,
boolean deviances,
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,
boolean reconstruct_train,
boolean project_archetypes,
boolean reverse_transform,
boolean leaf_node_assignment,
int exemplar_index,
boolean deviances,
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[] domain,
GenmodelutilsDistributionFamily distribution)
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,
boolean reconstruct_train,
boolean project_archetypes,
boolean reverse_transform,
boolean leaf_node_assignment,
int exemplar_index,
boolean deviances,
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,
@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="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="exemplar_index")
int exemplar_index,
@Field(value="deviances")
boolean deviances,
@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)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)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)_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="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="exemplar_index")
int exemplar_index,
@Field(value="deviances")
boolean deviances,
@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)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)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)_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="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="exemplar_index")
int exemplar_index,
@Field(value="deviances")
boolean deviances,
@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)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)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)_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="domain")
java.lang.String[] domain,
@Field(value="distribution")
GenmodelutilsDistributionFamily distribution)
predictions_frame - Predictions Frame.actuals_frame - Actuals Frame.domain - Domain (for classification).distribution - Distribution (for regression).@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)