Client Class¶
-
class
h2oai_client.protocol.
Client
(address: str, username: str, password: str, verify=True) → None Bases:
object
-
abort_autoreport
(key: str) → None
-
abort_custom_recipe_job
(key: str) → None
-
abort_experiment
(key: str) → None Abort the experiment.
Parameters: key – The experiment’s key.
-
abort_interpretation
(key: str) → None Abort MLI experiment
Parameters: key – The interpretation key.
-
build_mojo_pipeline
(model_key: str) → str
-
build_mojo_pipeline_sync
(model_key: str) → h2oai_client.messages.MojoPipeline Build MOJO pipeline.
Parameters: model_key – Model key. Returns: a new ScoringPipeline
instance.
-
build_scoring_pipeline
(model_key: str) → str
-
build_scoring_pipeline_sync
(model_key: str) → h2oai_client.messages.ScoringPipeline Build scoring pipeline.
Parameters: model_key – Model key. Returns: a new ScoringPipeline
instance.
-
change_sa_ws
(sa_key: str, action: str, target_col: str, target_row: int, value: str) → h2oai_client.messages.SaShape
-
check_rest_scorer_deployment_health
() → bool
-
clear_sa_history
(sa_key: str) → bool
-
copy_azr_blob_store_to_local
(src: str, dst: str) → bool
-
copy_dtap_to_local
(src: str, dst: str) → bool
-
copy_gcs_to_local
(src: str, dst: str) → bool
-
copy_hdfs_to_local
(src: str, dst: str) → bool
-
copy_minio_to_local
(src: str, dst: str) → bool
-
copy_s3_to_local
(src: str, dst: str) → bool
-
create_and_download_autoreport
(model_key: str, template_path: str = '', config_overrides: str = '', dest_dir: str = '.', **kwargs) Make and download an autoreport from a Driverless AI experiment.
Parameters: - model_key – Model key.
- template_path – Path to custom autoreport template, which will be uploaded and used during rendering
- config_overrides – TOML string format with configurations overrides for AutoDoc
- dest_dir – The directory where the AutoReport should be saved.
- **kwargs – See below
Keyword Arguments: - mli_key (
str
) – - MLI instance key
- mli_key (
- autoviz_key (
str
) – - Visualization key
- autoviz_key (
- individual_rows (
list
) – - List of row indices for rows of interest in training dataset, for which additional information can be shown (ICE, LOCO, KLIME)
- individual_rows (
- placeholders (
dict
) – - Additional text to be added to documentation in dict format, key is the name of the placeholder in template, value is the text content to be added in place of placeholder
- placeholders (
- external_dataset_keys (
list
) – - List of additional dataset keys, to be used for computing different statistics and generating plots.
- external_dataset_keys (
Returns: str: the path to the saved AutoReport
-
create_aws_lambda
(model_key: str, aws_credentials: h2oai_client.messages.AwsCredentials, aws_lambda_parameters: h2oai_client.messages.AwsLambdaParameters) → str Creates a new AWS lambda deployment for the specified model using the given AWS credentials.
-
create_bigquery_query
(dataset_id: str, dst: str, query: str) → str
-
create_csv_from_dataset
(key: str) → str Create csv version of dataset in it’s folder. Returns url to created file.
-
create_custom_recipe_from_url
(url: str) → str
-
create_dataset
(filepath: str) → str
-
create_dataset_from_azr_blob
(filepath: str) → str
-
create_dataset_from_azure_blob_store_sync
(filepath: str) → h2oai_client.messages.Dataset Import a dataset from Azure Blob Storage
Param: filepath: A path specifying the location of the data to upload. Returns: a new :class: Dataset instance.
-
create_dataset_from_bigquery_sync
(datasetid: str, dst: str, query: str) → h2oai_client.messages.Dataset Import a dataset using BigQuery Query
Parameters: - datasetid – Name of BQ Dataset to use for tmp tables
- dst – destination filepath within GCS (gs://<bucket>/<file.csv>)
- query – SQL query to pass to BQ
:returns a new
Dataset
instance.
-
create_dataset_from_dtap
(filepath: str) → str
-
create_dataset_from_dtap_sync
(filepath: str) → h2oai_client.messages.Dataset Import a dataset.
Parameters: filepath – A path specifying the location of the data to upload. Returns: a new Dataset
instance.
-
create_dataset_from_file
(filepath: str) → str
-
create_dataset_from_gbq
(filepath: str) → str
-
create_dataset_from_gcs
(filepath: str) → str
-
create_dataset_from_gcs_sync
(filepath: str) → h2oai_client.messages.Dataset Import a dataset from Google Cloud Storage.
Parameters: filepath – A path specifying the location of the data to upload. Returns: a new Dataset
instance.
-
create_dataset_from_hadoop
(filepath: str) → str
-
create_dataset_from_hadoop_sync
(filepath: str) → h2oai_client.messages.Dataset Import a dataset.
Parameters: filepath – A path specifying the location of the data to upload. Returns: a new Dataset
instance.
-
create_dataset_from_jdbc_sync
(jdbc_user: str, password: str, query: str, id_column: str, destination: str, db_name: str = '', jdbc_jar: str = '', jdbc_url: str = '', jdbc_driver: str = '') → h2oai_client.messages.Dataset Import a dataset using JDBC drivers and SQL Query
Parameters: - jdbc_user – (String) username to authenticate query with
- password – (String) password of user to authenticate query with
- query – (String) SQL query
- id_column – (String) name of id column in dataset
- destination – (String) name for resulting dataset. ex. my_dataset or credit_fraud_data_train
- db_name –
Optional (String) name of database configuration in config.toml to use. ex. {“postgres”: {configurations for postgres jdbc connection},
”sqlite”: {configurations for sqlite jdbc connection}}db_name could be “postgres” or “sqlite” If provided will ignore jdbc_jar, jdbc_url, jdbc_driver arguments. Takes these parameters directly from config.toml configuration
- jdbc_jar – Optional (String) path to JDBC driver jar. Uses this if db_name parameter not provided. Requires jdbc_url and jdbc_driver to be provided, in addition to this parameter
- jdbc_url – Optional (String) JDBC connection url. Uses this if db_name parameter not provided Requires jdbc_jar and jdbc_driver to be provided, in addition to this parameter
- jdbc_driver – Optional (String) classpath of JDBC driver. Uses this if db_name not provided Requires jdbc_jar and jdbc_url to be provided, in addition to this parameter
Returns: (Dataset) dataset object containing information regarding resultant dataset
-
create_dataset_from_kdb
(dst: str, query: str) → str
-
create_dataset_from_kdb_sync
(destination: str, query: str) Import a dataset using KDB+ Query.
Parameters: - destination – Destination for KDB+ Query to be stored on the local filesystem
- query – KDB query. Use standard q queries.
-
create_dataset_from_minio
(filepath: str) → str
-
create_dataset_from_minio_sync
(filepath: str) → h2oai_client.messages.Dataset Import a dataset from Minio.
Parameters: filepath – A path specifying the location of the data to upload. Returns: a new Dataset
instance.
-
create_dataset_from_recipe
(recipe_path: str) → str
-
create_dataset_from_s3
(filepath: str) → str
-
create_dataset_from_s3_sync
(filepath: str) → h2oai_client.messages.Dataset Import a dataset.
Parameters: filepath – A path specifying the location of the data to upload. Returns: a new Dataset
instance.
-
create_dataset_from_snow
(filepath: str) → str
-
create_dataset_from_snowflake_sync
(region: str, database: str, warehouse: str, schema: str, role: str, optional_file_formatting: str, dst: str, query: str) → h2oai_client.messages.Dataset Import a dataset using Snowflake Query.
Parameters: - region – (Optional) Region where Snowflake warehouse exists
- database – Name of Snowflake database to query
- warehouse – Name of Snowflake warehouse to query
- schema – Schema to use during query
- role – (Optional) Snowflake role to be used for query
- optional_file_formatting – (Optional) Additional arguments for formatting the output SQL query to csv file. See snowflake documentation for “Create File Format”
- dst – Destination within local file system for resulting dataset
- query – SQL query to pass to Snowflake
-
create_dataset_from_spark_jdbc
(dst: str, query: str, id_column: str, jdbc_user: str, password: str, spark_jdbc_config: h2oai_client.messages.SparkJDBCConfig) → str
-
create_dataset_from_upload
(filepath: str) → str
-
create_dataset_sync
(filepath: str) → h2oai_client.messages.Dataset Import a dataset.
Parameters: filepath – A path specifying the location of the data to upload. Returns: a new Dataset
instance.
-
create_entity_permission
(permission: h2oai_client.messages.Permission) → h2oai_client.messages.Permission Grant an access to an entity.
Parameters: permission – New access permission to grant to an entity within.
-
create_kdb_query
(dst: str, query: str) → bool
-
create_local_rest_scorer
(model_key: str, max_heap_size_gb: str, local_rest_scorer_parameters: h2oai_client.messages.LocalRestScorerParameters) → str Creates new local rest scorer deployment for specified model
-
create_local_rest_scorer_sync
(model_key: str, deployment_name: str, port_number: int, max_heap_size: int = None) Deploy REST server locally on Driverless AI server. NOTE: This function is primarily for testing & ci purposes.
Parameters: - model_key – Name of model generated by experiment
- deployment_name – Name to apply to deployment
- port_number – port number on which the deployment REST service will be exposed
Param: max_heap_size: maximum heap size (Gb) for rest server deployment. Used to set Xmx_g
Return Deployment: Class Deployment with attributes associated with the successful deployment of
local rest scorer to Driverless AI server
-
create_project
(name: str, description: str) → str
-
create_sa
(mli_key: str) → str
-
create_snowflake_query
(region: str, database: str, warehouse: str, schema: str, role: str, dst: str, query: str, optional_file_formatting: str) → str
-
create_spark_jdbc_query
(dst: str, query: str, id_column: str, username: str, password: str, spark_jdbc_config: h2oai_client.messages.SparkJDBCConfig) → bool
-
delete_autoviz_job
(key: str) → None
-
delete_dataset
(key: str) → None
-
delete_entity_permission
(permission_id: str) → None Revoke an access to an entity. An async job returning key of JobStatus.
Parameters: permission_id – The h2oai-storage ID of a permission to revoke.
-
delete_interpretation
(key: str) → None
-
delete_model
(key: str) → None
-
delete_model_diagnostic_job
(key: str) → None
-
delete_project
(key: str) → None
-
delete_storage_dataset
(dataset_id: str) → None Parameters: dataset_id – The h2oai-storage ID of a dataset to delete remotely.
-
delete_storage_model
(model_id: str) → None Parameters: model_id – The h2oai-storage ID of a model to delete remotely.
-
destroy_aws_lambda
(deployment_key: str) → str Shuts down an AWS lambda deployment removing it entirely from the associated AWS account. Any new deployment will result in a different endpoint URL using a different api_key.
-
destroy_local_rest_scorer
(deployment_key: str) → str
-
destroy_local_rest_scorer_sync
(deployment_key) Function to take down REST server that was deployed locally on Driverless AI server
Parameters: deployment_key – Name of deployment as generated by function create_local_rest_scorer_sync Returns: job status, should be 0
-
download_prediction
(model_key: str, dataset_type: str, include_columns: typing.List[str]) → str Parameters: - model_key – Model Key
- dataset_type – Type of dataset [train/valid/test]
- include_columns – List of columns, which should be included in predictions csv
-
download_prediction_sync
(dest_dir: str, model_key: str, dataset_type: str, include_columns: list) Downloads train/valid/test set predictions into csv file
Parameters: - dest_dir – Destination directory, where csv will be downloaded
- model_key – Model key for which predictions will be downloaded
- dataset_type – Type of dataset for which predictions will be downloaded. Available options are “train”, “valid” or “test”
- include_columns – List of columns from dataset, which will be included in predictions csv
Returns: Local path to csv
-
drop_local_rest_scorer_from_database
(key: str) → None
-
export_dataset_to_storage
(key: str, location: h2oai_client.messages.Location) → str Export a local dataset to the h2oai-storage location. An async job returning key of ExportEntityJob.
Parameters: key – Key of the dataset to export.
-
export_model_to_storage
(key: str, location: h2oai_client.messages.Location) → str Export a local model to the h2oai-storage location. An async job returning key of ExportEntityJob.
Parameters: key – Key of the model to export.
-
filter_sa_ws
(sa_key: str, row_from: int, row_to: int, expr_feature: str, expr_op: str, expr_value: str, f_expr: str) → h2oai_client.messages.SaShape filter the last history entry
-
fit_transform_batch
(model_key: str, training_dataset_key: str, validation_dataset_key: str, test_dataset_key: str, validation_split_fraction: float, seed: int, fold_column: str) → str
-
fit_transform_batch_sync
(model_key, training_dataset_key, validation_dataset_key, test_dataset_key, validation_split_fraction, seed, fold_column) → h2oai_client.messages.Transformation Use model feature engineering to transform provided dataset and get engineered feature in output CSV
Parameters: - model_key – Key of the model to use for transformation
- training_dataset_key – Dataset key which will be used for training
- validation_dataset_key – Dataset key which will be used for validation
- test_dataset_key – Dataset key which will be used for testing
- validation_split_fraction – If not having valid dataset, split ratio for splitting training dataset
- seed – Random seed for splitting
- fold_column – Fold column used for splitting
-
generate_local_rest_scorer_sample_data
(model_key: str) → str
-
get_1d_vega_plot
(dataset_key: str, plot_type: str, x_variable_name: str, kwargs: typing.Any) → str
-
get_2d_vega_plot
(dataset_key: str, plot_type: str, x_variable_name: str, y_variable_name: str, kwargs: typing.Any) → str
-
get_all_config_options
() → typing.List[h2oai_client.messages.ConfigItem] Get metadata and current value for all exposed options
-
get_all_dia_parity_ui
(dia_key: str, dia_variable: str, low_threshold: float, high_threshold: float, offset: int, count: int, sort_column: str, sort_order: str) → typing.List[h2oai_client.messages.DiaNamedMatrix]
-
get_app_version
() → h2oai_client.messages.AppVersion Returns the application version.
Returns: The application version.
-
get_autoreport_job
(key: str) → h2oai_client.messages.AutoReportJob
-
get_autoviz
(dataset_key: str, maximum_number_of_plots: int) → str
-
get_autoviz_job
(key: str) → h2oai_client.messages.AutoVizJob
-
get_autoviz_summary
(key: str) → h2oai_client.messages.AutoVizSummary
-
get_barchart
(dataset_key: str, variable_name: str) → str
-
get_barchart_job
(key: str) → h2oai_client.messages.BarchartJob
-
get_boxplot
(dataset_key: str, variable_name: str) → str
-
get_boxplot_job
(key: str) → h2oai_client.messages.BoxplotJob
-
get_config_options
(keys: typing.List[str]) → typing.List[h2oai_client.messages.ConfigItem] Get metadata and current value for specified options
-
get_configurable_options
() → typing.List[h2oai_client.messages.ConfigItem] Get all config options configurable through expert settings
-
get_create_csv_job
(key: str) → h2oai_client.messages.CreateCsvJob
-
get_create_deployment_job
(key: str) → h2oai_client.messages.CreateDeploymentJob
-
get_current_user_info
() → h2oai_client.messages.UserInfo
-
get_custom_recipe_job
(key: str) → h2oai_client.messages.CustomRecipeJob
-
get_custom_recipes_acceptance_jobs
() → typing.List[h2oai_client.messages.CustomRecipeJob]
-
get_dai_feat_imp_status
(importance_type: str, mli_key: str) → h2oai_client.messages.JobStatus
-
get_data_preview_job
(key: str) → h2oai_client.messages.DataPreviewJob
-
get_data_recipe_preview
(dataset_key: str, code: str) → str Gets the preview of recipe on subset of data Returns DataPreviewJob
Parameters: - dataset_key – Dataset key on which recipe is run
- code – Raw code of the recipe
-
get_dataset_job
(key: str) → h2oai_client.messages.DatasetJob
-
get_dataset_split_job
(key: str) → h2oai_client.messages.DatasetSplitJob
-
get_dataset_summary
(key: str) → h2oai_client.messages.DatasetSummary
-
get_datasets_for_project
(project_key: str, dataset_type: str) → typing.List[h2oai_client.messages.DatasetSummary]
-
get_deployment
(key: str) → h2oai_client.messages.Deployment
-
get_destroy_deployment_job
(key: str) → h2oai_client.messages.DestroyDeploymentJob
-
get_dia
(dia_key: str, dia_variable: str, dia_ref_levels: typing.List[str], offset: int, count: int, sort_column: str, sort_order: str) → h2oai_client.messages.Dia
-
get_dia_avp
(key: str, dia_variable: str) → h2oai_client.messages.DiaAvp
-
get_dia_parity_ui
(dia_key: str, dia_variable: str, ref_level: str, low_threshold: float, high_threshold: float, offset: int, count: int, sort_column: str, sort_order: str) → h2oai_client.messages.DiaMatrix
-
get_dia_status
(key: str) → h2oai_client.messages.JobStatus
-
get_dia_summary
(key: str) → h2oai_client.messages.DiaSummary
-
get_diagnostic_cm_for_threshold
(diagnostic_key: str, threshold: float) → str Returns Model diagnostic Job, where only argmax_cm will be populated
-
get_disk_stats
() → h2oai_client.messages.DiskStats Returns the server’s disk usage as if called by diskinfo (systemutils)
-
get_dotplot
(key: str, variable_name: str, digits: int) → str
-
get_dotplot_job
(key: str) → h2oai_client.messages.DotplotJob
-
get_exemplar_rows
(key: str, exemplar_id: int, offset: int, limit: int, variable_id: int) → h2oai_client.messages.ExemplarRowsResponse
-
get_experiment_preview
(dataset_key: str, validset_key: str, classification: bool, dropped_cols: typing.List[str], target_col: str, is_time_series: bool, time_col: str, enable_gpus: bool, accuracy: int, time: int, interpretability: int, config_overrides: str, reproducible: bool, resumed_experiment_id: str) → str
-
get_experiment_preview_job
(key: str) → h2oai_client.messages.ExperimentPreviewJob
-
get_experiment_preview_sync
(dataset_key: str, validset_key: str, classification: bool, dropped_cols: typing.List[str], target_col: str, is_time_series: bool, time_col: str, enable_gpus: bool, accuracy: int, time: int, interpretability: int, reproducible: bool, resumed_experiment_id: str, config_overrides: str) Get explanation text for experiment settings
Parameters: - dataset_key (str) – Training dataset key
- validset_key (str) – Validation dataset key if any
- classification (bool) – Indicating whether problem is classification or regression. Pass True for classification
- dropped_cols (list of strings) – List of column names, which won’t be used in training
- target_col (str) – Name of the target column for training
- is_time_series (bool) – Whether it’s a time-series problem
- enable_gpus (bool) – Specifies whether experiment will use GPUs for training
- accuracy (int) – Accuracy parameter value
- time (int) – Time parameter value
- interpretability (int) – Interpretability parameter value
- reproducbile (bool) – Set experiment to be reproducible
- resumed_experiment_id (str) – Name of resumed experiment
- config_overrides (str) – Raw config.toml file content (UTF8-encoded string)
Returns: List of strings describing the experiment properties
Return type: list of strings
-
get_experiment_summary_for_mli_key
(mli_job_key: str) → str
-
get_experiment_tuning_suggestion
(dataset_key: str, target_col: str, is_classification: bool, is_time_series: bool, config_overrides: str, cols_to_drop: typing.List[str]) → h2oai_client.messages.ModelParameters
-
get_experiments_for_project
(project_key: str) → typing.List[h2oai_client.messages.ModelSummaryWithDiagnostics]
-
get_experiments_stats
() → h2oai_client.messages.ExperimentsStats Returns stats about experiments
-
get_export_entity_job
(key: str) → h2oai_client.messages.ExportEntityJob
-
get_frame_row_by_value
(frame_name: str, feature_name: str, feature_value: str, num_rows: int, mli_job_key: str) → str
-
get_frame_row_offset_by_value
(feature_name: str, feature_value: str, mli_job_key: str) → int
-
get_frame_rows
(frame_name: str, row_offset: int, num_rows: int, mli_job_key: str, orig_feat_shapley: bool) → str
-
get_gpu_stats
() → h2oai_client.messages.GPUStats Returns gpu stats as if called by get_gpu_info_safe (systemutils)
-
get_grouped_boxplot
(datset_key: str, variable_name: str, group_variable_name: str) → str
-
get_grouped_boxplot_job
(key: str) → h2oai_client.messages.BoxplotJob
-
get_heatmap
(key: str, variable_names: typing.List[str], matrix_type: str, normalize: bool, permute: bool, missing: bool) → str
-
get_heatmap_job
(key: str) → h2oai_client.messages.HeatMapJob
-
get_histogram
(dataset_key: str, variable_name: str, number_of_bars: typing.Any, transform: str) → str
-
get_histogram_job
(key: str) → h2oai_client.messages.HistogramJob
-
get_import_entity_job
(key: str) → h2oai_client.messages.ImportEntityJob
-
get_importmodel_job
(key: str) → h2oai_client.messages.ImportModelJob
-
get_individual_conditional_expectation
(row_offset: int, mli_job_key: str) → str
-
get_interpret_timeseries_job
(key: str) → h2oai_client.messages.InterpretTimeSeriesJob
-
get_interpret_timeseries_summary
(key: str) → h2oai_client.messages.InterpretTimeSeriesSummary
-
get_interpretation_job
(key: str) → h2oai_client.messages.InterpretationJob
-
get_interpretation_summary
(key: str) → h2oai_client.messages.InterpretSummary
-
get_iteration_data
(key: str) → h2oai_client.messages.AutoDLProgress
-
get_jdbc_config
(db_name: str) → h2oai_client.messages.SparkJDBCConfig
-
get_json
(json_name: str, job_key: str) → str
-
get_mli_importance
(model_type: str, importance_type: str, mli_key: str, row_idx: int, code_offset: int, number_of_codes: int) → typing.List[h2oai_client.messages.MliVarImpTable]
-
get_mli_nlp_status
(key: str) → h2oai_client.messages.JobStatus
-
get_mli_nlp_tokens_status
(key: str) → h2oai_client.messages.JobStatus
-
get_mli_variable_importance
(key: str, mli_job_key: str, original: bool) → h2oai_client.messages.VarImpTable
-
get_model_diagnostic
(model_key: str, dataset_key: str) → str Makes model diagnostic from DAI model, containing logic for creating the predictions
-
get_model_diagnostic_job
(key: str) → h2oai_client.messages.ModelDiagnosticJob
-
get_model_job
(key: str) → h2oai_client.messages.ModelJob
-
get_model_job_partial
(key: str, from_iteration: int) → h2oai_client.messages.ModelJob
-
get_model_summary
(key: str) → h2oai_client.messages.ModelSummary
-
get_model_summary_with_diagnostics
(key: str) → h2oai_client.messages.ModelSummaryWithDiagnostics
-
get_model_trace
(key: str, offset: int, limit: int) → h2oai_client.messages.ModelTraceEvents
-
get_mojo_pipeline_job
(key: str) → h2oai_client.messages.MojoPipelineJob
-
get_multinode_stats
() → h2oai_client.messages.MultinodeStats Return stats about multinode
-
get_network
(dataset_key: str, matrix_type: str, normalize: bool) → str
-
get_network_job
(key: str) → h2oai_client.messages.NetworkJob
-
get_original_mli_frame_rows
(row_offset: int, num_rows: int, mli_job_key: str) → str
-
get_original_model_ice
(row_offset: int, mli_job_key: str) → str
-
get_outliers
(dataset_key: str, variable_names: typing.List[str], alpha: float) → str
-
get_outliers_job
(key: str) → h2oai_client.messages.OutliersJob
-
get_parallel_coordinates_plot
(key: str, variable_names: typing.List[str]) → str
-
get_parallel_coordinates_plot_job
(key: str) → h2oai_client.messages.ParallelCoordinatesPlotJob
-
get_prediction_job
(key: str) → h2oai_client.messages.PredictionJob
-
get_project
(key: str) → h2oai_client.messages.Project
-
get_raw_data
(key: str, offset: int, limit: int) → h2oai_client.messages.ExemplarRowsResponse
-
get_sa
(sa_key: str, hist_entry: int, ws_features: typing.List[str], main_chart_feature: str) → h2oai_client.messages.Sa
-
get_sa_create_progress
(sa_key: str) → int
-
get_sa_dataset_summary
(sa_key: str) → h2oai_client.messages.SaDatasetSummary
-
get_sa_history
(sa_key: str) → h2oai_client.messages.SaHistory
-
get_sa_history_entry
(sa_key: str, hist_entry: int) → h2oai_client.messages.SaHistoryItem
-
get_sa_main_chart_data
(sa_key: str, hist_entry: int, feature: str, page_offset: int, page_size: int, aggregate: bool) → h2oai_client.messages.SaMainChartData
-
get_sa_predictions
(sa_key: str, hist_entry: int) → h2oai_client.messages.SaWorkingSetPreds
-
get_sa_preds_history_chart_data
(sa_key: str) → h2oai_client.messages.SaPredsHistoryChartData
-
get_sa_score_progress
(sa_key: str, hist_entry: int) → int
-
get_sa_statistics
(sa_key: str, hist_entry: int) → h2oai_client.messages.SaStatistics
-
get_sa_ws
(sa_key: str, hist_entry: int, features: typing.List[str], page_offset: int, page_size: int) → h2oai_client.messages.SaWorkingSet
-
get_sa_ws_summary
(sa_key: str, hist_entry: int) → h2oai_client.messages.SaWorkingSetSummary
-
get_sa_ws_summary_for_column
(sa_key: str, hist_entry: int, column: str) → h2oai_client.messages.SaFeatureMeta
-
get_sa_ws_summary_for_row
(sa_key: str, hist_entry: int, row: int) → h2oai_client.messages.SaWorkingSetRow
-
get_sa_ws_summary_row
(sa_key: str, hist_entry: int, features: typing.List[str]) → h2oai_client.messages.SaWorkingSetRow
-
get_sas_for_mli
(mli_key: str) → typing.List[str]
-
get_scale
(dataset_key: str, data_min: float, data_max: float) → h2oai_client.messages.H2OScale
-
get_scatterplot
(dataset_key: str, x_variable_name: str, y_variable_name: str) → str
-
get_scatterplot_job
(key: str) → h2oai_client.messages.ScatterPlotJob
-
get_scoring_pipeline_job
(key: str) → h2oai_client.messages.ScoringPipelineJob
-
get_timeseries_split_suggestion
(train_key: str, time_col: str, time_groups_columns: typing.List[str], test_key: str, config_overrides: str) → str
-
get_timeseries_split_suggestion_job
(key: str) → h2oai_client.messages.TimeSeriesSplitSuggestionJob
-
get_transformation_job
(key: str) → h2oai_client.messages.TransformationJob
-
get_users
() → typing.List[str]
-
get_variable_importance
(key: str) → h2oai_client.messages.VarImpTable
-
get_vega_plot
(dataset_key: str, plot_type: str, variable_names: typing.List[str], kwargs: typing.Any) → str
-
get_vega_plot_job
(key: str) → h2oai_client.messages.VegaPlotJob
-
get_vis_stats
(dataset_key: str) → str
-
get_vis_stats_job
(key: str) → h2oai_client.messages.VisStatsJob
-
have_valid_license
() → h2oai_client.messages.License
-
import_model
(filepath: str) → str
-
import_storage_dataset
(dataset_id: str) → str Import dataset from the h2oai-storage locally. An async job returning key of ImportEntityJob.
Parameters: dataset_id – The h2oai-storage ID of the dataset to import.
-
import_storage_model
(model_id: str) → str Import model from the h2oai-storage locally. An async job returning key of ImportEntityJob.
Parameters: model_id – The h2oai-storage ID of the model to import.
-
is_autoreport_active
(key: str) → bool Indicates whether there is some active autoreport job with such key
-
is_original_model_pd_available
(mli_job_key: str) → bool
-
is_original_shapley_available
(mli_job_key: str) → bool
-
is_sa_enabled
() → bool Sensitivity analysis: REST RPC
-
is_valid_license_key
(license_key: str) → h2oai_client.messages.License
-
link_dataset_to_project
(project_key: str, dataset_key: str, dataset_type: str) → bool
-
link_experiment_to_project
(project_key: str, experiment_key: str) → bool
-
list_allowed_file_systems
(offset: int, limit: int) → typing.List[str]
-
list_aws_regions
(aws_credentials: h2oai_client.messages.AwsCredentials) → typing.List[str] List supported AWS regions.
-
list_azr_blob_store_buckets
(offset: int, limit: int) → typing.List[str]
-
list_datasets
(offset: int, limit: int, include_inactive: bool) → h2oai_client.messages.ListDatasetQueryResponse Parameters: include_inactive – Whether to include datasets in failed, cancelled or in-progress state.
-
list_datasets_with_similar_name
(name: str) → typing.List[str]
-
list_deployments
(offset: int, limit: int) → typing.List[h2oai_client.messages.Deployment]
-
list_entity_permissions
(entity_id: str) → typing.List[h2oai_client.messages.Permission] List permissions of a h2oai-storage entity.
Parameters: entity_id – The h2oai-storage ID of the entity to list the permissions of.
-
list_gcs_buckets
(offset: int, limit: int) → typing.List[str]
-
list_interpret_timeseries
(offset: int, limit: int) → typing.List[h2oai_client.messages.InterpretTimeSeriesSummary]
-
list_interpretations
(offset: int, limit: int) → typing.List[h2oai_client.messages.InterpretSummary]
-
list_keys_by_name
(kind: str, display_name: str) → typing.List[str] List all keys of caller’s entities with the given display_name and kind. Note that display_names are not unique so this call returns a list of keys.
Parameters: - kind – Kind of entities to be listed.
- display_name – Display name of the entities to be listed.
-
list_minio_buckets
(offset: int, limit: int) → typing.List[str]
-
list_model_diagnostic
(offset: int, limit: int) → typing.List[h2oai_client.messages.ModelDiagnosticJob]
-
list_model_estimators
() → typing.List[h2oai_client.messages.ModelEstimatorWrapper]
-
list_model_iteration_data
(key: str, offset: int, limit: int) → typing.List[h2oai_client.messages.AutoDLProgress]
-
list_models
(offset: int, limit: int) → h2oai_client.messages.ListModelQueryResponse
-
list_models_with_similar_name
(name: str) → typing.List[str] List all model names with display_name similar as name, e.g. to prevent display_name collision :returns: List of similar model names
-
list_projects
(offset: int, limit: int) → typing.List[h2oai_client.messages.Project]
-
list_s3_buckets
(offset: int, limit: int) → typing.List[str]
-
list_scorers
() → typing.List[h2oai_client.messages.Scorer]
-
list_storage_datasets
(offset: int, limit: int, location: h2oai_client.messages.Location) → h2oai_client.messages.ListDatasetQueryResponse List datasets based on the h2oai-storage location.
-
list_storage_models
(offset: int, limit: int, location: h2oai_client.messages.Location) → h2oai_client.messages.ListModelQueryResponse List models based on the h2oai-storage location.
-
list_storage_projects
(offset: int, limit: int) → h2oai_client.messages.ListProjectQueryResponse List h2oai-storage projects from USER_PROJECTS root location.
-
list_storage_users
(offset: int, limit: int) → typing.List[h2oai_client.messages.StorageUser] List users known to h2oai-storage.
-
list_transformers
() → typing.List[h2oai_client.messages.TransformerWrapper]
-
list_visualizations
(offset: int, limit: int) → typing.List[h2oai_client.messages.AutoVizSummary]
-
make_autoreport
(model_key: str, mli_key: str, individual_rows: typing.List[int], autoviz_key: str, template_path: str, placeholders: typing.Any, external_dataset_keys: typing.List[str], config_overrides: str) → str
-
make_autoreport_sync
(model_key: str, template_path: str = '', config_overrides: str = '', **kwargs) Make an autoreport from a Driverless AI experiment.
Parameters: - model_key – Model key.
- template_path – Path to custom autoreport template, which will be uploaded and used during rendering
- config_overrides – TOML string format with configurations overrides for AutoDoc
- **kwargs – See below
Keyword Arguments: - mli_key (
str
) – - MLI instance key
- mli_key (
- autoviz_key (
str
) – - Visualization key
- autoviz_key (
- individual_rows (
list
) – - List of row indices for rows of interest in training dataset, for which additional information can be shown (ICE, LOCO, KLIME)
- individual_rows (
- placeholders (
dict
) – - Additional text to be added to documentation in dict format, key is the name of the placeholder in template, value is the text content to be added in place of placeholder
- placeholders (
- external_dataset_keys (
list
) – - List of additional dataset keys, to be used for computing different statistics and generating plots.
- external_dataset_keys (
Returns: a new
AutoReport
instance.
-
make_dataset_split
(dataset_key: str, output_name1: str, output_name2: str, target: str, fold_col: str, time_col: str, ratio: float, seed: int) → str
-
make_model_diagnostic_sync
(model_key: str, dataset_key: str) → h2oai_client.messages.Dataset Make model diagnostics from a model and dataset
Parameters: - model_key – Model key.
- dataset_key – Dataset key
Returns: a new
ModelDiagnostic
instance.
-
make_prediction
(model_key: str, dataset_key: str, output_margin: bool, pred_contribs: bool, keep_non_missing_actuals: bool, include_columns: typing.List[str]) → str
-
make_prediction_sync
(model_key: str, dataset_key: str, output_margin: bool, pred_contribs: bool, keep_non_missing_actuals: bool = False, include_columns: list = []) Make a prediction from a model.
Parameters: - model_key – Model key.
- dataset_key – Dataset key on which prediction will be made
- output_margin – Whether to return predictions as margins (in link space)
- pred_contribs – Whether to return prediction contributions
- keep_non_missing_actuals –
- include_columns – List of column names, which should be included in output csv
Returns: a new
Predictions
instance.
-
modify_dataset_by_recipe_file
(key: str, recipe_path: str) → str Returns custom recipe job key
Parameters: - key – Dataset key
- recipe_path – Recipe file path
-
modify_dataset_by_recipe_url
(key: str, recipe_url: str) → str Returns custom recipe job key
Parameters: - key – Dataset key
- recipe_url – Url of the recipe
-
pop_sa_history
(sa_key: str) → bool
-
query_datatable
(frame_name: str, query_str: str, job_key: str) → str
-
remove_sa_history_entry
(sa_key: str, hist_entry: int) → bool
-
reset_sa_ws
(sa_key: str) → h2oai_client.messages.SaShape
-
run_custom_recipes_acceptance_checks
() → None
-
run_interpret_timeseries
(interpret_timeseries_params: h2oai_client.messages.InterpretTimeSeriesParameters) → str
-
run_interpret_timeseries_sync
(dai_model_key: str, **kwargs) Run Interpretation for Time Series
Parameters: - dai_model_key – Driverless AI Time Series Model key, which will be interpreted
- **kwargs – See below
- :Keyword Arguments
- sample_num_rows (
int
) – - Number of rows to sample to generate metrics. Default -1 (All rows)
- sample_num_rows (
Returns: a new :class: InterpretTimeSeries instance.
-
run_interpretation
(interpret_params: h2oai_client.messages.InterpretParameters) → str
-
run_interpretation_sync
(dai_model_key: str, dataset_key: str, target_col: str, **kwargs) Run MLI.
Parameters: - dai_model_key – Driverless AI Model key, which will be interpreted
- dataset_key – Dataset key
- target_col – Target column name
- **kwargs – See below
Keyword Arguments: - use_raw_features (
bool
) – - Show interpretation based on the original columns. Default True
- use_raw_features (
- weight_col (
str
) – - Weight column used by Driverless AI experiment
- weight_col (
- drop_cols (
list
) – - List of columns not used for interpretation
- drop_cols (
- klime_cluster_col (
str
) – - Column used to split data into k-LIME clusters
- klime_cluster_col (
- nfolds (
int
) – - Number of folds used by the surrogate models. Default 0
- nfolds (
- sample (
bool
) – - Whether the training dataset should be sampled down for the interpretation
- sample (
- sample_num_rows (
int
) – - Number of sampled rows. Default -1 == specified by config.toml
- sample_num_rows (
- qbin_cols (
list
) – - List of numeric columns to convert to quantile bins (can help fit surrogate models)
- qbin_cols (
- qbin_count (
int
) – - Number of quantile bins for the quantile bin columns. Default 0
- qbin_count (
- lime_method (
str
) – - LIME method type from [‘k-LIME’, ‘LIME_SUP’]. Default ‘k-LIME’
- lime_method (
- dt_tree_depth (
int
) – - Max depth of decision tree surrogate model. Default 3
- dt_tree_depth (
- config_overrides (
str
) – - Driverless AI config overrides for separate experiment in TOML string format
- config_overrides (
Returns: a new
Interpretation
instance.
-
save_license_key
(license_key: str) → h2oai_client.messages.License
-
score_sa
(sa_key: str, hist_entry: int) → int
-
search_azr_blob_store_files
(pattern: str) → h2oai_client.messages.FileSearchResults
-
search_dtap_files
(pattern: str) → h2oai_client.messages.FileSearchResults
-
search_files
(pattern: str) → h2oai_client.messages.FileSearchResults
-
search_gcs_files
(pattern: str) → h2oai_client.messages.FileSearchResults
-
search_hdfs_files
(pattern: str) → h2oai_client.messages.FileSearchResults
-
search_minio_files
(pattern: str) → h2oai_client.messages.FileSearchResults
-
search_s3_files
(pattern: str) → h2oai_client.messages.FileSearchResults
-
set_config_option
(key: str, value: typing.Any) → typing.List[h2oai_client.messages.ConfigItem] Set value for a given option Returns list of settings modified byt config rules application
-
set_config_option_dummy
(key: str, value: typing.Any, config_overrides: str) → typing.List[h2oai_client.messages.ConfigItem] Set value for a given option on local copy of config, without touching the global config Returns list of settings modified byt config rules application
Parameters: config_overrides – Used to initialize local config
-
start_echo
(message: str, repeat: int) → str
-
start_experiment
(req: h2oai_client.messages.ModelParameters, experiment_name: str) → str Start a new experiment.
Parameters: - req – The experiment’s parameters.
- experiment_name – Display name of newly started experiment
Returns: The experiment’s key.
-
start_experiment_sync
(dataset_key: str, target_col: str, is_classification: bool, accuracy: int, time: int, interpretability: int, scorer=None, score_f_name: str = None, **kwargs) → h2oai_client.messages.Model Start an experiment.
Parameters: - dataset_key (
str
) – Training dataset key - target_col (
str
) – Name of the targed column - is_classification (
bool
) – True for classification problem, False for regression - accuracy – Accuracy setting [1-10]
- time – Time setting [1-10]
- interpretability – Interpretability setting [1-10]
- score (
str
) – <same as score_f_name> for backwards compatibiilty - score_f_name (
str
) – Name of one of the available scorers Default None - automatically decided - **kwargs – See below
Keyword Arguments: - validset_key (
str
) – - Validation daset key
- validset_key (
- testset_key (
str
) – - Test daset key
- testset_key (
- weight_col (
str
) – - Weights column name
- weight_col (
- fold_col (
str
) – - Fold column name
- fold_col (
- cols_to_drop (
list
) – - List of column to be dropped
- cols_to_drop (
- enable_gpus (
bool
) – - Allow GPU usage in experiment. Default True
- enable_gpus (
- seed (
int
) – - Seed for PRNG. Default False
- seed (
- time_col (
str
) – - Time column name, containing time ordering for timeseries problems
- time_col (
- is_timeseries (
bool
) – - Specifies whether problem is timeseries. Default False
- is_timeseries (
- time_groups_columns (
list
) – - List of column names, contributing to time ordering
- time_groups_columns (
- unavailable_columns_at_prediction_time (
list
) – - List of column names, which won’t be present at prediction time in the testing dataset
- unavailable_columns_at_prediction_time (
- time_period_in_seconds (
int
) – - Size of Lag features in seconds, used in timeseries problems
- time_period_in_seconds (
- num_prediction_periods (
int
) – - Timeseries forecast horizont in time period units
- num_prediction_periods (
- num_gap_periods (
int
) – - Number of time periods after which forecast starts
- num_gap_periods (
- config_overrides (
str
) – - Driverless AI config overrides for separate experiment in TOML string format
- config_overrides (
- resumed_model_key (
str
) – - Experiment key, used for retraining/re-ensembling/starting from checkpoint
- resumed_model_key (
- force_skip_acceptance_tests (
bool
) – - Force experiment to skip custom recipes acceptance tests to finish, which may lead to not having all expected custom recipes
- force_skip_acceptance_tests (
- experiment_name (
str
) – - Display name of newly started experiment
- experiment_name (
Returns: a new
Model
instance.- dataset_key (
-
stop_echo
(key: str) → None
-
stop_experiment
(key: str) → None Stop the experiment.
Parameters: key – The experiment’s key.
-
track_subsystem_event
(subsystem_name: str, event_name: str) → None
-
type_of_mli
(mli_job_key: str) → str
-
unlink_dataset_from_project
(project_key: str, dataset_key: str, dataset_type: str) → bool
-
unlink_experiment_from_project
(project_key: str, experiment_key: str) → bool
-
update_dataset_col_format
(key: str, colname: str, datetime_format: str) → None
-
update_dataset_col_logical_types
(key: str, colname: str, logical_types: typing.List[str]) → None
-
update_dataset_name
(key: str, new_name: str) → None
-
update_mli_description
(key: str, new_description: str) → None
-
update_model_description
(key: str, new_description: str) → None
-
update_project_name
(key: str, name: str) → bool
-
upload_custom_recipe_sync
(file_path: str) → h2oai_client.messages.CustomRecipe Upload a custom recipe
Parameters: file_path – A path specifying the location of the python file containing custom transformer classes Returns: CustomRecipe: which contains models, transformers and scorers lists to see newly loaded recipes
-
upload_dataset
(file_path: str) → str Upload a dataset
Parameters: file_path – A path specifying the location of the data to upload. Returns: str: REST response
-
upload_dataset_sync
(file_path) Upload a dataset and wait for the upload to complete.
Parameters: file_path – A path specifying the location of the file to upload. Returns: a Dataset instance.
-
upload_file_sync
(file_path: str) Upload a file.
Parameters: file_path – A path specifying the location of the file to upload. Returns: str: Absolute server-side path to the uploaded file.
-