Client Class¶

class h2oai_client.protocol.Client(address: str, username: str, password: str, verify=True)

Bases: object

__init__(address: str, username: str, password: str, verify=True) → None: Initialize self. See help(type(self)) for accurate signature.

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.

property autoviz

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
autoviz_key (str) –
Visualization 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)
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
external_dataset_keys (list) –
List of additional dataset keys, to be used for computing different statistics and generating plots.

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

do_tornado_upload(filename, skip_parse=False)

download(src_path: str, dest_dir: str) → str

download_prediction(model_key: str, dataset_type: str, include_columns: 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: Any) → str

get_2d_vega_plot(dataset_key: str, plot_type: str, x_variable_name: str, y_variable_name: str, kwargs: Any) → str

get_all_config_options() → 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) → 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_column_stat(dataset_key: str, column_name: str, stat_type: str, meta: Any) → str

Gets column statistics like mean, median or specific percentile

Parameters

stat_type – Statistics type ref. h2oaicore/imputation_utils.py:ImputationType
meta – Can be e.g. percentile rank

get_config_options(keys: List[str]) → List[h2oai_client.messages.ConfigItem]: Get metadata and current value for specified options

get_configurable_options() → 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() → 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) → 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: 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: 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: 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: List[str]) → h2oai_client.messages.ModelParameters

get_experiments_for_project(project_key: str) → 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: 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: 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) → 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: List[str], alpha: float) → str

get_outliers_job(key: str) → h2oai_client.messages.OutliersJob

get_parallel_coordinates_plot(key: str, variable_names: 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: 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: 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: List[str]) → h2oai_client.messages.SaWorkingSetRow

get_sas_for_mli(mli_key: str) → 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: 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() → List[str]

get_variable_importance(key: str) → h2oai_client.messages.VarImpTable

get_vega_plot(dataset_key: str, plot_type: str, variable_names: List[str], kwargs: 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) → List[str]

list_aws_regions(aws_credentials: h2oai_client.messages.AwsCredentials) → List[str]: List supported AWS regions.

list_azr_blob_store_buckets(offset: int, limit: int) → 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) → List[str]

list_deployments(offset: int, limit: int) → List[h2oai_client.messages.Deployment]

list_entity_permissions(entity_id: str) → 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) → List[str]

list_interpret_timeseries(offset: int, limit: int) → List[h2oai_client.messages.InterpretTimeSeriesSummary]

list_interpretations(offset: int, limit: int) → List[h2oai_client.messages.InterpretSummary]

list_keys_by_name(kind: str, display_name: str) → 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) → List[str]

list_model_diagnostic(offset: int, limit: int) → List[h2oai_client.messages.ModelDiagnosticJob]

list_model_estimators() → List[h2oai_client.messages.ModelEstimatorWrapper]

list_model_iteration_data(key: str, offset: int, limit: int) → List[h2oai_client.messages.AutoDLProgress]

list_models(offset: int, limit: int) → h2oai_client.messages.ListModelQueryResponse

list_models_with_similar_name(name: str) → 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) → List[h2oai_client.messages.Project]

list_s3_buckets(offset: int, limit: int) → List[str]

list_scorers() → 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) → List[h2oai_client.messages.StorageUser]: List users known to h2oai-storage.

list_transformers() → List[h2oai_client.messages.TransformerWrapper]

list_visualizations(offset: int, limit: int) → List[h2oai_client.messages.AutoVizSummary]

make_autoreport(model_key: str, mli_key: str, individual_rows: List[int], autoviz_key: str, template_path: str, placeholders: Any, external_dataset_keys: 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
autoviz_key (str) –
Visualization 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)
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
external_dataset_keys (list) –
List of additional dataset keys, to be used for computing different statistics and generating plots.

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_dataset_split_sync(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: 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

perform_chunked_upload(file_path, skip_parse=False)

perform_stream_upload(file_path, skip_parse=False)

perform_upload(file_path, skip_parse=False)

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)

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
weight_col (str) –
Weight column used by Driverless AI experiment
drop_cols (list) –
List of columns not used for interpretation
klime_cluster_col (str) –
Column used to split data into k-LIME clusters
nfolds (int) –
Number of folds used by the surrogate models. Default 0
sample (bool) –
Whether the training dataset should be sampled down for the interpretation
sample_num_rows (int) –
Number of sampled rows. Default -1 == specified by config.toml
qbin_cols (list) –
List of numeric columns to convert to quantile bins (can help fit surrogate models)
qbin_count (int) –
Number of quantile bins for the quantile bin columns. Default 0
lime_method (str) –
LIME method type from [‘k-LIME’, ‘LIME_SUP’]. Default ‘k-LIME’
dt_tree_depth (int) –
Max depth of decision tree surrogate model. Default 3
config_overrides (str) –
Driverless AI config overrides for separate experiment in TOML string format

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: Any) → 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: Any, config_overrides: str) → 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
testset_key (str) –
Test daset key
weight_col (str) –
Weights column name
fold_col (str) –
Fold column name
cols_to_drop (list) –
List of column to be dropped
enable_gpus (bool) –
Allow GPU usage in experiment. Default True
seed (int) –
Seed for PRNG. Default False
time_col (str) –
Time column name, containing time ordering for timeseries problems
is_timeseries (bool) –
Specifies whether problem is timeseries. Default False
time_groups_columns (list) –
List of column names, contributing to time ordering
unavailable_columns_at_prediction_time (list) –
List of column names, which won’t be present at prediction time in the testing dataset
time_period_in_seconds (int) –
The length of the time period in seconds, used in timeseries problems
num_prediction_periods (int) –
Timeseries forecast horizont in time period units
num_gap_periods (int) –
Number of time periods after which forecast starts
config_overrides (str) –
Driverless AI config overrides for separate experiment in TOML string format
resumed_model_key (str) –
Experiment key, used for retraining/re-ensembling/starting from checkpoint
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
experiment_name (str) –
Display name of newly started experiment
cols_imputation (List[ColumnImputation]) –
List of column imputations for dataset. Ref messages::ColumnImputation

Returns

a new Model instance.

stop_echo(key: str) → None

stop_experiment(key: str) → None

Stop the experiment.

Parameters: key – The experiment’s key.

tornado_raw_producer(filename, write)

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: 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.