Package index

Utilities for submitting teams

Functions to help teams create valid submission model output files.

validate_submission(): Validate a submitted model data file.

submission_tmpl(): Create a model output submission file template

reexports create_hub_schema coerce_to_hub_schema is_hive_partitioned_path extract_hive_partitions read_config read_config_file get_hub_derived_task_ids: Objects exported from other packages

Higher level validation functions

Functions combining appropriate check unit test used for higher level validations. All return hub_validations or target_validations S3 class objects.

validate_model_data(): Validate the contents of a submitted model data file

validate_model_file(): Valid file level properties of a submitted model output file.

validate_model_metadata(): Valid properties of a metadata file.

validate_pr(): Validate Pull Request

validate_submission(): Validate a submitted model data file.

validate_submission_time(): Validate a submitted model data file submission time.

validate_target_data(): Validate the contents of a submitted target data file.

validate_target_dataset(): Validate dataset level properties of a given target type

validate_target_file(): Validate file level properties of a target data file.

validate_target_pr(): Validate Target Data Pull Request

validate_target_submission(): Validate a submitted target data file.

check_for_errors(): Raise conditions stored in a hub_validations S3 object

Unit validation checks

Unit validation checks. All return hub_check S3 class objects.

check_config_hub_valid(): Check hub correctly configured

check_file_exists(): Check file exists at the file path specified

check_file_format(): Check file format is accepted by hub.

check_file_location(): Check file is being submitted to the correct folder

check_file_n(): Check number of files submitted per round does not exceed the allowed number of submissions per team.

check_file_name(): Check a model output file name can be correctly parsed.

check_file_read(): Check file can be read successfully

check_for_errors(): Raise conditions stored in a hub_validations S3 object

check_metadata_file_exists(): Check whether a metadata schema file exists

check_metadata_file_ext(): Check file is being submitted to the correct folder

check_metadata_file_location(): Check that the metadata file is being submitted to the correct folder

check_metadata_file_name(): Check whether the file name of a metadata file matches the model_id or combination of team_abbr and model_abbr specified within the metadata file

check_metadata_matches_schema(): Check whether a metadata file matches the schema provided by the hub

check_metadata_schema_exists(): Check whether a metadata schema file exists

check_submission_metadata_file_exists(): Check whether a metadata file for the given model exists

check_submission_time(): Checks submission is within the valid submission window for a given round.

check_target_dataset_exists(): Check target dataset can be detected for a given target type

check_target_dataset_file_ext_unique(): Check that all files of a given target type share a single unique file format

check_target_dataset_rows_unique(): Check target dataset rows are all unique

check_target_dataset_unique(): Check that a single unique target dataset exists for a given target type.

check_target_file_ext_valid(): Check that a target data file has a valid extension.

check_target_file_name(): Check that a hive-partitioned target data file name can be correctly parsed.

check_target_file_read(): Check target file can be read successfully

check_target_tbl_colnames(): Check that a target data file has the correct column names according to target type

check_target_tbl_coltypes(): Check that a target data file has the correct column types according to target type

check_target_tbl_oracle_value(): Check that oracle values in an oracle output target data file are valid

check_target_tbl_output_type_ids(): Check that output type ID values in a target data file are valid and complete

check_target_tbl_rows_unique(): Check target data rows are all unique

check_target_tbl_ts_targets(): Check that targets in a time-series target data file are valid

check_target_tbl_values(): Check that task ID columns in a target data file have valid task ID values

check_tbl_col_types(): Check model data column data types

check_tbl_colnames(): Check column names of model output data

check_tbl_derived_task_id_vals(): Check derived task ID columns contain valid values

check_tbl_match_round_id(): Check model output data tbl round ID matches submission round ID.

check_tbl_rows_unique(): Check model data rows are all unique

check_tbl_spl_compound_taskid_set(): Check model output data tbl sample compound task id sets for each modeling task match or are coarser than the expected set defined in the config.

check_tbl_spl_compound_tid(): Check model output data tbl samples contain single unique values for each compound task ID within individual samples

check_tbl_spl_n(): Check model output data tbl samples contain the appropriate number of samples for a given compound idx.

check_tbl_spl_non_compound_tid(): Check model output data tbl samples contain single unique combination of non-compound task ID values across all samples

check_tbl_unique_round_id(): Check model output data tbl contains a single unique round ID.

check_tbl_value_col(): Check output type values of model output data against config

check_tbl_value_col_ascending(): Check that quantile and cdf output type values of model output data are non-descending

check_tbl_value_col_sum1(): Check that pmf output type values of model output data sum to 1.

check_tbl_values(): Check model output data tbl contains valid value combinations

check_tbl_values_required(): Check all required task ID/output type/output type ID value combinations present in model data.

check_valid_round_id(): Check whether the round_id determined for the submission is valid

check_valid_round_id_col(): Check that any round_id_col name provided or extracted from the hub config is valid.

Optional unit validation checks

Optional unit validation checks. All return hub_check S3 class objects and need to be deployed via validations.yml file.

opt_check_metadata_team_max_model_n(): Check that submitting team does not exceed maximum number of allowed models per team

opt_check_tbl_col_timediff(): Check time difference between values in two date columns equal a defined period.

opt_check_tbl_counts_lt_popn(): Check that predicted values per location are less than total location population.

opt_check_tbl_horizon_timediff(): Check time difference between values in two date columns equals a defined time period defined by values in a horizon column

Custom unit validation checks

Functions to support creation and deployment of custom unit validation checks

create_custom_check(): Create a custom validation check function template file.

Lower level functions

Lower level functions for assisting users in developing custom unit checks.

capture_check_cnd(): Capture a condition of the result of validation check.

capture_check_info(): Capture a simple info message condition

capture_exec_error(): Capture an execution error condition

capture_exec_warning(): Capture an execution warning condition

is_success() is_failure() is_error() is_info() not_pass() is_exec_error() is_exec_warn() is_any_error(): Get status of a hub check

parse_file_name(): Parse model output file metadata from file name

read_model_out_file(): Read a model output file

read_target_file(): Read a single target data file

try_check(): Wrap check expression in try to capture check execution errors

expand_model_out_grid(): Create expanded grid of valid task ID and output type value combinations

get_tbl_compound_taskid_set(): Detect the compound_taskid_set for a tbl for each modeling task in a given round.

get_config_derived_task_ids(): Get hub configuration fields from a <config> class object

match_tbl_to_model_task(): Match model output tbl data to their model tasks in config_tasks.

get_target_task_id(): Get Unique Target Task ID

`<hub_validations>` methods

Methods for <hub_validations> S3 objects and subclasses.

print(<hub_validations>): Print results of validate_...() function as a bullet list

print(<pr_hub_validations>): Print results of validate_pr() function as a bullet list

combine(): Concatenate hub_validations S3 class objects

combine(<target_validations>): Concatenate target_validations S3 class objects

new_hub_validations() as_hub_validations(): Create new or convert list to hub_validations S3 class object

new_target_validations() as_target_validations(): Create new or convert list to target_validations S3 class object