Validate a submitted target data file.

Checks both file level properties like file name, extension, location etc as well as target data, i.e. the contents of the file.

Usage

validate_target_submission(
  hub_path,
  file_path,
  target_type = c("time-series", "oracle-output"),
  date_col = NULL,
  round_id = "default",
  na = c("NA", ""),
  output_type_id_datatype = c("from_config", "auto", "character", "double", "integer",
    "logical", "Date"),
  validations_cfg_path = NULL,
  skip_check_config = FALSE
)

Arguments

hub_path

Either a character string path to a local Modeling Hub directory or an object of class <SubTreeFileSystem> created using functions s3_bucket() or gs_bucket() by providing a string S3 or GCS bucket name or path to a Modeling Hub directory stored in the cloud. For more details consult the Using cloud storage (S3, GCS) in the arrow package. The hub must be fully configured with valid admin.json and tasks.json files within the hub-config directory.

file_path

A character string representing the path to the target data file relative to the target-data directory.

target_type

Type of target data to retrieve matching files. One of "time-series" or "oracle-output". Defaults to "time-series".

date_col

Optional column name to be interpreted as date. Default is NULL. Useful when the required date column is a partitioning column in the target data and does not have the same name as a date typed task ID variable in the config.

round_id

Character string. Not generally relevant to target datasets but can be used to specify a specific block of custom validation checks. Otherwise best set to "default" which will deploy the default custom validation checks.

na

A character vector of strings to interpret as missing values. Only applies to CSV files. The default is c("NA", ""). Useful when actual character string "NA" values are used in the data. In such a case, use empty cells to indicate missing values in your files and set na = "".

output_type_id_datatype

character string. One of "from_config", "auto", "character", "double", "integer", "logical", "Date". Defaults to "from_config" which uses the setting in the output_type_id_datatype property in the tasks.json config file if available. If the property is not set in the config, the argument falls back to "auto" which determines the output_type_id data type automatically from the tasks.json config file as the simplest data type required to represent all output type ID values across all output types in the hub. When only point estimate output types (where output_type_ids are NA,) are being collected by a hub, the output_type_id column is assigned a character data type when auto-determined. Other data type values can be used to override automatic determination. Note that attempting to coerce output_type_id to a data type that is not valid for the data (e.g. trying to coerce"character" values to "double") will likely result in an error or potentially unexpected behaviour so use with care.

validations_cfg_path

Path to YAML file configuring custom validation checks. If NULL defaults to standard hub-config/validations.yml path. For more details see article on custom validation checks.

skip_check_config

Logical. Whether to skip the hub config validation check.

Value

An object of class hub_validations. Each named element contains a hub_check class object reflecting the result of a given check. Function will return early if a check returns an error.

For more details on the structure of <hub_validations> objects, including how to access more information on individual checks, see article on <hub_validations> S3 class objects.

Details

Details of checks performed by validate_target_submission()

Name	Check	Early return	Fail output	Extra info
valid_config	Hub config valid	TRUE	check_error
target_file_exists	File exists at `file_path` provided.	TRUE	check_error
target_partition_file_name	Hive-style partition file path segments are valid and can be parsed successfully. Skipped if target dataset not hive-partitioned.	TRUE	check_error
target_file_ext	Target data file extension is valid.	TRUE	check_error
target_file_read	Target data file can be read successfully.	TRUE	check_error
target_tbl_colnames	Target data file has the correct column names according to target type.	TRUE	check_error
target_tbl_coltypes	Target data file has the correct column types according to target type.	TRUE	check_error
target_tbl_ts_targets	Targets in a time-series target data file are valid. Only performed on `time-series` data files.	TRUE	check_error
target_tbl_rows_unique	Target data file rows are all unique.	FALSE	check_failure
target_tbl_values	Task ID columns in a target data file have valid task ID values.	TRUE	check_error
target_tbl_output_type_ids	Output type ID values in a target data file are valid and complete. Only performed when the target data file contains an `output_type_id` column.	TRUE	check_error
target_tbl_oracle_value	Oracle values in a target data file are valid. Only performed on `oracle output` data files.	FALSE	check_failure

Examples

hub_path <- system.file("testhubs/v5/target_file", package = "hubUtils")
validate_target_submission(
  hub_path,
  file_path = "time-series.csv",
  target_type = "time-series"
)
#> 
#> ── target_file ────
#> 
#> ✔ [valid_config]: All hub config files are valid.
#> 
#> ── time-series.csv ────
#> 
#> ✔ [target_file_exists]: File exists at path target-data/time-series.csv.
#> ℹ [target_partition_file_name]: Target file path not hive-partitioned. Check
#>   skipped.
#> ✔ [target_file_ext]: Target data file extension is valid.
#> ✔ [target_file_read]: target file could be read successfully.
#> ✔ [target_tbl_colnames]: Column names are consistent with expected column names
#>   for time-series target type data.
#> ✔ [target_tbl_coltypes]: Column data types match time-series target schema.
#> ✔ [target_tbl_ts_targets]: time-series targets are all valid.
#> ✔ [target_tbl_rows_unique]: time-series target data rows are unique.
#> ✔ [target_tbl_values]: `target_tbl_chr` contains valid values/value
#>   combinations.
#> ℹ [target_tbl_output_type_ids]: Check not applicable to time-series target
#>   data. Skipped.
#> ℹ [target_tbl_oracle_value]: Check not applicable to time-series target data.
#>   Skipped.
# Example with partitioned data
hub_path <- system.file("testhubs/v5/target_dir", package = "hubUtils")
validate_target_submission(
  hub_path,
  file_path = "time-series/target=wk%20flu%20hosp%20rate/part-0.parquet",
  target_type = "time-series"
)
#> 
#> ── target_dir ────
#> 
#> ✔ [valid_config]: All hub config files are valid.
#> 
#> ── time-series/target=wk%20flu%20hosp%20rate/part-0.parquet ────
#> 
#> ✔ [target_file_exists]: File exists at path
#>   target-data/time-series/target=wk%20flu%20hosp%20rate/part-0.parquet.
#> ✔ [target_partition_file_name]: Hive-style partition file path segments are
#>   valid.
#> ✔ [target_file_ext]: Hive-partitioned target data file extension is valid.
#> ✔ [target_file_read]: target file could be read successfully.
#> ✔ [target_tbl_colnames]: Column names are consistent with expected column names
#>   for time-series target type data.
#> ✔ [target_tbl_coltypes]: Column data types match time-series target schema.
#> ✔ [target_tbl_ts_targets]: time-series targets are all valid.
#> ✔ [target_tbl_rows_unique]: time-series target data rows are unique.
#> ✔ [target_tbl_values]: `target_tbl_chr` contains valid values/value
#>   combinations.
#> ℹ [target_tbl_output_type_ids]: Check not applicable to time-series target
#>   data. Skipped.
#> ℹ [target_tbl_oracle_value]: Check not applicable to time-series target data.
#>   Skipped.