train_lgbm
This document has been machine translated.
Execute model training for LGBM.
Use JSON-RPC v2.0 as the execution method.
Example request
To perform LGBM model training, specify train_lgbm
as the method
in the parameter prov.process
of the Provenance API.
An example JSON-RPC request is as follows
{
"jsonrpc": "2.0",
"method": "prov.process",
"params": {
"method": "train_lgbm",
"params": {
"output_ddc": "ddc:model",
"input_ddc": "ddc:train_data",
"param_json": "{ (see parameter entry) }",
"no_exec": true
},
},
"id": "provenance_jsonrpc_id"
}
Parameters
The following parameters can be specified for train_lgbm
.
パラメータ名 | 内容 |
---|---|
output_ddc |
output ddc name |
output_mode |
output mode (overwrite or error ) |
input_ddc |
input ddc name |
param_json |
Parameters for model training |
In param_json
you can specify detailed parameters that control the model learning method.
source.date_parts
.
Enumerates the column names to be used as time information in the table specified by input_ddc
.
source.id_column
.
Specify a column name that uniquely identifies a measurement point in the table specified by input_ddc
.
target.column
.
Specifies the name of a column in the table specified by input_ddc
to be used for forecasting.
target.prediction_time
.
Specifies how many steps ahead the value should be predicted.
lgbm.features
.
Enumerate the column names and time ranges to be used for the features in the table specified by input_ddc
in the following format
"features": [
{"column": "nox", "range": 24},
{"column": "temp", "range": [24, -3]},
...
]
Specify column names in columns
and time ranges in range
. The time range can be specified in the following format.
Specified format | Example | Meaning |
---|---|---|
positive integer | 24 |
use base time and values from last 24 hours |
0 |
use only base time values (past values are not used for prediction) | |
negative integer | -3 |
use base time and 3 hours in the future |
2-element array | [24, -3] |
use values from 24 hours before to 3 hours after (including both ends) |
lgbm.regressor
.
You can specify parameters to be passed to LightGBM's LGBMRegressor.
Input data
The ddc specified for input_ddc
must have the following schema
column name | contents | notes |
---|---|---|
start_datetime |
start time | required column in event table |
end_datetime |
end time | required column of event table |
(measurement point id) | id representing the measurement point | |
(Attribute 1) | any attribute value (numeric type) | |
(Attribute 2) | any attribute value (numeric type) | |
(...) | Any attribute value (numeric type) |
Output data
The schema of the ddc output to output_ddc
is as follows
This ddc is needed when performing LGBM forecasting.
column name | contents | notes |
---|---|---|
method |
learning method | fixed to lgbm string |
train_data |
input data name | the real table name will be listed instead of ddc |
param_json |
parameter | parameter string when learning |
location_code` | id representing the measurement location | |
target |
prediction target | value in `target.column |
prediction_time |
estimated time | value in target.prediction_time |
model_path` | model file path | the model file entity is stored on the compute node |
Return value
Output ddc information