Description:
Perform data scoring.
Syntax:
ym_predict(pd,data)
Note:
The external library function (See External Library Guide) performs data scoring based on model object pd. Parameter data is the to-be-scored data, which can be stored in a table sequence, a cursor, a CSV file or an mtx file. When data’s value is a file, the file’s path can be absolute or relative; it is [sAppHome]/store/predict if relative. If data’s value is a CSV file, the content must be comma-separated structured data.
Return a table sequence of scoring results when the data to be scored is a single record or concurrent records;
Return scoring result object when the data to be scored contains multiple records or it is a file.
Parameter:
|
pd |
A model object |
|
data |
To-be-scored data |
Option:
|
@m |
The function @m(pd,data;duration) performs data scoring with a parallel processing. Parameter duration defines a time period (Unit: millisecond) during which the data is to be scored |
Return value:
A scoring result object/table sequence
Example:
|
|
A |
|
|
1 |
=ym_env() |
Start Python service |
|
2 |
=ym_load_pcf("model.pcf") |
Generate a model object based on mode file model.pcf |
|
3 |
=ym_predict(A2,"train_2.csv") |
Score data in train_2.csv according to A2’s model object and return the scoring result object |
|
4 |
>ym_close(A1) |
Release resources |
|
|
A |
B |
|
|
1 |
=file("D:/train_t.csv").import@tcq() |
|
Get the data file to be scored |
|
2 |
=ym_env() |
|
Start the Python service |
|
3 |
=ym_load_pcf("model.pcf") |
|
Generate a model object from an existing model file model.pcf |
|
4 |
fork A1 |
=ym_predict@m(A3,A4;100) |
Use @m option to perform the scoring with concurrent processing and return scoring result table sequence |
|
5 |
>ym_close(A2) |
|
Release resources |