4paradigm / 4paradigm/OpenMLDB

The continuous feature values in the gcformat sample data generated by the OpenMLDB SQL feature extraction script are incorrect

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Beschreibung

**Bug Description**
Service Version: 0.9.0
The gcformat sample data generated by the OpenMLDB SQL feature extraction script contains incorrect continuous feature values, all of which are set to 0.

**Expected Behavior**
Current incorrect format: label| slot:sign:origin-value
Correct format: label index| slot:sign:origin-value

**Relation Case**
OpenMLDB SQL Feature Extraction Example:
```
0| 1:0:1 2:4599670039981440374 3:6365000770384461703 4:0:93.200000
1| 1:0:2 2:5613161932270271752 3:-1384602352766124944 4:0:93.075000
0| 1:0:3 2:4599670039981440374 3:-6239076729344379818 4:0:92.893000
```
PICO Feature Extraction Example:
```
0 0| 2:-8773247204422130117:1 3:4042412524814531440 4:6048373541161169225 5:4681710344575317709:0x1.74ccccccccccdp6
1 1| 2:-8773247204422130117:2 3:6142047291687075953 4:1461111459061395210 5:4681710344575317709:0x1.744cccccccccdp6
0 2| 2:-8773247204422130117:3 3:4042412524814531440 4:3353218529862650678 5:4681710344575317709:0x1.73926e978d4fep6
```

**Steps to Reproduce**
1. data schema:
```
id[Int],age[Int],job[String],cons_price_idx[Double],y[Int]
```
2. PICO Feature Extraction Script:
```
target_y = binary_label(y)
f_id = continuous(id)
f_age = discrete(age)
f_job = discrete(job)
f_cons_price_idx = continuous(cons_price_idx)
```
4. OpenMLDB SQL Feature Extraction Script:
```
select gcformat(
binary_label(bool(y)),
continuous(id),
discrete(age),
discrete(job),
continuous(cons_price_idx)
) as instance from main_table
```

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Rechercherichtung

The issue is about the `gcformat` function in OpenMLDB's SQL feature extraction script incorrectly outputting continuous feature values as 0. Look at the SQL function implementation for `gcformat`, likely in the SQL engine or UDF code. Compare the output format with the expected PICO format. The fix involves adjusting the formatting logic for continuous values. Test with the provided sample schema and SQL query to verify the corrected output.

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Bewertung

Tech-Stack
sql
Bereich
databases, machine-learning
Issue-Typ
Bug
Schwierigkeit
3/5
Geschätzter Aufwand
1-2 Tage
Aktivitätsstatus
Veraltet
Klarheit
Klar beschrieben
Anfängerfreundlichkeit
45/100

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