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InCore Ops Document
===
## About Document
<font class='apiok'>Tested API</font>
<font class='apitesting'>Untest API</font>
<font class='building'>Building API</font>
<font class='methodok'>Tested Method</font>
<font class='extend'>Extendable Method</font>
<font class='scheduled'>Scheduled</font>
## About Package
### Database
### Framework
### Data
### Visualize
### Analytic
## Database
- Engine`MYSQL`
- Connection in this project
```python=
from utils import sql
db=sql()
#write
db.cursor.execute("some sql here")
db.conn.commit()
#rollback
db.conn.rollback()
#read
db.cursor.execute("select * from ...")
data=[[dd for dd in d] for d in db.conn.fetchall()]
#close
db.conn.close()
```
- Tables and columns
## Before Run
- run `python`
- clone the repository
- build the database
- Under root folder, create `db`, `images`, `log`, `models`, `tmp` foler
- Under root folder, create a `secret.cfg` which is the config of the system
```=cfg
[DEFAULT]
host=the_IP_of_incore
port=the_port_of_incore
secretkey=the_Secret_Key(can be any string)
dbhost=the_IP_of_sqlDB
dbuser=the_username_of_sqlDB
dbpwd=the_password_of_sqlDB
dbschema=the_schema_of_sqlDB
```
## Run
- Under root folder, run `python3 ./src/apps.py`
## About Maintain
Two ways to enable debug mode (auto clone, auto restart)
- Under root folder, run `python3 ./src/apps.py --debug`
- Change `self.maintaining` to `True` in `src/params.py`
## About Token
When requesting a service, the request should contain a jwt token `token` to validate the request source.
**Contact DevOps for the audience in jwt.**
The validator is `tokenValidator` in `src/utils.py`
## Parameters setting
- file location: `src/params.py`
- Description: File folder, model folder, acceptable files, acceptable projects are defined in this file.
- Usage: In each py
- use `from params import params` to import
- create instance `param=params()`
- Then get var with `param.var`
## System variable API
- Folder location: `src/controller/getConfig.py`
- Description: get system settings
<details>
<summary class='apiok'>[API] getDataProject</summary>
- Description: Get supported project of one dataType
- ==**Usage**==: `GET http://host/sys/dataproject`
```
{
"status": Enum("success","error"),
"msg": "error msg",
"data": {
"num": [
"regression",
"classification",
"abnormal",
"clustering"
],
"cv": [
"regression",
"classification"
],
"nlp": [
"regression",
"classification"
]
}
}
```
</details>
<details>
<summary class='apiok'>[API] getDataExtension</summary>
- Description: Get supported extension of one dataType
- ==**Usage**==: `GET http://host/sys/dataextension` with param
```
{
"datatype": Enum("num", "cv", "nlp")
}
```
and get response
```
{
"status": Enum("success", "error")
"msg": "error msg",
"data": {
[
".ext1", ".ext2" ....
]
}
}
```
</details>
## Data collection service
- Folder location: `src/service/dataService`
- Description: this service contains **upload**, **download**, **getColumn**, **getFileStatus**, **delete** API
<details>
<summary class='apiok'>[API] dataUpload</summary>
- File location: `src/service/dataService/controller/upload.py`
- Description: This py is a upload API. When uploading a file, the service will check the file type and project type. Then generate a file UID. After that, the service will check the file content with checkers in `src/resources/dataService/fileChecker.py`.
- ==**Usage**==: `POST http://host/data/upload` with a form
```
{
"file": binaryFile,
"type": "dataType ( num/cv/nlp)",
"token": "token_string"
}
```
and get a response
```
{
"status": Enum("success", "error"),
"msg": "error_msg",
"data":{
"fileUid": "the_generated_file_uid"
}
}
```
- Acceptable file types and their rules:
- **Numerical project**: A `csv` with column name and their values. The values should be numerical data (classifiable text will be supported in v2.0). For example:
```
temp,humidity,quantity
30.57,43,6
22.89,99,30
```
- **NLP project**: A `tsv` with column name. For project with label, there should be at least one column which contains the numerical value. For example:
```
Sentence1 value value2
I am happy 1 1
I am sad 0 0
```
or
```
Sentence1 Sentence2 value
I am happy So am I :) 1
I am happy I am a student 0
```
- **CV project**: A `zip` file. There should be a (only one) `csv` file in the zip directly, not in a folder. For project with label, here should be at least one column that contains the numerical value. Other columns are the image file path (related path in zip). For example:
```
file.zip
|--lables.csv
|--folder_foo
| |--imga.jpg
| |--imgb.png
|
|--folder_bar
| |--imgk.JPEG
| |--imgl.png
|--imgt.jpg
```
and the csv is
```
filepath,value
folder_foo/imga.jpg,1
folder_foo/imgb.png,1
folder_bar/imgk.JPEG,0
folder_bar/imgl.png,0
imgt.jpg,1
```
</details>
<details>
<summary class='apiok'>[API] dataDownload</summary>
- File location: `src/service/dataService/controller/download.py`
- Description: Download file
- ==**Usage**==: `GET http://host/data/download` with a form
```
{
"fileUid": "file_id"
}
```
and get a binary response
</details>
<details>
<summary class='apiok'>[API] dataDelete</summary>
- File location: `src/service/dataService/controller/delete.py`
- Description: Delete file
- ==**Usage**==: `POST http://host/data/delete` with a form
```
{
"fileUid": "file_id",
"token": "token_string"
}
```
get a json
```
{
"status": Enum("success", "error"),
"msg": "error_msg",
"data":{}
}
```
</details>
<details>
<summary class='apiok'>[API] getColumn</summary>
- File location: `src/service/dataService/controller/getColumn.py`
- Description: Get column names and types
- ==**Usage**==: `POST http://host/data/getcol` with a form
```
{
"fileUid": "file_id",
"token": "token_string"
}
```
get a json
```
{
"status": Enum("success", "error"),
"msg": "error_msg",
"data":{
"cols":[
{
"name": "col1_name",
"type": Enum("int", "float", "path", "string"),
"classifiable": Enum(1,0)
}
]
}
}
```
</details>
<details>
<summary class='apiok'>[API] getFileStatus</summary>
- File location: `src/service/dataService/controller/getFileStatus.py`
- Description: Get file (batch) status
- ==**Usage**==: `POST http://host/data/getstatus` with a form
```
{
"fileUid":(a json list string) "["file_id1", "file_id2"]",
"token": "token_string"
}
```
get a json
```
{
"status": Enum("success", "error"),
"msg": "error_msg",
"data":{
"status":[Enum(0, 1), Enum(0, 1)]
}
}
```
0 for not in-use, 1 for in-use
</details>
<details>
<summary class='methodok'>[Method] fileUidGenerator</summary>
- File location: `src/service/dataService/utils.py`
- Description: Generate unique file id
- Usage:
```python
from service.dataService.utils import fileUidGenerator
uid=fileUidGenerator().uid
```
</details>
<details>
<summary class='methodok'>[Method] fileChecker</summary>
- File location: `src/service/dataService/utils.py`
- Description: Validate file content
- Usage:
```python
from service.dataService.utils import fileChecker
fileCheck=fileChecker(savedPath,dataType).check()
```
</details>
<details>
<summary class='methodok'>[Method] getColType</summary>
- File location: `src/service/dataService/utils.py`
- Description: Get column names and type
- Usage:
```python
from service.dataService.utils import getColType
coltype=getColType(savedPath,dataType).check()
```
This is how `coltype` looks like:
```
[
{
'name':col1_name,
'type':col1_type Enum("int", "float", "string", "path"),
'classifiable': Enum(1, 0)
},
{
'name':col2_name,
'type':col1_type Enum("int", "float", "string", "path"),
'classifiable': Enum(1, 0)
}...
]
```
</details>
<details>
<summary class='methodok'>[Method] getDf</summary>
- File location: `src/service/dataService/utils.py`
- Description: Get column names and type
- Usage:
```python
from service.dataService.utils import getColType
data=getDf(savedPath,dataType).get()['data']
```
Data is a dataframe.
</details>
## Visualize Service
- filelocation: `src/resources/visualizationService`
- Description: Use `bokeh` to show data and image. If the data is not supported by bokeh, it will return the image result of `matplotlib` and shown by bokeh.<br>For showing `bokeh` with `js`, please refer to _section 2_ of [this article](https://blog.csdn.net/cooldiok/article/details/85273652?fbclid=IwAR1fdXZ9k5FdqXq82sEWd3Lexe1vmiPr1ZDMad2Qvvv9xAakJWwozIWeRZo).
<details>
<summary class='apiok'> [API] getDataVizAlgoList</summary>
- File location: `src/service/visualizeService/controller/dataviz.py`
- Description: get data visualize algorithm
- ==**Usage**==: `GET http://host/viz/data/getalgo`
get a json
```
{
"status": Enum("success", "error"),
"msg": "error_msg",
"data":{
{
"algos":[
{
"friendlyname":"algoname1",
"lib":"used lib",
"algoname":"algo1",
"data":{
"x":"float", -> need a x col and must be float or int
"y":"string", -> need a y col and must be string
"value":"none" -> not needed
},
"description":"2D line plot" -> algo description
},
{
"friendlyname":"algoname2",
"lib":"used lib",
"algoname":"algo2",
"data":{
"x":"int", -> need a x col and must be int
"y":"path", -> need a y col and must be path
"value":"float" -> need a value col and must be float
},
"description":"2D scatter plot" -> algo description
}
]
}
}
}
```
</details>
<details>
<summary class='apiok'> [API] dataViz</summary>
- File location: `src/service/visualizeService/controller/dataViz.py`
- Description: Visualizing data
- ==**Usage**==: `POST http://host/viz/data/do` with param
```
{
"fileUid": "fileID",
"algoname": "algoname",
"datacol": (a json string) "{
"x":"col_name1",
"y":"col_name2",
"value":"col_name3"
}"
"token": "token_string"
}
```
and get a response
```
{
"status": Enum("success", "error"),
"msg": "error_msg",
"data":{
"div": "div of bokeh",
"script": "script of bokeh"
}
}
```
</details>
<details>
<summary class='apiok'> [API] getImg</summary>
- File location: `src/service/visualizeService/controller/getImg.py`
- Description: Get binary img
- ==**Usage**==: `GET http://host/viz/getimg` with param
```
uid='imguid'
action='get'/'download'
```
</details>
<details>
<summary class='extend'> [CORE] [EXTENDABLE] dataViz</summary>
- BaseClass File location: `src/service/visualizeService/core/dataViz.py`
</details>
## Analytic Service
### Preprocess and Data info
<details>
<summary class='methodok'> [CORE] missingFiltering</summary>
- File location: `src/service/analuticService/core/preprocess/missingFiltering.py`
- Discription: Filt missing value of number, string and path.
- ==**Usage**==:
- `filtCols` get a filted data
Args:
- data: A 2D array of data
- coltype: colType Enum("int", "float", "string", "path")
- doList: filt the column or not
- pathBase: the base folder of CV file. (OPTIONAL)
```
filted_data=missingFitering().filtCols(
[[dataCol1],[dataCol2]..],
["dataColType1","dataColType1"],
[True,False],
path_of_file (cv folder)
)
```
get a 2D array of filted data
- `getRetainIndex` get which row to be retained
Args:
- data: A 2D array of data to check missing value
- coltype: their colType
- pathBase: the base folder of CV file. (OPTIONAL)
```
retainIndex=missingFiltering().getRetainIndex(
[[dataCol1],[dataCol2]..],
["dataColType1","dataColType1"],
path_of_file (cv folder)
)
```
get a 1D np array of retain or not
</details>
<details>
<summary class='extend'> [CORE] [EXTENDABLE] normalize</summary>
- File location: `src/service/analuticService/core/preprocess/normalize.py`
- Discription: Normalize the column
- ==**Usage**==:
Call imeplemented algo class, `do` to normalize
Args:
- data: A 1D array of data
```
data=normalizeAlgo(data).do()
```
get a 1D array of normalized data
</details>
<details>
<summary class='extend'> [CORE] [EXTENDABLE] outlierFiltering</summary>
- File location: `src/service/analuticService/core/preprocess/outlierFiltering.py`
- Discription: Filt outlier
- ==**Usage**==:
Call imeplemented algo class, `getRetainIndex` get which row to be retained
Args:
- data: A 1D array of data to check missing value
```
retainIndex=outlierFilteringAlgo(data,algoName).getRetainIndex()
```
get a 1D np array of retain or not
</details>
<details>
<summary class='extend'> [CORE] [EXTENDABLE] stringCleaning</summary>
- File location: `src/service/analuticService/core/preprocess/stringCleaning.py`
- Discription: Clean string
- ==**Usage**==:
Call imeplemented algo class, `do` to get a clean string
Args:
- data: A string
```
cleanedString=stringCleaningAlgo(data,algoName).do()
```
get a 1D np array of retain or not
</details>
<details>
<summary class='extend'> [CORE] [EXTENDABLE] correlation</summary>
- File location: `src/service/analuticService/core/correlation.py`
- Discription: Clean string
- ==**Usage**==:
Call imeplemented algo class, `do` to get a clean string
Args:
- fileUid
```
result=correlationAlgo(fileUid).do()
```
get a correlation dataframe and its bokeh div and script
```
{
"div": "bokeh div",
"script": "bokeh script",
"dataframe": dataframe
}
```
</details>
<details>
<summary class='apiok'> [API] getPreprocessAlgo</summary>
- File location: `src/service/analyticService/controller/preprocess.py`
- Description: get preprocess algorithm list
- ==**Usage**==: `GET http://host/preprocess/getalgo`
get a json
```
{
"status": Enum("success", "error"),
"msg": "error msg",
"data": {
"normalize": [
{
"friendlyname": "Min-Max to 0~1",
"algoname": "minmax01"
}
],
"outlierFiltering": [
{
"friendlyname": "1st standard deviation ",
"algoname": "std1"
},
{
"friendlyname": "2nd standard deviation ",
"algoname": "std2"
},
{
"friendlyname": "3rd standard deviation ",
"algoname": "std3"
}
],
"stringCleaning": [
{
"friendlyname": "remove punctuation",
"algoname": "punctuation"
}
]
}
}
```
</details>
<details>
<summary class='apiok'> [API] doPreprocess</summary>
- File location: `src/service/analyticService/controller/preprocess.py`
- Description: preprocess a file and save it to another file
- ==**Usage**==: `POST http://host/preprocess/do` with param
```
{
"fileUid": "fileID",
"action": (a json string) "[
{
"col":"colname1",
"missingFiltering": "0", -> no needed
"outliterFiltering": "0", -> no needed
"normalize": "0", -> no needed
"stringCleaning": ["0"] -> no needed
},
{
"col":"colname2",
"missingFiltering": "1", -> filt missing value
"outliterFiltering": "algoname", -> use algoname to filt outlier
"normalize": "algoname", -> normalize using algoname
"stringCleaning": ["0"] -> a numerical column, no needed
},
{
"col":"colname3",
"missingFiltering": "1",
"outliterFiltering": "0", -> string column, no needed
"normalize": "0", -> string column, no needed
"stringCleaning": ["algoname1","algoname2"] -> use algo1 and algo2 to clean the string
}
]",
"token": "token"
}
```
and get a response
```
{
"status": Enum("success", "error"),
"msg": "error_msg",
"data":{
"fileUid": "uid of file after preprocessing"
}
}
```
</details>
<details>
<summary class="apiok"> [API] previewPreprocess</summary>
- File location: `src/service/analyticService/controller/preprocess.py`
- Description: Preview the result of preprocessed ==numerical== column (classifiable text will be supported in v2.0)
- ==**Usage**==: `POST http://host/preprocess/preview` with param
```
{
"fileUid": "fileID",
"action": (a json string) "[
{
"col":"colname1",
"missingFiltering": "0",
"outliterFiltering": "0",
"normalize": "minmax01",
"stringCleaning": ["0"]
}
]",
"token": "token_string"
}
```
and get a response
1. if figure is plotted:
```
{
"status": "success" or "error",
"msg": "error_msg",
"data":{
"msg":"preprocess result message",
"beforeComp":{
"div":"the bokeh div of before",
"script":"the bokeh script of before"
},
"afterComp":{
"div":"the bokeh div of after",
"script":"the bokeh script of after"
},
}
}
```
2. if no figure is plotted:
```
{
"status": "success" or "error",
"msg": "error_msg",
"data":{
"msg":"preprocess result message",
"beforeComp":"None",
"afterComp":"None"
}
}
```
</details>
<details>
<summary class="apiok"> [API] getPreprocessParameter</summary>
- File location: `src/service/analyticService/controller/preprocess.py`
- Description: get the parameter (called action in code) of a preprocessed file
- ==**Usage**==: `GET http://host/preprocess/get/param` with form
```
{
"token": "token",
"fileUid": "fileUid"
}
```
and get a response
```
[
{
"col":"colname1",
"missingFiltering": "0", -> no needed
"outliterFiltering": "0", -> no needed
"normalize": "0", -> no needed
"stringCleaning": ["0"] -> no needed
},
{
"col":"colname2",
"missingFiltering": "1", -> filt missing value
"outliterFiltering": "algoname", -> use algoname to filt outlier
"normalize": "algoname", -> normalize using algoname
"stringCleaning": ["0"] -> a numerical column, no needed
},
{
"col":"colname3",
"missingFiltering": "1",
"outliterFiltering": "0", -> string column, no needed
"normalize": "0", -> string column, no needed
"stringCleaning": ["algoname1","algoname2"] -> use algo1 and algo2 to clean the string
}
]
```
</details>
<details>
<summary class="apiok"> [API] getCorrelationAlgoList</summary>
- File location: `src/service/analyticService/controller/correlation.py`
- Description: get data correlation algorithm
- ==**Usage**==: `GET http://host/correlation/getalgo`
get a json
```
{
"status": "success",
"msg": "",
"data": [
{
"friendlyname": "Pearson Correlation",
"algoname": "pearson"
}
]
}
```
</details>
<details>
<summary class="apiok"> [API] doCorrelation</summary>
- File location: `src/service/analyticService/controller/correlation.py`
- Description: get correlation of a data (ONLY FOR **NUM** PROJECT)
- ==**Usage**==: `POST http://host/correlation/do` with param
```
{
"token": "token"
"fileUid": "fileID",
"algoname": "the algo name from getCorrelationAlgo response"
}
```
and get a response
```
{
"status": "success" or "error",
"msg": "error_msg",
"data":{
"div": "bokeh div",
"script": "bokeh script"
}
}
```
</details>
D:\NTU\MS\InCore\src\params.py
### Training
<details>
<summary class='extend'> [CORE][EXTEND] Analytic Core</summary>
```graphviz
digraph hierarchy {
//nodesep=1.0 // increases the separation between nodes
node [color=Red,fontname=Courier,shape=box] //All nodes will this shape and colour
edge [color=Blue, style=dashed] //All the lines look like this
analyticBase->{regressionBase classificationBase abnormalBase clusteringBase}
regressionBase->{regAlgo1 regAlgo2 regAlgoN}
classificationBase->{claAlgo1 claAlgo2 claAlgoN}
abnormalBase->{abnAlgo1 abnAlgo2 abnAlgoN}
clusteringBase->{cluAlgo1 cluAlgo2 cluAlgoN}
}
```
- All classes are child classes of `analyticBase`
- Four child PROJECT classes `regressionBase`, `classificationBase`, `abnormalBase`, `clusteringBase` are designed to adapt each kind of training purpose
- To train:
```python=1
alg=algo(algoInfo,fileID,'train') # algoInfo is defined in doModelTrain
modelID=algo.train()
```
- To predict:
```python=1
alg=algo(algoInfo,fileID,'predict',modelID)
alg.predictAlgo()
processedFileID,predictedFileID=alg.predict()
```
- To test:
```python=1
alg=algo(algoInfo,fileID,'test',modelID)
alg.predictAlgo()
result=alg.test()
'''
result={
"text": "The testing result",
"fig":{
"fig1Name":{
"div": "bokeh div",
"script": "bokeh script"
},
"fig1Name":{
"div": "bokeh div",
"script": "bokeh script"
}
}
}
'''
```
- To develop a new PROJECT class, implement:
- **Test**: Generate testing result (loss, accuracy....) as text by `self.outputData` and `self.result`. Save the string to `self.txtRes`
- **projectVisualize**: Generate visualization of model and result by `self.outputData`, `self.result`, `self.model`. The bokeh figures should be saved to `self.vizRes` as
```
{
"fig1Name":{
"div": "bokeh div",
"script": "bokeh script"
},
"fig2Name":{
"div": "bokeh div",
"script": "bokeh script"
}
}
```
</details>
<details>
<summary class="apiok"> [API] getAnalyticAlgoList</summary>
- File location: `src/service/analyticService/controller/analytic.py`
- Description: get analytic algorithm list
- ==**Usage**==: `GET http://host/analytic/getalgo` with param
```
{
"dataType": "cv",
"projectType": "classification"
}
```
get a response
```
{
"status": "status",
"msg": "error msg",
"data": ["algo1","algo2"]
}
```
</details>
<details>
<summary class="apiok"> [API] getAnalyticAlgoParam</summary>
- File location: `src/service/analyticService/controller/analytic.py`
- Description: get parameter of an analytic algorithm
- ==**Usage**==: `GET http://host/analytic/getparam` with param
```
{
"dataType":"num",
"projectType":"regression",
"algoName": "algonameYouWantToKnow"
}
```
get a response
```
{
"status": "status",
"msg": "error msg",
"data": {
"dataType": "num",
"projectType":"regression",
"algoName": "algonameYouWantToKnow",
"description": "the description"
"lib":"sklearn" / "keras",
"param":[
{
"name": "param1Name",
"description": "param1 Description",
"type": "int",
"upperBound": upperBound,
"lowerBound": lowerBound,
"default":20
},
{
"name": "param2Name",
"description": "param2 Description",
"type": "float",
"upperBound": upperBound,
"lowerBound": lowerBound,
"default":23.2
},
{
"name": "param3Name",
"description": "param3 Description",
"type": "bool",
"default":1
},
{
"name": "param4Name",
"description": "param4 Description",
"type": "enum",
"list": ["option1","option2","option3"],
"default":"option2"
},
{
"name": "param5Name",
"description": "param5 Description",
"type": "string",
"default":"default string
"
}
],
"input":[
{
"name": "input1Name",
"description": "input1 description",
"type": "float",
"amount": "multiple",
"required":
},
{
"name": "input2Name",
"description": "input2 description",
"type": "classifiable",
"amount": "single"
},
{
"name": "input3Name",
"description": "input3 description",
"type": "string",
"amount": "single"
},
{
"name": "input4Name",
"description": "input4 description",
"type": "path",
"amount": "single"
}
],
"output":[
{
"name": "output1Name",
"description": "output1 description",
"type": "float"
},
{
"name": "output2Name",
"description": "output2 description",
"type": "classifiable"
},
{
"name": "output3Name",
"description": "output3 description",
"type": "string"
},
{
"name": "output4Name",
"description": "output4 description",
"type": "path"
}
] # For unsupervised project, its a empty list
}
}
```
</details>
<details>
<summary class="apiok"> [API] doModelTrain</summary>
- File location: `src/service/analyticService/controller/analytic.py`
- Description: perform a model training
- ==**Usage**==: `POST http://host/analytic/train` with param (algoInfo in core)
```
{
"token": "token",
"fileUid": "file id",
"dataType": "num",
"projectType": "classification",
"algoName": "the algoname from getAlgoList",
"param": (A json string) "{
"param1Name" : 0.87, # float example
"param2Name" : 30, # int example
"param3Name" : 0, # bool example
"param4Name" : "option1", # enum example
"param5Name" : "string" # string example
}",
"input": (A json string) "{
"input1Name" : ["col1","col2"], # multiple input example
"input2Name" : ["col3"], # single input example
}",
"output": (A json string. Pass "{}" for unsupervised project) "{
"output1Name" : "col4",
"output2Name" : "col5"
}"
}
```
and get a response
```
{
"status": "success"/"error",
"msg": "error msg",
"data": {
"modelUid": "modelUid"
}
}
```
</details>
<details>
<summary class="apiok"> [API] stopTraining </summary>
- File location: `src/service/analyticService/controller/analytic.py`
- Description: stop a model training
- ==**Usage**==: `DELETE http://host/analytic/stop` with form
```
{
"token": "token",
"modelUid": "modelUid"
}
```
and get a response
```
{
"status": "success" / "error",
"msg": "error msg",
"data": {}
}
```
</details>
### After training
<details>
<summary class="apiok"> [API] getModelPreview</summary>
- File location: `src/service/analyticService/controller/analytic.py`
- Description: get the preview of model
- ==**Usage**==: `GET http://host/analytic/preview` with form
```
{
"token": "token",
"modelUid": "modelUid"
}
```
and get a response
```
{
"status": "success" / "error",
"msg": "error msg",
"data": {
"text": "the preview text",
"fig": {
"fig1Name":{
"div": "fig1 div",
"script": "fig1 script"
}
}
}
}
```
</details>
<details>
<summary class="apiok"> [API] doModelPredict</summary>
- File location: `src/service/analyticService/controller/analytic.py`
- Description: perform tprediction on a model using another file
- ==**Usage**==: `POST http://host/analytic/predict` with form
```
{
"token": "token",
"modelUid": "modelUid",
"fileUid": "fileUid",
"preprocess": Enum(1, 0)
}
```
and get a response
```
{
"status": "success" / "error",
"msg": "error msg",
"data": {
"preprocessedFileUid": "preprocessedFid", ("None" for no preprocess)
"predictedFileUid": "predictedFid"
}
}
```
</details>
<details>
<summary class="apiok"> [API] doModelTest</summary>
- File location: `src/service/analyticService/controller/analytic.py`
- Description: perform test on a model using another file
- ==**Usage**==: `POST http://host/analytic/test` with form
```
{
"token": "token",
"modelUid": "modelUid",
"fileUid": "fileUid",
"label": "label of abnormal detection testing"
}
```
and get a response
```
{
"status": "success" / "error",
"msg": "error msg",
"data": {
"text": "the test result text",
"fig": {
"fig1":{
"div": "fig1 div",
"script": "fig1 script"
}
}
}
}
```
</details>
<details>
<summary class="apiok"> [API] deleteModel</summary>
- File location: `src/service/analyticService/controller/analytic.py`
- Description: delete model
- ==**Usage**==: `POST http://host/analytic/delete` with form
```
{
"token": "token",
"modelUid": "modelUid"
}
```
and get a response
```
{
"status": "success" / "error",
"msg": "error msg",
"data": {}
}
```
</details>
<details>
<summary class="apiok"> [API] getModelStatus</summary>
- File location: `src/service/analyticService/controller/analytic.py`
- Description: get the status of a model
- ==**Usage**==: `GET http://host/analytic/get/status` with form
```
{
"token": "token",
"modelUid": "modelUid"
}
```
and get a response
```
{
"status": Enum("success", "error"),
"msg": "error msg",
"data": Enum("success", "train", "fail")
}
```
</details>
<details>
<summary class="apiok"> [API] getModelParameter</summary>
- File location: `src/service/analyticService/controller/analytic.py`
- Description: get the parameter (called algoInfo in code) of a model
- ==**Usage**==: `GET http://host/analytic/get/param` with form
```
{
"token": "token",
"modelUid": "modelUid"
}
```
and get a response
```
{
"dataType": "num",
"projectType": "classification",
"algoName": "the algoname from getAlgoList",
"param": (A json string) "{
"paramName1" : 0.87, # float example
"paramName2" : 30, # int example
"paramName3" : 0, # bool example
"paramName4" : "option1", # enum example
"paramName5" : "string" # string example
}",
"input": (A json string) "{
"input1" : ["col1","col2"], # multiple input example
"input2" : ["col3"], # single input example
}",
"output": (A json string. Pass "{}" for unsupervised project) "{
"output1" : "col4",
"output2" : "col5"
}"
}
```
</details>
<details>
<summary class="apiok"> [API] getModelFailReason</summary>
- File location: `src/service/analyticService/controller/analytic.py`
- Description: get the fail reason of a model
- ==**Usage**==: `GET http://host/analytic/get/fail` with form
```
{
"token": "token",
"modelUid": "modelUid"
}
```
and get a response
```
{
"status": "success" / "error",
"msg": "error msg",
"data": "the reason"
}
```
</details>
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