Integration
Optiscorer is using REST API and the data format is provided in CSV or JSON. You should send files for model training and prediction in API usage. For example; For the training of your machine learning model, a file is placed in the “train_file” key in CSV format. The data set shown in Table-1 should be converted to csv format in Table-2 and sent.
The data set to be sent to the Optiscorer API for training and prediction methods, column name order should be sent by following the same order as in Table 3.
Hint : The first column of the data file you provide is always ID column and the last column of the training data set is always score you want to train (teach to machine). In the test file you provide the same column format as in the training set without the last column (the score column). The reason of missing the last column in the test is, predicting this column during the test phase.
Further information about methods is below:
Methods
Optiscorer Train
- optiscorer/train
for training the model
Method | URL |
POST | sunucu_IP/optiscorer/train |
Type | Key | Value |
POST | train_file | file Train veri seti |
Response:
Type | Descriptive |
json | “Done” |
Optiscorer Predict
1. optiscorer/predict
Returns a test set for the test data from the trained model.
Rquirements: 1. A Trained model with the train method. 2. A test data set without the last column (the scores)
Request:
Method | URL |
POST | sunucu_IP/optiscorer/predict |
Type | Key | Value |
POST | file | file Skoru tahmin edilmesi istenilen veri seti |
Response:
Type | Description |
string | ID |
integer | Score |
2. optiscorer/predict_line
Returns the prediction score for a single line:
Requirements: 1. A trained model with the train method, 2. A test data line without the score
Request:
Method | URL |
POST | sunucu_IP/optiscorer/predict_line |
Type | Key | Value |
POST | line | data Skoru tahmin edilmesi istenilen veriler. |
Response:
Type | Description |
string | ID |
integer | Score |
Samples
CURL
1. optiscrorer/train
Request:
curl -X POST -F “train_file=@churn_train.csv” http://127.0.0.1:5000/optiscorer/train |
Response:
“Done” |
2. optiscrorer/predict
Request:
curl -X POST -F file=@”churn_predict.csv” http://127.0.0.1:5000/optiscorer/predict |
Response:
{“8999”: 0.2546779407600896, “9000”: 0.0038931861612941827, “9001”: 0.3305867704110245, “9002”: 0.04050216968690639, “9003”: 0.22248843761573855, “9004”: 0.13911811329647242 } |
3. optiscrorer/predict_line
Request:
curl \ -d ‘{“line”: “8999,15692577,Tomlinson,674,Germany,Female,38,10,83727.68,1,1,0,45418.12”}’ \ -H “Content-Type: application/json” \ -X POST http://127.0.0.1:5000/optiscorer/predict_line |
Response:
{“8999”: 0.2546779407600896 } |
Postman
1. optiscrorer/train
Step by Step
Select POST method
Provide the {server_ip}/optiscorer/train to the URL
Click on body
Click on Form_data
Open File (When you hover the 5 as indicated in the screen shot below, the file will appear)
Provide the key value of the data loaded for training themodel as: “train_file”
Load the data for the model
Send a request to OptiScorer Rest API
2. optiscrorer/predict
Adım Adım:
Select POST as the method
Provide the URL: {server_ip}/optiscorer/predict
Click on Body
Click on Form_data
Open File (When you hover the 5 as indicated in the screen shot below, the file will appear)
Provide the key value of the data loaded for test the model as: “file”
Load the data for the model
Send a request to OptiScorer Rest API
3. optiscrorer/predict_line
Step by Step
Select POST as the method
URL olarak {sunucu_ip}/optiscorer/predict_line içeren adresi yaz.
- Provide the URL: {server_ip}/optiscorer/predict
- Click on Body
Selecdt JSON(application/json)
Provide “line” for the key and “csv” for the value of the json file you will load for the prediction.
Send a request to OptiScorer Rest API