Open caojianneng opened 2 years ago
Description of the problem:
from kale.common.serveutils import serve kfserver = serve(rf_model) #model is deployed
data = [row.tolist() for _, row in train_df[predictor_var].head(10).iterrows()] data_json = json.dumps({"instances": data})
pred = kfserver.predict(data_json)
Question 1: The returned pred is class labels: 0/1. How to return probabilities?
I tried the following way after studying: kale.common.serveutils.predict
headers = {"content-type": "application/json", "Host": HOST} pred_2 = requests.post(url = URL, data=data_json, headers=headers)
Question 2: But not clear, where to set parameter, so pred_2 will return probabilities?
Description of the problem:
Let the random-forest model be: rf_model
from kale.common.serveutils import serve kfserver = serve(rf_model) #model is deployed
prepare data for prediction
data = [row.tolist() for _, row in train_df[predictor_var].head(10).iterrows()] data_json = json.dumps({"instances": data})
prediciton:
pred = kfserver.predict(data_json)
Question 1: The returned pred is class labels: 0/1. How to return probabilities?
I tried the following way after studying: kale.common.serveutils.predict
let HOST be the host name of deployed model
let the URL of calling the deployed model be: http://xxx:predict
headers = {"content-type": "application/json", "Host": HOST} pred_2 = requests.post(url = URL, data=data_json, headers=headers)
Question 2: But not clear, where to set parameter, so pred_2 will return probabilities?