In the Azure ML Studio I prepared a model with AutoML for time series forecasting. The data have some rare gaps in all data sets. I am using the following code to call for a deployed Azure AutoML model as a web service:
import requests
import json
import pandas as pd
# URL for the web service
scoring_uri = 'http://xxxxxx-xxxxxx-xxxxx-xxxx.xxxxx.azurecontainer.io/score'
# Two sets of data to score, so we get two results back
new_data = pd.DataFrame([
['2020-10-04 19:30:00',1.29281,1.29334,1.29334,1.29334,1],
['2020-10-04 19:45:00',1.29334,1.29294,1.29294,1.29294,1],
['2020-10-04 21:00:00',1.29294,1.29217,1.29334,1.29163,34],
['2020-10-04 21:15:00',1.29217,1.29257,1.29301,1.29115,195]],
columns=['1','2','3','4','5','6']
)
# Convert to JSON string
input_data = json.dumps({'data': new_data.to_dict(orient='records')})
# Set the content type
headers = {'Content-Type': 'application/json'}
# Make the request and display the response
resp = requests.post(scoring_uri, input_data, headers=headers)
print(resp.text)
I am getting an error:
{\"error\": \"DataException:\\n\\tMessage: No y values were provided. We expected non-null target values as prediction context because there is a gap between train and test and the forecaster depends on previous values of target. If it is expected, please run forecast() with ignore_data_errors=True. In this case the values in the gap will be imputed.\\n\\tInnerException: None\\n\\tErrorResponse \\n{\\n
I tried to add "ignore_data_errors=True" to different parts of the code without a success, hence, getting another error:
TypeError: __init__() got an unexpected keyword argument 'ignore_data_errors'
I would very much appreciate any help as I am stuck at this.
