Home/ Questions/Recommendation for the valueerror: only 2 non-keyword arguments accepted error
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Kylie Garcia
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Asked: 2022-05-18T18:13:36+00:00 In:

Recommendation for the valueerror: only 2 non-keyword arguments accepted error

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I get the error message:

ValueError: only 2 non-keyword arguments accepted

Has anyone ever faced this problem? How to troubleshoot the “valueerror: only 2 non-keyword arguments accepted.” The problem appears when I try to operate the following program:

from sklearn.linear_model import LinearRegression
  import numpy as np
 
  trainingData = np.array([[861, 16012018], [860, 12012018], [859, 9012018], [858, 5012018], [857, 2012018], [856, 29122017], [855, 26122017], [854, 22122017], [853, 19122017]])
  trainingScores = np.array([11,18,23,33,34,6],[10,19,21,33,34,1], [14,18,22,23,31,6],[16,22,29,31,33,10],[21,24,27,30,31,6],[1,14,15,20,27,7],[1,9,10,11,15,8],[2,9,27,31,35,1],[7,13,18,22,33,2])
 
  clf = LinearRegression(fit_intercept=True)
  clf.fit(trainingScores,trainingData)
 
  predictionData = np.array([862, 19012018 ])
  x=clf.predict(predictionData)
  print(x)

valueerror
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2 Answers

  1. Best Answer
    Expert

    The cause: The matrix construction format was not distinguished clearly, which was the cause of the valueerror: only 2 non-keyword arguments accepted.

    Solution: You are performing a linear regression in ML, it needs to consider replacing this line with:

    trainingScores = np.array(
[11,18,23,33,34,6],
[10,19,21,33,34,1],
[14,18,22,23,31,6],
[16,22,29,31,33,10],
[21,24,27,30,31,6],
[1,14,15,20,27,7],
[1,9,10,11,15,8],
[2,9,27,31,35,1],
[7,13,18,22,33,2]
)
    • 0

  2. Although I don’t know what you’re trying to accomplish here, please change this line. 

    trainingScores = np.array([11,18,23,33,34,6],[10,19,21,33,34,1], [14,18,22,23,31,6],[16,22,29,31,33,10],[21,24,27,30,31,6],[1,14,15,20,27,7],[1,9,10,11,15,8],[2,9,27,31,35,1],[7,13,18,22,33,2])

    This is how it looks (notice the extra brackets around your data). 

    trainingScores = np.array([[11,18,23,33,34,6],[10,19,21,33,34,1], [14,18,22,23,31,6],[16,22,29,31,33,10],[21,24,27,30,31,6],[1,14,15,20,27,7],[1,9,10,11,15,8],[2,9,27,31,35,1],[7,13,18,22,33,2]])

    Next, change the order in fit() as follows: 

    clf.fit(trainingData,trainingScores)

    Finally, you can change prediction data as shown below (again, take a look at the extra square brackets). 

    predictionData = np.array([[862, 19012018]])

    The code will then run.

    • 17

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