calculate sklearn metrics from 2d array

Question

I have the following frame of actual value,

[[0.1,0.2,0.3,0.4,0.5],
 [0.1,0.1,0.3,0.4,0.5],
 [0.1,0.1,0.3,0.4,0.1],
 [0.1,0.3,0.3,0.4,0.5],
 [0.1,0.2,0.2,0.4,0.4],
]

And I built my own model which predicted value as following:

[[0.2,0.4,0.3,0.4,0.1],
 [0.1,0.1,0.3,0.4,0.5],
 [0.2,0.2,0.2,0.4,0.1],
 [0.3,0.3,0.4,0.4,0.2],
 [0.5,0.2,0.2,0.4,0.4],
]

Each of one is in a csv file, I read both of them as a pandas frame and I processing them as following:

   arr1 = df1.values
   arr2 = df2.values
   import numpy as np
   from sklearn.metrics import hamming_loss, accuracy_score, precision_score, 
   recall_score, f1_score 
   from sklearn.metrics import multilabel_confusion_matrix
   y_true = np.array(arr1)
   y_pred = np.array(arr2)
   conf_mat=multilabel_confusion_matrix(y_true, y_pred)

and I get the following error,

if y_type not in ["binary", "multiclass", "multilabel-indicator"]:
--> 104         raise ValueError("{0} is not supported".format(y_type))
    105 
    106     if y_type in ["binary", "multiclass"]:
ValueError: continuous-multioutput is not supported

How can I get the sklearn report for my values?

Answer 1

Based on the metrics you import, I am guessing that your input data represents some sort of probability. However, your data is not normalized, so I might be wrong here.
In any case, Sklearn cannot calculate your metrics from data that is not a class-label or binary input. If you want to for example calculate the accuracy of your prediction, you have to transform your data from a probability (i.e. 80% a, 20% b) to a class (label: a) so that for each prediction Sklearn can compare your prediction to the ground truth data.