python中KNN的数据预处理

Question

preprocessing take a lot of time-consuming to understand, tuple, list, float, array structure. I have data that looks like

<bound method NDFrame.head of                                                       X                                 Y
0     [1.9902, 1.9902, 1.9902, 1.9902, 1.9902, 0.034...      [0.097, 0.097, 0.097, 0.094]
1     [1.9902, 0.034, 0.034, 0.034, 0.034, 0.034, 0....      [0.094, 0.094, 0.094, 0.094]
2     [0.034, 0.034, 0.097, 0.097, 0.097, 0.097, 0.0...  [1.0882, 1.0882, 1.0882, 1.0882]
3     [0.097, 0.097, 0.097, 0.094, 0.094, 0.094, 0.0...  [1.0882, 1.2382, 1.2382, 1.2382]
4     [0.094, 0.094, 0.094, 0.094, 1.0882, 1.0882, 1...  [1.2382, 1.2382, 1.2182, 1.2182]
...                                                 ...                               ...
3395  [0.136, 0.286, 0.286, 0.286, 0.286, 0.286, 0.2...  [0.1276, 0.1276, 0.1276, 0.1276]
3396  [0.286, 0.286, 0.266, 0.266, 0.266, 0.266, 0.2...   [1.1423, 1.2923, 1.2723, 3.672]
3397  [0.266, 0.266, 0.266, 0.1276, 0.1276, 0.1276, ...      [3.672, 3.672, 3.772, 3.772]
3398  [0.1276, 0.1276, 0.1276, 0.1276, 1.1423, 1.292...      [3.772, 3.802, 3.802, 3.802]
3399  [1.1423, 1.2923, 1.2723, 3.672, 3.672, 3.672, ...      [1.021, 1.021, 1.021, 1.021]

I am doing data split using

x=csv_data['X']
y=csv_data['Y']
#print(x)
x_train, x_test, y_train, y_test = train_test_split(x,y)

Fitting to KNN model

K = []
training = []
test = []
scores = {}
  
for k in range(2, 21):
    clf = KNeighborsClassifier(n_neighbors = k)
    clf.fit(x_train, y_train)
  
    training_score = clf.score(x_train, y_train)
    test_score = clf.score(x_test, y_test)
    K.append(k)
  
    training.append(training_score)
    test.append(test_score)
    scores[k] = [training_score, test_score]

Getting error

TypeError                                 Traceback (most recent call last)
TypeError: float() argument must be a string or a number, not 'list'

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
<ipython-input-93-906aa771beda> in <module>()
      6 for k in range(2, 21):
      7     clf = KNeighborsClassifier(n_neighbors = k)
----> 8     clf.fit(x_train, y_train)
      9 
     10     training_score = clf.score(x_train, y_train)

7 frames
/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
     81 
     82     """
---> 83     return array(a, dtype, copy=False, order=order)
     84 
     85 

ValueError: setting an array element with a sequence.

I have been trying few methods such as preprocessing or StandardScaler dint work for me. Kindly help in running KNN. Thanks

Answer 1

The problem is that while using KNN your y is of the shape (n, 4) while the KNN.fit method wants your y to be of shape (n,1) . So in short you can only predict 1 value from y . So in short you either use KNN 4 times for each column in y or don't use KNN .

The code will be like this

# Import KNN for regression

y1 = y.iloc[:, 0]
y2 = y.iloc[:, 1]
y3 = y.iloc[:, 2]
y4 = y.iloc[:, 3]

regressor1 = KNeighborsRegressor(n_neighbors=k).fit(x, y1)
regressor2 = KNeighborsRegressor(n_neighbors=k).fit(x, y2)
regressor3 = KNeighborsRegressor(n_neighbors=k).fit(x, y3)
regressor4 = KNeighborsRegressor(n_neighbors=k).fit(x, y4)

OMG!! Now that I see you were using KNN for classification where in fact your problem is regression. Your fundamentals are really really poor.

Also, Just don't even use that. You won't get any good results from it and it's also computationally expensive.