使用sklearn数组进行线性回归

Question

Just trying to set up a simple linear regression test based on the following example .

Here is my code:

# Normalize customer data
x_array = np.array(CustomerRFM['recency'])
normalized_X = preprocessing.normalize([x_array])
y_array = np.array(CustomerRFM['monetary_value'])
normalized_Y = preprocessing.normalize([y_array])

print('normalized_X: ' + str(np.count_nonzero(normalized_X)))
print('normalized_Y: ' + str(np.count_nonzero(normalized_Y)))

X_train, X_test = train_test_split(normalized_X, test_size=0.2)
Y_train, Y_test = train_test_split(normalized_Y, test_size=0.2)

print('X_train: ' + str(np.count_nonzero(X_train)))
print('Y_train: ' + str(np.count_nonzero(Y_train)))

regr = LinearRegression()
regr.fit(X_train, Y_train)

I have added the four print() lines as I am getting a strange issue. The console print of these four lines is:

normalized_X: 4304
normalized_Y: 4338
X_train: 0
Y_train: 0

For some reason when I am splitting the data between training and testing data I get no values?

I get the following error on the regr.fit() line:

ValueError: Found array with 0 sample(s) (shape=(0, 4339)) while a minimum of 1 is required.

This tells me there is something wrong with the X values but I don't know what

UPDATE: Change to print(array.shape)

If I change my code to use

print('normalized_X: ' + str(normalized_X.shape))
print('normalized_Y: ' + str(normalized_Y.shape))

and this:

print('X_train: ' + str(X_train.shape))
print('Y_train: ' + str(Y_train.shape))

I get:

normalized_X: (1, 4339)
normalized_Y: (1, 4339)

and this:

X_train: (0, 4339)
Y_train: (0, 4339)

Answer 1

It looks like you're using preprocessing.normalize incorrectly. By wrapping [x_array] in square brackets, you're creating an array of shape (1, 4339) .

According to the docs , preprocessing.normalize expects an array of shape [n_samples, n_features] . In your example, n_samples is 1 and n_features is 4339 which I don't think is what you want! You're then asking train_test_split to split a data set of one sample, so it understandably returns an empty array.