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How to fix "ValueError: Expected 2D array, got 1D array instead" in sklearn/python?

I there. I just started with the machine learning with a simple example to try and learn. So, I want to classify the files in my disk based on the file type by making use of a classifier. The code I have written is,

import sklearn
import numpy as np


#Importing a local data set from the desktop
import pandas as pd
mydata = pd.read_csv('file_format.csv',skipinitialspace=True)
print mydata


x_train = mydata.script
y_train = mydata.label

#print x_train
#print y_train
x_test = mydata.script

from sklearn import tree
classi = tree.DecisionTreeClassifier()

classi.fit(x_train, y_train)

predictions = classi.predict(x_test)
print predictions

And I am getting the error as,

  script  class  div   label
0       5      6    7    html
1       0      0    0  python
2       1      1    1     csv
Traceback (most recent call last):
  File "newtest.py", line 21, in <module>
  classi.fit(x_train, y_train)
  File "/home/initiouser2/.local/lib/python2.7/site-
packages/sklearn/tree/tree.py", line 790, in fit
    X_idx_sorted=X_idx_sorted)
  File "/home/initiouser2/.local/lib/python2.7/site-
packages/sklearn/tree/tree.py", line 116, in fit
    X = check_array(X, dtype=DTYPE, accept_sparse="csc")
  File "/home/initiouser2/.local/lib/python2.7/site-
packages/sklearn/utils/validation.py", line 410, in check_array
    "if it contains a single sample.".format(array))
ValueError: Expected 2D array, got 1D array instead:
array=[ 5.  0.  1.].
Reshape your data either using array.reshape(-1, 1) if your data has a 
single feature or array.reshape(1, -1) if it contains a single sample.

If anyone can help me with the code, it would be so helpful to me !!

like image 335
Karthik Bhojaraj Avatar asked Dec 03 '22 12:12

Karthik Bhojaraj


2 Answers

When passing your input to the classifiers, pass 2D arrays (of shape (M, N) where N >= 1), not 1D arrays (which have shape (N,)). The error message is pretty clear,

Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

from sklearn.model_selection import train_test_split

# X.shape should be (N, M) where M >= 1
X = mydata[['script']]  
# y.shape should be (N, 1)
y = mydata['label'] 
# perform label encoding if "label" contains strings
# y = pd.factorize(mydata['label'])[0].reshape(-1, 1) 
X_train, X_test, y_train, y_test = train_test_split(
                      X, y, test_size=0.33, random_state=42)
...

clf.fit(X_train, y_train) 
print(clf.score(X_test, y_test))

Some other helpful tips -

  1. split your data into valid train and test portions. Do not use your training data to test - that leads to inaccurate estimations of your classifier's strength
  2. I'd recommend factorizing your labels, so you're dealing with integers. It's just easier.
like image 199
cs95 Avatar answered Jan 20 '23 07:01

cs95


X=dataset.iloc[:, 0].values
y=dataset.iloc[:, 1].values

regressor=LinearRegression()
X=X.reshape(-1,1)
regressor.fit(X,y)

I had the following code. The reshape operator is not an inplace operator. So we have to replace it's value by the value after reshaping like given above.

like image 40
Ameya Marathe Avatar answered Jan 20 '23 08:01

Ameya Marathe