Why am I getting a score of 0.0 when finding the score of test data using Gaussian NB classifier?

Question

I have two different data sets. One for training my classifier and the other one is for testing. Both the datasets are text files with two columns separated by a ",". FIrst column (numbers) is for the independent variable (group) and the second column is for the dependent variable.

Training data set

(just few lines for example. there are no empty lines between each row):

EMI3776438,1
EMI3776438,1
EMI3669492,1
EMI3752004,1

Testing data setup

(as you can see, i have picked data from the training data to be sure that the score surely can't be zero)

EMI3776438,1

Code in Python 3.6:

# #all the import statements have been ignored to keep the code short
# #loading the training data set

training_file_path=r'C:\Users\yyy\Desktop\my files\python\Machine learning\Carepack\modified_columns.txt'

from sklearn import preprocessing
le = preprocessing.LabelEncoder()

training_file_data =  pandas.read_table(training_file_path, 
                                        header=None, 
                                        names=['numbers','group'],
                                        sep=',')

training_file_data = training_file_data.apply(le.fit_transform)

features = ['numbers']

x = training_file_data[features]
y = training_file_data["group"]

from sklearn.model_selection import train_test_split
training_x,testing_x, training_y, testing_y = train_test_split(x, y, 
                                                        random_state=0,
                                                        test_size=0.1)

from sklearn.naive_bayes import GaussianNB

gnb= GaussianNB()
gnb.fit(training_x, training_y)

# #loading the testing data 
testing_final_path=r"C:\Users\yyy\Desktop\my files\python\Machine learning\Carepack\testing_final.txt"
testing_sample_data=pandas.read_table(testing_final_path, 
                                      sep=',',
                                      header=None, 
                                      names=['numbers','group'])

testing_sample_data = testing_sample_data.apply(le.fit_transform)

category = ["numbers"]

testing_sample_data_x = testing_sample_data[category]

# #finding the score of the test data
print(gnb.score(testing_sample_data_x, testing_sample_data["group"]))

Answer 1

First, the above data samples dont show how many classes are there in it. You need to describe more about it.

Secondly, you are calling le.fit_transform again on test data which will forget all the training samples mappings from strings to numbers. The LabelEncoder le will start encoding the test data again from scratch, which will not be equal to how it mapped training data. So the input to GaussianNB is now incorrect and hence incorrect results.

Change that to:

testing_sample_data = testing_sample_data.apply(le.transform)

---

UPDATE:

I'm sorry I overlooked the fact that you had two columns in your data. LabelEncoder only works on a single column of data. For making it work on multiple pandas columns at once, look at the answers of following question:

• Label encoding across multiple columns in scikit-learn

If you are using the latest version of scikit (0.20) or can update to it, then you would not need any such hacks and directly use the OrdinalEncoder:

from sklearn.preprocessing import OrdinalEncoder
enc = OrdinalEncoder()

training_file_data = enc.fit_transform(training_file_data)

And during testing:

training_file_data = enc.transform(training_file_data)