Getting same value for Precision and Recall (K-NN) using sklearn

Updated question: I did this, but I am getting the same result for both precision and recall is it because I am using average ='binary'?

But when I use average='macro' I get this error message:

Test a custom review messageC:\Python27\lib\site-packages\sklearn\metrics\classification.py:976: DeprecationWarning: From version 0.18, binary input will not be handled specially when using averaged precision/recall/F-score. Please use average='binary' to report only the positive class performance.
'positive class performance.', DeprecationWarning)

Here is my updated code:

path = 'opinions.tsv'
data = pd.read_table(path,header=None,skiprows=1,names=['Sentiment','Review'])
X = data.Review
y = data.Sentiment
#Using CountVectorizer to convert text into tokens/features
vect = CountVectorizer(stop_words='english', ngram_range = (1,1), max_df = .80, min_df = 4)
X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=1, test_size= 0.2)
#Using training data to transform text into counts of features for each message
vect.fit(X_train)
X_train_dtm = vect.transform(X_train) 
X_test_dtm = vect.transform(X_test)




#Accuracy using KNN Model
KNN = KNeighborsClassifier(n_neighbors = 3)
KNN.fit(X_train_dtm, y_train)
y_pred = KNN.predict(X_test_dtm)
print('\nK Nearest Neighbors (NN = 3)')



#Naive Bayes Analysis
tokens_words = vect.get_feature_names()
print '\nAnalysis'
print'Accuracy Score: %f %%'% (metrics.accuracy_score(y_test,y_pred)*100)
print "Precision Score: %f%%" %  precision_score(y_test,y_pred, average='binary')
print "Recall Score: %f%%" %  recall_score(y_test,y_pred, average='binary')

By using the code above I get same value for precision and recall.

Thank you for answering my question, much appreciated.

Solution

To calculate precision and recall metrics, you should import the according methods from sklearn.metrics.

As stated in the documentation, their parameters are 1-d arrays of true and predicted labels:

from sklearn.metrics import precision_score
from sklearn.metrics import recall_score

y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 1, 0, 0, 1]

print('Calculating the metrics...')

recision_score(y_true, y_pred, average='macro')
>>> 0.22

recall_score(y_true, y_pred, average='macro')
>>> 0.33