Using cosine distance with scikit learn KNeighborsClassifier
The cosine similarity is generally defined as xT y / (||x|| * ||y||), and outputs 1 if they are the same and goes to -1 if they are completely different. This definition is not technically a metric, and so you can't use accelerating structures like ball and kd trees with it. If you force scikit learn to use the brute force approach, you should be able to use it as a distance if you pass it your own custom distance metric object. There are methods of transforming the cosine similarity into a valid distance metric if you would like to use ball trees (you can find one in the JSAT library)
Notice though, that xT y / (||x|| * ||y||) = (x/||x||)T (y/||y||). The euclidean distance can be equivalently written as sqrt(xTx + yTy − 2 xTy). If we normalize every datapoint before giving it to the KNeighborsClassifier, then x^T x = 1 for all x. So the euclidean distance will degrade to sqrt(2 − 2x^T y). For completely the same inputs, we would get sqrt(2-2*1) = 0 and for complete opposites sqrt(2-2*-1)= 2. And it is clearly a simple shape, so you can get the same ordering as the cosine distance by normalizing your data and then using the euclidean distance. So long as you use the uniform weights option, the results will be identical to having used a correct Cosine Distance.
KNN family class constructors have a parameter called metric, you can switch between different distance metrics you want to use in nearest neighbour model.
A list of available distance metrics can be found here
If you want to use cosine metric for ranking and classification problem, you can use norm 2 Euclidean distance on normalized feature vector, that gives you same ranking/classification (predictions that made by argmax or argmin operations) results.