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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Using dstoolbox models
Table of contents
Text
W2VClassifier
Imports
Step1:  Text
W2VClassifier
Mock the fit method so that we don't depend on an external file.
Step2:  Using the most_similar method
The most_similar method works similarly to the gensim method of the same name but does not support multiple positive terms or any negative terms.
Step3:  Using the predict method
The predict method works similarly to what would be expected from an sklearn classifier. The classes corresponding to the indices can be found in the classes_ attribute.
A predict_proba method does not exist, since it is not well defined for this case.
Step4:  The kneighbors method
Similarly to KNeighborsClassifier et al., the W2VClassifier supports the kneighbors method.
Step5:  most_similar can be called with multiple positive words
Step6:  The new search will result in an update in the dictionary and can thus be retrieved at a later point in time
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Python Code:
import numpy as np
from dstoolbox.models import W2VClassifier
Explanation: Using dstoolbox models
Table of contents
Text
W2VClassifier
Imports
End of explanation
word2idx = {'herren': 0, 'damen': 1, 'stiefel': 2, 'rock': 3}
word_embeddings = np.array([
    [0.1, 0.1, 0.1],
    [10.0, 10.0, 10.1],
    [-0.1, -0.1, -0.1],
    [-0.1, -0.1, 0.1],
])
def mock_fit(self, X=None, y=None):
    from dstoolbox.utils import normalize_matrix
    self.word2idx = word2idx
    idx2word = {val: key for key, val in word2idx.items()}
    self.classes_ = np.array([idx2word[i] for i in range(len(idx2word))])
    self.idx2word = idx2word
    self.syn0 = normalize_matrix(word_embeddings)
    return self
print(word2idx)
print(word_embeddings)
setattr(W2VClassifier, 'fit', mock_fit)
clf = W2VClassifier('a/path', topn=3).fit()
Explanation: Text
W2VClassifier
Mock the fit method so that we don't depend on an external file.
End of explanation
clf.classes_
clf.most_similar('herren')
clf.most_similar(['damen'])
clf.most_similar('rock')
Explanation: Using the most_similar method
The most_similar method works similarly to the gensim method of the same name but does not support multiple positive terms or any negative terms.
End of explanation
clf.predict(['herren', 'damen', 'rock'])
clf.classes_[clf.predict(['herren', 'damen', 'rock'])]
Explanation: Using the predict method
The predict method works similarly to what would be expected from an sklearn classifier. The classes corresponding to the indices can be found in the classes_ attribute.
A predict_proba method does not exist, since it is not well defined for this case.
End of explanation
clf.kneighbors(['herren', 'rock'], return_distance=False)
clf.kneighbors(['herren', 'rock'])
Explanation: The kneighbors method
Similarly to KNeighborsClassifier et al., the W2VClassifier supports the kneighbors method.
End of explanation
clf.most_similar(['herren', 'rock'])
Explanation: most_similar can be called with multiple positive words
End of explanation
clf.classes_
clf.most_similar('rock')
Explanation: The new search will result in an update in the dictionary and can thus be retrieved at a later point in time
End of explanation
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