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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:
 word2vec
This notebook is equivalent to demo-word.sh, demo-analogy.sh, demo-phrases.sh and demo-classes.sh from Google.
Training
Download some data, for example
Step1:  Run word2phrase to group up similar words "Los Angeles" to "Los_Angeles"
Step2:  This will create a text8-phrases that we can use as a better input for word2vec.
Note that you could easily skip this previous step and use the origial data as input for word2vec.
Train the model using the word2phrase output.
Step3:  That generated a text8.bin file containing the word vectors in a binary format.
Do the clustering of the vectors based on the trained model.
Step4:  That created a text8-clusters.txt with the cluster for every word in the vocabulary
Predictions
Step5:  Import the word2vec binary file created above
Step6:  We can take a look at the vocabulaty as a numpy array
Step7:  Or take a look at the whole matrix
Step8:  We can retreive the vector of individual words
Step9:  We can do simple queries to retreive words similar to "socks" based on cosine similarity
Step10:  This returned a tuple with 2 items
Step11:  There is a helper function to create a combined response
Step12:  Is easy to make that numpy array a pure python response
Step13:  Phrases
Since we trained the model with the output of word2phrase we can ask for similarity of "phrases"
Step14:  Analogies
Its possible to do more complex queries like analogies such as
Step15:  Clusters
Step16:  We can see get the cluster number for individual words
Step17:  We can see get all the words grouped on an specific cluster
Step18:  We can add the clusters to the word2vec model and generate a response that includes the clusters
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Python Code:
import word2vec
Explanation: word2vec
This notebook is equivalent to demo-word.sh, demo-analogy.sh, demo-phrases.sh and demo-classes.sh from Google.
Training
Download some data, for example: http://mattmahoney.net/dc/text8.zip
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word2vec.word2phrase('/Users/drodriguez/Downloads/text8', '/Users/drodriguez/Downloads/text8-phrases', verbose=True)
Explanation: Run word2phrase to group up similar words "Los Angeles" to "Los_Angeles"
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word2vec.word2vec('/Users/drodriguez/Downloads/text8-phrases', '/Users/drodriguez/Downloads/text8.bin', size=100, verbose=True)
Explanation: This will create a text8-phrases that we can use as a better input for word2vec.
Note that you could easily skip this previous step and use the origial data as input for word2vec.
Train the model using the word2phrase output.
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word2vec.word2clusters('/Users/drodriguez/Downloads/text8', '/Users/drodriguez/Downloads/text8-clusters.txt', 100, verbose=True)
Explanation: That generated a text8.bin file containing the word vectors in a binary format.
Do the clustering of the vectors based on the trained model.
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import word2vec
Explanation: That created a text8-clusters.txt with the cluster for every word in the vocabulary
Predictions
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model = word2vec.load('/Users/drodriguez/Downloads/text8.bin')
Explanation: Import the word2vec binary file created above
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model.vocab
Explanation: We can take a look at the vocabulaty as a numpy array
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model.vectors.shape
model.vectors
Explanation: Or take a look at the whole matrix
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model['dog'].shape
model['dog'][:10]
Explanation: We can retreive the vector of individual words
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indexes, metrics = model.cosine('socks')
indexes, metrics
Explanation: We can do simple queries to retreive words similar to "socks" based on cosine similarity:
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model.vocab[indexes]
Explanation: This returned a tuple with 2 items:
1. numpy array with the indexes of the similar words in the vocabulary
2. numpy array with cosine similarity to each word
Its possible to get the words of those indexes
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model.generate_response(indexes, metrics)
Explanation: There is a helper function to create a combined response: a numpy record array
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model.generate_response(indexes, metrics).tolist()
Explanation: Is easy to make that numpy array a pure python response:
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indexes, metrics = model.cosine('los_angeles')
model.generate_response(indexes, metrics).tolist()
Explanation: Phrases
Since we trained the model with the output of word2phrase we can ask for similarity of "phrases"
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indexes, metrics = model.analogy(pos=['king', 'woman'], neg=['man'], n=10)
indexes, metrics
model.generate_response(indexes, metrics).tolist()
Explanation: Analogies
Its possible to do more complex queries like analogies such as: king - man + woman = queen 
This method returns the same as cosine the indexes of the words in the vocab and the metric
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clusters = word2vec.load_clusters('/Users/drodriguez/Downloads/text8-clusters.txt')
Explanation: Clusters
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clusters['dog']
Explanation: We can see get the cluster number for individual words
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clusters.get_words_on_cluster(90).shape
clusters.get_words_on_cluster(90)[:10]
Explanation: We can see get all the words grouped on an specific cluster
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model.clusters = clusters
indexes, metrics = model.analogy(pos=['paris', 'germany'], neg=['france'], n=10)
model.generate_response(indexes, metrics).tolist()
Explanation: We can add the clusters to the word2vec model and generate a response that includes the clusters
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