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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:
 Visualizing text
Step1:  In this example, we will download some text from wikipedia, split it up into chunks and then plot it. We will use the wikipedia package to retrieve the wiki pages for 'dog' and 'cat'.
Step2:  Below is a snippet of some of the text from the dog wikipedia page.  As you can see, the word dog appears in many of the sentences, but also words related to dog like wolf and carnivore appear.
Step3:  Now we will simply pass the text samples as a list to hyp.plot.  By default hypertools will transform the text data using a topic model that was fit on a variety of wikipedia pages.  Specifically, the text is vectorized using the scikit-learn CountVectorizer and then passed on to a LatentDirichletAllocation to estimate topics.  As can be seen below, the 5 chunks of text from the dog/cat wiki pages cluster together, suggesting they are made up of distint topics.
Step4:  Now, let's add a third very different topic to the plot.
Step5:  As you might expect, the cat and dog text chunks are closer to each other than to basketball in this topic space. Since cats and dogs are both animals, they share many more features (and thus are described with similar text) than basketball.
Visualizing NIPS papers
The next example is a dataset of all NIPS papers published from 1987.  They are fit and transformed using the text from each paper. This example dataset can be loaded using the code below.
Step6:  Visualizing Wikipedia pages
Here, we will plot a collection of wikipedia pages, transformed using a topic
model (the default 'wiki' model) that was fit on the same articles. We will
reduce the dimensionality of the data with TSNE, and then discover cluster with
the 'HDBSCAN' algorithm.
Step7:  Visualizing State of the Union Addresses
In this example we will plot each state of the union address from 1989 to present.  The dots are colored and labeled by president.  The semantic model that was used to transform is the default 'wiki' model, which is a CountVectorizer->LatentDirichletAllocation pipeline fit with a selection of wikipedia pages. As you can see below, the points generally seem to cluster by president, but also by party affiliation (democrats mostly on the left and republicans mostly on the right).
Step8:  Changing the reduction model
These data are reduce with PCA.  Want to visualize using a different algorithm? Simply change the reduce parameter. This gives a different, but equally interesting lower dimensional representation of the data.
Step9:  Defining a corpus
Now let's change the corpus used to train the text model.  Specifically, we'll use the 'nips' text, a collection of scientific papers.  To do this, set corpus='nips'.  You can also specify your own text (as a list of text samples) to train the model.
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Python Code:
import hypertools as hyp
import wikipedia as wiki
%matplotlib inline
Explanation: Visualizing text
End of explanation
def chunk(s, count):
    return [''.join(x) for x in zip(*[list(s[z::count]) for z in range(count)])]
chunk_size = 5
dog_text = wiki.page('Dog').content
cat_text = wiki.page('Cat').content
dog = chunk(dog_text, int(len(dog_text)/chunk_size))
cat = chunk(cat_text, int(len(cat_text)/chunk_size))
Explanation: In this example, we will download some text from wikipedia, split it up into chunks and then plot it. We will use the wikipedia package to retrieve the wiki pages for 'dog' and 'cat'.
End of explanation
dog[0][:1000]
Explanation: Below is a snippet of some of the text from the dog wikipedia page.  As you can see, the word dog appears in many of the sentences, but also words related to dog like wolf and carnivore appear.
End of explanation
hue=['dog']*chunk_size+['cat']*chunk_size
geo = hyp.plot(dog + cat, 'o', hue=hue, size=[8, 6])
Explanation: Now we will simply pass the text samples as a list to hyp.plot.  By default hypertools will transform the text data using a topic model that was fit on a variety of wikipedia pages.  Specifically, the text is vectorized using the scikit-learn CountVectorizer and then passed on to a LatentDirichletAllocation to estimate topics.  As can be seen below, the 5 chunks of text from the dog/cat wiki pages cluster together, suggesting they are made up of distint topics.
End of explanation
bball_text = wiki.page('Basketball').content
bball = chunk(bball_text, int(len(bball_text)/chunk_size))
hue=['dog']*chunk_size+['cat']*chunk_size+['bball']*chunk_size
geo = hyp.plot(dog + cat + bball, 'o', hue=hue, labels=hue, size=[8, 6])
Explanation: Now, let's add a third very different topic to the plot.
End of explanation
nips = hyp.load('nips')
nips.plot(size=[8, 6])
Explanation: As you might expect, the cat and dog text chunks are closer to each other than to basketball in this topic space. Since cats and dogs are both animals, they share many more features (and thus are described with similar text) than basketball.
Visualizing NIPS papers
The next example is a dataset of all NIPS papers published from 1987.  They are fit and transformed using the text from each paper. This example dataset can be loaded using the code below.
End of explanation
wiki = hyp.load('wiki')
wiki.plot(size=[8, 6])
Explanation: Visualizing Wikipedia pages
Here, we will plot a collection of wikipedia pages, transformed using a topic
model (the default 'wiki' model) that was fit on the same articles. We will
reduce the dimensionality of the data with TSNE, and then discover cluster with
the 'HDBSCAN' algorithm.
End of explanation
sotus = hyp.load('sotus')
sotus.plot(size=[10,8])
Explanation: Visualizing State of the Union Addresses
In this example we will plot each state of the union address from 1989 to present.  The dots are colored and labeled by president.  The semantic model that was used to transform is the default 'wiki' model, which is a CountVectorizer->LatentDirichletAllocation pipeline fit with a selection of wikipedia pages. As you can see below, the points generally seem to cluster by president, but also by party affiliation (democrats mostly on the left and republicans mostly on the right).
End of explanation
sotus.plot(reduce='UMAP', size=[10, 8])
Explanation: Changing the reduction model
These data are reduce with PCA.  Want to visualize using a different algorithm? Simply change the reduce parameter. This gives a different, but equally interesting lower dimensional representation of the data.
End of explanation
sotus.plot(reduce='UMAP', corpus='nips', size=[10, 8])
Explanation: Defining a corpus
Now let's change the corpus used to train the text model.  Specifically, we'll use the 'nips' text, a collection of scientific papers.  To do this, set corpus='nips'.  You can also specify your own text (as a list of text samples) to train the model.
End of explanation
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