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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:
 Exploring and undertanding documental databases with topic models and graph analysis
Exercise notebook
Version 1.0
Date
Step1:  1. Corpus acquisition
In this block we will work with collections of text documents. The objectives will be
Step2:  1.1.2. Parsing XML
XML is an inherently hierarchical data format, and the most natural way to represent it is with a tree. The ElementTree module has two classes for this purpose
Step3:  or directly reading a string
Step4:  fromstring() parses XML from a string directly into an Element, which is the root element of the parsed tree. Other parsing functions may create an ElementTree, but we will not cover them here.
As an Element, root has a tag and a dictionary of attributes
Step5:  It also has children nodes over which we can iterate
Step6:  Children are nested, and we can access specific child nodes by index. We can also access the text of specified elements. For instance
Step8:  The presented classes and functions are all you need to solve the following exercise. However, there are many other interesting functions that can probably make it easier for you to work with XML files. For more information, please refer to the ElementTree API.
1.1.3. Exercise
Step9:  1.2. Building the dataset
Now, we will use the function you just implemented, to create a database that we will use throughout this module.
For simplicity, and given that the dataset is not too large, we will keep all projects in the RAM. The dataset will consist of a list containing the dictionaries associated to each of the considered projects in a time interval.
Step10:  We will extract some characteristics of the constructed dataset
Step11:  Exercise
Compute the average length of the abstracts of all projects in the dataset
Step12:  2. Corpus Processing
Topic modelling algorithms process vectorized data. In order to apply them, we need to transform the raw text input data into a vector representation. To do so, we will remove irrelevant information from the text data and preserve as much relevant information as possible to capture the semantic content in the document collection.
Thus, we will proceed with the following steps
Step13:  We will create a list that contains just the abstracts in the dataset. As the order of the elements in a list is fixed, it will be later straightforward to match the processed abstracts to metadata associated to their corresponding projects.
Step14:  2.2. Homogeneization
By looking at the tokenized corpus you may verify that there are many tokens that correspond to punktuation signs and other symbols that are not relevant to analyze the semantic content. They can be removed using the stemming or lemmatization tools from nltk.
The homogeneization process will consist of
Step15:  2.2.1. Stemming vs Lemmatization
At this point, we can choose between applying a simple stemming or ussing lemmatization. We will try both to test their differences.
The lemmatizer from NLTK is based on WordNet. If you have not used wordnet before, you will likely need to download it from nltk (use the nltk.download() command)
Step16:  One of the advantages of the lemmatizer method is that the result of lemmmatization is still a true word, which is more advisable for the presentation of text processing results and lemmatization.
However, without using contextual information, lemmatize() does not remove grammatical differences. This is the reason why "is" or "are" are preserved and not replaced by infinitive "be".
As an alternative, we can apply .lemmatize(word, pos), where 'pos' is a string code specifying the part-of-speech (pos), i.e. the grammatical role of the words in its sentence. For instance, you can check the difference between wnl.lemmatize('is') and wnl.lemmatize('is, pos='v').
Exercise
Complete the following code fragment to lemmatize all abstracts in the NSF dataset
Step17:  2.3. Cleaning
The third step consists of removing those words that are very common in language and do not carry out usefull semantic content (articles, pronouns, etc).
Once again, we might need to load the stopword files using the download tools from nltk
Exercise
In the second line below we read a list of common english stopwords. Clean lemmatized_abstracts by removing all tokens in the stopword list.
Step18:  2.4. Vectorization
Up to this point, we have transformed the raw text collection of articles in a list of articles, where each article is a collection of the word roots that are most relevant for semantic analysis. Now, we need to convert these data (a list of token lists) into a numerical representation (a list of vectors, or a matrix). To do so, we will start using the tools provided by the gensim library. 
As a first step, we create a dictionary containing all tokens in our text corpus, and assigning an integer identifier to each one of them.
Step19:  We can also filter out terms that appear in too few or too many of the documents in the dataset
Step20:  In the second step, let us create a numerical version of our corpus using the doc2bow method. In general, D.doc2bow(token_list) transforms any list of tokens into a list of tuples (token_id, n), one per each token in token_list, where token_id is the token identifier (according to dictionary D) and n is the number of occurrences of such token in token_list.
Step21:  At this point, it is good to make sure to understand what has happened. In clean_abstracts we had a list of token lists. With it, we have constructed a Dictionary, D, which assigns an integer identifier to each token in the corpus.
After that, we have transformed each article (in clean_abstracts) in a list tuples (id, n).
Step22:  Note that we can interpret each element of corpus_bow as a sparse_vector. For example, a list of tuples 
[(0, 1), (3, 3), (5,2)]
for a dictionary of 10 elements can be represented as a vector, where any tuple (id, n) states that position id must take value n. The rest of positions must be zero.
[1, 0, 0, 3, 0, 2, 0, 0, 0, 0]
These sparse vectors will be the inputs to the topic modeling algorithms.
As a summary, the following variables will be relevant for the next chapters
Step23:  3. Topic Modeling
3.1. Training a topic model using Gensim LDA
Since we already have computed the dictionary and documents BoW representation using Gensim, computing the topic model is straightforward using the LdaModel() function. Please, refer to Gensim API documentation for more information on the different parameters accepted by the function
Step24:  3.2. LDA model visualization
Gensim provides a basic visualization of the obtained topics
Step25:  A more useful visualization is provided by the python LDA visualization library, pyLDAvis.
Before executing the next code fragment you need to install pyLDAvis
Step26:  3.3. Gensim utility functions
In addition to visualization purposes, topic models are useful to obtain a semantic representation of documents that can later be used with some other purpose
Step27:  An alternative to the use of the get_document_topics() function is to directly transform a dataset using the ldag object as follows. You can apply this transformation to several documents at once, but then the result is an iterator from which you can build the corresponding list if necessary
Step28:  Finally, Gensim provides some useful functions to convert between formats, and to simplify interaction with numpy and scipy. The following code fragment converts a corpus in sparse format to a full numpy matrix
Step30:  Exercise
Build a function that returns the most relevant projects for a given topic
Step32:  Exercise
Build a function that computes the semantic distance between two documents. For this, you can use the functions (or code fragments) provided in the library dist_utils.py.
Step33:  Function that creates the Node CSV file for Gephi
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Python Code:
# Common imports 
import numpy as np
# import pandas as pd
# import os
from os.path import isfile, join
# import scipy.io as sio
# import scipy
import zipfile as zp
# import shutil
# import difflib
Explanation: Exploring and undertanding documental databases with topic models and graph analysis
Exercise notebook
Version 1.0
Date: Aug 31, 2017
Authors: 
Jerónimo Arenas-García (jeronimo.arenas@uc3m.es)
Jesús Cid-Sueiro (jcid@tsc.uc3m.es)
End of explanation
xmlfile = '../data/1600057.xml'
with open(xmlfile,'r') as fin:
    
    print(fin.read())
Explanation: 1. Corpus acquisition
In this block we will work with collections of text documents. The objectives will be:
Find the most important topics in the collection and assign documents to topics
Analyze the structure of the collection by means of graph analysis
We will work with a collection of research projects funded by the US National Science Foundation, that you can find under the ./data directory. These files are publicly available from the NSF website.
(As a side note, there are many other available text collections to work with. In particular, the NLTK library has many examples, that you can explore using the nltk.download() tool.
import nltk
nltk.download()
for instance, you can take the gutemberg dataset
Mycorpus = nltk.corpus.gutenberg
text_name = Mycorpus.fileids()[0]
raw = Mycorpus.raw(text_name)
Words = Mycorpus.words(text_name)
Also, tools like Gensim or Sci-kit learn include text databases to work with).
1.1. Exploring file structure
NSF project information is provided in XML files. Projects are yearly grouped in .zip files, and each project is saved in a different XML file. To explore the structure of such files, we will use the file 160057.xml. Parsing XML files in python is rather easy using the ElementTree module. 
To introduce some common functions to work with XML files we will follow <a href=http://docs.python.org/3.4/library/xml.etree.elementtree.html#module-xml.etree.ElementTree>this tutorial</a>.
1.1.1. File format
To start with, you can have a look at the contents of the example file. We are interested on the following information of each project:
Project identifier
Project Title
Project Abstract
Budget
Starting Year (we will ignore project duration)
Institution (name, zipcode, and state)
End of explanation
import xml.etree.ElementTree as ET
tree = ET.parse(xmlfile)
root = tree.getroot
Explanation: 1.1.2. Parsing XML
XML is an inherently hierarchical data format, and the most natural way to represent it is with a tree. The ElementTree module has two classes for this purpose:
ElementTree represents the whole XML document as a tree
Element represents a single node in this tree
We can import XLM data by reading an XML file:
End of explanation
root = ET.fromstring(open(xmlfile,'r').read())
Explanation: or directly reading a string:
End of explanation
print(root.tag)
print(root.attrib)
Explanation: fromstring() parses XML from a string directly into an Element, which is the root element of the parsed tree. Other parsing functions may create an ElementTree, but we will not cover them here.
As an Element, root has a tag and a dictionary of attributes:
End of explanation
for child in root:
    print(child.tag, child.attrib)
Explanation: It also has children nodes over which we can iterate:
End of explanation
for child in root[0]:
    print(child.tag, child.attrib, child.text)
Explanation: Children are nested, and we can access specific child nodes by index. We can also access the text of specified elements. For instance:
End of explanation
def parse_xmlproject(xml_string):
    This function processess the specified XML field,
    and outputs a dictionary with the desired project information
    
    :xml_string: String with XML content
    :Returns: Dictionary with indicated files
    
    
    #<SOL>
    #</SOL>
    
parse_xmlproject(open(xmlfile,'r').read())
Explanation: The presented classes and functions are all you need to solve the following exercise. However, there are many other interesting functions that can probably make it easier for you to work with XML files. For more information, please refer to the ElementTree API.
1.1.3. Exercise: Parsing the XML project files
Implement a function that parses the XML files and provides as its output a dictionary with fields:
project_code      (string)
title             (string)
abstract          (string)
budget            (float)
year              (string)
institution       (tuple with elements: name, zipcode, and statecode)
End of explanation
# Construct an iterator (or a list) for the years you want to work with
years = range(2015,2017)
datafiles_path = '../data/'
NSF_data = []
for year in years:
    
    zpobj = zp.ZipFile(join(datafiles_path, str(year)+'.zip'))
    for fileinzip in zpobj.namelist():
        if fileinzip.endswith('xml'):
            
            #Some files seem to be incorrectly parsed
            try:
                project_dictio = parse_xmlproject(zpobj.read(fileinzip))
                if project_dictio['abstract']:
                    NSF_data.append(project_dictio)
            except:
                pass
Explanation: 1.2. Building the dataset
Now, we will use the function you just implemented, to create a database that we will use throughout this module.
For simplicity, and given that the dataset is not too large, we will keep all projects in the RAM. The dataset will consist of a list containing the dictionaries associated to each of the considered projects in a time interval.
End of explanation
print('Number of projects in dataset:', len(NSF_data))
####
budget_data = list(map(lambda x: x['budget'], NSF_data))
print('Average budget of projects in dataset:', np.mean(budget_data))
####
insti_data = list(map(lambda x: x['institution'], NSF_data))
print('Number of unique institutions in dataset:', len(set(insti_data)))
####
counts = dict()
for project in NSF_data:
    counts[project['year']] = counts.get(project['year'],0) + 1
print('Breakdown of projects by starting year:')
for el in counts:
    print(el, ':', counts[el])
Explanation: We will extract some characteristics of the constructed dataset:
End of explanation
#<SOL>
#</SOL>
Explanation: Exercise
Compute the average length of the abstracts of all projects in the dataset
End of explanation
import nltk
# You should comment this code fragment if the package is already available.
# Select option "d) Download", and identifier "punkt"
# nltk.download()
Explanation: 2. Corpus Processing
Topic modelling algorithms process vectorized data. In order to apply them, we need to transform the raw text input data into a vector representation. To do so, we will remove irrelevant information from the text data and preserve as much relevant information as possible to capture the semantic content in the document collection.
Thus, we will proceed with the following steps:
Tokenization
Homogeneization
Cleaning
Vectorization
2.1. Tokenization
For the first steps, we will use some of the powerful methods available from the Natural Language Toolkit. In order to use the word_tokenize method from nltk, you might need to get the appropriate libraries using nltk.download(). You must select option "d) Download", and identifier "punkt"
End of explanation
from nltk.tokenize import word_tokenize
NSF_abstracts = list(map(lambda x: x['abstract'], NSF_data))
tokenized_abstracts = []
nprojects = len(NSF_abstracts)
for n, abstract in enumerate(NSF_abstracts):
    if not n%100:
        print('\rTokenizing abstract', n, 'out of', nprojects, end='', flush=True)
    tokenized_abstracts.append(word_tokenize(abstract))
print('\n\n The corpus has been tokenized. Check the result for the first abstract:')
print(NSF_abstracts[0])
print(tokenized_abstracts[0])
Explanation: We will create a list that contains just the abstracts in the dataset. As the order of the elements in a list is fixed, it will be later straightforward to match the processed abstracts to metadata associated to their corresponding projects.
End of explanation
filtered_abstracts = []
for n, abstract in enumerate(tokenized_abstracts):
    if not n%100:
        print('\rFiltering abstract', n, 'out of', nprojects, end='', flush=True)
    #<SOL>
    #</SOL>
print('\n',filtered_abstracts[0])
Explanation: 2.2. Homogeneization
By looking at the tokenized corpus you may verify that there are many tokens that correspond to punktuation signs and other symbols that are not relevant to analyze the semantic content. They can be removed using the stemming or lemmatization tools from nltk.
The homogeneization process will consist of:
Removing capitalization: capital alphabetic characters will be transformed to their corresponding lowercase characters.
Removing non alphanumeric tokens (e.g. punktuation signs)
Stemming/Lemmatization: removing word terminations to preserve the root of the words and ignore grammatical information.
Exercise
Convert all tokens in tokenized_abstracts to lowercase (using the .lower() method) and remove non alphanumeric tokens (that you can detect with .isalnum() method). You can complete the following code fragment with a single line of code ...
End of explanation
stemmer = nltk.stem.SnowballStemmer('english')
from nltk.stem import WordNetLemmatizer
wnl = WordNetLemmatizer()
print('Result for the first abstract in dataset applying stemming')
print([stemmer.stem(el) for el in filtered_abstracts[0]])
print('Result for the first abstract in the dataset applying lemmatization')
print([wnl.lemmatize(el) for el in filtered_abstracts[0]])
Explanation: 2.2.1. Stemming vs Lemmatization
At this point, we can choose between applying a simple stemming or ussing lemmatization. We will try both to test their differences.
The lemmatizer from NLTK is based on WordNet. If you have not used wordnet before, you will likely need to download it from nltk (use the nltk.download() command)
End of explanation
lemmatized_abstracts = []
for n, abstract in enumerate(filtered_abstracts):
    if not n%100:
        print('\rLemmatizing abstract', n, 'out of', nprojects, end='', flush=True)
    #<SOL>
    #</SOL>
print('Result for the first abstract in the dataset applying lemmatization')
print('\n',lemmatized_abstracts[0])
Explanation: One of the advantages of the lemmatizer method is that the result of lemmmatization is still a true word, which is more advisable for the presentation of text processing results and lemmatization.
However, without using contextual information, lemmatize() does not remove grammatical differences. This is the reason why "is" or "are" are preserved and not replaced by infinitive "be".
As an alternative, we can apply .lemmatize(word, pos), where 'pos' is a string code specifying the part-of-speech (pos), i.e. the grammatical role of the words in its sentence. For instance, you can check the difference between wnl.lemmatize('is') and wnl.lemmatize('is, pos='v').
Exercise
Complete the following code fragment to lemmatize all abstracts in the NSF dataset
End of explanation
from nltk.corpus import stopwords
stopwords_en = stopwords.words('english')
clean_abstracts = []
for n, abstract in enumerate(lemmatized_abstracts):
    if not n%100:
        print('\rCleaning abstract', n, 'out of', nprojects, end='', flush=True)
        
    # Remove all tokens in the stopwords list and append the result to clean_abstracts
    # <SOL>
    # </SOL>
    clean_abstracts.append(clean_tokens)
    
print('\n Let us check tokens after cleaning:')
print(clean_abstracts[0])
Explanation: 2.3. Cleaning
The third step consists of removing those words that are very common in language and do not carry out usefull semantic content (articles, pronouns, etc).
Once again, we might need to load the stopword files using the download tools from nltk
Exercise
In the second line below we read a list of common english stopwords. Clean lemmatized_abstracts by removing all tokens in the stopword list.
End of explanation
import gensim
# Create dictionary of tokens
D = gensim.corpora.Dictionary(clean_abstracts)
n_tokens = len(D)
print('The dictionary contains', n_tokens, 'terms')
print('First terms in the dictionary:')
for n in range(10):
    print(str(n), ':', D[n])
Explanation: 2.4. Vectorization
Up to this point, we have transformed the raw text collection of articles in a list of articles, where each article is a collection of the word roots that are most relevant for semantic analysis. Now, we need to convert these data (a list of token lists) into a numerical representation (a list of vectors, or a matrix). To do so, we will start using the tools provided by the gensim library. 
As a first step, we create a dictionary containing all tokens in our text corpus, and assigning an integer identifier to each one of them.
End of explanation
no_below = 5 #Minimum number of documents to keep a term in the dictionary
no_above = .75 #Maximum proportion of documents in which a term can appear to be kept in the dictionary
D.filter_extremes(no_below=no_below,no_above=no_above, keep_n=25000)
n_tokens = len(D)
print('The dictionary contains', n_tokens, 'terms')
print('First terms in the dictionary:')
for n in range(10):
    print(str(n), ':', D[n])
Explanation: We can also filter out terms that appear in too few or too many of the documents in the dataset:
End of explanation
corpus_bow = [D.doc2bow(doc) for doc in clean_abstracts]
Explanation: In the second step, let us create a numerical version of our corpus using the doc2bow method. In general, D.doc2bow(token_list) transforms any list of tokens into a list of tuples (token_id, n), one per each token in token_list, where token_id is the token identifier (according to dictionary D) and n is the number of occurrences of such token in token_list.
End of explanation
print('Original article (after cleaning):')
print(clean_abstracts[0])
print('Sparse vector representation (first 10 components):')
print(corpus_bow[0][:10])
print('Word counts for the first project (first 10 components):')
print(list(map(lambda x: (D[x[0]], x[1]), corpus_bow[0][:10])))
Explanation: At this point, it is good to make sure to understand what has happened. In clean_abstracts we had a list of token lists. With it, we have constructed a Dictionary, D, which assigns an integer identifier to each token in the corpus.
After that, we have transformed each article (in clean_abstracts) in a list tuples (id, n).
End of explanation
all_counts = [(D[el], D.dfs[el]) for el in D.dfs]
all_counts = sorted(all_counts, key=lambda x: x[1])
Explanation: Note that we can interpret each element of corpus_bow as a sparse_vector. For example, a list of tuples 
[(0, 1), (3, 3), (5,2)]
for a dictionary of 10 elements can be represented as a vector, where any tuple (id, n) states that position id must take value n. The rest of positions must be zero.
[1, 0, 0, 3, 0, 2, 0, 0, 0, 0]
These sparse vectors will be the inputs to the topic modeling algorithms.
As a summary, the following variables will be relevant for the next chapters:
D: A gensim dictionary. Term strings can be accessed using the numeric identifiers. For instance, D[0] contains the string corresponding to the first position in the BoW representation.
corpus_bow: BoW corpus. A list containing an entry per project in the dataset, and consisting of the (sparse) BoW representation for the abstract of that project.
NSF_data: A list containing an entry per project in the dataset, and consisting of metadata for the projects in the dataset
The way we have constructed the corpus_bow variable guarantees that the order is preserved, so that the projects are listed in the same order in the lists corpus_bow and NSF_data.
2.5. Dictionary properties
In the following code fragment, we build a list all_counts that contains tuples (terms, document_counts). You can use this list to calculate some statistics about the vocabulary of the dataset
End of explanation
import gensim
num_topics = 50
ldag = gensim.models.ldamodel.LdaModel(corpus=corpus_bow, id2word=D, num_topics=num_topics)
Explanation: 3. Topic Modeling
3.1. Training a topic model using Gensim LDA
Since we already have computed the dictionary and documents BoW representation using Gensim, computing the topic model is straightforward using the LdaModel() function. Please, refer to Gensim API documentation for more information on the different parameters accepted by the function:
End of explanation
ldag.print_topics(num_topics=-1, num_words=10)
Explanation: 3.2. LDA model visualization
Gensim provides a basic visualization of the obtained topics:
End of explanation
import pyLDAvis.gensim as gensimvis
import pyLDAvis
vis_data = gensimvis.prepare(ldag, corpus_bow, D)
pyLDAvis.display(vis_data)
Explanation: A more useful visualization is provided by the python LDA visualization library, pyLDAvis.
Before executing the next code fragment you need to install pyLDAvis:
&gt;&gt; pip install (--user) pyLDAvis
End of explanation
ldag.get_topic_terms(topicid=0)
ldag.get_document_topics(corpus_bow[0])
Explanation: 3.3. Gensim utility functions
In addition to visualization purposes, topic models are useful to obtain a semantic representation of documents that can later be used with some other purpose:
In document classification problems
In content-based recommendations systems
Essentially, the idea is that the topic model provides a (semantic) vector representation of documents, and use probability divergences to measure document similarity. The following functions of the LdaModel class will be useful in this context:
get_topic_terms(topic_id): Gets vector of the probability distribution among words for the indicated topic
get_document_topics(bow_vector): Gets (sparse) vector with the probability distribution among topics for the provided document
End of explanation
print(ldag[corpus_bow[0]])
print('When applied to a dataset it will provide an iterator')
print(ldag[corpus_bow[:3]])
print('We can rebuild the list from the iterator with a one liner')
print([el for el in ldag[corpus_bow[:3]]])
Explanation: An alternative to the use of the get_document_topics() function is to directly transform a dataset using the ldag object as follows. You can apply this transformation to several documents at once, but then the result is an iterator from which you can build the corresponding list if necessary
End of explanation
reduced_corpus = [el for el in ldag[corpus_bow[:3]]]
reduced_corpus = gensim.matutils.corpus2dense(reduced_corpus, num_topics).T
print(reduced_corpus)
Explanation: Finally, Gensim provides some useful functions to convert between formats, and to simplify interaction with numpy and scipy. The following code fragment converts a corpus in sparse format to a full numpy matrix
End of explanation
def most_relevant_projects(ldag, topicid, corpus_bow, nprojects=10):
    This function returns the most relevant projects in corpus_bow
    
    : ldag: The trained topic model object provided by gensim
    : topicid: The topic for which we want to find the most relevant documents
    : corpus_bow: The BoW representation of documents in Gensim format
    : nprojects: Number of most relevant projects to identify
    
    : Returns: A list with the identifiers of the most relevant projects
    
    print('Computing most relevant projects for Topic', topicid)
    print('Topic composition is:')
    print(ldag.show_topic(topicid))
    
    #<SOL>
    #</SOL>
    
#To test the function we will find the most relevant projects for a subset of the NSF dataset
project_id = most_relevant_projects(ldag, 17, corpus_bow[:10000])
#Print titles of selected projects
for idproject in project_id:
    print(NSF_data[idproject]['title'])
Explanation: Exercise
Build a function that returns the most relevant projects for a given topic
End of explanation
def pairwase_dist(doc1, doc2):
    This function returns the Jensen-Shannon
    distance between the corresponding vectors of the documents
    
    : doc1: Semantic representation for the doc1 (a vector of length ntopics)
    : doc2: Semantic representation for the doc2 (a vector of length ntopics)
    : Returns: The JS distance between doc1 and doc2 (a number)
    
    #<SOL>
    #</SOL>
Explanation: Exercise
Build a function that computes the semantic distance between two documents. For this, you can use the functions (or code fragments) provided in the library dist_utils.py.
End of explanation
#print(NSF_data[0].keys())
#print(NSF_data[0]['institution'])
def strNone(str_to_convert):
    if str_to_convert is None:
        return ''
    else:
        return str_to_convert
with open('NSF_nodes.csv','w') as fout:
    fout.write('Id;Title;Year;Budget;UnivName;UnivZIP;State\n')
    for project in NSF_data:
        fout.write(project['project_code']+';'+project['title']+';')
        fout.write(project['year']+';'+str(project['budget'])+';')
        fout.write(project['institution'][0]+';')
        fout.write(strNone(project['institution'][1])+';')
        fout.write(strNone(project['institution'][2])+'\n')
Explanation: Function that creates the Node CSV file for Gephi
End of explanation
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