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This module implements a basic naive classifier model for repository descriptions.
import os
import csv
import json
class Loader:
""" Loader class reads the source data files and format the data for further use.
def __init__(self, source_file, corpus, colander):
self.corpus = corpus
self.colander = colander
self.source_file = os.path.join(os.path.dirname(__file__), source_file)
def read_data(self):
""" read the csv data files accordingly
with open(os.path.abspath(self.source_file), 'rb') as csvfile:
# read the row content accordint to particular columns
reader = csv.DictReader(csvfile)
for row in reader:
wordlist = self.colander.process("".join([row[' description'], row[' repository']]))
self.corpus.learn_words(wordlist, row['organization'])
class Corpus:
""" Corpus class implements corpus object that contains words
and facilities function to operate on the content.
def __init__(self, group_file):
self.group_file = os.path.join(os.path.dirname(__file__), group_file)
# contains information about each groups and its word counts
self.groups = {}
# contains information about each groups and its words
self.words = {}
def learn_groups(self):
""" learn the total groups available
with open(os.path.abspath(self.group_file), 'r') as jsonfile:
groups = json.loads(
for group in groups:
self.groups.setdefault(group['handle'].lower(), {})
self.groups[group['handle'].lower()].setdefault('count', 0)
self.words.setdefault(group['handle'].lower(), {})
def learn_words(self, wordlist, group):
""" learn the words for a particular group/organization
for word in wordlist:
self.words[group].setdefault(word, 0)
self.words[group][word] += 1
self.groups[group]['count'] += 1