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Commit d9f19f56 authored by Jayesh's avatar Jayesh
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task1 get len of comments and attachments

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Assignment_2/Week1/task1.ipynb
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%% Cell type:code id: tags:
``` python
import pandas as pd
from jira import JIRA, JIRAError
```
%% Cell type:code id: tags:
``` python
issue_list = pd.read_excel("E:\DSSE\DSSE-Group-7\Assignment_2\Issues_2nd.xlsx", sheet_name="Yarn")
```
%% Cell type:code id: tags:
``` python
issue_keys = issue_list["Issue key"].to_list()
len(issue_keys)
```
%% Cell type:markdown id: tags:
# Point 1 & 2
%% Cell type:code id: tags:
``` python
APACHE_JIRA_SERVER = 'https://issues.apache.org/jira/'
jira = JIRA(APACHE_JIRA_SERVER)
```
%% Cell type:code id: tags:
``` python
jql_query = 'issuekey in ({}) AND project = YARN'
fields = "parent,summary,description,issuetype,status,comment"
fields = "parent,summary,description,issuetype,status,comment,attachment"
fields_arr = fields.split(',')
```
%% Cell type:code id: tags:
``` python
jira_raw_data_key_map = {
"issuetype": "name",
"parent": "key",
"status": "name"
}
```
%% Cell type:code id: tags:
``` python
issue_data = []
key_window = 300
for idx in range(0, len(issue_keys), key_window):
issue_keys_subset = issue_keys[idx:idx+key_window]
query = jql_query.format(','.join(issue_keys_subset))
response = jira.search_issues(query, fields=fields, maxResults=key_window)
for issue in response:
data = [issue.key]
for field in fields_arr:
if field in issue.raw['fields']:
if field == 'comment':
comments = issue.raw['fields'][field]['comments']
comment_text = []
for comment in comments:
comment_text.append(comment['body'])
data.append(comment_text)
data.append(len(comments))
continue
if field == 'attachment':
attachments = issue.raw['fields'][field]
data.append(len(attachments))
continue
if field in jira_raw_data_key_map:
data.append(issue.raw['fields'][field][jira_raw_data_key_map[field]])
else:
data.append(issue.raw['fields'][field])
else:
data.append(None)
issue_data.append(data)
```
%% Cell type:code id: tags:
``` python
issue_data_df = pd.DataFrame(issue_data, columns=["Issue key", "Parent", "Summary", "Description", "Issue Type", "Status", "Comments"])
issue_data_df = pd.DataFrame(issue_data, columns=["Issue key", "Parent", "Summary", "Description", "Issue Type", "Status", "Comments", "Attachments"])
issue_data_df.head()
```
%% Cell type:code id: tags:
``` python
import re
```
%% Cell type:code id: tags:
``` python
def remove_illegal_characters(cell):
"""
Function to remove illegal characters from a cell.
"""
# Regex to match illegal characters
ILLEGAL_CHARACTERS_RE = re.compile(r'[\x00-\x08\x0b-\x0c\x0e-\x1f]')
if isinstance(cell, str):
return ILLEGAL_CHARACTERS_RE.sub('', cell)
return cell
```
%% Cell type:code id: tags:
``` python
issue_data_df = issue_data_df.applymap(remove_illegal_characters)
```
%% Cell type:code id: tags:
``` python
issue_data_df.to_excel("E:\DSSE\DSSE-Group-7\Assignment_2\Week1\yarn_issue_data.xlsx", index=False)
```
%% Cell type:markdown id: tags:
# Point 3
%% Cell type:code id: tags:
``` python
import re
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer, PorterStemmer
from nltk import pos_tag
```
%% Cell type:code id: tags:
``` python
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
```
%% Cell type:code id: tags:
``` python
nltk.download('averaged_perceptron_tagger')
```
%% Cell type:code id: tags:
``` python
# merge summary and description
issue_data_df['Summary_Description'] = issue_data_df['Summary'] + ' ' + issue_data_df['Description']
```
%% Cell type:code id: tags:
``` python
import re
# Perform text cleaning and tokenization for the concatenation of issue summary and description
def clean_text(text):
"""
Function to clean text by removing unwanted characters and converting to lowercase.
"""
# Convert to string
text = str(text)
# Convert to lowercase
text = text.lower()
# Remove punctuation, numbers, and special characters
text = re.sub(r'[^a-z\s]', '', text)
# Remove extra whitespace
text = re.sub(r'\s+', ' ', text).strip()
return text
def replace_abbr(text):
"""
Replace specific words if they appear as standalone words.
"""
replacements = {
r"\brm\b": "ResourceManager",
r"\bnm\b": "NodeManager",
r"\bam\b": "ApplicationMaster",
r"\bacl\b": "AccessControlList",
r"\bha\b": "HighAvailability"
}
for pattern, replacement in replacements.items():
text = re.sub(pattern, replacement, text)
return text
def tokenize_text(text):
"""
Function to tokenize cleaned text.
"""
return word_tokenize(text)
```
%% Cell type:code id: tags:
``` python
issue_data_df['Summary_Description'] = issue_data_df['Summary_Description'].apply(clean_text)
issue_data_df['Summary_Description'] = issue_data_df['Summary_Description'].apply(replace_abbr)
issue_data_df['Summary_Description_Tokens'] = issue_data_df['Summary_Description'].apply(tokenize_text)
```
%% Cell type:code id: tags:
``` python
def remove_stopwords(tokens):
"""
Function to remove stop words from tokenized text.
"""
stop_words = set(stopwords.words('english'))
return [word for word in tokens if word not in stop_words]
```
%% Cell type:code id: tags:
``` python
issue_data_df['Summary_Description_Tokens'] = issue_data_df['Summary_Description_Tokens'].apply(remove_stopwords)
```
%% Cell type:code id: tags:
``` python
# pos_tags = pos_tag(issue_data_df['Summary_Description_Tokens'].tolist()[0])
# issue_data_df['Summary_Description_Tokens'].tolist()[0]
```
%% Cell type:code id: tags:
``` python
pos_tag(['running'])
```
%% Cell type:code id: tags:
``` python
pos_tag_mapping = lambda e: ('a' if e[0].lower() == 'j' else e[0].lower()) if e[0].lower() in ['n', 'r', 'v'] else 'n'
```
%% Cell type:code id: tags:
``` python
def lemmatize_text(tokens):
"""
Function to lemmatize tokenized text.
"""
lemmatizer = WordNetLemmatizer()
return [lemmatizer.lemmatize(word, pos=pos_tag_mapping(pos_tag([word])[0][1])) for word in tokens]
```
%% Cell type:code id: tags:
``` python
lemmatize_text(['running'])
```
%% Cell type:code id: tags:
``` python
issue_data_df['Summary_Description_Tokens'] = issue_data_df['Summary_Description_Tokens'].apply(remove_stopwords)
```
%% Cell type:code id: tags:
``` python
issue_data_df['Summary_Description_Tokens'] = issue_data_df['Summary_Description_Tokens'].apply(lemmatize_text)
```
%% Cell type:code id: tags:
``` python
issue_data_df.drop(columns=['Summary_Description'], inplace=True)
```
%% Cell type:code id: tags:
``` python
issue_data_df.to_excel("E:\DSSE\DSSE-Group-7\Assignment_2\Week1\yarn_issue_data_cleaned.xlsx", index=False)
```
%% Cell type:markdown id: tags:
# Point 4
%% Cell type:code id: tags:
``` python
# Create a list of unique words from the tokenized text and their count
unique_words = {}
for tokens in issue_data_df['Summary_Description_Tokens']:
for word in tokens:
if word in unique_words:
unique_words[word] += 1
else:
unique_words[word] = 1
unique_words_df = pd.DataFrame(list(unique_words.items()), columns=['Word', 'Count'])
unique_words_df = unique_words_df.sort_values('Count', ascending=False).reset_index(drop=True)
unique_words_df.head(10)
```
%% Cell type:code id: tags:
``` python
unique_words_df.to_excel("E:\DSSE\DSSE-Group-7\Assignment_2\Week1\yarn_unique_words.xlsx", index=False)
```
%% Cell type:code id: tags:
``` python
unique_words_df.head(20)
```
......
Assignment_2/Week1/yarn_issue_data.xlsx
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Assignment_2/Week1/yarn_issue_data_cleaned.xlsx
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Assignment_2/Week1/yarn_unique_words.xlsx
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