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<main>
<article id="content">
<header>
<h1 class="title">Module <code>mcat.github_data</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">import argparse
import pandas as pd
import utils
FILE_NAME_SUFFIX = "unannotated"
class GitHubData:
"""
Reformat raw data for annotation
"""
def __init__(self, raw_filename):
self.raw_filename = raw_filename
self.raw_data = None
def read_raw_data(self):
"""
Function to read raw data stored as csv
"""
self.raw_data = pd.read_csv(self.raw_filename)
# Convert Comments and Review Comments to dictionary
self.raw_data['Comments'] = self.raw_data['Comments'].apply(lambda comment: utils.string_to_dict(comment))
self.raw_data['Review_Comments'] = self.raw_data['Review_Comments'].apply(lambda comment: utils.string_to_dict(comment))
def reformat_data(self):
"""
Function to reformat raw data as form conversation strings given communication on a pull requests
"""
# Store each interaction and pull URL for export
conversations = []
pull_urls = []
pull_numbers = []
for index, row in self.raw_data.iterrows():
# Make pull message
conversations.append(self.merge_comments(row))
pull_urls.append(row["URL"])
pull_numbers.append(row["Number"])
# Export conversation field dataset
export_df = pd.DataFrame()
export_df["Number"] = pull_numbers
export_df["URL"] = pull_urls
export_df["Thread"] = conversations
return export_df
def merge_comments(self, row):
"""
merge comments and review comments to form a conversation
"""
conversation = "{} ({}) : {}\n{}".format(row["User"], row["Created_At"], row["Title"], row["Body"])
temp_df_comments = pd.DataFrame(row['Comments'])
temp_df_review_comments = pd.DataFrame(row["Review_Comments"])
if len(temp_df_comments) > 0 or len(temp_df_review_comments) > 0:
all_comments = temp_df_comments.append(temp_df_review_comments)
all_comments['Created_At'] = pd.to_datetime(all_comments['Created_At'])
all_comments = all_comments.sort_values(by=['Created_At']) # Sort data so as to export in order
for comment_index, comment_row in all_comments.iterrows():
conversation = conversation + "\n{} ({}) : {}".format(comment_row["User"],
comment_row["Created_At"],
comment_row["Body"])
return conversation.encode("ascii", "ignore").decode()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Reformat raw data for annotation.')
parser.add_argument('rawdatafile', help='Raw Data Filename')
parser.add_argument('-n', '--name', required=False, help='Output file name. If not specified, the name is '
'constructed like this: <rawdatafile>{'
'suffix}.csv'.format(suffix=FILE_NAME_SUFFIX))
args = parser.parse_args()
data_reformat = GitHubData(args.rawdatafile)
data_reformat.read_raw_data()
df = data_reformat.reformat_data()
file_name = utils.construct_file_name(args.name, args.rawdatafile, FILE_NAME_SUFFIX)
utils.export_to_cvs(df, file_name)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="mcat.github_data.GitHubData"><code class="flex name class">
<span>class <span class="ident">GitHubData</span></span>
<span>(</span><span>raw_filename)</span>
</code></dt>
<dd>
<div class="desc"><p>Reformat raw data for annotation</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class GitHubData:
"""
Reformat raw data for annotation
"""
def __init__(self, raw_filename):
self.raw_filename = raw_filename
self.raw_data = None
def read_raw_data(self):
"""
Function to read raw data stored as csv
"""
self.raw_data = pd.read_csv(self.raw_filename)
# Convert Comments and Review Comments to dictionary
self.raw_data['Comments'] = self.raw_data['Comments'].apply(lambda comment: utils.string_to_dict(comment))
self.raw_data['Review_Comments'] = self.raw_data['Review_Comments'].apply(lambda comment: utils.string_to_dict(comment))
def reformat_data(self):
"""
Function to reformat raw data as form conversation strings given communication on a pull requests
"""
# Store each interaction and pull URL for export
conversations = []
pull_urls = []
pull_numbers = []
for index, row in self.raw_data.iterrows():
# Make pull message
conversations.append(self.merge_comments(row))
pull_urls.append(row["URL"])
pull_numbers.append(row["Number"])
# Export conversation field dataset
export_df = pd.DataFrame()
export_df["Number"] = pull_numbers
export_df["URL"] = pull_urls
export_df["Thread"] = conversations
return export_df
def merge_comments(self, row):
"""
merge comments and review comments to form a conversation
"""
conversation = "{} ({}) : {}\n{}".format(row["User"], row["Created_At"], row["Title"], row["Body"])
temp_df_comments = pd.DataFrame(row['Comments'])
temp_df_review_comments = pd.DataFrame(row["Review_Comments"])
if len(temp_df_comments) > 0 or len(temp_df_review_comments) > 0:
all_comments = temp_df_comments.append(temp_df_review_comments)
all_comments['Created_At'] = pd.to_datetime(all_comments['Created_At'])
all_comments = all_comments.sort_values(by=['Created_At']) # Sort data so as to export in order
for comment_index, comment_row in all_comments.iterrows():
conversation = conversation + "\n{} ({}) : {}".format(comment_row["User"],
comment_row["Created_At"],
comment_row["Body"])
return conversation.encode("ascii", "ignore").decode()</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="mcat.github_data.GitHubData.merge_comments"><code class="name flex">
<span>def <span class="ident">merge_comments</span></span>(<span>self, row)</span>
</code></dt>
<dd>
<div class="desc"><p>merge comments and review comments to form a conversation</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def merge_comments(self, row):
"""
merge comments and review comments to form a conversation
"""
conversation = "{} ({}) : {}\n{}".format(row["User"], row["Created_At"], row["Title"], row["Body"])
temp_df_comments = pd.DataFrame(row['Comments'])
temp_df_review_comments = pd.DataFrame(row["Review_Comments"])
if len(temp_df_comments) > 0 or len(temp_df_review_comments) > 0:
all_comments = temp_df_comments.append(temp_df_review_comments)
all_comments['Created_At'] = pd.to_datetime(all_comments['Created_At'])
all_comments = all_comments.sort_values(by=['Created_At']) # Sort data so as to export in order
for comment_index, comment_row in all_comments.iterrows():
conversation = conversation + "\n{} ({}) : {}".format(comment_row["User"],
comment_row["Created_At"],
comment_row["Body"])
return conversation.encode("ascii", "ignore").decode()</code></pre>
</details>
</dd>
<dt id="mcat.github_data.GitHubData.read_raw_data"><code class="name flex">
<span>def <span class="ident">read_raw_data</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Function to read raw data stored as csv</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def read_raw_data(self):
"""
Function to read raw data stored as csv
"""
self.raw_data = pd.read_csv(self.raw_filename)
# Convert Comments and Review Comments to dictionary
self.raw_data['Comments'] = self.raw_data['Comments'].apply(lambda comment: utils.string_to_dict(comment))
self.raw_data['Review_Comments'] = self.raw_data['Review_Comments'].apply(lambda comment: utils.string_to_dict(comment))</code></pre>
</details>
</dd>
<dt id="mcat.github_data.GitHubData.reformat_data"><code class="name flex">
<span>def <span class="ident">reformat_data</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Function to reformat raw data as form conversation strings given communication on a pull requests</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def reformat_data(self):
"""
Function to reformat raw data as form conversation strings given communication on a pull requests
"""
# Store each interaction and pull URL for export
conversations = []
pull_urls = []
pull_numbers = []
for index, row in self.raw_data.iterrows():
# Make pull message
conversations.append(self.merge_comments(row))
pull_urls.append(row["URL"])
pull_numbers.append(row["Number"])
# Export conversation field dataset
export_df = pd.DataFrame()
export_df["Number"] = pull_numbers
export_df["URL"] = pull_urls
export_df["Thread"] = conversations
return export_df</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="mcat" href="index.html">mcat</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="mcat.github_data.GitHubData" href="#mcat.github_data.GitHubData">GitHubData</a></code></h4>
<ul class="">
<li><code><a title="mcat.github_data.GitHubData.merge_comments" href="#mcat.github_data.GitHubData.merge_comments">merge_comments</a></code></li>
<li><code><a title="mcat.github_data.GitHubData.read_raw_data" href="#mcat.github_data.GitHubData.read_raw_data">read_raw_data</a></code></li>
<li><code><a title="mcat.github_data.GitHubData.reformat_data" href="#mcat.github_data.GitHubData.reformat_data">reformat_data</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
</main>
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