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<main>
<article id="content">
<header>
<h1 class="title">Module <code>mcat.run</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright 2021 VMware, Inc.
# SPDX-License-Identifier: Apache-2.0
import argparse
import tarfile
import os
from sklearn.model_selection import train_test_split
from baseCNN import BaseCNN
from baseLSTM import BaseLSTM
from preProcessedDataset import PreProcessedDataset
model_directory = 'models'
def save_model(model, name, version):
if not os.path.exists(model_directory):
os.makedirs(model_directory)
model_path = "{}/{}".format(model_directory, name)
tar_file_name = "{}-{}.tar.gz".format(name, version)
model.saveModel(name=model_path, version=version)
os.chdir(model_path)
tar = tarfile.open(tar_file_name, "w:gz")
tar.add(version)
tar.close()
os.chdir("../../")
print("Model saved in {}/{}; {}/{}".format(model_path, version, model_path, tar_file_name))
def run(annotated_filename, dataset_filename, outcome, encoding_type, model_type, padding, save_name, model_ver):
# Setup dataset
data = PreProcessedDataset()
data.setupPreProcess(annotated_filename, dataset_filename)
data.encodeData()
# Create models
if model_type == 'LSTM':
model = BaseLSTM()
else:
model = BaseCNN()
# Get data for training
if encoding_type == 'role':
obs, res = data.getRoleMatrix(outcome, padding)
model.makeModel2D(obs[0].shape)
else:
obs, res = data.getRoleAgnosticMatrix(outcome, padding)
model.makeModel(obs[0].shape)
# Train model
train_obs, test_obs, train_res, test_res = train_test_split(obs, res, stratify=res, test_size=0.2)
model.trainModel(train_obs, train_res)
# Score model
scores = model.scoreModel(test_obs, test_res)
# Save model
if save_name is not None and len(save_name) > 0:
save_model(model=model, name=save_name+"-"+outcome, version=model_ver)
return scores
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Obtain models to determine constructive and inclusive feedback in Open source communities")
parser.add_argument('annotated_filename', help='File location of annotated file')
parser.add_argument('dataset_filename', help='File location of extracted dataset')
parser.add_argument('model', help='Model type to use for training, supported CNN and LSTM')
parser.add_argument('outcome', help='Inclusive, Constructive, or Both')
parser.add_argument('-save', metavar='NAME', help='Save the model using given NAME')
parser.add_argument('-save_version', metavar='VERSION', default='001',
help='Together with -save NAME: save the model using given NAME and VERSION. '\
'If omitted, 001 is used. The parameter is ignored if -save is missing.')
parser.add_argument('-roleRelevant', action='store_true', default=False,
help='Encoding method differentiates b/w conversation roles')
parser.add_argument('-pad', action='store_true', default=False, help='Pad total length of each pull')
args = parser.parse_args()
if args.model != 'CNN' and args.model != 'LSTM':
raise Exception("Model must be either CNN or LSTM")
encodingType = 'role'
if not args.roleRelevant:
encodingType = 'role-agnostic'
if args.outcome != 'Both':
run_res = run(args.annotated_filename, args.dataset_filename, args.outcome, encodingType,
args.model, args.pad, args.save, args.save_version)
print(run_res)
else:
run_res_constructive = run(args.annotated_filename, args.dataset_filename, 'Constructive', encodingType,
args.model, args.pad, args.save, args.save_version)
print("Constructive: {}".format(run_res_constructive))
run_res_inclusive = run(args.annotated_filename, args.dataset_filename, 'Inclusive', encodingType,
args.model, args.pad, args.save, args.save_version)
print("Inclusvie: {}".format(run_res_inclusive))</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="mcat.run.run"><code class="name flex">
<span>def <span class="ident">run</span></span>(<span>annotated_filename, dataset_filename, outcome, encoding_type, model_type, padding, save_name, model_ver)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def run(annotated_filename, dataset_filename, outcome, encoding_type, model_type, padding, save_name, model_ver):
# Setup dataset
data = PreProcessedDataset()
data.setupPreProcess(annotated_filename, dataset_filename)
data.encodeData()
# Create models
if model_type == 'LSTM':
model = BaseLSTM()
else:
model = BaseCNN()
# Get data for training
if encoding_type == 'role':
obs, res = data.getRoleMatrix(outcome, padding)
model.makeModel2D(obs[0].shape)
else:
obs, res = data.getRoleAgnosticMatrix(outcome, padding)
model.makeModel(obs[0].shape)
# Train model
train_obs, test_obs, train_res, test_res = train_test_split(obs, res, stratify=res, test_size=0.2)
model.trainModel(train_obs, train_res)
# Score model
scores = model.scoreModel(test_obs, test_res)
# Save model
if save_name is not None and len(save_name) > 0:
save_model(model=model, name=save_name+"-"+outcome, version=model_ver)
return scores</code></pre>
</details>
</dd>
<dt id="mcat.run.save_model"><code class="name flex">
<span>def <span class="ident">save_model</span></span>(<span>model, name, version)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def save_model(model, name, version):
if not os.path.exists(model_directory):
os.makedirs(model_directory)
model_path = "{}/{}".format(model_directory, name)
tar_file_name = "{}-{}.tar.gz".format(name, version)
model.saveModel(name=model_path, version=version)
os.chdir(model_path)
tar = tarfile.open(tar_file_name, "w:gz")
tar.add(version)
tar.close()
os.chdir("../../")
print("Model saved in {}/{}; {}/{}".format(model_path, version, model_path, tar_file_name))</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</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-functions">Functions</a></h3>
<ul class="">
<li><code><a title="mcat.run.run" href="#mcat.run.run">run</a></code></li>
<li><code><a title="mcat.run.save_model" href="#mcat.run.save_model">save_model</a></code></li>
</ul>
</li>
</ul>
</nav>
</main>
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