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</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>mcat.baseLSTM</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 numpy as np
import tensorflow as tf
from sklearn.metrics import precision_recall_fscore_support
from tensorflow import keras
class BaseLSTM:
def __init__(self):
"""
Constructor creates model
"""
self.dimension2 = True
self.input_shape = ()
self.model = None
self.model_ready = False
def makeModel(self, input_shape):
"""
Make lstm model for role agnostic data
"""
self.input_shape = input_shape
self.model = keras.models.Sequential()
self.model.add(keras.layers.LSTM(8, input_shape=input_shape))
self.model.add(keras.layers.Dropout(0.2))
self.model.add(keras.layers.Dense(1, activation='sigmoid'))
self.dimension2 = False
self.model_ready = True
def makeModel2D(self, input_shape):
"""
Made lstm model for role relevant layers data
"""
# Inputs
self.input_shape = input_shape
inputs = keras.Input(shape=(None, 512, 2), dtype="float32")
# Pipe output of author and reviewer layer to two lstm
author = keras.layers.Lambda(lambda x: x[:, :, :, 0])(inputs)
reviewer = keras.layers.Lambda(lambda x: x[:, :, :, 1])(inputs)
# Create author lstm
author_lstm = keras.layers.LSTM(4, activation='relu', return_sequences=False)(author)
author_dropout = keras.layers.Dropout(0.2)(author_lstm)
author_output = keras.layers.Dense(1, activation='relu')(author_dropout)
# Create reviewer lstm
reviewer_lstm = keras.layers.LSTM(4, activation='relu', return_sequences=False)(reviewer)
reviewer_dropout = keras.layers.Dropout(0.2)(reviewer_lstm)
reviewer_output = keras.layers.Dense(1, activation='relu')(reviewer_dropout)
# Concatenate author and reviewer output
combine = keras.layers.Concatenate(axis=1)([author_output, reviewer_output])
output = keras.layers.Dense(1, activation='sigmoid')(combine)
self.model = keras.models.Model(inputs=inputs, outputs=output)
self.dimension2 = True
self.model_ready = True
def trainModel(self, obs, res, val_split=0.3, val_set=None, epochs=10, batch_size=32):
self.model.compile(optimizer=keras.optimizers.Adam(), loss='binary_crossentropy', metrics=['accuracy'])
if val_set:
train_hist = self.model.fit(tf.ragged.stack(obs), tf.convert_to_tensor(res), epochs=epochs,
batch_size=batch_size,
validation_data=(tf.ragged_stack(val_set[0]), tf.convert_to_tensor(val_set[1])),
verbose=1)
return train_hist
else:
train_hist = self.model.fit(tf.ragged.stack(obs), tf.convert_to_tensor(res), epochs=epochs,
batch_size=batch_size, verbose=1)
return train_hist
def saveModel(self, name, version):
self.model.save("{}/{}".format(name, version))
def scoreModel(self, obs, res):
"""
Score model for accuracy, precision and recall
"""
evaluation = {}
evaluation['Loss_Acc'] = self.model.evaluate(tf.ragged.stack(obs), tf.convert_to_tensor(res))
evaluation['Precision_Recall_Fscore_Support'] = precision_recall_fscore_support(res, self.predict(obs, True),
average='binary')
print("Accuracy: {}".format(evaluation['Loss_Acc'][1]))
return evaluation
def predict(self, obs, labels=False):
predictions = self.model.predict(tf.ragged.stack(obs))
if labels:
return [1 if x > 0.5 else 0 for x in predictions]
return predictions
def explain(self, obs):
"""
Explain prediction for obs by calculating gradients
"""
grads = self._gradientImportance(obs)
imp = grads[:, 0] + grads[:, 1]
return imp
def _gradientImportance(self, seq):
"""
Get gradients
"""
seq = tf.Variable(seq[np.newaxis, :, :])
with tf.GradientTape() as tape:
predictions = self.model(seq)
grads = tape.gradient(predictions, seq)
grads = tf.reduce_mean(grads, axis=1).numpy()[0]
return grads</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="mcat.baseLSTM.BaseLSTM"><code class="flex name class">
<span>class <span class="ident">BaseLSTM</span></span>
</code></dt>
<dd>
<div class="desc"><p>Constructor creates model</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class BaseLSTM:
def __init__(self):
"""
Constructor creates model
"""
self.dimension2 = True
self.input_shape = ()
self.model = None
self.model_ready = False
def makeModel(self, input_shape):
"""
Make lstm model for role agnostic data
"""
self.input_shape = input_shape
self.model = keras.models.Sequential()
self.model.add(keras.layers.LSTM(8, input_shape=input_shape))
self.model.add(keras.layers.Dropout(0.2))
self.model.add(keras.layers.Dense(1, activation='sigmoid'))
self.dimension2 = False
self.model_ready = True
def makeModel2D(self, input_shape):
"""
Made lstm model for role relevant layers data
"""
# Inputs
self.input_shape = input_shape
inputs = keras.Input(shape=(None, 512, 2), dtype="float32")
# Pipe output of author and reviewer layer to two lstm
author = keras.layers.Lambda(lambda x: x[:, :, :, 0])(inputs)
reviewer = keras.layers.Lambda(lambda x: x[:, :, :, 1])(inputs)
# Create author lstm
author_lstm = keras.layers.LSTM(4, activation='relu', return_sequences=False)(author)
author_dropout = keras.layers.Dropout(0.2)(author_lstm)
author_output = keras.layers.Dense(1, activation='relu')(author_dropout)
# Create reviewer lstm
reviewer_lstm = keras.layers.LSTM(4, activation='relu', return_sequences=False)(reviewer)
reviewer_dropout = keras.layers.Dropout(0.2)(reviewer_lstm)
reviewer_output = keras.layers.Dense(1, activation='relu')(reviewer_dropout)
# Concatenate author and reviewer output
combine = keras.layers.Concatenate(axis=1)([author_output, reviewer_output])
output = keras.layers.Dense(1, activation='sigmoid')(combine)
self.model = keras.models.Model(inputs=inputs, outputs=output)
self.dimension2 = True
self.model_ready = True
def trainModel(self, obs, res, val_split=0.3, val_set=None, epochs=10, batch_size=32):
self.model.compile(optimizer=keras.optimizers.Adam(), loss='binary_crossentropy', metrics=['accuracy'])
if val_set:
train_hist = self.model.fit(tf.ragged.stack(obs), tf.convert_to_tensor(res), epochs=epochs,
batch_size=batch_size,
validation_data=(tf.ragged_stack(val_set[0]), tf.convert_to_tensor(val_set[1])),
verbose=1)
return train_hist
else:
train_hist = self.model.fit(tf.ragged.stack(obs), tf.convert_to_tensor(res), epochs=epochs,
batch_size=batch_size, verbose=1)
return train_hist
def saveModel(self, name, version):
self.model.save("{}/{}".format(name, version))
def scoreModel(self, obs, res):
"""
Score model for accuracy, precision and recall
"""
evaluation = {}
evaluation['Loss_Acc'] = self.model.evaluate(tf.ragged.stack(obs), tf.convert_to_tensor(res))
evaluation['Precision_Recall_Fscore_Support'] = precision_recall_fscore_support(res, self.predict(obs, True),
average='binary')
print("Accuracy: {}".format(evaluation['Loss_Acc'][1]))
return evaluation
def predict(self, obs, labels=False):
predictions = self.model.predict(tf.ragged.stack(obs))
if labels:
return [1 if x > 0.5 else 0 for x in predictions]
return predictions
def explain(self, obs):
"""
Explain prediction for obs by calculating gradients
"""
grads = self._gradientImportance(obs)
imp = grads[:, 0] + grads[:, 1]
return imp
def _gradientImportance(self, seq):
"""
Get gradients
"""
seq = tf.Variable(seq[np.newaxis, :, :])
with tf.GradientTape() as tape:
predictions = self.model(seq)
grads = tape.gradient(predictions, seq)
grads = tf.reduce_mean(grads, axis=1).numpy()[0]
return grads</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="mcat.baseLSTM.BaseLSTM.explain"><code class="name flex">
<span>def <span class="ident">explain</span></span>(<span>self, obs)</span>
</code></dt>
<dd>
<div class="desc"><p>Explain prediction for obs by calculating gradients</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def explain(self, obs):
"""
Explain prediction for obs by calculating gradients
"""
grads = self._gradientImportance(obs)
imp = grads[:, 0] + grads[:, 1]
return imp</code></pre>
</details>
</dd>
<dt id="mcat.baseLSTM.BaseLSTM.makeModel"><code class="name flex">
<span>def <span class="ident">makeModel</span></span>(<span>self, input_shape)</span>
</code></dt>
<dd>
<div class="desc"><p>Make lstm model for role agnostic data</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def makeModel(self, input_shape):
"""
Make lstm model for role agnostic data
"""
self.input_shape = input_shape
self.model = keras.models.Sequential()
self.model.add(keras.layers.LSTM(8, input_shape=input_shape))
self.model.add(keras.layers.Dropout(0.2))
self.model.add(keras.layers.Dense(1, activation='sigmoid'))
self.dimension2 = False
self.model_ready = True</code></pre>
</details>
</dd>
<dt id="mcat.baseLSTM.BaseLSTM.makeModel2D"><code class="name flex">
<span>def <span class="ident">makeModel2D</span></span>(<span>self, input_shape)</span>
</code></dt>
<dd>
<div class="desc"><p>Made lstm model for role relevant layers data</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def makeModel2D(self, input_shape):
"""
Made lstm model for role relevant layers data
"""
# Inputs
self.input_shape = input_shape
inputs = keras.Input(shape=(None, 512, 2), dtype="float32")
# Pipe output of author and reviewer layer to two lstm
author = keras.layers.Lambda(lambda x: x[:, :, :, 0])(inputs)
reviewer = keras.layers.Lambda(lambda x: x[:, :, :, 1])(inputs)
# Create author lstm
author_lstm = keras.layers.LSTM(4, activation='relu', return_sequences=False)(author)
author_dropout = keras.layers.Dropout(0.2)(author_lstm)
author_output = keras.layers.Dense(1, activation='relu')(author_dropout)
# Create reviewer lstm
reviewer_lstm = keras.layers.LSTM(4, activation='relu', return_sequences=False)(reviewer)
reviewer_dropout = keras.layers.Dropout(0.2)(reviewer_lstm)
reviewer_output = keras.layers.Dense(1, activation='relu')(reviewer_dropout)
# Concatenate author and reviewer output
combine = keras.layers.Concatenate(axis=1)([author_output, reviewer_output])
output = keras.layers.Dense(1, activation='sigmoid')(combine)
self.model = keras.models.Model(inputs=inputs, outputs=output)
self.dimension2 = True
self.model_ready = True</code></pre>
</details>
</dd>
<dt id="mcat.baseLSTM.BaseLSTM.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, obs, labels=False)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, obs, labels=False):
predictions = self.model.predict(tf.ragged.stack(obs))
if labels:
return [1 if x > 0.5 else 0 for x in predictions]
return predictions</code></pre>
</details>
</dd>
<dt id="mcat.baseLSTM.BaseLSTM.saveModel"><code class="name flex">
<span>def <span class="ident">saveModel</span></span>(<span>self, 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 saveModel(self, name, version):
self.model.save("{}/{}".format(name, version))</code></pre>
</details>
</dd>
<dt id="mcat.baseLSTM.BaseLSTM.scoreModel"><code class="name flex">
<span>def <span class="ident">scoreModel</span></span>(<span>self, obs, res)</span>
</code></dt>
<dd>
<div class="desc"><p>Score model for accuracy, precision and recall</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def scoreModel(self, obs, res):
"""
Score model for accuracy, precision and recall
"""
evaluation = {}
evaluation['Loss_Acc'] = self.model.evaluate(tf.ragged.stack(obs), tf.convert_to_tensor(res))
evaluation['Precision_Recall_Fscore_Support'] = precision_recall_fscore_support(res, self.predict(obs, True),
average='binary')
print("Accuracy: {}".format(evaluation['Loss_Acc'][1]))
return evaluation</code></pre>
</details>
</dd>
<dt id="mcat.baseLSTM.BaseLSTM.trainModel"><code class="name flex">
<span>def <span class="ident">trainModel</span></span>(<span>self, obs, res, val_split=0.3, val_set=None, epochs=10, batch_size=32)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def trainModel(self, obs, res, val_split=0.3, val_set=None, epochs=10, batch_size=32):
self.model.compile(optimizer=keras.optimizers.Adam(), loss='binary_crossentropy', metrics=['accuracy'])
if val_set:
train_hist = self.model.fit(tf.ragged.stack(obs), tf.convert_to_tensor(res), epochs=epochs,
batch_size=batch_size,
validation_data=(tf.ragged_stack(val_set[0]), tf.convert_to_tensor(val_set[1])),
verbose=1)
return train_hist
else:
train_hist = self.model.fit(tf.ragged.stack(obs), tf.convert_to_tensor(res), epochs=epochs,
batch_size=batch_size, verbose=1)
return train_hist</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
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<li><h3>Super-module</h3>
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<li><code><a title="mcat" href="index.html">mcat</a></code></li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<h4><code><a title="mcat.baseLSTM.BaseLSTM" href="#mcat.baseLSTM.BaseLSTM">BaseLSTM</a></code></h4>
<ul class="two-column">
<li><code><a title="mcat.baseLSTM.BaseLSTM.explain" href="#mcat.baseLSTM.BaseLSTM.explain">explain</a></code></li>
<li><code><a title="mcat.baseLSTM.BaseLSTM.makeModel" href="#mcat.baseLSTM.BaseLSTM.makeModel">makeModel</a></code></li>
<li><code><a title="mcat.baseLSTM.BaseLSTM.makeModel2D" href="#mcat.baseLSTM.BaseLSTM.makeModel2D">makeModel2D</a></code></li>
<li><code><a title="mcat.baseLSTM.BaseLSTM.predict" href="#mcat.baseLSTM.BaseLSTM.predict">predict</a></code></li>
<li><code><a title="mcat.baseLSTM.BaseLSTM.saveModel" href="#mcat.baseLSTM.BaseLSTM.saveModel">saveModel</a></code></li>
<li><code><a title="mcat.baseLSTM.BaseLSTM.scoreModel" href="#mcat.baseLSTM.BaseLSTM.scoreModel">scoreModel</a></code></li>
<li><code><a title="mcat.baseLSTM.BaseLSTM.trainModel" href="#mcat.baseLSTM.BaseLSTM.trainModel">trainModel</a></code></li>
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