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models.html
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models.html
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<article id="content">
<header>
<h1 class="title">Module <code>ktrain.graph.models</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .. import utils as U
from ..imports import *
GRAPHSAGE = "graphsage"
NODE_CLASSIFIERS = {GRAPHSAGE: "GraphSAGE: https://arxiv.org/pdf/1706.02216.pdf"}
LINK_PREDICTORS = {GRAPHSAGE: "GraphSAGE: https://arxiv.org/pdf/1706.02216.pdf"}
def print_node_classifiers():
for k, v in NODE_CLASSIFIERS.items():
print("%s: %s" % (k, v))
def print_link_predictors():
for k, v in LINK_PREDICTORS.items():
print("%s: %s" % (k, v))
def graph_node_classifier(name, train_data, layer_sizes=[32, 32], verbose=1):
"""
```
Build and return a neural node classification model.
Notes: Only mutually-exclusive class labels are supported.
Args:
name (string): one of:
- 'graphsage' for GraphSAGE model
(only GraphSAGE currently supported)
train_data (NodeSequenceWrapper): a ktrain.graph.sg_wrappers.NodeSequenceWrapper object
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
from .sg_wrappers import NodeSequenceWrapper
# check argument
if not isinstance(train_data, NodeSequenceWrapper):
err = """
train_data must be a ktrain.graph.sg_wrappers.NodeSequenceWrapper object
"""
raise Exception(err)
if len(layer_sizes) != 2:
raise ValueError("layer_sizes must be of length 2")
num_classes = U.nclasses_from_data(train_data)
# determine multilabel
multilabel = U.is_multilabel(train_data)
if multilabel:
raise ValueError(
"Multi-label classification not currently supported for graphs."
)
U.vprint("Is Multi-Label? %s" % (multilabel), verbose=verbose)
# set loss and activations
loss_func = "categorical_crossentropy"
activation = "softmax"
# import stellargraph
try:
import stellargraph as sg
from stellargraph.layer import GraphSAGE
except:
raise Exception(SG_ERRMSG)
if version.parse(sg.__version__) < version.parse("0.8"):
raise Exception(SG_ERRMSG)
# build a GraphSAGE node classification model
graphsage_model = GraphSAGE(
layer_sizes=layer_sizes,
generator=train_data,
bias=True,
dropout=0.5,
)
# x_inp, x_out = graphsage_model.default_model(flatten_output=True)
x_inp, x_out = graphsage_model.build()
prediction = keras.layers.Dense(units=num_classes, activation=activation)(x_out)
model = keras.Model(inputs=x_inp, outputs=prediction)
model.compile(optimizer="adam", loss=loss_func, metrics=["accuracy"])
U.vprint("done", verbose=verbose)
return model
def graph_link_predictor(name, train_data, preproc, layer_sizes=[20, 20], verbose=1):
"""
```
Build and return a neural link prediction model.
Args:
name (string): one of:
- 'graphsage' for GraphSAGE model
(only GraphSAGE currently supported)
train_data (LinkSequenceWrapper): a ktrain.graph.sg_wrappers.LinkSequenceWrapper object
preproc(LinkPreprocessor): a LinkPreprocessor instance
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
from .sg_wrappers import LinkSequenceWrapper
# check argument
if not isinstance(train_data, LinkSequenceWrapper):
err = """
train_data must be a ktrain.graph.sg_wrappers.LinkSequenceWrapper object
"""
raise Exception(err)
if len(layer_sizes) != len(preproc.sample_sizes):
raise ValueError("number of layer_sizes must match len(preproc.sample_sizes)")
num_classes = U.nclasses_from_data(train_data)
# set loss and activations
loss_func = "categorical_crossentropy"
activation = "softmax"
# import stellargraph
try:
import stellargraph as sg
from stellargraph.layer import GraphSAGE, link_classification
except:
raise Exception(SG_ERRMSG)
if version.parse(sg.__version__) < version.parse("0.8"):
raise Exception(SG_ERRMSG)
# build a GraphSAGE link prediction model
graphsage = GraphSAGE(
layer_sizes=layer_sizes, generator=train_data, bias=True, dropout=0.3
)
x_inp, x_out = graphsage.build()
prediction = link_classification(
output_dim=1, output_act="relu", edge_embedding_method="ip"
)(x_out)
model = keras.Model(inputs=x_inp, outputs=prediction)
model.compile(
optimizer=U.DEFAULT_OPT, loss="binary_crossentropy", metrics=["accuracy"]
)
return model</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.graph.models.graph_link_predictor"><code class="name flex">
<span>def <span class="ident">graph_link_predictor</span></span>(<span>name, train_data, preproc, layer_sizes=[20, 20], verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Build and return a neural link prediction model.
Args:
name (string): one of:
- 'graphsage' for GraphSAGE model
(only GraphSAGE currently supported)
train_data (LinkSequenceWrapper): a ktrain.graph.sg_wrappers.LinkSequenceWrapper object
preproc(LinkPreprocessor): a LinkPreprocessor instance
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def graph_link_predictor(name, train_data, preproc, layer_sizes=[20, 20], verbose=1):
"""
```
Build and return a neural link prediction model.
Args:
name (string): one of:
- 'graphsage' for GraphSAGE model
(only GraphSAGE currently supported)
train_data (LinkSequenceWrapper): a ktrain.graph.sg_wrappers.LinkSequenceWrapper object
preproc(LinkPreprocessor): a LinkPreprocessor instance
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
from .sg_wrappers import LinkSequenceWrapper
# check argument
if not isinstance(train_data, LinkSequenceWrapper):
err = """
train_data must be a ktrain.graph.sg_wrappers.LinkSequenceWrapper object
"""
raise Exception(err)
if len(layer_sizes) != len(preproc.sample_sizes):
raise ValueError("number of layer_sizes must match len(preproc.sample_sizes)")
num_classes = U.nclasses_from_data(train_data)
# set loss and activations
loss_func = "categorical_crossentropy"
activation = "softmax"
# import stellargraph
try:
import stellargraph as sg
from stellargraph.layer import GraphSAGE, link_classification
except:
raise Exception(SG_ERRMSG)
if version.parse(sg.__version__) < version.parse("0.8"):
raise Exception(SG_ERRMSG)
# build a GraphSAGE link prediction model
graphsage = GraphSAGE(
layer_sizes=layer_sizes, generator=train_data, bias=True, dropout=0.3
)
x_inp, x_out = graphsage.build()
prediction = link_classification(
output_dim=1, output_act="relu", edge_embedding_method="ip"
)(x_out)
model = keras.Model(inputs=x_inp, outputs=prediction)
model.compile(
optimizer=U.DEFAULT_OPT, loss="binary_crossentropy", metrics=["accuracy"]
)
return model</code></pre>
</details>
</dd>
<dt id="ktrain.graph.models.graph_node_classifier"><code class="name flex">
<span>def <span class="ident">graph_node_classifier</span></span>(<span>name, train_data, layer_sizes=[32, 32], verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Build and return a neural node classification model.
Notes: Only mutually-exclusive class labels are supported.
Args:
name (string): one of:
- 'graphsage' for GraphSAGE model
(only GraphSAGE currently supported)
train_data (NodeSequenceWrapper): a ktrain.graph.sg_wrappers.NodeSequenceWrapper object
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def graph_node_classifier(name, train_data, layer_sizes=[32, 32], verbose=1):
"""
```
Build and return a neural node classification model.
Notes: Only mutually-exclusive class labels are supported.
Args:
name (string): one of:
- 'graphsage' for GraphSAGE model
(only GraphSAGE currently supported)
train_data (NodeSequenceWrapper): a ktrain.graph.sg_wrappers.NodeSequenceWrapper object
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
```
"""
from .sg_wrappers import NodeSequenceWrapper
# check argument
if not isinstance(train_data, NodeSequenceWrapper):
err = """
train_data must be a ktrain.graph.sg_wrappers.NodeSequenceWrapper object
"""
raise Exception(err)
if len(layer_sizes) != 2:
raise ValueError("layer_sizes must be of length 2")
num_classes = U.nclasses_from_data(train_data)
# determine multilabel
multilabel = U.is_multilabel(train_data)
if multilabel:
raise ValueError(
"Multi-label classification not currently supported for graphs."
)
U.vprint("Is Multi-Label? %s" % (multilabel), verbose=verbose)
# set loss and activations
loss_func = "categorical_crossentropy"
activation = "softmax"
# import stellargraph
try:
import stellargraph as sg
from stellargraph.layer import GraphSAGE
except:
raise Exception(SG_ERRMSG)
if version.parse(sg.__version__) < version.parse("0.8"):
raise Exception(SG_ERRMSG)
# build a GraphSAGE node classification model
graphsage_model = GraphSAGE(
layer_sizes=layer_sizes,
generator=train_data,
bias=True,
dropout=0.5,
)
# x_inp, x_out = graphsage_model.default_model(flatten_output=True)
x_inp, x_out = graphsage_model.build()
prediction = keras.layers.Dense(units=num_classes, activation=activation)(x_out)
model = keras.Model(inputs=x_inp, outputs=prediction)
model.compile(optimizer="adam", loss=loss_func, metrics=["accuracy"])
U.vprint("done", verbose=verbose)
return model</code></pre>
</details>
</dd>
<dt id="ktrain.graph.models.print_link_predictors"><code class="name flex">
<span>def <span class="ident">print_link_predictors</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def print_link_predictors():
for k, v in LINK_PREDICTORS.items():
print("%s: %s" % (k, v))</code></pre>
</details>
</dd>
<dt id="ktrain.graph.models.print_node_classifiers"><code class="name flex">
<span>def <span class="ident">print_node_classifiers</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def print_node_classifiers():
for k, v in NODE_CLASSIFIERS.items():
print("%s: %s" % (k, v))</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="ktrain.graph" href="index.html">ktrain.graph</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="ktrain.graph.models.graph_link_predictor" href="#ktrain.graph.models.graph_link_predictor">graph_link_predictor</a></code></li>
<li><code><a title="ktrain.graph.models.graph_node_classifier" href="#ktrain.graph.models.graph_node_classifier">graph_node_classifier</a></code></li>
<li><code><a title="ktrain.graph.models.print_link_predictors" href="#ktrain.graph.models.print_link_predictors">print_link_predictors</a></code></li>
<li><code><a title="ktrain.graph.models.print_node_classifiers" href="#ktrain.graph.models.print_node_classifiers">print_node_classifiers</a></code></li>
</ul>
</li>
</ul>
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