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predictor.html
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predictor.html
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.graph.predictor</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from ..imports import *
from ..predictor import Predictor
from .preprocessor import NodePreprocessor, LinkPreprocessor
from .. import utils as U
class NodePredictor(Predictor):
"""
```
predicts graph node's classes
```
"""
def __init__(self, model, preproc, batch_size=U.DEFAULT_BS):
if not isinstance(model, Model):
raise ValueError('model must be of instance Model')
if not isinstance(preproc, NodePreprocessor):
raise ValueError('preproc must be a NodePreprocessor object')
self.model = model
self.preproc = preproc
self.c = self.preproc.get_classes()
self.batch_size = batch_size
def get_classes(self):
return self.c
def predict(self, node_ids, return_proba=False):
return self.predict_transductive(node_ids, return_proba=return_proba)
def predict_transductive(self, node_ids, return_proba=False):
"""
```
Performs transductive inference.
If return_proba is True, returns probabilities of each class.
```
"""
gen = self.preproc.preprocess_valid(node_ids)
gen.batch_size = self.batch_size
# *_generator methods are deprecated from TF 2.1.0
#preds = self.model.predict_generator(gen)
preds = self.model.predict(gen)
result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
return result
def predict_inductive(self, df, G, return_proba=False):
"""
```
Performs inductive inference.
If return_proba is True, returns probabilities of each class.
```
"""
gen = self.preproc.preprocess(df, G)
gen.batch_size = self.batch_size
# *_generator methods are deprecated from TF 2.1.0
#preds = self.model.predict_generator(gen)
preds = self.model.predict(gen)
result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
return result
class LinkPredictor(Predictor):
"""
```
predicts graph node's classes
```
"""
def __init__(self, model, preproc, batch_size=U.DEFAULT_BS):
if not isinstance(model, Model):
raise ValueError('model must be of instance Model')
if not isinstance(preproc, LinkPreprocessor):
raise ValueError('preproc must be a LinkPreprocessor object')
self.model = model
self.preproc = preproc
self.c = self.preproc.get_classes()
self.batch_size = batch_size
def get_classes(self):
return self.c
def predict(self, G, edge_ids, return_proba=False):
"""
```
Performs link prediction
If return_proba is True, returns probabilities of each class.
```
"""
gen = self.preproc.preprocess(G, edge_ids)
gen.batch_size = self.batch_size
# *_generator methods are deprecated from TF 2.1.0
#preds = self.model.predict_generator(gen)
preds = self.model.predict(gen)
preds = np.squeeze(preds)
if return_proba:
return [[1-pred, pred] for pred in preds]
result = np.where(preds > 0.5, self.c[1], self.c[0])
return result</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.graph.predictor.LinkPredictor"><code class="flex name class">
<span>class <span class="ident">LinkPredictor</span></span>
<span>(</span><span>model, preproc, batch_size=32)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>predicts graph node's classes
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class LinkPredictor(Predictor):
"""
```
predicts graph node's classes
```
"""
def __init__(self, model, preproc, batch_size=U.DEFAULT_BS):
if not isinstance(model, Model):
raise ValueError('model must be of instance Model')
if not isinstance(preproc, LinkPreprocessor):
raise ValueError('preproc must be a LinkPreprocessor object')
self.model = model
self.preproc = preproc
self.c = self.preproc.get_classes()
self.batch_size = batch_size
def get_classes(self):
return self.c
def predict(self, G, edge_ids, return_proba=False):
"""
```
Performs link prediction
If return_proba is True, returns probabilities of each class.
```
"""
gen = self.preproc.preprocess(G, edge_ids)
gen.batch_size = self.batch_size
# *_generator methods are deprecated from TF 2.1.0
#preds = self.model.predict_generator(gen)
preds = self.model.predict(gen)
preds = np.squeeze(preds)
if return_proba:
return [[1-pred, pred] for pred in preds]
result = np.where(preds > 0.5, self.c[1], self.c[0])
return result</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.predictor.Predictor" href="../predictor.html#ktrain.predictor.Predictor">Predictor</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.graph.predictor.LinkPredictor.get_classes"><code class="name flex">
<span>def <span class="ident">get_classes</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_classes(self):
return self.c</code></pre>
</details>
</dd>
<dt id="ktrain.graph.predictor.LinkPredictor.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, G, edge_ids, return_proba=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Performs link prediction
If return_proba is True, returns probabilities of each class.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, G, edge_ids, return_proba=False):
"""
```
Performs link prediction
If return_proba is True, returns probabilities of each class.
```
"""
gen = self.preproc.preprocess(G, edge_ids)
gen.batch_size = self.batch_size
# *_generator methods are deprecated from TF 2.1.0
#preds = self.model.predict_generator(gen)
preds = self.model.predict(gen)
preds = np.squeeze(preds)
if return_proba:
return [[1-pred, pred] for pred in preds]
result = np.where(preds > 0.5, self.c[1], self.c[0])
return result</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.predictor.Predictor" href="../predictor.html#ktrain.predictor.Predictor">Predictor</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.predictor.Predictor.create_onnx_session" href="../predictor.html#ktrain.predictor.Predictor.create_onnx_session">create_onnx_session</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.export_model_to_onnx" href="../predictor.html#ktrain.predictor.Predictor.export_model_to_onnx">export_model_to_onnx</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.export_model_to_tflite" href="../predictor.html#ktrain.predictor.Predictor.export_model_to_tflite">export_model_to_tflite</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.save" href="../predictor.html#ktrain.predictor.Predictor.save">save</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="ktrain.graph.predictor.NodePredictor"><code class="flex name class">
<span>class <span class="ident">NodePredictor</span></span>
<span>(</span><span>model, preproc, batch_size=32)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>predicts graph node's classes
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class NodePredictor(Predictor):
"""
```
predicts graph node's classes
```
"""
def __init__(self, model, preproc, batch_size=U.DEFAULT_BS):
if not isinstance(model, Model):
raise ValueError('model must be of instance Model')
if not isinstance(preproc, NodePreprocessor):
raise ValueError('preproc must be a NodePreprocessor object')
self.model = model
self.preproc = preproc
self.c = self.preproc.get_classes()
self.batch_size = batch_size
def get_classes(self):
return self.c
def predict(self, node_ids, return_proba=False):
return self.predict_transductive(node_ids, return_proba=return_proba)
def predict_transductive(self, node_ids, return_proba=False):
"""
```
Performs transductive inference.
If return_proba is True, returns probabilities of each class.
```
"""
gen = self.preproc.preprocess_valid(node_ids)
gen.batch_size = self.batch_size
# *_generator methods are deprecated from TF 2.1.0
#preds = self.model.predict_generator(gen)
preds = self.model.predict(gen)
result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
return result
def predict_inductive(self, df, G, return_proba=False):
"""
```
Performs inductive inference.
If return_proba is True, returns probabilities of each class.
```
"""
gen = self.preproc.preprocess(df, G)
gen.batch_size = self.batch_size
# *_generator methods are deprecated from TF 2.1.0
#preds = self.model.predict_generator(gen)
preds = self.model.predict(gen)
result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
return result</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.predictor.Predictor" href="../predictor.html#ktrain.predictor.Predictor">Predictor</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.graph.predictor.NodePredictor.get_classes"><code class="name flex">
<span>def <span class="ident">get_classes</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_classes(self):
return self.c</code></pre>
</details>
</dd>
<dt id="ktrain.graph.predictor.NodePredictor.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, node_ids, return_proba=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, node_ids, return_proba=False):
return self.predict_transductive(node_ids, return_proba=return_proba)</code></pre>
</details>
</dd>
<dt id="ktrain.graph.predictor.NodePredictor.predict_inductive"><code class="name flex">
<span>def <span class="ident">predict_inductive</span></span>(<span>self, df, G, return_proba=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Performs inductive inference.
If return_proba is True, returns probabilities of each class.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_inductive(self, df, G, return_proba=False):
"""
```
Performs inductive inference.
If return_proba is True, returns probabilities of each class.
```
"""
gen = self.preproc.preprocess(df, G)
gen.batch_size = self.batch_size
# *_generator methods are deprecated from TF 2.1.0
#preds = self.model.predict_generator(gen)
preds = self.model.predict(gen)
result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
return result</code></pre>
</details>
</dd>
<dt id="ktrain.graph.predictor.NodePredictor.predict_transductive"><code class="name flex">
<span>def <span class="ident">predict_transductive</span></span>(<span>self, node_ids, return_proba=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Performs transductive inference.
If return_proba is True, returns probabilities of each class.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_transductive(self, node_ids, return_proba=False):
"""
```
Performs transductive inference.
If return_proba is True, returns probabilities of each class.
```
"""
gen = self.preproc.preprocess_valid(node_ids)
gen.batch_size = self.batch_size
# *_generator methods are deprecated from TF 2.1.0
#preds = self.model.predict_generator(gen)
preds = self.model.predict(gen)
result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
return result</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.predictor.Predictor" href="../predictor.html#ktrain.predictor.Predictor">Predictor</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.predictor.Predictor.create_onnx_session" href="../predictor.html#ktrain.predictor.Predictor.create_onnx_session">create_onnx_session</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.export_model_to_onnx" href="../predictor.html#ktrain.predictor.Predictor.export_model_to_onnx">export_model_to_onnx</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.export_model_to_tflite" href="../predictor.html#ktrain.predictor.Predictor.export_model_to_tflite">export_model_to_tflite</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.save" href="../predictor.html#ktrain.predictor.Predictor.save">save</a></code></li>
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<h1>Index</h1>
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<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.graph" href="index.html">ktrain.graph</a></code></li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<h4><code><a title="ktrain.graph.predictor.LinkPredictor" href="#ktrain.graph.predictor.LinkPredictor">LinkPredictor</a></code></h4>
<ul class="">
<li><code><a title="ktrain.graph.predictor.LinkPredictor.get_classes" href="#ktrain.graph.predictor.LinkPredictor.get_classes">get_classes</a></code></li>
<li><code><a title="ktrain.graph.predictor.LinkPredictor.predict" href="#ktrain.graph.predictor.LinkPredictor.predict">predict</a></code></li>
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<h4><code><a title="ktrain.graph.predictor.NodePredictor" href="#ktrain.graph.predictor.NodePredictor">NodePredictor</a></code></h4>
<ul class="">
<li><code><a title="ktrain.graph.predictor.NodePredictor.get_classes" href="#ktrain.graph.predictor.NodePredictor.get_classes">get_classes</a></code></li>
<li><code><a title="ktrain.graph.predictor.NodePredictor.predict" href="#ktrain.graph.predictor.NodePredictor.predict">predict</a></code></li>
<li><code><a title="ktrain.graph.predictor.NodePredictor.predict_inductive" href="#ktrain.graph.predictor.NodePredictor.predict_inductive">predict_inductive</a></code></li>
<li><code><a title="ktrain.graph.predictor.NodePredictor.predict_transductive" href="#ktrain.graph.predictor.NodePredictor.predict_transductive">predict_transductive</a></code></li>
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