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predictor.py
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predictor.py
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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