/
predictor.py
100 lines (87 loc) · 3.33 KB
/
predictor.py
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from .. import utils as U
from ..imports import *
from ..predictor import Predictor
from .preprocessor import LinkPreprocessor, NodePreprocessor
class NodePredictor(Predictor):
"""
```
predicts graph node's classes
```
"""
def __init__(self, model, preproc, batch_size=U.DEFAULT_BS):
if not isinstance(model, keras.Model):
raise ValueError("model must be of instance keras.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, verbose=0):
return self.predict_transductive(
node_ids, return_proba=return_proba, verbose=verbose
)
def predict_transductive(self, node_ids, return_proba=False, verbose=0):
"""
```
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, verbose=verbose)
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, verbose=0):
"""
```
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, verbose=verbose)
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, keras.Model):
raise ValueError("model must be of instance keras.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, verbose=0):
"""
```
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, verbose=verbose)
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