This method doesn't work properly after migrating to Tensorlfow 2

[soran-ghaderi]

This method is bound to the _calibrate method working properly.

Emgraph/emgraph/models/EmbeddingModel.py

Lines 2137 to 2171 in 3926ad7

	def _predict_proba(self, X):
	"""Predicts probabilities using the Platt scaling model (after calibration).
	Model must be calibrated beforehand with the ``calibrate`` method.
	:param X: Numpy array of triples to be evaluated.
	:type X: ndarray, shape [n, 3]
	:return: Probability of each triple to be true according to the Platt scaling calibration.
	:rtype: ndarray, shape [n, 3]
	"""
	if not self.is_calibrated:
	msg = "Model has not been calibrated. Please call `model.calibrate(...)` before predicting probabilities."
	logger.error(msg)
	raise RuntimeError(msg)
	# tf.reset_default_graph()
	self._load_model_from_trained_params()
	w = tf.Variable(self.calibration_parameters[0], dtype=tf.float32, trainable=False)
	b = tf.Variable(self.calibration_parameters[1], dtype=tf.float32, trainable=False)
	x_idx = to_idx(X, ent_to_idx=self.ent_to_idx, rel_to_idx=self.rel_to_idx)
	x_tf = tf.Variable(x_idx, dtype=tf.int32, trainable=False)
	e_s, e_p, e_o = self._lookup_embeddings(x_tf)
	scores = self._fn(e_s, e_p, e_o)
	logits = -(w * scores + b)
	probas = tf.sigmoid(logits)
	# with tf.Session(config=self.tf_config) as sess:
	# sess.run(tf.global_variables_initializer())
	# return sess.run(probas)
	return probas