Added wrapper for tensorflow 2.2.0

wrappers/tensorflow2.2.0_wrapper.py

@@ -0,0 +1,55 @@
+import pandas
+import tensorflow
+
+import tensorflow_hub
+
+
+class TensorFlowWrapper:
+    """
+    Wrapper object for TensorFlow graph and helps use it.
+    """
+
+    def __init__(self, embedding_layer_hub_name: str) -> None:
+        g = tensorflow.Graph()
+        with g.as_default():
+            # Import the Universal Sentence Encoder's TF Hub module
+            embedding_layer = tensorflow_hub.Module(embedding_layer_hub_name)
+
+            self._sts_input1 = tensorflow.compat.v1.placeholder(tensorflow.string, shape=None)
+            self._sts_input2 = tensorflow.compat.v1.placeholder(tensorflow.string, shape=None)
+
+            # For evaluation we use exactly normalized rather than approximately normalized.
+            sts_encode1 = tensorflow.math.l2_normalize(embedding_layer(self._sts_input1), axis=1)
+            sts_encode2 = tensorflow.math.l2_normalize(embedding_layer(self._sts_input2), axis=1)
+            cosine_similarities = tensorflow.math.reduce_sum(tensorflow.multiply(sts_encode1, sts_encode2),
+                                                        axis=1)
+            clip_cosine_similarities = tensorflow.clip_by_value(cosine_similarities, -1.0, 1.0)
+            self._sim_scores = 1.0 - tensorflow.math.acos(clip_cosine_similarities)
+            init_op = tensorflow.group([tensorflow.compat.v1.global_variables_initializer(), tensorflow.compat.v1.tables_initializer()])
+        g.finalize()
+
+        self._session = tensorflow.compat.v1.Session(graph=g)
+        self._session.run(init_op)
+
+    def append_scores(self, sentence_pairs: pandas.DataFrame) -> None:
+        """
+        Appending scoring of cosine similarity based on the given embedding layer.
+
+        :param sentence_pairs: DataFrame matrix of paired sentences with the columns ["sent_1", "sent_2"] where each row
+            is a paired sentences.
+        :return: None; it append to given DataFrame new column "score" with the cosine similarity score for each pair in
+            each row.
+        """
+
+        text_a = sentence_pairs["sent_1"].fillna("").tolist()
+        text_b = sentence_pairs["sent_2"].fillna("").tolist()
+
+        scores = self._session.run(self._sim_scores, feed_dict={self._sts_input1: text_a, self._sts_input2: text_b})
+
+        sentence_pairs["score"] = scores
+
+    def close(self):
+        """
+        closes the TensorFlow session.
+        """
+        self._session.close()