Use Sentence Transformers to Encode, Query Schedule.org Headings

.gitignore

@@ -0,0 +1 @@
+.*

requirements.txt

@@ -0,0 +1,6 @@
+torch==1.6.0
+transformers==3.3.1
+sentence-transformers==0.3.8
+pandas==1.1.2
+faiss-cpu==1.6.1
+numpy==1.18.5

similarity.py

@@ -0,0 +1,97 @@
+import pandas as pd
+import faiss
+import numpy as np
+
+from sentence_transformers import SentenceTransformer
+
+import argparse
+import os
+
+def create_index(
+        model,
+        dataset_path,
+        index_path,
+        column_name,
+        recreate):
+    # Load Dataset
+    dataset = pd.read_csv(dataset_path)
+
+    # Clean Dataset
+    dataset = dataset.dropna()
+    dataset[column_name] = dataset[column_name].str.strip()
+
+    # Create Index or Load it if it already exists
+    if os.path.exists(index_path) and not recreate:
+        index = faiss.read_index(index_path)
+    else:
+        # Create Embedding Vectors of Documents
+        embeddings = model.encode(dataset[column_name].to_list(), show_progress_bar=True)
+        embeddings = np.array([embedding for embedding in embeddings]).astype("float32")
+
+        index = faiss.IndexIDMap(
+            faiss.IndexFlatL2(
+                embeddings.shape[1]))
+
+        index.add_with_ids(embeddings, dataset.index.values)
+
+        faiss.write_index(index, index_path)
+
+    return index, dataset
+
+
+def resolve_column(dataset, Id, column):
+    return [list(dataset[dataset.index == idx][column]) for idx in Id[0]]
+
+
+def vector_search(query, index, dataset, column_name, num_results=10):
+    query_vector = np.array(query).astype("float32")
+    D, Id = index.search(query_vector, k=num_results)
+
+    return zip(D[0], Id[0], resolve_column(dataset, Id, column_name))
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Find most suitable match based on users exclude, include preferences")
+    parser.add_argument('positives', type=str, help="Terms to find closest match to")
+    parser.add_argument('--negatives', '-n', type=str, help="Terms to find farthest match from")
+
+    parser.add_argument('--recreate', action='store_true', default=False, help="Recreate index at index_path from dataset at dataset path")
+    parser.add_argument('--index', type=str, default="./.faiss_index", help="Path to index for storing vector embeddings")
+    parser.add_argument('--dataset', type=str, default="./.dataset", help="Path to dataset to generate index from")
+    parser.add_argument('--column', type=str, default="DATA", help="Name of dataset column to index")
+    parser.add_argument('--num_results', type=int, default=10, help="Number of most suitable matches to show")
+    parser.add_argument('--model_name', type=str, default='paraphrase-distilroberta-base-v1', help="Specify name of the SentenceTransformer model to use for encoding")
+    args = parser.parse_args()
+
+    model = SentenceTransformer(args.model_name)
+
+    if args.positives and not args.negatives:
+        # Get index, create it from dataset if doesn't exist
+        index, dataset = create_index(model, args.dataset, args.index, args.column, args.recreate)
+
+        # Create vector to represent user's stated positive preference
+        preference_vector = model.encode([args.positives])
+
+        # Find and display most suitable matches for users preferences in the dataset
+        results = vector_search(preference_vector, index, dataset, args.column, args.num_results)
+
+        print("Most Suitable Matches:")
+        for similarity, id_, data in results:
+            print(f"Id: {id_}\nSimilarity: {similarity}\n{args.column}: {data[0]}")
+
+    elif args.positives and args.negatives:
+        # Get index, create it from dataset if doesn't exist
+        index, dataset = create_index(model, args.dataset, args.index, args.column, args.recreate)
+
+        # Create vector to represent user's stated preference
+        positives_vector = np.array(model.encode([args.positives])).astype("float32")
+        negatives_vector = np.array(model.encode([args.negatives])).astype("float32")
+
+        # preference_vector = np.mean([positives_vector, -1 * negatives_vector], axis=0)
+        preference_vector = np.add(positives_vector, -1 * negatives_vector)
+
+        # Find and display most suitable matches for users preferences in the dataset
+        results = vector_search(preference_vector, index, dataset, args.column, args.num_results)
+
+        print("Most Suitable Matches:")
+        for similarity, id_, data in results:
+            print(f"Id: {id_}\nSimilarity: {similarity}\n{args.column}: {data[0]}")