Add ANN algorithm for large number of records for similarity strategy

.pre-commit-config.yaml

@@ -2,7 +2,7 @@ default_language_version:
     python: python3
 repos:
     - repo: https://github.com/ambv/black
-      rev: 22.3.0
+      rev: 24.10.0
       hooks:
           - id: black
     - repo: https://github.com/pre-commit/pre-commit-hooks
@@ -18,12 +18,12 @@ repos:
           - id: rst-linter
             exclude: "docs"
     - repo: https://github.com/pycqa/isort
-      rev: 5.12.0
+      rev: 5.13.2
       hooks:
           - id: isort
             args: ["--profile", "black", "--filter-files"]
     - repo: https://github.com/pre-commit/mirrors-prettier
-      rev: v2.5.1
+      rev: v4.0.0-alpha.8
       hooks:
           - id: prettier
     - repo: local

cumulusci/tasks/bulkdata/select_utils.py

@@ -2,6 +2,12 @@
 import re
 import typing as T
 
+import numpy as np
+import pandas as pd
+from annoy import AnnoyIndex
+from sklearn.feature_extraction.text import HashingVectorizer
+from sklearn.preprocessing import StandardScaler
+
 from cumulusci.core.enums import StrEnum
 from cumulusci.tasks.bulkdata.extract_dataset_utils.hardcoded_default_declarations import (
     DEFAULT_DECLARATIONS,
@@ -159,14 +165,115 @@ def similarity_generate_query(
 
 
 def similarity_post_process(
-    load_records: list, query_records: list, sobject: str
+    load_records, query_records: list, sobject: str
 ) -> T.Tuple[T.List[dict], T.Union[str, None]]:
     """Processes the query results for the similarity selection strategy"""
     # Handle case where query returns 0 records
     if not query_records:
         error_message = f"No records found for {sobject} in the target org."
         return [], error_message
 
+    load_records = list(load_records)
+    load_record_count, query_record_count = len(load_records), len(query_records)
+
+    complexity_constant = load_record_count * query_record_count
+
+    print(complexity_constant)
+
+    closest_records = []
+
+    if complexity_constant < 1000:
+        closest_records = annoy_post_process(load_records, query_records)
+    else:
+        closest_records = levenshtein_post_process(load_records, query_records)
+
+    print(closest_records)
+
+    return closest_records
+
+
+def annoy_post_process(
+    load_records: list, query_records: list
+) -> T.Tuple[T.List[dict], T.Union[str, None]]:
+    """Processes the query results for the similarity selection strategy using Annoy algorithm for large number of records"""
+
+    query_records = replace_empty_strings_with_missing(query_records)
+    load_records = replace_empty_strings_with_missing(load_records)
+
+    print("Query records: ")
+    print(query_records)
+
+    print("Load records: ")
+    print(load_records)
+
+    print("\n\n\n\n")
+
+    hash_features = 100
+    num_trees = 10
+
+    query_record_ids = [record[0] for record in query_records]
+    query_record_data = [record[1:] for record in query_records]
+
+    record_to_id_map = {
+        tuple(query_record_data[i]): query_record_ids[i]
+        for i in range(len(query_records))
+    }
+
+    final_load_vectors, final_query_vectors = vectorize_records(
+        load_records, query_record_data, hash_features=hash_features
+    )
+
+    # Create Annoy index for nearest neighbor search
+    vector_dimension = final_query_vectors.shape[1]
+    annoy_index = AnnoyIndex(vector_dimension, "euclidean")
+
+    for i in range(len(final_query_vectors)):
+        annoy_index.add_item(i, final_query_vectors[i])
+
+    # Build the index
+    annoy_index.build(num_trees)
+
+    # Find nearest neighbors for each query vector
+    n_neighbors = 1
+
+    closest_records = []
+
+    for i, load_vector in enumerate(final_load_vectors):
+        # Get nearest neighbors' indices and distances
+        nearest_neighbors = annoy_index.get_nns_by_vector(
+            load_vector, n_neighbors, include_distances=True
+        )
+        neighbor_indices = nearest_neighbors[0]  # Indices of nearest neighbors
+        distances = nearest_neighbors[1]  # Distances to nearest neighbors
+
+        load_record = load_records[i]  # Get the query record for the current index
+        print(f"Load record {i + 1}: {load_record}\n")  # Print the query record
+
+        # Print the nearest neighbors for the current query
+        print(f"Nearest neighbors for load record {i + 1}:")
+
+        for j, neighbor_index in enumerate(neighbor_indices):
+            # Retrieve the corresponding record from the database
+            record = query_record_data[neighbor_index]
+            distance = distances[j]
+
+            # Print the record and its distance
+            print(f"  Neighbor {j + 1}: {record}, Distance: {distance:.6f}")
+            closest_record_id = record_to_id_map[tuple(record)]
+            print("Record id:" + closest_record_id)
+            closest_records.append(
+                {"id": closest_record_id, "success": True, "created": False}
+            )
+
+        print("\n")  # Add a newline for better readability between query results
+
+    return closest_records, None
+
+
+def levenshtein_post_process(
+    load_records: list, query_records: list
+) -> T.Tuple[T.List[dict], T.Union[str, None]]:
+    """Processes the query results for the similarity selection strategy using Levenshtein algorithm for small number of records"""
     closest_records = []
 
     for record in load_records:
@@ -300,3 +407,97 @@ def add_limit_offset_to_user_filter(
         filter_clause += f" OFFSET {offset_clause}"
 
     return f" {filter_clause}"
+
+
+def determine_field_types(df):
+    numerical_features = []
+    boolean_features = []
+    categorical_features = []
+
+    for col in df.columns:
+        # Check if the column can be converted to numeric
+        try:
+            # Attempt to convert to numeric
+            df[col] = pd.to_numeric(df[col], errors="raise")
+            numerical_features.append(col)
+        except ValueError:
+            # Check for boolean values
+            if df[col].str.lower().isin(["true", "false"]).all():
+                # Map to actual boolean values
+                df[col] = df[col].str.lower().map({"true": True, "false": False})
+                boolean_features.append(col)
+            else:
+                categorical_features.append(col)
+
+    return numerical_features, boolean_features, categorical_features
+
+
+def vectorize_records(db_records, query_records, hash_features):
+    # Convert database records and query records to DataFrames
+    df_db = pd.DataFrame(db_records)
+    df_query = pd.DataFrame(query_records)
+
+    # Dynamically determine field types
+    numerical_features, boolean_features, categorical_features = determine_field_types(
+        df_db
+    )
+
+    # Fit StandardScaler on the numerical features of the database records
+    scaler = StandardScaler()
+    if numerical_features:
+        df_db[numerical_features] = scaler.fit_transform(df_db[numerical_features])
+        df_query[numerical_features] = scaler.transform(df_query[numerical_features])
+
+    # Use HashingVectorizer to transform the categorical features
+    hashing_vectorizer = HashingVectorizer(
+        n_features=hash_features, alternate_sign=False
+    )
+
+    # For db_records
+    hashed_categorical_data_db = []
+    for col in categorical_features:
+        hashed_db = hashing_vectorizer.fit_transform(df_db[col]).toarray()
+        hashed_categorical_data_db.append(hashed_db)
+
+    # For query_records
+    hashed_categorical_data_query = []
+    for col in categorical_features:
+        hashed_query = hashing_vectorizer.transform(df_query[col]).toarray()
+        hashed_categorical_data_query.append(hashed_query)
+
+    # Combine all feature types into a single vector for the database records
+    db_vectors = []
+    if numerical_features:
+        db_vectors.append(df_db[numerical_features].values)
+    if boolean_features:
+        db_vectors.append(
+            df_db[boolean_features].astype(int).values
+        )  # Convert boolean to int
+    if hashed_categorical_data_db:
+        db_vectors.append(np.hstack(hashed_categorical_data_db))
+
+    # Concatenate database vectors
+    final_db_vectors = np.hstack(db_vectors)
+
+    # Combine all feature types into a single vector for the query records
+    query_vectors = []
+    if numerical_features:
+        query_vectors.append(df_query[numerical_features].values)
+    if boolean_features:
+        query_vectors.append(
+            df_query[boolean_features].astype(int).values
+        )  # Convert boolean to int
+    if hashed_categorical_data_query:
+        query_vectors.append(np.hstack(hashed_categorical_data_query))
+
+    # Concatenate query vectors
+    final_query_vectors = np.hstack(query_vectors)
+
+    return final_db_vectors, final_query_vectors
+
+
+def replace_empty_strings_with_missing(records):
+    return [
+        [(field if field != "" else "missing") for field in record]
+        for record in records
+    ]