Removed testing code

d3l/__init__.py

@@ -1,72 +0,0 @@
-import numpy as np
-from d3l.indexing.similarity_indexes import NameIndex, FormatIndex, ValueIndex, EmbeddingIndex, \
-    DistributionIndex
-from d3l.input_output.dataloaders import PostgresDataLoader, CSVDataLoader
-from d3l.querying.query_engine import QueryEngine
-from d3l.utils.functions import pickle_python_object, unpickle_python_object
-
-if __name__ == "__main__":
-    # pdl = PostgresDataLoader(
-    #     db_host="localhost",
-    #     db_password="postgres",
-    #     db_username="postgres",
-    #     db_name="postgres",
-    #     db_port=5432,
-    # )
-
-    csvdl = CSVDataLoader(
-        root_path='/Users/alexteodor/Downloads/evaluation/groundTruth/',
-        header=0,
-        sep=','
-    )
-    name_index = unpickle_python_object('./name.lsh')
-    for ht in name_index.lsh_index._hashtables:
-        for _, bucket in ht.items():
-            if len(bucket) > 1:
-                print(bucket)
-
-    name_index = NameIndex(
-        dataloader=csvdl
-    )
-    pickle_python_object(name_index, './name.lsh')
-    print("Name: SAVED!")
-
-    format_index = FormatIndex(
-        dataloader=csvdl
-    )
-    pickle_python_object(format_index, './format.lsh')
-    print("Format: SAVED!")
-
-    value_index = ValueIndex(
-        dataloader=csvdl
-    )
-    pickle_python_object(value_index, './value.lsh')
-    print("Value: SAVED!")
-
-    embedding_index = EmbeddingIndex(
-        dataloader=csvdl
-    )
-    pickle_python_object(embedding_index, './embedding.lsh')
-    print("Embedding: SAVED!")
-
-    distribution_index = DistributionIndex(
-        dataloader=csvdl
-    )
-    pickle_python_object(distribution_index, './distribution.lsh')
-    print("Distribution: SAVED!")
-
-
-    name_index = unpickle_python_object('./name.lsh')
-    format_index = unpickle_python_object('./format.lsh')
-    value_index = unpickle_python_object('./value.lsh')
-    embedding_index = unpickle_python_object('./embedding.lsh')
-    distribution_index = unpickle_python_object('./distribution.lsh')
-
-
-    qe = QueryEngine(name_index, format_index, value_index, embedding_index, distribution_index)
-
-    results = qe.table_query(table=csvdl.read_table(table_name='TEgbBqq_Food_Hygiene_Data'),
-                             aggregator=lambda scores: np.mean(scores),
-                             k=10,
-                             verbose=False)
-    print(results)