SYMBOL INDEX (67 symbols across 16 files)

FILE: retail/recommendation-system/bqml-mlops/part_2/pipeline.py
  function run_bigquery_ddl (line 6) | def run_bigquery_ddl(project_id: str, query_string: str, location: str) ...
  function train_matrix_factorization_model (line 35) | def train_matrix_factorization_model(ddlop, project_id: str, dataset: str):
  function evaluate_matrix_factorization_model (line 56) | def evaluate_matrix_factorization_model(project_id:str, mf_model:str, lo...
  function create_user_features (line 74) | def create_user_features(ddlop, project_id:str, dataset:str, mf_model:str):
  function create_hotel_features (line 99) | def create_hotel_features(ddlop, project_id:str, dataset:str, mf_model:s...
  function combine_features (line 124) | def combine_features(ddlop, project_id:str, dataset:str, mf_model:str, h...
  function train_xgboost_model (line 146) | def train_xgboost_model(ddlop, project_id:str, dataset:str, total_featur...
  function evaluate_class (line 162) | def evaluate_class(project_id:str, dataset:str, class_model:str, total_f...
  function export_bqml_model (line 187) | def export_bqml_model(project_id:str, model:str, destination:str) -> Nam...
  function training_pipeline (line 205) | def training_pipeline(project_id:str, dataset_name:str, model_storage:st...
  function main (line 266) | def main(**args):

FILE: retail/recommendation-system/bqml-scann/ann_grpc/match_pb2_grpc.py
  class MatchServiceStub (line 8) | class MatchServiceStub(object):
    method __init__ (line 13) | def __init__(self, channel):
  class MatchServiceServicer (line 31) | class MatchServiceServicer(object):
    method Match (line 36) | def Match(self, request, context):
    method BatchMatch (line 44) | def BatchMatch(self, request, context):
  function add_MatchServiceServicer_to_server (line 53) | def add_MatchServiceServicer_to_server(servicer, server):
  class MatchService (line 72) | class MatchService(object):
    method Match (line 78) | def Match(request,
    method BatchMatch (line 95) | def BatchMatch(request,

FILE: retail/recommendation-system/bqml-scann/embeddings_exporter/pipeline.py
  function get_query (line 21) | def get_query(dataset_name, table_name):
  function to_csv (line 32) | def to_csv(entry):
  function run (line 40) | def run(bq_dataset_name, embeddings_table_name, output_dir, pipeline_args):

FILE: retail/recommendation-system/bqml-scann/embeddings_exporter/runner.py
  function get_args (line 22) | def get_args(argv):
  function main (line 41) | def main(argv=None):

FILE: retail/recommendation-system/bqml-scann/embeddings_lookup/lookup_creator.py
  class EmbeddingLookup (line 22) | class EmbeddingLookup(tf.keras.Model):
    method __init__ (line 24) | def __init__(self, embedding_files_prefix, **kwargs):
    method __call__ (line 64) | def __call__(self, inputs):
  function export_saved_model (line 77) | def export_saved_model(embedding_files_path, model_output_dir):

FILE: retail/recommendation-system/bqml-scann/index_builder/builder/indexer.py
  function load_embeddings (line 31) | def load_embeddings(embedding_files_pattern):
  function build_index (line 56) | def build_index(embeddings, num_leaves):
  function save_index (line 78) | def save_index(index, tokens, output_dir):
  function build (line 93) | def build(embedding_files_pattern, output_dir, num_leaves=None):

FILE: retail/recommendation-system/bqml-scann/index_builder/builder/task.py
  function get_args (line 18) | def get_args():
  function main (line 49) | def main():

FILE: retail/recommendation-system/bqml-scann/index_server/lookup.py
  class EmbeddingLookup (line 19) | class EmbeddingLookup(object):
    method __init__ (line 21) | def __init__(self, project, region, model_name, version):
    method lookup (line 29) | def lookup(self, instances):

FILE: retail/recommendation-system/bqml-scann/index_server/main.py
  function health (line 39) | def health(model, version):
  function predict (line 44) | def predict(model, version):
  function validate_request (line 68) | def validate_request(query, show):

FILE: retail/recommendation-system/bqml-scann/index_server/matching.py
  class ScaNNMatcher (line 24) | class ScaNNMatcher(object):
    method __init__ (line 26) | def __init__(self, index_dir):
    method match (line 35) | def match(self, vector, num_matches=10):

FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/bq_components.py
  function compute_pmi (line 33) | def compute_pmi(
  function train_item_matching_model (line 66) | def train_item_matching_model(
  function extract_embeddings (line 96) | def extract_embeddings(

FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/item_matcher.py
  class ScaNNMatcher (line 26) | class ScaNNMatcher(object):
    method __init__ (line 28) | def __init__(self, index_dir):
    method match (line 37) | def match(self, vector, num_matches=10):
  class ExactMatcher (line 45) | class ExactMatcher(object):
    method __init__ (line 47) | def __init__(self, embeddings, tokens):
    method match (line 53) | def match(self, vector, num_matches=10):

FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/lookup_creator.py
  class EmbeddingLookup (line 24) | class EmbeddingLookup(tf.keras.Model):
    method __init__ (line 26) | def __init__(self, embedding_files_prefix, schema_file_path, **kwargs):
    method __call__ (line 75) | def __call__(self, inputs):
  function run_fn (line 89) | def run_fn(params):

FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/pipeline.py
  function create_pipeline (line 44) | def create_pipeline(pipeline_name: Text,

FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/scann_evaluator.py
  class IndexEvaluatorSpec (line 52) | class IndexEvaluatorSpec(tfx.types.ComponentSpec):
  class ScaNNIndexEvaluatorExecutor (line 71) | class ScaNNIndexEvaluatorExecutor(base_executor.BaseExecutor):
    method Do (line 73) | def Do(self,
  class IndexEvaluator (line 172) | class IndexEvaluator(base_component.BaseComponent):
    method __init__ (line 177) | def __init__(self,

FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/scann_indexer.py
  function load_embeddings (line 36) | def load_embeddings(embedding_files_pattern, schema_file_path):
  function build_index (line 71) | def build_index(embeddings, num_leaves):
  function save_index (line 91) | def save_index(index, tokens, output_dir):
  function run_fn (line 108) | def run_fn(params):