td-ml-probabilistic-unification 0.0.5

Probabilistic Unification

td-ml-probabilistic-unification

Introduction

Python package for Probabilistic Unification

Declare ENV Variables from YML file

• apikey = os.environ['TD_API_KEY']
• tdserver = os.environ['TD_API_SERVER']
• sink_database = os.environ['SINK_DB']
• output_table = os.environ['OUTPUT_TABLE

#####----Defining Variables-------##### TD_SINK_DATABASE=os.environ.get('TD_SINK_DATABASE') TD_API_KEY=os.environ.get('TD_API_KEY') TD_API_SERVER=os.environ.get('TD_API_SERVER')

id_col=os.environ.get('id_col') cluster_col_name=os.environ.get('cluster_col_name') convergence_threshold=float(os.environ.get('convergence_threshold')) cluster_threshold=float(os.environ.get('cluster_threshold')) string_type=os.environ.get('string_type') fill_missing=os.environ.get('fill_missing') feature_dict=json.loads(os.environ.get('feature_dict')) blocking_table=os.environ.get('blocking_table') output_table=os.environ.get('output_table')

record_limit=int(os.environ.get('record_limit')) lower_limit=int(os.environ.get('lower_limit')) upper_limit=int(os.environ.get('upper_limit')) range_index=os.environ.get('range_index') paralelism = os.environ.get('paralelism')

input_table=blocking_table

Expected Input Table

Copyright © 2022 Treasure Data, Inc. (or its affiliates). All rights reserved