datomize 2.3.20

Datomize python client

Welcome to Datomize Python SDK

Datomize is a Data-Driven Solution to machine learning. Datomize augments source data with synthetic data of exceptional quality, and can be used to generate synthetic replicas, optimize training data with balanced and richer data, and address the data bias challenge.

Getting Started

Getting your application user & password

In order to use the Datomize Python SDK client, you first need to register the Datomize solution. Once registering Datomize, you will be provided with username and password, which get passed to datomize.Datomizer() when starting your application.

Please register the Datomize solution on Datomize Registration.

Installation

pip install datomize

Important links

• Documentation

Usage Example

# Import relevant packages
from datomizer import Datomizer, DatoMapper, DatoTrainer, DatoGenerator, DatoEnhancer
from sklearn.datasets import load_iris
import pandas as pd

# load input data:
data = load_iris(return_X_y=False, as_frame=True)
df = pd.concat([data.data, data.target], axis=1)

# Create a Datomizer with your credentials:
datomizer = Datomizer(username=username, password=password)

# Create a DatoMapper and analyze the data structure:
mapper = DatoMapper(datomizer)
mapper.discover(df_map={"df1": df})

# Create a DatoTrainer and train the generative model:
trainer = DatoTrainer(mapper)
trainer.train()

# Create a DatoGenerator and generate a syntheyic replica:
generator = DatoGenerator(trainer)
generator.generate(output_ratio=5)  # 5 times the original number of records will be created
synth_data = pd.read_csv(generator.get_generated_data_csv())

# Create a DatoEnhancer for a spedific prediction task and generate a balanced and augmented data to enhance your training data:
enhancer = DatoEnhancer(mapper)
enhancer.generate(target_column="target")
train_enhanced = pd.read_csv(enhancer.get_generated_data_csv())

Async Usage Example

from datomizer import Datomizer, DatoMapper, DatoTrainer, DatoGenerator

datomizer = Datomizer(username=username, password=password)

mapper = DatoMapper(datomizer)
mapper.discover(df_map={"df1": df}, title="Some Title", wait=False)
...
do something...
mapper.wait()

trainer = DatoTrainer(mapper)
trainer.train(wait=False)
...
do something...
trainer.wait()

generator = DatoGenerator(trainer)
generator.generate(wait=False)
...
do something...
generator.wait()

dato_df = pd.read_csv(generator.get_generated_data_csv())

Misc examples

# Import relevant packages
from datomizer import Datomizer, DatoMapper, DatoTrainer, DatoGenerator, DatoEnhancer
from datomizer.helpers.wrapper.schema_wrapper import SchemaWrapper
import pandas as pd
from sqlalchemy import create_engine
# load input data:
db_connection_str = 'mysql+pymysql://guest:[e-mail]/financial'
db_connection = create_engine(db_connection_str)
account = pd.read_sql('SELECT * FROM account', con=db_connection)
loan = pd.read_sql('SELECT * FROM loan', con=db_connection)

# Create a Datomizer with your credentials:
datomizer = Datomizer(username=username, password=password)

# Create a DatoMapper and analyze the data structure:
mapper = DatoMapper(datomizer)
mapper.discover(df_map={"account": account, "loan": loan})

schema: SchemaWrapper = mapper.get_schema()
schema.add_relation_one2many(table_one="account", column_one="account_id",
                             table_many="loan", column_many="account_id")
mapper.schema = schema
mapper.set_schema()
# Create a DatoTrainer and train the generative model:
trainer = DatoTrainer(mapper)
trainer.train()

# Create a DatoGenerator and generate a syntheyic replica:
generator = DatoGenerator(trainer)
generator.generate(output_ratio=1)  # 5 times the original number of records will be created
synth_account = pd.read_csv(generator.get_generated_data_csv("account"))
synth_loan = pd.read_csv(generator.get_generated_data_csv("loan"))