Create sample_conditions() method

[npatki]

Problem Description

Let's make sampling more user friendly. We can create multiple methods for different user needs.

A new sample_conditions method can address conditional sampling needs.

Expected behavior

Parameters:

• (required) conditions : A list of Condition objects (see Create Condition object #689) -- this specifies num_rows
• max_tries: renamed from existing max_retries param (default: 100)
• batch_size_per_try: Number of rows to sample per try (default: 10x requested num)
• randomize_samples will determine whether or not there should be a fixed seed (default: True)

# works with any tabular model
from sdv.tabular import CTGAN
model = CTGAN()
model.fit(data)

# see Issue #689 for Condition object details
from sdv.tabular.sampling import Conditions
female_users = Condition(column_values={'sex': 'F', 'active_user': True}, num_rows=50)
inactive_users = Condition(column_values={'active_user': False}, num_rows=100)
conditions = [female_users, inactive_users]

# pass in list of conditions
model.sample_conditions(conditions, max_retries=200, randomize_sample=False)

Error Handling

Running out of tries

# Always gracefully reject sample (ie return any rows that are sampled)
>>> synthetic_data = model.sample_conditions(conditions)
Warning: Only able to sample 75 rows for the given conditions. To sample more rows, try increasing max_tries
(currently: 100) or increasing batch_size_per_try (currently: 10000). Note that increasing these values will also
increase the sampling time.

# Error if we weren't able to sample any rows
>>> synthetic_data = model.sample_conditions(conditions)
Error: Unable to sample any rows for the given conditions. Try increasing max_tries
(currently: 100) or increasing batch_size_per_try (currently: 10000). Note that increasing these values will also
increase the sampling time.

Checking for invalid input

# Unexpected column name
>>> c = Condition(column_values={'New_Column': 42})
>>> synthetic_data = model.sample_conditions([c])
Error: Unexpected column name 'New_Column'. Use a column name that was present in the original data.