＊ DayZSynthesizer
Last updated
Last updated
The Day Z Synthesizer produces synthetic data from scratch using only the metadata. This allows you start generating synthetic data from day zero: no real data or machine learning required!
from sdv.multi_table import DayZSynthesizer
synthesizer = DayZSynthesizer(metadata)
synthetic_data = synthesizer.sample(num_rows=1000)When creating your synthesizer, you are required to pass in a Metadata object as the first argument. Other parameters are optional.
locales: A list of locale strings. Any PII columns will correspond to the locales that you provide.
(default) ['en_US']
Generate PII values in English corresponding to US-based concepts (eg. addresses, phone numbers, etc.)
<list>
Create data from the list of locales. Each locale string consists of a 2-character code for the language and 2-character code for the country, separated by an underscore.
For example ["en_US", "fr_CA"].
For all options, see the Faker docs.
synthesizer = DayZSynthesizer(
metadata,
locales=['en_US', 'en_CA', 'fr_CA']
)By default, this synthesizer will randomly generate multi table data that conforms to your metadata specification. This includes referential integrity: All connections between tables will make sense.
If you'd like to generate more realistic data, you can use the methods below to add guidance.
Use this method to set lower and upper bounds for numerical columns
Parameters
(required) table_name: A string with the name of the table
(required) column_name: A string with the name of the column. This must be a numerical column referenced in your metadata.
(required) min_value: A float or int representing the minimum value.
(required) max_value: A float or int representing the max value
Output (None) The sampled synthetic data will follow the min and max bounds
synthesizer.add_numerical_bounds(
table_name='guests',
column_name='room_rate',
min_value=30.00,
max_value=5000.00
)Use this method to set lower and upper bounds for datetime columns
Parameters
(required) table_name: A string with the name of the table
(required) column_name: A string with the name of the column. This must be a datetime column referenced in your metadata.
(required) start_timestamp: A string representing the earliest allowed datetime. The string must be in the same datetime format as referenced in your metadata.
(required) end_timestamp: A string representing the latest allowed datetime. The string must be in the same datetime format as referenced in your metadata.
Output (None) The sampled synthetic data will follow start and end bounds
synthesizer.add_datetime_bounds(
table_name='guests',
column_name='checkin_date',
start_timestamp='01 Jan 2020',
end_timestamp='31 Dec 2020'
)Use this method to set the different values that are possible for categorical columns.
Parameters
(required) table_name: A string with the name of the table
(required) column_name: A string with the name of the column. This must be a categorical column referenced in your metadata.
(required) category_values: A list of strings representing the different unique category values that are possible. (If missing values are allowed, use the set_missing_values method instead of listing it here.)
Output (None) The sampled synthetic data will include the category values
synthesizer.set_category_values(
table_name='guests',
column_name='room_type',
category_values=['BASIC', 'DELUXE', 'SUITE']
)Use this method to set the proportion of missing values to generate in a column
Parameters
(required) table_name: A string representing the name of the table
(required) column_name: A string representing the name of the column. This column cannot be a primary or foreign key.
(required) missing_values_proportion: A float representing the proportion of missing values
Any float between 0.0 and 1.0: Randomly create this proportion of missing values in the column
synthesizer.set_missing_values(
table_name='guests',
column_name='room_type',
missing_values_proportion=0.1
)Output (None) Sets the proportion of the missing values
Use this function to set the cardinality of a parent/child relationship. The cardinality refers to the number of children that each parent row is allowed to have. This can be anywhere from 0 to infinity.
This function can help you create realistic data for many relationship types such as 1-1, 1-to-many, etc.
# each hotel must have 1 or more guests
synthesizer.set_cardinality(
parent_table_name='hotels',
child_table_name='guests',
parent_primary_key='hotel_id',
child_foreign_key='hotel_id',
min_cardinality=1,
max_cardinality=None
)Parameters
(required) parent_table_name: The name of the parent table
(required) child_table_name: The name of the child table
(required) parent_primary_key: The name of the primary key in the parent
(required) child_foreign_key: The name of the foreign key in the child that refers to the primary key of the parent
min_cardinality: The minimum # of children each parent must have, must be an integer >=0
(default) 0: A parent row must have 0 or more children
<integer>: An integer representing the minimum # of children
max_cardinality: The maximum # of children each parent must have, must be an integer >0
(default) None: Do not enforce a maximum (i.e. the maximum # of children can be infinite)
<integer>: An integer > min_cardinality representing the maximum # of children
Note that If min cardinality = max cardinality, then that means there is a fixed # of children for each parent.
Output (None) Sets the min and max cardinality of the parent/child relationship, or updates it if the cardinality was already set.
Use this function to access the all parameters your synthesizer uses -- those you have provided as well as the default ones.
Parameters
output_filepath: A string representing the name of the file to write the parameters to. We recommend storing this as a JSON file. Defaults to None, meaning that no output filepath is written.
Output A dictionary with the table names and parameters for each table.
synthesizer.get_parameters(){
'locales': ['en_US', 'fr_CA'],
...
}Use this function to access the all parameters a table synthesizer uses -- those you have provided as well as the default ones.
Parameters
(required) table_name: A string describing the name of the table
Output A dictionary with the parameter names and the values
synthesizer.get_table_parameters(table_name='users'){
'synthesizer_name': 'DayZSynthesizer',
'synthesizer_parameters': {
'columns': {
...
}
}
}Save your synthesizer for future use
Use this function to save your synthesizer as a Python pickle file.
Parameters
(required) filepath: A string describing the filepath where you want to save your synthesizer. Make sure this ends in .pkl
Output (None) The file will be saved at the desired location
synthesizer.save(
filepath='my_synthesizer.pkl'
)Use this utility function to load a trained synthesizer from a Python pickle file. After loading your synthesizer, you'll be able to sample synthetic data from it.
Parameters
(required) filepath: A string describing the filepath of your saved synthesizer
Output Your synthesizer object
from sdv.utils import load_synthesizer
synthesizer = load_synthesizer(
filepath='my_synthesizer.pkl'
)This utility function works for any SDV synthesizer.
Sample any amount of synthetic data
Use this method to sample synthetic data
Parameters
num_rows: An integer >0 that specifies the number of rows to synthesize for each table by default. Defaults to 1000, meaning 1000 rows will be generated for each table
num_rows_per_table: A dictionary mapping each table name to the number of rows to generate for the table. If a table is not in this dictionary, we'll fall back to the num_rows
Output A dictionary that maps each table name (string) to a pandas DataFrame object with synthetic data for that table. The synthetic data mimics the real data.
synthetic_data = synthesizer.sample(
num_rows_per_table={
'hotels': 1000,
'guests': 2500,
}
)