# ＊ DayZSynthesizer {% hint style="info" %} **＊SDV Enterprise Feature.** This feature is only available for licensed, enterprise users. For more information, visit our page to [Compare SDV Features](https://docs.sdv.dev/sdv/explore/sdv-enterprise/compare-features). {% endhint %} The Day Z Synthesizer produces synthetic data from scratch using the metadata. This allows you start generating synthetic data from **day zero**: no machine learning required! ```python from sdv.single_table import DayZSynthesizer synthesizer = DayZSynthesizer(metadata) synthetic_data = synthesizer.sample(num_rows=10) ``` ## Estimate parameters For more realistic data, we recommend estimating some basic DayZ parameters using the real data. This includes information such as the min/max range of numerical columns and the possible category values in categorical columns. {% hint style="success" %} **SDV Community users can complete this step.** You may be asked share the DayZ parameters file to the SDV team for help in performance testing or debugging. {% endhint %} ### Create Parameters Use the **`create_parameters`** function to estimate the parameters and save them as a JSON file. ```python from sdv.single_table import DayZSynthesizer my_parameters = DayZSynthesizer.create_parameters( data=my_data, metadata=my_metadata, output_filename='dayz_parameters.json' ) ``` **Parameters:** * (required) `data`: A pd.DataFrame object containing the data to use for estimating parameters * (required) `metadata`: A SDV Metadata object that describes the data * `output_filepath`: A string with the name of the file in which to save the parameters. This should end in a `.json` suffix. **Returns**: A Python dictionary representation of the parameters (that are also saved in the JSON). ### Validate Parameters Use the **`validate_parameters`** to validate that the parameters accurately reflect the metadata. This is important if you've modified any of the parameters in the file. ```python DayZSynthesizer.validate_parameters( metadata=my_metadata, parameters=my_parameters ) ``` **Parameters**: * (required) `metadata`: An SDV Metadata object that describes the data * (required) `parameters`: The parameters dictionary **Returns**: (None) If there are any issues with the parameters, you'll see an error. ## Creating a synthesizer When creating your synthesizer, you are required to pass in a [Metadata](https://docs.sdv.dev/sdv/single-table-data/data-preparation/creating-metadata) object as the first argument. We also recommend setting the parameters at this time. ```python synthesizer = DayZSynthesizer( metadata, parameters=my_parameters, locales=['en_US', 'en_CA', 'fr_CA'] ) ``` ### Parameter Reference **`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.

**`parameters`**: A dictionary of DayZ parameters. Use this to set all the parameters that DayZ needs to create realistic data. Use the `create_parameters` function [described above](#estimate-parameters) and instantiate your DayZ synthesizer with it. ```python from sdv.single_table import DayZSynthesizer my_parameters = DayZSynthesizer.create_parameters( data=my_data, metadata=my_metadata, output_filename='dayz_parameters.json' ) synthesizer = DayZSynthesizer( metadata, parameters=my_parameters, locales=['en_US', 'en_CA', 'fr_CA'] ) ``` ### Programmatic Parameters API We recommend setting the parameters all at once. However, we also offer a programmatic, Python API to set the parameters one column at a time. Expand the sections below to learn more.
set_numerical_bounds Use this method to set lower and upper bounds for numerical columns **Parameters** * (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 ```python synthesizer.set_numerical_bounds( column_name='room_rate', min_value=30.00, max_value=5000.00 ) ```
set_rounding_scheme Use this method to set the rounding scheme (# of decimal digits) for a numerical column. **Parameters:** * (required) `column_name`: A string with the name of the column. This must be a numerical column referenced in your metadata. * (required) `num_decimal_digits`: An integer that is >= 0, that specifies how to round the generated values * `0` means that the generated values should be whole numbers * Any higher number describes the # of digits to round. So `2` would mean rounding to 2 decimal digits (eg. `12.23`)
set_datetime_bounds Use this method to set lower and upper bounds for datetime columns **Parameters** * (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 ```python synthesizer.set_datetime_bounds( column_name='checkin_date', start_timestamp='01 Jan 2020', end_timestamp='31 Dec 2020' ) ```
set_category_values Use this method to set the different values that are possible for categorical columns. **Parameters** * (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 ```python synthesizer.set_category_values( column_name='room_type', category_values=['BASIC', 'DELUXE', 'SUITE'], ) ```
set_missing_values Use this method to set the proportion of missing values to generate in a column **Parameters** * (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 ```python synthesizer.set_missing_values( column_name='room_type', missing_values_proportion=0.1 ) ```
### get\_parameters Use this method to get a dictionary of all the parameters used to make synthetic data -- 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 representing all the parameters the synthesizer uses to generate data. ```python { 'locales': ['en_US', 'en_CA', 'fr_CA'], 'columns': { 'room_rate': { 'min_value': 30.00, 'max_value': 500.00 }, 'checkin_date': { 'start_timestamp':'01 Jan 2020', 'end_timestamp':'31 Dec 2020' }, 'room_type': { 'category_values': ['BASIC', 'DELUXE', 'SUITE'], 'missing_values_proportion': 0.1 } }, ... } ``` ## Saving your synthesizer Save your synthesizer for future use ### save 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 ```python synthesizer.save( filepath='my_synthesizer.pkl' ) ``` ### load (utility function) 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 ```python from sdv.utils import load_synthesizer synthesizer = load_synthesizer( filepath='my_synthesizer.pkl' ) ``` *This utility function works for any SDV synthesizer.* ## What's next? After training your synthesizer, you can now sample synthetic data. See the [Sampling](https://docs.sdv.dev/sdv/single-table-data/sampling) section for more details. ```python synthetic_data = synthesizer.sample(num_rows=10) ``` {% hint style="info" %} **Want to improve your synthesizer?** Input logical rules in the form of constraints. For more details, see [Customizations](https://docs.sdv.dev/sdv/single-table-data/modeling/customizations). {% endhint %}