# ＊ 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 %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.sdv.dev/SDV/single-table-data/modeling/synthesizers/dayzsynthesizer.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.