# ＊ 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.multi_table import DayZSynthesizer synthesizer = DayZSynthesizer(metadata) synthetic_data = synthesizer.sample(num_rows=1000) ``` ## 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.multi_table import DayZSynthesizer my_parameters = DayZSynthesizer.create_parameters( data=my_data, metadata=my_metadata, output_filename='dayz_parameters.json' ) ``` **Parameters:** * (required) `data`: A dictionary mapping each table name to a [pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) containing the real data that the machine learning model will learn from * (required) `metadata`: A [SDV Metadata](https://docs.sdv.dev/sdv/concepts/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/~/changes/328/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](#create-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) `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 ```python synthesizer.set_numerical_bounds( table_name='guests', 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) `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) `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) `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 ```python synthesizer.set_datetime_bounds( table_name='guests', 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) `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 ```python synthesizer.set_category_values( table_name='guests', 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) `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 ```python 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
set_cardinality 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. ```python # 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 * ``: 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) * ``: 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.
set_table_sizes Use this function to set the (relative) table sizes of each table in the dataset. When sampling, you can scale the entire dataset up or down. ```python synthesizer = DayZSynthesizer(metadata) synthesizer.set_table_sizes( num_rows_per_table={ 'hotels': 1000, 'guests': 2500 } ) ``` **Parameters:** * (required) `num_rows_per_table`: A dictionary containing the number of rows to set per table. The keys are the names of the tables, and the values are integers representing the number of rows.
### get\_parameters 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. {% hint style="info" %} These parameters are only for the multi-table synthesizer. To get individual table-level parameters, use the `get_table_parameters` function. The returned parameters are a copy. Changing them will not affect the synthesizer. {% endhint %} ```python synthesizer.get_parameters() ``` ```python { 'locales': ['en_US', 'fr_CA'], ... } ``` ### get\_table\_parameters 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 ```python synthesizer.get_table_parameters(table_name='users') ``` ```python { 'synthesizer_name': 'DayZSynthesizer', 'synthesizer_parameters': { 'columns': { ... } } } ``` ## 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/multi-table-data/sampling) section for more details. *(This synthesizer does not yet offer support for conditional sampling.)* ```python synthetic_data = synthesizer.sample(scale=1.0) ``` {% hint style="info" %} **Want to improve your synthesizer?** Input logical rules in the form of constraints, and customize the transformations used for pre- and post-processing the data. 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/multi-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.