# ＊ 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 %}