This release adds multiple improvements to handling premium transformers and column relationships, including using premium transformers even if the PII flag is set to true. Additionally, the SDV now warns users to save the metadata after auto-detection has been used. Semantic sdtype detection has also been improved to tokenize column names to prevent unexpected substring matches.
This release also fixes a few warning bugs and fixes an issue that would cause metadata.to_dict to fail for metadata loaded from older versions of the SDV. A few synthesizer bugs were also resolved. The quality of the sequence_index for the PARSynthesizer has been improved, and an issue that would cause CTGANSynthesizer, TVAESynthesizer, and CopulaGANSynthesizer to crash if all columns were to be generated from scratch has been fixed.
- HMASynthesizer sometimes creates null values (out-of-bounds parameters synthesized) - Issue #1691 by @fealho
- Unable to conditionally sample some rows when using a
ScalarRangeconstraint - Issue #1737 by @fealho - Metadata.to_dict fails on metadata instances pre 1.9.0 - Issue #1739 by @amontanez24
- Metadata auto-detection should not assign a primary key if there are NaN values in it - Issue #1740 by @R-Palazzo
- '' object has no attribute '_model' - Issue #1741 by @fealho
- Column relationship warning should be raised during synthesizer initialization only - Issue #1750 by @R-Palazzo
- Improve quality of
sequence_index: Move the start dates into the context model - Issue #1760 by @frances-h - Add-ons warning is raised twice for multi table synthesizers. - Issue #1768 by @R-Palazzo
- Metadata auto-detection should tokenize words before determining PII - Issue #1725 by @fealho
- Provide a friendlier error if data is stored as dtype
'category'(CTGAN, TVAE) - Issue #1735 by @frances-h - Allow the ability to easily remove primary keys - Issue #1742 by @frances-h
- Constraint should not be set on columns inside a gps relationship - Issue #1748 by @R-Palazzo
- Set the default transformer for GPS column relationship - Issue #1749 by @R-Palazzo
- Add a
versionmodule to align with SDV Enterprise - Issue #1761 by @R-Palazzo - Warn users to save their metadata file after auto-detecting/updating it - Issue #1762 by @R-Palazzo
- Set the GPSNoiser as default transformer for GPS column relationship - Issue #1767 by @R-Palazzo
- Update transformer assignment logic for handling pii - Issue #1775 by @R-Palazzo
This release makes a number of improvements. It introduces a new concept to the metadata known as column relationships! Column relationships can be used to define when certain groups of columns in a table should be treated as a special concept (eg. address). You can add a column relationship by using the new add_column_relationship method. The metadata detection was also improved by allowing semantic sdtypes (eg. 'email', 'phone_number') to be detected as primary keys.
This release also patches some bugs. An issue messing up the likelihood matching in the HMASynthesizer was resolved. The CTGANSynthesizer no longer fails when using the FixedCombinations constraint. The Inequality constraint was also patched to handle datetimes better.
- The
set_address_columnsmethod is deprecated in favor ofadd_column_relationship.
- Improve error messages for composite keys - Issue #1684 by @frances-h
- Add column relationship validation to single table metadata - Issue #1698 by @frances-h
- Add add_column_relationship method to single table metadata - Issue #1699 by @frances-h
- Make synthesizers work with column_relationships - Issue #1700 by @R-Palazzo
- Metadata auto-detection should find primary keys of semantic sdtypes - Issue #1724 by @fealho
- InvalidDataError for Inequality constraint (even though data is valid) - Issue #1692 by @fealho
BaseIndependentSamplercrashes because it tries to cast id columns - Issue #1712 by @pvk-developer- KeyError in
CTGANSynthesizerwhen applyingFixedCombinationsconstraint - Issue #1717 by @pvk-developer - Fix _get_likelihoods not generating likelihood values - Issue #1720 by @frances-h
This release adds support for the new Diagnostic Report from SDMetrics. This report calculates scores for three basic but important properties of your data: data validity, data structure and in the multi table case, relationship validity. Data validity checks that the columns of your data are valid (eg. correct range or values). Data structure makes sure the synthetic data has the correct columns. Relationship validity checks to make sure key references are correct and the cardinality is within ranges seen in the real data.
Additionally, a few bugs were fixed and functionality was improved around synthesizers. It is now possible to access the loss values for the TVAESynthesizer and CTGANSynthesizer by using the get_loss_values method. The get_parameters method is now more detailed and returns all the parameters used to make a synthesizer. The metadata is now capable of detecting some common pii sdtypes. Finally, a bug that made every parent row generated by the HMASynthesizer have at least one child row was patched. This should improve cardinality.
- Address
SettingWithCopyWarning(HMASynthesizer) - Issue #1557 by @pvk-developer - Bump SDMetrics version - Issue #1702 by @amontanez24
- Allow me to access loss values for GAN-based synthesizers - Issue #1671 by @frances-h
- Create a unified
get_parametersmethod for all multi-table synthesizers - Issue #1674 by @frances-h - Set credentials key as variables - Issue #1680 by @R-Palazzo
- Identifying PII Sdtypes in Metadata - Issue #1683 by @R-Palazzo
- Make SDV compatible with the latest SDMetrics - Issue #1687 by @fealho
- SingleTablePreset uses FrequencyEncoder - Issue #1695 by @fealho
- HMASynthesizer creates too much synthetic data (always creates a child for every parent row) - Issue #1673 by @frances-h
This release adds an alert to the CTGANSynthesizer during preprocessing. The alert informs the user if the fitting of the synthesizer is likely to be slow on their schema. Additionally, it is now possible to enforce that sampled datetime values stay within the range of the fitted data!
This release also makes internal changes to support address data in SDV Enterprise.
- Add set_address_columns method - Issue #1593 by @R-Palazzo
- Update_transformers should raise error on address columns - Issue #1594 by @R-Palazzo
- add_constraints should raise error on address columns - Issue #1595 by @R-Palazzo
- Print alert if CTGANSynthesizer is likely to be slow - Issue #1658 by @fealho
- Set enforce_min_max_values to True for datetime transformers - Issue #1676 by @R-Palazzo
- Unable to visualize metadata (
Error: bad label formatandCalledProcessError) - Issue #1625 by @fealho - Can't set address columns after fitting - Issue #1661 by @R-Palazzo
This release improves user messaging in multiple ways. The most notable is that users will now see an alert if the HMASynthesizer is likely to be slow for their data's schema. Additionally, the logger messaging for constraints and the error messaging when setting distributions on non-parametric models was made more detailed.
The visualization plots in the sdv.evaluation sub-package all got a new parameter called plot_type, allowing the users to specify the plot type to use if the one being inferred is not useful. The sdv.datasets.local.load_csvs method now has a parameter called read_csv_parameters, that allow users to specify how the csvs should be read during loading. The same change was also made to the sdv.metadata.multi_table.detect_table_from_csv, sdv.metadata.multi_table.detect_from_csvs and sdv.metadata.single_table.detect_from_csv methods.
Multiple bugs were resolved including one that caused new categories to be created during the sample step of CTGANSynthesizer.
- Improve debug messages when a constraint falls back to reject sampling approach - Issue #1478 by @amontanez24
- Constraints should work with timezone-aware datetime columns - Issue #1576 by @fealho
- Better error message when trying to get distributions from non-parametric models - PR #1633 by @frances-h
- Add options to read CSV files - Issue #1644 by @lajohn4747
- Print alert if HMASynthesizer is likely to be slow - Issue #1646 by @lajohn4747
- Make SDV compatible with SDMetrics 0.12.1 - Issue #1650 by @pvk-developer
- Make function to estimate number of columns CTGAN produces - Issue #1657 by @fealho
- In get_available_demos, the num_tables column should be an int - Issue #1420 by @lajohn4747
- AttributeError when using specific locale strings (es_AR, fr_BE) - Issue #1439 by @lajohn4747
- Confusing error when passing in an empty dataframe (with constraints) - Issue #1455 by @lajohn4747
- HMASynthesizer: Better error message for learned distributions (misleading fit error) - Issue #1579 by @fealho
- Fix tests in SDV after update in RDT 1.7.1 - Issue #1638 by @lajohn4747
- CTGAN sometimes creates new categories (int data) - Issue #1647 by @pvk-developer
- CTGAN sometimes creates new categories (object data) - Issue #1648 by @pvk-developer
- Better error message if I provide an incompatible sdtype/locale combo - Issue #1653 by @pvk-developer
Several improvements and bug fixes were made in this release. Most notably, the metadata detection was substantially improved. Support for the 'unknown' sdtype was added, providing more flexibility in data representation. The software now attempts to intelligently detect primary keys and identify parent-child relationships in the metadata, streamlining the metadata creation process.
Additionally, issues related to conditional sampling with negative float values, the inability to update transformers for columns created by constraints, and compatibility with numpy version 1.25 and higher were addressed. The default branch was also switched from 'master' to 'main' for better development practices. Various bugs and errors, including those involving HMA and datetime format detection, were also resolved.
- Improve metadata detection - Issue #1515 by @R-Palazzo
- Support 'unknown' sdtype - Issue #1516 by @R-Palazzo
- Detect primary keys in metadata - Issue #1521 by @frances-h
- Detect relationships in MultiTableMetadata - Issue #1522 by @frances-h
- Make function to estimate number of columns HMA produces. - Issue #1572 by @fealho
- Add wrapper for get_cardinalty_plot - Issue #1573 by @frances-h
- [Metadata detection] Add a cardinality cap when choosing between categorical vs. numerical - Issue #1584 by @pvk-developer
- [Metadata Detection] Only make primary/foreign keys sdtype
id(leave others asunknown) - Issue #1598 by @amontanez24 - Check and supply a more descriptive error when trying to use
'gaussian_kde'with HMA - Issue #1604 by @frances-h
- Conditional sampling with negative float values doesn't work - Issue #1161 by @fealho
- Cannot update transformers for columns that get created by constraints (
KeyError) - Issue #1454 by @frances-h - HMA produces KeyError for a schema with 3+ levels of depth - Issue #1558 by @fealho
- Columns consisting of only Nones are being detected as datetime - Issue #1589 by @pvk-developer
- HMASynthesizer throws an error when sampling multi table models with three levels of depths - Issue #1600 by @amontanez24
ValueError: Invalid distribution specificationwhen setting numerical_distributions on child table (HMA) - Issue #1605 by @fealho- Bug: updating transformers in DataProcessor resets warning filters - Issue #1618 by @rwedge
- Investigate how to get numpy >1.25 to pass - Issue #1501 by @rwedge
- Switch default branch from master to main - Issue #1550 by @amontanez24
This release makes multiple improvements to the metadata. Both the single and multi table metadata classes now have a validate_data method. This method runs checks to validate the data against the current specifications in the metadata. The SingleTableMetadata.visualize is also improved. The sequence index is now shown in the same section as the sequence key. It also now shows all key and index information (eg. sequence key, primary key, sequence index) in one section.
The CTGANSynthesizer has been made more efficient in the following ways:
- Boolean columns are now being skipped during
preprocesslike categorial columns are. - It is possible to apply other transformations to categorical columns and have
CTGANskip the one-hot encoding step.
Additional changes include that the columns labeled with the sdtype id will now go through the IDGenerator transformer by default and constraint transformations that were being overwritten during sampling will now be respected.
- Add validate_data method to Metadata - Issue #1518 by @fealho
- Use IDGenerator for ID columns - Issue #1519 by @frances-h
- Metadata visualization for sequential data: Only create 2 sections - Issue #1543 by @frances-h
- Inefficient CTGAN modeling when adding categorical transformers - Issue #1450 by @fealho
- CTGANSynthesizer is assigning LabelEncoder to boolean columns (instead of None) - Issue #1530 by @fealho
- Metadata visualization for sequential data: Missing sequence index - Issue #1542 by @frances-h
- Constraint outputs are being overwritten in DataProcessor.reverse_transform - Issue #1551 by @amontanez24
This release adds two new methods to the MultiTableMetadata: detect_from_csvs and detect_From_dataframes. These methods allow you to detect metadata for a whole dataset at once by either loading them from a folder or a dictionary mapping table names to the pandas.DataFrames. The SingleTableMetadata can now be visualized! Additionally, there is now a summarized option in the show_table_details parameter of the visualize methods. This will print each sdtype in the table and the number of columns that have that sdtype.
Additionally, this release patches a bug that prevented custom constraints from working on columns that were primary or alternate keys. It also adds support for Python 3.11!
- Align default transformers between SDV and RDT - Issue #1484 by @R-Palazzo
- Add visualize method to SingleTableMetadata - Issue #1517 by @pvk-developer
- Add detect_from_csvs and detect_from_dataframes methods to MultiTableMetadata - Issue #1520 by @R-Palazzo
- Allow empty tables to be fitted using fit_processed_data - Issue #1524 by @fealho
- Summarized metadata visualization - Issue #1525 by @pvk-developer
- Cannot use custom constraint transforms for certain columns (inconsistent ordering in forward vs. reverse) - Issue #1476 by @fealho
- Cannot create custom constraint with primary key - Issue #1514 by @amontanez24
- Add support for Python 3.11 - Issue #1459 by @fealho
This release fixes a bug that caused the Inequality constraint and others to fail if there were None values in a datetime column.
- Inequality fails with None and datetime - Issue #1471 by @pvk-developer
- Drop support for Python 3.7 - Issue #1487 by @pvk-developer
- Make HMA use hierarchical sampling mixin - Issue #1428 by @frances-h
- Move progress bar out of base multi table synthesizer - Issue #1486 by @R-Palazzo
This release adds a parameter called verbose to the HMASynthesizer. Setting it to True will show progress bars during the fitting steps. Additionally, performance optimizations were made to the modeling and initialization of the HMASynthesizer.
Multiple changes were made to enhance constraints. The Range constraint was improved to be able to generate more accurate data when null values are provided. Constraints are also now validated against the data when running validate() on any synthesizer.
Finally, some warnings were resolved.
- Report fitting progress for the HMASynthesizer - Issue #1440 by @pvk-developer
- Range constraint does not produce cases of missing values & may create violative data - Issue #1393 by @R-Palazzo
- Synthesizers don't validate constraints during validate() - Issue #1402 by @pvk-developer
- Confusing error during metadata validation - Issue #1417 by @frances-h
- SettingWithCopyWarning when conditional sampling - #1436 by @pvk-developer
- HMASynthesizer is modeling child tables - Issue #1442 by @pvk-developer
- ValueError when sampling PII columns - Issue #1445 by @pvk-developer
- Add BaseHierarchicalSampler Mixin - Issue #1394 by @frances-h
- Add BaseIndependentSampler Mixin - Issue #1395 by @frances-h
- Synthesizers created twice during HMA init - Issue #1418 by @frances-h
- Get rid of unnecessary methods for single table sampling - Issue #1430 by @amontanez24
- Detect all addons from top level init - PR #1453 by @frances-h
- Upgrade to torch 2.0 - Issue #1365 by @fealho
- During fit, there is a FutureWarning (due to RDT 1.5.0) - Issue #1456 by @amontanez24
This release adds a new initialization parameter to synthesizers called locales that allows users to set the locales to use for all columns that have a locale based sdtype (eg. address or phone_number). Additionally, it adds support for Pandas 2.0!
Multiple enhancements were made to improve the performance of data and metadata validation in synthesizers. The Inequality constraint was improved to be able to generate more scenarios of data concerning the presence of NaNs. Finally, many warnings have been resolved.
- Add add-on detection for new constraints - Issue #1397 by @frances-h
- Add add-on detection for multi and single table synthesizers - Issue #1385 by @frances-h
- Setting a locale for all my anonymized (PII) columns - Issue #1371 by @frances-h
- Skip checking for Faker function if its a default sdtype - PR #1410 by @pvk-developer
- Inequality constraint does not produce all possibilities of missing values - Issue #1392 by @R-Palazzo
- Deprecated locale warning - Issue #1400 by @frances-h
- Use cached Faker instance to discover if an sdtype is a Faker function. - Issue #1405 by @pvk-developer
- Upgrade to pandas 2.0 - Issue #1366 by @pvk-developer
- Refactor Multi Table Modeling - Issue #1387 by @pvk-developer
- PytestConfigWarning: Unknown config option: collect_ignore - Issue #1376 by @amontanez24
- Pandas FutureWarning: Could not cast to int64 - Issue #1357 by @R-Palazzo
- RuntimeWarning: invalid value encountered in cast. - Issue #1369 by @amontanez24
This release improves the load_custom_constraint_classes method by removing the table_name parameter and just loading the constraint
for all tables instead. It also improves some error messages as well as removes some of the warnings that have been surfacing.
Support for sdtypes is enhanced by resolving a bug that was incorrecttly specifying Faker functions for some of them. Support for datetime formats has also been improved. Finally, the path argument in some save and load methods was changed to filepath for consistency.
- Method load_custom_constraint_classes does not need table_name parameter - Issue #1354 by @R-Palazzo
- Improve error message for invalid primary keys - Issue #1341 by @R-Palazzo
- Add functionality to find version add-on - Issue #1309 by @frances-h
- Certain sdtypes cause Faker to raise error - Issue #1346 by @frances-h
- Change path to filepath for load and save methods - Issue #1352 by @fealho
- Some datetime formats cause InvalidDataError, even if the datetime matches the format - Issue #1136 by @amontanez24
- Inequality constraint raises RuntimeWarning (invalid value encountered in log) - Issue #1275 by @frances-h
- Pandas FutureWarning: Default dtype for Empty Series will be 'object' - Issue #1355 by @R-Palazzo
- Pandas FutureWarning: Length 1 tuple will be returned - Issue #1356 by @R-Palazzo
This is a major release that introduces a new API to the SDV aimed at streamlining the process of synthetic data generation! To achieve this, this release includes the addition of several large features.
Some of the most notable additions are the new SingleTableMetadata and MultiTableMetadata classes. These classes enable a number of features that make it easier to synthesize your data correctly such as:
- Automatic data detection - Calling
metadata.detect_from_dataframe()ormetadata.detect_from_csv()will populate the metadata autonomously with values it thinks represent the data. - Easy updating - Once an instance of the metadata is created, values can be easily updated using a number of methods defined in the API. For more info, view the docs.
- Metadata validation - Calling
metadata.validate()will return a report of any invalid definitions in the metadata specification. - Upgrading - Users with the previous metadata format can easily update to the new specification using the
upgrade_metadata()method. - Saving and loading - The metadata itself can easily be saved to a json file and loaded back up later.
Another major change is the renaming of our core modeling classes and modules. The name changes are meant to highlight the difference between the underlying machine learning models, and the objects responsible for the end-to-end workflow of generating synthetic data. The main name changes are as follows:
tabular->single_tablerelational->multi_tabletimeseries->sequentialBaseTabularModel->BaseSingleTableSynthesizerGaussianCopula->GaussianCopulaSynthesizerCTGAN->CTGANSynthesizerTVAE->TVAESynthesizerCopulaGan->CopulaGANSynthesizerPAR->PARSynthesizerHMA1->HMASynthesizer
In SDV 1.0, synthesizers are classes that take in metadata and handle data preprocessing, model training and model sampling. This is similar to the previous BaseTabularModel in SDV <1.0.
Synthesizers in SDV 1.0 define a clear workflow for generating synthetic data.
- Synthesizers are initialized with a metadata class.
- They can then be used to transform the data and apply constraints using the
synthesizer.preprocess()method. This step also validates that the data matches the provided metadata to avoid errors in fitting or sampling. - The processed data can then be fed into the underlying machine learning model using
synthesizer.fit_processed_data(). (Alternatively, data can be preprocessed and fit to the model usingsynthesizer.fit().) - Data can then be sampled using
synthesizer.sample().
Each synthesizer class also provides a series of methods to help users customize the transformations their data goes through. Read more about that here.
Notice that the preprocessing and model fitting steps can now be separated. This can be helpful if preprocessing is time consuming or if the data has been processed externally.
Another major addition is control over randomization. In SDV <1.0, users could set a seed to control the randomization for only some columns. In SDV 1.0, randomization is controlled for all columns. Every new call to sample generates new data, but the synthesizer's seed can be reset to the original state using synthesizer.reset_randomization(), enabling reproducibility.
SDV 1.0 adds accessibility and transparency into the transformers used for preprocessing and underlying machine learning models.
- Using the
synthesizer.get_transformers()method, you can access the transformers used to preprocess each column and view their properties. This can be useful for debugging and accessing privacy information like mappings used to mask data. - Distribution parameters learned by copula models can be accessed using the
synthesizer.get_learned_distributions()method.
PII handling is improved by the following features:
- Primary keys can be set to natural sdtypes (eg. SSN, email, name). Previously they could only be numerical or text.
- The
PseudoAnonymizedFakercan be used to provide consistent mapping to PII columns. As mentioned before, the mapping itself can be accessed by viewing the transformers for the column usingsynthesizer.get_transformers(). - A bug causing PII columns to slow down modeling is patched.
Finally, the synthetic data can now be easily evaluated using the evaluate_quality() and run_diagnostic() methods. The data can be compared visually to the actual data using the get_column_plot() and get_column_pair_plot() methods. For more info on how to visualize or interpret the synthetic data evaluation, read the docs here.
- Change auto_assign_transformers to handle id types - Issue #1325 by @pvk-developer
- Change 'text' sdtype to 'id' - Issue #1324 by @frances-h
- In
upgrade_metadata, return the object instead of writing it to a JSON file - Issue #1319 by @frances-h - In
upgrade_metadataindex primary keys should be converted totext- Issue #1318 by @amontanez24 - Add
load_from_dictto SingleTableMetadata and MultiTableMetadata - Issue #1314 by @amontanez24 - Throw a
SynthesizerInputErrorifFixedCombinationsconstraint is applied to a column that is notbooleanorcategorical- Issue #1306 by @frances-h - Missing
saveandloadmethods forHMASynthesizer- Issue #1262 by @amontanez24 - Better input validation when creating single and multi table synthesizers - Issue #1242 by @fealho
- Better input validation on
HMASynthesizer.sample- Issue #1241 by @R-Palazzo - Validate that relationship must be between a
primary keyandforeign key- Issue #1236 by @fealho - Improve
update_columnvalidation forpiiattribute - Issue #1226 by @pvk-developer - Order the output of
get_transformers()based on the metadata - Issue #1222 by @pvk-developer - Log if any
numerical_distributionswill not be applied - Issue #1212 by @fealho - Improve error handling for
GaussianCopulaSynthesizer:numerical_distributions- Issue #1211 by @fealho - Improve error handling when validating
constraints- Issue #1210 by @fealho - Add
fake_companiesdemo - Issue #1209 by @amontanez24 - Allow me to create a custom constraint class and use it in the same file - Issue #1205 by @amontanez24
- Sampling should reset after retraining the model - Issue #1201 by @pvk-developer
- Change function name
HMASynthesizer.update_table_parameters-->set_table_parameters- Issue #1200 by @pvk-developer - Add
get_infomethod to synthesizers - Issue #1199 by @fealho - Add evaluation methods to synthesizer - Issue #1190 by @fealho
- Update
evaluate.pyto work with the newmetadata- Issue #1186 by @fealho - Remove old code - Issue #1181 by @pvk-developer
- Drop support for python 3.6 and add support for 3.10 - Issue #1176 by @fealho
- Add constraint methods to MultiTableSynthesizers - Issue #1171 by @fealho
- Update custom constraint workflow - Issue #1169 by @pvk-developer
- Add get_constraints method to synthesizers - Issue #1168 by @pvk-developer
- Migrate adding and validating constraints to BaseSynthesizer - Issue #1163 by @pvk-developer
- Change metadata
"SCHEMA_VERSION"-->"METADATA_SPEC_VERSION"- Issue #1139 by @amontanez24 - Add ability to reset random sampling - Issue #1130 by @pvk-developer
- Add get_available_demos - Issue #1129 by @fealho
- Add demo loading functionality - Issue #1128 by @fealho
- Use logging instead of printing in detect methods - Issue #1107 by @fealho
- Add save and load methods to synthesizers - Issue #1106 by @pvk-developer
- Add sampling methods to PARSynthesizer - Issue #1083 by @amontanez24
- Add transformer methods to PARSynthesizer - Issue #1082 by @fealho
- Add validate to PARSynthesizer - Issue #1081 by @amontanez24
- Add preprocess and fit methods to PARSynthesizer - Issue #1080 by @amontanez24
- Create SingleTablePreset - Issue #1079 by @amontanez24
- Add sample method to multi-table synthesizers - Issue #1078 by @pvk-developer
- Add get_learned_distributions method to synthesizers - Issue #1075 by @pvk-developer
- Add preprocess and fit methods to multi-table synthesizers - Issue #1074 by @pvk-developer
- Add transformer related methods to BaseMultiTableSynthesizer - Issue #1072 by @fealho
- Add validate method to
BaseMultiTableSynthesizer- Issue #1071 by @pvk-developer - Create BaseMultiTableSynthesizer and HMASynthesizer classes - Issue #1070 by @pvk-developer
- Create PARSynthesizer - Issue #1055 by @amontanez24
- Raise an error if an invalid sdtype is provided to the metadata - Issue #1042 by @amontanez24
- Only allow datetime and numerical sdtypes to be set as the sequence index - Issue #1030 by @amontanez24
- Change set_alternate_keys to add_alternate_keys and add error handling - Issue #1029 by @amontanez24
- Create
MultiTableMetadata.add_tablemethod - Issue #1024 by @amontanez24 - Add update_transformers to synthesizers - Issue #1021 by @fealho
- Add assign_transformers and get_transformers methods to synthesizers - Issue #1020 by @pvk-developer
- Add fit and fit_processed_data methods to synthesizers - Issue #1019 by @pvk-developer
- Add preprocess method to synthesizers - Issue #1018 by @pvk-developer
- Add sampling to synthesizer classes - Issue #1015 by @pvk-developer
- Add validate method to synthesizer - Issue #1014 by @fealho
- Create GaussianCopula, CTGAN, TVAE and CopulaGAN synthesizer classes - Issue #1013 by @pvk-developer
- Create BaseSynthesizer class - Issue #1012 by @pvk-developer
- Add constraint conversion to upgrade_metadata - Issue #1005 by @amontanez24
- Add method to generate keys to DataProcessor - Issue #994 by @pvk-developer
- Create formatter - Issue #970 by @fealho
- Create a utility to load multiple CSV files at once - Issue #969 by @amontanez24
- Create a utility to convert old --> new metadata format - Issue #966 by @amontanez24
- Add validation check that
primary_key,alternate_keysandsequence_keycannot be sdtype categorical - Issue #963 by @fealho - Add anonymization to DataProcessor - Issue #950 by @pvk-developer
- Add utility methods to DataProcessor - Issue #948 by @fealho
- Add fit, transform and reverse_transform to DataProcessor - Issue #947 by @amontanez24
- Create DataProcessor class - Issue #946 by @amontanez24
- Add add_constraint method to MultiTableMetadata - Issue #895 by @amontanez24
- Add key related methods to MultiTableMetadata - Issue #894 by @fealho
- Add update_column and add_column methods to MultiTableMetadata - Issue #893 by @amontanez24
- Add detect methods to MultiTableMetadata - Issue #892 by @amontanez24
- Add load_from_json and save_to_json methods to the MultiTableMetadata - Issue #891 by @fealho
- Add add_relationship method to MultiTableMetadata - Issue #890 by @pvk-developer
- Add validate method to MultiTableMetadata - Issue #888 by @pvk-developer
- Add visualize method to MultiTableMetadata class - Issue #884 by @amontanez24
- Create MultiTableMetadata class - Issue #883 by @pvk-developer
- Add add_constraint method to SingleTableMetadata - Issue #881 by @amontanez24
- Add key related methods to SingleTableMetadata - Issue #880 by @fealho
- Add validate method to SingleTableMetadata - Issue #879 by @fealho
- Add _validate_inputs class method to each constraint - Issue #878 by @fealho
- Add update_column and add_column methods to SingleTableMetadata - Issue #877 by @pvk-developer
- Add detect methods to SingleTableMetadata - Issue #876 by @pvk-developer
- Add load_from_json and save_to_json methods to SingleTableMetadata - Issue #874 by @pvk-developer
- Create SingleTableMetadata class - Issue #873 by @pvk-developer
- In
upgrade_metadata, PII values are being converted to generic categorical columns - Issue #1317 by @frances-h PARSynthesizeris missingsaveandloadmethods - Issue #1289 by @amontanez24- Confusing warning when updating transformers - Issue #1272 by @frances-h
- When adding constraints,
auto_assign_transformersis showing columns that should no longer exist - Issue #1260 by @pvk-developer - Cannot fit twice if I modify transformers:
ValueError: There are non-numerical values in your data.- Issue #1259 by @frances-h - Cannot fit twice if I add constraints:
ValueError: There are non-numerical values in your data.- Issue #1258 by @frances-h HMASynthesizererrors out when fitting a dataset that has a table which holds primary key and foreign keys only - Issue #1257 by @pvk-developer- Change ValueErrors to InvalidMetadataErrors - Issue #1251 by @frances-h
- Multi-table should show foreign key transformers as None - Issue #1249 by @frances-h
- Cannot use
HMASynthesizer.fit_processed_datamore than once (KeyError) - Issue #1240 by @frances-h - Function
get_available_demoscrashes if a dataset'snum-tablesorsize-MBcannot be found - Issue #1215 by @amontanez24 - Cannot supply a natural key to
HMASynthesizer(wheresdtypeis custom): Error insample- Issue #1214 by @pvk-developer - Unable to sample when using a
PseudoAnonymizedFaker- Issue #1207 by @pvk-developer - Incorrect
sdtypespecified in demo datasetstudent_placements_pii- Issue #1206 by @amontanez24 - Auto assigned transformers for datetime columns don't have the right parameters - Issue #1204 by @pvk-developer
- Cannot apply
Inequalityconstraint on demo dataset's datetime columns - Issue #1203 by @pvk-developer - pii should not be required to auto-assign faker transformers - Issue #1194 by @pvk-developer
- Misc. bug fixes for SDV 1.0.0 - Issue #1193 by @pvk-developer
- Small bug fixes in demo module - Issue #1192 by @pvk-developer
- Foreign Keys are added as Alternate Keys when upgrading - Issue #1143 by @pvk-developer
- Alternate keys not unique when assigned to a semantic type - Issue #1111 by @pvk-developer
- Synthesizer errors if column is semantic type and pii is False - Issue #1110 by @fealho
- Sampled values not unique if primary key is numerical - Issue #1109 by @pvk-developer
- Validate not called during synthesizer creation - Issue #1105 by @pvk-developer
- SingleTableSynthesizer fit doesn't update rounding - Issue #1104 by @amontanez24
- Method
auto_assign_tranformersalways setsenforce_min_max_values=True- Issue #1095 by @fealho - Sampled context columns in PAR must be in the same order - Issue #1052 by @amontanez24
- Incorrect schema version printing during detect_table_from_dataframe - Issue #1038 by @amontanez24
- Same relationship can be added twice to MultiTableMetadata - Issue #1031 by @amontanez24
- Miscellaneous metadata bugs - Issue #1026 by @amontanez24
- SDV Package Maintenance Updates - Issue #1140 by @amontanez24
- Add integration tests for 'Synthesize Sequences' demo - Issue #1295 by @pvk-developer
- Add integration tests for 'Adding Constraints' demo - Issue #1280 by @pvk-developer
- Add integration tests to the 'Use Your Own Data' demo - Issue #1278 by @frances-h
- Add integration tests for 'Synthesize Multi Tables' demo - Issue #1277 by @pvk-developer
- Add integration tests for 'Synthesize a Table' demo - Issue #1276 by @frances-h
- Update
get_available_demostests - Issue #1247 by @fealho - Make private attributes public in the metadata - Issue #1245 by @fealho
This release adds suppport for Python 3.10 and drops support for 3.6.
- Drop support for python 3.6 - Issue #1177 by @amontanez24
- Support for python 3.10 - Issue #939 by @amontanez24
- Support Python >=3.10,<4 - Issue #1000 by @amontanez24
This release fixes a bug in the demo module related to loading the demo data with constraints. It also adds a name to the demo datasets. Finally, it bumps the version of SDMetrics used.
- Upgrade SDMetrics requirement to 0.8.0 - Issue #1125 by @katxiao
- Provide a name for the default demo datasets - Issue #1124 by @amontanez24
- Cannot load_tabular_demo with metadata - Issue #1123 by @amontanez24
This release bumps the dependency requirements to use the latest version of SDMetrics.
- Patch release: Bump required version for SDMetrics - Issue #1010 by @katxiao
This release updates the code to use RDT version 1.2.0 and greater, so that those new features are now available in SDV. This changes the transformers that are available in SDV models to be those that are in RDT version 1.2.0. As a result, some arguments for initializing models have changed.
Additionally, this release fixes bugs related to loading models with custom constraints. It also fixes a bug that added NaNs to the index of sampled data when using sample_remaining_columns.
- Incorrect rounding in Custom Constraint example - Issue #941 by @amontanez24
- Can't save the model if use the custom constraint - Issue #928 by @pvk-developer
- User Guide code fixes - Issue #983 by @amontanez24
- Index contains NaNs when using sample_remaining_columns - Issue #985 by @amontanez24
- Cannot sample after loading a model with custom constraints: TypeError - Issue #984 by @pvk-developer
- Set HyperTransformer config manually, based on Metadata if given - Issue #982 by @pvk-developer
- Change default metrics for evaluate - Issue #949 by @fealho
- Update the RDT version to 1.0 - Issue #897 by @pvk-developer
This release brings user friendly improvements and bug fixes on the SDV constraints, to help
users generate their synthetic data easily.
Some predefined constraints have been renamed and redefined to be more user friendly & consistent.
The custom constraint API has also been updated for usability. The SDV now automatically determines
the best handling_strategy to use for each constraint, attempting transform by default and
falling back to reject_sampling otherwise. The handling_strategy parameters are no longer
included in the API.
Finally, this version of SDV also unifies the parameters for all sampling related methods for
all models (including TabularPreset).
-
GreatherThanconstraint is now separated in two new constraints:Inequality, which is intended to be used between two columns, andScalarInequality, which is intended to be used between a column and a scalar. -
Betweenconstraint is now separated in two new constraints:Range, which is intended to be used between three columns, andScalarRange, which is intended to be used between a column and low and high scalar values. -
FixedIncrementsa new constraint that makes the data increment by a certain value. -
New
create_custom_constraintfunction available to create custom constraints.
RoundingRounding is automatically being handled by therdt.HyperTransformer.ColumnFormulathecreate_custom_constrainttakes place over this one and allows more advanced usage for the end users.
- Improve error message for invalid constraints - Issue #801 by @fealho
- Numerical Instability in Constrained GaussianCopula - Issue #806 by @fealho
- Unify sampling params for reject sampling - Issue #809 by @amontanez24
- Split
GreaterThanconstraint intoInequalityandScalarInequality- Issue #814 by @fealho - Split
Betweenconstraint intoRangeandScalarRange- Issue #815 @pvk-developer - Change
columnstocolumn_namesinOneHotEncodingandUniqueconstraints - Issue #816 by @amontanez24 - Update columns parameter in
PositiveandNegativeconstraint - Issue #817 by @fealho - Create
FixedIncrementsconstraint - Issue #818 by @amontanez24 - Improve datetime handling in
ScalarInequalityandScalarRangeconstraints - Issue #819 by @pvk-developer - Support strict boundaries even when transform strategy is used - Issue #820 by @fealho
- Add
create_custom_constraintfactory method - Issue #836 by @fealho
- Remove
handling_strategyparameter - Issue #833 by @amontanez24 - Remove
fit_columns_modelparameter - Issue #834 by @pvk-developer - Remove the
ColumnFormulaconstraint - Issue #837 by @amontanez24 - Move
table_data.copyto base class of constraints - Issue #845 by @fealho
- Numerical Instability in Constrained GaussianCopula - Issue #801 by @tlranda and @fealho
- Fix error message for
FixedIncrements- Issue #865 by @pvk-developer - Fix constraints with conditional sampling - Issue #866 by @amontanez24
- Fix error message in
ScalarInequality- Issue #868 by @pvk-developer - Cannot use
max_tries_per_batchon sample:TypeError: sample() got an unexpected keyword argument 'max_tries_per_batch'- Issue #885 by @amontanez24 - Conditional sampling + batch size:
ValueError: Length of values (1) does not match length of index (5)- Issue #886 by @amontanez24 TabularPresetdoesn't support new sampling parameters - Issue #887 by @fealho- Conditional Sampling:
batch_sizeis being set toNoneby default? - Issue #889 by @amontanez24 - Conditional sampling using GaussianCopula inefficient when categories are noised - Issue #910 by @amontanez24
- Show the
APIforTabularPresetmodels - Issue #854 by @katxiao - Update handling constraints doc - Pull Request #856 by @amontanez24
- Update custom costraints documentation - Pull Request #857 by @pvk-developer
This release improves the speed of the GaussianCopula model by removing logic that previously searched for the appropriate distribution to
use. It also fixes a bug that was happening when conditional sampling was used with the TabularPreset.
The rest of the release focuses on making changes to improve constraints including changing the UniqueCombinations constraint to FixedCombinations,
making the Unique constraint work with missing values and erroring when null values are seen in the OneHotEncoding constraint.
- Silence warnings coming from univariate fit in copulas - Issue #769 by @pvk-developer
- Remove parameters related to distribution search and change default - Issue #767 by @fealho
- Update the UniqueCombinations constraint - Issue #793 by @fealho
- Make Unique constraint works with nans - Issue #797 by @fealho
- Error out if nans in OneHotEncoding - Issue #800 by @amontanez24
- Unable to sample conditionally in Tabular_Preset model - Issue #796 by @katxiao
- Support GPU computing and progress track? - Issue #478 by @fealho
This release adds a TabularPreset, available in the sdv.lite module, which allows users to easily optimize a tabular model for speed.
In this release, we also include bug fixes for sampling with conditions, an unresolved warning, and setting field distributions. Finally,
we include documentation updates for sampling and the new TabularPreset.
- Sampling with conditions={column: 0.0} for float columns doesn't work - Issue #525 by @shlomihod and @tssbas
- resolved FutureWarning with Pandas replaced append by concat - Issue #759 by @Deathn0t
- Field distributions bug in CopulaGAN - Issue #747 by @katxiao
- Field distributions bug in GaussianCopula - Issue #746 by @katxiao
- Set default transformer to categorical_fuzzy - Issue #768 by @amontanez24
- Model nulls normally when tabular preset has constraints - Issue #764 by @katxiao
- Don't modify my metadata object - Issue #754 by @amontanez24
- Presets should be able to handle constraints - Issue #753 by @katxiao
- Change preset optimize_for --> name - Issue #749 by @katxiao
- Create a speed optimized Preset - Issue #716 by @katxiao
- Add tabular preset docs - Issue #777 by @katxiao
- sdv.sampling module is missing from the API - Issue #740 by @katxiao
This release updates the sampling API and splits the existing functionality into three methods - sample, sample_conditions,
and sample_remaining_columns. We also add support for sampling in batches, displaying a progress bar when sampling with more than one batch,
sampling deterministically, and writing the sampled results to an output file. Finally, we include fixes for sampling with conditions
and updates to the documentation.
- Fix write to file in sampling - Issue #732 by @katxiao
- Conditional sampling doesn't work if the model has a CustomConstraint - Issue #696 by @katxiao
- Updates to GaussianCopula conditional sampling methods - Issue #729 by @katxiao
- Update conditional sampling errors - Issue #730 by @katxiao
- Enable Batch Sampling + Progress Bar - Issue #693 by @katxiao
- Create sample_remaining_columns() method - Issue #692 by @katxiao
- Create sample_conditions() method - Issue #691 by @katxiao
- Improve sample() method - Issue #690 by @katxiao
- Create Condition object - Issue #689 by @katxiao
- Is it possible to generate data with new set of primary keys? - Issue #686 by @katxiao
- No way to fix the random seed? - Issue #157 by @katxiao
- Can you set a random state for the sdv.tabular.ctgan.CTGAN.sample method? - Issue #515 by @katxiao
- generating different synthetic data while training the model multiple times. - Issue #299 by @katxiao
- Typo in the document documentation - Issue #680 by @katxiao
This release adds support for passing tabular constraints to the HMA1 model, and adds more explicit error handling for metric evaluation. It also includes a fix for using categorical columns in the PAR model and documentation updates for metadata and HMA1.
- Categorical column after sequence_index column - Issue #314 by @fealho
- Support passing tabular constraints to the HMA1 model - Issue #296 by @katxiao
- Metric evaluation error handling metrics - Issue #638 by @katxiao
- Make true/false values lowercase in Metadata Schema specification - Issue #664 by @katxiao
- Update docstrings for hma1 methods - Issue #642 by @katxiao
This release makes multiple improvements to different Constraint classes. The Unique constraint can now
handle columns with the name index and no longer crashes on subsets of the original data. The Between
constraint can now handle columns with nulls properly. The memory of all constraints was also improved.
Various other features and fixes were added. Conditional sampling no longer crashes when the num_rows argument
is not provided. Multiple localizations can now be used for PII fields. Scaffolding for integration tests was added
and the workflows now run pip check.
Additionally, this release adds support for Python 3.9!
- Gaussian Copula – Memory Issue in Release 0.10.0 - Issue #459 by @xamm
- Applying Unique Constraint errors when calling model.fit() on a subset of data - Issue #610 by @xamm
- Calling sampling with conditions and without num_rows crashes - Issue #614 by @xamm
- Metadata.visualize with path parameter throws AttributeError - Issue #634 by @xamm
- The Unique constraint crashes when the data contains a column called index - Issue #616 by @xamm
- The Unique constraint cannot handle non-default index - Issue #617 by @xamm
- ConstraintsNotMetError when applying Between constraint on datetime columns containing null values - Issue #632 by @katxiao
- Adds Multi localisations feature for PII fields defined in #308 - PR #609 by @xamm
- Support latest version of Faker - Issue #621 by @katxiao
- Add scaffolding for Metadata integration tests - Issue #624 by @katxiao
- Add support for Python 3.9 - Issue #631 by @amontanez24
- Add pip check to CI workflows - Issue #626 by @pvk-developer
- Anonymizing PII in single table tutorials states address field as e-mail type - Issue #604 by @xamm
Special thanks to @xamm, @katxiao, @pvk-developer and @amontanez24 for all the work that made this release possible!
This release fixes bugs in constraints, metadata behavior, and SDV documentation. Specifically, we added proper handling of data containing null values for constraints and timeseries data, and updated the default metadata detection behavior.
- ValueError: The parameter loc has invalid values - Issue #353 by @fealho
- Gaussian Copula is generating different data with metadata and without metadata - Issue #576 by @katxiao
- Make pomegranate an optional dependency - Issue #567 by @katxiao
- Small wording change for Question Issue Template - Issue #571 by @katxiao
- ConstraintsNotMetError when using GreaterThan constraint with datetime - Issue #590 by @katxiao
- GreaterThan constraint crashing with NaN values - Issue #592 by @katxiao
- Null values in GreaterThan constraint raises error - Issue #589 by @katxiao
- ColumnFormula raises ConstraintsNotMetError when checking NaN values - Issue #593 by @katxiao
- GreaterThan constraint raises TypeError when using datetime - Issue #596 by @katxiao
- Fix repository language - Issue #464 by @fealho
- Update init.py - Issue #578 by @dyuliu
- IndexingError: Unalignable boolean - Issue #446 by @fealho
This release focuses on improving and expanding upon the existing constraints. More specifically, the users can now
(1) specify multiple columns in Positive and Negative constraints, (2) use the new Uniqueconstraint and
(3) use datetime data with the Between constraint. Additionaly, error messages have been added and updated
to provide more useful feedback to the user.
Besides the added features, several bugs regarding the UniqueCombinations and ColumnFormula constraints have been fixed,
and an error in the metadata.json for the student_placements dataset was corrected. The release also added documentation
for the fit_columns_model which affects the majority of the available constraints.
- Change default fit_columns_model to False - Issue #550 by @katxiao
- Support multi-column specification for positive and negative constraint - Issue #545 by @sarahmish
- Raise error when multiple constraints can't be enforced - Issue #541 by @amontanez24
- Create Unique Constraint - Issue #532 by @amontanez24
- Passing invalid conditions when using constraints produces unreadable errors - Issue #511 by @katxiao
- Improve error message for ColumnFormula constraint when constraint column used in formula - Issue #508 by @katxiao
- Add datetime functionality to Between constraint - Issue #504 by @katxiao
- UniqueCombinations constraint with handling_strategy = 'transform' yields synthetic data with nan values - Issue #521 by @katxiao and @csala
- UniqueCombinations constraint outputting wrong data type - Issue #510 by @katxiao and @csala
- UniqueCombinations constraint on only one column gets stuck in an infinite loop - Issue #509 by @katxiao
- Conditioning on a non-constraint column using the ColumnFormula constraint - Issue #507 by @katxiao
- Conditioning on the constraint column of the ColumnFormula constraint - Issue #506 by @katxiao
- Update metadata.json for duration of student_placements dataset - Issue #503 by @amontanez24
- Unit test for HMA1 when working with a single child row per parent row - Issue #497 by @pvk-developer
- UniqueCombinations constraint for more than 2 columns - Issue #494 by @katxiao and @csala
- Add explanation of fit_columns_model to API docs - Issue #517 by @katxiao
This release primarily addresses bugs and feature requests related to using constraints for the single-table models.
Users can now enforce scalar comparison with the existing GreaterThan constraint and apply 5 new constraints: OneHotEncoding, Positive, Negative, Between and Rounding.
Additionally, the SDV will now auto-apply constraints for rounding numerical values, and for keeping the data within the observed bounds.
All related user guides are updated with the new functionality.
- Add OneHotEncoding Constraint - Issue #303 by @fealho
- GreaterThan Constraint should apply to scalars - Issue #410 by @amontanez24
- Improve GreaterThan constraint - Issue #368 by @amontanez24
- Add Non-negative and Positive constraints across multiple columns- Issue #409 by @amontanez24
- Add Between values constraint - Issue #367 by @fealho
- Ensure values fall within the specified range - Issue #423 by @amontanez24
- Add Rounding constraint - Issue #482 by @katxiao
- Add rounding and min/max arguments that are passed down to the NumericalTransformer - Issue #491 by @amontanez24
- GreaterThan constraint between Date columns rasises TypeError - Issue #421 by @amontanez24
- GreaterThan constraint's transform strategy fails on columns that are not float - Issue #448 by @amontanez24
- AttributeError on UniqueCombinations constraint with non-strings - Issue #196 by @katxiao
- Use reject sampling to sample missing columns for constraints - Issue #435 by @amontanez24
- Ensure privacy metrics are available in the API docs - Issue #458 by @fealho
- Ensure forumla constraint is called ColumnFormula everywhere in the docs - Issue #449 by @fealho
This release changes the way we sample conditions to not only group by the conditions passed by the user, but also by the transformed conditions that result from them.
- Conditionally sampling on variable in constraint should have variety for other variables - Issue #440 by @amontanez24
This release improves the constraint functionality by allowing constraints and conditions at the same time. Additional changes were made to update tutorials.
- Not able to use constraints and conditions in the same time - Issue #379 by @amontanez24
- Update benchmarking user guide for reading private datasets - Issue #427 by @katxiao
This release broadens the constraint functionality by allowing for the ColumnFormula
constraint to take lambda functions and returned functions as an input for its formula.
It also improves conditional sampling by ensuring that any id fields generated by the
model remain unique throughout the sampled data.
The CTGAN model was improved by adjusting a default parameter to be more mathematically
correct.
Additional changes were made to improve tutorials as well as fix fragile tests.
- Tutorials test sometimes fails - Issue #355 by @fealho
- Duplicate IDs when using reject-sampling - Issue #331 by @amontanez24 and @csala
- discriminator_decay should be initialized at 1e-6 but it's 0 - Issue #401 by @fealho and @YoucefZemmouri
- Tutorial typo - Issue #380 by @fealho
- Request for sdv.constraint.ColumnFormula for a wider range of function - Issue #373 by @amontanez24 and @JetfiRex
This release brings new privacy metrics to the evaluate framework which help to determine
if the real data could be obtained or deduced from the synthetic samples.
Additionally, now there is a normalized score for the metrics, which stays between 0 and 1.
There are improvements that reduce the usage of memory ram when sampling new data. Also there
is a new parameter to control the reject sampling crash, graceful_reject_sampling, which if
set to True and if it's not possible to generate all the requested rows, it will just issue a
warning and return whatever it was able to generate.
The Metadata object can now be visualized using different combinations of names and details,
which can be set to True or False in order to display only the table names with details or
without. There is also an improvement on the validation, which now will display all the errors
found at the end of the validation instead of only the first one.
This version also exposes all the hyperparameters of the models CTGAN and TVAE to allow a more
advanced usage. There is also a fix for the TVAE model on small datasets and it's performance
with NaN values has been improved. There is a fix for when using
UniqueCombinationConstraint with the transform strategy.
- Memory Usage Gaussian Copula Trained Model consuming high memory when generating synthetic data - Issue #304 by @pvk-developer and @AnupamaGangadhar
- Add option to visualize metadata with only table names - Issue #347 by @csala
- Add sample parameter to control reject sampling crash - Issue #343 by @fealho
- Verbose metadata validation - Issue #348 by @csala
- Missing the introduction of custom specification for hyperparameters in the TVAE model - Issue #344 by @imkhoa99 and @pvk-developer
This version adds conditional sampling for tabular models by combining a reject-sampling strategy with the native conditional sampling capabilities from the gaussian copulas.
It also introduces several upgrades on the HMA1 algorithm that improve data quality and robustness in the multi-table scenarios by making changes in how the parameters of the child tables are aggregated on the parent tables, including a complete rework of how the correlation matrices are modeled and rebuild after sampling.
- Fix probabilities contain NaN error - Issue #326 by @csala
- Conditional Sampling for tabular models - Issue #316 by @fealho and @csala
- HMA1: LinAlgError: SVD did not converge - Issue #240 by @csala
This release introduces a few changes in the HMA1 relational algorithm to decrease modeling and sampling times, while also ensuring that correlations are properly kept across tables and also adding support for some relational schemas that were not supported before.
A few changes in constraints and tabular models also ensure that situations that produced errors before now work without errors.
- Fix unique key generation - Issue #306 by @fealho
- Ensure tables that contain nothing but ids can be modeled - Issue #302 by @csala
- Metadata visualization improvements - Issue #301 by @csala
- Multi-parent re-model and re-sample issue - Issue #298 by @csala
- Support datetimes in GreaterThan constraint - Issue #266 by @rollervan
- Support for multiple foreign keys in one table - Issue #185 by @csala
SDMetrics version is updated to include the new Time Series metrics, which have also been added to the API Reference and User Guides documentation. Additionally, a few code has been refactored to reduce external dependencies and a few minor bugs related to single table constraints have been fixed
- Add timeseries metrics and user guides - Issue #289 by @csala
- Add functions to generate regex ids - Issue #288 by @csala
- Saving a fitted tabular model with UniqueCombinations constraint raises PicklingError - Issue #286 by @csala
- Constraints:
handling_strategy='reject_sampling'causes'ZeroDivisionError: division by zero'- Issue #285 by @csala
This release updates to the latest CTGAN, RDT and SDMetrics libraries to introduce a new TVAE model, multiple new metrics for single table and multi table, and fixes issues in the re-creation of tabular models from a metadata dict.
- Upgrade to SDMetrics v0.1.0 and add
sdv.metricsmodule - Issue #281 by @csala - Upgrade to CTGAN 0.3.0 and add TVAE model - Issue #278 by @fealho
- Add
dtype_transformerstoTable.from_dict- Issue #276 by @csala - Fix Metadata
from_dictbehavior - Issue #275 by @csala
This version updates the dependencies and makes a few internal changes in order to ensure that SDV works properly on Windows Systems, making this the first release to be officially supported on Windows.
Apart from this, some more internal changes have been made to solve a few minor issues from the older versions while also improving the processing speed when processing relational datasets with the default parameters.
- The
distributionargument of theGaussianCopulahas been renamed tofield_distributions. - The
HMA1andSDVclasses now use thecategorical_fuzzytransformer by default instead of theone_hot_encodingone.
- GaussianCopula: rename
distributionargument tofield_distributions- Issue #237 by @csala - GaussianCopula: Improve error message if an invalid distribution name is passed - Issue #220 by csala
- Import urllib.request explicitly - Issue #227 by @csala
- TypeError: cannot astype a datetimelike from [datetime64[ns]] to [int32] - Issue #218 by @csala
- Change default categorical transformer to
categorical_fuzzyin HMA1 - Issue #214 by @csala - Integer categoricals being sampled as strings instead of integer values - Issue #194 by @csala
In this version a new family of models for Synthetic Time Series Generation is introduced
under the sdv.timeseries sub-package. The new family of models now includes a new class
called PAR, which implements a Probabilistic AutoRegressive model.
This version also adds support for composite primary keys and regex based generation of id fields in tabular models and drops Python 3.5 support.
- Drop python 3.5 support - Issue #204 by @csala
- Support composite primary keys in tabular models - Issue #207 by @csala
- Add the option to generate string
idfields based on regex on tabular models - Issue #208 by @csala - Synthetic Time Series - Issue #142 by @csala
This version adds a new tabular model based on combining the CTGAN model with the reversible transformation applied in the GaussianCopula model that converts random variables with arbitrary distributions to new random variables with standard normal distribution.
The reversible transformation is handled by the GaussianCopulaTransformer recently added to RDT.
- Add CopulaGAN Model - Issue #202 by @csala
This release moves the models and algorithms related to generation of synthetic
relational data to a new sdv.relational subpackage (Issue #198)
As part of the change, also the old sdv.models have been removed and now
relational model is based on the recently introduced sdv.tabular models.
In this release the sdv.evaluation module has been reworked to include 4 different
metrics and in all cases return a normalized score between 0 and 1.
Included metrics are:
cstestkstestlogistic_detectionsvc_detection
This release fixes a couple of minor issues and introduces an important rework of the User Guides section of the documentation.
- Error Message: "make sure the Graphviz executables are on your systems' PATH" - Issue #182 by @csala
- Anonymization mappings leak - Issue #187 by @csala
In this release SDV gets new documentation, new tutorials, improvements to the Tabular API and broader python and dependency support.
Complete list of changes:
- New Documentation site based on the
pydata-sphinx-theme. - New User Guides and Notebook tutorials.
- New Developer Guides section within the docs with details about the SDV architecture, the ecosystem libraries and how to extend and contribute to the project.
- Improved API for the Tabular models with focus on ease of use.
- Support for Python 3.8 and the newest versions of pandas, scipy and scikit-learn.
- New Slack Workspace for development discussions and community support.
This release introduces a new concept of Constraints, which allow the user to define
special relationships between columns that will not be handled via modeling.
This is done via a new sdv.constraints subpackage which defines some well-known pre-defined
constraints, as well as a generic framework that allows the user to customize the constraints
to their needs as much as necessary.
- Support for Constraints - Issue #169 by @csala
This release introduces a new subpackage sdv.tabular with models designed specifically
for single table modeling, while still providing all the usual conveniences from SDV, such
as:
- Seamless multi-type support
- Missing data handling
- PII anonymization
Currently implemented models are:
- GaussianCopula: Multivariate distributions modeled using copula functions. This is stronger version, with more marginal distributions and options, than the one used to model multi-table datasets.
- CTGAN: GAN-based data synthesizer that can generate synthetic tabular data with high fidelity.
- Support for Multiple Parents - Issue #162 by @csala
- Sample by default the same number of rows as in the original table - Issue #163 by @csala
- Add benchmark - Issue #165 by @csala
- Use SDMetrics for evaluation - Issue #159 by @csala
- Improve metadata visualization - Issue #151 by @csala @JDTheRipperPC
-
Add Metadata Validation - Issue #134 by @csala @JDTheRipperPC
-
Add Metadata Visualization - Issue #135 by @JDTheRipperPC
-
Add path to metadata JSON - Issue #143 by @JDTheRipperPC
-
Use new Copulas and RDT versions - Issue #147 by @csala @JDTheRipperPC
- Create sdv.models subpackage - Issue #141 by @JDTheRipperPC
-
Adapt evaluation to the different data types - Issue #128 by @csala @JDTheRipperPC
-
Extend
load_demofunctionality to load other datasets - Issue #136 by @JDTheRipperPC
- Methods to generate Metadata from DataFrames - Issue #126 by @csala @JDTheRipperPC
- compatibility with rdt issue 72 - Issue #120 by @csala @JDTheRipperPC
- Error docstring sampler.__fill_text_columns - Issue #144 by @JDTheRipperPC
- Reach 90% coverage - Issue #112 by @JDTheRipperPC
- Review unittests - Issue #111 by @JDTheRipperPC
- Time required for sample_all function? - Issue #118 by @csala @JDTheRipperPC
- Add option to model the amount of child rows - Issue 93 by @ManuelAlvarezC
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Add Evaluation Metrics - Issue 52 by @ManuelAlvarezC
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Ensure unicity on primary keys on different calls - Issue 63 by @ManuelAlvarezC
- executing readme: 'not supported between instances of 'int' and 'NoneType' - Issue 104 by @csala
- Add warnings when trying to model an unsupported dataset structure. GH#73
- Add option to anonymize data. GH#51
- Add support for modeling data with different distributions, when using
GaussianMultivariatemodel. GH#68 - Add support for
VineCopulasas a model. GH#71 - Improve
GaussianMultivariateparameter sampling, avoiding warnings and unvalid parameters. GH#58 - Fix issue that caused that sampled categorical values sometimes got numerical values mixed. GH#81
- Improve the validation of extensions. GH#69
- Update examples. GH#61
- Replaced
Tableclass with aNamedTuple. GH#92 - Fix inconsistent dependencies and add upper bound to dependencies. GH#96
- Fix error when merging extension in
Modeler.CPAwhen running examples. GH#86
- First release on PyPI.