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make release-tag: Merge branch 'master' into stable
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HISTORY.md

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# History
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## 0.1.3 (2020-10-16)
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This version includes several minor improvements to the PAR model and the
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way the sequences are generated:
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* Sequences can now be generated without dropping the sequence index.
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* The PAR model learns the min and max length of the sequence from the input data.
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* NaN values are properly supported for both categorical and numerical columns.
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* NaN values are generated for numerical columns only if there were NaNs in the input data.
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* Constant columns can now be modeled.
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## 0.1.2 (2020-09-15)
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Add BasicGAN Model and additional benchmarking results.

Makefile

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dist: clean ## builds source and wheel package
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python setup.py sdist
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python setup.py bdist_wheel
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cd benchmark && python setup.py sdist
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cd benchmark && python setup.py bdist_wheel
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mv benchmark/dist/* dist && rmdir benchmark/dist
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# cd benchmark && python setup.py sdist
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# cd benchmark && python setup.py bdist_wheel
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# mv benchmark/dist/* dist && rmdir benchmark/dist
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ls -l dist
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.PHONY: publish-confirm
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@echo "A new release can be made"
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.PHONY: release
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release: check-release bumpversion-release bumpversion-patch
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release: check-release bumpversion-release publish bumpversion-patch
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.PHONY: release-test
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release-test: check-release bumpversion-release-test bumpversion-revert
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release-test: check-release bumpversion-release-test publish-test bumpversion-revert
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.PHONY: release-candidate
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release-candidate: check-master bumpversion-candidate
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release-candidate: check-master publish bumpversion-candidate
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.PHONY: release-candidate-test
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release-candidate-test: check-clean check-master
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release-candidate-test: check-clean check-master publish-test
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.PHONY: release-minor
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release-minor: check-release bumpversion-minor release

README.md

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# Define data types for all the columns
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data_types = {
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'season': 'categorical',
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'region': 'categorical',
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'day_of_week': 'categorical',
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'total_sales': 'continuous',
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'nb_customers': 'count',

benchmark/deepecho/__init__.py

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__author__ = 'MIT Data To AI Lab'
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__email__ = '[email protected]'
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__version__ = '0.1.2'
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__version__ = '0.1.3.dev2'
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__path__ = __import__('pkgutil').extend_path(__path__, __name__)
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from deepecho.demo import load_demo

benchmark/deepecho/benchmark/dataset.py

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table_meta = self.metadata.get_table_meta(self.table_name)
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self.entity_columns = table_meta.get('entity_columns') or []
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self.sequence_index = table_meta.get('sequence_index')
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self.context_columns = self._get_context_columns()
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if 'context_columns' in table_meta:
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self.context_columns = table_meta['context_columns']
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else:
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self.context_columns = self._get_context_columns()
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self.model_columns = [
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column for column in self.data.columns
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if column not in self.entity_columns + self.context_columns + [self.sequence_index]

benchmark/setup.py

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"""The setup script."""
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__version__ = '0.1.2'
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__version__ = '0.1.3.dev2'
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from setuptools import setup, find_packages
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deepecho/__init__.py

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__author__ = 'MIT Data To AI Lab'
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__email__ = '[email protected]'
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__version__ = '0.1.2'
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__version__ = '0.1.3.dev2'
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__path__ = __import__('pkgutil').extend_path(__path__, __name__)
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from deepecho.demo import load_demo

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