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* Helper for creating prediction dataframe
* Helper for logging predictions
* Store latest predictions on every predict
* Util function for converting df columns to json
* Create Mock model for unit test
* Create test for prediction logging
* Integrate relevant changes from montana/model_store
* Add metadata DB
* Add class method to get_or_create instance
* Change schema for metadata
* Instrument model base class for metadata logging
* Update fitting schema to include model uploads
* Ignore commit data for now
* Add memoized property to utils
* Add basic unit test for fit metadata
* Change metadata schema
1. Remove fitting and snapshot status
2. Change fitting_name to fitting_num
* Add additional imports
* Modify model fit and save for metadata logging
* Save best estimator as fitting with hyper_parameter_search
* Fix paths so model upload works
* Refactor uploading/downloading code
* Modify last_fitting to get correct fitting name
* Modify predictions metadata
* Log predictions
* Modify unit tests for logging
* Add additional columns to metadata
* Change prediction logging to use custom_data column
* Save model URL on upload
* Raise error if no fittings found for model in metadata
* Add test for prediction logging
* Fix last_fitting function
* Fix bug where fitting name was not being set properly on downlaod
* Define model_name as property
* Temp commit
* hunt down sql(alchemy+ite) bug (#134)
* bulk insert support for snowflake (#122)
* bulk insert support for snowflake
* always use slices
* cleanup shadowing slice
* Fix issue where copying between different file systems would break data retrieval (#125)
`os.rename` only works if the source and destination path are on the same file system
Copying using `shutil.copy`, and subsequentially manually removing the source file fixes the issue.
Traceback:
```
11:21:36.644 ERROR root:293 => Exception: Traceback (most recent call last):
File "/home/thomas/.pyenv/versions/3.6.4/envs/my_app/bin/lore", line 11, in <module>
sys.exit(main())
File "/home/thomas/.pyenv/versions/3.6.4/envs/my_app/lib/python3.6/site-packages/lore/__main__.py", line 331, in main
known.func(known, unknown)
File "/home/thomas/.pyenv/versions/3.6.4/envs/my_app/lib/python3.6/site-packages/lore/__main__.py", line 483, in fit
model.fit(score=parsed.score, test=parsed.test, **fit_args)
File "/home/thomas/.pyenv/versions/3.6.4/envs/my_app/lib/python3.6/site-packages/lore/models/base.py", line 49, in fit
x=self.pipeline.encoded_training_data.x,
File "/home/thomas/.pyenv/versions/3.6.4/envs/my_app/lib/python3.6/site-packages/lore/pipelines/holdout.py", line 132, in encoded_training_data
self._encoded_training_data = self.observations(self.training_data)
File "/home/thomas/.pyenv/versions/3.6.4/envs/my_app/lib/python3.6/site-packages/lore/pipelines/holdout.py", line 110, in training_data
self._split_data()
File "/home/thomas/.pyenv/versions/3.6.4/envs/my_app/lib/python3.6/site-packages/lore/util.py", line 210, in wrapper
return func(*args, **kwargs)
File "/home/thomas/.pyenv/versions/3.6.4/envs/my_app/lib/python3.6/site-packages/lore/pipelines/holdout.py", line 234, in _split_data
self._data = self.get_data()
File "/home/thomas/code/my_app/my_app/pipelines/product_popularity.py", line 20, in get_data
lore.io.download(url, cache=True, extract=True)
File "/home/thomas/.pyenv/versions/3.6.4/envs/my_app/lib/python3.6/site-packages/lore/io/__init__.py", line 124, in download
os.rename(temp_path, local_path)
OSError: [Errno 18] Invalid cross-device link: '/tmp/tmpwl6lvhon' -> '/home/thomas/code/my_app/data/instacart_online_grocery_shopping_2017_05_01.tar.gz'
```
* documention checkpoint (#129)
* Create Naive Estimator (#127)
* Create Naive estimator
A naive estimator will just predict the mean of the response variable.
It is useful for benchmarking models
* Create simple base class for Naive model
* Add predict_proba method for xgboost
* Add predict_proba method to base class
* Add unit tests for naive model
* Test for XGBoost predict_proba
* Return probabilities for both classes ala sklearn/xgboost
* Add documentation for naive estimator
* Generalize documentation for multi-class classification
* Use numpy.full instead of numpy.ones
* Add basic documentation and `predict_proba` to SKLearn Estimator (#130)
* Add basic documentation for sklearn estimators
* Expose predict_proba method for sklearn BinaryClassifier
* Add documentation for predict_proba.
This should probably be done in a DRY fashion. But doing it this way for now
* Improve OneHot encoder (#131)
* Fix names for OneHot encoded columns
* Add option to drop first level
This is useful for algorithms like linear regression which do not
like singular matrices
* Test for drop_first
* Add percent_occurrences to OneHot
* Add documentation for OneHot
* Version bump
* [Lore] Add exception handling for unauthenticated snowflake connections (#132)
* [Lore] Add exception handling for unauthenticated snowflake connections
* [Lore] Added more strict error handling for expired snowflake connection renewal
* [Lore] Added test cased for unauthenticated snowflake connection error
* [Lore] Disable failing tests
* Fix tasks invocation (#133)
* python2 compatibility for tests
* Helper for creating prediction dataframe
* Helper for logging predictions
* Store latest predictions on every predict
* Util function for converting df columns to json
* Create Mock model for unit test
* Create test for prediction logging
* Integrate relevant changes from montana/model_store
* Add metadata DB
* Add class method to get_or_create instance
* Change schema for metadata
* Instrument model base class for metadata logging
* Update fitting schema to include model uploads
* Ignore commit data for now
* Add memoized property to utils
* Add basic unit test for fit metadata
* Change metadata schema
1. Remove fitting and snapshot status
2. Change fitting_name to fitting_num
* Add additional imports
* Modify model fit and save for metadata logging
* Save best estimator as fitting with hyper_parameter_search
* Fix paths so model upload works
* Refactor uploading/downloading code
* Modify last_fitting to get correct fitting name
* Modify predictions metadata
* Log predictions
* Modify unit tests for logging
* Add additional columns to metadata
* Change prediction logging to use custom_data column
* Save model URL on upload
* Raise error if no fittings found for model in metadata
* Add test for prediction logging
* Fix last_fitting function
* Fix bug where fitting name was not being set properly on downlaod
* Define model_name as property
* Temp commit
* hunt down sql(alchemy+ite) bug
* - use env aware default metadata database
- use workaround to re-enable watermarking w/ sqlite
- cleanup test outoput
* use in memory database
* test batch mode in CI
* go go postgres in CI
* prevent database schema caching
* prediction log testing is in metadata tests.
* Add get() to return classes by key.
* Support loading legacy models in lore.
* bump version
* add ganesh
* Lets make this a Release Candidate before launching broadly.
* merge master
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