Add ARIMA to AutoML #2009
Add ARIMA to AutoML #2009
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@@ Coverage Diff @@
## main #2009 +/- ##
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- Coverage 99.9% 99.9% -0.0%
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Files 280 280
Lines 24105 24288 +183
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+ Hits 24080 24260 +180
- Misses 25 28 +3
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Performance tests here |
ParthivNaresh
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# Conflicts: # docs/source/release_notes.rst # evalml/tests/automl_tests/test_automl_search_regression.py # evalml/tests/component_tests/test_arima_regressor.py # evalml/tests/component_tests/test_estimators.py
| X_t = self._component_obj.transform(X) | ||
| X_t_df = pd.DataFrame(X_t, columns=X.columns, index=X.index) | ||
| return _retain_custom_types_and_initalize_woodwork(X_ww, X_t_df, ltypes_to_ignore=[Integer, Categorical]) | ||
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| def fit_transform(self, X, y=None): | ||
| X_ww = infer_feature_types(X) |
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Necessary otherwise datetime columns are standardized which results in an error.
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How did this come up? Is this a bug that we've always had or triggered by another change in this PR?
ParthivNaresh
requested review from
angela97lin,
dsherry,
bchen1116,
chukarsten,
freddyaboulton and
jeremyliweishih
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This PR looks good to me - just left a clarifying question. I wonder if there's a way to clean up the tests due to the exception in pipeline generation for ARIMA but I couldn't think of anything off the top of my head. Moving forward with TS problems for performance testing, I'm hoping that we can add datasets with seasonality or other characteristics that models like ARIMA can leverage.
…valml into 1946-Add-ARIMA-To-AutoML
# Conflicts: # docs/source/release_notes.rst
…valml into 1946-Add-ARIMA-To-AutoML
# Conflicts: # docs/source/release_notes.rst
…valml into 1946-Add-ARIMA-To-AutoML # Conflicts: # docs/source/release_notes.rst
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LGTM! Let me know when we've got the corresponding conda-forge change approved/merged and are ready to merge this PR and I will gladly do so :)
Other comments blocking merge:
- Please add a note to the ARIMA docstring mentioning that component isn't supported yet on conda.
- I left one question about pmdarima version
| @@ -84,40 +91,56 @@ def _match_indices(self, X, y, date_col): | |||
| y.index = date_col | |||
| return X, y | |||
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| def _format_dates(self, dates, X, y, predict=False): | |||
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Makes sense. Heads up @freddyaboulton we'll eventually need to update this ARIMA impl on your accessor feature branch once this is merged.
evalml/pipelines/components/estimators/regressors/arima_regressor.py
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| @pytest.fixture | ||
| def has_minimal_dependencies(pytestconfig): | ||
| return pytestconfig.getoption("--has-minimal-dependencies") | ||
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| @pytest.fixture | ||
| def is_using_conda(pytestconfig): | ||
| return pytestconfig.getoption("--is-using-conda") |
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We have a separate conda feedstock PR to add this there, right?
I'm guessing we need that to be merged first in order for CI to work properly, yes?
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@dsherry Yes we'll have a separate PR for the feedstock that adds --is-using-conda to the command
| @@ -11,4 +11,5 @@ seaborn>=0.11.1 | |||
| category_encoders>=2.0.0 | |||
| statsmodels >= 0.12.2 | |||
| imbalanced-learn>=0.8.0 | |||
| pmdarima==1.8.0 | |||
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Now that we've disabled arima for conda installs while pmdarima sorts out the dependency issue: is there any reason we shouldn't do
pmdarima>=1.8.0here?
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This unfortunately causes latest_dependency_versions to throw an error as it expects 1.8.2, but that version pins numpy to a lower version than what we can accept.
Fixes #1946