Release v1.0.11 (#353)

* remove enum34 dependency (#301) * add test_lgt_full_reproducibility (#303) * Integrating LGT/DLT into ETS Base (#280) * first draft of ets * ets temp * refresh banner and icon * read me link * more banners * testing new dim of banner * Predict fix (#281) * fix regressor matrix extraction * unit test for cases with mixed pos&reg regressors * adding logo and some style for tutorial and docs * favicon * refactoring DLT onto ets * refine DLT and pass all unit tests * passed all unit tests * unit test fix * fixed unit test * add test_ets.py * Additional refinement on ETS Refactoring (#296) * fixed some OO duplicated methods -[x] remove duplicated modules as suggested -[x] remove `_get_regresso_matrix()` as suggested * docstring update * removing fit method in lgt * add negative regressor test cases * add negative regressor test * Update test_lgt.py * updated the test data and tests logic * fixed numerical issues due to initialization * minor * bug fix * Update conftest.py * Create iclaims.example.csv * temp * Create orbit-adhoc-test[TEST].ipynb * fixed regression issue * Delete orbit-adhoc-test[TEST].ipynb * removing negative test for lgt * add test_lgt_full_reproducibility (#303) * introducing * small fix on refactoring ets - refine/remove comments - remove unused methods Co-authored-by: Zhishi Wang <wangzhishi@users.noreply.github.com> Co-authored-by: Yifeng Wu <yifeng.wu@uber.com> Co-authored-by: Steve Yang <steven.s.yang@gmail.com> * Create RELEASE.md (#307) * utility for hyper-parameter tuning (#324) * utility for hyper-parameter tuning * minor * demo for hyper tuning * Organize Plotting functions and enhancement (#327) * Tutorial Update - diagnostics/backtest plotting (#332) * add ploting tutorials * back test tutorial * back test tutorial * fix commets * suppress warnings * quick fix * badge update * Update tutorials notebooks (#329) * Update tutorials notebooks * add the notebook and dataset for regression * push the new icalim dataset * update the regression notebook and modify the data load function * update for regression tutorials * regression notebook * Update regression.ipynb * notebooks update * refine verbiage; regression change * minor cosmetics * remove log transform since it's done in new iclaims data * remove log transform since it's done in the new iclaims data * filter first then standardize * filter first then standardize Co-authored-by: Edwin Ng <edwinng@uber.com> Co-authored-by: Zhishi Wang <zhishiw@uber.com> Co-authored-by: Zhishi Wang <wangzhishi@users.noreply.github.com> * fix data url in load_iclaims() (#337) * fix data url in load_iclaims() * update the tutorials catalogue to reflect the changes; fix typos * Minor updates in tutorial notebooks (#339) * fix tutorials notebook * update title level in regression notebook * Feat pickle fix (#342) * first pickle fix attempt * better attempt by using initializer creating an initializer class to tackle problem of #340 fixed #40 * ETS Initializer add ets initializer and change some wording from stan to generic * change wording from stan to generic [minor] minor wording * Add deprecating warnings for LGT model with regressor (#341) * add deprecation warning for LGT regression * remove wrong notebook * remove wrong notebook * minor cosmetic change (#343) * Dev unit test (#348) * validation for global_trend_option and regression_penalty * unit test for fixed sm input and regression penalty * unit test for fixed sm input * validation for global_trend_option and regression_penalty * unit test for fixed sm input and regression penalty * warning message * more smoothing input tests * more smoothing input tests * change level_sm_input lower bound to 0.001 from 0 * raise error message for level_sm_input lower than 0.0001 * Docstring inheritance (#351) * add docstring inheritance * add example jupyter notebook * add requirements * minor changes * minor formatting * add lower bound for common substring * Update stan_estimator.py * Update docstring_inheritance_check.ipynb * Update backtest.py Co-authored-by: Edwin Ng <edwinng@uber.com> * iclaims data de-meaning (#352) * pumping version to 1.0.11 pumping version to 1.0.11 * bug fix for following sphinx in README.rst * Update setup.py Co-authored-by: CrossNox <ijmermet@gmail.com> Co-authored-by: Steve Yang <steven.s.yang@gmail.com> Co-authored-by: Zhishi Wang <wangzhishi@users.noreply.github.com> Co-authored-by: Yifeng Wu <yifeng.wu@uber.com> Co-authored-by: ppstacy <ppstacy@gmail.com> Co-authored-by: Zhishi Wang <zhishiw@uber.com>
uber · Feb 13, 2021 · 7334ba5 · 7334ba5
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diff --git a/README.rst b/README.rst
@@ -13,7 +13,7 @@ This project
 - requires PyStan as a system dependency. PyStan is licensed under `GPLv3 <https://www.gnu.org/licenses/gpl-3.0.html>`__, which is a free, copyleft license for software.
 
 Orbit: A Python package for Bayesian forecasting models
-====================
+=======================================================
 
 Orbit is a Python package for Bayesian forecasting models developed under object-oriented design. It provides a
 familiar and intuitive initialize-fit-predict interface for working with

diff --git a/RELEASE.md b/RELEASE.md
@@ -0,0 +1,40 @@
+# Orbit Release Process
+
+## Full Release
+1. Create a release branch from `dev`
+    - e.g. `release/v1.0.15`
+2. Update the version number in `orbit/__init__.py`. This version number will propagate to `docs/conf.py`, `setup.cfg`, and `setup.py`.
+3. Commit changes
+4. If necessary, additional PRs may be merged to the release branch directly, but this should be reserved for bug fixes only and should not add or change any features
+5. Merge the release branch to both `dev` and `master`
+6. Draft a new release: https://github.com/uber/orbit/releases/new
+    - Select the master branch as the target branch
+    - Use version number for both the tag and title
+    - Add a bulleted list of changes in the description
+7. Click `Publish Release` once all changes are finalized and description is updated
+
+
+## Quick Release
+Sometimes we just want to release a patch, and no subsequent commits are needed on the release branch.
+In this case, we can avoid creating the branch and create a release directly from dev.
+
+1. From `dev`, update the version number in `orbit/__init__.py`.
+2. Commit changes
+3. Merge to `master`
+4. Draft a new release: https://github.com/uber/orbit/releases/new
+    - Select the master branch as the target branch
+    - Use version number for both the tag and title
+    - Add a bulleted list of changes in the description
+
+
+## Hotfix
+Sometimes we may need to address a bug fix directly from master after a release, but `dev` may have moved on with new commits.
+
+1. Create a hotfix branch from master and update the version number
+2. Make fix
+3. Merge changes into `master`
+4. Draft a new release: https://github.com/uber/orbit/releases/new
+    - Select the master branch as the target branch
+    - Use version number for both the tag and title
+    - Add a bulleted list of changes in the description
+5. Merge changes into `dev`
diff --git a/docs/tutorials.rst b/docs/tutorials.rst
@@ -4,11 +4,11 @@ Tutorials
 .. toctree::
    :maxdepth: 1
 
-   tutorials/quick_start
-   tutorials/lgt
-   tutorials/dlt
+   tutorials/quick_start_DLT
+   tutorials/quick_start_LGT
    tutorials/regression
    tutorials/decompose_prediction
    tutorials/pyro_basic
    tutorials/model_diagnostics
    tutorials/backtest
+   tutorials/utilities_simulation
diff --git a/docs/tutorials/backtest.ipynb b/docs/tutorials/backtest.ipynb
diff --git a/docs/tutorials/decompose_prediction.ipynb b/docs/tutorials/decompose_prediction.ipynb
diff --git a/docs/tutorials/dlt.ipynb b/docs/tutorials/dlt.ipynb
diff --git a/docs/tutorials/lgt.ipynb b/docs/tutorials/lgt.ipynb
diff --git a/docs/tutorials/model_diagnostics.ipynb b/docs/tutorials/model_diagnostics.ipynb
diff --git a/docs/tutorials/pyro_basic.ipynb b/docs/tutorials/pyro_basic.ipynb
@@ -20,11 +20,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-14T01:02:03.673300Z",
-     "start_time": "2020-10-14T01:02:01.954298Z"
+     "end_time": "2021-01-28T21:53:20.816128Z",
+     "start_time": "2021-01-28T21:53:18.895302Z"
     }
    },
    "outputs": [],
@@ -41,11 +41,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-14T01:02:08.020386Z",
-     "start_time": "2020-10-14T01:02:08.017595Z"
+     "end_time": "2021-01-28T21:53:20.821909Z",
+     "start_time": "2021-01-28T21:53:20.818613Z"
     }
    },
    "outputs": [],
@@ -62,26 +62,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-14T01:02:19.234379Z",
-     "start_time": "2020-10-14T01:02:19.226006Z"
+     "end_time": "2021-01-28T21:53:30.197479Z",
+     "start_time": "2021-01-28T21:53:29.951038Z"
     }
    },
    "outputs": [],
    "source": [
-    "df = load_iclaims()\n",
-    "df[\"claims\"] = np.log(df[\"claims\"])"
+    "df = load_iclaims()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-14T01:02:19.614756Z",
-     "start_time": "2020-10-14T01:02:19.610808Z"
+     "end_time": "2021-01-28T21:53:30.203929Z",
+     "start_time": "2021-01-28T21:53:30.199787Z"
     }
    },
    "outputs": [],
@@ -107,11 +106,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-14T01:02:23.310264Z",
-     "start_time": "2020-10-14T01:02:23.306846Z"
+     "end_time": "2021-01-28T21:53:31.885441Z",
+     "start_time": "2021-01-28T21:53:31.881952Z"
     }
    },
    "outputs": [],
@@ -127,20 +126,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-14T01:02:33.627525Z",
-     "start_time": "2020-10-14T01:02:23.582567Z"
+     "end_time": "2021-01-28T21:53:42.550294Z",
+     "start_time": "2021-01-28T21:53:32.096916Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 9.88 s, sys: 130 ms, total: 10 s\n",
-      "Wall time: 10 s\n"
+      "CPU times: user 10.3 s, sys: 130 ms, total: 10.4 s\n",
+      "Wall time: 10.4 s\n"
      ]
     }
    ],
@@ -151,11 +150,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-14T01:02:33.873416Z",
-     "start_time": "2020-10-14T01:02:33.629694Z"
+     "end_time": "2021-01-28T21:53:49.078952Z",
+     "start_time": "2021-01-28T21:53:48.850112Z"
     }
    },
    "outputs": [
@@ -172,7 +171,7 @@
    ],
    "source": [
     "predicted_df = lgt_map.predict(df=test_df)\n",
-    "plot_predicted_data(training_actual_df=train_df, predicted_df=predicted_df, \n",
+    "_ = plot_predicted_data(training_actual_df=train_df, predicted_df=predicted_df, \n",
     "                    date_col=lgt_map.date_col, actual_col=lgt_map.response_col, \n",
     "                    test_actual_df=test_df)"
    ]
@@ -193,11 +192,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-14T01:02:33.878749Z",
-     "start_time": "2020-10-14T01:02:33.875433Z"
+     "end_time": "2021-01-28T21:53:51.688900Z",
+     "start_time": "2021-01-28T21:53:51.685180Z"
     }
    },
    "outputs": [],
@@ -217,11 +216,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-14T01:02:45.942312Z",
-     "start_time": "2020-10-14T01:02:33.880710Z"
+     "end_time": "2021-01-28T21:54:05.996596Z",
+     "start_time": "2021-01-28T21:53:51.865111Z"
     }
    },
    "outputs": [
@@ -236,8 +235,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 11.8 s, sys: 244 ms, total: 12 s\n",
-      "Wall time: 12.1 s\n"
+      "CPU times: user 13.7 s, sys: 261 ms, total: 14 s\n",
+      "Wall time: 14.1 s\n"
      ]
     }
    ],
@@ -248,11 +247,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-14T01:02:45.979904Z",
-     "start_time": "2020-10-14T01:02:45.943968Z"
+     "end_time": "2021-01-28T21:54:06.032273Z",
+     "start_time": "2021-01-28T21:54:05.998652Z"
     }
    },
    "outputs": [],
@@ -265,8 +264,8 @@
    "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-14T01:02:46.183371Z",
-     "start_time": "2020-10-14T01:02:45.981678Z"
+     "end_time": "2021-01-28T21:54:06.435012Z",
+     "start_time": "2021-01-28T21:54:06.234729Z"
     }
    },
    "outputs": [
@@ -282,7 +281,7 @@
     }
    ],
    "source": [
-    "plot_predicted_data(training_actual_df=train_df, predicted_df=predicted_df, \n",
+    "_ = plot_predicted_data(training_actual_df=train_df, predicted_df=predicted_df, \n",
     "                    date_col=lgt_vi.date_col, actual_col=lgt_vi.response_col, \n",
     "                    test_actual_df=test_df)"
    ]
@@ -304,7 +303,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.7"
   },
   "toc": {
    "base_numbering": 1,

diff --git a/docs/tutorials/quick_start.ipynb b/docs/tutorials/quick_start.ipynb