make release-tag: Merge branch 'master' into stable

sintel-dev · May 23, 2023 · 7e1056e · 7e1056e
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diff --git a/HISTORY.md b/HISTORY.md
@@ -1,6 +1,20 @@
 History
 =======
 
+## 0.5.0 - 2023-05-23
+
+This version uses ``ml-stars`` package instead of ``mlprimitives``.
+
+### Issues resolved
+
+* Migrate to ml-stars – [Issue #418](https://github.com/signals-dev/Orion/issues/418) by @sarahmish
+* Updating ``best_cost`` in ``find_anomalies`` primitive – [Issue #403](https://github.com/signals-dev/Orion/issues/403) by @sarahmish
+* Retire ``lstm_dynamic_threshold_gpu`` and ``lstm_autoencoder_gpu`` pipeline maintenance – [Issue #373](https://github.com/signals-dev/Orion/issues/373) by @sarahmish
+* Typo in xlsxwriter dependency specification – [Issue #394](https://github.com/signals-dev/Orion/issues/394) by @sarahmish
+* ``orion.evaluate`` uses fails when fitting – [Issue #384](https://github.com/signals-dev/Orion/issues/384) by @sarahmish
+* AER pipeline with visualization option – [Issue #379](https://github.com/signals-dev/Orion/issues/379) by @sarahmish
+
+
 ## 0.4.1 - 2023-01-31
 
 ### Issues resolved
@@ -10,6 +24,7 @@ History
 * Pin ``scikit-learn`` – [Issue #367](https://github.com/signals-dev/Orion/issues/367) by @sarahmish
 * Fix VAE documentation – [Issue #360](https://github.com/signals-dev/Orion/issues/360) by @sarahmish
 
+
 ## 0.4.0 - 2022-11-08
 
 This version introduces several new enhancements:

diff --git a/README.md b/README.md
@@ -18,22 +18,20 @@
 
 A machine learning library for unsupervised time series anomaly detection.
 
-| Important Links                     |                                                                      |
-| ----------------------------------- | -------------------------------------------------------------------- |
-| :computer: **[Website]**            | Check out the Sintel Website for more information about the project. |
-| :book: **[Documentation]**          | Quickstarts, User and Development Guides, and API Reference.         |
-| :star: **[Tutorials]**              | Checkout our notebooks                                               |
-| :octocat: **[Repository]**          | The link to the Github Repository of this library.                   |
-| :scroll: **[License]**              | The repository is published under the MIT License.                   |
-| :keyboard: **[Development Status]** | This software is in its Pre-Alpha stage.                             |
+| Important Links                               |                                                                      |
+| --------------------------------------------- | -------------------------------------------------------------------- |
+| :computer: **[Website]**                      | Check out the Sintel Website for more information about the project. |
+| :book: **[Documentation]**                    | Quickstarts, User and Development Guides, and API Reference.         |
+| :star: **[Tutorials]**                        | Checkout our notebooks                                               |
+| :octocat: **[Repository]**                    | The link to the Github Repository of this library.                   |
+| :scroll: **[License]**                        | The repository is published under the MIT License.                   |
 | [![][Slack Logo] **Community**][Community]    | Join our Slack Workspace for announcements and discussions.          |
 
 [Website]: https://sintel.dev/
 [Documentation]: https://sintel-dev.github.io/Orion
 [Tutorials]: https://github.com/sintel-dev/Orion/tree/master/tutorials
 [Repository]: https://github.com/sintel-dev/Orion
 [License]: https://github.com/sintel-dev/Orion/blob/master/LICENSE
-[Development Status]: https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha
 [Community]: https://join.slack.com/t/sintel-space/shared_invite/zt-q147oimb-4HcphcxPfDAM0O9_4PaUtw
 [Slack Logo]: https://github.com/sintel-dev/Orion/blob/master/docs/images/slack.png
 
@@ -87,20 +85,20 @@ which should show a signal with `timestamp` and `value`.
 4  1222905600 -0.370746
 ```
 
-In this example we use `lstm_dynamic_threshold` pipeline and set some hyperparameters (in this case training epochs as 5).
+In this example we use `aer` pipeline and set some hyperparameters (in this case training epochs as 5).
 
 ```python3
 from orion import Orion
 
 hyperparameters = {
-    'keras.Sequential.LSTMTimeSeriesRegressor#1': {
+    'orion.primitives.aer.AER#1': {
         'epochs': 5,
         'verbose': True
     }
 }
 
 orion = Orion(
-    pipeline='lstm_dynamic_threshold',
+    pipeline='aer',
     hyperparameters=hyperparameters
 )
 
@@ -120,7 +118,7 @@ The output of the previous command will be a ``pandas.DataFrame`` containing a t
 
 ```
         start         end  severity
-0  1394323200  1399701600  0.673494
+0  1402012800  1403870400  0.122539
 ```
 
 # Leaderboard
@@ -132,11 +130,12 @@ We run the benchmark on **11** datasets with their known grounth truth. We recor
 |---------------------------|--------------------|
 | AER                       |         10         |
 | TadGAN                    |          7         |
-| LSTM Dynamic Thresholding |          8         |
+| LSTM Dynamic Thresholding |          7         |
 | LSTM Autoencoder          |          6         |
 | Dense Autoencoder         |          6         |
 | VAE                       |          6         |
-| Azure                     |          0         |
+| [GANF](https://arxiv.org/pdf/2202.07857.pdf)                                                  |          6         |
+| [Azure](https://azure.microsoft.com/en-us/products/cognitive-services/anomaly-detector/)      |          0         |
 
 
 You can find the scores of each pipeline on every signal recorded in the [details Google Sheets document](https://docs.google.com/spreadsheets/d/1HaYDjY-BEXEObbi65fwG0om5d8kbRarhpK4mvOZVmqU/edit?usp=sharing). The summarized results can also be browsed in the following [summary Google Sheets document](https://docs.google.com/spreadsheets/d/1ZPUwYH8LhDovVeuJhKYGXYny7472HXVCzhX6D6PObmg/edit?usp=sharing).
@@ -150,24 +149,22 @@ Additional resources that might be of interest:
 
 # Citation
 
-If you use **Orion** which is part of the **Sintel** ecosystem for your research, please consider citing the following paper:
+If you use **AER** for your research, please consider citing the following paper:
+
+Lawrence Wong, Dongyu Liu, Laure Berti-Equille, Sarah Alnegheimish, Kalyan Veeramachaneni. [AER: Auto-Encoder with Regression for Time Series Anomaly Detection](https://arxiv.org/pdf/2212.13558.pdf).
 
-Sarah Alnegheimish, Dongyu Liu, Carles Sala, Laure Berti-Equille, Kalyan Veeramachaneni. [Sintel: A Machine Learning Framework to Extract Insights from Signals](https://dl.acm.org/doi/pdf/10.1145/3514221.3517910).
 ```
-@inproceedings{alnegheimish2022sintel,
-  title={Sintel: A Machine Learning Framework to Extract Insights from Signals},
-  author={Alnegheimish, Sarah and Liu, Dongyu and Sala, Carles and Berti-Equille, Laure and Veeramachaneni, Kalyan},  
-  booktitle={Proceedings of the 2022 International Conference on Management of Data},
-  pages = {1855–1865},
-  numpages = {11},
-  publisher={Association for Computing Machinery},
-  doi = {10.1145/3514221.3517910},
-  series = {SIGMOD '22},
+@inproceedings{wong2022aer,
+  title={AER: Auto-Encoder with Regression for Time Series Anomaly Detection},
+  author={Wong, Lawrence and Liu, Dongyu and Berti-Equille, Laure and Alnegheimish, Sarah and Veeramachaneni, Kalyan},
+  booktitle={2022 IEEE International Conference on Big Data (IEEE BigData)},
+  pages={1152-1161},
+  doi={10.1109/BigData55660.2022.10020857},
+  organization={IEEE},
   year={2022}
 }
 ```
 
-
 If you use **TadGAN** for your research, please consider citing the following paper:
 
 Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni. [TadGAN - Time Series Anomaly Detection Using Generative Adversarial Networks](https://arxiv.org/pdf/2009.07769v3.pdf).
@@ -183,3 +180,20 @@ Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan
   year={2020}
 }
 ```
+
+If you use **Orion** which is part of the **Sintel** ecosystem for your research, please consider citing the following paper:
+
+Sarah Alnegheimish, Dongyu Liu, Carles Sala, Laure Berti-Equille, Kalyan Veeramachaneni. [Sintel: A Machine Learning Framework to Extract Insights from Signals](https://dl.acm.org/doi/pdf/10.1145/3514221.3517910).
+```
+@inproceedings{alnegheimish2022sintel,
+  title={Sintel: A Machine Learning Framework to Extract Insights from Signals},
+  author={Alnegheimish, Sarah and Liu, Dongyu and Sala, Carles and Berti-Equille, Laure and Veeramachaneni, Kalyan},  
+  booktitle={Proceedings of the 2022 International Conference on Management of Data},
+  pages={1855–1865},
+  numpages={11},
+  publisher={Association for Computing Machinery},
+  doi={10.1145/3514221.3517910},
+  series={SIGMOD '22},
+  year={2022}
+}
+```
diff --git a/benchmark/leaderboard.md b/benchmark/leaderboard.md
@@ -2,8 +2,9 @@
 |---------------------------|--------------------|
 | AER					    |          10        |
 | TadGAN					|          7         |
-| LSTM Dynamic Thresholding |          8         |
+| LSTM Dynamic Thresholding |          7         |
 | LSTM Autoencoder			|          6         |
 | Dense Autoencoder			|          6         |
 | VAE					    |          6         |
+| GANF					    |          6         |
 | Azure						|          0         |
diff --git a/benchmark/leaderboard.xlsx b/benchmark/leaderboard.xlsx