Aliro rebranding

README.md

@@ -1,25 +1,24 @@
 [![Logo](./docs/source/_static/logo_blank_small.png)]()
 
-[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://github.com/EpistasisLab/pennai/blob/master/LICENSE) [![PennAI CI/CD](https://github.com/EpistasisLab/pennai/actions/workflows/pennai_tests.yml/badge.svg)](https://github.com/EpistasisLab/pennai/actions/workflows/pennai_tests.yml) [![Coverage Status](https://coveralls.io/repos/github/EpistasisLab/pennai/badge.svg)](https://coveralls.io/github/EpistasisLab/pennai)
+[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://github.com/EpistasisLab/Aliro/blob/master/LICENSE) [![Aliro CI/CD](https://github.com/EpistasisLab/Aliro/actions/workflows/pennai_tests.yml/badge.svg)](https://github.com/EpistasisLab/Aliro/actions/workflows/pennai_tests.yml) [![Coverage Status](https://coveralls.io/repos/github/EpistasisLab/pennai/badge.svg)](https://coveralls.io/github/EpistasisLab/pennai)
 
 News
 ==================================
+**04/18/2022: PennAI** is becoming **Aliro**<br/>
+Over the next few weeks, PennAI will be rebranded as **Aliro.** The repository name will be updated on **TBD** and thus the URL for this project will change as well.
 
-**PennAI** is becoming **Aliro**
-**04/18/2022:** Over the next few weeks, PennAI will be rebranded as **Aliro.** The repository name will be updated on **TBD** and thus the URL for this project will change as well.
 
-
-PennAI: AI-Driven Data Science
+Aliro: AI-Driven Data Science
 ==================================
 
-**PennAI** is an easy-to-use data science assistant.
+**Aliro** is an easy-to-use data science assistant.
 It allows researchers without machine learning or coding expertise to run supervised machine learning analysis through a clean web interface. 
 It provides results visualization and reproducible scripts so that the analysis can be taken anywhere. 
-And, it has an *AI* assistant that can choose the analysis to run for you.   Dataset profiles are generated and added to a knowledgebase as experiments are run, and the AI assistant learns from this to give more informed recommendations as it is used.   PennAI comes with an initial knowledgebase generated from the [PMLB benchmark suite](https://github.com/EpistasisLab/penn-ml-benchmarks).
+And, it has an *AI* assistant that can choose the analysis to run for you.   Dataset profiles are generated and added to a knowledgebase as experiments are run, and the AI assistant learns from this to give more informed recommendations as it is used.   Aliro comes with an initial knowledgebase generated from the [PMLB benchmark suite](https://github.com/EpistasisLab/penn-ml-benchmarks).
 
-[**Documentation**](https://epistasislab.github.io/pennai/) 
+[**Documentation**](https://epistasislab.github.io/Aliro/) 
 
-[**Latest Production Release**](https://github.com/EpistasisLab/pennai/releases/latest)
+[**Latest Production Release**](https://github.com/EpistasisLab/Aliro/releases/latest)
 
 Browse the repo:
  - [User Guide](./docs/guides/userGuide.md)
@@ -28,7 +27,7 @@ Browse the repo:
 About the Project
 =================
 
-PennAI is actively developed by the [Institute for Biomedical Informatics](http://upibi.org) at the University of Pennsylvania.
+Aliro is actively developed by the [Institute for Biomedical Informatics](http://upibi.org) at the University of Pennsylvania.
 Contributors include Heather Williams, Weixuan Fu, William La Cava, Josh Cohen,
 Steve Vitale, Sharon Tartarone, Randal Olson, Patryk Orzechowski, and Jason Moore. 
 

ai/recommender/README.md

@@ -97,9 +97,9 @@ You should now be able to start the AI with your recommender.
 The easiest way to do so is to add your recommender to the `config/ai.env` file.
 Edit this file so that `AI_RECOMMENDER=myrec`.
 
-Then when PennAI is launched, it will run with your recommender. 
+Then when Aliro is launched, it will run with your recommender. 
 
-For more control and for testing, launch PennAI with `AI_AUTOSTART=0` set in the
+For more control and for testing, launch Aliro with `AI_AUTOSTART=0` set in the
 `config/ai.env` file. 
 Then, attach to the `pennai_lab_1` docker container with the command
 

data/knowledgebases/README.md

@@ -1,9 +1,9 @@
 # Knowledgebases
 
 Knowledgebases are collections of previous results from machine learning analyses 
-that are used to bootstrap PennAI. 
+that are used to bootstrap Aliro. 
 
-The results are stored in a .tsv.gz file. By default PennAI loads results from the
+The results are stored in a .tsv.gz file. By default Aliro loads results from the
 benchmark of scikit-learn described in these papers:
 
  - Olson, Randal S., William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and 

data/recommenders/pennaiweb/README.md

@@ -1,3 +1,3 @@
-# Serialized recommenders for use with the PennAI web interface
+# Serialized recommenders for use with the Aliro web interface
 
 Pretrained recommenders are currently provided for the SVD recommender, one for regression and one for classification.

data/recommenders/scikitlearn/README.md

@@ -1 +1 @@
-Serialized recommenders for use with the scikit-learn PennAI interface
+Serialized recommenders for use with the scikit-learn Aliro interface

docker/lab/Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.7.4-stretch
+FROM python:3.7.11-stretch
 
 #nodejs
 RUN wget --quiet https://nodejs.org/dist/v11.14.0/node-v11.14.0-linux-x64.tar.xz -O ~/node.tar.xz && \

docs/guides/Scikit_Learn_API_Guide.md

@@ -1,7 +1,7 @@
 #  User Guide of PennAIpy
 
 ### Installation of AI engine as a standalone python package ###
-PennAI AI engine is built on top of several existing Python libraries, including:
+Aliro AI engine is built on top of several existing Python libraries, including:
 
 * [NumPy](http://www.numpy.org/)
 
@@ -20,15 +20,15 @@ PennAI AI engine is built on top of several existing Python libraries, including
 
 Most of the necessary Python packages can be installed via the [Anaconda Python distribution](https://www.anaconda.com/products/individual), which we strongly recommend that you use.
 
-You can install PennAI AI engine using `pip`.
+You can install Aliro AI engine using `pip`.
 
 NumPy, SciPy, scikit-learn, pandas and joblib can be installed in Anaconda via the command:
 
 ```Shell
 conda install numpy scipy scikit-learn pandas joblib simplejson
 ```
 
-Surprise was tweaked for the PennAI AI engine and it can be install with `pip` via the command below. **Note: [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) is required for building the surprise package in Windows OS. Please download and run the installer with selecting "C++ Build tools". Additionally, the latest version of [`cython`](https://cython.org) is required and it can be installed/updated via `pip install --upgrade cython`.**
+Surprise was tweaked for the Aliro AI engine and it can be install with `pip` via the command below. **Note: [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) is required for building the surprise package in Windows OS. Please download and run the installer with selecting "C++ Build tools". Additionally, the latest version of [`cython`](https://cython.org) is required and it can be installed/updated via `pip install --upgrade cython`.**
 
 ```Shell
 pip install --no-cache-dir git+https://github.com/lacava/surprise.git@1.1.1.1
@@ -40,9 +40,9 @@ Finally to install AI engine itself, run the following command:
 pip install pennaipy
 ```
 
-### Example of using PennAI AI engine ###
+### Example of using Aliro AI engine ###
 
-The following code illustrates how PennAI can be employed for performing a simple _classification task_ over the Iris dataset.
+The following code illustrates how Aliro can be employed for performing a simple _classification task_ over the Iris dataset.
 
 ```Python
 from pennai.sklearn import PennAIClassifier
@@ -68,17 +68,17 @@ print(pennai.score(X_test, y_test))
 
 ```
 
-### Default knowledgebase/metafeatures of PennAI AI engine
+### Default knowledgebase/metafeatures of Aliro AI engine
 
-If you don't specify `knowledgebase` and `kb_metafeatures` in `PennAIClassifier` or `PennAIRegressor`, PennAI AI engine will use default knowledgebase based on [pmlb](https://github.com/EpistasisLab/penn-ml-benchmarks)(version0.3).
+If you don't specify `knowledgebase` and `kb_metafeatures` in `PennAIClassifier` or `PennAIRegressor`, Aliro AI engine will use default knowledgebase based on [pmlb](https://github.com/EpistasisLab/penn-ml-benchmarks)(version0.3).
 
 |                | Default Knowledgebase                          | Default Metafeatures                    |
 |----------------|------------------------------------------------|-----------------------------------------|
-| Classification | [sklearn-benchmark-data-knowledgebase-r6.tsv.gz](https://github.com/EpistasisLab/pennai/blob/master/data/knowledgebases/sklearn-benchmark-data-knowledgebase-r6.tsv.gz) | [pmlb_classification_metafeatures.csv.gz](https://github.com/EpistasisLab/pennai/blob/master/data/knowledgebases/pmlb_classification_metafeatures.csv.gz) |
-| Regression     | [pmlb_regression_results.tsv.gz](https://github.com/EpistasisLab/pennai/blob/master/data/knowledgebases/pmlb_regression_results.tsv.gz)                 | [pmlb_regression_metafeatures.csv.gz](https://github.com/EpistasisLab/pennai/blob/master/data/knowledgebases/pmlb_regression_metafeatures.csv.gz)     |
+| Classification | [sklearn-benchmark-data-knowledgebase-r6.tsv.gz](https://github.com/EpistasisLab/Aliro/blob/master/data/knowledgebases/sklearn-benchmark-data-knowledgebase-r6.tsv.gz) | [pmlb_classification_metafeatures.csv.gz](https://github.com/EpistasisLab/Aliro/blob/master/data/knowledgebases/pmlb_classification_metafeatures.csv.gz) |
+| Regression     | [pmlb_regression_results.tsv.gz](https://github.com/EpistasisLab/Aliro/blob/master/data/knowledgebases/pmlb_regression_results.tsv.gz)                 | [pmlb_regression_metafeatures.csv.gz](https://github.com/EpistasisLab/Aliro/blob/master/data/knowledgebases/pmlb_regression_metafeatures.csv.gz)     |
 
 
-### Example of using PennAI AI engine with non-default knowledgebase/metafeature. ###
+### Example of using Aliro AI engine with non-default knowledgebase/metafeature. ###
 
 
 ```Python
@@ -92,8 +92,8 @@ iris = load_iris()
 X_train, X_test, y_train, y_test = train_test_split(iris.data.astype(np.float64),
     iris.target.astype(np.float64), train_size=0.75, test_size=0.25, random_state=42)
 
-classification_kb = "https://github.com/EpistasisLab/pennai/raw/ai_sklearn_api/data/knowledgebases/sklearn-benchmark5-data-knowledgebase-small.tsv.gz"
-classification_metafeatures="https://github.com/EpistasisLab/pennai/raw/ai_sklearn_api/data/knowledgebases/pmlb_classification_metafeatures.csv.gz"
+classification_kb = "https://github.com/EpistasisLab/Aliro/raw/ai_sklearn_api/data/knowledgebases/sklearn-benchmark5-data-knowledgebase-small.tsv.gz"
+classification_metafeatures="https://github.com/EpistasisLab/Aliro/raw/ai_sklearn_api/data/knowledgebases/pmlb_classification_metafeatures.csv.gz"
 
 pennai = PennAIClassifier(
               rec_class=KNNMetaRecommender,
@@ -110,9 +110,9 @@ print(pennai.score(X_test, y_test))
 
 ```
 
-### Example of using PennAI AI engine with pre-trained SVG recommender ###
+### Example of using Aliro AI engine with pre-trained SVG recommender ###
 
-The pre-trained SVG recommender is provided for saving computational time for initializing the recommender with default knowledgebase in PennAI. The following code illustrates how to use the pre-trained SVG recommender:
+The pre-trained SVG recommender is provided for saving computational time for initializing the recommender with default knowledgebase in Aliro. The following code illustrates how to use the pre-trained SVG recommender:
 
 ```Python
 from pennai.sklearn import PennAIClassifier
@@ -129,7 +129,7 @@ X_train, X_test, y_train, y_test = train_test_split(iris.data.astype(np.float64)
     iris.target.astype(np.float64), train_size=0.75, test_size=0.25, random_state=42)
 
 # download pre-trained SVG recommender for pennai's github
-urllib.request.urlretrieve("https://github.com/EpistasisLab/pennai/raw/ai_sklearn_api/data/recommenders/scikitlearn/SVDRecommender_classifier_accuracy_pmlb.pkl.gz", "SVDRecommender_classifier_accuracy_pmlb.pkl.gz")
+urllib.request.urlretrieve("https://github.com/EpistasisLab/Aliro/raw/ai_sklearn_api/data/recommenders/scikitlearn/SVDRecommender_classifier_accuracy_pmlb.pkl.gz", "SVDRecommender_classifier_accuracy_pmlb.pkl.gz")
 serialized_rec = "SVDRecommender_classifier_accuracy_pmlb.pkl.gz"
 
 pennai = PennAIClassifier(