Merge branch 'develop'

CHANGELOG.md

@@ -6,6 +6,18 @@ Most recent releases are shown at the top. Each release shows:
 - **Changed**: Additional parameters, changes to inputs or outputs, etc
 - **Fixed**: Bug fixes that don't change documented behaviour
 
+## 0.27.0 (2021-07-20)
+
+### New:
+- Added `tabular.causal_inference_model` function for causal inference support.
+
+### Changed
+- N/A
+
+### Fixed:
+- N/A
+
+
 ## 0.26.5 (2021-07-15)
 
 ### New:

README.md

@@ -10,6 +10,8 @@
 
 
 ### News and Announcements
+- **2021-07-20**
+  - **ktrain v0.27.x** is released and now supports causal inference using [meta-learners](https://arxiv.org/abs/1706.03461). See the [example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/tabular/causal_inference_example.ipynb) for more information.
 - **2021-07-15**
   - **ktrain** was used to train machine learning models for [CoronaCentral.ai](https://coronacentral.ai/), a machine-learning-enhanced search engine for COVID publications at Stanford University. The CoronaCentral document classifier, **CoronaBERT**, is [available on the Hugging Face model hub](https://huggingface.co/jakelever/coronabert).  CoronaCentral.ai was developed by Jake Lever and Russ Altman and funded by the Chan Zuckerberg Biohub. Check out [their paper](https://www.biorxiv.org/content/10.1101/2020.12.21.423860v1).
 ----
@@ -42,6 +44,7 @@
   - `tabular` data:
     - **tabular classification** (e.g., Titanic survival prediction) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/tutorials/tutorial-08-tabular_classification_and_regression.ipynb)]</sup></sub>
     - **tabular regression** (e.g., predicting house prices) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/tabular/HousePricePrediction-MLP.ipynb)]</sup></sub>
+    - **causal inference** using meta-learners <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/tabular/causal_inference_example.ipynb)]</sup></sub>
 
 - estimate an optimal learning rate for your model given your data using a Learning Rate Finder
 - utilize learning rate schedules such as the [triangular policy](https://arxiv.org/abs/1506.01186), the [1cycle policy](https://arxiv.org/abs/1803.09820), and [SGDR](https://arxiv.org/abs/1608.03983) to effectively minimize loss and improve generalization
@@ -296,16 +299,24 @@ The above should be all you need on Linux systems and cloud computing environmen
 [more detailed instructions](https://github.com/amaiya/ktrain/blob/master/FAQ.md#how-do-i-install-ktrain-on-a-windows-machine) that include some extra steps.
 
 **Some important things to note about installation:**
-- If using **ktrain** with `tensorflow<=2.1`, you must also downgrade the **transformers** library to `transformers==3.1`.
 - If `load_predictor` fails with the error "`AttributeError: 'str' object has no attribute 'decode'`", then downgrade **h5py**: `pip install h5py==2.10.0`
-- As of v0.21.x, **ktrain** no longer installs TensorFlow 2 automatically.  As indicated above, you should install TensorFlow 2 yourself before installing and using **ktrain**.  On Google Colab, TensorFlow 2 should be already installed.  You should be able to use **ktrain**  with any version of [TensorFlow 2](https://www.tensorflow.org/install/pip?lang=python3). Note, however, that there is a bug in TensorFlow 2.2 and 2.3 that affects the *Learning-Rate-Finder* [that will not be fixed until TensorFlow 2.4](https://github.com/tensorflow/tensorflow/issues/41174#issuecomment-656330268).  The bug causes the learning-rate-finder to complete all epochs even after loss has diverged (i.e., no automatic-stopping).
+- There is a bug in TensorFlow 2.2 and 2.3 that affects the *Learning-Rate-Finder* [that was not fixed until TensorFlow 2.4](https://github.com/tensorflow/tensorflow/issues/41174#issuecomment-656330268).  The bug causes the learning-rate-finder to complete all epochs even after loss has diverged (i.e., no automatic-stopping).
 - If using **ktrain** on a local machine with a GPU (versus Google Colab, for example), you'll need to [install GPU support for TensorFlow 2](https://www.tensorflow.org/install/gpu).
-- Since some **ktrain** dependencies have not yet been migrated to `tf.keras` in TensorFlow 2 (or may have other issues), 
-  **ktrain** is temporarily using forked versions of some libraries. Specifically, **ktrain** uses forked versions of the `eli5` and `stellargraph` libraries.  If not installed, **ktrain** will complain  when a method or function needing either of these libraries is invoked.  To install these forked versions, you can do the following:
-```
+- Some optional, aditional libraries to install if needed are:
+```python
+# for ktrain.text.TextPredictor.explain and ktrain.vision.ImagePredictor.explain
 pip install https://github.com/amaiya/eli5/archive/refs/heads/tfkeras_0_10_1.zip
+# for ktrain.graph
 pip install https://github.com/amaiya/stellargraph/archive/refs/heads/no_tf_dep_082.zip
+# for ktrain.text.ZeroShotClassifier, ktrain.text.TransformerSummarizer, ktrain.text.Translator
+pip install torch
+# for ktrain.tabular.TabularPredictor.explain
+pip install shap
+# for ktrain.tabular.causal_inference_model
+pip install causalnlp
 ```
+If the above libaries are not installed, **ktrain** will complain  when a method or function needing either any of the above is invoked.
+Notice that **ktrain** is using forked versions of the `eli5` and `stellargraph` libraries above in order to support TensorFlow2.
 
 This code was tested on Ubuntu 18.04 LTS using TensorFlow 2.3.1 and Python 3.6.9.