Merge pull request #10 from manujosephv/develop

-- added documentation

Author: manujosephv

docs/history.md

@@ -25,3 +25,7 @@ History
 -   Refactored the classes to separate backbones from the head of the models
 -   Changed the saving and loading model to work for custom parameters that you pass in `fit`
 
+0.5.0 (2021-03-18)
+------------------
+-   Added more documentation
+-   Added Zenodo citation

docs/other_features.md

@@ -0,0 +1,31 @@
+Apart from training and using Deep Networks for tabular data, PyTorch Tabular also has some cool features which can help your classical ML/ sci-kit learn pipelines
+
+## Categorical Embeddings
+
+The CategoryEmbedding Model can also be used as a way to encode your categorical columns. instead of using a One-hot encoder or a variant of TargetMean Encoding, you can use a learned embedding to encode your categorical features. And all this can be done using a scikit-learn style Transformer.
+
+### Usage Example
+
+```python
+# passing the trained model as an argument
+transformer = CategoricalEmbeddingTransformer(tabular_model)
+# passing the train dataframe to extract the embeddings and replace categorical features
+# defined in the trained tabular_model
+train_transformed = transformer.fit_transform(train)
+# using the extracted embeddings on new dataframe
+val_transformed = transformer.transform(val)
+```
+
+## Feature Extractor
+
+What if you want to use the features learnt by the Neural Network in your ML model? Pytorch Tabular let's you do that as well, and with ease. Again, a scikit-learn style Transformer does the job for you.
+
+```python
+# passing the trained model as an argument
+dt = DeepFeatureExtractor(tabular_model)
+# passing the train dataframe to extract the last layer features
+# here `fit` is there only for compatibility and does not do anything
+enc_df = dt.fit_transform(train)
+# using the extracted embeddings on new dataframe
+val_transformed = transformer.transform(val)
+```

mkdocs.yml

@@ -1,7 +1,7 @@
 site_name: PyTorch Tabular
 nav:
     - Getting Started: 
-      - " ": index.md
+      - "PyTorch Tabular": index.md
     - Tutorials:
       - Basic Usage: "tutorials/01-Basic_Usage.ipynb"
       - Advanced Usage: "tutorials/02-Advanced_Usage.ipynb"
@@ -18,6 +18,8 @@ nav:
       - Experiment Tracking: experiment_tracking.md
     - Tabular Model:
       - TabularModel: tabular_model.md
+    - Other Features:
+      - "Other Features": other_features.md
     # - FAQ: faq.md
     - API: 
       - API: apidocs.md