Metadata-Version: 2.1
Name: deeptables
Version: 0.2.3
Summary: Deep-learning Toolkit for Tabular datasets
Home-page: UNKNOWN
Author: DeepTables Community
Author-email: yangjian@zetyun.com
License: Apache License 2.0
Description: # DeepTables
        
        
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        ## We Are Hiring！
        Dear folks, we are opening several precious positions based in Beijing both for professionals and interns avid in AutoML/NAS, please send your resume/cv to yangjian@zetyun.com. (Application deadline: TBD.) 
        
        ## DeepTables: Deep-learning Toolkit for Tabular data
        DeepTables(DT) is a easy-to-use toolkit that enables deep learning to unleash great power on tabular data.
        
        
        ## Overview
        
        MLP (also known as Fully-connected neural networks) have been shown inefficient in learning distribution representation. The "add" operations of the perceptron layer have been proven poor performance to exploring multiplicative feature interactions. In most cases, manual feature engineering is necessary and this work requires extensive domain knowledge and very cumbersome. How learning feature interactions efficiently in neural networks becomes the most important problem.
        
        Various models have been proposed to CTR prediction and continue to outperform existing state-of-the-art approaches to the late years. Well-known examples include FM, DeepFM, Wide&Deep, DCN, PNN, etc. These models can also provide good performance on tabular data under reasonable utilization.
        
        DT aims to utilize the latest research findings to provide users with an end-to-end toolkit on tabular data.
        
        DT has been designed with these key goals in mind:
        
        * Easy to use, non-experts can also use.
        * Provide good performance out of the box.
        * Flexible architecture and easy expansion by user.
        
        ## Tutorials
        Please refer to the official docs at [https://deeptables.readthedocs.io/en/latest/](https://deeptables.readthedocs.io/en/latest/).
        * [Quick Start](https://deeptables.readthedocs.io/en/latest/quick_start.html)
        * [Examples](https://deeptables.readthedocs.io/en/latest/examples.html)
        * [ModelConfig](https://deeptables.readthedocs.io/en/latest/model_config.html)
        * [Models](https://deeptables.readthedocs.io/en/latest/models.html)
        * [Layers](https://deeptables.readthedocs.io/en/latest/layers.html)
        * [AutoML](https://deeptables.readthedocs.io/en/latest/automl.html)
        
        ## Installation
        
        `pip` is recommended to install DeepTables:
        
        ```shell script
        pip install deeptables
        ```
        
        Note:
        * If you installed TensorFlow before DeepTables, the DeepTables installer will reuse it, otherwise 'tensorflow>=2.0.0,<2.5.0' will be added to the requirements. 
        * DeepTables was tested with TensorFlow version 2.0 to 2.4, install the tested version please.
        
        **GPU** Setup (Optional)
        
        To use DeepTables with GPU devices, install `tensorflow-gpu` before install DeepTables please.
        
        ```shell script
        pip install tensorflow-gpu==2.4.2
        pip install deeptables
        ```
        
        
        ***Verify the installation***:
        
        ```shell script
        python -c "from deeptables.utils.quicktest import test; test()"
        ```
        
        ## Optional dependencies
        Following libraries are not hard dependencies and are not automatically installed when you install DeepTables. To use all functionalities of DT, these optional dependencies must be installed.
        
        ```shell script
        pip install shap
        ```
        
        ## Example：
        
        ### A simple binary classification example
        ``` python
        import numpy as np
        from deeptables.models import deeptable, deepnets
        from deeptables.datasets import dsutils
        from sklearn.model_selection import train_test_split
        
        #loading data
        df = dsutils.load_bank()
        df_train, df_test = train_test_split(df, test_size=0.2, random_state=42)
        
        y = df_train.pop('y')
        y_test = df_test.pop('y')
        
        #training
        config = deeptable.ModelConfig(nets=deepnets.DeepFM)
        dt = deeptable.DeepTable(config=config)
        model, history = dt.fit(df_train, y, epochs=10)
        
        #evaluation
        result = dt.evaluate(df_test,y_test, batch_size=512, verbose=0)
        print(result)
        
        #scoring
        preds = dt.predict(df_test)
        ```
        
        ### A solution using DeepTables to win the 1st place in Kaggle Categorical Feature Encoding Challenge II
        
        [Click here](https://github.com/DataCanvasIO/DeepTables/blob/master/examples/Kaggle%20-%20Categorical%20Feature%20Encoding%20Challenge%20II.ipynb)
        
        ## DataCanvas
        
        ![](docs/source/images/dc_logo_1.png)
        
        DeepTables is an open source project created by [DataCanvas](https://www.datacanvas.com/). 
        
Platform: UNKNOWN
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6.*
Description-Content-Type: text/markdown
Provides-Extra: tests
