Metadata-Version: 2.1
Name: textualheatmap
Version: 1.0.0
Summary: Create interactive textual heat maps for Jupiter notebooks
Home-page: https://github.com/AndreasMadsen/python-textualheatmap
Author: Andreas Madsen
Author-email: amwebdk@gmail.com
License: MIT
Description: # textualheatmap
        
        **Create interactive textual heatmaps for Jupiter notebooks.**
        
        There is a framework agnostic interface `lrcurve.PlotLearningCurve`
        that works well with PyTorch and Tensorflow and a keras wrapper
        `lrcurve.KerasLearningCurve` that uses the keras callback interface.
        
        `textualheatmap` works with python 3.6 or newer and is distributed under the
        MIT license.
        
        ![Gif of textualheatmap](gifs/show_meta.gif)
        
        ## Install
        
        ```bash
        pip install -U textualheatmap
        ```
        
        ## API
        
        * [`textualheatmap.TextualHeatmap`](textualheatmap/textual_heatmap.py)
        
        ## Example
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AndreasMadsen/python-textualheatmap/blob/master/notebooks/general_example.ipynb)
        
        ```python
        from textualheatmap import TextualHeatmap
        
        data = [[
            # GRU data
            {"token":" ",
             "meta":["the","one","of"],
             "heat":[1,0,0,0,0,0,0,0,0]},
            {"token":"c",
             "meta":["can","called","century"],
             "heat":[1,0.22,0,0,0,0,0,0,0]},
            {"token":"o",
             "meta":["country","could","company"],
             "heat":[0.57,0.059,1,0,0,0,0,0,0]},
            {"token":"n",
             "meta":["control","considered","construction"],
             "heat":[1,0.20,0.11,0.84,0,0,0,0,0]},
            {"token":"t",
             "meta":["control","continued","continental"],
             "heat":[0.27,0.17,0.052,0.44,1,0,0,0,0]},
            {"token":"e",
             "meta":["context","content","contested"],
             "heat":[0.17,0.039,0.034,0.22,1,0.53,0,0,0]},
            {"token":"x",
             "meta":["context","contexts","contemporary"],
             "heat":[0.17,0.0044,0.021,0.17,1,0.90,0.48,0,0]},
            {"token":"t",
             "meta":["context","contexts","contentious"],
             "heat":[0.14,0.011,0.034,0.14,0.68,1,0.80,0.86,0]},
            {"token":" ",
             "meta":["of","and","the"],
             "heat":[0.014,0.0063,0.0044,0.011,0.034,0.10,0.32,0.28,1]},
            # ...
        ],[
            # LSTM data
            # ...
        ]]
        
        heatmap = TextualHeatmap(
            width = 600,
            show_meta = True,
            facet_titles = ['GRU', 'LSTM']
        )
        # Set data and render plot, this can be called again to replace
        # the data.
        heatmap.set_data(data)
        # Focus on the token with the given index. Especially useful when
        # `interactive=False` is used in `TextualHeatmap`.
        heatmap.highlight(159)
        ```
        
        ![Gif of learning-curve for keras example](gifs/show_meta.gif)
        
        ```python
        heatmap = TextualHeatmap(
            show_meta = False,
            facet_titles = ['LSTM', 'GRU'],
            rotate_facet_titles = True
        )
        heatmap.set_data(data)
        heatmap.highlight(159)
        ```
        
        ![Gif of learning-curve for keras example](gifs/no_meta_and_rotated.gif)
        
        ## Citation
        
        If you use this in a publication, please cite my [Distill publication](https://distill.pub/2019/memorization-in-rnns/) where I first demonstrated this visualization method.
        
        ```bib
        @article{madsen2019visualizing,
          author = {Madsen, Andreas},
          title = {Visualizing memorization in RNNs},
          journal = {Distill},
          year = {2019},
          note = {https://distill.pub/2019/memorization-in-rnns},
          doi = {10.23915/distill.00016}
        }
        ```
        
        ## Sponsor
        
        Sponsored by <a href="https://www.nearform.com/research/">NearForm Research</a>.
        
Keywords: saliency heatmap text textual jupyter colab interactive
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
