Bokeh chart fixes and improvements

CHANGELOG.md

@@ -5,6 +5,19 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.5.1] - 2020-05-28
+
+### Added
+
+- Bokeh colors, scaling, and hover tooltip (see [this PR for screenshots](https://github.com/stared/livelossplot/pull/106)).
+- Tests for Neptune, better tests in general.
+- Nice Issue templates.
+
+### Fixed
+
+- Bokeh example.
+- Made it working in general.
+
 ## [0.5.0] - 2020-03-20
 
 It is a major rewrite, with breaking API changes.
@@ -19,8 +32,8 @@ It requires Python 3.5+, and ideally 3.6+.
 - Continuous integration (using GitHub Actions).
 - Type hints, mostly for better development and more explicit documentation.
 - Way to write new output plugins (see `outputs` directory).
-- Examples are readily runnable on Colab. 
-- A more general way generate groups for plots based on Regex patterns. 
+- Examples are readily runnable on Colab.
+- A more general way generate groups for plots based on Regex patterns.
 - Bokeh plot - an initial version.
 
 ### Refactored
@@ -29,18 +42,16 @@ It requires Python 3.5+, and ideally 3.6+.
 - `PlotLosses` is no longer a god object.
 - Organized input plugins in the `inputs` directory.
 
-
 ### Fixed
 
 - Updated `neptune` and `tensorboard` plugins to support current APIs of the respective libraries.
 
 ### Removed
 
-- Python 2.7 support. 
+- Python 2.7 support.
 - Python 3.4 support - due to type hints.
 - Python 3.5 support priority. Right it installs an older version of `matplotlib`, 3.0 (the last working with Python 3.5). The current version of `matplotlib` is 3.2, see https://matplotlib.org/3.2.0/users/installing.html. Support for Python 3.5 for `livelossplot` may be dropped at any moment.
 
-
 ## [0.4.2] - 2020-03-06
 
 ### Added
@@ -53,15 +64,13 @@ It requires Python 3.5+, and ideally 3.6+.
 - Tensorboard path for Windows.
 - Changed PyToune to Poutyne to reflect the API name change of the respective library.
 
-
 ## [0.4.1] - 2019-05-26
 
 ### Added
 
 - Custom `matplotlib` subplots.
 - 2d plot prediction.
 
-
 ## [0.4.0] - 2019-05-06
 
-For now, it is prehistory. 
+For now, it is prehistory.

README.md

@@ -10,14 +10,11 @@
 
 Don't train deep learning models blindfolded! Be impatient and look at each epoch of your training!
 
-**🎉 New release 0.5.0 (20 Mar 2020). See [CHANGELOG](CHANGELOG.md) and updated [EXAMPLES IN COLAB](https://colab.research.google.com/github/stared/livelossplot). There are some API changes, to make it better, cleaner, and more modular. 🎉**
+**🎉 New release 0.5.0 (20 Mar 2020). See [CHANGELOG](CHANGELOG.md) and updated [EXAMPLES IN COLAB](https://colab.research.google.com/github/stared/livelossplot). There are some API changes, to make it better, cleaner, and more modular.From 0.5.1 (28 May 2020) there is [Bokeh](https://docs.bokeh.org/en/latest/#) support. 🎉**
 
 A live training loss plot in [Jupyter Notebook](http://jupyter.org/) for [Keras](https://keras.io/), [PyTorch](http://pytorch.org/) and other frameworks. An open-source Python package by [Piotr Migdał](https://p.migdal.pl/), [Bartłomiej Olechno](https://github.com/Bartolo1024/) and [others](https://github.com/stared/livelossplot/graphs/contributors). **Open for collaboration!** (Some tasks are as simple as writing code docstrings, so - no excuses! :))
 
-This project supported by [Jacek Migdał](http://jacek.migdal.pl/), [Marek Cichy](https://medium.com/@marekkcichy/). [Join the sponsors - show your ❤️ and support, and appear on the list](https://github.com/sponsors/stared)! It will give me time and energy to work on this project.
-
-
-```
+```python
 from livelossplot import PlotLossesKeras
 
 model.fit(X_train, Y_train,
@@ -29,36 +26,35 @@ model.fit(X_train, Y_train,
 
 ![](livelossplot.gif)
 
-So remember, [log your loss](https://twitter.com/pmigdal/status/943764924983017473)!
-
-* (The most FA)Q: Why not TensorBoard?
-* A: Jupyter Notebook compatibility (for exploration and teaching). The simplicity of use.
+- (The most FA)Q: Why not TensorBoard?
+- A: Jupyter Notebook compatibility (for exploration and teaching). The simplicity of use.
 
 ## Installation
 
 To install [this version from PyPI](https://pypi.org/project/livelossplot/), type:
 
-```
+```bash
 pip install livelossplot
 ```
 
 To get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:
 
-```
+```bash
 pip install git+git://github.com/stared/livelossplot.git
 ```
 
 ## Examples
 
 Look at notebook files with full working [examples](https://github.com/stared/livelossplot/blob/master/examples/):
 
-* [keras.ipynb](https://github.com/stared/livelossplot/blob/master/examples/keras.ipynb) - a Keras callback
-* [minimal.ipynb](https://github.com/stared/livelossplot/blob/master/examples/minimal.ipynb) - a bare API, to use anywhere
-* [pytorch.ipynb](https://github.com/stared/livelossplot/blob/master/examples/pytorch.ipynb) - a bare API, as applied to PyTorch
-* [2d_prediction_maps.ipynb](https://github.com/stared/livelossplot/blob/master/examples/2d_prediction_maps.ipynb) - example of custom plots - 2d prediction maps (0.4.1+)
-* [poutyne.ipynb](https://github.com/stared/livelossplot/blob/master/examples/poutyne.ipynb) - a Poutyne callback ([Poutyne](https://poutyne.org/) is a Keras-like framework for PyTorch)
-* [torchbearer.ipynb](https://github.com/stared/livelossplot/blob/master/examples/torchbearer.ipynb) - an example using the built in functionality from torchbearer ([torchbearer](https://github.com/ecs-vlc/torchbearer) is a model fitting library for PyTorch)
-* [neptune.py](https://github.com/stared/livelossplot/blob/master/examples/neptune.py)  and [neptune.ipynb](https://github.com/stared/livelossplot/blob/master/examples/neptune.ipynb) - a [Neptune.AI](https://neptune.au/)
+- [keras.ipynb](https://github.com/stared/livelossplot/blob/master/examples/keras.ipynb) - a Keras callback
+- [minimal.ipynb](https://github.com/stared/livelossplot/blob/master/examples/minimal.ipynb) - a bare API, to use anywhere
+- [bokeh.ipynb](https://github.com/stared/livelossplot/blob/master/examples/minimal.ipynb) - a bare API, plots with Bokeh
+- [pytorch.ipynb](https://github.com/stared/livelossplot/blob/master/examples/pytorch.ipynb) - a bare API, as applied to PyTorch
+- [2d_prediction_maps.ipynb](https://github.com/stared/livelossplot/blob/master/examples/2d_prediction_maps.ipynb) - example of custom plots - 2d prediction maps (0.4.1+)
+- [poutyne.ipynb](https://github.com/stared/livelossplot/blob/master/examples/poutyne.ipynb) - a Poutyne callback ([Poutyne](https://poutyne.org/) is a Keras-like framework for PyTorch)
+- [torchbearer.ipynb](https://github.com/stared/livelossplot/blob/master/examples/torchbearer.ipynb) - an example using the built in functionality from torchbearer ([torchbearer](https://github.com/ecs-vlc/torchbearer) is a model fitting library for PyTorch)
+- [neptune.py](https://github.com/stared/livelossplot/blob/master/examples/neptune.py) and [neptune.ipynb](https://github.com/stared/livelossplot/blob/master/examples/neptune.ipynb) - a [Neptune.AI](https://neptune.au/)
 
 You [run examples in Colab](https://colab.research.google.com/github/stared/livelossplot).
 
@@ -67,10 +63,9 @@ You [run examples in Colab](https://colab.research.google.com/github/stared/live
 Text logs are easy, but it's easy to miss the most crucial information: is it learning, doing nothing or overfitting?
 Visual feedback allows us to keep track of the training process. Now there is one for Jupyter.
 
-If you want to get serious - use [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), . 
+If you want to get serious - use [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), .
 But what if you just want to train a small model in Jupyter Notebook? Here is a way to do so, using `livelossplot` as a plug&play component
 
-
 ### from livelossplot import ...
 
 `PlotLosses` for a generic API.
@@ -86,10 +81,9 @@ There are callbacks for common libraries and frameworks: `PlotLossesKeras`, `Plo
 Feel invited to write, and contribute, your adapter.
 If you want to use a bare logger, there is `MainLogger`.
 
-
 ### from livelossplot.outputs import ...
 
-Plots: `MatplotlibPlot`, `BokehPlot`. 
+Plots: `MatplotlibPlot`, `BokehPlot`.
 
 Loggers: `ExtremaPrinter` (to standard output), `TensorboardLogger`, `TensorboardTFLogger`, `NeptuneLogger`.
 
@@ -101,6 +95,15 @@ plotlosses = PlotLosses(outputs=[MatplotlibPlot(), TensorboardLogger()])
 
 There are custom `matplotlib` plots in `livelossplot.outputs.matplotlib_subplots` you can pass in `MatplotlibPlot` arguments.
 
+If you like to plot with [Bokeh](https://docs.bokeh.org/en/latest/) instead of [matplotlib](https://matplotlib.org/), use
+
+```{python}
+plotlosses = PlotLosses(outputs=[BokehPlot()])
+```
+
+## Sponsors
+
+This project supported by [Jacek Migdał](http://jacek.migdal.pl/), [Marek Cichy](https://medium.com/@marekkcichy/). [Join the sponsors - show your ❤️ and support, and appear on the list](https://github.com/sponsors/stared)! It will give me time and energy to work on this project.
 
 ## Trivia