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more viz + build fixes (#703)

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* viz docs

* writing vizualization docs

* documenting plot_util

* docstrings in plot_util

* autopep8 and flake8

* spelling (using default vim spellchecker and ingoring things like dataframe, docstring and etc)

* rephrased viz.md a little bit

* more examples of viz code usage in the docs
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pzhokhov
2018-11-06 17:02:20 -08:00
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commit 8b95576a92
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@@ -27,9 +27,8 @@ And you can now start TensorBoard with:
tensorboard --logdir=$OPENAI_LOGDIR
```
## Loading summaries of the results
## Loading summaries of the results ([notebook](https://colab.research.google.com/drive/1Wez1SA9PmNkCoYc8Fvl53bhU3F8OffGm))
If the summary overview provided by tensorboard is not sufficient, and you would like to either access to raw environment episode data, or use complex post-processing notavailable in tensorboard, you can load results into python as [pandas](https://pandas.pydata.org/) dataframes.
The colab notebook with the full version of the code is available [here](https://colab.research.google.com/drive/1Wez1SA9PmNkCoYc8Fvl53bhU3F8OffGm) (use "Open in playground" button to get a runnable version)
For instance, the following snippet:
```python
@@ -106,12 +105,32 @@ The results are split into two groups based on batch size and are plotted on a s
<img src="https://storage.googleapis.com/baselines/assets/viz/Screen%20Shot%202018-10-29%20at%205.53.45%20PM.png" width="700">
Showing all seeds on the same plot may be somewhat hard to comprehend and analyse. We can instead average over all seeds via the following command:
<img src="https://storage.googleapis.com/baselines/assets/viz/Screen%20Shot%202018-11-02%20at%204.42.52%20PM.png" width="720">
The lighter shade shows the standard deviation of data, and darker shade -
error in estimate of the mean (that is, standard deviation divided by square root of number of seeds)
error in estimate of the mean (that is, standard deviation divided by square root of number of seeds).
Note that averaging over seeds requires resampling to a common grid, which, in turn, requires smoothing
(using language of signal processing, we need to do low-pass filtering before resampling to avoid aliasing effects).
You can change the amount of smoothing by adjusting `resample` and `smooth_step` arguments to achieve desired smoothing effect
See the docstring of `plot_util` function for more info.
To plot both groups on the same graph, we can use the following:
```python
pu.plot_results(results, average_group=True, split_fn=lambda _: '')
```
Option `split_fn=labmda _:'' ` effectively disables splitting, so that all curves end up on the same panel.
<img src="https://storage.googleapis.com/baselines/assets/viz/Screen%20Shot%202018-11-06%20at%203.11.51%20PM.png" width=720>
Now, with many groups the overlapping shaded regions may start looking messy. We can disable either
light shaded region (corresponding to standard deviation of the curves in the group) or darker shaded region
(corresponding to the error in mean estimate) by using `shaded_std=False` or `shaded_err=False` options respectively.
For instance,
```python
pu.plot_results(results, average_group=True, split_fn=lambda _: '', shaded_std=False)
```
produces the following plot:
<img src="https://storage.googleapis.com/baselines/assets/viz/Screen%20Shot%202018-11-06%20at%203.12.02%20PM.png" width=820>