gaspersic/baselines
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

more examples of viz code usage in the docs

Browse Source
This commit is contained in:
Peter Zhokhov
2018-11-06 15:25:17 -08:00
parent b8bc0f8791
commit db9563ebf6
2 changed files with 31 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
baselines/common/plot_util.py
View File

@@ -263,9 +263,15 @@ def plot_results(
Curves in the same group have the same color (if average_group is False), or averaged over
(if average_group is True). The default value is the same as default value for split_fn
average_group: bool - if True, will average the curves in the same group. The mean of the result is plotted, with lighter
shaded region around corresponding to the standard deviation, and darker shaded region corresponding to
the error of mean estimate (that is, standard deviation over square root of number of samples)
average_group: bool - if True, will average the curves in the same group and plot the mean. Enables resampling
(if resample = 0, will use 512 steps)
shaded_std: bool - if True (default), the shaded region corresponding to standard deviation of the group of curves will be
shown (only applicable if average_group = True)
shaded_err: bool - if True (default), the shaded region corresponding to error in mean estimate of the group of curves
(that is, standard deviation divided by square root of number of curves) will be
shown (only applicable if average_group = True)
figsize: tuple or None - size of the resulting figure (including sub-panels). By default, width is 6 and height is 6 times number of
sub-panels.

25
docs/viz/viz.md
View File

@@ -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>