Refactor Prediction #60
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src/prog_algs/metrics/samples.py
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| @@ -88,7 +88,7 @@ def mean_square_error(values, ground_truth): | |||
| Returns: | |||
| float: mean square error of eol predictions | |||
| """ | |||
| return sum([(x.mean() - ground_truth)**2 for x in values])/len(values) | |||
| return sum([(sum(x)/len(x) - ground_truth)**2 for x in values])/len(values) | |||
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This change supports the case where x is not unweighted_samples, but instead is a list (some users keep doing that).
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Python has a builtin mean function: https://docs.python.org/3/library/statistics.html#statistics.mean. I've never used it, but I expect it would work here.
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I updated to use numpy::mean so it'll be compatible with the upcoming vectorization changes.
| @@ -40,7 +40,7 @@ def future_loading(t, x = None): | |||
| if isinstance(filt, state_estimators.UnscentedKalmanFilter): | |||
| samples = filt.x.sample(20) | |||
| else: # Particle Filter | |||
| samples = filt.x.raw_samples() | |||
| samples = filt.x | |||
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With the refactor I make raw_samples an depreciated call, so I'm updating the example to not use it
| fig = states.snapshot(0).plot_scatter(label = "t={}".format(int(times[0][0]))) | ||
| states.snapshot(10).plot_scatter(fig = fig, label = "t={}".format(int(times[0][10]))) | ||
| states.snapshot(50).plot_scatter(fig = fig, label = "t={}".format(int(times[0][50]))) | ||
| fig = states.snapshot(0).plot_scatter(label = "t={}".format(int(times[0]))) |
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times returned from prediction is now times[time_index] instead of times[sample_id][time_index]. This is a breaking change that requires users to update how they're using it (like I do here).
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Because of other project deadlines the review for this will be delayed by 1 week. |
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src/prog_algs/metrics/samples.py
Outdated
| @@ -88,7 +88,7 @@ def mean_square_error(values, ground_truth): | |||
| Returns: | |||
| float: mean square error of eol predictions | |||
| """ | |||
| return sum([(x.mean() - ground_truth)**2 for x in values])/len(values) | |||
| return sum([(sum(x)/len(x) - ground_truth)**2 for x in values])/len(values) | |||
There was a problem hiding this comment.
Python has a builtin mean function: https://docs.python.org/3/library/statistics.html#statistics.mean. I've never used it, but I expect it would work here.
Co-authored-by: Jason Watkins <jason.watkins@nasa.gov>
Co-authored-by: Jason Watkins <jason.watkins@nasa.gov>
Refactor Prediction to support non-sample based predictors.
Refactor Prediction (an unreleased feature, so breaking changes are acceptable) to support non-sample based predictor such as the Unscented Kalman Predictor in PR #40. In this case predicted states would be of the type MultivariateNormalDistribution, not Unweighted samples.
New prediction class includes "mean" function. This is most useful for the MultivariateNormalPrediction, but has use for sample-based as well.
This change also fixes a bug in UnweightedSamplesPrediction where getting a snapshot at later timesteps after some samples have reached EOL results in an exception. Implementation fills them with none, so the result will look like this:
With this change I updated the predict step to return a single list of times (times[time_index]), rather than a list of list of times of format times[sample][time_index]. NOTE: THIS IS A BREAKING CHANGE THAT WILL REQUIRE USERS TO MAKE CHANGES. But the previous implementation makes no sense for a non sample-based approach.