Incorrect display smooth feature #3837
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Labels
A: plots
Area: plots webview, side panel and everything related
bug
Something isn't working
priority-p1
Regular product backlog
Comments
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It's more related to dvc-render to be honest, but let's keep it here for now (cc @daavoo @dberenbaum ) . Some customers (Studio) also were mentioning broken and over aggressive smoothing which forced them to stay with TB. We need to got back and research the templates again I guess. Making |
shcheklein
added
the
A: plots
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It's more related to dvc-render to be honest, but .... -->Hey thanks for this. From now on I´ll make the effort to explore codebases to report bugs more efficiently! |
shcheklein
changed the title
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My $0.02 is (even on a laptop) when the plot is given the screen's entire space that the experience isn't that bad: Screen.Recording.2023-05-09.at.4.25.09.pm.movOn a wide screen you get an even better sense of what is going on: Screen.Recording.2023-05-09.at.4.29.15.pm.mov |
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I think the underlying problems here are:
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Generally, there seems to be some aggressive smoothing for what I'd expect, especially at the edges of plots. If there's interpolation with linear, where smoothness of 1 is linear, then I can definitely see why that is. I would not expect linear--I guess I'm someone expecting more like the "TensorBoard behavior". @daavoo , I think that first bullet is exactly where I'm coming from For an example of what I'd expect, I'm attaching examples what tensorboard does
And this is studio: |
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I'm looking into this (trying different Vega hacks). For the record, this is the Vega dump that can be used in the Vega editor: {
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},
"title": "dvclive/plots/metrics/accuracy_train.tsv",
"width": 300,
"height": 300,
"params": [
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"value": 0.001,
"bind": { "input": "range", "min": 0.001, "max": 1, "step": 0.001 }
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],
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},
"color": { "field": "rev", "type": "nominal" }
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"transform": [
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"groupby": ["rev", "filename", "field", "filename::field"],
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},
{
"mark": { "type": "point", "tooltip": { "content": "data" } },
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"color": { "field": "rev", "type": "nominal" }
}
}
]
} |
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I think we need this vega/vega#3686 (+change the way we show lines a bit). + use |
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This seems solved . Therefore closing it |
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@SoyGema hey, we've improved it, but it's not solved yet :( we need to fix the way smooth actually works via vega/vega#3686 |
shcheklein
added
the
blocked
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Hey, Thanks ! Have a nice day! |
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Opened iterative/dvc-render#135 since I don't think there's much VS Code can do. Since we are waiting on vega/vega#3686 and it doesn't appear to be moving right now, is it worth considering other options? The quickest fix is to move to a simple (non-exponential) moving average, which is similar to what we want except for having an unweighted, fixed window. Here's how it looks in comparison to the current smoothing (old smoothing shown by Screen.Recording.2023-06-19.at.4.03.08.PM.movWe could also consider starting to move towards plotly (see iterative/dvc-render#7). It already has triangular moving average, which is probably close enough to exponential moving average. |
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Also note that the tensorboard example @SoyGema also looks off to me. In the last row with the max smoothing parameter, the smoothed line looks way below the actual trend of the points:
|
8 tasks
shcheklein
removed
the
blocked
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This still hasn't propagated to Vega Lite and is blocked. |
sroy3
added
the
blocked
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I think it's on us to propagate it. It seemed more or less straightforward the last time I checked and the vega-light is moving faster. Let's take a look please. |
In that case, I don't think I'm the person for that job as I am quite lost in all this. I would not know if I'm doing the right thing or not. I'll unassign myself. |
shcheklein
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the
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CONTEXT :
Neural Machine Translation training scenario.
1.7.82.56.00.8.7The smoothing feature shows two lines instead of one. The thicker green line should be transformed as the UI smoothing basr is moving through the bar but it´s not.
SmoothSmoothing.mp4
From conversation in Discord
Increasing plot size test seems to have the same issue
Machine Learning Epicstemics coming from the conversation in discord
The signal or number of datapoints can increase significantly when we change the
batch size parameterduring experiments : batch size controls the accuracy of the estimate of the error gradient when training Neural Networks. Too large batch sizes -less data points- may cause bad generalization. Many data points (smaller batch sizes ) take more training time but might have better generalization.Iterating over this parameter can be a common case of experimentation scenario.
NOTE for machine learning practitioners: this parameter starts with 8 and increases by powers of 2. Experimenting with this parameter is key for better generalization by having other rational hypotheses in mind. This article gives good hints.
For developers
Nice work !
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