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zer0n
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zer0n
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Why don't I see any deletion?
| return window | ||
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| def nab_score(y_true, y_pred): |
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Let's not call it nab score. It's a fairly standard metric.
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I haven't deleted the earlier evaluation (mAP) module. For the new metric, I have created an additional file. I'll change the name from nab_score to weighted_EarlyDetection_score.
added buffer to windows based on sparsity
zer0n
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zer0n
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@satyanshukla Please update this
nab_evaluation.py
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| @param window_scale_limit (float): The largest amount the windows to be expanded by. For example, for | ||
| window_scale_limit=2, the new windows will be at most | ||
| 2 * (current window size). | ||
| @param goal_sparsity (int): The goal sparsity of the window after increasing the window size. |
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max_sparsity would be more precise
nab_evaluation.py
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| """ | ||
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| for i, window in enumerate(windows): | ||
| if index <= window[1] and index >= window[0]: |
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window[0] <= index and index <= window[1] is easier to read.
| if index <= window[1] and index >= window[0]: | ||
| return window | ||
| elif index > window[1] and index < windows[i+1][0]: | ||
| return window |
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You return window in both cases? Don't you want to return None here?
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What do you return at the end of the loop?
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- I had these two cases for clarity, have combined them into one.
- There will never be the end of the loop, those cases have already been taken care before calling this function.
| import numpy as np | ||
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| def scaledSigmoid(relativePositionInWindow): | ||
| if relativePositionInWindow > 3.0: |
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Will it ever happen? The condition for this function to be called is that the prediction point in properly within the window, right?
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Yes, if the point is predicted right outside the window, the scaled sigmoid is used to calculate its score.
| sparsity = sum(y_true)/float(len(y_true)) | ||
| label_windows = add_buffer_to_label(sparsity, label_windows, 0, len(y_true)) | ||
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| detection_info = {} |
nab_evaluation.py
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| tp_score = 0 | ||
| fp_score = 0 | ||
| fn_score = 0 | ||
| for i in range(len(y_pred)): |
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Is i the index of the time series? If so, I would name it t to be clearer.
nab_evaluation.py
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| if i < label_windows[0][0]: | ||
| fp_score += -1.0*fp_weight | ||
| elif i > label_windows[-1][1]: | ||
| position = abs(label_windows[-1][1]-i)/float(label_windows[-1][1]-label_windows[-1][0]) |
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Based on the latest dicussion, should it be fp_weight score?
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Yeah, you can not give the same score to fp_weight and fn_weight. For standard score, NAB suggests tp_weight = 1.0, fp_weight = 0.11 and fn_weight = 1.0. This is to compensate for the fact that it is okay to have a couple of false positives if the algorithm predicts some true positives.
nab_evaluation.py
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| else: | ||
| cWindow = getCorrespondingWindow(i, label_windows) | ||
| if i <= cWindow[1] and i >= cWindow[0] and detection_info[cWindow] == 0: |
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If there there are several points falling in the same window, we should not over count them.
A cleaner implementation would be:
1. Go through the windows
2. For each window:
- Find the first prediction index in that window (from window[0] to window[1])
- Change the subsequent 1's in the predictions, in the window range, to 0
- Compute the score; if no anomaly prediction found, return the false negative weight
- Return the score
3. The remaining prediction points would be false positives. Add those to the total score.
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For false positives also, we need to find the preceding window. If there are several points falling in the same window, we take care using the detection_info dictionary. If a window is detected once, detection_info for that window turns to 1 and subsequent points falling in that window will not contribute to the score.
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It's unclear whether you we explaining that the existing code works or you're going to submit an update commit.
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