I got following error for only interest experiment on R2 region:
WARNING There are 3 nodes that have not run. runner.py:205
You can resume the pipeline run from the nearest nodes with persisted inputs by adding the
following argument to your previous command:
--from-nodes ""
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ C:\Users\ysami\.conda\envs\venv_lc\lib\runpy.py:194 in _run_module_as_main │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\runpy.py:87 in _run_code │
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│ in <module>:7 │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\framework\cli\cli.py:211 in main │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\click\core.py:1157 in __call__ │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\framework\cli\cli.py:139 in main │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\click\core.py:1078 in main │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\click\core.py:1688 in invoke │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\click\core.py:1434 in invoke │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\click\core.py:783 in invoke │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\framework\cli\project.py:455 in run │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\framework\session\session.py:421 in │
│ run │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\runner\runner.py:91 in run │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\runner\sequential_runner.py:70 in │
│ _run │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\runner\runner.py:319 in run_node │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\runner\runner.py:415 in │
│ _run_node_sequential │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\runner\runner.py:381 in │
│ _call_node_run │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\runner\runner.py:371 in │
│ _call_node_run │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\pipeline\node.py:355 in run │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\pipeline\node.py:348 in run │
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│ C:\Users\ysami\.conda\envs\venv_lc\lib\site-packages\kedro\pipeline\node.py:398 in │
│ _run_with_dict │
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│ C:\Local\LAAS\projets\sampler\src\sampler\pipelines\metrics\nodes.py:114 in get_metrics │
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│ 111 │ │ │ "features_targets": np.array([0]*n_interest[key]) │
│ 112 │ │ } │
│ 113 │ │ if params_voronoi["compute_voronoi"]["features"]: │
│ ❱ 114 │ │ │ volume_voronoi[key]["features"] = get_volume_voronoi( │
│ 115 │ │ │ │ scaled_data_interest_f, │
│ 116 │ │ │ │ len(features),tol=params_voronoi["tol"], isFilter=params_voronoi["isFilt │
│ 117 │ │ │ ) │
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│ C:\Local\LAAS\projets\sampler\src\sampler\pipelines\metrics\voronoi.py:111 in get_volume_voronoi │
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│ 108 │ │ │
│ 109 │ │ │
│ 110 │ │ # * Add points of the hypercube inside the polytope (region) if within the regio │
│ ❱ 111 │ │ delaunay = Delaunay(polytope) │
│ 112 │ │ for p in hypercube_points: │
│ 113 │ │ │ if delaunay.find_simplex(p)>=0: │
│ 114 │ │ │ │ polytope = np.vstack([polytope, p]) │
│ │
│ in scipy.spatial._qhull.Delaunay.__init__:1841 │
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│ in scipy.spatial._qhull._Qhull.__init__:353 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
QhullError: QH6154 Qhull precision error: Initial simplex is flat (facet 1 is coplanar with the interior point)
While executing: | qhull d Qz Q12 Qbb Qt Qc
Options selected for Qhull 2019.1.r 2019/06/21:
run-id 1483190572 delaunay Qz-infinity-point Q12-allow-wide Qbbound-last
Qtriangulate Qcoplanar-keep _pre-merge _zero-centrum Qinterior-keep
Pgood _max-width 0.56 Error-roundoff 1.8e-15 _one-merge 1.7e-14
Visible-distance 1.1e-14 U-max-coplanar 1.1e-14 Width-outside 2.2e-14
_wide-facet 6.6e-14 _maxoutside 2.2e-14
The input to qhull appears to be less than 4 dimensional, or a
computation has overflowed.
Qhull could not construct a clearly convex simplex from points:
- p7(v5): 0.38 0.76 0.055 0.39
- p4(v4): 0.28 0.67 0.055 0.093
- p12(v3): 0.27 0.78 0.055 0.91
- p8(v2): 0.38 0.9 0.055 0.75
- p1(v1): -0.18 0.65 0.055 0.00055
The center point is coplanar with a facet, or a vertex is coplanar
with a neighboring facet. The maximum round off error for
computing distances is 1.8e-15. The center point, facets and distances
to the center point are as follows:
center point 0.2229 0.7524 0.055 0.429
facet p4 p12 p8 p1 distance= 1.9e-18
facet p7 p12 p8 p1 distance= -8.5e-18
facet p7 p4 p8 p1 distance= -6.9e-18
facet p7 p4 p12 p1 distance= -7.8e-18
facet p7 p4 p12 p8 distance= 2.7e-18
These points either have a maximum or minimum x-coordinate, or
they maximize the determinant for k coordinates. Trial points
are first selected from points that maximize a coordinate.
The min and max coordinates for each dimension are:
0: -0.185 0.379 difference= 0.564
1: 0.651 0.912 difference= 0.261
2: 0.055 0.055 difference= 1.388e-17
3: 0 0.912 difference= 0.912
If the input should be full dimensional, you have several options that
may determine an initial simplex:
- use 'QJ' to joggle the input and make it full dimensional
- use 'QbB' to scale the points to the unit cube
- use 'QR0' to randomly rotate the input for different maximum points
- use 'Qs' to search all points for the initial simplex
- use 'En' to specify a maximum roundoff error less than 1.8e-15.
- trace execution with 'T3' to see the determinant for each point.
If the input is lower dimensional:
- use 'QJ' to joggle the input and make it full dimensional
- use 'Qbk:0Bk:0' to delete coordinate k from the input. You should
pick the coordinate with the least range. The hull will have the
correct topology.
- determine the flat containing the points, rotate the points
into a coordinate plane, and delete the other coordinates.
- add one or more points to make the input full dimensional.
I tried to add a noise as suggested by chatGPT but it didn't solve it.
But when you check only interest results, they are in fact too condesed in some regions. We could try to remove close points from the scaled_data_interest_f.
import numpy as np
from scipy.spatial import distance_matrix
def remove_close_points(data, threshold=1e-10):
"""
Remove points that are too close to each other based on a threshold distance.
Parameters:
- data (np.ndarray): The input data array.
- threshold (float): The minimum allowable distance between points.
Returns:
- np.ndarray: The data with close points removed.
"""
# Compute the distance matrix
dist_matrix = distance_matrix(data, data)
# Identify points to keep
keep_indices = []
for i in range(len(data)):
# Check if the point is too close to any previously kept point
if all(dist_matrix[i, j] >= threshold for j in keep_indices):
keep_indices.append(i)
return data[keep_indices]
yassersami
commented
1 month ago
I got following error for only interest experiment on R2 region:
I tried to add a noise as suggested by chatGPT but it didn't solve it.
But when you check only interest results, they are in fact too condesed in some regions. We could try to remove close points from the
scaled_data_interest_f.