Note
Go to the end to download the full example code
TreeEnsemble optimization#
The execution of a TreeEnsembleRegressor can lead to very different results depending on how the computation is parallelized. By trees, by rows, by both, for only one row, for a short batch of rows, a longer one. The implementation in onnxruntime does not let the user changed the predetermined settings but a custom kernel might. That’s what this example is measuring.
The default set of optimized parameters is very short and is meant to be executed fast. Many more parameters can be tried.
python plot_optim_tree_ensemble --scenario=LONG
To change the training parameters:
python plot_optim_tree_ensemble.py
--n_trees=100
--max_depth=10
--n_features=50
--batch_size=100000
Another example with a full list of parameters:
- python plot_optim_tree_ensemble.py
–n_trees=100 –max_depth=10 –n_features=50 –batch_size=100000 –tries=3 –scenario=CUSTOM –parallel_tree=80,40 –parallel_tree_N=128,64 –parallel_N=50,25 –batch_size_tree=1,2 –batch_size_rows=1,2 –use_node3=0
Another example:
python plot_optim_tree_ensemble.py
--n_trees=100 --n_features=10 --batch_size=10000 --max_depth=8 -s SHORT
import os
import timeit
import numpy
import onnx
from onnx.reference import ReferenceEvaluator
import matplotlib.pyplot as plt
from pandas import DataFrame, concat
from sklearn.datasets import make_regression
from sklearn.ensemble import RandomForestRegressor
from skl2onnx import to_onnx
from onnxruntime import InferenceSession, SessionOptions
from onnx_array_api.plotting.text_plot import onnx_simple_text_plot
from onnx_extended.reference import CReferenceEvaluator
from onnx_extended.ortops.optim.cpu import get_ort_ext_libs
from onnx_extended.ortops.optim.optimize import (
change_onnx_operator_domain,
get_node_attribute,
optimize_model,
)
from onnx_extended.ext_test_case import get_parsed_args, unit_test_going
script_args = get_parsed_args(
"plot_optim_tree_ensemble",
description=__doc__,
scenarios={
"SHORT": "short optimization (default)",
"LONG": "test more options",
"CUSTOM": "use values specified by the command line",
},
n_features=(2 if unit_test_going() else 5, "number of features to generate"),
n_trees=(3 if unit_test_going() else 10, "number of trees to train"),
max_depth=(2 if unit_test_going() else 5, "max_depth"),
batch_size=(1000 if unit_test_going() else 10000, "batch size"),
parallel_tree=("80,160,40", "values to try for parallel_tree"),
parallel_tree_N=("256,128,64", "values to try for parallel_tree_N"),
parallel_N=("100,50,25", "values to try for parallel_N"),
batch_size_tree=("2,4,8", "values to try for batch_size_tree"),
batch_size_rows=("2,4,8", "values to try for batch_size_rows"),
use_node3=("0,1", "values to try for use_node3"),
expose="",
n_jobs=("-1", "number of jobs to train the RandomForestRegressor"),
)
Training a model#
batch_size = script_args.batch_size
n_features = script_args.n_features
n_trees = script_args.n_trees
max_depth = script_args.max_depth
filename = f"plot_optim_tree_ensemble-f{n_features}-" f"t{n_trees}-d{max_depth}.onnx"
if not os.path.exists(filename):
X, y = make_regression(
batch_size + max(batch_size, 2 ** (max_depth + 1)),
n_features=n_features,
n_targets=1,
)
print(f"Training to get {filename!r} with X.shape={X.shape}")
X, y = X.astype(numpy.float32), y.astype(numpy.float32)
model = RandomForestRegressor(
n_trees, max_depth=max_depth, verbose=2, n_jobs=int(script_args.n_jobs)
)
model.fit(X[:-batch_size], y[:-batch_size])
onx = to_onnx(model, X[:1])
with open(filename, "wb") as f:
f.write(onx.SerializeToString())
else:
X, y = make_regression(batch_size, n_features=n_features, n_targets=1)
X, y = X.astype(numpy.float32), y.astype(numpy.float32)
Xb, yb = X[-batch_size:].copy(), y[-batch_size:].copy()
Training to get 'plot_optim_tree_ensemble-f5-t10-d5.onnx' with X.shape=(20000, 5)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
building tree 1 of 10
building tree 2 of 10
building tree 3 of 10
building tree 4 of 10
building tree 5 of 10
building tree 6 of 10
building tree 7 of 10
building tree 8 of 10
building tree 9 of 10
building tree 10 of 10
[Parallel(n_jobs=-1)]: Done 7 out of 10 | elapsed: 2.4min remaining: 1.0min
[Parallel(n_jobs=-1)]: Done 10 out of 10 | elapsed: 2.4min finished
2023-09-28 12:14:34,717 skl2onnx [DEBUG] - [Var] +Variable('X', 'X', type=FloatTensorType(shape=[None, 5]))
2023-09-28 12:14:34,717 skl2onnx [DEBUG] - [Var] update is_root=True for Variable('X', 'X', type=FloatTensorType(shape=[None, 5]))
2023-09-28 12:14:34,717 skl2onnx [DEBUG] - [parsing] found alias='SklearnRandomForestRegressor' for type=<class 'sklearn.ensemble._forest.RandomForestRegressor'>.
2023-09-28 12:14:34,717 skl2onnx [DEBUG] - [Op] +Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='', outputs='', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2))
2023-09-28 12:14:34,719 skl2onnx [DEBUG] - [Op] add In Variable('X', 'X', type=FloatTensorType(shape=[None, 5])) to Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2))
2023-09-28 12:14:34,719 skl2onnx [DEBUG] - [Var] +Variable('variable', 'variable', type=FloatTensorType(shape=[]))
2023-09-28 12:14:34,719 skl2onnx [DEBUG] - [Var] set parent for Variable('variable', 'variable', type=FloatTensorType(shape=[])), parent=Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2))
2023-09-28 12:14:34,719 skl2onnx [DEBUG] - [Op] add Out Variable('variable', 'variable', type=FloatTensorType(shape=[])) to Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='variable', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2))
2023-09-28 12:14:34,720 skl2onnx [DEBUG] - [Var] update is_leaf=True for Variable('variable', 'variable', type=FloatTensorType(shape=[]))
2023-09-28 12:14:34,720 skl2onnx [DEBUG] - [Var] update is_fed=True for Variable('X', 'X', type=FloatTensorType(shape=[None, 5])), parent=None
2023-09-28 12:14:34,720 skl2onnx [DEBUG] - [Var] update is_fed=False for Variable('variable', 'variable', type=FloatTensorType(shape=[])), parent=Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='variable', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2))
2023-09-28 12:14:34,720 skl2onnx [DEBUG] - [Op] update is_evaluated=False for Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='variable', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2))
2023-09-28 12:14:34,720 skl2onnx [DEBUG] - [Shape2] call infer_types for Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='variable', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2))
2023-09-28 12:14:34,721 skl2onnx [DEBUG] - [Shape-a] Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='variable', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2)) fed 'True' - 'False'
2023-09-28 12:14:34,721 skl2onnx [DEBUG] - [Shape-b] Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='variable', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2)) inputs=[Variable('X', 'X', type=FloatTensorType(shape=[None, 5]))] - outputs=[Variable('variable', 'variable', type=FloatTensorType(shape=[None, 1]))]
2023-09-28 12:14:34,721 skl2onnx [DEBUG] - [Conv] call Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='variable', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2)) fed 'True' - 'False'
2023-09-28 12:14:34,726 skl2onnx [DEBUG] - [Node] 'TreeEnsembleRegressor' - 'X' -> 'variable' (name='TreeEnsembleRegressor')
2023-09-28 12:14:34,728 skl2onnx [DEBUG] - [Conv] end - Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='variable', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2))
2023-09-28 12:14:34,728 skl2onnx [DEBUG] - [Op] update is_evaluated=True for Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='variable', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2))
2023-09-28 12:14:34,728 skl2onnx [DEBUG] - [Var] update is_fed=True for Variable('variable', 'variable', type=FloatTensorType(shape=[None, 1])), parent=Operator(type='SklearnRandomForestRegressor', onnx_name='SklearnRandomForestRegressor', inputs='X', outputs='variable', raw_operator=RandomForestRegressor(max_depth=5, n_estimators=10, n_jobs=-1, verbose=2))
Rewrite the onnx file to use a different kernel#
The custom kernel is mapped to a custom operator with the same name the attributes and domain = “onnx_extented.ortops.optim.cpu”. We call a function to do that replacement. First the current model.
opset: domain='ai.onnx.ml' version=1
opset: domain='' version=19
input: name='X' type=dtype('float32') shape=['', 5]
TreeEnsembleRegressor(X, n_targets=1, nodes_falsenodeids=630:[32,17,10...62,0,0], nodes_featureids=630:[1,0,2...1,0,0], nodes_hitrates=630:[1.0,1.0...1.0,1.0], nodes_missing_value_tracks_true=630:[0,0,0...0,0,0], nodes_modes=630:[b'BRANCH_LEQ',b'BRANCH_LEQ'...b'LEAF',b'LEAF'], nodes_nodeids=630:[0,1,2...60,61,62], nodes_treeids=630:[0,0,0...9,9,9], nodes_truenodeids=630:[1,2,3...61,0,0], nodes_values=630:[-0.16108554601669312,0.062494851648807526...0.0,0.0], post_transform=b'NONE', target_ids=320:[0,0,0...0,0,0], target_nodeids=320:[5,6,8...59,61,62], target_treeids=320:[0,0,0...9,9,9], target_weights=320:[-28.71653938293457,-19.51543426513672...17.91996192932129,24.008808135986328]) -> variable
output: name='variable' type=dtype('float32') shape=['', 1]
And then the modified model.
def transform_model(onx, **kwargs):
att = get_node_attribute(onx.graph.node[0], "nodes_modes")
modes = ",".join(map(lambda s: s.decode("ascii"), att.strings))
return change_onnx_operator_domain(
onx,
op_type="TreeEnsembleRegressor",
op_domain="ai.onnx.ml",
new_op_domain="onnx_extented.ortops.optim.cpu",
nodes_modes=modes,
**kwargs,
)
print("Tranform model to add a custom node.")
onx_modified = transform_model(onx)
print(f"Save into {filename + 'modified.onnx'!r}.")
with open(filename + "modified.onnx", "wb") as f:
f.write(onx_modified.SerializeToString())
print("done.")
print(onnx_simple_text_plot(onx_modified))
Tranform model to add a custom node.
Save into 'plot_optim_tree_ensemble-f5-t10-d5.onnxmodified.onnx'.
done.
opset: domain='ai.onnx.ml' version=1
opset: domain='' version=19
opset: domain='onnx_extented.ortops.optim.cpu' version=1
input: name='X' type=dtype('float32') shape=['', 5]
TreeEnsembleRegressor[onnx_extented.ortops.optim.cpu](X, nodes_modes=b'BRANCH_LEQ,BRANCH_LEQ,BRANCH_LEQ,BRANC...LEAF,LEAF', n_targets=1, nodes_falsenodeids=630:[32,17,10...62,0,0], nodes_featureids=630:[1,0,2...1,0,0], nodes_hitrates=630:[1.0,1.0...1.0,1.0], nodes_missing_value_tracks_true=630:[0,0,0...0,0,0], nodes_nodeids=630:[0,1,2...60,61,62], nodes_treeids=630:[0,0,0...9,9,9], nodes_truenodeids=630:[1,2,3...61,0,0], nodes_values=630:[-0.16108554601669312,0.062494851648807526...0.0,0.0], post_transform=b'NONE', target_ids=320:[0,0,0...0,0,0], target_nodeids=320:[5,6,8...59,61,62], target_treeids=320:[0,0,0...9,9,9], target_weights=320:[-28.71653938293457,-19.51543426513672...17.91996192932129,24.008808135986328]) -> variable
output: name='variable' type=dtype('float32') shape=['', 1]
Comparing onnxruntime and the custom kernel#
print(f"Loading {filename!r}")
sess_ort = InferenceSession(filename, providers=["CPUExecutionProvider"])
r = get_ort_ext_libs()
print(f"Creating SessionOptions with {r!r}")
opts = SessionOptions()
if r is not None:
opts.register_custom_ops_library(r[0])
print(f"Loading modified {filename!r}")
sess_cus = InferenceSession(
onx_modified.SerializeToString(), opts, providers=["CPUExecutionProvider"]
)
print(f"Running once with shape {Xb.shape}.")
base = sess_ort.run(None, {"X": Xb})[0]
print(f"Running modified with shape {Xb.shape}.")
got = sess_cus.run(None, {"X": Xb})[0]
print("done.")
Loading 'plot_optim_tree_ensemble-f5-t10-d5.onnx'
Creating SessionOptions with ['/home/xadupre/github/onnx-extended/onnx_extended/ortops/optim/cpu/libortops_optim_cpu.so']
Loading modified 'plot_optim_tree_ensemble-f5-t10-d5.onnx'
Running once with shape (10000, 5).
Running modified with shape (10000, 5).
done.
Discrepancies?
Discrepancies: 3.0517578125e-05
Simple verification#
Baseline with onnxruntime.
t1 = timeit.timeit(lambda: sess_ort.run(None, {"X": Xb}), number=50)
print(f"baseline: {t1}")
baseline: 0.08234070000253269
The custom implementation.
t2 = timeit.timeit(lambda: sess_cus.run(None, {"X": Xb}), number=50)
print(f"new time: {t2}")
new time: 0.04308429999946384
The same implementation but ran from the onnx python backend.
ref = CReferenceEvaluator(filename)
ref.run(None, {"X": Xb})
t3 = timeit.timeit(lambda: ref.run(None, {"X": Xb}), number=50)
print(f"CReferenceEvaluator: {t3}")
CReferenceEvaluator: 0.17407249999814667
The python implementation but from the onnx python backend.
ReferenceEvaluator: 3.7600438999979815 (only 5 times instead of 50)
Time for comparison#
The custom kernel supports the same attributes as TreeEnsembleRegressor plus new ones to tune the parallelization. They can be seen in tree_ensemble.cc. Let’s try out many possibilities. The default values are the first ones.
if unit_test_going():
optim_params = dict(
parallel_tree=[40], # default is 80
parallel_tree_N=[128], # default is 128
parallel_N=[50, 25], # default is 50
batch_size_tree=[1], # default is 1
batch_size_rows=[1], # default is 1
use_node3=[0], # default is 0
)
elif script_args.scenario in (None, "SHORT"):
optim_params = dict(
parallel_tree=[80, 40], # default is 80
parallel_tree_N=[128, 64], # default is 128
parallel_N=[50, 25], # default is 50
batch_size_tree=[1], # default is 1
batch_size_rows=[1], # default is 1
use_node3=[0], # default is 0
)
elif script_args.scenario == "LONG":
optim_params = dict(
parallel_tree=[80, 160, 40],
parallel_tree_N=[256, 128, 64],
parallel_N=[100, 50, 25],
batch_size_tree=[1, 2, 4, 8],
batch_size_rows=[1, 2, 4, 8],
use_node3=[0, 1],
)
elif script_args.scenario == "CUSTOM":
optim_params = dict(
parallel_tree=list(int(i) for i in script_args.parallel_tree.split(",")),
parallel_tree_N=list(int(i) for i in script_args.parallel_tree_N.split(",")),
parallel_N=list(int(i) for i in script_args.parallel_N.split(",")),
batch_size_tree=list(int(i) for i in script_args.batch_size_tree.split(",")),
batch_size_rows=list(int(i) for i in script_args.batch_size_rows.split(",")),
use_node3=list(int(i) for i in script_args.use_node3.split(",")),
)
else:
raise ValueError(
f"Unknown scenario {script_args.scenario!r}, use --help to get them."
)
cmds = []
for att, value in optim_params.items():
cmds.append(f"--{att}={','.join(map(str, value))}")
print("Full list of optimization parameters:")
print(" ".join(cmds))
Full list of optimization parameters:
--parallel_tree=80,40 --parallel_tree_N=128,64 --parallel_N=50,25 --batch_size_tree=1 --batch_size_rows=1 --use_node3=0
Then the optimization.
def create_session(onx):
opts = SessionOptions()
r = get_ort_ext_libs()
if r is None:
raise RuntimeError("No custom implementation available.")
opts.register_custom_ops_library(r[0])
return InferenceSession(
onx.SerializeToString(), opts, providers=["CPUExecutionProvider"]
)
res = optimize_model(
onx,
feeds={"X": Xb},
transform=transform_model,
session=create_session,
baseline=lambda onx: InferenceSession(
onx.SerializeToString(), providers=["CPUExecutionProvider"]
),
params=optim_params,
verbose=True,
number=script_args.number,
repeat=script_args.repeat,
warmup=script_args.warmup,
sleep=script_args.sleep,
n_tries=script_args.tries,
)
0%| | 0/16 [00:00<?, ?it/s]
i=1/16 TRY=0 parallel_tree=80 parallel_tree_N=128 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 0%| | 0/16 [00:00<?, ?it/s]
i=1/16 TRY=0 parallel_tree=80 parallel_tree_N=128 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 6%|▋ | 1/16 [00:00<00:08, 1.75it/s]
i=2/16 TRY=0 parallel_tree=80 parallel_tree_N=128 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 6%|▋ | 1/16 [00:00<00:08, 1.75it/s]
i=2/16 TRY=0 parallel_tree=80 parallel_tree_N=128 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 12%|█▎ | 2/16 [00:00<00:05, 2.67it/s]
i=3/16 TRY=0 parallel_tree=80 parallel_tree_N=64 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 12%|█▎ | 2/16 [00:00<00:05, 2.67it/s]
i=3/16 TRY=0 parallel_tree=80 parallel_tree_N=64 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 19%|█▉ | 3/16 [00:01<00:03, 3.30it/s]
i=4/16 TRY=0 parallel_tree=80 parallel_tree_N=64 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 19%|█▉ | 3/16 [00:01<00:03, 3.30it/s]
i=4/16 TRY=0 parallel_tree=80 parallel_tree_N=64 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 25%|██▌ | 4/16 [00:01<00:03, 3.40it/s]
i=5/16 TRY=0 parallel_tree=40 parallel_tree_N=128 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 25%|██▌ | 4/16 [00:01<00:03, 3.40it/s]
i=5/16 TRY=0 parallel_tree=40 parallel_tree_N=128 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 31%|███▏ | 5/16 [00:01<00:03, 3.47it/s]
i=6/16 TRY=0 parallel_tree=40 parallel_tree_N=128 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 31%|███▏ | 5/16 [00:01<00:03, 3.47it/s]
i=6/16 TRY=0 parallel_tree=40 parallel_tree_N=128 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 38%|███▊ | 6/16 [00:01<00:02, 3.47it/s]
i=7/16 TRY=0 parallel_tree=40 parallel_tree_N=64 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 38%|███▊ | 6/16 [00:01<00:02, 3.47it/s]
i=7/16 TRY=0 parallel_tree=40 parallel_tree_N=64 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 44%|████▍ | 7/16 [00:02<00:02, 3.46it/s]
i=8/16 TRY=0 parallel_tree=40 parallel_tree_N=64 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 44%|████▍ | 7/16 [00:02<00:02, 3.46it/s]
i=8/16 TRY=0 parallel_tree=40 parallel_tree_N=64 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 50%|█████ | 8/16 [00:02<00:02, 3.42it/s]
i=9/16 TRY=1 parallel_tree=80 parallel_tree_N=128 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 50%|█████ | 8/16 [00:02<00:02, 3.42it/s]
i=9/16 TRY=1 parallel_tree=80 parallel_tree_N=128 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 56%|█████▋ | 9/16 [00:02<00:01, 3.50it/s]
i=10/16 TRY=1 parallel_tree=80 parallel_tree_N=128 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 56%|█████▋ | 9/16 [00:02<00:01, 3.50it/s]
i=10/16 TRY=1 parallel_tree=80 parallel_tree_N=128 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 62%|██████▎ | 10/16 [00:03<00:01, 3.49it/s]
i=11/16 TRY=1 parallel_tree=80 parallel_tree_N=64 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 62%|██████▎ | 10/16 [00:03<00:01, 3.49it/s]
i=11/16 TRY=1 parallel_tree=80 parallel_tree_N=64 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 69%|██████▉ | 11/16 [00:03<00:01, 3.44it/s]
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i=12/16 TRY=1 parallel_tree=80 parallel_tree_N=64 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 75%|███████▌ | 12/16 [00:03<00:01, 3.53it/s]
i=13/16 TRY=1 parallel_tree=40 parallel_tree_N=128 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 75%|███████▌ | 12/16 [00:03<00:01, 3.53it/s]
i=13/16 TRY=1 parallel_tree=40 parallel_tree_N=128 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 81%|████████▏ | 13/16 [00:03<00:00, 3.51it/s]
i=14/16 TRY=1 parallel_tree=40 parallel_tree_N=128 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 81%|████████▏ | 13/16 [00:03<00:00, 3.51it/s]
i=14/16 TRY=1 parallel_tree=40 parallel_tree_N=128 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 88%|████████▊ | 14/16 [00:04<00:00, 3.51it/s]
i=15/16 TRY=1 parallel_tree=40 parallel_tree_N=64 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 88%|████████▊ | 14/16 [00:04<00:00, 3.51it/s]
i=15/16 TRY=1 parallel_tree=40 parallel_tree_N=64 parallel_N=50 batch_size_tree=1 batch_size_rows=1 use_node3=0: 94%|█████████▍| 15/16 [00:04<00:00, 3.52it/s]
i=16/16 TRY=1 parallel_tree=40 parallel_tree_N=64 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 94%|█████████▍| 15/16 [00:04<00:00, 3.52it/s]
i=16/16 TRY=1 parallel_tree=40 parallel_tree_N=64 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 100%|██████████| 16/16 [00:04<00:00, 3.56it/s]
i=16/16 TRY=1 parallel_tree=40 parallel_tree_N=64 parallel_N=25 batch_size_tree=1 batch_size_rows=1 use_node3=0: 100%|██████████| 16/16 [00:04<00:00, 3.39it/s]
And the results.
df = DataFrame(res)
df.to_csv("plot_optim_tree_ensemble.csv", index=False)
df.to_excel("plot_optim_tree_ensemble.xlsx", index=False)
print(df.columns)
print(df.head(5))
Index(['average', 'deviation', 'min_exec', 'max_exec', 'repeat', 'number',
'ttime', 'context_size', 'warmup_time', 'n_exp', 'n_exp_name',
'short_name', 'TRY', 'name', 'parallel_tree', 'parallel_tree_N',
'parallel_N', 'batch_size_tree', 'batch_size_rows', 'use_node3'],
dtype='object')
average deviation min_exec max_exec repeat number ttime context_size warmup_time n_exp n_exp_name short_name TRY name parallel_tree parallel_tree_N parallel_N batch_size_tree batch_size_rows use_node3
0 0.001653 0.000043 0.001581 0.001719 10 10 0.016532 64 0.008145 0 TRY=0,baseline 0,baseline 0.0 baseline NaN NaN NaN NaN NaN NaN
1 0.001812 0.000354 0.001203 0.002526 10 10 0.018125 64 0.006668 0 TRY=0,parallel_tree=80,parallel_tree_N=128,par... 0,80,128,50,1,1,0 NaN 80,128,50,1,1,0 80.0 128.0 50.0 1.0 1.0 0.0
2 0.001275 0.000142 0.001077 0.001501 10 10 0.012754 64 0.004669 1 TRY=0,parallel_tree=80,parallel_tree_N=128,par... 0,80,128,25,1,1,0 NaN 80,128,25,1,1,0 80.0 128.0 25.0 1.0 1.0 0.0
3 0.001065 0.000100 0.000969 0.001272 10 10 0.010655 64 0.005027 2 TRY=0,parallel_tree=80,parallel_tree_N=64,para... 0,80,64,50,1,1,0 NaN 80,64,50,1,1,0 80.0 64.0 50.0 1.0 1.0 0.0
4 0.001611 0.000824 0.000766 0.002874 10 10 0.016114 64 0.015396 3 TRY=0,parallel_tree=80,parallel_tree_N=64,para... 0,80,64,25,1,1,0 NaN 80,64,25,1,1,0 80.0 64.0 25.0 1.0 1.0 0.0
Sorting#
small_df = df.drop(
[
"min_exec",
"max_exec",
"repeat",
"number",
"context_size",
"n_exp_name",
],
axis=1,
).sort_values("average")
print(small_df.head(n=10))
average deviation ttime warmup_time n_exp short_name TRY name parallel_tree parallel_tree_N parallel_N batch_size_tree batch_size_rows use_node3
3 0.001065 0.000100 0.010655 0.005027 2 0,80,64,50,1,1,0 NaN 80,64,50,1,1,0 80.0 64.0 50.0 1.0 1.0 0.0
2 0.001275 0.000142 0.012754 0.004669 1 0,80,128,25,1,1,0 NaN 80,128,25,1,1,0 80.0 128.0 25.0 1.0 1.0 0.0
12 0.001443 0.000892 0.014426 0.017050 11 1,80,64,25,1,1,0 NaN 80,64,25,1,1,0 80.0 64.0 25.0 1.0 1.0 0.0
9 0.001540 0.001089 0.015395 0.012873 8 1,80,128,50,1,1,0 NaN 80,128,50,1,1,0 80.0 128.0 50.0 1.0 1.0 0.0
16 0.001551 0.000906 0.015506 0.013941 15 1,40,64,25,1,1,0 NaN 40,64,25,1,1,0 40.0 64.0 25.0 1.0 1.0 0.0
5 0.001604 0.000748 0.016043 0.012097 4 0,40,128,50,1,1,0 NaN 40,128,50,1,1,0 40.0 128.0 50.0 1.0 1.0 0.0
4 0.001611 0.000824 0.016114 0.015396 3 0,80,64,25,1,1,0 NaN 80,64,25,1,1,0 80.0 64.0 25.0 1.0 1.0 0.0
15 0.001653 0.000950 0.016527 0.013219 14 1,40,64,50,1,1,0 NaN 40,64,50,1,1,0 40.0 64.0 50.0 1.0 1.0 0.0
0 0.001653 0.000043 0.016532 0.008145 0 0,baseline 0.0 baseline NaN NaN NaN NaN NaN NaN
6 0.001708 0.001056 0.017081 0.014329 5 0,40,128,25,1,1,0 NaN 40,128,25,1,1,0 40.0 128.0 25.0 1.0 1.0 0.0
Worst#
print(small_df.tail(n=10))
average deviation ttime warmup_time n_exp short_name TRY name parallel_tree parallel_tree_N parallel_N batch_size_tree batch_size_rows use_node3
0 0.001653 0.000043 0.016532 0.008145 0 0,baseline 0.0 baseline NaN NaN NaN NaN NaN NaN
6 0.001708 0.001056 0.017081 0.014329 5 0,40,128,25,1,1,0 NaN 40,128,25,1,1,0 40.0 128.0 25.0 1.0 1.0 0.0
14 0.001711 0.000925 0.017113 0.010606 13 1,40,128,25,1,1,0 NaN 40,128,25,1,1,0 40.0 128.0 25.0 1.0 1.0 0.0
13 0.001714 0.001003 0.017142 0.014240 12 1,40,128,50,1,1,0 NaN 40,128,50,1,1,0 40.0 128.0 50.0 1.0 1.0 0.0
10 0.001716 0.001044 0.017165 0.014097 9 1,80,128,25,1,1,0 NaN 80,128,25,1,1,0 80.0 128.0 25.0 1.0 1.0 0.0
7 0.001774 0.000984 0.017736 0.008249 6 0,40,64,50,1,1,0 NaN 40,64,50,1,1,0 40.0 64.0 50.0 1.0 1.0 0.0
1 0.001812 0.000354 0.018125 0.006668 0 0,80,128,50,1,1,0 NaN 80,128,50,1,1,0 80.0 128.0 50.0 1.0 1.0 0.0
8 0.001841 0.001108 0.018413 0.011121 7 0,40,64,25,1,1,0 NaN 40,64,25,1,1,0 40.0 64.0 25.0 1.0 1.0 0.0
11 0.001856 0.001381 0.018558 0.009867 10 1,80,64,50,1,1,0 NaN 80,64,50,1,1,0 80.0 64.0 50.0 1.0 1.0 0.0
17 0.002144 0.001527 0.021441 0.032011 0 1,baseline 1.0 baseline NaN NaN NaN NaN NaN NaN
Plot#
dfm = (
df[["name", "average"]]
.groupby(["name"], as_index=False)
.agg(["mean", "min", "max"])
.copy()
)
if dfm.shape[1] == 3:
dfm = dfm.reset_index(drop=False)
dfm.columns = ["name", "average", "min", "max"]
dfi = (
dfm[["name", "average", "min", "max"]].sort_values("average").reset_index(drop=True)
)
baseline = dfi[dfi["name"].str.contains("baseline")]
not_baseline = dfi[~dfi["name"].str.contains("baseline")].reset_index(drop=True)
if not_baseline.shape[0] > 50:
not_baseline = not_baseline[:50]
merged = concat([baseline, not_baseline], axis=0)
merged = merged.sort_values("average").reset_index(drop=True).set_index("name")
skeys = ",".join(optim_params.keys())
print(merged.columns)
fig, ax = plt.subplots(1, 1, figsize=(10, merged.shape[0] / 2))
err_min = merged["average"] - merged["min"]
err_max = merged["max"] - merged["average"]
merged[["average"]].plot.barh(
ax=ax,
title=f"TreeEnsemble tuning, n_tries={script_args.tries}"
f"\n{skeys}\nlower is better",
xerr=[err_min, err_max],
)
b = df.loc[df["name"] == "baseline", "average"].mean()
ax.plot([b, b], [0, df.shape[0]], "r--")
ax.set_xlim(
[
(df["min_exec"].min() + df["average"].min()) / 2,
(df["average"].max() + df["average"].max()) / 2,
]
)
# ax.set_xscale("log")
fig.tight_layout()
fig.savefig("plot_optim_tree_ensemble.png")
Index(['average', 'min', 'max'], dtype='object')
2023-09-28 12:14:45,042 matplotlib.font_manager [DEBUG] - findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.
2023-09-28 12:14:45,043 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05
2023-09-28 12:14:45,043 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,043 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,043 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,043 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05
2023-09-28 12:14:45,043 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,043 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,043 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,044 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335
2023-09-28 12:14:45,045 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-R.ttf', name='Ubuntu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-Th.ttf', name='Ubuntu', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-ExtraLight.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=200, stretch='normal', size='scalable')) = 0.24
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-R.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-RI.ttf', name='Ubuntu', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-BI.ttf', name='Ubuntu', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-LI.ttf', name='Ubuntu', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,046 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-B.ttf', name='Ubuntu', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-C.ttf', name='Ubuntu Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-B.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-RI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 0.25
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 1.25
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-M.ttf', name='Ubuntu', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 0.5349999999999999
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,047 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,048 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335
2023-09-28 12:14:45,048 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-MI.ttf', name='Ubuntu', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145
2023-09-28 12:14:45,048 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-BI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,048 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,048 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535
2023-09-28 12:14:45,048 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-L.ttf', name='Ubuntu', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145
2023-09-28 12:14:45,048 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 1.535
2023-09-28 12:14:45,048 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuMathTeXGyre.ttf', name='DejaVu Math TeX Gyre', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,048 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05
2023-09-28 12:14:45,048 matplotlib.font_manager [DEBUG] - findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.
2023-09-28 12:14:45,156 matplotlib.font_manager [DEBUG] - findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0.
2023-09-28 12:14:45,156 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05
2023-09-28 12:14:45,156 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,156 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,157 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,158 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-R.ttf', name='Ubuntu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-Th.ttf', name='Ubuntu', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-ExtraLight.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=200, stretch='normal', size='scalable')) = 0.24
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-R.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-RI.ttf', name='Ubuntu', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-BI.ttf', name='Ubuntu', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-LI.ttf', name='Ubuntu', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-B.ttf', name='Ubuntu', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-C.ttf', name='Ubuntu Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-B.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,159 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-RI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 0.25
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 1.25
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-M.ttf', name='Ubuntu', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 0.5349999999999999
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-MI.ttf', name='Ubuntu', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-BI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-L.ttf', name='Ubuntu', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145
2023-09-28 12:14:45,160 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 1.535
2023-09-28 12:14:45,161 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuMathTeXGyre.ttf', name='DejaVu Math TeX Gyre', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05
2023-09-28 12:14:45,161 matplotlib.font_manager [DEBUG] - findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05
2023-09-28 12:14:45,161 matplotlib.font_manager [DEBUG] - findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to DejaVu Sans ('/home/xadupre/.local/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.
Total running time of the script: (2 minutes 43.621 seconds)