cui-unige / mcc4mcc Goto Github PK
View Code? Open in Web Editor NEWModel Checker Collection for the Model Checking Contest @ Petri nets
Home Page: https://cui-unige.github.io/mcc4mcc/
License: MIT License
Model Checker Collection for the Model Checking Contest @ Petri nets
Home Page: https://cui-unige.github.io/mcc4mcc/
License: MIT License
It may be interesting to look at the shape of the tree in order to identify useful or useless characteristics.
Maybe the "don't care" pattern of decision diagrams appears!
For instance:
pycodingstyle
on python code;autopep8
if needed;pylint
on python code;When i run:
$ ./extract.py --iterations=1 --duplicates=false
I get the following error. It does not happen with other algorithms.
INFO: Learning using algorithm: 'custom-algo'.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 4.13it/s]
INFO: Algorithm: custom-algo
INFO: Min : 0.49337490257209665
INFO: Max : 0.49337490257209665
INFO: Mean : 0.49337490257209665
INFO: Median : 0.49337490257209665
Traceback (most recent call last):
File "./extract.py", line 685, in <module>
analyze_learned()
File "./extract.py", line 667, in analyze_learned
SCORES[name] = mcc_score(algorithm)
File "./extract.py", line 566, in mcc_score
alg_or_tool.predict(pandas.DataFrame([test]))[0]
File "./extract.py", line 123, in predict
y_pred[mask] = self.multi.predict(test_x[mask])
File "/usr/local/lib/python3.6/site-packages/sklearn/ensemble/forest.py", line 538, in predict
proba = self.predict_proba(X)
File "/usr/local/lib/python3.6/site-packages/sklearn/ensemble/forest.py", line 578, in predict_proba
X = self._validate_X_predict(X)
File "/usr/local/lib/python3.6/site-packages/sklearn/ensemble/forest.py", line 357, in _validate_X_predict
return self.estimators_[0]._validate_X_predict(X, check_input=True)
File "/usr/local/lib/python3.6/site-packages/sklearn/tree/tree.py", line 373, in _validate_X_predict
X = check_array(X, dtype=DTYPE, accept_sparse="csr")
File "/usr/local/lib/python3.6/site-packages/sklearn/utils/validation.py", line 462, in check_array
context))
ValueError: Found array with 0 sample(s) (shape=(0, 23)) while a minimum of 1 is required.
For @mencattini , in branch issue-7
.
@mencattini look at 17f9b47, it seems that you removed some of my code, that was about distance between tools. Is it intentional?
Select tools that are within a % of the best, instead of the best only.
See for instance this post.
It should compute the score given the scoring algorithm of the model checking contest.
It's more a request than an issue. Can we convert the code to the Pep8 standard. It's a syntaxe standard used in most of the python project.
The errors to correct are :
And also improve the mcc-head
scripts.
diff = x_1 - x_2
return max(diff, 2)
Try to enhance results by voting among the results of several algorithms.
When i run:
$ ./extract.py
The results for regression algorithms are:
INFO: Algorithm: decision-tree-regression
INFO: Min : -0.23408958008379654
INFO: Max : -0.013248075792894998
INFO: Mean : -0.10488969127417243
INFO: Median : -0.10611363409432173
INFO: Stdev : 0.06583227749288169
INFO: Variance: 0.004333888759899777
INFO: Learning using algorithm: 'knn-regression'.
INFO: Algorithm: knn-regression
INFO: Min : -0.10861812564105898
INFO: Max : 0.030489299735053432
INFO: Mean : -0.04368667835772075
INFO: Median : -0.05275172799947181
INFO: Stdev : 0.04813321043108172
INFO: Variance: 0.002316805946402794
INFO: Learning using algorithm: 'random-forest-regression'.
INFO: Algorithm: random-forest-regression
INFO: Min : -0.05989802856982962
INFO: Max : 0.06089070070317171
INFO: Mean : 0.003811517055560587
INFO: Median : -0.003230270254699741
INFO: Stdev : 0.03777773047303064
INFO: Variance: 0.0014271569196929476
The problem only happens with the option duplicates=false
.
@saucisson
I have noticed there are less datas than before.
INFO: Select 9366 best entries.
And before it was :
INFO: Select 10261 best entries.
Is it normal ?
The vector for learning algorithms could contain a distance from the fastest tool, together with the tool name. Can it allow us to find the fastest tool instead of just a correct tool?
We could try to find the interesting characteristics, by removing each characteristic and evaluating the algorithms.
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