Record the progress and auto disable the obviously slow candidate(never use in future trainning)

[eromoe]

features:
X.shape (9516, 24956)
code:

tpot = TPOTClassifier(generations=5, population_size=500, verbosity=2, max_eval_time_mins=20)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))

log:

Optimization Progress:   1%|          | 24/3000 [50:06<143:35:50, 173.71s/pipeline]rogress:   0%|          | 1/3000 [01:29<74:23:16, 89.30s/pipeline]          Optimization Progress:   1%|          | 25/3000 [50:06<397:45:25, 481.32s/pipeline]

Timeout during evaluation of pipeline #25. Skipping to the next pipeline.

Optimization Progress:   1%|▏         | 44/3000 [1:21:41<40:07:44, 48.87s/pipeline]
Optimization Progress:   1%|          | 28/3000 [50:16<195:53:14, 237.28s/pipeline]          
Optimization Progress:   2%|▏         | 45/3000 [1:21:41<323:34:33, 394.20s/pipeline]

Timeout during evaluation of pipeline #45. Skipping to the next pipeline.

Optimization Progress:   2%|▏         | 57/3000 [1:45:48<39:40:12, 48.53s/pipeline]  
Optimization Progress:   2%|▏         | 48/3000 [1:22:15<163:14:27, 199.07s/pipeline]          
Optimization Progress:   2%|▏         | 58/3000 [1:45:48<125:42:37, 153.83s/pipeline]

Timeout during evaluation of pipeline #58. Skipping to the next pipeline.

Optimization Progress:   2%|▏         | 66/3000 [1:46:12<16:09:53, 19.83s/pipeline]] 
Optimization Progress:   2%|▏         | 61/3000 [1:46:05<63:41:23, 78.01s/pipeline] 

It looks like some specifical models/ models combination are very slow, so the pipeline which contain that modle would hit timeout and failed every time. I think tpot could log the info, when failed count hit a certain number, then don't use that model or delay it .(Also can use some machine learning model to select the obviously slow candidate )

PS: The log message didn't look well in jupyter notebook:

[rhiever]

@weixuanfu2016, can you please confirm that this issue is addressed in the 0.7 release? IIRC models that failed to finish evaluating due to timeouts have their "timeout score" recorded in the lookup dictionary, and are thus overlooked on all future evaluations, right?

[weixuanfu]

Nope, this is not addressed in the 0.7 release since we do not check some specifical models/ models combination in optimization processes. The slow pipeline would have -inf fitness score due to timeout and it would be selected for next generation with updated NSGA-II selection operator in version 0.7 but these slow models and combination may be generated in later generations due to crossover and mutation. I think we may need an intelligent way to adapt the operator list or operator combinations during optimization process

[yishairasowsky]

if you have a chance to reply
was there ever an answer to this? @spiros @bollwyvl @zarch @cottrell @mrocklin
because I have a similar issue