Fixed Convergence Warnings On MLP Training Curves #14144
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@martinoywa tests failing |
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An example shows users how to use our library, and a ConvergenceWarning is there to protect our users. I think it's appropriate that if you ignore a warning in an example, you leave a comment explaining where those warnings come up and why you might want to ignore them in this context... I.e. say that they might rather increase iterations. (I also don't know the internals of how these examples are executed by sphinx-gallery to determine if there is a chance this warning filter will apply globally to other examples.) |
| from sklearn.neural_network import MLPClassifier | ||
| from sklearn.preprocessing import MinMaxScaler | ||
| from sklearn import datasets | ||
| from sklearn.exceptions import ConvergenceWarning | ||
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| warnings.filterwarnings("ignore", category=ConvergenceWarning, |
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Maybe add a comment like
Some of the parameter combinations will not converge as can be seen on the plots so we ignore them here
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Let me get to it
probably a |
| for ax, data, name in zip(axes.ravel(), data_sets, ['iris', 'digits', | ||
| # some parameter combinations will not converge as can be seen on the | ||
| # plots so they are ignored here | ||
| with warnings.catch_warnings(): |
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only mlp.fit needs to be inside of the context manager I think
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Just seeing that now. Thanks
There are some changes I've made, is that what you were aiming for? |
| print("training: %s" % label) | ||
| mlp = MLPClassifier(verbose=0, random_state=0, | ||
| max_iter=max_iter, **param) | ||
| mlp.fit(X, y) |
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I think the context manager should just be for this line
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I was putting it before the preprocessing step instead of the model fitting. Sorry for that.
| @@ -64,12 +70,19 @@ def plot_on_dataset(X, y, ax, name): | |||
| print("training: %s" % label) | |||
| mlp = MLPClassifier(verbose=0, random_state=0, | |||
| max_iter=max_iter, **param) | |||
| mlp.fit(X, y) | |||
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this line has trailing white spaces but that's not really important. LGTM anyway.
thanks @martinoywa
…4144) * fix convergence warnings * fix convergence warnings * PEP8 * PEP8 * Fix Convergence Warning by changing the Optimization Algorithm * PEP8 * Fixed Future Warnings by explicitly defining n_estimators. * PEP8 * deleted all * Fixed Convergence Warnings * removed changes on unrelated examples * add comment and with statement * PEP8 * context manager fix * fixed indentation * PEP8 * flake8
…4144) * fix convergence warnings * fix convergence warnings * PEP8 * PEP8 * Fix Convergence Warning by changing the Optimization Algorithm * PEP8 * Fixed Future Warnings by explicitly defining n_estimators. * PEP8 * deleted all * Fixed Convergence Warnings * removed changes on unrelated examples * add comment and with statement * PEP8 * context manager fix * fixed indentation * PEP8 * flake8
Referencing PR / Issue
This addresses #14117 examples/neural_networks/plot_mlp_training_curves.py
Note
There were a lot of Convergence Warnings on the Stochastic Optimizer.
This can be solved by increasing the maximum iterations, but there will be
a trade-off in time taken. So I filtered the warnings by ignoring them.
#WiMLDS_Nairobi
cc: @lagvier @mcecily @toopurity @felexkemboi