MLPEstimator do not report the proper n_iter_ after successive call of fit #24764
Description
Describe the bug
It seems that MLPRegressor and MLPClassifier does not take into account max_iter with warm_start.
Steps/Code to Reproduce
model = MLPRegressor(warm_start=True, early_stopping=True, max_iter=10)
model.fit(X_iris, y_iris)
assert model.n_iter_ <= 10
model.set_params(max_iter=20)
model.fit(X_iris, y_iris)
assert model.n_iter_ <= 20You can also try MLPClassifier which leads to over iterating.
Expected Results
n_iter_ <= 20
Actual Results
def test_xxx():
model = MLPRegressor(warm_start=True, early_stopping=False, max_iter=10)
model.fit(X_iris, y_iris)
assert model.n_iter_ <= 10
model.set_params(max_iter=20)
model.fit(X_iris, y_iris)
> assert model.n_iter_ <= 20
E assert 30 <= 20
E + where 30 = MLPRegressor(max_iter=20, warm_start=True).n_iter_Versions
System:
python: 3.8.12 | packaged by conda-forge | (default, Sep 16 2021, 01:38:21) [Clang 11.1.0 ]
executable: /Users/glemaitre/mambaforge/envs/dev/bin/python
machine: macOS-12.6-arm64-arm-64bit
Python dependencies:
sklearn: 1.2.dev0
pip: 21.3
setuptools: 58.2.0
numpy: 1.21.6
scipy: 1.8.0
Cython: 0.29.24
pandas: 1.5.0
matplotlib: 3.4.3
joblib: 1.2.0
threadpoolctl: 2.2.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
prefix: libopenblas
filepath: /Users/glemaitre/mambaforge/envs/dev/lib/libopenblas_vortexp-r0.3.18.dylib
version: 0.3.18
threading_layer: openmp
architecture: VORTEX
num_threads: 8
user_api: openmp
internal_api: openmp
prefix: libomp
filepath: /Users/glemaitre/mambaforge/envs/dev/lib/libomp.dylib
version: None
num_threads: 8