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kdey within the new grid search

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Alejandro Moreo Fernandez 2023-12-18 15:43:36 +01:00
parent c56fe9c09c
commit b882c23477
1 changed files with 5 additions and 5 deletions
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examples/model_selection.py
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@ -1,7 +1,7 @@
import quapy as qp import quapy as qp
from method.kdey import KDEyML from method.kdey import KDEyML
from quapy.method.non_aggregative import DMx from quapy.method.non_aggregative import DMx
from quapy.protocol import APP from quapy.protocol import APP, UPP
from quapy.method.aggregative import DMy from quapy.method.aggregative import DMy
from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LogisticRegression
from examples.comparing_gridsearch import OLD_GridSearchQ from examples.comparing_gridsearch import OLD_GridSearchQ
@ -18,7 +18,7 @@ qp.environ['SAMPLE_SIZE'] = 100
qp.environ['N_JOBS'] = -1 qp.environ['N_JOBS'] = -1
# training, test = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5).train_test # training, test = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5).train_test
training, test = qp.datasets.fetch_UCIMulticlassDataset('dry-bean').train_test training, test = qp.datasets.fetch_UCIMulticlassDataset('letter').train_test
with qp.util.temp_seed(0): with qp.util.temp_seed(0):
@ -30,7 +30,7 @@ with qp.util.temp_seed(0):
# values in the entire range of values from a grid (e.g., [0, 0.1, 0.2, ..., 1]). # values in the entire range of values from a grid (e.g., [0, 0.1, 0.2, ..., 1]).
# We devote 30% of the dataset for this exploration. # We devote 30% of the dataset for this exploration.
training, validation = training.split_stratified(train_prop=0.7) training, validation = training.split_stratified(train_prop=0.7)
protocol = APP(validation) protocol = UPP(validation)
# We will explore a classification-dependent hyper-parameter (e.g., the 'C' # We will explore a classification-dependent hyper-parameter (e.g., the 'C'
# hyper-parameter of LogisticRegression) and a quantification-dependent hyper-parameter # hyper-parameter of LogisticRegression) and a quantification-dependent hyper-parameter
@ -53,7 +53,7 @@ with qp.util.temp_seed(0):
protocol=protocol, protocol=protocol,
error='mae', # the error to optimize is the MAE (a quantification-oriented loss) error='mae', # the error to optimize is the MAE (a quantification-oriented loss)
refit=False, # retrain on the whole labelled set once done refit=False, # retrain on the whole labelled set once done
raise_errors=False, # raise_errors=False,
verbose=True # show information as the process goes on verbose=True # show information as the process goes on
).fit(training) ).fit(training)
@ -64,7 +64,7 @@ model = model.best_model_
# evaluation in terms of MAE # evaluation in terms of MAE
# we use the same evaluation protocol (APP) on the test set # we use the same evaluation protocol (APP) on the test set
mae_score = qp.evaluation.evaluate(model, protocol=APP(test), error_metric='mae') mae_score = qp.evaluation.evaluate(model, protocol=UPP(test), error_metric='mae')
print(f'MAE={mae_score:.5f}') print(f'MAE={mae_score:.5f}')
print(f'model selection took {tend-tinit:.1f}s') print(f'model selection took {tend-tinit:.1f}s')