lucianolorenti
commented
4 months ago Hi!
I wasn't able to reproduce the error. Which version of scikit-learn are you using? I am using (1.4.1.post1)
I am using the following snippet to test it:
from ceruleo.dataset.catalog.CMAPSS import CMAPSSDataset, sensor_indices
from ceruleo.transformation import Transformer
from ceruleo.transformation.functional.pipeline.pipeline import make_pipeline
from ceruleo.transformation.features.selection import ByNameFeatureSelector
from ceruleo.transformation.features.scalers import MinMaxScaler
from ceruleo.models.sklearn import (
CeruleoRegressor,
EstimatorWrapper,
TimeSeriesWindowTransformer,
predict,
train_model,
CeruleoMetricWrapper
)
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor
train_dataset = CMAPSSDataset(train=True, models='FD001')
test_dataset = CMAPSSDataset(train=False, models='FD001')[15:30]
# Define list of sensor measurement features
FEATURES = [train_dataset[0].columns[i] for i in sensor_indices]
transformer = Transformer(
pipelineX=make_pipeline(
ByNameFeatureSelector(features=FEATURES),
MinMaxScaler(range=(-1, 1))
),
pipelineY=make_pipeline(
ByNameFeatureSelector(features=['RUL']),
)
)
regressor_gs = CeruleoRegressor(
TimeSeriesWindowTransformer(
transformer,
window_size=32,
padding=True,
step=1),
Ridge(alpha=15))
grid_search = GridSearchCV(
estimator=regressor_gs,
param_grid={
'ts_window_transformer__window_size': [5, 10],
'regressor': [Ridge(alpha=15), RandomForestRegressor(max_depth=5)]
},
scoring=CeruleoMetricWrapper('neg_mean_absolute_error'),
verbose=5
)
grid_search.fit(train_dataset)
FrizzoDavide
The
GridSearchCVclass, that works together withCeruleoMetricWrappergives an error when a dataset including a Scaler in itsTransformeris used.Let's consider the following example:
CMAPSSDatasetand define theFEATURESas the most relevant sensor measurement features:TransformerNote that here I am using the
MinMaxScalerto scale the data in the range(-1,1)GridSearchCVto find compare different Regression modelsThe output returned after the
grid_search.fit(train_dataset)command is launched is:And then the final error message is:
So probably there are two operands that should be subtracted one to the other but being both strings this results in an error.
Looking at additional details in the long error message returned we can find:
As suggested in the error message I added the
error_score='raise'input argument toGridSearchCVto get a more detailed error explanation.Looking at the new error message I think that the source of the error is in the
transformmethod of theMinMaxScalerclass contained inceruleo.transformation.features.scalers:The one reported above is the subtraction where two strings are found in correspondence of
self.data_maxandself.data_minthus creating theTypeError: unsupported operand type(s) for -: 'str' and 'str'reported above.I tried to run the code again after placing an
import ipdb; ipdb.set_trace()inside thetransformfunction but for some reason the code did not stop as it is supposed to happen when usingipdb.I was also able to access the
data_maxanddata_minattributes withtransformer.pipelineX.final_step.data_maxandtransformer.pipelineX.final_step.data_minand I was also able to do:without any error.
So I do not really have a clue why this bug appears.
Obviously running the code without
MinMaxScaler, so with:it works without any errors.