MAPIE is not able to one-hot encode columns when estimator is an scikit learn pipeline with a preprocessor step #128
Comments
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Hi @edgBR , and thanks for spotting this ! Could you provide a complete, minimal and reproducible example ? Typically with a dataframe of 3-4 hand-made lines and minimal number of lines of code (I think less than 10 would suffice). |
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Hi @gmartinonQM unfortunately I can not share the data but I have tried with another dataset attached here: Which I got from this kaggle kernel: https://www.kaggle.com/riantowibisono/loan-classification-machine-learning/notebook After doing: import numpy as nb
import pandas as pd
df = pd.read_csv('kaggle_dataset.csv')
df_1 = df.dropna(axis=1, thresh=int(0.70*len(df)))
df_1.head()
df_clean = df_1[[
'loan_status_fullyPaid', 'term','int_rate',
'installment','grade', 'annual_inc',
'verification_status','dti' # These features are just initial guess, you can try to choose any other combination
]].copy()
df_clean.head()
from sklearn.preprocessing import LabelEncoder
label = LabelEncoder()
df_clean['term'] = label.fit_transform(df_clean['term'])
df_clean['grade'] = label.fit_transform(df_clean['grade'])
df_clean['verification_status'] = label.fit_transform(df_clean['verification_status'])
x = df_clean.drop(['loan_status_fullyPaid'], axis=1)
y = df_clean['loan_status_fullyPaid']
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.compose import ColumnTransformer, TransformedTargetRegressor, make_column_selector as selector
from sklearn.feature_selection import VarianceThreshold
from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
import numpy as np
categorical_transformer = OneHotEncoder(handle_unknown="ignore", categories='auto')
numeric_transformer = Pipeline(
steps=[
("imputer", SimpleImputer(strategy="mean")),
("scaler", StandardScaler()),
("variance_selector", VarianceThreshold(threshold=0.03))
]
)
preprocessor = ColumnTransformer(
transformers=[
("numeric_only", numeric_transformer, [2,4,6]),
("get_dummies", categorical_transformer, [0,3,5])],
remainder = 'passthrough'
)
from sklearn.model_selection import train_test_split
xtr, xts, ytr, yts = train_test_split(
x,
y,
test_size = .2
)
from sklearn.metrics import roc_auc_score, plot_roc_curve, roc_curve, confusion_matrix, classification_report, ConfusionMatrixDisplay
from sklearn.ensemble import RandomForestClassifier, HistGradientBoostingRegressor, HistGradientBoostingClassifier
pipeline_hist_boost_clf = Pipeline([('preprocessor', preprocessor),
('estimator', HistGradientBoostingClassifier())])
pipeline_hist_boost_clf.fit(xtr, ytr)
from mapie.classification import MapieClassifier
mapie_classifier = MapieClassifier(pipeline_hist_boost_clf)
mapie_classifier.fit(xtr, ytr)This time with a classification model following the example of: https://mapie.readthedocs.io/en/latest/examples_classification/plot_sadinle2019_example.html I get a similar error: ---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-27-b25355019e0d> in <module>
2
3 mapie_classifier = MapieClassifier(pipeline_hist_boost_clf)
----> 4 mapie_classifier.fit(xtr, ytr)
/anaconda/envs/azureml_py38/lib/python3.8/site-packages/mapie/classification.py in fit(self, X, y, sample_weight)
511 X, y, force_all_finite=False, dtype=["float64", "int", "object"]
512 )
--> 513 assert type_of_target(y) == "multiclass"
514 self.n_features_in_ = check_n_features_in(X, cv, estimator)
515 sample_weight, X, y = check_null_weight(sample_weight, X, y)
AssertionError: It seems that this force_all_finite is the main issue. I will be happy to contribute to the debugging, if needed I could offer myself for a teams call. BR |
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Thanks again @edgBR ! Actually, this is not a minimal example. There are two efficient ways of solving the issue :
Which option would you prefer ? For option 1., could you create a toy dataset (3-4 lines, 2-3 columns) and juste create a minimalistic scikit-learn pipeline reproducing the bug ? This could help create a unit test in the future in order to ensure non-regression of the bug fix. |
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Hi @gmartinonQM, Unfortunately my knowledge of scikit-learn pipelines is not that great yet (I was using R an recipes in the past and my scikit learn usages was limited to the classical way, a.k.a no pipelines objects). Therfore I will prefer option 1. I will attach the example dataset in a couple of hours and I will update the original bug issue. BR |
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Hi @gmartinonQM Minimal example below: import pandas as pd
test_df = pd.DataFrame({'loan_status_fullyPaid':[1,1,1,0],
'term':['0','1','0','1'],
'int_rate': [19.20,19.99,6.49,30.94],
'installment':[739.74, 233.10, 597.57, 426.49],
'grade':['3','4','0','6'],
'annual_inc': [45000,66000,125000,36000],
'verification_status': ['2','2','0','2'],
'dti':[10.16,10.95,6.57,18.19]
})
x = test_df.drop(['loan_status_fullyPaid'], axis=1)
y = test_df['loan_status_fullyPaid']
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.compose import ColumnTransformer, TransformedTargetRegressor, make_column_selector as selector
from sklearn.feature_selection import VarianceThreshold
from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
import numpy as np
categorical_transformer = OneHotEncoder(handle_unknown="ignore", categories='auto')
numeric_transformer = Pipeline(
steps=[
("imputer", SimpleImputer(strategy="mean")),
("scaler", StandardScaler()),
("variance_selector", VarianceThreshold(threshold=0.03))
]
)
preprocessor = ColumnTransformer(
transformers=[
("numeric_only", numeric_transformer, [2,4,6]),
("get_dummies", categorical_transformer, [0,3,5])],
remainder = 'passthrough'
)
from sklearn.model_selection import train_test_split
xtr, xts, ytr, yts = train_test_split(
x,
y,
test_size = .1
)
from sklearn.ensemble import HistGradientBoostingClassifier
pipeline_hist_boost_clf = Pipeline([('preprocessor', preprocessor),
('estimator', HistGradientBoostingClassifier())])
pipeline_hist_boost_clf.fit(xtr, ytr)
from mapie.classification import MapieClassifier
mapie_classifier = MapieClassifier(pipeline_hist_boost_clf)
mapie_classifier.fit(xtr, ytr) |
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Hi @gmartinonQM Did you manage to get the a hint of why this could be happening? |
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Hi @edgBR , here is a minimal working example, that abstracts away all your use case particularities : import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.naive_bayes import GaussianNB
from mapie.classification import MapieClassifier
np.random.seed(2)
n = 20
x = pd.DataFrame(
{
"x_cat": np.random.choice(["A", "B", "C"], size=n),
"x_num": np.random.randn(n)
}
)
y = pd.Series(np.random.choice([0, 1, 2], size=n))
categorical_transformer = OneHotEncoder(handle_unknown="ignore", categories="auto")
preprocessor = ColumnTransformer(
transformers=[
("get_dummies", categorical_transformer, [0])
],
remainder="passthrough"
)
estimator = GaussianNB()
model = Pipeline(
[
("preprocessor", preprocessor),
("estimator", estimator)
]
)
mapie_classifier = MapieClassifier(model)
mapie_classifier.fit(x, y)This code executes perfectly right, so I cannot reproduce your bug. At this point, few comments may be useful for you :
If you think we should iterate further and change the code base all the same, please start from the minimal working example I just provided to expose your diagnostic. Happy to help, and feel free to ask other questions. |
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Dear @gmartinonQM The information regarding the classifier is clear and understood but still I am not able to make it work with the regression use case. I have created another example that is showing a similar error than my original issue (being not able to encode columns): from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, OrdinalEncoder
from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.datasets import fetch_openml
from mapie.regression import MapieRegressor
df = fetch_openml(data_id=41214, as_frame=True).frame
df["Frequency"] = df["ClaimNb"] / df["Exposure"]
df_train, df_test = train_test_split(df, test_size=0.33, random_state=0)
log_scale_transformer = make_pipeline(
FunctionTransformer(np.log, validate=False), StandardScaler()
)
model_preprocessor = ColumnTransformer(
[
("passthrough_numeric", "passthrough", ["BonusMalus"]),
("binned_numeric", KBinsDiscretizer(n_bins=10), ["VehAge", "DrivAge"]),
("log_scaled_numeric", log_scale_transformer, ["Density"]),
(
"categorical",
OrdinalEncoder(),
["VehBrand", "VehPower", "VehGas", "Region", "Area"],
)
],
remainder="drop",
)
poisson_gbrt = Pipeline(
[
("preprocessor", model_preprocessor),
(
"regressor",
HistGradientBoostingRegressor(loss="poisson", max_leaf_nodes=128),
),
]
)
mapie = MapieRegressor(poisson_gbrt)
mapie.fit(
df_train, df_train["Frequency"]
)Error as follows: ValueError Traceback (most recent call last)
<ipython-input-34-5fa6526911b2> in <module>
----> 1 mapie.fit(
2 df_train, df_train["Frequency"]
3 )
/anaconda/envs/azureml_py38/lib/python3.8/site-packages/mapie/regression.py in fit(self, X, y, sample_weight)
457 cv = self._check_cv(self.cv)
458 estimator = self._check_estimator(self.estimator)
--> 459 X, y = check_X_y(
460 X, y, force_all_finite=False, dtype=["float64", "int", "object"]
461 )
/anaconda/envs/azureml_py38/lib/python3.8/site-packages/sklearn/utils/validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
962 raise ValueError("y cannot be None")
963
--> 964 X = check_array(
965 X,
966 accept_sparse=accept_sparse,
/anaconda/envs/azureml_py38/lib/python3.8/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
744 array = array.astype(dtype, casting="unsafe", copy=False)
745 else:
--> 746 array = np.asarray(array, order=order, dtype=dtype)
747 except ComplexWarning as complex_warning:
748 raise ValueError(
/anaconda/envs/azureml_py38/lib/python3.8/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order, like)
100 return _asarray_with_like(a, dtype=dtype, order=order, like=like)
101
--> 102 return array(a, dtype, copy=False, order=order)
103
104
/anaconda/envs/azureml_py38/lib/python3.8/site-packages/pandas/core/generic.py in __array__(self, dtype)
1991
1992 def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
-> 1993 return np.asarray(self._values, dtype=dtype)
1994
1995 def __array_wrap__(
/anaconda/envs/azureml_py38/lib/python3.8/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order, like)
100 return _asarray_with_like(a, dtype=dtype, order=order, like=like)
101
--> 102 return array(a, dtype, copy=False, order=order)
103
104
ValueError: could not convert string to float: 'A' |
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Here you can see that if I encode the values first mapie works just fine: from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, OrdinalEncoder
from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.linear_model import Ridge
from mapie.regression import MapieRegressor
from sklearn.datasets import fetch_openml
df = fetch_openml(data_id=41214, as_frame=True).frame
df["Frequency"] = df["ClaimNb"] / df["Exposure"]
df_final = pd.concat([df.drop(columns=["VehBrand", "VehGas", "Region", "Area"]),
pd.get_dummies(df[["VehBrand", "VehGas", "Region", "Area"]], sparse=True)], axis=1)
df_train, df_test = train_test_split(df_final, test_size=0.33, random_state=0)
hist_reg = Pipeline(
[
("regressor", HistGradientBoostingRegressor(loss='poisson')),
]
)
from mapie.regression import MapieRegressor
mapie = MapieRegressor(hist_reg)
mapie.fit(
df_train, df_train["Frequency"]
)
|
|
Hi, where you able to solve this? I'm having a similar problem when making use of ColumnTransformer and Pipeline. Not really an expert in pipelines, but I have the following set up of transformation methods: numeric_preprocessor = Pipeline(
steps = [
('imputation_mean', SimpleImputer(strategy='mean')),
('scaler', RobustScaler())
]
)
categorical_preprocessor = Pipeline(
steps = [
('inputation_mode', SimpleImputer(strategy='most_frequent')),
('ohe',OneHotEncoder(handle_unknown='ignore'))
]
)
preprocessor = ColumnTransformer([
('cat_preprocessor', categorical_preprocessor, vars_cat),
('num_preprocessor', numeric_preprocessor, vars_num)])
catboost_pipe = make_pipeline(preprocessor, CatBoostRegressor(random_state=123, verbose=0))
catboost_pipe.fit(train_df[vars_cat + vars_num], train_df[target])This part of the code works fine. However, when I try to use mapie = MapieRegressor(catboost_pipe)
mapie.fit(train_df[vars_cat + vars_num], train_df[target])I get the following error: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\utils\__init__.py in _get_column_indices(X, key)
424 try:
--> 425 all_columns = X.columns
426 except AttributeError:
AttributeError: 'numpy.ndarray' object has no attribute 'columns'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
C:\Users\FRANCI~1.PAR\AppData\Local\Temp/ipykernel_25908/3938012699.py in <module>
1 mapie = MapieRegressor(catboost_pipe)
----> 2 mapie.fit(train_df[vars_cat + vars_num], train_df[target])
~\Anaconda3\envs\ktp_explore\lib\site-packages\mapie\regression.py in fit(self, X, y, sample_weight)
489 self.n_samples_val_ = [X.shape[0]]
490 else:
--> 491 self.single_estimator_ = fit_estimator(
492 clone(estimator), X, y, sample_weight
493 )
~\Anaconda3\envs\ktp_explore\lib\site-packages\mapie\utils.py in fit_estimator(estimator, X, y, sample_weight)
112 estimator.fit(X, y, sample_weight=sample_weight)
113 else:
--> 114 estimator.fit(X, y)
115 return estimator
116
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\pipeline.py in fit(self, X, y, **fit_params)
328 """
329 fit_params_steps = self._check_fit_params(**fit_params)
--> 330 Xt = self._fit(X, y, **fit_params_steps)
331 with _print_elapsed_time('Pipeline',
332 self._log_message(len(self.steps) - 1)):
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\pipeline.py in _fit(self, X, y, **fit_params_steps)
290 cloned_transformer = clone(transformer)
291 # Fit or load from cache the current transformer
--> 292 X, fitted_transformer = fit_transform_one_cached(
293 cloned_transformer, X, y, None,
294 message_clsname='Pipeline',
~\Anaconda3\envs\ktp_explore\lib\site-packages\joblib\memory.py in __call__(self, *args, **kwargs)
347
348 def __call__(self, *args, **kwargs):
--> 349 return self.func(*args, **kwargs)
350
351 def call_and_shelve(self, *args, **kwargs):
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
738 with _print_elapsed_time(message_clsname, message):
739 if hasattr(transformer, 'fit_transform'):
--> 740 res = transformer.fit_transform(X, y, **fit_params)
741 else:
742 res = transformer.fit(X, y, **fit_params).transform(X)
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\compose\_column_transformer.py in fit_transform(self, X, y)
527 self._validate_transformers()
528 self._validate_column_callables(X)
--> 529 self._validate_remainder(X)
530
531 result = self._fit_transform(X, y, _fit_transform_one)
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\compose\_column_transformer.py in _validate_remainder(self, X)
325 cols = []
326 for columns in self._columns:
--> 327 cols.extend(_get_column_indices(X, columns))
328
329 remaining_idx = sorted(set(range(self._n_features)) - set(cols))
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\utils\__init__.py in _get_column_indices(X, key)
425 all_columns = X.columns
426 except AttributeError:
--> 427 raise ValueError("Specifying the columns using strings is only "
428 "supported for pandas DataFrames")
429 if isinstance(key, str):
ValueError: Specifying the columns using strings is only supported for pandas DataFramesIs this an error on my code? Not sure because Thanks for the help! |
Hi @gmartinonQM , would you be able to show/give an example similar to these, but with more than 1 categorical feature? And I see that in the Is it possible to refer to it with their name? For example: I have made this small dataframe/example with some sample values and 2 categorical features, 1 numerical and target column, alongside two different transformations that are used within # Sample df
sample_df = x = pd.DataFrame(
{
"x_cat_1": ['type_1', 'type_2', 'type_3','type_1', 'type_2',np.nan],
"x_cat_2": ['size_1', 'size_1', 'size_2',np.nan,'size_1', 'size_2'],
"x_num_1": [0,1,1,4,np.nan,5],
'target': [5,7,3,9,10,8]
}
)
vars_cat = ['x_cat_1','x_cat_2']
vars_num = ['x_num_1']
target = ['target']
# Transformations
numeric_preprocessor = Pipeline(
steps = [
('imputation_mean', SimpleImputer(strategy='mean')),
('scaler', RobustScaler())
]
)
categorical_preprocessor = Pipeline(
steps = [
('imputation_mode', SimpleImputer(strategy='most_frequent')),
('ohe',OneHotEncoder(handle_unknown='ignore'))
]
)
# Define a column transformer that uses your previous transformers and applies them to specific columns defined.
preprocessor = ColumnTransformer([
('cat_preprocessor', categorical_preprocessor, vars_cat),
('num_preprocessor', numeric_preprocessor, vars_num)])
# Pipeline
catboost_pipe = make_pipeline(preprocessor, CatBoostRegressor(random_state=123, verbose=0))
# MAPIE Regressor
mapie = MapieRegressor(catboost_pipe)
mapie.fit(sample_df[vars_cat + vars_num], sample_df[target])Error: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\utils\__init__.py in _get_column_indices(X, key)
424 try:
--> 425 all_columns = X.columns
426 except AttributeError:
AttributeError: 'numpy.ndarray' object has no attribute 'columns'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
C:\Users\FRANCI~1.PAR\AppData\Local\Temp/ipykernel_25908/3044912112.py in <module>
1 mapie = MapieRegressor(catboost_pipe)
----> 2 mapie.fit(sample_df[vars_cat + vars_num], sample_df[target])
~\Anaconda3\envs\ktp_explore\lib\site-packages\mapie\regression.py in fit(self, X, y, sample_weight)
489 self.n_samples_val_ = [X.shape[0]]
490 else:
--> 491 self.single_estimator_ = fit_estimator(
492 clone(estimator), X, y, sample_weight
493 )
~\Anaconda3\envs\ktp_explore\lib\site-packages\mapie\utils.py in fit_estimator(estimator, X, y, sample_weight)
112 estimator.fit(X, y, sample_weight=sample_weight)
113 else:
--> 114 estimator.fit(X, y)
115 return estimator
116
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\pipeline.py in fit(self, X, y, **fit_params)
328 """
329 fit_params_steps = self._check_fit_params(**fit_params)
--> 330 Xt = self._fit(X, y, **fit_params_steps)
331 with _print_elapsed_time('Pipeline',
332 self._log_message(len(self.steps) - 1)):
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\pipeline.py in _fit(self, X, y, **fit_params_steps)
290 cloned_transformer = clone(transformer)
291 # Fit or load from cache the current transformer
--> 292 X, fitted_transformer = fit_transform_one_cached(
293 cloned_transformer, X, y, None,
294 message_clsname='Pipeline',
~\Anaconda3\envs\ktp_explore\lib\site-packages\joblib\memory.py in __call__(self, *args, **kwargs)
347
348 def __call__(self, *args, **kwargs):
--> 349 return self.func(*args, **kwargs)
350
351 def call_and_shelve(self, *args, **kwargs):
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
738 with _print_elapsed_time(message_clsname, message):
739 if hasattr(transformer, 'fit_transform'):
--> 740 res = transformer.fit_transform(X, y, **fit_params)
741 else:
742 res = transformer.fit(X, y, **fit_params).transform(X)
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\compose\_column_transformer.py in fit_transform(self, X, y)
527 self._validate_transformers()
528 self._validate_column_callables(X)
--> 529 self._validate_remainder(X)
530
531 result = self._fit_transform(X, y, _fit_transform_one)
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\compose\_column_transformer.py in _validate_remainder(self, X)
325 cols = []
326 for columns in self._columns:
--> 327 cols.extend(_get_column_indices(X, columns))
328
329 remaining_idx = sorted(set(range(self._n_features)) - set(cols))
~\Anaconda3\envs\ktp_explore\lib\site-packages\sklearn\utils\__init__.py in _get_column_indices(X, key)
425 all_columns = X.columns
426 except AttributeError:
--> 427 raise ValueError("Specifying the columns using strings is only "
428 "supported for pandas DataFrames")
429 if isinstance(key, str):
ValueError: Specifying the columns using strings is only supported for pandas DataFrames |
Were you able to solve this by not transforming/encoding explicitly before using MAPIE? |
|
Hi @fjpa121197 Edit: No, I have noticed that if I refer the columns by name for any preprocessing step I am getting the same error than you. |
Hi @edgBR, thanks for letting me know. I'm waiting for @gmartinonQM, maybe we are refering to the columns in a wrong way. Thanks anyway! |
|
Hi @edgBR @fjpa121197 , indeed, all your problems have the same cause : MAPIE, at some point, require that input data is (or is convertible to) a numpy array. When using pipelines based on column names, a bug comes out because the names disappear during the conversion. I have begun a pull request about this : #136 This fixes the issue for regression (not for classification yet), but breaks scikit-learn estimator compatibility. Some additional work is needed to make all unit tests passing. Feel free to suggest changes to this pull request. In the meantime, I will try to converge to a solution during next weeks. |
|
Hi @fjpa121197 A dirty trick to bypass this:
This seems a point of friction and I am not sure if pipeline.transform() is transforming the data with the right parameters (like if we have a simple mean imputer I am not sure if it is transforming with the mean of the training set or if it is refitting again). Anyhow, great hopes for the fix @gmartinonQM. |
|
Hi @edgBR I did this, and I was able to use MAPIE without a problem. When calling pipeline.transform() on the test set, it should not refit the transformers again. Thanks for helping with these @gmartinonQM, looking forward to these fix! Thanks in advance! |
|
Good news @fjpa121197 @edgBR , I have managed to resolve all side effects and unit tests. The linked PR will be merged soon, and the bug will disappear in next MAPIE release |
|
Thanks @gmartinonQM! |
Describe the bug
Dear colleagues, I am creating a system to classify customers in 2 binary classes and then apply a regression model to one of the classes.
Some of my features are string that I obviously need to encode. In this case with one hot encoding.
To Reproduce
My code is as follows:
Expected behavior
After this I will expect that I can run:
y_pred, y_pis = mapie_estimator.predict(data_test)
Screenshots
Being this value part of one of the categorical columns which its being encoded by the preprocessor.
When training the model without mapie everything works correctly:
Desktop (please complete the following information):
Scikit learn dependencies:
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