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C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release. from numpy.core.umath_tests import inner1d #13568

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mounaBoo opened this issue May 15, 2019 · 5 comments

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@mounaBoo
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mounaBoo commented May 15, 2019

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
from numpy.core.umath_tests import inner1d

`import numpy as np
import pandas as pd
from sklearn.kernel_approximation import RBFSampler
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.metrics import classification_report
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import (precision_score, recall_score,f1_score, accuracy_score,mean_squared_error,mean_absolute_error)
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import Normalizer
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix
from sklearn.metrics import (precision_score, recall_score,f1_score, accuracy_score,mean_squared_error,mean_absolute_error, roc_curve, classification_report,auc)

from sklearn.preprocessing import OneHotEncoder, LabelEncoder`

Numpy/Python version information: Python 3.7.0 Numpy 1.16.3

@bashtage
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This is a scikit-learn issue since they are using a module in a way that they should not.

@mhvk
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mhvk commented May 15, 2019

@mounaBoo - does skikit-learn use inner1d? If so, how? It was meant only as an example custom ufunc, tested to ensure that we do not accidentally break ufunc creation. But if it does something beyond the regular np.inner, perhaps we should expose it...

@charris
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charris commented May 15, 2019

This issue has come up before, don't recall exactly when.

@ksunden
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ksunden commented May 15, 2019

This has been fixed in scikit-learn scikit-learn/scikit-learn#10880

See also #10815

@seberg
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seberg commented May 15, 2019

Sounds like scikit-learn has fixed the issue on their end, and that is fine for the moment. If someone wants inner1d exposed, please open a new issue for that. I will close this one. Thanks all.

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