check_is_fitted

sklearn.utils.validation.check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=<built-in function all>)[source]

Выполнить проверку is_fitted для оценщика.

Проверяет, что оценщик обучен, проверяя наличие атрибутов, завершающихся подчёркиванием, и в противном случае вызывает исключение NotFittedError с указанным сообщением.

Если оценщик не задаёт атрибуты с подчёркиванием в конце, он может определить метод __sklearn_is_fitted__, возвращающий булево значение, чтобы указать, обучен ли оценщик или нет. Смотрите __sklearn_is_fitted__ как API для разработчиков для примера использования API.

Если не переданы attributes, эта функция будет пропускать проверку, если оценщик является бессостоятельным. Оценщик может указать, что он бессостоятельный, установив тег requires_fit. См. Теги оценщика для получения дополнительной информации. Обратите внимание, что тег requires_fit игнорируется, если переданы attributes.

Parameters:

estimatorэкземпляр оценщика

Экземпляр оценщика, для которого выполняется проверка.

attributesстрока, список или кортеж строк, по умолчанию=None

Имя(а) атрибута(ов), заданное(ые) строкой или списком/кортежем строк. Пример: ["coef_", "estimator_", ...], "coef_"

Если None, оценщик считается обученным, если существует атрибут, который заканчивается подчёркиванием и не начинается с двойного подчёркивания.

msgстрока, по умолчанию=None

По умолчанию сообщение об ошибке: “Этот экземпляр %(name)s ещё не обучен. Вызовите ‘fit’ с соответствующими аргументами перед использованием этого оценщика.”

Для пользовательских сообщений, если в строке сообщения присутствует “%(name)s”, она подставляется именем оценщика.

Пример: “Оценщик, %(name)s, должен быть обучен перед сжатием.”

all_or_anyвызываемая функция, {all, any}, по умолчанию=all

Указывает, должны ли существовать все или любые из указанных атрибутов.

Raises:

TypeError: Если оценщик является классом или не является экземпляром оценщика
NotFittedError: Если атрибуты не найдены.

Примеры

>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.utils.validation import check_is_fitted
>>> from sklearn.exceptions import NotFittedError
>>> lr = LogisticRegression()
>>> try:
...     check_is_fitted(lr)
... except NotFittedError as exc:
...     print(f"Model is not fitted yet.")
Model is not fitted yet.
>>> lr.fit([[1, 2], [1, 3]], [1, 0])
LogisticRegression()
>>> check_is_fitted(lr)

Примеры галереи

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

Индуктивная кластеризация

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BcEzb8c8q6ROO1qgB/CAmQLJS1kNshFHCxXtNUxIbIKh1INwcJb9ue5S4Wy4GD0LPQ1kJiwYNIVLK+tNwj5jGlnLsFXEgJdtD7fxB0TQxHThpBpCLQIrqYus+OmYMcb91FxwMe9BpB3DMDP0EXyE3OYiRAmJzsF+YgSZ0l0YiAcENRBvI8ioUECC0k6jvz9267FBppjQAIuSyuC1XBxR+L0hxtwINIrRJ2/30KyPUlDL0CLMfkekiL8Ayqwu/566sTMAiACFjBD4rzTR+tsMYz16MTkQBJqb16EVUgK2+1FxIALuGa8oGIzIxzDVtN+UBr22rKPKl6OWgPPfrW1CsP5cZaqcCDYASs0h/Kw35+a95XO8vIEHgQa7wx6wRVIOE77nOYRNDRLJXGRrA4/k6zacvfop5NfDIeBj0LUeCpQclV6ehKlNsxEEURgAfBEChp99+kMEZs35jpjgAIRKYXoA2k4/7yDJoEdLcuwxD7Djf0mkBWtRXQHYCDKLRUzyUbUZa5mNN8qCjYR6IDD+Kz/MAvIOeT6VQceBAHv8C7p/NQBWJffOw5j6VSgx73ijAxdbl73oDU/KUeEoMGUY6SuaGrI9ahaqaOtf3yZl0CBjyIiJm6dMgEcqFmVxACIFr/ltocWyCnOQDd0lbtK5BiQINImKkbhkw51xeubvbD60GDyHHsuFa0bWkzzJp7l3KYGIwKMIiUxmnfawLpulsRKZZPjIHk0FvjoOhdNRdQB3Ly3sDhxQKMDDCIzoq3b69po0jXjS1suQIsiFwPJS7ZtDMCdSAeHT+UnnhJlIJtIqpxQWzTG5BPWkvYWi3gHkTuwA6t+ioPdSDezdeaeCyCGjDIT4TY6iO/gMQxXgEHScmMa+tYizoQu/yPBg/7CsZVQONZkv8SQ8xAhHGer4GCTIhHlZEBnRfDnVEHYhj5HtuXypQiCRIurAUOosXTd32bBKrSgJ6XRRoaiKcQbTRARRD+ZMkncO5Zp2qOoxLEpbe79ASTKAUKYt6pYw2dOmMUKIhygsoI2NkWjkoQr+Khwd2OVmoNWBBeqdmwF/Qoa1TM4O+/kueNShDsArt/FHFiQLYQiUg9fR4CLsUBFmshetwlby9bVIIYyqP2+AwCRiIBODF05ZiBGGbqeoAgCvEXKS0F/S7gKgw0SF53bojPDA8a/30QDY3TPmQeOsHBwGJZsNaaHFRRdc8btSDYteWfRQUzwYJMCS4K/fAAQUahyCxhDYqPGp9nn//kXB1LAiz95Odorz0i0V54bNS9TPg43MMLvZ+syQStYBoRVN61RKRiRv1qPQQYiAJHZdRevQj0ZjDwIOv2fP59BlMGaC1Xhpm6Ynge4IqhfBRicPvPRqAcxCNvTxGPZQMMhNi33DzJAeCaulxJ5tb2pzd6oRvEJT/i81JfK+O1hED2UBEo2d1T8rLc8XIQqaSwYgzPLkm+UulNQjcIdl7+rQNRiwVAMq8NIALWlES5U0vIEBAQuQJKzEw+tYgE9uI8JG5pczq2p4jjRhxXgQAhMjOKDqWbhr2Nza18TyoIEL1Sm8IP6H+cA7iykACxtVs2WMpzBdGLqKSEvhM3Hxbbmdbxi89c9iODOPtSL8cFVbR2uXjNAZCREdKjwe/dXhJUAEAEboUPwpeZNgbYOR+/Tk8BAALj3BmXO1fPkatXR7BOJx8cDKapJBKVpT9ZVgm7v/vgzd3FtguWXS/ztPhx7zAsx0cGDh93nj9HQAzlRdNyN1eizOIgNN8pW9qw24Zb2GJLgyjEkH/Zrs5tCNQTUiC2D7tzOW5WOssOtSQqHZN37pH58TPL9sf5WVt2WzQMa3H+l4U95UlzCGT+iqMN8TwKwcbCILKYqKYnZlvLsEnfCuvxFgYRf2G8T72qfN0cAiHlLGpuaI/tI6h1EktGsmwoablHppx1creAT5VbEkTxSkyO3rL99IpipzkEYpy0HR1IK/Sx5BYeiQxDcKtumHKgcWXNpiwy3pJHa8Bah6CWjV83OyNSSwiCeKTfWloa0icQaSy1gGiYFr4Mbj86Za9f5entAQwqrLRU9wHLIXJQXMHVrats5xoIyat4x0B2IUUgshSIRi1g8dqvTdkNW1l+Ko6eAmstBTKBT4wO3VS1xmk+dq6BGObRa8rbqzluTJFOZhES6TitMGpPeqN5tM87b2tnFhu3yAFmsFyrpXpyS5JrmouRqiNEQRbYNx66s4HjRlBLLSKiG/dJLXpkb2f+253vdHx1XL31K0skOsDyUbk/v2Jnz4qwhXMSZGTEZc2OQ0W8jD5XokgzcxOV8VTSb6a940pylvuMDxqHYQWMpyayuftb28rDkashhEFIHo0Pe9dnc9xoonH1DPO1jFesh+Q2TMtCDzvbU8Km4mZ2kDKs0L42fK74ccKnV1bluMxZEENZuaJ3/WFeAsWVQLRRzSS2Jf0P0zF1acdHv36B99NTFVwGpJ0ByBgsxkPUFD9+gLB1zVoXJKsHeRCndSu/ObJvX10IyxWjHte9Y28i0Uh/0rHqzh0pnhaAtauM6NzCp77WKt/pOP4JWK4cfaWE/Oktmwv6uyrzwa4Qzj7I5DpSx/2mqA8zfKyIRJt3DKZgMERacHzRj2+N01y8EeoJQbh3bB84PB4is+mBWzbtTLdDumpmB8R50cMXD4qql8c69llhpGq1TmqYLP6hlmL4M41Up1bLCExH3sEnH7999JN05lILn0HFaUdHtUo5DI9N/IGGYejGlcZ/0EOJkdxQoXC469lfK7HvBwiWRCI5f9p9IDu1kMUk2Mgkf2ahRCKRiWwEPhmppYNhv7FXfB7pwo0tXDYZb60fg8f+1LzcGu/uGZ1VktzTdsHWlmQ78n6AGItXxKdDT/4Vn8bxdVwcY0XAyKRSqUY2WSTTi+GpRiM1FhGBRnFLCInKPrf3499eL0p61llREpgZzU6h4uVapVapkBsKPL0YnioUCrlSqVRM4Klkhh+9rHZz/7PVT9M9ZqVaZg8Ea+f0wcJjR+/kVtfFJrBiXAkY4/FXhnp/C8nkU4nK2IqIghg339S00vvdvWF2v/1Bmb9wVVvVRmFtVj2DDOFxer3eWPPTTSY5lIZWZG0YViUygvihW5J7vj67zMXJZcF7BjJZwu/1rB+8eftgXWpsYUIGixJDExCIhoLBGHmMAhhjMTwh0F72UVgZjsEhqQdv334+eKt80e9/T/Kf1jx9PHx9ODSQWx8dyfZMIVMh/GTB4Yw3DE923sYCQQ6Jnmy/6Ex+YG3A8L9Xby0/vcJu1mpklkFcPNZ5hP2PvTMPairL4nCxBNmSsEhIWANhkZ1ggLCENQKZBMKOCaiRfUcEQ3BBBwRbQKQREXBBEFFEWlBsUSwQqUK0R2bArWwtmabIELYEEQeH6T/mJWiLbduivNRIyK+SSt6t1HlV78t99757zj3n0pkTpaWVifG/zj5YnaZhDQ0NbQmGoiKfClSRAUhbW1tjnaWHjb2zr3t5ZVvFnUuXzuh5wf58wFVw8nKy279/oLiY5pnstj7B31qXiEajldXU1CCQBRBqaspoJSUlM1uCxV63Xd6ZxR2NF5D7jb1iUXKrFcjb4SSipuZg9anzTx7toNN37KBjsR516lSqjo4OlapeV1eHxdJN6E+fdj16Eh5XUViT47R007IhuVe3nesvarn2Y2srmbzBX4vJJFhbmzo6mloTmFpa/hvIFuTWH6+1FP31ytmrqUmh/+9r8U0AkdXbxMrGbw4dGtp38lRlZTAu8UB5fHyau7t7Wnx8efmBRFxl5e7Bx0NnhkI3p2RvCvqSG4px9iZ8UK2Xk9N3hy421mfSaN7enp4luwCVeHp60zIzdxbQLjfUOnkFpeA3sVI0xUA+WOqKxfSU3qqpycoqLMThqqurcbjgwsKsrJrSW0kOxsuIwQHubsiUvJqOo8UF9bmNjY3bADU25tYXFB9N7cj1y0Z+M5fg2wLyL81YVkhUCAaDcXBwiBEI+AIchkTF6Cks7yFNFYUPicIsmN4ikMAyYNohxU5VDEQsMRAxELHEQMRAxFoNQKQMUHqxC9IzhiHgYFpG2r03rQkXA1na84heSM7Zgwf5r55oTAoMPMuqsTF+OUkLppOisxFiIJ/658INNJFITQRCARAChn9wteM6Xx1Hz+ZhvASNCE1N4CcwBdkvZYCEIcPCwhZMo/ZF3CwsvQfo+b3S6tNOYW9NhyGRiLWyYiC/aa3dpugH0QNbm5sbGnp7e38q2lVSUiJ49xe1vOjtbXjR/HDrwOW8nB7Ml67EBpX1lH3ffeTk6cG+vrt9N56EB6btTnNPA3T+Z6Chr29w8OSR7rKykFiEGAi/a6yFw+XgyCC/gtR6mmfGHrd08/V7AxLIrVpMJlOrlWyREGC13jw9MrnEO7O++PovOXZycnJwuOxSTAOSk8WUPi/NCk6Md/fd6Eyx93G1odOxdep1dVj6DlefKorz9uOBaQeC20pvsowN5OBLMy3CQFAYv6jmC/n5vS3JuyLNrRIEC7IEU1t9MxKJSCKZ6evamloztfw3WATsTXdL9vQuys9vwEREhS5hHSbG72/P7j6+sbvc3Xc7xd7VBIv1UKdaGqo0uQwDcmky1KGqe2CxJjY+lO2B7uFdd/9x99lpv4iUVQ0k5eD1gisZ6VZkJgCBiIa8dSJ9LIiaEklf15HQGpDe3ph6FPN507FJt7LKz1ftqNOxVHExklhwfUElF0vQAlVkaLioWKpjD1cd3427dSsCvkqBGMSyLgQ1X9npyXcfmeqjIfITU6/+A+jVq1dTU1NzfAGfU8ChoJU7D1BB62olmCfTaFtrD20J+rSX1ZjFenw/DRe/0d5EZ5jBgEqOT/8X0MzMzDSgNwIBX2ZmgMZff50ZWwNluFiqm9hvD0wM7ryzj4WHSa06IFJ2EbmZ/cesNpjqk4hoNQjftTq/yN39kq/Fx2y2tDQfCdHMlrAh4NjtgrxPz4djDgYDfcOD74BkKC44hBf56HkCLWoQxHkxJDSGXQzrbKrCKwpz8AarC4iqU61X/uVMb7cERyXIxNwclx/2wfnzkB0Ohx+mMAn8WAZCYu4tqb994ROZq1Ch3cGJG00MNaCjgkiV2fHPxBmNjo7Pjo29AV4jEk1Yijtu+7PQ0FjkKgKCGjhX9NM1Cy1bkjJEmvM2FuT152NuF/oMWxqC1mdaRd7O++Od/A430ygmlsMM6MhCf1hC1Nfb/jI+IskwUlE3OXz457i47FUCRAGFQtb20/YEEEiQ+am5Sc6XxuBy+OG3cxB9q8xtW+w+eiyBw0JPVAZStUemx75i8xw/MHJMUtuQ7hxfPhiGskPKiT4Q/IMHD19cIxN0iRD2JJc7wfmKzRvAXW4SYp3evi3no78xKiKpItBm+C+zX8MD6CnAvWtUUmKdDtamq6vzVIiCqANZqxBVUL8nQRciw1letsq5OSWmW2rHR2Uj9M4+P2CjAp1exu4T3uj4+MgaIw/nxLabXqqyog0k6GH/LjeyozIXGJwnOa+XAwRilvAHhVWcbrb5WkrwlrMffhSYC7yZhjZ5UNzjjsQgRRoI4iFtT6suWhqMHbJsiGny7UO/P4Ndd7mPxggoaVAVXaiU8rhQEQaiWttwLJJfPhgYypddOOI1l6trfvGjOgWIG75YKBjV4caB0V1DfXv4jX0wkQUC29q+3pEI+QGseoKkgBav35/j730U6hqQyvXxJCVU6IFxd0QUiIFT/rFIf2Xg0Y4DEhA006IBb7x41A3bfL/LRAWsgpbAFBhqSDk+OIQUSSD7txZZOCqBlwqcw4Xot7bnflACNagHV2WpMQJSPkFgurWGoVJX1XlHBIEo2OUXpZvKcydBSwzDL6NNoHVEL14/yb7aRhkGsWz46Oz4uJEr0EfCRC61htdAUYK10ksuiGmOJ6R/sM2o/yDXyeZqnA2DB2Jmc2AKrKiCrQqPWStaQAw0G9r3WAOP5qCmFnvJJaV7Ny++VKFxaR6Sb0DN/T/LkzSyL+88oyArSkBSBorMyaSJSXDTgHO4xL3JDYuBDHUep46Am5F2dPSNItU+vNMBITJApOQUohozCEryXJCzsnO4Shbp+Ys9fGceUSxHwE6iPcZjqFBwp0LhUqICBB/Vn2FlJs+ZBBvInLK/1XeLTxXW5arCAxsIMLAzPHzjuh1QIgJEM6+ghKwL4YJetYAzp2ZN/gDIpafYdaPgl4UbW2Ok7hxcjRcJIJr4kHM0c1019hz4QLgQXa0XWza/m/gas549pRoJA8gYdJgen9jN0pNa+UBie4rbrUzV/smeB5sHe34CQiRkFJe9W2yKKcS5NjF4QqmMAV1H3xhciJEVASDbUiNN0RNTbLYQgMirOXr+kmT39lQh99pcjRSFAmRsWrHJNbg0RARuWaiLnglEYEAXApCXk9IQ3eTU31K4OtQEm2hAhVI2cXxWkqHjW3F/xQNRhdW2mFvLT7I5QgKi7/Y+Hiim8ABWQjh1LPlREMOuzvdRiBUOxNiv34pAlBFG/+CP6jLypPSd74FUx9cpjgipsChvnGFIDz/BWtFA4ApbGjMtlCAywiotKDNPNG+8/A4IqzqNCuUJC8i/JRmWgVnfowxkVywQKXz0Oe9IU/kJtrCAsCeU1mdefueuuF/hTpUcFRYQHm/MyCe+ogwDW7FAZCOuH3XT+h97dx7U1J0HAHzIofJIhKABOQMICHJKCDeC3Ajhlg3iEQ6LQRAUL1BEiqNT6jF1qaAIeIG0dF22VtS1h9fWStXRadWZ1pGVSXy5eFxhk/LXhp1FQZHleN/M/HZ8f

__sklearn_is_fitted__ как API для разработчиков

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

Маршрутизация метаданных

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Licensed under the 3-clause BSD License.
https://scikit-learn.org/1.6/modules/generated/sklearn.utils.validation.check_is_fitted.html