:Sh8dZddlmZddlmZddlmZmZmZddl m Z Gdde Z Gdd e Z Gd d Z d Zd e fdZdZdZdS)aV This module borrows and adapts `Pipeline` from `sklearn.pipeline` and `TransformerMixin` from `sklearn.base` in the scikit-learn framework (commit hash d205638475ca542dc46862652e3bb0be663a8eac) to be precise). Both are BSD licensed and allow for this sort of thing; attribution is given as a comment above each class. License reproduced below: BSD 3-Clause License Copyright (c) 2007-2022 The scikit-learn developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ) defaultdict)islice)AnyCallableOptional)ProtocolcBeZdZUeded<eegefed<dS)TransformerProtocol).r fit transformN)__name__ __module__ __qualname__r__annotations__r\/var/www/html/movieo_spanner_bot/venv/lib/python3.11/site-packages/ffsubsync/sklearn_shim.pyr r -s; , ----s ######rr c 8eZdZdZddedeededefdZdS) TransformerMixinz!Mixin class for all transformers.NXy fit_paramsreturnc |!|j|fi||S|j||fi||S)a6 Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters ---------- X : ndarray of shape (n_samples, n_features) Training set. y : ndarray of shape (n_samples,), default=None Target values. **fit_params : dict Additional fit parameters. Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array. )r r )selfrrrs r fit_transformzTransformerMixin.fit_transform7s]( 948A,,,,66q99 948Aq//J//99!<< >$*S/22 2 ) 3ID## ) ) )#C( ( ( ( ) sA A:9A:c2|jddjS)Nr'r3)r"_estimator_typer>s rrKzPipeline._estimator_typesz"~a 00rc*t|jSr)dictr"r>s rrFzPipeline.named_stepssDJrc4|jdd}|dn|S)Nr'r3r()r")rr0s r_final_estimatorzPipeline._final_estimators!JrN1% ) 1}}y@rcn|jsdS|j|\}}d|dzt|j|fzS)Nz(step %d of %d) Processing %sr3)r#r"r4)rstep_idxr:rCs r _log_messagezPipeline._log_messages@| 4Z) d.(Q,DJQU1VVVrNc 2t|j|_|d|jD}|D]O\}}d|vr"t d||dd\}}||||<P|ddD]4\} } } | | dkrt| ||dfi|| \}} | | f|j| <5|j dkr|ifS|||jdd fS) Nci|] \}}||i Srr).0r:rCs r z!Pipeline._fit..s#VVVtTEUD"EUEUEUr__zPipeline.fit does not accept the {} parameter. You can pass parameters to specific steps of your pipeline using the stepname__parameter format, e.g. `Pipeline.fit(X, y, logisticregression__sample_weight=sample_weight)`.r3F)r6r7r(r'r) listr"r$itemsrDformatsplitr<_fit_transform_onerO) rrrrfit_params_stepspnamepvalrCparamrQr: transformerfitted_transformers r_fitz Pipeline._fitst$*%%  VVtzVVV%++-- 1 1KE45   ()/u  ++dA..KD%,0 T "5 ) )+/::,6, , > > 'HdK"k]&B&B%7Q4%%+;D+A%% !A! %)*<#=DJx  M 1 1b5L"4:b>!#4555rc h|j||fi|\}}|jdkr|jj||fi||S)aFit the model Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. Returns ------- self : Pipeline This estimator r()rcrOr )rrrrXts rr z Pipeline.fitsV4#1a66:66J  M 1 1 %D ! %b! : :z : : : rc |j}|j||fi|\}}|dkr|St|dr|j||fi|S|j||fi||S)aFit the model and transform with the final estimator Fits all the transforms one after the other and transforms the data, then uses fit_transform on transformed data with the final estimator. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. Returns ------- Xt : array-like of shape (n_samples, n_transformed_features) Transformed samples r(r)rOrcr*rr r )rrrr last_stepres rrzPipeline.fit_transforms6) "1a66:66J  % %I 9o . . D*9*2q??J?? ? 9=Q55*55??CC Crc>|jdkr |jj|jS)aApply transforms, and transform with the final estimator This also works where final estimator is ``None``: all prior transformations are applied. Parameters ---------- X : iterable Data to transform. Must fulfill input requirements of first step of the pipeline. Returns ------- Xt : array-like of shape (n_samples, n_transformed_features) r()rOr _transformr>s rr zPipeline.transform&s&&  M 1 1  ! + +rcj|}|D]\}}}||}|Sr)r<r )rrre_r s rrizPipeline._transform=s> #zz|| ) )OAq)$$R((BB rc2|jddjS)Nr')r"classes_r>s rrmzPipeline.classes_Csz"~b!**rcFt|jddddS)Nrr3 _pairwiseF)getattrr"r>s rrozPipeline._pairwiseGs!tz!}Q'e<<s rrrzPipeline.n_features_in_Lsz!}Q..r)F)TTr)r rrr%r$r<intr?rIpropertyrKrFrOrRrcr rr rirmrorrrrrr r Ys @''''" (11X1  X AAXAWWW!6!6!6!6F>"D"D"D"DHX, ++X+==X=//X///rr cd|D}tt}t||D]\}}||xxdz cc<t|D]\}}|dkr||=t t t|D]7}||}||vr)||xxd||zz cc<||xxdzcc<8tt||S)zGenerate names for estimators.cg|]?}t|tr|n%t|j@Sr)rAstrr,r lower)rUr0s r z$_name_estimators..UsP     3//U T)__5M5S5S5U5U   rr3z-%d)rrsr)rXrYreversedranger4)r.r- namecountrHr:kvis r_name_estimatorsrRs!  #   EC  IU++ T$1Y__&&''1 66! eC OO,, - -!!Qx 9   !HHH $/ /HHH dOOOq OOO E:&& ' ''rrc|dd}|rGtdt|dt t ||S)aConstruct a Pipeline from the given estimators. This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically. Parameters ---------- *steps : list of estimators. verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed. Returns ------- p : Pipeline r#FzUnknown keyword arguments: "{}"r)r#)popr+rZrXkeysr r)r"kwargsr#s r make_pipelinerjsu&jjE**G   - 4 4T&++--5H5H5K L L    $U++W = = ==rc >||}||S||zSr)r rarrweightrress r_transform_oners)    " "C ~ <rc t|dr|j||fi|}n"|j||fi||}|||fS||z|fS)z Fits ``transformer`` to ``X`` and ``y``. The transformed result is returned with the fitted transformer. If ``weight`` is not ``None``, the result will be multiplied by ``weight``. r)r*rr r rs rr\r\s| {O,,?'k'1;; ;;koa11j11;;A>> ~K < $$rN)r collectionsr itertoolsrtypingrrrtyping_extensionsrr rr rrrr\rrrrs?$$J$#####**********&&&&&&$$$$$($$$=====*===Jv/v/v/v/v/v/v/v/r(((0>x>>>>6 % % % % %r