B d!:@sdZddlZddlZddlZddlZddlmZddlm Z ddl m Z ddl m Z ddlmZddlmZGd d d Zed ejGd d d eZedejGdddeZdS)z9Wrapper for using the Scikit-Learn API with Keras models.N)losses) Sequential)has_arg)to_categorical) keras_export) doc_controlsc@sDeZdZdZdddZddZddZd d Zd d Zdd dZ dS) BaseWrappera Base class for the Keras scikit-learn wrapper. Warning: This class should not be used directly. Use descendant classes instead. Args: build_fn: callable function or class instance **sk_params: model parameters & fitting parameters The `build_fn` should construct, compile and return a Keras model, which will then be used to fit/predict. One of the following three values could be passed to `build_fn`: 1. A function 2. An instance of a class that implements the `__call__` method 3. None. This means you implement a class that inherits from either `KerasClassifier` or `KerasRegressor`. The `__call__` method of the present class will then be treated as the default `build_fn`. `sk_params` takes both model parameters and fitting parameters. Legal model parameters are the arguments of `build_fn`. Note that like all other estimators in scikit-learn, `build_fn` should provide default values for its arguments, so that you could create the estimator without passing any values to `sk_params`. `sk_params` could also accept parameters for calling `fit`, `predict`, `predict_proba`, and `score` methods (e.g., `epochs`, `batch_size`). fitting (predicting) parameters are selected in the following order: 1. Values passed to the dictionary arguments of `fit`, `predict`, `predict_proba`, and `score` methods 2. Values passed to `sk_params` 3. The default values of the `keras.models.Sequential` `fit`, `predict` methods. When using scikit-learn's `grid_search` API, legal tunable parameters are those you could pass to `sk_params`, including fitting parameters. In other words, you could use `grid_search` to search for the best `batch_size` or `epochs` as well as the model parameters. NcKs||_||_||dS)N)build_fn sk_params check_params)selfr r r M/var/www/html/venv/lib/python3.7/site-packages/keras/wrappers/scikit_learn.py__init__KszBaseWrapper.__init__cCstjtjtjg}|jdkr(||jn8t|jtj sTt|jtj sT||jjn ||jx<|D]4}x.|D]}t ||rpPqpW|dkrft |dqfWdS)zChecks for user typos in `params`. Args: params: dictionary; the parameters to be checked Raises: ValueError: if any member of `params` is not a valid argument. NZnb_epochz is not a legal parameter) rfitpredictevaluater append__call__ isinstancetypes FunctionType MethodTyper ValueError)r paramsZlegal_params_fnsZ params_namefnr r rr Ps"       zBaseWrapper.check_paramscKs|j}|d|ji|S)zGets parameters for this estimator. Args: **params: ignored (exists for API compatibility). 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N)r rfilter_sk_paramsmodelrrrrrZis_categorical_crossentropylosslenshaperrdeepcopyrrr)r xykwargsZfit_argshistoryr r rrs   zBaseWrapper.fitcCsJ|pi}i}x.|jD] \}}t||r|||iqW|||S)a5Filters `sk_params` and returns those in `fn`'s arguments. Args: fn : arbitrary function override: dictionary, values to override `sk_params` Returns: res : dictionary containing variables in both `sk_params` and `fn`'s arguments. )r itemsrr)r roverridernamevaluer r rr"s   zBaseWrapper.filter_sk_params)N)N) __name__ __module__ __qualname____doc__rr rr rr"r r r rr"s' !  (rz+keras.wrappers.scikit_learn.KerasClassifiercsFeZdZdZd fdd ZfddZddZd d Zd d ZZ S)KerasClassifierzImplementation of the scikit-learn classifier API for Keras. DEPRECATED. Use [Sci-Keras](https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating. Nc s$tjdtddtj|f|dS)NzKerasClassifier is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. 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Raises: ValueError: In case of invalid shape for `y` argument. r!zInvalid shape for y: ) nparrayr%r&Zarangeclasses_unique searchsortedrstrZ n_classes_r9r)r r(r)r*)r:r rrs *  zKerasClassifier.fitcKsD|jj|f|}|jddkr,|jdd}n|dkd}|j|S)aReturns the class predictions for the given test data. Args: x: array-like, shape `(n_samples, n_features)` Test samples where `n_samples` is the number of samples and `n_features` is the number of features. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.predict`. Returns: preds: array-like, shape `(n_samples,)` Class predictions. r;)Zaxisg?Zint32)r#rr&ZargmaxZastyper>)r r(r*Zprobaclassesr r rrs zKerasClassifier.predictcKs4|jj|f|}|jddkr0td||g}|S)aReturns class probability estimates for the given test data. 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You should pass `metrics=["accuracy"]` to the `model.compile()` method.N)r<r@r>r"rrr#r$hasattrr0r%r&rrlistzipZ metrics_namesr)r r(r)r*Z loss_nameoutputsr.outputr r rscore+s  zKerasClassifier.score)N) r0r1r2r3rrrrDrK __classcell__r r )r:rr4s  r4z*keras.wrappers.scikit_learn.KerasRegressorcs8eZdZdZejd fdd ZddZddZZ S) KerasRegressorzImplementation of the scikit-learn regressor API for Keras. DEPRECATED. Use [Sci-Keras](https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating. Nc s$tjdtddtj|f|dS)NzKerasRegressor is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.r!)r5)r6r7r8r9r)r r r )r:r rr`s zKerasRegressor.__init__cKs$|tj|}t|jj|f|S)aReturns predictions for the given test data. 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Returns: score: float Mean accuracy of predictions on `x` wrt. `y`. r)r"rrr#rrG)r r(r)r*r$r r rrK}s   zKerasRegressor.score)N) r0r1r2r3rZdo_not_doc_inheritablerrrKrLr r )r:rrMVs  rM)r3rrr6numpyr<ZkerasrZ keras.modelsrZkeras.utils.generic_utilsrZkeras.utils.np_utilsrZ tensorflow.python.util.tf_exportrZtensorflow.tools.docsrrZdo_not_generate_docsr4rMr r r rs&      &