o ={c!:@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||_||_||dSN)build_fn sk_params check_paramsselfr r r;lib/python3.10/site-packages/keras/wrappers/scikit_learn.py__init__KszBaseWrapper.__init__cCstjtjtjg}|jdur||jnt|jtj s*t|jtj s*||jjn||j|D]}|D] }t ||r?n q6|dkrKt |dq2dS)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)rparamsZlegal_params_fnsZ params_namefnrrrr Ps0     zBaseWrapper.check_paramscKs|j}|d|ji|S)zGets parameters for this estimator. Args: **params: ignored (exists for API compatibility). Returns: Dictionary of parameter names mapped to their values. r )r copyupdater )rrresrrr get_paramsqs zBaseWrapper.get_paramscKs|||j||S)zSets the parameters of this estimator. Args: **params: Dictionary of parameter names mapped to their values. Returns: self )r r r)rrrrr set_params~s zBaseWrapper.set_paramscKs|jdur|jdi||j|_n*t|jtjs0t|jtjs0|jdi||jj|_n |jdi||j|_t |jj rOt |j dkrOt |}t|tj}|||jj||fi|}|S)asConstructs a new model with `build_fn` & fit the model to `(x, y)`. Args: x : array-like, shape `(n_samples, n_features)` Training samples where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like, shape `(n_samples,)` or `(n_samples, n_outputs)` True labels for `x`. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.fit` Returns: history : object details about the training history at each epoch. Nr)r rfilter_sk_paramsmodelrrrrrZis_categorical_crossentropylosslenshaperrZdeepcopyrrr)rxykwargsZfit_argshistoryrrrrs&      zBaseWrapper.fitcCsF|pi}i}|jD]\}}t||r|||iq |||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)rroverrider namevaluerrrr$s   zBaseWrapper.filter_sk_paramsr ) __name__ __module__ __qualname____doc__rr r!r"rr$rrrrr"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 (tjdtddtj|fi|dS)NzKerasClassifier is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.r# stacklevelwarningswarnDeprecationWarningsuperrr  __class__rrrs zKerasClassifier.__init__c st|}t|jdkr|jddkrt|jd|_n,t|jdkr+|jddks2t|jdkr@t||_t|j|}n tdt |jt|j|_ t j ||fi|S)aConstructs a new model with `build_fn` & fit the model to `(x, y)`. Args: x : array-like, shape `(n_samples, n_features)` Training samples where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like, shape `(n_samples,)` or `(n_samples, n_outputs)` True labels for `x`. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.fit` Returns: history : object details about the training history at each epoch. Raises: ValueError: In case of invalid shape for `y` argument. r#zInvalid shape for y: ) npZarrayr'r(Zarangeclasses_unique searchsortedrstrZ n_classes_r=r)rr)r*r+r>rrrs *  zKerasClassifier.fitcKsH|jj|fi|}|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(ZargmaxZastyperB)rr)r+ZprobaZclassesrrrrs  zKerasClassifier.predictcKs8|jj|fi|}|jddkrtd||g}|S)aReturns class probability estimates 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: proba: array-like, shape `(n_samples, n_outputs)` Class probability estimates. In the case of binary classification, to match the scikit-learn API, will return an array of shape `(n_samples, 2)` (instead of `(n_sample, 1)` as in Keras). r@)r%rr(rAZhstack)rr)r+Zprobsrrr predict_probaszKerasClassifier.predict_probacKst|j|}|tj|}|jj}t|dr|j }|dkr)t |j dkr)t |}|jj||fi|}t |ts<|g}t|jj|D] \}}|dvrO|SqCtd)a;Returns the mean accuracy on the given test data and labels. 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. y: array-like, shape `(n_samples,)` or `(n_samples, n_outputs)` True labels for `x`. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.evaluate`. Returns: score: float Mean accuracy of predictions on `x` wrt. `y`. Raises: ValueError: If the underlying model isn't configured to compute accuracy. You should pass `metrics=["accuracy"]` to the `.compile()` method of the model. r1Zcategorical_crossentropyr#)ZaccuracyZacczxThe model is not configured to compute accuracy. You should pass `metrics=["accuracy"]` to the `model.compile()` method.)rArDrBr$rrr%r&hasattrr1r'r(rrlistzipZ metrics_namesr)rr)r*r+Z loss_nameZoutputsr/outputrrrscore+s"  zKerasClassifier.scorer ) r1r2r3r4rrrrGrL __classcell__rrr>rr5s r5z*keras.wrappers.scikit_learn.KerasRegressorcs8eZdZdZejd fdd ZddZddZZ S) KerasRegressorzImplementation of the scikit-learn regressor API for Keras. DEPRECATED. Use [Sci-Keras](https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating. Nc r6)NzKerasRegressor is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.r#r7r9r r>rrr`s zKerasRegressor.__init__cKs(|tj|}t|jj|fi|S)aReturns predictions for the given test data. Args: x: array-like, shape `(n_samples, n_features)` Test samples where `n_samples` is the number of samples and `n_features` is the number of features. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.predict`. Returns: preds: array-like, shape `(n_samples,)` Predictions. )r$rrrAZsqueezer%)rr)r+rrrrlszKerasRegressor.predictcKs>|tj|}|jj||fi|}t|tr|d S| S)aDReturns the mean loss on the given test data and labels. 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. y: array-like, shape `(n_samples,)` True labels for `x`. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.evaluate`. Returns: score: float Mean accuracy of predictions on `x` wrt. `y`. r)r$rrr%rrI)rr)r*r+r&rrrrL}s   zKerasRegressor.scorer ) r1r2r3r4rZdo_not_doc_inheritablerrrLrMrrr>rrNVs  rN)r4rrr:ZnumpyrAZkerasrZ keras.modelsrZkeras.utils.generic_utilsrZkeras.utils.np_utilsrZ tensorflow.python.util.tf_exportrZtensorflow.tools.docsrrZdo_not_generate_docsr5rNrrrrs(      &