a Sic:@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_paramsselfr r rW/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/wrappers/scikit_learn.py__init__KszBaseWrapper.__init__cCstjtjtjg}|jdur(||jn8t|jtj sTt|jtj sT||jjn ||j|D]2}|D]}t ||rlqdql|dkrdt d |qddS)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. Nnb_epochz{} is not a legal parameter)rfitpredictevaluater append__call__ isinstancetypes FunctionType MethodTyper ValueErrorformat)r paramsZlegal_params_fnsZ params_namefnrrrr Ps*     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 )r rresrrr 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 )r rrrr set_params~s  zBaseWrapper.set_paramscKs|jdur&|jfi||j|_nTt|jtjs`t|jtjs`|jfi||jj|_n|jfi||j|_t |jj rt |j dkrt |}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. N)r rfilter_sk_paramsmodelrrrrris_categorical_crossentropylosslenshaperrdeepcopyrrr )r xykwargsZfit_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 )r roverrider!namevaluerrrr%s   zBaseWrapper.filter_sk_params)N)N) __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 s(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:|jddkr:t|jd|_nXt|jdkrV|jddksdt|jdkrt||_t|j|}ntdt |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: ) nparrayr)r*arangeclasses_unique searchsortedrstrZ n_classes_r?r)r r,r-r.r@rrrs *  zKerasClassifier.fitcKsH|jj|fi|}|jddkr0|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. rB)axisg?int32)r&rr*argmaxastyperF)r r,r.probaclassesrrrrs zKerasClassifier.predictcKs8|jj|fi|}|jddkr4td||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). rB)r&rr*rChstack)r r,r.probsrrr predict_probaszKerasClassifier.predict_probacKst|j|}|tj|}|jj}t|dr4|j }|dkrRt |j dkrRt |}|jj||fi|}t |tsx|g}t|jj|D]\}}|dvr|SqtddS)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. r4categorical_crossentropyr$)accuracyacczxThe model is not configured to compute accuracy. You should pass `metrics=["accuracy"]` to the `model.compile()` method.N)rCrHrFr%rrr&r(hasattrr4r)r*rrlistzip metrics_namesr)r r,r-r. loss_nameoutputsr2outputrrrscore+s    zKerasClassifier.score)N) r4r5r6r7rrrrSr^ __classcell__rrr@rr8s  r8z*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|fi|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$r9r;r 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%rrrCsqueezer&)r r,r.rrrrlszKerasRegressor.predictcKs>|tj|}|jj||fi|}t|tr8|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&rrX)r r,r-r.r(rrrr^}s   zKerasRegressor.score)N) r4r5r6r7rdo_not_doc_inheritablerrr^r_rrr@rr`Vs  r`)r7rrr<numpyrCkerasrZ keras.modelsrkeras.utils.generic_utilsrkeras.utils.np_utilsr tensorflow.python.util.tf_exportrtensorflow.tools.docsrrdo_not_generate_docsr8r`rrrrs&      &