ZPh$(dZdZe\ZZddlmZddlmZddlm Z ddlm Z ddl m Z dd lmZdd lmZdd lmZGd d geRZeedddddddZ dS)z?Implement generators for ``keras`` which will balance the data.cvd}d}|\}}|\}}|p|}|r|r|}n |}ntf}||fS)zzTry to import keras from keras and tensorflow. This is possible to import the sequence from keras or tensorflow. c ddl}t|jdr|jjfdfS|jjjfdfS#t $rt dfcYSwxYw)NSequenceTF)kerashasattrutilsr data_utils ImportErrortuplers Y/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/imblearn/keras/_generator.pyimport_from_kerasz'import_keras..import_from_kerass} " LLLu{J// @ ,.44 .794?? " " "77E> ! ! ! "s'>>AAc ddlm}t|jdr|jjfdfS|jjjfdfS#t $rtdfcYSwxYw)Nrr rTF) tensorflowrrrrr r r r s r import_from_tensforflowz-import_keras..import_from_tensforflows " ( ( ( ( ( (u{J// @ ,.44 .794?? " " "77E> ! ! ! "s)AAAA)object)rrParentClassKeras has_keras_kParentClassTensorflow has_keras_tf has_keras ParentClasss r import_kerasr s " " " " " "%6$5$7$7!k*A*A*C*C'<+|I  0*KK/KKi  !!r)issparse)clone)_safe_indexing)check_random_state)balanced_batch_generator)RandomUnderSampler) Substitution)_random_state_docstringc<eZdZdZdZdddddddZdZd Zd ZdS) BalancedBatchGeneratora Create balanced batches when training a keras model. Create a keras ``Sequence`` which is given to ``fit``. The sampler defines the sampling strategy used to balance the dataset ahead of creating the batch. The sampler should have an attribute ``sample_indices_``. .. versionadded:: 0.4 Parameters ---------- X : ndarray of shape (n_samples, n_features) Original imbalanced dataset. y : ndarray of shape (n_samples,) or (n_samples, n_classes) Associated targets. sample_weight : ndarray of shape (n_samples,) Sample weight. sampler : sampler object, default=None A sampler instance which has an attribute ``sample_indices_``. By default, the sampler used is a :class:`~imblearn.under_sampling.RandomUnderSampler`. batch_size : int, default=32 Number of samples per gradient update. keep_sparse : bool, default=False Either or not to conserve or not the sparsity of the input (i.e. ``X``, ``y``, ``sample_weight``). By default, the returned batches will be dense. random_state : int, RandomState instance or None, default=None Control the randomization of the algorithm: - If int, ``random_state`` is the seed used by the random number generator; - If ``RandomState`` instance, random_state is the random number generator; - If ``None``, the random number generator is the ``RandomState`` instance used by ``np.random``. Attributes ---------- sampler_ : sampler object The sampler used to balance the dataset. indices_ : ndarray of shape (n_samples, n_features) The indices of the samples selected during sampling. Examples -------- >>> from sklearn.datasets import load_iris >>> iris = load_iris() >>> from imblearn.datasets import make_imbalance >>> class_dict = dict() >>> class_dict[0] = 30; class_dict[1] = 50; class_dict[2] = 40 >>> X, y = make_imbalance(iris.data, iris.target, sampling_strategy=class_dict) >>> import tensorflow >>> y = tensorflow.keras.utils.to_categorical(y, 3) >>> model = tensorflow.keras.models.Sequential() >>> model.add( ... tensorflow.keras.layers.Dense( ... y.shape[1], input_dim=X.shape[1], activation='softmax' ... ) ... ) >>> model.compile(optimizer='sgd', loss='categorical_crossentropy', ... metrics=['accuracy']) >>> from imblearn.keras import BalancedBatchGenerator >>> from imblearn.under_sampling import NearMiss >>> training_generator = BalancedBatchGenerator( ... X, y, sampler=NearMiss(), batch_size=10, random_state=42) >>> callback_history = model.fit(training_generator, epochs=10, verbose=0) TN F sample_weightsampler batch_size keep_sparse random_statectstd||_||_||_||_||_||_||_| dS)Nz'No module named 'keras') HAS_KERASr Xyr(r)r*r+r,_sample)selfr/r0r(r)r*r+r,s r __init__zBalancedBatchGenerator.__init__s` :899 9* $&( rct|j}|jt||_nt |j|_|j|j|jt|jdstd|jj |_ | |j dS)Nr,sample_indices_z7'sampler' needs to have an attribute 'sample_indices_'.)rr,r)r!sampler_r fit_resampler/r0r ValueErrorr6indices_shuffle)r2r,s r r1zBalancedBatchGenerator._samples)$*;<< < .LIIIDMM!$,//DM ""4646222t}&788 XVWW W 5 T]+++++rcDt|jj|jzS)N)intr:sizer*)r2s r __len__zBalancedBatchGenerator.__len__s4=%8999rct|j|j||jz|dz|jz}t|j|j||jz|dz|jz}t |r|js|}|j5t|j|j||jz|dz|jz}|j||fS|||fS)N) rr/r:r*r0rr+toarrayr()r2index X_resampled y_resampledsample_weight_resampleds r __getitem__z"BalancedBatchGenerator.__getitem__s$ F M%$/1UQY$/4QQ R  % F M%$/1UQY$/4QQ R   K  0)9 0%--//K   )&4" edo5do8UUV'' #   % + + -DD Dr) __name__ __module__ __qualname____doc__use_sequence_apir3r1r?rGrr r%r%@sJJZ, , , ,:::EEEEErr%r5Nr&Fr'c .t|||||||S)a Create a balanced batch generator to train keras model. Returns a generator --- as well as the number of step per epoch --- which is given to ``fit``. The sampler defines the sampling strategy used to balance the dataset ahead of creating the batch. The sampler should have an attribute ``sample_indices_``. Parameters ---------- X : ndarray of shape (n_samples, n_features) Original imbalanced dataset. y : ndarray of shape (n_samples,) or (n_samples, n_classes) Associated targets. sample_weight : ndarray of shape (n_samples,), default=None Sample weight. sampler : sampler object, default=None A sampler instance which has an attribute ``sample_indices_``. By default, the sampler used is a :class:`~imblearn.under_sampling.RandomUnderSampler`. batch_size : int, default=32 Number of samples per gradient update. keep_sparse : bool, default=False Either or not to conserve or not the sparsity of the input (i.e. ``X``, ``y``, ``sample_weight``). By default, the returned batches will be dense. {random_state} Returns ------- generator : generator of tuple Generate batch of data. The tuple generated are either (X_batch, y_batch) or (X_batch, y_batch, sampler_weight_batch). steps_per_epoch : int The number of samples per epoch. Required by ``fit_generator`` in keras. Examples -------- >>> from sklearn.datasets import load_iris >>> X, y = load_iris(return_X_y=True) >>> from imblearn.datasets import make_imbalance >>> class_dict = dict() >>> class_dict[0] = 30; class_dict[1] = 50; class_dict[2] = 40 >>> from imblearn.datasets import make_imbalance >>> X, y = make_imbalance(X, y, sampling_strategy=class_dict) >>> import tensorflow >>> y = tensorflow.keras.utils.to_categorical(y, 3) >>> model = tensorflow.keras.models.Sequential() >>> model.add( ... tensorflow.keras.layers.Dense( ... y.shape[1], input_dim=X.shape[1], activation='softmax' ... ) ... ) >>> model.compile(optimizer='sgd', loss='categorical_crossentropy', ... metrics=['accuracy']) >>> from imblearn.keras import balanced_batch_generator >>> from imblearn.under_sampling import NearMiss >>> training_generator, steps_per_epoch = balanced_batch_generator( ... X, y, sampler=NearMiss(), batch_size=10, random_state=42) >>> callback_history = model.fit(training_generator, ... steps_per_epoch=steps_per_epoch, ... epochs=10, verbose=0) r/r0r(r)r*r+r,)tf_bbgrOs r r r s1d  #!   r)rKrrr. scipy.sparser sklearn.baser sklearn.utilsrrrr rPunder_samplingr!rr"utils._docstringr#r%rMrr rVs\EE&"&"&"R& Y!!!!!!((((((,,,,,,;;;;;;////// 666666JEJEJEJEJEJEkJEJEJEJEZ2333  YYYY43YYYr