scikeras.wrappers.BaseWrapper¶
- class scikeras.wrappers.BaseWrapper(model=None, *, build_fn=None, warm_start=False, random_state=None, optimizer='rmsprop', loss=None, metrics=None, batch_size=None, validation_batch_size=None, verbose=1, callbacks=None, validation_split=0.0, shuffle=True, run_eagerly=False, epochs=1, **kwargs)[source]¶
Implementation of the scikit-learn classifier API for Keras.
Below are a list of SciKeras specific parameters. For details on other parameters, please see the see the tf.keras.Model documentation.
- Parameters:
- modelUnion[None, Callable[…, tf.keras.Model], tf.keras.Model], default None
Used to build the Keras Model. When called, must return a compiled instance of a Keras Model to be used by fit, predict, etc. If None, you must implement
_keras_build_fn
.- optimizerUnion[str, tf.keras.optimizers.Optimizer, Type[tf.keras.optimizers.Optimizer]], default “rmsprop”
This can be a string for Keras’ built in optimizers, an instance of tf.keras.optimizers.Optimizer or a class inheriting from tf.keras.optimizers.Optimizer. Only strings and classes support parameter routing.
- lossUnion[Union[str, tf.keras.losses.Loss, Type[tf.keras.losses.Loss], Callable], None], default None
The loss function to use for training. This can be a string for Keras’ built in losses, an instance of tf.keras.losses.Loss or a class inheriting from tf.keras.losses.Loss . Only strings and classes support parameter routing.
- random_stateUnion[int, np.random.RandomState, None], default None
Set the Tensorflow random number generators to a reproducible deterministic state using this seed. Pass an int for reproducible results across multiple function calls.
- warm_startbool, default False
If True, subsequent calls to fit will _not_ reset the model parameters but will reset the epoch to zero. If False, subsequent fit calls will reset the entire model. This has no impact on partial_fit, which always trains for a single epoch starting from the current epoch.
- batch_sizeUnion[int, None], default None
Number of samples per gradient update. This will be applied to both fit and predict. To specify different numbers, pass fit__batch_size=32 and predict__batch_size=1000 (for example). To auto-adjust the batch size to use all samples, pass batch_size=-1.
- Attributes:
- model_tf.keras.Model
The instantiated and compiled Keras Model. For pre-built models, this will just be a reference to the passed Model instance.
- history_Dict[str, List[Any]]
Dictionary of the format
{metric_str_name: [epoch_0_data, epoch_1_data, ..., epoch_n_data]}
.initialized_
boolChecks if the estimator is intialized.
- target_encoder_sklearn-transformer
Transformer used to pre/post process the target y.
- feature_encoder_sklearn-transformer
Transformer used to pre/post process the features/input X.
- n_outputs_expected_int
The number of outputs the Keras Model is expected to have, as determined by
target_transformer_
.- target_type_str
One of:
‘continuous’: y is an array-like of floats that are not all integers, and is 1d or a column vector.
‘continuous-multioutput’: y is a 2d array of floats that are not all integers, and both dimensions are of size > 1.
‘binary’: y contains <= 2 discrete values and is 1d or a column vector.
‘multiclass’: y contains more than two discrete values, is not a sequence of sequences, and is 1d or a column vector.
‘multiclass-multioutput’: y is a 2d array that contains more than two discrete values, is not a sequence of sequences, and both dimensions are of size > 1.
‘multilabel-indicator’: y is a label indicator matrix, an array of two dimensions with at least two columns, and at most 2 unique values.
‘unknown’: y is array-like but none of the above, such as a 3d array, sequence of sequences, or an array of non-sequence objects.
- y_shape_Tuple[int]
Shape of the target y that the estimator was fitted on.
- y_dtype_np.dtype
Dtype of the target y that the estimator was fitted on.
- X_shape_Tuple[int]
Shape of the input X that the estimator was fitted on.
- X_dtype_np.dtype
Dtype of the input X that the estimator was fitted on.
- n_features_in_int
The number of features seen during fit.
- Parameters:
- property current_epoch: int¶
Returns the current training epoch.
- Returns:
- int
Current training epoch.
- property feature_encoder¶
Retrieve a transformer for features / X.
Metadata will be collected from
get_metadata
if the transformer implements that method. Override this method to implement a custom data transformer for the features.- Returns:
- sklearn transformer
Transformer implementing the sklearn transformer interface.
- fit(X, y, sample_weight=None, **kwargs)[source]¶
Constructs a new model with
model
& fit the model to(X, y)
.- Parameters:
- XUnion[array-like, sparse matrix, dataframe, of shape (n_samples, n_features)
Training samples, where n_samples is the number of samples and n_features is the number of features.
- yUnion[array-like, dataframe of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
- sample_weightarray-like of shape (n_samples,), default=None
Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.
- **kwargsDict[str, Any]
Extra arguments to route to
Model.fit
.
- Returns:
- BaseWrapper
A reference to the instance that can be chain called (
est.fit(X,y).transform(X)
).
- Return type:
Warning
Passing estimator parameters as keyword arguments (aka as
**kwargs
) tofit
is not supported by the Scikit-Learn API, and will be removed in a future version of SciKeras. These parameters can also be specified by prefixingfit__
to a parameter at initialization (BaseWrapper(..., fit__batch_size=32, predict__batch_size=1000)
) or by usingset_params
(est.set_params(fit__batch_size=32, predict__batch_size=1000)
).
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- initialize(X, y=None)[source]¶
Initialize the model without any fitting.
You only need to call this model if you explicitly do not want to do any fitting (for example with a pretrained model). You should _not_ call this right before calling
fit
, callingfit
will do this automatically.- Parameters:
- XUnion[array-like, sparse matrix, dataframe, of shape (n_samples, n_features)
Training samples where n_samples is the number of samples and n_features is the number of features.
- yUnion[array-like, dataframe,, of shape (n_samples,) or (n_samples, n_outputs), default None
True labels for X.
- Returns:
- BaseWrapper
A reference to the BaseWrapper instance for chained calling.
- Return type:
- property initialized_: bool¶
Checks if the estimator is intialized.
- Returns:
- bool
True if the estimator is initialized (i.e., it can be used for inference or is ready to train), otherwise False.
- partial_fit(X, y, sample_weight=None, **kwargs)[source]¶
Fit the estimator for a single epoch, preserving the current training history and model parameters.
- Parameters:
- XUnion[array-like, sparse matrix, dataframe, of shape (n_samples, n_features)
Training samples where n_samples is the number of samples and n_features is the number of features.
- yUnion[array-like, dataframe,, of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
- sample_weightarray-like of shape (n_samples,), default=None
Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.
- **kwargsDict[str, Any]
Extra arguments to route to
Model.fit
.
- Returns:
- BaseWrapper
A reference to the instance that can be chain called (ex: instance.partial_fit(X, y).transform(X) )
- Return type:
- predict(X, **kwargs)[source]¶
Returns predictions for the given test data.
- Parameters:
- XUnion[array-like, sparse matrix, dataframe, of shape (n_samples, n_features)
Training samples where n_samples is the number of samples and n_features is the number of features.
- **kwargsDict[str, Any]
Extra arguments to route to
Model.predict
.
- Returns:
- array-like
Predictions, of shape shape (n_samples,) or (n_samples, n_outputs).
Warning
Passing estimator parameters as keyword arguments (aka as
**kwargs
) topredict
is not supported by the Scikit-Learn API, and will be removed in a future version of SciKeras. These parameters can also be specified by prefixingpredict__
to a parameter at initialization (BaseWrapper(..., fit__batch_size=32, predict__batch_size=1000)
) or by usingset_params
(est.set_params(fit__batch_size=32, predict__batch_size=1000)
).
- score(X, y, sample_weight=None)[source]¶
Returns the score on the given test data and labels.
No default scoring function is implemented in BaseWrapper, you must subclass and implement one.
- Parameters:
- XUnion[array-like, sparse matrix, dataframe, of shape (n_samples, n_features)
Test input samples, where n_samples is the number of samples and n_features is the number of features.
- yUnion[array-like, dataframe,, of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
- sample_weightarray-like of shape (n_samples,), default=None
Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.
- Returns:
- float
Score for the test data set.
- Return type:
- static scorer(y_true, y_pred, **kwargs)[source]¶
Scoring function for model.
This is not implemented in BaseWrapper, it exists as a stub for documentation.
- Parameters:
- y_truearray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels.
- y_predarray-like of shape (n_samples,) or (n_samples, n_outputs)
Predicted labels.
- **kwargs: dict
Extra parameters passed to the scorer.
- Returns:
- float
Score for the test data set.
- Return type:
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object. This also supports routed parameters, eg:classifier__optimizer__learning_rate
.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- BaseWrapper
Estimator instance.
- Return type:
- property target_encoder¶
Retrieve a transformer for targets / y.
Metadata will be collected from
get_metadata
if the transformer implements that method. Override this method to implement a custom data transformer for the target.- Returns:
- target_encoder
Transformer implementing the sklearn transformer interface.