snowflake.ml.modeling.neural_network.MLPClassifier¶

class snowflake.ml.modeling.neural_network.MLPClassifier(*, hidden_layer_sizes=(100,), activation='relu', solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Multi-layer Perceptron classifier For more details on this class, see sklearn.neural_network.MLPClassifier

hidden_layer_sizes: array-like of shape(n_layers - 2,), default=(100,)

The ith element represents the number of neurons in the ith hidden layer.

activation: {‘identity’, ‘logistic’, ‘tanh’, ‘relu’}, default=’relu’

Activation function for the hidden layer.

‘identity’, no-op activation, useful to implement linear bottleneck, returns f(x) = x
‘logistic’, the logistic sigmoid function, returns f(x) = 1 / (1 + exp(-x)).
‘tanh’, the hyperbolic tan function, returns f(x) = tanh(x).
‘relu’, the rectified linear unit function, returns f(x) = max(0, x)

solver: {‘lbfgs’, ‘sgd’, ‘adam’}, default=’adam’

The solver for weight optimization.

‘lbfgs’ is an optimizer in the family of quasi-Newton methods.
‘sgd’ refers to stochastic gradient descent.
‘adam’ refers to a stochastic gradient-based optimizer proposed by Kingma, Diederik, and Jimmy Ba

Note: The default solver ‘adam’ works pretty well on relatively large datasets (with thousands of training samples or more) in terms of both training time and validation score. For small datasets, however, ‘lbfgs’ can converge faster and perform better.

alpha: float, default=0.0001

Strength of the L2 regularization term. The L2 regularization term is divided by the sample size when added to the loss.

batch_size: int, default=’auto’

Size of minibatches for stochastic optimizers. If the solver is ‘lbfgs’, the classifier will not use minibatch. When set to “auto”, batch_size=min(200, n_samples).

learning_rate: {‘constant’, ‘invscaling’, ‘adaptive’}, default=’constant’

Learning rate schedule for weight updates.

‘constant’ is a constant learning rate given by ‘learning_rate_init’.
‘invscaling’ gradually decreases the learning rate at each time step ‘t’ using an inverse scaling exponent of ‘power_t’. effective_learning_rate = learning_rate_init / pow(t, power_t)
‘adaptive’ keeps the learning rate constant to ‘learning_rate_init’ as long as training loss keeps decreasing. Each time two consecutive epochs fail to decrease training loss by at least tol, or fail to increase validation score by at least tol if ‘early_stopping’ is on, the current learning rate is divided by 5.

Only used when solver='sgd'.

learning_rate_init: float, default=0.001

The initial learning rate used. It controls the step-size in updating the weights. Only used when solver=’sgd’ or ‘adam’.

power_t: float, default=0.5

The exponent for inverse scaling learning rate. It is used in updating effective learning rate when the learning_rate is set to ‘invscaling’. Only used when solver=’sgd’.

max_iter: int, default=200

Maximum number of iterations. The solver iterates until convergence (determined by ‘tol’) or this number of iterations. For stochastic solvers (‘sgd’, ‘adam’), note that this determines the number of epochs (how many times each data point will be used), not the number of gradient steps.

shuffle: bool, default=True

Whether to shuffle samples in each iteration. Only used when solver=’sgd’ or ‘adam’.

random_state: int, RandomState instance, default=None

Determines random number generation for weights and bias initialization, train-test split if early stopping is used, and batch sampling when solver=’sgd’ or ‘adam’. Pass an int for reproducible results across multiple function calls. See Glossary.

tol: float, default=1e-4

Tolerance for the optimization. When the loss or score is not improving by at least tol for n_iter_no_change consecutive iterations, unless learning_rate is set to ‘adaptive’, convergence is considered to be reached and training stops.

verbose: bool, default=False

Whether to print progress messages to stdout.

warm_start: bool, default=False

When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.

momentum: float, default=0.9

Momentum for gradient descent update. Should be between 0 and 1. Only used when solver=’sgd’.

nesterovs_momentum: bool, default=True

Whether to use Nesterov’s momentum. Only used when solver=’sgd’ and momentum > 0.

early_stopping: bool, default=False

Whether to use early stopping to terminate training when validation score is not improving. If set to true, it will automatically set aside 10% of training data as validation and terminate training when validation score is not improving by at least tol for n_iter_no_change consecutive epochs. The split is stratified, except in a multilabel setting. If early stopping is False, then the training stops when the training loss does not improve by more than tol for n_iter_no_change consecutive passes over the training set. Only effective when solver=’sgd’ or ‘adam’.

validation_fraction: float, default=0.1

The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True.

beta_1: float, default=0.9

Exponential decay rate for estimates of first moment vector in adam, should be in [0, 1). Only used when solver=’adam’.

beta_2: float, default=0.999

Exponential decay rate for estimates of second moment vector in adam, should be in [0, 1). Only used when solver=’adam’.

epsilon: float, default=1e-8

Value for numerical stability in adam. Only used when solver=’adam’.

n_iter_no_change: int, default=10

Maximum number of epochs to not meet tol improvement. Only effective when solver=’sgd’ or ‘adam’.

max_fun: int, default=15000

Only used when solver=’lbfgs’. Maximum number of loss function calls. The solver iterates until convergence (determined by ‘tol’), number of iterations reaches max_iter, or this number of loss function calls. Note that number of loss function calls will be greater than or equal to the number of iterations for the MLPClassifier.

input_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.

label_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).

output_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.

sample_weight_col: Optional[str]

A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

Methods

`fit`(dataset)	Fit the model to data matrix X and target(s) y For more details on this function, see sklearn.neural_network.MLPClassifier.fit
`predict`(dataset)	Predict using the multi-layer perceptron classifier For more details on this function, see sklearn.neural_network.MLPClassifier.predict
`predict_log_proba`(dataset[, output_cols_prefix])	Probability estimates For more details on this function, see sklearn.neural_network.MLPClassifier.predict_proba
`predict_proba`(dataset[, output_cols_prefix])	Probability estimates For more details on this function, see sklearn.neural_network.MLPClassifier.predict_proba
`score`(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.neural_network.MLPClassifier.score
`set_input_cols`(input_cols)	Input columns setter.
`to_sklearn`()	Get sklearn.neural_network.MLPClassifier object.

Attributes

model_signatures

Returns model signature of current class.