Class: MLPRegressor
Multi-layer Perceptron regressor.
This model optimizes the squared error using LBFGS or stochastic gradient descent.
Constructors
new MLPRegressor()
new MLPRegressor(
opts?):MLPRegressor
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.activation? | "identity" | "logistic" | "tanh" | "relu" | Activation function for the hidden layer. |
opts.alpha? | number | Strength of the L2 regularization term. The L2 regularization term is divided by the sample size when added to the loss. |
opts.batch_size? | number | Size of minibatches for stochastic optimizers. If the solver is ‘lbfgs’, the regressor will not use minibatch. When set to “auto”, batch_size=min(200, n_samples). |
opts.beta_1? | number | Exponential decay rate for estimates of first moment vector in adam, should be in [0, 1). Only used when solver=’adam’. |
opts.beta_2? | number | Exponential decay rate for estimates of second moment vector in adam, should be in [0, 1). Only used when solver=’adam’. |
opts.early_stopping? | boolean | Whether to use early stopping to terminate training when validation score is not improving. If set to true, it will automatically set aside validation_fraction 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. Only effective when solver=’sgd’ or ‘adam’. |
opts.epsilon? | number | Value for numerical stability in adam. Only used when solver=’adam’. |
opts.hidden_layer_sizes? | any | The ith element represents the number of neurons in the ith hidden layer. |
opts.learning_rate? | "constant" | "invscaling" | "adaptive" | Learning rate schedule for weight updates. |
opts.learning_rate_init? | number | The initial learning rate used. It controls the step-size in updating the weights. Only used when solver=’sgd’ or ‘adam’. |
opts.max_fun? | number | Only used when solver=’lbfgs’. Maximum number of function calls. The solver iterates until convergence (determined by tol), number of iterations reaches max_iter, or this number of function calls. Note that number of function calls will be greater than or equal to the number of iterations for the MLPRegressor. |
opts.max_iter? | number | 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. |
opts.momentum? | number | Momentum for gradient descent update. Should be between 0 and 1. Only used when solver=’sgd’. |
opts.n_iter_no_change? | number | Maximum number of epochs to not meet tol improvement. Only effective when solver=’sgd’ or ‘adam’. |
opts.nesterovs_momentum? | boolean | Whether to use Nesterov’s momentum. Only used when solver=’sgd’ and momentum > 0. |
opts.power_t? | number | 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’. |
opts.random_state? | number | 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. |
opts.shuffle? | boolean | Whether to shuffle samples in each iteration. Only used when solver=’sgd’ or ‘adam’. |
opts.solver? | "lbfgs" | "sgd" | "adam" | The solver for weight optimization. |
opts.tol? | number | 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. |
opts.validation_fraction? | number | 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. |
opts.verbose? | boolean | Whether to print progress messages to stdout. |
opts.warm_start? | boolean | When set to true, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary. |
Returns MLPRegressor
Defined in generated/neural_network/MLPRegressor.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/neural_network/MLPRegressor.ts:21 |
_isInitialized | boolean | false | generated/neural_network/MLPRegressor.ts:20 |
_py | PythonBridge | undefined | generated/neural_network/MLPRegressor.ts:19 |
id | string | undefined | generated/neural_network/MLPRegressor.ts:16 |
opts | any | undefined | generated/neural_network/MLPRegressor.ts:17 |
Accessors
best_loss_
Get Signature
get best_loss_():
Promise<number>
The minimum loss reached by the solver throughout fitting. If early_stopping=True, this attribute is set to undefined. Refer to the best_validation_score_ fitted attribute instead. Only accessible when solver=’sgd’ or ‘adam’.
Returns Promise<number>
Defined in generated/neural_network/MLPRegressor.ts:501
best_validation_score_
Get Signature
get best_validation_score_():
Promise<number>
The best validation score (i.e. R2 score) that triggered the early stopping. Only available if early_stopping=True, otherwise the attribute is set to undefined. Only accessible when solver=’sgd’ or ‘adam’.
Returns Promise<number>
Defined in generated/neural_network/MLPRegressor.ts:576
coefs_
Get Signature
get coefs_():
Promise<any[]>
The ith element in the list represents the weight matrix corresponding to layer i.
Returns Promise<any[]>
Defined in generated/neural_network/MLPRegressor.ts:624
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/neural_network/MLPRegressor.ts:697
intercepts_
Get Signature
get intercepts_():
Promise<any[]>
The ith element in the list represents the bias vector corresponding to layer i + 1.
Returns Promise<any[]>
Defined in generated/neural_network/MLPRegressor.ts:647
loss_
Get Signature
get loss_():
Promise<number>
The current loss computed with the loss function.
Returns Promise<number>
Defined in generated/neural_network/MLPRegressor.ts:478
loss_curve_
Get Signature
get loss_curve_():
Promise<any[]>
Loss value evaluated at the end of each training step. The ith element in the list represents the loss at the ith iteration. Only accessible when solver=’sgd’ or ‘adam’.
Returns Promise<any[]>
Defined in generated/neural_network/MLPRegressor.ts:526
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/neural_network/MLPRegressor.ts:672
n_iter_
Get Signature
get n_iter_():
Promise<number>
The number of iterations the solver has run.
Returns Promise<number>
Defined in generated/neural_network/MLPRegressor.ts:722
n_layers_
Get Signature
get n_layers_():
Promise<number>
Number of layers.
Returns Promise<number>
Defined in generated/neural_network/MLPRegressor.ts:745
n_outputs_
Get Signature
get n_outputs_():
Promise<number>
Number of outputs.
Returns Promise<number>
Defined in generated/neural_network/MLPRegressor.ts:770
out_activation_
Get Signature
get out_activation_():
Promise<string>
Name of the output activation function.
Returns Promise<string>
Defined in generated/neural_network/MLPRegressor.ts:795
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/neural_network/MLPRegressor.ts:185
t_
Get Signature
get t_():
Promise<number>
The number of training samples seen by the solver during fitting. Mathematically equals n_iters \* X.shape\[0\], it means time_step and it is used by optimizer’s learning rate scheduler.
Returns Promise<number>
Defined in generated/neural_network/MLPRegressor.ts:601
validation_scores_
Get Signature
get validation_scores_():
Promise<any[]>
The score at each iteration on a held-out validation set. The score reported is the R2 score. Only available if early_stopping=True, otherwise the attribute is set to undefined. Only accessible when solver=’sgd’ or ‘adam’.
Returns Promise<any[]>
Defined in generated/neural_network/MLPRegressor.ts:551
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/neural_network/MLPRegressor.ts:237
fit()
fit(
opts):Promise<any>
Fit the model to data matrix X and target(s) y.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input data. |
opts.y? | ArrayLike | The target values (class labels in classification, real numbers in regression). |
Returns Promise<any>
Defined in generated/neural_network/MLPRegressor.ts:254
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/neural_network/MLPRegressor.ts:293
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/neural_network/MLPRegressor.ts:198
partial_fit()
partial_fit(
opts):Promise<any>
Update the model with a single iteration over the given data.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input data. |
opts.y? | ArrayLike | The target values. |
Returns Promise<any>
Defined in generated/neural_network/MLPRegressor.ts:327
predict()
predict(
opts):Promise<ArrayLike[]>
Predict using the multi-layer perceptron model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input data. |
Returns Promise<ArrayLike[]>
Defined in generated/neural_network/MLPRegressor.ts:364
score()
score(
opts):Promise<number>
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true \- y_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y_true \- y_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.X? | ArrayLike[] | Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator. |
opts.y? | ArrayLike | True values for X. |
Returns Promise<number>
Defined in generated/neural_network/MLPRegressor.ts:398
set_score_request()
set_score_request(
opts):Promise<any>
Request metadata passed to the score method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in score. |
Returns Promise<any>
Defined in generated/neural_network/MLPRegressor.ts:444