MLPRegressor

Multi-layer Perceptron for regression.

This model is still work in progress. Here are some features that still need implementing:

• learn_one and predict_one just cast the input dict to a single row dataframe and then

  call learn_many and predict_many respectively. This is very inefficient. - Not all of the optimizers in the optim module can be used as they are not all vectorised.

• Emerging and disappearing features are not supported. Each instance/batch has to have the

  same features. - The gradient haven't been numerically checked.

Parameters

• hidden_dims

  The dimensions of the hidden layers. For example, specifying (10, 20) means that there are two hidden layers with 10 and 20 neurons, respectively. Note that the number of layers the network contains is equal to the number of hidden layers plus two (to account for the input and output layers).

• activations

  The activation functions to use at each layer, including the input and output layers. Therefore you need to specify three activation if you specify one hidden layer.

• loss

  Type → optim.losses.Loss | None

  Default → None

  Loss function. Defaults to optim.losses.Squared.

• optimizer

  Type → optim.base.Optimizer | None

  Default → None

  Optimizer. Defaults to optim.SGD with the learning rate set to 0.01.

• seed

  Type → int | None

  Default → None

  Random number generation seed. Set this for reproducibility.

Attributes

• n_layers

  Return the number of layers in the network. The number of layers is equal to the number of hidden layers plus 2. The 2 accounts for the input layer and the output layer.

Examples

from river import datasets
from river import evaluate
from river import neural_net as nn
from river import optim
from river import preprocessing as pp
from river import metrics

model = (
    pp.StandardScaler() |
    nn.MLPRegressor(
        hidden_dims=(5,),
        activations=(
            nn.activations.ReLU,
            nn.activations.ReLU,
            nn.activations.Identity
        ),
        optimizer=optim.SGD(1e-3),
        seed=42
    )
)

dataset = datasets.TrumpApproval()

metric = metrics.MAE()

evaluate.progressive_val_score(dataset, model, metric)

MAE: 1.580578

You can also use this to process mini-batches of data.

model = (
    pp.StandardScaler() |
    nn.MLPRegressor(
        hidden_dims=(10,),
        activations=(
            nn.activations.ReLU,
            nn.activations.ReLU,
            nn.activations.ReLU
        ),
        optimizer=optim.SGD(1e-4),
        seed=42
    )
)

dataset = datasets.TrumpApproval()
batch_size = 32

for epoch in range(10):
    for xb in pd.read_csv(dataset.path, chunksize=batch_size):
        yb = xb.pop('five_thirty_eight')
        y_pred = model.predict_many(xb)
        model.learn_many(xb, yb)

model.predict_many(xb)

      five_thirty_eight
992           39.405231
993           46.447481
994           42.121865
995           40.251148
996           40.836378
997           40.893153
998           40.949927
999           48.416504
1000          42.077830

Methods

call

Make predictions.

Parameters

• X — 'pd.DataFrame'

learn_many

Train the network.

Parameters

• X — 'pd.DataFrame'
• y — 'pd.DataFrame'

learn_one

Fits to a set of features x and a real-valued target y.

Parameters

• x — 'dict'
• y — 'base.typing.RegTarget'

predict_many

predict_one

Predict the output of features x.

Parameters

• x — 'dict'

Returns

base.typing.RegTarget: The prediction.