Neural Network
- class statinf.ml.neuralnetwork.Layer(n_in, n_out, activation='linear', W=None, b=None, init_weights='xavier', init_bias='zeros', seed=DeviceArray([0, 0], dtype=uint32))[source]
Bases:
object
- class statinf.ml.neuralnetwork.MLP(loss='MSE', random=None)[source]
Bases:
objectMulti-Layer Perceptron
- References
Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386.
- fit(data, X, Y='Y', epochs=100, optimizer='SGD', batch_size=1, training_size=0.8, test_set=None, learning_rate=0.05, L1_reg=0.0, L2_reg=0.0, early_stop=True, patience=10, improvement_threshold=0.995, restore_weights=True, verbose=True, verbose_all=False, plot=False, *args)[source]
Train the Neural Network.
- Parameters
data (
pandas.DataFrame) – Layer id from which to fetch the parameters.X (
list) – List of X input variables.Y (
str, optional) – Variable to predict, defaults to ‘Y’.epochs (
int, optional) – Number of epochs to train the network, defaults to 100.optimizer (
str, optional) – Algortihm to use to minimize the loss function, defaults to ‘SGD’ (see optimizers).batch_size (
int, optional) – Size of each batch to be trained, defaults to 1.training_size (
float, optional) – Ratio of the data to be used for training set (\(\in (0, 1)\)) the remainder is used for test set, defaults to 0.8.test_set (
pandas.DataFrame, optional) – Data frame to use as test set (overrides training_size if provided), defaults to None.learning_rate (
float, optional) – Learning rate (step size) for gradient descent, defaults to 0.01.L1_reg (
float, optional) – Coefficient \(\lambda_{1} \in (0,1)\) for the L1 penalty, defaults to 0.L2_reg (
float, optional) – Coefficient \(\lambda_{2} \in (0,1)\) for the L2 penalty, defaults to 0.early_stop (
bool, optional) – Apply early-stopping method to avoid over-fitting. Stop the training sequence after the patience (see below) with no improvement, defaults to True.patience (
int, optional) – Number of iterations to wait after a minimum has been found. If no improvement, training sequence is stopped, defaults to 10.improvement_threshold (
float, optional) – Change level of the loss from which we consider it is an improvement, defaults to 0.995.restore_weights (
bool, optional) – If training is early-stopped, restore the weights of the best iteration, defaults to True.verbose (
int, optional) – Verbosity level (True or 1 for minimal display, 2 for details), defaults to True.plot (
bool, optional) – Plot the evolution of the training and test loss for each epoch, defaults to False.*args (
str) – Arguments to be passed to the optimizer.
- References
Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). New York: Springer series in statistics.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
- get_weights(layer='all', param='all')[source]
Fetches the parameters from the network.
- Parameters
layer (
int, optional) – Layer id from which to fetch the parameters, defaults to ‘all’param (
str, optional) – What parameter we need to fetch (can either be ‘weight’, ‘bias’ or ‘all’), defaults to ‘all’
- Returns
Weights and Bias used in the Neural Network.
- Return type
dict
- predict(new_data, binary=False, threshold=0.5)[source]
Generates output prediction after feedforward pass.
- Parameters
new_data (
pandas.DataFrame) – Input data.binary (
bool, optional) – Boolean for returning a binary output (not probability), defaults to False.threshold (
float, optional) – Probability threshold for binary response, defaults to 0.5.
- Returns
Predicted values.
- Return type
list