# -*- coding: utf-8 -*-
import numpy as np
from ztlearn.utils import LogIfBusy
from ztlearn.utils import computebar
from ztlearn.initializers import InitializeWeights as init
from ztlearn.objectives import ObjectiveFunction as objective
from ztlearn.optimizers import OptimizationFunction as optimize
from ztlearn.regularizers import RegularizationFunction as regularize
[docs]class Regression(object):
def __init__(self,
epochs,
loss = 'mean_squared_error',
init_method = 'he_uniform',
optimizer = {},
penalty = 'ridge',
penalty_weight = 0.5,
l1_ratio = 0.5):
self.epochs = epochs
self.loss = objective(loss)
self.init_method = init(init_method)
self.optimizer = optimize(optimizer)
self.regularization = regularize(penalty, penalty_weight, l1_ratio = l1_ratio)
[docs] @LogIfBusy
def fit(self, inputs, targets, verbose = False):
fit_stats = {"train_loss": [], "train_acc": [], "valid_loss": [], "valid_acc": []}
inputs = np.column_stack((np.ones(inputs.shape[0]), inputs))
self.weights = self.init_method.initialize_weights((inputs.shape[1], ))
for i in range(self.epochs):
predictions = inputs.dot(self.weights)
mse = self.loss.forward(np.expand_dims(predictions, axis = 1), np.expand_dims(targets, axis = 1)) + self.regularization.regulate(self.weights)
acc = self.loss.accuracy(predictions, targets)
fit_stats["train_loss"].append(np.mean(mse))
fit_stats["train_acc"].append(np.mean(acc))
cost_gradient = self.loss.backward(predictions, targets)
d_weights = cost_gradient.dot(inputs) + self.regularization.derivative(self.weights)
self.weights = self.optimizer.update(self.weights, d_weights, i, 1, 1)
if verbose:
print('TRAINING: Epoch-{} loss: {:2.4f} acc: {:2.4f}'.format(i+1, mse, acc))
else:
computebar(self.epochs, i)
return fit_stats
[docs] def predict(self, inputs):
inputs = np.column_stack((np.ones(inputs.shape[0]), inputs))
return inputs.dot(self.weights)