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Edit and run



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import sys
print(sys.version)



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import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import time

import pandas as pd
import seaborn as sns



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import sys
sys.path.append('../code/')



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from least_squares_sgd import LeastSquaresSGD

from rbf_kernel import RBFKernel



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from sklearn.datasets import make_classification
X, y = make_classification(n_samples=100, n_features=60, 
                           n_informative=60, n_redundant=0, n_repeated=0, 
                           n_classes=5, n_clusters_per_class=1, 
                           weights=None, flip_y=0.001, class_sep=1.0, 
                           hypercube=True, shift=0.0, scale=1.0, 
                           shuffle=True, random_state=None)



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model = LeastSquaresSGD(X=X, y=y, batch_size=10, kernel=RBFKernel,
                        progress_monitoring_freq=100, max_epochs=1000)



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model.eta0



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model.eta0 # = model.eta0/10



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model.run()



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model.results



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model.plot_01_loss()



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model.plot_01_loss(logx=True)



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model.plot_square_loss(logx=False)



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fig, ax = plt.subplots(1, 1, figsize=(4,3))
plot_data = model.results[model.results['step'] > 1]
plot_x = 'step'
plot_y = 'training (0/1 loss)/N'
colors = ['gray']
plt.plot(plot_data[plot_x], plot_data[plot_y],
             linestyle='--', marker='o',
             color=colors[0])



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model.plot_w_hat_history()



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model.results.columns



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model.plot_loss_and_eta()

Should diverge:



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model_diverge = LeastSquaresSGD(X=X, y=y, batch_size=2, eta0 = model.eta0*10,
                                kernel=RBFKernel, 
                                progress_monitoring_freq=100, max_epochs=500)
model_diverge.run()



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model_diverge.plot_square_loss()