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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 NoKernel


    

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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=5, kernel=NoKernel,
                        progress_monitoring_freq=100, max_epochs=1000)


    

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model.find_good_learning_rate(starting_eta0=1e-4)


    

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model.eta0


    

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model.eta0 =  model.eta0/100
model.eta = model.eta0


    

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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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model.plot_square_loss(logx=False, logy=False, head_n=15)


    

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model.plot_w_hat_history()


    

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model.plot_loss_and_eta()


    

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model.results.columns


    

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model.plot_loss_and_eta()


    

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


    

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model_diverge.plot_square_loss()