23 lines
617 B
Python
23 lines
617 B
Python
import joblib
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import numpy as np
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from keras import models
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import scipy.stats
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# Import the accuracy of both models
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e_a = 0.856333315372467 # without augmentation
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e_b = 0.843666672706604 # with data augmentation
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# # of data points in both test sets
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L = 3000
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# Compute classification variance for both models
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s_a = e_a * (1 - e_a)
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s_b = e_b * (1 - e_b)
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# Compute Student's T-test
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T = (e_a - e_b) / np.sqrt((s_a / L) + (s_b / L))
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print("T test:\t\t\t %1.06f" % T)
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print("P-value:\t\t %1.06f" % (scipy.stats.t.sf(abs(T), df=L) * 2))
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print("No aug variance:\t %1.06f" % s_a)
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print("With aug variance:\t %1.06f" % s_b)
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