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# This code is supporting material for the book

# Building Machine Learning Systems with Python

# by Willi Richert and Luis Pedro Coelho

# published by PACKT Publishing

#

# It is made available under the MIT License

from sklearn.datasets import load_iris

data = load_iris()

features = data.data

labels = data.target_names[data.target]

is_setosa = (labels == 'setosa')

features = features[~is_setosa]

labels = labels[~is_setosa]

is_virginica = (labels == 'virginica')

# Initialize to a value that is worse than any possible test

best_acc = -1.0

# Loop over all the features

for fi in range(features.shape[1]):

# Test every possible threshold value for feature fi

thresh = features[:, fi].copy()

# Test them in order

thresh.sort()

for t in thresh:

# Generate predictions using t as a threshold

pred = (features[:, fi] > t)

# Accuracy is the fraction of predictions that match reality

acc = (pred == is_virginica).mean()

# We test whether negating the test is a better threshold:

acc_neg = ((~pred) == is_virginica).mean()

if acc_neg > acc:

acc = acc_neg

negated = True

else:

negated = False

# If this is better than previous best, then this is now the new best:

if acc > best_acc:

best_acc = acc

best_fi = fi

best_t = t

best_is_negated = negated

print('Best threshold is {0} on feature {1} (index {2}), which achieves accuracy of {3:.1%}.'.format(

best_t, data.feature_names[best_fi], best_fi, best_acc))