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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

import os

import sys

from matplotlib import pylab

import numpy as np

DATA_DIR = os.path.join(

os.path.dirname(os.path.realpath(__file__)), "data")

CHART_DIR = os.path.join(

os.path.dirname(os.path.realpath(__file__)), "charts")

for d in [DATA_DIR, CHART_DIR]:

if not os.path.exists(d):

os.mkdir(d)

# Put your directory to the different music genres here

GENRE_DIR = None

GENRE_LIST = ["classical", "jazz", "country", "pop", "rock", "metal"]

# Put your directory to the test dir here

TEST_DIR = None

if GENRE_DIR is None or TEST_DIR is None:

print("Please set GENRE_DIR and TEST_DIR in utils.py")

sys.exit(1)

def plot_confusion_matrix(cm, genre_list, name, title):

pylab.clf()

pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0)

ax = pylab.axes()

ax.set_xticks(range(len(genre_list)))

ax.set_xticklabels(genre_list)

ax.xaxis.set_ticks_position("bottom")

ax.set_yticks(range(len(genre_list)))

ax.set_yticklabels(genre_list)

pylab.title(title)

pylab.colorbar()

pylab.grid(False)

pylab.show()

pylab.xlabel('Predicted class')

pylab.ylabel('True class')

pylab.grid(False)

pylab.savefig(

os.path.join(CHART_DIR, "confusion_matrix_%s.png" % name), bbox_inches="tight")

def plot_pr(auc_score, name, precision, recall, label=None):

pylab.clf()

pylab.figure(num=None, figsize=(5, 4))

pylab.grid(True)

pylab.fill_between(recall, precision, alpha=0.5)

pylab.plot(recall, precision, lw=1)

pylab.xlim([0.0, 1.0])

pylab.ylim([0.0, 1.0])

pylab.xlabel('Recall')

pylab.ylabel('Precision')

pylab.title('P/R curve (AUC = %0.2f) / %s' % (auc_score, label))

filename = name.replace(" ", "_")

pylab.savefig(

os.path.join(CHART_DIR, "pr_" + filename + ".png"), bbox_inches="tight")

def plot_roc(auc_score, name, tpr, fpr, label=None):

pylab.clf()

pylab.figure(num=None, figsize=(5, 4))

pylab.grid(True)

pylab.plot([0, 1], [0, 1], 'k--')

pylab.plot(fpr, tpr)

pylab.fill_between(fpr, tpr, alpha=0.5)

pylab.xlim([0.0, 1.0])

pylab.ylim([0.0, 1.0])

pylab.xlabel('False Positive Rate')

pylab.ylabel('True Positive Rate')

pylab.title('ROC curve (AUC = %0.2f) / %s' %

(auc_score, label), verticalalignment="bottom")

pylab.legend(loc="lower right")

filename = name.replace(" ", "_")

pylab.savefig(

os.path.join(CHART_DIR, "roc_" + filename + ".png"), bbox_inches="tight")

def show_most_informative_features(vectorizer, clf, n=20):

c_f = sorted(zip(clf.coef_[0], vectorizer.get_feature_names()))

top = zip(c_f[:n], c_f[:-(n + 1):-1])

for (c1, f1), (c2, f2) in top:

print("\t%.4f\t%-15s\t\t%.4f\t%-15s" % (c1, f1, c2, f2))

def plot_log():

pylab.clf()

x = np.arange(0.001, 1, 0.001)

y = np.log(x)

pylab.title('Relationship between probabilities and their logarithm')

pylab.plot(x, y)

pylab.grid(True)

pylab.xlabel('P')

pylab.ylabel('log(P)')

filename = 'log_probs.png'

pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")

def plot_feat_importance(feature_names, clf, name):

pylab.clf()

coef_ = clf.coef_

important = np.argsort(np.absolute(coef_.ravel()))

f_imp = feature_names[important]

coef = coef_.ravel()[important]

inds = np.argsort(coef)

f_imp = f_imp[inds]

coef = coef[inds]

xpos = np.array(range(len(coef)))

pylab.bar(xpos, coef, width=1)

pylab.title('Feature importance for %s' % (name))

ax = pylab.gca()

ax.set_xticks(np.arange(len(coef)))

labels = ax.set_xticklabels(f_imp)

for label in labels:

label.set_rotation(90)

filename = name.replace(" ", "_")

pylab.savefig(os.path.join(

CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight")

def plot_feat_hist(data_name_list, filename=None):

pylab.clf()

num_rows = 1 + (len(data_name_list) - 1) / 2

num_cols = 1 if len(data_name_list) == 1 else 2

pylab.figure(figsize=(5 * num_cols, 4 * num_rows))

for i in range(num_rows):

for j in range(num_cols):

pylab.subplot(num_rows, num_cols, 1 + i * num_cols + j)

x, name = data_name_list[i * num_cols + j]

pylab.title(name)

pylab.xlabel('Value')

pylab.ylabel('Density')

# the histogram of the data

max_val = np.max(x)

if max_val <= 1.0:

bins = 50

elif max_val > 50:

bins = 50

else:

bins = max_val

n, bins, patches = pylab.hist(

x, bins=bins, normed=1, facecolor='green', alpha=0.75)

pylab.grid(True)

if not filename:

filename = "feat_hist_%s.png" % name

pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")

def plot_bias_variance(data_sizes, train_errors, test_errors, name):

pylab.clf()

pylab.ylim([0.0, 1.0])

pylab.xlabel('Data set size')

pylab.ylabel('Error')

pylab.title("Bias-Variance for '%s'" % name)

pylab.plot(

data_sizes, train_errors, "-", data_sizes, test_errors, "--", lw=1)

pylab.legend(["train error", "test error"], loc="upper right")

pylab.grid(True)

pylab.savefig(os.path.join(CHART_DIR, "bv_" + name + ".png"))