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utils.py
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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"))