BuildingMachineLearningSystemsWithPython/ch05/utils.py at master · luispedro/BuildingMachineLearningSystemsWithPython · GitHub

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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

try:

import ujson as json # UltraJSON if available

except:

import json

from matplotlib import pylab

import numpy as np

from data import CHART_DIR

def fetch_data(filename, col=None, line_count=-1, only_questions=False):

count = 0

for line in open(filename, "r"):

count += 1

if line_count > 0 and count > line_count:

break

data = Id, ParentId, IsQuestion, IsAccepted, TimeToAnswer, Score, Text, NumTextTokens, NumCodeLines, LinkCount, MisSpelledFraction = line.split(

"\t")

IsQuestion = int(IsQuestion)

if only_questions and not IsQuestion:

continue

if col:

if col < 6:

val = int(data[col])

else:

val = data[col]

yield val

else:

Id = int(Id)

assert Id >= 0, line

ParentId = int(ParentId)

IsAccepted = int(IsAccepted)

assert not IsQuestion == IsAccepted == 1, "%i %i --- %s" % (

IsQuestion, IsAccepted, line)

assert (ParentId == -1 and IsQuestion) or (

ParentId >= 0 and not IsQuestion), "%i %i --- %s" % (ParentId, IsQuestion, line)

TimeToAnswer = int(TimeToAnswer)

Score = int(Score)

NumTextTokens = int(NumTextTokens)

NumCodeLines = int(NumCodeLines)

LinkCount = int(LinkCount)

MisSpelledFraction = float(MisSpelledFraction)

yield Id, ParentId, IsQuestion, IsAccepted, TimeToAnswer, Score, Text, NumTextTokens, NumCodeLines, LinkCount, MisSpelledFraction

def fetch_posts(filename, with_index=True, line_count=-1):

count = 0

for line in open(filename, "r"):

count += 1

if line_count > 0 and count > line_count:

break

Id, Text = line.split("\t")

Text = Text.strip()

if with_index:

yield int(Id), Text

else:

yield Text

def load_meta(filename):

meta = json.load(open(filename, "r"))

keys = list(meta.keys())

# JSON only allows string keys, changing that to int

for key in keys:

meta[int(key)] = meta[key]

del meta[key]

# post Id to index in vectorized

id_to_idx = {}

# and back

idx_to_id = {}

for PostId, Info in meta.items():

id_to_idx[PostId] = idx = Info['idx']

idx_to_id[idx] = PostId

return meta, id_to_idx, idx_to_id

def plot_roc(auc_score, name, fpr, tpr):

pylab.figure(num=None, figsize=(6, 5))

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

pylab.xlim([0.0, 1.0])

pylab.ylim([0.0, 1.0])

pylab.xlabel('False Positive Rate')

pylab.ylabel('True Positive Rate')

pylab.title('Receiver operating characteristic (AUC=%0.2f)\n%s' % (

auc_score, name))

pylab.legend(loc="lower right")

pylab.grid(True, linestyle='-', color='0.75')

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

pylab.plot(fpr, tpr, lw=1)

pylab.savefig(

os.path.join(CHART_DIR, "roc_" + name.replace(" ", "_") + ".png"))

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

pylab.figure(num=None, figsize=(6, 5))

pylab.xlim([0.0, 1.0])

pylab.ylim([0.0, 1.0])

pylab.xlabel('Recall')

pylab.ylabel('Precision')

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

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

pylab.grid(True, linestyle='-', color='0.75')

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

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

pylab.savefig(os.path.join(CHART_DIR, "pr_" + filename + ".png"))

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

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

top = list(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_feat_importance(feature_names, clf, name):

pylab.figure(num=None, figsize=(6, 5))

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(list(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):

if len(data_name_list) > 1:

assert filename is not None

pylab.figure(num=None, figsize=(8, 6))

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

num_cols = int(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('Fraction')

# 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, alpha=0.75)

pylab.grid(True)

if not filename:

filename = "feat_hist_%s.png" % name.replace(" ", "_")

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

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

pylab.figure(num=None, figsize=(6, 5))

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

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

pylab.grid(True, linestyle='-', color='0.75')

pylab.savefig(

os.path.join(CHART_DIR, "bv_" + name.replace(" ", "_") + ".png"), bbox_inches="tight")

def plot_k_complexity(ks, train_errors, test_errors):

pylab.figure(num=None, figsize=(6, 5))

pylab.ylim([0.0, 1.0])

pylab.xlabel('k')

pylab.ylabel('Error')

pylab.title('Errors for for different values of $k$')

pylab.plot(

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

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

pylab.grid(True, linestyle='-', color='0.75')

pylab.savefig(

os.path.join(CHART_DIR, "kcomplexity.png"), bbox_inches="tight")

BuildingMachineLearningSystemsWithPython/ch05/utils.py at master · luispedro/BuildingMachineLearningSystemsWithPython · GitHub

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