"""

======================================================

Out-of-core classification of text documents

======================================================

This is an example showing how scikit-learn can be used for classification

using an out-of-core approach: learning from data that doesn't fit into main

memory. We make use of an online classifier, i.e., one that supports the

partial_fit method, that will be fed with batches of examples. To guarantee

that the features space remains the same over time we leverage a

HashingVectorizer that will project each example into the same feature space.

This is especially useful in the case of text classification where new

features (words) may appear in each batch.

The dataset used in this example is Reuters-21578 as provided by the UCI ML

repository. It will be automatically downloaded and uncompressed on first run.

The plot represents the learning curve of the classifier: the evolution

of classification accuracy over the course of the mini-batches. Accuracy is

measured on the first 1000 samples, held out as a validation set.

To limit the memory consumption, we queue examples up to a fixed amount before

feeding them to the learner.

"""

# Authors: Eustache Diemert <eustache@diemert.fr>

#          @FedericoV <https://github.com/FedericoV/>

# License: BSD 3 clause

from __future__ import print_function

from glob import glob

import itertools

import os.path

import re

import tarfile

import time

import numpy as np

import matplotlib.pyplot as plt

from matplotlib import rcParams

from sklearn.externals.six.moves import html_parser

from sklearn.externals.six.moves import urllib

from sklearn.datasets import get_data_home

from sklearn.feature_extraction.text import HashingVectorizer

from sklearn.linear_model import SGDClassifier

from sklearn.linear_model import PassiveAggressiveClassifier

from sklearn.linear_model import Perceptron

from sklearn.naive_bayes  import MultinomialNB

def _not_in_sphinx():

    # Hack to detect whether we are running by the sphinx builder

    return '__file__' in globals()

###############################################################################

# Reuters Dataset related routines

# --------------------------------

#

comment

class ReutersParser(html_parser.HTMLParser):

    """Utility class to parse a SGML file and yield documents one at a time."""

    def __init__(self, encoding='latin-1'):

        html_parser.HTMLParser.__init__(self)

        self._reset()

        self.encoding = encoding

    def handle_starttag(self, tag, attrs):

        method = 'start_' + tag

        getattr(self, method, lambda x: None)(attrs)

    def handle_endtag(self, tag):

        method = 'end_' + tag

        getattr(self, method, lambda: None)()

    def _reset(self):

        self.in_title = 0

        self.in_body = 0

        self.in_topics = 0

        self.in_topic_d = 0

        self.title = ""

        self.body = ""

        self.topics = []

        self.topic_d = ""

    def parse(self, fd):

        self.docs = []

        for chunk in fd:

            self.feed(chunk.decode(self.encoding))

            for doc in self.docs:

                yield doc

            self.docs = []

        self.close()

    def handle_data(self, data):

        if self.in_body:

            self.body += data

        elif self.in_title:

            self.title += data

        elif self.in_topic_d:

            self.topic_d += data

    def start_reuters(self, attributes):

        pass

    def end_reuters(self):

        self.body = re.sub(r'\s+', r' ', self.body)

        self.docs.append({'title': self.title,

                          'body': self.body,

                          'topics': self.topics})

        self._reset()

    def start_title(self, attributes):

        self.in_title = 1

    def end_title(self):

        self.in_title = 0

    def start_body(self, attributes):

        self.in_body = 1

    def end_body(self):

        self.in_body = 0

    def start_topics(self, attributes):

        self.in_topics = 1

    def end_topics(self):

        self.in_topics = 0

    def start_d(self, attributes):

        self.in_topic_d = 1

    def end_d(self):

        self.in_topic_d = 0

        self.topics.append(self.topic_d)

        self.topic_d = ""

class ReutersParser

def stream_reuters_documents(data_path=None):

    """Iterate over documents of the Reuters dataset.

    The Reuters archive will automatically be downloaded and uncompressed if

    the `data_path` directory does not exist.

    Documents are represented as dictionaries with 'body' (str),

    'title' (str), 'topics' (list(str)) keys.

    """

    DOWNLOAD_URL = ('http://archive.ics.uci.edu/ml/machine-learning-databases/reuters21578-mld/reuters21578.tar.gz')

    ARCHIVE_FILENAME = 'reuters21578.tar.gz'

    if data_path is None:

        data_path = os.path.join(get_data_home(), "reuters")

    if not os.path.exists(data_path):

        """Download the dataset."""

        print("downloading dataset (once and for all) into %s" %

              data_path)

        os.mkdir(data_path)

        def progress(blocknum, bs, size):

            total_sz_mb = '%.2f MB' % (size / 1e6)

            current_sz_mb = '%.2f MB' % ((blocknum * bs) / 1e6)

            if _not_in_sphinx():

                print('\rdownloaded %s / %s' % (current_sz_mb, total_sz_mb),

                      end='')

        archive_path = os.path.join(data_path, ARCHIVE_FILENAME)

        urllib.request.urlretrieve(DOWNLOAD_URL, filename=archive_path,

                                   reporthook=progress)

        if _not_in_sphinx():

            print('\r', end='')

        print("untarring Reuters dataset...")

        tarfile.open(archive_path, 'r:gz').extractall(data_path)

        print("done.")

    parser = ReutersParser()

    for filename in glob(os.path.join(data_path, "*.sgm")):

        for doc in parser.parse(open(filename, 'rb')):

            yield doc

stream_reuters_documents

###############################################################################

# Main

# ----

#

# Create the vectorizer and limit the number of features to a reasonable

# maximum

vectorizer = HashingVectorizer(decode_error='ignore', n_features=2 ** 18, non_negative=True)

# Iterator over parsed Reuters SGML files.

data_stream = stream_reuters_documents()

# We learn a binary classification between the "acq" class and all the others.

# "acq" was chosen as it is more or less evenly distributed in the Reuters

# files. For other datasets, one should take care of creating a test set with

# a realistic portion of positive instances.

all_classes    = np.array([0, 1])

positive_class = 'acq'

# Here are some classifiers that support the `partial_fit` method

partial_fit_classifiers = {

    'SGD': SGDClassifier(),

    'Perceptron': Perceptron(),

    'NB Multinomial': MultinomialNB(alpha=0.01),

    'Passive-Aggressive': PassiveAggressiveClassifier(),

}

def get_minibatch(doc_iter, size, pos_class=positive_class):

    """Extract a minibatch of examples, return a tuple X_text, y.

    Note: size is before excluding invalid docs with no topics assigned.

    """

    data = [(u'{title}\n\n{body}'.format(**doc), pos_class in doc['topics'])

            for doc in itertools.islice(doc_iter, size)

            if doc['topics']]

    if not len(data):

        return np.asarray([], dtype=int), np.asarray([], dtype=int)

    X_text, y = zip(*data)

    return X_text, np.asarray(y, dtype=int)

def iter_minibatches(doc_iter, minibatch_size):

    """Generator of minibatches."""

    X_text, y = get_minibatch(doc_iter, minibatch_size)

    while len(X_text):

        yield X_text, y

        X_text, y = get_minibatch(doc_iter, minibatch_size)

# test data statistics

test_stats = {'n_test': 0, 'n_test_pos': 0}

# First we hold out a number of examples to estimate accuracy

n_test_documents    = 1000

tick                = time.time()

X_test_text, y_test = get_minibatch(data_stream, 1000)

parsing_time        = time.time() - tick

tick   = time.time()

X_test = vectorizer.transform(X_test_text)

vectorizing_time          = time.time() - tick

test_stats['n_test']     += len(y_test)

test_stats['n_test_pos'] += sum(y_test)

print("Test set is %d documents (%d positive)" % (len(y_test), sum(y_test)))



def progress(cls_name, stats):

    """Report progress information, return a string."""

    duration = time.time() - stats['t0']

    s  = "%20s classifier : \t" % cls_name

    s += "%(n_train)6d train docs (%(n_train_pos)6d positive) " % stats

    s += "%(n_test)6d test docs (%(n_test_pos)6d positive) " % test_stats

    s += "accuracy: %(accuracy).3f " % stats

    s += "in %.2fs (%5d docs/s)" % (duration, stats['n_train'] / duration)

    return s

cls_stats = {}

for cls_name in partial_fit_classifiers:

    stats = {'n_train':     0, 
             'n_train_pos': 0,

             'accuracy':    0.0, 
             'accuracy_history': [(0, 0)], 
             't0':               time.time(),

             'runtime_history':  [(0, 0)], 
             'total_fit_time':   0.0 }

    cls_stats[cls_name] = stats

get_minibatch(data_stream, n_test_documents)

# Discard test set

# We will feed the classifier with mini-batches of 1000 documents; this means

# we have at most 1000 docs in memory at any time.  The smaller the document

# batch, the bigger the relative overhead of the partial fit methods.

minibatch_size = 1000

# Create the data_stream that parses Reuters SGML files and iterates on

# documents as a stream.

minibatch_iterators = iter_minibatches(data_stream, minibatch_size)

total_vect_time = 0.0

# Main loop : iterate on mini-batches of examples
# 来一批，大家各自训练一次；再来一批，大家各自再训练一次

for i, (X_train_text, y_train) in enumerate(minibatch_iterators):

    tick = time.time()

    X_train = vectorizer.transform(X_train_text)

    total_vect_time += time.time() - tick

    for cls_name, cls in partial_fit_classifiers.items():

        tick = time.time()

        # update estimator with examples in the current mini-batch

        cls.partial_fit(X_train, y_train, classes=all_classes)

        # accumulate test accuracy stats

        cls_stats[cls_name]['total_fit_time'] += time.time() - tick

        cls_stats[cls_name]['n_train']        += X_train.shape[0]

        cls_stats[cls_name]['n_train_pos']    += sum(y_train)

        tick = time.time()

        cls_stats[cls_name]['accuracy']        = cls.score(X_test, y_test)

        cls_stats[cls_name]['prediction_time'] = time.time() - tick

        acc_history = (cls_stats[cls_name]['accuracy'], cls_stats[cls_name]['n_train'])

        cls_stats[cls_name]['accuracy_history'].append(acc_history)

        run_history = (cls_stats[cls_name]['accuracy'], total_vect_time + cls_stats[cls_name]['total_fit_time'])

        cls_stats[cls_name]['runtime_history'].append(run_history)

        if i % 3 == 0:

            print(progress(cls_name, cls_stats[cls_name]))

    if i % 3 == 0:

        print('\n')

###############################################################################

# Plot results

# ------------

def plot_accuracy(x, y, x_legend):

    """Plot accuracy as a function of x."""

    x = np.array(x)

    y = np.array(y)

    plt.title('Classification accuracy as a function of %s' % x_legend)

    plt.xlabel('%s' % x_legend)

    plt.ylabel('Accuracy')

    plt.grid(True)

    plt.plot(x, y)

rcParams['legend.fontsize'] = 10

cls_names = list(sorted(cls_stats.keys()))



# Plot accuracy evolution

plt.figure()

for _, stats in sorted(cls_stats.items()):

    # Plot accuracy evolution with #examples

    accuracy, n_examples = zip(*stats['accuracy_history'])

    plot_accuracy(n_examples, accuracy, "training examples (#)")

    ax = plt.gca()

    ax.set_ylim((0.8, 1))

plt.legend(cls_names, loc='best')

plt.figure()

for _, stats in sorted(cls_stats.items()):

    # Plot accuracy evolution with runtime

    accuracy, runtime = zip(*stats['runtime_history'])

    plot_accuracy(runtime, accuracy, 'runtime (s)')

    ax = plt.gca()

    ax.set_ylim((0.8, 1))

plt.legend(cls_names, loc='best')

# Plot fitting times

plt.figure()

fig = plt.gcf()

cls_runtime = []

for cls_name, stats in sorted(cls_stats.items()):

    cls_runtime.append(stats['total_fit_time'])

cls_runtime.append(total_vect_time)

cls_names.append('Vectorization')

bar_colors = ['b', 'g', 'r', 'c', 'm', 'y']

ax = plt.subplot(111)

rectangles = plt.bar(range(len(cls_names)), cls_runtime, width=0.5,

                     color=bar_colors)

ax.set_xticks(np.linspace(0.25, len(cls_names) - 0.75, len(cls_names)))

ax.set_xticklabels(cls_names, fontsize=10)

ymax = max(cls_runtime) * 1.2

ax.set_ylim((0, ymax))

ax.set_ylabel('runtime (s)')

ax.set_title('Training Times')

def autolabel(rectangles):

    """attach some text vi autolabel on rectangles."""

    for rect in rectangles:

        height = rect.get_height()

        ax.text(rect.get_x() + rect.get_width() / 2.,

                1.05 * height, '%.4f' % height,

                ha='center', va='bottom')

autolabel(rectangles)

plt.show()

# Plot prediction times

plt.figure()

cls_runtime = []

cls_names = list(sorted(cls_stats.keys()))

for cls_name, stats in sorted(cls_stats.items()):

    cls_runtime.append(stats['prediction_time'])

cls_runtime.append(parsing_time)

cls_names.append('Read/Parse\n+Feat.Extr.')

cls_runtime.append(vectorizing_time)

cls_names.append('Hashing\n+Vect.')

ax = plt.subplot(111)

rectangles = plt.bar(range(len(cls_names)), cls_runtime, width=0.5,

                     color=bar_colors)

ax.set_xticks(np.linspace(0.25, len(cls_names) - 0.75, len(cls_names)))

ax.set_xticklabels(cls_names, fontsize=8)

plt.setp(plt.xticks()[1], rotation=30)

ymax = max(cls_runtime) * 1.2

ax.set_ylim((0, ymax))

ax.set_ylabel('runtime (s)')

ax.set_title('Prediction Times (%d instances)' % n_test_documents)

autolabel(rectangles)

plt.show()