try:
    import nltk
except ModuleNotFoundError:
    !pip install nltk

## This code downloads the required packages.
## You can run `nltk.download('all')` to download everything.

nltk_packages = [
    ("reuters", "corpora/reuters.zip")
]

for pid, fid in nltk_packages:
    try:
        nltk.data.find(fid)
    except LookupError:
        nltk.download(pid)

from nltk.corpus import reuters

train_documents, train_categories = zip(*[(reuters.raw(i), reuters.categories(i)) for i in reuters.fileids() if i.startswith('training/')])
test_documents, test_categories = zip(*[(reuters.raw(i), reuters.categories(i)) for i in reuters.fileids() if i.startswith('test/')])

all_categories = sorted(list(set(reuters.categories())))

from nltk.stem.porter import PorterStemmer
def tokenize(text):
    tokens = nltk.word_tokenize(text)
    stems = []
    for item in tokens:
        stems.append(PorterStemmer().stem(item))
    return stems

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(tokenizer = tokenize, stop_words = 'english')

vectorised_train_documents = vectorizer.fit_transform(train_documents)
vectorised_test_documents = vectorizer.transform(test_documents)

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()
train_labels = mlb.fit_transform(train_categories)
test_labels = mlb.transform(test_categories)

%%capture
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC

classifier = OneVsRestClassifier(LinearSVC())
classifier.fit(vectorised_train_documents, train_labels)

%%capture
from sklearn.model_selection import KFold, cross_val_score

kf = KFold(n_splits=10, random_state = 42, shuffle = True)
scores = cross_val_score(classifier, vectorised_train_documents, train_labels, cv = kf)

print('Cross-validation scores:', scores)
print('Cross-validation accuracy: {:.4f} (+/- {:.4f})'.format(scores.mean(), scores.std() * 2))

%%capture
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix

predictions = classifier.predict(vectorised_test_documents)

accuracy = accuracy_score(test_labels, predictions)

macro_precision = precision_score(test_labels, predictions, average='macro')
macro_recall = recall_score(test_labels, predictions, average='macro')
macro_f1 = f1_score(test_labels, predictions, average='macro')

micro_precision = precision_score(test_labels, predictions, average='micro')
micro_recall = recall_score(test_labels, predictions, average='micro')
micro_f1 = f1_score(test_labels, predictions, average='micro')

cm = confusion_matrix(test_labels.argmax(axis = 1), predictions.argmax(axis = 1))

print("Accuracy: {:.4f}\nPrecision:\n- Macro: {:.4f}\n- Micro: {:.4f}\nRecall:\n- Macro: {:.4f}\n- Micro: {:.4f}\nF1-measure:\n- Macro: {:.4f}\n- Micro: {:.4f}".format(accuracy, macro_precision, micro_precision, macro_recall, micro_recall, macro_f1, micro_f1))

import matplotlib.pyplot as plt
import seaborn as sb
import pandas as pd

cm_plt = pd.DataFrame(cm[:73])

plt.figure(figsize = (25, 25))
ax = plt.axes()

sb.heatmap(cm_plt, annot=True)

ax.xaxis.set_ticks_position('top')

plt.show()

example_text = '''The coconut tree (Cocos nucifera) is a member of the family Arecaceae (palm family) and the only species of the genus Cocos.
The term coconut can refer to the whole coconut palm or the seed, or the fruit, which, botanically, is a drupe, not a nut.
The spelling cocoanut is an archaic form of the word.
The term is derived from the 16th-century Portuguese and Spanish word coco meaning "head" or "skull", from the three indentations on the coconut shell that resemble facial features.
Coconuts are known for their versatility ranging from food to cosmetics.
They form a regular part of the diets of many people in the tropics and subtropics.
Coconuts are distinct from other fruits for their endosperm containing a large quantity of water (also called "milk"), and when immature, may be harvested for the potable coconut water.
When mature, they can be used as seed nuts or processed for oil, charcoal from the hard shell, and coir from the fibrous husk.
When dried, the coconut flesh is called copra.
The oil and milk derived from it are commonly used in cooking and frying, as well as in soaps and cosmetics.
The husks and leaves can be used as material to make a variety of products for furnishing and decorating.
The coconut also has cultural and religious significance in certain societies, particularly in India, where it is used in Hindu rituals.'''

example_preds = classifier.predict(vectorizer.transform([example_text]))
example_labels = mlb.inverse_transform(example_preds)
print("Example labels: {}".format(example_labels))