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107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
5 years ago
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from __future__ import print_function
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from __future__ import unicode_literals
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from __future__ import division
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from builtins import str, bytes, dict, int
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import os
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
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import random
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from pattern.db import Datasheet
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from pattern.nl import tag, predicative
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from pattern.vector import SVM, KNN, NB, count, shuffled
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# This example demonstrates a Support Vector Machine (SVM).
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# SVM is a robust classifier that uses "kernel" functions.
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# See: http://www.clips.ua.ac.be/pages/pattern-vector#svm
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#
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# As a metaphor, imagine the following game:
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# - The ground is scattered with red and blue marbles.
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# - It is your task to separate them using a single, straight line.
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#
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# The separation is going to be a rough approximation, obviously.
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#
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# Now imagine the following game:
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# - The room is filled with static, floating red and blue marbles.
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# - It is your task to separate them by inserting a glass panel between them.
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#
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# The 3-D space gives a lot more options. Adding more dimensions add even more options.
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# This is roughly what a SVM does, using kernel functions to push the separation
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# to a higher dimension.
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# Pattern includes precompiled C binaries of libsvm.
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# If these do not work on your system you have to compile libsvm manually.
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# You can also change the "SVM()" statement below with "KNN()",
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# so you can still follow the rest of the example.
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classifier = SVM()
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# We'll build a classifier to predict sentiment in Dutch movie reviews.
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# For example, "geweldige film!" (great movie) indicates a positive sentiment.
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# The CSV file at pattern/test/corpora/polarity-nl-bol.com.csv
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# contains 1,500 positive and 1,500 negative reviews.
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# The pattern.vector module has a shuffled() function
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# which we use to randomly arrange the reviews in the list:
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print("loading data...")
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data = os.path.join(os.path.dirname(__file__), "..", "..", "test", "corpora", "polarity-nl-bol.com.csv")
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data = Datasheet.load(data)
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data = shuffled(data)
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# We do not necessarily need Document objects as in the previous examples.
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# We can train any classifier on simple Python dictionaries too.
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# This is sometimes easier if you want full control over the data.
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# The instance() function below returns a train/test instance for a given review:
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# 1) parse the review for part-of-speech tags,
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# 2) keep adjectives, adverbs and exclamation marks (these mainly carry sentiment),
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# 3) lemmatize the Dutch adjectives, e.g., "goede" => "goed" (good).
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# 4) count the distinct words in the list, map it to a dictionary.
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def instance(review): # "Great book!"
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v = tag(review) # [("Great", "JJ"), ("book", "NN"), ("!", "!")]
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v = [word for (word, pos) in v if pos in ("JJ", "RB") or word in ("!")]
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v = [predicative(word) for word in v] # ["great", "!", "!"]
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v = count(v) # {"great": 1, "!": 1}
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return v
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# We can add any kind of features to a custom instance dict.
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# For example, in a deception detection experiment
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# we may want to populate the dict with PRP (pronouns), punctuation marks,
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# average sentence length, a score for word diversity, etc.
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# Use 1,000 random instances as training material.
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print("training...")
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for score, review in data[:1000]:
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classifier.train(instance(review), type=int(score) > 0)
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#classifier.save("sentiment-nl-svm.p")
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#classifier = SVM.load("sentiment-nl-svm.p")
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# Use 500 random instances as test.
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print("testing...")
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i = n = 0
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for score, review in data[1000:1500]:
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if classifier.classify(instance(review)) == (int(score) > 0):
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i += 1
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n += 1
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# The overall accuracy is around 82%.
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# A Naieve Bayes classifier has about 78% accuracy.
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# A KNN classifier has about 80% accuracy.
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# Careful: to get a reliable score you need to calculate precision and recall,
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# study the documentation at:
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# http://www.clips.ua.ac.be/pages/pattern-metrics#accuracy
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print(float(i) / n)
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# The work is not done here.
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# Low accuracy is disappointing, but high accuracy is often suspicious.
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# Things to look out for:
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# - distinction between train and test set,
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# - overfitting: http://en.wikipedia.org/wiki/Overfitting
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