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file mode 100644 index 0000000000..d87f82cad6 --- /dev/null +++ b/river/compression/__init__.py @@ -0,0 +1,11 @@ +"""Compression-based text classification. This module contains wrappers to enable compression-based +text classification using different algorithms.""" +from __future__ import annotations + +from .text_compression_classifier import ( + TextCompressionClassifier, +) + +__all__ = [ + "TextCompressionClassifier", +] diff --git a/river/compression/test-compression.ipynb b/river/compression/test-compression.ipynb new file mode 100644 index 0000000000..db32c31b06 --- /dev/null +++ b/river/compression/test-compression.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from sklearn.datasets import fetch_20newsgroups\n", + "from river import compression\n", + "from sklearn.metrics import classification_report\n", + "\n", + "# prepare train dataset\n", + "categories = [\n", + " 'alt.atheism',\n", + " 'talk.religion.misc',\n", + " 'comp.graphics',\n", + " 'sci.space'\n", + "]\n", + "\n", + "train = fetch_20newsgroups(subset='train', categories=categories)\n", + "test = fetch_20newsgroups(subset='test', categories=categories)\n", + "\n", + "model = compression.TextCompressionClassifier()\n", + "\n", + "for i, text in enumerate(train['data']):\n", + " x = text \n", + " y = train['target_names'][train['target'][i]]\n", + " model = model.learn_one(x, y)\n", + "\n", + "predictions = []\n", + "\n", + "for text in test['data']:\n", + " y_pred = model.predict_one(text)\n", + " predictions.append(y_pred)\n", + "\n", + "test_labels = [\n", + " test['target_names'][label]\n", + " for label in test['target']\n", + "]\n", + "\n", + "print(classification_report(\n", + " test_labels,\n", + " predictions,\n", + " digits=3\n", + "))" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/river/compression/teste.ipynb b/river/compression/teste.ipynb new file mode 100644 index 0000000000..9b9a6a5609 --- /dev/null +++ b/river/compression/teste.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n", + "estou na classe !! hello world\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from river import compression\n", + "model = compression.TextCompressionClassifier()\n", + "\n", + "model.test(\"hello world\")" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "1c919c39556be274582a1f16bb0a84a92481bcfbe155bed1bcf4102511c6f533" + }, + "kernelspec": { + "display_name": "Python 3.8.6rc1 ('.venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6rc1" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/river/compression/text_compression_classifier.py b/river/compression/text_compression_classifier.py new file mode 100644 index 0000000000..49eb9e5dbf --- /dev/null +++ b/river/compression/text_compression_classifier.py @@ -0,0 +1,135 @@ +from __future__ import annotations +from collections import deque + +import zstandard # type: ignore + +from river import base + + +class TextCompressionClassifier(base.Classifier): + """Classifier based on text compression techniques. + + The classifier utilizes the `zstandard` compression library to classify text by measuring + how well a new piece of data compresses with existing data of each class. + + Attributes: + compression_level (int): The level of compression. Default is 3. + k (int): The maximum number of documents to be stored per label. Default is 150. + label_documents (dict): Stores concatenated documents for each label. + compression_contexts (dict): Stores Zstd compression contexts for each label. + + """ + def __init__(self, compression_level=3, k=150): + """ + Initializes the TextCompressionClassifier. + + Args: + compression_level (int, optional): The desired compression level. Defaults to 3. + k (int, optional): The maximum number of documents to store per label. Defaults to 150. + """ + self.compression_level = compression_level + self.k = k + self.label_documents = {} # Concatenated documents for each label + self.compression_contexts = {} # Zstd compression contexts for each label + + def __getstate__(self): + return {'compression_level': self.compression_level, + 'k': self.k, + 'label_documents': self.label_documents} + + def __setstate__(self, state): + self.compression_level = state['compression_level'] + self.k = state['k'] + self.label_documents = state['label_documents'] + self.compression_contexts = self.recreate_contexts(self.label_documents) + + def recreate_contexts(self, label_documents): + new_compression_contexts = {} + for label, documents in label_documents.items(): + concatenated_documents = " ".join(documents).encode("utf-8") + compression_dict = zstandard.ZstdCompressionDict(concatenated_documents) + + zstd_compressor = zstandard.ZstdCompressor( + level=self.compression_level, dict_data=compression_dict + ) + + new_compression_contexts[label] = zstd_compressor + + return new_compression_contexts + + def _unit_test_skips(self): + return {"check_shuffle_features_no_impact"} + + def learn_one(self, x, y): + """Updates the classifier with a new sample. + + Args: + x (dict): The input sample. + y (str): The label of the input sample. + + Returns: + TextCompressionClassifier: The classifier instance (for chaining). + + """ + # Convert your input 'x' to a string representation if it's not already + # For the sake of example, let's assume 'x' is a dictionary of features + x_str = str(x) + + # Initialize if label is new + if y not in self.label_documents: + self.label_documents[y] = deque(maxlen=self.k) + + # Append the new document and remove the oldest if length > k + self.label_documents[y].append(x_str) + + # Concatenate documents in the deque into a single string + concatenated_documents = " ".join(self.label_documents[y]) + + # Create a dictionary with encoded concatenated text + compression_dict = zstandard.ZstdCompressionDict(concatenated_documents.encode("utf-8")) + + # Create a Zstandard compression context for this label using the dictionary + zstd_compressor = zstandard.ZstdCompressor( + level=self.compression_level, dict_data=compression_dict + ) + + # Update the compression context for this label with the new compressor + self.compression_contexts[y] = zstd_compressor + + return self + + def predict_one(self, x): + """Predict the output label for the input `x`. + + Parameters + ---------- + x : any type that can be converted to string + The input to be classified. + + Returns + ------- + best_label: string + The label corresponding to the smallest increase in compressed size after + adding the new data `x`. + """ + min_size_increase = float("inf") + best_label = None + + # Convert your input 'x' to a string representation if it's not already + x_str = str(x) + + for label, compressor in self.compression_contexts.items(): + concatenated_doc = (" ".join(self.label_documents[label]) + " " + x_str).encode("utf-8") + compressed_size = len(compressor.compress(concatenated_doc)) + + previous_size = len( + compressor.compress(" ".join(self.label_documents[label]).encode("utf-8")) + ) + + size_increase = compressed_size - previous_size + + if size_increase < min_size_increase: + min_size_increase = size_increase + best_label = label + + return best_label diff --git a/river/test_estimators.py b/river/test_estimators.py index 18aacf32f2..513f363b18 100644 --- a/river/test_estimators.py +++ b/river/test_estimators.py @@ -12,6 +12,7 @@ checks, compat, compose, + compression, facto, feature_extraction, feature_selection, @@ -105,6 +106,7 @@ def can_be_tested(estimator): ) for estimator in list(iter_estimators_which_can_be_tested()) + [ + compression.TextCompressionClassifier(), preprocessing.StandardScaler() | linear_model.LinearRegression(), preprocessing.StandardScaler() | linear_model.PAClassifier(), (