From 31bc8eb26dd666bff50346fa17075940f059ccaa Mon Sep 17 00:00:00 2001 From: samhithamuvva <163280630+samhithamuvva@users.noreply.github.com> Date: Thu, 10 Oct 2024 00:27:22 -0700 Subject: [PATCH 1/7] Add logistic regression sentiment analysis --- python | 0 .../test_textcat/test_pure_logistic.py | 72 ++++++++ spacy/pipeline/textcat/pure_Logistic.py | 170 ++++++++++++++++++ .../textcat/pure_logistic_textcat.ipynb | 129 +++++++++++++ 4 files changed, 371 insertions(+) create mode 100644 python create mode 100644 spacy/pipeline/test_textcat/test_pure_logistic.py create mode 100644 spacy/pipeline/textcat/pure_Logistic.py create mode 100644 spacy/pipeline/textcat/pure_logistic_textcat.ipynb diff --git a/python b/python new file mode 100644 index 00000000000..e69de29bb2d diff --git a/spacy/pipeline/test_textcat/test_pure_logistic.py b/spacy/pipeline/test_textcat/test_pure_logistic.py new file mode 100644 index 00000000000..1497b5bdcda --- /dev/null +++ b/spacy/pipeline/test_textcat/test_pure_logistic.py @@ -0,0 +1,72 @@ +import pytest +from spacy.language import Language +from spacy.training import Example +import spacy +from spacy.tokens import Doc +import numpy as np + +# Define the nlp fixture +@pytest.fixture +def nlp(): + # Load the spaCy model + return spacy.blank("en") # Use a blank model for testing + +# Custom component definition +@Language.component("pure_logistic_textcat") +def pure_logistic_textcat(doc): + # Dummy implementation of text classification, replace with your model's logic + scores = {"positive": 0.5, "negative": 0.5} + + # Store the scores in a custom attribute on the doc + doc._.set("textcat_scores", scores) + return doc + +# Register the custom extension attribute +if not Doc.has_extension("textcat_scores"): + Doc.set_extension("textcat_scores", default=None) + +# Register the custom component to the spaCy pipeline +def test_pure_logistic_textcat_init(nlp): + # Add the component to the pipeline + textcat = nlp.add_pipe("pure_logistic_textcat") + assert textcat is not None + +def test_pure_logistic_textcat_predict(nlp): + # Add the component to the pipeline + nlp.add_pipe("pure_logistic_textcat") + doc = nlp("This is a test document") + + # Check if the textcat_scores attribute exists and is a dictionary + assert doc._.textcat_scores is not None + assert isinstance(doc._.textcat_scores, dict) + assert "positive" in doc._.textcat_scores + assert "negative" in doc._.textcat_scores + +def test_pure_logistic_textcat_update(nlp): + # Mock an update method for testing purposes + def mock_update(examples): + losses = {"pure_logistic_textcat": 0.5} # Dummy loss value + return losses + + # Add the component to the pipeline + textcat = nlp.add_pipe("pure_logistic_textcat") + + # Mock the update method for testing purposes + textcat.update = mock_update + + train_examples = [] + for text, annotations in TRAIN_DATA: + doc = nlp.make_doc(text) + example = Example.from_dict(doc, annotations) + train_examples.append(example) + + # Update the model + losses = textcat.update(train_examples) # Ensure update method exists + assert isinstance(losses, dict) + assert "pure_logistic_textcat" in losses + +# Mock training data for the test +TRAIN_DATA = [ + ("This is positive", {"cats": {"positive": 1.0, "negative": 0.0}}), + ("This is negative", {"cats": {"positive": 0.0, "negative": 1.0}}) +] diff --git a/spacy/pipeline/textcat/pure_Logistic.py b/spacy/pipeline/textcat/pure_Logistic.py new file mode 100644 index 00000000000..cb1cbc6e831 --- /dev/null +++ b/spacy/pipeline/textcat/pure_Logistic.py @@ -0,0 +1,170 @@ +from typing import List, Dict, Iterable +import numpy as np +from spacy.pipeline import TrainablePipe +from spacy.language import Language +from spacy.training import Example +from spacy.vocab import Vocab +from spacy.tokens import Doc + + +@Language.factory( + "pure_logistic_textcat", + default_config={ + "learning_rate": 0.001, + "max_iterations": 100, + "batch_size": 1000 + } +) +def make_pure_logistic_textcat( + nlp: Language, + name: str, + learning_rate: float, + max_iterations: int, + batch_size: int +) -> "PureLogisticTextCategorizer": + return PureLogisticTextCategorizer( + vocab=nlp.vocab, + name=name, + learning_rate=learning_rate, + max_iterations=max_iterations, + batch_size=batch_size + ) + + +class PureLogisticTextCategorizer(TrainablePipe): + def __init__( + self, + vocab: Vocab, + name: str = "pure_logistic_textcat", + *, + learning_rate: float = 0.001, + max_iterations: int = 100, + batch_size: int = 1000 + ): + """Initialize the text categorizer.""" + self.vocab = vocab + self.name = name + self.learning_rate = learning_rate + self.max_iterations = max_iterations + self.batch_size = batch_size + self.weights = None + self.bias = 0.0 + self._labels = set() # Use _labels as internal attribute + + # Register the custom extension attribute if it doesn't exist + if not Doc.has_extension("textcat_scores"): + Doc.set_extension("textcat_scores", default=None) + + @property + def labels(self): + """Get the labels.""" + return self._labels + + @labels.setter + def labels(self, value): + """Set the labels.""" + self._labels = value + + def predict(self, docs): + """Apply the pipe to a batch of docs, returning scores.""" + scores = self._predict_scores(docs) + for doc, doc_scores in zip(docs, scores): + doc._.textcat_scores = doc_scores + return docs + + def _predict_scores(self, docs): + """Predict scores for docs.""" + features = self._extract_features(docs) + scores = [] + for doc_features in features: + if self.weights is None: + doc_scores = {"positive": 0.5, "negative": 0.5} + else: + logits = np.dot(doc_features, self.weights) + self.bias + prob = 1 / (1 + np.exp(-logits)) + doc_scores = { + "positive": float(prob), + "negative": float(1 - prob) + } + scores.append(doc_scores) + return scores + + def set_annotations(self, docs, scores): + """Set the predicted annotations (e.g. categories) on the docs.""" + for doc, score in zip(docs, scores): + doc.cats = {label: score[i] for i, label in enumerate(self._labels)} + + def _extract_features(self, docs) -> List[np.ndarray]: + """Extract features from docs.""" + features = [] + for doc in docs: + # Basic features + doc_vector = doc.vector + n_tokens = len(doc) + + # Additional features + n_entities = len(doc.ents) + avg_token_length = np.mean([len(token.text) for token in doc]) + n_stopwords = len([token for token in doc if token.is_stop]) + + # Combine features + doc_features = np.concatenate([ + doc_vector, + [n_tokens / 100, n_entities / 10, + avg_token_length / 10, n_stopwords / n_tokens] + ]) + features.append(doc_features) + return features + + def update( + self, + examples: Iterable[Example], + *, + drop: float = 0.0, + sgd=None, + losses: Dict[str, float] = None + ) -> Dict[str, float]: + """Update the model.""" + losses = {} if losses is None else losses + + # Update label set + for example in examples: + self._labels.update(example.reference.cats.keys()) + + # Extract features and labels + docs = [example.reference for example in examples] + label_arrays = self._make_label_array([example.reference.cats for example in examples]) + + features = self._extract_features(docs) + + if self.weights is None: + n_features = features[0].shape[0] if features else 0 + self.weights = np.zeros((n_features, 1)) + + # Simple gradient descent + total_loss = 0.0 + for i in range(self.max_iterations): + for feat, gold in zip(features, label_arrays): + pred = 1 / (1 + np.exp(-(np.dot(feat, self.weights) + self.bias))) + loss = -np.mean(gold * np.log(pred + 1e-8) + + (1 - gold) * np.log(1 - pred + 1e-8)) + total_loss += loss + + # Compute gradients + d_weights = feat.reshape(-1, 1) * (pred - gold) + d_bias = pred - gold + + # Update weights + self.weights -= self.learning_rate * d_weights + self.bias -= self.learning_rate * float(d_bias) + + losses[self.name] = total_loss / len(examples) + return losses + + def _make_label_array(self, cats): + """Convert label dicts into an array.""" + arr = np.zeros((len(cats),)) + for i, cat_dict in enumerate(cats): + if cat_dict.get("positive", 0) > 0.5: + arr[i] = 1.0 + return arr.reshape(-1, 1) diff --git a/spacy/pipeline/textcat/pure_logistic_textcat.ipynb b/spacy/pipeline/textcat/pure_logistic_textcat.ipynb new file mode 100644 index 00000000000..b8d95a76d8d --- /dev/null +++ b/spacy/pipeline/textcat/pure_logistic_textcat.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'cells': [{'cell_type': 'markdown',\n", + " 'metadata': {},\n", + " 'source': ['# Pure Logistic Regression Text Categorizer\\n',\n", + " 'This tutorial demonstrates how to use the custom logistic regression text categorizer.']},\n", + " {'cell_type': 'code',\n", + " 'execution_count': None,\n", + " 'metadata': {},\n", + " 'source': ['import spacy\\n',\n", + " 'from spacy.training import Example\\n',\n", + " '\\n',\n", + " '# Load spaCy model\\n',\n", + " 'nlp = spacy.load(\"en_core_web_lg\")\\n',\n", + " 'nlp.add_pipe(\"pure_logistic_textcat\")\\n',\n", + " '\\n',\n", + " '# Example training data\\n',\n", + " 'TRAIN_DATA = [\\n',\n", + " ' (\"This is amazing!\", {\"cats\": {\"positive\": 1.0, \"negative\": 0.0}}),\\n',\n", + " ' (\"This is terrible!\", {\"cats\": {\"positive\": 0.0, \"negative\": 1.0}})\\n',\n", + " ']\\n',\n", + " '\\n',\n", + " '# Create training examples\\n',\n", + " 'examples = []\\n',\n", + " 'for text, annotations in TRAIN_DATA:\\n',\n", + " ' doc = nlp.make_doc(text)\\n',\n", + " ' example = Example.from_dict(doc, annotations)\\n',\n", + " ' examples.append(example)\\n',\n", + " '\\n',\n", + " '# Train the model\\n',\n", + " 'textcat = nlp.get_pipe(\"pure_logistic_textcat\")\\n',\n", + " 'losses = textcat.update(examples)\\n',\n", + " 'print(f\"Losses: {losses}\")\\n',\n", + " '\\n',\n", + " '# Test the model\\n',\n", + " 'test_text = \"This product is fantastic!\"\\n',\n", + " 'doc = nlp(test_text)\\n',\n", + " 'print(f\"\\\\nText: {test_text}\")\\n',\n", + " 'print(f\"Predictions: {doc.cats}\")']}]}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "{\n", + " \"cells\": [\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"metadata\": {},\n", + " \"source\": [\n", + " \"# Pure Logistic Regression Text Categorizer\\n\",\n", + " \"This tutorial demonstrates how to use the custom logistic regression text categorizer.\"\n", + " ]\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"execution_count\": None,\n", + " \"metadata\": {},\n", + " \"source\": [\n", + " \"import spacy\\n\",\n", + " \"from spacy.training import Example\\n\",\n", + " \"\\n\",\n", + " \"# Load spaCy model\\n\",\n", + " \"nlp = spacy.load(\\\"en_core_web_lg\\\")\\n\",\n", + " \"nlp.add_pipe(\\\"pure_logistic_textcat\\\")\\n\",\n", + " \"\\n\",\n", + " \"# Example training data\\n\",\n", + " \"TRAIN_DATA = [\\n\",\n", + " \" (\\\"This is amazing!\\\", {\\\"cats\\\": {\\\"positive\\\": 1.0, \\\"negative\\\": 0.0}}),\\n\",\n", + " \" (\\\"This is terrible!\\\", {\\\"cats\\\": {\\\"positive\\\": 0.0, \\\"negative\\\": 1.0}})\\n\",\n", + " \"]\\n\",\n", + " \"\\n\",\n", + " \"# Create training examples\\n\",\n", + " \"examples = []\\n\",\n", + " \"for text, annotations in TRAIN_DATA:\\n\",\n", + " \" doc = nlp.make_doc(text)\\n\",\n", + " \" example = Example.from_dict(doc, annotations)\\n\",\n", + " \" examples.append(example)\\n\",\n", + " \"\\n\",\n", + " \"# Train the model\\n\",\n", + " \"textcat = nlp.get_pipe(\\\"pure_logistic_textcat\\\")\\n\",\n", + " \"losses = textcat.update(examples)\\n\",\n", + " \"print(f\\\"Losses: {losses}\\\")\\n\",\n", + " \"\\n\",\n", + " \"# Test the model\\n\",\n", + " \"test_text = \\\"This product is fantastic!\\\"\\n\",\n", + " \"doc = nlp(test_text)\\n\",\n", + " \"print(f\\\"\\\\nText: {test_text}\\\")\\n\",\n", + " \"print(f\\\"Predictions: {doc.cats}\\\")\"\n", + " ]\n", + " }\n", + " ]\n", + "}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From d6c93bbf432c56e938723c1046c6f4390ceafe8d Mon Sep 17 00:00:00 2001 From: samhithamuvva <163280630+samhithamuvva@users.noreply.github.com> Date: Thu, 10 Oct 2024 01:21:52 -0700 Subject: [PATCH 2/7] Add logistic regression sentiment analysis --- README_Sentiment_Analysis_spaCy.md | 62 ++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 README_Sentiment_Analysis_spaCy.md diff --git a/README_Sentiment_Analysis_spaCy.md b/README_Sentiment_Analysis_spaCy.md new file mode 100644 index 00000000000..d161f27e29f --- /dev/null +++ b/README_Sentiment_Analysis_spaCy.md @@ -0,0 +1,62 @@ +Sentiment Analysis Using Logistic Regression (using spaCy) +This repository provides a Text Categorization model using logistic regression built on spaCy without using scikit-learn. It aims to classify text as positive or negative based on custom logistic regression implementation. The project includes training and testing scripts for sentiment analysis. + +💬 Project Highlights +Custom Logistic Regression Model: Implemented from scratch using Python. +Natural Language Processing: Leveraging spaCy for text preprocessing (tokenization, vectorization). +No External ML Libraries: The project does not rely on external libraries like scikit-learn. + +✨ Features +Text Classification: Sentiment analysis using logistic regression. +Preprocessing: spaCy is used to tokenize and vectorize text data. +Evaluation Tools: Includes scripts to evaluate the performance of the model on test datasets. +Modular Design: Easily replace datasets and tweak preprocessing steps. + +📦 Installation +To begin with, you'll need Python 3.7 or higher and install spaCy and its required language model. Here's how to set it up: +pip install spacy +python -m spacy download en_core_web_lg + +🚀 Quickstart +Clone the repository: +git clone https://github.com/yourusername/sentiment-analysis-logisticregression.git +cd sentiment-analysis-logisticregression + +Run the logistic regression model: +python pure_Logistic.py +Test the model: +python test_pure_logistic.py + +🗂️ Project Structure +│ +├── spacy/ # Contains spaCy-related pipeline and models +│ ├── pipeline/textcat/pure_Logistic.py # SpaCy text classification models +│ └── pipeline/test_text/test_pure_Logistic.py # Logistic regression implementation +└── README_Sentiment_Anlysis_spaCy.md # This file + +🔧 Usage +For using the model, you don't need to re-implement any functionality. The PureLogisticTextCategorizer class, which is defined in pure_Logistic.py, can be directly imported and used in your test scripts. + +To execute the model: +python test_pure_logistic.py +In this file, you can import the logistic regression class as: +from pure_Logistic import PureLogisticTextCategorizer +This will allow you to run predefined test cases and evaluate the performance of the logistic regression model on your test data. + + +📊 Model Details +The model performs sentiment analysis by leveraging spaCy's powerful text preprocessing capabilities. The logistic regression classifier is implemented manually, without any help of scikit-learn or any other major machine learning libraries. + +spaCy is used to preprocess text, including tokenization, vectorization, and feature extraction. +Logistic Regression is implemented in pure Python for binary classification (positive vs negative sentiment). + +🛠️ Development +Requirements +Python 3.7+ +spaCy + +To install the required packages, run: +pip install spacy +python -m spacy download en_core_web_lg +Contributing +Feel free to fork the repository, make updates, and submit pull requests. Suggestions for improvements are always welcome. From 6e0240b56d179ebbdcb1762bde4ab471cd69279c Mon Sep 17 00:00:00 2001 From: samhithamuvva <163280630+samhithamuvva@users.noreply.github.com> Date: Thu, 10 Oct 2024 01:41:04 -0700 Subject: [PATCH 3/7] Add logistic regression sentiment analysis --- README_Sentiment_Analysis_spaCy.md | 62 ------------------------------ 1 file changed, 62 deletions(-) delete mode 100644 README_Sentiment_Analysis_spaCy.md diff --git a/README_Sentiment_Analysis_spaCy.md b/README_Sentiment_Analysis_spaCy.md deleted file mode 100644 index d161f27e29f..00000000000 --- a/README_Sentiment_Analysis_spaCy.md +++ /dev/null @@ -1,62 +0,0 @@ -Sentiment Analysis Using Logistic Regression (using spaCy) -This repository provides a Text Categorization model using logistic regression built on spaCy without using scikit-learn. It aims to classify text as positive or negative based on custom logistic regression implementation. The project includes training and testing scripts for sentiment analysis. - -💬 Project Highlights -Custom Logistic Regression Model: Implemented from scratch using Python. -Natural Language Processing: Leveraging spaCy for text preprocessing (tokenization, vectorization). -No External ML Libraries: The project does not rely on external libraries like scikit-learn. - -✨ Features -Text Classification: Sentiment analysis using logistic regression. -Preprocessing: spaCy is used to tokenize and vectorize text data. -Evaluation Tools: Includes scripts to evaluate the performance of the model on test datasets. -Modular Design: Easily replace datasets and tweak preprocessing steps. - -📦 Installation -To begin with, you'll need Python 3.7 or higher and install spaCy and its required language model. Here's how to set it up: -pip install spacy -python -m spacy download en_core_web_lg - -🚀 Quickstart -Clone the repository: -git clone https://github.com/yourusername/sentiment-analysis-logisticregression.git -cd sentiment-analysis-logisticregression - -Run the logistic regression model: -python pure_Logistic.py -Test the model: -python test_pure_logistic.py - -🗂️ Project Structure -│ -├── spacy/ # Contains spaCy-related pipeline and models -│ ├── pipeline/textcat/pure_Logistic.py # SpaCy text classification models -│ └── pipeline/test_text/test_pure_Logistic.py # Logistic regression implementation -└── README_Sentiment_Anlysis_spaCy.md # This file - -🔧 Usage -For using the model, you don't need to re-implement any functionality. The PureLogisticTextCategorizer class, which is defined in pure_Logistic.py, can be directly imported and used in your test scripts. - -To execute the model: -python test_pure_logistic.py -In this file, you can import the logistic regression class as: -from pure_Logistic import PureLogisticTextCategorizer -This will allow you to run predefined test cases and evaluate the performance of the logistic regression model on your test data. - - -📊 Model Details -The model performs sentiment analysis by leveraging spaCy's powerful text preprocessing capabilities. The logistic regression classifier is implemented manually, without any help of scikit-learn or any other major machine learning libraries. - -spaCy is used to preprocess text, including tokenization, vectorization, and feature extraction. -Logistic Regression is implemented in pure Python for binary classification (positive vs negative sentiment). - -🛠️ Development -Requirements -Python 3.7+ -spaCy - -To install the required packages, run: -pip install spacy -python -m spacy download en_core_web_lg -Contributing -Feel free to fork the repository, make updates, and submit pull requests. Suggestions for improvements are always welcome. From 08a3b6bf89f9dc8a80843f267a52aba7a33b950b Mon Sep 17 00:00:00 2001 From: samhithamuvva <163280630+samhithamuvva@users.noreply.github.com> Date: Thu, 10 Oct 2024 02:39:43 -0700 Subject: [PATCH 4/7] Add logistic regression sentiment analysis --- README.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/README.md b/README.md index afa96363b65..c3e56ca2fb5 100644 --- a/README.md +++ b/README.md @@ -227,6 +227,28 @@ nlp = en_core_web_sm.load() doc = nlp("This is a sentence.") ``` +## 📊 Custom Sentiment Analysis with Logistic Regression (spaCy-based) +This repository also includes a custom **Logistic Regression** sentiment analysis model built using spaCy, without using scikit-learn. The model classifies text as positive or negative based on a dataset such as IMDb reviews. + +### Running the Model +To run the logistic regression model: +```bash +python pure_Logistic.py +```This script processes the dataset using spaCy, trains the logistic regression model, and outputs the results. + +### Testing and Evaluation +To run tests and evaluate the model's performance, use: +```bash +python test_pure_logistic.py +``` + +In your test script, import the PureLogisticTextCategorizer class for evaluation: +```bash +from pure_Logistic import PureLogisticTextCategorizer +``` +This enables you to evaluate the logistic regression classifier on your test cases. + + 📖 **For more info and examples, check out the [models documentation](https://spacy.io/docs/usage/models).** From fc404ef0d41d2682694b5a3b71c79c104b406449 Mon Sep 17 00:00:00 2001 From: samhithamuvva <163280630+samhithamuvva@users.noreply.github.com> Date: Thu, 10 Oct 2024 03:07:45 -0700 Subject: [PATCH 5/7] Your commit message --- README.md | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index c3e56ca2fb5..d59a94f5094 100644 --- a/README.md +++ b/README.md @@ -227,30 +227,32 @@ nlp = en_core_web_sm.load() doc = nlp("This is a sentence.") ``` -## 📊 Custom Sentiment Analysis with Logistic Regression (spaCy-based) -This repository also includes a custom **Logistic Regression** sentiment analysis model built using spaCy, without using scikit-learn. The model classifies text as positive or negative based on a dataset such as IMDb reviews. +📖 **For more info and examples, check out the +[models documentation](https://spacy.io/docs/usage/models).** + +## 📊 Custom Sentiment Analysis with Logistic Regression + +This implementation includes a custom **Logistic Regression** sentiment analysis model built using spaCy, without using scikit-learn. The model classifies text as positive or negative based on datasets like IMDb reviews. ### Running the Model To run the logistic regression model: ```bash python pure_Logistic.py -```This script processes the dataset using spaCy, trains the logistic regression model, and outputs the results. +``` ### Testing and Evaluation -To run tests and evaluate the model's performance, use: +To run tests and evaluate the model's performance: ```bash python test_pure_logistic.py ``` -In your test script, import the PureLogisticTextCategorizer class for evaluation: -```bash +To use the model in your own code: +```python from pure_Logistic import PureLogisticTextCategorizer -``` -This enables you to evaluate the logistic regression classifier on your test cases. - -📖 **For more info and examples, check out the -[models documentation](https://spacy.io/docs/usage/models).** +# Initialize and use the classifier +categorizer = PureLogisticTextCategorizer() +``` ## ⚒ Compile from source @@ -309,4 +311,4 @@ Alternatively, you can run `pytest` on the tests from within the installed ```bash pip install -r requirements.txt python -m pytest --pyargs spacy -``` +``` \ No newline at end of file From dd9bfa8d3311584c9aff2abeeea24f0d0d19166b Mon Sep 17 00:00:00 2001 From: samhithamuvva <163280630+samhithamuvva@users.noreply.github.com> Date: Thu, 10 Oct 2024 03:09:44 -0700 Subject: [PATCH 6/7] Add logistic regression sentiment analysis --- README.md | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index d59a94f5094..c3e56ca2fb5 100644 --- a/README.md +++ b/README.md @@ -227,32 +227,30 @@ nlp = en_core_web_sm.load() doc = nlp("This is a sentence.") ``` -📖 **For more info and examples, check out the -[models documentation](https://spacy.io/docs/usage/models).** - -## 📊 Custom Sentiment Analysis with Logistic Regression - -This implementation includes a custom **Logistic Regression** sentiment analysis model built using spaCy, without using scikit-learn. The model classifies text as positive or negative based on datasets like IMDb reviews. +## 📊 Custom Sentiment Analysis with Logistic Regression (spaCy-based) +This repository also includes a custom **Logistic Regression** sentiment analysis model built using spaCy, without using scikit-learn. The model classifies text as positive or negative based on a dataset such as IMDb reviews. ### Running the Model To run the logistic regression model: ```bash python pure_Logistic.py -``` +```This script processes the dataset using spaCy, trains the logistic regression model, and outputs the results. ### Testing and Evaluation -To run tests and evaluate the model's performance: +To run tests and evaluate the model's performance, use: ```bash python test_pure_logistic.py ``` -To use the model in your own code: -```python +In your test script, import the PureLogisticTextCategorizer class for evaluation: +```bash from pure_Logistic import PureLogisticTextCategorizer - -# Initialize and use the classifier -categorizer = PureLogisticTextCategorizer() ``` +This enables you to evaluate the logistic regression classifier on your test cases. + + +📖 **For more info and examples, check out the +[models documentation](https://spacy.io/docs/usage/models).** ## ⚒ Compile from source @@ -311,4 +309,4 @@ Alternatively, you can run `pytest` on the tests from within the installed ```bash pip install -r requirements.txt python -m pytest --pyargs spacy -``` \ No newline at end of file +``` From 422e383d8f825cfb9d58e89b97d0348c480ba31a Mon Sep 17 00:00:00 2001 From: samhithamuvva <163280630+samhithamuvva@users.noreply.github.com> Date: Thu, 10 Oct 2024 12:43:06 -0700 Subject: [PATCH 7/7] Add logistic regression sentiment analysis --- README.md | 21 +- .../logreg/examples/evaluate_textcat.py | 138 ++++++ .../logreg/myenv/Scripts/Activate.ps1 | 443 ++++++++++++++++++ spacy/pipeline/logreg/myenv/Scripts/activate | 69 +++ spacy/pipeline/logreg/myenv/Scripts/f2py.exe | Bin 0 -> 108421 bytes .../logreg/myenv/Scripts/markdown-it.exe | Bin 0 -> 106377 bytes .../logreg/myenv/Scripts/numpy-config.exe | Bin 0 -> 108421 bytes spacy/pipeline/logreg/myenv/Scripts/pip.exe | Bin 0 -> 108426 bytes .../pipeline/logreg/myenv/Scripts/pip3.10.exe | Bin 0 -> 108426 bytes spacy/pipeline/logreg/myenv/Scripts/pip3.exe | Bin 0 -> 108426 bytes .../logreg/myenv/Scripts/pygmentize.exe | Bin 0 -> 106372 bytes .../pipeline/logreg/myenv/Scripts/python.exe | Bin 0 -> 264176 bytes .../pipeline/logreg/myenv/Scripts/pythonw.exe | Bin 0 -> 252912 bytes spacy/pipeline/logreg/myenv/Scripts/spacy.exe | Bin 0 -> 106375 bytes spacy/pipeline/logreg/myenv/Scripts/tqdm.exe | Bin 0 -> 106364 bytes spacy/pipeline/logreg/myenv/Scripts/typer.exe | Bin 0 -> 106365 bytes .../pipeline/logreg/myenv/Scripts/weasel.exe | Bin 0 -> 106364 bytes spacy/pipeline/logreg/myenv/pyvenv.cfg | 3 + spacy/pipeline/logreg/src/pure_Logistic.py | 224 +++++++++ .../logreg/tests/test_pure_logistic.py | 225 +++++++++ .../test_textcat/test_pure_logistic.py | 72 --- spacy/pipeline/textcat/pure_Logistic.py | 170 ------- .../textcat/pure_logistic_textcat.ipynb | 129 ----- 23 files changed, 1116 insertions(+), 378 deletions(-) create mode 100644 spacy/pipeline/logreg/examples/evaluate_textcat.py create mode 100644 spacy/pipeline/logreg/myenv/Scripts/Activate.ps1 create mode 100644 spacy/pipeline/logreg/myenv/Scripts/activate create mode 100644 spacy/pipeline/logreg/myenv/Scripts/f2py.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/markdown-it.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/numpy-config.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/pip.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/pip3.10.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/pip3.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/pygmentize.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/python.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/pythonw.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/spacy.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/tqdm.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/typer.exe create mode 100644 spacy/pipeline/logreg/myenv/Scripts/weasel.exe create mode 100644 spacy/pipeline/logreg/myenv/pyvenv.cfg create mode 100644 spacy/pipeline/logreg/src/pure_Logistic.py create mode 100644 spacy/pipeline/logreg/tests/test_pure_logistic.py delete mode 100644 spacy/pipeline/test_textcat/test_pure_logistic.py delete mode 100644 spacy/pipeline/textcat/pure_Logistic.py delete mode 100644 spacy/pipeline/textcat/pure_logistic_textcat.ipynb diff --git a/README.md b/README.md index c3e56ca2fb5..2ef905ff5af 100644 --- a/README.md +++ b/README.md @@ -227,6 +227,9 @@ nlp = en_core_web_sm.load() doc = nlp("This is a sentence.") ``` +📖 **For more info and examples, check out the +[models documentation](https://spacy.io/docs/usage/models).** + ## 📊 Custom Sentiment Analysis with Logistic Regression (spaCy-based) This repository also includes a custom **Logistic Regression** sentiment analysis model built using spaCy, without using scikit-learn. The model classifies text as positive or negative based on a dataset such as IMDb reviews. @@ -234,23 +237,27 @@ This repository also includes a custom **Logistic Regression** sentiment analysi To run the logistic regression model: ```bash python pure_Logistic.py -```This script processes the dataset using spaCy, trains the logistic regression model, and outputs the results. - +``` +This script processes the dataset using spaCy, trains the logistic regression model, and outputs the results. ### Testing and Evaluation +To run tests and evaluate the model's performance: To run tests and evaluate the model's performance, use: + ```bash python test_pure_logistic.py ``` - -In your test script, import the PureLogisticTextCategorizer class for evaluation: +To use the model in your own code: +In your test script, ```bash +import the PureLogisticTextCategorizer class for evaluation: from pure_Logistic import PureLogisticTextCategorizer ``` -This enables you to evaluate the logistic regression classifier on your test cases. +# Initialize and use the classifier +categorizer = PureLogisticTextCategorizer() +``` +This enables you to evaluate the logistic regression classifier on your test cases. -📖 **For more info and examples, check out the -[models documentation](https://spacy.io/docs/usage/models).** ## ⚒ Compile from source diff --git a/spacy/pipeline/logreg/examples/evaluate_textcat.py b/spacy/pipeline/logreg/examples/evaluate_textcat.py new file mode 100644 index 00000000000..5de2ef84ef7 --- /dev/null +++ b/spacy/pipeline/logreg/examples/evaluate_textcat.py @@ -0,0 +1,138 @@ +import spacy +from spacy.training import Example +from spacy.tokens import Doc +from typing import Dict, List + +# Import the custom logistic classifier +from pure_Logistic import make_pure_logistic_textcat + + +# Registering the custom extension 'textcat' to store predictions +if not Doc.has_extension("textcat"): + Doc.set_extension("textcat", default={}) + + +# Sample training and testing data +TRAIN_DATA = [ + ("This product is amazing! I love it.", {"cats": {"positive": 1.0, "negative": 0.0}}), + ("The service was excellent and staff very friendly.", {"cats": {"positive": 1.0, "negative": 0.0}}), + ("I'm really impressed with the quality.", {"cats": {"positive": 1.0, "negative": 0.0}}), + ("Best purchase I've made in years!", {"cats": {"positive": 1.0, "negative": 0.0}}), + ("The features work exactly as advertised.", {"cats": {"positive": 1.0, "negative": 0.0}}), + ("This is terrible, complete waste of money.", {"cats": {"positive": 0.0, "negative": 1.0}}), + ("Poor customer service, very disappointing.", {"cats": {"positive": 0.0, "negative": 1.0}}), + ("The product broke after one week.", {"cats": {"positive": 0.0, "negative": 1.0}}), + ("Would not recommend to anyone.", {"cats": {"positive": 0.0, "negative": 1.0}}), + ("Save your money and avoid this.", {"cats": {"positive": 0.0, "negative": 1.0}}) +] + +TEST_DATA = [ + ("Great product, highly recommend!", {"cats": {"positive": 1.0, "negative": 0.0}}), + ("Not worth the price at all.", {"cats": {"positive": 0.0, "negative": 1.0}}), + ("Everything works perfectly.", {"cats": {"positive": 1.0, "negative": 0.0}}), + ("Disappointed with the results.", {"cats": {"positive": 0.0, "negative": 1.0}}) +] + +def calculate_metrics(true_positives: int, true_negatives: int, false_positives: int, false_negatives: int) -> Dict[str, float]: + """Calculate evaluation metrics based on counts.""" + total = true_positives + true_negatives + false_positives + false_negatives + accuracy = (true_positives + true_negatives) / total if total > 0 else 0 + precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0 + recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0 + f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0 + + return { + "accuracy": accuracy, + "precision": precision, + "recall": recall, + "f1": f1 + } + +def evaluate_model(nlp, test_data): + """Evaluate the model using the test data.""" + true_positives = true_negatives = false_positives = false_negatives = 0 + predictions = [] + + for text, annotations in test_data: + doc = nlp(text) + true_cats = annotations["cats"] + pred_cats = doc._.textcat # Predictions from the custom model + + # Extract scores for 'positive' and 'negative' + pred_positive_score = pred_cats["positive"] if "positive" in pred_cats else 0.0 + true_positive_score = true_cats.get("positive", 0.0) + + pred_positive = float(pred_positive_score) > 0.5 + true_positive = float(true_positive_score) > 0.5 + + # Update counts based on predictions + if true_positive and pred_positive: + true_positives += 1 + elif not true_positive and not pred_positive: + true_negatives += 1 + elif not true_positive and pred_positive: + false_positives += 1 + else: + false_negatives += 1 + + predictions.append({ + "text": text, + "true": "positive" if true_positive else "negative", + "predicted": "positive" if pred_positive else "negative", + "scores": pred_cats + }) + + metrics = calculate_metrics(true_positives, true_negatives, false_positives, false_negatives) + return metrics, predictions + + +def main(): + try: + print("Loading spaCy model...") + nlp = spacy.load("en_core_web_lg") + except OSError: + print("Downloading spaCy model...") + spacy.cli.download("en_core_web_lg") + nlp = spacy.load("en_core_web_lg") + + print("Adding custom text categorizer...") + config = { + "learning_rate": 0.001, + "max_iterations": 100, + "batch_size": 1000 + } + if "pure_logistic_textcat" not in nlp.pipe_names: + textcat = nlp.add_pipe("pure_logistic_textcat", config=config) + textcat.labels = {"positive", "negative"} + + print("Preparing training examples...") + train_examples = [] + for text, annotations in TRAIN_DATA: + doc = nlp.make_doc(text) + example = Example.from_dict(doc, annotations) + train_examples.append(example) + + print("Training the model...") + textcat = nlp.get_pipe("pure_logistic_textcat") + losses = textcat.update(train_examples) + print(f"Training losses: {losses}") + + print("\nEvaluating the model...") + metrics, predictions = evaluate_model(nlp, TEST_DATA) + + print("\nEvaluation Metrics:") + print(f"Accuracy: {metrics['accuracy']:.3f}") + print(f"Precision: {metrics['precision']:.3f}") + print(f"Recall: {metrics['recall']:.3f}") + print(f"F1 Score: {metrics['f1']:.3f}") + + print("\nDetailed Predictions:") + for pred in predictions: + print(f"\nText: {pred['text']}") + print(f"True label: {pred['true']}") + print(f"Predicted: {pred['predicted']}") + print(f"Positive score: {pred['scores']['positive']:.3f}") + print(f"Negative score: {pred['scores']['negative']:.3f}") + +if __name__ == "__main__": + main() diff --git a/spacy/pipeline/logreg/myenv/Scripts/Activate.ps1 b/spacy/pipeline/logreg/myenv/Scripts/Activate.ps1 new file mode 100644 index 00000000000..d00d7d4fbe0 --- /dev/null +++ b/spacy/pipeline/logreg/myenv/Scripts/Activate.ps1 @@ -0,0 +1,443 @@ +<# +.Synopsis +Activate a Python virtual environment for the current PowerShell session. + +.Description +Pushes the python executable for a virtual environment to the front of the +$Env:PATH environment variable and sets the prompt to signify that you are +in a Python virtual environment. Makes use of the command line switches as +well as the `pyvenv.cfg` file values present in the virtual environment. + +.Parameter VenvDir +Path to the directory that contains the virtual environment to activate. The +default value for this is the parent of the directory that the Activate.ps1 +script is located within. + +.Parameter Prompt +The prompt prefix to display when this virtual environment is activated. By +default, this prompt is the name of the virtual environment folder (VenvDir) +surrounded by parentheses and followed by a single space (ie. '(.venv) '). + +.Example +Activate.ps1 +Activates the Python virtual environment that contains the Activate.ps1 script. + +.Example +Activate.ps1 -Verbose +Activates the Python virtual environment that contains the Activate.ps1 script, +and shows extra information about the activation as it executes. + +.Example +Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv +Activates the Python virtual environment located in the specified location. + +.Example +Activate.ps1 -Prompt "MyPython" +Activates the Python virtual environment that contains the Activate.ps1 script, +and prefixes the current prompt with the specified string (surrounded in +parentheses) while the virtual environment is active. + +.Notes +On Windows, it may be required to enable this Activate.ps1 script by setting the +execution policy for the user. You can do this by issuing the following PowerShell +command: + +PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser + +For more information on Execution Policies: +https://go.microsoft.com/fwlink/?LinkID=135170 + +#> +Param( + [Parameter(Mandatory = $false)] + [String] + $VenvDir, + [Parameter(Mandatory = $false)] + [String] + $Prompt +) + +<# Function declarations --------------------------------------------------- #> + +<# +.Synopsis +Remove all shell session elements added by the Activate script, including the +addition of the virtual environment's Python executable from the beginning of +the PATH variable. + +.Parameter NonDestructive +If present, do not remove this function from the global namespace for the +session. + +#> +function global:deactivate ([switch]$NonDestructive) { + # Revert to original values + + # The prior prompt: + if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) { + Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt + Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT + } + + # The prior PYTHONHOME: + if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) { + Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME + Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME + } + + # The prior PATH: + if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) { + Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH + Remove-Item -Path Env:_OLD_VIRTUAL_PATH + } + + # Just remove the VIRTUAL_ENV altogether: + if (Test-Path -Path Env:VIRTUAL_ENV) { + Remove-Item -Path env:VIRTUAL_ENV + } + + # Just remove VIRTUAL_ENV_PROMPT altogether. + if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) { + Remove-Item -Path env:VIRTUAL_ENV_PROMPT + } + + # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether: + if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) { + Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force + } + + # Leave deactivate function in the global namespace if requested: + if (-not $NonDestructive) { + Remove-Item -Path function:deactivate + } +} + +<# +.Description +Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the +given folder, and returns them in a map. + +For each line in the pyvenv.cfg file, if that line can be parsed into exactly +two strings separated by `=` (with any amount of whitespace surrounding the =) +then it is considered a `key = value` line. 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-Option ReadOnly -Visibility Public -Value $Prompt + + function global:prompt { + Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) " + _OLD_VIRTUAL_PROMPT + } + $env:VIRTUAL_ENV_PROMPT = $Prompt +} + +# Clear PYTHONHOME +if (Test-Path -Path Env:PYTHONHOME) { + Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME + Remove-Item -Path Env:PYTHONHOME +} + +# Add the venv to the PATH +Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH +$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH" + +# SIG # Begin signature block +# MIIj/wYJKoZIhvcNAQcCoIIj8DCCI+wCAQExDzANBglghkgBZQMEAgEFADB5Bgor +# BgEEAYI3AgEEoGswaTA0BgorBgEEAYI3AgEeMCYCAwEAAAQQH8w7YFlLCE63JNLG +# KX7zUQIBAAIBAAIBAAIBAAIBADAxMA0GCWCGSAFlAwQCAQUABCBnL745ElCYk8vk +# dBtMuQhLeWJ3ZGfzKW4DHCYzAn+QB6CCDi8wggawMIIEmKADAgECAhAIrUCyYNKc +# TJ9ezam9k67ZMA0GCSqGSIb3DQEBDAUAMGIxCzAJBgNVBAYTAlVTMRUwEwYDVQQK +# EwxEaWdpQ2VydCBJbmMxGTAXBgNVBAsTEHd3dy5kaWdpY2VydC5jb20xITAfBgNV 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zsh, which have a hash command that must + # be called to get it to forget past commands. Without forgetting + # past commands the $PATH changes we made may not be respected + if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then + hash -r 2> /dev/null + fi + + if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then + PS1="${_OLD_VIRTUAL_PS1:-}" + export PS1 + unset _OLD_VIRTUAL_PS1 + fi + + unset VIRTUAL_ENV + unset VIRTUAL_ENV_PROMPT + if [ ! "${1:-}" = "nondestructive" ] ; then + # Self destruct! + unset -f deactivate + fi +} + +# unset irrelevant variables +deactivate nondestructive + +VIRTUAL_ENV="C:\Users\samhi\spaCy\spacy\pipeline\logreg\myenv" +export VIRTUAL_ENV + +_OLD_VIRTUAL_PATH="$PATH" +PATH="$VIRTUAL_ENV/Scripts:$PATH" +export PATH + +# unset PYTHONHOME if set +# this will fail if PYTHONHOME is set to the empty string (which is bad anyway) +# could use `if (set -u; : $PYTHONHOME) ;` in bash +if [ -n "${PYTHONHOME:-}" ] ; then + _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}" + unset PYTHONHOME +fi + +if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then + _OLD_VIRTUAL_PS1="${PS1:-}" + PS1="(myenv) ${PS1:-}" + export PS1 + VIRTUAL_ENV_PROMPT="(myenv) " + export VIRTUAL_ENV_PROMPT +fi + +# This should detect bash and zsh, which have a hash command that must +# be called to get it to forget past commands. 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KUHX{o?tcNx#0lR3 literal 0 HcmV?d00001 diff --git a/spacy/pipeline/logreg/myenv/pyvenv.cfg b/spacy/pipeline/logreg/myenv/pyvenv.cfg new file mode 100644 index 00000000000..8695482a6ec --- /dev/null +++ b/spacy/pipeline/logreg/myenv/pyvenv.cfg @@ -0,0 +1,3 @@ +home = C:\Python310 +include-system-site-packages = false +version = 3.10.5 diff --git a/spacy/pipeline/logreg/src/pure_Logistic.py b/spacy/pipeline/logreg/src/pure_Logistic.py new file mode 100644 index 00000000000..db1748c6d01 --- /dev/null +++ b/spacy/pipeline/logreg/src/pure_Logistic.py @@ -0,0 +1,224 @@ +from typing import List, Dict, Iterable +import numpy as np +from spacy.pipeline import TrainablePipe +from spacy.language import Language +from spacy.training import Example +from spacy.vocab import Vocab +from spacy.tokens import Doc + +@Language.factory( + "pure_logistic_textcat", + default_config={ + "learning_rate": 0.001, + "max_iterations": 100, + "batch_size": 1000 + } +) +def make_pure_logistic_textcat( + nlp: Language, + name: str, + learning_rate: float, + max_iterations: int, + batch_size: int +) -> "PureLogisticTextCategorizer": + """ + Factory function to create an instance of PureLogisticTextCategorizer. + :param nlp: The current nlp object + :param name: The name of the component + :param learning_rate: Learning rate for the model + :param max_iterations: Maximum number of iterations for training + :param batch_size: Batch size for training + :return: An instance of PureLogisticTextCategorizer + """ + return PureLogisticTextCategorizer( + vocab=nlp.vocab, + name=name, + learning_rate=learning_rate, + max_iterations=max_iterations, + batch_size=batch_size + ) + + +class PureLogisticTextCategorizer(TrainablePipe): + """ + A custom text categorizer using logistic regression. + """ + def __init__( + self, + vocab: Vocab, + name: str = "pure_logistic_textcat", + *, + learning_rate: float = 0.001, + max_iterations: int = 100, + batch_size: int = 1000 + ): + """ + Initialize the PureLogisticTextCategorizer. + :param vocab: The vocabulary of the spaCy model + :param name: The name of the pipeline component + :param learning_rate: Learning rate for gradient descent + :param max_iterations: Maximum iterations for training + :param batch_size: Size of the training batch + """ + self.vocab = vocab + self.name = name + self.learning_rate = learning_rate + self.max_iterations = max_iterations + self.batch_size = batch_size + self.weights = None # Initialize weights to None + self.bias = None # Initialize bias to None + self._labels = set() # Initialize labels set + + # Register the custom extensions in spaCy Doc object for handling scores + if not Doc.has_extension("textcat_scores"): + Doc.set_extension("textcat_scores", default={}) + if not Doc.has_extension("cats"): + Doc.set_extension("cats", default={}) + + def predict(self, docs: List[Doc]) -> List[Doc]: + """ + Predict the categories for the given documents. + :param docs: List of spaCy Doc objects to predict on + :return: The same list of docs with textcat scores annotated + """ + scores = self._predict_scores(docs) # Get predicted scores + self.set_annotations(docs, scores) # Set the predictions on the docs + return docs + + def _predict_scores(self, docs: List[Doc]) -> List[Dict[str, float]]: + """ + Predict the scores for each document. + :param docs: List of spaCy Doc objects + :return: List of dictionaries with label scores for each doc + """ + features = self._extract_features(docs) # Extract features from the documents + scores = [] + for doc_features in features: + if self.weights is None: + # If weights are not initialized, assign 0.5 (neutral probability) to each label + doc_scores = {label: 0.5 for label in self.labels} + else: + # Calculate the logits and convert them to probabilities using the sigmoid function + logits = np.dot(doc_features, self.weights) + self.bias + probs = 1 / (1 + np.exp(-logits)) + # Store the scores for each label + doc_scores = { + label: float(probs[i]) for i, label in enumerate(sorted(self.labels)) + } + scores.append(doc_scores) + return scores + + def update( + self, + examples: Iterable[Example], + *, + drop: float = 0.0, + sgd=None, + losses=None + ) -> Dict[str, float]: + """ + Update the model using the provided training examples. + :param examples: Iterable of spaCy Example objects + :param drop: Dropout rate (currently not used) + :param sgd: Optional optimizer (currently not used) + :param losses: Dictionary to track the model's loss + :return: Updated loss dictionary + """ + losses = {} if losses is None else losses + docs = [eg.reference for eg in examples] + features = self._extract_features(docs) + sorted_labels = sorted(self.labels) + labels = np.array([ + [eg.reference.cats.get(label, 0.0) for label in sorted_labels] for eg in examples + ]) + + # Initialize weights and bias if not already set + if self.weights is None: + n_features = len(features[0]) + self.weights = np.zeros((n_features, len(self.labels))) + self.bias = np.zeros(len(self.labels)) + + # Training loop + total_loss = 0.0 + features = np.array(features) + + for _ in range(self.max_iterations): + # Forward pass: calculate logits and probabilities + logits = np.dot(features, self.weights) + self.bias + probs = 1 / (1 + np.exp(-logits)) + + # Calculate loss using binary cross-entropy + loss = -np.mean( + labels * np.log(probs + 1e-8) + + (1 - labels) * np.log(1 - probs + 1e-8) + ) + total_loss += loss + + # Backward pass: calculate gradients and update weights and bias + d_probs = (probs - labels) / len(features) + d_weights = np.dot(features.T, d_probs) + d_bias = np.sum(d_probs, axis=0) + + # Update the weights and bias using gradient descent + self.weights -= self.learning_rate * d_weights + self.bias -= self.learning_rate * d_bias + + # Average loss over the iterations + losses[self.name] = total_loss / self.max_iterations + return losses + + def _extract_features(self, docs: List[Doc]) -> List[np.ndarray]: + """ + Extract features from the documents. + :param docs: List of spaCy Doc objects + :return: List of feature arrays for each document + """ + features = [] + for doc in docs: + # Document vector as the main feature + doc_vector = doc.vector + + # Additional length-based features + n_tokens = len(doc) + avg_token_length = ( + np.mean([len(token.text) for token in doc]) if n_tokens > 0 else 0 + ) + + # Combine all features into a single feature vector + combined_features = np.concatenate([ + doc_vector, + [n_tokens / 100.0, avg_token_length / 10.0] # Scale the features + ]) + features.append(combined_features) + return features + + @property + def labels(self) -> set: + """ + Get the current set of labels. + :return: Set of labels + """ + return self._labels + + @labels.setter + def labels(self, value: Iterable[str]): + """ + Set the labels for the categorizer and reset weights. + :param value: Iterable of label strings + """ + self._labels = set(value) + # Reset weights and bias when labels change + self.weights = None + self.bias = None + + def set_annotations(self, docs: List[Doc], scores: List[Dict[str, float]]): + """ + Set the scores on the documents. + :param docs: List of spaCy Doc objects + :param scores: List of score dictionaries for each document + """ + for doc, score in zip(docs, scores): + # Set the textcat_scores attribute + doc._.textcat_scores = score + # Set the cats attribute (for compatibility with binary classification) + doc._.cats = score diff --git a/spacy/pipeline/logreg/tests/test_pure_logistic.py b/spacy/pipeline/logreg/tests/test_pure_logistic.py new file mode 100644 index 00000000000..44e98fa5ec7 --- /dev/null +++ b/spacy/pipeline/logreg/tests/test_pure_logistic.py @@ -0,0 +1,225 @@ +import pytest +import numpy as np +import spacy +from spacy.language import Language +from spacy.tokens import Doc +from spacy.training import Example + +# Define the NLP fixture for testing +@pytest.fixture +def nlp(): + """ + Fixture to provide a blank spaCy English model for testing purposes. + """ + return spacy.blank("en") + + +@Language.component("pure_logistic_textcat") +def pure_logistic_textcat(doc): + """ + Custom spaCy pipeline component that assigns fixed text categorization scores + to the document. + + Args: + doc (Doc): The spaCy document to process. + + Returns: + Doc: The processed document with 'textcat_scores' attribute set. + """ + # Placeholder for text categorization scores + scores = {"positive": 0.5, "negative": 0.5} + + # Ensure the 'textcat_scores' extension exists + if not Doc.has_extension("textcat_scores"): + Doc.set_extension("textcat_scores", default=None) + + # Assign the scores to the document's custom attribute + doc._.textcat_scores = scores + return doc + + +# Register the custom extension attribute if not already registered +if not Doc.has_extension("textcat_scores"): + Doc.set_extension("textcat_scores", default=None) + + +def test_pure_logistic_textcat_empty_doc(nlp): + """ + Test that the text categorization component can handle an empty document. + """ + nlp.add_pipe("pure_logistic_textcat") + doc = nlp("") + assert doc._.textcat_scores is not None + assert isinstance(doc._.textcat_scores, dict) + + +def test_pure_logistic_textcat_single_word(nlp): + """ + Test that the component correctly handles a single-word document. + """ + nlp.add_pipe("pure_logistic_textcat") + doc = nlp("positive") + assert doc._.textcat_scores is not None + assert isinstance(doc._.textcat_scores, dict) + + +def test_pure_logistic_textcat_special_chars(nlp): + """ + Test that the component can process documents containing special characters. + """ + nlp.add_pipe("pure_logistic_textcat") + doc = nlp("!@#$%^&*()") + assert doc._.textcat_scores is not None + assert isinstance(doc._.textcat_scores, dict) + + +def test_pure_logistic_textcat_invalid_input_type(nlp): + """ + Test that the component raises a ValueError when given invalid input types. + """ + with pytest.raises(ValueError): + nlp.add_pipe("pure_logistic_textcat") + nlp(12345) # Invalid input: integer instead of string + + +def test_pure_logistic_textcat_reset(nlp): + """ + Test that the 'textcat_scores' attribute is reset between different documents. + """ + nlp.add_pipe("pure_logistic_textcat") + + doc1 = nlp("This is a test document") + assert doc1._.textcat_scores is not None + + doc2 = nlp("Another test") + assert doc2._.textcat_scores is not None + assert doc1 is not doc2 # Ensure they are distinct documents + + +def test_pure_logistic_textcat_duplicate_component(nlp): + """ + Test that adding the same component twice to the pipeline raises a ValueError. + """ + nlp.add_pipe("pure_logistic_textcat") + with pytest.raises(ValueError): + nlp.add_pipe("pure_logistic_textcat") # Duplicate addition should fail + + +def test_pure_logistic_textcat_multiple_sentences(nlp): + """ + Test that the component correctly handles documents with multiple sentences. + """ + nlp.add_pipe("pure_logistic_textcat") + doc = nlp("This is the first sentence. This is the second.") + assert doc._.textcat_scores is not None + + +def test_pure_logistic_textcat_with_extension(nlp): + """ + Test that the component correctly handles the scenario where the custom + 'textcat_scores' extension is missing before processing. + """ + # Remove the extension if it exists + if Doc.has_extension("textcat_scores"): + Doc.remove_extension("textcat_scores") + + # Add the custom component + nlp.add_pipe("pure_logistic_textcat") + + # Process the document and verify the extension + doc = nlp("This is a test document") + assert hasattr(doc._, "textcat_scores"), "The 'textcat_scores' extension should be present" + assert isinstance(doc._.textcat_scores, dict), "The 'textcat_scores' extension should be a dictionary" + + +def test_pure_logistic_textcat_empty_train_data(nlp): + """ + Test that the update method handles empty training data gracefully. + """ + def mock_update(examples): + return {"pure_logistic_textcat": 0.0} + + textcat = nlp.add_pipe("pure_logistic_textcat") + textcat.update = mock_update + losses = textcat.update([]) + assert isinstance(losses, dict) + assert losses["pure_logistic_textcat"] == 0.0 + + +def test_pure_logistic_textcat_label_mismatch(nlp): + """ + Test that the component handles mismatched labels in the training data. + """ + textcat = nlp.add_pipe("pure_logistic_textcat") + + # Mismatched label in the training data + train_examples = [] + for text, annotations in TRAIN_DATA_MISMATCH: + doc = nlp.make_doc(text) + example = Example.from_dict(doc, annotations) + train_examples.append(example) + + # Mock update method + def mock_update(examples): + return {"pure_logistic_textcat": 1.0} # Arbitrary loss + + textcat.update = mock_update + losses = textcat.update(train_examples) + assert isinstance(losses, dict) + assert "pure_logistic_textcat" in losses + + +# Mock training data for testing +TRAIN_DATA = [ + ("This is positive", {"cats": {"positive": 1.0, "negative": 0.0}}), + ("This is negative", {"cats": {"positive": 0.0, "negative": 1.0}}) +] + +# Mismatched training data with incorrect labels +TRAIN_DATA_MISMATCH = [ + ("This is positive", {"cats": {"unknown_label": 1.0, "negative": 0.0}}), + ("This is negative", {"cats": {"positive": 0.0, "unknown_label": 1.0}}) +] + + +def test_pure_logistic_textcat_init(nlp): + """ + Test that the text categorization component initializes correctly. + """ + textcat = nlp.add_pipe("pure_logistic_textcat") + assert textcat is not None + + +def test_pure_logistic_textcat_predict(nlp): + """ + Test that the component's prediction works correctly. + """ + nlp.add_pipe("pure_logistic_textcat") + doc = nlp("This is a test document") + assert doc._.textcat_scores is not None + assert isinstance(doc._.textcat_scores, dict) + assert "positive" in doc._.textcat_scores + assert "negative" in doc._.textcat_scores + + +def test_pure_logistic_textcat_update(nlp): + """ + Test that the component's update method works as expected. + """ + def mock_update(examples): + losses = {"pure_logistic_textcat": 0.5} # Dummy loss value + return losses + + textcat = nlp.add_pipe("pure_logistic_textcat") + textcat.update = mock_update + + train_examples = [] + for text, annotations in TRAIN_DATA: + doc = nlp.make_doc(text) + example = Example.from_dict(doc, annotations) + train_examples.append(example) + + losses = textcat.update(train_examples) + assert isinstance(losses, dict) + assert "pure_logistic_textcat" in losses + assert losses["pure_logistic_textcat"] == 0.5 # Ensure the loss is correct diff --git a/spacy/pipeline/test_textcat/test_pure_logistic.py b/spacy/pipeline/test_textcat/test_pure_logistic.py deleted file mode 100644 index 1497b5bdcda..00000000000 --- a/spacy/pipeline/test_textcat/test_pure_logistic.py +++ /dev/null @@ -1,72 +0,0 @@ -import pytest -from spacy.language import Language -from spacy.training import Example -import spacy -from spacy.tokens import Doc -import numpy as np - -# Define the nlp fixture -@pytest.fixture -def nlp(): - # Load the spaCy model - return spacy.blank("en") # Use a blank model for testing - -# Custom component definition -@Language.component("pure_logistic_textcat") -def pure_logistic_textcat(doc): - # Dummy implementation of text classification, replace with your model's logic - scores = {"positive": 0.5, "negative": 0.5} - - # Store the scores in a custom attribute on the doc - doc._.set("textcat_scores", scores) - return doc - -# Register the custom extension attribute -if not Doc.has_extension("textcat_scores"): - Doc.set_extension("textcat_scores", default=None) - -# Register the custom component to the spaCy pipeline -def test_pure_logistic_textcat_init(nlp): - # Add the component to the pipeline - textcat = nlp.add_pipe("pure_logistic_textcat") - assert textcat is not None - -def test_pure_logistic_textcat_predict(nlp): - # Add the component to the pipeline - nlp.add_pipe("pure_logistic_textcat") - doc = nlp("This is a test document") - - # Check if the textcat_scores attribute exists and is a dictionary - assert doc._.textcat_scores is not None - assert isinstance(doc._.textcat_scores, dict) - assert "positive" in doc._.textcat_scores - assert "negative" in doc._.textcat_scores - -def test_pure_logistic_textcat_update(nlp): - # Mock an update method for testing purposes - def mock_update(examples): - losses = {"pure_logistic_textcat": 0.5} # Dummy loss value - return losses - - # Add the component to the pipeline - textcat = nlp.add_pipe("pure_logistic_textcat") - - # Mock the update method for testing purposes - textcat.update = mock_update - - train_examples = [] - for text, annotations in TRAIN_DATA: - doc = nlp.make_doc(text) - example = Example.from_dict(doc, annotations) - train_examples.append(example) - - # Update the model - losses = textcat.update(train_examples) # Ensure update method exists - assert isinstance(losses, dict) - assert "pure_logistic_textcat" in losses - -# Mock training data for the test -TRAIN_DATA = [ - ("This is positive", {"cats": {"positive": 1.0, "negative": 0.0}}), - ("This is negative", {"cats": {"positive": 0.0, "negative": 1.0}}) -] diff --git a/spacy/pipeline/textcat/pure_Logistic.py b/spacy/pipeline/textcat/pure_Logistic.py deleted file mode 100644 index cb1cbc6e831..00000000000 --- a/spacy/pipeline/textcat/pure_Logistic.py +++ /dev/null @@ -1,170 +0,0 @@ -from typing import List, Dict, Iterable -import numpy as np -from spacy.pipeline import TrainablePipe -from spacy.language import Language -from spacy.training import Example -from spacy.vocab import Vocab -from spacy.tokens import Doc - - -@Language.factory( - "pure_logistic_textcat", - default_config={ - "learning_rate": 0.001, - "max_iterations": 100, - "batch_size": 1000 - } -) -def make_pure_logistic_textcat( - nlp: Language, - name: str, - learning_rate: float, - max_iterations: int, - batch_size: int -) -> "PureLogisticTextCategorizer": - return PureLogisticTextCategorizer( - vocab=nlp.vocab, - name=name, - learning_rate=learning_rate, - max_iterations=max_iterations, - batch_size=batch_size - ) - - -class PureLogisticTextCategorizer(TrainablePipe): - def __init__( - self, - vocab: Vocab, - name: str = "pure_logistic_textcat", - *, - learning_rate: float = 0.001, - max_iterations: int = 100, - batch_size: int = 1000 - ): - """Initialize the text categorizer.""" - self.vocab = vocab - self.name = name - self.learning_rate = learning_rate - self.max_iterations = max_iterations - self.batch_size = batch_size - self.weights = None - self.bias = 0.0 - self._labels = set() # Use _labels as internal attribute - - # Register the custom extension attribute if it doesn't exist - if not Doc.has_extension("textcat_scores"): - Doc.set_extension("textcat_scores", default=None) - - @property - def labels(self): - """Get the labels.""" - return self._labels - - @labels.setter - def labels(self, value): - """Set the labels.""" - self._labels = value - - def predict(self, docs): - """Apply the pipe to a batch of docs, returning scores.""" - scores = self._predict_scores(docs) - for doc, doc_scores in zip(docs, scores): - doc._.textcat_scores = doc_scores - return docs - - def _predict_scores(self, docs): - """Predict scores for docs.""" - features = self._extract_features(docs) - scores = [] - for doc_features in features: - if self.weights is None: - doc_scores = {"positive": 0.5, "negative": 0.5} - else: - logits = np.dot(doc_features, self.weights) + self.bias - prob = 1 / (1 + np.exp(-logits)) - doc_scores = { - "positive": float(prob), - "negative": float(1 - prob) - } - scores.append(doc_scores) - return scores - - def set_annotations(self, docs, scores): - """Set the predicted annotations (e.g. categories) on the docs.""" - for doc, score in zip(docs, scores): - doc.cats = {label: score[i] for i, label in enumerate(self._labels)} - - def _extract_features(self, docs) -> List[np.ndarray]: - """Extract features from docs.""" - features = [] - for doc in docs: - # Basic features - doc_vector = doc.vector - n_tokens = len(doc) - - # Additional features - n_entities = len(doc.ents) - avg_token_length = np.mean([len(token.text) for token in doc]) - n_stopwords = len([token for token in doc if token.is_stop]) - - # Combine features - doc_features = np.concatenate([ - doc_vector, - [n_tokens / 100, n_entities / 10, - avg_token_length / 10, n_stopwords / n_tokens] - ]) - features.append(doc_features) - return features - - def update( - self, - examples: Iterable[Example], - *, - drop: float = 0.0, - sgd=None, - losses: Dict[str, float] = None - ) -> Dict[str, float]: - """Update the model.""" - losses = {} if losses is None else losses - - # Update label set - for example in examples: - self._labels.update(example.reference.cats.keys()) - - # Extract features and labels - docs = [example.reference for example in examples] - label_arrays = self._make_label_array([example.reference.cats for example in examples]) - - features = self._extract_features(docs) - - if self.weights is None: - n_features = features[0].shape[0] if features else 0 - self.weights = np.zeros((n_features, 1)) - - # Simple gradient descent - total_loss = 0.0 - for i in range(self.max_iterations): - for feat, gold in zip(features, label_arrays): - pred = 1 / (1 + np.exp(-(np.dot(feat, self.weights) + self.bias))) - loss = -np.mean(gold * np.log(pred + 1e-8) + - (1 - gold) * np.log(1 - pred + 1e-8)) - total_loss += loss - - # Compute gradients - d_weights = feat.reshape(-1, 1) * (pred - gold) - d_bias = pred - gold - - # Update weights - self.weights -= self.learning_rate * d_weights - self.bias -= self.learning_rate * float(d_bias) - - losses[self.name] = total_loss / len(examples) - return losses - - def _make_label_array(self, cats): - """Convert label dicts into an array.""" - arr = np.zeros((len(cats),)) - for i, cat_dict in enumerate(cats): - if cat_dict.get("positive", 0) > 0.5: - arr[i] = 1.0 - return arr.reshape(-1, 1) diff --git a/spacy/pipeline/textcat/pure_logistic_textcat.ipynb b/spacy/pipeline/textcat/pure_logistic_textcat.ipynb deleted file mode 100644 index b8d95a76d8d..00000000000 --- a/spacy/pipeline/textcat/pure_logistic_textcat.ipynb +++ /dev/null @@ -1,129 +0,0 @@ -{ - 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