Add logistic regression sentiment analysis

spacy/pipeline/test_textcat/test_pure_logistic.py

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+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}})
+]

spacy/pipeline/textcat/pure_Logistic.py

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+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)

spacy/pipeline/textcat/pure_logistic_textcat.ipynb

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+{
+ "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
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