Sentiment analysis logregspacy

README.md

@@ -230,6 +230,35 @@ 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.
+
+### 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:
+In your test script, 
+```bash
+import the PureLogisticTextCategorizer class for evaluation:
+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.
+
+
 ## ⚒ Compile from source
 
 The other way to install spaCy is to clone its

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()