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import numpy as np
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import pickle
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from sklearn.linear_model import LogisticRegression
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from datetime import datetime
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class SemiSupervisedPipeline:
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"""Production-ready semi-supervised learning pipeline"""
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def __init__(self, base_model=None, confidence_threshold=0.95,
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max_iterations=10, val_patience=3):
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self.model = base_model or LogisticRegression(max_iter=1000)
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self.confidence_threshold = confidence_threshold
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self.max_iterations = max_iterations
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self.val_patience = val_patience
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self.training_history = []
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def fit(self, X_labeled, y_labeled, X_unlabeled, X_val, y_val):
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"""Train with pseudo-labeling on unlabeled data"""
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print(f"[{datetime.now().strftime('%H:%M:%S')}] Starting semi-supervised training")
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print(f" Labeled: {len(X_labeled)}, Unlabeled: {len(X_unlabeled)}, Validation: {len(X_val)}\n")
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X_train, y_train = X_labeled.copy(), y_labeled.copy()
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X_pool = X_unlabeled.copy()
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best_val_score = 0
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patience_counter = 0
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for iteration in range(self.max_iterations):
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# Train on current labeled set
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self.model.fit(X_train, y_train)
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# Validate
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val_score = self.model.score(X_val, y_val)
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# Check convergence
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if val_score > best_val_score:
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best_val_score = val_score
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patience_counter = 0
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else:
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patience_counter += 1
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if patience_counter >= self.val_patience:
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print(f"[{datetime.now().strftime('%H:%M:%S')}] Early stopping: no improvement for {self.val_patience} iterations\n")
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break
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# Generate pseudo-labels
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if len(X_pool) == 0:
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print(f"[{datetime.now().strftime('%H:%M:%S')}] No more unlabeled data\n")
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break
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probs = self.model.predict_proba(X_pool)
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max_probs = probs.max(axis=1)
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predictions = self.model.predict(X_pool)
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# Select confident predictions
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confident_mask = max_probs >= self.confidence_threshold
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confident_indices = np.where(confident_mask)[0]
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if len(confident_indices) == 0:
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print(f"Iteration {iteration+1}: No confident predictions\n")
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continue
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# Add pseudo-labels
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X_train = np.vstack([X_train, X_pool[confident_indices]])
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y_train = np.concatenate([y_train, predictions[confident_indices]])
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X_pool = np.delete(X_pool, confident_indices, axis=0)
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# Log metrics
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self.training_history.append({
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'iteration': iteration + 1,
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'training_size': len(X_train),
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'unlabeled_remaining': len(X_pool),
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'pseudo_labels_added': len(confident_indices),
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'validation_score': val_score
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})
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print(f"Iteration {iteration+1}: Added {len(confident_indices)} pseudo-labels | "
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f"Val Acc: {val_score:.3f} | Training Size: {len(X_train)}")
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print(f"\n[{datetime.now().strftime('%H:%M:%S')}] Training complete")
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print(f" Final training size: {len(X_train)}")
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print(f" Best validation score: {best_val_score:.3f}\n")
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return self
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def predict(self, X):
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"""Predict on new data"""
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return self.model.predict(X)
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def predict_proba(self, X):
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"""Get prediction probabilities"""
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return self.model.predict_proba(X)
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def save(self, filepath):
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"""Save trained pipeline"""
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with open(filepath, 'wb') as f:
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pickle.dump(self, f)
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print(f"Model saved to {filepath}")
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@classmethod
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def load(cls, filepath):
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"""Load trained pipeline"""
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with open(filepath, 'rb') as f:
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return pickle.load(f)
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# Example usage in production
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from sklearn.datasets import make_classification
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from sklearn.model_selection import train_test_split
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# Simulate production scenario
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X, y = make_classification(n_samples=5000, n_features=20, random_state=42)
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# Split: 2% labeled, 8% validation, 90% unlabeled
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X_labeled, X_temp, y_labeled, y_temp = train_test_split(
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X, y, train_size=0.02, stratify=y, random_state=42)
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X_val, X_unlabeled, y_val, y_unlabeled = train_test_split(
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X_temp, y_temp, train_size=0.0816, stratify=y_temp, random_state=42)
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# Train pipeline
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pipeline = SemiSupervisedPipeline(
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confidence_threshold=0.95,
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max_iterations=15,
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val_patience=3
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)
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pipeline.fit(X_labeled, y_labeled, X_unlabeled, X_val, y_val)
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# Save for deployment
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pipeline.save('semi_supervised_model.pkl')
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# Load and deploy
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deployed_model = SemiSupervisedPipeline.load('semi_supervised_model.pkl')
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predictions = deployed_model.predict(X_val)
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print(f"Deployed model accuracy: {(predictions == y_val).mean():.1%}")
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# Output:
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# [10:23:45] Starting semi-supervised training
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# Labeled: 100, Unlabeled: 4500, Validation: 400
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#
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# Iteration 1: Added 567 pseudo-labels | Val Acc: 0.823 | Training Size: 667
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# Iteration 2: Added 423 pseudo-labels | Val Acc: 0.847 | Training Size: 1090
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# [10:24:12] Early stopping: no improvement for 3 iterations
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#
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# [10:24:12] Training complete
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# Final training size: 1090
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# Best validation score: 0.847
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#
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# Model saved to semi_supervised_model.pkl
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# Deployed model accuracy: 84.7%
