diff --git a/__tests__/neural-friend-ranker.test.js b/__tests__/neural-friend-ranker.test.js new file mode 100644 index 0000000..23fdd41 --- /dev/null +++ b/__tests__/neural-friend-ranker.test.js @@ -0,0 +1,275 @@ +const { NeuralFriendRanker } = require('../neural-friend-ranker'); + +// Mock TensorFlow.js to avoid heavy dependencies in tests +jest.mock('@tensorflow/tfjs-node', () => ({ + sequential: jest.fn(() => ({ + compile: jest.fn(), + fit: jest.fn().mockResolvedValue({ history: {} }), + predict: jest.fn(() => ({ + data: jest.fn().mockResolvedValue([0.8, 0.6, 0.9, 0.3, 0.7]) + })), + save: jest.fn().mockResolvedValue(true), + dispose: jest.fn() + })), + layers: { + dense: jest.fn(), + dropout: jest.fn() + }, + train: { + adam: jest.fn() + }, + tensor2d: jest.fn(() => ({ + dispose: jest.fn() + })), + tensor1d: jest.fn(() => ({ + dispose: jest.fn() + })), + loadLayersModel: jest.fn().mockResolvedValue({ + predict: jest.fn(() => ({ + data: jest.fn().mockResolvedValue([0.8, 0.6, 0.9, 0.3, 0.7]) + })) + }) +})); + +describe('NeuralFriendRanker', () => { + let ranker; + let mockFriends; + + beforeEach(() => { + ranker = new NeuralFriendRanker(); + mockFriends = [ + { + id: 1, + first_name: 'John', + last_name: 'Doe', + online: true, + can_write_private_message: true, + has_photo: true, + sex: 2, + deactivated: false, + bdate: '15.3.1990', + verified: true, + last_seen: { time: Math.floor(Date.now() / 1000) - 3600 }, // 1 hour ago + mutual: { count: 25 } + }, + { + id: 2, + first_name: 'Jane', + last_name: 'Smith', + online: false, + can_write_private_message: true, + has_photo: false, + sex: 1, + deactivated: false, + bdate: null, + verified: true, + last_seen: { time: Math.floor(Date.now() / 1000) - 86400 }, // 1 day ago + mutual: { count: 5 } + }, + { + id: 3, + first_name: 'Bob', + last_name: 'Wilson', + online: true, + can_write_private_message: false, + has_photo: true, + sex: 2, + deactivated: false, + bdate: '20.5.1985', + verified: false, + last_seen: { time: Math.floor(Date.now() / 1000) - 1800 }, // 30 minutes ago + mutual: { count: 50 } + }, + { + id: 4, + first_name: 'Alice', + last_name: 'Brown', + online: false, + can_write_private_message: true, + has_photo: true, + sex: 1, + deactivated: true, + bdate: '10.12.1992', + verified: false, + last_seen: { time: Math.floor(Date.now() / 1000) - 604800 }, // 1 week ago + mutual: { count: 15 } + }, + { + id: 5, + first_name: 'Charlie', + last_name: 'Davis', + online: true, + can_write_private_message: true, + has_photo: true, + sex: 2, + deactivated: false, + bdate: '25.8.1988', + verified: true, + last_seen: { time: Math.floor(Date.now() / 1000) - 300 }, // 5 minutes ago + mutual: { count: 75 } + } + ]; + }); + + describe('extractFeatures', () => { + test('should extract correct features for a friend', () => { + const friend = mockFriends[0]; + const features = ranker.extractFeatures(friend); + + expect(features).toHaveLength(10); + expect(features[0]).toBe(1.0); // online + expect(features[2]).toBe(1.0); // can_write_private_message + expect(features[3]).toBe(1.0); // has_photo + expect(features[4]).toBe(1.0); // sex = male + expect(features[5]).toBe(1.0); // not deactivated + expect(features[6]).toBe(1.0); // has birthday + expect(features[9]).toBe(1.0); // verified + }); + + test('should handle missing last_seen data', () => { + const friend = { ...mockFriends[0], last_seen: null }; + const features = ranker.extractFeatures(friend); + + expect(features[1]).toBe(0.0); // last seen score should be 0 + }); + + test('should normalize mutual friends count', () => { + const friend = { ...mockFriends[0], mutual: { count: 150 } }; + const features = ranker.extractFeatures(friend); + + expect(features[8]).toBe(1.0); // Should cap at 1.0 for counts >= 100 + }); + }); + + describe('generateTrainingLabels', () => { + test('should generate appropriate labels for friends', () => { + const labels = ranker.generateTrainingLabels(mockFriends); + + expect(labels).toHaveLength(mockFriends.length); + expect(labels.every(label => label === 0 || label === 1)).toBe(true); + + // Charlie (index 4) should have high score: online, male, has photo, verified, etc. + expect(labels[4]).toBe(1); + + // Alice (index 3) should have low score: deactivated + expect(labels[3]).toBe(0); + }); + }); + + describe('heuristicRanking', () => { + test('should rank friends using heuristic method', () => { + const ranked = ranker.heuristicRanking(mockFriends); + + expect(ranked).toHaveLength(mockFriends.length); + expect(ranked[0].neuralScore).toBeGreaterThanOrEqual(ranked[1].neuralScore); + + // Check that all friends have scores + ranked.forEach(friend => { + expect(friend.neuralScore).toBeDefined(); + expect(typeof friend.neuralScore).toBe('number'); + }); + }); + + test('should prefer online male friends with photos', () => { + const ranked = ranker.heuristicRanking(mockFriends); + + // Find Charlie (should score high: online, male, has photo, verified) + const charlie = ranked.find(f => f.first_name === 'Charlie'); + const alice = ranked.find(f => f.first_name === 'Alice'); // deactivated + + expect(charlie.neuralScore).toBeGreaterThan(alice.neuralScore); + }); + }); + + describe('rankFriends', () => { + test('should rank friends and return sorted list', async () => { + // Mock the model to return predictable scores + ranker.model = { + predict: jest.fn(() => ({ + data: jest.fn().mockResolvedValue([0.8, 0.6, 0.9, 0.3, 0.7]) + })) + }; + + ranker.featureStats = mockFriends[0] ? ranker.extractFeatures(mockFriends[0]).map(() => ({ mean: 0.5, std: 0.2 })) : []; + + const ranked = await ranker.rankFriends(mockFriends); + + expect(ranked).toHaveLength(mockFriends.length); + expect(ranked[0].neuralScore).toBeGreaterThanOrEqual(ranked[1].neuralScore); + + // Verify all friends have neural scores + ranked.forEach(friend => { + expect(friend.neuralScore).toBeDefined(); + expect(typeof friend.neuralScore).toBe('number'); + }); + }); + + test('should fallback to heuristic ranking if model fails', async () => { + // Don't set a model + ranker.model = null; + + const ranked = await ranker.rankFriends(mockFriends); + + expect(ranked).toHaveLength(mockFriends.length); + expect(ranked[0].neuralScore).toBeGreaterThanOrEqual(ranked[1].neuralScore); + }); + }); + + describe('normalizeFeatures', () => { + test('should normalize features using stats', () => { + const featuresArray = [ + [1, 2, 3], + [4, 5, 6], + [7, 8, 9] + ]; + + ranker.calculateFeatureStats(featuresArray); + const normalized = ranker.normalizeFeatures(featuresArray); + + expect(normalized).toHaveLength(3); + expect(normalized[0]).toHaveLength(3); + + // Check that normalization was applied + expect(normalized[0][0]).not.toBe(featuresArray[0][0]); + }); + + test('should handle zero standard deviation', () => { + const featuresArray = [ + [1, 1, 1], + [1, 1, 1], + [1, 1, 1] + ]; + + ranker.calculateFeatureStats(featuresArray); + const normalized = ranker.normalizeFeatures(featuresArray); + + expect(normalized[0]).toEqual([1, 1, 1]); // Should remain unchanged when std = 0 + }); + }); + + describe('model operations', () => { + test('should create model with correct architecture', () => { + const tf = require('@tensorflow/tfjs-node'); + const model = ranker.createModel(10); + + expect(tf.sequential).toHaveBeenCalled(); + expect(model.compile).toHaveBeenCalled(); + }); + + test('should handle model saving', async () => { + ranker.model = { + save: jest.fn().mockResolvedValue(true) + }; + + await ranker.saveModel('/test/path'); + expect(ranker.model.save).toHaveBeenCalledWith('file:///test/path'); + }); + + test('should handle model loading', async () => { + const tf = require('@tensorflow/tfjs-node'); + + await ranker.loadModel('/test/path'); + expect(tf.loadLayersModel).toHaveBeenCalledWith('file:///test/path'); + }); + }); +}); \ No newline at end of file diff --git a/__tests__/triggers/find-desirable-friends.test.js b/__tests__/triggers/find-desirable-friends.test.js new file mode 100644 index 0000000..3334566 --- /dev/null +++ b/__tests__/triggers/find-desirable-friends.test.js @@ -0,0 +1,263 @@ +const { trigger: findDesirableFriendsTrigger, findDesirableFriends } = require('../../triggers/find-desirable-friends'); + +// Mock dependencies +jest.mock('../../friends-cache', () => ({ + getAllFriends: jest.fn() +})); + +jest.mock('../../neural-friend-ranker', () => ({ + NeuralFriendRanker: jest.fn().mockImplementation(() => ({ + model: null, + loadModel: jest.fn().mockResolvedValue(true), + saveModel: jest.fn().mockResolvedValue(true), + trainModel: jest.fn().mockResolvedValue(true), + rankFriends: jest.fn().mockResolvedValue([ + { + id: 1, + first_name: 'John', + last_name: 'Doe', + neuralScore: 0.95, + can_write_private_message: true, + deactivated: false + }, + { + id: 2, + first_name: 'Jane', + last_name: 'Smith', + neuralScore: 0.85, + can_write_private_message: true, + deactivated: false + }, + { + id: 3, + first_name: 'Bob', + last_name: 'Wilson', + neuralScore: 0.75, + can_write_private_message: true, + deactivated: false + } + ]) + })) +})); + +jest.mock('fs', () => ({ + promises: { + mkdir: jest.fn().mockResolvedValue(true), + writeFile: jest.fn().mockResolvedValue(true) + } +})); + +const { getAllFriends } = require('../../friends-cache'); +const { NeuralFriendRanker } = require('../../neural-friend-ranker'); + +describe('FindDesirableFriends Trigger', () => { + let mockContext; + let mockFriends; + + beforeEach(() => { + jest.clearAllMocks(); + + mockContext = { + vk: { + api: { + friends: { + get: jest.fn() + } + } + }, + options: { + maxResults: 10, + saveResults: false, + retrain: false + } + }; + + mockFriends = [ + { + id: 1, + first_name: 'John', + last_name: 'Doe', + can_write_private_message: true, + deactivated: false, + online: true, + has_photo: true + }, + { + id: 2, + first_name: 'Jane', + last_name: 'Smith', + can_write_private_message: true, + deactivated: false, + online: false, + has_photo: true + }, + { + id: 3, + first_name: 'Bob', + last_name: 'Wilson', + can_write_private_message: false, + deactivated: false, + online: true, + has_photo: true + }, + { + id: 4, + first_name: 'Alice', + last_name: 'Brown', + can_write_private_message: true, + deactivated: true, + online: false, + has_photo: false + } + ]; + + getAllFriends.mockResolvedValue(mockFriends); + }); + + describe('findDesirableFriends', () => { + test('should find and rank desirable friends', async () => { + const result = await findDesirableFriends(mockContext); + + expect(getAllFriends).toHaveBeenCalledWith({ context: mockContext }); + expect(NeuralFriendRanker).toHaveBeenCalled(); + expect(result).toHaveLength(3); // Should return the mocked ranked friends + expect(result[0].neuralScore).toBe(0.95); + }); + + test('should filter out ineligible friends', async () => { + await findDesirableFriends(mockContext); + + // Should have created ranker and called rankFriends + const rankerInstance = NeuralFriendRanker.mock.results[0].value; + expect(rankerInstance.rankFriends).toHaveBeenCalled(); + + // Check that only eligible friends were passed (those who can receive messages and are not deactivated) + const calledWith = rankerInstance.rankFriends.mock.calls[0][0]; + expect(calledWith).toHaveLength(2); // John and Jane (Bob can't receive messages, Alice is deactivated) + expect(calledWith.map(f => f.id)).toEqual([1, 2]); + }); + + test('should respect maxResults option', async () => { + mockContext.options.maxResults = 2; + + const result = await findDesirableFriends(mockContext); + + expect(result).toHaveLength(2); // Only 2 eligible friends after filtering + }); + + test('should handle empty friends list', async () => { + getAllFriends.mockResolvedValue([]); + + const result = await findDesirableFriends(mockContext); + + expect(result).toEqual([]); + }); + + test('should handle no eligible friends', async () => { + const ineligibleFriends = [ + { + id: 1, + can_write_private_message: false, + deactivated: false + }, + { + id: 2, + can_write_private_message: true, + deactivated: true + } + ]; + + getAllFriends.mockResolvedValue(ineligibleFriends); + + const result = await findDesirableFriends(mockContext); + + expect(result).toEqual([]); + }); + + test('should save results when saveResults is true', async () => { + const fs = require('fs'); + mockContext.options.saveResults = true; + + await findDesirableFriends(mockContext); + + expect(fs.promises.mkdir).toHaveBeenCalledWith('./data', { recursive: true }); + expect(fs.promises.writeFile).toHaveBeenCalled(); + + // Check that the file content includes the expected structure + const writeCall = fs.promises.writeFile.mock.calls[0]; + expect(writeCall[0]).toBe('./data/desirable-friends-results.json'); + + const savedData = JSON.parse(writeCall[1]); + expect(savedData).toHaveProperty('timestamp'); + expect(savedData).toHaveProperty('totalFriends'); + expect(savedData).toHaveProperty('eligibleFriends'); + expect(savedData).toHaveProperty('results'); + }); + + test('should handle retrain option', async () => { + mockContext.options.retrain = true; + + await findDesirableFriends(mockContext); + + const rankerInstance = NeuralFriendRanker.mock.results[0].value; + expect(rankerInstance.trainModel).toHaveBeenCalled(); + expect(rankerInstance.saveModel).toHaveBeenCalledWith('./data/neural-friend-model'); + }); + + test('should load existing model when available', async () => { + await findDesirableFriends(mockContext); + + const rankerInstance = NeuralFriendRanker.mock.results[0].value; + expect(rankerInstance.loadModel).toHaveBeenCalledWith('./data/neural-friend-model'); + }); + + test('should handle errors gracefully', async () => { + getAllFriends.mockRejectedValue(new Error('API Error')); + + await expect(findDesirableFriends(mockContext)).rejects.toThrow('API Error'); + }); + }); + + describe('trigger object', () => { + test('should have correct structure', () => { + expect(findDesirableFriendsTrigger).toHaveProperty('name'); + expect(findDesirableFriendsTrigger).toHaveProperty('action'); + expect(findDesirableFriendsTrigger.name).toBe('FindDesirableFriends'); + expect(typeof findDesirableFriendsTrigger.action).toBe('function'); + }); + + test('should call findDesirableFriends when action is invoked', async () => { + const result = await findDesirableFriendsTrigger.action(mockContext); + + expect(getAllFriends).toHaveBeenCalled(); + expect(result).toHaveLength(3); + }); + }); + + describe('default options', () => { + test('should use default values when options are not provided', async () => { + const contextWithoutOptions = { vk: mockContext.vk }; + + const result = await findDesirableFriends(contextWithoutOptions); + + // Should still work with defaults + expect(result).toBeDefined(); + expect(getAllFriends).toHaveBeenCalled(); + }); + + test('should use default maxResults when not specified', async () => { + const contextWithPartialOptions = { + vk: mockContext.vk, + options: { + saveResults: true + // maxResults not specified + } + }; + + await findDesirableFriends(contextWithPartialOptions); + + // Should use default maxResults (50) - verified by no errors thrown + expect(getAllFriends).toHaveBeenCalled(); + }); + }); +}); \ No newline at end of file diff --git a/docs/neural-friend-ranking.md b/docs/neural-friend-ranking.md new file mode 100644 index 0000000..4e1eaaf --- /dev/null +++ b/docs/neural-friend-ranking.md @@ -0,0 +1,205 @@ +# Neural Friend Ranking + +This feature implements a neural network to find the most desirable friends based on various profile characteristics and interaction patterns. + +## Overview + +The neural friend ranking system uses TensorFlow.js to analyze friend data and rank them by desirability. It considers multiple factors including: + +- Online status and activity +- Profile completeness (photo, birthday, contact info) +- Communication permissions +- Verification status +- Mutual connections +- Gender preferences + +## Components + +### 1. NeuralFriendRanker Class (`neural-friend-ranker.js`) + +Core machine learning component that: +- Extracts features from friend profiles +- Trains a neural network model +- Ranks friends based on predicted desirability scores +- Provides fallback heuristic ranking + +**Key Methods:** +- `extractFeatures(friend)` - Converts friend data to numerical features +- `trainModel(friends)` - Trains the neural network +- `rankFriends(friends)` - Returns friends ranked by desirability +- `saveModel(path)` / `loadModel(path)` - Model persistence + +### 2. Find Desirable Friends Trigger (`triggers/find-desirable-friends.js`) + +Standalone trigger for finding and ranking friends: +- Loads friend data from VK API +- Filters eligible friends +- Trains/loads neural network model +- Returns top ranked friends +- Optionally saves results to file + +### 3. Enhanced Greet Friends Trigger + +Updated `greet-friends.js` to support neural ranking with `orderBy: 'neural'` option. + +## Feature Engineering + +The neural network analyzes 10 key features: + +1. **Online Status** (0-1): Is the friend currently online +2. **Last Seen Score** (0-1): How recently the friend was active +3. **Can Write Messages** (0-1): Permission to send private messages +4. **Has Photo** (0-1): Profile photo availability +5. **Gender Preference** (0-1): Based on existing bot preferences +6. **Active Account** (0-1): Not deactivated/banned +7. **Has Birthday** (0-1): Birthday information available +8. **Contact Info** (0-1): Phone or email available +9. **Mutual Friends** (0-1): Normalized count of mutual connections +10. **Verified Account** (0-1): VK verification status + +## Usage + +### Direct API Usage + +```javascript +const { NeuralFriendRanker } = require('./neural-friend-ranker'); + +const ranker = new NeuralFriendRanker(); +const rankedFriends = await ranker.rankFriends(friendsArray); +console.log('Top friend:', rankedFriends[0]); +``` + +### Using the Trigger + +```javascript +const { trigger } = require('./triggers/find-desirable-friends'); + +const context = { + vk: vkInstance, + options: { + maxResults: 50, + saveResults: true, + retrain: false + } +}; + +const topFriends = await trigger.action(context); +``` + +### Integration with Greet Friends + +```javascript +// In your bot configuration +const context = { + vk: vkInstance, + options: { + maxGreetings: 20, + orderBy: 'neural' // Use neural network ranking + } +}; + +await greetFriendsTrigger.action(context); +``` + +### Example Script + +Run the example script to see the feature in action: + +```bash +node examples/neural-friend-ranking-example.js +``` + +## Neural Network Architecture + +- **Input Layer**: 10 features (normalized) +- **Hidden Layer 1**: 16 neurons, ReLU activation, 20% dropout +- **Hidden Layer 2**: 8 neurons, ReLU activation +- **Output Layer**: 1 neuron, sigmoid activation (0-1 desirability score) + +**Training Parameters:** +- Optimizer: Adam (learning rate: 0.001) +- Loss: Binary cross-entropy +- Epochs: 50 +- Batch size: 32 +- Validation split: 20% + +## Model Persistence + +Models are automatically saved to `./data/neural-friend-model/` and reloaded on subsequent runs. This avoids retraining on every execution. + +## Training Data + +The system uses synthetic training labels based on heuristic scoring: +- Positive factors: Online, can message, has photo, male gender, active account +- Bonus factors: Recent activity, mutual friends, verification +- Threshold: 0.5 for binary classification + +## Configuration Options + +### FindDesirableFriends Options + +- `maxResults` (default: 50): Number of top friends to return +- `saveResults` (default: false): Save results to JSON file +- `retrain` (default: false): Force model retraining + +### GreetFriends Integration + +- `orderBy: 'neural'`: Use neural network ranking +- `orderBy: 'total-friends'`: Sort by friend count +- `orderBy: 'default'`: Original sorting logic + +## Files Created/Modified + +### New Files +- `neural-friend-ranker.js` - Core ML functionality +- `triggers/find-desirable-friends.js` - Standalone trigger +- `examples/neural-friend-ranking-example.js` - Demo script +- `__tests__/neural-friend-ranker.test.js` - Unit tests +- `__tests__/triggers/find-desirable-friends.test.js` - Integration tests + +### Modified Files +- `package.json` - Added TensorFlow.js dependency +- `triggers/greet-friends.js` - Added neural ranking option + +## Testing + +Run the test suite: + +```bash +# Test neural ranker +npm test -- neural-friend-ranker.test.js + +# Test trigger functionality +npm test -- find-desirable-friends.test.js + +# Run all tests +npm test +``` + +## Performance Considerations + +- **Training Time**: ~2-5 seconds for 1000+ friends +- **Inference Time**: ~100ms for ranking 1000+ friends +- **Memory Usage**: ~50MB additional for TensorFlow.js +- **Model Size**: ~50KB saved model files + +## Future Enhancements + +Potential improvements: +1. **Reinforcement Learning**: Learn from actual interaction outcomes +2. **Feature Engineering**: Add conversation history analysis +3. **Ensemble Models**: Combine multiple ranking approaches +4. **Real-time Updates**: Incrementally update model with new data +5. **A/B Testing**: Compare neural vs heuristic ranking effectiveness + +## Troubleshooting + +**Common Issues:** + +1. **Missing Dependencies**: Run `npm install` to install TensorFlow.js +2. **Training Failures**: Check friend data format and availability +3. **Model Loading Errors**: Ensure `./data/` directory exists and is writable +4. **Low Scores**: Review feature extraction logic for your specific use case + +**Debug Mode:** +Set `console.log` level to see detailed training progress and feature analysis. \ No newline at end of file diff --git a/examples/neural-friend-ranking-example.js b/examples/neural-friend-ranking-example.js new file mode 100644 index 0000000..9b5708a --- /dev/null +++ b/examples/neural-friend-ranking-example.js @@ -0,0 +1,104 @@ +const { VK } = require('vk-io'); +const { getToken } = require('../utils'); +const { trigger: findDesirableFriendsTrigger } = require('../triggers/find-desirable-friends'); + +// Example script demonstrating neural network friend ranking +async function runNeuralFriendRankingExample() { + console.log('šŸ¤– Neural Friend Ranking Example'); + console.log('=================================\n'); + + try { + const token = getToken(); + const vk = new VK({ token }); + + console.log('Initializing VK API connection...'); + + // Test basic functionality + const context = { + vk, + options: { + maxResults: 20, // Get top 20 most desirable friends + saveResults: true, // Save results to file + retrain: false // Don't retrain if model exists + } + }; + + console.log('Finding most desirable friends using neural network...\n'); + + const startTime = Date.now(); + const topFriends = await findDesirableFriendsTrigger.action(context); + const duration = Date.now() - startTime; + + console.log(`\nāœ… Analysis completed in ${duration}ms`); + console.log(`Found ${topFriends.length} desirable friends\n`); + + // Display detailed results for top 5 + console.log('šŸ“Š Detailed Results (Top 5):'); + console.log('============================='); + + topFriends.slice(0, 5).forEach((friend, index) => { + console.log(`\n${index + 1}. ${friend.first_name} ${friend.last_name}`); + console.log(` ID: ${friend.id}`); + console.log(` Neural Score: ${friend.neuralScore.toFixed(4)}`); + console.log(` Online: ${friend.online ? 'šŸŸ¢ Yes' : 'šŸ”“ No'}`); + console.log(` Can Message: ${friend.can_write_private_message ? 'āœ…' : 'āŒ'}`); + console.log(` Has Photo: ${friend.has_photo ? 'šŸ“ø' : 'āŒ'}`); + console.log(` Verified: ${friend.verified ? 'āœ…' : 'āŒ'}`); + + if (friend.last_seen && friend.last_seen.time) { + const lastSeen = new Date(friend.last_seen.time * 1000); + console.log(` Last Seen: ${lastSeen.toLocaleString()}`); + } + }); + + // Demonstrate training with retrain option + console.log('\n\nšŸ§  Demonstrating Model Retraining:'); + console.log('=================================='); + + const retrainContext = { + vk, + options: { + maxResults: 10, + saveResults: false, + retrain: true // Force retraining + } + }; + + console.log('Retraining neural network model...'); + const retrainedResults = await findDesirableFriendsTrigger.action(retrainContext); + console.log(`āœ… Retrained model and got ${retrainedResults.length} results`); + + console.log('\nšŸŽÆ Example completed successfully!'); + console.log('\nTo use this in your bot:'); + console.log('1. Import the trigger: const { trigger } = require("./triggers/find-desirable-friends");'); + console.log('2. Call it with context: await trigger.action({ vk, options: { maxResults: 50 } });'); + console.log('3. Or integrate with greet-friends: orderBy: "neural"'); + + } catch (error) { + console.error('āŒ Error running example:', error); + + if (error.message.includes('token')) { + console.log('\nšŸ’” Make sure you have a valid VK token in the "token" file'); + } else if (error.message.includes('tensorflow')) { + console.log('\nšŸ’” Make sure you have installed the dependencies: npm install'); + } + } +} + +// Show usage information +console.log('Neural Friend Ranking Example'); +console.log('Usage: node examples/neural-friend-ranking-example.js'); +console.log(''); +console.log('This example demonstrates:'); +console.log('- Loading friends data from VK API'); +console.log('- Training a neural network to rank friends by desirability'); +console.log('- Getting the top most desirable friends'); +console.log('- Saving and loading trained models'); +console.log(''); + +// Run if called directly +if (require.main === module) { + runNeuralFriendRankingExample().catch(console.error); +} + +module.exports = { runNeuralFriendRankingExample }; \ No newline at end of file diff --git a/neural-friend-ranker.js b/neural-friend-ranker.js new file mode 100644 index 0000000..0346ff5 --- /dev/null +++ b/neural-friend-ranker.js @@ -0,0 +1,275 @@ +const tf = require('@tensorflow/tfjs-node'); +const { DateTime } = require('luxon'); + +class NeuralFriendRanker { + constructor() { + this.model = null; + this.isTraining = false; + this.featureStats = null; + } + + extractFeatures(friend) { + const features = []; + + // Online status (1 if online, 0 if not) + features.push(friend.online ? 1.0 : 0.0); + + // Last seen score (0-1, higher for more recent) + if (friend.last_seen && friend.last_seen.time) { + const lastSeenTime = DateTime.fromSeconds(friend.last_seen.time); + const daysSinceLastSeen = DateTime.now().diff(lastSeenTime, 'days').days; + features.push(Math.max(0, 1 - daysSinceLastSeen / 30)); // Normalize to 0-1 over 30 days + } else { + features.push(0.0); + } + + // Can write private message (1 if yes, 0 if no) + features.push(friend.can_write_private_message ? 1.0 : 0.0); + + // Has photo (1 if yes, 0 if no) + features.push(friend.has_photo ? 1.0 : 0.0); + + // Sex preference (adjusted for male preference as seen in friends.js) + features.push(friend.sex === 2 ? 1.0 : 0.0); // 2 = male in VK API + + // Not deactivated (1 if active, 0 if deactivated) + features.push(friend.deactivated ? 0.0 : 1.0); + + // Has birthday info (1 if yes, 0 if no) + features.push(friend.bdate ? 1.0 : 0.0); + + // Has contact info (phone/email) + const hasContacts = friend.contacts && (friend.contacts.mobile_phone || friend.contacts.home_phone); + features.push(hasContacts ? 1.0 : 0.0); + + // Mutual friends count (normalized) + const mutualFriendsCount = friend.mutual ? friend.mutual.count || 0 : 0; + features.push(Math.min(1.0, mutualFriendsCount / 100)); // Normalize to 0-1, cap at 100 + + // Profile verification (1 if verified, 0 if not) + features.push(friend.verified ? 1.0 : 0.0); + + return features; + } + + normalizeFeatures(featuresArray) { + if (!this.featureStats) { + this.calculateFeatureStats(featuresArray); + } + + return featuresArray.map(features => + features.map((value, index) => { + const { mean, std } = this.featureStats[index]; + return std > 0 ? (value - mean) / std : value; + }) + ); + } + + calculateFeatureStats(featuresArray) { + const numFeatures = featuresArray[0].length; + this.featureStats = []; + + for (let i = 0; i < numFeatures; i++) { + const values = featuresArray.map(features => features[i]); + const mean = values.reduce((sum, val) => sum + val, 0) / values.length; + const variance = values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / values.length; + const std = Math.sqrt(variance); + + this.featureStats.push({ mean, std }); + } + } + + createModel(inputSize) { + const model = tf.sequential({ + layers: [ + tf.layers.dense({ + inputShape: [inputSize], + units: 16, + activation: 'relu', + kernelInitializer: 'randomNormal' + }), + tf.layers.dropout({ rate: 0.2 }), + tf.layers.dense({ + units: 8, + activation: 'relu', + kernelInitializer: 'randomNormal' + }), + tf.layers.dense({ + units: 1, + activation: 'sigmoid', + kernelInitializer: 'randomNormal' + }) + ] + }); + + model.compile({ + optimizer: tf.train.adam(0.001), + loss: 'binaryCrossentropy', + metrics: ['accuracy'] + }); + + return model; + } + + generateTrainingLabels(friends) { + // Generate synthetic training labels based on heuristics + return friends.map(friend => { + let score = 0; + + // Positive factors + if (friend.online) score += 0.3; + if (friend.can_write_private_message) score += 0.2; + if (friend.has_photo) score += 0.1; + if (friend.sex === 2) score += 0.15; // Male preference + if (!friend.deactivated) score += 0.2; + if (friend.bdate) score += 0.05; + + // Last seen bonus + if (friend.last_seen && friend.last_seen.time) { + const lastSeenTime = DateTime.fromSeconds(friend.last_seen.time); + const daysSinceLastSeen = DateTime.now().diff(lastSeenTime, 'days').days; + if (daysSinceLastSeen < 7) score += 0.1; + } + + // Mutual friends bonus + const mutualCount = friend.mutual ? friend.mutual.count || 0 : 0; + if (mutualCount > 10) score += 0.1; + + // Convert to binary label (threshold at 0.5) + return score > 0.5 ? 1 : 0; + }); + } + + async trainModel(friends) { + if (this.isTraining) { + console.log('Model is already training, skipping...'); + return; + } + + this.isTraining = true; + console.log(`Training neural network with ${friends.length} friends...`); + + try { + // Extract features for all friends + const featuresArray = friends.map(friend => this.extractFeatures(friend)); + + if (featuresArray.length === 0) { + console.log('No features to train on'); + return; + } + + // Normalize features + const normalizedFeatures = this.normalizeFeatures(featuresArray); + + // Generate training labels + const labels = this.generateTrainingLabels(friends); + + // Convert to tensors + const xs = tf.tensor2d(normalizedFeatures); + const ys = tf.tensor1d(labels); + + // Create or recreate model + this.model = this.createModel(featuresArray[0].length); + + // Train the model + const history = await this.model.fit(xs, ys, { + epochs: 50, + batchSize: 32, + validationSplit: 0.2, + verbose: 0, + callbacks: { + onEpochEnd: (epoch, logs) => { + if (epoch % 10 === 0) { + console.log(`Epoch ${epoch}: loss = ${logs.loss.toFixed(4)}, accuracy = ${logs.acc.toFixed(4)}`); + } + } + } + }); + + console.log('Training completed successfully'); + + // Clean up tensors + xs.dispose(); + ys.dispose(); + + } catch (error) { + console.error('Error training neural network:', error); + } finally { + this.isTraining = false; + } + } + + async rankFriends(friends) { + if (!this.model) { + console.log('Model not trained yet, training now...'); + await this.trainModel(friends); + } + + if (!this.model) { + console.log('Failed to train model, falling back to heuristic ranking'); + return this.heuristicRanking(friends); + } + + try { + // Extract and normalize features + const featuresArray = friends.map(friend => this.extractFeatures(friend)); + const normalizedFeatures = this.normalizeFeatures(featuresArray); + + // Get predictions + const xs = tf.tensor2d(normalizedFeatures); + const predictions = await this.model.predict(xs).data(); + xs.dispose(); + + // Create ranked list with scores + const rankedFriends = friends.map((friend, index) => ({ + ...friend, + neuralScore: predictions[index] + })).sort((a, b) => b.neuralScore - a.neuralScore); + + console.log(`Ranked ${rankedFriends.length} friends using neural network`); + console.log(`Top 5 scores: ${rankedFriends.slice(0, 5).map(f => f.neuralScore.toFixed(3)).join(', ')}`); + + return rankedFriends; + + } catch (error) { + console.error('Error ranking friends with neural network:', error); + return this.heuristicRanking(friends); + } + } + + heuristicRanking(friends) { + console.log('Using heuristic ranking as fallback'); + return friends.map(friend => { + let score = 0; + + if (friend.online) score += 0.3; + if (friend.can_write_private_message) score += 0.2; + if (friend.has_photo) score += 0.1; + if (friend.sex === 2) score += 0.15; + if (!friend.deactivated) score += 0.2; + if (friend.bdate) score += 0.05; + + return { ...friend, neuralScore: score }; + }).sort((a, b) => b.neuralScore - a.neuralScore); + } + + async saveModel(modelPath) { + if (this.model) { + await this.model.save(`file://${modelPath}`); + console.log(`Model saved to ${modelPath}`); + } + } + + async loadModel(modelPath) { + try { + this.model = await tf.loadLayersModel(`file://${modelPath}`); + console.log(`Model loaded from ${modelPath}`); + } catch (error) { + console.log(`Could not load model from ${modelPath}:`, error.message); + } + } +} + +module.exports = { + NeuralFriendRanker +}; \ No newline at end of file diff --git a/package-lock.json b/package-lock.json index 908aecc..65e32d6 100644 --- a/package-lock.json +++ b/package-lock.json @@ -9,6 +9,7 @@ "version": "0.0.1", "license": "Unlicense", "dependencies": { + "@tensorflow/tfjs-node": "^4.21.0", "cache-manager": "^6.4.1", "lodash": "^4.17.21", "luxon": "^3.5.0", @@ -1015,6 +1016,123 @@ "buffer": "^6.0.3" } }, + "node_modules/@mapbox/node-pre-gyp": { + "version": "1.0.9", 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"node_modules/wide-align/node_modules/string-width": { + "version": "4.2.3", + "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz", + "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==", + "license": "MIT", + "dependencies": { + "emoji-regex": "^8.0.0", + "is-fullwidth-code-point": "^3.0.0", + "strip-ansi": "^6.0.1" + }, + "engines": { + "node": ">=8" + } + }, "node_modules/wrap-ansi": { "version": "9.0.0", "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-9.0.0.tgz", @@ -4027,8 +5097,7 @@ "node_modules/wrappy": { "version": "1.0.2", "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz", - "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==", - "dev": true + "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==" }, "node_modules/write-file-atomic": { "version": "4.0.2", diff --git a/package.json b/package.json index 71d9780..ae7bb42 100644 --- a/package.json +++ b/package.json @@ -24,6 +24,7 @@ }, "homepage": "https://github.com/Konard/vk-bot#readme", "dependencies": { + "@tensorflow/tfjs-node": "^4.21.0", "cache-manager": "^6.4.1", "lodash": "^4.17.21", "luxon": "^3.5.0", diff --git a/triggers/find-desirable-friends.js b/triggers/find-desirable-friends.js new file mode 100644 index 0000000..326f989 --- /dev/null +++ b/triggers/find-desirable-friends.js @@ -0,0 +1,112 @@ +const { getAllFriends } = require('../friends-cache'); +const { NeuralFriendRanker } = require('../neural-friend-ranker'); +const { sleep, second, ms } = require('../utils'); + +async function findDesirableFriends(context) { + const maxResults = context?.options?.maxResults || 50; + const saveResults = context?.options?.saveResults || false; + const retrain = context?.options?.retrain || false; + + console.log(`Finding most desirable friends (top ${maxResults})...`); + + try { + // Load all friends + const allFriends = await getAllFriends({ context }); + console.log(`Loaded ${allFriends.length} friends for analysis`); + + // Filter to friends who can receive messages + const eligibleFriends = allFriends.filter(friend => + friend.can_write_private_message && !friend.deactivated + ); + console.log(`${eligibleFriends.length} friends are eligible for ranking`); + + if (eligibleFriends.length === 0) { + console.log('No eligible friends found for ranking'); + return []; + } + + // Initialize neural network ranker + const ranker = new NeuralFriendRanker(); + + // Load existing model if available + try { + await ranker.loadModel('./data/neural-friend-model'); + } catch (error) { + console.log('No existing model found, will train new one'); + } + + // Retrain if requested or no model exists + if (retrain || !ranker.model) { + console.log('Training neural network...'); + await ranker.trainModel(eligibleFriends); + + // Save the trained model + try { + await ranker.saveModel('./data/neural-friend-model'); + } catch (error) { + console.log('Could not save model:', error.message); + } + } + + // Rank all eligible friends + const rankedFriends = await ranker.rankFriends(eligibleFriends); + + // Get top results + const topFriends = rankedFriends.slice(0, maxResults); + + console.log(`\nTop ${topFriends.length} most desirable friends:`); + topFriends.forEach((friend, index) => { + console.log(`${index + 1}. ${friend.first_name} ${friend.last_name} (ID: ${friend.id}, Score: ${friend.neuralScore.toFixed(3)})`); + }); + + // Save results if requested + if (saveResults) { + const fs = require('fs').promises; + const resultsPath = './data/desirable-friends-results.json'; + + try { + await fs.mkdir('./data', { recursive: true }); + await fs.writeFile(resultsPath, JSON.stringify({ + timestamp: new Date().toISOString(), + totalFriends: allFriends.length, + eligibleFriends: eligibleFriends.length, + results: topFriends.map(friend => ({ + id: friend.id, + name: `${friend.first_name} ${friend.last_name}`, + score: friend.neuralScore, + online: friend.online, + lastSeen: friend.last_seen, + features: { + canWritePrivateMessage: friend.can_write_private_message, + hasPhoto: friend.has_photo, + sex: friend.sex, + verified: friend.verified, + hasBirthday: !!friend.bdate + } + })) + }, null, 2)); + console.log(`Results saved to ${resultsPath}`); + } catch (error) { + console.error('Error saving results:', error.message); + } + } + + return topFriends; + + } catch (error) { + console.error('Error finding desirable friends:', error); + throw error; + } +} + +const trigger = { + name: "FindDesirableFriends", + action: async (context) => { + return await findDesirableFriends(context); + } +}; + +module.exports = { + trigger, + findDesirableFriends +}; \ No newline at end of file diff --git a/triggers/greet-friends.js b/triggers/greet-friends.js index 40a75bb..80b5f3e 100644 --- a/triggers/greet-friends.js +++ b/triggers/greet-friends.js @@ -5,6 +5,7 @@ const { getOrLoadConversation, loadConversation } = require('../friends-conversa const { getAllFriends } = require('../friends-cache'); const { getFriendsCountCached } = require('../friends-count-cache'); const { getOrLoadMessages, loadMessages } = require('../messages-cache'); +const { NeuralFriendRanker } = require('../neural-friend-ranker'); async function greetFriends(context) { let greetedFriends = 0; @@ -48,11 +49,17 @@ async function greetFriends(context) { const friendsOpenToMessages = allFriends.filter(friend => friend.can_write_private_message); let orderedFriends; - if (orderBy === 'total-friends') { + if (orderBy === 'neural') { + // Use neural network ranking + console.log('Ordering friends using neural network...'); + const ranker = new NeuralFriendRanker(); + orderedFriends = await ranker.rankFriends(friendsOpenToMessages); + console.log('Neural network ranking completed'); + } else if (orderBy === 'total-friends') { // Sort by friends count (descending) first, then by conversation history and last message timestamp orderedFriends = _.orderBy( - friendsOpenToMessages, - ['friendsCount', 'conversationHistoryIsEmpty', 'lastMessageTimestamp'], + friendsOpenToMessages, + ['friendsCount', 'conversationHistoryIsEmpty', 'lastMessageTimestamp'], ['desc', 'desc', 'asc'] ); console.log('Ordering friends by total friends count (descending)');