fix netwrk rebuild
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Dobromir Popov
187
test_fixed_input_size.py
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187
test_fixed_input_size.py
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#!/usr/bin/env python3
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"""
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Test Fixed Input Size
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Verify that the CNN model now receives consistent input dimensions
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"""
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import numpy as np
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from datetime import datetime
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from core.data_models import BaseDataInput, OHLCVBar
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from core.enhanced_cnn_adapter import EnhancedCNNAdapter
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def create_test_data(with_cob=True, with_indicators=True):
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"""Create test BaseDataInput with varying data completeness"""
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# Create basic OHLCV data
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ohlcv_bars = []
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for i in range(100): # Less than 300 to test padding
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bar = OHLCVBar(
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symbol="ETH/USDT",
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timestamp=datetime.now(),
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open=100.0 + i,
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high=101.0 + i,
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low=99.0 + i,
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close=100.5 + i,
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volume=1000 + i,
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timeframe="1s"
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)
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ohlcv_bars.append(bar)
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# Create test data
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base_data = BaseDataInput(
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symbol="ETH/USDT",
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timestamp=datetime.now(),
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ohlcv_1s=ohlcv_bars,
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ohlcv_1m=ohlcv_bars[:50], # Even less data
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ohlcv_1h=ohlcv_bars[:20],
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ohlcv_1d=ohlcv_bars[:10],
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btc_ohlcv_1s=ohlcv_bars[:80], # Incomplete BTC data
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technical_indicators={'rsi': 50.0, 'macd': 0.1} if with_indicators else {},
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last_predictions={}
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)
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# Add COB data if requested (simplified for testing)
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if with_cob:
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# Create a simple mock COB data object
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class MockCOBData:
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def __init__(self):
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self.price_buckets = {
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2500.0: {'bid_volume': 100, 'ask_volume': 90, 'total_volume': 190, 'imbalance': 0.05},
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2501.0: {'bid_volume': 80, 'ask_volume': 120, 'total_volume': 200, 'imbalance': -0.2}
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}
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self.ma_1s_imbalance = {2500.0: 0.1, 2501.0: -0.1}
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self.ma_5s_imbalance = {2500.0: 0.05, 2501.0: -0.05}
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base_data.cob_data = MockCOBData()
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return base_data
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def test_consistent_feature_size():
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"""Test that feature vectors are always the same size"""
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print("=== Testing Consistent Feature Size ===")
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# Test different data scenarios
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scenarios = [
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("Full data", True, True),
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("No COB data", False, True),
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("No indicators", True, False),
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("Minimal data", False, False)
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]
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feature_sizes = []
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for name, with_cob, with_indicators in scenarios:
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base_data = create_test_data(with_cob, with_indicators)
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features = base_data.get_feature_vector()
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print(f"{name}: {len(features)} features")
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feature_sizes.append(len(features))
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# Check if all sizes are the same
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if len(set(feature_sizes)) == 1:
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print(f"✅ All feature vectors have consistent size: {feature_sizes[0]}")
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return feature_sizes[0]
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else:
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print(f"❌ Inconsistent feature sizes: {feature_sizes}")
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return None
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def test_cnn_adapter():
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"""Test that CNN adapter works with fixed input size"""
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print("\n=== Testing CNN Adapter ===")
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try:
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# Create CNN adapter
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adapter = EnhancedCNNAdapter()
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print(f"CNN model initialized with feature_dim: {adapter.model.feature_dim}")
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# Test with different data scenarios
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scenarios = [
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("Full data", True, True),
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("No COB data", False, True),
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("Minimal data", False, False)
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]
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for name, with_cob, with_indicators in scenarios:
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try:
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base_data = create_test_data(with_cob, with_indicators)
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# Make prediction
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result = adapter.predict(base_data)
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print(f"✅ {name}: Prediction successful - {result.action} (conf={result.confidence:.3f})")
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except Exception as e:
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print(f"❌ {name}: Prediction failed - {e}")
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return True
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except Exception as e:
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print(f"❌ CNN adapter initialization failed: {e}")
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return False
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def test_no_network_rebuilding():
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"""Test that network doesn't rebuild during runtime"""
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print("\n=== Testing No Network Rebuilding ===")
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try:
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adapter = EnhancedCNNAdapter()
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original_feature_dim = adapter.model.feature_dim
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print(f"Original feature_dim: {original_feature_dim}")
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# Make multiple predictions with different data
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for i in range(5):
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base_data = create_test_data(with_cob=(i % 2 == 0), with_indicators=(i % 3 == 0))
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try:
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result = adapter.predict(base_data)
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current_feature_dim = adapter.model.feature_dim
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if current_feature_dim != original_feature_dim:
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print(f"❌ Network was rebuilt! Original: {original_feature_dim}, Current: {current_feature_dim}")
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return False
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print(f"✅ Prediction {i+1}: No rebuilding, feature_dim stable at {current_feature_dim}")
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except Exception as e:
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print(f"❌ Prediction {i+1} failed: {e}")
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return False
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print("✅ Network architecture remained stable throughout all predictions")
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return True
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except Exception as e:
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print(f"❌ Test failed: {e}")
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return False
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def main():
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"""Run all tests"""
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print("=== Fixed Input Size Test Suite ===\n")
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# Test 1: Consistent feature size
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fixed_size = test_consistent_feature_size()
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if fixed_size:
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# Test 2: CNN adapter works
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adapter_works = test_cnn_adapter()
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if adapter_works:
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# Test 3: No network rebuilding
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no_rebuilding = test_no_network_rebuilding()
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if no_rebuilding:
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print("\n✅ ALL TESTS PASSED!")
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print("✅ Feature vectors have consistent size")
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print("✅ CNN adapter works with fixed input")
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print("✅ No runtime network rebuilding")
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print(f"✅ Fixed feature size: {fixed_size}")
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else:
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print("\n❌ Network rebuilding test failed")
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else:
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print("\n❌ CNN adapter test failed")
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else:
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print("\n❌ Feature size consistency test failed")
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if __name__ == "__main__":
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main()
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