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Features: - 16 checkpoint test suite validating ML systems capabilities - Integration tests covering complete learning progression - Rich CLI progress tracking with visual timelines - Capability-driven assessment from environment to production Checkpoints: - Environment setup through full ML system deployment - Each checkpoint validates integrated functionality - Progressive capability building with clear success criteria - Professional CLI interface with status/timeline/test commands
68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
"""
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Checkpoint 1: Foundation (After Module 2 - Tensor)
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Question: "Can I create and manipulate the building blocks of ML?"
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"""
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import numpy as np
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import pytest
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def test_checkpoint_01_foundation():
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"""
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Checkpoint 1: Foundation
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Validates that students can create and manipulate multi-dimensional tensors,
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perform arithmetic operations, and understand tensor shapes - the foundation
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of all machine learning computations.
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"""
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print("\n🏁 Checkpoint 1: Foundation")
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print("=" * 50)
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try:
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from tinytorch.core.tensor import Tensor
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except ImportError:
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pytest.fail("❌ Cannot import Tensor - complete Module 2 first")
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# Test 1: Basic tensor creation
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print("📊 Testing tensor creation...")
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x = Tensor([[1, 2], [3, 4]])
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y = Tensor([[5, 6], [7, 8]])
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assert x.shape == (2, 2), f"Expected shape (2, 2), got {x.shape}"
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assert y.shape == (2, 2), f"Expected shape (2, 2), got {y.shape}"
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print(f"✅ Created tensors with shapes: {x.shape}")
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# Test 2: Arithmetic operations
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print("🧮 Testing arithmetic operations...")
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result = x + y * 2 # Should be [[1+10, 2+12], [3+14, 4+16]] = [[11, 14], [17, 20]]
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expected = np.array([[11, 14], [17, 20]])
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assert np.allclose(result.data, expected), f"Expected {expected}, got {result.data}"
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print(f"✅ Arithmetic operations working: {result.data}")
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# Test 3: Different tensor shapes
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print("📐 Testing different shapes...")
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vector = Tensor([1, 2, 3, 4, 5])
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scalar = Tensor(42)
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matrix_3x3 = Tensor(np.random.randn(3, 3))
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assert vector.shape == (5,), f"Vector shape should be (5,), got {vector.shape}"
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assert scalar.shape == (), f"Scalar shape should be (), got {scalar.shape}"
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assert matrix_3x3.shape == (3, 3), f"Matrix shape should be (3, 3), got {matrix_3x3.shape}"
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print(f"✅ Multiple shapes supported: vector{vector.shape}, scalar{scalar.shape}, matrix{matrix_3x3.shape}")
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# Test 4: Data type handling
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print("🔢 Testing data types...")
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float_tensor = Tensor([1.5, 2.7, 3.14])
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int_tensor = Tensor([1, 2, 3])
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assert hasattr(float_tensor, 'dtype'), "Tensor should have dtype attribute"
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assert hasattr(int_tensor, 'dtype'), "Tensor should have dtype attribute"
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print(f"✅ Data types: float_tensor.dtype={float_tensor.dtype}, int_tensor.dtype={int_tensor.dtype}")
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print("\n🎉 Foundation Complete!")
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print("📝 You can now create and manipulate the building blocks of ML")
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print("🔧 Built capabilities: Tensor creation, arithmetic, shapes, dtypes")
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print("🎯 Next: Add intelligence with activation functions")
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if __name__ == "__main__":
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test_checkpoint_01_foundation() |