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Initial phase of adding PyTorch support for data analysis. Added conversion nodes.

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HowToSD
2025-02-24 21:50:18 -08:00
parent 4a0d561d8f
commit 17a40d7798
30 changed files with 1042 additions and 1 deletions
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tests/numpy_wrapper/__init__.py
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tests/numpy_wrapper/numpy_float_create_test.py
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import os
import sys
import unittest
import numpy as np
PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
sys.path.append(MODULE_ROOT)
from numpy_wrapper.numpy_float_create import NumpyFloatCreate
class TestNumpyFloatCreate(unittest.TestCase):
def setUp(self):
"""Set up test data."""
self.node = NumpyFloatCreate()
# Test cases covering rank 0 (scalar) to rank 5 tensors
self.test_cases = [
# Rank 0 (scalar)
("12345", np.array(12345, dtype=np.float32)),
("12345.6", np.array(12345.6, dtype=np.float32)),
# Rank 1 (1D array)
("[1, 2, 3]", np.array([1, 2, 3], dtype=np.float32)),
("[1.1, 2.2, 3.3]", np.array([1.1, 2.2, 3.3], dtype=np.float32)),
# Rank 2 (2D matrix)
("[[1, 2, 3], [4, 5, 6]]", np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)),
("[[1.1, 2.2], [3.3, 4.4]]", np.array([[1.1, 2.2], [3.3, 4.4]], dtype=np.float32)),
# Rank 3 (3D tensor)
("[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]",
np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.float32)),
# Rank 4 (4D tensor)
("[[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]]",
np.array([[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]], dtype=np.float32)),
# Rank 5 (5D tensor)
("[[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]]",
np.array([[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]], dtype=np.float32)),
]
def test_create_ndarray(self):
"""Test NumPy ndarray creation from various structured inputs."""
for data_str, expected_array in self.test_cases:
with self.subTest(data=data_str):
retval = self.node.f(data_str)[0]
# Ensure the returned value is a NumPy array
self.assertTrue(isinstance(retval, np.ndarray), f"Expected ndarray, got {type(retval)}")
# Ensure the dtype is float32
self.assertEqual(retval.dtype, np.float32, f"Dtype mismatch for input: {data_str}")
# Ensure the shape matches
self.assertEqual(retval.shape, expected_array.shape,
f"Shape mismatch for input: {data_str} (Expected {expected_array.shape}, got {retval.shape})")
# Ensure values match
np.testing.assert_array_equal(retval, expected_array,
f"Value mismatch for input: {data_str}")
if __name__ == "__main__":
unittest.main()
tests/numpy_wrapper/numpy_int_create_test.py
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import os
import sys
import unittest
import numpy as np
PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
sys.path.append(MODULE_ROOT)
from numpy_wrapper.numpy_int_create import NumpyIntCreate
class TestNumpyIntCreate(unittest.TestCase):
def setUp(self):
"""Set up test data."""
self.node = NumpyIntCreate()
# Test cases covering rank 0 (scalar) to rank 5 tensors
self.test_cases = [
# Rank 0 (scalar)
("12345", np.array(12345, dtype=np.int32)),
# Rank 1 (1D array)
("[1, 2, 3]", np.array([1, 2, 3], dtype=np.int32)),
# Rank 2 (2D matrix)
("[[1, 2, 3], [4, 5, 6]]", np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)),
# Rank 3 (3D tensor)
("[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]",
np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.int32)),
# Rank 4 (4D tensor)
("[[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]]",
np.array([[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]], dtype=np.int32)),
# Rank 5 (5D tensor)
("[[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]]",
np.array([[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]], dtype=np.int32)),
]
def test_create_ndarray(self):
"""Test NumPy int32 ndarray creation from various structured inputs."""
for data_str, expected_array in self.test_cases:
with self.subTest(data=data_str):
retval = self.node.f(data_str)[0]
# Ensure the returned value is a NumPy array
self.assertTrue(isinstance(retval, np.ndarray), f"Expected ndarray, got {type(retval)}")
# Ensure the dtype is int32
self.assertEqual(retval.dtype, np.int32, f"Dtype mismatch for input: {data_str}")
# Ensure the shape matches
self.assertEqual(retval.shape, expected_array.shape,
f"Shape mismatch for input: {data_str} (Expected {expected_array.shape}, got {retval.shape})")
# Ensure values match
np.testing.assert_array_equal(retval, expected_array,
f"Value mismatch for input: {data_str}")
if __name__ == "__main__":
unittest.main()
tests/pandas_wrapper/pandas_create_from_numpy_test.py
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import os
import sys
import unittest
import numpy as np
import pandas as pd
PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
sys.path.append(MODULE_ROOT)
from pandas_wrapper.pandas_create_from_numpy import PandasCreateFromNumpy
class TestPandasCreateFromNumpy(unittest.TestCase):
def setUp(self):
"""Set up test data."""
self.node = PandasCreateFromNumpy()
self.test_cases = [
# Rank 0
(np.array(42), ValueError),
(np.array(3.14), ValueError),
(np.array("hello"), ValueError),
(np.array(True), ValueError),
# Rank 1
(np.array([1, 2, 3]), pd.DataFrame([1, 2, 3])),
(np.array([1.1, 2.2, 3.3]), pd.DataFrame([1.1, 2.2, 3.3])),
(np.array(["a", "b", "c"]), pd.DataFrame(["a", "b", "c"])),
(np.array([True, False, True]), pd.DataFrame([True, False, True])),
# Rank 2
(np.array([[1, 2], [3, 4]]), pd.DataFrame([[1, 2], [3, 4]])),
(np.array([[1.1, 2.2], [3.3, 4.4]]), pd.DataFrame([[1.1, 2.2], [3.3, 4.4]])),
(np.array([["a", "b"], ["c", "d"]]), pd.DataFrame([["a", "b"], ["c", "d"]])),
(np.array([[True, False], [False, True]]), pd.DataFrame([[True, False], [False, True]])),
# Rank 3 (Invalid)
(np.array([[[1, 2], [3, 4]]]), ValueError),
(np.array([[["x", "y"], ["z", "w"]]]), ValueError),
# Rank 4 (Invalid)
(np.random.rand(2, 2, 2, 2), ValueError)
]
def test_create_dataframe(self):
"""Test DataFrame creation from various NumPy arrays."""
for data, expected in self.test_cases:
with self.subTest(data=data):
if isinstance(expected, type) and issubclass(expected, Exception):
with self.assertRaises(expected):
self.node.f(data)
else:
retval = self.node.f(data)[0]
# Test if DataFrame structure matches
self.assertTrue(isinstance(retval, pd.DataFrame))
# Test if data matches
pd.testing.assert_frame_equal(retval, expected)
if __name__ == "__main__":
unittest.main()
tests/pandas_wrapper/pandas_create_to_numpy_test.py
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import os
import sys
import unittest
import numpy as np
import pandas as pd
PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
sys.path.append(MODULE_ROOT)
from pandas_wrapper.pandas_to_numpy import PandasToNumpy
class TestPandasToNumpy(unittest.TestCase):
def setUp(self):
"""Set up test data."""
self.node = PandasToNumpy()
self.test_cases = [
# Rank 1
(pd.DataFrame([1, 2, 3]), np.array([[1], [2], [3]])),
(pd.DataFrame([1.1, 2.2, 3.3]), np.array([[1.1], [2.2], [3.3]])),
(pd.DataFrame(["a", "b", "c"]), np.array([["a"], ["b"], ["c"]], dtype=object)),
(pd.DataFrame([True, False, True]), np.array([[True], [False], [True]])),
# Rank 2
(pd.DataFrame([[1, 2], [3, 4]]), np.array([[1, 2], [3, 4]])),
(pd.DataFrame([[1.1, 2.2], [3.3, 4.4]]), np.array([[1.1, 2.2], [3.3, 4.4]])),
(pd.DataFrame([["a", "b"], ["c", "d"]]), np.array([["a", "b"], ["c", "d"]], dtype=object)),
(pd.DataFrame([[True, False], [False, True]]), np.array([[True, False], [False, True]])),
# Invalid cases
(None, TypeError),
("not a DataFrame", TypeError),
(np.array([[1, 2], [3, 4]]), TypeError)
]
def test_dataframe_to_numpy(self):
"""Test NumPy array conversion from various Pandas DataFrames."""
for dataframe, expected in self.test_cases:
with self.subTest(dataframe=dataframe):
if isinstance(expected, type) and issubclass(expected, Exception):
with self.assertRaises(expected):
self.node.f(dataframe)
else:
retval = self.node.f(dataframe)[0]
# Ensure the return type is a NumPy array
self.assertTrue(isinstance(retval, np.ndarray))
# Check array equality
np.testing.assert_array_equal(retval, expected)
if __name__ == "__main__":
unittest.main()
tests/pytorch_wrapper/__init__.py
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tests/pytorch_wrapper/pt_float_create_test.py
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import os
import sys
import unittest
import torch
PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
sys.path.append(MODULE_ROOT)
from pytorch_wrapper.pt_float_create import PtFloatCreate
class TestPtFloatCreate(unittest.TestCase):
def setUp(self):
"""Set up test data."""
self.node = PtFloatCreate()
# Test cases covering rank 0 (scalar) to rank 5 tensors
self.test_cases = [
# Rank 0 (scalar)
("12345", torch.tensor(12345, dtype=torch.float32)),
("12345.6", torch.tensor(12345.6, dtype=torch.float32)),
# Rank 1 (1D array)
("[1, 2, 3]", torch.tensor([1, 2, 3], dtype=torch.float32)),
("[1.1, 2.2, 3.3]", torch.tensor([1.1, 2.2, 3.3], dtype=torch.float32)),
# Rank 2 (2D matrix)
("[[1, 2, 3], [4, 5, 6]]", torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.float32)),
("[[1.1, 2.2], [3.3, 4.4]]", torch.tensor([[1.1, 2.2], [3.3, 4.4]], dtype=torch.float32)),
# Rank 3 (3D tensor)
("[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]",
torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=torch.float32)),
# Rank 4 (4D tensor)
("[[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]]",
torch.tensor([[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]], dtype=torch.float32)),
# Rank 5 (5D tensor)
("[[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]]",
torch.tensor([[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]], dtype=torch.float32)),
]
def test_create_tensor(self):
"""Test PyTorch tensor creation from various structured inputs."""
for data_str, expected_tensor in self.test_cases:
with self.subTest(data=data_str):
retval = self.node.f(data_str)[0]
# Ensure the returned value is a PyTorch tensor
self.assertTrue(isinstance(retval, torch.Tensor), f"Expected tensor, got {type(retval)}")
# Ensure the dtype is float32
self.assertEqual(retval.dtype, torch.float32, f"Dtype mismatch for input: {data_str}")
# Ensure the shape matches
self.assertEqual(retval.shape, expected_tensor.shape,
f"Shape mismatch for input: {data_str} (Expected {expected_tensor.shape}, got {retval.shape})")
# Ensure values match
torch.testing.assert_close(retval, expected_tensor,
msg=f"Value mismatch for input: {data_str}")
if __name__ == "__main__":
unittest.main()
tests/pytorch_wrapper/pt_from_numpy_test.py
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import os
import sys
import unittest
import torch
import numpy as np
PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
sys.path.append(MODULE_ROOT)
from pytorch_wrapper.pt_from_numpy import PtFromNumpy
class TestPtFromNumpy(unittest.TestCase):
def setUp(self):
"""Set up test data."""
self.node = PtFromNumpy()
# Test cases covering rank 0 (scalar) to rank 3 tensors
self.test_cases = [
# Rank 0 (scalar) int32
(np.array(12345, dtype=np.int32), torch.tensor(12345, dtype=torch.int32)),
# Rank 0 (scalar) float32
(np.array(12345.6, dtype=np.float32), torch.tensor(12345.6, dtype=torch.float32)),
# Rank 1 (1D array) int32
(np.array([1, 2, 3], dtype=np.int32), torch.tensor([1, 2, 3], dtype=torch.int32)),
# Rank 1 (1D array) float32
(np.array([1.1, 2.2, 3.3], dtype=np.float32), torch.tensor([1.1, 2.2, 3.3], dtype=torch.float32)),
# Rank 2 (2D matrix) int32
(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32), torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.int32)),
# Rank 2 (2D matrix) float32
(np.array([[1.1, 2.2], [3.3, 4.4]], dtype=np.float32), torch.tensor([[1.1, 2.2], [3.3, 4.4]], dtype=torch.float32)),
# Rank 3 (3D tensor) int32
(np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.int32),
torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=torch.int32)),
# Rank 3 (3D tensor) float32
(np.array([[[1.1, 2.2], [3.3, 4.4]], [[5.5, 6.6], [7.7, 8.8]]], dtype=np.float32),
torch.tensor([[[1.1, 2.2], [3.3, 4.4]], [[5.5, 6.6], [7.7, 8.8]]], dtype=torch.float32)),
]
def test_convert_numpy_to_tensor(self):
"""Test conversion of NumPy ndarray to PyTorch tensor."""
for np_array, expected_tensor in self.test_cases:
with self.subTest(data=np_array):
retval = self.node.f(np_array)[0]
# Ensure the returned value is a PyTorch tensor
self.assertTrue(isinstance(retval, torch.Tensor), f"Expected tensor, got {type(retval)}")
# Ensure the dtype matches
self.assertEqual(retval.dtype, expected_tensor.dtype, f"Dtype mismatch for input: {np_array}")
# Ensure the shape matches
self.assertEqual(retval.shape, expected_tensor.shape,
f"Shape mismatch for input: {np_array} (Expected {expected_tensor.shape}, got {retval.shape})")
# Ensure values match
torch.testing.assert_close(retval, expected_tensor,
msg=f"Value mismatch for input: {np_array}")
if __name__ == "__main__":
unittest.main()
tests/pytorch_wrapper/pt_int_create_test.py
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import os
import sys
import unittest
import torch
PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
sys.path.append(MODULE_ROOT)
from pytorch_wrapper.pt_int_create import PtIntCreate
class TestPtIntCreate(unittest.TestCase):
def setUp(self):
"""Set up test data."""
self.node = PtIntCreate()
# Test cases covering rank 0 (scalar) to rank 5 tensors
self.test_cases = [
# Rank 0 (scalar)
("12345", torch.tensor(12345, dtype=torch.int32)),
# Rank 1 (1D array)
("[1, 2, 3]", torch.tensor([1, 2, 3], dtype=torch.int32)),
# Rank 2 (2D matrix)
("[[1, 2, 3], [4, 5, 6]]", torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.int32)),
# Rank 3 (3D tensor)
("[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]",
torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=torch.int32)),
# Rank 4 (4D tensor)
("[[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]]",
torch.tensor([[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]], dtype=torch.int32)),
# Rank 5 (5D tensor)
("[[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]]",
torch.tensor([[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]], dtype=torch.int32)),
]
def test_create_tensor(self):
"""Test PyTorch tensor creation from various structured inputs."""
for data_str, expected_tensor in self.test_cases:
with self.subTest(data=data_str):
retval = self.node.f(data_str)[0]
# Ensure the returned value is a PyTorch tensor
self.assertTrue(isinstance(retval, torch.Tensor), f"Expected tensor, got {type(retval)}")
# Ensure the dtype is int32
self.assertEqual(retval.dtype, torch.int32, f"Dtype mismatch for input: {data_str}")
# Ensure the shape matches
self.assertEqual(retval.shape, expected_tensor.shape,
f"Shape mismatch for input: {data_str} (Expected {expected_tensor.shape}, got {retval.shape})")
# Ensure values match
torch.testing.assert_close(retval, expected_tensor,
msg=f"Value mismatch for input: {data_str}")
if __name__ == "__main__":
unittest.main()
tests/pytorch_wrapper/pt_to_numpy_test.py
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import os
import sys
import unittest
import torch
import numpy as np
PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
sys.path.append(MODULE_ROOT)
from pytorch_wrapper.pt_to_numpy import PtToNumpy
class TestPtToNumpy(unittest.TestCase):
def setUp(self):
"""Set up test data."""
self.node = PtToNumpy()
# Test cases covering rank 0 (scalar) to rank 5 tensors
self.test_cases = [
# Rank 0 (scalar) int32
(torch.tensor(12345, dtype=torch.int32), np.array(12345, dtype=np.int32)),
# Rank 0 (scalar) float32
(torch.tensor(12345.6, dtype=torch.float32), np.array(12345.6, dtype=np.float32)),
# Rank 1 (1D array) int32
(torch.tensor([1, 2, 3], dtype=torch.int32), np.array([1, 2, 3], dtype=np.int32)),
# Rank 1 (1D array) float32
(torch.tensor([1.1, 2.2, 3.3], dtype=torch.float32), np.array([1.1, 2.2, 3.3], dtype=np.float32)),
# Rank 2 (2D matrix) int32
(torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.int32), np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)),
# Rank 2 (2D matrix) float32
(torch.tensor([[1.1, 2.2], [3.3, 4.4]], dtype=torch.float32), np.array([[1.1, 2.2], [3.3, 4.4]], dtype=np.float32)),
# Rank 3 (3D tensor) int32
(torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=torch.int32),
np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.int32)),
# Rank 3 (3D tensor) float32
(torch.tensor([[[1.1, 2.2], [3.3, 4.4]], [[5.5, 6.6], [7.7, 8.8]]], dtype=torch.float32),
np.array([[[1.1, 2.2], [3.3, 4.4]], [[5.5, 6.6], [7.7, 8.8]]], dtype=np.float32)),
]
def test_convert_tensor_to_numpy(self):
"""Test conversion of PyTorch tensor to NumPy ndarray."""
for torch_tensor, expected_array in self.test_cases:
with self.subTest(data=torch_tensor):
retval = self.node.f(torch_tensor)[0]
# Ensure the returned value is a NumPy array
self.assertTrue(isinstance(retval, np.ndarray), f"Expected ndarray, got {type(retval)}")
# Ensure the dtype matches
self.assertEqual(retval.dtype, expected_array.dtype, f"Dtype mismatch for input: {torch_tensor}")
# Ensure the shape matches
self.assertEqual(retval.shape, expected_array.shape,
f"Shape mismatch for input: {torch_tensor} (Expected {expected_array.shape}, got {retval.shape})")
# Ensure values match
np.testing.assert_array_equal(retval, expected_array,
f"Value mismatch for input: {torch_tensor}")
if __name__ == "__main__":
unittest.main()