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[Minor] Fixed sampling generating counterfactuals bug in causal_model…
….py and introduced tests for causal model code;, updated DAS main introduction to match new causal model schematic; created notebook for MQNLI dataset exploring DAS on a nested heirarchical causal structure
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,208 @@ | ||
| import unittest | ||
| import random | ||
| import torch | ||
| from pyvene import CausalModel | ||
| random.seed(42) | ||
|
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|
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| class CasualModelTestCase(unittest.TestCase): | ||
| @classmethod | ||
| def setUpClass(self): | ||
| print("=== Test Suite: CausalModelTestCase ===") | ||
| self.variables = ['A', 'B', 'C'] | ||
| self.values = { | ||
| 'A': [False, True], | ||
| 'B': [False, True], | ||
| 'C': [False, True] | ||
| } | ||
|
|
||
| self.parents = { | ||
| 'A': [], | ||
| 'B': [], | ||
| 'C': ['A', 'B'] | ||
| } | ||
|
|
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| self.functions = { | ||
| "A": lambda: True, | ||
| "B": lambda: True, | ||
| "C": lambda a, b: a and b | ||
| } | ||
|
|
||
| self.causal_model = CausalModel( | ||
| self.variables, | ||
| self.values, | ||
| self.parents, | ||
| self.functions | ||
| ) | ||
|
|
||
| def test_initialization(self): | ||
| inputs = ['A', 'B'] | ||
| outputs = ['C'] | ||
| timesteps = { | ||
| 'A': 0, | ||
| 'B': 0, | ||
| 'C': 1 | ||
| } | ||
| equivalence_classes = { | ||
| 'C': { | ||
| False: [ | ||
| {'A': False, 'B': False}, | ||
| {'A': False, 'B': True}, | ||
| {'A': True, 'B': False} | ||
| ], | ||
| True: [ | ||
| {'A': True, 'B': True} | ||
| ] | ||
| } | ||
| } | ||
|
|
||
| self.assertEqual(set(self.causal_model.inputs), set(inputs)) | ||
| self.assertEqual(set(self.causal_model.outputs), set(outputs)) | ||
| self.assertEqual(self.causal_model.timesteps, timesteps) | ||
| self.assertEqual(self.causal_model.equiv_classes, equivalence_classes) | ||
|
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||
| def test_run_forward(self): | ||
| # test run forward with default values (A and B set to True) | ||
| self.assertEqual( | ||
| self.causal_model.run_forward(), | ||
| {'A': True, 'B': True, 'C': True} | ||
| ) | ||
|
|
||
| # test run forward on all possible input values | ||
| for a in [False, True]: | ||
| for b in [False, True]: | ||
| input_setting = { | ||
| 'A': a, | ||
| 'B': b | ||
| } | ||
| output_setting = { | ||
| 'A': a, | ||
| 'B': b, | ||
| 'C': a and b | ||
| } | ||
| self.assertEqual(self.causal_model.run_forward(input_setting), output_setting) | ||
|
|
||
| # test run forward on fully specified setting | ||
| output_setting = {'A': False, 'B': False, 'C': True} | ||
| self.assertEqual(self.causal_model.run_forward(output_setting), output_setting) | ||
|
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||
| def test_run_interchange(self): | ||
| # interchange intervention on input | ||
| base = {'A': True, 'B': False} | ||
| source = {'A': False, 'B': True} | ||
| self.assertEqual(self.causal_model.run_forward(base)['C'], False) | ||
| self.assertEqual(self.causal_model.run_forward(source)['C'], False) | ||
| self.assertEqual( | ||
| self.causal_model.run_interchange(base, {'B': source})['C'], | ||
| True | ||
| ) | ||
|
|
||
| # interchange intervention on output | ||
| base = {'A': False, 'B': False} | ||
| source = {'A': True, 'B': True} | ||
| self.assertEqual(self.causal_model.run_forward(base)['C'], False) | ||
| self.assertEqual( | ||
| self.causal_model.run_interchange(base, {'B': source})['C'], | ||
| False | ||
| ) | ||
| self.assertEqual( | ||
| self.causal_model.run_interchange(base, {'C': source})['C'], | ||
| True | ||
| ) | ||
|
|
||
| def test_sample_input_tree_balanced(self): | ||
| # NOTE: not quite sure how to test a function with random behavior | ||
| # right now, fixing seed and assuming approximate behavior | ||
| # (taking balanced to be less than 30-70 split) | ||
|
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| K = 100 | ||
| # test sampling by output value | ||
| outputs = [] | ||
| for _ in range(K): | ||
| sample = self.causal_model.sample_input_tree_balanced() | ||
| output = self.causal_model.run_forward(sample) | ||
| outputs.append(output['C']) | ||
| self.assertGreaterEqual(sum(outputs), 30) | ||
| self.assertLessEqual(sum(outputs), 70) | ||
|
|
||
| # test sampling by input value | ||
| inputs = [] | ||
| for _ in range(K): | ||
| sample = self.causal_model.sample_input_tree_balanced() | ||
| inputs.append(sample['A']) | ||
| self.assertGreaterEqual(sum(outputs), 30) | ||
| self.assertLessEqual(sum(outputs), 70) | ||
|
|
||
| def test_generate_factual_dataset(self): | ||
| def sampler(): | ||
| return {'A': False, 'B': False} | ||
|
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| size = 4 | ||
| factual_dataset = self.causal_model.generate_factual_dataset( | ||
| size=size, | ||
| sampler=sampler, | ||
| return_tensors=False | ||
| ) | ||
| self.assertEqual(len(factual_dataset), size) | ||
|
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| self.assertEqual(factual_dataset[0]['input_ids'], {'A': False, 'B': False}) | ||
| self.assertEqual(factual_dataset[0]['labels']['C'], False) | ||
|
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| factual_dataset_tensors = self.causal_model.generate_factual_dataset( | ||
| size=size, | ||
| sampler=sampler, | ||
| return_tensors=True | ||
| ) | ||
| self.assertEqual(len(factual_dataset_tensors), size) | ||
| X = torch.stack([example['input_ids'] for example in factual_dataset_tensors]) | ||
| y = torch.stack([example['labels'] for example in factual_dataset_tensors]) | ||
| self.assertEqual(X.shape, (size, 2)) | ||
| self.assertEqual(y.shape, (size, 1)) | ||
| self.assertTrue(torch.equal(X[0], torch.tensor([0., 0.]))) | ||
| self.assertTrue(torch.equal(y[0], torch.tensor([0.]))) | ||
|
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| def test_generate_counterfactual_dataset(self): | ||
| def sampler(*args, **kwargs): | ||
| if kwargs.get('output_var', None): | ||
| return {'A': True, 'B': True} | ||
|
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| return {'A': True, 'B': False} | ||
|
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| def intervention_sampler(*args, **kwargs): | ||
| return {'B': True} | ||
|
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| def intervention_id(*args, **kwargs): | ||
| return 0 | ||
|
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| size = 4 | ||
| counterfactual_dataset = self.causal_model.generate_counterfactual_dataset( | ||
| size=size, | ||
| batch_size=1, | ||
| intervention_id=intervention_id, | ||
| sampler=sampler, | ||
| intervention_sampler=intervention_sampler, | ||
| return_tensors=False | ||
| ) | ||
| self.assertEqual(len(counterfactual_dataset), size) | ||
| example = counterfactual_dataset[0] | ||
| self.assertEqual(example['input_ids'], {'A': True, 'B': False}) | ||
| self.assertEqual(example['source_input_ids'][0]['B'], True) | ||
| self.assertEqual(example['intervention_id'], [0]) | ||
| self.assertEqual(example['base_labels']['C'], False) # T and F | ||
| self.assertEqual(example['labels']['C'], True) # T and T | ||
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| def suite(): | ||
| suite = unittest.TestSuite() | ||
| suite.addTest(CasualModelTestCase("test_initialization")) | ||
| suite.addTest(CasualModelTestCase("test_run_forward")) | ||
| suite.addTest(CasualModelTestCase("test_run_interchange")) | ||
| suite.addTest(CasualModelTestCase("test_sample_input_tree_balanced")) | ||
| suite.addTest(CasualModelTestCase("test_generate_factual_dataset")) | ||
| suite.addTest(CasualModelTestCase("test_generate_counterfactual_dataset")) | ||
| return suite | ||
|
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|
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| if __name__ == "__main__": | ||
| runner = unittest.TextTestRunner() | ||
| runner.run(suite()) |
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