mirror of
https://github.com/facebookresearch/ReAgent.git
synced 2026-06-16 12:44:41 +00:00
Kittipat Virochsiri
4efafe9f99
Summary: Code mod by running ``` hg grep -l "from ml.\rl" | xargs sed -i 's/from ml.\rl/from reagent/g' hg grep -l "import ml.\rl" | xargs sed -i 's/import ml.\rl/import reagent/g' arc f ``` Added `base_module` properties to buck targets Then run `buck test ml/rl:` & manually fixed edge cases Reviewed By: czxttkl Differential Revision: D20951338 fbshipit-source-id: f13b1ef5aa938faa9f5e499c3c97feb7c913db7b
73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import unittest
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import torch
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from reagent.preprocessing import normalization
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from reagent.preprocessing.sparse_to_dense import (
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PythonSparseToDenseProcessor,
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StringKeySparseToDenseProcessor,
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)
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class TestSparseToDense(unittest.TestCase):
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def setUp(self):
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self.sorted_features = [1, 2, 5, 4]
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self.str_keyed_sparse_data = [
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{},
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{"2": 0.3},
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{"4": 0.5, "5": 0.4},
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{"5": 0.3, "1": 0.5, "2": 0.1, "4": 0.7},
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]
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self.int_keyed_sparse_data = [
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{int(k): v for k, v in d.items()} for d in self.str_keyed_sparse_data
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]
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self.expected_value_0 = torch.tensor(
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[[0, 0, 0, 0], [0, 0.3, 0, 0], [0, 0, 0.4, 0.5], [0.5, 0.1, 0.3, 0.7]]
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)
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self.expected_presence_0 = torch.ones(4, 4).bool()
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MISSING = normalization.MISSING_VALUE
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self.expected_value_missing = torch.tensor(
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[
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[MISSING, MISSING, MISSING, MISSING],
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[MISSING, 0.3, MISSING, MISSING],
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[MISSING, MISSING, 0.4, 0.5],
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[0.5, 0.1, 0.3, 0.7],
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]
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)
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self.expected_presence_missing = torch.tensor(
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[
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[False, False, False, False],
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[False, True, False, False],
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[False, False, True, True],
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[True, True, True, True],
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]
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)
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def test_int_key_sparse_to_dense(self):
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# int keys, set_missing_value_to_zero=False
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processor = PythonSparseToDenseProcessor(
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self.sorted_features, set_missing_value_to_zero=False
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)
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value, presence = processor.process(self.int_keyed_sparse_data)
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assert torch.allclose(value, self.expected_value_missing)
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assert torch.all(presence == self.expected_presence_missing)
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def test_str_key_sparse_to_dense(self):
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# string keys, set_missing_value_to_zero=True
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processor = StringKeySparseToDenseProcessor(
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self.sorted_features, set_missing_value_to_zero=True
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)
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value, presence = processor.process(self.str_keyed_sparse_data)
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assert torch.allclose(value, self.expected_value_0)
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assert torch.all(presence == self.expected_presence_0)
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# string keys, set_missing_value_to_zero=False
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processor = StringKeySparseToDenseProcessor(
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self.sorted_features, set_missing_value_to_zero=False
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)
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value, presence = processor.process(self.str_keyed_sparse_data)
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assert torch.allclose(value, self.expected_value_missing)
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assert torch.all(presence == self.expected_presence_missing)
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