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7934b29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import string
import numpy as np
import pytest
from nemo.collections.common.tokenizers.column_coder import ColumnCodes
from nemo.collections.common.tokenizers.tabular_tokenizer import TabularTokenizer
class TestTabularTokenizer:
def setup_method(self, test_method):
column_configs = [
{
"name": "col_a",
"code_type": "float",
"args": {"code_len": 4, "base": 16, "fillall": False, "hasnan": True, "transform": 'yeo-johnson'},
},
{
"name": "col_b",
"code_type": "float",
"args": {"code_len": 4, "base": 177, "fillall": True, "hasnan": True, "transform": 'quantile'},
},
{
"name": "col_c",
"code_type": "int",
"args": {"code_len": 3, "base": 12, "fillall": True, "hasnan": True},
},
{"name": "col_d", "code_type": "category",},
]
example_arrays = {}
np.random.seed(1234)
array = np.random.random(100)
example_arrays['col_a'] = array
array = np.random.random(100)
example_arrays['col_b'] = array
array = np.random.randint(3, 1000, 100)
example_arrays['col_c'] = array
ALPHABET = np.array(list(string.ascii_lowercase + ' '))
array = np.char.add(np.random.choice(ALPHABET, 1000), np.random.choice(ALPHABET, 1000))
example_arrays['col_d'] = array
self.cc = ColumnCodes.get_column_codes(column_configs, example_arrays)
@pytest.mark.unit
def test_tabular_tokenizer(self):
tab = TabularTokenizer(self.cc, delimiter=',')
text = "0.323, 0.1, 232, xy\n0.323, 0.1, 232, xy<|endoftext|>"
r = tab.text_to_tokens(text)
assert len(r) == 10
assert tab.eod == 1351
assert tab.eor == 1352
assert tab.num_columns == 4
assert self.cc.vocab_size == 1351
assert tab.vocab_size == 1353
r = tab.text_to_ids(text)
assert (sum(self.cc.sizes) + 1) * 2 == len(r)
assert np.array_equal(
np.array(r[0:13]), np.array([49, 32, 29, 15, 584, 417, 305, 76, 787, 780, 773, 1313, 1352])
)
assert np.array_equal(
np.array(r[13:]), np.array([49, 32, 29, 15, 584, 417, 305, 76, 787, 780, 773, 1313, 1351])
)
reversed_text = tab.ids_to_text(r)
assert reversed_text == '0.3230,0.0999998,232,xy\n0.3230,0.0999998,232,xy<|endoftext|>'
text = "xy\n0.323, 0.1, 232, xy<|endoftext|>"
r = tab.text_to_tokens(text)
assert len(r) == 7
r = tab.text_to_ids(text)
assert sum(self.cc.sizes) + 1 + 2 == len(r)
assert np.array_equal(np.array(r[0:2]), np.array([1313, 1352]))
assert np.array_equal(
np.array(r[2:15]), np.array([49, 32, 29, 15, 584, 417, 305, 76, 787, 780, 773, 1313, 1351])
)
reversed_text = tab.ids_to_text(r)
assert reversed_text == 'xy\n0.3230,0.0999998,232,xy<|endoftext|>'
text = "\n0.323, 0.1, 232, xy<|endoftext|>"
r = tab.text_to_tokens(text)
assert len(r) == 5
r = tab.text_to_ids(text)
assert sum(self.cc.sizes) + 1 == len(r)
assert np.array_equal(
np.array(r[0:13]), np.array([49, 32, 29, 15, 584, 417, 305, 76, 787, 780, 773, 1313, 1351])
)
reversed_text = tab.ids_to_text(r)
assert reversed_text == '0.3230,0.0999998,232,xy<|endoftext|>'
text = "232, xy\n0.323, 0.1, 232, xy<|endoftext|>"
r = tab.text_to_tokens(text)
assert len(r) == 8
r = tab.text_to_ids(text)
assert sum(self.cc.sizes) + 1 + 5 == len(r)
assert np.array_equal(np.array(r[0:5]), np.array([787, 780, 773, 1313, 1352]))
assert np.array_equal(
np.array(r[5:18]), np.array([49, 32, 29, 15, 584, 417, 305, 76, 787, 780, 773, 1313, 1351])
)
reversed_text = tab.ids_to_text(r)
assert reversed_text == '232,xy\n0.3230,0.0999998,232,xy<|endoftext|>'
text = "0.1, 232, xy\n0.323, 0.1, 232, xy<|endoftext|>"
r = tab.text_to_tokens(text)
assert len(r) == 9
r = tab.text_to_ids(text)
assert sum(self.cc.sizes) + 1 + 9 == len(r)
assert np.array_equal(np.array(r[0:9]), np.array([584, 417, 305, 76, 787, 780, 773, 1313, 1352]))
assert np.array_equal(
np.array(r[9:22]), np.array([49, 32, 29, 15, 584, 417, 305, 76, 787, 780, 773, 1313, 1351])
)
reversed_text = tab.ids_to_text(r)
assert reversed_text == '0.0999998,232,xy\n0.3230,0.0999998,232,xy<|endoftext|>'
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