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Fix numpy stacking #494

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Merged
merged 2 commits into from
Aug 11, 2020
Merged

Fix numpy stacking #494

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b207133
fix numpy stacking when getting with a column as a key
lhoestq Aug 11, 2020
d845413
style
lhoestq Aug 11, 2020
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71 changes: 28 additions & 43 deletions datasets/crd3/crd3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,9 +17,9 @@
"""CRD3 dataset"""

from __future__ import absolute_import, division, print_function
import logging

import json
import logging
import os

import nlp
Expand All @@ -46,10 +46,11 @@

_URL = "https://github.com/RevanthRameshkumar/CRD3/archive/master.zip"


def get_train_test_dev_files(files, test_split, train_split, dev_split):
test_files = dev_files = train_files = []
for file in files:
filename = os.path.split(file)[1].split('_')[0]
filename = os.path.split(file)[1].split("_")[0]
if filename in test_split:
test_files.append(file)
elif filename in train_split:
Expand All @@ -59,25 +60,23 @@ def get_train_test_dev_files(files, test_split, train_split, dev_split):
else:
logging.info("skipped file {}".format(file))
return test_files, train_files, dev_files


class CRD3(nlp.GeneratorBasedBuilder):

def _info(self):
return nlp.DatasetInfo(
description=_DESCRIPTION,
features=nlp.Features({
"chunk": nlp.Value("string"),
"chunk_id": nlp.Value("int32"),
"turn_start": nlp.Value("int32"),
"turn_end": nlp.Value("int32"),
"alignment_score": nlp.Value("float32"),
"turn_num": nlp.Value("int32"),
"turns":nlp.features.Sequence({
"names": nlp.Value("string"),
"utterances": nlp.Value("string"),
}),
}),
features=nlp.Features(
{
"chunk": nlp.Value("string"),
"chunk_id": nlp.Value("int32"),
"turn_start": nlp.Value("int32"),
"turn_end": nlp.Value("int32"),
"alignment_score": nlp.Value("float32"),
"turn_num": nlp.Value("int32"),
"turns": nlp.features.Sequence({"names": nlp.Value("string"), "utterances": nlp.Value("string"),}),
}
),
homepage="https://github.com/RevanthRameshkumar/CRD3",
citation=_CITATION,
)
Expand All @@ -89,7 +88,7 @@ def _split_generators(self, dl_manager):
dev_file = os.path.join(path, "CRD3-master", "data", "aligned data", "val_files")
with open(test_file) as f:
test_splits = [file.replace("\n", "") for file in f.readlines()]

with open(train_file) as f:
train_splits = [file.replace("\n", "") for file in f.readlines()]
with open(dev_file) as f:
Expand All @@ -98,29 +97,20 @@ def _split_generators(self, dl_manager):
c3 = "CRD3-master/data/aligned data/c=3"
c4 = "CRD3-master/data/aligned data/c=4"
files = [os.path.join(path, c2, file) for file in sorted(os.listdir(os.path.join(path, c2)))]
files.extend([os.path.join(path, c3, file) for file in sorted(os.listdir(os.path.join(path, c3)))])
files.extend([os.path.join(path, c4, file) for file in sorted(os.listdir(os.path.join(path, c4)))])
files.extend([os.path.join(path, c3, file) for file in sorted(os.listdir(os.path.join(path, c3)))])
files.extend([os.path.join(path, c4, file) for file in sorted(os.listdir(os.path.join(path, c4)))])

test_files, train_files, dev_files = get_train_test_dev_files(files, test_splits, train_splits, dev_splits)

return [
nlp.SplitGenerator(
name=nlp.Split.TRAIN,
gen_kwargs={"files_path": train_files},
),
nlp.SplitGenerator(
name=nlp.Split.TEST,
gen_kwargs={"files_path": test_files},
),
nlp.SplitGenerator(
name=nlp.Split.VALIDATION,
gen_kwargs={"files_path": dev_files},
)
nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"files_path": train_files},),
nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={"files_path": test_files},),
nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"files_path": dev_files},),
]

def _generate_examples(self, files_path):
"""Yields examples."""

for file in files_path:
with open(file) as f:
data = json.load(f)
Expand All @@ -130,21 +120,16 @@ def _generate_examples(self, files_path):
turn_start = row["ALIGNMENT"]["TURN START"]
turn_end = row["ALIGNMENT"]["TURN END"]
score = row["ALIGNMENT"]["ALIGNMENT SCORE"]
for id2, turn in enumerate(row['TURNS']):
for id2, turn in enumerate(row["TURNS"]):
turn_names = turn["NAMES"]
turn_utterances = turn["UTTERANCES"]
turn_num = turn["NUMBER"]
yield str(id1)+'_'+str(id2), {
"chunk":chunk,
yield str(id1) + "_" + str(id2), {
"chunk": chunk,
"chunk_id": chunk_id,
"turn_start": turn_start,
"turn_end": turn_end,
"alignment_score": score,
"turn_num": turn_num,
"turns": {
"names": turn_names,
"utterances": turn_utterances,
},
"turns": {"names": turn_names, "utterances": turn_utterances,},
}


9 changes: 3 additions & 6 deletions src/nlp/arrow_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -583,8 +583,6 @@ def _convert_outputs(

map_nested_kwargs = {}
if format_type == "numpy":
import numpy as np

if "copy" not in format_kwargs:
format_kwargs["copy"] = False
command = partial(np.array, **format_kwargs)
Expand All @@ -603,8 +601,7 @@ def identity(x):
return x

command = identity

if isinstance(outputs, (list, tuple)):
if isinstance(outputs, (list, tuple, np.ndarray)):
return command(outputs)
elif isinstance(outputs, pd.DataFrame):
if format_columns is not None and not output_all_columns:
Expand Down Expand Up @@ -678,7 +675,7 @@ def _getitem(
if format_type == "pandas":
outputs = self._data[key].to_pandas(split_blocks=True)
elif format_type in ("numpy", "torch", "tensorflow"):
outputs = self._data[key].to_pandas(split_blocks=True).to_numpy()
outputs = self._data.to_pandas(split_blocks=True).to_dict("list")[key]
else:
outputs = self._data[key].to_pylist()
else:
Expand All @@ -698,7 +695,7 @@ def _getitem(
else:
raise ValueError("Can only get row(s) (int or slice or list[int]) or columns (string).")

if (format_type is not None or format_columns is not None) and not isinstance(key, str):
if format_type is not None or format_columns is not None:
outputs = self._convert_outputs(
outputs,
format_type=format_type,
Expand Down
20 changes: 19 additions & 1 deletion tests/test_arrow_dataset.py
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Expand Up @@ -26,6 +26,13 @@ def _create_dummy_dataset(self, multiple_columns=False):
def test_dummy_dataset(self):
dset = self._create_dummy_dataset()
self.assertDictEqual(dset.features, Features({"filename": Value("string")}))
self.assertEqual(dset[0]["filename"], "my_name-train_0")
self.assertEqual(dset["filename"][0], "my_name-train_0")

dset = self._create_dummy_dataset(multiple_columns=True)
self.assertDictEqual(dset.features, Features({"col_1": Value("int64"), "col_2": Value("string")}))
self.assertEqual(dset[0]["col_1"], 3)
self.assertEqual(dset["col_1"][0], 3)

def test_from_pandas(self):
data = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
Expand Down Expand Up @@ -78,20 +85,26 @@ def test_from_dict(self):
features = Features({"col_1": Value("string"), "col_2": Value("string")})
self.assertRaises(pa.ArrowTypeError, Dataset.from_dict, data, features=features)

def test_set_format_numpy(self):
def test_set_format_numpy_multiple_columns(self):
dset = self._create_dummy_dataset(multiple_columns=True)
dset.set_format(type="numpy", columns=["col_1"])
self.assertEqual(len(dset[0]), 1)
self.assertIsInstance(dset[0]["col_1"], np.ndarray)
self.assertListEqual(list(dset[0]["col_1"].shape), [])
self.assertEqual(dset[0]["col_1"].item(), 3)
self.assertIsInstance(dset["col_1"], np.ndarray)
self.assertListEqual(list(dset["col_1"].shape), [4])
np.testing.assert_array_equal(dset["col_1"], np.array([3, 2, 1, 0]))

dset.reset_format()
with dset.formated_as(type="numpy", columns=["col_1"]):
self.assertEqual(len(dset[0]), 1)
self.assertIsInstance(dset[0]["col_1"], np.ndarray)
self.assertListEqual(list(dset[0]["col_1"].shape), [])
self.assertEqual(dset[0]["col_1"].item(), 3)
self.assertIsInstance(dset["col_1"], np.ndarray)
self.assertListEqual(list(dset["col_1"].shape), [4])
np.testing.assert_array_equal(dset["col_1"], np.array([3, 2, 1, 0]))

self.assertEqual(dset.format["type"], "python")
self.assertEqual(dset.format["format_kwargs"], {})
Expand All @@ -115,6 +128,7 @@ def test_set_format_torch(self):
dset.set_format(type="torch", columns=["col_1"])
self.assertEqual(len(dset[0]), 1)
self.assertIsInstance(dset[0]["col_1"], torch.Tensor)
self.assertIsInstance(dset["col_1"], torch.Tensor)
self.assertListEqual(list(dset[0]["col_1"].shape), [])
self.assertEqual(dset[0]["col_1"].item(), 3)

Expand Down Expand Up @@ -694,21 +708,25 @@ def test_format_vectors(self):
for col in columns:
self.assertIsInstance(dset[0][col], (tf.Tensor, tf.RaggedTensor))
self.assertIsInstance(dset[:2][col][0], (tf.Tensor, tf.RaggedTensor)) # not stacked
self.assertIsInstance(dset[col][0], (tf.Tensor, tf.RaggedTensor)) # not stacked

dset.set_format("numpy")
self.assertIsNotNone(dset[0])
self.assertIsNotNone(dset[:2])
for col in columns:
self.assertIsInstance(dset[0][col], np.ndarray)
self.assertIsInstance(dset[:2][col], np.ndarray) # stacked
self.assertIsInstance(dset[col], np.ndarray) # stacked
self.assertEqual(dset[:2]["vec"].shape, (2, 3)) # stacked
self.assertEqual(dset["vec"][:2].shape, (2, 3)) # stacked

dset.set_format("torch", columns=["vec"])
self.assertIsNotNone(dset[0])
self.assertIsNotNone(dset[:2])
# torch.Tensor is only for numerical columns
self.assertIsInstance(dset[0]["vec"], torch.Tensor)
self.assertIsInstance(dset[:2]["vec"][0], torch.Tensor) # not stacked
self.assertIsInstance(dset["vec"][0], torch.Tensor) # not stacked

def test_format_nested(self):
dset = self._create_dummy_dataset()
Expand Down