How does this work? The docs should really be better
Browse files- .gitattributes +2 -0
- json-files-Jan2016.tar +3 -0
- metadata--Jan2016--2021-02-10.feather +3 -0
- test-dataset-debug.py +305 -0
.gitattributes
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@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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json-files-Jan2016.tar filter=lfs diff=lfs merge=lfs -text
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metadata--Jan2016--2021-02-10.feather filter=lfs diff=lfs merge=lfs -text
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json-files-Jan2016.tar
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version https://git-lfs.github.com/spec/v1
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oid sha256:4a7d7923941e39255112d2b40a40e8dae8579d9150459c1f0599ffe8a4cfb5a5
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size 2024540160
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metadata--Jan2016--2021-02-10.feather
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:5ce14560b1f610436f0f6810e38b28a71803aa2b995b27220578ed870e8bc620
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size 10639266
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test-dataset-debug.py
ADDED
@@ -0,0 +1,305 @@
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1 |
+
"""TODO: Add a description here."""
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from __future__ import absolute_import, division, print_function
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5 |
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import json
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import os
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7 |
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import datetime
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8 |
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import pandas as pd
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9 |
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import numpy as np
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10 |
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from pathlib import Path
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11 |
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# from sklearn.utils import shuffle
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12 |
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|
13 |
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import datasets
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|
15 |
+
|
16 |
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# TODO: Add BibTeX citation
|
17 |
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_CITATION = """\
|
18 |
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@InProceedings{huggingface:dataset,
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19 |
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title = {A great new dataset},
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20 |
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authors={huggingface, Inc.
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21 |
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},
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22 |
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year={2020}
|
23 |
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}
|
24 |
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"""
|
25 |
+
|
26 |
+
# TODO: Add description of the dataset here
|
27 |
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_DESCRIPTION = """TODO: Add description"""
|
28 |
+
|
29 |
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# URLs for production
|
30 |
+
_METADATA_URL = "https://patentdiag.blob.core.windows.net/patent-data/metadata-2021-02-10.feather"
|
31 |
+
# _METADATA_URL = "https://patentdiag.blob.core.windows.net/patent-data/metadata-2021-01-21.feather"
|
32 |
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_DATA_URL = "https://patentdiag.blob.core.windows.net/patent-data/distilled-2021-01-07.tar"
|
33 |
+
_DATA_SUBFOLDER_NAME = 'distilled'
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34 |
+
|
35 |
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# # URLs for debugging
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36 |
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# _METADATA_URL = _DEBUG_METADATA_URL = "https://patentdiag.blob.core.windows.net/patent-data/metadata_debug-2021-02-10.feather"
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37 |
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# _DATA_URL = _DEBUG_DATA_URL = "https://patentdiag.blob.core.windows.net/patent-data/distilled_debug-2021-01-07.tar"
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38 |
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# _DATA_SUBFOLDER_NAME = _DATA_SUBFOLDER_NAME = 'debug_distilled'
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39 |
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|
40 |
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RANDOM_STATE = 1729
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41 |
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|
42 |
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|
43 |
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# Names of features
|
44 |
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_FEATURES = [
|
45 |
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"patent_number",
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46 |
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"decision",
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47 |
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"title",
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48 |
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"abstract",
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49 |
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"claims",
|
50 |
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"background",
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51 |
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"summary",
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52 |
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"description",
|
53 |
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"cpc_label",
|
54 |
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"ipc_label",
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55 |
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"filing_date",
|
56 |
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"patent_issue_date",
|
57 |
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"date_published",
|
58 |
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"examiner_id"
|
59 |
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]
|
60 |
+
|
61 |
+
|
62 |
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def str_to_date(s):
|
63 |
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"""A helper function to convert strings to dates"""
|
64 |
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return datetime.datetime.strptime(s, '%Y-%m-%d')
|
65 |
+
|
66 |
+
|
67 |
+
class PatentsConfig(datasets.BuilderConfig):
|
68 |
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"""BuilderConfig for Patents"""
|
69 |
+
|
70 |
+
def __init__(
|
71 |
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self,
|
72 |
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ipcr_label: str = None, # 'G06F',
|
73 |
+
cpc_label: str = None, # 'G06F',
|
74 |
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train_filing_start_date: str = None,
|
75 |
+
train_filing_end_date: str = None,
|
76 |
+
val_filing_start_date: str = None,
|
77 |
+
val_filing_end_date: str = None,
|
78 |
+
query_string: str = None,
|
79 |
+
val_set_balancer=False,
|
80 |
+
uniform_split=False,
|
81 |
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train_only=False,
|
82 |
+
**kwargs
|
83 |
+
):
|
84 |
+
"""
|
85 |
+
If train_filing_end_date is None, then a random train-val split will be used. If it is
|
86 |
+
specified, then the specified date range will be used for the split. If train_filing_end_date
|
87 |
+
if specified and val_filing_start_date is not specifed, then val_filing_start_date defaults to
|
88 |
+
train_filing_end_date.
|
89 |
+
|
90 |
+
Args:
|
91 |
+
ipcr_label: International Patent Classification code
|
92 |
+
cpc_label: Cooperative Patent Classification code
|
93 |
+
train_filing_start_date: Start date for patents in train set (and val set if random split is used)
|
94 |
+
train_filing_end_date: End date for patents in train set
|
95 |
+
val_filing_start_date: Start date for patents in val set
|
96 |
+
val_filing_end_date: End date for patents in val set (and train set if random split is used)
|
97 |
+
**kwargs: keyword arguments forwarded to super.
|
98 |
+
"""
|
99 |
+
super().__init__(**kwargs)
|
100 |
+
self.ipcr_label = ipcr_label
|
101 |
+
self.cpc_label = cpc_label
|
102 |
+
self.train_filing_start_date = train_filing_start_date
|
103 |
+
self.train_filing_end_date = train_filing_end_date
|
104 |
+
self.val_filing_start_date = val_filing_start_date
|
105 |
+
self.val_filing_end_date = val_filing_end_date
|
106 |
+
self.query_string = query_string
|
107 |
+
self.val_set_balancer = val_set_balancer
|
108 |
+
self.uniform_split = uniform_split
|
109 |
+
self.train_only = train_only
|
110 |
+
|
111 |
+
|
112 |
+
class Patents(datasets.GeneratorBasedBuilder):
|
113 |
+
"""TODO: Add description"""
|
114 |
+
|
115 |
+
VERSION = datasets.Version("1.0.1")
|
116 |
+
|
117 |
+
# This is an example of a dataset with multiple configurations.
|
118 |
+
# If you don't want/need to define several sub-sets in your dataset,
|
119 |
+
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
|
120 |
+
BUILDER_CONFIG_CLASS = PatentsConfig
|
121 |
+
# BUILDER_CONFIGS = [
|
122 |
+
# PatentsConfig(name="my_dataset_" + size, description="A small dataset", data_size=size)
|
123 |
+
# for size in ["small", "medium", "large"]
|
124 |
+
# ]
|
125 |
+
|
126 |
+
def _info(self):
|
127 |
+
return datasets.DatasetInfo(
|
128 |
+
# This is the description that will appear on the datasets page.
|
129 |
+
description=_DESCRIPTION,
|
130 |
+
# This defines the different columns of the dataset and their types
|
131 |
+
features=datasets.Features(
|
132 |
+
{k: datasets.Value("string") for k in _FEATURES}
|
133 |
+
),
|
134 |
+
# If there's a common (input, target) tuple from the features,
|
135 |
+
# specify them here. They'll be used if as_supervised=True in
|
136 |
+
# builder.as_dataset.
|
137 |
+
supervised_keys=("claims", "decision"),
|
138 |
+
# TODO: Homepage of the dataset for documentation
|
139 |
+
homepage="https://huggingface.co/great-new-dataset",
|
140 |
+
citation=_CITATION,
|
141 |
+
)
|
142 |
+
|
143 |
+
def _split_generators(self, dl_manager: datasets.DownloadManager):
|
144 |
+
"""Returns SplitGenerators."""
|
145 |
+
print(f'Loading dataset with config: {self.config}')
|
146 |
+
|
147 |
+
# Download metadata
|
148 |
+
# NOTE: data_files is a path to a pickled pandas DataFrame
|
149 |
+
if self.config.data_files is None:
|
150 |
+
print(f'Loading / downloading metadata file: {_METADATA_URL}')
|
151 |
+
metadata_file = dl_manager.download_and_extract(_METADATA_URL)
|
152 |
+
else:
|
153 |
+
print(f'Using metadata file: {self.config.data_files}')
|
154 |
+
metadata_file = Path(self.config.data_files)
|
155 |
+
|
156 |
+
# Download data
|
157 |
+
# NOTE: data_dir is a path to a directory of json files, with one
|
158 |
+
# json file per patent application
|
159 |
+
if self.config.data_dir is None:
|
160 |
+
print('Loading / downloading data. This is a big file (360GB)!')
|
161 |
+
json_dir = Path(dl_manager.download_and_extract(_DATA_URL))
|
162 |
+
# NOTE: The extracted path contains a subfolder
|
163 |
+
json_dir = json_dir / _DATA_SUBFOLDER_NAME
|
164 |
+
else:
|
165 |
+
json_dir = Path(self.config.data_dir)
|
166 |
+
|
167 |
+
# Load metadata file
|
168 |
+
print(f'Reading metadata file: {metadata_file}')
|
169 |
+
df = pd.read_feather(metadata_file) # pd.read_pickle(metadata_file) #
|
170 |
+
|
171 |
+
# Filter based on ICPR / CPC label
|
172 |
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if self.config.ipcr_label:
|
173 |
+
print(f'Filtering by IPCR label: {self.config.ipcr_label}')
|
174 |
+
df = df[df['main_ipcr_label'].str.startswith(self.config.ipcr_label)]
|
175 |
+
elif self.config.cpc_label:
|
176 |
+
print(f'Filtering by CPC label: {self.config.cpc_label}')
|
177 |
+
df = df[df['main_cpc_label'].str.startswith(self.config.cpc_label)]
|
178 |
+
|
179 |
+
# Filter metadata based on arbitrary query string
|
180 |
+
# TODO(suproteem): remove for production
|
181 |
+
if self.config.query_string:
|
182 |
+
df = df.query(self.config.query_string)
|
183 |
+
|
184 |
+
# Return only one dataset
|
185 |
+
if self.config.train_only:
|
186 |
+
if self.config.train_filing_start_date:
|
187 |
+
print(f'Filtering by train filing start date: {self.config.train_filing_start_date}')
|
188 |
+
df = df[df['filing_date'] >= self.config.train_filing_start_date]
|
189 |
+
if self.config.train_filing_end_date:
|
190 |
+
print(f'Filtering by train filing end date: {self.config.train_filing_end_date}')
|
191 |
+
df = df[df['filing_date'] <= self.config.train_filing_end_date]
|
192 |
+
|
193 |
+
return [
|
194 |
+
datasets.SplitGenerator(
|
195 |
+
name=datasets.Split.TRAIN,
|
196 |
+
gen_kwargs=dict( # kwargs passed to _generate_examples
|
197 |
+
df=df,
|
198 |
+
json_dir=json_dir,
|
199 |
+
split='train',
|
200 |
+
),
|
201 |
+
)
|
202 |
+
]
|
203 |
+
|
204 |
+
# Train-validation split (either uniform or by date)
|
205 |
+
if self.config.uniform_split:
|
206 |
+
|
207 |
+
# Assumes that training_start_data < val_end_date
|
208 |
+
if self.config.train_filing_start_date:
|
209 |
+
df = df[df['filing_date'] >= self.config.train_filing_start_date]
|
210 |
+
if self.config.val_filing_end_date:
|
211 |
+
df = df[df['filing_date'] <= self.config.val_filing_end_date]
|
212 |
+
df = df.sample(frac=1.0, random_state=RANDOM_STATE)
|
213 |
+
num_train_samples = int(len(df) * 0.85)
|
214 |
+
train_df = df.iloc[0:num_train_samples]
|
215 |
+
val_df = df.iloc[num_train_samples:-1]
|
216 |
+
|
217 |
+
else:
|
218 |
+
|
219 |
+
# Does not assume that training_start_data < val_end_date
|
220 |
+
if self.config.train_filing_start_date:
|
221 |
+
print(f'Filtering by train filing start date: {self.config.train_filing_start_date}')
|
222 |
+
tdf = df[df['filing_date'] >= self.config.train_filing_start_date]
|
223 |
+
if self.config.train_filing_end_date:
|
224 |
+
print(f'Filtering by train filing end date: {self.config.train_filing_end_date}')
|
225 |
+
train_df = tdf[tdf['filing_date'] <= self.config.train_filing_end_date]
|
226 |
+
|
227 |
+
if self.config.val_filing_start_date:
|
228 |
+
print(f'Filtering by val filing start date: {self.config.val_filing_start_date}')
|
229 |
+
vdf = df[df['filing_date'] >= self.config.val_filing_start_date]
|
230 |
+
if self.config.val_filing_end_date:
|
231 |
+
print(f'Filtering by val filing end date: {self.config.val_filing_end_date}')
|
232 |
+
val_df = vdf[vdf['filing_date'] <= self.config.val_filing_end_date]
|
233 |
+
|
234 |
+
# TODO: Can make this step faster
|
235 |
+
if self.config.val_set_balancer:
|
236 |
+
rejected_df = val_df[val_df.status == 'REJECTED']
|
237 |
+
num_rejected = len(rejected_df)
|
238 |
+
accepted_df = val_df[val_df.status == 'ACCEPTED']
|
239 |
+
num_accepted = len(accepted_df)
|
240 |
+
if num_rejected < num_accepted:
|
241 |
+
accepted_df = accepted_df.sample(frac=1.0, random_state=RANDOM_STATE) # shuffle(accepted_df)
|
242 |
+
accepted_df = accepted_df[:num_rejected]
|
243 |
+
else:
|
244 |
+
rejected_df = rejected_df.sample(frac=1.0, random_state=RANDOM_STATE) # shuffle(rejected_df)
|
245 |
+
rejected_df = rejected_df[:num_accepted]
|
246 |
+
val_df = pd.concat([rejected_df, accepted_df])
|
247 |
+
|
248 |
+
return [
|
249 |
+
datasets.SplitGenerator(
|
250 |
+
name=datasets.Split.TRAIN,
|
251 |
+
gen_kwargs=dict( # kwargs passed to _generate_examples
|
252 |
+
df=train_df,
|
253 |
+
json_dir=json_dir,
|
254 |
+
split='train',
|
255 |
+
),
|
256 |
+
),
|
257 |
+
datasets.SplitGenerator(
|
258 |
+
name=datasets.Split.VALIDATION,
|
259 |
+
gen_kwargs=dict(
|
260 |
+
df=val_df,
|
261 |
+
json_dir=json_dir,
|
262 |
+
split='val',
|
263 |
+
),
|
264 |
+
),
|
265 |
+
]
|
266 |
+
|
267 |
+
def _generate_examples(self, df, json_dir, split):
|
268 |
+
""" Yields examples by loading JSON files containing patent applications. """
|
269 |
+
|
270 |
+
# NOTE: df.itertuples() is way faster than df.iterrows()
|
271 |
+
for id_, x in enumerate(df.itertuples()):
|
272 |
+
|
273 |
+
# JSON files are named by application number (unique)
|
274 |
+
application_number = x.application_number
|
275 |
+
filepath = json_dir / (application_number + '.json')
|
276 |
+
try:
|
277 |
+
with open(filepath, 'r') as f:
|
278 |
+
patent = json.load(f)
|
279 |
+
except Exception as e:
|
280 |
+
print('------------')
|
281 |
+
print(f'ERROR WITH {filepath}\n')
|
282 |
+
print(repr(e))
|
283 |
+
print()
|
284 |
+
yield id_, {k: "error" for k in _FEATURES}
|
285 |
+
|
286 |
+
# Most up-to-date-decision in meta dataframe
|
287 |
+
decision = x.decision
|
288 |
+
yield id_, {
|
289 |
+
"patent_number": application_number,
|
290 |
+
"decision": decision,
|
291 |
+
"title": patent["title"],
|
292 |
+
"abstract": patent["abstract"],
|
293 |
+
"claims": patent["claims"],
|
294 |
+
"description": patent["full_description"],
|
295 |
+
"background": patent["background"],
|
296 |
+
"summary": patent["summary"],
|
297 |
+
"cpc_label": patent["main_cpc_label"],
|
298 |
+
'filing_date': patent['filing_date'],
|
299 |
+
'patent_issue_date': patent['patent_issue_date'],
|
300 |
+
'date_published': patent['date_published'],
|
301 |
+
'examiner_id': patent['examiner_id'],
|
302 |
+
"ipc_label": patent["main_ipcr_label"],
|
303 |
+
# "all_cpc_labels": patent["cpc_labels"], # these are lists, ignoring for now
|
304 |
+
# 'inventor_list': patent['inventor_list'], # these are lists, ignoring for now
|
305 |
+
}
|