lukemelas commited on
Commit
6fad1a6
1 Parent(s): e42403e

Adding datasets

Browse files
Files changed (4) hide show
  1. .gitignore +2 -1
  2. hupd.py +10 -9
  3. json-files-Jan2016.tar +0 -3
  4. tests/tests.py +179 -0
.gitignore CHANGED
@@ -1 +1,2 @@
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- tmp
 
 
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+ tmp
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+ *.pyc
hupd.py CHANGED
@@ -119,7 +119,7 @@ class PatentsConfig(datasets.BuilderConfig):
119
  class Patents(datasets.GeneratorBasedBuilder):
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  _DESCRIPTION
121
 
122
- VERSION = datasets.Version("1.0.1")
123
 
124
  # This is an example of a dataset with multiple configurations.
125
  # If you don't want/need to define several sub-sets in your dataset,
@@ -129,16 +129,16 @@ class Patents(datasets.GeneratorBasedBuilder):
129
  PatentsConfig(
130
  name="sample",
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  description="Patent data from January 2016, for debugging",
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- metadata_url="https://huggingface.co/datasets/HUPD/hupd-subset/resolve/main/metadata--Jan2016--2021-11-07.pkl",
133
- data_url="https://huggingface.co/datasets/HUPD/hupd-subset/resolve/main/json-files-Jan2016.tar",
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- data_dir="json-files-Jan2016",
135
  ),
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  PatentsConfig(
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  name="all",
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- description="Patent data from January 2016, for debugging",
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- metadata_url="https://patentdiag.blob.core.windows.net/patent-data/metadata-2021-11-07.pkl",
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- data_url="https://patentdiag.blob.core.windows.net/patent-data/distilled-2021-01-07.tar",
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- data_dir="distilled",
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  ),
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  ]
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@@ -274,8 +274,9 @@ class Patents(datasets.GeneratorBasedBuilder):
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  for id_, x in enumerate(df.itertuples()):
275
 
276
  # JSON files are named by application number (unique)
 
277
  application_number = x.application_number
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- filepath = os.path.join(json_dir, application_number + '.json')
279
  try:
280
  with open(filepath, 'r') as f:
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  patent = json.load(f)
 
119
  class Patents(datasets.GeneratorBasedBuilder):
120
  _DESCRIPTION
121
 
122
+ VERSION = datasets.Version("1.0.2")
123
 
124
  # This is an example of a dataset with multiple configurations.
125
  # If you don't want/need to define several sub-sets in your dataset,
 
129
  PatentsConfig(
130
  name="sample",
131
  description="Patent data from January 2016, for debugging",
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+ metadata_url="https://huggingface.co/datasets/HUPD/hupd/resolve/main/hupd_metadata_jan16_2022-02-22.feather",
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+ data_url="https://huggingface.co/datasets/HUPD/hupd/resolve/main/data/sample-jan-2016.tar.gz",
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+ data_dir="data", # this will unpack to data/sample/2016
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  ),
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  PatentsConfig(
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  name="all",
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+ description="Patent data from all years (2004-2018)",
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+ metadata_url="https://huggingface.co/datasets/HUPD/hupd/resolve/main/hupd_metadata_2022-02-22.feather",
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+ data_url="https://huggingface.co/datasets/HUPD/hupd/resolve/main/data/all-years.tar.gz",
141
+ data_dir="data", # this will unpack to data/{year}
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  ),
143
  ]
144
 
 
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  for id_, x in enumerate(df.itertuples()):
275
 
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  # JSON files are named by application number (unique)
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+ application_year = str(x.filing_date.year)
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  application_number = x.application_number
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+ filepath = os.path.join(json_dir, application_year, application_number + '.json')
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  try:
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  with open(filepath, 'r') as f:
282
  patent = json.load(f)
json-files-Jan2016.tar DELETED
@@ -1,3 +0,0 @@
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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
 
 
 
 
tests/tests.py ADDED
@@ -0,0 +1,179 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ """
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+ Dataset loading tests. Run with:
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+ PYTHONPATH=. pytest tests/tests.py -vvrP
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+
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+ Additional notes about pytest:
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+ - Skip a test with @pytest.mark.skip(reason='skipping')
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+ - Use `-vvrP` to print stdout
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+ """
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+ import pdb
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+ import os
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+ from pathlib import Path
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+ from pprint import pprint
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+
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+ import pytest
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+ import torch
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+ import torch.nn.functional as F
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+ import torch.utils.data
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+ from datasets import load_dataset
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+
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+
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+ def test_dataset_sample():
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+ """Load the sample dataset"""
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+ root = os.getcwd()
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+ dataset_dict = load_dataset(
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+ 'hupd.py',
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+ name='sample',
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+ data_files=os.path.join(root, "hupd_metadata_jan16_2022-02-22.feather"),
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+ data_dir=os.path.join(root, "data/sample"),
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+ uniform_split=True
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+ )
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+ for name, dataset in dataset_dict.items():
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+ print(f'Dataset {name}: {len(dataset)}')
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+ import pdb; pdb.set_trace()
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+
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+
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+ if __name__ == '__main__':
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+ test_dataset_sample()
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+
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+
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+ # # # ----- Data loading example 1 ------
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+ # # # To load a dataset from files directly, pass in the
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+ # # # data_files and data_dir parameters. For example:
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+
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+ # # # ----- Data loading example 2 ------
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+ # # # It is simple to specify an IPCR or CPC label and
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+ # # # a date range for training/validation. For example:
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+ # # dataset_dict = load_dataset(
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+ # # 'patents.py',
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+ # # data_files="/blob/uspto/data/codebooks/data_link_new.pkl",
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+ # # data_dir="/blob/uspto/data/distilled",
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+ # # ipcr_label='G01T', #'G06F',
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+ # # cpc_label=None,
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+ # # train_filing_start_date=None,
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+ # # train_filing_end_date=None,
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+ # # val_filing_start_date=None,
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+ # # val_filing_end_date=None,
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+ # # )
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+
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+ # # # ----- Data loading example 3 ------
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+ # # If you do not specify the data_files and data_dir parameters, the
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+ # # dataset will be downloaded automatically for you. For example:
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+ # dataset_dict = load_dataset(
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+ # 'patents.py',
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+ # data_dir="/blob/uspto/data/distilled",
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+ # cache_dir='/blob/data/patents/distilled/distilled/huggingface-dataset/cache',
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+ # ipcr_label=None, # 'G01T', #'G06F', # cpc_label='G01T',
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+ # train_filing_start_date='2016-01-01',
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+ # train_filing_end_date='2016-01-05',
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+ # val_filing_start_date='2017-01-01',
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+ # val_filing_end_date='2017-01-05',
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+ # )
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+
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+
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+ # def combine_two_sections(tokenizer, dataset, s1, s2, new_tokens):
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+
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+ # # Add the seperation token
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+ # if tokenizer.sep_token != '[SEP]':
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+ # tokenizer.add_tokens(['[SEP]'], special_tokens=True)
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+ # tokenizer.sep_token = '[SEP]'
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+
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+ # print(f'[OLD] len(tokenizer.vocab) = {len(tokenizer)}')
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+ # tokenizer.add_tokens(new_tokens + [s1.upper(), 'TITLE', 'YEAR', s2.upper()])
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+ # print(f'[NEW] len(tokenizer.vocab) = {len(tokenizer)}')
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+ # dataset = dataset.map(
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+ # # lambda e: {f'{s1}_{s2}': f'[SEP] {s1.upper()} ' + e[s1 + '_label'][:4] + ' [SEP] ' + e[s2]})
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+ # lambda e: {f'{s1}_{s2}': f'[SEP] TITLE ' + e['title'] + '. YEAR ' + e['filing_date'][:4] + f'. {s1.upper()} ' + e[s1 + '_label'][:4] + f' [SEP] {s2.upper()} ' + e[s2]})
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+ # return tokenizer, dataset
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+
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+
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+ # def convert_ids_to_string(tokenizer, input):
91
+ # return ' '.join(tokenizer.convert_ids_to_tokens(input))
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+
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+
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+ # conditional = 'ipc'
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+ # section = 'abstract'
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+
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+ # # Print some metadata
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+ # print('Dataset dictionary contents:')
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+ # pprint(dataset_dict)
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+ # print('Dataset dictionary cached to:')
101
+ # pprint(dataset_dict.cache_files)
102
+ # print(f'Train dataset size: {dataset_dict["train"].shape}')
103
+ # print(f'Validation dataset size: {dataset_dict["validation"].shape}')
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+
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+ # # Example: preprocess dataset "decision" feature for classification
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+ # decision_to_str = {
107
+ # 'REJECTED': 0,
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+ # 'ACCEPTED': 1,
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+ # 'PENDING': 2,
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+ # 'CONT-REJECTED': 3,
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+ # 'CONT-ACCEPTED': 4,
112
+ # 'CONT-PENDING': 5
113
+ # }
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+
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+
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+ # def map_decision_to_string(example):
117
+ # # NOTE: returned dict updates the example
118
+ # return {'decision': decision_to_str[example['decision']]}
119
+
120
+
121
+ # # Performing the remapping means iterating over the dataset
122
+ # # NOTE: This stores the updated table in a cache file indexed
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+ # # by the current state and the mapping function
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+ # train_dataset = dataset_dict['train'].map(map_decision_to_string)
125
+ # print('Processed train dataset cached to: ')
126
+ # pprint(train_dataset.cache_files)
127
+
128
+ # # Example: preprocess dataset "abstract" field using huggingface
129
+ # # tokenizers for classification. We truncate at the max token length.
130
+ # from transformers import AutoTokenizer
131
+ # tokenizer = AutoTokenizer.from_pretrained('roberta-base')
132
+
133
+ # # def map_cpc_label(example):
134
+ # # # NOTE: returned dict updates the example
135
+ # # # print(tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids(example['cpc_label'][:4])))
136
+ # # return {'cpc_label': tokenizer.convert_tokens_to_ids(example['cpc_label'][:4])}
137
+ # # train_dataset = train_dataset.map(map_cpc_label)
138
+
139
+ # if conditional:
140
+ # f = open(f'{conditional}_labels.txt', 'r')
141
+ # new_tokens = f.read().split('\n')
142
+ # tokenizer, train_dataset = combine_two_sections(tokenizer, train_dataset, conditional, section, new_tokens)
143
+ # section = f'{conditional}_{section}'
144
+
145
+ # # We tokenize in batches, so it is actually quite fast
146
+ # print('Tokenizing')
147
+ # train_dataset = train_dataset.map(
148
+ # lambda e: tokenizer((e[section]), truncation=True, padding='max_length'),
149
+ # batched=True)
150
+ # print('Processed train dataset cached to: ')
151
+ # pprint(train_dataset.cache_files)
152
+ # print('Processed train dataset columns: ')
153
+ # pprint(train_dataset.column_names)
154
+
155
+ # # Convert to PyTorch Dataset
156
+ # # NOTE: If you also want to return string columns (as a list), just
157
+ # # pass `output_all_columns=True` to the dataset
158
+ # train_dataset.set_format(type='torch',
159
+ # columns=['input_ids', 'attention_mask', 'decision'])
160
+
161
+ # # Standard PyTorch DataLoader
162
+ # from torch.utils.data import DataLoader
163
+ # train_dataloader = DataLoader(train_dataset, batch_size=16)
164
+ # print('Shapes of items in batch from standard PyTorch DataLoader:')
165
+ # pprint({k: v.shape for k, v in next(iter(train_dataloader)).items()})
166
+ # print('Batch from standard PyTorch DataLoader:')
167
+ # batch = next(iter(train_dataloader))
168
+ # pprint(batch['input_ids'])
169
+ # pprint(batch['decision'])
170
+
171
+ # # Print examples
172
+ # print(convert_ids_to_string(tokenizer, batch['input_ids'][0]))
173
+ # pprint(batch['input_ids'][0][:20])
174
+ # # vocab = batch['input_ids'][0][:20]
175
+ # # for elt in vocab:
176
+ # # print(f'{elt}: {convert_ids_to_string(tokenizer, [elt])}')
177
+ # print(tokenizer.decode(batch['input_ids'][0]))
178
+
179
+ # print('All done')