Update

test-dataset-debug.py

@@ -1,45 +1,43 @@
-"""TODO: Add a description here."""
+"""
+The Harvard USPTO Patent Dataset (HUPD) is a large-scale, well-structured, and multi-purpose corpus 
+of English-language patent applications filed to the United States Patent and Trademark Office (USPTO) 
+between 2004 and 2018. With more than 4.5 million patent documents, HUPD is two to three times larger 
+than comparable corpora. Unlike other NLP patent datasets, HUPD contains the inventor-submitted versions 
+of patent applications, not the final versions of granted patents, allowing us to study patentability at 
+the time of filing using NLP methods for the first time.
+"""
 
 from __future__ import absolute_import, division, print_function
 
-import json
 import os
 import datetime
 import pandas as pd
 import numpy as np
 from pathlib import Path
+try:
+    import ujson as json
+except:
+    import json
 
 import datasets
 
 
-# TODO: Add BibTeX citation
 _CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-authors={huggingface, Inc.
-},
-year={2020}
+@InProceedings{suzgun2021:hupd,
+title = {The Harvard USPTO Patent Dataset},
+authors={Mirac Suzgun and Suproteem Sarkar and Luke Melas-Kyriazi and Scott Kominers and Stuart Shieber},
+year={2021}
 }
 """
 
-# TODO: Add description of the dataset here
-_DESCRIPTION = """TODO: Add description"""
-
-# # URLs for production
-# _METADATA_URL = "https://patentdiag.blob.core.windows.net/patent-data/metadata-2021-02-10.feather"
-# # _METADATA_URL = "https://patentdiag.blob.core.windows.net/patent-data/metadata-2021-01-21.feather"
-# _DATA_URL = "https://patentdiag.blob.core.windows.net/patent-data/distilled-2021-01-07.tar"
-# _DATA_SUBFOLDER_NAME = 'distilled'
-
-# # URLs for debugging
-# _METADATA_URL = _DEBUG_METADATA_URL = "https://patentdiag.blob.core.windows.net/patent-data/metadata_debug-2021-02-10.feather"
-# _DATA_URL = _DEBUG_DATA_URL = "https://patentdiag.blob.core.windows.net/patent-data/distilled_debug-2021-01-07.tar"
-# _DATA_SUBFOLDER_NAME = _DATA_SUBFOLDER_NAME = 'debug_distilled'
-
-# URLs for figuring out the Huggingface Hub
-_METADATA_URL = "https://huggingface.co/datasets/greeneggsandyaml/test-dataset-debug/resolve/main/metadata--Jan2016--2021-02-10.feather"
-_DATA_URL = "https://huggingface.co/datasets/greeneggsandyaml/test-dataset-debug/resolve/main/json-files-Jan2016.tar"
-_DATA_SUBFOLDER_NAME = 'json-files-Jan2016'
+_DESCRIPTION = """
+The Harvard USPTO Patent Dataset (HUPD) is a large-scale, well-structured, and multi-purpose corpus 
+of English-language patent applications filed to the United States Patent and Trademark Office (USPTO) 
+between 2004 and 2018. With more than 4.5 million patent documents, HUPD is two to three times larger 
+than comparable corpora. Unlike other NLP patent datasets, HUPD contains the inventor-submitted versions 
+of patent applications, not the final versions of granted patents, allowing us to study patentability at 
+the time of filing using NLP methods for the first time.
+"""
 
 RANDOM_STATE = 1729
 
@@ -73,8 +71,11 @@ class PatentsConfig(datasets.BuilderConfig):
 
     def __init__(
         self,
-        ipcr_label: str = None,  # 'G06F',
-        cpc_label: str = None,  # 'G06F',
+        metadata_url: str,
+        data_url: str,
+        data_dir: str,
+        ipcr_label: str = None,
+        cpc_label: str = None,
         train_filing_start_date: str = None,
         train_filing_end_date: str = None,
         val_filing_start_date: str = None,
@@ -82,7 +83,6 @@ class PatentsConfig(datasets.BuilderConfig):
         query_string: str = None,
         val_set_balancer=False,
         uniform_split=False,
-        train_only=False,
         **kwargs
     ):
         """
@@ -92,6 +92,9 @@ class PatentsConfig(datasets.BuilderConfig):
         train_filing_end_date. 
 
         Args:
+            metadata_url: `string`, url from which to download the metadata file
+            data_url: `string`, url from which to download the json files
+            data_dir: `string`, folder (in cache) in which downloaded json files are stored
             ipcr_label: International Patent Classification code
             cpc_label: Cooperative Patent Classification code
             train_filing_start_date: Start date for patents in train set (and val set if random split is used)
@@ -101,6 +104,9 @@ class PatentsConfig(datasets.BuilderConfig):
             **kwargs: keyword arguments forwarded to super.
         """
         super().__init__(**kwargs)
+        self.metadata_url = metadata_url
+        self.data_url = data_url
+        self.data_dir = data_dir
         self.ipcr_label = ipcr_label
         self.cpc_label = cpc_label
         self.train_filing_start_date = train_filing_start_date
@@ -110,11 +116,10 @@ class PatentsConfig(datasets.BuilderConfig):
         self.query_string = query_string
         self.val_set_balancer = val_set_balancer
         self.uniform_split = uniform_split
-        self.train_only = train_only
 
 
 class Patents(datasets.GeneratorBasedBuilder):
-    """TODO: Add description"""
+    _DESCRIPTION
 
     VERSION = datasets.Version("1.0.1")
 
@@ -122,10 +127,22 @@ class Patents(datasets.GeneratorBasedBuilder):
     # If you don't want/need to define several sub-sets in your dataset,
     # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
     BUILDER_CONFIG_CLASS = PatentsConfig
-    # BUILDER_CONFIGS = [
-    #     PatentsConfig(name="my_dataset_" + size, description="A small dataset", data_size=size)
-    #     for size in ["small", "medium", "large"]
-    # ]
+    BUILDER_CONFIGS = [
+        PatentsConfig(
+            name="sample", 
+            description="Patent data from January 2016, for debugging", 
+            metadata_url="https://huggingface.co/datasets/greeneggsandyaml/test-dataset-debug/resolve/main/metadata--Jan2016--2021-02-10.feather",
+            data_url="https://huggingface.co/datasets/greeneggsandyaml/test-dataset-debug/resolve/main/json-files-Jan2016.tar",
+            data_dir="json-files-Jan2016",
+        ),
+        PatentsConfig(
+            name="all", 
+            description="Patent data from January 2016, for debugging", 
+            metadata_url="https://patentdiag.blob.core.windows.net/patent-data/metadata-2021-02-10.feather",
+            data_url="https://patentdiag.blob.core.windows.net/patent-data/distilled-2021-01-07.tar",
+            data_dir="distilled",
+        ),
+    ]
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -139,8 +156,7 @@ class Patents(datasets.GeneratorBasedBuilder):
             # specify them here. They'll be used if as_supervised=True in
             # builder.as_dataset.
             supervised_keys=("claims", "decision"),
-            # TODO: Homepage of the dataset for documentation
-            homepage="https://huggingface.co/great-new-dataset",
+            homepage="https://github.com/suzgunmirac/hupd",
             citation=_CITATION,
         )
 
@@ -149,24 +165,16 @@ class Patents(datasets.GeneratorBasedBuilder):
         print(f'Loading dataset with config: {self.config}')
 
         # Download metadata
-        # NOTE: data_files is a path to a pickled pandas DataFrame
-        if self.config.data_files is None:
-            print(f'Loading or downloading metadata file: {_METADATA_URL}')
-            metadata_file = dl_manager.download_and_extract(_METADATA_URL)
-        else:
-            print(f'Using metadata file: {self.config.data_files}')
-            metadata_file = Path(self.config.data_files)
+        # NOTE: Metadata is stored as a Pandas DataFrame in Apache Feather format
+        metadata_file = dl_manager.download_and_extract(self.config.metadata_url)
+        metadata_file = Path(self.config.data_files)
+        print(f'Using metadata file: {self.config.data_files}')
 
         # Download data
-        # NOTE: data_dir is a path to a directory of json files, with one
-        # json file per patent application
-        if self.config.data_dir is None:
-            print('Loading or downloading data. If downloading, watch out! This is a huge file (360GB)!')
-            json_dir = Path(dl_manager.download_and_extract(_DATA_URL))
-            # NOTE: The extracted path contains a subfolder
-            json_dir = json_dir / _DATA_SUBFOLDER_NAME
-        else:
-            json_dir = Path(self.config.data_dir)
+        # NOTE: The extracted path contains a subfolder, data_dir. This directory holds
+        # a large number of json files (one json file per patent application).
+        download_dir = dl_manager.download_and_extract(self.config.data_url)
+        json_dir = os.path.join(download_dir, self.config.data_dir)
 
         # Load metadata file
         print(f'Reading metadata file: {metadata_file}')
@@ -181,32 +189,9 @@ class Patents(datasets.GeneratorBasedBuilder):
             df = df[df['main_cpc_label'].str.startswith(self.config.cpc_label)]
 
         # Filter metadata based on arbitrary query string
-        # TODO(suproteem): remove for production
         if self.config.query_string:
             df = df.query(self.config.query_string)
 
-        
-
-        # Return only one dataset
-        if self.config.train_only:
-            if self.config.train_filing_start_date:
-                print(f'Filtering by train filing start date: {self.config.train_filing_start_date}')
-                df = df[df['filing_date'] >= self.config.train_filing_start_date]
-            if self.config.train_filing_end_date:
-                print(f'Filtering by train filing end date: {self.config.train_filing_end_date}')
-                df = df[df['filing_date'] <= self.config.train_filing_end_date]
-
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs=dict(  # kwargs passed to _generate_examples
-                        df=df,
-                        json_dir=json_dir,
-                        split='train',
-                    ),
-                )
-            ]
-
         # Train-validation split (either uniform or by date)
         if self.config.uniform_split:
 
@@ -242,7 +227,7 @@ class Patents(datasets.GeneratorBasedBuilder):
                 (df['filing_date'] < self.config.val_filing_end_date)
             ]
 
-        # TODO: Can make this step faster
+        # TODO: We can probably make this step faster
         if self.config.val_set_balancer:
             rejected_df = val_df[val_df.status == 'REJECTED']
             num_rejected = len(rejected_df)
@@ -259,7 +244,7 @@ class Patents(datasets.GeneratorBasedBuilder):
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs=dict(  # kwargs passed to _generate_examples
+                gen_kwargs=dict(  # these kwargs are passed to _generate_examples
                     df=train_df,
                     json_dir=json_dir,
                     split='train',
@@ -311,6 +296,4 @@ class Patents(datasets.GeneratorBasedBuilder):
                 'date_published': patent['date_published'],
                 'examiner_id': patent['examiner_id'],
                 "ipc_label": patent["main_ipcr_label"],
-                # "all_cpc_labels": patent["cpc_labels"],  # these are lists, ignoring for now
-                # 'inventor_list': patent['inventor_list'],  # these are lists, ignoring for now
             }