added print statements.
Browse files- regulatory_comments.py +5 -1
regulatory_comments.py
CHANGED
@@ -62,6 +62,7 @@ class RegComments(datasets.GeneratorBasedBuilder):
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# ) #use pandas
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def _info(self):
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features = datasets.Features(
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{"docket_agency": datasets.Value("string"),
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"docket_title": datasets.Value("string"),
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@@ -90,6 +91,7 @@ class RegComments(datasets.GeneratorBasedBuilder):
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)
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def _split_generators(self, dl_manager):
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# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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@@ -97,7 +99,8 @@ class RegComments(datasets.GeneratorBasedBuilder):
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# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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urls = _URLS[self.config.name]
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-
data_dir = dl_manager.download_and_extract(urls)
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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@@ -107,6 +110,7 @@ class RegComments(datasets.GeneratorBasedBuilder):
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, filepath):
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with open(filepath, encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for key, row in enumerate(reader):
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# ) #use pandas
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def _info(self):
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+
print("info called")
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features = datasets.Features(
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{"docket_agency": datasets.Value("string"),
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"docket_title": datasets.Value("string"),
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)
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def _split_generators(self, dl_manager):
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print("split generators called")
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# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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urls = _URLS[self.config.name]
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data_dir = dl_manager.download_and_extract(urls)
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print("urls accessed")
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, filepath):
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print("generate examples called")
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with open(filepath, encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for key, row in enumerate(reader):
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