Datasets:
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1K<n<10K
Tags:
natural-language-understanding
ideology classification
text classification
natural language processing
License:
EricR401S
commited on
Commit
·
3fb5a0e
1
Parent(s):
c6be7ba
annoying colab
Browse files
Pill_Ideologies-Post_Titles.py
CHANGED
@@ -166,7 +166,23 @@ class SubRedditPosts(datasets.GeneratorBasedBuilder):
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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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data = pd.read_csv(data_dir)
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-
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# make splits
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# train, test = train_test_split(
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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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data = pd.read_csv(data_dir)
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def clean_data_nans(data):
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"""This function takes a dataframe and fills all NaNs with a value
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This is to appease google colab, because my local machine did not raise errors
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... and it's a windows. That should tell you a lot."""
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for col in data.columns:
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if col.dtype == "object":
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data[col].fillna("NAN -Nothing found", inplace=True)
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elif col.dtype == "int64" or col.dtype == "float64":
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data[col].fillna(0, inplace=True)
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else:
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data[col].fillna("NAN - problematic {col} found", inplace=True)
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return None
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clean_data_nans(data)
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# commented out the splits, due to google colab being uncooperative
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# raised too many errors that my local machine did not
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# make splits
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# train, test = train_test_split(
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