TEXT_DATASET
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Load one of the 20 newsgroup sample datasets from scikit-learn. The data is returned as a dataframe with one column containing the text and the other containing the category. Params: subset : "train" | "test" | "all", default="train" Select the dataset to load: "train" for the training set, "test" for the test set, "all" for both. categories : list of str Select the categories to load. By default, all categories are loaded.
The list of all categories is:
'alt.atheism',
'comp.graphics',
'comp.os.ms-windows.misc',
'comp.sys.ibm.pc.hardware',
'comp.sys.mac.hardware',
'comp.windows.x',
'misc.forsale',
'rec.autos',
'rec.motorcycles',
'rec.sport.baseball',
'rec.sport.hockey',
'sci.crypt',
'sci.electronics',
'sci.med',
'sci.space',
'soc.religion.christian',
'talk.politics.guns',
'talk.politics.mideast',
'talk.politics.misc',
'talk.religion.misc' remove_headers : boolean, default=false Remove the headers from the data. remove_footers : boolean, default=false Remove the footers from the data. remove_quotes : boolean, default=false Remove the quotes from the data. Returns: out : DataFrame
Python Code
from flojoy import flojoy, DataFrame, Array
from sklearn.datasets import fetch_20newsgroups
from sklearn.utils import Bunch
import pandas as pd
from typing import cast, Literal, Optional
# TODO: Add more datasets to this node.
@flojoy
def TEXT_DATASET(
subset: Literal["train", "test", "all"] = "train",
categories: Optional[Array] = None,
remove_headers: bool = False,
remove_footers: bool = False,
remove_quotes: bool = False,
) -> DataFrame:
"""Load one of the 20 newsgroup sample datasets from scikit-learn.
The data is returned as a dataframe with one column containing the text and the other containing the category.
Parameters
----------
subset : "train" | "test" | "all", default="train"
Select the dataset to load: "train" for the training set, "test" for the test set, "all" for both.
categories : list of str, optional
Select the categories to load. By default, all categories are loaded.
The list of all categories is:
'alt.atheism',
'comp.graphics',
'comp.os.ms-windows.misc',
'comp.sys.ibm.pc.hardware',
'comp.sys.mac.hardware',
'comp.windows.x',
'misc.forsale',
'rec.autos',
'rec.motorcycles',
'rec.sport.baseball',
'rec.sport.hockey',
'sci.crypt',
'sci.electronics',
'sci.med',
'sci.space',
'soc.religion.christian',
'talk.politics.guns',
'talk.politics.mideast',
'talk.politics.misc',
'talk.religion.misc'
remove_headers : boolean, default=false
Remove the headers from the data.
remove_footers : boolean, default=false
Remove the footers from the data.
remove_quotes : boolean, default=false
Remove the quotes from the data.
Returns
-------
DataFrame
"""
to_remove = tuple(
["headers" for remove_headers in [remove_headers] if remove_headers]
+ ["footers" for remove_footers in [remove_footers] if remove_footers]
+ ["quotes" for remove_quotes in [remove_quotes] if remove_quotes]
)
newsgroups = fetch_20newsgroups(
subset=subset,
categories=categories.unwrap() if categories else None,
remove=to_remove,
)
newsgroups = cast(Bunch, newsgroups)
data = newsgroups.data
labels = [newsgroups.target_names[i] for i in newsgroups.target]
df = pd.DataFrame({"Text": data, "Label": labels})
return DataFrame(df=df)
Example App
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In this example, the TEXT_DATASET node is used to load the 20 newsgroups dataset. Only the training subset is selected, and the two categories that are loaded are comp.graphics and alt.atheism.
REMOVE_HEADERS, REMOVE_FOOTERS, and REMOVE_QUOTES are also set to true in order to remove the headers, footers, and quotes from the data.