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README.md

@@ -1,3 +1,30 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- congress
+- tabular_classification
+- binary_classification
+pretty_name: Congress
+size_categories:
+- 100<n<1K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- voting
 ---
+# Congress
+The [Congress dataset](https://archive.ics.uci.edu/ml/datasets/Congress) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
+Congressmen of two different parties vote on a series of bills. Guess the party of each voter on the basis of their votes.
+
+# Configurations and tasks
+| **Configuration** | **Task**                  | **Description**                                               |
+|-------------------|---------------------------|---------------------------------------------------------------|
+| voting            | Binary classification     | What's the voter of the party?               |
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/congress", "voting")["train"]
+```

congress.py

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+"""Congress"""
+
+from typing import List
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+_BASE_FEATURE_NAMES = [
+    "party",
+    "pro_handicapped_infants",
+    "pro_water_project_cost_sharing",
+    "pro_adoption_of_the_budget_resolution",
+    "pro_physician_fee_freeze",
+    "pro_el_salvador_aid",
+    "pro_religious_groups_in_schools",
+    "pro_anti_satellite_test_ban",
+    "pro_aid_to_nicaraguan_contras",
+    "pro_mx_missile",
+    "pro_immigration",
+    "pro_synfuels_corporation_cutback",
+    "pro_education_spending",
+    "pro_superfund_right_to_sue",
+    "pro_crime",
+    "pro_duty_free_exports",
+    "pro_export_administration_act_south_africa",
+]
+
+DESCRIPTION = "Congress dataset from the UCI ML repository."
+_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Congress"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/105/congressional+voting+records")
+_CITATION = """
+@misc{misc_congressional_voting_records_105,
+  title        = {{Congressional Voting Records}},
+  year         = {1987},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5C01P}}
+}"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/congress/raw/main/house-votes-84.data"
+}
+features_types_per_config = {
+    "voting": {
+        "pro_handicapped_infants": datasets.Value("bool"),
+        "pro_water_project_cost_sharing": datasets.Value("bool"),
+        "pro_adoption_of_the_budget_resolution": datasets.Value("bool"),
+        "pro_physician_fee_freeze": datasets.Value("bool"),
+        "pro_el_salvador_aid": datasets.Value("bool"),
+        "pro_religious_groups_in_schools": datasets.Value("bool"),
+        "pro_anti_satellite_test_ban": datasets.Value("bool"),
+        "pro_aid_to_nicaraguan_contras": datasets.Value("bool"),
+        "pro_mx_missile": datasets.Value("bool"),
+        "pro_immigration": datasets.Value("bool"),
+        "pro_synfuels_corporation_cutback": datasets.Value("bool"),
+        "pro_education_spending": datasets.Value("bool"),
+        "pro_superfund_right_to_sue": datasets.Value("bool"),
+        "pro_crime": datasets.Value("bool"),
+        "pro_duty_free_exports": datasets.Value("bool"),
+        "pro_export_administration_act_south_africa": datasets.Value("bool"),
+        "party": datasets.ClassLabel(num_classes=2, names=("democrat", "republican")),
+    }
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class CongressConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(CongressConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Congress(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "voting"
+    BUILDER_CONFIGS = [
+        CongressConfig(name="voting",
+                    description="Binary classification of politician, either democrat or republican.")
+    ]
+
+
+    def _info(self):
+        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
+                                    features=features_per_config[self.config.name])
+
+        return info
+    
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        downloads = dl_manager.download_and_extract(urls_per_split)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
+        ]
+    
+    def _generate_examples(self, filepath: str):
+        data = pandas.read_csv(filepath, header=None)
+        data = self.preprocess(data, config=self.config.name)
+
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+
+            yield row_id, data_row
+
+
+    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
+        data.columns = _BASE_FEATURE_NAMES
+        vote_dictionary = {
+            "y": "pro",
+            "n": "against",
+            "?": "did_not_vote",
+        }
+        for feature in _BASE_FEATURE_NAMES[1:]:
+            data.loc[:, feature] = data[feature].apply(lambda x: vote_dictionary[x])
+        data.loc[:, "party"] = data["party"].apply(lambda x: 0 if x == "democrat" else 1)
+        data = data.astype({"party": "int8"})
+
+        data = data[list(features_types_per_config[config].keys())]
+
+        return data

house-votes-84.data

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