Transform the USCensus feature matrix into text data.

multi_categorical_gans/datasets/uscensus/reverse_transform.py

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+from __future__ import print_function
+
+import argparse
+import csv
+import json
+
+from scipy.sparse import load_npz
+
+
+def uscensus_reverse_transform(input_path, output_path, metadata_path):
+    with open(metadata_path, "r") as metadata_file:
+        metadata = json.load(metadata_file)
+
+    features = load_npz(input_path)
+
+    csv_file = open(output_path, "w")
+    output = csv.DictWriter(csv_file, ["caseid"] + metadata["variables"])
+    output.writeheader()
+
+    for row_value_indices in features:
+        _, selected_value_indices = row_value_indices.nonzero()
+        # there should be one value per variable
+        assert len(selected_value_indices) == len(metadata["variables"])
+
+        row_dict = dict()
+
+        for selected_value_index in selected_value_indices:
+            variable, value = metadata["index_to_value"][selected_value_index]
+            row_dict[variable] = value
+
+        output.writerow(row_dict)
+
+    csv_file.close()
+
+
+def main():
+    options_parser = argparse.ArgumentParser(
+        description="Transform the USCensus feature matrix into text data."
+                    + " Dataset: https://archive.ics.uci.edu/ml/datasets/US+Census+Data+(1990)."
+    )
+
+    options_parser.add_argument("input", type=str, help="Output features in sparse scipy matrix format.")
+    options_parser.add_argument("output", type=str, help="Input USCensus data in text format.")
+    options_parser.add_argument("metadata", type=str, help="Metadata in json format.")
+
+    options = options_parser.parse_args()
+
+    uscensus_reverse_transform(options.input, options.output, options.metadata)
+
+
+if __name__ == "__main__":
+    main()