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import hashlib
import logging
import pandas as pd
import os
import os.path
import wget
DATA_URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/image/segmentation.test"
DATA_SHA256 = "2e9e966479d54c6aaec309059376dd9c89c1b46bf3a23aceeefb36d20d93a189"
DATA_FILE = "segmentation.test"
if __name__ == "__main__":
logging.basicConfig(filename="segmentation_extract.log",
format="%(levelname)s:%(message)s",
level=logging.INFO)
if not os.path.exists(DATA_FILE):
logging.info("Downloading '{}'".format(DATA_URL))
wget.download(DATA_URL, DATA_FILE)
with open(DATA_FILE, "rb") as f:
if hashlib.sha256(f.read()).hexdigest() != DATA_SHA256:
logging.error("{} is corrupted; aborting".format(DATA_FILE))
df = pd.read_table(DATA_FILE, header=None, skiprows=4, delimiter=",")
# First column contains class names, which we convert to numbers using the
# 'class_labels' dict
classes = set(df[0])
numbers = [i for i in range(len(classes))]
class_labels = dict(zip(classes, numbers))
data = df.drop([0, 3], axis=1)
data.to_csv("segmentation.tbl", sep=" ", index=False, header=False)
labels = df[0].apply(lambda x: class_labels[x])
labels.to_csv("segmentation.labels", sep=" ", index=False, header=False)
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