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read_data.py
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read_data.py
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import sys
import os
import time
import argparse
import pickle
import numpy as np
import pandas as pd
import GEOparse
from data_generator import Dataset
from common import *
def parse_args():
''' parse command line arguments '''
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--seed',
type=int,
help='seed',
default=1)
parser.add_argument('--scale-y',
action='store_true')
parser.add_argument('--center-y',
action='store_true')
parser.add_argument('--test-proportion',
type=float,
default=0.2)
parser.add_argument('--in-file',
type=str,
default="../data/riboflavin.csv")
parser.add_argument('--out-file',
type=str,
default="_output/data.pkl")
args = parser.parse_args()
return args
def main(args=sys.argv[1:]):
args = parse_args()
np.random.seed(args.seed)
print(args)
X, y = read_data(args.in_file, has_header=True)
print(X.shape)
print(y.shape)
print(y.mean())
y = y.reshape(y.size, 1)
if args.center_y:
y -= np.mean(y)
if args.scale_y:
y /= np.sqrt(np.var(y))
shuffled_idx = np.random.choice(y.size, size=y.size, replace=False)
shuff_X = X[shuffled_idx, :]
shuff_y = y[shuffled_idx]
n_train = y.size - int(y.size * args.test_proportion)
train_data = Dataset(
shuff_X[:n_train, :],
shuff_y[:n_train,:],
shuff_y[:n_train,:])
test_data = Dataset(
shuff_X[n_train:, :],
shuff_y[n_train:,:],
shuff_y[n_train:,:])
print("data_file %s" % args.out_file)
with open(args.out_file, "wb") as f:
pickle.dump({
"train": train_data,
"test": test_data},
f)
if __name__ == "__main__":
main(sys.argv[1:])