# Import
import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt

# Import data
data = pd.read_csv('tmp/01_data/data_stocks.csv')
# Drop date variable
data = data.drop(['DATE'], 1)
# Dimensions of dataset
n = data.shape[0]
p = data.shape[1]
# Make data a numpy array
data = data.values

with tf.python_io.TFRecordWriter("csv.tfrecord") as writer:
    for row in data:
        features, label = row[1:], row[0]
        example = tf.train.Example()
        example.features.feature["features"].float_list.value.extend(features)
        example.features.feature["label"].float_list.value.append(label)

        writer.write(example.SerializeToString())


print ("ok")