import tensorflow
import pandas as pd
from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import cross_val_predict
from numpy import genfromtxt

data = genfromtxt('data.csv',delimiter=',')
data = np.ndarray(shape=(21,4),buffer=data)
data = np.delete(data,(0),axis=0)
data = np.delete(data,(0),axis=1) # not necessary
data = np.delete(data,(0),axis=1) # not necessary

x_data = data[:,[0]] # sleep
y_data = data[:,[1]]# grades

lr = linear_model.LinearRegression()

predicted = cross_val_predict(lr, x_data, y_data, cv=10)

fig1, ax1 = plt.subplots()
fig2, ax2 = plt.subplots()
ax1.plot(x_data, predicted,'.')
ax1.set_xlabel('Time of Sleep')
ax1.set_ylabel('Exam Grades')
ax2.scatter(y_data, predicted, edgecolors=(0, 0, 0))
ax2.plot([y_data.min(), y_data.max()], [y_data.min(), y_data.max()], 'k--', lw=1)
ax2.set_xlabel('Measured')
ax2.set_ylabel('Predicted')

plt.show()