import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# downloading dataset
#https: // s3 - api.us - geo.objectstorage.softlayer.net / cf - courses - data / CognitiveClass / ML0101ENv3 / labs / china_gdp.csv

df = pd.read_csv("china_gdp.csv")
df.head(10)


plt.figure(figsize=(8,5))
x_data, y_data = (df["Year"].values, df["Value"].values)
X = np.arange(-5.0, 5.0, 0.1)
Y = 1.0 / (1.0 + np.exp(-X))




def sigmoid(x, Beta_1, Beta_2):
    y = 1 / (1 + np.exp(-Beta_1 * (x - Beta_2)))
    return y

beta_1 = 0.10
beta_2 = 1990.0

#logistic function
Y_pred = sigmoid(x_data, beta_1 , beta_2)

#plot initial prediction against datapoints


xdata =x_data/max(x_data)
ydata =y_data/max(y_data)

from scipy.optimize import curve_fit
popt, pcov = curve_fit(sigmoid, xdata, ydata)
#print the final parameters
print(" beta_1 = %f, beta_2 = %f" % (popt[0], popt[1]))

x = np.linspace(1960, 2015, 55)
x = x/max(x)
plt.figure(figsize=(8,5))
y = sigmoid(x, *popt)
plt.plot(xdata, ydata, 'ro', label='data')
plt.plot(x,y, linewidth=3.0, label='fit')
plt.legend(loc='best')
plt.ylabel('GDP')
plt.xlabel('Year')
plt.show()

msk = np.random.rand(len(df)) < 0.8
train_x = xdata[msk]
test_x = xdata[~msk]
train_y = ydata[msk]
test_y = ydata[~msk]

popt, pcov = curve_fit(sigmoid, train_x, train_y)

y_hat = sigmoid(test_x, *popt)

print("Mean absolute error: %.2f" % np.mean(np.absolute(y_hat - test_y)))
print("Residual sum of squares (MSE): %.2f" % np.mean((y_hat - test_y) ** 2))
from sklearn.metrics import r2_score
print("R2-score: %.2f" % r2_score(y_hat , test_y) )

