linearreg_true, linearreg_pred = read_csv("LinearRegression_results.csv")

rfr_true, rfr_pred = read_csv("RandomForestRegressor_results.csv")

arima_true, arima_pred = read_csv("ARIMA_results.csv")

rnn_true, rnn_pred = read_csv("RNN_results.csv")

size=10

fig = plt.figure()

fig = plt.figure(dpi=100, figsize=(20, 7))

plt.subplot(221)

days = range(len(linearreg_true))

plt.plot(days, linearreg_true,color='r', label='truth sales' )

plt.plot(days, linearreg_pred,color='b', label='pred sales')

plt.yscale('log')

plt.xlabel("days")

plt.ylabel("sales")

plt.legend(loc='upper left', frameon=False)

plt.title('Linear Regression',fontsize=size)

plt.subplot(222)

days = range(len(rfr_true))

plt.plot(days, rfr_true,color='r')

plt.plot(days, rfr_pred,color='b')

plt.yscale('log')

plt.xlabel("days")

plt.ylabel("sales")

# plt.legend(loc='upper left', frameon=False)

plt.title('Random Forest Regressor',fontsize=size)

plt.subplot(223)

days = range(len(arima_true))

plt.plot(days, arima_true,color='r')

plt.plot(days, arima_pred,color='b')

plt.yscale('log')

plt.xlabel("days")

plt.ylabel("sales")

# plt.legend(loc='upper left', frameon=False)

plt.title('ARIMA',fontsize=size)

plt.subplot(224)

days = range(len(rnn_true))

plt.plot(days, rnn_true,color='r' )

plt.plot(days, rnn_pred,color='b' )

plt.yscale('log')

plt.xlabel("days")

plt.ylabel("sales")

# plt.legend(loc='upper left', frameon=False)

plt.title('LSTM',fontsize=size)

plt.savefig("store 285 subplots.png", format='png', bbox_inches='tight', transparent=False)

plt.show()

# plt.subplot

#

# f, axarr = plt.subplots(4, sharey=True)

# f.suptitle('Sales Prediction store 285')

#

# days = range(len(linearreg_true))

# axarr[0].plot(days, linearreg_true,color='r', label='truth sales' )

# axarr[0].plot(days, linearreg_pred,color='b', label='pred sales')

# axarr[0].plt.legend(loc='upper left', frameon=False)

# axarr[0].plt.yscale('log')

# axarr[0].plt.xlabel("days")

# axarr[0].plt.ylabel("sales")

# axarr[0].plt

#

#

#

# days = range(len(rfr_true))

# axarr[1].plot(days, rfr_true, color='r', label='truth sales')

# axarr[1].plot(days, rfr_pred, color='b', label='pred sales')

# axarr[1].plt.legend(loc='upper left', frameon=False)

# axarr[1].plt.yscale('log')

# axarr[1].plt.xlabel("days")

# axarr[1].plt.ylabel("sales")

#

# days = range(len(arima_true))

# axarr[2].plot(days, arima_true, color='r', label='truth sales')

# axarr[2].plot(days, arima_pred, color='b', label='pred sales')

# axarr[2].plt.legend(loc='upper left', frameon=False)

# axarr[2].plt.yscale('log')

# axarr[2].plt.xlabel("days")

# axarr[2].plt.ylabel("sales")

#

# days = range(len(rnn_true))

# axarr[3].plot(days, rnn_true, color='r', label='truth sales')

# axarr[3].plot(days, rnn_pred, color='b', label='pred sales')

# axarr[3].plt.legend(loc='upper left', frameon=False)

# axarr[3].plt.yscale('log')

# axarr[3].plt.xlabel("days")

# axarr[3].plt.ylabel("sales")

#

store_data = pd.read_csv(filename)

x = store_data.iloc[:,0]

y = store_data.iloc[:,1]