1.py

import os
from PIL import Image as im
import numpy as np
from matplotlib import pyplot as plt
import csv

machine_learning_R_models = ['decisionTree', 'randomForest', 'linearModel', 'neuralNetwork']
dataset = 'regressionDataSet.csv'
tail1 = '-Evaluation-Result.csv'
tail2 = '-ScatterPlot.png'

# running our models now

for model in machine_learning_R_models:
    os.system('Rscript ' + model + '.R')

plots_array = []
results_array = []
for i in machine_learning_R_models:
    plots_array.append(i + tail2)
    results_array.append(i + tail1)

# loading images
imagesarray = []
for img in plots_array:
    i = im.open(img)
    iar = np.array(i)
    imagesarray.append(iar)
c = 0
file = open('results.txt', 'w')
for i in results_array:
    with open(i, 'rb') as csvfile:
        reade = csv.reader(csvfile, delimiter=' ', quotechar='|')
        for row in reade:
            s = row[0].split(',')
            if c == 0 or c % 2 == 1:
                file.write(s[0] +"  " + s[4]+'\n')
            c += 1
os.system('gedit results.txt')

fig = plt.figure()
ax1 = plt.subplot2grid((8, 6), (0, 0), rowspan=4, colspan=3)
ax2 = plt.subplot2grid((8, 6), (4, 0), rowspan=4, colspan=3)
ax3 = plt.subplot2grid((8, 6), (0, 3), rowspan=4, colspan=3)
ax4 = plt.subplot2grid((8, 6), (4, 3), rowspan=4, colspan=3)

ax1.imshow(imagesarray[0])
ax2.imshow(imagesarray[1])
ax3.imshow(imagesarray[2])
ax4.imshow(imagesarray[3])
plt.show()