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117 changes: 117 additions & 0 deletions Parallel_coordinate_Script(2).py
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"""
Programmers: Zakaria Abdelmoiz Dahi, Gabriel Luque and Enrique Alba
Insitution: Malaga University
About:
- This code creates the parallel coordinates of two data series: # Employees 3-5, Empployees 1000-4999: non-normalized
- The results will be two figures for each data series: one non-normalised and the second normalised using min-max technique.
How to:
- Just execute the script.
"""


from yellowbrick.features import ParallelCoordinates
import sklearn.datasets as sd
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import matplotlib.pyplot as plt

"""
Here I set my new Map using Green, Yellow and Orange colors to indicate the profiles
Note: coding is in RGBA
Green: 0,128,0,1.0
Yelow: 255,255,0,1.0
Orange: 255, 138, 30, 1
"""
vals = [[255/256, 138/256, 30/256, 1],[255/256,251/256,0/256,1.0],[0/256,128/256,0/256,1.0]]
newcmp = ListedColormap(vals)

# generate random classification data: 3 samples: economic profiles, 30 features: economic data, 3 classes: economic profiles
X, y = sd.make_classification(n_samples=3, n_features=30, n_informative=2, n_redundant=0, n_repeated=0, n_classes=3, n_clusters_per_class=1, weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=0.0, scale=1.0, shuffle=True, random_state=None);


# Fill up the classification data with the real data
# Employees 3-5
a = [5772.10476190476, 5913.59047619048, 6076.01785714286, 6253.40357142857, 6371.44761904762, 6447.10476190476, 6588.88690476191, 6777.90119047619, 6924.87380952381, 7041.81666666667, 7118.93452380952, 7167.60595238095, 7104.36904761905, 6961.19761904762, 6816.61785714286, 6671.03214285714, 6580.89642857143, 6530.36785714286, 6363.61428571429, 6113.84285714286, 5967.12142857143, 5894.00714285714, 5891.42619047619, 5939.22619047619, 5974.86785714286, 6001.825, 6041.35714285714, 6089.87142857143, 6153.11904761905, 6226.89047619047];
b = [4638.6875, 4744.4375, 4858.64583333333, 4978.89583333333, 5065.89583333333, 5129.14583333333, 5212.375, 5309.875, 5393.52083333333, 5467.27083333333, 5550.35416666667, 5640.10416666667, 5631.27083333333, 5552.02083333333, 5484, 5424, 5372.02083333333, 5325.77083333333, 5271.35416666667, 5211.10416666667, 5067.14583333333, 4863.39583333333, 4765.66666666667, 4743.66666666667, 4753.16666666667, 4785.16666666667, 4845.89583333333, 4927.14583333333, 4943.5, 4913.5];
c = [4300.88888888889, 4419.55555555556, 4570.77222222222, 4745.23888888889, 4846.225, 4894.725, 5000.72222222222, 5147.78888888889, 5273.95277777778, 5385.18611111111, 5459.61111111111, 5507.74444444444, 5472.61666666667, 5378.01666666667, 5259.16944444444, 5123.00277777778, 5046.33611111111, 5012.16944444444, 4865.96388888889, 4639.73055555556, 4500.35555555556, 4423.02222222222, 4404.625, 4428.325, 4453.81388888889, 4480.58055555556, 4516.07777777778, 4557.81111111111, 4600.41944444444, 4643.65277777778];


# Print the centroids' min and maximum values throughout the 30 features.
print(min(a))
print(max(a))
print(min(b))
print(max(b))
print(min(c))
print(max(c))



for i in range(30):
X[0][i] = a[i];
X[1][i] = b[i];
X[2][i] = c[i];


# Specify the features of interest and the classes of the target
features = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30"]
classes = ["Profile 1", "Profile 2", "Profile 3"]


print("Empployees 3-5: non-normalized")
# Instantiate the visualizer with minmax normalisation
visualizer = ParallelCoordinates(classes=classes,vlines=False,colormap = newcmp, features=features)
# Fit and transform the data to the visualizer
visualizer.fit_transform(X, y)
# Finalize the title and axes then display the visualization
visualizer.show()


print("Empployees 3-5: normalized via minmax technique")
# Instantiate the visualizer with minmax normalisation
visualizer = ParallelCoordinates(classes=classes, features=features,vlines=False,colormap = newcmp, normalize = 'minmax')
# Fit and transform the data to the visualizer
visualizer.fit_transform(X, y)
# Finalize the title and axes then display the visualization
visualizer.show()


# Fill up the classification data with the real data
d = [11.3353658536585, 12.2865853658537, 12.9105691056911, 13.3008130081301, 13.7337398373984, 14.1971544715447, 14.5040650406504, 14.6991869918699, 14.9085365853659, 15.1280487804878, 15.4969512195122, 15.9725609756098, 15.8932926829268, 15.4176829268293, 15.0558943089431, 14.775406504065, 14.8434959349593, 15.1605691056911, 15.2286585365854, 15.1189024390244, 14.9095528455285, 14.6290650406504, 14.4908536585366, 14.4542682926829, 14.6880081300813, 15.114837398374, 15.2428861788618, 15.1575203252033, 15.3140243902439, 15.6432926829268];
e = [2.68518518518519, 2.90740740740741, 3.29166666666667, 3.79166666666667, 4.03240740740741, 4.08796296296296, 4.5, 5.16666666666667, 5.60648148148148, 5.88425925925926, 5.93518518518519, 5.82407407407407, 5.64814814814815, 5.42592592592593, 5.2037037037037, 4.98148148148148, 4.9537037037037, 5.06481481481482, 5.0462962962963, 4.93518518518519, 4.85648148148148, 4.80092592592593, 4.97222222222222, 5.30555555555556, 5.47685185185185, 5.53240740740741, 5.49074074074074, 5.37962962962963, 5.59259259259259, 6.03703703703704];
f = [0.791666666666667, 1.29166666666667, 1.64583333333333, 1.89583333333333, 1.85416666666667, 1.60416666666667, 1.5, 1.5, 1.9375, 2.6875, 3.58333333333333, 4.58333333333333, 4.41666666666667, 3.41666666666667, 3, 3, 3, 3, 3.14583333333333, 3.39583333333333, 3.20833333333333, 2.70833333333333, 2.5, 2.5, 2.64583333333333, 2.89583333333333, 3, 3, 3, 3];

# Print the centroids' min and maximum values throughout the 30 features.
print(min(d))
print(max(d))
print(min(e))
print(max(e))
print(min(f))
print(max(f))


for i in range(30):
X[0][i] = d[i];
X[1][i] = e[i];
X[2][i] = f[i];



# Specify the features of interest and the classes of the target
features = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30"]
classes = ["Profile 1", "Profile 2", "Profile 3"]

print("Empployees 1000-4999: non-normalized")
# Instantiate the visualizer with minmax normalisation
visualizer = ParallelCoordinates(classes=classes,vlines=False,colormap = newcmp, features=features)
# Fit and transform the data to the visualizer
visualizer.fit_transform(X, y)
# Finalize the title and axes then display the visualization
visualizer.show()


print("Empployees 1000-4999: normalized via minmax technique")
# Instantiate the visualizer with minmax normalisation
visualizer = ParallelCoordinates(classes=classes, features=features,vlines=False,colormap = newcmp, normalize = 'minmax')
# Fit and transform the data to the visualizer
visualizer.fit_transform(X, y)
# Finalize the title and axes then display the visualization
visualizer.show()