draw1.py

__author__ = 'amrit'

import matplotlib.pyplot as plt
import os, pickle
import operator
import numpy as np
import matplotlib.cm as cmx
import matplotlib.colors as colors
from collections import OrderedDict
#Contains all datasets
smote1={'synapse': {'AUC': [['KNN', 0.294, 0.294, 0.147, 0.265, 0.206, 0.294, 0.294, 0.118, 0.206, 0.265, 0.176, 0.206, 0.382, 0.324, 0.235], ['SVM', 0.441, 0.029, 0.324, 0.088, 0.265, 0.324, 0.206, 0.235, 0.294, 0.147, 0.147, 0.294, 0.235, 0.294, 0.206], ['NB', 0.324, 0.147, 0.294, 0.147, 0.206, 0.441, 0.324, 0.147, 0.147, 0.147, 0.147, 0.176, 0.294, 0.324, 0.059], ['RF', 0.147, 0.088, 0.147, 0.118, 0.059, 0.088, 0.118, 0.029, 0.147, 0.118, 0.059, 0.118, 0.088, 0.265, 0.088], ['LR', 0.176, 0.176, 0.265, 0.206, 0.265, 0.147, 0.324, 0.206, 0.176, 0.324, 0.147, 0.176, 0.118, 0.294, 0.294], ['DT', 0.265, 0.324, 0.059, 0.147, 0.206, 0.147, 0.088, 0.029, 0.294, 0.206, 0.206, 0.088, 0.235, 0.324, 0.235]], 'False_alarm': [['KNN', 0.294, 0.294, 0.147, 0.265, 0.206, 0.294, 0.294, 0.118, 0.206, 0.265, 0.176, 0.206, 0.382, 0.324, 0.235], ['SVM', 0.441, 0.029, 0.324, 0.088, 0.265, 0.324, 0.206, 0.235, 0.294, 0.147, 0.147, 0.294, 0.235, 0.294, 0.206], ['NB', 0.324, 0.147, 0.294, 0.147, 0.206, 0.441, 0.324, 0.147, 0.147, 0.147, 0.147, 0.176, 0.294, 0.324, 0.059], ['RF', 0.147, 0.088, 0.147, 0.118, 0.059, 0.088, 0.118, 0.029, 0.147, 0.118, 0.059, 0.118, 0.088, 0.265, 0.088], ['LR', 0.176, 0.176, 0.265, 0.206, 0.265, 0.147, 0.324, 0.206, 0.176, 0.324, 0.147, 0.176, 0.118, 0.294, 0.294], ['DT', 0.265, 0.324, 0.059, 0.147, 0.206, 0.147, 0.088, 0.029, 0.294, 0.206, 0.206, 0.088, 0.235, 0.324, 0.235]], 'Recall': [['KNN', 0.778, 0.588, 0.824, 0.471, 0.824, 0.778, 0.706, 0.588, 0.647, 0.471, 0.444, 0.647, 0.706, 0.706, 0.588], ['SVM', 0.722, 0.529, 0.588, 0.529, 0.529, 0.611, 0.647, 0.647, 0.647, 0.647, 0.778, 0.529, 0.529, 0.706, 0.529], ['NB', 0.556, 0.706, 0.706, 0.647, 0.529, 0.667, 0.588, 0.647, 0.471, 0.647, 0.333, 0.529, 0.529, 0.706, 0.588], ['RF', 0.333, 0.529, 0.529, 0.529, 0.529, 0.278, 0.412, 0.471, 0.412, 0.529, 0.611, 0.471, 0.235, 0.294, 0.294], ['LR', 0.556, 0.588, 0.647, 0.294, 0.765, 0.611, 0.765, 0.412, 0.765, 0.529, 0.667, 0.706, 0.529, 0.588, 0.471], ['DT', 0.389, 0.471, 0.353, 0.471, 0.588, 0.389, 0.235, 0.529, 0.529, 0.471, 0.667, 0.294, 0.471, 0.353, 0.294]], 'Precision': [['KNN', 0.583, 0.5, 0.737, 0.471, 0.667, 0.583, 0.545, 0.714, 0.611, 0.471, 0.571, 0.611, 0.48, 0.522, 0.556], ['SVM', 0.464, 0.9, 0.476, 0.75, 0.5, 0.5, 0.611, 0.579, 0.524, 0.688, 0.737, 0.474, 0.529, 0.545, 0.563], ['NB', 0.476, 0.706, 0.545, 0.688, 0.563, 0.444, 0.476, 0.688, 0.615, 0.688, 0.545, 0.6, 0.474, 0.522, 0.833], ['RF', 0.545, 0.75, 0.643, 0.692, 0.818, 0.625, 0.636, 0.889, 0.583, 0.692, 0.846, 0.667, 0.571, 0.357, 0.625], ['LR', 0.625, 0.625, 0.55, 0.417, 0.591, 0.688, 0.542, 0.5, 0.684, 0.45, 0.706, 0.667, 0.692, 0.5, 0.444], ['DT', 0.438, 0.421, 0.75, 0.615, 0.588, 0.583, 0.571, 0.9, 0.474, 0.533, 0.632, 0.625, 0.5, 0.353, 0.385]], 'F_score': [['KNN', 0.667, 0.541, 0.778, 0.471, 0.737, 0.667, 0.615, 0.645, 0.629, 0.471, 0.5, 0.629, 0.571, 0.6, 0.571], ['SVM', 0.565, 0.667, 0.526, 0.621, 0.514, 0.55, 0.629, 0.611, 0.579, 0.667, 0.757, 0.5, 0.529, 0.615, 0.545], ['NB', 0.513, 0.706, 0.615, 0.667, 0.545, 0.533, 0.526, 0.667, 0.533, 0.667, 0.414, 0.563, 0.5, 0.6, 0.69], ['RF', 0.414, 0.621, 0.581, 0.6, 0.643, 0.385, 0.5, 0.615, 0.483, 0.6, 0.71, 0.552, 0.333, 0.323, 0.4], ['LR', 0.588, 0.606, 0.595, 0.345, 0.667, 0.647, 0.634, 0.452, 0.722, 0.486, 0.686, 0.686, 0.6, 0.541, 0.457], ['DT', 0.412, 0.444, 0.48, 0.533, 0.588, 0.467, 0.333, 0.667, 0.5, 0.5, 0.649, 0.4, 0.485, 0.353, 0.333]], 'Accuracy': [['KNN', 0.731, 0.667, 0.843, 0.647, 0.804, 0.731, 0.706, 0.784, 0.745, 0.647, 0.692, 0.745, 0.647, 0.686, 0.706], ['SVM', 0.615, 0.824, 0.647, 0.784, 0.667, 0.654, 0.745, 0.725, 0.686, 0.784, 0.827, 0.647, 0.686, 0.706, 0.706], ['NB', 0.635, 0.804, 0.706, 0.784, 0.706, 0.596, 0.647, 0.784, 0.725, 0.784, 0.673, 0.725, 0.647, 0.686, 0.824], ['RF', 0.673, 0.784, 0.745, 0.765, 0.804, 0.692, 0.725, 0.804, 0.706, 0.765, 0.827, 0.745, 0.686, 0.588, 0.706], ['LR', 0.731, 0.745, 0.706, 0.627, 0.745, 0.769, 0.706, 0.667, 0.804, 0.627, 0.788, 0.784, 0.765, 0.667, 0.627], ['DT', 0.615, 0.608, 0.745, 0.725, 0.725, 0.692, 0.686, 0.824, 0.647, 0.686, 0.75, 0.706, 0.667, 0.569, 0.608]]}, 'xerces': {'AUC': [['KNN', 0.548, 0.333, 0.333, 0.3, 0.5, 0.452, 0.333, 0.267, 0.3, 0.3, 0.387, 0.3, 0.3, 0.267, 0.433], ['SVM', 0.194, 0.1, 0.233, 0.133, 0.3, 0.129, 0.167, 0.267, 0.233, 0.067, 0.161, 0.233, 0.2, 0.1, 0.2], ['NB', 0.161, 0.133, 0.133, 0.0, 0.067, 0.032, 0.1, 0.133, 0.167, 0.1, 0.129, 0.167, 0.0, 0.067, 0.167], ['RF', 0.258, 0.067, 0.1, 0.233, 0.233, 0.161, 0.3, 0.1, 0.133, 0.333, 0.323, 0.133, 0.167, 0.167, 0.133], ['LR', 0.194, 0.267, 0.133, 0.233, 0.067, 0.226, 0.233, 0.2, 0.2, 0.167, 0.29, 0.2, 0.067, 0.133, 0.233], ['DT', 0.161, 0.067, 0.133, 0.267, 0.233, 0.161, 0.167, 0.167, 0.2, 0.2, 0.161, 0.233, 0.3, 0.1, 0.133]], 'False_alarm': [['KNN', 0.548, 0.333, 0.333, 0.3, 0.5, 0.452, 0.333, 0.267, 0.3, 0.3, 0.387, 0.3, 0.3, 0.267, 0.433], ['SVM', 0.194, 0.1, 0.233, 0.133, 0.3, 0.129, 0.167, 0.267, 0.233, 0.067, 0.161, 0.233, 0.2, 0.1, 0.2], ['NB', 0.161, 0.133, 0.133, 0.0, 0.067, 0.032, 0.1, 0.133, 0.167, 0.1, 0.129, 0.167, 0.0, 0.067, 0.167], ['RF', 0.258, 0.067, 0.1, 0.233, 0.233, 0.161, 0.3, 0.1, 0.133, 0.333, 0.323, 0.133, 0.167, 0.167, 0.133], ['LR', 0.194, 0.267, 0.133, 0.233, 0.067, 0.226, 0.233, 0.2, 0.2, 0.167, 0.29, 0.2, 0.067, 0.133, 0.233], ['DT', 0.161, 0.067, 0.133, 0.267, 0.233, 0.161, 0.167, 0.167, 0.2, 0.2, 0.161, 0.233, 0.3, 0.1, 0.133]], 'Recall': [['KNN', 0.92, 0.955, 0.885, 0.943, 0.828, 0.875, 0.943, 0.897, 0.943, 0.897, 0.943, 0.92, 0.931, 0.931, 0.908], ['SVM', 0.932, 0.875, 0.943, 0.954, 0.931, 0.932, 0.92, 0.92, 0.954, 0.92, 0.932, 0.955, 0.908, 0.943, 0.931], ['NB', 0.659, 0.568, 0.563, 0.517, 0.563, 0.523, 0.602, 0.632, 0.598, 0.54, 0.5, 0.602, 0.586, 0.563, 0.621], ['RF', 0.966, 0.955, 0.989, 0.954, 0.966, 0.955, 0.955, 0.966, 0.931, 0.954, 0.955, 0.943, 0.943, 0.954, 0.966], ['LR', 0.92, 0.898, 0.851, 0.943, 0.954, 0.955, 0.966, 0.943, 0.897, 0.92, 0.932, 0.909, 0.931, 0.897, 0.931], ['DT', 0.92, 0.898, 0.897, 0.989, 0.943, 0.966, 0.898, 0.92, 0.954, 0.977, 0.886, 0.955, 0.966, 0.954, 0.943]], 'Precision': [['KNN', 0.827, 0.894, 0.885, 0.901, 0.828, 0.846, 0.892, 0.907, 0.901, 0.897, 0.874, 0.9, 0.9, 0.91, 0.859], ['SVM', 0.932, 0.963, 0.921, 0.954, 0.9, 0.953, 0.942, 0.909, 0.922, 0.976, 0.943, 0.923, 0.929, 0.965, 0.931], ['NB', 0.921, 0.926, 0.925, 1.0, 0.961, 0.979, 0.946, 0.932, 0.912, 0.94, 0.917, 0.914, 1.0, 0.961, 0.915], ['RF', 0.914, 0.977, 0.966, 0.922, 0.923, 0.944, 0.903, 0.966, 0.953, 0.892, 0.894, 0.954, 0.943, 0.943, 0.955], ['LR', 0.931, 0.908, 0.949, 0.921, 0.976, 0.923, 0.924, 0.932, 0.929, 0.941, 0.901, 0.93, 0.976, 0.951, 0.92], ['DT', 0.942, 0.975, 0.951, 0.915, 0.921, 0.944, 0.94, 0.941, 0.933, 0.934, 0.94, 0.923, 0.903, 0.965, 0.953]], 'F_score': [['KNN', 0.871, 0.923, 0.885, 0.921, 0.828, 0.86, 0.917, 0.902, 0.921, 0.897, 0.907, 0.91, 0.915, 0.92, 0.883], ['SVM', 0.932, 0.917, 0.932, 0.954, 0.915, 0.943, 0.931, 0.914, 0.938, 0.947, 0.937, 0.939, 0.919, 0.953, 0.931], ['NB', 0.768, 0.704, 0.7, 0.682, 0.71, 0.681, 0.736, 0.753, 0.722, 0.686, 0.647, 0.726, 0.739, 0.71, 0.74], ['RF', 0.939, 0.966, 0.977, 0.938, 0.944, 0.949, 0.928, 0.966, 0.942, 0.922, 0.923, 0.949, 0.943, 0.949, 0.96], ['LR', 0.926, 0.903, 0.897, 0.932, 0.965, 0.939, 0.944, 0.937, 0.912, 0.93, 0.916, 0.92, 0.953, 0.923, 0.926], ['DT', 0.931, 0.935, 0.923, 0.95, 0.932, 0.955, 0.919, 0.93, 0.943, 0.955, 0.912, 0.939, 0.933, 0.96, 0.948]], 'Accuracy': [['KNN', 0.798, 0.881, 0.829, 0.88, 0.744, 0.79, 0.873, 0.855, 0.88, 0.846, 0.857, 0.864, 0.872, 0.88, 0.821], ['SVM', 0.899, 0.881, 0.897, 0.932, 0.872, 0.916, 0.898, 0.872, 0.906, 0.923, 0.908, 0.907, 0.88, 0.932, 0.897], ['NB', 0.706, 0.644, 0.641, 0.641, 0.658, 0.639, 0.678, 0.692, 0.658, 0.632, 0.597, 0.661, 0.692, 0.658, 0.675], ['RF', 0.908, 0.949, 0.966, 0.906, 0.915, 0.924, 0.89, 0.949, 0.915, 0.88, 0.882, 0.924, 0.915, 0.923, 0.94], ['LR', 0.891, 0.856, 0.855, 0.897, 0.949, 0.908, 0.915, 0.906, 0.872, 0.897, 0.874, 0.881, 0.932, 0.889, 0.889], ['DT', 0.899, 0.907, 0.889, 0.923, 0.897, 0.933, 0.881, 0.897, 0.915, 0.932, 0.874, 0.907, 0.897, 0.94, 0.923]]}, 'tomcat': {'AUC': [['KNN', 0.223, 0.276, 0.231, 0.231, 0.199, 0.293, 0.244, 0.231, 0.224, 0.288, 0.21, 0.192, 0.256, 0.199, 0.224], ['SVM', 0.191, 0.154, 0.218, 0.218, 0.192, 0.134, 0.167, 0.205, 0.237, 0.244, 0.159, 0.205, 0.179, 0.205, 0.147], ['NB', 0.102, 0.154, 0.09, 0.154, 0.122, 0.134, 0.109, 0.192, 0.096, 0.115, 0.096, 0.103, 0.154, 0.167, 0.071], ['RF', 0.038, 0.077, 0.09, 0.083, 0.051, 0.07, 0.077, 0.045, 0.045, 0.071, 0.096, 0.064, 0.071, 0.077, 0.077], ['LR', 0.191, 0.141, 0.244, 0.167, 0.16, 0.197, 0.141, 0.147, 0.141, 0.199, 0.197, 0.205, 0.141, 0.141, 0.135], ['DT', 0.115, 0.147, 0.122, 0.096, 0.167, 0.108, 0.109, 0.115, 0.128, 0.109, 0.172, 0.147, 0.071, 0.077, 0.122]], 'False_alarm': [['KNN', 0.223, 0.276, 0.231, 0.231, 0.199, 0.293, 0.244, 0.231, 0.224, 0.288, 0.21, 0.192, 0.256, 0.199, 0.224], ['SVM', 0.191, 0.154, 0.218, 0.218, 0.192, 0.134, 0.167, 0.205, 0.237, 0.244, 0.159, 0.205, 0.179, 0.205, 0.147], ['NB', 0.102, 0.154, 0.09, 0.154, 0.122, 0.134, 0.109, 0.192, 0.096, 0.115, 0.096, 0.103, 0.154, 0.167, 0.071], ['RF', 0.038, 0.077, 0.09, 0.083, 0.051, 0.07, 0.077, 0.045, 0.045, 0.071, 0.096, 0.064, 0.071, 0.077, 0.077], ['LR', 0.191, 0.141, 0.244, 0.167, 0.16, 0.197, 0.141, 0.147, 0.141, 0.199, 0.197, 0.205, 0.141, 0.141, 0.135], ['DT', 0.115, 0.147, 0.122, 0.096, 0.167, 0.108, 0.109, 0.115, 0.128, 0.109, 0.172, 0.147, 0.071, 0.077, 0.122]], 'Recall': [['KNN', 0.563, 0.688, 0.667, 0.467, 0.8, 0.5, 0.688, 0.6, 0.6, 0.6, 0.625, 0.688, 0.6, 0.733, 0.6], ['SVM', 0.625, 0.5, 0.667, 0.8, 0.533, 0.688, 0.688, 0.667, 0.933, 0.667, 0.563, 0.875, 0.6, 0.667, 0.533], ['NB', 0.375, 0.688, 0.867, 0.533, 0.667, 0.563, 0.563, 0.667, 0.667, 0.467, 0.563, 0.438, 0.8, 0.533, 0.4], ['RF', 0.313, 0.25, 0.4, 0.533, 0.333, 0.375, 0.375, 0.333, 0.4, 0.467, 0.563, 0.375, 0.467, 0.267, 0.4], ['LR', 0.563, 0.438, 0.533, 0.733, 0.667, 0.688, 0.5, 0.867, 0.6, 0.533, 0.75, 0.688, 0.4, 0.6, 0.8], ['DT', 0.563, 0.5, 0.4, 0.467, 0.4, 0.625, 0.563, 0.2, 0.467, 0.467, 0.375, 0.563, 0.2, 0.2, 0.467]], 'Precision': [['KNN', 0.205, 0.204, 0.217, 0.163, 0.279, 0.148, 0.224, 0.2, 0.205, 0.167, 0.233, 0.268, 0.184, 0.262, 0.205], ['SVM', 0.25, 0.25, 0.227, 0.261, 0.211, 0.344, 0.297, 0.238, 0.275, 0.208, 0.265, 0.304, 0.243, 0.238, 0.258], ['NB', 0.273, 0.314, 0.481, 0.25, 0.345, 0.3, 0.346, 0.25, 0.4, 0.28, 0.375, 0.304, 0.333, 0.235, 0.353], ['RF', 0.455, 0.25, 0.3, 0.381, 0.385, 0.353, 0.333, 0.417, 0.462, 0.389, 0.375, 0.375, 0.389, 0.25, 0.333], ['LR', 0.231, 0.241, 0.174, 0.297, 0.286, 0.262, 0.267, 0.361, 0.29, 0.205, 0.279, 0.256, 0.214, 0.29, 0.364], ['DT', 0.333, 0.258, 0.24, 0.318, 0.188, 0.37, 0.346, 0.143, 0.259, 0.292, 0.182, 0.281, 0.214, 0.2, 0.269]], 'F_score': [['KNN', 0.3, 0.314, 0.328, 0.241, 0.414, 0.229, 0.338, 0.3, 0.305, 0.261, 0.339, 0.386, 0.281, 0.386, 0.305], ['SVM', 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0.033, 0.1, 0.1, 0.172, 0.2, 0.033, 0.067, 0.067, 0.172]], 'Recall': [['KNN', 0.333, 0.5, 0.6, 0.4, 0.8, 0.333, 0.0, 0.6, 0.4, 0.2, 0.667, 0.333, 0.4, 0.6, 0.6], ['SVM', 0.5, 0.5, 0.4, 0.6, 0.6, 0.667, 0.167, 0.6, 0.4, 0.6, 0.833, 0.5, 0.6, 0.4, 0.6], ['NB', 0.833, 0.333, 1.0, 1.0, 1.0, 1.0, 0.833, 1.0, 0.6, 1.0, 1.0, 0.833, 0.6, 1.0, 0.8], ['RF', 0.833, 0.333, 0.4, 0.2, 0.6, 0.333, 0.333, 0.6, 0.4, 1.0, 0.5, 0.5, 0.6, 0.6, 0.6], ['LR', 0.167, 0.667, 0.8, 0.4, 0.4, 0.5, 0.5, 0.6, 1.0, 0.6, 0.0, 0.833, 0.6, 0.6, 0.4], ['DT', 0.5, 0.667, 0.6, 0.4, 0.4, 0.667, 0.5, 0.4, 0.4, 0.6, 0.5, 0.5, 0.6, 0.4, 0.4]], 'Precision': [['KNN', 0.333, 0.273, 0.176, 0.286, 0.333, 0.222, 0.0, 0.188, 0.154, 0.1, 0.364, 0.25, 0.286, 0.176, 0.3], ['SVM', 0.273, 0.333, 0.333, 0.375, 0.429, 0.364, 0.25, 0.375, 0.286, 0.75, 0.5, 0.429, 0.375, 0.4, 0.3], ['NB', 0.208, 0.095, 0.192, 0.179, 0.192, 0.2, 0.185, 0.192, 0.136, 0.2, 0.2, 0.208, 0.167, 0.167, 0.167], ['RF', 1.0, 0.4, 1.0, 1.0, 0.6, 0.667, 0.667, 0.429, 0.4, 0.556, 0.375, 0.6, 0.5, 1.0, 0.5], ['LR', 0.5, 0.308, 0.5, 0.4, 0.4, 0.75, 0.6, 0.429, 0.417, 0.273, 0.0, 0.417, 0.333, 0.214, 0.333], ['DT', 0.75, 0.667, 0.5, 0.333, 0.5, 0.4, 0.75, 0.4, 0.4, 0.375, 0.333, 0.75, 0.6, 0.5, 0.286]], 'F_score': [['KNN', 0.333, 0.353, 0.273, 0.333, 0.471, 0.267, 0.0, 0.286, 0.222, 0.133, 0.471, 0.286, 0.333, 0.273, 0.4], ['SVM', 0.353, 0.4, 0.364, 0.462, 0.5, 0.471, 0.2, 0.462, 0.333, 0.667, 0.625, 0.462, 0.462, 0.4, 0.4], ['NB', 0.333, 0.148, 0.323, 0.303, 0.323, 0.333, 0.303, 0.323, 0.222, 0.333, 0.333, 0.333, 0.261, 0.286, 0.276], ['RF', 0.909, 0.364, 0.571, 0.333, 0.6, 0.444, 0.444, 0.5, 0.4, 0.714, 0.429, 0.545, 0.545, 0.75, 0.545], ['LR', 0.25, 0.421, 0.615, 0.4, 0.4, 0.6, 0.545, 0.5, 0.588, 0.375, 0.0, 0.556, 0.429, 0.316, 0.364], ['DT', 0.6, 0.667, 0.545, 0.364, 0.444, 0.5, 0.6, 0.4, 0.4, 0.462, 0.4, 0.6, 0.6, 0.444, 0.333]], 'Accuracy': [['KNN', 0.778, 0.694, 0.543, 0.771, 0.735, 0.694, 0.0, 0.571, 0.6, 0.618, 0.75, 0.722, 0.771, 0.543, 0.735], ['SVM', 0.694, 0.75, 0.8, 0.8, 0.824, 0.75, 0.778, 0.8, 0.771, 0.912, 0.833, 0.806, 0.8, 0.829, 0.735], ['NB', 0.444, 0.361, 0.4, 0.343, 0.382, 0.333, 0.361, 0.4, 0.4, 0.412, 0.333, 0.444, 0.514, 0.286, 0.382], ['RF', 0.972, 0.806, 0.914, 0.886, 0.882, 0.861, 0.861, 0.829, 0.829, 0.882, 0.778, 0.861, 0.857, 0.943, 0.853], ['LR', 0.833, 0.694, 0.857, 0.829, 0.824, 0.889, 0.861, 0.829, 0.8, 0.706, 0.0, 0.778, 0.771, 0.629, 0.794], ['DT', 0.889, 0.889, 0.857, 0.8, 0.853, 0.778, 0.889, 0.829, 0.829, 0.794, 0.75, 0.889, 0.886, 0.857, 0.765]]}, 'log4j': {'AUC': [['KNN', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['SVM', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['NB', 0.0, 0.0, 0.667, 0.0, 0.333, 0.25, 0.333, 0.0, 0.0, 1.0, 0.25, 0.0, 0.333, 0.0, 0.333], ['RF', 1.0, 1.0, 0.667, 0.667, 0.667, 1.0, 1.0, 0.667, 0.667, 0.667, 1.0, 0.667, 0.333, 0.667, 1.0], ['LR', 1.0, 1.0, 1.0, 1.0, 0.667, 1.0, 1.0, 1.0, 1.0, 0.667, 1.0, 1.0, 0.667, 1.0, 1.0], ['DT', 0.5, 1.0, 0.0, 0.333, 0.333, 1.0, 0.667, 0.333, 1.0, 0.333, 0.25, 0.667, 0.667, 0.333, 0.333]], 'False_alarm': [['KNN', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['SVM', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ['NB', 0.0, 0.0, 0.667, 0.0, 0.333, 0.25, 0.333, 0.0, 0.0, 1.0, 0.25, 0.0, 0.333, 0.0, 0.333], ['RF', 1.0, 1.0, 0.667, 0.667, 0.667, 1.0, 1.0, 0.667, 0.667, 0.667, 1.0, 0.667, 0.333, 0.667, 1.0], ['LR', 1.0, 1.0, 1.0, 1.0, 0.667, 1.0, 1.0, 1.0, 1.0, 0.667, 1.0, 1.0, 0.667, 1.0, 1.0], ['DT', 0.5, 1.0, 0.0, 0.333, 0.333, 1.0, 0.667, 0.333, 1.0, 0.333, 0.25, 0.667, 0.667, 0.333, 0.333]], 'Recall': [['KNN', 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.974, 0.973, 1.0, 1.0, 1.0, 0.974, 1.0], ['SVM', 1.0, 1.0, 0.947, 0.974, 0.946, 1.0, 1.0, 1.0, 1.0, 0.973, 0.974, 1.0, 1.0, 1.0, 1.0], ['NB', 0.447, 0.474, 0.684, 0.316, 0.568, 0.526, 0.474, 0.5, 0.368, 0.595, 0.526, 0.395, 0.526, 0.447, 0.703], ['RF', 1.0, 1.0, 0.921, 1.0, 1.0, 0.974, 1.0, 0.947, 0.974, 0.973, 0.974, 0.974, 0.974, 1.0, 1.0], ['LR', 0.947, 1.0, 0.974, 0.947, 1.0, 1.0, 1.0, 0.947, 0.974, 0.973, 0.974, 0.974, 0.974, 0.895, 1.0], ['DT', 0.947, 0.895, 0.921, 0.921, 0.946, 0.947, 0.921, 0.921, 1.0, 0.892, 0.895, 0.974, 0.947, 0.921, 0.919]], 'Precision': [['KNN', 0.905, 0.927, 0.927, 0.927, 0.925, 0.905, 0.927, 0.927, 0.925, 0.923, 0.905, 0.927, 0.927, 0.925, 0.925], ['SVM', 0.905, 0.927, 0.923, 0.925, 0.921, 0.905, 0.927, 0.927, 0.927, 0.923, 0.902, 0.927, 0.927, 0.927, 0.925], ['NB', 1.0, 1.0, 0.929, 1.0, 0.955, 0.952, 0.947, 1.0, 1.0, 0.88, 0.952, 1.0, 0.952, 1.0, 0.963], ['RF', 0.905, 0.927, 0.946, 0.95, 0.949, 0.902, 0.927, 0.947, 0.949, 0.947, 0.902, 0.949, 0.974, 0.95, 0.925], ['LR', 0.9, 0.927, 0.925, 0.923, 0.949, 0.905, 0.927, 0.923, 0.925, 0.947, 0.902, 0.925, 0.949, 0.919, 0.925], ['DT', 0.947, 0.919, 1.0, 0.972, 0.972, 0.9, 0.946, 0.972, 0.927, 0.971, 0.971, 0.949, 0.947, 0.972, 0.971]], 'F_score': [['KNN', 0.95, 0.962, 0.962, 0.962, 0.961, 0.95, 0.962, 0.962, 0.949, 0.947, 0.95, 0.962, 0.962, 0.949, 0.961], ['SVM', 0.95, 0.962, 0.935, 0.949, 0.933, 0.95, 0.962, 0.962, 0.962, 0.947, 0.937, 0.962, 0.962, 0.962, 0.961], ['NB', 0.618, 0.643, 0.788, 0.48, 0.712, 0.678, 0.632, 0.667, 0.538, 0.71, 0.678, 0.566, 0.678, 0.618, 0.813], ['RF', 0.95, 0.962, 0.933, 0.974, 0.974, 0.937, 0.962, 0.947, 0.961, 0.96, 0.937, 0.961, 0.974, 0.974, 0.961], ['LR', 0.923, 0.962, 0.949, 0.935, 0.974, 0.95, 0.962, 0.935, 0.949, 0.96, 0.937, 0.949, 0.961, 0.907, 0.961], ['DT', 0.947, 0.907, 0.959, 0.946, 0.959, 0.923, 0.933, 0.946, 0.962, 0.93, 0.932, 0.961, 0.947, 0.946, 0.944]], 'Accuracy': [['KNN', 0.905, 0.927, 0.927, 0.927, 0.925, 0.905, 0.927, 0.927, 0.902, 0.9, 0.905, 0.927, 0.927, 0.902, 0.925], ['SVM', 0.905, 0.927, 0.878, 0.902, 0.875, 0.905, 0.927, 0.927, 0.927, 0.9, 0.881, 0.927, 0.927, 0.927, 0.925], ['NB', 0.5, 0.512, 0.659, 0.366, 0.575, 0.548, 0.488, 0.537, 0.415, 0.55, 0.548, 0.439, 0.537, 0.488, 0.7], ['RF', 0.905, 0.927, 0.878, 0.951, 0.95, 0.881, 0.927, 0.902, 0.927, 0.925, 0.881, 0.927, 0.951, 0.951, 0.925], ['LR', 0.857, 0.927, 0.902, 0.878, 0.95, 0.905, 0.927, 0.878, 0.902, 0.925, 0.881, 0.902, 0.927, 0.829, 0.925], ['DT', 0.905, 0.829, 0.927, 0.902, 0.925, 0.857, 0.878, 0.902, 0.927, 0.875, 0.881, 0.927, 0.902, 0.902, 0.9]]}, 'jedit': {'AUC': [['KNN', 0.268, 0.24, 0.177, 0.083, 0.292, 0.268, 0.156, 0.25, 0.146, 0.177, 0.216, 0.208, 0.0, 0.188, 0.198], ['SVM', 0.0, 0.24, 0.198, 0.156, 0.26, 0.144, 0.208, 0.0, 0.219, 0.302, 0.103, 0.0, 0.146, 0.24, 0.156], ['NB', 0.0, 0.198, 0.75, 0.0, 0.0, 0.134, 0.708, 0.083, 0.0, 0.104, 0.134, 0.427, 0.0, 0.0, 0.0], ['RF', 0.062, 0.0, 0.104, 0.021, 0.031, 0.01, 0.083, 0.094, 0.0, 0.0, 0.062, 0.0, 0.031, 0.042, 0.0], ['LR', 0.072, 0.229, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.198, 0.144, 0.219, 0.0, 0.177, 0.094], ['DT', 0.082, 0.0, 0.104, 0.083, 0.083, 0.041, 0.146, 0.0, 0.063, 0.0, 0.082, 0.0, 0.0, 0.083, 0.052]], 'False_alarm': [['KNN', 0.268, 0.24, 0.177, 0.083, 0.292, 0.268, 0.156, 0.25, 0.146, 0.177, 0.216, 0.208, 0.0, 0.188, 0.198], ['SVM', 0.0, 0.24, 0.198, 0.156, 0.26, 0.144, 0.208, 0.0, 0.219, 0.302, 0.103, 0.0, 0.146, 0.24, 0.156], ['NB', 0.0, 0.198, 0.75, 0.0, 0.0, 0.134, 0.708, 0.083, 0.0, 0.104, 0.134, 0.427, 0.0, 0.0, 0.0], ['RF', 0.062, 0.0, 0.104, 0.021, 0.031, 0.01, 0.083, 0.094, 0.0, 0.0, 0.062, 0.0, 0.031, 0.042, 0.0], ['LR', 0.072, 0.229, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.198, 0.144, 0.219, 0.0, 0.177, 0.094], ['DT', 0.082, 0.0, 0.104, 0.083, 0.083, 0.041, 0.146, 0.0, 0.063, 0.0, 0.082, 0.0, 0.0, 0.083, 0.052]], 'Recall': [['KNN', 0.667, 0.5, 1.0, 0.5, 0.5, 0.667, 0.5, 1.0, 0.5, 0.5, 0.333, 0.5, 0.0, 1.0, 0.5], ['SVM', 0.0, 0.5, 1.0, 0.5, 0.5, 0.333, 1.0, 0.0, 0.5, 1.0, 0.333, 0.0, 0.5, 0.5, 0.5], ['NB', 0.0, 0.5, 0.5, 0.0, 0.0, 0.333, 1.0, 0.5, 0.0, 0.5, 0.333, 1.0, 0.0, 0.0, 0.0], ['RF', 0.333, 0.0, 0.5, 0.5, 0.5, 0.667, 0.5, 1.0, 0.0, 0.0, 0.333, 0.0, 0.5, 1.0, 0.0], ['LR', 0.333, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.667, 0.5, 0.0, 1.0, 1.0], ['DT', 0.333, 0.0, 1.0, 1.0, 0.5, 0.667, 0.5, 0.0, 0.5, 0.0, 0.667, 0.0, 0.0, 0.5, 0.5]], 'Precision': [['KNN', 0.071, 0.042, 0.105, 0.111, 0.034, 0.071, 0.063, 0.077, 0.067, 0.056, 0.045, 0.048, 0.0, 0.1, 0.05], ['SVM', 0.0, 0.042, 0.095, 0.063, 0.038, 0.067, 0.091, 0.0, 0.045, 0.065, 0.091, 0.0, 0.067, 0.042, 0.063], ['NB', 0.0, 0.05, 0.014, 0.0, 0.0, 0.071, 0.029, 0.111, 0.0, 0.091, 0.071, 0.047, 0.0, 0.0, 0.0], ['RF', 0.143, 0.0, 0.091, 0.333, 0.25, 0.667, 0.111, 0.182, 0.0, 0.0, 0.143, 0.0, 0.25, 0.333, 0.0], ['LR', 0.125, 0.043, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.095, 0.125, 0.045, 0.0, 0.105, 0.182], ['DT', 0.111, 0.0, 0.167, 0.2, 0.111, 0.333, 0.067, 0.0, 0.143, 0.0, 0.2, 0.0, 0.0, 0.111, 0.167]], 'F_score': [['KNN', 0.129, 0.077, 0.19, 0.182, 0.065, 0.129, 0.111, 0.143, 0.118, 0.1, 0.08, 0.087, 0.0, 0.182, 0.091], ['SVM', 0.0, 0.077, 0.174, 0.111, 0.071, 0.111, 0.167, 0.0, 0.083, 0.121, 0.143, 0.0, 0.118, 0.077, 0.111], ['NB', 0.0, 0.091, 0.027, 0.0, 0.0, 0.118, 0.056, 0.182, 0.0, 0.154, 0.118, 0.089, 0.0, 0.0, 0.0], ['RF', 0.2, 0.0, 0.154, 0.4, 0.333, 0.667, 0.182, 0.308, 0.0, 0.0, 0.2, 0.0, 0.333, 0.5, 0.0], ['LR', 0.182, 0.08, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.174, 0.211, 0.083, 0.0, 0.19, 0.308], ['DT', 0.167, 0.0, 0.286, 0.333, 0.182, 0.444, 0.118, 0.0, 0.222, 0.0, 0.308, 0.0, 0.0, 0.182, 0.25]], 'Accuracy': [['KNN', 0.73, 0.755, 0.827, 0.908, 0.704, 0.73, 0.837, 0.755, 0.847, 0.816, 0.77, 0.786, 0.0, 0.816, 0.796], ['SVM', 0.0, 0.755, 0.806, 0.837, 0.735, 0.84, 0.796, 0.0, 0.776, 0.704, 0.88, 0.0, 0.847, 0.755, 0.837], ['NB', 0.0, 0.796, 0.255, 0.0, 0.0, 0.85, 0.306, 0.908, 0.0, 0.888, 0.85, 0.582, 0.0, 0.0, 0.0], ['RF', 0.92, 0.0, 0.888, 0.969, 0.959, 0.98, 0.908, 0.908, 0.0, 0.0, 0.92, 0.0, 0.959, 0.959, 0.0], ['LR', 0.91, 0.765, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.806, 0.85, 0.776, 0.0, 0.827, 0.908], ['DT', 0.9, 0.0, 0.898, 0.918, 0.908, 0.95, 0.847, 0.0, 0.929, 0.0, 0.91, 0.0, 0.0, 0.908, 0.939]]}}

#Do Not Contain prop
base={'synapse': {'AUC': [['KNN', 0.088, 0.176, 0.088, 0.176, 0.235, 0.088, 0.088, 0.294, 0.118, 0.118, 0.118, 0.147, 0.206, 0.147, 0.176], ['SVM', 0.029, 0.088, 0.088, 0.176, 0.147, 0.029, 0.118, 0.118, 0.118, 0.176, 0.059, 0.088, 0.059, 0.206, 0.059], ['NB', 0.118, 0.118, 0.118, 0.147, 0.324, 0.147, 0.147, 0.118, 0.176, 0.206, 0.235, 0.118, 0.176, 0.088, 0.176], ['RF', 0.118, 0.147, 0.118, 0.118, 0.059, 0.265, 0.059, 0.147, 0.118, 0.118, 0.176, 0.088, 0.059, 0.206, 0.059], ['LR', 0.118, 0.176, 0.147, 0.118, 0.118, 0.029, 0.118, 0.118, 0.176, 0.088, 0.059, 0.147, 0.118, 0.118, 0.118], ['DT', 0.324, 0.147, 0.176, 0.176, 0.206, 0.235, 0.147, 0.353, 0.206, 0.235, 0.324, 0.118, 0.059, 0.294, 0.324]], 'False_alarm': [['KNN', 0.088, 0.176, 0.088, 0.176, 0.235, 0.088, 0.088, 0.294, 0.118, 0.118, 0.118, 0.147, 0.206, 0.147, 0.176], ['SVM', 0.029, 0.088, 0.088, 0.176, 0.147, 0.029, 0.118, 0.118, 0.118, 0.176, 0.059, 0.088, 0.059, 0.206, 0.059], ['NB', 0.118, 0.118, 0.118, 0.147, 0.324, 0.147, 0.147, 0.118, 0.176, 0.206, 0.235, 0.118, 0.176, 0.088, 0.176], ['RF', 0.118, 0.147, 0.118, 0.118, 0.059, 0.265, 0.059, 0.147, 0.118, 0.118, 0.176, 0.088, 0.059, 0.206, 0.059], ['LR', 0.118, 0.176, 0.147, 0.118, 0.118, 0.029, 0.118, 0.118, 0.176, 0.088, 0.059, 0.147, 0.118, 0.118, 0.118], ['DT', 0.324, 0.147, 0.176, 0.176, 0.206, 0.235, 0.147, 0.353, 0.206, 0.235, 0.324, 0.118, 0.059, 0.294, 0.324]], 'Recall': [['KNN', 0.5, 0.176, 0.471, 0.294, 0.706, 0.444, 0.412, 0.294, 0.471, 0.412, 0.5, 0.529, 0.471, 0.412, 0.294], ['SVM', 0.278, 0.235, 0.647, 0.412, 0.529, 0.389, 0.353, 0.353, 0.706, 0.294, 0.5, 0.471, 0.588, 0.412, 0.176], ['NB', 0.5, 0.471, 0.471, 0.647, 0.529, 0.556, 0.647, 0.353, 0.471, 0.588, 0.611, 0.529, 0.412, 0.471, 0.471], ['RF', 0.444, 0.353, 0.412, 0.471, 0.824, 0.778, 0.471, 0.471, 0.529, 0.176, 0.556, 0.353, 0.529, 0.647, 0.588], ['LR', 0.556, 0.412, 0.471, 0.412, 0.471, 0.389, 0.353, 0.412, 0.471, 0.706, 0.556, 0.588, 0.471, 0.235, 0.412], ['DT', 0.667, 0.294, 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0.667, 0.722, 0.722, 1.0, 0.778, 0.889, 0.944, 0.889, 0.889, 0.944, 0.778, 0.833], 'Precision': [0.55, 0.684, 0.515, 0.412, 0.48, 0.536, 0.556, 0.667, 0.6, 0.526, 0.533, 0.611, 0.63, 0.48, 0.519], 'F_score': [0.75, 0.6, 0.583, 0.667, 0.636, 0.706, 0.632, 0.682, 0.583, 0.683, 0.593, 0.605, 0.571, 0.7, 0.653], 'Accuracy': [0.654, 0.673, 0.596, 0.692, 0.731, 0.615, 0.692, 0.673, 0.596, 0.673, 0.654, 0.519, 0.731, 0.635, 0.673]}, 'NB': {'AUC': [0.662, 0.703, 0.732, 0.588, 0.704, 0.645, 0.675, 0.742, 0.758, 0.73, 0.732, 0.588, 0.662, 0.603, 0.704], 'False_alarm': [0.176, 0.265, 0.176, 0.441, 0.118, 0.088, 0.088, 0.265, 0.176, 0.176, 0.088, 0.147, 0.118, 0.206, 0.235], 'Recall': [0.389, 0.611, 0.667, 0.833, 0.611, 0.667, 0.611, 0.611, 0.556, 0.889, 0.778, 0.611, 0.611, 0.5, 0.556], 'Precision': [0.538, 0.643, 0.433, 0.75, 0.474, 0.591, 0.571, 0.667, 0.522, 0.55, 0.529, 0.563, 0.455, 0.6, 0.727], 'F_score': [0.629, 0.651, 0.706, 0.476, 0.706, 0.474, 0.516, 0.615, 0.688, 0.629, 0.474, 0.649, 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0.49484536082474229, 0.49484536082474229, 0.49484536082474229, 0.49484536082474229, 0.49484536082474229, 0.49484536082474229, 0.49484536082474229, 0.49484536082474229, 0.49484536082474229], 'False_alarm': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Recall': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Precision': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'F_score': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Accuracy': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'NB': {'AUC': [0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452], 'False_alarm': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Recall': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Precision': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'F_score': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Accuracy': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'RF': {'AUC': [0.49484536082474229, 0.5, 0.5, 0.5, 0.5, 0.49484536082474229, 0.49484536082474229, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 'False_alarm': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Recall': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Precision': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'F_score': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Accuracy': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'LR': {'AUC': [0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601, 0.78178694158075601], 'False_alarm': [0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463, 0.10309278350515463], 'Recall': [0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663, 0.66666666666666663], 'Precision': [0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666], 'F_score': [0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666, 0.26666666666666666], 'Accuracy': [0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001, 0.89000000000000001]}, 'DT': {'AUC': [0.48969072164948452, 0.49484536082474229, 0.49484536082474229, 0.49484536082474229, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.48969072164948452, 0.49484536082474229, 0.48969072164948452, 0.49484536082474229, 0.49484536082474229, 0.49484536082474229], 'False_alarm': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Recall': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Precision': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'F_score': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Accuracy': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}}}

if __name__ == '__main__':
    fileB=[]
    fileB1 = ['velocity', 'synapse', 'ant', 'redaktor', 'arc', 'ivy', 'tomcat','camel', 'jedit']

    font = {
        'size': 50}

    plt.rc('font', **font)
    paras = {'lines.linewidth': 8, 'legend.fontsize': 45, 'axes.labelsize': 70, 'legend.frameon': False,
             'figure.autolayout': True}
    plt.rcParams.update(paras)

    measure_med = {}
    measure_iqr = {}
    measure_med_tun={}
    smote=OrderedDict()
    nosmote=OrderedDict()
    tuning = OrderedDict()
    l = ["Recall", "Precision", "Accuracy", "F_score","False_alarm","AUC"]
    for i in l:
        measure_med[i] = {}
        measure_iqr[i] = {}
        measure_med_tun[i] = {}
    for x in fileB1:
        smote[x]=smote1[x]
        nosmote[x]=base[x]
        tuning[x]=smote2[x]

    for f, measures in smote.iteritems():
        fileB.append(f)
        #print(fileB)
        for mea, values in measures.iteritems():
            for i,k in enumerate(values):
                try:
                    measure_med_tun[mea][k[0]].append(np.median(tuning[f][k[0]][mea])-np.median(nosmote[f][mea][i][1:]))
                    measure_med[mea][k[0]].append(np.median(k[1:])-np.median(nosmote[f][mea][i][1:]))
                    measure_iqr[mea][k[0]].append((np.percentile(k[1:], 75) - np.percentile(k[1:], 25))-(np.percentile(nosmote[f][mea][i][1:], 75) - np.percentile(nosmote[f][mea][i][1:], 25)))
                except KeyError:
                    measure_med_tun[mea][k[0]]=[np.median(tuning[f][k[0]][mea])-np.median(nosmote[f][mea][i][1:])]
                    measure_med[mea][k[0]] = [np.median(k[1:])-np.median(nosmote[f][mea][i][1:])]
                    measure_iqr[mea][k[0]] = [(np.percentile(k[1:], 75) - np.percentile(k[1:], 25))-(np.percentile(nosmote[f][mea][i][1:], 75) - np.percentile(nosmote[f][mea][i][1:], 25))]
    X = range(len(fileB))

    fileB2 = [ '(34) velocity', '(33) synapse', '(22) ant', '(15) redaktor', '(11) arc', '(11) ivy', '(9) tomcat', '(4) camel', '(2) jedit']

    for i,j in enumerate(measure_iqr.keys()):
        plt.figure(num=i, figsize=(25, 15))
        plt.subplot(121)
        M = -1.0
        m = 1.0


        for k in measure_iqr[j].keys():
            if M < max(max(measure_med_tun[j][k]),max(measure_med[j][k])) :
                 M = max(max(measure_med_tun[j][k]),max(measure_med[j][k]))
            if m > min(min(measure_med_tun[j][k]),min(measure_med[j][k])):
                 m = min(min(measure_med_tun[j][k]),min(measure_med[j][k]))
            line,=plt.plot(X,measure_med_tun[j][k] ,marker='*', markersize=20, label=k+' SMOTE2')
            plt.plot(X, measure_med[j][k],linestyle="-.",color=line.get_color(), markersize=20,label=k+' SMOTE1')
            #plt.ytext(0.04, 0.5, va='center', rotation='vertical', fontsize=11)
            #plt.text(0.04, 0.5,"Rn (Raw Score)", labelpad=100)
        #box = ax.get_position()
        #ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
        plt.ylim(m, M)
        plt.xticks(X, fileB2,rotation=90)
        plt.ylabel(j +' (Delta) ', labelpad=30)
        plt.xlabel("Dataset Name", labelpad=30)
        #plt.axhline(0, linestyle="-.",color='black')
        #plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
        plt.legend(bbox_to_anchor=(1.05, 1.0), loc=2, borderaxespad=0.)
        plt.savefig("fault_prediction/fig/"+j+"_tuned2.png")