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102 lines
4.0 KiB
102 lines
4.0 KiB
6 years ago
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# MIT License
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# Copyright (c) 2018 Robby Muhammad Nst
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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import pandas
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from pandas.plotting import scatter_matrix
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import matplotlib.pyplot as plt
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from sklearn import model_selection
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from sklearn.metrics import classification_report
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from sklearn.metrics import confusion_matrix
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from sklearn.metrics import accuracy_score
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from sklearn.linear_model import LogisticRegression
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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from sklearn.naive_bayes import GaussianNB
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from sklearn.svm import SVC
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from model import Model
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class ModelsEvaluation:
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__models = []
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def __init__(self, x_train, y_train):
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""" Models Evaluation Constructor
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Initiallize 6 models """
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self.x_train = x_train
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self.y_train = y_train
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# 6 default algorith models
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# Maybe I will add more algorithm in the future
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self.__models.append(('Logistic Regression', LogisticRegression()))
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self.__models.append(('Linear Discrimination Analysis', LinearDiscriminantAnalysis()))
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self.__models.append(('DecissionTreeClassifier', DecisionTreeClassifier()))
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self.__models.append(('SVM', SVC()))
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self.__models.append(('Gaussian NB', GaussianNB()))
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self.__models.append(('K NeighborsClassifier', KNeighborsClassifier()))
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# Evaluate the accuracy of 6 models
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# K fold Validation model
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def evaluateAccuracy(self):
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Model.models[:] = []
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""" Evalueate the accuracy model by given data train.
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So it could get the best Algorithm to use """
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__results = []
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__names = []
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for name, model in self.__models:
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kfold = model_selection.KFold(n_splits = 10, random_state = 7)
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cv_results = model_selection.cross_val_score(model, self.x_train, self.y_train, cv = kfold, scoring = 'accuracy')
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__results.append(cv_results)
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__names.append(name)
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Model.models.append(Model(name, cv_results.mean(), cv_results.std()))
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# NOT IMPLEMENTED YET
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#
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# if ( len(Model.getHighestScore()) > 1 ):
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# if (Model.getHighestScore()[0].mean == Model.getHighestScore()[1].mean):
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# return Model.getHighestScore()[0]
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# else:
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# return Model.getHighestScore()
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# else:
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# return Model.getHighestScore()[0]
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return Model.getHighestScore()[0]
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# LOOCV Validation Model
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def leaveOneOutCrossValidationEvaluation(self):
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Model.models[:] = []
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__results = []
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__names = []
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__looCrossValidation = model_selection.LeaveOneOut()
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for name, model in self.__models:
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cv_results = model_selection.cross_val_score(model, self.x_train, self.y_train, cv = __looCrossValidation)
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__results.append(cv_results)
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__names.append(name)
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Model.models.append(Model(name, cv_results.mean(), cv_results.std()))
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return Model.getHighestScore()[0]
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