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main.py
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main.py
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import numpy as np
from scipy import stats
import pandas as pd
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import VotingClassifier
from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
class Classifier(object):
def __init__(self):
self.train_shape_view = None
self.train_rgb_view = None
return
def readViews(self):
self.train_shape_view = pd.read_csv('train.csv', header=0, index_col=None, usecols=[*range(0,10)])
self.train_rgb_view = pd.read_csv('train.csv', header=0, index_col=None, usecols=[0, *range(10,20)])
self.test_shape_view = pd.read_csv('test.csv', header=0, index_col=None, usecols=[*range(0,10)])
self.test_rgb_view = pd.read_csv('test.csv', header=0, index_col=None, usecols=[0, *range(10,20)])
return
# Some data for training and other part for test (90% and 10%)
def preProcess(self, train_df):
train_set, test_set = train_test_split(train_df, test_size= 0.1, random_state = 42)
train_set_predictors = train_set.drop('CLASS', axis=1)
train_set_labels = train_set['CLASS'].copy()
test_set_predictors = test_set.drop('CLASS', axis=1)
test_set_labels = test_set['CLASS'].copy()
le = preprocessing.LabelEncoder()
le.fit(train_set['CLASS'])
train_set_labels_numeric = le.transform(train_set_labels)
test_set_labels_numeric = le.transform(test_set_labels)
return train_set_predictors, train_set_labels_numeric, test_set_predictors, test_set_labels_numeric
# All data for training or test
def preProcessCompleteData(self, dataset_df):
train_set_predictors = dataset_df.drop('CLASS', axis=1)
train_set_labels = dataset_df['CLASS'].copy()
le = preprocessing.LabelEncoder()
le.fit(dataset_df['CLASS'])
train_set_labels_numeric = le.transform(train_set_labels)
return train_set_predictors, train_set_labels_numeric
def bayes(self, train_set_predictors, train_set_labels):
clf = GaussianNB()
clf.fit(train_set_predictors, train_set_labels)
return clf
def knn(self, train_set_predictors, train_set_labels):
k = self.findK(train_set_predictors, train_set_labels)
clf = KNeighborsClassifier(n_neighbors=k, p=1)
clf.fit(train_set_predictors, train_set_labels)
return clf
def testClassifier(self, clf, test_set_predictors, test_set_labels):
target_pred = clf.predict(test_set_predictors)
test_accuracy = accuracy_score(test_set_labels, target_pred)
print(test_accuracy)
return
def findK(self, train_set_predictors, train_set_labels):
cv_scores = []
cv_scores2 = []
scores2 = []
ks = [1, 3, 5, 7, 9, 11, 13]
for k in ks:
knn = KNeighborsClassifier(n_neighbors=k, p=1)
scores = cross_val_score(knn, train_set_predictors, train_set_labels, cv=10, scoring='accuracy')
mean = scores.mean()
cv_scores.append(mean)
mse = [1 - x for x in cv_scores]
optimal_k = ks[mse.index(min(mse))]
return optimal_k
def cross_validate(self, k, clf, train_set_predictors, train_set_labels):
skf = StratifiedKFold(n_splits=k)
scores = []
predictors_set = np.array(train_set_predictors)
label_set = np.array(train_set_labels)
for train_index, test_index in skf.split(predictors_set, label_set):
clf.fit(predictors_set[train_index], label_set[train_index])
target_pred = clf.predict(predictors_set[test_index])
score = accuracy_score(label_set[test_index], target_pred)
scores.append(score)
mean = np.mean(scores);
return mean
def combinationClassifier(self, train_set_predictors_v1, train_set_predictors_v2, train_set_labels):
#TODO: PARALELIZAR O TREINAMENTO
self.bayes_view1 = self.bayes(train_set_predictors_v1, train_set_labels)
self.bayes_view2 = self.bayes(train_set_predictors_v2, train_set_labels)
self.knn_view1 = self.knn(train_set_predictors_v1, train_set_labels)
self.knn_view2 = self.knn(train_set_predictors_v2, train_set_labels)
return
def testFinalClassifier(self, test_set_predictors_v1, test_set_predictors_v2, test_set_labels):
targets_pred = np.empty(shape=(0,test_set_labels.size))
targets_pred = np.vstack((targets_pred, self.bayes_view1.predict(test_set_predictors_v1)))
targets_pred = np.vstack((targets_pred, self.bayes_view2.predict(test_set_predictors_v2)))
targets_pred = np.vstack((targets_pred, self.knn_view1.predict(test_set_predictors_v1)))
targets_pred = np.vstack((targets_pred, self.knn_view2.predict(test_set_predictors_v2)))
target_pred = stats.mode(targets_pred).mode[0]
test_accuracy = accuracy_score(test_set_labels, target_pred)
print("Final Classifier: ")
print(test_accuracy)
return
def comparingModels(self, train_set_predictors_v1, train_set_predictors_v2, train_set_labels):
targets_pred = np.empty(shape=(0,train_set_labels.size))
print("Bayes:")
print("Shape View")
score1 = cross_val_score(GaussianNB(), train_set_predictors_v1, train_set_labels, cv=30)
print(np.mean(score1))
print("RGB View")
score2 = cross_val_score(GaussianNB(), train_set_predictors_v2, train_set_labels, cv=30)
print (np.mean(score2))
print("k-NN:")
print("Shape View")
k = self.findK(train_set_predictors_v1, train_set_labels)
clf = KNeighborsClassifier(n_neighbors=k, p=1)
score3 = cross_val_score(clf, train_set_predictors_v1, train_set_labels, cv=30)
print (np.mean(score3))
print("RGB View")
k = self.findK(train_set_predictors_v2, train_set_labels)
clf = KNeighborsClassifier(n_neighbors=k, p=1)
score4 = cross_val_score(clf, train_set_predictors_v2, train_set_labels, cv=30)
print (np.mean(score4))
return
def process(self):
self.readViews()
train_set_predictors_shape, train_set_labels_shape = classifier.preProcessCompleteData(classifier.train_shape_view)
train_set_predictors_rgb, train_set_labels_rgb = classifier.preProcessCompleteData(classifier.train_rgb_view)
self.comparingModels(train_set_predictors_shape, train_set_predictors_rgb, train_set_labels_shape)
self.combinationClassifier(train_set_predictors_shape, train_set_predictors_rgb, train_set_labels_shape)
test_set_predictors_shape, test_set_labels_shape = classifier.preProcessCompleteData(classifier.test_shape_view)
test_set_predictors_rgb, test_set_labels_rbg = classifier.preProcessCompleteData(classifier.test_rgb_view)
self.testFinalClassifier(test_set_predictors_shape, test_set_predictors_rgb, test_set_labels_shape)
return
def stratifiedCrossvalidate(self):
return
def majorityVote(self, train_set_predictors, train_set_labels, test_set_predictors, target_pred):
c_bayes = GaussianNB()
k = self.findK(train_set_predictors, train_set_labels)
c_knn = KNeighborsClassifier(n_neighbors=k, p=1)
final_classifier = VotingClassifier(estimators=[('bayes', c_bayes), ('knn', c_knn)], voting='hard')
final_classifier.fit(train_set_predictors, train_set_labels)
target_pred = final_classifier.predict(test_set_predictors)
test_accuracy = accuracy_score(test_set_labels, target_pred)
print(test_accuracy)
return
classifier = Classifier()
classifier.process()