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dogBreed.py
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import itertools
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import random
import seaborn as sns
from scipy import stats
from sklearn.cluster import KMeans
from math import sqrt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
dogDataset = pd.read_csv('AKCBreedInfo.csv',',')
#Peso em Kilos e altura em centimetros
#print(dogDataset['Breed'])
for i in dogDataset['weight_high_lbs']:
X = dogDataset['weight_high_lbs'] / 2.2
# Xa = dogDataset['weight_high_lbs'] / 2.2
#X = round(X,2)
# for i in dogDataset['weight_low_lbs']:
# Xb = dogDataset['weight_low_lbs'] / 2.2
for i in dogDataset['height_high_inches']:
# Ya = dogDataset['height_high_inches'] * 2.54
Y = dogDataset['height_high_inches'] * 2.54
# for i in dogDataset['height_low_inches']:
# Yb = dogDataset['height_low_inches'] * 2.54
for i in dogDataset['Breed']:
name = dogDataset['Breed']
# X = Xa - Xb
# Y = Ya - Yb
# print(X)
# print(Y)
data = np.array(list(zip(X, Y)))
data = pd.DataFrame(data)
data.columns = ['peso', 'altura']
model = KMeans(n_clusters=3)
model.fit(data)
#print data.peso[0]
#print data.altura[0]
#print(model.predict(data.peso[0]))
### CONFUSION MATRIX
y = model.labels_
vetor = np.array([data.peso,data.altura])
#print(vetor)
class_names = ['pequeno', 'medio', 'grande']
# Split the data into a training set and a test set
data_train, data_test, y_train, y_test = train_test_split(data, y, random_state=0)
# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(data_train, y_train).predict(data_test)
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
specificity1 = cm[1,1]/(cm[1,0]+cm[1,1])
print 'Specificity : ', specificity1
print(cm)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names,
title='Confusion matrix, without normalization')
plt.savefig('nom-normalizedCM.png')
# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
title='Normalized confusion matrix')
plt.savefig('normalizedCM.png')
#plt.show()
#print(model.labels_)
#labels = np.array([model.labels_])
#colors = np.array(['r', 'g', 'b'])
#LABEL_COLOR_MAP = {0 : 'r', 1 : 'g', 2 : 'b'}
#labelColor = [LABEL_COLOR_MAP[l] for l in labels]
data = np.array(list(zip(X, Y, model.labels_)))
data = pd.DataFrame(data)
data.columns = ['peso', 'altura','clusters']
sns.lmplot('peso', 'altura', data=data, fit_reg=False, hue='clusters', scatter_kws={"marker": "D", "s": 60},size = 5, aspect = 2)
plt.title('Cluster Dog Breed Size')
plt.ylabel('Altura em Centimetros')
plt.xlabel('Peso em Kilos')
#plt.legend()
plt.savefig('clusterDog.png')
plt.show()