|
|
@ -24,7 +24,6 @@ import pandas |
|
|
|
import numpy |
|
|
|
import numpy |
|
|
|
from pandas.plotting import scatter_matrix |
|
|
|
from pandas.plotting import scatter_matrix |
|
|
|
import matplotlib.pyplot as plt |
|
|
|
import matplotlib.pyplot as plt |
|
|
|
import sys |
|
|
|
|
|
|
|
from sklearn import model_selection |
|
|
|
from sklearn import model_selection |
|
|
|
from sklearn.metrics import classification_report |
|
|
|
from sklearn.metrics import classification_report |
|
|
|
from sklearn.metrics import confusion_matrix |
|
|
|
from sklearn.metrics import confusion_matrix |
|
|
@ -35,16 +34,45 @@ from sklearn.neighbors import KNeighborsClassifier |
|
|
|
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis |
|
|
|
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis |
|
|
|
from sklearn.naive_bayes import GaussianNB |
|
|
|
from sklearn.naive_bayes import GaussianNB |
|
|
|
from sklearn.svm import SVC |
|
|
|
from sklearn.svm import SVC |
|
|
|
sys.path.append('..') |
|
|
|
|
|
|
|
from utils.models_evaluation import ModelsEvaluation |
|
|
|
from utils.models_evaluation import ModelsEvaluation |
|
|
|
from utils.model import Model |
|
|
|
from utils.model import Model |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def getGlassesClassText(number): |
|
|
|
|
|
|
|
return { |
|
|
|
|
|
|
|
1.0 : 'Building windows float processed', |
|
|
|
|
|
|
|
2.0 : 'Building windows non-float processed', |
|
|
|
|
|
|
|
3.0 : 'Vehicle windows float processed', |
|
|
|
|
|
|
|
4.0 : 'Vehicle windows non-float processed', |
|
|
|
|
|
|
|
5.0 : 'Containers', |
|
|
|
|
|
|
|
6.0 : 'Tableware', |
|
|
|
|
|
|
|
7.0 : 'Headlamps' |
|
|
|
|
|
|
|
}.get(number, None) |
|
|
|
|
|
|
|
|
|
|
|
# Load the dataset |
|
|
|
# Load the dataset |
|
|
|
path = "../datasets/Glass Dataset/glass.data" |
|
|
|
path = "../datasets/Glass Dataset/glass.data" |
|
|
|
names = ['id', 'RI', 'Na', 'Mg', 'Al', 'Si', 'K', 'Ca', 'Ba', 'Fe', 'class'] |
|
|
|
names = ['id', 'RI', 'Na', 'Mg', 'Al', 'Si', 'K', 'Ca', 'Ba', 'Fe', 'class'] |
|
|
|
dataset = pandas.read_csv(path, names = names) |
|
|
|
dataset = pandas.read_csv(path, names = names) |
|
|
|
array = dataset.values |
|
|
|
array = dataset.values |
|
|
|
X = array[:, 1:9] |
|
|
|
X = array[:, 1:10] |
|
|
|
Y = array[:, 10] |
|
|
|
Y = array[:, 10] |
|
|
|
print(X) |
|
|
|
# validation_size = 0.20 |
|
|
|
print(Y) |
|
|
|
# seed = 7 |
|
|
|
|
|
|
|
# X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size = validation_size, random_state = seed) |
|
|
|
|
|
|
|
MODEL_EVALUATION = ModelsEvaluation(X, Y) |
|
|
|
|
|
|
|
HIGHEST_ACC_MODEL_NAME = MODEL_EVALUATION.evaluateAccuracy().name |
|
|
|
|
|
|
|
HIGHEST_ACC_MODEL_SCORE = MODEL_EVALUATION.evaluateAccuracy().mean |
|
|
|
|
|
|
|
HIGHEST_MODEL = Model.getAlgorithmModel(HIGHEST_ACC_MODEL_NAME) |
|
|
|
|
|
|
|
HIGHEST_MODEL.fit(X, Y) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
predict_data = [ |
|
|
|
|
|
|
|
[ 1.51755, 13.00, 3.60, 1.36, 72.99, 0.57, 8.40, 0.00, 0.11 ], |
|
|
|
|
|
|
|
[ 1.51574, 14.86, 3.67, 1.74, 71.87, 0.16, 7.36, 0.00, 0.12 ], |
|
|
|
|
|
|
|
[ 1.51593, 13.09, 3.59, 1.52, 73.10, 0.67, 7.83, 0.00, 0.00 ] |
|
|
|
|
|
|
|
] |
|
|
|
|
|
|
|
if ( HIGHEST_MODEL is not None ): |
|
|
|
|
|
|
|
prediction = HIGHEST_MODEL.predict(predict_data) |
|
|
|
|
|
|
|
print("Matching algorithm is " + HIGHEST_ACC_MODEL_NAME) |
|
|
|
|
|
|
|
for i in range(0, len(prediction)): |
|
|
|
|
|
|
|
print(getGlassesClassText(prediction[i])) |
|
|
|
|
|
|
|
else: |
|
|
|
|
|
|
|
print("Didn't get matching algorithm") |
|
|
|