Glass classification done

glass-classification/index.py

@@ -24,7 +24,6 @@ import pandas
 import numpy
 from pandas.plotting import scatter_matrix
 import matplotlib.pyplot as plt
-import sys
 from sklearn import model_selection
 from sklearn.metrics import classification_report
 from sklearn.metrics import confusion_matrix
@@ -35,16 +34,45 @@ from sklearn.neighbors import KNeighborsClassifier
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
 from sklearn.naive_bayes import GaussianNB
 from sklearn.svm import SVC
-sys.path.append('..')
 from utils.models_evaluation import ModelsEvaluation
 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
 path = "../datasets/Glass Dataset/glass.data"
 names = ['id', 'RI', 'Na', 'Mg', 'Al', 'Si', 'K', 'Ca', 'Ba', 'Fe', 'class']
 dataset = pandas.read_csv(path, names = names)
 array = dataset.values
-X = array[:, 1:9]
+X = array[:, 1:10]
 Y = array[:, 10]
-print(X)
-print(Y)
+# validation_size = 0.20
+# 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")