Writing Our First Classifier - Machine Learning Recipes #5 (Adapted from this video : https://www.youtube.com/watch?v=AoeEHqVSNOw&index=5&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)

sample-machine-learning-classifier.py

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from scipy.spatial import distance

def euc(a,b):
    return distance.euclidean(a,b)

#Creating a sample classifier class
class ScrappyKNN():
    def fit(self, X_train, y_train):
        self.X_train = X_train
        self.y_train = y_train

    def predict(self, X_test):
        predictions = []
        for row in X_test:
            label = self.closest(row)
            predictions.append(label)

        return predictions

    def closest(self, row):
        best_dist = euc(row, self.X_train[0])
        best_index = 0
        for i in range(1, len(self.X_train)):
            dist = euc(row, self.X_train[i])
            if dist < best_dist:
                best_dist = dist
                best_index = i 
        return self.y_train[best_index]

    
#Collecting the training data
iris = datasets.load_iris()

X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= .5) 

#Training the classifier
my_classifier = ScrappyKNN()
my_classifier.fit(X_train, y_train)

#Making the predictions
predictions = my_classifier.predict(X_test)

#Chacking the accuracy
print(accuracy_score(y_test, predictions))