import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from sklearn import datasets from sklearn.model_selection import train_test_split , KFold from sklearn.preprocessing import Normalizer from sklearn.metrics import accuracy_score from sklearn.neighbors import KNeighborsClassifier from collections import Counter

# import iris dataset iris = datasets.load_iris() # np.c_ is the numpy concatenate function iris_df = pd.DataFrame(data= np.c_[iris['data'], iris['target']], columns= iris['feature_names'] + ['target']) iris_df.head()

iris_df.describe()

x= iris_df.iloc[:, :-1] y= iris_df.iloc[:, -1]

x.head()

y.head()

# split the data into train and test sets x_train, x_test, y_train, y_test= train_test_split(x, y, test_size= 0.2, shuffle= True, #shuffle the data to avoid bias random_state= 0) x_train= np.asarray(x_train) y_train= np.asarray(y_train) x_test= np.asarray(x_test) y_test= np.asarray(y_test)

scaler= Normalizer().fit(x_train) # the scaler is fitted to the training set normalized_x_train= scaler.transform(x_train) # the scaler is applied to the training set normalized_x_test= scaler.transform(x_test) # the scaler is applied to the test set

print('x train before Normalization') print(x_train[0:5]) print('\nx train after Normalization') print(normalized_x_train[0:5])

## Before # View the relationships between variables; color code by species type di= {0.0: 'Setosa', 1.0: 'Versicolor', 2.0:'Virginica'} # dictionary before= sns.pairplot(iris_df.replace({'target': di}), hue= 'target') before.fig.suptitle('Pair Plot of the dataset Before normalization', y=1.08) ## After iris_df_2= pd.DataFrame(data= np.c_[normalized_x_train, y_train], columns= iris['feature_names'] + ['target']) di= {0.0: 'Setosa', 1.0: 'Versicolor', 2.0: 'Virginica'} after= sns.pairplot(iris_df_2.replace({'target':di}), hue= 'target') after.fig.suptitle('Pair Plot of the dataset After normalization', y=1.08)

def distance_ecu(x_train, x_test_point): """ Input: - x_train: corresponding to the training data - x_test_point: corresponding to the test point Output: -distances: The distances between the test point and each point in the training data. """ distances= [] ## create empty list called distances for row in range(len(x_train)): ## Loop over the rows of x_train current_train_point= x_train[row] #Get them point by point current_distance= 0 ## initialize the distance by zero for col in range(len(current_train_point)): ## Loop over the columns of the row current_distance += (current_train_point[col] - x_test_point[col]) **2 ## Or current_distance = current_distance + (x_train[i] - x_test_point[i])**2 current_distance= np.sqrt(current_distance) distances.append(current_distance) ## Append the distances # Store distances in a dataframe distances= pd.DataFrame(data=distances,columns=['dist']) return distances

def nearest_neighbors(distance_point, K): """ Input: -distance_point: the distances between the test point and each point in the training data. -K : the number of neighbors Output: -df_nearest: the nearest K neighbors between the test point and the training data. """ # Sort values using the sort_values function df_nearest= distance_point.sort_values(by=['dist'], axis=0) ## Take only the first K neighbors df_nearest= df_nearest[:K] return df_nearest

def voting(df_nearest, y_train): """ Input: -df_nearest: dataframe contains the nearest K neighbors between the full training dataset and the test point. -y_train: the labels of the training dataset. Output: -y_pred: the prediction based on Majority Voting """ ## Use the Counter Object to get the labels with K nearest neighbors. counter_vote= Counter(y_train[df_nearest.index]) y_pred= counter_vote.most_common()[0][0] # Majority Voting return y_pred

def KNN_from_scratch(x_train, y_train, x_test, K): """ Input: -x_train: the full training dataset -y_train: the labels of the training dataset -x_test: the full test dataset -K: the number of neighbors Output: -y_pred: the prediction for the whole test set based on Majority Voting. """ y_pred=[] ## Loop over all the test set and perform the three steps for x_test_point in x_test: distance_point = distance_ecu(x_train, x_test_point) ## Step 1 df_nearest_point= nearest_neighbors(distance_point, K) ## Step 2 y_pred_point = voting(df_nearest_point, y_train) ## Step 3 y_pred.append(y_pred_point) return y_pred

K=3 y_pred_scratch= KNN_from_scratch(normalized_x_train, y_train, normalized_x_test, K) print(y_pred_scratch)

knn=KNeighborsClassifier(K) knn.fit(normalized_x_train, y_train) y_pred_sklearn= knn.predict(normalized_x_test) print(y_pred_sklearn)

print(np.array_equal(y_pred_sklearn, y_pred_scratch))

print(f'The accuracy of our implementation is {accuracy_score(y_test, y_pred_scratch)}') print(f'The accuracy of sklearn implementation is {accuracy_score(y_test, y_pred_sklearn)}')

n_splits= 4 ## Choose the number of splits kf= KFold(n_splits= n_splits) ## Call the K Fold function accuracy_k= [] ## Keep track of the accuracy for each K k_values= list(range(1,30,2)) ## Search for the best value of K for k in k_values: ## Loop over the K values accuracy_fold= 0 for normalized_x_train_fold_idx, normalized_x_valid_fold_idx in kf.split(normalized_x_train): ## Loop over the splits normalized_x_train_fold= normalized_x_train[normalized_x_train_fold_idx] ## fetch the values y_train_fold= y_train[normalized_x_train_fold_idx] normalized_x_test_fold= normalized_x_train[normalized_x_valid_fold_idx] y_valid_fold= y_train[normalized_x_valid_fold_idx] y_pred_fold= KNN_from_scratch(normalized_x_train_fold, y_train_fold, normalized_x_test_fold, k) accuracy_fold+= accuracy_score (y_pred_fold, y_valid_fold) ## Accumulate the accuracy accuracy_fold= accuracy_fold/ n_splits ## Divide by the number of splits accuracy_k.append(accuracy_fold)

print(f'The accuracy for each K value was {list ( zip (accuracy_k, k_values))}') ## creates a tuple with accuracy corresponding to k value