Testing_Activities.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle
import itertools
from sklearn.metrics import f1_score,accuracy_score,classification_report,confusion_matrix
activities=["climbingdown","climbingup","jumping","lying","running","sitting","standing","walking"]
data=pd.read_csv('clean_data.csv')
data.drop(data.columns[[0]],axis=1,inplace=True)
def Range(a):
    return a.max()-a.min();
def plot_confusion_matrix(cm,classes,normalize=False,title='Confusion Matrix',cmap='YlOrRd'):
    plt.rcParams.update({'font.size': 18})
    plt.figure(figsize=(20,10),)
    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.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.tight_layout();
range_sample=data.groupby(['Activity','Subject','Sample_Num']).apply(Range).add_prefix('range_')
stdev_sample=data.groupby(['Activity','Subject','Sample_Num']).std().add_prefix('std_')
mean_sample=data.groupby(['Activity','Subject','Sample_Num']).mean().add_prefix('mean_')
total_data=pd.concat([stdev_sample,mean_sample,range_sample],axis=1)
total_data.reset_index(inplace=True)
columns=[
       'std_attr_x_gyro', 'std_attr_y_gyro', 'std_attr_z_gyro',
         'std_attr_x_acc', 'std_attr_y_acc', 'std_attr_z_acc', 
         'mean_attr_x_gyro', 'mean_attr_y_gyro', 'mean_attr_z_gyro', 
         'mean_attr_x_acc', 'mean_attr_y_acc', 'mean_attr_z_acc',
         'range_attr_x_gyro', 'range_attr_y_gyro','range_attr_z_gyro', 
         'range_attr_x_acc', 'range_attr_y_acc', 'range_attr_z_acc']
temp=total_data.Subject>=5
X_final=total_data[columns][temp]
Y_final=total_data.Activity[temp]
# Loading previously saved Logistic Regression model
pkl="Logistic_Regression_Model.pkl"
classifier=pickle.load(open(pkl,'rb'))
#Making predictions for last two unseen subjects
Y_predicted=classifier.predict(X_final)
print(f'Testing F1 score is: {f1_score(Y_final,Y_predicted,average="macro"):.3f}')
print(f'Testing accuracy is: {accuracy_score(Y_final,Y_predicted):.3f}')
print(classification_report(Y_final,Y_predicted))
np.set_printoptions(precision=2)
cm=confusion_matrix(Y_final,Y_predicted)
plt.figure()
plot_confusion_matrix(cm,classes=activities,title='Testing Confusion Matrix');