Learn.py

"""
CS501 Group 16
Fall 2018
Modified/adapted from code baseline by Thomas Shaw

This module imports the data and executes the algorithms.

Re-used code from: https://github.com/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping
credit: Mark Dregan

*Some modifications made for implementation in Python3 and custom data input, plotting

"""


import matplotlib.pyplot as plt
import numpy as np

from knndtw import KnnDtw
from knndtw import ProgressBar
from k_fold_cv import k_fold_cross_val

dataset = 'Data6/'
dataparam = 'mavlink_attitude_t_roll angle'
n_neighbors = 1
max_warping_window=100

plotdata = False

#get time for computatoin length
timestartalg = datetime.datetime.now()

trainingdatafile =  dataset + 'train_' + dataparam + '.txt'
traininglabelfile = dataset + 'train_labels.txt'

testdatafile =  dataset + 'test_' + dataparam + '.txt'
testlabelfile = dataset + 'test_labels.txt'

# Import the HAR dataset
x_train_file = open(trainingdatafile, 'r')
y_train_file = open(traininglabelfile, 'r')

x_test_file = open(testdatafile, 'r')
y_test_file = open(testlabelfile, 'r')

# Create empty lists
x_train = []
y_train = []
x_test = []
y_test = []

# Mapping table for classes
labels = {1:'Hover', 2:'Impact (Front Left)', 3:'Impact (Front Right)', 4:'Impact (Back Left)', 5:'Impact (Back Right)', 
          6:'Gust (from Left)', 7:'Gust (from Right)', 8: 'Gust (from front)' }

# Loop through datasets
for x in x_train_file:
    x_train.append([float(ts) for ts in x.split()])
    
for y in y_train_file:
    y_train.append(int(y.rstrip('\n')))

for x in x_test_file:
    x_test.append([float(ts) for ts in x.split()])
 
for y in y_test_file:
    y_test.append(int(y.rstrip('\n')))


#close data files
x_train_file.close()
y_train_file.close()
x_test_file.close()
y_test_file.close()

# Convert to numpy for efficiency
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)


#plot train data
if(plotdata):
    plt.figure(figsize=(11,7))
    colors = ['#D62728','#2C9F2C','#FD7F23','#1F77B4','#9467BD',
              '#8C564A','#7F7F7F','#1FBECF','#E377C2','#BCBD27',
              '#D62728','#2C9F2C']
    for i, r in enumerate([0,1,2,3,5,6,7,8,9,10,11,12]):
        plt.subplot(7,2,i+1)
        plt.plot(x_train[r], label=labels[y_train[r]], color=colors[i], linewidth=2)
        plt.xlabel('Samples @50Hz')
        plt.legend(loc='upper left')
        plt.tight_layout()

#Plot Test data
if(plotdata):
    plt.figure(figsize=(11,7))
    colors = ['#D62728','#2C9F2C','#FD7F23','#1F77B4','#9467BD',
              '#8C564A','#7F7F7F','#1FBECF','#E377C2','#BCBD27']
    for i, r in enumerate([0,1,2,3,4,5,6,7,8,9]):
        plt.subplot(5,2,i+1)
        plt.plot(x_test[r], label=labels[y_test[r]], color=colors[i], linewidth=2)
        plt.xlabel('Samples @50Hz')
        plt.legend(loc='upper left')
        plt.tight_layout()
        

#Analyze dataset
m = KnnDtw(n_neighbors, max_warping_window)
m.fit(x_train,y_train)
label, proba = m.predict(x_test)

#Classification report
from sklearn.metrics import classification_report, confusion_matrix
print(classification_report(label, y_test,
                            target_names=[l for l in labels.values()]))


#Confusion Matrix
conf_mat = confusion_matrix(label, y_test)

fig = plt.figure(figsize=(8,8))
width = np.shape(conf_mat)[1]
height = np.shape(conf_mat)[0]

res = plt.imshow(np.array(conf_mat), cmap=plt.cm.summer, interpolation='nearest')
for i, row in enumerate(conf_mat):
    for j, c in enumerate(row):
        if c>0:
            plt.text(j-.2, i+.1, c, fontsize=16)
            
#cb = fig.colorbar(res)
plt.title('Confusion Matrix for ' + dataparam)
plt.xlabel('Data')
plt.ylabel('ML Identification')
_ = plt.xticks(range(8), [l for l in labels.values()], rotation=90)
_ = plt.yticks(range(8), [l for l in labels.values()])


#get end time for computatoin length and compute total run time
timeendalg = datetime.datetime.now()
runtime = timeendalg - timestartalg
print('total algorithm computation time was %f seconds' % (runtime.total_seconds()))