Merge branch 'master' of https://github.com/CS501ProSCE/DroneProject

GUI/GUIFunctions.py

@@ -64,13 +64,16 @@ def csvParaExtract(file_name,para_name):
                     else:
                         mavlink_data[typ][param].append(float(row[mavlink_index[typ][param]-1]))
     data = []
+    count = 0
     for typ in range(len(mavlink_types)):
         for param in range(len(mavlink_param[typ])):
             if mavlink_param[typ][param] == para_name:
-                for i in range(len(mavlink_data[typ][param])):
-                    data.append(mavlink_data[typ][param][i])
-                rate = mavlink_rate[typ]
-                mav_type = mavlink_types[typ]
+                if count == 0:
+                    for i in range(len(mavlink_data[typ][param])):
+                        data.append(mavlink_data[typ][param][i])
+                    rate = mavlink_rate[typ]
+                    count = count + 1
+                    mav_type = mavlink_types[typ]
 
     print("Done collecting "+para_name+" data!")
     return data,rate,mav_type

find_best_k.py

@@ -0,0 +1,89 @@
+
+# -*- coding: utf-8 -*-
+"""
+A function to see the best k for each parameter
+
+@author: Jeff Xie
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+import time
+from knndtw import KnnDtw
+from knndtw import ProgressBar
+from scipy import stats
+
+from k_fold_cv import k_fold_cross_val
+from sklearn.metrics import classification_report, confusion_matrix
+from sklearn.metrics import precision_recall_fscore_support as score
+
+def find_best_k(param_list,k_range,warp_val,datapath,folds): 
+    start_time = time.time()
+    param_k = []
+    for dataparam in param_list:
+        trainingdatafile =  datapath + 'train_' + dataparam + '.txt'
+        traininglabelfile = datapath + 'train_labels.txt'
+
+        testdatafile =  datapath + 'test_' + dataparam + '.txt'
+        testlabelfile = datapath + 'test_labels.txt'
+
+        # Open training data file, x:data, y:label
+        x_train_file = open(trainingdatafile, 'r')
+        y_train_file = open(traininglabelfile, 'r')
+
+        #Open test data file, x:data, y:label
+        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)' }
+
+        i = 0
+        # 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 efficienc 
+        x_train = np.array(x_train)
+        y_train = np.array(y_train)
+        x_test = np.array(x_test)
+        y_test = np.array(y_test)
+
+        k_best = k_fold_cross_val(k_range,x_train,y_train,folds)    
+        param_k.append((dataparam,k_best))
+
+
+    print("k-fold cross val results for the included paramters")    
+    for kv in param_k:
+         print(kv[0],": ",kv[1])
+    print("--- %s seconds ---" % (time.time() - start_time)) #let's see how long this takes...
+#Testing 
+plist1 = ['mavlink_raw_imu_t_Xaccel','mavlink_raw_imu_t_Yaccel','mavlink_raw_imu_t_Zaccel','mavlink_raw_imu_t_XGyro','mavlink_raw_imu_t_YGyro','mavlink_raw_imu_t_ZGyro']
+plist2 = ['mavlink_attitude_t_pitch angle','mavlink_attitude_t_roll angle','mavlink_attitude_t_yaw angle','mavlink_attitude_t_pitch rate','mavlink_attitude_t_yaw rate','mavlink_attitude_t_roll rate']
+plist3 = ['mavlink_raw_imu_t_XMag','mavlink_raw_imu_t_YMag','mavlink_raw_imu_t_ZMag','mavlink_vibration_t_vibration_x','mavlink_vibration_t_vibration_y','mavlink_vibration_t_vibration_z']
+p_all = plist1+plist2+plist3
+k_list = list(range(1,6))
+find_best_k(p_all,k_list,100,'Data4/',5)       

k_fold_cv.py

@@ -23,6 +23,7 @@
 
 
 def k_fold_cross_val(k_list,train,label,folds):
+
     #Randomly shuffle the data and label in to the same sequence 
     seed = np.arange(train.shape[0])
     np.random.shuffle(seed)
@@ -32,7 +33,8 @@ def k_fold_cross_val(k_list,train,label,folds):
     k_scores = [] #averaged scores for each k value, num of scores = num of K
 
     #we want to split train data into test and train
-    label_name = {1:'Hover', 2:'Impact (tapping)', 3:'Wind'}
+    label_name = {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)' }
     clf = KnnDtw(n_neighbors=1, max_warping_window=100) #Initialize classifier
     kf = KFold(n_splits=folds)
     kf.get_n_splits(train)
@@ -54,8 +56,11 @@ def k_fold_cross_val(k_list,train,label,folds):
         score = np.average(scores) #averages the fold scores to a single socre for the k 
         k_scores.append(score)
     #Plot the average accuracy score for each k, recommend a besk (highest accuracy) k
+    k_best = k_list[np.argmax(k_scores)]
+
     plt.bar(k_list, k_scores,width=0.2)
     plt.xlabel('k (nearest neighbors)')
     plt.ylabel('Accuracy (average)')
     plt.xticks(k_list)
-    print('Best k value from list is:',k_list[np.argmax(k_scores)])
+    print('Best k value from list is:',k_best)
+    return k_best

param_ranking.py

@@ -0,0 +1,103 @@
+# -*- coding: utf-8 -*-
+"""
+A function to rank parameter based on precision (ability to not label a negative)
+
+@author: Jeff Xie
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+import time
+from knndtw import KnnDtw
+from knndtw import ProgressBar
+from scipy import stats
+
+from sklearn.metrics import classification_report, confusion_matrix
+from sklearn.metrics import precision_recall_fscore_support as score
+
+def param_ranking(param_list,k_val,warp_val,datapath,avg_type):
+    start_time = time.time()
+    p = []
+    r = []
+    f = []
+    for dataparam in param_list:
+        trainingdatafile =  datapath + 'train_' + dataparam + '.txt'
+        traininglabelfile = datapath + 'train_labels.txt'
+
+        testdatafile =  datapath + 'test_' + dataparam + '.txt'
+        testlabelfile = datapath + 'test_labels.txt'
+
+        # Open training data file, x:data, y:label
+        x_train_file = open(trainingdatafile, 'r')
+        y_train_file = open(traininglabelfile, 'r')
+
+        #Open test data file, x:data, y:label
+        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)' }
+
+        i = 0
+        # 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)
+
+        m = KnnDtw(n_neighbors=k_val, max_warping_window=warp_val)
+        m.fit(x_train, y_train)
+        label, proba = m.predict(x_test)
+
+        precision, recall, f_score, _ = score(y_test,label,average=avg_type)
+        p.append(precision)
+        r.append(recall)
+        f.append(f_score)
+
+    precision_rank = sorted(list(zip(param_list,p)),key=lambda x: x[1])
+    recall_rank = sorted(list(zip(param_list,r)),key=lambda x: x[1])
+    fscore_rank = sorted(list(zip(param_list,f)),key=lambda x: x[1])
+    #("Parameter rank by precision is:",precision_rank)
+    print('Ranking for k = %s, max warping window = %s' %(k_val,warp_val))
+    for rank in precision_rank[::-1]:
+        print(rank[0],": ",rank[1])
+    #print("Parameter rank by recall is:",recall_rank)
+    #print("Parameter rank by f-score is:",fscore_rank)
+    print("--- %s seconds ---" % (time.time() - start_time)) #let's see how long this takes...
+#Testing
+plist1 = ['mavlink_raw_imu_t_Xaccel','mavlink_raw_imu_t_Yaccel','mavlink_raw_imu_t_Zaccel','mavlink_raw_imu_t_XGyro','mavlink_raw_imu_t_YGyro','mavlink_raw_imu_t_ZGyro']
+plist2 = ['mavlink_attitude_t_pitch angle','mavlink_attitude_t_roll angle','mavlink_attitude_t_yaw angle','mavlink_attitude_t_pitch rate','mavlink_attitude_t_yaw rate','mavlink_attitude_t_roll rate']
+plist3 = ['mavlink_raw_imu_t_XMag','mavlink_raw_imu_t_YMag','mavlink_raw_imu_t_ZMag','mavlink_vibration_t_vibration_x','mavlink_vibration_t_vibration_y','mavlink_vibration_t_vibration_z']
+p_all = plist1+plist2+plist3
+param_ranking(p_all,1,100,'Data6/','weighted')
+#param_ranking(p_all,1,100,'Data4/','weighted')