Committing the implementation for calculating the correlations among the...

Committing the implementation for calculating the correlations among the student behavior components.

FirstApp/api.py

@@ -15,7 +15,6 @@ from random import Random
 
 from MonitorLecturerApp.models import LectureRecordedVideo, LecturerVideoMetaData
 from MonitorLecturerApp.serializers import LectureRecordedVideoSerializer, LecturerVideoMetaDataSerializer
-from .MongoModels import *
 from rest_framework.views import *
 from .logic import activity_recognition as ar
 from . import emotion_detector as ed
@@ -23,6 +22,7 @@ from .logic import id_generator as ig
 from .logic import pdf_file_generator as pdf
 from .logic import head_gaze_estimation as hge
 from .logic import video_extraction as ve
+from . logic import student_behavior_process as sbp
 from .serializers import *
 from braces.views import CsrfExemptMixin
 
@@ -114,8 +114,7 @@ class LecturerSubjectViewSet(APIView):
 
 
 # API for timetables
-class FacultyTimetableViewSet(CsrfExemptMixin, APIView):
-    # authentication_classes = []
+class FacultyTimetableViewSet(APIView):
 
     def get(self, request):
         timetable = FacultyTimetable.objects.all().filter()
@@ -1271,6 +1270,208 @@ class GetLectureGazeCorrelations(APIView):
         })
 
 
+# this class will handle the student activity-emotion correlations
+class GetStudentActivityEmotionCorrelations(APIView):
+
+    def get(self, request):
+        # get the day option
+        option = request.query_params.get('option')
+        # get the lecturer id
+        lecturer = request.query_params.get('lecturer')
+        int_option = int(option)
+        # initialize the student behavior count
+        student_behavior_count = 0
+
+
+        current_date = datetime.datetime.now().date()
+        option_date = datetime.timedelta(days=int_option)
+
+        # get the actual date
+        previous_date = current_date - option_date
+
+        # initialize the lists
+        individual_lec_activities = []
+        individual_lec_emotions = []
+        activity_emotion_correlations = []
+
+        # retrieving lecture activities
+        lec_activity = LectureActivity.objects.filter(
+            lecture_video_id__date__gte=previous_date,
+            lecture_video_id__date__lte=current_date,
+            lecture_video_id__lecturer=lecturer
+        )
+
+        # retrieving lecture emotions
+        lec_emotion = LectureEmotionReport.objects.filter(
+            lecture_video_id__date__gte=previous_date,
+            lecture_video_id__date__lte=current_date,
+            lecture_video_id__lecturer=lecturer
+        )
+
+
+        # if there are lecture activities
+        if len(lec_activity) > 0:
+            student_behavior_count += 1
+            activity_serializer = LectureActivitySerializer(lec_activity, many=True)
+            activity_data = activity_serializer.data
+            _, individual_lec_activities, _ = ar.get_student_activity_summary_for_period(activity_data)
+
+        # if there are lecture emotions
+        if len(lec_emotion) > 0:
+            student_behavior_count += 1
+            emotion_serializer = LectureEmotionSerializer(lec_emotion, many=True)
+            emotion_data = emotion_serializer.data
+            _, individual_lec_emotions, _ = ed.get_student_emotion_summary_for_period(emotion_data)
+
+
+        # if both student activity, emotion are available
+        if student_behavior_count == 2:
+
+            # find the correlations between student activity and gaze estimations
+            activity_emotion_correlations = sbp.calculate_student_activity_emotion_correlations(individual_lec_activities, individual_lec_emotions)
+
+
+        return Response({
+            "correlations": activity_emotion_correlations
+        })
+
+
+# this class will handle the student activity-emotion correlations
+class GetStudentActivityGazeCorrelations(APIView):
+
+    def get(self, request):
+        # get the day option
+        option = request.query_params.get('option')
+        # get the lecturer id
+        lecturer = request.query_params.get('lecturer')
+        int_option = int(option)
+        # initialize the student behavior count
+        student_behavior_count = 0
+
+
+        current_date = datetime.datetime.now().date()
+        option_date = datetime.timedelta(days=int_option)
+
+        # get the actual date
+        previous_date = current_date - option_date
+
+        # initialize the lists
+        individual_lec_activities = []
+        individual_lec_gaze = []
+        activity_gaze_correlations = []
+
+
+        # retrieving lecture gaze estimations
+        lec_gaze = LectureGazeEstimation.objects.filter(
+            lecture_video_id__date__gte=previous_date,
+            lecture_video_id__date__lte=current_date,
+            lecture_video_id__lecturer=lecturer
+        )
+
+
+        # retrieving lecture activities
+        lec_activity = LectureActivity.objects.filter(
+            lecture_video_id__date__gte=previous_date,
+            lecture_video_id__date__lte=current_date,
+            lecture_video_id__lecturer=lecturer
+        )
+
+
+        # if there are lecture activities
+        if len(lec_activity) > 0:
+            student_behavior_count += 1
+            activity_serializer = LectureActivitySerializer(lec_activity, many=True)
+            activity_data = activity_serializer.data
+            _, individual_lec_activities, _ = ar.get_student_activity_summary_for_period(activity_data)
+
+
+        # if there are gaze estimations
+        if len(lec_gaze) > 0:
+            student_behavior_count += 1
+            gaze_serializer = LectureGazeEstimationSerializer(lec_gaze, many=True)
+            gaze_data = gaze_serializer.data
+            _, individual_lec_gaze, _ = hge.get_student_gaze_estimation_summary_for_period(gaze_data)
+
+
+        # if there are any recorded lectures
+        if student_behavior_count == 2:
+
+            # find the correlations between student activity and gaze estimations
+            activity_gaze_correlations = sbp.calculate_student_activity_gaze_correlations(individual_lec_activities, individual_lec_gaze)
+
+
+        return Response({
+            "correlations": activity_gaze_correlations
+        })
+
+
+# this class will handle the student emotion-gaze correlations
+class GetStudentEmotionGazeCorrelations(APIView):
+
+    def get(self, request):
+        # get the day option
+        option = request.query_params.get('option')
+        # get the lecturer id
+        lecturer = request.query_params.get('lecturer')
+        int_option = int(option)
+        # initialize the student behavior count
+        student_behavior_count = 0
+
+
+        current_date = datetime.datetime.now().date()
+        option_date = datetime.timedelta(days=int_option)
+
+        # get the actual date
+        previous_date = current_date - option_date
+
+        # initialize the lists
+        individual_lec_emotions = []
+        individual_lec_gaze = []
+        emotion_gaze_correlations = []
+
+
+        # retrieving lecture gaze estimations
+        lec_gaze = LectureGazeEstimation.objects.filter(
+            lecture_video_id__date__gte=previous_date,
+            lecture_video_id__date__lte=current_date,
+            lecture_video_id__lecturer=lecturer
+        )
+
+        # retrieving lecture emotions
+        lec_emotion = LectureEmotionReport.objects.filter(
+            lecture_video_id__date__gte=previous_date,
+            lecture_video_id__date__lte=current_date,
+            lecture_video_id__lecturer=lecturer
+        )
+
+        # if there are lecture emotions
+        if len(lec_emotion) > 0:
+            student_behavior_count += 1
+            emotion_serializer = LectureEmotionSerializer(lec_emotion, many=True)
+            emotion_data = emotion_serializer.data
+            _, individual_lec_emotions, _ = ed.get_student_emotion_summary_for_period(emotion_data)
+
+        # if there are gaze estimations
+        if len(lec_gaze) > 0:
+            student_behavior_count += 1
+            gaze_serializer = LectureGazeEstimationSerializer(lec_gaze, many=True)
+            gaze_data = gaze_serializer.data
+            _, individual_lec_gaze, _ = hge.get_student_gaze_estimation_summary_for_period(gaze_data)
+
+
+
+        # if there are any recorded lectures
+        if student_behavior_count == 2:
+
+            # find the correlations between student activity and gaze estimations
+            emotion_gaze_correlations = sbp.calculate_student_emotion_gaze_correlations(individual_lec_emotions, individual_lec_gaze)
+
+
+        return Response({
+            "correlations": emotion_gaze_correlations
+        })
+
+
 ##### BATCH PROCESS SECTION #####
 
 # perform the student behavior analysis as a batch process
@@ -1319,4 +1520,5 @@ class TestRandom(APIView):
 
         return Response({
             "response": random
-        })
+        })
\ No newline at end of file
+

FirstApp/logic/student_behavior_process.py

+import pandas as pd
+from . import utilities as ut
+
+def calculate_student_activity_emotion_correlations(lec_activities, lec_emotions):
+    # this variable will be used to store the correlations
+    correlations = []
+
+    limit = 10
+
+    data_index = ['lecture-{}'.format(i + 1) for i in range(len(lec_activities))]
+
+    # student gaze labels
+    student_activity_labels = ['phone checking', 'listening', 'note taking']
+    student_emotion_labels = ['Happy', 'Sad', 'Angry', 'Surprise', 'Neutral']
+
+    # lecture activity data list (student)
+    phone_perct_list = []
+    note_perct_list = []
+    listen_perct_list = []
+
+    # lecture emotion data list (student)
+    happy_perct_list = []
+    sad_perct_list = []
+    angry_perct_list = []
+    surprise_perct_list = []
+    neutral_perct_list = []
+
+
+    # loop through the lecture activity data
+    for data in lec_activities:
+        phone_perct_list.append(int(data['phone_perct']))
+        listen_perct_list.append(int(data['listening_perct']))
+        note_perct_list.append(int(data['writing_perct']))
+
+    # loop through the lecture emotion data
+    for data in lec_emotions:
+        happy_perct_list.append(int(data['happy_perct']))
+        sad_perct_list.append(int(data['sad_perct']))
+        angry_perct_list.append(int(data['angry_perct']))
+        surprise_perct_list.append(int(data['surprise_perct']))
+        neutral_perct_list.append(int(data['neutral_perct']))
+
+
+    corr_data = {'phone checking': phone_perct_list, 'listening': listen_perct_list, 'note taking': note_perct_list,
+                 'Happy': happy_perct_list, 'Sad': sad_perct_list, 'Angry': angry_perct_list, 'Surprise': surprise_perct_list, 'Neutral': neutral_perct_list,
+                 }
+
+    # create the dataframe
+    df = pd.DataFrame(corr_data, index=data_index)
+
+    # calculate the correlation
+    pd_series = ut.get_top_abs_correlations(df, limit)
+
+    for i in range(limit):
+        # this dictionary will get the pandas.Series object's  indices and values separately
+        corr_dict = {}
+
+        index = pd_series.index[i]
+
+        # check whether the first index is a student activity
+        isStudentActivity = index[0] in student_activity_labels
+        # check whether the second index is a lecturer activity
+        isStudentEmotion = index[1] in student_emotion_labels
+
+        # if both are student and lecturer activities, add to the dictionary
+        if isStudentActivity & isStudentEmotion:
+            corr_dict['index'] = index
+            corr_dict['value'] = pd_series.values[i]
+
+            # append the dictionary to the 'correlations' list
+            correlations.append(corr_dict)
+
+    # return the list
+    return correlations
+
+
+# this method will calculate the student activity-gaze correlations
+def calculate_student_activity_gaze_correlations(lec_activities, lec_gaze):
+    # this variable will be used to store the correlations
+    correlations = []
+
+    limit = 10
+
+    data_index = ['lecture-{}'.format(i + 1) for i in range(len(lec_activities))]
+
+    # student gaze labels
+    student_activity_labels = ['phone checking', 'listening', 'note taking']
+    student_emotion_labels = ['Happy', 'Sad', 'Angry', 'Surprise', 'Neutral']
+    student_gaze_labels = ['Up and Right', 'Up and Left', 'Down and Right', 'Down and Left', 'Front']
+
+    # lecture activity data list (student)
+    phone_perct_list = []
+    note_perct_list = []
+    listen_perct_list = []
+
+    # lecture gaze estimation data list (student)
+    upright_perct_list = []
+    upleft_perct_list = []
+    downright_perct_list = []
+    downleft_perct_list = []
+    front_perct_list = []
+
+
+    # loop through the lecture activity data
+    for data in lec_activities:
+        phone_perct_list.append(int(data['phone_perct']))
+        listen_perct_list.append(int(data['listening_perct']))
+        note_perct_list.append(int(data['writing_perct']))
+
+    # loop through the lecture activity data
+    for data in lec_gaze:
+        upright_perct_list.append(int(data['looking_up_and_right_perct']))
+        upleft_perct_list.append(int(data['looking_up_and_left_perct']))
+        downright_perct_list.append(int(data['looking_down_and_right_perct']))
+        downleft_perct_list.append(int(data['looking_down_and_left_perct']))
+        front_perct_list.append(int(data['looking_front_perct']))
+
+
+    corr_data = {'phone checking': phone_perct_list, 'listening': listen_perct_list, 'note taking': note_perct_list,
+                 'Up and Right': upright_perct_list, 'Up and Left': upleft_perct_list, 'Down and Right': downright_perct_list,
+                 'Down and Left': downleft_perct_list, 'Front': front_perct_list
+                 }
+
+    # create the dataframe
+    df = pd.DataFrame(corr_data, index=data_index)
+
+    # calculate the correlation
+    pd_series = ut.get_top_abs_correlations(df, limit)
+
+    for i in range(limit):
+        # this dictionary will get the pandas.Series object's  indices and values separately
+        corr_dict = {}
+
+        index = pd_series.index[i]
+
+        # check whether the first index is a student activity
+        isStudentActivity = index[0] in student_activity_labels
+        # check whether the second index is a student gaze estimation
+        isStudentGaze = index[1] in student_gaze_labels
+
+        # if both are student and lecturer activities, add to the dictionary
+        if isStudentActivity & isStudentGaze:
+            corr_dict['index'] = index
+            corr_dict['value'] = pd_series.values[i]
+
+            # append the dictionary to the 'correlations' list
+            correlations.append(corr_dict)
+
+    # return the list
+    return correlations
+
+
+# this method will calculate the student activity-gaze correlations
+def calculate_student_emotion_gaze_correlations(lec_emotions, lec_gaze):
+    # this variable will be used to store the correlations
+    correlations = []
+
+    limit = 10
+
+    data_index = ['lecture-{}'.format(i + 1) for i in range(len(lec_emotions))]
+
+    student_emotion_labels = ['Happy', 'Sad', 'Angry', 'Surprise', 'Neutral']
+    student_gaze_labels = ['Up and Right', 'Up and Left', 'Down and Right', 'Down and Left', 'Front']
+
+
+    # lecture emotion data list (student)
+    happy_perct_list = []
+    sad_perct_list = []
+    angry_perct_list = []
+    surprise_perct_list = []
+    neutral_perct_list = []
+
+    # lecture gaze estimation data list (student)
+    upright_perct_list = []
+    upleft_perct_list = []
+    downright_perct_list = []
+    downleft_perct_list = []
+    front_perct_list = []
+
+
+    # loop through the lecture emotion data
+    for data in lec_emotions:
+        happy_perct_list.append(int(data['happy_perct']))
+        sad_perct_list.append(int(data['sad_perct']))
+        angry_perct_list.append(int(data['angry_perct']))
+        surprise_perct_list.append(int(data['surprise_perct']))
+        neutral_perct_list.append(int(data['neutral_perct']))
+
+    # loop through the lecture gaze data
+    for data in lec_gaze:
+        upright_perct_list.append(int(data['looking_up_and_right_perct']))
+        upleft_perct_list.append(int(data['looking_up_and_left_perct']))
+        downright_perct_list.append(int(data['looking_down_and_right_perct']))
+        downleft_perct_list.append(int(data['looking_down_and_left_perct']))
+        front_perct_list.append(int(data['looking_front_perct']))
+
+
+    corr_data = {'Happy': happy_perct_list, 'Sad': sad_perct_list, 'Angry': angry_perct_list, 'Surprise': surprise_perct_list, 'Neutral': neutral_perct_list,
+                 'Up and Right': upright_perct_list, 'Up and Left': upleft_perct_list, 'Down and Right': downright_perct_list,
+                 'Down and Left': downleft_perct_list, 'Front': front_perct_list
+                 }
+
+    # create the dataframe
+    df = pd.DataFrame(corr_data, index=data_index)
+
+    # calculate the correlation
+    pd_series = ut.get_top_abs_correlations(df, limit)
+
+    for i in range(limit):
+        # this dictionary will get the pandas.Series object's  indices and values separately
+        corr_dict = {}
+
+        index = pd_series.index[i]
+
+        # check whether the first index is a student activity
+        isStudentEmotion = index[0] in student_emotion_labels
+        # check whether the second index is a student gaze estimation
+        isStudentGaze = index[1] in student_gaze_labels
+
+        # if both are student and lecturer activities, add to the dictionary
+        if isStudentEmotion & isStudentGaze:
+            corr_dict['index'] = index
+            corr_dict['value'] = pd_series.values[i]
+
+            # append the dictionary to the 'correlations' list
+            correlations.append(corr_dict)
+
+    # return the list
+    return correlations
\ No newline at end of file

FirstApp/urls.py

@@ -151,21 +151,30 @@ urlpatterns = [
     # retrieves lecture activity summary
     url(r'^get-lecture-activity-summary/$', api.GetLectureActivitySummary.as_view()),
 
-    # retrieves lecture activity summary
+    # retrieves lecture emotion summary
     url(r'^get-lecture-emotion-summary/$', api.GetLectureEmotionSummary.as_view()),
 
-    # retrieves lecture activity summary
+    # retrieves lecture gaze estimation summary
     url(r'^get-lecture-gaze-summary/$', api.GetLectureGazeSummary.as_view()),
 
-    # retrieves lecture activity summary
+    # retrieves student activity correlations with lecturer activity
     url(r'^get-activity-correlations/$', api.GetLectureActivityCorrelations.as_view()),
 
-    # retrieves lecture activity summary
+    # retrieves student emotion correlations with lecturer activity
     url(r'^get-emotion-correlations/$', api.GetLectureEmotionCorrelations.as_view()),
 
-    # retrieves lecture activity summary
+    # retrieves student gaze estimation correlations with lecturer activity
     url(r'^get-gaze-correlations/$', api.GetLectureGazeCorrelations.as_view()),
 
+    # retrieves student activity-emotion correlations
+    url(r'^get-student-activity-emotion-correlations/$', api.GetStudentActivityEmotionCorrelations.as_view()),
+
+    # retrieves student activity-gaze correlations
+    url(r'^get-student-activity-gaze-correlations/$', api.GetStudentActivityGazeCorrelations.as_view()),
+
+    # retrieves student emotion-gaze correlations
+    url(r'^get-student-emotion-gaze-correlations/$', api.GetStudentEmotionGazeCorrelations.as_view()),
+
 
     ##### OTHERS #####