notebook.community

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In [1]:



    


import pandas as pd
import urllib
import time
import json



    











In [116]:



    


studentBehavior = pd.read_json("studentBehaviorInfoOver40Class_1213.json", orient="index")
csv_file = pd.read_csv("clusterVideoWordLevel3cluster.csv",header=None)



    











In [34]:



    


# print(studentBehavior[0])
# print studentBehavior[0]["memberId"]
# print (studentBehavior[0]["chosenVideo"])
# print (studentBehavior[0]["vocabularyList"])
print(video_to_cluster)

## Desired json structure
# [
#     {
#         "memberId": 12345,
#         "videoSequence":
#             [
#                 3980,
#                 7680,
#                 1235
#             ],
#         "skippingRatioSequence":
#             [
#                 0,
#                 0,
#                 0.3
#             ],
#         "scoreSequence":
#             [
#                 80,
#                 70,
#                 75
#             ],
#         "dictionarySequence":
#             [
#                 0,
#                 0,
#                 0
#             ],
#         "skippingIndex": 10,
#         "clusterSequence":
#             [
#                 "C1",
#                 "C1",
#                 "C2"
#             ]
        
#     }
# ]



    


















    






13354

















In [117]:



    


# Generate Users Profile File

# This method return a mapping of video(key) 
# and and array of scores(value)
def parse_video_scores(scores_object):
    result = {}
    for score in scores_object:
        if score['postId'] not in result:
            result[score['postId']] = []
        result[score['postId']].append(score['score'])
    return result
            
def parse_video_words(words_object):
    result = {}
    for word in words_object:
        if word['postId'] not in result:
            result[word['postId']] = []
        result[word['postId']].append(word['word'])
    return result

def parse_csv_file(csv_file):
    result = {}
    for index in range(1,len(csv_file[0])):
        result[csv_file[0][index]] = int(csv_file[1][index])
    return result

def get_cluster_cardinality(videos_cluster):
    result = {}
    for video in videos_cluster:
        if videos_cluster[video] in result:
            # Increase
            result[videos_cluster[video]] += 1
        else:
            result[videos_cluster[video]] = 1
    return result
    

def calc_skipping_ratio(scores):
    countSkipped = 0
    for score in scores:
        if score < 0:
            countSkipped += 1
    return round(countSkipped*1.0/len(scores)*1.0,2)

def calc_avg_score(scores):
    countSkipped = 0
    sumScores = 0
    for score in scores:
        if score < 0:
            countSkipped += 1
        else:
            sumScores += score
                
    if countSkipped > 0:
        return 0
    else:
        return round(sumScores*1.0/(len(scores)*1.0-countSkipped*1.0),2)
    
lenStudentBehavior = len(pd.read_json("studentBehaviorInfoOver40Class_1213.json"))
usersProfilesJsonWithIndexes = {}
video_to_cluster = parse_csv_file(csv_file)
cardinality = get_cluster_cardinality(video_to_cluster)
print(cardinality)
for index in range(lenStudentBehavior):
    memberId = studentBehavior[index]["memberId"]
    if memberId not in usersProfilesJsonWithIndexes:
        studentProfile                          = {}
        studentProfile["memberId"]              = memberId
        studentProfile["videoSequence"]         = []
        studentProfile["skippingRatioSequence"] = []
        studentProfile["avgScoreSequence"]         = []
        studentProfile["dictionarySequence"]    = []
        studentProfile["skippingIndex"]         = -1
        studentProfile["clusterSequence"]       = []
        studentProfile["scores"]                = []
        studentProfile["interaction"]           = {}
        usersProfilesJsonWithIndexes[memberId] = studentProfile
    
    # Addid values for every key
    # Video Sequence
    usersProfilesJsonWithIndexes[memberId]["videoSequence"].extend(studentBehavior[index]["chosenVideo"])
    
    
    videos_scores = parse_video_scores(studentBehavior[index]["listenScore"])
    video_words = parse_video_words(studentBehavior[index]["vocabularyList"])
    
    
    # SkippingRationSequence
    for video in studentBehavior[index]["chosenVideo"]:
        scores = videos_scores[video]
        usersProfilesJsonWithIndexes[memberId]["scores"].append(scores)
        usersProfilesJsonWithIndexes[memberId]["skippingRatioSequence"].append(calc_skipping_ratio(scores))
        # Average Score Sequence
        avg = calc_avg_score(scores)
        usersProfilesJsonWithIndexes[memberId]["avgScoreSequence"].append(avg)
        
        # DictionarySequence
        if str(video) in video_words:
            words = video_words[str(video)]
            usersProfilesJsonWithIndexes[memberId]["dictionarySequence"].append(len(words))
        else:
            usersProfilesJsonWithIndexes[memberId]["dictionarySequence"].append(0)
            
        # ClusterSequece
        usersProfilesJsonWithIndexes[memberId]["clusterSequence"].append(video_to_cluster[str(video)])
            
        
    # skippingIndex
    skip_index = [ n for n,i in enumerate(usersProfilesJsonWithIndexes[memberId]["skippingRatioSequence"]) if i>0.0 ][0]
    usersProfilesJsonWithIndexes[memberId]["skippingIndex"] = skip_index
    # Add cluster turning point
    usersProfilesJsonWithIndexes[memberId]["tp"] = usersProfilesJsonWithIndexes[memberId]["clusterSequence"][skip_index]
    
    # Calculate Interaction
    # Sum scores by cluster
    scores_by_cluster = {}
    for c_index in range(len(usersProfilesJsonWithIndexes[memberId]["clusterSequence"])):
        cluster = usersProfilesJsonWithIndexes[memberId]["clusterSequence"][c_index]
        if cluster in scores_by_cluster:
            scores_by_cluster[cluster] += usersProfilesJsonWithIndexes[memberId]["avgScoreSequence"][c_index]
        else:
            scores_by_cluster[cluster] = usersProfilesJsonWithIndexes[memberId]["avgScoreSequence"][c_index]
    # Add cluster with no interaction
    for cluster in cardinality:
        if cluster not in scores_by_cluster:
            scores_by_cluster[cluster] = 0
    # Divide sum by cardinality of the cluster
    for cluster in scores_by_cluster:
        scores_by_cluster[cluster] = round((scores_by_cluster[cluster]*1.0) / (cardinality[cluster] * 1.0),2)
    
    usersProfilesJsonWithIndexes[memberId]["interaction"] = scores_by_cluster
    
    
        
print(usersProfilesJsonWithIndexes['50679'])
fileStudentProfile = open("studentProfile_wordLevel3cluster.json", "w")
fileStudentProfile.write("[")
lenObjects = len(usersProfilesJsonWithIndexes)
it = 0
for key, value in usersProfilesJsonWithIndexes.items():
    json.dump(value,fileStudentProfile)
    if it != (lenObjects-1):
        fileStudentProfile.write(", ")
    it = it + 1
    


fileStudentProfile.write("]")
fileStudentProfile.close()



    


















    






{0: 69, 1: 42, 2: 124}
{'avgScoreSequence': [92.0, 93.33, 90.14, 90.17, 93.86, 92.0, 0, 0], 'videoSequence': [3913, 5186, 4974, 4802, 3711, 5881, 7126, 5797], 'memberId': '50679', 'dictionarySequence': [26, 15, 73, 32, 33, 17, 19, 40], 'scores': [[93, 91, 92, 92, 92], [93, 94, 93], [90, 80, 77, 96, 86, 97, 90, 95, 95, 98, 91, 86, 95, 86], [85, 90, 90, 93, 90, 93], [92, 88, 99, 94, 98, 91, 95], [90, 94], [77, 79, -1], [-1, -1, -1, -1, -1]], 'skippingIndex': 6, 'clusterSequence': [0, 0, 0, 2, 0, 0, 2, 2], 'tp': 2, 'interaction': {0: 6.69, 1: 0.0, 2: 0.73}, 'skippingRatioSequence': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.33, 1.0]}

Notes

  1. We have three different skipRatio behavior User that goes from 0 to 1 Users that goes from 0 to [a range gratter than 0] and stop Users that goes from 0 to [a range gratter than 0] and then skippRation 1

  2. The skipIndex shows the index where the skipRation is gratter than 0





In [118]:



    


# Generate csv file for classifier
interaction = open("users_interaction_wordLevel3cluster.csv", "w")
header = ""
for key in cardinality:
    header += "C" + str(key) 
    header += ","
header += "TP" # class label
header += "\n"
print(header)
interaction.write(header)
for user in usersProfilesJsonWithIndexes:
    s = ""
    for key in cardinality:
        s += str(usersProfilesJsonWithIndexes[user]["interaction"][key])+","
    s += "C"+str(usersProfilesJsonWithIndexes[user]["tp"])
    s += "\n"
    print(s)
    interaction.write(s)
interaction.close()



    


















    






C0,C1,C2,TP

6.35,14.59,3.61,C1

11.56,0.0,1.43,C0

4.83,0.0,2.27,C2

9.06,5.54,0.66,C0

24.79,0.0,0.0,C0

13.76,52.94,5.3,C2

4.53,0.0,0.43,C2

25.65,27.99,2.38,C1

4.22,9.32,0.79,C1

9.48,4.12,3.55,C1

3.83,5.95,1.4,C1

4.03,23.72,2.19,C1

13.29,0.0,7.42,C2

22.49,0.0,0.0,C0

11.66,0.0,0.73,C0

7.63,1.67,2.09,C1

14.15,0.0,1.86,C2

4.12,4.51,3.14,C2

13.7,0.0,8.34,C0

35.71,0.0,3.22,C0

15.81,0.0,1.63,C1

6.37,0.0,0.0,C2

37.83,8.89,5.39,C1

9.33,0.0,0.0,C2

16.93,0.0,0.0,C0

21.1,0.0,4.72,C2

8.23,0.0,0.0,C0

13.06,0.0,3.15,C2

4.92,3.86,1.86,C1

19.38,0.0,2.87,C2

4.11,15.25,0.75,C1

10.49,0.0,0.0,C0

6.2,9.18,3.09,C1

2.83,29.93,2.38,C1

31.86,0.0,0.69,C0

8.23,0.0,0.76,C1

7.08,0.0,1.34,C2

12.18,0.0,0.0,C0

12.68,0.0,0.0,C0

5.63,15.36,6.04,C1

4.17,0.0,3.79,C1

7.43,2.07,0.68,C1

17.35,0.0,1.51,C2

11.32,0.0,0.0,C0

5.22,0.0,0.73,C2

11.07,0.0,0.0,C0

8.38,2.25,15.74,C2

24.01,18.19,5.52,C2

4.12,7.97,2.16,C1

2.99,0.0,0.0,C0

5.46,22.53,2.18,C1

2.77,62.74,3.11,C2

9.71,6.45,0.75,C1

2.72,10.77,1.49,C1

4.16,15.58,2.35,C1

25.78,0.0,3.18,C0

3.58,73.59,0.72,C1

7.97,6.82,12.56,C1

17.88,0.0,0.0,C0

6.09,0.0,3.13,C2

9.16,0.0,0.0,C0

10.59,0.0,0.68,C2

9.38,0.0,2.36,C2

14.13,0.0,0.0,C0

11.17,1.86,0.73,C1

28.39,7.76,3.79,C1

14.35,0.0,1.15,C0

5.13,0.0,2.8,C2

15.11,0.0,1.93,C0

15.61,0.0,8.49,C2

10.82,0.0,0.72,C0

22.78,2.26,4.74,C0

11.59,0.0,0.0,C0

5.53,0.0,0.0,C0

9.39,4.46,3.71,C2

13.46,0.0,0.69,C2

5.58,0.0,2.35,C0

5.24,0.0,6.81,C2

4.52,1.88,1.47,C1

22.58,0.0,3.93,C2

9.04,16.4,3.44,C1

17.88,0.0,0.0,C0

9.87,0.0,0.0,C0

5.94,19.77,0.69,C1

16.57,0.0,3.93,C2

22.39,0.0,1.58,C2

5.63,13.78,3.15,C1

17.59,2.18,2.23,C2

7.22,0.0,4.22,C1

4.9,16.53,7.23,C0

11.36,0.0,0.0,C0

4.09,8.91,3.08,C1

18.2,25.16,6.95,C2

7.99,0.0,0.0,C0

9.1,0.0,0.0,C0

4.58,21.61,2.56,C1

4.21,0.0,0.79,C2

5.1,9.39,1.32,C1

19.78,0.0,0.77,C0

10.29,0.0,0.0,C0

9.87,9.21,2.72,C2

4.31,2.34,6.24,C2

11.96,0.0,1.88,C2

4.2,35.28,3.09,C1

15.09,0.0,0.0,C0

5.32,29.03,3.0,C2

2.68,19.3,3.04,C1

9.78,0.0,1.36,C0

6.99,15.27,4.71,C1

27.23,0.0,0.0,C0

5.36,82.91,9.62,C2

16.8,0.0,0.0,C0

4.86,0.0,2.93,C2

5.51,1.87,0.76,C1

20.53,0.0,8.54,C2

11.09,55.11,28.71,C1

6.94,9.2,4.67,C1

5.08,8.98,2.86,C2

4.07,4.1,2.96,C2

11.21,0.0,0.0,C0

2.75,41.27,5.55,C2

9.63,0.0,0.0,C0

11.43,0.0,0.0,C0

17.99,0.0,0.76,C2

2.76,0.0,0.76,C1

3.85,0.0,0.0,C0

12.6,0.0,0.0,C0

24.07,0.0,2.4,C0

13.45,0.0,1.71,C2

4.12,0.0,3.01,C2

10.24,11.64,4.45,C1

2.67,55.37,5.98,C1

2.51,1.55,1.99,C2

18.52,0.0,0.76,C2

7.15,3.77,1.38,C1

8.82,3.9,3.69,C1

16.53,1.38,5.61,C1

0.67,0.0,0.0,C2

19.33,8.15,0.78,C1

7.23,0.0,1.89,C2

4.25,11.27,6.21,C1

12.63,0.0,0.0,C0

8.6,0.0,1.42,C0

11.76,0.0,0.0,C0

21.76,0.0,0.64,C0

7.4,14.72,2.6,C1

2.59,6.24,2.83,C1

2.61,21.65,3.75,C1

4.13,2.35,5.24,C2

2.75,0.0,1.54,C1

17.2,0.0,1.47,C0

2.8,4.53,5.44,C1

4.29,28.19,4.04,C1

10.0,0.0,0.0,C2

16.46,0.0,0.75,C2

9.11,0.0,0.0,C2

12.64,55.09,1.56,C1

1.28,0.0,0.0,C0

11.17,0.0,0.0,C0

4.98,9.34,1.58,C1

35.69,0.0,0.0,C0

5.46,51.13,5.28,C2

2.67,0.0,0.69,C2

2.85,0.0,0.78,C1

2.79,4.27,1.56,C1

5.58,2.23,2.23,C0

5.49,0.0,2.17,C2

8.6,7.08,4.79,C1

7.64,0.0,0.62,C2

8.59,11.04,1.4,C1

2.23,2.2,4.37,C1

5.52,0.0,4.64,C2

9.7,4.05,0.76,C1

4.24,0.0,0.0,C0

1.37,0.0,0.0,C0

2.4,30.71,6.8,C2

23.37,0.0,0.72,C2

22.7,14.9,2.88,C1

13.82,0.0,2.18,C2

4.22,2.32,9.29,C2

6.35,0.0,1.45,C2

4.13,0.0,1.53,C2

22.45,2.05,6.96,C0

4.96,9.85,1.39,C1

14.63,0.0,2.94,C1

4.15,6.55,18.51,C1

4.17,12.94,4.19,C2

6.11,7.54,2.05,C1

2.49,9.07,3.0,C1

4.16,15.91,3.11,C1

6.92,0.0,2.95,C2

6.43,12.87,2.98,C1

4.15,34.64,6.82,C2

2.8,2.27,1.56,C1

3.95,18.88,2.97,C1

2.68,14.81,1.44,C1

4.01,8.44,1.44,C1

4.13,0.0,3.04,C0

4.27,2.3,3.12,C2

2.75,42.35,1.48,C1

15.77,0.0,0.0,C0

4.83,3.7,2.34,C1

4.08,10.73,3.75,C1

6.76,0.0,0.73,C1

5.18,0.0,0.73,C2

11.48,0.0,0.0,C0

4.2,4.66,1.53,C1

14.22,0.0,0.0,C0

8.55,0.0,3.17,C2

2.47,14.32,3.32,C1

2.8,90.36,16.12,C2

14.08,0.0,0.0,C0

11.06,0.0,0.71,C0

4.81,8.45,2.69,C2

6.54,3.1,3.85,C1

7.03,1.58,4.6,C2

4.18,0.0,1.48,C1

36.28,0.0,2.63,C0

11.15,0.0,4.09,C2

3.68,12.41,2.1,C1

14.2,0.0,8.54,C0

2.55,0.0,0.0,C2

10.66,0.0,0.56,C0

4.16,0.0,9.27,C0

2.81,79.93,3.09,C2

4.17,4.69,1.58,C1

2.78,23.25,3.06,C1

31.63,0.0,0.73,C0

26.92,0.0,0.0,C0

8.59,0.0,0.66,C0

6.17,0.0,1.37,C2

3.77,10.47,1.56,C1

12.54,0.0,0.0,C0

2.76,25.79,3.84,C1

3.93,15.13,4.14,C1

7.88,0.0,2.04,C2

5.56,2.12,0.75,C1

15.46,0.0,0.0,C0

2.54,5.65,0.68,C2

2.46,0.0,3.02,C2

23.76,3.72,1.28,C1

11.04,0.0,3.47,C2

10.24,0.0,0.67,C2

9.38,0.0,1.88,C2

8.4,4.4,1.42,C2

6.66,0.0,0.0,C0

14.7,0.0,1.47,C2

1.41,0.0,0.0,C0

15.05,0.0,0.0,C0

7.95,0.0,3.52,C2

9.79,11.71,2.64,C1

15.17,0.0,0.76,C0

2.29,16.16,3.62,C1

3.59,1.84,0.7,C1

2.74,13.55,1.54,C1

4.28,7.69,1.37,C1

5.35,0.0,0.77,C2

4.28,4.6,1.51,C1

20.2,0.0,0.0,C0

4.16,22.02,0.74,C1

6.81,15.3,3.0,C1

26.71,0.0,0.0,C0

17.48,21.08,6.01,C1

20.68,20.08,15.56,C0

2.34,0.0,0.61,C0

24.96,44.13,2.15,C0

8.05,5.93,1.34,C1

35.5,0.0,6.01,C0

2.54,6.12,1.39,C1

2.81,11.48,1.56,C1

11.69,0.0,0.0,C0

21.94,0.0,0.9,C2

10.17,0.0,0.0,C0

18.08,4.59,0.74,C1

4.87,0.0,3.05,C2

3.8,0.0,0.67,C0

34.89,0.0,4.31,C2

2.78,5.33,3.57,C1

2.52,5.93,1.37,C1

4.11,0.0,1.53,C1

10.17,0.0,4.25,C2

17.54,0.0,0.73,C2

26.38,27.57,8.38,C1

3.62,3.68,0.66,C1

18.52,0.0,0.0,C0

25.99,42.68,37.72,C1

10.79,0.0,4.51,C1

5.48,0.0,4.4,C1

6.96,0.0,0.0,C0

2.56,10.48,0.7,C1

7.96,2.05,8.79,C2

19.09,0.0,0.0,C0

15.33,50.22,7.84,C2

18.1,0.0,3.14,C0

6.48,6.99,2.15,C1

4.2,11.24,1.53,C1

10.12,0.0,0.0,C0

6.17,0.0,0.0,C0

4.29,0.0,0.95,C2

2.07,1.28,0.43,C1

4.16,0.0,5.25,C2

18.36,4.49,6.27,C1

2.77,28.38,2.32,C1

5.13,5.99,4.78,C1

10.6,0.0,0.0,C0

13.41,0.0,0.0,C0

11.34,0.0,3.07,C1

7.25,0.0,0.0,C0

14.06,9.26,3.18,C1

24.73,8.97,2.32,C1

5.28,0.0,1.25,C1

0.04,0.14,0.0,C1

5.17,24.1,6.25,C2

20.66,11.22,2.22,C1

2.48,19.63,0.75,C1

3.78,8.78,4.61,C2

4.24,0.0,0.0,C2

12.57,0.0,4.28,C2

11.31,0.0,2.3,C2

2.29,10.42,13.43,C1

25.05,4.26,1.51,C1

3.97,11.48,1.51,C1

9.74,0.0,0.42,C2

4.24,13.85,1.58,C1

2.61,1.27,1.29,C2

3.9,0.0,4.64,C2

34.03,0.0,6.21,C2

7.51,0.0,5.11,C2

11.97,0.0,0.0,C0

9.32,3.98,4.27,C1

3.97,0.0,0.37,C0

4.08,44.92,1.57,C1

6.54,0.0,0.0,C0

1.89,16.02,0.92,C1

5.13,10.21,3.34,C1

7.67,6.04,4.05,C2

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15.26,2.1,3.1,C1

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24.61,0.0,4.59,C0

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30.1,0.0,0.0,C0

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13.33,0.0,0.68,C0

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9.85,0.0,0.0,C0

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15.64,59.4,32.17,C1

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10.61,0.0,1.3,C2

2.6,15.15,0.71,C1

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6.91,0.0,0.0,C0

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15.34,25.17,0.76,C1

21.07,6.24,6.27,C2

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19.52,6.81,8.65,C1

31.17,0.0,7.53,C2

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17.23,1.59,1.25,C1

38.34,6.15,3.69,C0

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12.71,0.0,0.0,C0

16.35,0.0,0.0,C0

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4.88,0.0,3.36,C2

31.12,0.94,5.85,C1

3.67,0.0,0.0,C0

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6.66,49.3,9.78,C1

2.55,7.97,12.6,C1

3.98,1.67,1.41,C1

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3.78,0.0,0.7,C2

9.76,0.0,0.71,C1

5.24,8.8,2.25,C1

2.56,5.97,2.06,C1

12.55,0.0,1.45,C1

11.1,19.95,3.87,C1

28.64,12.64,2.19,C1

18.22,0.0,8.41,C2

11.21,5.77,2.13,C1

5.16,2.31,1.26,C1

5.03,0.0,0.77,C0

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5.3,0.0,3.79,C2

2.56,3.95,1.36,C1

4.21,29.29,0.76,C1

20.85,0.0,0.78,C2

16.83,0.0,0.0,C0

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2.82,0.0,3.93,C1

2.34,0.0,0.0,C0

11.07,17.96,6.18,C1

9.3,0.0,0.73,C0

8.26,0.0,0.0,C0

0.92,0.01,0.0,C1

29.1,0.0,0.77,C0

17.31,28.71,8.43,C1

6.73,0.0,1.94,C0

9.65,83.91,17.98,C1

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3.69,0.0,0.71,C2

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22.04,0.0,3.42,C0

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29.89,9.46,3.99,C2

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22.81,2.41,1.67,C2

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0.0,0.0,0.0,C0

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27.69,0.0,0.0,C0

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2.57,52.51,1.36,C1

9.41,0.0,0.0,C0

2.7,35.42,2.08,C1

3.62,0.0,0.75,C2

0.71,0.0,0.0,C1

7.98,35.63,4.17,C2

16.16,59.98,5.35,C1

2.14,3.08,4.88,C1

2.5,5.84,1.31,C1

29.83,77.2,41.14,C1

45.82,0.0,11.86,C2

11.35,5.13,3.64,C1

14.8,0.0,0.0,C0

3.72,13.55,7.94,C1

34.67,42.16,3.6,C0

5.41,5.33,3.34,C1

4.18,0.0,0.73,C1

7.88,6.38,3.98,C1

4.07,6.03,1.45,C1

1.04,0.0,0.0,C0

9.81,0.0,7.75,C2

6.72,6.34,2.03,C1

2.52,4.31,0.71,C1

15.58,0.0,0.0,C0

13.32,10.46,3.0,C1

3.75,13.11,1.39,C1

14.92,0.0,0.0,C0

5.23,3.51,0.73,C1

4.21,14.04,4.73,C1

13.79,18.8,6.4,C1

15.6,0.0,3.15,C2

19.1,0.0,1.45,C2

2.5,44.89,10.43,C2

8.15,0.0,0.0,C0

16.7,6.32,1.5,C2

23.49,11.57,13.2,C1

4.15,6.37,0.74,C1

3.33,0.0,3.56,C0

5.37,11.17,1.49,C1

3.91,0.0,0.77,C2

11.91,10.98,2.22,C1

15.7,0.0,4.08,C0

5.4,0.0,5.31,C2

15.1,0.0,0.48,C0

6.61,0.0,3.76,C0

25.29,10.58,3.59,C1

4.02,2.14,2.83,C1

2.28,23.73,2.05,C1

3.13,2.35,1.59,C1

11.0,0.0,0.83,C1

25.67,22.78,1.88,C1

10.69,0.0,0.0,C0

4.47,1.94,3.34,C2

18.84,3.93,2.75,C1

4.03,0.0,0.0,C0

11.98,0.0,0.0,C0

6.89,0.0,0.75,C1

2.8,13.69,1.56,C1

5.39,0.0,0.56,C0

3.3,0.0,1.16,C2

13.55,13.1,1.28,C1

8.33,0.0,1.55,C0

5.4,48.89,3.83,C2

2.61,12.22,3.52,C1

2.12,3.89,1.99,C1

12.86,0.0,0.0,C0

6.04,11.62,0.7,C1

6.87,0.0,1.05,C0

5.64,0.0,2.23,C1

9.65,0.0,1.17,C2

6.72,15.67,8.19,C1

5.82,2.33,0.78,C1

4.17,2.29,1.43,C1

8.37,13.29,17.78,C1

40.34,0.0,18.6,C0

2.8,0.0,1.56,C1

2.8,0.0,3.82,C2

8.27,3.46,1.86,C0

5.29,11.5,2.07,C1

2.84,87.17,14.17,C2

2.01,13.77,1.54,C1

41.89,0.0,8.28,C2

4.04,80.61,5.51,C1

2.81,24.77,1.53,C1

20.81,0.0,0.0,C0

5.21,0.0,1.99,C0

11.72,0.0,0.0,C0

4.17,2.13,3.76,C2

2.75,0.0,0.0,C0

5.01,9.67,1.34,C1

4.23,4.46,1.58,C1

10.26,0.0,5.49,C2

7.06,0.0,0.73,C2

4.05,0.0,3.57,C2

4.12,31.67,3.11,C1

17.77,0.0,2.3,C2

3.23,0.0,0.0,C0

14.03,6.59,5.36,C1

16.34,0.0,1.51,C0

4.18,19.22,0.7,C1

2.64,39.24,0.67,C1

16.36,14.93,0.71,C1

2.58,10.75,1.37,C1

7.38,0.0,2.13,C2

4.23,18.38,1.59,C1

13.27,0.0,1.4,C1

6.77,0.0,0.0,C0

5.55,0.0,1.3,C0

3.85,7.93,2.04,C1

26.61,2.18,1.54,C1

22.02,2.29,1.86,C1

2.62,5.21,2.39,C1

37.66,0.0,5.44,C1

17.81,0.0,0.0,C0

4.26,0.0,3.99,C2

3.81,0.0,0.0,C2

4.12,0.0,0.0,C0

4.1,0.0,0.0,C0

3.52,0.0,0.0,C0

7.0,55.56,3.14,C1

16.82,23.96,3.9,C1

29.13,0.0,0.73,C0

5.33,0.0,1.47,C2

25.17,20.95,4.15,C2

18.82,0.0,1.98,C2

17.47,6.86,2.3,C1

22.99,0.0,0.61,C0

9.57,0.0,0.0,C0

4.26,29.79,2.35,C2

13.74,18.26,3.08,C2

2.67,1.24,0.58,C1

2.69,2.0,1.97,C2

5.31,21.78,1.49,C1

7.49,0.0,0.0,C0

15.02,0.0,2.71,C0

4.18,0.0,5.48,C2

2.8,32.93,0.79,C1

3.57,1.69,1.07,C2

28.05,0.0,0.78,C2

2.4,0.0,1.52,C2

2.68,13.27,1.52,C1

2.89,51.06,5.49,C1

14.76,0.0,0.0,C0

8.89,0.0,0.0,C0

6.37,0.0,1.71,C2

8.29,0.0,1.55,C0

2.76,38.6,3.15,C1

17.82,26.53,1.41,C1

2.36,2.1,1.37,C1

14.13,92.91,24.49,C2

2.75,0.0,1.42,C1

6.82,0.0,0.78,C2

4.17,15.76,6.14,C2

2.54,25.31,1.53,C1

0.4,1.87,1.04,C1

2.8,11.28,1.51,C1

20.9,1.82,2.41,C2

4.19,13.15,2.25,C1

2.39,50.87,6.17,C1

7.03,19.14,5.62,C1

4.14,22.36,0.78,C1

4.32,40.61,1.43,C1

2.74,84.25,5.24,C2

17.02,0.0,0.0,C0

5.12,11.68,1.49,C1

2.82,2.23,0.77,C1

2.32,1.69,1.16,C1

3.88,10.87,3.28,C1

5.74,40.01,3.37,C1

10.07,15.82,2.08,C1

3.7,17.27,4.45,C2

10.48,0.0,4.74,C1

43.2,9.73,10.79,C1

4.29,29.78,0.78,C1

7.3,0.0,1.36,C1

4.08,12.15,2.8,C1

2.57,0.0,2.31,C2

11.83,0.0,4.38,C0

4.73,5.84,0.64,C2

6.57,0.0,3.53,C2

3.95,6.8,1.54,C2

12.92,9.33,3.17,C1

6.41,0.0,1.53,C1

3.28,0.0,0.3,C2

2.84,7.05,3.98,C1

5.59,0.0,4.66,C2

40.7,0.0,6.82,C2

25.92,0.0,0.0,C0

2.84,6.96,0.8,C1

17.62,0.0,1.49,C0

3.86,8.53,3.4,C1

6.79,0.0,6.85,C0

3.3,13.63,1.39,C1

48.45,2.33,27.77,C2

14.81,0.0,0.0,C0

12.74,13.87,0.79,C1

9.44,0.0,10.96,C2

18.23,24.71,1.11,C1

6.8,0.0,1.38,C2

2.74,38.74,2.2,C1

20.05,0.0,1.47,C1

2.74,2.32,5.26,C1

4.26,2.21,6.15,C1

1.37,0.0,0.87,C0

4.12,6.85,3.08,C1

18.11,0.0,2.22,C1

2.41,0.0,1.06,C1

9.2,29.11,6.02,C1

11.5,0.0,0.0,C0

12.86,0.0,0.8,C2

3.38,0.0,0.63,C2

4.13,0.0,1.53,C2

29.09,0.0,0.0,C0

12.12,0.0,2.09,C2

14.99,0.0,1.62,C0

11.49,0.0,0.0,C0

2.77,0.0,1.44,C1

5.63,38.98,3.94,C1

12.75,0.0,2.17,C2


















In [101]:



    


# Generate secuence of videos by user
interaction = open("users_video_sequence.csv", "w")
for user in usersProfilesJsonWithIndexes:
    s = (','.join(str(x) for x in usersProfilesJsonWithIndexes[user]["videoSequence"][:usersProfilesJsonWithIndexes[user]["skippingIndex"]+1]))
    s += "\n"
    interaction.write(s)
interaction.close()

# Generate secuence of videos by cluster
interaction = open("users_video_sequence_by_cluster.csv", "w")
for user in usersProfilesJsonWithIndexes:
    s = (','.join(str(x) for x in usersProfilesJsonWithIndexes[user]["clusterSequence"][:usersProfilesJsonWithIndexes[user]["skippingIndex"]+1]))
    s += "\n"
    interaction.write(s)
interaction.close()

# TP videos
interaction = open("tp_videos.csv", "w")
array = []
for user in usersProfilesJsonWithIndexes:
    index = usersProfilesJsonWithIndexes[user]["skippingIndex"]
    array.append(usersProfilesJsonWithIndexes[user]["videoSequence"][index])
result = list(set(array))
for v in result:
    interaction.write(str(v) + "\n")
interaction.close()

# TP clusters
interaction = open("tp_clusters.csv", "w")
array = []
for user in usersProfilesJsonWithIndexes:
    index = usersProfilesJsonWithIndexes[user]["skippingIndex"]
    array.append(usersProfilesJsonWithIndexes[user]["clusterSequence"][index])
result = list(set(array))
for v in result:
    interaction.write(str(v) + "\n")
interaction.close()

Content source: Hopenglish-Miners/UserProfile

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