```
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]}

```
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
18.47,0.0,6.33,C2
11.44,6.99,2.38,C1
4.23,15.21,0.77,C1
13.39,0.0,0.0,C0
2.55,8.57,4.25,C1
3.82,0.0,1.27,C1
15.41,0.0,0.0,C0
2.82,10.9,1.55,C1
8.29,4.27,6.01,C0
8.72,0.0,1.31,C2
34.3,0.0,4.66,C2
21.4,0.0,0.61,C0
7.32,10.93,2.29,C1
3.24,0.0,1.4,C2
15.52,18.28,2.35,C1
3.72,0.0,1.32,C1
3.14,0.0,2.84,C2
11.48,0.0,0.0,C0
19.08,26.9,8.19,C2
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14.21,0.0,0.0,C0
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2.68,10.78,2.26,C1
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19.1,0.0,0.0,C0
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8.93,8.85,5.46,C1
11.25,16.42,4.7,C1
10.26,0.0,0.93,C0
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29.1,0.0,0.77,C0
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14.29,11.69,2.07,C1
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5.4,0.0,4.85,C2
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7.34,0.4,1.06,C0
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44.46,13.82,5.58,C0
4.15,0.0,4.6,C1
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11.37,0.0,0.0,C0
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39.47,6.72,4.45,C1
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17.15,0.0,3.12,C2
6.72,13.21,11.04,C1
2.75,12.45,1.43,C1
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29.84,35.42,18.94,C1
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10.99,0.0,0.66,C0
4.02,22.06,5.29,C2
4.0,10.68,1.43,C2
9.07,11.75,4.45,C1
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5.48,13.54,2.33,C1
13.78,49.65,6.09,C2
11.05,7.38,1.25,C1
15.37,0.0,0.0,C0
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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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4.38,0.0,0.0,C0
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29.89,9.46,3.99,C2
9.95,0.0,0.0,C0
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6.69,0.0,0.73,C2
7.07,0.0,3.01,C2
11.82,0.0,0.0,C0
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23.58,0.0,0.0,C0
19.85,0.0,0.0,C0
22.1,82.11,15.92,C0
7.95,0.0,0.75,C2
23.56,0.0,3.02,C0
14.94,0.0,0.0,C2
2.52,6.28,1.41,C2
8.61,1.9,2.33,C1
11.16,11.39,9.28,C1
4.87,5.82,5.08,C1
12.35,2.37,10.07,C2
9.08,0.0,0.0,C2
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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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