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  1. amazon_apps/apps.md +17 -0
  2. amazon_apps/processed/apps.pkl +3 -0
  3. amazon_apps/raw/Apps_for_Android_5.json +3 -0
  4. amazon_apps/raw/meta_Apps_for_Android.json +3 -0
  5. amazon_apps/raw/process_final.ipynb +1342 -0
amazon_apps/apps.md ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Amazon-Apps Datasets
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+
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+ ## Dataset Description
4
+ The Amazon-Apps dataset is a shopping network. It includes information about items, users and reviews. Nodes represent items and users. Text on a item node is the description of the item. Text on a user node is the `reviwer`. The item text is `item`. Edges represent relationships between items and users. Text on an edge is a user's review of a item including following information: `Reviewer [reviewerName] left a review on [reviewTime], giving the product [rating] stars. In his/her review, he/she wrote: [reviewText]. His/Her summary was [summary].`.
5
+
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+
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+ ## Graph Machine Learning Tasks
8
+
9
+ ### Link Prediction
10
+ Link prediction in the Amazon-Apps dataset involves predicting potential connections between users and items. The goal is to predict whether a user will purchase a item.
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+
12
+ ### Node Classification
13
+ Node classification tasks in the Amazon-Apps dataset include predicting the items' category.
14
+
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+
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+ ## Dataset Source
17
+ http://jmcauley.ucsd.edu/data/amazon/links.html
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+ version https://git-lfs.github.com/spec/v1
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+ size 305378380
amazon_apps/raw/Apps_for_Android_5.json ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:55a30be054c139b900fd93fe3cffda17caf49b3a91760dc2e40eb84ea59ad958
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+ size 337389838
amazon_apps/raw/meta_Apps_for_Android.json ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:cdc12d4b8bfe4bc9c88b29752f6138ffd0ea41a9e3740388002ae64d2b5d6e81
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+ size 59955438
amazon_apps/raw/process_final.ipynb ADDED
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1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 1,
6
+ "id": "2b881572b62f8ce1",
7
+ "metadata": {
8
+ "ExecuteTime": {
9
+ "end_time": "2024-10-19T08:11:58.339331100Z",
10
+ "start_time": "2024-10-19T08:11:51.232136400Z"
11
+ },
12
+ "collapsed": false
13
+ },
14
+ "outputs": [],
15
+ "source": [
16
+ "import json\n",
17
+ "path = \"Apps_for_Android_5.json\"\n",
18
+ "dict_edge = {} #example: 8842281e1d1347389f2ab93d60773d4d|23310161 : One of my favorite books.\n",
19
+ "dict_num_to_id = {} # reorder the node's id\n",
20
+ "edge_score = []\n",
21
+ "count = 0\n",
22
+ "review_text = \"Reviewer [reviewerName] left a review on [reviewTime], giving the product [rating] stars. In his/her review, he/she wrote: [reviewText]. His/Her summary was [summary].\"\n",
23
+ "with open(path) as f:\n",
24
+ " for line in f:\n",
25
+ " d = json.loads(line)\n",
26
+ " edge = d[\"reviewerID\"] + \"|\" + d[\"asin\"]\n",
27
+ " try:\n",
28
+ " reviewtext = review_text.replace(\"[reviewerName]\", d[\"reviewerName\"])\n",
29
+ " except:\n",
30
+ " reviewtext = review_text.replace(\"[reviewerName]\", \"\")\n",
31
+ " if d[\"reviewTime\"] == \"\":\n",
32
+ " reviewtext = reviewtext.replace(\"[reviewTime]\", \"Unknown reviewtime\")\n",
33
+ " else:\n",
34
+ " reviewtext = reviewtext.replace(\"[reviewTime]\", d[\"reviewTime\"])\n",
35
+ " if d[\"overall\"] == \"\":\n",
36
+ " reviewtext = reviewtext.replace(\"[rating]\", \"Unknown\")\n",
37
+ " else:\n",
38
+ " reviewtext = reviewtext.replace(\"[rating]\", str(d[\"overall\"]))\n",
39
+ " reviewtext = reviewtext.replace(\"[reviewText]\", d[\"reviewText\"])\n",
40
+ " if d[\"summary\"] == \"\":\n",
41
+ " reviewtext = reviewtext.replace(\"[summary]\", \"Unknown\")\n",
42
+ " else:\n",
43
+ " reviewtext = reviewtext.replace(\"[summary]\", d[\"summary\"])\n",
44
+ " dict_edge[edge] = reviewtext\n",
45
+ " edge_score.append(d[\"overall\"])\n",
46
+ " if d[\"reviewerID\"] not in dict_num_to_id:\n",
47
+ " dict_num_to_id[d[\"reviewerID\"]] = count\n",
48
+ " count += 1\n",
49
+ " if d[\"asin\"] not in dict_num_to_id:\n",
50
+ " dict_num_to_id[d[\"asin\"]] = count\n",
51
+ " count += 1\n",
52
+ " "
53
+ ]
54
+ },
55
+ {
56
+ "cell_type": "code",
57
+ "execution_count": 14,
58
+ "id": "acb9e595af870544",
59
+ "metadata": {
60
+ "ExecuteTime": {
61
+ "end_time": "2024-10-19T08:15:01.303897900Z",
62
+ "start_time": "2024-10-19T08:15:00.542531700Z"
63
+ },
64
+ "collapsed": false
65
+ },
66
+ "outputs": [],
67
+ "source": [
68
+ "import json\n",
69
+ "dict_id_to_text = {}\n",
70
+ "dictid_to_label = {}\n",
71
+ "node_texts = \"item\"\n",
72
+ "#nodes_texts = \"The product titled '[title]' falls under the Movies & TV category, specifically [category]. It features [feature] and is about [description], making it a great fit for [fit]. This product is sold for [price] and is from the brand [brand]. This product has a rank of [rank] and was released on [date]. For more details, check out the [imageURL] or the high-resolution image [imageURLHighRes].\"\n",
73
+ "with open(\"meta_Apps_for_Android.json\") as f:\n",
74
+ " for line in f:\n",
75
+ " d = json.loads(line)\n",
76
+ " label_list = []\n",
77
+ " for x in d[\"categories\"]:\n",
78
+ " for label in x:\n",
79
+ " label_list.append(label)\n",
80
+ " dictid_to_label[d[\"asin\"]] = label_list\n",
81
+ " product_text = node_texts\n",
82
+ " '''\n",
83
+ " product_text = nodes_texts.replace(\"[title]\", d[\"title\"])\n",
84
+ " category_text = \", \".join(label_list[1:])\n",
85
+ " product_text = product_text.replace(\"[category]\", category_text)\n",
86
+ " if d[\"feature\"] == []:\n",
87
+ " product_text = product_text.replace(\"[feature]\",\"Unknown feature\")\n",
88
+ " else:\n",
89
+ " feature_text = \", \".join(d[\"feature\"])\n",
90
+ " product_text = product_text.replace(\"[feature]\",feature_text)\n",
91
+ " if d[\"description\"] == []:\n",
92
+ " product_text = product_text.replace(\"[description]\",\"Unknown description\")\n",
93
+ " else:\n",
94
+ " description_text = \", \".join(d[\"description\"])\n",
95
+ " product_text = product_text.replace(\"[description]\",description_text)\n",
96
+ " if d[\"fit\"] == \"\":\n",
97
+ " product_text = product_text.replace(\"[fit]\",\"Unknown fit\")\n",
98
+ " else:\n",
99
+ " product_text = product_text.replace(\"[fit]\",d[\"fit\"])\n",
100
+ " if d[\"price\"] == \"\":\n",
101
+ " product_text = product_text.replace(\"[price]\",\"Unknown price\")\n",
102
+ " else:\n",
103
+ " product_text = product_text.replace(\"[price]\",d[\"price\"])\n",
104
+ " if d[\"brand\"] == \"\":\n",
105
+ " product_text = product_text.replace(\"[brand]\",\"Unknown brand\")\n",
106
+ " else:\n",
107
+ " product_text = product_text.replace(\"[brand]\",d[\"brand\"])\n",
108
+ " if d[\"rank\"] == \"\":\n",
109
+ " product_text = product_text.replace(\"[rank]\",\"Unknown rank\")\n",
110
+ " else:\n",
111
+ " try:\n",
112
+ " product_text = product_text.replace(\"[rank]\",d[\"rank\"])\n",
113
+ " except:\n",
114
+ " product_text = product_text.replace(\"[rank]\",\"Unknown rank\")\n",
115
+ " if d[\"date\"] == \"\":\n",
116
+ " product_text = product_text.replace(\"[date]\",\"Unknown date\")\n",
117
+ " else:\n",
118
+ " product_text = product_text.replace(\"[date]\",d[\"date\"])\n",
119
+ " if d[\"imageURL\"] == []:\n",
120
+ " product_text = product_text.replace(\"[imageURL]\",\"Unknown imageURL\")\n",
121
+ " else:\n",
122
+ " imageURL_text = \", \".join(d[\"imageURL\"])\n",
123
+ " product_text = product_text.replace(\"[imageURL]\",imageURL_text)\n",
124
+ " if d[\"imageURLHighRes\"] == []:\n",
125
+ " product_text = product_text.replace(\"[imageURLHighRes]\",\"Unknown imageURLHighRes\")\n",
126
+ " else:\n",
127
+ " imageURLHighRes_text = \", \".join(d[\"imageURLHighRes\"])\n",
128
+ " product_text = product_text.replace(\"[imageURLHighRes]\",imageURLHighRes_text)\n",
129
+ " '''\n",
130
+ " dict_id_to_text[d[\"asin\"]] = product_text"
131
+ ]
132
+ },
133
+ {
134
+ "cell_type": "code",
135
+ "execution_count": 15,
136
+ "id": "5e69e274cb42bf36",
137
+ "metadata": {
138
+ "ExecuteTime": {
139
+ "end_time": "2024-10-19T08:15:01.999784800Z",
140
+ "start_time": "2024-10-19T08:15:01.989277300Z"
141
+ },
142
+ "collapsed": false
143
+ },
144
+ "outputs": [],
145
+ "source": [
146
+ "edge1 = [] \n",
147
+ "edge2 = [] # edge1 edge2 are to generate edge_index\n",
148
+ "text_nodes = [None] * len(dict_num_to_id)\n",
149
+ "text_edges = []\n",
150
+ "text_node_labels = [-1] * len(dict_num_to_id)"
151
+ ]
152
+ },
153
+ {
154
+ "cell_type": "code",
155
+ "execution_count": 16,
156
+ "id": "f2adedbc870feda",
157
+ "metadata": {
158
+ "ExecuteTime": {
159
+ "end_time": "2024-10-19T08:15:04.385748Z",
160
+ "start_time": "2024-10-19T08:15:02.732806400Z"
161
+ },
162
+ "collapsed": false
163
+ },
164
+ "outputs": [],
165
+ "source": [
166
+ "for edge, edge_text in dict_edge.items():\n",
167
+ " node1 = edge.split(\"|\")[0]\n",
168
+ " node2 = edge.split(\"|\")[1]\n",
169
+ " node1_id = int(dict_num_to_id[node1])\n",
170
+ " node2_id = int(dict_num_to_id[node2])\n",
171
+ " edge1.append(node1_id)\n",
172
+ " edge2.append(node2_id)\n",
173
+ " text_nodes[node1_id] = \"reviewer\"\n",
174
+ " try:\n",
175
+ " text_nodes[node2_id] = dict_id_to_text[node2]\n",
176
+ " except:\n",
177
+ " text_nodes[node2_id] = \"Unknown node texts\"\n",
178
+ " text_edges.append(edge_text)\n",
179
+ " try:\n",
180
+ " text_node_labels[node2_id] = dictid_to_label[node2]\n",
181
+ " except:\n",
182
+ " text_node_labels[node2_id] = -1"
183
+ ]
184
+ },
185
+ {
186
+ "cell_type": "code",
187
+ "execution_count": 17,
188
+ "id": "3305934f1a11caa7",
189
+ "metadata": {
190
+ "ExecuteTime": {
191
+ "end_time": "2024-10-19T08:15:04.410284900Z",
192
+ "start_time": "2024-10-19T08:15:04.384747Z"
193
+ },
194
+ "collapsed": false
195
+ },
196
+ "outputs": [],
197
+ "source": [
198
+ "from torch_geometric.data import Data\n",
199
+ "import torch"
200
+ ]
201
+ },
202
+ {
203
+ "cell_type": "code",
204
+ "execution_count": 18,
205
+ "id": "5030fa8672f2b177",
206
+ "metadata": {
207
+ "ExecuteTime": {
208
+ "end_time": "2024-10-19T08:15:04.678317100Z",
209
+ "start_time": "2024-10-19T08:15:04.398293500Z"
210
+ },
211
+ "collapsed": false
212
+ },
213
+ "outputs": [],
214
+ "source": [
215
+ "edge_index = torch.tensor([edge1,edge2])"
216
+ ]
217
+ },
218
+ {
219
+ "cell_type": "code",
220
+ "execution_count": 19,
221
+ "id": "21085a8a04df7062",
222
+ "metadata": {
223
+ "ExecuteTime": {
224
+ "end_time": "2024-10-19T08:15:04.696139900Z",
225
+ "start_time": "2024-10-19T08:15:04.683323900Z"
226
+ },
227
+ "collapsed": false
228
+ },
229
+ "outputs": [],
230
+ "source": [
231
+ "new_data = Data(\n",
232
+ " edge_index=edge_index,\n",
233
+ " text_nodes=text_nodes,\n",
234
+ " text_edges=text_edges,\n",
235
+ " text_node_labels=text_node_labels,\n",
236
+ " edge_score=edge_score\n",
237
+ ")"
238
+ ]
239
+ },
240
+ {
241
+ "cell_type": "code",
242
+ "execution_count": 21,
243
+ "id": "9034cd055a347411",
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+ "metadata": {
245
+ "ExecuteTime": {
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+ "end_time": "2024-10-19T08:15:41.052485Z",
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+ "start_time": "2024-10-19T08:15:41.019185400Z"
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+ },
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+ "collapsed": false
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+ },
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+ "outputs": [
252
+ {
253
+ "data": {
254
+ "text/plain": [
255
+ "['reviewer',\n",
256
+ " '',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " '',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " '',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " '',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
1226
+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
1229
+ " 'reviewer',\n",
1230
+ " 'reviewer',\n",
1231
+ " 'reviewer',\n",
1232
+ " 'reviewer',\n",
1233
+ " 'reviewer',\n",
1234
+ " 'reviewer',\n",
1235
+ " 'reviewer',\n",
1236
+ " 'reviewer',\n",
1237
+ " 'reviewer',\n",
1238
+ " 'reviewer',\n",
1239
+ " 'reviewer',\n",
1240
+ " 'reviewer',\n",
1241
+ " 'reviewer',\n",
1242
+ " 'reviewer',\n",
1243
+ " 'reviewer',\n",
1244
+ " 'reviewer',\n",
1245
+ " 'reviewer',\n",
1246
+ " 'reviewer',\n",
1247
+ " 'reviewer',\n",
1248
+ " 'reviewer',\n",
1249
+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
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+ " 'reviewer',\n",
1254
+ " 'reviewer',\n",
1255
+ " ...]"
1256
+ ]
1257
+ },
1258
+ "execution_count": 21,
1259
+ "metadata": {},
1260
+ "output_type": "execute_result"
1261
+ }
1262
+ ],
1263
+ "source": [
1264
+ "new_data.text_nodes"
1265
+ ]
1266
+ },
1267
+ {
1268
+ "cell_type": "code",
1269
+ "execution_count": null,
1270
+ "id": "c7169e97a39204ce",
1271
+ "metadata": {
1272
+ "collapsed": false,
1273
+ "is_executing": true
1274
+ },
1275
+ "outputs": [],
1276
+ "source": [
1277
+ "new_data.text_edges"
1278
+ ]
1279
+ },
1280
+ {
1281
+ "cell_type": "code",
1282
+ "execution_count": null,
1283
+ "id": "d39601d90a0171c5",
1284
+ "metadata": {
1285
+ "collapsed": false,
1286
+ "is_executing": true
1287
+ },
1288
+ "outputs": [],
1289
+ "source": [
1290
+ "import pickle\n",
1291
+ "output_file_path = '../processed/apps.pkl'\n",
1292
+ "with open(output_file_path, 'wb') as output_file:\n",
1293
+ " pickle.dump(new_data, output_file)\n",
1294
+ "\n",
1295
+ "print(f\"Data saved to {output_file_path}\")"
1296
+ ]
1297
+ },
1298
+ {
1299
+ "cell_type": "code",
1300
+ "execution_count": null,
1301
+ "id": "60f52e9317cfad61",
1302
+ "metadata": {
1303
+ "collapsed": false,
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+ "is_executing": true
1305
+ },
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+ "outputs": [],
1307
+ "source": []
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+ },
1309
+ {
1310
+ "cell_type": "code",
1311
+ "execution_count": null,
1312
+ "id": "4aaa10c4d649044a",
1313
+ "metadata": {
1314
+ "collapsed": false,
1315
+ "is_executing": true
1316
+ },
1317
+ "outputs": [],
1318
+ "source": []
1319
+ }
1320
+ ],
1321
+ "metadata": {
1322
+ "kernelspec": {
1323
+ "display_name": ".venv",
1324
+ "language": "python",
1325
+ "name": "python3"
1326
+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
1331
+ },
1332
+ "file_extension": ".py",
1333
+ "mimetype": "text/x-python",
1334
+ "name": "python",
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+ "nbconvert_exporter": "python",
1336
+ "pygments_lexer": "ipython3",
1337
+ "version": "3.10.12"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 5
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+ }