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rufimelo commited on
Commit
d0bb838
·
1 Parent(s): a53f165

yolo

Browse files
Files changed (7) hide show
  1. .DS_Store +0 -0
  2. app/.DS_Store +0 -0
  3. app/__pycache__/core.cpython-310.pyc +0 -0
  4. app/app.py +53 -7
  5. app/dashboard.ipynb +56 -17
  6. app/data/raw_simulated_ratings.csv +101 -0
  7. app/data/simulated_ratings.csv +100 -100
.DS_Store CHANGED
Binary files a/.DS_Store and b/.DS_Store differ
 
app/.DS_Store CHANGED
Binary files a/app/.DS_Store and b/app/.DS_Store differ
 
app/__pycache__/core.cpython-310.pyc CHANGED
Binary files a/app/__pycache__/core.cpython-310.pyc and b/app/__pycache__/core.cpython-310.pyc differ
 
app/app.py CHANGED
@@ -2,7 +2,7 @@ import os
2
  import plotly.express as px
3
  import plotly.graph_objects as go
4
  import pandas as pd
5
- from dash import Dash, html, dcc, Input, Output, callback
6
  import plotly.express as px
7
  import numpy as np
8
  from plotly.subplots import make_subplots
@@ -36,6 +36,11 @@ user_rating_df.set_index('user', inplace=True)
36
  user_rating_df['user'] = user_rating_df.index
37
  user_ids = user_rating_df['user']
38
 
 
 
 
 
 
39
 
40
 
41
  ## Layout ##
@@ -49,14 +54,51 @@ dashboard_layout = html.Div([
49
  dcc.Dropdown(['all'],'all', id='dropdown-wr'),
50
  dcc.Graph(id='world-map-fig'),
51
 
52
- html.H1(children='Wine Recommender', style={'textAlign':'center'}),
53
- dcc.Dropdown(wine_list, wine_list[0], id='dropdown-selection'),
54
- html.Div(id='wine-recommendation', children='', style={'textAlign':'center'}),
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
55
 
 
56
 
57
- html.H1(children='Wine Recommender based on feedback and other user', style={'textAlign':'center'}),
58
- dcc.Dropdown(user_ids, user_ids[0], id='dropdown-selection-user'),
59
- html.Div(id='wine-recommendation-from-user', children='', style={'textAlign':'center'}),
60
 
61
  ])
62
 
@@ -136,6 +178,7 @@ def update_graph(value,wr,wc, user_value):
136
  wine_recommendation_from_user = f"Based on user information, we recommend: "+"; ".join(wine_recommendation_from_user)
137
 
138
 
 
139
  ### World Map of wineyards ###
140
 
141
  geo_df = pd.read_csv('data/processed_wineyards.csv')
@@ -160,6 +203,9 @@ def update_graph(value,wr,wc, user_value):
160
  title='Wineyards around the world',
161
  )
162
 
 
 
 
163
 
164
 
165
  return recommended_wines, world_map_fig, wine_recommendation_from_user
 
2
  import plotly.express as px
3
  import plotly.graph_objects as go
4
  import pandas as pd
5
+ from dash import Dash, html, dcc, Input, Output, callback, dash_table
6
  import plotly.express as px
7
  import numpy as np
8
  from plotly.subplots import make_subplots
 
36
  user_rating_df['user'] = user_rating_df.index
37
  user_ids = user_rating_df['user']
38
 
39
+ raw_ratings = pd.read_csv('data/raw_simulated_ratings.csv')
40
+ # crop to 5 rows and 7 columns
41
+ raw_ratings = raw_ratings.iloc[:5, :7]
42
+
43
+
44
 
45
 
46
  ## Layout ##
 
54
  dcc.Dropdown(['all'],'all', id='dropdown-wr'),
55
  dcc.Graph(id='world-map-fig'),
56
 
57
+
58
+
59
+ html.Div(
60
+ [
61
+ html.Div(
62
+ [
63
+ html.H1(children='Wine Recommender', style={'textAlign':'center'}),
64
+ dcc.Dropdown(wine_list, wine_list[0], id='dropdown-selection'),
65
+ html.H1(id='wine-recommendation', style={'textAlign':'center'}),
66
+ ], className='six columns',
67
+ ),
68
+ html.Div(
69
+ [
70
+ html.H1(children='something here', style={'textAlign':'center'}),
71
+ ], className='six columns',
72
+ )
73
+ ], className='row'),
74
+
75
+
76
+ html.H1(children='Wine Recommender based on feedback and other user', style={'textAlign':'center'}),
77
+ html.Div(
78
+ [
79
+ html.Div(
80
+ [
81
+ dcc.Dropdown(user_ids, user_ids[0], id='dropdown-selection-user'),
82
+ html.H4(id='wine-recommendation-from-user', style={'textAlign':'center'}),
83
+ ], className='six columns',
84
+ ),
85
+
86
+ html.Div(
87
+ [
88
+ html.H4(children='Data Example', style={'textAlign':'left'}),
89
+ dash_table.DataTable(
90
+ data=raw_ratings.to_dict('records'),
91
+ columns=[{'id': c, 'name': c} for c in raw_ratings.columns]
92
+ )
93
+ ], className='six columns',
94
+ ),
95
+ ], className='row'),
96
+
97
+
98
 
99
+
100
 
101
+
 
 
102
 
103
  ])
104
 
 
178
  wine_recommendation_from_user = f"Based on user information, we recommend: "+"; ".join(wine_recommendation_from_user)
179
 
180
 
181
+
182
  ### World Map of wineyards ###
183
 
184
  geo_df = pd.read_csv('data/processed_wineyards.csv')
 
203
  title='Wineyards around the world',
204
  )
205
 
206
+ world_map_fig.update_layout(height=800, width=1200)
207
+
208
+
209
 
210
 
211
  return recommended_wines, world_map_fig, wine_recommendation_from_user
app/dashboard.ipynb CHANGED
@@ -2,14 +2,14 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 2,
6
  "metadata": {},
7
  "outputs": [
8
  {
9
  "name": "stderr",
10
  "output_type": "stream",
11
  "text": [
12
- "/var/folders/b4/lwfgccm95kqd2skcwvrt2fr00000gn/T/ipykernel_33833/349649994.py:58: FutureWarning:\n",
13
  "\n",
14
  "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
15
  "\n"
@@ -30,7 +30,7 @@
30
  " "
31
  ],
32
  "text/plain": [
33
- "<IPython.lib.display.IFrame at 0x2adb0eb30>"
34
  ]
35
  },
36
  "metadata": {},
@@ -40,13 +40,6 @@
40
  "name": "stderr",
41
  "output_type": "stream",
42
  "text": [
43
- "/Users/ruimelo/Documents/GitHub/eda/app/core.py:20: SettingWithCopyWarning:\n",
44
- "\n",
45
- "\n",
46
- "A value is trying to be set on a copy of a slice from a DataFrame\n",
47
- "\n",
48
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
49
- "\n",
50
  "/Users/ruimelo/Documents/GitHub/eda/app/core.py:20: SettingWithCopyWarning:\n",
51
  "\n",
52
  "\n",
@@ -62,7 +55,7 @@
62
  "import plotly.express as px\n",
63
  "import plotly.graph_objects as go\n",
64
  "import pandas as pd\n",
65
- "from dash import Dash, html, dcc, Input, Output, callback\n",
66
  "import plotly.express as px\n",
67
  "import numpy as np\n",
68
  "from plotly.subplots import make_subplots\n",
@@ -96,6 +89,11 @@
96
  "user_rating_df['user'] = user_rating_df.index\n",
97
  "user_ids = user_rating_df['user']\n",
98
  "\n",
 
 
 
 
 
99
  "\n",
100
  "\n",
101
  "## Layout ##\n",
@@ -109,14 +107,51 @@
109
  " dcc.Dropdown(['all'],'all', id='dropdown-wr'),\n",
110
  " dcc.Graph(id='world-map-fig'),\n",
111
  "\n",
112
- " html.H1(children='Wine Recommender', style={'textAlign':'center'}),\n",
113
- " dcc.Dropdown(wine_list, wine_list[0], id='dropdown-selection'),\n",
114
- " html.Div(id='wine-recommendation', children='', style={'textAlign':'center'}),\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
115
  "\n",
 
116
  "\n",
117
- " html.H1(children='Wine Recommender based on feedback and other user', style={'textAlign':'center'}),\n",
118
- " dcc.Dropdown(user_ids, user_ids[0], id='dropdown-selection-user'),\n",
119
- " html.Div(id='wine-recommendation-from-user', children='', style={'textAlign':'center'}),\n",
120
  "\n",
121
  "])\n",
122
  "\n",
@@ -196,6 +231,7 @@
196
  " wine_recommendation_from_user = f\"Based on user information, we recommend: \"+\"; \".join(wine_recommendation_from_user)\n",
197
  "\n",
198
  "\n",
 
199
  " ### World Map of wineyards ###\n",
200
  "\n",
201
  " geo_df = pd.read_csv('data/processed_wineyards.csv')\n",
@@ -220,6 +256,9 @@
220
  " title='Wineyards around the world',\n",
221
  " )\n",
222
  " \n",
 
 
 
223
  "\n",
224
  "\n",
225
  " return recommended_wines, world_map_fig, wine_recommendation_from_user\n",
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 17,
6
  "metadata": {},
7
  "outputs": [
8
  {
9
  "name": "stderr",
10
  "output_type": "stream",
11
  "text": [
12
+ "/var/folders/b4/lwfgccm95kqd2skcwvrt2fr00000gn/T/ipykernel_33833/3272723617.py:81: FutureWarning:\n",
13
  "\n",
14
  "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
15
  "\n"
 
30
  " "
31
  ],
32
  "text/plain": [
33
+ "<IPython.lib.display.IFrame at 0x2ae1f6cb0>"
34
  ]
35
  },
36
  "metadata": {},
 
40
  "name": "stderr",
41
  "output_type": "stream",
42
  "text": [
 
 
 
 
 
 
 
43
  "/Users/ruimelo/Documents/GitHub/eda/app/core.py:20: SettingWithCopyWarning:\n",
44
  "\n",
45
  "\n",
 
55
  "import plotly.express as px\n",
56
  "import plotly.graph_objects as go\n",
57
  "import pandas as pd\n",
58
+ "from dash import Dash, html, dcc, Input, Output, callback, dash_table\n",
59
  "import plotly.express as px\n",
60
  "import numpy as np\n",
61
  "from plotly.subplots import make_subplots\n",
 
89
  "user_rating_df['user'] = user_rating_df.index\n",
90
  "user_ids = user_rating_df['user']\n",
91
  "\n",
92
+ "raw_ratings = pd.read_csv('data/raw_simulated_ratings.csv')\n",
93
+ "# crop to 5 rows and 7 columns\n",
94
+ "raw_ratings = raw_ratings.iloc[:5, :7]\n",
95
+ "\n",
96
+ "\n",
97
  "\n",
98
  "\n",
99
  "## Layout ##\n",
 
107
  " dcc.Dropdown(['all'],'all', id='dropdown-wr'),\n",
108
  " dcc.Graph(id='world-map-fig'),\n",
109
  "\n",
110
+ " \n",
111
+ " \n",
112
+ " html.Div(\n",
113
+ " [\n",
114
+ " html.Div(\n",
115
+ " [\n",
116
+ " html.H1(children='Wine Recommender', style={'textAlign':'center'}),\n",
117
+ " dcc.Dropdown(wine_list, wine_list[0], id='dropdown-selection'),\n",
118
+ " html.H1(id='wine-recommendation', style={'textAlign':'center'}),\n",
119
+ " ], className='six columns',\n",
120
+ " ),\n",
121
+ " html.Div(\n",
122
+ " [\n",
123
+ " html.H1(children='something here', style={'textAlign':'center'}),\n",
124
+ " ], className='six columns',\n",
125
+ " )\n",
126
+ " ], className='row'),\n",
127
+ " \n",
128
+ " \n",
129
+ " html.H1(children='Wine Recommender based on feedback and other user', style={'textAlign':'center'}),\n",
130
+ " html.Div(\n",
131
+ " [\n",
132
+ " html.Div(\n",
133
+ " [\n",
134
+ " dcc.Dropdown(user_ids, user_ids[0], id='dropdown-selection-user'),\n",
135
+ " html.H4(id='wine-recommendation-from-user', style={'textAlign':'center'}),\n",
136
+ " ], className='six columns',\n",
137
+ " ),\n",
138
+ " \n",
139
+ " html.Div(\n",
140
+ " [ \n",
141
+ " html.H4(children='Data Example', style={'textAlign':'left'}),\n",
142
+ " dash_table.DataTable(\n",
143
+ " data=raw_ratings.to_dict('records'),\n",
144
+ " columns=[{'id': c, 'name': c} for c in raw_ratings.columns]\n",
145
+ " )\n",
146
+ " ], className='six columns',\n",
147
+ " ),\n",
148
+ " ], className='row'),\n",
149
+ " \n",
150
+ " \n",
151
  "\n",
152
+ " \n",
153
  "\n",
154
+ " \n",
 
 
155
  "\n",
156
  "])\n",
157
  "\n",
 
231
  " wine_recommendation_from_user = f\"Based on user information, we recommend: \"+\"; \".join(wine_recommendation_from_user)\n",
232
  "\n",
233
  "\n",
234
+ "\n",
235
  " ### World Map of wineyards ###\n",
236
  "\n",
237
  " geo_df = pd.read_csv('data/processed_wineyards.csv')\n",
 
256
  " title='Wineyards around the world',\n",
257
  " )\n",
258
  " \n",
259
+ " world_map_fig.update_layout(height=800, width=1200)\n",
260
+ "\n",
261
+ " \n",
262
  "\n",
263
  "\n",
264
  " return recommended_wines, world_map_fig, wine_recommendation_from_user\n",
app/data/raw_simulated_ratings.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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app/data/simulated_ratings.csv CHANGED
@@ -1,101 +1,101 @@
1
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1
  Azevedo,Gazela,Aura,Dedicado,Legado,Offley,Quinta dos Carvalhais,Série Ímpar,Antónia Adelaide Ferreira,Casa Ferreirinha,Finca Flichman,Herdade do peso,Marqués de Burgos,Porto Ferreira,Sandeman,Silk & Spice,Chateau Los Boldos,Framingham,LAN,Mateus,Quinta da Romeira,Santiago Ruiz,cluster,user
2
+ 0.0,2.0,0.0,4.0,0.0,0.0,3.0,4.0,2.0,0.0,5.0,3.0,0.0,0.0,0.0,4.0,2.0,0.0,0.0,3.0,5.0,0.0,0,User1
3
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5
+ 4.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,0.0,0.0,5.0,2.0,0.0,0.0,2,User4
6
+ 3.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,0.0,1.0,0.0,0.0,5.0,4.0,0.0,3.0,0.0,0.0,5.0,0.0,4.0,0.0,2,User5
7
+ 4.0,0.0,0.0,5.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,4.0,0.0,5.0,5.0,1.0,0.0,3.0,2,User6
8
+ 2.0,1.0,2.0,0.0,5.0,5.0,2.0,0.0,0.0,2.0,1.0,0.0,4.0,0.0,3.0,0.0,0.0,4.0,0.0,4.0,0.0,1.0,1,User7
9
+ 4.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,0.0,1.0,3.0,1.0,4.0,0.0,0.0,0.0,4.0,0.0,2.0,2.0,2.0,1.0,0,User8
10
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11
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12
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13
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14
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15
+ 2.0,1.0,0.0,4.0,0.0,4.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,2.0,0.0,4.0,1.0,0.0,1,User14
16
+ 1.0,0.0,3.0,0.0,4.0,1.0,0.0,2.0,3.0,0.0,5.0,0.0,3.0,0.0,2.0,5.0,0.0,0.0,5.0,0.0,0.0,0.0,2,User15
17
+ 5.0,4.0,3.0,2.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,1.0,0.0,3.0,4.0,0.0,0.0,0.0,0.0,2.0,5.0,0.0,2,User16
18
+ 0.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,5.0,0.0,0.0,1,User17
19
+ 3.0,0.0,1.0,0.0,5.0,5.0,0.0,0.0,1.0,3.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,1,User18
20
+ 1.0,3.0,4.0,1.0,0.0,4.0,0.0,3.0,3.0,0.0,1.0,3.0,0.0,0.0,0.0,0.0,5.0,5.0,1.0,0.0,3.0,0.0,3,User19
21
+ 1.0,0.0,0.0,5.0,0.0,3.0,1.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,2.0,3.0,0.0,5.0,2.0,1.0,1,User20
22
+ 3.0,1.0,3.0,0.0,4.0,1.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0,User21
23
+ 0.0,5.0,0.0,0.0,1.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,4.0,1.0,2.0,2.0,0.0,1,User22
24
+ 0.0,0.0,0.0,0.0,0.0,4.0,0.0,5.0,0.0,4.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,3,User23
25
+ 0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,5.0,4.0,5.0,3.0,0.0,3.0,0.0,2.0,0.0,0.0,0.0,4.0,2,User24
26
+ 0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,3.0,1.0,3.0,0.0,0.0,0.0,4.0,1.0,5.0,5.0,0.0,0.0,0.0,3,User25
27
+ 3.0,1.0,4.0,0.0,0.0,5.0,1.0,2.0,0.0,2.0,2.0,0.0,0.0,5.0,0.0,2.0,5.0,0.0,2.0,5.0,0.0,4.0,2,User26
28
+ 4.0,0.0,5.0,0.0,0.0,0.0,0.0,3.0,1.0,3.0,0.0,0.0,1.0,0.0,0.0,1.0,4.0,0.0,3.0,0.0,5.0,0.0,0,User27
29
+ 1.0,0.0,4.0,0.0,3.0,4.0,0.0,0.0,0.0,0.0,2.0,0.0,5.0,0.0,0.0,0.0,1.0,0.0,5.0,2.0,1.0,0.0,2,User28
30
+ 0.0,1.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,4.0,3.0,0.0,2,User29
31
+ 0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,1.0,0.0,4.0,0.0,0.0,0.0,0.0,4.0,2.0,0.0,3.0,0.0,2,User30
32
+ 3.0,5.0,2.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,5.0,0.0,2.0,0.0,0.0,3.0,0.0,0.0,0.0,4.0,4.0,1.0,2,User31
33
+ 0.0,0.0,0.0,0.0,4.0,1.0,0.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,3,User32
34
+ 0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,5.0,0.0,3.0,0.0,0.0,3.0,0.0,0.0,2.0,1.0,5.0,3,User33
35
+ 0.0,0.0,1.0,4.0,0.0,0.0,0.0,4.0,2.0,3.0,2.0,0.0,0.0,4.0,2.0,1.0,0.0,3.0,2.0,0.0,0.0,5.0,3,User34
36
+ 0.0,0.0,0.0,3.0,0.0,0.0,4.0,1.0,1.0,2.0,4.0,0.0,0.0,0.0,4.0,4.0,5.0,0.0,0.0,0.0,4.0,5.0,0,User35
37
+ 0.0,2.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0,User36
38
+ 0.0,0.0,0.0,0.0,4.0,3.0,0.0,2.0,0.0,1.0,0.0,5.0,0.0,1.0,0.0,2.0,5.0,0.0,0.0,0.0,1.0,1.0,3,User37
39
+ 4.0,3.0,5.0,1.0,2.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,1.0,0.0,4.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,0,User38
40
+ 0.0,2.0,3.0,4.0,0.0,2.0,0.0,0.0,3.0,0.0,5.0,0.0,5.0,2.0,2.0,1.0,0.0,4.0,1.0,0.0,3.0,0.0,2,User39
41
+ 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,4.0,0.0,0.0,3.0,0.0,0.0,0.0,3.0,0.0,0.0,3.0,3,User40
42
+ 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,3.0,0.0,4.0,0.0,4.0,1,User41
43
+ 0.0,2.0,5.0,0.0,5.0,2.0,0.0,4.0,3.0,3.0,0.0,4.0,0.0,0.0,2.0,0.0,2.0,2.0,0.0,0.0,4.0,3.0,3,User42
44
+ 0.0,0.0,0.0,1.0,3.0,2.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,3.0,2,User43
45
+ 3.0,0.0,0.0,3.0,5.0,0.0,4.0,0.0,4.0,5.0,0.0,0.0,0.0,0.0,5.0,4.0,0.0,0.0,4.0,0.0,0.0,5.0,0,User44
46
+ 4.0,0.0,3.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,5.0,5.0,3.0,2.0,4.0,0.0,1.0,3.0,5.0,0.0,0.0,2,User45
47
+ 0.0,0.0,0.0,0.0,0.0,4.0,0.0,4.0,0.0,4.0,3.0,2.0,2.0,3.0,0.0,4.0,3.0,1.0,0.0,1.0,5.0,0.0,3,User46
48
+ 2.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,5.0,4.0,4.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,3,User47
49
+ 0.0,0.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,3.0,1.0,3.0,0.0,2,User48
50
+ 3.0,0.0,3.0,2.0,5.0,2.0,0.0,4.0,0.0,1.0,0.0,4.0,0.0,0.0,1.0,0.0,0.0,5.0,0.0,3.0,0.0,0.0,3,User49
51
+ 0.0,0.0,4.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,3.0,1.0,1.0,3.0,0.0,0,User50
52
+ 0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,0.0,0.0,0.0,3.0,5.0,0.0,0.0,5.0,5.0,4.0,5.0,4.0,2,User51
53
+ 0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,4.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,3.0,4.0,2.0,4.0,3.0,2,User52
54
+ 0.0,0.0,1.0,0.0,0.0,0.0,3.0,0.0,0.0,3.0,0.0,3.0,1.0,2.0,0.0,0.0,1.0,5.0,0.0,0.0,0.0,3.0,3,User53
55
+ 0.0,0.0,0.0,0.0,1.0,0.0,3.0,2.0,0.0,1.0,0.0,2.0,1.0,0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,4.0,0,User54
56
+ 0.0,0.0,0.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,2.0,0.0,1.0,0.0,4.0,0.0,1.0,1,User55
57
+ 0.0,0.0,5.0,3.0,3.0,0.0,0.0,0.0,3.0,0.0,0.0,4.0,0.0,5.0,0.0,2.0,0.0,0.0,5.0,3.0,0.0,0.0,2,User56
58
+ 3.0,0.0,0.0,0.0,0.0,4.0,0.0,1.0,0.0,4.0,5.0,0.0,0.0,5.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,2,User57
59
+ 0.0,0.0,0.0,4.0,4.0,0.0,0.0,0.0,0.0,1.0,0.0,5.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,5.0,1.0,4.0,1,User58
60
+ 0.0,0.0,1.0,0.0,3.0,1.0,0.0,0.0,0.0,4.0,3.0,0.0,3.0,2.0,0.0,0.0,3.0,0.0,5.0,5.0,3.0,0.0,2,User59
61
+ 5.0,0.0,5.0,5.0,3.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,3.0,0.0,0.0,0.0,0.0,4.0,0.0,0,User60
62
+ 0.0,0.0,5.0,0.0,4.0,0.0,0.0,2.0,2.0,0.0,0.0,2.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3,User61
63
+ 0.0,1.0,0.0,3.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,5.0,1.0,5.0,1.0,3.0,3.0,3.0,0.0,3.0,0.0,3.0,1,User62
64
+ 2.0,3.0,0.0,1.0,3.0,0.0,0.0,4.0,0.0,0.0,1.0,0.0,3.0,0.0,0.0,3.0,1.0,0.0,1.0,1.0,1.0,0.0,0,User63
65
+ 2.0,0.0,0.0,0.0,0.0,5.0,2.0,1.0,2.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,3.0,4.0,0.0,5.0,0.0,0.0,1,User64
66
+ 0.0,0.0,2.0,5.0,0.0,3.0,0.0,0.0,0.0,4.0,4.0,5.0,0.0,0.0,4.0,0.0,0.0,5.0,4.0,0.0,3.0,0.0,3,User65
67
+ 0.0,0.0,2.0,1.0,0.0,0.0,4.0,2.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,5.0,0.0,0.0,0.0,5.0,0.0,0,User66
68
+ 2.0,2.0,0.0,5.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,5.0,0.0,1.0,0.0,0.0,0.0,3.0,2.0,0.0,4.0,3.0,3,User67
69
+ 3.0,0.0,0.0,0.0,2.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,3.0,4.0,0.0,0.0,0.0,0.0,0,User68
70
+ 0.0,4.0,0.0,1.0,0.0,1.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,4.0,4.0,4.0,3.0,0.0,5.0,0.0,5.0,0.0,0,User69
71
+ 0.0,0.0,4.0,0.0,5.0,0.0,4.0,0.0,0.0,3.0,0.0,0.0,5.0,2.0,2.0,3.0,3.0,5.0,1.0,4.0,4.0,1.0,0,User70
72
+ 4.0,5.0,0.0,1.0,2.0,3.0,1.0,4.0,0.0,0.0,2.0,1.0,2.0,0.0,1.0,0.0,0.0,1.0,5.0,0.0,1.0,0.0,2,User71
73
+ 0.0,0.0,3.0,1.0,3.0,0.0,2.0,3.0,0.0,3.0,0.0,3.0,4.0,1.0,5.0,0.0,1.0,3.0,0.0,0.0,1.0,0.0,3,User72
74
+ 5.0,3.0,2.0,0.0,3.0,0.0,3.0,1.0,3.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,3.0,1.0,2.0,0.0,1.0,0,User73
75
+ 4.0,5.0,0.0,2.0,0.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,2.0,0.0,5.0,0.0,2.0,2.0,5.0,0,User74
76
+ 2.0,1.0,0.0,3.0,0.0,0.0,5.0,0.0,3.0,0.0,4.0,3.0,0.0,0.0,0.0,3.0,2.0,2.0,0.0,1.0,0.0,5.0,0,User75
77
+ 0.0,4.0,0.0,0.0,1.0,0.0,3.0,2.0,0.0,0.0,3.0,0.0,0.0,4.0,4.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0,User76
78
+ 3.0,0.0,0.0,0.0,2.0,5.0,1.0,0.0,0.0,0.0,2.0,4.0,0.0,3.0,5.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,1,User77
79
+ 0.0,2.0,0.0,2.0,1.0,0.0,0.0,0.0,5.0,3.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,2,User78
80
+ 0.0,0.0,5.0,0.0,0.0,0.0,4.0,1.0,5.0,0.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,2.0,0.0,0.0,3.0,3.0,0,User79
81
+ 0.0,2.0,0.0,2.0,0.0,0.0,5.0,0.0,1.0,4.0,0.0,0.0,0.0,5.0,3.0,0.0,0.0,0.0,4.0,1.0,0.0,0.0,2,User80
82
+ 5.0,0.0,5.0,0.0,0.0,5.0,5.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,5.0,4.0,3.0,0.0,0.0,0.0,0.0,0,User81
83
+ 0.0,0.0,0.0,0.0,5.0,0.0,1.0,0.0,0.0,4.0,5.0,2.0,0.0,0.0,0.0,0.0,4.0,3.0,0.0,0.0,0.0,0.0,3,User82
84
+ 0.0,0.0,1.0,3.0,0.0,5.0,0.0,0.0,4.0,5.0,0.0,0.0,0.0,5.0,2.0,0.0,3.0,0.0,0.0,1.0,4.0,0.0,3,User83
85
+ 1.0,0.0,3.0,0.0,0.0,1.0,2.0,0.0,1.0,1.0,4.0,2.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,3.0,2,User84
86
+ 0.0,4.0,4.0,0.0,5.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,3,User85
87
+ 0.0,0.0,0.0,2.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,1.0,4.0,5.0,2.0,3.0,0.0,0.0,0.0,2.0,0,User86
88
+ 0.0,0.0,0.0,0.0,3.0,2.0,2.0,2.0,0.0,0.0,2.0,0.0,3.0,0.0,3.0,0.0,0.0,0.0,1.0,1.0,0.0,3.0,2,User87
89
+ 2.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,4.0,3.0,0.0,4.0,4.0,0.0,4.0,2,User88
90
+ 0.0,0.0,4.0,5.0,1.0,2.0,0.0,0.0,2.0,0.0,0.0,3.0,0.0,5.0,5.0,0.0,0.0,0.0,5.0,0.0,0.0,3.0,2,User89
91
+ 3.0,2.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,0.0,1.0,4.0,5.0,5.0,0.0,2,User90
92
+ 0.0,4.0,0.0,0.0,1.0,0.0,4.0,3.0,4.0,0.0,0.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,5.0,0,User91
93
+ 2.0,2.0,2.0,0.0,5.0,0.0,0.0,0.0,5.0,4.0,0.0,0.0,3.0,0.0,5.0,2.0,5.0,2.0,0.0,0.0,0.0,5.0,0,User92
94
+ 3.0,5.0,4.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,3.0,3.0,2.0,2.0,4.0,5.0,1.0,0.0,0.0,1,User93
95
+ 0.0,5.0,0.0,0.0,4.0,4.0,0.0,0.0,1.0,5.0,0.0,0.0,0.0,5.0,1.0,1.0,0.0,5.0,0.0,0.0,0.0,0.0,3,User94
96
+ 0.0,0.0,0.0,2.0,2.0,2.0,0.0,2.0,0.0,0.0,3.0,0.0,3.0,0.0,0.0,0.0,2.0,0.0,0.0,1.0,0.0,5.0,2,User95
97
+ 0.0,1.0,5.0,0.0,2.0,3.0,0.0,0.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,3,User96
98
+ 0.0,0.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,4.0,0.0,3.0,2.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,0,User97
99
+ 0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,0.0,2.0,0.0,0.0,0.0,0.0,0,User98
100
+ 0.0,0.0,0.0,2.0,4.0,5.0,3.0,0.0,0.0,1.0,2.0,0.0,0.0,1.0,2.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,1,User99
101
+ 0.0,0.0,0.0,3.0,1.0,0.0,4.0,3.0,2.0,4.0,0.0,0.0,0.0,4.0,4.0,0.0,1.0,4.0,0.0,3.0,3.0,1.0,3,User100