-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathcross_filt.py
336 lines (289 loc) · 15.8 KB
/
cross_filt.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
from textwrap import dedent as d
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import dash
from dash import Input, Output, html, dcc, dash_table
import pandas as pd
import plotly.express as px
import dash_bootstrap_components as dbc
import re
from operator import itemgetter
def remove_year(year):
# years= year.str.extract(r'([0-9]{4})')
expr = re.compile('[0-9]{4}')
line = re.sub(expr, '', year)
return line
def split_it(year):
return year.str.extract(r'([0-9]{4})')
# external_stylesheets = ['https://codepen.io/kaburelabs/pen/xxGRXWa.css']
# app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
# def dataset():
# data_path = 'data/wine_data_new.csv'
# ## Importing the dataset
# data = pd.read_csv(data_path, index_col=0)
# print(data)
# return data
# df_train = dataset()
# # print(df_train.columns)
# scatt = pd.read_csv("data/matrix_wines.csv", index_col=0)
# scatt['harvest'] = scatt['harvest'].astype(str)
# scatt['rating_cat'] = scatt['rating_cat'].astype(str)
# #scatt['clusters'] = scatt['clusters'].astype(str)
# df_train['rating_cat'] = df_train['rating_cat'].astype(str)
# print(scatt.columns)
# available_indicators = df['Indicator Name'].unique()
def _scatter_view(data, x_val, y_val, log):
fig = px.scatter(data, x='price', y='points',
title=f"Distribution of Wine Price and Ratings",
color=x_val, hover_name='variety', opacity=.4,
hover_data=['country'], height=600,
log_x=log)
fig.update_layout(showlegend=False, title_x=.5,
# title=f"Distribution of {x_val} <br>by {color}",
xaxis_title=f"Mean Price per Bottle (in USD)",
yaxis_title=f"Mean Points Range",
# xaxis={'type':'category'},
# margin=dict(t=100, l=50)
)
return fig
def tf_idf_words(df, country_var, variety, ngram, province):
if ngram == 1:
ngran_range = (1, 2)
title = (f"TfIdf Unigram for <br>{country_var}, {province}, {variety}")
elif ngram == 2:
ngran_range = (2, 2)
title = (f"TfIdf Unigram for <br>{country_var}, {province}, {variety}")
elif ngram == 3:
ngran_range = (3, 3)
title = (f"TfIdf Unigram for <br>{country_var}, {province}, {variety}")
vectorizer = TfidfVectorizer(ngram_range=ngran_range, min_df=1,
stop_words='english', max_features=10,
max_df=500)
# X2 = vectorizer.fit_transform(df_train.loc[(df_train.country == country_var)]['description'])
X2 = vectorizer.fit_transform(df['description_desc'])
features = (vectorizer.get_feature_names())
scores = (X2.toarray())
# Getting top ranking features
sums = X2.sum(axis=0)
data1 = []
for col, term in enumerate(features):
data1.append((term, sums[0, col]))
ranking = pd.DataFrame(data1, columns=['term', 'rank'])
words = (ranking.sort_values('rank', ascending=False))[:15]
fig = px.bar(words, x='term', y='rank', title=title, height=500)
fig.update_layout(autosize=False, title_x=.5)
return fig
columns = ['province', 'winery', 'title', 'harvest', 'points', 'price']
# print(scatt.columns)
layout2 = html.Div([
dbc.Row([
dbc.Col(
html.Div([
html.Div("Colored by: ", className="text-color font-md"),
dcc.Dropdown(
id='crossfilter-xaxis-column',
options=[ # {'label': 'Review Cluster', 'value': 'clusters'},
{'label': 'Country', 'value': 'country'},
{'label': 'Harvest', 'value': 'harvest'},
{'label': 'Rating', 'value': 'rating_cat'}],
value='rating_cat', placeholder='Select a feature')
], className="text-center"
), width={"size": 8, "offset": 2}, md={"size": 5, "offset": 1}, lg={"size": 2, "offset": 0},
className="bottom16"),
dbc.Col(
html.Div([
html.Div("Price Distribution",
className="text-color font-md"),
dcc.RadioItems(
id='crossfilter-yaxis-type',
options=[{'label': 'Linear', 'value': 0},
{'label': 'Log', 'value': 1}],
value=1,
labelStyle={'display': 'inline-block',
"marginRight": "16px"},
)], className="text-center"),
width={"size": 8, "offset": 2}, md={"size": 5, "offset": 0}, lg={"size": 2, "offset": 0},
className="bottom16"),
dbc.Col(html.Div([html.Div("Selection Data", className="bold"),
html.Div(["To get informations about an specific pair of ", html.Span("Country/Variety", className="bold"), " click on the points in scatter chart below."
# dcc.Markdown(d("""
# **Selection Data**
# To get informations about an specific pair of **Country/Variety**, click on the points in scatter chart below.
# """)),
])]),
width={"size": 12, "offset": 0}, md={"size": 10, "offset": 1}, lg={"size": 8, "offset": 0},
className="bottom16"
),
], style={'borderLeft': 'thin lightgrey solid',
'backgroundColor': '#50d890',
'padding': '32px 16px', 'margin': '0 auto'}),
# Gráficos
dbc.Row([
# Scatter plot
dbc.Col(html.Div([
# Gráfico principal que tem o hover
dcc.Graph(
id='crossfilter-indicator-scatter',
clickData={'points': [
{'customdata': 'Portugal', 'hovertext': 'Portuguese White'}]},
# hoverData={'points': [{'customdata': 'General', 'hovertext':'Ramisco'}]
# clear_on_unhover=True,
)
],), width={"size": 12, "offset": 0}, md={"size": 10, "offset": 1}, lg={"size": 7, "offset": 0}, className="bottom32"),
dbc.Col(
html.Div([
html.Div([
html.Div(
[
html.Div(
children=[
html.Div(className='title text-center padding16 font-md',
children="Information for Country/Variety"),
html.Div(
id='display-1', className="text-center bold font-lg")
]),
dbc.Row([
dbc.Col(html.Div(
children=[
html.Div(
id='display-2', className="text-center text-color bold font-lg"),
html.Div(
children="Reviews", className="text-center text-color font-md")
]), width={"size": 6, "offset": 3}, md={"size": 6, "offset": 3}),
]),
dbc.Row([
dbc.Col(html.Div(
children=[
html.Div(
id='display-10', className="text-center text-color bold font-lg"),
html.Div(
children="Provinces", className="text-center text-color font-md")
]), width={"size": 4, "offset": 0}, md={"size": 4, "offset": 0}),
dbc.Col(html.Div(
children=[
html.Div(
id='display-3', className="text-center text-color bold font-lg"),
html.Div(
children="Wineries", className="text-center text-color font-md")
]), width={"size": 4, "offset": 0}, md={"size": 4, "offset": 0}),
dbc.Col(html.Div(
children=[
html.Div(
id='display-4', className="text-center text-color bold font-lg"),
html.Div(
children="Titles", className="text-center text-color font-md")
]), width={"size": 4, "offset": 0}, md={"size": 4, "offset": 0})], style={"padding": "0 12px"})
], id="number-plate", className="BgDisplay",
style={'padding': '24px 0', 'height': 'auto'}),
html.Div([
html.Div(id='title-table'),
dash_table.DataTable(id='table',
columns=[{"name": i, "id": i}
for i in columns],
page_current=0,
page_size=5,
page_action='custom',
filter_action='custom',
sort_by=[],
sort_mode='single',
sort_action='custom',
# sort_by='points', sort_mode='single',
# sortDirection='ASC',
filter_query='',
# style_data={'whiteSpace':'normal'},
# style_table={'overflowX': 'scroll'},
fixed_columns={
'headers': True, 'data': 0},
fixed_rows={
'headers': True, 'data': 5},
# style_table={'height': '240px',
# #'minHeight': '150px', 'maxHeight': '150px',
# #'minWidth': '525px', 'width': '525px', 'maxWidth': '525px',
# # 'overflowX': 'scroll'
# },
style_cell={
# 'minWidth': '15px', 'maxWidth': '150px',
# , 'maxHeight':'50px',
# # 'textOverflow': 'ellipsis',
},
style_cell_conditional=[
{
'if': {'column_id': c},
'maxWidth': '90px',
'textAlign': 'left',
# 'overflowY': 'hidden',
'textOverflow': 'hidden'
} for c in ['province', 'winery', 'title']
],
)], style={'margin': '0 auto'})
], style={"height": "600px"}),
]), width={"size": 12, "offset": 0}, md={"size": 10, "offset": 1}, lg={"size": 5, "offset": 0})
], style={'padding': '24px 0'}),
dbc.Row(
html.Div([
html.Div("Reviews Specification",
className="font-xl text-center bottom16"),
html.Div(["You can select in the Scatter plot on the right the Titles and see some aspects of the reviews of sommeliers."],
className="font-md text-center bottom32"),
html.Div("This section is composed by three parts: ",
className="font-lg text-center bottom16 top16"),
html.Div(dbc.Row([
dbc.Col(
html.Div([
html.Div("TFIDF", className="font-lg"),
html.Div(
"TFIDF - You can select the N-gram that you desire for each Country/Variety pair.", className="font-md bottom16")
], style={"height": "100%"}, className="BgDisplay2 radius12 padding16"), width=4),
dbc.Col(html.Div([
html.Div("Scatter Plot", className="font-lg"),
html.Div(
"Scatter Plot Chart where you can select an specific variety to see some informations and main aspects of sommelier reviews.", className="font-md bottom16")
], style={"height": "100%"}, className="BgDisplay2 radius12 padding16"), width=4),
dbc.Col(html.Div([
html.Div("Aspects - SpaCy", className="font-lg"),
html.Div(
"The Aspects of reviews was extracted by SpaCy using some POS tagging techniques", className="font-md bottom16")
], style={"height": "100%"}, className="BgDisplay2 radius12 padding16"), width=4),
], className="boxed margin-auto")),
], className="BgDisplay padding32 bottom32"),
),
dbc.Row([
dbc.Col(html.Div([
html.Div("TfIdf N-grams: ", className="font-md text-color"),
dcc.RadioItems(
id='crossfilter-xaxis-type',
options=[{'label': 'Unigram', 'value': 1},
{'label': 'Bigram', 'value': 2},
{'label': 'Trigram', 'value': 3}],
value=1,
labelStyle={'display': 'inline-block', "marginLeft": "16px"})
], className="text-center"), width=3, className="bottom32"),
dbc.Col(html.Div([
html.Div("Select the Province(s): ",
className="font-md text-color"),
dcc.Dropdown(
id='province-selector1',
multi=False, # labelStyle={'display': 'inline-block'}
)
], className="text-center"), width=3, className="bottom32")
], style={'borderBottom': 'thin lightgrey solid',
'backgroundColor': '#50d890',
'padding': '30px 0 0'}, className="bottom32"),
# Table and tfidf plot
dbc.Row([
dbc.Col(
dcc.Graph(id='fig-1'), width=7), # style={'width':'75%', 'margin':'0 auto'},
dbc.Col(
dcc.Graph(id='fig-2',
clickData={'points': [
{'customdata': 'Portugal', 'hovertext': 'Portuguese White'}]},
hoverData={'points': [{'customdata': ['Seacampo Reserva Red (Dão)', 'Portugal']}]},),
width=5)
]),
html.Div([
html.Div(id='inf-1')
# style={'width':'75%', 'margin':'0 auto'},
# html.Div([
# dcc.Graph(id='fig-2')
# ], className='four columns')
], style={'padding': '24px 0'}, className="BgDisplay")
])