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Code Issues Projects Releases Wiki Activity

- csv exporter

Browse Source 
- set weights to include 1
- csv viewer (par-cord)
- added some csv results
This commit is contained in:
Benedikt Kromer
2018-01-02 17:31:53 +01:00
parent 035702ca2c
commit 4608dd4ea4
15 changed files with 22024 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
.gitignore vendored
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@@ -157,4 +157,5 @@ venv.bak/
# Giveme5W:
runtime-resources/
examples/misc/learn_weights_new/queue_caches/
*.pyc
*.pyc
*/*+/temp-plot.html

38
examples/misc/learn_weights_new/csv_to_parallel_coordinates_plotter.py Normal file
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@@ -0,0 +1,38 @@
import plotly.graph_objs as go
import plotly
import pandas as pd
df = pd.read_csv("result/training_final_result_what.csv")
dimensions = []
for dimension in df.columns.values:
dimensions.append(
dict(range=[0, 1], label=dimension, values=df[dimension])
)
line = dict(color = df['score'],
colorscale = 'Viridis',
showscale = True,
reversescale = True,
cmin=0,
cmax=1
)
data = [
go.Parcoords(
line = line,
dimensions = dimensions
)
]
layout = go.Layout(
plot_bgcolor = '#E5E5E5',
paper_bgcolor = '#E5E5E5'
)
fig = go.Figure(data = data, layout = layout)
plotly.offline.plot(fig)
#plotly.offline.plot(fig, filename = 'parcoords-basic.html')
#py.iplot(fig, filename = 'parcoords-basic')

33
examples/misc/learn_weights_new/evaluate.py
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@@ -10,6 +10,8 @@ from itertools import groupby
import os
from tools import mapper
def weights_to_string(weights):
"""
@@ -298,14 +300,31 @@ def evaluate(score_results, write_full: bool=False, praefix=''):
data_file.write(json.dumps(final_result[question], sort_keys=False, indent=4))
data_file.close()
# write it as CSV
for question in final_result:
pass
# with open(os.path.dirname(__file__) + '/final_result.csv','w') as csv_file:
# writer = csv.writer(csv_file)
# for line in outputs:
# writer.writerow(line)
# write combinations per extractor to CSV
for question in score_results:
csv_results = []
weights = list(score_results[question][1]['weights'].values())
extractor_name = mapper.question_to_extractor(question)
headerline = []
# take first weights to form a header line
for i, weight in enumerate(weights[1]['weights']):
headerline.append(mapper.weight_to_string(extractor_name, i))
headerline.append('score')
csv_results.append(headerline)
for weight in weights:
# average score over all document for these weights
csv_results.append( list(weight['weights']) + [weight['norm_avg']])
print('test')
with open('result/' + praefix + '_final_result_' + question + '.csv', 'w') as csv_file:
writer = csv.writer(csv_file)
for line in csv_results:
writer.writerow(line)
if __name__ == '__main__':
process_files('queue_caches/*_processed*/', praefix='training')

6
examples/misc/learn_weights_new/learn.py
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@@ -8,7 +8,7 @@ from threading import Thread
import dateutil.parser
from dateutil.relativedelta import relativedelta as rd
from geopy.distance import vincenty
from geopy.distance import great_circle
from nltk import word_tokenize
from nltk.corpus import wordnet
@@ -259,7 +259,7 @@ class Learn(object):
# retrieved answer couldn't be parsed
return -3
return vincenty(annotation.point, location.point).kilometers
return great_circle(annotation.point, location.point).kilometers
def load_documents(self, extractors, combined_scorer, sampling):
inputPath = path(self._input_path)
@@ -279,7 +279,7 @@ class Learn(object):
# set a path to save an load preprocessed documents
.set_preprocessed_path(preprocessedPath)
# limit the the to process documents (nice for development)
.set_limit(1)
#.set_limit(1)
# add an optional extractor (it would do basically just copying without...)
.set_sampling(sampling)
.set_extractor(extractor_object)

9205
examples/misc/learn_weights_new/result/training_final_result_how.csv Normal file
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File diff suppressed because it is too large Load Diff

812
examples/misc/learn_weights_new/result/training_final_result_how_1.json Normal file
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@@ -0,0 +1,812 @@
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822
examples/misc/learn_weights_new/result/training_final_result_what.csv Normal file
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@@ -0,0 +1,822 @@
position,frequency,named_entity,score
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0.9,0.5,0.1,0.6277880048608356
0.1,0.6,0.9,0.4899457208701132
0.9,0.9,0.1,0.5844611456777249
0.1,0.4,0.6,0.49107726817016034
0.1,0.1,0.5,0.7955424990683091
0.9,0.1,0.6,1.0
0.9,0.6,0.4,0.6690440975541606
0.1,0.1,0.6,1.0
0.9,0.2,0.6,1.0
0.9,0.7,0.7,0.7503377372360198
0.9,0.8,0.8,0.8636949993788727
0.1,0.5,0.0,0.5150217939574043
0.1,0.4,0.8,0.5654331090974125
0.1,0.5,0.2,0.5270980134756814
0.1,0.5,0.4,0.5524521049595581
0.1,0.5,0.6,0.5019913549407683
0.1,0.5,0.8,0.5124839225384381
0.1,0.6,0.0,0.5659609094140274
0.1,0.6,0.2,0.5335317840891889
0.1,0.6,0.4,0.5548408381979596
0.1,0.6,0.6,0.5566802398510661
0.1,0.6,0.8,0.5469238063685252
0.1,0.7,0.0,0.5850436384086756
1 position frequency named_entity score
2 0.0 0.0 0.1 0.4380389410657441
3 0.0 0.1 0.0 0.493884251036021
4 0.0 0.3 0.5 0.47056734632127967
5 0.2 0.5 0.5 0.5492493387863854
6 0.1 0.7 0.7 0.4788073268322726
7 0.2 0.0 0.9 0.5202437302620984
8 0.1 0.8 0.5 0.4637556358493378
9 0.1 0.7 0.9 0.4788073268322726
10 0.1 0.1 0.7 0.46296270672233475
11 0.1 0.8 0.3 0.4759397451278808
12 0.1 0.8 0.1 0.5159586120744105
13 0.3 0.9 0.7 0.47543628411524935
14 0.3 0.8 0.7 0.47880732683227245
15 0.1 0.9 0.9 0.4910772681701603
16 0.2 0.9 0.8 0.47880732683227256
17 0.2 0.1 0.2 0.43596663802318103
18 0.3 0.2 0.5 0.4418324443874049
19 0.2 0.2 0.9 0.47377633502764577
20 0.5 0.0 0.1 0.4347190743906035
21 0.6 0.9 0.0 0.47645898859021724
22 0.3 0.4 0.2 0.4764589885902173
23 0.2 0.7 0.8 0.4940680043461102
24 0.0 0.3 0.1 0.48094766335331623
25 0.2 0.1 0.5 0.45445020502818617
26 0.1 0.7 0.2 0.6053805977520288
27 0.2 0.1 0.8 0.43596663802318103
28 0.2 0.8 0.7 0.4788073268322726
29 0.2 0.4 0.7 0.4788073268322729
30 0.3 0.0 0.7 0.4347190743906034
31 0.3 0.1 0.9 0.4210896939674765
32 0.2 0.0 0.5 0.5432485507045787
33 0.2 0.6 0.1 0.482191092472262
34 0.2 0.5 0.2 0.47880732683227273
35 0.1 0.9 0.7 0.47880732683227273
36 0.9 0.4 0.3 0.43171525344277634
37 0.0 0.2 0.7 0.4753796316755852
38 0.1 0.7 0.4 0.6053805977520288
39 0.5 0.3 0.8 0.46034156417207295
40 0.4 0.4 0.7 0.47377633502764543
41 0.5 0.6 0.0 0.47645898859021724
42 0.5 0.2 0.0 0.42412869291902666
43 0.2 0.7 0.4 0.4940680043461103
44 0.5 0.8 0.2 0.480148277885126
45 0.6 0.1 0.2 0.4259871149546935
46 0.2 0.9 0.9 0.5055790485754897
47 0.4 0.3 0.1 0.4455578720659077
48 0.1 0.7 0.6 0.5856886014424414
49 0.2 0.1 0.0 0.4616494987949885
50 0.8 0.7 0.6 0.4455578720659079
51 0.0 0.3 0.4 0.4878504731448196
52 0.2 0.4 0.1 0.46354003012355166
53 0.4 0.6 0.5 0.4764589885902171
54 0.1 0.7 0.8 0.6109820868172063
55 0.1 0.9 0.5 0.45061452563013377
56 0.2 0.9 0.4 0.4788073268322726
57 0.0 0.2 0.5 0.4782168023609608
58 0.1 0.8 0.0 0.6131926352824967
59 0.7 0.2 0.8 0.4210896939674767
60 0.1 0.8 0.2 0.6277880048608356
61 0.3 0.1 0.4 0.4694147319155854
62 0.2 0.4 0.9 0.4940680043461103
63 0.2 0.1 0.6 0.5477134551773803
64 0.2 0.1 0.9 0.47390314916015863
65 0.2 0.6 0.3 0.4940680043461102
66 0.8 0.1 0.5 0.4247582513451611
67 0.7 0.6 0.6 0.4455578720659077
68 0.5 0.4 0.0 0.4455578720659077
69 0.1 0.9 0.3 0.5320291770553768
70 0.1 0.4 0.1 0.47880732683227256
71 0.2 0.5 0.7 0.4940680043461102
72 0.3 0.2 0.7 0.48144923523586997
73 0.5 0.2 0.2 0.42412869291902666
74 0.2 0.7 0.5 0.4976532038675429
75 0.2 0.0 0.7 0.4347190743906033
76 0.1 0.7 0.3 0.45015872301487986
77 0.4 0.1 0.6 0.42741329536538414
78 0.1 0.9 0.8 0.4940680043461101
79 0.4 0.8 0.7 0.47880732683227245
80 0.3 0.6 0.1 0.4704936094336783
81 0.3 0.5 0.2 0.48014827788512615
82 0.1 0.9 0.1 0.47880732683227273
83 0.0 0.3 0.8 0.4567445704339565
84 0.2 0.3 0.4 0.5375704514478242
85 0.4 0.3 0.3 0.4455578720659077
86 0.3 0.0 0.9 0.4347190743906034
87 0.2 0.4 0.3 0.47880732683227284
88 0.1 0.7 0.5 0.5448881023390082
89 0.2 0.1 0.1 0.4633771261169364
90 0.3 0.1 0.5 0.42735397092152405
91 0.2 0.7 0.1 0.47880732683227273
92 0.3 0.8 0.1 0.47880732683227245
93 0.1 0.8 0.9 0.47880732683227273
94 0.6 0.9 0.6 0.47645898859021724
95 0.2 0.3 0.1 0.4764589885902175
96 0.4 0.0 0.3 0.46872355343530553
97 0.0 0.4 0.5 0.5288159158934341
98 0.1 0.9 0.6 0.5442345738744367
99 0.5 0.6 0.4 0.47645898859021724
100 0.1 0.8 0.7 0.5019224721571678
101 0.3 0.9 0.1 0.47880732683227245
102 0.2 0.2 0.7 0.48421357697276673
103 0.2 0.1 0.7 0.43596234008708484
104 0.2 0.5 0.4 0.47880732683227273
105 0.0 0.5 0.1 0.475620122806916
106 0.8 0.1 0.9 0.4247582513451611
107 0.1 0.6 0.7 0.47880732683227256
108 0.3 0.2 0.2 0.47608327169636305
109 0.1 0.9 0.4 0.452085632614737
110 0.6 0.5 0.7 0.4562227625801785
111 0.2 0.8 0.3 0.4576940458676577
112 0.1 0.5 0.3 0.47880732683227256
113 0.2 0.9 0.1 0.4940680043461102
114 0.2 0.3 0.5 0.4760909969791543
115 0.8 0.8 0.9 0.4737763350276456
116 0.3 0.2 0.9 0.46746204957012855
117 0.6 0.1 0.8 0.4259871149546935
118 0.7 0.3 0.4 0.4326593426868578
119 0.3 0.6 0.4 0.48128594770040145
120 0.5 0.6 0.6 0.47645898859021724
121 0.3 0.5 0.4 0.48014827788512615
122 0.5 0.4 0.4 0.4455578720659077
123 0.3 0.0 0.4 0.46872355343530564
124 0.2 0.3 0.2 0.4816006457508894
125 0.3 0.8 0.3 0.48128594770040134
126 0.2 0.7 0.7 0.4735496123761792
127 0.2 0.2 0.3 0.47767621369959445
128 0.3 0.1 0.7 0.449732482093649
129 0.2 0.9 0.2 0.4788073268322726
130 0.7 0.3 0.6 0.4326593426868578
131 0.3 0.3 0.1 0.4842135769727668
132 0.3 0.4 0.0 0.4764589885902173
133 0.5 0.1 0.0 0.41934441830237235
134 0.5 0.9 0.0 0.47880732683227245
135 0.0 0.5 0.3 0.47359465558658476
136 0.9 0.5 0.7 0.4417887842796155
137 0.4 0.7 0.3 0.47880732683227245
138 0.3 0.0 0.5 0.46066819973942275
139 0.5 0.4 0.6 0.4562227625801785
140 0.4 0.3 0.7 0.4455578720659077
141 0.0 0.2 0.9 0.4705673463212796
142 0.2 0.0 0.1 0.5365425858231306
143 0.2 0.6 0.9 0.45015872301487986
144 0.7 0.0 0.3 0.4347190743906035
145 0.8 0.3 0.0 0.42373344246011907
146 0.2 0.1 0.3 0.5144094414944862
147 0.3 0.4 0.8 0.4764589885902173
148 0.0 0.4 0.3 0.494719160941061
149 0.3 0.6 0.7 0.47880732683227245
150 0.7 0.7 0.8 0.47377633502764543
151 0.3 0.3 0.2 0.4737763350276457
152 0.0 0.3 0.2 0.5052616564944266
153 0.4 0.1 0.0 0.42741329536538414
154 0.8 0.9 0.5 0.4760951746072446
155 0.3 0.8 0.5 0.47880732683227245
156 0.1 0.1 0.9 0.4737763350276456
157 0.5 0.7 0.0 0.47645898859021724
158 0.4 0.5 0.6 0.4764589885902171
159 0.7 0.0 0.5 0.4347190743906035
160 0.6 0.6 0.9 0.4737763350276456
161 0.9 0.6 0.2 0.4542641287119915
162 0.2 0.3 0.7 0.45224057584161953
163 0.4 0.7 0.5 0.4812859477004013
164 0.7 0.8 0.0 0.4760951746072446
165 0.0 0.3 0.7 0.4694222347894346
166 0.1 0.4 0.3 0.47880732683227256
167 0.2 0.0 0.3 0.5266911228111769
168 0.2 0.1 0.4 0.4544460802158692
169 0.5 0.9 0.4 0.47880732683227245
170 0.7 0.4 0.2 0.4414400553395309
171 0.0 0.4 0.1 0.513329748866741
172 0.4 0.9 0.6 0.47880732683227245
173 0.3 0.5 0.0 0.47779463187947874
174 0.2 0.2 0.1 0.4605234655218372
175 0.5 0.5 0.1 0.4737763350276455
176 0.2 0.3 0.8 0.49961679845872453
177 0.4 0.7 0.7 0.47880732683227245
178 0.5 0.3 0.2 0.4414400553395308
179 0.9 0.0 0.2 0.4347190743906036
180 0.7 0.8 0.4 0.4760951746072446
181 0.2 0.5 0.8 0.48471133781600634
182 0.6 0.7 0.3 0.4605270724705822
183 0.9 0.7 0.1 0.4455578720659079
184 0.8 0.3 0.8 0.42373344246011907
185 0.1 0.8 0.4 0.6690440975541606
186 0.3 0.2 0.1 0.4266599450578959
187 0.5 0.1 0.6 0.434650542147953
188 0.7 0.1 0.2 0.4247582513451611
189 0.2 0.5 0.1 0.48536963919962417
190 0.5 0.3 0.4 0.4414400553395308
191 0.6 0.7 0.5 0.4760951746072446
192 0.9 0.0 0.8 0.4347190743906036
193 0.3 0.1 0.0 0.4160791328969484
194 0.3 0.3 0.8 0.471431520327058
195 0.6 0.3 0.8 0.43596663802318103
196 0.7 0.4 0.8 0.4414400553395309
197 0.2 0.2 0.5 0.5124839225384381
198 0.7 0.9 0.0 0.47645898859021724
199 0.3 0.1 0.1 0.4501098858706284
200 0.6 0.4 0.1 0.44183244438740504
201 0.4 0.5 0.0 0.47543628411524935
202 0.6 0.0 0.7 0.4347190743906035
203 0.1 0.7 0.1 0.47880732683227256
204 0.7 0.5 0.2 0.4455578720659077
205 0.0 0.1 0.1 0.4917400016200394
206 0.6 0.8 0.3 0.46572934573324165
207 0.4 0.1 0.8 0.4656969592871014
208 0.9 0.1 0.7 0.419968963321137
209 0.8 0.5 0.3 0.4414400553395309
210 0.9 0.8 0.3 0.4476286716905708
211 0.7 0.5 0.4 0.4455578720659077
212 0.3 0.9 0.3 0.4847113378160064
213 0.0 0.4 0.7 0.4899856360400152
214 0.9 0.8 0.5 0.4476286716905708
215 0.7 0.9 0.6 0.47645898859021724
216 0.7 0.2 0.2 0.4210896939674767
217 0.4 0.2 0.1 0.44236942684063973
218 0.7 0.9 0.8 0.47645898859021724
219 0.0 0.3 0.9 0.4849715052124407
220 0.2 0.9 0.5 0.5155499967548633
221 0.7 0.5 0.8 0.4612857559961583
222 0.3 0.9 0.5 0.48128594770040134
223 0.9 0.2 0.3 0.42118995231018425
224 0.3 0.7 0.7 0.48128594770040145
225 0.4 0.9 0.2 0.4812859477004013
226 0.5 0.2 0.6 0.47603247742520766
227 0.8 0.6 0.3 0.4455578720659077
228 0.9 0.2 0.7 0.42118995231018425
229 0.1 0.8 0.6 0.7503377372360198
230 0.1 0.3 0.7 0.47880732683227245
231 0.9 0.9 0.8 0.4737763350276456
232 0.1 0.2 0.9 0.47880732683227245
233 0.3 0.4 0.5 0.4866894257562926
234 0.1 0.4 0.7 0.47880732683227256
235 0.6 0.9 0.8 0.4605270724705822
236 0.9 0.3 0.8 0.4210896939674767
237 0.1 0.2 0.3 0.47880732683227256
238 0.7 0.7 0.1 0.45811470698919804
239 0.3 0.4 0.9 0.4866894257562926
240 0.2 0.9 0.6 0.4365743089994643
241 0.0 0.5 0.6 0.48150889804249175
242 0.1 0.4 0.9 0.47880732683227256
243 0.3 0.3 0.7 0.4410637244155005
244 0.4 0.7 0.1 0.4915264442984948
245 0.1 0.8 0.8 0.8636949993788727
246 0.2 0.3 0.0 0.5954777861920978
247 0.3 0.7 0.1 0.48892516077529435
248 0.2 0.6 0.7 0.5134872053183669
249 0.7 0.1 0.6 0.4531305630124317
250 0.3 0.0 0.1 0.5223674687231941
251 0.3 0.7 0.3 0.4573098042454443
252 0.3 0.2 0.6 0.5040730508369703
253 0.5 0.1 0.2 0.46166929959903963
254 0.7 0.9 0.2 0.4605270724705822
255 0.5 0.8 0.8 0.4605270724705821
256 0.2 0.3 0.3 0.5335317840891889
257 0.3 0.4 0.4 0.47543359234145294
258 0.2 0.7 0.2 0.4755880652055101
259 0.1 0.9 0.0 0.7955424990683091
260 0.5 0.1 0.8 0.46166929959903963
261 0.4 0.9 0.0 0.4621694521819146
262 0.4 0.9 0.4 0.4605270724705822
263 0.0 0.4 0.9 0.45566176383767715
264 0.0 0.1 0.3 0.47071307372561405
265 0.2 0.3 0.6 0.5548408381979596
266 0.8 0.2 0.3 0.45168160721826156
267 0.1 0.9 0.2 1.0
268 0.6 0.1 0.4 0.44643749931604215
269 0.3 0.7 0.2 0.4940680043461102
270 0.8 0.3 0.2 0.4362532580487615
271 0.3 0.5 0.7 0.4981989497523047
272 0.3 0.5 0.8 0.47939119881765463
273 0.3 0.7 0.6 0.4940680043461102
274 0.8 0.4 0.5 0.43508619451335423
275 0.1 0.3 0.1 0.4759397451278808
276 0.6 0.2 0.9 0.4362532580487616
277 0.5 0.7 0.8 0.501384795664055
278 0.0 0.1 0.5 0.4811302556277716
279 0.2 0.3 0.9 0.5576660105869832
280 0.4 0.5 0.2 0.48178302604377077
281 0.3 0.1 0.3 0.4677180612692774
282 0.8 0.5 0.9 0.4535245600282508
283 0.5 0.4 0.8 0.4714315203270579
284 0.0 0.1 0.9 0.48113025562777145
285 0.2 0.8 0.1 0.5150217939574043
286 0.6 0.5 0.1 0.463795254107462
287 0.5 0.5 0.6 0.4714315203270579
288 0.3 0.8 0.8 0.4940680043461102
289 0.8 0.7 0.8 0.4612857559961583
290 0.2 0.4 0.5 0.5718743938792334
291 0.0 0.5 0.2 0.4934688092285288
292 0.4 0.1 0.2 0.4729601692710307
293 0.5 0.0 0.3 0.4608199051243059
294 0.3 0.1 0.8 0.5189192298236676
295 0.5 0.3 0.6 0.44968241140034587
296 0.3 0.9 0.0 0.463693109982937
297 0.9 0.1 0.1 0.44295229365077815
298 0.4 0.0 0.1 0.46946237103759125
299 0.5 0.7 0.4 0.4739073691105837
300 0.9 0.1 0.5 0.4478815423287709
301 0.9 0.2 0.1 0.4385749285106799
302 0.2 0.5 0.0 0.5752068462266912
303 0.3 0.7 0.5 0.4942235859008704
304 0.0 0.5 0.8 0.4693618632749693
305 0.9 0.2 0.9 0.4405231716785396
306 0.3 0.2 0.3 0.5254903280130518
307 0.4 0.1 0.3 0.46920087192654225
308 0.2 0.5 0.3 0.5894348161635052
309 0.0 0.5 0.4 0.4469445943423825
310 0.4 0.1 0.7 0.49181476758023596
311 0.5 0.9 0.2 0.4812859477004013
312 0.2 0.8 0.9 0.5025398508800099
313 0.3 0.5 0.3 0.5412786821880908
314 0.4 0.1 0.1 0.436245730041982
315 0.1 0.6 0.1 0.47210175451535696
316 0.9 0.5 0.5 0.4474665414881804
317 0.4 0.0 0.5 0.5394523791440353
318 0.2 0.9 0.7 0.5182435968322509
319 0.2 0.5 0.6 0.6053805977520288
320 0.4 0.3 0.0 0.4842135769727668
321 0.6 0.1 0.0 0.47376643985049954
322 0.4 0.3 0.5 0.5293027430711539
323 0.0 0.6 0.1 0.4793966818121918
324 0.9 0.8 0.9 0.4562227625801785
325 0.2 0.5 0.9 0.5856886014424414
326 0.3 0.0 0.2 0.5794746945668598
327 0.0 0.5 0.7 0.4774146956213576
328 0.4 0.4 0.5 0.4842135769727668
329 0.6 0.3 0.5 0.4505950694712274
330 0.3 0.3 0.5 0.49695178058683004
331 0.4 0.5 0.1 0.4866894257562926
332 0.4 0.4 0.1 0.4546484007438056
333 0.2 0.6 0.5 0.6109820868172063
334 0.5 0.9 0.6 0.4769265657795589
335 0.7 0.1 0.0 0.4915373722044724
336 0.3 0.6 0.8 0.48536963919962417
337 0.5 0.6 0.8 0.4484017361964743
338 0.1 0.2 0.5 0.4704936094336783
339 0.3 0.8 0.2 0.5004176871676632
340 0.6 0.5 0.9 0.4714315203270579
341 0.3 0.0 0.8 0.6456553221058687
342 0.6 0.7 0.1 0.4739073691105837
343 0.0 0.5 0.9 0.4858165871850674
344 0.4 0.2 0.7 0.4530633907606626
345 0.3 0.9 0.6 0.496196027224849
346 0.3 0.1 0.2 0.47953581795451994
347 0.4 0.7 0.6 0.4940680043461102
348 0.2 0.7 0.0 0.6277880048608356
349 0.4 0.8 0.1 0.4940680043461102
350 0.5 0.0 0.8 0.48819965506931257
351 0.2 0.7 0.3 0.6277880048608356
352 0.3 0.4 0.6 0.5471373253432184
353 0.4 0.5 0.8 0.4904153658345584
354 0.0 0.6 0.7 0.5288159158934341
355 0.4 0.6 0.7 0.471481750625734
356 0.4 0.9 0.3 0.4940680043461102
357 0.3 0.1 0.6 0.5164385843578777
358 0.3 0.4 0.1 0.5873309154485463
359 0.4 0.0 0.7 0.522898083610721
360 0.7 0.0 0.8 0.46872355343530564
361 0.4 0.8 0.5 0.463693109982937
362 0.4 0.9 0.7 0.4940680043461102
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0.9,0.2,0.8,0.48615534101264746
0.7,0.0,0.6,0.5751835966113495
0.5,0.9,0.1,0.4488721135510362
0.9,0.7,0.9,0.5187138179284019
0.5,0.2,0.1,0.5629154024151714
0.9,0.3,0.4,0.4542240519324181
0.5,0.2,0.7,0.5629154024151714
0.0,0.9,0.2,0.4471353103584191
0.6,0.7,0.8,0.4839789370085178
0.9,0.0,0.1,0.46369292377529353
0.7,0.8,0.1,0.5359261816700928
0.6,0.3,0.9,0.4442223315785375
0.9,0.4,0.8,0.45012960752144393
0.1,0.1,0.8,0.49098772358345133
0.5,0.6,0.5,0.470459038400133
0.9,0.3,0.1,0.46172523801531373
0.9,0.5,0.6,0.48285864401592116
0.5,0.9,0.9,0.4095058359475388
0.9,0.4,0.2,0.4567062416405713
0.7,0.1,0.8,0.5117906881983383
0.7,0.9,0.5,0.5031824912310006
0.9,0.6,0.7,0.4531151931034449
0.5,0.3,0.3,0.6651082350358932
0.7,0.7,0.3,0.4904916746413452
0.1,0.6,0.5,0.4350053649475644
0.9,0.7,0.6,0.4593189643523495
0.6,0.5,0.3,0.47531343999976855
0.5,0.7,0.2,0.48693806538114937
0.8,0.9,0.3,0.41771818534932265
0.9,0.5,0.8,0.4603659917088305
0.9,0.4,0.6,0.4181091566687879
0.9,0.7,0.0,0.46797061453006844
0.9,0.9,0.4,0.4593189643523495
0.9,0.4,0.9,0.4533921717900566
0.5,0.3,0.9,0.770904382553698
0.5,0.7,0.9,0.5112289034251621
0.9,0.6,0.1,0.42536974310407294
0.0,0.9,0.4,0.49156735644628735
0.0,0.9,0.5,0.493068909111418
0.7,0.0,0.4,0.6053970121176447
0.1,0.3,0.9,0.5039292662092655
0.6,0.2,0.3,0.39589930665438283
0.0,0.1,0.6,0.42565450793506787
0.1,0.2,0.0,0.4274590659254803
0.5,0.4,0.5,0.8950979073664068
0.6,0.6,0.7,0.5814003094097855
0.7,0.5,0.9,0.4697286399737012
0.9,0.8,0.2,0.4311865185435539
0.8,0.5,0.7,0.4563265315502658
0.7,0.4,0.0,0.44281635930695645
0.9,0.4,0.4,0.41903762995097016
0.5,0.8,0.6,0.5301113297509121
0.6,0.7,0.7,0.43180369916906386
0.8,0.3,0.3,0.503979818470946
0.1,0.2,0.2,0.4157572681591707
0.9,0.9,0.7,0.4274590659254803
0.0,0.9,0.7,0.4549547249505325
0.7,0.1,0.5,0.5146795931815485
0.7,0.7,0.2,0.4736033740873457
0.5,0.5,0.2,0.8426468610496103
0.6,0.3,0.7,0.40529127667704046
0.8,0.0,0.1,0.4989509058315627
0.5,0.9,0.3,0.5391637252487268
0.8,0.9,0.8,0.45664123673407614
0.6,0.9,0.1,0.3936066149782151
0.5,0.5,0.9,1.0
0.0,0.9,0.8,0.4559883766254276
0.7,0.2,0.5,0.4545966097138876
0.9,0.9,0.2,0.45382043694819113
0.8,0.1,0.8,0.5086391738069127
0.6,0.0,0.1,0.6894840230490532
0.9,0.1,0.8,0.46005893149421456
0.6,0.5,0.0,0.4891817880605871
0.7,0.0,0.1,0.5293713333029206
0.7,0.5,0.1,0.5009026243481076
0.7,0.6,0.2,0.5272859885368663
0.6,0.5,0.8,0.48693806538114937
0.8,0.9,0.7,0.5687827912297083
0.1,0.2,0.4,0.4734208465480632
0.6,0.1,0.3,0.6652203409195623
0.8,0.3,0.6,0.45068578607535537
0.1,0.0,0.0,0.6101356525805551
0.9,0.5,0.2,0.4853135959605981
0.1,0.2,0.6,0.40400333385903964
0.6,0.7,0.2,0.5112289034251621
0.7,0.7,0.4,0.4742931482849001
0.1,0.0,0.1,0.5222390862556039
0.7,0.4,0.5,0.4327943025069131
0.6,0.2,0.1,0.7408930610672486
0.9,0.7,0.3,0.42221422493069627
0.8,0.7,0.1,0.5011880712856821
0.8,0.5,0.4,0.4466795316885041
0.7,0.8,0.6,0.45018781969260974
0.6,0.3,0.4,0.855082812051141
0.1,0.0,0.2,0.49038835867264413
0.7,0.5,0.5,0.4095058359475388
0.7,0.2,0.0,0.4537228594489555
0.6,0.8,0.1,0.5391637252487268
0.1,0.2,0.8,0.4209298884996478
0.1,0.4,0.5,0.551468643525136
0.9,0.3,0.2,0.44954564467329183
0.6,0.9,0.3,0.5391637252487268
0.7,0.2,0.9,0.4453667477974655
0.9,0.0,0.7,0.5546295525280927
0.6,0.4,0.5,0.8426468610496103
0.8,0.1,0.4,0.5885520942099559
0.1,0.0,0.3,0.5293713333029206
0.7,0.0,0.2,0.6894840230490532
0.8,0.4,0.7,0.4793730887541584
0.7,0.3,0.8,0.4508168202203848
0.6,0.5,0.4,1.0
0.8,0.1,0.3,0.45804764511591767
0.1,0.0,0.4,0.5510360085962979
0.7,0.7,0.6,0.42368487985497094
0.0,0.1,0.8,0.4298038641002472
0.1,0.5,0.1,0.5367259575546622
0.7,0.1,0.1,0.6652203409195623
0.8,0.6,0.1,0.45964991673441347
0.9,0.6,0.8,0.4504165733644705
0.8,0.9,0.0,0.5009026243481076
0.7,0.4,0.7,0.5318735851487412
0.9,0.0,0.5,0.49758513015803985
0.1,0.0,0.5,0.5709949724990819
0.7,0.8,0.7,0.4678189774345865
0.8,0.2,0.9,0.38370494654699266
0.1,0.3,0.0,0.5306708637400053
0.1,0.3,0.2,0.43810701558803355
0.9,0.7,0.4,0.4864955362527584
0.7,0.1,0.9,0.7649936028484808
0.7,0.5,0.6,0.5301113297509121
0.1,0.0,0.6,0.5898328910568217
0.8,0.4,0.1,0.37620498236749167
0.9,0.2,0.0,0.4287656307704186
0.7,0.2,0.7,0.855082812051141
0.1,0.3,0.4,0.41445531381986694
0.7,0.9,0.7,0.5103080308341368
0.8,0.9,0.4,0.430305492355232
0.7,0.6,0.5,0.5829229500728043
0.1,0.0,0.7,0.6151911899249054
0.8,0.1,0.2,0.5039611852027978
0.7,0.3,0.5,0.7826242180767115
0.8,0.5,0.6,0.40513355941944423
0.9,0.3,0.5,0.42941789200915786
0.7,0.7,0.5,0.5829229500728043
0.9,0.5,0.4,0.48616866763195943
0.1,0.5,0.7,0.4611817508884852
0.9,0.1,0.0,0.47620659166999024
0.9,0.8,0.4,0.5763933251472528
0.7,0.4,0.3,1.0
0.1,0.0,0.8,0.6195818849758469
0.8,0.2,0.5,0.4710941865798682
0.8,0.7,0.2,0.43539231431101094
0.7,0.8,0.5,0.6651082350358932
0.9,0.7,0.2,0.5499974519294579
0.9,0.8,0.6,0.47300002342960035
0.1,0.0,0.9,0.5861664887772793
0.8,0.3,0.7,0.5147242585850558
0.9,0.2,0.2,0.46150643563537347
0.8,0.8,0.7,0.470459038400133
0.7,0.9,0.4,0.770904382553698
0.1,0.3,0.8,0.4834859790330009
0.9,0.6,0.5,0.40744274926603763
0.1,0.1,0.0,0.5391637252487268
0.8,0.5,0.2,0.5887037386294022
0.9,0.3,0.7,0.41958589293751325
0.0,0.2,0.1,0.46149369320340244
0.8,0.0,0.7,0.8739332146487881
0.8,0.9,0.9,0.48693806538114937
0.9,0.8,0.0,0.3840167435945633
0.1,0.4,0.0,0.44220598224602575
0.1,0.1,0.1,0.5829229500728043
0.8,0.6,0.5,0.5829229500728043
0.1,0.6,0.3,0.44677225683510646
0.8,0.1,0.7,0.8108998219731822
0.9,0.1,0.4,0.4959929908401717
0.1,0.3,0.6,0.48994251763453817
0.9,0.9,0.5,0.4039905555680798
0.1,0.1,0.2,0.6651082350358932
0.8,0.7,0.7,0.6651082350358932
0.8,0.3,0.1,1.0
0.1,0.4,0.2,0.47826010000571473
0.9,0.5,0.0,0.48351167909393156
0.9,0.2,0.5,0.48495977329995643
0.1,0.1,0.3,0.770904382553698
0.8,0.9,0.2,0.770904382553698
0.9,0.7,0.8,0.48693806538114937
0.9,0.4,0.0,0.5220143986850413
0.1,0.4,0.4,0.45836330193189817
0.1,0.1,0.4,0.8950979073664068
0.9,0.0,0.4,0.8739332146487881
0.9,0.5,0.1,0.5829229500728043
0.1,0.6,0.9,0.4351103039372217
0.9,0.9,0.1,0.5167894461950312
0.1,0.4,0.6,0.43031257775974313
0.1,0.1,0.5,0.8426468610496103
0.9,0.1,0.6,0.8108998219731822
0.9,0.6,0.4,0.6651082350358932
0.1,0.1,0.6,1.0
0.9,0.2,0.6,1.0
0.9,0.7,0.7,0.770904382553698
0.9,0.8,0.8,0.8950979073664068
0.1,0.5,0.0,0.4584004752784965
0.1,0.4,0.8,0.5195834476794378
0.1,0.5,0.2,0.5009026243481076
0.1,0.5,0.4,0.5284383753172531
0.1,0.5,0.6,0.46281600318364424
0.1,0.5,0.8,0.4642863925381568
0.1,0.6,0.0,0.5122183316158576
0.1,0.6,0.2,0.42251569711990306
0.1,0.6,0.4,0.3840167435945633
0.1,0.6,0.6,0.4401863072941097
0.1,0.6,0.8,0.42368487985497094
0.1,0.7,0.0,0.43539231431101094
1 position frequency named_entity score
2 0.0 0.0 0.1 0.5083437448470514
3 0.0 0.1 0.0 0.4383261054822027
4 0.0 0.3 0.5 0.42123555399093365
5 0.2 0.5 0.5 0.44220598224602575
6 0.1 0.7 0.7 0.43788854919224535
7 0.2 0.0 0.9 0.5023290388788725
8 0.1 0.8 0.5 0.43313204607793665
9 0.1 0.7 0.9 0.43788854919224535
10 0.1 0.1 0.7 0.4256132561328083
11 0.1 0.8 0.3 0.425180450380071
12 0.1 0.8 0.1 0.4384243415015093
13 0.3 0.9 0.7 0.41402244766756846
14 0.3 0.8 0.7 0.4378885491922452
15 0.1 0.9 0.9 0.4303125777597432
16 0.2 0.9 0.8 0.4378885491922452
17 0.2 0.1 0.2 0.4273884900753904
18 0.3 0.2 0.5 0.4226585707477626
19 0.2 0.2 0.9 0.4340562834993896
20 0.5 0.0 0.1 0.41057308533638776
21 0.6 0.9 0.0 0.441915492686006
22 0.3 0.4 0.2 0.4419154926860059
23 0.2 0.7 0.8 0.44225674459875847
24 0.0 0.3 0.1 0.4066876004784904
25 0.2 0.1 0.5 0.468795497522303
26 0.1 0.7 0.2 0.48693806538114937
27 0.2 0.1 0.8 0.4273884900753902
28 0.2 0.8 0.7 0.43788854919224507
29 0.2 0.4 0.7 0.4378885491922452
30 0.3 0.0 0.7 0.41057308533638787
31 0.3 0.1 0.9 0.42521463545410537
32 0.2 0.0 0.5 0.49168007808331826
33 0.2 0.6 0.1 0.4259258080653142
34 0.2 0.5 0.2 0.4378885491922452
35 0.1 0.9 0.7 0.43788854919224535
36 0.9 0.4 0.3 0.427439716536499
37 0.0 0.2 0.7 0.46850808946619515
38 0.1 0.7 0.4 0.48693806538114937
39 0.5 0.3 0.8 0.42345218361783654
40 0.4 0.4 0.7 0.43405628349938946
41 0.5 0.6 0.0 0.44191549268600605
42 0.5 0.2 0.0 0.42055354386867805
43 0.2 0.7 0.4 0.44225674459875847
44 0.5 0.8 0.2 0.43300401274511474
45 0.6 0.1 0.2 0.45733040258615043
46 0.2 0.9 0.9 0.4522504474254864
47 0.4 0.3 0.1 0.4257604563722149
48 0.1 0.7 0.6 0.511228903425162
49 0.2 0.1 0.0 0.5213241525104696
50 0.8 0.7 0.6 0.4257604563722149
51 0.0 0.3 0.4 0.4273218947305689
52 0.2 0.4 0.1 0.5098682479623462
53 0.4 0.6 0.5 0.4419154926860058
54 0.1 0.7 0.8 0.5301113297509121
55 0.1 0.9 0.5 0.44261500919061
56 0.2 0.9 0.4 0.4378885491922452
57 0.0 0.2 0.5 0.47066610140157
58 0.1 0.8 0.0 0.5391637252487268
59 0.7 0.2 0.8 0.42521463545410537
60 0.1 0.8 0.2 0.5829229500728043
61 0.3 0.1 0.4 0.4657551464613721
62 0.2 0.4 0.9 0.4422567445987586
63 0.2 0.1 0.6 0.4803361288495707
64 0.2 0.1 0.9 0.42980295327545026
65 0.2 0.6 0.3 0.44225674459875847
66 0.8 0.1 0.5 0.45595262090658456
67 0.7 0.6 0.6 0.4257604563722149
68 0.5 0.4 0.0 0.42576045637221493
69 0.1 0.9 0.3 0.42158769391415
70 0.1 0.4 0.1 0.4378885491922453
71 0.2 0.5 0.7 0.4422567445987586
72 0.3 0.2 0.7 0.532183165254778
73 0.5 0.2 0.2 0.42055354386867805
74 0.2 0.7 0.5 0.4126863425043738
75 0.2 0.0 0.7 0.4105730853363878
76 0.1 0.7 0.3 0.43398322773016634
77 0.4 0.1 0.6 0.42872706477692163
78 0.1 0.9 0.8 0.4422567445987586
79 0.4 0.8 0.7 0.4378885491922452
80 0.3 0.6 0.1 0.39548806610964793
81 0.3 0.5 0.2 0.43300401274511463
82 0.1 0.9 0.1 0.43788854919224535
83 0.0 0.3 0.8 0.44671614481031496
84 0.2 0.3 0.4 0.4178109477100248
85 0.4 0.3 0.3 0.4257604563722149
86 0.3 0.0 0.9 0.41057308533638787
87 0.2 0.4 0.3 0.4378885491922452
88 0.1 0.7 0.5 0.45135042703723743
89 0.2 0.1 0.1 0.45002715459922643
90 0.3 0.1 0.5 0.4539819151835552
91 0.2 0.7 0.1 0.4378885491922452
92 0.3 0.8 0.1 0.43788854919224507
93 0.1 0.8 0.9 0.43788854919224535
94 0.6 0.9 0.6 0.441915492686006
95 0.2 0.3 0.1 0.4419154926860059
96 0.4 0.0 0.3 0.48286728967913356
97 0.0 0.4 0.5 0.46347983954670724
98 0.1 0.9 0.6 0.43796972014540314
99 0.5 0.6 0.4 0.44191549268600605
100 0.1 0.8 0.7 0.4454962480044107
101 0.3 0.9 0.1 0.4378885491922452
102 0.2 0.2 0.7 0.4593189643523495
103 0.2 0.1 0.7 0.4316853377592799
104 0.2 0.5 0.4 0.4378885491922452
105 0.0 0.5 0.1 0.4850287936114454
106 0.8 0.1 0.9 0.45595262090658456
107 0.1 0.6 0.7 0.4378885491922453
108 0.3 0.2 0.2 0.4531151931034449
109 0.1 0.9 0.4 0.49108162362228003
110 0.6 0.5 0.7 0.3942717643951014
111 0.2 0.8 0.3 0.41697402225910646
112 0.1 0.5 0.3 0.43788854919224535
113 0.2 0.9 0.1 0.4422567445987586
114 0.2 0.3 0.5 0.5220248951339999
115 0.8 0.8 0.9 0.4340562834993897
116 0.3 0.2 0.9 0.4600071652679549
117 0.6 0.1 0.8 0.45733040258615043
118 0.7 0.3 0.4 0.4196236947439287
119 0.3 0.6 0.4 0.4472447414688254
120 0.5 0.6 0.6 0.44191549268600605
121 0.3 0.5 0.4 0.43300401274511463
122 0.5 0.4 0.4 0.42576045637221493
123 0.3 0.0 0.4 0.4828672896791335
124 0.2 0.3 0.2 0.5111913013854592
125 0.3 0.8 0.3 0.4472447414688253
126 0.2 0.7 0.7 0.48722495592727455
127 0.2 0.2 0.3 0.4286824356161872
128 0.3 0.1 0.7 0.4591095958802673
129 0.2 0.9 0.2 0.43788854919224507
130 0.7 0.3 0.6 0.4196236947439287
131 0.3 0.3 0.1 0.4593189643523495
132 0.3 0.4 0.0 0.441915492686006
133 0.5 0.1 0.0 0.4300974365994895
134 0.5 0.9 0.0 0.4378885491922452
135 0.0 0.5 0.3 0.4898798621572795
136 0.9 0.5 0.7 0.4289325093274995
137 0.4 0.7 0.3 0.43788854919224507
138 0.3 0.0 0.5 0.5620175567843863
139 0.5 0.4 0.6 0.39427176439510137
140 0.4 0.3 0.7 0.4257604563722149
141 0.0 0.2 0.9 0.4212355539909336
142 0.2 0.0 0.1 0.5314413395369155
143 0.2 0.6 0.9 0.4339832277301664
144 0.7 0.0 0.3 0.41057308533638776
145 0.8 0.3 0.0 0.4226007660741652
146 0.2 0.1 0.3 0.5101467714313425
147 0.3 0.4 0.8 0.4419154926860059
148 0.0 0.4 0.3 0.45137391517894954
149 0.3 0.6 0.7 0.4378885491922453
150 0.7 0.7 0.8 0.4340562834993897
151 0.3 0.3 0.2 0.4340562834993897
152 0.0 0.3 0.2 0.44546116167870814
153 0.4 0.1 0.0 0.4287270647769215
154 0.8 0.9 0.5 0.43411611693927266
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"standard_deviation": 0.28252826800556124,
"harmonic_mean": 0.0,
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"median_high": 0.5,
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{
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"standard_deviation": 0.2831850101965145,
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"median": 0.5,
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"mode": 0.7000000000000001
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"data": [
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]
},
{
"stats": {
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"variance": 0.06748263888888889,
"standard_deviation": 0.2597742075127723,
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"median": 0.5,
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"median_grouped": 0.3333333333333333,
"mode": 0.5
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"data": [
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}
]
}

17
examples/misc/learn_weights_new/run_training.py
View File

@@ -7,8 +7,8 @@ import queue
import threading
import tracemalloc
from combined_scoring import distance_of_candidate
from extractor.root import path
from extractor.combined_scoring import distance_of_candidate
from extractors import environment_extractor, action_extractor, cause_extractor, method_extractor
from misc.learn_weights_new.learn import Learn, Worker
from misc.learn_weights_new.work_queue import WorkQueue
@@ -132,16 +132,11 @@ if __name__ == '__main__':
lock = threading.Lock() # Wordnet is not threadsave
#q.put(action(lock))
q.put(environment(lock))
#q.put(cause(lock))
#q.put(method(lock))
#q.put(default_combined_scoring(lock))
q.put(action(lock))
#q.put(environment(lock))
q.put(cause(lock))
q.put(method(lock))
q.put(default_combined_scoring(lock))
for i in range(4):
t = Worker(q)

6
examples/misc/learn_weights_new/work_queue.py
View File

@@ -45,7 +45,7 @@ class WorkQueue(object):
def get_id(self):
return self._id
def setup_scoring_parameters(self, weight_start: float = 0.0, weight_stop: float = 1,
def setup_scoring_parameters(self, weight_start: float = 0.0, weight_stop: float = 1.1,
weight_step_size: float = 0.1):
self._weights_range = np.arange(weight_start, weight_stop, weight_step_size)
@@ -154,7 +154,7 @@ class WorkQueue(object):
def _generate_pre_calculated(self, pre_calculated_weights):
for pre_calculated_weight in pre_calculated_weights:
self._queue.append({
'extracting_parameters_id': 0,
'extracting_parameters_id': 1,
'scoring_parameters': {
'weights': pre_calculated_weight
},
@@ -197,7 +197,7 @@ class WorkQueue(object):
def _generate_environment(self):
weight_start = 0
weight_step_size = 0.2
weight_step_size = 0.1
weight_stop = 1
# (0.5, 0.8), (0.8, 0.7, 0.5, 0.5, 0.5)
for i in self._weights_range:

2
extractor/tools/mapper.py
View File

@@ -44,7 +44,7 @@ def weight_to_string(extractor, weight_index):
return 'frequency'
elif weight_index == 1:
return 'conjunction'
elif weight_index == 1:
elif weight_index == 2:
return 'adjectives_adverbs'
else:
return 'no_mapping'