rgithubRUN/rgithub_pp_MDrun.R at master · BitQueryD3/rgithubRUN · GitHub

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

library(rgithubS)
## for TM analysis: from https://github.com/lborke/TManalyzer_dev
library(TManalyzer)


setwd("c:/r/github")

source("BQSearch/BitQuerySearchExport.R")

source("BQSearch/gh_login.R")
# "gh_login.R" führt nur diesen Code aus, Authentifizierungs-Parameter müssen dort zu Beginn manuell eingefügt werden
# ctx = interactive.login("XXX", "YYY", scopes=c("repo"))

# ctx$user$login
# ctx$user$id


## Readme's [MD]

readme_search = 'filename:"readme.md" path:"/"'


spec_search_term = "user:d3"
spec_search_term = "tensorflow tutorials"
spec_search_term = "tensorflow arXiv"
spec_search_term = "tensorflow arXiv cran"
spec_search_term = "tensorflow arXiv image"
spec_search_term = "tensorflow rstudio"
spec_search_term = "tensorflow R-CNN"
spec_search_term = "tensorflow segmentation mask"
spec_search_term = "tensorflow edge detection"

spec_search_term = "user:ddionrails user:mhebing user:paneldata"

spec_search_term = "user:cscheid"

spec_search_term = "ross girshick"

# > neue ideen
# coco dataset
# _ rg

( search_query = paste(spec_search_term, readme_search) )


### I: Search API layer:

## Full search

sr <- search.code.full(search_query, delay_l = 1)

sr <- search.code.full(search_query, delay_l = 1, print_stats = F)

sr <- search.code.full(search_query, delay_l = 1, max_items = 100)

sr <- search.code.full(search_query, delay_l = 1, max_items = 250)

# "preview" with 100 items, equivalent to simple "search.code"
sr <- search.code.full(search_query, delay_l = 1, max_items = 100, print_stats = F)

# default: gets maximally 1000 items with printing statistics
system.time( sr <- search.code.full(search_query, delay_l = 1) )


sort(table(sr$gh_login), decreasing = T)[1:10]
sort(table(sr$repo_name), decreasing = T)[1:10]
boxplot(sr$scores)


### II: Parser layer - V3 version

# md
system.time( parser_res <- flat.search.parser(sr) )


# some parser evaluation
parser_res$type

parser_res$ok_vec
parser_res$path_vec
parser_res$score_vec

table(parser_res$ok_vec)


### III: Metadata extraction and analysis layer

Metainfos = parser_res$parsed_list

length(Metainfos)


### IV: to TManalyzer

( doc_names = parser_res$path_vec )


t_vec = meta.list.extract(Metainfos, doc_names)


# check
t_vec$t_vec
t_vec$doc_names


# add more stopwords by hand
more_stopwords = c("instal", "you", "your", "sudo", "will", "librari", "how", "run", "ansibl", "user", "our", "width", "each", "file",
					"have", "youll", "need", "like", "want", "not", "let", "pleas")

# system.time( A_list <- tm.create.models(t_vec, models = c("lsa")) )
# system.time( A_list <- tm.create.models(t_vec, stopwords_select = "cran", models = c("tt", "lsa")) )
system.time( A_list <- tm.create.models(t_vec, stopwords_select = "cran", add_stopwords = more_stopwords, models = c("tt", "lsa")) )
# take LSA standard (50% sv weight)
A = A_list$lsa


### V: TM applications : output to BitQueryS

## general for all data types

d = dist(A)

k = 5
# k = 6
# k = 16
# k = 24
# k = 40
# k = 48
# k = 50
# k = 64

# graphics.off()

# k-means
system.time( cl_r <- D3Visu_ClusterTopic_kmeans(A, k, 5, mds_plot = FALSE, topic_print = TRUE, d_mat = d) )


# pam
# system.time( cl_r <- D3Visu_ClusterTopic_pam(A, k, 5, mds_plot = F, topic_print = TRUE) )
system.time( cl_r <- D3Visu_ClusterTopic_pam(A, k, 5, mds_plot = F, topic_print = TRUE, d_mat = d) )


## MD output
search_query

# ( gh_path = sr$gh_path[parser_res$ok_vec] )
# ( gh_org = sr$gh_login[parser_res$ok_vec] )
# ( full_link = sapply( sr$full_search[parser_res$ok_vec], function(item){ item$repository$html_url } ) )
( full_link = sapply( sr$full_search, function(item){ item$repository$html_url } ) )

export_l = createD3_PreJSON_MD(Metainfos, sr$gh_path, sr$gh_login, full_link, cl_r$cl_topic_vec, cl_r$cluster)

d_out = list(nodes = export_l, links = list())

# message_str = "tensorflow_tutorials"
# message_str = "user_d3"
# message_str = "tensorflow_arXiv"
# message_str = "tensorflow_arXiv_image"
# message_str = "tensorflow_arXiv_image_1000_PAM"
# message_str = "tensorflow_rstudio_PAM_better_cnames"
# message_str = "tensorflow_R-CNN_PAM_better_cnames"
# message_str = "tensorflow_segmentation mask_PAM"
# message_str = "tensorflow_edge_detection_PAM"
# message_str = "test2_1911"
# message_str = "diw_t1"
# message_str = "user_cscheid"
# message_str = "ross_girshick"


( fname = paste("BQsearch/output/md_github_", tolower(message_str), "_", k, "cl.json", sep="") )


save_JSON(d_out, fname, showPretty = TRUE)