hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
621473cc0dea916695c341578ae6bebd99ab30cf
| 173
|
py
|
Python
|
tests/unit/output/schema/__init__.py
|
jaebradley/draftkings_client
|
8db4484f293df3c65c48d62d972b71df95f5ea3d
|
[
"MIT"
] | 111
|
2017-01-07T13:32:00.000Z
|
2022-03-07T22:58:11.000Z
|
tests/unit/output/schema/__init__.py
|
jaebradley/draftkings_client
|
8db4484f293df3c65c48d62d972b71df95f5ea3d
|
[
"MIT"
] | 56
|
2016-11-14T05:50:44.000Z
|
2022-01-18T23:27:44.000Z
|
tests/unit/output/schema/__init__.py
|
jaebradley/draftkings_client
|
8db4484f293df3c65c48d62d972b71df95f5ea3d
|
[
"MIT"
] | 39
|
2017-01-25T01:57:09.000Z
|
2021-12-29T06:57:31.000Z
|
"""
Represents tests defined in the draft_kings.output.schema module.
Most tests center around serializing / deserializing output objects using the marshmallow library
"""
| 28.833333
| 97
| 0.809249
| 22
| 173
| 6.318182
| 0.863636
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|
0
| 4
|
6234529bcbfa06980d8cc0d8e4a9cc3d90c397ba
| 210,875
|
py
|
Python
|
unick.py
|
j3-141592/unick-leaked-scraping
|
b46ddde6ee01caf10f950650cccf1f66663e4a0d
|
[
"MIT"
] | null | null | null |
unick.py
|
j3-141592/unick-leaked-scraping
|
b46ddde6ee01caf10f950650cccf1f66663e4a0d
|
[
"MIT"
] | null | null | null |
unick.py
|
j3-141592/unick-leaked-scraping
|
b46ddde6ee01caf10f950650cccf1f66663e4a0d
|
[
"MIT"
] | null | null | null |
import requests
from bs4 import BeautifulSoup
import time
N_TO_BREATH = 10 #Number of lines processed to take a breath (breath to bypass the firewall)
TIME_TO_BREATH = 30 #Number in seconds of the breath (Caution on trying to be faster)
#Base URL from the Unick Forex Leaked page
base_url = 'https://unickbgv.000webhostapp.com/?x='
#All users codes (leaked)
all_codes = 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#Open (create if not exists) the file to save all
f= open("fools.txt","a+")
for counter,user_code in enumerate(all_codes):
if(counter%N_TO_BREATH==0):
time.sleep(TIME_TO_BREATH) # Every 10 (N_TO_BREATH) lines of requests, stop 30 (TIME_TO_BREATH) seconds for a breath
print(user_code) #print user_code to see the progress
code_url = base_url + str(user_code) #build the final URL with the user_code
r = requests.get(code_url) #request the URL with the leaked data
soup = BeautifulSoup(r.text,features='html.parser') #open the data
final = "" #The final string of each user
rows = soup.findAll("td") #Search for 'td' (cells of user's data)
for i,cell in enumerate(rows):
if(i>6 and i%2!=0): #The first 6 lines and odd lines are trash
final += cell.get_text().replace(';',',') + ';' #append the cell to the row of data. (replace ; by , to cooperate with CSV)
final += "\n" #Concludes the line
f.write(final) #append to the file
print(final) #print the line to see the progress
f.close() #close the file when finish
| 6,025
| 209,424
| 0.823905
| 36,948
| 210,875
| 4.701662
| 0.99613
| 0.000276
| 0.000155
| 0.000184
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.820206
| 0.001347
| 210,875
| 35
| 209,425
| 6,025
| 0.004696
| 0.003504
| 0
| 0
| 0
| 0
| 0.000319
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.125
| 0
| 0.125
| 0.083333
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
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| null | 0
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0
| 4
|
623752449739933b8792bc8b9f35324d3a0132a2
| 171
|
py
|
Python
|
src/main.py
|
rodfloripa/py-greenhouse
|
daf0d6e064f3ca3e7eafcd15a81c9183156b43fc
|
[
"Apache-2.0"
] | null | null | null |
src/main.py
|
rodfloripa/py-greenhouse
|
daf0d6e064f3ca3e7eafcd15a81c9183156b43fc
|
[
"Apache-2.0"
] | null | null | null |
src/main.py
|
rodfloripa/py-greenhouse
|
daf0d6e064f3ca3e7eafcd15a81c9183156b43fc
|
[
"Apache-2.0"
] | null | null | null |
import data_sourcing
from prefect import Flow, task
@task
def sourcing():
return data_sourcing.get()
with Flow("greenhouse") as flow:
sourcing()
flow.run()
| 10.6875
| 32
| 0.701754
| 23
| 171
| 5.130435
| 0.608696
| 0.20339
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.192982
| 171
| 15
| 33
| 11.4
| 0.855072
| 0
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| 0
| 0
| 0.05848
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.125
| true
| 0
| 0.25
| 0.125
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
|
0
| 4
|
6242e6e1c497f5f3784c1d52e801640726f28ea2
| 65
|
py
|
Python
|
client/verta/verta/integrations/__init__.py
|
fool-sec-review/modeldb
|
44e7f3c1af6768c4c23a2d134f9a322fcf0320b5
|
[
"Apache-2.0"
] | 835
|
2017-02-08T20:14:24.000Z
|
2020-03-12T17:37:49.000Z
|
client/verta/verta/integrations/__init__.py
|
fool-sec-review/modeldb
|
44e7f3c1af6768c4c23a2d134f9a322fcf0320b5
|
[
"Apache-2.0"
] | 651
|
2019-04-18T12:55:07.000Z
|
2022-03-31T23:45:09.000Z
|
client/verta/verta/integrations/__init__.py
|
fool-sec-review/modeldb
|
44e7f3c1af6768c4c23a2d134f9a322fcf0320b5
|
[
"Apache-2.0"
] | 170
|
2017-02-13T14:49:22.000Z
|
2020-02-19T17:59:12.000Z
|
"""Automated logging support for common scientific libraries."""
| 32.5
| 64
| 0.784615
| 7
| 65
| 7.285714
| 1
| 0
| 0
| 0
| 0
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| 0.107692
| 65
| 1
| 65
| 65
| 0.87931
| 0.892308
| 0
| null | 0
| null | 0
| 0
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| null | true
| 0
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| null | null | null | 1
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| null | 0
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| 1
| 0
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| 0
| 0
| 0
|
0
| 4
|
624ff6a5353a55142445b0f904f36097e9f3a48e
| 146
|
py
|
Python
|
code2code/main.py
|
liukunup/workspace
|
4198da23c6b9b4e12b0f4092c19cd1622e9bedc4
|
[
"MIT"
] | 1
|
2021-08-29T04:35:21.000Z
|
2021-08-29T04:35:21.000Z
|
code2code/main.py
|
liukunup/workspace
|
4198da23c6b9b4e12b0f4092c19cd1622e9bedc4
|
[
"MIT"
] | null | null | null |
code2code/main.py
|
liukunup/workspace
|
4198da23c6b9b4e12b0f4092c19cd1622e9bedc4
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
# -*- coding: UTF-8 -*-
# author : Liu Kun
# date : 2021-09-19 23:39:01
import json
import re
import time
import requests
| 11.230769
| 30
| 0.643836
| 24
| 146
| 3.916667
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12931
| 0.205479
| 146
| 12
| 31
| 12.166667
| 0.681034
| 0.575342
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| 0
| 0
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| 0
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| 1
| 0
| true
| 0
| 1
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| 1
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| null | 0
| 0
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| 0
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| 0
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| null | 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
625ffba842f0bb96e5a81a9f336bf62d17f7a5de
| 43,092
|
py
|
Python
|
catboost/python-package/ut/medium/canondata/test.test_export_to_python_no_cat_features_CPU-40_/model.py
|
ZhekehZ/catboost
|
3f774da539b8e57cca25686b89c473cbd1f61a6c
|
[
"Apache-2.0"
] | null | null | null |
catboost/python-package/ut/medium/canondata/test.test_export_to_python_no_cat_features_CPU-40_/model.py
|
ZhekehZ/catboost
|
3f774da539b8e57cca25686b89c473cbd1f61a6c
|
[
"Apache-2.0"
] | 6
|
2020-02-18T22:12:29.000Z
|
2020-02-18T22:31:26.000Z
|
catboost/python-package/ut/medium/canondata/test.test_export_to_python_no_cat_features_CPU-40_/model.py
|
ZhekehZ/catboost
|
3f774da539b8e57cca25686b89c473cbd1f61a6c
|
[
"Apache-2.0"
] | null | null | null |
### Model data
class catboost_model(object):
float_features_index = [
0, 1, 2, 4, 5, 6, 8, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23, 24, 27, 28, 31, 33, 35, 37, 38, 39, 46, 47, 48,
]
float_feature_count = 50
cat_feature_count = 0
binary_feature_count = 29
tree_count = 40
float_feature_borders = [
[0.000758085982, 0.178914994, 0.1888735, 0.194539994, 0.34700349, 0.44264698, 0.780139983, 0.813149452],
[0.00132575491, 0.0216720998, 0.0395833515, 0.0403138474, 0.102008, 0.156479999, 0.213277996, 0.281994998, 0.312178016, 0.384054512, 0.474032521, 0.585997999, 0.681437016, 0.815984011],
[0.00109145499, 0.00861673057, 0.0365923494, 0.0686140954, 0.419130981, 0.428588986, 0.879231513, 0.937812984],
[0.5],
[0.5],
[0.5],
[0.5],
[0.5],
[0.5],
[0.358823478, 0.421568513, 0.433333516, 0.52352953, 0.554902017, 0.558823466, 0.621568501, 0.7156865],
[0.148551002, 0.183333501, 0.384469509, 0.436507493, 0.506097496, 0.578900516, 0.587584972, 0.674775004, 0.748417497, 0.76047051, 0.800989985, 0.866219521, 0.867108464, 0.908883512, 0.950919986],
[0.0207247995, 0.0343967006, 0.0504557006, 0.158435494, 0.216674, 0.247377992, 0.269448996, 0.318728, 0.333916008, 0.37875849, 0.39003402, 0.422357976, 0.594812512, 0.795647502],
[0.0398375988, 0.0653211027, 0.115491495, 0.120285496, 0.124523498, 0.133076996, 0.136280507, 0.140028998, 0.142480001, 0.14288801, 0.155889004, 0.199276, 0.2121225, 0.240777999, 0.260086477, 0.276386499, 0.280552983, 0.297052979, 0.355903506],
[0.5],
[0.252941012, 0.276470482, 0.331372499, 0.335294008, 0.413725495, 0.437254995, 0.496078491, 0.694117486, 0.699999988, 0.750980496, 0.852941036, 0.929412007, 0.968627512],
[0.5],
[0.0416666493],
[0.0225447994, 0.0226299986, 0.0261416994, 0.0633649006, 0.182705492, 0.187063992, 0.211840004, 0.213952005, 0.241398007, 0.29707399, 0.937534451, 0.939258993, 0.93988049, 0.946740508],
[0.5],
[0.5],
[0.0867389515, 0.16316551, 0.693239987],
[0.185759991, 0.297058523, 0.363489985, 0.402247012, 0.793442488, 0.84256053],
[-0.0383633971, 0.00895375013, 0.193027496, 0.220256999, 0.342289001, 0.423586994, 0.434064507, 0.476337016, 0.623547494, 0.957795024],
[0.000421158999, 0.000926548964, 0.00227425992, 0.00513814017, 0.0282176994, 0.0325976983, 0.0403470509],
[0.283847004, 0.650285006, 0.6519925, 0.654693007, 0.661086977, 0.682412982, 0.726830006, 0.784554005, 0.821318984, 0.950830996],
[4.17586998e-05, 8.0244994e-05, 0.000137109499, 0.00141531997, 0.00250496017, 0.00326151494, 0.00393318012, 0.005463365, 0.00693041505, 0.00947646052, 0.0113535002, 0.0157128982, 0.01822575, 0.0689947009, 0.0747110993],
[0.0761536956, 0.103404, 0.148530498, 0.164992496, 0.214888006, 0.404017985, 0.451396525, 0.535629511, 0.665955007, 0.811691999],
[0.60571146, 0.711432993, 0.981393516],
[0.1105005, 0.175806999, 0.340994507, 0.346906006, 0.458132505, 0.504471004, 0.544902503, 0.547486544, 0.569079995, 0.670499027, 0.726330996, 0.774749517, 0.776835024, 0.787591517, 0.870769978, 0.911834955],
]
tree_depth = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]
tree_split_border = [1, 6, 8, 9, 4, 6, 8, 5, 7, 9, 3, 1, 2, 13, 5, 1, 5, 7, 16, 1, 1, 7, 10, 6, 14, 11, 7, 8, 14, 12, 2, 5, 4, 7, 6, 14, 6, 7, 19, 8, 5, 1, 2, 1, 6, 13, 2, 5, 13, 10, 8, 1, 15, 10, 5, 8, 10, 2, 2, 9, 1, 9, 2, 3, 9, 7, 1, 1, 8, 7, 8, 8, 1, 15, 1, 8, 15, 7, 1, 8, 4, 4, 10, 4, 13, 3, 1, 4, 5, 1, 14, 1, 6, 4, 14, 10, 4, 8, 8, 2, 1, 7, 12, 16, 5, 3, 7, 9, 4, 3, 11, 8, 18, 8, 14, 14, 5, 1, 4, 9, 1, 3, 3, 4, 1, 11, 12, 10, 13, 9, 13, 13, 1, 3, 13, 6, 3, 3, 8, 3, 1, 1, 13, 8, 2, 3, 3, 4, 2, 12, 15, 6, 11, 9, 14, 7, 14, 12, 1, 10, 2, 1, 6, 3, 5, 3, 4, 5, 7, 10, 4, 1, 1, 1, 2, 7, 10, 2, 12, 11, 14, 12, 6, 1, 6, 9, 10, 12, 2, 9, 1, 6, 15, 5, 17, 10, 6, 1, 11, 9, 8, 2, 1, 2, 15, 2, 1, 1, 3, 3, 13, 12, 1, 6, 12, 5, 6, 3, 1, 5, 4, 6, 11, 6, 1, 9, 7, 4, 2, 10, 11, 14, 12, 5, 2, 16, 7, 3, 11, 4]
tree_split_feature_index = [20, 1, 25, 10, 23, 22, 2, 22, 14, 22, 11, 21, 27, 11, 28, 18, 14, 23, 28, 4, 24, 2, 12, 12, 11, 17, 10, 0, 12, 25, 25, 12, 21, 24, 11, 17, 17, 25, 12, 11, 9, 27, 17, 13, 2, 12, 21, 17, 14, 24, 2, 26, 28, 25, 21, 2, 24, 22, 9, 25, 15, 11, 2, 9, 28, 26, 8, 1, 12, 12, 28, 9, 15, 10, 2, 22, 12, 9, 0, 17, 25, 24, 11, 9, 14, 17, 28, 24, 10, 19, 1, 8, 21, 14, 11, 26, 12, 1, 24, 28, 4, 25, 14, 28, 23, 10, 0, 24, 0, 24, 10, 26, 12, 10, 10, 11, 25, 15, 26, 17, 18, 26, 25, 2, 23, 28, 14, 14, 17, 26, 1, 25, 10, 12, 10, 14, 23, 21, 2, 20, 22, 25, 28, 14, 1, 27, 22, 22, 11, 10, 28, 23, 12, 14, 28, 22, 1, 1, 3, 1, 20, 5, 28, 28, 24, 2, 17, 0, 17, 17, 10, 13, 9, 18, 0, 28, 10, 12, 11, 11, 25, 28, 0, 14, 12, 12, 22, 17, 24, 12, 6, 12, 25, 1, 12, 28, 25, 6, 12, 12, 12, 9, 17, 14, 25, 26, 12, 11, 14, 1, 14, 1, 3, 10, 12, 2, 24, 26, 16, 26, 1, 9, 14, 26, 7, 1, 11, 11, 23, 26, 1, 17, 17, 11, 10, 12, 1, 0, 25, 28]
tree_split_xor_mask = [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, 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, 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, 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, 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, 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, 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, 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]
cat_features_index = []
one_hot_cat_feature_index = []
one_hot_hash_values = [
]
ctr_feature_borders = [
]
## Aggregated array of leaf values for trees. Each tree is represented by a separate line:
leaf_values = [
-0.01452223061650176, 0.003220391872338723, -0.005535119676446759, -0.01465273981215404, -0.00855547415566176, 0.002037667919068398, 0.0001120632868695247, 0.03840825802757053, -0.01533940293562541, 0.007951286482469178, 0.02893911799022985, 0, 0.02292931274542596, 0.02265389319360762, 0.02038603740012057, 0.01812879496613256, -0.00714521746238457, 0.001950130990137022, -0.007326369906077018, 0.002300279908813374, 0.008621431815437664, 0.03554284328040242, 0.03512996404078633, 0.01981997355782309, -0.0179565158733794, 0.01325214413744863, 0.01360229305612498, 0.009626649814890392, 0.01925329962978078, 0.02543763356059014, 0.01559011467674228, 0.03281872682860205, -0.009945571873778352, -0.001362905044225135, -0.01304254387598467, 0, -0.006594462653496712, -0.01190226843647107, 0.007182689275421724, 0.01535789817241634, -0.00410502282758085, 0.007472611244634545, 0.04089836809158642, 0, 0.006062759240253598, 0.03643488793168285, 0.003512268152670903, 0.007951286482469178, -0.00983941490470884, 0.07047121835364896, 0, 0, 0, 0.02766033534093435, 0, 0.05719577328378828, -0.0149153515011613, -0.007326369906077018, 0, 0, 0, 0.03080527940764925, 0.001150139954406687, -0.004940983961336265,
-0.004290491016358114, 0, -0.005142491159808076, 0, -0.01631414563094668, 0, -0.01583305362655595, 0, 0, 0, -0.004223387993378917, 0, 0, 0, -0.009901576725822188, 0, 0.001550563113605264, 0.001390731497052868, 0.00226873941224347, 0.005896346192859286, -0.007258726327461003, 0, 0.003358357795086376, 0, 0, 0, 0.00837589032594164, 0, 0, 0, -0.01059649838552386, 0, -0.009781645896798577, -0.007562517113173503, 0.02214271375734489, 0.05394157562038105, -0.0002138456496420421, 0.008021907958099189, -0.005188739401065129, 0, 0, 0, 0.04905615915828059, 0, 0, 0, -0.005303802232773693, 0, 0.0117081010746993, 0.03062085933581888, 0.02235340547823699, 0.1227133227722861, 0.007862897175603121, 0, 0.001608888337109428, 0, 0, 0, 0.007699061410369978, 0.00687759070158362, 0, 0, -0.01342322142795995, 0,
-0.0008628659065023804, 0.009204875414304065, -0.01426102453631723, 0, 0.001937670583401637, 0.0156650165407804, 0.003739955634906077, -0.01011840625499374, -0.006940214177145987, 0, 0.0880356242859759, 0, 0.004817009991483832, -0.01025097578144819, 0.01005740748032615, 0, -0.007628195609257056, 0.01666370432348902, -0.005875549236929858, 0, -0.005698779283673799, 0.02285690403407965, 0.007392561165114673, -0.0003019701652545931, -0.007486785321659697, 0, 0.02840589257042851, 0, 0.02333489281990855, -0.01496735226052822, 0.02076800095251007, 0, -0.001649031678139777, 0, 0, 0, -0.008155449138096203, 0.004641872952715348, 0, 0, -0.001232876227576032, 0, 0, 0, 0, 0.05206643187070074, 0, 0, -0.006966526173996213, 0, 0, 0, 0.006788599608527733, 0.03295161256656724, 0, 0, -0.004321213678543653, 0, 0, 0, 0, 0, 0, 0,
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]
scale = 1
bias = 0.06050201133
cat_features_hashes = {
}
def hash_uint64(string):
return cat_features_hashes.get(str(string), 0x7fFFffFF)
### Applicator for the CatBoost model
def apply_catboost_model(float_features, cat_features=[], ntree_start=0, ntree_end=catboost_model.tree_count):
"""
Applies the model built by CatBoost.
Parameters
----------
float_features : list of float features
cat_features : list of categorical features
You need to pass float and categorical features separately in the same order they appeared in train dataset.
For example if you had features f1,f2,f3,f4, where f2 and f4 were considered categorical, you need to pass here float_features=f1,f3, cat_features=f2,f4
Returns
-------
prediction : formula value for the model and the features
"""
if ntree_end == 0:
ntree_end = catboost_model.tree_count
else:
ntree_end = min(ntree_end, catboost_model.tree_count)
model = catboost_model
assert len(float_features) >= model.float_feature_count
assert len(cat_features) >= model.cat_feature_count
# Binarise features
binary_features = [0] * model.binary_feature_count
binary_feature_index = 0
for i in range(len(model.float_feature_borders)):
for border in model.float_feature_borders[i]:
binary_features[binary_feature_index] += 1 if (float_features[model.float_features_index[i]] > border) else 0
binary_feature_index += 1
transposed_hash = [0] * model.cat_feature_count
for i in range(model.cat_feature_count):
transposed_hash[i] = hash_uint64(cat_features[i])
if len(model.one_hot_cat_feature_index) > 0:
cat_feature_packed_indexes = {}
for i in range(model.cat_feature_count):
cat_feature_packed_indexes[model.cat_features_index[i]] = i
for i in range(len(model.one_hot_cat_feature_index)):
cat_idx = cat_feature_packed_indexes[model.one_hot_cat_feature_index[i]]
hash = transposed_hash[cat_idx]
for border_idx in range(len(model.one_hot_hash_values[i])):
binary_features[binary_feature_index] |= (1 if hash == model.one_hot_hash_values[i][border_idx] else 0) * (border_idx + 1)
binary_feature_index += 1
if hasattr(model, 'model_ctrs') and model.model_ctrs.used_model_ctrs_count > 0:
ctrs = [0.] * model.model_ctrs.used_model_ctrs_count;
calc_ctrs(model.model_ctrs, binary_features, transposed_hash, ctrs)
for i in range(len(model.ctr_feature_borders)):
for border in model.ctr_feature_borders[i]:
binary_features[binary_feature_index] += 1 if ctrs[i] > border else 0
binary_feature_index += 1
# Extract and sum values from trees
result = 0.
tree_splits_index = 0
current_tree_leaf_values_index = 0
for tree_id in range(ntree_start, ntree_end):
current_tree_depth = model.tree_depth[tree_id]
index = 0
for depth in range(current_tree_depth):
border_val = model.tree_split_border[tree_splits_index + depth]
feature_index = model.tree_split_feature_index[tree_splits_index + depth]
xor_mask = model.tree_split_xor_mask[tree_splits_index + depth]
index |= ((binary_features[feature_index] ^ xor_mask) >= border_val) << depth
result += model.leaf_values[current_tree_leaf_values_index + index]
tree_splits_index += current_tree_depth
current_tree_leaf_values_index += (1 << current_tree_depth)
return model.scale * result + model.bias
| 232.92973
| 1,209
| 0.738954
| 5,682
| 43,092
| 5.56811
| 0.291271
| 0.092989
| 0.084866
| 0.087237
| 0.056198
| 0.047664
| 0.036317
| 0.027846
| 0.020766
| 0.018775
| 0
| 0.761169
| 0.117446
| 43,092
| 184
| 1,210
| 234.195652
| 0.070732
| 0.015734
| 0
| 0.10274
| 0
| 0
| 0.000236
| 0
| 0
| 0
| 0.000236
| 0
| 0.013699
| 1
| 0.013699
| false
| 0
| 0
| 0.006849
| 0.150685
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
65a3c3090b54b05efc139063e2874deec1d50341
| 117
|
py
|
Python
|
billy2pupa/fl.py
|
backwardn/billy
|
07ac788d25a6c79d03dd0e3d55459bbb55e22439
|
[
"BSD-3-Clause"
] | 33
|
2016-11-05T07:25:48.000Z
|
2022-01-31T03:40:43.000Z
|
billy2pupa/fl.py
|
backwardn/billy
|
07ac788d25a6c79d03dd0e3d55459bbb55e22439
|
[
"BSD-3-Clause"
] | 16
|
2015-02-05T21:25:58.000Z
|
2015-09-18T20:27:06.000Z
|
billy2pupa/fl.py
|
backwardn/billy
|
07ac788d25a6c79d03dd0e3d55459bbb55e22439
|
[
"BSD-3-Clause"
] | 22
|
2015-03-23T07:13:20.000Z
|
2016-06-10T04:41:06.000Z
|
from openstatesapi.jurisdiction import make_jurisdiction
J = make_jurisdiction('fl')
J.url = 'http://myflorida.com'
| 23.4
| 56
| 0.786325
| 15
| 117
| 6
| 0.733333
| 0.355556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.094017
| 117
| 4
| 57
| 29.25
| 0.849057
| 0
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| 0
| 0
| 0.188034
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
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| null | 1
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
65ad2a278ad6df49cd892cd3302f8669dd739afd
| 219
|
py
|
Python
|
src/generate_artificial.py
|
ozansener/egocentric-object
|
ea7cd43e2e795f16dd3df80843b11f0e6443839d
|
[
"MIT"
] | null | null | null |
src/generate_artificial.py
|
ozansener/egocentric-object
|
ea7cd43e2e795f16dd3df80843b11f0e6443839d
|
[
"MIT"
] | null | null | null |
src/generate_artificial.py
|
ozansener/egocentric-object
|
ea7cd43e2e795f16dd3df80843b11f0e6443839d
|
[
"MIT"
] | null | null | null |
from artificial_mnist import ArtificialMnist
data_generator = ArtificialMnist()
data_generator.get_n_random_samples(60000, './MNIST_a/train_data/')
data_generator.get_n_random_samples(10000, './MNIST_a/test_data/')
| 24.333333
| 67
| 0.826484
| 30
| 219
| 5.566667
| 0.533333
| 0.233533
| 0.335329
| 0.203593
| 0.359281
| 0.359281
| 0
| 0
| 0
| 0
| 0
| 0.04902
| 0.068493
| 219
| 8
| 68
| 27.375
| 0.769608
| 0
| 0
| 0
| 0
| 0
| 0.188073
| 0.09633
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.25
| 0
| 0.25
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
65c003852412c52e42ff0d1125159af4573229b5
| 957
|
py
|
Python
|
packages/w3af/w3af/core/ui/api/resources/index.py
|
ZooAtmosphereGroup/HelloPackages
|
0ccffd33bf927b13d28c8f715ed35004c33465d9
|
[
"Apache-2.0"
] | 3
|
2019-04-09T22:59:33.000Z
|
2019-06-14T09:23:24.000Z
|
tools/w3af/w3af/core/ui/api/resources/index.py
|
sravani-m/Web-Application-Security-Framework
|
d9f71538f5cba6fe1d8eabcb26c557565472f6a6
|
[
"MIT"
] | null | null | null |
tools/w3af/w3af/core/ui/api/resources/index.py
|
sravani-m/Web-Application-Security-Framework
|
d9f71538f5cba6fe1d8eabcb26c557565472f6a6
|
[
"MIT"
] | null | null | null |
"""
index.py
Copyright 2015 Andres Riancho
This file is part of w3af, http://w3af.org/ .
w3af is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation version 2 of the License.
w3af is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with w3af; if not, write to the Free Software
Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
"""
from w3af.core.ui.api import app
from w3af.core.ui.api.utils.auth import requires_auth
from flask import jsonify
@app.route('/', methods=['GET'])
@requires_auth
def index():
return jsonify({'docs': 'http://docs.w3af.org/en/latest/api/index.html'})
| 30.870968
| 77
| 0.763845
| 160
| 957
| 4.55625
| 0.61875
| 0.049383
| 0.053498
| 0.078189
| 0.159122
| 0.076818
| 0
| 0
| 0
| 0
| 0
| 0.029814
| 0.15883
| 957
| 30
| 78
| 31.9
| 0.875776
| 0.724138
| 0
| 0
| 0
| 0
| 0.207843
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| true
| 0
| 0.428571
| 0.142857
| 0.714286
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 4
|
02c9558c0a1e62e9dd5dce1034cec40ff106e1f2
| 203
|
py
|
Python
|
crud/admin.py
|
OuroborosD/03-PiggoV2
|
0fdabfeca3a29cf0cfb87f120506ad517ee75ce6
|
[
"MIT"
] | null | null | null |
crud/admin.py
|
OuroborosD/03-PiggoV2
|
0fdabfeca3a29cf0cfb87f120506ad517ee75ce6
|
[
"MIT"
] | null | null | null |
crud/admin.py
|
OuroborosD/03-PiggoV2
|
0fdabfeca3a29cf0cfb87f120506ad517ee75ce6
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
# Register your models here.
from .models import Receita,Despesa,Emprestimo
admin.site.register(Receita)
admin.site.register(Despesa)
admin.site.register(Emprestimo)
| 18.454545
| 46
| 0.812808
| 27
| 203
| 6.111111
| 0.481481
| 0.163636
| 0.309091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.098522
| 203
| 11
| 47
| 18.454545
| 0.901639
| 0.128079
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.4
| 0
| 0.4
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
02d4f4b5c7bb4b905f0307569c85891ef2aa2a2c
| 714
|
py
|
Python
|
poky-dunfell/bitbake/lib/toaster/tests/browser/selenium_helpers.py
|
lacie-life/YoctoPi
|
3412e78468a9b84da50bb1aadb12b459001a3712
|
[
"MIT"
] | 14
|
2021-11-04T07:47:37.000Z
|
2022-03-21T10:10:30.000Z
|
poky-dunfell/bitbake/lib/toaster/tests/browser/selenium_helpers.py
|
lacie-life/YoctoPi
|
3412e78468a9b84da50bb1aadb12b459001a3712
|
[
"MIT"
] | null | null | null |
poky-dunfell/bitbake/lib/toaster/tests/browser/selenium_helpers.py
|
lacie-life/YoctoPi
|
3412e78468a9b84da50bb1aadb12b459001a3712
|
[
"MIT"
] | 6
|
2021-11-02T10:56:19.000Z
|
2022-03-06T11:58:20.000Z
|
#! /usr/bin/env python3
#
# BitBake Toaster Implementation
#
# Copyright (C) 2013-2016 Intel Corporation
#
# SPDX-License-Identifier: GPL-2.0-only
#
# The Wait class and some of SeleniumDriverHelper and SeleniumTestCase are
# modified from Patchwork, released under the same licence terms as Toaster:
# https://github.com/dlespiau/patchwork/blob/master/patchwork/tests.browser.py
"""
Helper methods for creating Toaster Selenium tests which run within
the context of Django unit tests.
"""
from django.contrib.staticfiles.testing import StaticLiveServerTestCase
from tests.browser.selenium_helpers_base import SeleniumTestCaseBase
class SeleniumTestCase(SeleniumTestCaseBase, StaticLiveServerTestCase):
pass
| 32.454545
| 78
| 0.810924
| 88
| 714
| 6.556818
| 0.761364
| 0.041594
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.017378
| 0.113445
| 714
| 21
| 79
| 34
| 0.894155
| 0.644258
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.25
| 0.5
| 0
| 0.75
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 4
|
02e65bd48691c61957de2ecbdc08f085bb744a1c
| 273
|
py
|
Python
|
Visualization/EndGameScreen.py
|
frosthamster/Tower-defense
|
0bf373d935265e4941d1ff1b7c78a15522eef548
|
[
"Unlicense"
] | 1
|
2020-02-20T21:30:20.000Z
|
2020-02-20T21:30:20.000Z
|
Visualization/EndGameScreen.py
|
frosthamster/Tower-defense
|
0bf373d935265e4941d1ff1b7c78a15522eef548
|
[
"Unlicense"
] | null | null | null |
Visualization/EndGameScreen.py
|
frosthamster/Tower-defense
|
0bf373d935265e4941d1ff1b7c78a15522eef548
|
[
"Unlicense"
] | null | null | null |
from kivy.properties import StringProperty, ObjectProperty
from kivy.uix.screenmanager import Screen
class EndGameScreen(Screen):
message_image = StringProperty('')
background = StringProperty('res/end_game_background.jpg')
game_screen = ObjectProperty(None)
| 30.333333
| 62
| 0.794872
| 29
| 273
| 7.344828
| 0.655172
| 0.075117
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.124542
| 273
| 8
| 63
| 34.125
| 0.891213
| 0
| 0
| 0
| 0
| 0
| 0.098901
| 0.098901
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
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| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
02ec0e53f313ad03382d4d41ade5f8e1a3137611
| 11,880
|
py
|
Python
|
cuhk03/model.py
|
cwpeng-cn/TorchReID
|
e6cf1d38bfc3100ea19e3e92aa4306b79fd3517b
|
[
"MIT"
] | null | null | null |
cuhk03/model.py
|
cwpeng-cn/TorchReID
|
e6cf1d38bfc3100ea19e3e92aa4306b79fd3517b
|
[
"MIT"
] | null | null | null |
cuhk03/model.py
|
cwpeng-cn/TorchReID
|
e6cf1d38bfc3100ea19e3e92aa4306b79fd3517b
|
[
"MIT"
] | null | null | null |
from . import init_env
from torch import nn
from torch.nn import functional as F
from torch.nn import init
import torchvision
import torch
import zipfile
from resnet_ibn_b import *
from reid.utils.model_save_restore import *
class STN(nn.Module):
def __init__(self):
super(STN, self).__init__()
self.localization = nn.Sequential(
nn.Conv2d(1024, 2048, kernel_size=3),
nn.BatchNorm2d(2048),
nn.ReLU(True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.fc_loc = nn.Sequential(
nn.Linear(2048, 512),
nn.ReLU(True),
nn.Linear(512, 2 * 3),
)
# Initialize the weights/bias with identity transformation
self.fc_loc[2].weight.data.zero_()
self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
# Spatial transformer network forward function
def forward(self, x):
xs = self.localization(x)
xs = F.adaptive_avg_pool2d(xs, (1, 1))
xs = xs.view(xs.size(0), -1) # N,4096
theta = self.fc_loc(xs) # N,6
theta = theta.view(-1, 2, 3) # N,2,3
grid = F.affine_grid(theta, x.size())
x = F.grid_sample(x, grid)
return x
class ResNet(nn.Module):
__factory = {
18: torchvision.models.resnet18,
34: torchvision.models.resnet34,
50: torchvision.models.resnet50,
101: torchvision.models.resnet101,
152: torchvision.models.resnet152,
}
def __init__(self, depth=50, pretrained=True, cut_at_pooling=False,
num_features=1024, dropout=0.5, num_classes=0):
super(ResNet, self).__init__()
self.depth = depth
self.pretrained = pretrained
self.cut_at_pooling = cut_at_pooling
# Construct base (pretrained) resnet
base = resnet50_ibn_b(pretrained=pretrained)
base_stn = resnet50_ibn_b(pretrained=pretrained)
self.stn = STN()
self.conv1 = base.conv1
self.bn1 = base.bn1
self.relu = base.relu
self.maxpool = base.maxpool
self.layer1 = base.layer1
self.layer2 = base.layer2
self.layer3 = base.layer3
self.layer4 = base.layer4
self.layer4_stn = base_stn.layer4
for mo in self.layer4[0].modules():
if isinstance(mo, nn.Conv2d):
mo.stride = (1, 1)
for mo in self.layer4_stn[0].modules():
if isinstance(mo, nn.Conv2d):
mo.stride = (1, 1)
self.mmaxpool = nn.AdaptiveMaxPool2d((1, 1))
if not self.cut_at_pooling:
self.num_features = num_features
self.dropout = dropout
self.has_embedding = num_features > 0
self.num_classes = num_classes
out_planes = base.fc.in_features
# Append new layers
if self.has_embedding:
feat = nn.Linear(out_planes, self.num_features)
feat_bn = nn.BatchNorm1d(self.num_features)
init.kaiming_normal_(feat.weight, mode='fan_out')
init.constant_(feat.bias, 0)
init.normal_(feat_bn.weight, 1, 0.02)
init.constant_(feat_bn.bias, 0.0)
embed_layer = [feat, feat_bn]
self.embed_layer = nn.Sequential(*embed_layer)
feat = nn.Linear(out_planes, self.num_features)
feat_bn = nn.BatchNorm1d(self.num_features)
init.kaiming_normal_(feat.weight, mode='fan_out')
init.constant_(feat.bias, 0)
init.normal_(feat_bn.weight, 1, 0.02)
init.constant_(feat_bn.bias, 0.0)
embed_layer = [feat, feat_bn]
self.embed_layer_stn = nn.Sequential(*embed_layer)
else:
# Change the num_features to CNN output channels
self.num_features = out_planes
if self.dropout > 0:
self.drop = nn.Dropout(self.dropout)
if self.num_classes > 0:
self.last_fc = nn.Linear(self.num_features, self.num_classes)
init.normal_(self.last_fc.weight, std=0.001)
init.constant_(self.last_fc.bias, 0.0)
self.last_fc_stn = nn.Linear(self.num_features, self.num_classes)
init.normal_(self.last_fc_stn.weight, std=0.001)
init.constant_(self.last_fc_stn.bias, 0.0)
if not self.pretrained:
self.reset_params()
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x_stn = self.stn(x)
x = self.layer4(x)
x_stn = self.layer4_stn(x_stn)
# 如果是测试，则取这一结果作为特征
# if not self.training:
# result_sum=torch.sum(x,1) # N,H,W
# result_mean=torch.mean(result_sum,1).mean(1) # N
# result_mean=result_mean.view(-1,1,1)
# mask=result_sum>result_mean #N,H,W
# mask=mask.unsqueeze(1).repeat(1,2048,1,1).float() #N,C,H,W
# x2=x*mask #N,C,H,W
# x2=self.mmaxpool(x2) #N,C,1,1
# x2 = x2.view(x2.size(0), x2.size(1)) #N,C
# triplet_out = self.embed_layer(x2)
# result_sum_stn=torch.sum(x_stn,1) # N,H,W
# result_mean_stn=torch.mean(result_sum_stn,1).mean(1) # N
# result_mean_stn=result_mean_stn.view(-1,1,1)
# mask_stn=result_sum_stn>result_mean_stn #N,H,W
# mask_stn=mask_stn.unsqueeze(1).repeat(1,2048,1,1).float() #N,C,H,W
# x2_stn=x_stn*mask_stn #N,C,H,W
# x2_stn=self.mmaxpool(x2_stn) #N,C,1,1
# x2_stn = x2_stn.view(x2_stn.size(0), x2_stn.size(1)) #N,C
# triplet_out_stn = self.embed_layer_stn(x2_stn)
# triplet_out=self.normalize(triplet_out)
# triplet_out_stn=self.normalize(triplet_out_stn)
# return torch.cat((triplet_out,triplet_out_stn),1) #N,2C
if self.cut_at_pooling:
return x
x = F.max_pool2d(x, x.size()[2:]).view(x.size()[:2])
x_stn = F.max_pool2d(x_stn, x_stn.size()[2:]).view(x_stn.size()[:2])
if self.has_embedding:
triplet_out = self.embed_layer(x)
triplet_out_stn = self.embed_layer_stn(x_stn)
if not self.training:
triplet_out = self.normalize(triplet_out)
triplet_out_stn = self.normalize(triplet_out_stn)
return torch.cat((triplet_out, triplet_out_stn), 1) # N,2C
if self.num_classes > 0:
x = self.last_fc(triplet_out)
x_stn = self.last_fc_stn(triplet_out_stn)
return triplet_out, x, triplet_out_stn, x_stn
def reset_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.normal_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)
def normalize(self, x, axis=-1):
"""Normalizing to unit length along the specified dimension.
Args:
x: pytorch Variable
Returns:
x: pytorch Variable, same shape as input
"""
x = 1. * x / (torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12)
return x
class MNet(nn.Module):
def __init__(self, net, depth=50, pretrained=True, cut_at_pooling=False,
num_features=512, dropout=0.5, num_classes=0):
super(MNet, self).__init__()
self.depth = depth
self.pretrained = pretrained
self.cut_at_pooling = cut_at_pooling
# Construct base (pretrained) resnet
base = net
self.conv1 = base.conv1
self.bn1 = base.bn1
self.relu = base.relu
self.maxpool = base.maxpool
self.layer1 = base.layer1
self.layer2 = base.layer2
self.layer3 = base.layer3
self.layer4 = base.layer4
self.layer4_stn = base.layer4_stn
self.stn = base.stn
self.mmaxpool = base.mmaxpool
if not self.cut_at_pooling:
self.num_features = num_features
self.dropout = dropout
self.has_embedding = num_features > 0
self.num_classes = num_classes
out_planes = 2048
# Append new layers
if self.has_embedding:
self.embed_layer = base.embed_layer
self.embed_layer_stn = base.embed_layer_stn
else:
# Change the num_features to CNN output channels
self.num_features = 2048
if self.dropout > 0:
self.drop = nn.Dropout(self.dropout)
if self.num_classes > 0:
self.last_fc = nn.Linear(self.num_features, self.num_classes)
init.normal_(self.last_fc.weight, std=0.001)
init.constant_(self.last_fc.bias, 0.0)
self.last_fc_stn = nn.Linear(self.num_features, self.num_classes)
init.normal_(self.last_fc_stn.weight, std=0.001)
init.constant_(self.last_fc_stn.bias, 0.0)
if not self.pretrained:
self.reset_params()
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x_stn = self.stn(x)
x = self.layer4(x)
x_stn = self.layer4_stn(x_stn)
if self.cut_at_pooling:
return x
x = F.max_pool2d(x, x.size()[2:]).view(x.size()[:2])
x_stn = F.max_pool2d(x_stn, x_stn.size()[2:]).view(x_stn.size()[:2])
if self.has_embedding:
triplet_out = self.embed_layer(x)
triplet_out_stn = self.embed_layer_stn(x_stn)
if not self.training:
triplet_out = self.normalize(triplet_out)
triplet_out_stn = self.normalize(triplet_out_stn)
return torch.cat((triplet_out, triplet_out_stn), 1) # N,2C
if self.num_classes > 0:
x = self.last_fc(triplet_out)
x_stn = self.last_fc_stn(triplet_out_stn)
return triplet_out, x, triplet_out_stn, x_stn
def reset_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.normal_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)
def normalize(self, x, axis=-1):
"""Normalizing to unit length along the specified dimension.
Args:
x: pytorch Variable
Returns:
x: pytorch Variable, same shape as input
"""
x = 1. * x / (torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12)
return x
def get_model():
net = ResNet(num_classes=4101, num_features=1024)
net = restore_network("./", 149, net).cuda()
return net
| 35.675676
| 91
| 0.55968
| 1,608
| 11,880
| 3.93097
| 0.122512
| 0.050625
| 0.025312
| 0.016453
| 0.750989
| 0.72093
| 0.705426
| 0.682329
| 0.682329
| 0.682329
| 0
| 0.040614
| 0.330556
| 11,880
| 332
| 92
| 35.783133
| 0.754181
| 0.152104
| 0
| 0.706897
| 0
| 0
| 0.003021
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.047414
| false
| 0
| 0.038793
| 0
| 0.146552
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
02f345cf2ece698df9f67f871baccd7defff1ec2
| 79
|
py
|
Python
|
pub_data_visualization/auctions/plot/subplot/__init__.py
|
l-leo/pub-data-visualization
|
68eea00491424581b057495a7f0f69cf74e16e7d
|
[
"MIT"
] | 1
|
2021-01-22T16:47:20.000Z
|
2021-01-22T16:47:20.000Z
|
pub_data_visualization/auctions/plot/subplot/__init__.py
|
l-leo/pub-data-visualization
|
68eea00491424581b057495a7f0f69cf74e16e7d
|
[
"MIT"
] | null | null | null |
pub_data_visualization/auctions/plot/subplot/__init__.py
|
l-leo/pub-data-visualization
|
68eea00491424581b057495a7f0f69cf74e16e7d
|
[
"MIT"
] | null | null | null |
"""
Module for the subplots of auctions data.
"""
from .price import *
| 9.875
| 45
| 0.620253
| 10
| 79
| 4.9
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.265823
| 79
| 8
| 46
| 9.875
| 0.844828
| 0.518987
| 0
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| 0
| 0
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| 1
| 0
| true
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| 1
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
f301c34a7ede06d952c51d79da1ba17c1c54ef35
| 150
|
py
|
Python
|
virtual/bin/django-admin.py
|
LoiseMwarangu/Instagram
|
1bb0791a69350e0b9a3d2864d7132c6a605360d7
|
[
"MIT"
] | null | null | null |
virtual/bin/django-admin.py
|
LoiseMwarangu/Instagram
|
1bb0791a69350e0b9a3d2864d7132c6a605360d7
|
[
"MIT"
] | 5
|
2020-06-05T19:59:40.000Z
|
2021-09-08T00:53:31.000Z
|
virtual/bin/django-admin.py
|
LoiseMwarangu/Instagram
|
1bb0791a69350e0b9a3d2864d7132c6a605360d7
|
[
"MIT"
] | null | null | null |
#!/home/loise/instagram/virtual/bin/python3
from django.core import management
if __name__ == "__main__":
management.execute_from_command_line()
| 25
| 43
| 0.786667
| 19
| 150
| 5.631579
| 0.894737
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007407
| 0.1
| 150
| 5
| 44
| 30
| 0.785185
| 0.28
| 0
| 0
| 0
| 0
| 0.074766
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
f3032f65417094eb571da930a991579fc5ad3730
| 152
|
py
|
Python
|
chat_example/server.py
|
h-hirokawa/swampdragon
|
064ee71a5838e1363b69eef9af6b1a56be96fe23
|
[
"BSD-3-Clause"
] | null | null | null |
chat_example/server.py
|
h-hirokawa/swampdragon
|
064ee71a5838e1363b69eef9af6b1a56be96fe23
|
[
"BSD-3-Clause"
] | null | null | null |
chat_example/server.py
|
h-hirokawa/swampdragon
|
064ee71a5838e1363b69eef9af6b1a56be96fe23
|
[
"BSD-3-Clause"
] | null | null | null |
import os
from swampdragon.swampdragon_server import run_server
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "chat_example.settings")
run_server()
| 21.714286
| 72
| 0.842105
| 20
| 152
| 6.1
| 0.65
| 0.147541
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.072368
| 152
| 6
| 73
| 25.333333
| 0.865248
| 0
| 0
| 0
| 0
| 0
| 0.282895
| 0.282895
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
f309ce833b5249c5d49bd3ee39165b5ebedc79bc
| 353
|
py
|
Python
|
workspace/pynb_dag_runner/tests/tasks/jupytext_test_notebooks/notebook_ok.py
|
pynb-dag-runner/pynb-dag-runner
|
52473a743e82080217e7924bd35fcc3362bf2335
|
[
"MIT"
] | 4
|
2021-10-01T17:12:39.000Z
|
2021-12-12T17:36:10.000Z
|
workspace/pynb_dag_runner/tests/tasks/jupytext_test_notebooks/notebook_ok.py
|
pynb-dag-runner/pynb-dag-runner
|
52473a743e82080217e7924bd35fcc3362bf2335
|
[
"MIT"
] | 1
|
2021-09-19T18:06:59.000Z
|
2021-09-19T18:06:59.000Z
|
workspace/pynb_dag_runner/tests/tasks/jupytext_test_notebooks/notebook_ok.py
|
pynb-dag-runner/pynb-dag-runner
|
52473a743e82080217e7924bd35fcc3362bf2335
|
[
"MIT"
] | null | null | null |
# %%
P = {"task.variable_a": "value-used-during-interactive-development"}
# %% tags=["parameters"]
# ---- During automated runs parameters will be injected in this cell ---
# %%
# -----------------------------------------------------------------------
# %%
# Example comment
print(1 + 12 + 123)
# %%
print(f"""variable_a={P["task.variable_a"]}""")
# %%
| 27.153846
| 73
| 0.495751
| 35
| 353
| 4.914286
| 0.742857
| 0.156977
| 0.151163
| 0.162791
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.019231
| 0.116147
| 353
| 12
| 74
| 29.416667
| 0.532051
| 0.558074
| 0
| 0
| 0
| 0
| 0.609589
| 0.506849
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.666667
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
f3167643eab720bd99949066e57d017b77cad801
| 184
|
py
|
Python
|
graphit/graph_io/__init__.py
|
codacy-badger/graphit
|
7fcfed114875466179ed3d4848dd9098fa3e60fb
|
[
"Apache-2.0"
] | null | null | null |
graphit/graph_io/__init__.py
|
codacy-badger/graphit
|
7fcfed114875466179ed3d4848dd9098fa3e60fb
|
[
"Apache-2.0"
] | null | null | null |
graphit/graph_io/__init__.py
|
codacy-badger/graphit
|
7fcfed114875466179ed3d4848dd9098fa3e60fb
|
[
"Apache-2.0"
] | null | null | null |
#TODO: make walk and serialization methods to customize format export
#TODO: add support for import/export of LEMON Graph Format (LGF) http://lemon.cs.elte.hu/pub/doc/1.2.3/a00002.html
| 92
| 114
| 0.782609
| 33
| 184
| 4.363636
| 0.878788
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.04878
| 0.108696
| 184
| 2
| 114
| 92
| 0.829268
| 0.983696
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0.5
| null | 1
| null | true
| 0
| 0
| null | null | null | 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
f324f66ac6894ed748a8ce97c513c4ce9b3559a2
| 419
|
py
|
Python
|
sort_radix.py
|
rachitmishra/45
|
c38650f4fa2ea1857848b95320cdc37929b39197
|
[
"MIT"
] | null | null | null |
sort_radix.py
|
rachitmishra/45
|
c38650f4fa2ea1857848b95320cdc37929b39197
|
[
"MIT"
] | null | null | null |
sort_radix.py
|
rachitmishra/45
|
c38650f4fa2ea1857848b95320cdc37929b39197
|
[
"MIT"
] | null | null | null |
"""
Shell Sort
Approach: Divide and Conquer
Complexity: O(n2)
"""
def sort_shell(input_arr):
print("""""""""""""""""""""""""")
print("input " + str(input_arr))
print("""""""""""""""""""""""""")
print("""""""""""""""""""""""""")
print("result " + str(input_arr))
print("""""""""""""""""""""""""")
if __name__ == '__main__':
arr = [21, 4, 1, 3, 9, 20, 25, 6, 21, 14]
sort_shell(arr)
| 19.045455
| 45
| 0.434368
| 45
| 419
| 3.755556
| 0.6
| 0.142012
| 0.230769
| 0.213018
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.047059
| 0.188544
| 419
| 21
| 46
| 19.952381
| 0.45
| 0.138425
| 0
| 0.4
| 0
| 0
| 0.05949
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.1
| false
| 0
| 0
| 0
| 0.1
| 0.6
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
b8955c63b0d450843dbf880da44478e43ca9276c
| 62
|
py
|
Python
|
tests/benchmarks/cli/commands/test_help.py
|
iterative/dvc-benchmark
|
f6f1e682bea6de40f999be8beb2fc38fab37e5bc
|
[
"Apache-2.0"
] | null | null | null |
tests/benchmarks/cli/commands/test_help.py
|
iterative/dvc-benchmark
|
f6f1e682bea6de40f999be8beb2fc38fab37e5bc
|
[
"Apache-2.0"
] | null | null | null |
tests/benchmarks/cli/commands/test_help.py
|
iterative/dvc-benchmark
|
f6f1e682bea6de40f999be8beb2fc38fab37e5bc
|
[
"Apache-2.0"
] | null | null | null |
def test_help(bench_dvc):
bench_dvc("--help", rounds=100)
| 20.666667
| 35
| 0.693548
| 10
| 62
| 4
| 0.7
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.055556
| 0.129032
| 62
| 2
| 36
| 31
| 0.685185
| 0
| 0
| 0
| 0
| 0
| 0.096774
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
b8a3ee19ec5d63b3a174af2a7503b3d1bbe2f1ca
| 1,746
|
py
|
Python
|
slise/__init__.py
|
vishalbelsare/pyslise
|
3d81fe62a58755fa71755a0c02e56b9aa3e15e96
|
[
"MIT"
] | 3
|
2021-05-06T11:31:29.000Z
|
2022-03-18T19:20:33.000Z
|
slise/__init__.py
|
vishalbelsare/pyslise
|
3d81fe62a58755fa71755a0c02e56b9aa3e15e96
|
[
"MIT"
] | null | null | null |
slise/__init__.py
|
vishalbelsare/pyslise
|
3d81fe62a58755fa71755a0c02e56b9aa3e15e96
|
[
"MIT"
] | 1
|
2021-08-20T13:46:31.000Z
|
2021-08-20T13:46:31.000Z
|
"""
__ SLISE - Sparse Linear Subset Explanations __
The SLISE algorithm can be used for both robust regression and to explain outcomes from black box models.
In robust regression we fit regression models that can handle data that
contains outliers. SLISE accomplishes this by fitting a model such that
the largest possible subset of the data items have an error less than a
given value. All items with an error larger than that are considered
potential outliers and do not affect the resulting model.
SLISE can also be used to provide local model-agnostic explanations for
outcomes from black box models. To do this we replace the ground truth
response vector with the predictions from the complex model. Furthermore, we
force the model to fit a selected item (making the explanation local). This
gives us a local approximation of the complex model with a simpler linear
model. In contrast to other methods SLISE creates explanations using real
data (not some discretised and randomly sampled data) so we can be sure that
all inputs are valid (i.e. in the correct data manifold, and follows the
constraints used to generate the data, e.g., the laws of physics).
More in-depth details about the algorithm can be found in the paper:
Björklund A., Henelius A., Oikarinen E., Kallonen K., Puolamäki K.
Sparse Robust Regression for Explaining Classifiers.
Discovery Science (DS 2019).
Lecture Notes in Computer Science, vol 11828, Springer.
https://doi.org/10.1007/978-3-030-33778-0_27
"""
from slise.slise import SliseRegression, regression, SliseExplainer, explain
from slise.utils import limited_logit as logit
from slise.data import normalise_robust
| 47.189189
| 109
| 0.761168
| 268
| 1,746
| 4.932836
| 0.544776
| 0.011346
| 0.02118
| 0.030257
| 0.039334
| 0
| 0
| 0
| 0
| 0
| 0
| 0.021505
| 0.201031
| 1,746
| 36
| 110
| 48.5
| 0.926165
| 0.84937
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
b8b1bb8fba88fd9d17eefe426df853b1c29a99bd
| 205
|
py
|
Python
|
borrowingMoneyManagement/urls.py
|
520MianXiangDuiXiang520/FamilyPropertyManageSystem
|
b4f9d681a96a6547c6755d0229f420b4112076c5
|
[
"MIT"
] | 7
|
2019-11-24T08:24:33.000Z
|
2021-11-07T20:25:51.000Z
|
borrowingMoneyManagement/urls.py
|
520MianXiangDuiXiang520/FamilyPropertyManageSystem
|
b4f9d681a96a6547c6755d0229f420b4112076c5
|
[
"MIT"
] | 6
|
2020-02-12T02:58:28.000Z
|
2022-02-10T08:52:38.000Z
|
borrowingMoneyManagement/urls.py
|
520MianXiangDuiXiang520/FamilyPropertyManageSystem
|
b4f9d681a96a6547c6755d0229f420b4112076c5
|
[
"MIT"
] | 1
|
2019-11-30T03:11:32.000Z
|
2019-11-30T03:11:32.000Z
|
from django.urls import path
from .views import BorrowingView
from .views import PayBackView
urlpatterns = [
path('borrow/', BorrowingView.as_view()),
path('payBack/', PayBackView.as_view())
]
| 25.625
| 45
| 0.721951
| 24
| 205
| 6.083333
| 0.541667
| 0.123288
| 0.205479
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.156098
| 205
| 8
| 46
| 25.625
| 0.843931
| 0
| 0
| 0
| 0
| 0
| 0.072816
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.428571
| 0
| 0.428571
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
b217bdfeed738074a1529c020bf0c432f5237937
| 44
|
py
|
Python
|
test/scripts/starterr.py
|
codders/mitmproxy
|
4f9deae8fc2b5f8b0519f82b1f3cdda6c115b475
|
[
"MIT"
] | 3
|
2016-10-08T05:19:11.000Z
|
2020-05-29T20:08:56.000Z
|
test/scripts/starterr.py
|
huyphan/mitmproxy
|
5259aeb3264423204e84072d9e1b3ace4f6e6031
|
[
"MIT"
] | null | null | null |
test/scripts/starterr.py
|
huyphan/mitmproxy
|
5259aeb3264423204e84072d9e1b3ace4f6e6031
|
[
"MIT"
] | 1
|
2015-08-20T02:20:27.000Z
|
2015-08-20T02:20:27.000Z
|
def start(ctx, argv):
raise ValueError
| 11
| 21
| 0.681818
| 6
| 44
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.227273
| 44
| 3
| 22
| 14.666667
| 0.882353
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
b21a2e92a0903ea9c334f4be5faa7c94c1bd7867
| 30,621
|
py
|
Python
|
tests/test_registration_embargoes.py
|
bdyetton/prettychart
|
e8b33a7dfdc8c33d15969586be7f68172795f76d
|
[
"Apache-2.0"
] | null | null | null |
tests/test_registration_embargoes.py
|
bdyetton/prettychart
|
e8b33a7dfdc8c33d15969586be7f68172795f76d
|
[
"Apache-2.0"
] | null | null | null |
tests/test_registration_embargoes.py
|
bdyetton/prettychart
|
e8b33a7dfdc8c33d15969586be7f68172795f76d
|
[
"Apache-2.0"
] | null | null | null |
"""Tests related to embargoes of registrations"""
import datetime
import json
import mock
from nose.tools import * #noqa
from tests.base import fake, OsfTestCase
from tests.factories import (
AuthUserFactory, EmbargoFactory, NodeFactory, ProjectFactory,
RegistrationFactory, UserFactory, UnconfirmedUserFactory
)
from framework.exceptions import PermissionsError
from modularodm.exceptions import ValidationValueError
from website.exceptions import (
InvalidEmbargoDisapprovalToken, InvalidEmbargoApprovalToken, NodeStateError,
)
from website.models import Embargo, Node
from website.project.model import ensure_schemas
class RegistrationEmbargoModelsTestCase(OsfTestCase):
def setUp(self):
super(RegistrationEmbargoModelsTestCase, self).setUp()
self.user = UserFactory()
self.project = ProjectFactory(creator=self.user)
self.registration = RegistrationFactory(project=self.project)
self.embargo = EmbargoFactory(user=self.user)
self.valid_embargo_end_date = datetime.datetime.utcnow() + datetime.timedelta(days=3)
# Validator tests
def test_invalid_state_raises_ValidationValueError(self):
with assert_raises(ValidationValueError):
self.embargo.state = 'not a valid state'
self.embargo.save()
# Node#_initiate_embargo tests
def test__initiate_embargo_does_not_save_embargo(self):
initial_count = Embargo.find().count()
self.registration._initiate_embargo(
self.user,
self.valid_embargo_end_date,
for_existing_registration=True
)
self.assertEqual(Embargo.find().count(), initial_count)
def test__initiate_embargo_does_not_create_tokens_for_unregistered_admin(self):
unconfirmed_user = UnconfirmedUserFactory()
self.registration.contributors.append(unconfirmed_user)
self.registration.add_permission(unconfirmed_user, 'admin', save=True)
assert_true(self.registration.has_permission(unconfirmed_user, 'admin'))
embargo = self.registration._initiate_embargo(
self.user,
self.valid_embargo_end_date,
for_existing_registration=True
)
assert_true(self.user._id in embargo.approval_state)
assert_false(unconfirmed_user._id in embargo.approval_state)
def test__initiate_embargo_with_save_does_save_embargo(self):
initial_count = Embargo.find().count()
self.registration._initiate_embargo(
self.user,
self.valid_embargo_end_date,
for_existing_registration=True,
save=True
)
self.assertEqual(Embargo.find().count(), initial_count + 1)
# Backref tests
def test_embargo_initiator_has_backref(self):
self.registration.embargo_registration(
self.user,
self.valid_embargo_end_date
)
self.registration.save()
self.registration.reload()
assert_equal(len(self.user.embargo__embargoed), 1)
# Node#embargo_registration tests
def test_embargo_from_non_admin_raises_PermissionsError(self):
self.registration.remove_permission(self.user, 'admin')
self.registration.save()
self.registration.reload()
with assert_raises(PermissionsError):
self.registration.embargo_registration(self.user, self.valid_embargo_end_date)
def test_embargo_end_date_in_past_raises_ValidationValueError(self):
with assert_raises(ValidationValueError):
self.registration.embargo_registration(
self.user,
datetime.datetime(1999, 1, 1)
)
def test_embargo_end_date_today_raises_ValidationValueError(self):
with assert_raises(ValidationValueError):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow()
)
def test_embargo_end_date_in_far_future_raises_ValidationValueError(self):
with assert_raises(ValidationValueError):
self.registration.embargo_registration(
self.user,
datetime.datetime(2099, 1, 1)
)
def test_embargo_with_valid_end_date_starts_pending_embargo(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
def test_embargo_public_project_makes_private_pending_embargo(self):
self.registration.is_public = True
assert_true(self.registration.is_public)
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
assert_false(self.registration.is_public)
def test_embargo_non_registration_raises_NodeStateError(self):
self.registration.is_registration = False
self.registration.save()
with assert_raises(NodeStateError):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
assert_false(self.registration.pending_embargo)
# Embargo#approve_embargo tests
def test_invalid_approval_token_raises_InvalidEmbargoApprovalToken(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
invalid_approval_token = 'not a real token'
with assert_raises(InvalidEmbargoApprovalToken):
self.registration.embargo.approve_embargo(self.user, invalid_approval_token)
assert_true(self.registration.pending_embargo)
assert_false(self.registration.embargo_end_date)
def test_non_admin_approval_token_raises_PermissionsError(self):
non_admin = UserFactory()
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
approval_token = self.registration.embargo.approval_state[self.user._id]['approval_token']
with assert_raises(PermissionsError):
self.registration.embargo.approve_embargo(non_admin, approval_token)
assert_true(self.registration.pending_embargo)
def test_one_approval_with_one_admin_embargoes(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
approval_token = self.registration.embargo.approval_state[self.user._id]['approval_token']
self.registration.embargo.approve_embargo(self.user, approval_token)
assert_true(self.registration.embargo_end_date)
assert_false(self.registration.pending_embargo)
def test_approval_adds_to_parent_projects_log(self):
initial_project_logs = len(self.registration.registered_from.logs)
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
approval_token = self.registration.embargo.approval_state[self.user._id]['approval_token']
self.registration.embargo.approve_embargo(self.user, approval_token)
# Logs: Created, registered, embargo initiated, embargo approved
assert_equal(len(self.registration.registered_from.logs), initial_project_logs + 2)
def test_one_approval_with_two_admins_stays_pending(self):
admin2 = UserFactory()
self.registration.contributors.append(admin2)
self.registration.add_permission(admin2, 'admin', save=True)
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
# First admin approves
approval_token = self.registration.embargo.approval_state[self.user._id]['approval_token']
self.registration.embargo.approve_embargo(self.user, approval_token)
assert_true(self.registration.pending_embargo)
num_of_approvals = sum([val['has_approved'] for val in self.registration.embargo.approval_state.values()])
assert_equal(num_of_approvals, 1)
# Second admin approves
approval_token = self.registration.embargo.approval_state[admin2._id]['approval_token']
self.registration.embargo.approve_embargo(admin2, approval_token)
assert_true(self.registration.embargo_end_date)
assert_false(self.registration.pending_embargo)
num_of_approvals = sum([val['has_approved'] for val in self.registration.embargo.approval_state.values()])
assert_equal(num_of_approvals, 2)
# Embargo#disapprove_embargo tests
def test_invalid_disapproval_token_raises_InvalidEmbargoDisapprovalToken(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
with assert_raises(InvalidEmbargoDisapprovalToken):
self.registration.embargo.disapprove_embargo(self.user, fake.sentence())
assert_true(self.registration.pending_embargo)
assert_false(self.registration.embargo_end_date)
def test_non_admin_disapproval_token_raises_PermissionsError(self):
non_admin = UserFactory()
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
disapproval_token = self.registration.embargo.approval_state[self.user._id]['disapproval_token']
with assert_raises(PermissionsError):
self.registration.embargo.disapprove_embargo(non_admin, disapproval_token)
assert_true(self.registration.pending_embargo)
def test_one_disapproval_cancels_embargo(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
disapproval_token = self.registration.embargo.approval_state[self.user._id]['disapproval_token']
self.registration.embargo.disapprove_embargo(self.user, disapproval_token)
assert_equal(self.registration.embargo.state, Embargo.CANCELLED)
assert_false(self.registration.pending_embargo)
assert_false(self.registration.embargo_end_date)
def test_disapproval_adds_to_parent_projects_log(self):
initial_project_logs = len(self.registration.registered_from.logs)
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
disapproval_token = self.registration.embargo.approval_state[self.user._id]['disapproval_token']
registered_from = self.registration.registered_from
self.registration.embargo.disapprove_embargo(self.user, disapproval_token)
# Logs: Created, registered, embargo initiated, embargo cancelled
assert_equal(len(registered_from.logs), initial_project_logs + 2)
def test_cancelling_embargo_deletes_parent_registration(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
disapproval_token = self.registration.embargo.approval_state[self.user._id]['disapproval_token']
self.registration.embargo.disapprove_embargo(self.user, disapproval_token)
assert_equal(self.registration.embargo.state, Embargo.CANCELLED)
assert_true(self.registration.is_deleted)
def test_cancelling_embargo_for_existing_registration_does_not_delete_registration(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10),
for_existing_registration=True
)
self.registration.save()
disapproval_token = self.registration.embargo.approval_state[self.user._id]['disapproval_token']
self.registration.embargo.disapprove_embargo(self.user, disapproval_token)
assert_equal(self.registration.embargo.state, Embargo.CANCELLED)
assert_false(self.registration.is_deleted)
# Embargo property tests
def test_new_registration_is_pending_registration(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_registration)
def test_existing_registration_is_not_pending_registration(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10),
for_existing_registration=True
)
self.registration.save()
assert_false(self.registration.pending_registration)
class RegistrationWithChildNodesEmbargoModelTestCase(OsfTestCase):
def setUp(self):
super(RegistrationWithChildNodesEmbargoModelTestCase, self).setUp()
self.user = AuthUserFactory()
self.auth = self.user.auth
self.valid_embargo_end_date = datetime.datetime.utcnow() + datetime.timedelta(days=3)
self.project = ProjectFactory(title='Root', is_public=False, creator=self.user)
self.component = NodeFactory(
creator=self.user,
parent=self.project,
title='Component'
)
self.subproject = ProjectFactory(
creator=self.user,
parent=self.project,
title='Subproject'
)
self.subproject_component = NodeFactory(
creator=self.user,
parent=self.subproject,
title='Subcomponent'
)
self.registration = RegistrationFactory(project=self.project)
# Reload the registration; else tests won't catch failures to save
self.registration.reload()
def test_approval_embargoes_descendant_nodes(self):
# Initiate embargo for parent registration
self.registration.embargo_registration(
self.user,
self.valid_embargo_end_date
)
self.registration.save()
assert_true(self.registration.pending_embargo)
# Ensure descendant nodes are pending embargo
descendants = self.registration.get_descendants_recursive()
for node in descendants:
assert_true(node.pending_embargo)
# Approve parent registration's embargo
approval_token = self.registration.embargo.approval_state[self.user._id]['approval_token']
self.registration.embargo.approve_embargo(self.user, approval_token)
assert_true(self.registration.embargo.embargo_end_date)
# Ensure descendant nodes are in embargo
descendants = self.registration.get_descendants_recursive()
for node in descendants:
assert_true(node.embargo_end_date)
def test_disapproval_cancels_embargo_on_descendant_nodes(self):
# Initiate embargo on parent registration
self.registration.embargo_registration(
self.user,
self.valid_embargo_end_date
)
self.registration.save()
assert_true(self.registration.pending_embargo)
# Ensure descendant nodes are pending embargo
descendants = self.registration.get_descendants_recursive()
for node in descendants:
assert_true(node.pending_embargo)
# Disapprove parent registration's embargo
disapproval_token = self.registration.embargo.approval_state[self.user._id]['disapproval_token']
self.registration.embargo.disapprove_embargo(self.user, disapproval_token)
assert_false(self.registration.pending_embargo)
assert_false(self.registration.embargo_end_date)
assert_equal(self.registration.embargo.state, Embargo.CANCELLED)
# Ensure descendant nodes' embargoes are cancelled
descendants = self.registration.get_descendants_recursive()
for node in descendants:
assert_false(node.pending_embargo)
assert_false(node.embargo_end_date)
class RegistrationEmbargoApprovalDisapprovalViewsTestCase(OsfTestCase):
def setUp(self):
super(RegistrationEmbargoApprovalDisapprovalViewsTestCase, self).setUp()
self.user = AuthUserFactory()
self.registration = RegistrationFactory(creator=self.user)
# node_registration_embargo_approve tests
def test_GET_from_unauthorized_user_raises_HTTPForbidden(self):
unauthorized_user = AuthUserFactory()
res = self.app.get(
self.registration.web_url_for('node_registration_embargo_approve', token=fake.sentence()),
auth=unauthorized_user.auth,
expect_errors=True
)
assert_equal(res.status_code, 403)
def test_GET_approve_registration_without_embargo_raises_HTTPBad_Request(self):
assert_false(self.registration.pending_embargo)
res = self.app.get(
self.registration.web_url_for('node_registration_embargo_approve', token=fake.sentence()),
auth=self.user.auth,
expect_errors=True
)
assert_equal(res.status_code, 400)
def test_GET_approve_with_invalid_token_returns_HTTPBad_Request(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
res = self.app.get(
self.registration.web_url_for('node_registration_embargo_approve', token=fake.sentence()),
auth=self.user.auth,
expect_errors=True
)
assert_equal(res.status_code, 400)
def test_GET_approve_with_wrong_token_returns_HTTPBad_Request(self):
admin2 = UserFactory()
self.registration.contributors.append(admin2)
self.registration.add_permission(admin2, 'admin', save=True)
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
wrong_approval_token = self.registration.embargo.approval_state[admin2._id]['approval_token']
res = self.app.get(
self.registration.web_url_for('node_registration_embargo_approve', token=wrong_approval_token),
auth=self.user.auth,
expect_errors=True
)
assert_equal(res.status_code, 400)
def test_GET_approve_with_wrong_admins_token_returns_HTTPBad_Request(self):
admin2 = UserFactory()
self.registration.contributors.append(admin2)
self.registration.add_permission(admin2, 'admin', save=True)
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
wrong_approval_token = self.registration.embargo.approval_state[admin2._id]['approval_token']
res = self.app.get(
self.registration.web_url_for('node_registration_embargo_approve', token=wrong_approval_token),
auth=self.user.auth,
expect_errors=True
)
assert_true(self.registration.pending_embargo)
assert_equal(res.status_code, 400)
def test_GET_approve_with_valid_token_returns_redirect(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
approval_token = self.registration.embargo.approval_state[self.user._id]['approval_token']
res = self.app.get(
self.registration.web_url_for('node_registration_embargo_approve', token=approval_token),
auth=self.user.auth,
)
self.registration.embargo.reload()
assert_true(self.registration.embargo_end_date)
assert_false(self.registration.pending_embargo)
assert_equal(res.status_code, 302)
# node_registration_embargo_disapprove tests
def test_GET_from_unauthorized_user_returns_HTTPForbidden(self):
unauthorized_user = AuthUserFactory()
res = self.app.get(
self.registration.web_url_for('node_registration_embargo_disapprove', token=fake.sentence()),
auth=unauthorized_user.auth,
expect_errors=True
)
assert_equal(res.status_code, 403)
def test_GET_disapprove_registration_without_embargo_HTTPBad_Request(self):
assert_false(self.registration.pending_embargo)
res = self.app.get(
self.registration.web_url_for('node_registration_embargo_disapprove', token=fake.sentence()),
auth=self.user.auth,
expect_errors=True
)
assert_equal(res.status_code, 400)
def test_GET_disapprove_with_invalid_token_returns_HTTPBad_Request(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
res = self.app.get(
self.registration.web_url_for('node_registration_embargo_disapprove', token=fake.sentence()),
auth=self.user.auth,
expect_errors=True
)
self.registration.embargo.reload()
assert_true(self.registration.pending_embargo)
assert_equal(res.status_code, 400)
def test_GET_disapprove_with_wrong_admins_token_returns_HTTPBad_Request(self):
admin2 = UserFactory()
self.registration.contributors.append(admin2)
self.registration.add_permission(admin2, 'admin', save=True)
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
self.registration.save()
assert_true(self.registration.pending_embargo)
wrong_disapproval_token = self.registration.embargo.approval_state[admin2._id]['disapproval_token']
res = self.app.get(
self.registration.web_url_for('node_registration_embargo_disapprove', token=wrong_disapproval_token),
auth=self.user.auth,
expect_errors=True
)
assert_true(self.registration.pending_embargo)
assert_equal(res.status_code, 400)
def test_GET_disapprove_with_valid_token_returns_redirect_to_parent(self):
project = ProjectFactory(creator=self.user)
registration = RegistrationFactory(project=project)
registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10)
)
registration.save()
assert_true(registration.pending_embargo)
disapproval_token = registration.embargo.approval_state[self.user._id]['disapproval_token']
res = self.app.get(
registration.web_url_for('node_registration_embargo_disapprove', token=disapproval_token),
auth=self.user.auth,
)
registration.embargo.reload()
assert_equal(registration.embargo.state, Embargo.CANCELLED)
assert_false(registration.embargo_end_date)
assert_false(registration.pending_embargo)
assert_equal(res.status_code, 302)
assert_true(project._id in res.location)
def test_GET_disapprove_for_existing_registration_with_valid_token_returns_redirect_to_registration(self):
self.registration.embargo_registration(
self.user,
datetime.datetime.utcnow() + datetime.timedelta(days=10),
for_existing_registration=True
)
self.registration.save()
assert_true(self.registration.pending_embargo)
disapproval_token = self.registration.embargo.approval_state[self.user._id]['disapproval_token']
res = self.app.get(
self.registration.web_url_for('node_registration_embargo_disapprove', token=disapproval_token),
auth=self.user.auth,
)
self.registration.embargo.reload()
assert_equal(self.registration.embargo.state, Embargo.CANCELLED)
assert_false(self.registration.embargo_end_date)
assert_false(self.registration.pending_embargo)
assert_equal(res.status_code, 302)
assert_true(self.registration._id in res.location)
class RegistrationEmbargoViewsTestCase(OsfTestCase):
def setUp(self):
super(RegistrationEmbargoViewsTestCase, self).setUp()
ensure_schemas()
self.user = AuthUserFactory()
self.project = ProjectFactory(creator=self.user)
self.registration = RegistrationFactory(project=self.project, creator=self.user)
current_month = datetime.datetime.now().strftime("%B")
current_year = datetime.datetime.now().strftime("%Y")
self.valid_make_public_payload = json.dumps({
u'embargoEndDate': u'Fri, 01, {month} {year} 00:00:00 GMT'.format(
month=current_month,
year=current_year
),
u'registrationChoice': 'immediate',
u'summary': unicode(fake.sentence())
})
valid_date = datetime.datetime.now() + datetime.timedelta(days=180)
self.valid_embargo_payload = json.dumps({
u'embargoEndDate': unicode(valid_date.strftime('%a, %d, %B %Y %H:%M:%S')) + u' GMT',
u'registrationChoice': 'embargo',
u'summary': unicode(fake.sentence())
})
self.invalid_embargo_date_payload = json.dumps({
u'embargoEndDate': u"Thu, 01 {month} {year} 05:00:00 GMT".format(
month=current_month,
year=str(int(current_year)-1)
),
u'registrationChoice': 'embargo',
u'summary': unicode(fake.sentence())
})
@mock.patch('framework.tasks.handlers.enqueue_task')
def test_POST_register_make_public_immediately_creates_public_registration(self, mock_enqueue):
res = self.app.post(
self.project.api_url_for('node_register_template_page_post', template=u'Open-Ended_Registration'),
self.valid_make_public_payload,
content_type='application/json',
auth=self.user.auth
)
assert_equal(res.status_code, 201)
registration = Node.find().sort('-registered_date')[0]
assert_true(registration.is_registration)
assert_true(registration.is_public)
@mock.patch('framework.tasks.handlers.enqueue_task')
def test_POST_register_make_public_immediately_makes_children_public(self, mock_enqueue):
component = NodeFactory(
creator=self.user,
parent=self.project,
title='Component'
)
subproject = ProjectFactory(
creator=self.user,
parent=self.project,
title='Subproject'
)
subproject_component = NodeFactory(
creator=self.user,
parent=subproject,
title='Subcomponent'
)
res = self.app.post(
self.project.api_url_for('node_register_template_page_post', template=u'Open-Ended_Registration'),
self.valid_make_public_payload,
content_type='application/json',
auth=self.user.auth
)
self.project.reload()
# Last node directly registered from self.project
registration = Node.load(self.project.node__registrations[-1])
assert_true(registration.is_public)
for node in registration.get_descendants_recursive():
assert_true(node.is_registration)
assert_true(node.is_public)
@mock.patch('framework.tasks.handlers.enqueue_task')
def test_POST_register_embargo_is_not_public(self, mock_enqueue):
res = self.app.post(
self.project.api_url_for('node_register_template_page_post', template=u'Open-Ended_Registration'),
self.valid_embargo_payload,
content_type='application/json',
auth=self.user.auth
)
assert_equal(res.status_code, 201)
registration = Node.find().sort('-registered_date')[0]
assert_true(registration.is_registration)
assert_false(registration.is_public)
assert_true(registration.pending_registration)
assert_is_not_none(registration.embargo)
@mock.patch('framework.tasks.handlers.enqueue_task')
def test_POST_invalid_embargo_end_date_returns_HTTPBad_Request(self, mock_enqueue):
res = self.app.post(
self.project.api_url_for('node_register_template_page_post', template=u'Open-Ended_Registration'),
self.invalid_embargo_date_payload,
content_type='application/json',
auth=self.user.auth,
expect_errors=True
)
assert_equal(res.status_code, 400)
@mock.patch('framework.tasks.handlers.enqueue_task')
def test_valid_POST_embargo_adds_to_parent_projects_log(self, mock_enquque):
initial_project_logs = len(self.project.logs)
res = self.app.post(
self.project.api_url_for('node_register_template_page_post', template=u'Open-Ended_Registration'),
self.valid_embargo_payload,
content_type='application/json',
auth=self.user.auth
)
self.project.reload()
# Logs: Created, registered, embargo initiated
assert_equal(len(self.project.logs), initial_project_logs + 1)
| 42.76676
| 114
| 0.694327
| 3,266
| 30,621
| 6.223209
| 0.075628
| 0.150357
| 0.090529
| 0.043493
| 0.79941
| 0.758573
| 0.723936
| 0.701156
| 0.669668
| 0.656581
| 0
| 0.006438
| 0.218869
| 30,621
| 715
| 115
| 42.826573
| 0.843304
| 0.031612
| 0
| 0.627288
| 0
| 0
| 0.05618
| 0.029508
| 0
| 0
| 0
| 0
| 0.183028
| 1
| 0.079867
| false
| 0
| 0.018303
| 0
| 0.104825
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
b21d3033aa002597cfd5442d454bee6146b1eabf
| 83
|
py
|
Python
|
tests/Firefly.PSCloudFormation.Tests.Unit/Resources/LambdaDependencies/PythonLambda/Lambda/my_lambda.py
|
fireflycons/PSCloudFormation
|
4f2a3388789ee83e1cf0896395925a40055e2b9c
|
[
"MIT"
] | 3
|
2019-12-13T02:58:22.000Z
|
2020-07-01T14:18:11.000Z
|
tests/Firefly.PSCloudFormation.Tests.Unit/Resources/LambdaDependencies/PythonLambda/Lambda/my_lambda.py
|
fireflycons/PSCloudFormation
|
4f2a3388789ee83e1cf0896395925a40055e2b9c
|
[
"MIT"
] | 82
|
2019-02-21T08:58:14.000Z
|
2021-12-24T08:10:27.000Z
|
tests/Firefly.PSCloudFormation.Tests.Unit/Resources/LambdaDependencies/PythonLambda/Lambda/my_lambda.py
|
fireflycons/PSCloudFormation
|
4f2a3388789ee83e1cf0896395925a40055e2b9c
|
[
"MIT"
] | null | null | null |
def handler(event: dict, context, debug_var=1, **kwargs) -> None:
print('hi')
| 27.666667
| 66
| 0.638554
| 13
| 83
| 4.076923
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.014286
| 0.156627
| 83
| 2
| 67
| 41.5
| 0.728571
| 0
| 0
| 0
| 0
| 0
| 0.02439
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.5
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
b241cb9e24a1bde3d20727f989aa533032131766
| 319
|
py
|
Python
|
exercicios/aula8-ex016.py
|
anildoferreira/CursoPython-PyCharm
|
56c3962d6cc7d43482a9649a482619adba780d81
|
[
"MIT"
] | null | null | null |
exercicios/aula8-ex016.py
|
anildoferreira/CursoPython-PyCharm
|
56c3962d6cc7d43482a9649a482619adba780d81
|
[
"MIT"
] | null | null | null |
exercicios/aula8-ex016.py
|
anildoferreira/CursoPython-PyCharm
|
56c3962d6cc7d43482a9649a482619adba780d81
|
[
"MIT"
] | null | null | null |
'''from math import trunc
num = float(input('Digite um número para mostrar sua porção: '))
print('a porção do seu número é: {}'.format(trunc(num)))'''
num = float(input('Digite um numero para se tornar uma integral zozinha: '))
print('o número flutuante {}, se tornou um número integral {:.0f}'.format(num, int(num)))
| 45.571429
| 89
| 0.69906
| 50
| 319
| 4.46
| 0.62
| 0.071749
| 0.116592
| 0.170404
| 0.188341
| 0
| 0
| 0
| 0
| 0
| 0
| 0.00365
| 0.141066
| 319
| 6
| 90
| 53.166667
| 0.810219
| 0.451411
| 0
| 0
| 0
| 0
| 0.662722
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
b245ababefa8374f938222add079b33a6c2afbfb
| 10,776
|
py
|
Python
|
tensorflow_federated/python/core/templates/estimation_process_test.py
|
j35tor/federated
|
d92bfa6b8e3c9ebbac51ff7a3a180c2baaa08730
|
[
"Apache-2.0"
] | 1
|
2021-04-01T08:35:06.000Z
|
2021-04-01T08:35:06.000Z
|
tensorflow_federated/python/core/templates/estimation_process_test.py
|
j35tor/federated
|
d92bfa6b8e3c9ebbac51ff7a3a180c2baaa08730
|
[
"Apache-2.0"
] | null | null | null |
tensorflow_federated/python/core/templates/estimation_process_test.py
|
j35tor/federated
|
d92bfa6b8e3c9ebbac51ff7a3a180c2baaa08730
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2020, The TensorFlow Federated Authors.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tensorflow as tf
from tensorflow_federated.python.core.api import computation_types
from tensorflow_federated.python.core.api import computations
from tensorflow_federated.python.core.api import intrinsics
from tensorflow_federated.python.core.api import placements
from tensorflow_federated.python.core.api import test_case
from tensorflow_federated.python.core.templates import errors
from tensorflow_federated.python.core.templates import estimation_process
@computations.tf_computation()
def test_initialize_fn():
return tf.constant(0, tf.int32)
@computations.tf_computation(tf.int32)
def test_next_fn(state):
return state
@computations.tf_computation(tf.int32)
def test_report_fn(state):
return tf.cast(state, tf.float32)
@computations.tf_computation(tf.float32)
def test_map_fn(estimate):
return tf.stack([estimate, estimate])
class EstimationProcessTest(test_case.TestCase):
def test_construction_does_not_raise(self):
try:
estimation_process.EstimationProcess(test_initialize_fn, test_next_fn,
test_report_fn)
except: # pylint: disable=bare-except
self.fail('Could not construct a valid EstimationProcess.')
def test_construction_with_empty_state_does_not_raise(self):
initialize_fn = computations.tf_computation()(lambda: ())
next_fn = computations.tf_computation(())(lambda x: (x, 1.0))
report_fn = computations.tf_computation(())(lambda x: x)
try:
estimation_process.EstimationProcess(initialize_fn, next_fn, report_fn)
except: # pylint: disable=bare-except
self.fail('Could not construct an EstimationProcess with empty state.')
def test_construction_with_unknown_dimension_does_not_raise(self):
initialize_fn = computations.tf_computation()(
lambda: tf.constant([], dtype=tf.string))
@computations.tf_computation(
computation_types.TensorType(shape=[None], dtype=tf.string))
def next_fn(strings):
return tf.concat([strings, tf.constant(['abc'])], axis=0)
@computations.tf_computation(
computation_types.TensorType(shape=[None], dtype=tf.string))
def report_fn(strings):
return strings
try:
estimation_process.EstimationProcess(initialize_fn, next_fn, report_fn)
except: # pylint: disable=bare-except
self.fail('Could not construct an EstimationProcess with parameter types '
'with statically unknown shape.')
def test_init_not_tff_computation_raises(self):
with self.assertRaisesRegex(TypeError, r'Expected .*\.Computation, .*'):
estimation_process.EstimationProcess(
initialize_fn=lambda: 0,
next_fn=test_next_fn,
report_fn=test_report_fn)
def test_next_not_tff_computation_raises(self):
with self.assertRaisesRegex(TypeError, r'Expected .*\.Computation, .*'):
estimation_process.EstimationProcess(
initialize_fn=test_initialize_fn,
next_fn=lambda state: state,
report_fn=test_report_fn)
def test_report_not_tff_computation_raises(self):
with self.assertRaisesRegex(TypeError, r'Expected .*\.Computation, .*'):
estimation_process.EstimationProcess(
initialize_fn=test_initialize_fn,
next_fn=test_next_fn,
report_fn=lambda state: state)
def test_init_param_not_empty_raises(self):
one_arg_initialize_fn = computations.tf_computation(tf.int32)(lambda x: x)
with self.assertRaises(errors.TemplateInitFnParamNotEmptyError):
estimation_process.EstimationProcess(one_arg_initialize_fn, test_next_fn,
test_report_fn)
def test_init_state_not_assignable(self):
float_initialize_fn = computations.tf_computation()(lambda: 0.0)
with self.assertRaises(errors.TemplateStateNotAssignableError):
estimation_process.EstimationProcess(float_initialize_fn, test_next_fn,
test_report_fn)
def test_federated_init_state_not_assignable(self):
initialize_fn = computations.federated_computation()(
lambda: intrinsics.federated_value(0, placements.SERVER))
next_fn = computations.federated_computation(
computation_types.FederatedType(
tf.int32, placements.CLIENTS))(lambda state: state)
report_fn = computations.federated_computation(
initialize_fn.type_signature.result)(lambda state: state)
with self.assertRaises(errors.TemplateStateNotAssignableError):
estimation_process.EstimationProcess(initialize_fn, next_fn, report_fn)
def test_next_state_not_assignable(self):
float_next_fn = computations.tf_computation(
tf.float32)(lambda state: tf.cast(state, tf.float32))
with self.assertRaises(errors.TemplateStateNotAssignableError):
estimation_process.EstimationProcess(test_initialize_fn, float_next_fn,
test_report_fn)
def test_federated_next_state_not_assignable(self):
initialize_fn = computations.federated_computation()(
lambda: intrinsics.federated_value(0, placements.SERVER))
next_fn = computations.federated_computation(
initialize_fn.type_signature.result)(
intrinsics.federated_broadcast)
report_fn = computations.federated_computation(
initialize_fn.type_signature.result)(lambda state: state)
with self.assertRaises(errors.TemplateStateNotAssignableError):
estimation_process.EstimationProcess(initialize_fn, next_fn, report_fn)
def test_next_state_not_assignable_tuple_result(self):
float_next_fn = computations.tf_computation(
tf.float32,
tf.float32)(lambda state, x: (tf.cast(state, tf.float32), x))
with self.assertRaises(errors.TemplateStateNotAssignableError):
estimation_process.EstimationProcess(test_initialize_fn, float_next_fn,
test_report_fn)
# Tests specific only for the EstimationProcess contract below.
def test_report_state_not_assignable(self):
report_fn = computations.tf_computation(
tf.float32)(lambda estimate: estimate)
with self.assertRaises(errors.TemplateStateNotAssignableError):
estimation_process.EstimationProcess(test_initialize_fn, test_next_fn,
report_fn)
def test_federated_report_state_not_assignable(self):
initialize_fn = computations.federated_computation()(
lambda: intrinsics.federated_value(0, placements.SERVER))
next_fn = computations.federated_computation(
initialize_fn.type_signature.result)(lambda state: state)
report_fn = computations.federated_computation(
computation_types.FederatedType(
tf.int32, placements.CLIENTS))(lambda state: state)
with self.assertRaises(errors.TemplateStateNotAssignableError):
estimation_process.EstimationProcess(initialize_fn, next_fn, report_fn)
def test_mapped_process_as_expected(self):
process = estimation_process.EstimationProcess(test_initialize_fn,
test_next_fn, test_report_fn)
mapped_process = process.map(test_map_fn)
self.assertIsInstance(mapped_process, estimation_process.EstimationProcess)
self.assertEqual(process.initialize, mapped_process.initialize)
self.assertEqual(process.next, mapped_process.next)
self.assertEqual(process.report.type_signature.parameter,
mapped_process.report.type_signature.parameter)
self.assertEqual(test_map_fn.type_signature.result,
mapped_process.report.type_signature.result)
def test_federated_mapped_process_as_expected(self):
initialize_fn = computations.federated_computation()(
lambda: intrinsics.federated_value(0, placements.SERVER))
next_fn = computations.federated_computation(
initialize_fn.type_signature.result)(lambda state: state)
report_fn = computations.federated_computation(
initialize_fn.type_signature.result)(
lambda state: intrinsics.federated_map(test_report_fn, state))
process = estimation_process.EstimationProcess(initialize_fn, next_fn,
report_fn)
map_fn = computations.federated_computation(
report_fn.type_signature.result)(
lambda estimate: intrinsics.federated_map(test_map_fn, estimate))
mapped_process = process.map(map_fn)
self.assertIsInstance(mapped_process, estimation_process.EstimationProcess)
self.assertEqual(process.initialize, mapped_process.initialize)
self.assertEqual(process.next, mapped_process.next)
self.assertEqual(process.report.type_signature.parameter,
mapped_process.report.type_signature.parameter)
self.assertEqual(map_fn.type_signature.result,
mapped_process.report.type_signature.result)
def test_map_estimate_not_assignable(self):
map_fn = computations.tf_computation(tf.int32)(lambda estimate: estimate)
process = estimation_process.EstimationProcess(test_initialize_fn,
test_next_fn, test_report_fn)
with self.assertRaises(estimation_process.EstimateNotAssignableError):
process.map(map_fn)
def test_federated_map_estimate_not_assignable(self):
initialize_fn = computations.federated_computation()(
lambda: intrinsics.federated_value(0, placements.SERVER))
next_fn = computations.federated_computation(
initialize_fn.type_signature.result)(lambda state: state)
report_fn = computations.federated_computation(
initialize_fn.type_signature.result)(
lambda state: intrinsics.federated_map(test_report_fn, state))
process = estimation_process.EstimationProcess(initialize_fn, next_fn,
report_fn)
map_fn = computations.federated_computation(
computation_types.FederatedType(
tf.int32, placements.CLIENTS))(lambda estimate: estimate)
with self.assertRaises(estimation_process.EstimateNotAssignableError):
process.map(map_fn)
if __name__ == '__main__':
test_case.main()
| 45.277311
| 80
| 0.737751
| 1,219
| 10,776
| 6.224774
| 0.133716
| 0.060095
| 0.089615
| 0.076173
| 0.801924
| 0.75369
| 0.741829
| 0.653532
| 0.638245
| 0.624012
| 0
| 0.005912
| 0.183742
| 10,776
| 237
| 81
| 45.468354
| 0.856753
| 0.06663
| 0
| 0.595628
| 0
| 0
| 0.028984
| 0
| 0
| 0
| 0
| 0
| 0.125683
| 1
| 0.131148
| false
| 0
| 0.043716
| 0.032787
| 0.213115
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
b24bf3f0f808b974876e556d94b824a5902ddd73
| 81
|
py
|
Python
|
nes/processors/cpu/instructions/flags/sed.py
|
Hexadorsimal/pynes
|
dbb3d40c1240fa27f70fa798bcec09188755eec2
|
[
"MIT"
] | 1
|
2017-05-13T18:57:09.000Z
|
2017-05-13T18:57:09.000Z
|
nes/processors/cpu/instructions/flags/sed.py
|
Hexadorsimal/py6502
|
dbb3d40c1240fa27f70fa798bcec09188755eec2
|
[
"MIT"
] | 7
|
2020-10-24T17:16:56.000Z
|
2020-11-01T14:10:23.000Z
|
nes/processors/cpu/instructions/flags/sed.py
|
Hexadorsimal/pynes
|
dbb3d40c1240fa27f70fa798bcec09188755eec2
|
[
"MIT"
] | null | null | null |
from .set import SetInstruction
class Sed(SetInstruction):
flag_name = 'd'
| 13.5
| 31
| 0.728395
| 10
| 81
| 5.8
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.185185
| 81
| 5
| 32
| 16.2
| 0.878788
| 0
| 0
| 0
| 0
| 0
| 0.012346
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
b24ef81aeb720d24ded2e6df60f77295f24af1be
| 371
|
py
|
Python
|
planar_ising/common_utils.py
|
ValeryTyumen/planar_ising
|
5a1803487e1dd59c5d5e790cc949b7234bf52ac8
|
[
"MIT"
] | 8
|
2019-05-02T20:27:21.000Z
|
2020-11-01T20:41:38.000Z
|
planar_ising/common_utils.py
|
ValeryTyumen/planar_ising
|
5a1803487e1dd59c5d5e790cc949b7234bf52ac8
|
[
"MIT"
] | 1
|
2019-09-03T18:15:53.000Z
|
2019-09-06T16:41:12.000Z
|
planar_ising/common_utils.py
|
ValeryTyumen/planar_ising
|
5a1803487e1dd59c5d5e790cc949b7234bf52ac8
|
[
"MIT"
] | 3
|
2019-08-11T23:08:58.000Z
|
2022-03-19T09:09:50.000Z
|
import numpy as np
def repeat_int(value, count):
array = np.zeros(count, dtype=np.int32)
array[:] = value
return array
def repeat_bool(value, count):
array = np.zeros(count, dtype=np.bool_)
array[:] = value
return array
def repeat_float(value, count):
array = np.zeros(count, dtype=np.float32)
array[:] = value
return array
| 15.458333
| 45
| 0.644205
| 52
| 371
| 4.519231
| 0.326923
| 0.114894
| 0.191489
| 0.217021
| 0.689362
| 0.689362
| 0.434043
| 0.434043
| 0
| 0
| 0
| 0.014085
| 0.234501
| 371
| 23
| 46
| 16.130435
| 0.81338
| 0
| 0
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.230769
| false
| 0
| 0.076923
| 0
| 0.538462
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
b277ad8bb485a328d0fb6df423dc9afba869a596
| 775
|
py
|
Python
|
Surf_counter/spot_urls.py
|
SimonHollands/crowdfactor3
|
efc4721e7884c23d96f8004d3e0447a2d24d932a
|
[
"MIT"
] | 2
|
2019-10-31T22:17:57.000Z
|
2021-09-19T20:11:25.000Z
|
Surf_counter/spot_urls.py
|
SimonHollands/crowdfactor3
|
efc4721e7884c23d96f8004d3e0447a2d24d932a
|
[
"MIT"
] | 6
|
2019-10-31T20:09:22.000Z
|
2022-02-10T01:05:29.000Z
|
Surf_counter/spot_urls.py
|
SimonHollands/crowdfactor3
|
efc4721e7884c23d96f8004d3e0447a2d24d932a
|
[
"MIT"
] | 1
|
2022-01-28T21:24:30.000Z
|
2022-01-28T21:24:30.000Z
|
class SpotUrls:
token='85392160'
CFID='459565'
venice_morning_good='https://camrewinds.cdn-surfline.com/live/wc-venicebeachclose.stream.20191103T162900647.mp4'
venice_static='https://camrewinds.cdn-surfline.com/live/wc-venicebeachclose.stream.20191027T235900139.mp4'
lookup={'breakwater': 'http://www.surfline.com/surfdata/video-rewind/video_rewind.cfm?id=150603&CFID=459565&CFTOKEN=85392160',
'topanga': 'http://www.surfline.com/surfdata/video-rewind/video_rewind.cfm?id=150605&CFID=491164&CFTOKEN=82948697'}
lookupmp4={'breakwater': "https://camrewinds.cdn-surfline.com/live/wc-venicebeachclose.stream.",
'topanga': "https://camrewinds.cdn-surfline.com/live/wc-topangaclose.stream."}
| 64.583333
| 132
| 0.713548
| 89
| 775
| 6.157303
| 0.438202
| 0.120438
| 0.131387
| 0.189781
| 0.569343
| 0.569343
| 0.569343
| 0.505474
| 0.505474
| 0.193431
| 0
| 0.135417
| 0.132903
| 775
| 11
| 133
| 70.454545
| 0.68006
| 0
| 0
| 0
| 0
| 0.444444
| 0.726098
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0.777778
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
b2aa3b81ce40646b2b5ced0272d45e40ff05e4ae
| 518
|
py
|
Python
|
Curso-Em-Video-Python/2Exercicios/009_Tabuada.py
|
pedrohd21/Cursos-Feitos
|
b223aad83867bfa45ad161d133e33c2c200d42bd
|
[
"MIT"
] | null | null | null |
Curso-Em-Video-Python/2Exercicios/009_Tabuada.py
|
pedrohd21/Cursos-Feitos
|
b223aad83867bfa45ad161d133e33c2c200d42bd
|
[
"MIT"
] | null | null | null |
Curso-Em-Video-Python/2Exercicios/009_Tabuada.py
|
pedrohd21/Cursos-Feitos
|
b223aad83867bfa45ad161d133e33c2c200d42bd
|
[
"MIT"
] | null | null | null |
x = int(input('Qual tabuada multiplicar: '))
print('-' * 15)
print('{} x {:2} = {}'.format(x, 1, (x*1)))
print('{} x {:2} = {}'.format(x, 2, (x*2)))
print('{} x {:2} = {}'.format(x, 3, (x*3)))
print('{} x {:2} = {}'.format(x, 4, (x*4)))
print('{} x {:2} = {}'.format(x, 5, (x*5)))
print('{} x {:2} = {}'.format(x, 6, (x*6)))
print('{} x {:2} = {}'.format(x, 7, (x*7)))
print('{} x {:2} = {}'.format(x, 8, (x*8)))
print('{} x {:2} = {}'.format(x, 9, (x*9)))
print('{} x {:2} = {}'.format(x, 10, (x*10)))
print('-' * 15)
| 39.846154
| 45
| 0.418919
| 90
| 518
| 2.411111
| 0.211111
| 0.110599
| 0.322581
| 0.599078
| 0.645161
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08238
| 0.156371
| 518
| 13
| 46
| 39.846154
| 0.414188
| 0
| 0
| 0.153846
| 0
| 0
| 0.323699
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.923077
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
b2af942332ef7793dbc1d36cd9e6bf7de75b8073
| 180
|
py
|
Python
|
data_files/LearningPython/if1.py
|
PmagPy/PmagPy-notebooks
|
490cb13e3a78e1323368125de9a503bd327c7174
|
[
"BSD-3-Clause"
] | 2
|
2020-07-05T01:11:33.000Z
|
2020-07-05T01:11:39.000Z
|
data_files/LearningPython/if1.py
|
schwehr/PmagPy
|
5e9edc5dc9a7a243b8e7f237fa156e0cd782076b
|
[
"BSD-3-Clause"
] | 1
|
2018-08-27T22:59:09.000Z
|
2018-08-27T22:59:09.000Z
|
data_files/LearningPython/if1.py
|
PmagPy/PmagPy-notebooks
|
490cb13e3a78e1323368125de9a503bd327c7174
|
[
"BSD-3-Clause"
] | null | null | null |
#!/usr/bin/env python
from __future__ import print_function
if (2+2)==4: # note the use of '==' and parentheses in comparison statement
print("I can put two and two together!")
| 36
| 75
| 0.722222
| 30
| 180
| 4.166667
| 0.866667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.019868
| 0.161111
| 180
| 4
| 76
| 45
| 0.807947
| 0.45
| 0
| 0
| 0
| 0
| 0.319588
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0.666667
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 4
|
a25cd3c9ab8ee69e6283e126daf4ec3d2408b150
| 107
|
py
|
Python
|
python/py-functionals/reduce-function.py
|
feliposz/hackerrank-solutions
|
fb1d63ca12a0d289362c9b3fb4cb0b79ef73f72f
|
[
"MIT"
] | null | null | null |
python/py-functionals/reduce-function.py
|
feliposz/hackerrank-solutions
|
fb1d63ca12a0d289362c9b3fb4cb0b79ef73f72f
|
[
"MIT"
] | null | null | null |
python/py-functionals/reduce-function.py
|
feliposz/hackerrank-solutions
|
fb1d63ca12a0d289362c9b3fb4cb0b79ef73f72f
|
[
"MIT"
] | null | null | null |
def product(fracs):
t = reduce(lambda x, y: x * y, fracs, 1)
return t.numerator, t.denominator
| 13.375
| 44
| 0.616822
| 17
| 107
| 3.882353
| 0.705882
| 0.060606
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.0125
| 0.252336
| 107
| 7
| 45
| 15.285714
| 0.8125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
a2798b2c78bed914f523662514fff931c8e873a8
| 21,533
|
py
|
Python
|
test/mount_efs_test/test_publish_cloudwatch_log.py
|
daisuke-yoshimoto/efs-utils
|
cd27177d80d7a3a5f44bc3412c0b275c6ba3eece
|
[
"MIT"
] | null | null | null |
test/mount_efs_test/test_publish_cloudwatch_log.py
|
daisuke-yoshimoto/efs-utils
|
cd27177d80d7a3a5f44bc3412c0b275c6ba3eece
|
[
"MIT"
] | null | null | null |
test/mount_efs_test/test_publish_cloudwatch_log.py
|
daisuke-yoshimoto/efs-utils
|
cd27177d80d7a3a5f44bc3412c0b275c6ba3eece
|
[
"MIT"
] | 12
|
2020-10-22T03:47:51.000Z
|
2022-03-19T18:09:59.000Z
|
#
# Copyright 2017-2018 Amazon.com, Inc. and its affiliates. All Rights Reserved.
#
# Licensed under the MIT License. See the LICENSE accompanying this file
# for the specific language governing permissions and limitations under
# the License.
#
import mount_efs
from .. import utils
from botocore.exceptions import ClientError, NoCredentialsError
from mock import MagicMock
try:
import ConfigParser
except ImportError:
from configparser import ConfigParser
DEFAULT_CLOUDWATCH_LOG_GROUP = '/aws/efs/utils'
DEFAULT_CLOUDWATCH_ENABLED = 'true'
DEFAULT_CLOUDWATCH_DISABLED = 'false'
DEFAULT_RETENTION_DAYS = 14
FS_ID = 'fs-deadbeef'
INSTANCE = 'i-12345678'
DEFAULT_CLOUDWATCH_LOG_STREAM = '%s - %s - mount.log' % (FS_ID, INSTANCE)
MOCK_AGENT = {
'client': 'fake-agent',
'log_group_name': DEFAULT_CLOUDWATCH_LOG_GROUP,
'log_stream_name': '%s - %s - mount.log' % (FS_ID, INSTANCE)
}
def _get_mock_config(enabled, log_group_name, retention_in_days):
def config_get_side_effect(section, field):
if section == mount_efs.CLOUDWATCH_LOG_SECTION and field == 'log_group_name':
return log_group_name
elif section == mount_efs.CLOUDWATCH_LOG_SECTION and field == 'retention_in_days':
return retention_in_days
else:
raise ValueError('Unexpected arguments')
def config_getboolean_side_effect(section, field):
if section == mount_efs.CLOUDWATCH_LOG_SECTION and field == 'enabled':
return True if enabled == 'true' else False
else:
raise ValueError('Unexpected arguments')
mock_config = MagicMock()
mock_config.get.side_effect = config_get_side_effect
mock_config.getboolean.side_effect = config_getboolean_side_effect
return mock_config
"""
cloudwatch-log config unit tests
"""
def test_get_cloudwatchlog_config_without_fsid_with_instance_id(mocker):
config = _get_mock_config(DEFAULT_CLOUDWATCH_ENABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
enabled = mount_efs.check_if_cloudwatch_log_enabled(config)
assert enabled == True
mocker.patch('mount_efs.get_instance_identity_info_from_instance_metadata', return_value=INSTANCE)
cloudwatchlog_agent = mount_efs.get_cloudwatchlog_config(config)
assert cloudwatchlog_agent.get('log_group_name') == DEFAULT_CLOUDWATCH_LOG_GROUP
assert cloudwatchlog_agent.get('retention_days') == DEFAULT_RETENTION_DAYS
assert cloudwatchlog_agent.get('log_stream_name') == '%s - mount.log' % INSTANCE
def test_get_cloudwatchlog_config_with_fsid_with_instance_id(mocker):
config = _get_mock_config(DEFAULT_CLOUDWATCH_ENABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
enabled = mount_efs.check_if_cloudwatch_log_enabled(config)
assert enabled == True
mocker.patch('mount_efs.get_instance_identity_info_from_instance_metadata', return_value=INSTANCE)
cloudwatchlog_agent = mount_efs.get_cloudwatchlog_config(config, FS_ID)
assert cloudwatchlog_agent.get('log_group_name') == DEFAULT_CLOUDWATCH_LOG_GROUP
assert cloudwatchlog_agent.get('retention_days') == DEFAULT_RETENTION_DAYS
assert cloudwatchlog_agent.get('log_stream_name') == '%s - %s - mount.log' % (FS_ID, INSTANCE)
def test_get_cloudwatchlog_config_with_fsid_without_instance_id(mocker):
config = _get_mock_config(DEFAULT_CLOUDWATCH_ENABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
enabled = mount_efs.check_if_cloudwatch_log_enabled(config)
assert enabled == True
mocker.patch('mount_efs.get_instance_identity_info_from_instance_metadata', return_value=None)
cloudwatchlog_agent = mount_efs.get_cloudwatchlog_config(config, FS_ID)
assert cloudwatchlog_agent.get('log_group_name') == DEFAULT_CLOUDWATCH_LOG_GROUP
assert cloudwatchlog_agent.get('retention_days') == DEFAULT_RETENTION_DAYS
assert cloudwatchlog_agent.get('log_stream_name') == '%s - mount.log' % (FS_ID)
def test_get_cloudwatchlog_config_without_fsid_without_instance_id(mocker):
config = _get_mock_config(DEFAULT_CLOUDWATCH_ENABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
enabled = mount_efs.check_if_cloudwatch_log_enabled(config)
assert enabled == True
mocker.patch('mount_efs.get_instance_identity_info_from_instance_metadata', return_value=None)
cloudwatchlog_agent = mount_efs.get_cloudwatchlog_config(config)
assert cloudwatchlog_agent.get('log_group_name') == DEFAULT_CLOUDWATCH_LOG_GROUP
assert cloudwatchlog_agent.get('retention_days') == DEFAULT_RETENTION_DAYS
assert cloudwatchlog_agent.get('log_stream_name') == 'default - mount.log'
# When config set enabled = false, or there is no enabled section, call the bootstrap_cloudwatch_logging, the get_botocore_client
# is not called
def test_botocore_not_called_when_feature_not_enabled(mocker):
config = _get_mock_config(DEFAULT_CLOUDWATCH_DISABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
enabled = mount_efs.check_if_cloudwatch_log_enabled(config)
assert enabled == False
get_botocore_client_mock = mocker.patch('mount_efs.get_botocore_client')
cloudwatchlog_agent = mount_efs.bootstrap_cloudwatch_logging(config, FS_ID)
utils.assert_not_called(get_botocore_client_mock)
assert cloudwatchlog_agent == None
# When config set enabled = true, call the bootstrap_cloudwatch_logging, the get_botocore_client is called
def test_cloudwatchlog_agent_none_when_botocore_agent_is_none(mocker):
config = _get_mock_config(DEFAULT_CLOUDWATCH_ENABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
get_botocore_client_mock = mocker.patch('mount_efs.get_botocore_client', return_value=None)
cloudwatchlog_agent = mount_efs.bootstrap_cloudwatch_logging(config, FS_ID)
utils.assert_called_once(get_botocore_client_mock)
assert cloudwatchlog_agent == None
"""
bootstrap cloud watch log unit tests
"""
def test_bootstrap_cloudwatch_log(mocker):
config = _get_mock_config(DEFAULT_CLOUDWATCH_ENABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
mocker.patch('mount_efs.get_instance_identity_info_from_instance_metadata', return_value=INSTANCE)
get_botocore_client_mock = mocker.patch('mount_efs.get_botocore_client', return_value='fake-agent')
create_log_group_mock = mocker.patch('mount_efs.create_cloudwatch_log_group', return_value=True)
put_retention_policy_mock = mocker.patch('mount_efs.put_cloudwatch_log_retention_policy', return_value=True)
create_log_stream_mock = mocker.patch('mount_efs.create_cloudwatch_log_stream', return_value=True)
cloudwatchlog_agent = mount_efs.bootstrap_cloudwatch_logging(config, FS_ID)
utils.assert_called_once(get_botocore_client_mock)
utils.assert_called_once(create_log_group_mock)
utils.assert_called_once(put_retention_policy_mock)
utils.assert_called_once(create_log_stream_mock)
assert cloudwatchlog_agent == MOCK_AGENT
def test_bootstrap_cloudwatch_log_create_log_group_failed(mocker):
config = _get_mock_config(DEFAULT_CLOUDWATCH_ENABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
mocker.patch('mount_efs.get_instance_identity_info_from_instance_metadata', return_value=INSTANCE)
get_botocore_client_mock = mocker.patch('mount_efs.get_botocore_client', return_value='fake-agent')
create_log_group_mock = mocker.patch('mount_efs.create_cloudwatch_log_group', return_value=False)
put_retention_policy_mock = mocker.patch('mount_efs.put_cloudwatch_log_retention_policy')
create_log_stream_mock = mocker.patch('mount_efs.create_cloudwatch_log_stream')
cloudwatchlog_agent = mount_efs.bootstrap_cloudwatch_logging(config, FS_ID)
utils.assert_called_once(get_botocore_client_mock)
utils.assert_called_once(create_log_group_mock)
utils.assert_not_called(put_retention_policy_mock)
utils.assert_not_called(create_log_stream_mock)
assert cloudwatchlog_agent == None
def test_bootstrap_cloudwatch_log_put_retention_days_failed(mocker):
config = _get_mock_config(DEFAULT_CLOUDWATCH_ENABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
mocker.patch('mount_efs.get_instance_identity_info_from_instance_metadata', return_value=INSTANCE)
get_botocore_client_mock = mocker.patch('mount_efs.get_botocore_client', return_value='fake-agent')
create_log_group_mock = mocker.patch('mount_efs.create_cloudwatch_log_group', return_value=True)
put_retention_policy_mock = mocker.patch('mount_efs.put_cloudwatch_log_retention_policy', return_value=False)
create_log_stream_mock = mocker.patch('mount_efs.create_cloudwatch_log_stream')
cloudwatchlog_agent = mount_efs.bootstrap_cloudwatch_logging(config, FS_ID)
utils.assert_called_once(get_botocore_client_mock)
utils.assert_called_once(create_log_group_mock)
utils.assert_called_once(put_retention_policy_mock)
utils.assert_not_called(create_log_stream_mock)
assert cloudwatchlog_agent == None
def test_bootstrap_cloudwatch_log_create_log_stream_failed(mocker):
config = _get_mock_config(DEFAULT_CLOUDWATCH_ENABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
mocker.patch('mount_efs.get_instance_identity_info_from_instance_metadata', return_value=INSTANCE)
get_botocore_client_mock = mocker.patch('mount_efs.get_botocore_client', return_value='fake-agent')
create_log_group_mock = mocker.patch('mount_efs.create_cloudwatch_log_group', return_value=True)
put_retention_policy_mock = mocker.patch('mount_efs.put_cloudwatch_log_retention_policy', return_value=True)
create_log_stream_mock = mocker.patch('mount_efs.create_cloudwatch_log_stream', return_value=False)
cloudwatchlog_agent = mount_efs.bootstrap_cloudwatch_logging(config, FS_ID)
utils.assert_called_once(get_botocore_client_mock)
utils.assert_called_once(create_log_group_mock)
utils.assert_called_once(put_retention_policy_mock)
utils.assert_called_once(create_log_stream_mock)
assert cloudwatchlog_agent == None
"""
botocore client unit tests
"""
def test_botocore_none_if_botocore_not_present(mocker):
config = _get_mock_config(DEFAULT_CLOUDWATCH_ENABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
mount_efs.BOTOCORE_PRESENT = False
client = mount_efs.get_botocore_client(config, 'logs')
assert client == None
def _test_botocore_client_established(mocker, iam_name):
config = _get_mock_config(DEFAULT_CLOUDWATCH_ENABLED, DEFAULT_CLOUDWATCH_LOG_GROUP, DEFAULT_RETENTION_DAYS)
mount_efs.BOTOCORE_PRESENT = True
mocker.patch('mount_efs.get_target_region', return_value='us-east-1')
mocker.patch('mount_efs.get_iam_role_name', return_value=iam_name)
mocker.patch('mount_efs.get_aws_security_credentials_from_instance_metadata',
return_value=({
'AccessKeyId': '123456',
'SecretAccessKey': '123456',
'Token': '123456'
}, ''))
boto_session_mock = MagicMock()
boto_session_mock.create_client.return_value = 'fake-client'
mocker.patch('botocore.session.get_session', return_value=boto_session_mock)
client = mount_efs.get_botocore_client(config, 'logs')
assert client == 'fake-client'
def test_botocore_client_established_if_iam_name_is_present(mocker):
_test_botocore_client_established(mocker, 'default')
def test_botocore_client_established_if_iam_name_is_not_present(mocker):
_test_botocore_client_established(mocker, None)
"""
create_log_group api call exception unit tests
"""
def _test_create_log_group_client_error(mocker, exception, desired_result=False):
operation_name = 'CreateLogGroup'
response = {
'Error': {
'Code': exception
}
}
mocker.patch('mount_efs.cloudwatch_create_log_group_helper', side_effect=[ClientError(response, operation_name)])
is_completed = mount_efs.create_cloudwatch_log_group(MOCK_AGENT, DEFAULT_CLOUDWATCH_LOG_GROUP)
assert is_completed == desired_result
def test_create_log_group_no_credentials_error(mocker):
mocker.patch('mount_efs.cloudwatch_create_log_group_helper', side_effect=[NoCredentialsError()])
is_completed = mount_efs.create_cloudwatch_log_group(MOCK_AGENT, DEFAULT_CLOUDWATCH_LOG_GROUP)
assert is_completed == False
def test_create_log_group_resource_already_exist(mocker):
_test_create_log_group_client_error(mocker, 'ResourceAlreadyExistsException', True)
def test_create_log_group_limit_exceed(mocker):
_test_create_log_group_client_error(mocker, 'LimitExceededException')
def test_create_log_group_operation_aborted(mocker):
_test_create_log_group_client_error(mocker, 'OperationAbortedException')
def test_create_log_group_invalid_parameter(mocker):
_test_create_log_group_client_error(mocker, 'InvalidParameterException')
def test_create_log_group_service_unavailable_exception(mocker):
_test_create_log_group_client_error(mocker, 'ServiceUnavailableException')
def test_create_log_group_access_denied_exception(mocker):
_test_create_log_group_client_error(mocker, 'AccessDeniedException')
def test_create_log_group_unexpected_client_error(mocker):
_test_create_log_group_client_error(mocker, 'Unknown exception')
"""
put_retention_policy api call exception unit tests
"""
def _test_put_retention_policy_client_error(mocker, exception, desired_result=False):
operation_name = 'PutRetentionPolicy'
response = {
'Error': {
'Code': exception
}
}
mocker.patch('mount_efs.cloudwatch_put_retention_policy_helper', side_effect=[ClientError(response, operation_name)])
is_completed = mount_efs.put_cloudwatch_log_retention_policy(MOCK_AGENT['client'],
DEFAULT_CLOUDWATCH_LOG_GROUP,
DEFAULT_RETENTION_DAYS)
assert is_completed == desired_result
def test_put_retention_policy_no_credentials_error(mocker):
mocker.patch('mount_efs.cloudwatch_put_retention_policy_helper', side_effect=[NoCredentialsError()])
is_completed = mount_efs.put_cloudwatch_log_retention_policy(MOCK_AGENT['client'], DEFAULT_CLOUDWATCH_LOG_GROUP,
DEFAULT_RETENTION_DAYS)
assert is_completed == False
def test_put_retention_policy_resource_not_found(mocker):
_test_put_retention_policy_client_error(mocker, 'ResourceNotFoundException')
def test_put_retention_policy_operation_aborted(mocker):
_test_put_retention_policy_client_error(mocker, 'OperationAbortedException')
def test_put_retention_policy_invalid_parameter(mocker):
_test_put_retention_policy_client_error(mocker, 'InvalidParameterException')
def test_put_retention_policy_service_unavailable_exception(mocker):
_test_put_retention_policy_client_error(mocker, 'ServiceUnavailableException')
def test_put_retention_policy_access_denied_exception(mocker):
_test_put_retention_policy_client_error(mocker, 'AccessDeniedException')
def test_put_retention_policy_unexpected_client_error(mocker):
_test_put_retention_policy_client_error(mocker, 'Unknown exception')
"""
create_log_stream api call exception unit tests
"""
def _test_create_log_stream_client_error(mocker, exception, desired_result=False):
operation_name = 'CreateLogStream'
response = {
'Error': {
'Code': exception
}
}
mocker.patch('mount_efs.cloudwatch_create_log_stream_helper', side_effect=[ClientError(response, operation_name)])
is_completed = mount_efs.create_cloudwatch_log_stream(MOCK_AGENT['client'], DEFAULT_CLOUDWATCH_LOG_GROUP,
DEFAULT_CLOUDWATCH_LOG_STREAM)
assert is_completed == desired_result
def test_create_log_stream_no_credentials_error(mocker):
mocker.patch('mount_efs.cloudwatch_create_log_stream_helper', side_effect=[NoCredentialsError()])
is_completed = mount_efs.create_cloudwatch_log_stream(MOCK_AGENT['client'], DEFAULT_CLOUDWATCH_LOG_GROUP,
DEFAULT_CLOUDWATCH_LOG_STREAM)
assert is_completed == False
def test_create_log_stream_resource_already_exist(mocker):
_test_create_log_stream_client_error(mocker, 'ResourceAlreadyExistsException', True)
def test_create_log_stream_resource_not_found(mocker):
_test_create_log_stream_client_error(mocker, 'ResourceNotFoundException')
def test_create_log_stream_invalid_parameter(mocker):
_test_create_log_stream_client_error(mocker, 'InvalidParameterException')
def test_create_log_stream_service_unavailable_exception(mocker):
_test_create_log_stream_client_error(mocker, 'ServiceUnavailableException')
def test_create_log_stream_access_denied_exception(mocker):
_test_create_log_stream_client_error(mocker, 'AccessDeniedException')
def test_create_log_stream_unexpected_client_error(mocker):
_test_create_log_stream_client_error(mocker, 'Unknown exception')
"""
put_log_events api call exception unit tests
"""
def _test_put_log_events_client_error(mocker, exception, desired_result=False):
operation_name = 'PutLogEvents'
response = {
'Error': {
'Code': exception
}
}
mocker.patch('mount_efs.get_log_stream_next_token', return_value='ABCDEF')
mocker.patch('mount_efs.cloudwatch_put_log_events_helper', side_effect=[ClientError(response, operation_name)])
is_completed = mount_efs.publish_cloudwatch_log(MOCK_AGENT, 'Test')
assert is_completed == desired_result
def test_put_log_events_no_credentials_error(mocker):
mocker.patch('mount_efs.get_log_stream_next_token', return_value='ABCDEF')
mocker.patch('mount_efs.cloudwatch_put_log_events_helper', side_effect=[NoCredentialsError()])
is_completed = mount_efs.publish_cloudwatch_log(MOCK_AGENT, 'Test')
assert is_completed == False
def test_put_log_events_resource_not_found(mocker):
_test_put_log_events_client_error(mocker, 'ResourceNotFoundException')
def test_put_log_events_invalid_sequence_token(mocker):
_test_put_log_events_client_error(mocker, 'InvalidSequenceTokenException')
def test_put_log_events_invalid_parameter(mocker):
_test_put_log_events_client_error(mocker, 'InvalidParameterException')
def test_put_log_events_data_already_accepted(mocker):
_test_put_log_events_client_error(mocker, 'DataAlreadyAcceptedException')
def test_put_log_events_unrecognized_client(mocker):
_test_put_log_events_client_error(mocker, 'UnrecognizedClientException')
def test_put_log_events_service_unavailable_exception(mocker):
_test_put_log_events_client_error(mocker, 'ServiceUnavailableException')
def test_put_log_events_access_denied_exception(mocker):
_test_put_log_events_client_error(mocker, 'AccessDeniedException')
def test_put_log_events_unexpected_client_error(mocker):
_test_put_log_events_client_error(mocker, 'Unknown exception')
"""
describe_log_stream api call exception unit tests
"""
def _test_get_log_stream_next_token_client_error(mocker, exception, desired_result=None):
operation_name = 'DescribeLogStream'
response = {
'Error': {
'Code': exception
}
}
mocker.patch('mount_efs.cloudwatch_describe_log_streams_helper', side_effect=[ClientError(response, operation_name)])
token = mount_efs.get_log_stream_next_token(MOCK_AGENT)
assert token == desired_result
def test_get_log_stream_next_token_no_credentials_error(mocker):
mocker.patch('mount_efs.cloudwatch_describe_log_streams_helper', side_effect=[NoCredentialsError()])
token = mount_efs.get_log_stream_next_token(MOCK_AGENT)
assert token == None
def test_get_log_stream_next_token_resource_not_found(mocker):
_test_put_log_events_client_error(mocker, 'ResourceNotFoundException')
def test_get_log_stream_next_token_invalid_parameter(mocker):
_test_get_log_stream_next_token_client_error(mocker, 'InvalidParameterException')
def test_get_log_stream_next_token_service_unavailable_exception(mocker):
_test_get_log_stream_next_token_client_error(mocker, 'ServiceUnavailableException')
def test_get_log_stream_next_token_access_denied_exception(mocker):
_test_get_log_stream_next_token_client_error(mocker, 'AccessDeniedException')
def test_get_log_stream_next_token_unexpected_client_error(mocker):
_test_get_log_stream_next_token_client_error(mocker, 'Unknown exception')
def _test_get_log_stream_token_response(mocker, response, desired_token=None):
mocker.patch('mount_efs.cloudwatch_describe_log_streams_helper', return_value=response)
token = mount_efs.get_log_stream_next_token(MOCK_AGENT)
assert token == desired_token
def test_get_log_stream_token_index_error(mocker):
response = {
'logStreams': []
}
_test_get_log_stream_token_response(mocker, response)
def test_get_log_stream_token_key_error(mocker):
response = {}
_test_get_log_stream_token_response(mocker, response)
def test_get_log_stream_token_type_error(mocker):
response = None
_test_get_log_stream_token_response(mocker, response)
def test_get_log_stream_token_return_correct(mocker):
token = 'ABCDEF'
response = {
'logStreams': [
{
'uploadSequenceToken': token
}
]
}
_test_get_log_stream_token_response(mocker, response, token)
| 42.138943
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0
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a2ab0cdd89b054fb5109db4c024387dec53b6cf5
| 191
|
py
|
Python
|
cmemsapi/__init__.py
|
copernicusmarine/cmemsapi
|
b2b2f8e9c80d989fe1aa1374d8174a30c819847e
|
[
"MIT"
] | null | null | null |
cmemsapi/__init__.py
|
copernicusmarine/cmemsapi
|
b2b2f8e9c80d989fe1aa1374d8174a30c819847e
|
[
"MIT"
] | null | null | null |
cmemsapi/__init__.py
|
copernicusmarine/cmemsapi
|
b2b2f8e9c80d989fe1aa1374d8174a30c819847e
|
[
"MIT"
] | null | null | null |
"""Top-level package for Copernicus Marine ToolBox."""
__author__ = """E.U. Copernicus Marine Service Information"""
__email__ = 'servicedesk.cmems@mercator-ocean.eu'
__version__ = '0.1.17'
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0
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a2dc0973dc47ae7d7ab1aa134ee67dbeb6ca91f0
| 152
|
py
|
Python
|
examples/rest-api-python/src/list.py
|
drewfish/serverless-stack
|
155353ed7daf3ba2d4daeb9096f6c7638cb404fc
|
[
"MIT"
] | 5,922
|
2020-08-19T05:27:43.000Z
|
2022-03-31T23:29:17.000Z
|
examples/rest-api-python/src/list.py
|
Dzan001/serverless-stack
|
69fb992f31ac098b644f50cbddf3aaec4db054cd
|
[
"MIT"
] | 980
|
2020-09-17T03:09:42.000Z
|
2022-03-31T20:21:43.000Z
|
examples/rest-api-python/src/list.py
|
Dzan001/serverless-stack
|
69fb992f31ac098b644f50cbddf3aaec4db054cd
|
[
"MIT"
] | 458
|
2020-09-02T13:47:17.000Z
|
2022-03-31T12:14:32.000Z
|
import json
from db.notes import getNotes
def main(event, context):
return {
"statusCode": 200,
"body": json.dumps(getNotes(), indent=2)
}
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| 4
|
a2e0a3d88c4002f4d628216c77bf7762d39e81eb
| 602
|
py
|
Python
|
tests/integration/pybaseball/test_statcast_batter.py
|
mwisnie5/pybaseball
|
0a2a84d757e478aa79619100872ef48cf7da52c5
|
[
"MIT"
] | 1
|
2021-01-09T01:44:07.000Z
|
2021-01-09T01:44:07.000Z
|
tests/integration/pybaseball/test_statcast_batter.py
|
mwisnie5/pybaseball
|
0a2a84d757e478aa79619100872ef48cf7da52c5
|
[
"MIT"
] | null | null | null |
tests/integration/pybaseball/test_statcast_batter.py
|
mwisnie5/pybaseball
|
0a2a84d757e478aa79619100872ef48cf7da52c5
|
[
"MIT"
] | null | null | null |
import pandas as pd
from pybaseball.statcast_batter import statcast_batter, statcast_batter_exitvelo_barrels
def test_statcast_batter_exitvelo_barrels() -> None:
result: pd.DataFrame = statcast_batter_exitvelo_barrels(2019)
assert result is not None
assert not result.empty
assert len(result.columns) == 19
assert len(result) == 250
def test_statcast_batter() -> None:
result: pd.DataFrame = statcast_batter('2019-01-01', '2019-12-31', 642715)
assert result is not None
assert not result.empty
assert len(result.columns) == 89
assert len(result) == 2418
| 26.173913
| 88
| 0.73588
| 83
| 602
| 5.156627
| 0.361446
| 0.228972
| 0.140187
| 0.203271
| 0.457944
| 0.457944
| 0.294393
| 0.294393
| 0.294393
| 0.294393
| 0
| 0.074747
| 0.177741
| 602
| 22
| 89
| 27.363636
| 0.789899
| 0
| 0
| 0.285714
| 0
| 0
| 0.033223
| 0
| 0
| 0
| 0
| 0
| 0.571429
| 1
| 0.142857
| true
| 0
| 0.142857
| 0
| 0.285714
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
a2e8576c830fbf4be7678294ec7ac2e86823fb25
| 78
|
py
|
Python
|
python/testData/inspections/PyRedundantParenthesesInspection/NestedParentheses.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/inspections/PyRedundantParenthesesInspection/NestedParentheses.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/inspections/PyRedundantParenthesesInspection/NestedParentheses.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
x = (<weak_warning descr="Remove redundant parentheses">((42))</weak_warning>)
| 78
| 78
| 0.74359
| 10
| 78
| 5.6
| 0.8
| 0.392857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027397
| 0.064103
| 78
| 1
| 78
| 78
| 0.739726
| 0
| 0
| 0
| 0
| 0
| 0.35443
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0c0bec567195e560969a40e85958c41082afeac9
| 334
|
py
|
Python
|
pylas/errors.py
|
weyerhaeuser/pylas
|
8b0e266bf65e40906128979546de97093aaeadeb
|
[
"BSD-3-Clause"
] | null | null | null |
pylas/errors.py
|
weyerhaeuser/pylas
|
8b0e266bf65e40906128979546de97093aaeadeb
|
[
"BSD-3-Clause"
] | null | null | null |
pylas/errors.py
|
weyerhaeuser/pylas
|
8b0e266bf65e40906128979546de97093aaeadeb
|
[
"BSD-3-Clause"
] | null | null | null |
""" All the custom exceptions types
"""
class PylasError(Exception):
pass
class UnknownExtraType(PylasError):
pass
class PointFormatNotSupported(PylasError):
pass
class FileVersionNotSupported(PylasError):
pass
class LazPerfNotFound(PylasError):
pass
class IncompatibleDataFormat(PylasError):
pass
| 12.37037
| 42
| 0.745509
| 29
| 334
| 8.586207
| 0.482759
| 0.180723
| 0.305221
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.182635
| 334
| 26
| 43
| 12.846154
| 0.912088
| 0.092814
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
0c29b32808fd38a1cab0e595d78d8ab343d3e28d
| 148
|
py
|
Python
|
src/Advanced/tuplas.py
|
Thiago18l/Python-Projects
|
a8a748e41762269fc01474bb2ff0546d2902cf91
|
[
"MIT"
] | null | null | null |
src/Advanced/tuplas.py
|
Thiago18l/Python-Projects
|
a8a748e41762269fc01474bb2ff0546d2902cf91
|
[
"MIT"
] | null | null | null |
src/Advanced/tuplas.py
|
Thiago18l/Python-Projects
|
a8a748e41762269fc01474bb2ff0546d2902cf91
|
[
"MIT"
] | null | null | null |
t1 = ('OI', 2.0, [40, 50])
print(t1[2:])
t = 1, 4, "THiago"
tupla1 = 1, 2, 3, 4, 5
tulpla2 = 6, 7, 8, 9, 10
print(tupla1 + tulpla2) # concatena
| 14.8
| 36
| 0.527027
| 29
| 148
| 2.689655
| 0.724138
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.230089
| 0.236486
| 148
| 9
| 37
| 16.444444
| 0.460177
| 0.060811
| 0
| 0
| 0
| 0
| 0.058394
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0c3e9fda76a22fd599e09c9fbe18474bb8920e35
| 91
|
py
|
Python
|
Python scripts/Rookie/KeywordArguments.py
|
shartrooper/My-python-scripts
|
5c3a8db4ed9a75bd9ab4b29153a788d9e6c5d28c
|
[
"MIT"
] | null | null | null |
Python scripts/Rookie/KeywordArguments.py
|
shartrooper/My-python-scripts
|
5c3a8db4ed9a75bd9ab4b29153a788d9e6c5d28c
|
[
"MIT"
] | null | null | null |
Python scripts/Rookie/KeywordArguments.py
|
shartrooper/My-python-scripts
|
5c3a8db4ed9a75bd9ab4b29153a788d9e6c5d28c
|
[
"MIT"
] | null | null | null |
print('MyMy',end=' ')
print('Popsicle')
print('Balloon','Helium','Blimp',sep=' and ')
| 18.2
| 46
| 0.593407
| 11
| 91
| 4.909091
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.120879
| 91
| 4
| 47
| 22.75
| 0.675
| 0
| 0
| 0
| 0
| 0
| 0.413793
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
0c3eb7fa2051f6adf0fe421ca6d82b35a2944bed
| 42
|
py
|
Python
|
step_1.py
|
GreeeNic/lesson_201128
|
abf7ed6d8d2b545e1da86368116993fc27e63050
|
[
"Apache-2.0"
] | null | null | null |
step_1.py
|
GreeeNic/lesson_201128
|
abf7ed6d8d2b545e1da86368116993fc27e63050
|
[
"Apache-2.0"
] | null | null | null |
step_1.py
|
GreeeNic/lesson_201128
|
abf7ed6d8d2b545e1da86368116993fc27e63050
|
[
"Apache-2.0"
] | null | null | null |
print('hello, world!')
hol = 3
print(hol)
| 10.5
| 22
| 0.642857
| 7
| 42
| 3.857143
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027778
| 0.142857
| 42
| 3
| 23
| 14
| 0.722222
| 0
| 0
| 0
| 0
| 0
| 0.309524
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.666667
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
a75b691b65b86cfb1bac01db945795a5d8eece23
| 118
|
py
|
Python
|
conftest.py
|
JakeRoggenbuck/snow_script
|
052ec29f6d109977a64dc14b10d94659b5bafae2
|
[
"MIT"
] | null | null | null |
conftest.py
|
JakeRoggenbuck/snow_script
|
052ec29f6d109977a64dc14b10d94659b5bafae2
|
[
"MIT"
] | null | null | null |
conftest.py
|
JakeRoggenbuck/snow_script
|
052ec29f6d109977a64dc14b10d94659b5bafae2
|
[
"MIT"
] | null | null | null |
import sys
import os
root = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'src')
sys.path.insert(0, root)
| 19.666667
| 70
| 0.728814
| 21
| 118
| 3.904762
| 0.571429
| 0.219512
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.009346
| 0.09322
| 118
| 5
| 71
| 23.6
| 0.757009
| 0
| 0
| 0
| 0
| 0
| 0.025424
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
a78f73f309b86b32cb02b27f9b22fd5608b4454d
| 235
|
py
|
Python
|
python3/berrymq_singlethread/__init__.py
|
shibukawa/berrymq
|
ab2f7127b80957e01e756f431554d3d8c8c85bb8
|
[
"MIT"
] | null | null | null |
python3/berrymq_singlethread/__init__.py
|
shibukawa/berrymq
|
ab2f7127b80957e01e756f431554d3d8c8c85bb8
|
[
"MIT"
] | null | null | null |
python3/berrymq_singlethread/__init__.py
|
shibukawa/berrymq
|
ab2f7127b80957e01e756f431554d3d8c8c85bb8
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from .berrymq import (following,
following_function,
auto_twitter,
cond,
Follower,
twitter)
| 26.111111
| 42
| 0.361702
| 14
| 235
| 5.928571
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.009804
| 0.565957
| 235
| 8
| 43
| 29.375
| 0.803922
| 0.089362
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.166667
| 0
| 0.166667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
a7a5391e1f76f2570fd09cefbc183c8554459c46
| 497
|
py
|
Python
|
tests/legacy/test_utils.py
|
NeoLight1010/strawberry-graphql-django
|
86d0dbb606a1dd0d96bb79a4cdd6902c6a515b2f
|
[
"MIT"
] | 18
|
2020-11-10T10:12:11.000Z
|
2021-03-10T18:51:01.000Z
|
tests/legacy/test_utils.py
|
NeoLight1010/strawberry-graphql-django
|
86d0dbb606a1dd0d96bb79a4cdd6902c6a515b2f
|
[
"MIT"
] | 8
|
2020-11-19T18:05:14.000Z
|
2021-03-10T19:06:33.000Z
|
tests/legacy/test_utils.py
|
NeoLight1010/strawberry-graphql-django
|
86d0dbb606a1dd0d96bb79a4cdd6902c6a515b2f
|
[
"MIT"
] | 2
|
2021-02-20T11:18:03.000Z
|
2021-03-10T07:14:34.000Z
|
from strawberry_django.legacy import utils
def test_basic_filters():
filter, exclude = utils.process_filters(['id__gt=5', 'name="you"', 'name__contains!="me"'])
assert filter == { 'id__gt': 5, 'name': 'you' }
assert exclude == { 'name__contains': 'me' }
def test_is_in_filter():
filter, exclude = utils.process_filters(['id__in=[1, 2, 3]', 'group__in!=["a", "b", "x y z"]'])
assert filter == { 'id__in': [1, 2, 3] }
assert exclude == { 'group__in': ['a', 'b', 'x y z'] }
| 41.416667
| 99
| 0.605634
| 73
| 497
| 3.794521
| 0.438356
| 0.050542
| 0.129964
| 0.180505
| 0.454874
| 0.33213
| 0.086643
| 0
| 0
| 0
| 0
| 0.019512
| 0.17505
| 497
| 11
| 100
| 45.181818
| 0.656098
| 0
| 0
| 0
| 0
| 0
| 0.27163
| 0
| 0
| 0
| 0
| 0
| 0.444444
| 1
| 0.222222
| true
| 0
| 0.111111
| 0
| 0.333333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
a7c2b384e7cce9524bad254293ab9a9c47d7b2b4
| 191
|
py
|
Python
|
bin/agent.py
|
johnblackford/agent
|
dd303f658d483317154aada1109ca5b742f8f094
|
[
"MIT"
] | 2
|
2019-04-27T14:13:42.000Z
|
2022-03-23T06:51:44.000Z
|
bin/agent.py
|
johnblackford/agent
|
dd303f658d483317154aada1109ca5b742f8f094
|
[
"MIT"
] | 1
|
2019-01-22T07:32:51.000Z
|
2019-03-01T08:59:47.000Z
|
bin/agent.py
|
johnblackford/agent
|
dd303f658d483317154aada1109ca5b742f8f094
|
[
"MIT"
] | 4
|
2018-01-25T19:41:47.000Z
|
2021-04-30T12:57:41.000Z
|
#! /usr/bin/env python
import sys
import runpy
sys.path.insert(0, "/Users/jblackford/Development/agent")
if __name__ == '__main__':
runpy.run_module("agent.main", run_name="__main__")
| 19.1
| 57
| 0.722513
| 27
| 191
| 4.592593
| 0.703704
| 0.129032
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005917
| 0.115183
| 191
| 9
| 58
| 21.222222
| 0.727811
| 0.109948
| 0
| 0
| 0
| 0
| 0.360947
| 0.207101
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.4
| 0
| 0.4
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
a7cc6877e9d225378a53cbad71b61398d82cd3fa
| 6,309
|
py
|
Python
|
tensorflow_checkpoint_reader/pb/tensorflow/core/protobuf/master_service_pb2.py
|
shawwn/tensorflow-checkpoint-reader
|
f0e65548411e3bd66a07e36bb1850907a05952d0
|
[
"MIT"
] | 1
|
2021-12-02T15:06:09.000Z
|
2021-12-02T15:06:09.000Z
|
tensorflow_checkpoint_reader/pb/tensorflow/core/protobuf/master_service_pb2.py
|
shawwn/tensorflow-checkpoint-reader
|
f0e65548411e3bd66a07e36bb1850907a05952d0
|
[
"MIT"
] | null | null | null |
tensorflow_checkpoint_reader/pb/tensorflow/core/protobuf/master_service_pb2.py
|
shawwn/tensorflow-checkpoint-reader
|
f0e65548411e3bd66a07e36bb1850907a05952d0
|
[
"MIT"
] | null | null | null |
'Generated protocol buffer code.'
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
_sym_db = _symbol_database.Default()
from ....tensorflow.core.protobuf import master_pb2 as tensorflow_dot_core_dot_protobuf_dot_master__pb2
DESCRIPTOR = _descriptor.FileDescriptor(name='tensorflow/core/protobuf/master_service.proto', package='tensorflow.grpc', syntax='proto3', serialized_options=b'\n\x1aorg.tensorflow.distruntimeB\x13MasterServiceProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto', create_key=_descriptor._internal_create_key, serialized_pb=b'\n-tensorflow/core/protobuf/master_service.proto\x12\x0ftensorflow.grpc\x1a%tensorflow/core/protobuf/master.proto2\xbb\x06\n\rMasterService\x12T\n\rCreateSession\x12 .tensorflow.CreateSessionRequest\x1a!.tensorflow.CreateSessionResponse\x12T\n\rExtendSession\x12 .tensorflow.ExtendSessionRequest\x1a!.tensorflow.ExtendSessionResponse\x12Z\n\x0fPartialRunSetup\x12".tensorflow.PartialRunSetupRequest\x1a#.tensorflow.PartialRunSetupResponse\x12B\n\x07RunStep\x12\x1a.tensorflow.RunStepRequest\x1a\x1b.tensorflow.RunStepResponse\x12Q\n\x0cCloseSession\x12\x1f.tensorflow.CloseSessionRequest\x1a .tensorflow.CloseSessionResponse\x12N\n\x0bListDevices\x12\x1e.tensorflow.ListDevicesRequest\x1a\x1f.tensorflow.ListDevicesResponse\x12<\n\x05Reset\x12\x18.tensorflow.ResetRequest\x1a\x19.tensorflow.ResetResponse\x12Q\n\x0cMakeCallable\x12\x1f.tensorflow.MakeCallableRequest\x1a .tensorflow.MakeCallableResponse\x12N\n\x0bRunCallable\x12\x1e.tensorflow.RunCallableRequest\x1a\x1f.tensorflow.RunCallableResponse\x12Z\n\x0fReleaseCallable\x12".tensorflow.ReleaseCallableRequest\x1a#.tensorflow.ReleaseCallableResponseB\x8a\x01\n\x1aorg.tensorflow.distruntimeB\x13MasterServiceProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protob\x06proto3', dependencies=[tensorflow_dot_core_dot_protobuf_dot_master__pb2.DESCRIPTOR])
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
DESCRIPTOR._options = None
_MASTERSERVICE = _descriptor.ServiceDescriptor(name='MasterService', full_name='tensorflow.grpc.MasterService', file=DESCRIPTOR, index=0, serialized_options=None, create_key=_descriptor._internal_create_key, serialized_start=106, serialized_end=933, methods=[_descriptor.MethodDescriptor(name='CreateSession', full_name='tensorflow.grpc.MasterService.CreateSession', index=0, containing_service=None, input_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._CREATESESSIONREQUEST, output_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._CREATESESSIONRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key), _descriptor.MethodDescriptor(name='ExtendSession', full_name='tensorflow.grpc.MasterService.ExtendSession', index=1, containing_service=None, input_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._EXTENDSESSIONREQUEST, output_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._EXTENDSESSIONRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key), _descriptor.MethodDescriptor(name='PartialRunSetup', full_name='tensorflow.grpc.MasterService.PartialRunSetup', index=2, containing_service=None, input_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._PARTIALRUNSETUPREQUEST, output_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._PARTIALRUNSETUPRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key), _descriptor.MethodDescriptor(name='RunStep', full_name='tensorflow.grpc.MasterService.RunStep', index=3, containing_service=None, input_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._RUNSTEPREQUEST, output_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._RUNSTEPRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key), _descriptor.MethodDescriptor(name='CloseSession', full_name='tensorflow.grpc.MasterService.CloseSession', index=4, containing_service=None, input_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._CLOSESESSIONREQUEST, output_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._CLOSESESSIONRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key), _descriptor.MethodDescriptor(name='ListDevices', full_name='tensorflow.grpc.MasterService.ListDevices', index=5, containing_service=None, input_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._LISTDEVICESREQUEST, output_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._LISTDEVICESRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key), _descriptor.MethodDescriptor(name='Reset', full_name='tensorflow.grpc.MasterService.Reset', index=6, containing_service=None, input_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._RESETREQUEST, output_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._RESETRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key), _descriptor.MethodDescriptor(name='MakeCallable', full_name='tensorflow.grpc.MasterService.MakeCallable', index=7, containing_service=None, input_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._MAKECALLABLEREQUEST, output_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._MAKECALLABLERESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key), _descriptor.MethodDescriptor(name='RunCallable', full_name='tensorflow.grpc.MasterService.RunCallable', index=8, containing_service=None, input_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._RUNCALLABLEREQUEST, output_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._RUNCALLABLERESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key), _descriptor.MethodDescriptor(name='ReleaseCallable', full_name='tensorflow.grpc.MasterService.ReleaseCallable', index=9, containing_service=None, input_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._RELEASECALLABLEREQUEST, output_type=tensorflow_dot_core_dot_protobuf_dot_master__pb2._RELEASECALLABLERESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key)])
_sym_db.RegisterServiceDescriptor(_MASTERSERVICE)
DESCRIPTOR.services_by_name['MasterService'] = _MASTERSERVICE
| 420.6
| 4,005
| 0.890315
| 758
| 6,309
| 6.941953
| 0.183377
| 0.041049
| 0.071076
| 0.083618
| 0.564044
| 0.490878
| 0.475675
| 0.465032
| 0.465032
| 0.424363
| 0
| 0.022078
| 0.023617
| 6,309
| 14
| 4,006
| 450.642857
| 0.832143
| 0.004914
| 0
| 0
| 1
| 0.153846
| 0.333386
| 0.302315
| 0
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| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.384615
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| 0.384615
| 0
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| null | 0
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| 0
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| 1
| 0
| 0
| 0
|
0
| 4
|
ac2650a322b42e5e0b13615d9a39c1db1062a28d
| 221
|
py
|
Python
|
moogle/search/snooper/facebook.py
|
nimiq/moogle-project
|
f1fcac13fba43a6e93baf172571003359f4b725c
|
[
"Apache-2.0"
] | 4
|
2018-06-25T15:57:26.000Z
|
2021-06-07T23:53:31.000Z
|
moogle/search/snooper/facebook.py
|
puntonim/moogle-project
|
f1fcac13fba43a6e93baf172571003359f4b725c
|
[
"Apache-2.0"
] | null | null | null |
moogle/search/snooper/facebook.py
|
puntonim/moogle-project
|
f1fcac13fba43a6e93baf172571003359f4b725c
|
[
"Apache-2.0"
] | 1
|
2017-06-17T09:53:21.000Z
|
2017-06-17T09:53:21.000Z
|
from ..snooper import BaseSolrSnooper
from tokens.models import Provider
class FacebookSnooper(BaseSolrSnooper):
def __init__(self, user):
self.user = user
self.provider_name = Provider.NAME_FACEBOOK
| 27.625
| 51
| 0.751131
| 25
| 221
| 6.4
| 0.6
| 0.1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.180995
| 221
| 8
| 51
| 27.625
| 0.883978
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
ac48fa560daf995254c53535d5989aa92d48eb0b
| 67
|
py
|
Python
|
{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/server/handlers/graphql/__init__.py
|
Maximilien-R/cookiecutter-tartiflette-aiohttp
|
66e7e0897b315df6a1908c6c31ec58b74e0b3a6f
|
[
"MIT"
] | 3
|
2020-06-01T14:16:19.000Z
|
2021-11-07T19:54:08.000Z
|
{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/server/handlers/graphql/__init__.py
|
Maximilien-R/cookiecutter-tartiflette-aiohttp
|
66e7e0897b315df6a1908c6c31ec58b74e0b3a6f
|
[
"MIT"
] | 88
|
2019-11-15T17:35:54.000Z
|
2021-08-02T04:50:51.000Z
|
{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/server/handlers/graphql/__init__.py
|
Maximilien-R/cookiecutter-tartiflette-aiohttp
|
66e7e0897b315df6a1908c6c31ec58b74e0b3a6f
|
[
"MIT"
] | 2
|
2020-05-04T08:35:34.000Z
|
2020-10-22T17:47:26.000Z
|
from .handler import handle_graphql
__all__ = ("handle_graphql",)
| 16.75
| 35
| 0.776119
| 8
| 67
| 5.75
| 0.75
| 0.565217
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.119403
| 67
| 3
| 36
| 22.333333
| 0.779661
| 0
| 0
| 0
| 0
| 0
| 0.208955
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
ac4c64769c023fe3caed57369f65456d9909c191
| 23
|
py
|
Python
|
samtranslator/__init__.py
|
armaciej/serverless-application-model
|
e3a4641600ba1d042e021258fd540de8b3f3a927
|
[
"Apache-2.0"
] | 24
|
2015-01-31T14:25:35.000Z
|
2022-02-24T01:53:53.000Z
|
samtranslator/__init__.py
|
sriharikadali/serverless-application-model
|
e3a4641600ba1d042e021258fd540de8b3f3a927
|
[
"Apache-2.0"
] | 64
|
2015-02-12T12:04:04.000Z
|
2021-06-28T10:51:58.000Z
|
samtranslator/__init__.py
|
sriharikadali/serverless-application-model
|
e3a4641600ba1d042e021258fd540de8b3f3a927
|
[
"Apache-2.0"
] | 22
|
2015-07-07T23:29:00.000Z
|
2021-03-09T18:32:29.000Z
|
__version__ = "1.31.0"
| 11.5
| 22
| 0.652174
| 4
| 23
| 2.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 0.130435
| 23
| 1
| 23
| 23
| 0.35
| 0
| 0
| 0
| 0
| 0
| 0.26087
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
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| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
ac4cdee27c4d9f2d7fb68bae29c170724f60d9c5
| 442
|
py
|
Python
|
exercises/pt/test_04_04.py
|
tuanducdesign/spacy-course
|
f8d092c5fa2997fccb3f367d174dce8667932b3d
|
[
"MIT"
] | null | null | null |
exercises/pt/test_04_04.py
|
tuanducdesign/spacy-course
|
f8d092c5fa2997fccb3f367d174dce8667932b3d
|
[
"MIT"
] | null | null | null |
exercises/pt/test_04_04.py
|
tuanducdesign/spacy-course
|
f8d092c5fa2997fccb3f367d174dce8667932b3d
|
[
"MIT"
] | null | null | null |
def test():
assert (
'spacy.blank("en")' in __solution__
), "Você inicializou um fluxo de processamento em Inglês vazio?"
assert "DocBin(docs=docs)" in __solution__, "Você criou o DocBin corretamente?"
assert "doc_bin.to_disk(" in __solution__, "Você utilizou o método to_disk?"
assert "train.spacy" in __solution__, "Você criou um arquivo com o nome correto?"
__msg__.good("Muito bem! Tudo certo por aqui.")
| 44.2
| 85
| 0.699095
| 62
| 442
| 4.612903
| 0.66129
| 0.13986
| 0.195804
| 0.132867
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.19457
| 442
| 9
| 86
| 49.111111
| 0.803371
| 0
| 0
| 0
| 0
| 0
| 0.579186
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.125
| true
| 0
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| 0.125
| 0
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| null | 0
| 1
| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
ac5430084f29010f2f9e74e3f5a47a5dd9d18619
| 3,510
|
py
|
Python
|
uiautomator2/_init.py
|
snakx/uiautomator2
|
470af5f627838ed2c9195110b4659f7d34ce9343
|
[
"MIT"
] | 1
|
2021-10-31T05:31:51.000Z
|
2021-10-31T05:31:51.000Z
|
uiautomator2/_init.py
|
snakx/uiautomator2
|
470af5f627838ed2c9195110b4659f7d34ce9343
|
[
"MIT"
] | null | null | null |
uiautomator2/_init.py
|
snakx/uiautomator2
|
470af5f627838ed2c9195110b4659f7d34ce9343
|
[
"MIT"
] | null | null | null |
import requests
import _main
import _pk as p
import _x86 as j
import os
import logging
class _Service():
# Instrumentation
def _apk_cache_i(self):
try:
logging.debug("Download instrumentation apk {}".format(p._apk()))
url = 'https://github.com/snakx/x86-uiautomator2-server/raw/main/bin/{}'.format(p._apk())
logging.debug(url)
r = requests.get(url, allow_redirects=True)
except Exception as e:
logging.error(e.__context__)
return False
try:
open(p._apk(), 'wb').write(r.content)
logging.debug('Download instrumentation apk successfully completed')
return True
except Exception as e:
logging.error(e.__context__)
return False
# Release
def _apk_cache_r(self):
try:
logging.debug("Download release apk {}".format(p._apk2()))
url = 'https://github.com/snakx/x86-uiautomator2-server/raw/main/bin/{}'.format(p._apk2())
logging.debug(url)
r = requests.get(url, allow_redirects=True)
except Exception as e:
logging.error(e.__context__)
return False
try:
open(p._apk2(), 'wb').write(r.content)
logging.debug('Download release apk successfully completed')
return True
except Exception as e:
logging.error(e.__context__)
return False
# Jar
def _jar_cache(self):
try:
logging.debug("Download x86 jar {}".format(p._apk2()))
url = 'https://github.com/snakx/x86-uiautomator2-server/raw/main/out/artifacts/x86_uiautomator2_server_jar/{}'.format(j._jar())
logging.debug(url)
r = requests.get(url, allow_redirects=True)
except Exception as e:
logging.error(e.__context__)
return False
try:
open(j._jar(), 'wb').write(r.content)
logging.debug('Download x86 jar successfully completed')
return True
except Exception as e:
logging.error(e.__context__)
return False
# vbs
def _vbs_cache(self):
try:
logging.debug("Download vbs script {}".format(p._apk2()))
url = 'https://github.com/snakx/x86-uiautomator2-server/raw/main/bin/uiautomator2.vbs'
logging.debug(url)
r = requests.get(url, allow_redirects=True)
except Exception as e:
logging.error(e.__context__)
return False
try:
open('uiautomator2.vbs', 'wb').write(r.content)
logging.debug('Download vbs script successfully completed')
return True
except Exception as e:
logging.error(e.__context__)
return False
# bat
def _bat_cache(self):
try:
logging.debug("Download shell script {}".format(p._apk2()))
url = 'https://github.com/snakx/x86-uiautomator2-server/raw/main/bin/uiautomator2.bat'
logging.debug(url)
r = requests.get(url, allow_redirects=True)
except Exception as e:
logging.error(e.__context__)
return False
try:
open('uiautomator2.bat', 'wb').write(r.content)
logging.debug('Download shell script successfully completed')
return True
except Exception as e:
logging.error(e.__context__)
return False
| 35.816327
| 139
| 0.580342
| 398
| 3,510
| 4.929648
| 0.145729
| 0.091743
| 0.101937
| 0.107034
| 0.878186
| 0.810907
| 0.761978
| 0.672783
| 0.672783
| 0.672783
| 0
| 0.014061
| 0.311111
| 3,510
| 98
| 140
| 35.816327
| 0.797353
| 0.009972
| 0
| 0.632184
| 0
| 0.034483
| 0.220749
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.057471
| false
| 0
| 0.068966
| 0
| 0.310345
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
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| 0
| 1
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| 0
| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
ac617e9ebea5fe73b16443828f248c35f2ed2013
| 165
|
py
|
Python
|
luminoth/tools/dataset/cli.py
|
PiterPentester/luminoth
|
da0186515586291fbb9544c98240979480355f7a
|
[
"BSD-3-Clause"
] | 2
|
2018-01-25T10:05:10.000Z
|
2020-05-16T13:01:24.000Z
|
luminoth/tools/dataset/cli.py
|
macressler/luminoth
|
da0186515586291fbb9544c98240979480355f7a
|
[
"BSD-3-Clause"
] | null | null | null |
luminoth/tools/dataset/cli.py
|
macressler/luminoth
|
da0186515586291fbb9544c98240979480355f7a
|
[
"BSD-3-Clause"
] | null | null | null |
import click
from .transform import transform
@click.group(help='Groups of commands to manage datasets')
def dataset():
pass
dataset.add_command(transform)
| 13.75
| 58
| 0.763636
| 22
| 165
| 5.681818
| 0.772727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.151515
| 165
| 11
| 59
| 15
| 0.892857
| 0
| 0
| 0
| 0
| 0
| 0.224242
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| true
| 0.166667
| 0.333333
| 0
| 0.5
| 0
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| 0
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| null | 0
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| 1
| 1
| 0
| 0
| 0
|
0
| 4
|
3ba702f8f624f6bd6daa6ddf7d1d724ddbfddf9a
| 154
|
py
|
Python
|
gs/group/messages/add/base/__init__.py
|
groupserver/gs.group.messages.add.base
|
914f692659c56c0af3f759ee962a724ce2a0ae99
|
[
"ZPL-2.1"
] | null | null | null |
gs/group/messages/add/base/__init__.py
|
groupserver/gs.group.messages.add.base
|
914f692659c56c0af3f759ee962a724ce2a0ae99
|
[
"ZPL-2.1"
] | null | null | null |
gs/group/messages/add/base/__init__.py
|
groupserver/gs.group.messages.add.base
|
914f692659c56c0af3f759ee962a724ce2a0ae99
|
[
"ZPL-2.1"
] | null | null | null |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
#lint:disable
from .addapost import add_a_post
from .base import ListInfoForm
#lint:enable
| 22
| 38
| 0.779221
| 22
| 154
| 5.136364
| 0.727273
| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
| 0.007407
| 0.123377
| 154
| 6
| 39
| 25.666667
| 0.82963
| 0.285714
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| null | 0
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| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
3be9613bd13eae258eb6e21aa1fa43052dae4b04
| 407
|
py
|
Python
|
test/hlt/pytest/python/com/huawei/iotplatform/client/dto/NorthInDTO.py
|
yuanyi-thu/AIOT-
|
27f67d98324593c4c6c66bbd5e2a4aa7b9a4ac1e
|
[
"BSD-3-Clause"
] | 128
|
2018-10-29T04:11:47.000Z
|
2022-03-07T02:19:14.000Z
|
test/hlt/pytest/python/com/huawei/iotplatform/client/dto/NorthInDTO.py
|
yuanyi-thu/AIOT-
|
27f67d98324593c4c6c66bbd5e2a4aa7b9a4ac1e
|
[
"BSD-3-Clause"
] | 40
|
2018-11-02T00:40:48.000Z
|
2021-12-07T09:33:56.000Z
|
test/hlt/pytest/python/com/huawei/iotplatform/client/dto/NorthInDTO.py
|
yuanyi-thu/AIOT-
|
27f67d98324593c4c6c66bbd5e2a4aa7b9a4ac1e
|
[
"BSD-3-Clause"
] | 118
|
2018-10-29T08:43:57.000Z
|
2022-01-07T06:49:25.000Z
|
class NorthInDTO(object):
def __init__(self):
self.platformIp = None
self.platformPort = None
def getPlatformIp(self):
return self.platformIp
def setPlatformIp(self, platformIp):
self.platformIp = platformIp
def getPlatformPort(self):
return self.platformPort
def setPlatformPort(self, platformPort):
self.platformPort = platformPort
| 22.611111
| 44
| 0.673219
| 38
| 407
| 7.105263
| 0.368421
| 0.207407
| 0.103704
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.253071
| 407
| 17
| 45
| 23.941176
| 0.888158
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.416667
| false
| 0
| 0
| 0.166667
| 0.666667
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
ce01fa0b70ea3129bde7690a32e4e1d17927c2c1
| 5,087
|
py
|
Python
|
tests/unit/models/test_contact.py
|
nethad/moco-wrapper
|
012f9aab6e9fa60e3ccdf7254f0366b108651899
|
[
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null |
tests/unit/models/test_contact.py
|
nethad/moco-wrapper
|
012f9aab6e9fa60e3ccdf7254f0366b108651899
|
[
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null |
tests/unit/models/test_contact.py
|
nethad/moco-wrapper
|
012f9aab6e9fa60e3ccdf7254f0366b108651899
|
[
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null |
import pytest
from .. import UnitTest
class TestContact(UnitTest):
def test_create(self):
firstname = "Peter"
lastname = "Muster"
gender = "F"
organization_id = 123
title ="Dr. med."
job_position = "Account Manager"
mobile_phone = "+49 177 123 45 67"
work_fax = "+49 30 123 45 67"
work_phone = "+49 30 123 45 67"
work_email = "bestellung@lieferant.de"
work_address = "Lieferant AG\nBeispielstrasse 123\n12345 Berlin"
home_email = "privat@home.ch"
home_address = "Peter Muster\nZu Hause"
birthday = "1959-05-22"
info = "Information for this company"
tags = ["Christmas Card", "Project Lead"]
response = self.moco.Contact.create(firstname, lastname, gender, customer_id=organization_id, title=title, job_position=job_position, mobile_phone=mobile_phone, work_fax=work_fax, work_phone=work_phone, work_email=work_email, work_address=work_address, home_email=home_email, home_address=home_address, birthday=birthday, info=info, tags=tags)
data = response["data"]
assert data["firstname"] == firstname
assert data["lastname"] == lastname
assert data["gender"] == gender
assert data["customer_id"] == organization_id
assert data["title"] == title
assert data["job_position"] == job_position
assert data["mobile_phone"] == mobile_phone
assert data["work_fax"] == work_fax
assert data["work_phone"] == work_phone
assert data["work_email"] == work_email
assert data["work_address"] == work_address
assert data["home_email"] == home_email
assert data["home_address"] == home_address
assert data["birthday"] == birthday
assert data["info"] == info
assert data["tags"] == tags
assert response["method"] == "POST"
def test_update(self):
contact_id = 123
firstname = "Peter"
lastname = "Muster"
gender = "F"
organization_id = 123
title ="Dr. med."
job_position = "Account Manager"
mobile_phone = "+49 177 123 45 67"
work_fax = "+49 30 123 45 67"
work_phone = "+49 30 123 45 67"
work_email = "bestellung@lieferant.de"
work_address = "Lieferant AG\nBeispielstrasse 123\n12345 Berlin"
home_email = "privat@home.ch"
home_address = "Peter Muster\nZu Hause"
birthday = "1959-05-22"
info = "Information for this company"
tags = ["Christmas Card", "Project Lead"]
response = self.moco.Contact.update(contact_id, firstname=firstname, lastname=lastname, job_position=job_position, gender=gender, customer_id=organization_id, title=title, mobile_phone=mobile_phone, work_fax=work_fax, work_phone=work_phone, work_email=work_email, work_address=work_address, home_email=home_email, home_address=home_address, birthday=birthday, info=info, tags=tags)
data = response["data"]
assert data["firstname"] == firstname
assert data["lastname"] == lastname
assert data["gender"] == gender
assert data["customer_id"] == organization_id
assert data["title"] == title
assert data["job_position"] == job_position
assert data["mobile_phone"] == mobile_phone
assert data["work_fax"] == work_fax
assert data["work_phone"] == work_phone
assert data["work_email"] == work_email
assert data["work_address"] == work_address
assert data["home_email"] == home_email
assert data["home_address"] == home_address
assert data["birthday"] == birthday
assert data["info"] == info
assert data["tags"] == tags
assert response["method"] == "PUT"
def test_get(self):
contact_id = 1234
response = self.moco.Contact.get(contact_id)
assert response["method"] == "GET"
def test_getlist(self):
tags = ["eins", "zwei", "drei", "polizei"]
response = self.moco.Contact.getlist(tags=tags)
params = response["params"]
assert params["tags"] == tags
assert response["method"] == "GET"
def test_getlist_sort_default(self):
sort_by = "testfield to sort by"
response = self.moco.Contact.getlist(sort_by=sort_by)
assert response["params"]["sort_by"] == "{} asc".format(sort_by)
def test_getlist_sort_overwrite(self):
sort_by = "testfield to sort by"
sort_order = "desc"
response = self.moco.Contact.getlist(sort_by=sort_by, sort_order=sort_order)
assert response["params"]["sort_by"] == "{} {}".format(sort_by, sort_order)
def test_getlist_page_default(self):
page_default = 1
response = self.moco.Contact.getlist()
assert response["params"]["page"] == page_default
def test_getlist_page_overwrite(self):
page_overwrite = 22
response = self.moco.Contact.getlist(page=page_overwrite)
assert response["params"]["page"] == page_overwrite
| 37.962687
| 389
| 0.634166
| 612
| 5,087
| 5.060458
| 0.151961
| 0.103326
| 0.04133
| 0.059412
| 0.811753
| 0.745237
| 0.745237
| 0.678076
| 0.678076
| 0.650953
| 0
| 0.030303
| 0.247494
| 5,087
| 133
| 390
| 38.24812
| 0.778736
| 0
| 0
| 0.679612
| 0
| 0
| 0.189699
| 0.009043
| 0
| 0
| 0
| 0
| 0.398058
| 1
| 0.07767
| false
| 0
| 0.019417
| 0
| 0.106796
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
ce0592be5c2f4572b47ca3764346de0b0e30c5f5
| 1,869
|
py
|
Python
|
phonenumbers/data/region_GA.py
|
ayushgoel/FixGoogleContacts
|
e49e58db6718bef8f95b6f767241605441c7fe41
|
[
"MIT"
] | 2
|
2019-02-22T05:27:22.000Z
|
2020-12-30T19:33:18.000Z
|
phonenumbers/data/region_GA.py
|
ayushgoel/FixGoogleContacts
|
e49e58db6718bef8f95b6f767241605441c7fe41
|
[
"MIT"
] | null | null | null |
phonenumbers/data/region_GA.py
|
ayushgoel/FixGoogleContacts
|
e49e58db6718bef8f95b6f767241605441c7fe41
|
[
"MIT"
] | null | null | null |
"""Auto-generated file, do not edit by hand. GA metadata"""
from ..phonemetadata import NumberFormat, PhoneNumberDesc, PhoneMetadata
PHONE_METADATA_GA = PhoneMetadata(id='GA', country_code=241, international_prefix='00',
general_desc=PhoneNumberDesc(national_number_pattern='0\\d{7}', possible_number_pattern='\\d{8}'),
fixed_line=PhoneNumberDesc(national_number_pattern='01\\d{6}', possible_number_pattern='\\d{8}', example_number='01441234'),
mobile=PhoneNumberDesc(national_number_pattern='0[2-7]\\d{6}', possible_number_pattern='\\d{8}', example_number='06031234'),
toll_free=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
premium_rate=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
shared_cost=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
personal_number=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
voip=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
pager=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
uan=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
emergency=PhoneNumberDesc(national_number_pattern='1730|18|13\\d{2}', possible_number_pattern='\\d{2,4}', example_number='1730'),
voicemail=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
short_code=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
standard_rate=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
no_international_dialling=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
number_format=[NumberFormat(pattern='(0\\d)(\\d{2})(\\d{2})(\\d{2})', format='\\1 \\2 \\3 \\4')],
leading_zero_possible=True)
| 84.954545
| 133
| 0.779026
| 235
| 1,869
| 5.855319
| 0.293617
| 0.28343
| 0.239826
| 0.392442
| 0.619186
| 0.548692
| 0.548692
| 0.548692
| 0.548692
| 0.094477
| 0
| 0.032165
| 0.068486
| 1,869
| 21
| 134
| 89
| 0.758185
| 0.028357
| 0
| 0
| 1
| 0
| 0.100552
| 0.016575
| 0
| 0
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| 0
| 0
| 1
| 0
| false
| 0
| 0.052632
| 0
| 0.052632
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
ce06f91505e8f6162f9b831ea1a231c01b447adc
| 314
|
py
|
Python
|
Script/Commands/Messages/Creators/refresh_dbl.py
|
AIDRI/Clash-Of-Clans-Discord-Bot
|
2a9f0495e30ae22ca487886fa48f9d206a545b99
|
[
"BSD-3-Clause"
] | null | null | null |
Script/Commands/Messages/Creators/refresh_dbl.py
|
AIDRI/Clash-Of-Clans-Discord-Bot
|
2a9f0495e30ae22ca487886fa48f9d206a545b99
|
[
"BSD-3-Clause"
] | null | null | null |
Script/Commands/Messages/Creators/refresh_dbl.py
|
AIDRI/Clash-Of-Clans-Discord-Bot
|
2a9f0495e30ae22ca487886fa48f9d206a545b99
|
[
"BSD-3-Clause"
] | null | null | null |
from Script.import_emojis import Emojis
from Script.Clients.top_gg import Dbl_client
from Script.Clients.discord import Clash_info
async def refresh_dbl(ctx):
await Dbl_client.update_stats(len(Clash_info.guilds))
await ctx.send(str(Emojis["Yes"]) + " (https://top.gg/bot/704688212832026724)")
return
| 31.4
| 83
| 0.773885
| 47
| 314
| 5
| 0.595745
| 0.12766
| 0.144681
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.064982
| 0.117834
| 314
| 9
| 84
| 34.888889
| 0.783394
| 0
| 0
| 0
| 0
| 0
| 0.136943
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.428571
| 0
| 0.571429
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
ce4475bce28b53ec3043700ef6fe4d35783c84b0
| 1,838
|
py
|
Python
|
hsploit/searcher/engine/string.py
|
nicolas-carolo/hsploit
|
66dede8549b418a2485b88dd6da28e96d60e74f5
|
[
"BSD-3-Clause"
] | 10
|
2020-04-17T11:38:46.000Z
|
2021-11-12T01:07:50.000Z
|
hsploit/searcher/engine/string.py
|
nicolas-carolo/HoundSploitBash
|
66dede8549b418a2485b88dd6da28e96d60e74f5
|
[
"BSD-3-Clause"
] | null | null | null |
hsploit/searcher/engine/string.py
|
nicolas-carolo/HoundSploitBash
|
66dede8549b418a2485b88dd6da28e96d60e74f5
|
[
"BSD-3-Clause"
] | 3
|
2019-03-13T22:17:37.000Z
|
2019-04-11T10:37:22.000Z
|
import re
def str_contains_numbers(str):
"""
Check if a string contains at least one number.
:param str: the string to check.
:return: true if the string contains at least one number, false else.
"""
return bool(re.search(r'\d', str))
def str_is_num_version(str):
"""
Check if a string contains a number of version.
:param str: the string to check.
:return: true if the string contains a number of version, false else.
"""
return bool(re.search(r' \d+((\.\d+)+)?', str))
def word_is_num_version(str):
"""
Check if a word contains a number of version.
:param str: the word to check.
:return: true if the word contains a number of version, false else.
"""
return bool(re.search(r'\d+((\.\d+)+)?', str))
def str_contains_num_version_range(str):
"""
Check if a string contains a range of number version.
:param str: the string to check.
:return: true if the string contains a a range of number version, false else.
"""
return bool(re.search(r'\d+((\.\d+)+)? < \d+((\.\d+)+)?', str))
def str_contains_num_version_range_with_x(str):
"""
Check if a string contains a range of number version with x.
:param str: the string to check.
:return: true if the string contains a a range of number version with x, false else.
"""
return bool(re.search(r'\d+((\.\d+)+)?(\.x)? < \d+((\.\d+)+)?(\.x)?', str))
def get_vulnerability_extension(vulnerability_file):
"""
Get the extension of the vulnerability passed as parameter.
:param vulnerability_file: the vulnerability we want to get its extension.
:return: the extension of the vulnerability passed as parameter.
"""
regex = re.search(r'\.(?P<extension>\w+)', vulnerability_file)
extension = '.' + regex.group('extension')
return extension
| 31.689655
| 88
| 0.648531
| 275
| 1,838
| 4.250909
| 0.170909
| 0.095808
| 0.046193
| 0.047049
| 0.779299
| 0.779299
| 0.704876
| 0.651839
| 0.502139
| 0.42515
| 0
| 0
| 0.214363
| 1,838
| 57
| 89
| 32.245614
| 0.809557
| 0.538629
| 0
| 0.133333
| 0
| 0
| 0.190409
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0.066667
| 0
| 0.866667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
02109eec69deb135be5b21cdb8238b14eee16ef3
| 242
|
py
|
Python
|
tests/test_version.py
|
grdorin/mopidy
|
76db44088c102d7ad92a3fc6a15a938e66b99b0d
|
[
"Apache-2.0"
] | 6,700
|
2015-01-01T03:57:59.000Z
|
2022-03-30T09:31:31.000Z
|
tests/test_version.py
|
pnijhara/mopidy
|
7168787ea6c82b66e138fc2b388d78fa1c7661ba
|
[
"Apache-2.0"
] | 1,141
|
2015-01-02T09:48:59.000Z
|
2022-03-28T22:25:30.000Z
|
tests/test_version.py
|
pnijhara/mopidy
|
7168787ea6c82b66e138fc2b388d78fa1c7661ba
|
[
"Apache-2.0"
] | 735
|
2015-01-01T21:15:50.000Z
|
2022-03-20T16:13:44.000Z
|
import unittest
from distutils.version import StrictVersion
from mopidy import __version__
class VersionTest(unittest.TestCase):
def test_current_version_is_parsable_as_a_strict_version_number(self):
StrictVersion(__version__)
| 24.2
| 74
| 0.834711
| 29
| 242
| 6.37931
| 0.689655
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.123967
| 242
| 9
| 75
| 26.888889
| 0.872642
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.5
| 0
| 0.833333
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
021d7b748c457e069b81b442cffe5bcd26c444b1
| 390
|
py
|
Python
|
src/translators/Translator.py
|
StrandHQ/strand-slack
|
44d63df66c7f26b516b1e823dbba6bd6afa49fee
|
[
"MIT"
] | null | null | null |
src/translators/Translator.py
|
StrandHQ/strand-slack
|
44d63df66c7f26b516b1e823dbba6bd6afa49fee
|
[
"MIT"
] | null | null | null |
src/translators/Translator.py
|
StrandHQ/strand-slack
|
44d63df66c7f26b516b1e823dbba6bd6afa49fee
|
[
"MIT"
] | null | null | null |
from src.utilities.logging import get_logger
class Translator:
def __init__(self, slack_client_wrapper=None, strand_api_client_wrapper=None):
self.logger = get_logger(self.__class__.__name__)
self.slack_client_wrapper = slack_client_wrapper
self.strand_api_client_wrapper = strand_api_client_wrapper
def translate(self):
raise NotImplementedError
| 32.5
| 82
| 0.771795
| 49
| 390
| 5.55102
| 0.44898
| 0.286765
| 0.198529
| 0.242647
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.169231
| 390
| 11
| 83
| 35.454545
| 0.839506
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.125
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0224ad86662da7e0921e9fbb06852fb87331dbb9
| 129
|
py
|
Python
|
accounts/admin.py
|
mariuslihet/CRM
|
1323dc358a016d027717466f946ffd3af74897f2
|
[
"MIT"
] | 2
|
2018-07-25T13:11:19.000Z
|
2019-04-19T03:45:40.000Z
|
accounts/admin.py
|
mariuslihet/CRM
|
1323dc358a016d027717466f946ffd3af74897f2
|
[
"MIT"
] | 2
|
2020-06-05T19:05:09.000Z
|
2021-06-10T21:08:49.000Z
|
accounts/admin.py
|
mariuslihet/CRM
|
1323dc358a016d027717466f946ffd3af74897f2
|
[
"MIT"
] | 12
|
2017-11-02T22:32:32.000Z
|
2018-04-12T05:13:25.000Z
|
from django.contrib import admin
from accounts.models import Account
# Register your models here.
admin.site.register(Account)
| 18.428571
| 35
| 0.813953
| 18
| 129
| 5.833333
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.124031
| 129
| 6
| 36
| 21.5
| 0.929204
| 0.20155
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
023dd6b6d62f9fdab6855552383128ecf9f7e361
| 53
|
py
|
Python
|
lacrm/__init__.py
|
HighMileage/lacrm
|
2783040e20583d0b6493bf8f09ad8dbe21febdba
|
[
"MIT"
] | 1
|
2018-04-04T01:47:42.000Z
|
2018-04-04T01:47:42.000Z
|
lacrm/__init__.py
|
HighMileage/lacrm
|
2783040e20583d0b6493bf8f09ad8dbe21febdba
|
[
"MIT"
] | 1
|
2017-02-15T05:54:18.000Z
|
2017-02-15T05:54:18.000Z
|
lacrm/__init__.py
|
HighMileage/lacrm
|
2783040e20583d0b6493bf8f09ad8dbe21febdba
|
[
"MIT"
] | 2
|
2018-04-04T01:47:45.000Z
|
2020-12-30T03:53:44.000Z
|
"lacrm package"
from lacrm.api import Lacrm # noqa
| 13.25
| 35
| 0.735849
| 8
| 53
| 4.875
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.188679
| 53
| 3
| 36
| 17.666667
| 0.906977
| 0.358491
| 0
| 0
| 0
| 0
| 0.276596
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
023eed7e520ee27f88edb16dd64289c6944b98fe
| 126
|
py
|
Python
|
src/data/image_validation.py
|
ehudbaumatz/enhance
|
2195ead204e9195bb02d7b512d8597508d7210f3
|
[
"FTL"
] | null | null | null |
src/data/image_validation.py
|
ehudbaumatz/enhance
|
2195ead204e9195bb02d7b512d8597508d7210f3
|
[
"FTL"
] | null | null | null |
src/data/image_validation.py
|
ehudbaumatz/enhance
|
2195ead204e9195bb02d7b512d8597508d7210f3
|
[
"FTL"
] | null | null | null |
def is_before_after(img_path):
"""
check weather image is a before after
:param img_path:
:return:
"""
| 12.6
| 41
| 0.603175
| 17
| 126
| 4.235294
| 0.705882
| 0.305556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.293651
| 126
| 10
| 42
| 12.6
| 0.808989
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| false
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
0255dfa2f1e1a378ae51bbb2c67654c848a7bd99
| 369
|
py
|
Python
|
socfaker/__init__.py
|
atstpls/soc-faker
|
119fcb9c4329a918ef9001ac5eaa36251b862bf0
|
[
"MIT"
] | null | null | null |
socfaker/__init__.py
|
atstpls/soc-faker
|
119fcb9c4329a918ef9001ac5eaa36251b862bf0
|
[
"MIT"
] | null | null | null |
socfaker/__init__.py
|
atstpls/soc-faker
|
119fcb9c4329a918ef9001ac5eaa36251b862bf0
|
[
"MIT"
] | null | null | null |
from .socfaker import SocFaker
#from .vulnerability import Vulnerability
#from .application import Application
#from .computer import Computer
#from .employee import Employee
#from .file import File
#from .network import Network
#from .organization import Organization
#from .vulnerabilityhost import VulnerabilityHost
#from .vulnerabilityscan import VulnerabilityScan
| 33.545455
| 49
| 0.840108
| 40
| 369
| 7.75
| 0.3
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108401
| 369
| 11
| 50
| 33.545455
| 0.942249
| 0.867209
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
5a046cad2ac389ed6445a8b8d17f41ec6eef53e2
| 193
|
py
|
Python
|
sumo/phonon/__init__.py
|
zhubonan/sumo
|
eb964951b1e4573717c9b13a82a01452f48d1a39
|
[
"MIT"
] | 1
|
2019-08-21T02:28:08.000Z
|
2019-08-21T02:28:08.000Z
|
sumo/phonon/__init__.py
|
zhubonan/sumo
|
eb964951b1e4573717c9b13a82a01452f48d1a39
|
[
"MIT"
] | null | null | null |
sumo/phonon/__init__.py
|
zhubonan/sumo
|
eb964951b1e4573717c9b13a82a01452f48d1a39
|
[
"MIT"
] | null | null | null |
# coding: utf-8
# Copyright (c) Scanlon Materials Theory Group
# Distributed under the terms of the MIT License.
"""
Package containing functions for loading and manipulating phonon data.
"""
| 24.125
| 70
| 0.761658
| 26
| 193
| 5.653846
| 0.961538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006173
| 0.160622
| 193
| 7
| 71
| 27.571429
| 0.901235
| 0.92228
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
5a0d4505072492c2777ca1f08838d11980f54d49
| 33
|
py
|
Python
|
ssh2net/core/cisco_iosxr/__init__.py
|
carlmontanari/ssh2net
|
55e969b6d44ec3f2bd2ebbd8dedd68b99bee4c5b
|
[
"MIT"
] | 10
|
2020-01-13T03:28:33.000Z
|
2022-02-08T17:05:59.000Z
|
ssh2net/core/cisco_iosxr/__init__.py
|
carlmontanari/ssh2net
|
55e969b6d44ec3f2bd2ebbd8dedd68b99bee4c5b
|
[
"MIT"
] | null | null | null |
ssh2net/core/cisco_iosxr/__init__.py
|
carlmontanari/ssh2net
|
55e969b6d44ec3f2bd2ebbd8dedd68b99bee4c5b
|
[
"MIT"
] | 1
|
2020-05-26T13:35:46.000Z
|
2020-05-26T13:35:46.000Z
|
"""ssh2net cisco iosxr driver"""
| 16.5
| 32
| 0.69697
| 4
| 33
| 5.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.034483
| 0.121212
| 33
| 1
| 33
| 33
| 0.758621
| 0.787879
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
5a15e25f3f27ef8b345c1016024dac5a731b9812
| 41
|
py
|
Python
|
pychemia/code/fireball/task/__init__.py
|
petavazohi/PyChemia
|
e779389418771c25c830aed360773c63bb069372
|
[
"MIT"
] | 67
|
2015-01-31T07:44:55.000Z
|
2022-03-21T21:43:34.000Z
|
pychemia/code/fireball/task/__init__.py
|
petavazohi/PyChemia
|
e779389418771c25c830aed360773c63bb069372
|
[
"MIT"
] | 13
|
2016-06-03T19:07:51.000Z
|
2022-03-31T04:20:40.000Z
|
pychemia/code/fireball/task/__init__.py
|
petavazohi/PyChemia
|
e779389418771c25c830aed360773c63bb069372
|
[
"MIT"
] | 37
|
2015-01-22T15:37:23.000Z
|
2022-03-21T15:38:10.000Z
|
__author__ = 'Guillermo Avendano-Franco'
| 20.5
| 40
| 0.804878
| 4
| 41
| 7.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097561
| 41
| 1
| 41
| 41
| 0.783784
| 0
| 0
| 0
| 0
| 0
| 0.609756
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
5a37c4e60c76ad71bbf4eeb47d6ce6c44856038a
| 39
|
py
|
Python
|
algorithm/common.py
|
VatsalP/algorithm
|
56c449962734241298aa6a4751ed9e4dd3d69ffa
|
[
"MIT"
] | null | null | null |
algorithm/common.py
|
VatsalP/algorithm
|
56c449962734241298aa6a4751ed9e4dd3d69ffa
|
[
"MIT"
] | null | null | null |
algorithm/common.py
|
VatsalP/algorithm
|
56c449962734241298aa6a4751ed9e4dd3d69ffa
|
[
"MIT"
] | null | null | null |
from typing import *
T = TypeVar('T')
| 9.75
| 20
| 0.641026
| 6
| 39
| 4.166667
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205128
| 39
| 3
| 21
| 13
| 0.806452
| 0
| 0
| 0
| 0
| 0
| 0.025641
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
5a4e2094c07cee091725dd0fedb04ecf84ef265b
| 7,236
|
py
|
Python
|
tools/fileinfo/features/visual-basic-parser/test.py
|
stepanek-m/retdec-regression-tests
|
12b834b14ede2826fec451368fa8192ab00ddadf
|
[
"MIT"
] | null | null | null |
tools/fileinfo/features/visual-basic-parser/test.py
|
stepanek-m/retdec-regression-tests
|
12b834b14ede2826fec451368fa8192ab00ddadf
|
[
"MIT"
] | null | null | null |
tools/fileinfo/features/visual-basic-parser/test.py
|
stepanek-m/retdec-regression-tests
|
12b834b14ede2826fec451368fa8192ab00ddadf
|
[
"MIT"
] | null | null | null |
from regression_tests import *
# https://github.com/avast-tl/retdec/issues/138
# Test for proper Visual Basic metadata parsing
class Test1(Test):
settings = TestSettings(
tool='fileinfo',
input='3e7126c600eb3d73c9b470aa98f2a416',
args='--verbose --json'
)
def test_visual_basic_presented(self):
assert self.fileinfo.succeeded
self.assertEqual(self.fileinfo.output['visualBasicInfo']['backupLanguageDLL'], '*')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['isPCode'], 'yes')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['languageDLL'], 'VB6DE.DLL')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['languageDLLPrimaryLCID'], 'German - Germany')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['languageDLLSecondaryLCID'], 'English - United States')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['projectDescription'], 'Projekt1')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['projectExeName'], 'my_st0re_loader_____')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['projectName'], 'muschmusch')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['projectPath'], 'C:\\Users\\Tix\\Desktop\\Sell_Tools\\iProtect\\load\\asdasd.vbp')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['projectPrimaryLCID'], 'English - United States')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['projectSecondaryLCID'], 'German - Austria')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['typeLibCLSID'], 'AB656C18-7E7D-2A48-90D0-CC26EBE49DE4')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['typeLibLCID'], 'Unspecified')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['typeLibMajorVersion'], '1')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['typeLibMinorVersion'], '0')
def test_visual_basic_extern_table(self):
assert self.fileinfo.succeeded
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['crc32'], '4647fd66')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['md5'], '038528f5da1ca95d66de9ffb558a8fad')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['sha256'], '8903e14d38862749270803180fc2240bce4610e28b2e4f4bfdaec55a6cfaa3ff')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][0]['apiName'], 'ARgopzWRvwdj')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][0]['moduleName'], 'netapi32')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][1]['apiName'], 'PYZXczGNsFE')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][1]['moduleName'], 'netapi32')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][2]['apiName'], 'HMxqxbooEHKCbqjT')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][2]['moduleName'], 'mapi32')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][3]['apiName'], 'eiIwtnFCZvUZW')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][3]['moduleName'], 'mapi32')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][4]['apiName'], 'CallWindowProcW')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][4]['moduleName'], 'UsEr32')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][5]['apiName'], 'pNfrfdXpmJsDJFRi')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][5]['moduleName'], 'netapi32')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][6]['apiName'], 'KnSCymHxoCMv')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][6]['moduleName'], 'netapi32')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][7]['apiName'], 'zVWgpkOdwQje')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][7]['moduleName'], 'shell32')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][8]['apiName'], 'ylMihJrIuyYyKDWTq')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][8]['moduleName'], 'version.dll')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][9]['apiName'], 'BegNhmukPYZXczGN')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['externTable']['externs'][9]['moduleName'], 'mapi32')
def test_visual_basic_object_table(self):
assert self.fileinfo.succeeded
self.assertEqual(self.fileinfo.output['visualBasicInfo']['objectTable']['guid'], '005CD394-A073-944E-8831-0A6EFC7D3AF0')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['objectTable']['crc32'], '0b86b7f1')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['objectTable']['md5'], 'f6c85535feafadb74306afc874c516a0')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['objectTable']['sha256'], 'ae05250c967d1f55105322454ada56db6990bd74a41a2cc63ce4e2f458a85616')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['objectTable']['objects'][0]['name'], 'acnaAA')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['objectTable']['objects'][0]['methods'][0], 'RunPE')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['objectTable']['objects'][0]['methods'][1], 'Invoke')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['objectTable']['objects'][0]['methods'][2], 'sDecryptName')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['objectTable']['objects'][0]['methods'][3], 'InjPath')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['objectTable']['objects'][0]['methods'][4], 'nand')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['objectTable']['objects'][0]['methods'][5], 'xori')
# Test for proper COM Visual Basic metadata parsing
class Test2(Test):
settings = TestSettings(
tool='fileinfo',
input='c4affaea94863009d90668c9d86291864cd6027d798a20085b5110f6473450b7',
args='--verbose --json'
)
def test_visual_basic_com_data_presented(self):
assert self.fileinfo.succeeded
self.assertEqual(self.fileinfo.output['visualBasicInfo']['comObjectCLSID'], '13A84C25-CDF1-F24D-9338-CEF08CAAF469')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['comObjectEventsCLSID'], '3490B97E-F7E7-8847-8A6F-97AB39FC9C97')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['comObjectInterfaceCLSID'], '1A2ADBEC-0944-C944-A046-F535D14B4E10')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['comObjectName'], 'usrReverseRelay')
self.assertEqual(self.fileinfo.output['visualBasicInfo']['comObjectType'], 'ActiveXUserControl')
| 82.227273
| 158
| 0.720564
| 659
| 7,236
| 7.874052
| 0.251897
| 0.13413
| 0.197726
| 0.280979
| 0.719599
| 0.707651
| 0.553093
| 0.468876
| 0.468876
| 0.248217
| 0
| 0.056563
| 0.100884
| 7,236
| 87
| 159
| 83.172414
| 0.741008
| 0.019486
| 0
| 0.133333
| 0
| 0
| 0.418277
| 0.084614
| 0
| 0
| 0
| 0
| 0.773333
| 1
| 0.053333
| false
| 0
| 0.013333
| 0
| 0.12
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
ce9d27092e781bc4b5071fb38634b84dfe069529
| 706
|
py
|
Python
|
tests/test_layout.py
|
up2cat/flask_extras
|
7888da0ca2793e49a803a256b405fa43e6e64ae2
|
[
"MIT"
] | 19
|
2016-08-03T07:10:23.000Z
|
2022-03-03T16:37:11.000Z
|
tests/test_layout.py
|
christabor/jinja2_template_pack
|
f57300bc2922aa4105d1aa393351b63c86c26048
|
[
"MIT"
] | 7
|
2016-11-11T21:54:53.000Z
|
2018-11-21T04:33:46.000Z
|
tests/test_layout.py
|
christabor/jinja2_template_pack
|
f57300bc2922aa4105d1aa393351b63c86c26048
|
[
"MIT"
] | 3
|
2016-12-30T10:34:02.000Z
|
2021-04-08T05:40:09.000Z
|
"""Tests for 'layout' filters."""
from flask_extras.filters import layout
class TestBs3Col:
"""All tests for bs3 col function."""
def test_returns_right_width(self):
"""Test the return value for a valid type."""
assert layout.bs3_cols(1) == 12
assert layout.bs3_cols(2) == 6
assert layout.bs3_cols(3) == 4
assert layout.bs3_cols(4) == 3
assert layout.bs3_cols(5) == 2
assert layout.bs3_cols(6) == 2
def test_returns_right_width_bad_data(self):
"""Test the return value for an invalid type."""
assert layout.bs3_cols(None) == 12
assert layout.bs3_cols('foo') == 12
assert layout.bs3_cols(dict()) == 12
| 30.695652
| 56
| 0.626062
| 102
| 706
| 4.156863
| 0.411765
| 0.254717
| 0.318396
| 0.403302
| 0.488208
| 0.117925
| 0
| 0
| 0
| 0
| 0
| 0.056711
| 0.250708
| 706
| 22
| 57
| 32.090909
| 0.744802
| 0.201133
| 0
| 0
| 0
| 0
| 0.005525
| 0
| 0
| 0
| 0
| 0
| 0.692308
| 1
| 0.153846
| false
| 0
| 0.076923
| 0
| 0.307692
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
cea98b4701aca9f25c1610186d2c0feab883feac
| 191
|
py
|
Python
|
src/test/blocked_bad_ip.py
|
jalapenopuzzle/rr
|
6f8b3c73868d9fd3e6ac14a1322b92dbe9958807
|
[
"BSD-1-Clause"
] | 5,156
|
2015-01-01T06:10:28.000Z
|
2020-11-13T15:12:34.000Z
|
src/test/blocked_bad_ip.py
|
jalapenopuzzle/rr
|
6f8b3c73868d9fd3e6ac14a1322b92dbe9958807
|
[
"BSD-1-Clause"
] | 1,214
|
2015-01-02T02:32:13.000Z
|
2020-11-09T04:36:26.000Z
|
src/test/blocked_bad_ip.py
|
jalapenopuzzle/rr
|
6f8b3c73868d9fd3e6ac14a1322b92dbe9958807
|
[
"BSD-1-Clause"
] | 402
|
2015-01-13T22:54:32.000Z
|
2020-11-05T15:02:25.000Z
|
from util import *
send_gdb('c')
expect_rr('EXIT-SUCCESS')
expect_gdb('SIGSEGV')
send_gdb('reverse-stepi')
expect_gdb('SIGSEGV')
send_gdb('reverse-stepi')
expect_gdb('start_thread')
ok()
| 13.642857
| 26
| 0.73822
| 29
| 191
| 4.586207
| 0.551724
| 0.157895
| 0.240602
| 0.300752
| 0.593985
| 0.593985
| 0.593985
| 0.593985
| 0.593985
| 0
| 0
| 0
| 0.08377
| 191
| 13
| 27
| 14.692308
| 0.76
| 0
| 0
| 0.444444
| 0
| 0
| 0.340314
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.111111
| 0
| 0.111111
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0c8ca5aa09cd7f6d43b1725a7e8f55b68ac7b6b4
| 182,832
|
py
|
Python
|
3algo/homo/0_16_4datap.py
|
allengrr/deadlock_project
|
933878077c45a7df04daa087407bb2620c064617
|
[
"MIT"
] | null | null | null |
3algo/homo/0_16_4datap.py
|
allengrr/deadlock_project
|
933878077c45a7df04daa087407bb2620c064617
|
[
"MIT"
] | null | null | null |
3algo/homo/0_16_4datap.py
|
allengrr/deadlock_project
|
933878077c45a7df04daa087407bb2620c064617
|
[
"MIT"
] | 1
|
2021-03-21T17:54:26.000Z
|
2021-03-21T17:54:26.000Z
|
wt0_16_4 = {'192.168.122.111': [8.3103, 8.0392, 7.6612, 7.6962, 7.5176, 7.5481, 7.5683, 7.5518, 7.8167, 7.7147, 7.6347, 7.5667, 7.5034, 7.9934, 7.9606, 7.9429, 7.9216, 7.8997, 7.8826, 8.0898, 8.037, 8.36, 8.3285, 8.4888, 8.4126, 8.3655, 8.2986, 8.258, 8.2224, 8.1874, 8.1354, 8.1046, 8.2392, 8.2046, 8.1844, 8.1706, 8.1582, 8.1317, 8.1214, 8.0948, 8.0548, 8.0183, 8.0002, 7.973, 7.9512, 7.9496, 7.9225, 7.9308, 7.9368, 7.9226, 7.9, 7.8689, 7.8479, 7.827, 7.8126, 7.8134, 7.8033, 7.7925, 7.7745, 7.6912, 7.692200000000001, 7.6813, 7.6008, 7.514, 7.5196, 7.587, 7.5832, 7.4953, 7.4972, 7.5021, 7.4376, 7.4318, 7.4258, 7.4227, 7.3766, 7.3647, 7.3642, 7.3937, 7.3842, 7.375, 7.3711, 7.3137, 7.2711, 7.2118, 7.1764, 7.1216, 7.1177, 7.1787, 7.1659, 7.1548, 7.1514, 7.0893, 7.0323, 7.0305, 7.0315, 7.032500000000001, 7.036, 7.0324, 7.0408, 6.9961, 7.0036, 7.0046, 7.0123, 7.0146, 6.9612, 6.9161, 6.9252, 6.9307, 6.9758, 6.9971, 6.9868, 7.3222, 7.3241, 7.3192, 7.3183, 7.3191, 7.3195, 7.313, 7.3118, 7.2631, 7.2276, 7.231, 7.2273, 7.2219, 7.2211, 7.2201, 7.221100000000001, 7.2208, 7.2237, 7.2276, 7.2312, 7.2293, 7.2287, 7.2276, 7.2224, 7.2216, 7.2268, 7.227, 7.2259, 7.2255, 7.225, 7.2188, 7.1922, 7.4323, 7.4251, 7.4253, 7.4242, 7.4207, 7.4152, 7.608, 7.6008, 7.6082, 7.6017, 7.6633, 7.6438, 7.6153, 7.6123, 7.6369, 7.6612, 7.6281, 7.5977, 7.5924, 7.5876, 7.619, 7.614, 7.6269, 7.6209, 7.6193, 7.6142, 7.6115, 7.6096, 7.6515, 7.6493, 7.6187, 7.5851, 7.6309, 7.6646, 7.659, 7.6557, 7.6658, 7.6687, 7.669700000000001, 7.670700000000001, 7.671700000000001, 7.6685, 7.6363, 7.6417, 7.6421, 7.6393, 7.6113, 7.5857, 7.6111, 7.6244, 7.6324, 7.7465, 7.7412, 7.7363, 7.7106, 7.691, 7.6631, 7.6609, 7.659, 7.6583, 7.6332, 7.6334, 7.6321, 7.6072, 7.5831, 7.5814, 7.605, 7.6031, 7.5991, 7.5972, 7.5955, 7.5964, 7.5925, 7.5906, 7.5892, 7.5976, 7.5943, 7.5715, 7.5449, 7.5455, 7.544, 7.5539, 7.5616, 7.5601, 7.5587, 7.5545, 7.5525, 7.5535000000000005, 7.5995, 7.5738, 7.5468, 7.5228, 7.4982, 7.4992, 7.4982, 7.4987, 7.5391, 7.5388, 7.524, 7.5244, 7.523, 7.4996, 7.4904, 7.4895, 7.5056, 7.5309, 7.5319, 7.5309, 7.5317, 7.5308, 7.5476, 7.545, 7.5439, 7.5404, 7.5365, 7.5582, 7.5565, 7.6475, 7.6256, 7.6268, 7.6246, 7.6206, 7.6002, 7.607, 7.605, 7.6047, 7.6043, 7.5841, 7.5727, 7.5707, 7.5702, 7.5687, 7.5731, 7.574, 7.5742, 7.6595, 7.6572, 7.6531, 7.6502, 7.6487, 7.6501, 7.6666, 7.6481, 7.6498, 7.7605, 7.761500000000001, 7.762500000000001, 7.763500000000001, 7.7624, 7.7588, 7.7413, 7.738, 7.7385, 7.7525, 7.7531, 7.7326, 7.7235, 7.7197, 7.7157, 7.7219, 7.718, 7.7161, 7.7162, 7.7142, 7.7128, 7.7118, 7.7139, 7.7117, 7.7131, 7.7109, 7.7092, 7.7073, 7.7074, 7.7055, 7.7056, 7.7067, 7.7038, 7.7044, 7.7045, 7.7043, 7.7015, 7.702500000000001, 7.703500000000001, 7.7037, 7.687, 7.6881, 7.6867, 7.6887, 7.6897, 7.6875, 7.6885, 7.689500000000001, 7.6912, 7.69, 7.6987, 7.6944, 7.6933, 7.6903, 7.6909, 7.6988, 7.7039, 7.7101, 7.7196, 7.7207, 7.7215, 7.7205, 7.7207, 7.7195, 7.7182, 7.7199, 7.7189, 7.7178, 7.7192, 7.7187, 7.7189, 7.7191, 7.7194, 7.717, 7.7149, 7.7128, 7.7731, 7.7712, 7.7713, 7.769, 7.7692, 7.7682, 7.7669, 7.7497, 7.7488, 7.7498000000000005, 7.750800000000001, 7.7497, 7.7486, 7.7344, 7.7346, 7.7342, 7.7367, 7.7372, 7.7235, 7.7092, 7.7077, 7.7101, 7.6968, 7.7012, 7.7018, 7.6873, 8.3206, 8.3092, 8.3087, 8.2932, 8.3091, 8.3003, 8.3361, 8.3353, 8.3646, 8.3505, 8.3511, 8.3529, 8.3514, 8.3495, 8.3327, 8.3314, 8.3278, 8.3264, 8.3236, 8.3215, 8.3237, 8.32, 8.3187, 8.3166, 8.3033, 8.3008, 8.2848, 8.2707, 8.2558, 8.2535, 8.2384, 8.224, 8.2219, 8.2196, 8.2267, 8.2254, 8.2222, 8.2207, 8.2052, 8.2064, 8.2038, 8.2011, 8.1978, 8.2009, 8.1878, 8.1852, 8.1841, 8.1829, 8.1818, 8.1806, 8.1765, 8.1637, 8.1628, 8.1617, 8.147, 8.1461, 8.142, 8.143, 8.144, 8.1415, 8.1391, 8.1393, 8.1386, 8.1387, 8.126, 8.1122, 8.1101, 8.1097, 8.0971, 8.0824, 8.0799, 8.0775, 8.077, 8.0649, 8.0628, 8.0602, 8.0579, 8.0454, 8.0449, 8.0435, 8.0439, 8.0434, 8.0408, 8.0376, 8.0355, 8.0359, 8.0258, 8.0166, 8.0171, 8.0148, 8.0126, 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cpu0_16_4 = [62.0, 38.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 0.0, 0.0]
off_mec0_16_4 = 233
off_cloud0_16_4 = 286
inward_mec0_16_4 = 0
loc0_16_4 = 1828
deadlock0_16_4 = [10]
memory0_16_4 = [8.5979, 8.921, 8.9898, 9.0629, 9.6961, 9.7501, 9.807, 9.7012, 9.7016, 9.706, 9.706, 9.706, 9.7132, 9.7593, 9.8213, 9.8221, 9.8232, 9.8236, 9.824, 9.8244, 9.8248, 9.8256, 9.8395, 9.8399, 9.8399, 9.8407, 9.8407, 9.8415, 9.8415, 9.8419, 9.8435, 9.8439, 9.8443, 9.8447, 9.8447, 9.8447, 9.8447, 9.8447, 9.8495, 9.8503, 9.8507, 9.8507, 9.8507, 9.8507, 9.8507, 9.8515, 9.8523, 9.8523, 9.8523, 9.8523, 9.8523, 9.8523, 9.8523, 9.8642, 9.865, 9.865, 9.865, 9.865, 9.865, 9.865, 9.865, 9.8849, 9.886, 9.8872, 9.8872, 9.8872, 9.8872, 9.8872, 9.8872, 9.8872, 9.8872, 9.8872, 9.8872, 9.8872, 9.8872, 9.8872, 9.8936, 9.9019, 9.9019, 9.9019, 9.9019, 9.9019, 9.9019, 9.9019, 9.9067, 9.9067, 9.9067, 9.9067, 9.9067, 9.9071, 9.9071, 9.9071, 9.9071, 9.9071, 9.9071, 9.9075, 9.9075, 9.9075, 9.9075, 9.9242, 9.925, 9.925, 9.925, 9.925, 9.925, 9.925, 9.925, 9.925, 9.9262, 9.9262, 9.9262, 9.9262, 9.9262, 9.9262, 9.9429, 9.9437, 9.9437, 9.9437, 9.9437, 9.9437, 9.9437, 9.95, 9.952, 9.952, 9.952, 9.952, 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task_received0_16_4 = 2347
sent_t0_16_4 = {'124': 856, '126': 740, '125': 751}
cooperate0_16_4 = {'mec': 233, 'cloud': 286}
task_record0_16_4 = {}
outward_mec0_16_4 = 0
offload_check0_16_4 = [0, 0]
| 11,427
| 158,080
| 0.620356
| 46,262
| 182,832
| 2.450888
| 0.142622
| 0.022843
| 0.034211
| 0.04558
| 0.463403
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| 0.241271
| 0.126112
| 0.126112
| 0.077093
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| 0.709337
| 0.126614
| 182,832
| 16
| 158,081
| 11,427
| 0.000714
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0
| 4
|
0ca7fd4b6546eaff9dcc2302399c2635ed68bca3
| 158,339
|
py
|
Python
|
kernel_bootstrap.py
|
RamsteinWR/Diabetic-Retinopathy-Blindness-Detection
|
24390aeefd197600255a961189872dd4dfc77092
|
[
"MIT"
] | 68
|
2019-09-08T20:04:23.000Z
|
2021-05-05T10:05:14.000Z
|
kernel_bootstrap.py
|
RamsteinWR/Diabetic-Retinopathy-Blindness-Detection
|
24390aeefd197600255a961189872dd4dfc77092
|
[
"MIT"
] | 1
|
2019-09-24T06:40:33.000Z
|
2019-10-04T09:13:35.000Z
|
kernel_bootstrap.py
|
RamsteinWR/Diabetic-Retinopathy-Blindness-Detection
|
24390aeefd197600255a961189872dd4dfc77092
|
[
"MIT"
] | 25
|
2019-09-09T04:42:51.000Z
|
2022-03-28T15:01:30.000Z
|
import base64
import os
def decode_archive(archive_name, content):
with open(archive_name, "wb") as f:
f.write(base64.b64decode(content))
decode_archive('pytorch_toolbelt-0.1.3.tar.gz', 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')
os.system('pip install pytorch_toolbelt-0.1.3.tar.gz')
# Imports
import os
import math
import cv2
import torch
import pandas as pd
import numpy as np
import multiprocessing
import albumentations as A
from tqdm import tqdm
from torch.utils.data import Dataset
from torch import nn
from functools import partial
from pytorch_toolbelt.utils import fs
from pytorch_toolbelt.utils.torch_utils import to_numpy
from torch.utils.data import DataLoader
from pytorch_toolbelt.inference.tta import *
from pytorch_toolbelt.modules.encoders import *
from pytorch_toolbelt.modules.activations import swish
from pytorch_toolbelt.modules.pooling import *
from pytorch_toolbelt.modules.scse import *
import torch.nn.functional as F
from pytorch_toolbelt.modules import ABN
from torch.autograd import Variable
from torchvision.models import densenet169, densenet121, densenet201
import torch.utils.model_zoo as model_zoo
from multiprocessing.pool import Pool
from collections import OrderedDict, defaultdict
from albumentations.augmentations.functional import longest_max_size
import pytorch_toolbelt.inference.functional as FF
from pytorch_toolbelt.modules.backbone.efficient_net import efficient_net_b0, efficient_net_b1, efficient_net_b2, efficient_net_b3, efficient_net_b4, efficient_net_b5, efficient_net_b6, efficient_net_b7
from pytorch_toolbelt.modules.encoders import EfficientNetEncoder
from typing import List
from skimage.measure import label
from skimage.morphology import remove_small_objects
from pytorch_toolbelt.modules.decoders import FPNDecoder
from pytorch_toolbelt.modules.fpn import FPNBottleneckBlockBN
from pytorch_toolbelt.modules.hypercolumn import HyperColumn
from pytorch_toolbelt.modules.coord_conv import AddCoords, append_coords
from sklearn import metrics
import scipy as sp
from scipy.stats import trim_mean
UNLABELED_CLASS = -100
pretrained_settings = None
pretrained_settings_dilated = None
# Functions
def tensor_from_rgb_image(image: np.ndarray) -> torch.Tensor:
image = np.moveaxis(image, -1, 0)
image = np.ascontiguousarray(image)
image = torch.from_numpy(image)
return image
def id_from_fname(fname: str):
return os.path.splitext(os.path.basename(fname))[0]
def read_rgb_image(fname: str) -> np.ndarray:
"""Read RGB image from filesystem. This function uses PIL to load image since PIL respects EXIF image orientation flag.
:param fname: Image file path
:return: A numpy array with a loaded image in RGB format
"""
from PIL import Image
im = Image.open(fname)
if im.mode != 'RGB':
im = im.convert('RGB')
image = np.asarray(im)
return image
def get_class_names(coarse_grading=False):
if coarse_grading:
return [
'No DR',
'DR (Mild/Moderate/Severe)',
'Proliferative DR'
]
CLASS_NAMES = [
'No DR',
'Mild',
'Moderate',
'Severe',
'Proliferative DR'
]
return CLASS_NAMES
class RetinopathyDataset(Dataset):
def __init__(self, images, targets,
transform: A.Compose,
target_as_array=False,
dtype=int,
meta_features=False):
if targets is not None:
targets = np.array(targets)
unique_targets = set(targets)
if len(unique_targets.difference({0, 1, 2, 3, 4, UNLABELED_CLASS})):
raise ValueError('Unexpected targets in Y ' + str(unique_targets))
self.meta_features = meta_features
self.images = np.array(images)
self.targets = targets
self.transform = transform
self.target_as_array = target_as_array
self.dtype = dtype
def __len__(self):
return len(self.images)
def __getitem__(self, item):
image = cv2.imread(self.images[item]) # Read with OpenCV instead PIL. It's faster
if image is None:
raise FileNotFoundError(self.images[item])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
height, width = image.shape[:2]
diagnosis = UNLABELED_CLASS
if self.targets is not None:
diagnosis = self.targets[item]
data = self.transform(image=image, diagnosis=diagnosis)
diagnosis = data['diagnosis']
data = {'image': tensor_from_rgb_image(data['image']),
'image_id': id_from_fname(self.images[item])}
if self.meta_features:
log_height = math.log(height)
log_width = math.log(width)
aspect_ratio = log_height / log_width
mean = np.mean(image, axis=(0, 1))
meta_features = np.array([
log_height,
log_width,
aspect_ratio,
mean[0],
mean[1],
mean[2]
])
data['meta_features'] = meta_features
diagnosis = self.dtype(diagnosis)
if self.target_as_array:
data['targets'] = np.array([diagnosis])
else:
data['targets'] = diagnosis
return data
class RMSPool2d(nn.Module):
"""
Root mean square pooling
"""
def __init__(self, eps=1e-9):
super().__init__()
self.eps = eps
self.avg_pool = GlobalAvgPool2d()
def forward(self, x):
x_mean = torch.mean(x, dim=[2, 3], keepdim=True)
avg_pool = self.avg_pool((x - x_mean) ** 2)
return (avg_pool + self.eps).sqrt()
class DenseNet121Encoder(EncoderModule):
def __init__(self, pretrained=True):
densenet = densenet121(pretrained=pretrained)
super().__init__([1024], [32], [0])
self.features = densenet.features
def forward(self, x):
x = self.features(x)
x = F.relu(x, inplace=True)
return [x]
class DenseNet169Encoder(EncoderModule):
def __init__(self, pretrained=True):
densenet = densenet169(pretrained=pretrained)
super().__init__([1664], [32], [0])
self.features = densenet.features
def forward(self, x):
x = self.features(x)
x = F.relu(x, inplace=True)
return [x]
class DenseNet201Encoder(EncoderModule):
def __init__(self, pretrained=True):
densenet = densenet201(pretrained=pretrained)
super().__init__([1920], [32], [0])
self.features = densenet.features
def forward(self, x):
x = self.features(x)
x = F.relu(x, inplace=True)
return [x]
def drop_connect(inputs, p, training):
"""
Drop connect implementation.
"""
if not training:
return inputs
batch_size = inputs.shape[0]
keep_prob = 1 - p
random_tensor = keep_prob
random_tensor += torch.rand([batch_size, 1, 1, 1],
dtype=inputs.dtype, device=inputs.device) # uniform [0,1)
binary_tensor = torch.floor(random_tensor)
output = (inputs / keep_prob) * binary_tensor
return output
def initialize_pretrained_model_dilated(model, num_classes, settings):
assert num_classes == settings['num_classes'], \
'num_classes should be {}, but is {}'.format(
settings['num_classes'], num_classes)
model.load_state_dict(model_zoo.load_url(settings['url']))
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1,
padding=0)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1,
padding=0)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
module_input = x
x = self.avg_pool(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.sigmoid(x)
return module_input * x
class BottleneckD(nn.Module):
"""
Base class for bottlenecks that implements `forward()` method.
"""
def __init__(self, drop_connect_rate=0.):
super().__init__()
self.drop_connect_rate = drop_connect_rate
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out = self.se_module(out)
if self.drop_connect_rate:
out = drop_connect(out, p=self.drop_connect_rate,
training=self.training)
out = out + residual
out = self.relu(out)
return out
class SEBottleneckD(BottleneckD):
"""
Bottleneck for SENet154.
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1,
downsample=None, dilation=1, drop_connect_rate=0.):
super(SEBottleneckD, self).__init__(drop_connect_rate)
self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes * 2)
self.conv2 = nn.Conv2d(planes * 2, planes * 4, kernel_size=3,
stride=stride, padding=dilation, groups=groups,
bias=False, dilation=dilation)
self.bn2 = nn.BatchNorm2d(planes * 4)
self.conv3 = nn.Conv2d(planes * 4, planes * 4, kernel_size=1,
bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SEResNetBottleneckD(BottleneckD):
"""
ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe
implementation and uses `stride=stride` in `conv1` and not in `conv2`
(the latter is used in the torchvision implementation of ResNet).
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1,
downsample=None, dilation=1, drop_connect_rate=0.):
super(SEResNetBottleneckD, self).__init__(drop_connect_rate)
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False,
stride=stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=dilation,
groups=groups, bias=False, dilation=dilation)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SEResNeXtBottleneckD(BottleneckD):
"""
ResNeXt bottleneck type C with a Squeeze-and-Excitation module.
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1,
downsample=None, base_width=4, dilation=1, drop_connect_rate=0.):
super(SEResNeXtBottleneckD, self).__init__(drop_connect_rate)
width = math.floor(planes * (base_width / 64)) * groups
self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False,
stride=1)
self.bn1 = nn.BatchNorm2d(width)
self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=dilation,
groups=groups, bias=False, dilation=dilation)
self.bn2 = nn.BatchNorm2d(width)
self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SENetD(nn.Module):
def __init__(self, block, layers, groups, reduction, dropout_p=0.1,
inplanes=128, input_3x3=True, dilation=(1, 1, 2, 4), downsample_kernel_size=3,
downsample_padding=1, num_classes=1000):
"""
Parameters
----------
block (nn.Module): Bottleneck class.
- For SENet154: SEBottleneck
- For SE-ResNet models: SEResNetBottleneck
- For SE-ResNeXt models: SEResNeXtBottleneck
layers (list of ints): Number of residual blocks for 4 layers of the
network (layer1...layer4).
groups (int): Number of groups for the 3x3 convolution in each
bottleneck block.
- For SENet154: 64
- For SE-ResNet models: 1
- For SE-ResNeXt models: 32
reduction (int): Reduction ratio for Squeeze-and-Excitation modules.
- For all models: 16
dropout_p (float or None): Drop probability for the Dropout layer.
If `None` the Dropout layer is not used.
- For SENet154: 0.2
- For SE-ResNet models: None
- For SE-ResNeXt models: None
inplanes (int): Number of input channels for layer1.
- For SENet154: 128
- For SE-ResNet models: 64
- For SE-ResNeXt models: 64
input_3x3 (bool): If `True`, use three 3x3 convolutions instead of
a single 7x7 convolution in layer0.
- For SENet154: True
- For SE-ResNet models: False
- For SE-ResNeXt models: False
downsample_kernel_size (int): Kernel size for downsampling convolutions
in layer2, layer3 and layer4.
- For SENet154: 3
- For SE-ResNet models: 1
- For SE-ResNeXt models: 1
downsample_padding (int): Padding for downsampling convolutions in
layer2, layer3 and layer4.
- For SENet154: 1
- For SE-ResNet models: 0
- For SE-ResNeXt models: 0
num_classes (int): Number of outputs in `last_linear` layer.
- For all models: 1000
"""
super(SENetD, self).__init__()
self.inplanes = inplanes
if input_3x3:
layer0_modules = [
('conv1', nn.Conv2d(3, 64, 3, stride=2, padding=1,
bias=False)),
('bn1', nn.BatchNorm2d(64)),
('relu1', nn.ReLU(inplace=True)),
('conv2', nn.Conv2d(64, 64, 3, stride=1, padding=1,
bias=False)),
('bn2', nn.BatchNorm2d(64)),
('relu2', nn.ReLU(inplace=True)),
('conv3', nn.Conv2d(64, inplanes, 3, stride=1, padding=1,
bias=False)),
('bn3', nn.BatchNorm2d(inplanes)),
('relu3', nn.ReLU(inplace=True)),
]
else:
layer0_modules = [
('conv1', nn.Conv2d(3, inplanes, kernel_size=7, stride=2,
padding=3, bias=False)),
('bn1', nn.BatchNorm2d(inplanes)),
('relu1', nn.ReLU(inplace=True)),
]
# To preserve compatibility with Caffe weights `ceil_mode=True`
# is used instead of `padding=1`.
layer0_modules.append(('pool', nn.MaxPool2d(3, stride=2,
ceil_mode=True)))
self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
self.layer1 = self._make_layer(
block,
planes=64,
blocks=layers[0],
groups=groups,
reduction=reduction,
downsample_kernel_size=1,
downsample_padding=0,
drop_connect_rate=dropout_p,
dilation=dilation[0]
)
self.layer2 = self._make_layer(
block,
planes=128,
blocks=layers[1],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding,
drop_connect_rate=dropout_p,
dilation=dilation[1]
)
self.layer3 = self._make_layer(
block,
planes=256,
blocks=layers[2],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding,
drop_connect_rate=dropout_p,
dilation=dilation[2]
)
self.layer4 = self._make_layer(
block,
planes=512,
blocks=layers[3],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding,
drop_connect_rate=dropout_p,
dilation=dilation[3]
)
self.avg_pool = nn.AvgPool2d(7, stride=1)
self.dropout = nn.Dropout(dropout_p) if dropout_p is not None else None
self.last_linear = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
downsample_kernel_size=1, downsample_padding=0, dilation=1, drop_connect_rate=0.):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=downsample_kernel_size, stride=stride,
padding=downsample_padding, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, groups, reduction, stride,
downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
d = dilation
if i == blocks - 1:
d = 1 # Do not apply dillation on last block
layers.append(block(self.inplanes, planes, groups, reduction,
dilation=d, drop_connect_rate=drop_connect_rate))
return nn.Sequential(*layers)
def features(self, x):
x = self.layer0(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def logits(self, x):
x = self.avg_pool(x)
if self.dropout is not None:
x = self.dropout(x)
x = x.view(x.size(0), -1)
x = self.last_linear(x)
return x
def forward(self, x):
x = self.features(x)
x = self.logits(x)
return x
def dilated_se_resnext50_32x4d(num_classes=1000, pretrained='imagenet', dilation=(1, 1, 2, 4), dropout_p=0.1):
model = SENetD(SEResNeXtBottleneckD, [3, 4, 6, 3], groups=32, reduction=16, dilation=dilation,
dropout_p=dropout_p, inplanes=64, input_3x3=False,
downsample_kernel_size=1, downsample_padding=0,
num_classes=num_classes)
if pretrained is not None:
settings = pretrained_settings_dilated['se_resnext50_32x4d'][pretrained]
initialize_pretrained_model_dilated(model, num_classes, settings)
return model
class DilatedSEResNeXt50Encoder(SEResnetEncoder):
def __init__(self, pretrained=True, layers=[1, 2, 3, 4], dropout=0.):
encoder = dilated_se_resnext50_32x4d(pretrained='imagenet' if pretrained else None, dropout_p=dropout)
super().__init__(encoder, [64, 256, 512, 1024, 2048], [2, 4, 8, 16, 32], layers)
def dilated_se_resnext101_32x4d(num_classes=1000, pretrained='imagenet', dilation=(1, 1, 2, 4), dropout_p=0.1):
model = SENetD(SEResNeXtBottleneckD, [3, 4, 23, 3], groups=32, reduction=16, dilation=dilation,
dropout_p=dropout_p, inplanes=64, input_3x3=False,
downsample_kernel_size=1, downsample_padding=0,
num_classes=num_classes)
if pretrained is not None:
settings = pretrained_settings_dilated['se_resnext101_32x4d'][pretrained]
initialize_pretrained_model_dilated(model, num_classes, settings)
return model
class DilatedSEResNeXt101Encoder(SEResnetEncoder):
def __init__(self, pretrained=True, layers=[1, 2, 3, 4], dropout=0.):
encoder = dilated_se_resnext101_32x4d(pretrained='imagenet' if pretrained else None,
dilation=(1, 1, 4, 8),
dropout_p=dropout)
super().__init__(encoder, [64, 256, 512, 1024, 2048], [2, 4, 8, 16, 32], layers)
class GlobalWeightedAvgPoolHead(nn.Module):
"""
1) Squeeze last feature map in num_classes
2) Compute global average
"""
def __init__(self, feature_maps, num_classes: int, dropout=0.):
super().__init__()
self.features_size = feature_maps[-1]
self.gwap = GWAP(self.features_size)
self.dropout = nn.Dropout(dropout)
self.logits = nn.Linear(self.features_size, num_classes)
# Regression to grade using SSD-like module
self.regression = nn.Sequential(
nn.Linear(self.features_size, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, 1),
nn.ELU(inplace=True),
)
self.ordinal = nn.Sequential(
nn.Linear(self.features_size, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, num_classes - 1),
)
def forward(self, feature_maps):
# Take last feature map
features = feature_maps[-1]
features = self.gwap(features)
features = features.view(features.size(0), features.size(1))
features = self.dropout(features)
logits = self.logits(features)
regression = self.regression(features)
if regression.size(1) == 1:
regression = regression.squeeze(1)
ordinal = self.ordinal(features).sigmoid().sum(dim=1)
return {
'features': features,
'logits': logits,
'regression': regression,
'ordinal': ordinal
}
class GlobalAvgPoolHead(nn.Module):
"""
1) Squeeze last feature map in num_classes
2) Compute global average
"""
def __init__(self, feature_maps, num_classes: int, dropout=0.):
super().__init__()
self.features_size = feature_maps[-1]
self.dropout = nn.Dropout(dropout)
self.bottleneck = nn.Conv2d(self.features_size, num_classes, kernel_size=1)
# Regression to grade using SSD-like module
self.regression = nn.Sequential(
GlobalAvgPool2d(),
Flatten(),
nn.Linear(self.features_size, 64),
nn.ReLU(inplace=True),
nn.Linear(64, 16),
nn.ReLU(inplace=True),
nn.Linear(16, 1),
nn.ReLU6()
)
self.ordinal = nn.Sequential(
GlobalAvgPool2d(),
Flatten(),
nn.Linear(self.features_size, 64),
nn.LeakyReLU(inplace=True),
nn.Linear(64, num_classes - 1))
def forward(self, feature_maps):
# Take last feature map
features = feature_maps[-1]
features = self.dropout(features)
# Squeeze to num_classes
logits = self.bottleneck(features)
# Compute average
logits = F.adaptive_avg_pool2d(logits, output_size=1)
# Flatten
logits = logits.view(logits.size(0), logits.size(1))
regression = self.regression(features)
if regression.size(1) == 1:
regression = regression.squeeze(1)
ordinal = self.ordinal(features).sigmoid().sum(dim=1)
return {
'features': features.mean(dim=(2, 3)),
'logits': logits,
'regression': regression,
'ordinal': ordinal
}
class BasicConv2d(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_planes, out_planes,
kernel_size=kernel_size, stride=stride,
padding=padding, bias=False) # verify bias false
self.bn = nn.BatchNorm2d(out_planes,
eps=0.001, # value found in tensorflow
momentum=0.1, # default pytorch value
affine=True)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class Mixed_3a(nn.Module):
def __init__(self):
super(Mixed_3a, self).__init__()
self.maxpool = nn.MaxPool2d(3, stride=2)
self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)
def forward(self, x):
x0 = self.maxpool(x)
x1 = self.conv(x)
out = torch.cat((x0, x1), 1)
return out
class Mixed_4a(nn.Module):
def __init__(self):
super(Mixed_4a, self).__init__()
self.branch0 = nn.Sequential(
BasicConv2d(160, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1)
)
self.branch1 = nn.Sequential(
BasicConv2d(160, 64, kernel_size=1, stride=1),
BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)),
BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)),
BasicConv2d(64, 96, kernel_size=(3, 3), stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
out = torch.cat((x0, x1), 1)
return out
class Mixed_5a(nn.Module):
def __init__(self):
super(Mixed_5a, self).__init__()
self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
self.maxpool = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.conv(x)
x1 = self.maxpool(x)
out = torch.cat((x0, x1), 1)
return out
class Reduction_A(nn.Module):
def __init__(self):
super(Reduction_A, self).__init__()
self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)
self.branch1 = nn.Sequential(
BasicConv2d(384, 192, kernel_size=1, stride=1),
BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
BasicConv2d(224, 256, kernel_size=3, stride=2)
)
self.branch2 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
return out
class Inception_A(nn.Module):
def __init__(self):
super(Inception_A, self).__init__()
self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(384, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1)
)
self.branch2 = nn.Sequential(
BasicConv2d(384, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
)
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
BasicConv2d(384, 96, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Inception_B(nn.Module):
def __init__(self):
super(Inception_B, self).__init__()
self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
BasicConv2d(224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0))
)
self.branch2 = nn.Sequential(
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)),
BasicConv2d(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
BasicConv2d(224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)),
BasicConv2d(224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3))
)
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
BasicConv2d(1024, 128, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Reduction_B(nn.Module):
def __init__(self):
super(Reduction_B, self).__init__()
self.branch0 = nn.Sequential(
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 192, kernel_size=3, stride=2)
)
self.branch1 = nn.Sequential(
BasicConv2d(1024, 256, kernel_size=1, stride=1),
BasicConv2d(256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)),
BasicConv2d(256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)),
BasicConv2d(320, 320, kernel_size=3, stride=2)
)
self.branch2 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
return out
class Inception_C(nn.Module):
def __init__(self):
super(Inception_C, self).__init__()
self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)
self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
self.branch1_1a = BasicConv2d(384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
self.branch1_1b = BasicConv2d(384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
self.branch2_1 = BasicConv2d(384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0))
self.branch2_2 = BasicConv2d(448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1))
self.branch2_3a = BasicConv2d(512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
self.branch2_3b = BasicConv2d(512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
BasicConv2d(1536, 256, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1_0 = self.branch1_0(x)
x1_1a = self.branch1_1a(x1_0)
x1_1b = self.branch1_1b(x1_0)
x1 = torch.cat((x1_1a, x1_1b), 1)
x2_0 = self.branch2_0(x)
x2_1 = self.branch2_1(x2_0)
x2_2 = self.branch2_2(x2_1)
x2_3a = self.branch2_3a(x2_2)
x2_3b = self.branch2_3b(x2_2)
x2 = torch.cat((x2_3a, x2_3b), 1)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class InceptionV4(nn.Module):
def __init__(self, num_classes=1001):
super(InceptionV4, self).__init__()
# Special attributs
self.input_space = None
self.input_size = (299, 299, 3)
self.mean = None
self.std = None
# Modules
self.features = nn.Sequential(
BasicConv2d(3, 32, kernel_size=3, stride=2),
BasicConv2d(32, 32, kernel_size=3, stride=1),
BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
Mixed_3a(),
Mixed_4a(),
Mixed_5a(),
Inception_A(),
Inception_A(),
Inception_A(),
Inception_A(),
Reduction_A(), # Mixed_6a
Inception_B(),
Inception_B(),
Inception_B(),
Inception_B(),
Inception_B(),
Inception_B(),
Inception_B(),
Reduction_B(), # Mixed_7a
Inception_C(),
Inception_C(),
Inception_C()
)
self.last_linear = nn.Linear(1536, num_classes)
def logits(self, features):
# Allows image of any size to be processed
adaptiveAvgPoolWidth = features.shape[2]
x = F.avg_pool2d(features, kernel_size=adaptiveAvgPoolWidth)
x = x.view(x.size(0), -1)
x = self.last_linear(x)
return x
def forward(self, input):
x = self.features(input)
x = self.logits(x)
return x
def inceptionv4(num_classes=1000, pretrained='imagenet'):
if pretrained:
settings = pretrained_settings['inceptionv4'][pretrained]
assert num_classes == settings['num_classes'], \
"num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
# both 'imagenet'&'imagenet+background' are loaded from same parameters
model = InceptionV4(num_classes=1001)
model.load_state_dict(model_zoo.load_url(settings['url']))
if pretrained == 'imagenet':
new_last_linear = nn.Linear(1536, 1000)
new_last_linear.weight.data = model.last_linear.weight.data[1:]
new_last_linear.bias.data = model.last_linear.bias.data[1:]
model.last_linear = new_last_linear
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
else:
model = InceptionV4(num_classes=num_classes)
return model
class InceptionV4Encoder(EncoderModule):
def __init__(self, pretrained=True):
inception = inceptionv4(pretrained='imagenet' if pretrained else None)
super().__init__([1536], [32], [0])
self.features = inception.features
def forward(self, x):
x = self.features(x)
return [x]
class LSTMBottleneck(nn.Module):
def __init__(self, in_channels, hidden_size, dropout=0.1, num_layers=2):
super().__init__()
self.lstm = nn.LSTM(input_size=in_channels + 3,
hidden_size=hidden_size,
num_layers=num_layers,
dropout=dropout,
batch_first=True,
bidirectional=True)
def forward(self, input):
input = append_coords(input, with_r=True)
batch_size = input.size(0)
in_channels = input.size(1)
rows = input.size(2)
cols = input.size(3)
input = input.permute((0, 3, 1, 2)).reshape(batch_size, -1, in_channels)
self.lstm.flatten_parameters()
lstm_out, hidden = self.lstm(input)
lstm_out = lstm_out.view(batch_size, rows * cols, 2, -1) # (batch, seq_len, num_directions, hidden_size)
lstm_left = lstm_out[:, :, 0, :]
lstm_right = lstm_out[:, :, 1, :]
lstm_out = lstm_left + lstm_right
last_out = lstm_out[:, -1, :] # Many to one
return last_out
class EfficientNetB0ReLUEncoder(EfficientNetEncoder):
def __init__(self, activation='leaky_relu', layers=[1, 2, 4, 6], abn_block=ABN, pretrained=False):
abn_params = {
'activation': activation,
'momentum': 0.1,
'eps': 1e-5
}
super().__init__(efficient_net_b0(num_classes=1, abn_block=abn_block, abn_params=abn_params),
[16, 24, 40, 80, 112, 192, 320],
[2, 4, 8, 16, 16, 32, 32], layers)
class EfficientNetB1ReLUEncoder(EfficientNetEncoder):
def __init__(self, activation='leaky_relu', layers=[1, 2, 4, 6], abn_block=ABN, pretrained=False):
abn_params = {
'activation': activation,
'momentum': 0.1,
'eps': 1e-5
}
super().__init__(efficient_net_b1(num_classes=1, abn_block=abn_block, abn_params=abn_params),
[16, 24, 40, 80, 112, 192, 320],
[2, 4, 8, 16, 16, 32, 32], layers)
class EfficientNetB2ReLUEncoder(EfficientNetEncoder):
def __init__(self, activation='leaky_relu', layers=[1, 2, 4, 6], abn_block=ABN, pretrained=False):
abn_params = {
'activation': activation,
'momentum': 0.1,
'eps': 1e-5
}
super().__init__(efficient_net_b2(num_classes=1, abn_block=abn_block, abn_params=abn_params),
[16, 24, 48, 88, 120, 208, 352],
[2, 4, 8, 16, 16, 32, 32], layers)
class EfficientNetB3ReLUEncoder(EfficientNetEncoder):
def __init__(self, activation='leaky_relu', layers=[1, 2, 4, 6], abn_block=ABN, pretrained=False):
abn_params = {
'activation': activation,
'momentum': 0.1,
'eps': 1e-5
}
super().__init__(efficient_net_b3(num_classes=1, abn_block=abn_block, abn_params=abn_params),
[24, 32, 48, 96, 136, 232, 384],
[2, 4, 8, 16, 16, 32, 32], layers)
class EfficientNetB4ReLUEncoder(EfficientNetEncoder):
def __init__(self, activation='leaky_relu', layers=[1, 2, 4, 6], abn_block=ABN, pretrained=False):
abn_params = {
'activation': activation,
'momentum': 0.1,
'eps': 1e-5
}
super().__init__(efficient_net_b4(num_classes=1, abn_block=abn_block, abn_params=abn_params),
[24, 32, 56, 112, 160, 272, 448],
[2, 4, 8, 16, 16, 32, 32], layers)
class EfficientNetB5ReLUEncoder(EfficientNetEncoder):
def __init__(self, activation='leaky_relu', layers=[1, 2, 4, 6], abn_block=ABN, pretrained=False):
abn_params = {
'activation': activation,
'momentum': 0.1,
'eps': 1e-5
}
super().__init__(efficient_net_b5(num_classes=1, abn_block=abn_block, abn_params=abn_params),
[24, 40, 64, 128, 176, 304, 512],
[2, 4, 8, 16, 16, 32, 32], layers)
class EfficientNetB6ReLUEncoder(EfficientNetEncoder):
def __init__(self, activation='leaky_relu', layers=[1, 2, 4, 6], abn_block=ABN, pretrained=False):
abn_params = {
'activation': activation,
'momentum': 0.1,
'eps': 1e-5
}
super().__init__(efficient_net_b6(num_classes=1, abn_block=abn_block, abn_params=abn_params),
[32, 40, 72, 144, 200, 344, 576],
[2, 4, 8, 16, 16, 32, 32], layers)
class EfficientNetB7ReLUEncoder(EfficientNetEncoder):
def __init__(self, activation='leaky_relu', layers=[1, 2, 4, 6], abn_block=ABN, pretrained=False):
abn_params = {
'activation': activation,
'momentum': 0.1,
'eps': 1e-5
}
super().__init__(efficient_net_b7(num_classes=1, abn_block=abn_block, abn_params=abn_params),
[32, 48, 80, 160, 224, 384, 640],
[2, 4, 8, 16, 16, 32, 32], layers)
def pnasnet5large(num_classes=1001, pretrained='imagenet'):
r"""PNASNet-5 model architecture from the
`"Progressive Neural Architecture Search"
<https://arxiv.org/abs/1712.00559>`_ paper.
"""
if pretrained:
settings = pretrained_settings['pnasnet5large'][pretrained]
assert num_classes == settings[
'num_classes'], 'num_classes should be {}, but is {}'.format(
settings['num_classes'], num_classes)
# both 'imagenet'&'imagenet+background' are loaded from same parameters
model = PNASNet5Large(num_classes=1001)
model.load_state_dict(model_zoo.load_url(settings['url']))
if pretrained == 'imagenet':
new_last_linear = nn.Linear(model.last_linear.in_features, 1000)
new_last_linear.weight.data = model.last_linear.weight.data[1:]
new_last_linear.bias.data = model.last_linear.bias.data[1:]
model.last_linear = new_last_linear
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
else:
model = PNASNet5Large(num_classes=num_classes)
return model
class CoordDoubleConvBNRelu(nn.Module):
def __init__(self, in_dec_filters: int, out_filters: int, abn_block=ABN):
super().__init__()
self.add_coords = AddCoords(with_r=True)
self.conv1 = nn.Conv2d(in_dec_filters + 3, out_filters, kernel_size=3, padding=1, stride=1, bias=False)
self.abn1 = abn_block(out_filters)
self.conv2 = nn.Conv2d(out_filters + 3, out_filters, kernel_size=3, padding=1, stride=1, bias=False)
self.abn2 = abn_block(out_filters)
def forward(self, x):
x = self.add_coords(x)
x = self.conv1(x)
x = self.abn1(x)
x = self.add_coords(x)
x = self.conv2(x)
x = self.abn2(x)
return x
class PNasnet5LargeEncoder(EncoderModule):
def __init__(self, pretrained=False):
model = pnasnet5large(pretrained='imagenet+background' if pretrained else None)
super().__init__([4320], [32], [0])
model.last_linear = None # Remove last linear block as we have our own
self.extractor = model
def forward(self, x):
x = F.relu(self.extractor.features(x))
return [x]
class FlipLRMultiheadTTA(nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, image):
output = self.model(image)
# Flip image input
output2 = self.model(FF.torch_fliplr(image))
if len(output['features'].size()) == 4:
output2['features'] = FF.torch_fliplr(output2['features'])
output['logits'] = (output['logits'] + output2['logits']) * 0.5
output['ordinal'] = (output['ordinal'] + output2['ordinal']) * 0.5
output['regression'] = (output['regression'] + output2['regression']) * 0.5
output['features'] = (output['features'] + output2['features']) * 0.5
return output
class Flip4MultiheadTTA(nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, image):
outputs = []
outputs.append(self.model(image))
image_fliplr = FF.torch_fliplr(image)
outputs.append(self.model(image_fliplr))
image_flipud = FF.torch_flipud(image)
outputs.append(self.model(image_flipud))
image_fliplr_ud = FF.torch_fliplr(image_flipud)
outputs.append(self.model(FF.torch_fliplr(image_fliplr_ud)))
for key in {'logits', 'features', 'regression', 'ordinal'}:
for i in range(1, len(outputs)):
outputs[0][key] += outputs[i][key]
outputs[0][key] /= len(outputs)
return outputs[0]
class MultiscaleFlipLRMultiheadTTA(nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, image):
rows = image.size(2)
cols = image.size(3)
outputs = []
for scale in [1.0, 1.15, 0.87]:
image_i = F.interpolate(image, size=(int(rows * scale), int(cols * scale)), mode='bilinear', align_corners=True)
output = self.model(image_i)
outputs.append(output)
# Flip image input
output2 = self.model(FF.torch_fliplr(image_i))
outputs.append(output2)
for key in {'logits', 'features', 'regression', 'ordinal'}:
for i in range(1, len(outputs)):
outputs[0][key] += outputs[i][key]
outputs[0][key] /= len(outputs)
return outputs[0]
class ApplySoftmaxToLogits(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
input['logits'] = input['logits'].softmax(dim=1)
return input
class OrdinalEncoderHeadModel(nn.Module):
def __init__(self, encoder: EncoderModule, head, num_classes):
super().__init__()
self.encoder = encoder
self.head = head
self.link = LogisticCumulativeLink(num_classes,
init_cutpoints='ordered')
@property
def features_size(self):
return self.head.features_size
def forward(self, input):
feature_maps = self.encoder(input)
features, logits = self.head(feature_maps)
logits = self.link(logits)
return {'features': features, 'logits': logits}
class GlobalRankPooling(nn.Module):
def __init__(self, num_features, spatial_size):
super().__init__()
self.conv = nn.Conv1d(num_features, num_features, spatial_size, groups=num_features)
def forward(self, x: torch.Tensor):
spatial_size = x.size(2) * x.size(3)
assert spatial_size == self.conv.kernel_size[0], f'Expected spatial size {self.conv.kernel_size[0]}, ' \
f'got {x.size(2)}x{x.size(3)}'
x = x.view(x.size(0), x.size(1), -1) # Flatten spatial dimensions
x_sorted, index = x.topk(spatial_size, dim=2)
x = self.conv(x_sorted) # [B, C, 1]
return x.squeeze(2)
class GlobalAvgPoolHeadV2(nn.Module):
"""
"""
def __init__(self, feature_maps, num_classes: int, dropout=0.):
super().__init__()
self.features_size = feature_maps[-1]
self.avgpool = GlobalAvgPool2d()
self.dropout = nn.Dropout(dropout)
self.logits = nn.Linear(self.features_size, num_classes)
self.regression = nn.Linear(self.features_size, 1)
self.ordinal = nn.Linear(self.features_size, num_classes - 1)
def forward(self, feature_maps):
# Take last feature map
features = self.avgpool(feature_maps[-1])
features = features.view(features.size(0), features.size(1))
features = self.dropout(features)
# Squeeze to num_classes
logits = self.logits(features)
regression = (self.regression(features) + 2.).log()
ordinal = self.ordinal(features).sigmoid().sum(dim=1)
if regression.size(1) == 1:
regression = regression.squeeze(1)
return {
'features': features,
'logits': logits,
'regression': regression,
'ordinal': ordinal
}
class GlobalMaxPoolHeadV2(nn.Module):
"""
"""
def __init__(self, feature_maps, num_classes: int, dropout=0.):
super().__init__()
self.features_size = feature_maps[-1]
self.maxpool = GlobalMaxPool2d()
self.dropout = nn.Dropout(dropout)
self.logits = nn.Linear(self.features_size, num_classes)
self.regression = nn.Sequential(nn.Linear(self.features_size, self.features_size // 4),
nn.BatchNorm1d(self.features_size // 4),
nn.ReLU(inplace=True),
nn.Linear(self.features_size // 4, self.features_size // 8),
nn.BatchNorm1d(self.features_size // 8),
nn.ReLU(inplace=True),
nn.Linear(self.features_size // 8, 1))
self.ordinal = nn.Sequential(nn.Linear(self.features_size, self.features_size // 4),
nn.BatchNorm1d(self.features_size // 4),
nn.ReLU(inplace=True),
nn.Linear(self.features_size // 4, self.features_size // 8),
nn.BatchNorm1d(self.features_size // 8),
nn.ReLU(inplace=True),
nn.Linear(self.features_size // 8, num_classes - 1))
def forward(self, feature_maps):
# Take last feature map
features = self.maxpool(feature_maps[-1])
features = features.view(features.size(0), features.size(1))
features = self.dropout(features)
# Squeeze to num_classes
logits = self.logits(features)
regression = self.regression(features)
ordinal = self.ordinal(features).sigmoid().sum(dim=1)
if regression.size(1) == 1:
regression = regression.squeeze(1)
return {
'features': features,
'logits': logits,
'regression': regression,
'ordinal': ordinal
}
class RankPoolingHeadModel(nn.Module):
def __init__(self, feature_maps, num_classes: int, dropout=0.):
super().__init__()
self.features_size = feature_maps[-1]
self.rank_pool = GlobalRankPooling(self.features_size, 16 * 16)
self.dropout = nn.AlphaDropout(dropout)
self.logits = nn.Linear(self.features_size, num_classes)
# Regression to grade using SSD-like module
self.regression = nn.Sequential(
nn.Linear(self.features_size, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, 1)
)
self.ordinal = nn.Linear(self.features_size, num_classes - 1)
def forward(self, features):
features = self.rank_pool(features[-1])
features = self.dropout(features)
logits = self.logits(features)
regression = self.regression(features)
if regression.size(1) == 1:
regression = regression.squeeze(1)
ordinal = self.ordinal(features).sigmoid().sum(dim=1)
return {
'features': features,
'logits': logits,
'regression': regression,
'ordinal': ordinal
}
class RankPoolingHeadModelV2(nn.Module):
def __init__(self, feature_maps, num_classes: int, dropout=0.):
super().__init__()
self.features_size = 512
self.bottleneck = nn.Conv2d(feature_maps[-1], self.features_size, kernel_size=1)
self.rank_pool = GlobalRankPooling(self.features_size, 16 * 16)
self.dropout = nn.AlphaDropout(dropout)
self.logits = nn.Linear(self.features_size, num_classes)
# Regression to grade using SSD-like module
self.regression = nn.Sequential(
nn.Linear(self.features_size, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, 1)
)
self.ordinal = nn.Linear(self.features_size, num_classes - 1)
def forward(self, features):
features = self.bottleneck(self.dropout(features[-1]))
features = self.rank_pool(features)
logits = self.logits(features)
regression = self.regression(features)
if regression.size(1) == 1:
regression = regression.squeeze(1)
ordinal = self.ordinal(features).sigmoid().sum(dim=1)
return {
'features': features,
'logits': logits,
'regression': regression,
'ordinal': ordinal
}
class LogisticCumulativeLink(nn.Module):
"""
Converts a single number to the proportional odds of belonging to a class.
Parameters
----------
num_classes : int
Number of ordered classes to partition the odds into.
init_cutpoints : str (default='ordered')
How to initialize the cutpoints of the model. Valid values are
- ordered : cutpoints are initialized to halfway between each class.
- random : cutpoints are initialized with random values.
"""
def __init__(self, num_classes: int,
init_cutpoints: str = 'ordered') -> None:
assert num_classes > 2, (
'Only use this model if you have 3 or more classes'
)
super().__init__()
self.num_classes = num_classes
self.init_cutpoints = init_cutpoints
if init_cutpoints == 'ordered':
num_cutpoints = self.num_classes - 1
cutpoints = torch.arange(num_cutpoints).float() - num_cutpoints / 2
self.cutpoints = nn.Parameter(cutpoints)
elif init_cutpoints == 'random':
cutpoints = torch.rand(self.num_classes - 1).sort()[0]
self.cutpoints = nn.Parameter(cutpoints)
else:
raise ValueError(f'{init_cutpoints} is not a valid init_cutpoints '
f'type')
def forward(self, X: torch.Tensor) -> torch.Tensor:
"""
Equation (11) from
"On the consistency of ordinal regression methods", Pedregosa et. al.
"""
sigmoids = torch.sigmoid(self.cutpoints - X)
link_mat = sigmoids[:, 1:] - sigmoids[:, :-1]
link_mat = torch.cat((
sigmoids[:, [0]],
link_mat,
(1 - sigmoids[:, [-1]])
),
dim=1
)
return link_mat
class RMSPoolHead(nn.Module):
"""
"""
def __init__(self, feature_maps, num_classes: int, dropout=0.):
super().__init__()
self.features_size = feature_maps[-1]
self.rms_pooling = RMSPool2d(self.features_size)
self.dropout = nn.Dropout(dropout)
self.logits = nn.Linear(self.features_size, num_classes)
self.regression = nn.Sequential(
nn.Linear(self.features_size, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, 1),
)
self.ordinal = nn.Sequential(
nn.Linear(self.features_size, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, num_classes - 1),
)
def forward(self, feature_maps):
# Take last feature map
features = feature_maps[-1]
features = self.rms_pooling(features)
features = features.view(features.size(0), features.size(1))
features = self.dropout(features)
logits = self.logits(features)
regression = self.regression(features)
ordinal = self.ordinal(features).sigmoid().sum(dim=1)
if regression.size(1) == 1:
regression = regression.squeeze(1)
return {
'features': features,
'logits': logits,
'regression': regression,
'ordinal': ordinal
}
class RNNHead(nn.Module):
def __init__(self, feature_maps, num_classes: int, dropout=0.):
super().__init__()
self.features_size = feature_maps[-1] // 8
self.rnn_pool = LSTMBottleneck(feature_maps[-1] + 3, self.features_size, dropout=dropout)
self.logits = nn.Linear(self.features_size, num_classes)
self.regression = nn.Sequential(
nn.Linear(self.features_size, self.features_size),
nn.LeakyReLU(inplace=True),
nn.Linear(self.features_size, self.features_size),
nn.LeakyReLU(inplace=True),
nn.Linear(self.features_size, 1),
)
self.ordinal = nn.Sequential(
nn.Linear(self.features_size, self.features_size),
nn.LeakyReLU(inplace=True),
nn.Linear(self.features_size, num_classes - 1),
nn.Sigmoid())
def forward(self, feature_maps):
# Take last feature map
features = feature_maps[-1]
features = append_coords(features, with_r=True)
features = self.rnn_pool(features)
# Squeeze to num_classes
logits = self.logits(features)
regression = self.regression(features)
ordinal = self.ordinal(features).sum(dim=1)
if regression.size(1) == 1:
regression = regression.squeeze(1)
return {
'features': features,
'logits': logits,
'regression': regression,
'ordinal': ordinal
}
class GlobalMaxPoolHead(nn.Module):
"""
1) Squeeze last feature map in num_classes
2) Compute global average
"""
def __init__(self, feature_maps, num_classes: int, dropout=0., reduction=8):
super().__init__()
self.features_size = feature_maps[-1] // reduction
self.bottleneck = nn.Sequential(
nn.Conv2d(feature_maps[-1], self.features_size, kernel_size=1, bias=False),
nn.BatchNorm2d(self.features_size),
nn.ReLU(inplace=True))
self.maxpool = GlobalMaxPool2d()
self.dropout = nn.Dropout(dropout)
self.logits = nn.Linear(self.features_size, num_classes)
# Regression to grade using SSD-like module
self.regression = nn.Sequential(
nn.Linear(self.features_size, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, 1),
nn.ELU(inplace=True),
)
self.ordinal = nn.Sequential(
nn.Linear(self.features_size, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, 16),
nn.ELU(inplace=True),
nn.Linear(16, num_classes - 1),
)
def forward(self, feature_maps):
# Take last feature map
features = feature_maps[-1]
features = self.bottleneck(features)
features = self.maxpool(features)
features = features.view(features.size(0), features.size(1))
features = self.dropout(features)
logits = self.logits(features)
regression = self.regression(features)
if regression.size(1) == 1:
regression = regression.squeeze(1)
ordinal = self.ordinal(features).sigmoid().sum(dim=1)
return {
'features': features,
'logits': logits,
'regression': regression,
'ordinal': ordinal
}
class FPNHeadModel(nn.Module):
def __init__(self, feature_maps, num_classes: int, dropout=0., reduction=8):
super().__init__()
self.decoder = FPNDecoder(features=feature_maps[1:],
bottleneck=FPNBottleneckBlockBN,
prediction_block=CoordDoubleConvBNRelu,
fpn_features=128,
prediction_features=128)
self.hypercolumn = HyperColumn(mode='nearest',align_corners=None)
self.maxpool = GlobalMaxPool2d()
self.features_size = sum(self.decoder.output_filters)
self.logits = nn.Linear(self.features_size, num_classes)
self.dropout = nn.Dropout(dropout)
self.logits = nn.Linear(self.features_size, num_classes)
self.regression = nn.Sequential(nn.Linear(self.features_size, self.features_size // 4),
nn.BatchNorm1d(self.features_size // 4),
nn.ReLU(inplace=True),
nn.Linear(self.features_size // 4, self.features_size // 8),
nn.BatchNorm1d(self.features_size // 8),
nn.ReLU(inplace=True),
nn.Linear(self.features_size // 8, 1))
self.ordinal = nn.Sequential(nn.Linear(self.features_size, self.features_size),
nn.BatchNorm1d(self.features_size),
nn.LeakyReLU(inplace=True),
nn.Linear(self.features_size, self.features_size),
nn.BatchNorm1d(self.features_size),
nn.LeakyReLU(inplace=True),
nn.Linear(self.features_size, num_classes - 1),
nn.Sigmoid())
def forward(self, features):
features = self.decoder(features[1:])
features = self.hypercolumn(*features)
features = self.maxpool(features)
features = features.view(features.size(0), features.size(1))
features = self.dropout(features)
logits = self.logits(features)
regression = self.regression(features)
if regression.size(1) == 1:
regression = regression.squeeze(1)
ordinal = self.ordinal(features).sum(dim=1)
return {
'features': features,
'logits': logits,
'regression': regression,
'ordinal': ordinal
}
class Flatten(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x.view(x.shape[0], -1)
class EncoderHeadModel(nn.Module):
def __init__(self, encoder: EncoderModule, head: nn.Module):
super().__init__()
self.encoder = encoder
self.head = head
@property
def features_size(self):
return self.head.features_size
def forward(self, image):
feature_maps = self.encoder(image)
result = self.head(feature_maps)
return result
def crop_black(image, tolerance=5):
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
cv2.threshold(gray, tolerance, 255, type=cv2.THRESH_BINARY, dst=gray)
# cv2.threshold(gray, tolerance, 255, type=cv2.THRESH_BINARY | cv2.THRESH_OTSU, dst=gray)
cv2.medianBlur(gray, 7, gray)
# Remove small objects that occupy less than 5% of an image
min_size = 0.05 * int(image.shape[0] * image.shape[1])
label_image = label(gray)
label_image = remove_small_objects(label_image, min_size=min_size)
gray = (label_image > 0).astype(np.uint8)
x, y, w, h = cv2.boundingRect(gray)
# Sanity check for very dark images
non_black_area = w * h
image_area = image.shape[0] * image.shape[1]
fg_ratio = non_black_area / image_area
# If area of black region is more than half of the whole image area,
# we do not crop it.
if fg_ratio < 0.5:
return image
return image[y:y + h, x:x + w]
class CropBlackRegions(A.ImageOnlyTransform):
def __init__(self, tolerance=5, p=1.):
super().__init__(p=p)
self.tolerance = tolerance
def apply(self, img, **params):
return crop_black(img, self.tolerance)
def get_transform_init_args_names(self):
return ('tolerance',)
def unsharp_mask(image, sigmaX=10):
image = cv2.addWeighted(image, 4, cv2.GaussianBlur(image, (0, 0), sigmaX), -4, 128)
return image
class UnsharpMask(A.ImageOnlyTransform):
def __init__(self, p=1.0):
super().__init__(p=p)
def apply(self, img, **params):
return unsharp_mask(img)
def get_transform_init_args_names(self):
return tuple()
def clahe_preprocessing(image, clip_limit=4.0, tile_grid_size=(18, 18)):
image_norm = image.copy()
clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size)
image_norm[:, :, 0] = clahe.apply(image[:, :, 0])
image_norm[:, :, 1] = clahe.apply(image[:, :, 1])
image_norm[:, :, 2] = clahe.apply(image[:, :, 2])
# image_norm = cv2.addWeighted(image, 0.5, image_norm, 0.5, 0)
return image_norm
class ChannelIndependentCLAHE(A.ImageOnlyTransform):
def __init__(self, p=1.0):
super().__init__(p=p)
def apply(self, img, **params):
return clahe_preprocessing(img)
def get_transform_init_args_names(self):
return tuple()
def unsharp_mask_v2(image):
filter = cv2.bilateralFilter(image,
d=32,
sigmaColor=75,
sigmaSpace=15)
multiplier = 6
difference = cv2.addWeighted(image, multiplier, filter, -multiplier, 0, dtype=cv2.CV_32F)
a_max = np.max(difference)
a_min = np.min(difference)
rng = max(a_max, -a_min, 1)
scale = 127. / rng
difference = difference * scale + 127
difference = difference.astype(np.uint8)
return difference
class UnsharpMaskV2(A.ImageOnlyTransform):
def __init__(self, p=1.0):
super().__init__(p=p)
def apply(self, img, **params):
return unsharp_mask_v3(img)
def get_transform_init_args_names(self):
return tuple()
class RedFree(A.ImageOnlyTransform):
def __init__(self, p=1):
super().__init__(p=p)
def apply(self, img, **params):
return red_free(img)
def red_free(image):
"""
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4944099/
The red-free version of this photo shows the new vessels at the optic disc more clearly.
Altering the images, e.g. by using red-free, is a valuable tool for detecting retinopathy
:param image:
:return:
"""
image = image.copy()
image[..., 0] = 0
return image
def get_model(model_name, num_classes, pretrained=True, dropout=0.0, **kwargs):
keys = model_name.split('_')
if len(keys) == 2:
encoder_name, head_name = keys
model = 'baseline'
else:
model, encoder_name, head_name = keys
abn_block = ABN
try:
from inplace_abn import InPlaceABN
abn_block = InPlaceABN
print('Using InPlaceABN')
except:
print('InplaceABN not available, using classic BatchNorm+Act')
ENCODERS = {
'resnet18': Resnet18Encoder,
'resnet34': Resnet34Encoder,
'resnet50': Resnet50Encoder,
'resnet101': Resnet101Encoder,
'resnet152': Resnet152Encoder,
'seresnext50': SEResNeXt50Encoder,
'seresnext50d': partial(DilatedSEResNeXt50Encoder, dropout=0.25),
'seresnext101': SEResNeXt101Encoder,
'seresnext101d': partial(DilatedSEResNeXt101Encoder, dropout=0.25),
'seresnet152': SEResnet152Encoder,
'senet154': SENet154Encoder,
'densenet121': DenseNet121Encoder,
'densenet169': DenseNet169Encoder,
'densenet201': DenseNet201Encoder,
'inceptionv4': InceptionV4Encoder,
'efficientb0': partial(EfficientNetB0ReLUEncoder, abn_block=abn_block),
'efficientb1': partial(EfficientNetB1ReLUEncoder, abn_block=abn_block),
'efficientb2': partial(EfficientNetB2ReLUEncoder, abn_block=abn_block),
'efficientb3': partial(EfficientNetB3ReLUEncoder, abn_block=abn_block),
'efficientb4': partial(EfficientNetB4ReLUEncoder, abn_block=abn_block),
'efficientb5': partial(EfficientNetB5ReLUEncoder, abn_block=abn_block),
'efficientb6': partial(EfficientNetB6ReLUEncoder, abn_block=abn_block),
'efficientb7': partial(EfficientNetB7ReLUEncoder, abn_block=abn_block),
'pnasnet5': PNasnet5LargeEncoder
}
encoder = ENCODERS[encoder_name](pretrained=pretrained)
HEADS = {
'gap': GlobalAvgPoolHead,
'gapv2': GlobalAvgPoolHeadV2,
'gwap': GlobalWeightedAvgPoolHead,
'rms': RMSPoolHead,
'max': GlobalMaxPoolHead,
'maxv2': GlobalMaxPoolHeadV2,
'fpn': FPNHeadModel,
'rank': RankPoolingHeadModel,
'rankv2': RankPoolingHeadModelV2,
'rnn': RNNHead
}
MODELS = {
'baseline': EncoderHeadModel,
}
head = HEADS[head_name](feature_maps=encoder.output_filters, num_classes=num_classes, dropout=dropout)
model = MODELS[model](encoder, head)
return model
def get_preprocessing_transform(preprocessing: str) -> A.ImageOnlyTransform:
assert preprocessing in {None, 'unsharp', 'unsharpv2', 'iclahe', 'clahe', 'redfree'}
if preprocessing is None:
return A.NoOp()
if preprocessing == 'unsharp':
return UnsharpMask(p=1)
if preprocessing == 'unsharpv2':
return UnsharpMaskV2(p=1)
if preprocessing == 'iclahe':
return ChannelIndependentCLAHE(p=1)
if preprocessing == 'clahe':
return A.CLAHE(p=1)
if preprocessing == 'redfree':
return RedFree(p=1)
raise KeyError(f'Unsupported preprocessing method {preprocessing}')
def get_test_transform(image_size, preprocessing: str = None, crop_black=True):
longest_size = max(image_size[0], image_size[1])
return A.Compose([
CropBlackRegions(tolerance=5) if crop_black else A.NoOp(always_apply=True),
A.LongestMaxSize(longest_size, interpolation=cv2.INTER_CUBIC),
A.PadIfNeeded(image_size[0], image_size[1],
border_mode=cv2.BORDER_CONSTANT, value=0),
get_preprocessing_transform(preprocessing),
A.Normalize()
])
def preprocess(image_fname, output_dir, image_size=768):
image = cv2.imread(image_fname)
image = crop_black(image, tolerance=5)
image = longest_max_size(image, max_size=image_size, interpolation=cv2.INTER_CUBIC)
image_id = fs.id_from_fname(image_fname)
dst_fname = os.path.join(output_dir, image_id + '.png')
cv2.imwrite(dst_fname, image)
return
def convert_dir(input_dir, output_dir, image_size=768, workers=32):
os.makedirs(output_dir, exist_ok=True)
images = fs.find_images_in_dir(input_dir)
processing_fn = partial(preprocess, output_dir=output_dir, image_size=image_size)
with Pool(workers) as wp:
for image_id in tqdm(wp.imap_unordered(processing_fn, images), total=len(images)):
pass
def report_checkpoint(checkpoint):
print('Epoch :', checkpoint['epoch'])
print('Metrics (Train):', checkpoint['epoch_metrics']['train'])
print('Metrics (Valid):', checkpoint['epoch_metrics']['valid'])
@torch.no_grad()
def run_models_inference_via_dataset(model_checkpoints: List[str],
dataset: RetinopathyDataset,
batch_size=1,
coarse_grading=False,
tta=None,
need_features=True,
apply_softmax=True,
workers=None) -> List[pd.DataFrame]:
if workers is None:
workers = multiprocessing.cpu_count()
models = []
models_predictions = []
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Load models
for model_checkpoint in model_checkpoints:
checkpoint = torch.load(model_checkpoint)
model_name = checkpoint['checkpoint_data']['cmd_args']['model']
print(model_checkpoint, model_name)
report_checkpoint(checkpoint)
num_classes = len(get_class_names(coarse_grading=coarse_grading))
model = get_model(model_name, pretrained=False, num_classes=num_classes)
model.load_state_dict(checkpoint['model_state_dict'], strict=True)
del checkpoint
if apply_softmax:
model = nn.Sequential(model, ApplySoftmaxToLogits())
if tta == 'flip' or tta == 'fliplr':
model = FlipLRMultiheadTTA(model)
if tta == 'flip4':
model = Flip4MultiheadTTA(model)
if tta == 'fliplr_ms':
model = MultiscaleFlipLRMultiheadTTA(model)
model = model.cuda()
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model = model.eval()
models.append(model)
models_predictions.append(defaultdict(list))
data_loader = DataLoader(dataset, batch_size,
pin_memory=True,
num_workers=workers)
for batch in tqdm(data_loader):
input = batch['image'].cuda(non_blocking=True)
for model, predictions in zip(models, models_predictions):
outputs = model(input)
predictions['image_id'].extend(batch['image_id'])
if 'targets' in batch:
predictions['diagnosis'].extend(to_numpy(batch['targets']).tolist())
predictions['logits'].extend(to_numpy(outputs['logits']).tolist())
predictions['regression'].extend(to_numpy(outputs['regression']).tolist())
predictions['ordinal'].extend(to_numpy(outputs['ordinal']).tolist())
if need_features:
predictions['features'].extend(to_numpy(outputs['features']).tolist())
models_predictions = [pd.DataFrame.from_dict(p) for p in models_predictions]
del data_loader, models
return models_predictions
def run_models_inference(model_checkpoints: List[str],
test_csv: pd.DataFrame,
data_dir,
images_dir='test_images',
preprocessing=None,
image_size=None,
crop_black=True,
**kwargs) -> List[pd.DataFrame]:
checkpoint = torch.load(model_checkpoints[0])
if preprocessing is None:
preprocessing = checkpoint['checkpoint_data']['cmd_args'].get('preprocessing', None)
if image_size is None:
image_size = checkpoint['checkpoint_data']['cmd_args'].get('image_size', 512)
image_size = (image_size, image_size)
image_fnames = test_csv['id_code'].apply(lambda x: image_with_name_in_dir(os.path.join(data_dir, images_dir), x))
if 'diagnosis' in test_csv:
targets = test_csv['diagnosis'].values
else:
targets = None
test_ds = RetinopathyDataset(image_fnames, targets, get_test_transform(image_size,
preprocessing=preprocessing,
crop_black=crop_black))
return run_models_inference_via_dataset(model_checkpoints, test_ds, **kwargs)
def average_predictions(predictions: List[pd.DataFrame], column: str,
method='mean', min=None, max=None) -> pd.DataFrame:
preds = []
for p in predictions:
pred = to_numpy(p[column].values.tolist())
preds.append(pred)
preds = np.row_stack(preds)
if min is not None or max is not None:
preds = np.clip(preds, min, max)
if method == 'mean':
y_pred = np.mean(preds, axis=0)
elif method == 'trim_mean':
y_pred = trim_mean(preds, proportiontocut=0.1, axis=0)
elif method == 'median':
y_pred = np.median(preds, axis=0)
else:
raise KeyError(method)
result = pd.DataFrame.from_dict({'id_code': predictions[0]['image_id'].values,
'diagnosis': y_pred.tolist()})
return result
def cls_predictions_to_submission(predictions) -> pd.DataFrame:
predictions = predictions.copy()
predictions['diagnosis'] = predictions['diagnosis'].apply(lambda x: np.argmax(x))
return predictions
def reg_predictions_to_submission(predictions, rounding_coefficients=None) -> pd.DataFrame:
rounder = partial(regression_to_class, rounding_coefficients=rounding_coefficients)
predictions = predictions.copy()
predictions['diagnosis'] = rounder(predictions['diagnosis'].values)
predictions['diagnosis'] = predictions['diagnosis'].apply(int)
return predictions
def regression_to_class(value: torch.Tensor, min=0, max=4, rounding_coefficients=None):
if isinstance(value, np.ndarray):
value = torch.from_numpy(value)
if isinstance(value, (int, float)):
value = torch.tensor(value)
if rounding_coefficients is None:
value = torch.round(value)
value = torch.clamp(value, min, max)
else:
rounded = torch.zeros(len(value))
rounded[value < rounding_coefficients[0]] = 0
rounded[(value >= rounding_coefficients[0]) & (value < rounding_coefficients[1])] = 1
rounded[(value >= rounding_coefficients[1]) & (value < rounding_coefficients[2])] = 2
rounded[(value >= rounding_coefficients[2]) & (value < rounding_coefficients[3])] = 3
rounded[value >= rounding_coefficients[3]] = 4
value = rounded.long()
return value.long()
def image_with_name_in_dir(dirname, image_id):
for ext in ['png', 'jpg', 'jpeg', 'tif']:
image_fname = os.path.join(dirname, f'{image_id}.{ext}')
if os.path.isfile(image_fname):
return image_fname
raise FileNotFoundError(image_fname)
class OptimizedRounder(object):
def __init__(self):
self.coef_ = 0
def _kappa_loss(self, coef, X, y):
X_p = np.copy(X)
for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
elif pred >= coef[0] and pred < coef[1]:
X_p[i] = 1
elif pred >= coef[1] and pred < coef[2]:
X_p[i] = 2
elif pred >= coef[2] and pred < coef[3]:
X_p[i] = 3
else:
X_p[i] = 4
ll = metrics.cohen_kappa_score(y, X_p, weights='quadratic')
return -ll
def fit(self, X, y):
loss_partial = partial(self._kappa_loss, X=X, y=y)
initial_coef = [0.5, 1.5, 2.5, 3.5]
self.coef_ = sp.optimize.minimize(loss_partial, initial_coef, method='nelder-mead')
return self.coefficients()
def predict(self, X, coef):
X_p = np.copy(X)
for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
elif pred >= coef[0] and pred < coef[1]:
X_p[i] = 1
elif pred >= coef[1] and pred < coef[2]:
X_p[i] = 2
elif pred >= coef[2] and pred < coef[3]:
X_p[i] = 3
else:
X_p[i] = 4
return X_p
def coefficients(self):
return self.coef_['x']
| 70.030517
| 79,427
| 0.779732
| 11,992
| 158,339
| 10.152602
| 0.287692
| 0.008049
| 0.010382
| 0.006735
| 0.201873
| 0.182333
| 0.170144
| 0.159121
| 0.153766
| 0.149832
| 0
| 0.109744
| 0.145296
| 158,339
| 2,260
| 79,428
| 70.061504
| 0.789889
| 0.030125
| 0
| 0.434652
| 0
| 0.0006
| 0.537266
| 0.520126
| 0
| 1
| 0
| 0
| 0.003597
| 1
| 0.089928
| false
| 0.0006
| 0.026978
| 0.008993
| 0.211031
| 0.003597
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0cba4744ef3c8e00a3c5c2ee6f5f2a65b9d8cf3c
| 91
|
py
|
Python
|
Testing/gg.py
|
DEADSEC-SECURITY/CODEX
|
cbd1a6c1a0d7fe0dbb682d055861b57eb14cb393
|
[
"MIT"
] | 30
|
2019-05-04T00:52:54.000Z
|
2022-03-14T23:28:32.000Z
|
Testing/gg.py
|
userid9708/CODEX
|
9e39103271502a4ec0da129b29425990784f83b1
|
[
"MIT"
] | 4
|
2019-04-08T12:10:27.000Z
|
2021-06-12T22:55:43.000Z
|
Testing/gg.py
|
userid9708/CODEX
|
9e39103271502a4ec0da129b29425990784f83b1
|
[
"MIT"
] | 5
|
2019-10-19T01:37:56.000Z
|
2022-02-15T07:23:24.000Z
|
x = "'/home/deadsec/Desktop/CODEX/Data/HandShakes/PWF1717189-01.cap'"
x = x[1:-1]
print(x)
| 22.75
| 69
| 0.692308
| 16
| 91
| 3.9375
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130952
| 0.076923
| 91
| 3
| 70
| 30.333333
| 0.619048
| 0
| 0
| 0
| 0
| 0
| 0.692308
| 0.692308
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0cc3d5b459ba1833c6f85c362840370af24eaf85
| 467
|
py
|
Python
|
obeflix_back/serializer.py
|
luigiMinardi/alurachallenge-backend
|
b68eaab29cdb583930687f19662a35fa84376abc
|
[
"MIT"
] | 3
|
2021-07-28T11:48:13.000Z
|
2021-08-05T15:10:13.000Z
|
obeflix_back/serializer.py
|
luigiMinardi/alurachallenge-backend
|
b68eaab29cdb583930687f19662a35fa84376abc
|
[
"MIT"
] | 1
|
2021-08-24T18:21:44.000Z
|
2021-08-24T18:21:44.000Z
|
obeflix_back/serializer.py
|
luigiMinardi/alurachallenge-backend
|
b68eaab29cdb583930687f19662a35fa84376abc
|
[
"MIT"
] | 1
|
2022-02-28T01:16:23.000Z
|
2022-02-28T01:16:23.000Z
|
from rest_framework import serializers
from obeflix_back.models import Video, Categoria
class VideoSerializer(serializers.ModelSerializer):
class Meta:
model = Video
fields = '__all__'
class CategoriaSerializer(serializers.ModelSerializer):
class Meta:
model = Categoria
fields = '__all__'
class ListaVideoPorCategoriaSerializer(serializers.ModelSerializer):
class Meta:
model = Video
fields = '__all__'
| 25.944444
| 68
| 0.719486
| 42
| 467
| 7.666667
| 0.452381
| 0.242236
| 0.28882
| 0.326087
| 0.459627
| 0.335404
| 0.335404
| 0.335404
| 0
| 0
| 0
| 0
| 0.218415
| 467
| 18
| 69
| 25.944444
| 0.882192
| 0
| 0
| 0.571429
| 0
| 0
| 0.044872
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.142857
| 0
| 0.571429
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
0cd16e8d0f36c79c0b719aafb1a4cfe1db97861e
| 616
|
py
|
Python
|
auto_tag/exception.py
|
mateimicu/auto-tag
|
541e11b1d3cf583093cdc826983ec13833b26833
|
[
"MIT"
] | 6
|
2020-06-05T20:34:51.000Z
|
2022-01-24T12:20:16.000Z
|
auto_tag/exception.py
|
mateimicu/auto-tag
|
541e11b1d3cf583093cdc826983ec13833b26833
|
[
"MIT"
] | 230
|
2019-09-01T21:59:47.000Z
|
2022-03-30T02:07:16.000Z
|
auto_tag/exception.py
|
mateimicu/auto-tag
|
541e11b1d3cf583093cdc826983ec13833b26833
|
[
"MIT"
] | 1
|
2019-09-16T11:32:09.000Z
|
2019-09-16T11:32:09.000Z
|
#!/usr/bin/env python3
"""
Exception used in the AutoTag project
"""
class BaseAutoTagException(Exception):
"""Base exception for the AutoTag project."""
class DetectorValidationException(BaseAutoTagException):
"""Validation failed on a detector"""
class DetectorNotFound(BaseAutoTagException):
"""Validation failed on a detector"""
class ConfigurationError(BaseAutoTagException):
"""Validation failed on a detector"""
class CantFindBranch(BaseAutoTagException):
"""Can't find a specific branch"""
class UnknowkSearchStrategy(BaseAutoTagException):
"""Invalid search strategy."""
| 21.241379
| 56
| 0.748377
| 58
| 616
| 7.948276
| 0.551724
| 0.195228
| 0.234273
| 0.247289
| 0.338395
| 0.338395
| 0.338395
| 0
| 0
| 0
| 0
| 0.00189
| 0.141234
| 616
| 28
| 57
| 22
| 0.869565
| 0.404221
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
0cd1eca777efb1b70621def99741636554017bae
| 96,520
|
py
|
Python
|
pyglm/inference/gibbs.py
|
slinderman/theano_pyglm
|
c7fc11069dfe91b0eb7c96b8464fcd0f57442952
|
[
"MIT"
] | 37
|
2015-04-08T15:11:51.000Z
|
2022-01-05T19:32:58.000Z
|
pyglm/inference/gibbs.py
|
slinderman/theano_pyglm
|
c7fc11069dfe91b0eb7c96b8464fcd0f57442952
|
[
"MIT"
] | 2
|
2016-03-08T02:39:58.000Z
|
2017-03-29T08:51:08.000Z
|
pyglm/inference/gibbs.py
|
slinderman/theano_pyglm
|
c7fc11069dfe91b0eb7c96b8464fcd0f57442952
|
[
"MIT"
] | 10
|
2015-02-25T21:47:50.000Z
|
2020-03-06T00:21:48.000Z
|
""" Fit a Network GLM with MAP estimation. For some models, the log posterior
is concave and has a unique maximum.
"""
import copy
from scipy.misc import logsumexp
from scipy.integrate import cumtrapz
from pyglm.utils.theano_func_wrapper import seval, _flatten
from pyglm.utils.packvec import *
from pyglm.utils.grads import *
from hips.inference.ars import adaptive_rejection_sample
from hips.inference.hmc import hmc
from pyglm.inference.log_sum_exp import log_sum_exp_sample
from pyglm.inference.coord_descent import coord_descent
class MetropolisHastingsUpdate(object):
"""
Base class for MH updates. Each update targets a specific model component
and requires certain configuration. For example, an update for the standard GLM
might require differentiable parameters. Typical updates include:
- Gibbs updates (sample from conditional distribution)
- Hamiltonian Monte Carlo (uses gradient info to sample unconstrained cont. vars)
- Slice sampling (good for correlaed multivariate Gaussians)
"""
def __init__(self):
self._target_components = []
@property
def target_components(self):
# Return a list of components that this update applies to
return self._target_components
@property
def target_variables(self):
# Return a list of variables that this update applies to
return []
def preprocess(self, population):
""" Do any req'd preprocessing
"""
pass
def update(self, x_curr):
""" Take a MH step
"""
return x_curr
class ParallelMetropolisHastingsUpdate(MetropolisHastingsUpdate):
""" Extending this class indicates that the updates can be
performed in parallel over n, the index of the neuron.
"""
def update(self, x_curr, n):
""" Take a MH step for the n-th neuron. This can be performed in parallel
over other n' \in [N]
"""
pass
# class HmcGlmUpdate(ParallelMetropolisHastingsUpdate):
# """
# Update the continuous and unconstrained GLM parameters using Hamiltonian
# Monte Carlo. Stochastically follow the gradient of the parameters using
# Hamiltonian dynamics.
# """
# def __init__(self):
# super(HmcGlmUpdate, self).__init__()
#
# self.avg_accept_rate = 0.9
# self.step_sz = 0.05
#
# def preprocess(self, population):
# """ Initialize functions that compute the gradient and Hessian of
# the log probability with respect to the differentiable GLM
# parameters, e.g. the weight matrix if it exists.
# """
# self.population = population
# self.glm = population.glm
# self.syms = population.get_variables()
# self.glm_syms = differentiable(self.syms['glm'])
#
# # Compute gradients of the log prob wrt the GLM parameters
# self.glm_logp = self.glm.log_p
# self.g_glm_logp_wrt_glm, _ = grad_wrt_list(self.glm_logp,
# _flatten(self.glm_syms))
#
# # Get the shape of the parameters from a sample of variables
# self.glm_shapes = get_shapes(self.population.extract_vars(self.population.sample(),0)['glm'],
# self.glm_syms)
#
# def _glm_logp(self, x_vec, x_all):
# """
# Compute the log probability (or gradients and Hessians thereof)
# of the given GLM variables. We also need the rest of the population variables,
# i.e. those that are not being sampled currently, in order to evaluate the log
# probability.
# """
# # Extract the glm parameters
# x_glm = unpackdict(x_vec, self.glm_shapes)
# set_vars(self.glm_syms, x_all['glm'], x_glm)
# lp = seval(self.glm_logp,
# self.syms,
# x_all)
# return lp
#
# def _grad_glm_logp(self, x_vec, x_all):
# """
# Compute the negative log probability (or gradients and Hessians thereof)
# of the given GLM variables. We also need the rest of the population variables,
# i.e. those that are not being sampled currently, in order to evaluate the log
# probability.
# """
# # Extract the glm parameters
# x_glm = unpackdict(x_vec, self.glm_shapes)
# set_vars(self.glm_syms, x_all['glm'], x_glm)
# glp = seval(self.g_glm_logp_wrt_glm,
# self.syms,
# x_all)
# return glp
#
# def update(self, x, n):
# """ Gibbs sample the GLM parameters. These are mostly differentiable
# so we use HMC wherever possible.
# """
#
# xn = self.population.extract_vars(x, n)
#
# # Get the differentiable variables suitable for HMC
# dxn = get_vars(self.glm_syms, xn['glm'])
# x_glm_0, shapes = packdict(dxn)
#
# # Create lambda functions to compute the nll and its gradient
# nll = lambda x_glm_vec: -1.0*self._glm_logp(x_glm_vec, xn)
# grad_nll = lambda x_glm_vec: -1.0*self._grad_glm_logp(x_glm_vec, xn)
#
# # HMC with automatic parameter tuning
# n_steps = 2
# x_glm, new_step_sz, new_accept_rate = hmc(nll,
# grad_nll,
# self.step_sz,
# n_steps,
# x_glm_0,
# adaptive_step_sz=True,
# avg_accept_rate=self.avg_accept_rate)
#
# # Update step size and accept rate
# self.step_sz = new_step_sz
# self.avg_accept_rate = new_accept_rate
# # print "GLM step sz: %.3f\tGLM_accept rate: %.3f" % (new_step_sz, new_accept_rate)
#
#
# # Unpack the optimized parameters back into the state dict
# x_glm_n = unpackdict(x_glm, shapes)
# set_vars(self.glm_syms, xn['glm'], x_glm_n)
#
#
# x['glms'][n] = xn['glm']
# return x
class HmcBiasUpdate(ParallelMetropolisHastingsUpdate):
"""
Update the continuous and unconstrained bias parameters using Hamiltonian
Monte Carlo. Stochastically follow the gradient of the parameters using
Hamiltonian dynamics.
"""
def __init__(self):
super(HmcBiasUpdate, self).__init__()
self.n_steps = 10
self.avg_accept_rate = 0.9
self.step_sz = 0.1
def preprocess(self, population):
""" Initialize functions that compute the gradient and Hessian of
the log probability with respect to the differentiable GLM
parameters, e.g. the weight matrix if it exists.
"""
self.population = population
self.glm = population.glm
self.bias_model = self.glm.bias_model
self.syms = population.get_variables()
self.bias_syms = differentiable(self.syms['glm']['bias'])
# Compute gradients of the log prob wrt the GLM parameters
self.glm_logp = self.glm.log_p
# self.g_glm_logp_wrt_bias, _ = grad_wrt_list(self.glm_logp,
# _flatten(self.bias_syms))
self.g_glm_ll_wrt_bias, _ = grad_wrt_list(self.glm.ll,
_flatten(self.bias_syms))
self.g_bias_logp_wrt_bias, _ = grad_wrt_list(self.bias_model.log_p,
_flatten(self.bias_syms))
# Get the shape of the parameters from a sample of variables
self.glm_shapes = get_shapes(self.population.extract_vars(self.population.sample(),0)['glm']['bias'],
self.bias_syms)
def _precompute_vars(self, x, n):
""" Precompute currents for sampling A and W
"""
nvars = self.population.extract_vars(x, n)
I_stim = seval(self.glm.bkgd_model.I_stim,
self.syms,
nvars)
I_net = seval(self.glm.I_net,
self.syms,
nvars)
return I_stim, I_net
def _glm_logp(self, x_vec, x_all, I_stim, I_net):
"""
Compute the log probability (or gradients and Hessians thereof)
of the given GLM variables. We also need the rest of the population variables,
i.e. those that are not being sampled currently, in order to evaluate the log
probability.
"""
# Extract the glm parameters
x_bias = unpackdict(x_vec, self.glm_shapes)
set_vars(self.bias_syms, x_all['glm']['bias'], x_bias)
lp = seval(self.bias_model.log_p,
self.syms,
x_all)
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
lp += seval(self.glm.ll,
{'I_stim' : self.glm.bkgd_model.I_stim,
'I_net' : self.glm.I_net,
'bias' : self.bias_model.bias,
'n' : self.glm.n
},
{'I_stim' : I_stim,
'I_net' : I_net,
'bias' : x_vec,
'n' : x_all['glm']['n']
}
)
return lp
def _grad_glm_logp(self, x_vec, x_all, I_stim, I_net):
"""
Compute the negative log probability (or gradients and Hessians thereof)
of the given GLM variables. We also need the rest of the population variables,
i.e. those that are not being sampled currently, in order to evaluate the log
probability.
"""
# Extract the glm parameters
x_bias = unpackdict(x_vec, self.glm_shapes)
set_vars(self.bias_syms, x_all['glm']['bias'], x_bias)
# glp = seval(self.g_glm_logp_wrt_bias,
# self.syms,
# x_all)
#
glp = seval(self.g_bias_logp_wrt_bias,
self.syms,
x_all)
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
glp += seval(self.g_glm_ll_wrt_bias,
{'I_stim' : self.glm.bkgd_model.I_stim,
'I_net' : self.glm.I_net,
'bias' : self.bias_model.bias,
'n' : self.glm.n
},
{'I_stim' : I_stim,
'I_net' : I_net,
'bias' : x_vec,
'n' : x_all['glm']['n']
}
)
return glp
def update(self, x, n):
""" Gibbs sample the GLM parameters. These are mostly differentiable
so we use HMC wherever possible.
"""
xn = self.population.extract_vars(x, n)
# # Get the differentiable variables suitable for HMC
# dxn = get_vars(self.bias_syms, xn['glm']['bias'])
# x_glm_0, shapes = packdict(dxn)
I_stim, I_net = self._precompute_vars(x, n)
x_bias_0 = xn['glm']['bias']['bias']
# Create lambda functions to compute the nll and its gradient
nll = lambda x_glm_vec: -1.0 * self._glm_logp(x_glm_vec, xn, I_stim, I_net)
grad_nll = lambda x_glm_vec: -1.0 * self._grad_glm_logp(x_glm_vec, xn, I_stim, I_net)
# HMC with automatic parameter tuning
x_bias, new_step_sz, new_accept_rate = hmc(nll,
grad_nll,
self.step_sz,
self.n_steps,
x_bias_0,
adaptive_step_sz=True,
avg_accept_rate=self.avg_accept_rate)
# Update step size and accept rate
self.step_sz = new_step_sz
self.avg_accept_rate = new_accept_rate
# print "GLM step sz: %.3f\tGLM_accept rate: %.3f" % (new_step_sz, new_accept_rate)
xn['glm']['bias']['bias'] = x_bias
x['glms'][n] = xn['glm']
return x
class HmcBkgdUpdate(ParallelMetropolisHastingsUpdate):
"""
Update the continuous and unconstrained bkgd parameters using Hamiltonian
Monte Carlo. Stochastically follow the gradient of the parameters using
Hamiltonian dynamics.
"""
def __init__(self):
super(HmcBkgdUpdate, self).__init__()
self.n_steps = 2
self.avg_accept_rate = 0.9
self.step_sz = 0.1
def preprocess(self, population):
""" Initialize functions that compute the gradient and Hessian of
the log probability with respect to the differentiable GLM
parameters, e.g. the weight matrix if it exists.
"""
self.population = population
self.glm = population.glm
self.syms = population.get_variables()
self.bkgd_syms = differentiable(self.syms['glm']['bkgd'])
# Compute gradients of the log prob wrt the GLM parameters
self.glm_logprior = self.glm.log_prior
self.g_glm_logprior_wrt_bkgd, _ = grad_wrt_list(self.glm_logprior,
_flatten(self.bkgd_syms))
self.glm_ll = self.glm.ll
self.g_glm_ll_wrt_bkgd, _ = grad_wrt_list(self.glm_ll,
_flatten(self.bkgd_syms))
# Get the shape of the parameters from a sample of variables
self.glm_shapes = get_shapes(self.population.extract_vars(self.population.sample(),0)['glm']['bkgd'],
self.bkgd_syms)
def _glm_logp(self, x_vec, x_all):
"""
Compute the log probability (or gradients and Hessians thereof)
of the given GLM variables. We also need the rest of the population variables,
i.e. those that are not being sampled currently, in order to evaluate the log
probability.
"""
# Extract the glm parameters
x_imp = unpackdict(x_vec, self.glm_shapes)
set_vars(self.bkgd_syms, x_all['glm']['bkgd'], x_imp)
lp = seval(self.glm_logprior,
self.syms,
x_all)
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
lp += seval(self.glm_ll,
self.syms,
x_all)
return lp
def _grad_glm_logp(self, x_vec, x_all):
"""
Compute the negative log probability (or gradients and Hessians thereof)
of the given GLM variables. We also need the rest of the population variables,
i.e. those that are not being sampled currently, in order to evaluate the log
probability.
"""
# Extract the glm parameters
x_imp = unpackdict(x_vec, self.glm_shapes)
set_vars(self.bkgd_syms, x_all['glm']['bkgd'], x_imp)
glp = seval(self.g_glm_logprior_wrt_bkgd,
self.syms,
x_all)
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
glp = seval(self.g_glm_ll_wrt_bkgd,
self.syms,
x_all)
return glp
def update(self, x, n):
""" Gibbs sample the GLM parameters. These are mostly differentiable
so we use HMC wherever possible.
"""
xn = self.population.extract_vars(x, n)
# Get the differentiable variables suitable for HMC
dxn = get_vars(self.bkgd_syms, xn['glm']['bkgd'])
x_glm_0, shapes = packdict(dxn)
# Return if nothing to do
if len(dxn) == 0:
return x
# Create lambda functions to compute the nll and its gradient
nll = lambda x_glm_vec: -1.0*self._glm_logp(x_glm_vec, xn)
grad_nll = lambda x_glm_vec: -1.0*self._grad_glm_logp(x_glm_vec, xn)
# HMC with automatic parameter tuning
x_bkgd, new_step_sz, new_accept_rate = hmc(nll,
grad_nll,
self.step_sz,
self.n_steps,
x_glm_0,
adaptive_step_sz=True,
avg_accept_rate=self.avg_accept_rate)
# Update step size and accept rate
self.step_sz = new_step_sz
self.avg_accept_rate = new_accept_rate
# print "GLM step sz: %.3f\tGLM_accept rate: %.3f" % (new_step_sz, new_accept_rate)
# Unpack the optimized parameters back into the state dict
x_bkgd_n = unpackdict(x_bkgd, shapes)
set_vars(self.bkgd_syms, xn['glm']['bkgd'], x_bkgd_n)
x['glms'][n] = xn['glm']
return x
class HmcImpulseUpdate(ParallelMetropolisHastingsUpdate):
"""
Update the continuous and unconstrained bias parameters using Hamiltonian
Monte Carlo. Stochastically follow the gradient of the parameters using
Hamiltonian dynamics.
"""
def __init__(self):
super(HmcImpulseUpdate, self).__init__()
self.avg_accept_rate = 0.9
self.step_sz = 0.1
def preprocess(self, population):
""" Initialize functions that compute the gradient and Hessian of
the log probability with respect to the differentiable GLM
parameters, e.g. the weight matrix if it exists.
"""
self.population = population
self.glm = population.glm
self.syms = population.get_variables()
self.impulse_syms = differentiable(self.syms['glm']['imp'])
# Compute gradients of the log prob wrt the GLM parameters
self.glm_logprior = self.glm.log_prior
self.g_glm_logprior_wrt_imp, _ = grad_wrt_list(self.glm_logprior,
_flatten(self.impulse_syms))
self.glm_ll = self.glm.ll
self.g_glm_ll_wrt_imp, _ = grad_wrt_list(self.glm_ll,
_flatten(self.impulse_syms))
# Get the shape of the parameters from a sample of variables
self.glm_shapes = get_shapes(self.population.extract_vars(self.population.sample(),0)['glm']['imp'],
self.impulse_syms)
def _glm_logp(self, x_vec, x_all):
"""
Compute the log probability (or gradients and Hessians thereof)
of the given GLM variables. We also need the rest of the population variables,
i.e. those that are not being sampled currently, in order to evaluate the log
probability.
"""
# Extract the glm parameters
x_imp = unpackdict(x_vec, self.glm_shapes)
set_vars(self.impulse_syms, x_all['glm']['imp'], x_imp)
lp = seval(self.glm_logprior,
self.syms,
x_all)
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
lp += seval(self.glm_ll,
self.syms,
x_all)
return lp
def _grad_glm_logp(self, x_vec, x_all):
"""
Compute the negative log probability (or gradients and Hessians thereof)
of the given GLM variables. We also need the rest of the population variables,
i.e. those that are not being sampled currently, in order to evaluate the log
probability.
"""
# Extract the glm parameters
x_imp = unpackdict(x_vec, self.glm_shapes)
set_vars(self.impulse_syms, x_all['glm']['imp'], x_imp)
glp = seval(self.g_glm_logprior_wrt_imp,
self.syms,
x_all)
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
glp = seval(self.g_glm_ll_wrt_imp,
self.syms,
x_all)
return glp
def update(self, x, n):
""" Gibbs sample the GLM parameters. These are mostly differentiable
so we use HMC wherever possible.
"""
xn = self.population.extract_vars(x, n)
# Get the differentiable variables suitable for HMC
dxn = get_vars(self.impulse_syms, xn['glm']['imp'])
x_glm_0, shapes = packdict(dxn)
# Create lambda functions to compute the nll and its gradient
nll = lambda x_glm_vec: -1.0*self._glm_logp(x_glm_vec, xn)
grad_nll = lambda x_glm_vec: -1.0*self._grad_glm_logp(x_glm_vec, xn)
# HMC with automatic parameter tuning
n_steps = 2
x_imp, new_step_sz, new_accept_rate = hmc(nll,
grad_nll,
self.step_sz,
n_steps,
x_glm_0,
adaptive_step_sz=True,
avg_accept_rate=self.avg_accept_rate)
# Update step size and accept rate
self.step_sz = new_step_sz
self.avg_accept_rate = new_accept_rate
# print "GLM step sz: %.3f\tGLM_accept rate: %.3f" % (new_step_sz, new_accept_rate)
# Unpack the optimized parameters back into the state dict
x_imp_n = unpackdict(x_imp, shapes)
set_vars(self.impulse_syms, xn['glm']['imp'], x_imp_n)
x['glms'][n] = xn['glm']
return x
class HmcDirichletImpulseUpdate(ParallelMetropolisHastingsUpdate):
"""
Update the Dirichlet impulse response parameters using Hamiltonian
Monte Carlo. Stochastically follow the gradient of the parameters using
Hamiltonian dynamics.
"""
def __init__(self):
super(HmcDirichletImpulseUpdate, self).__init__()
self.avg_accept_rate = 0.9
self.step_sz = 0.1
def preprocess(self, population):
""" Initialize functions that compute the gradient and Hessian of
the log probability with respect to the differentiable GLM
parameters, e.g. the weight matrix if it exists.
"""
self.population = population
self.glm = population.glm
self.network = self.population.network
self.syms = population.get_variables()
# Compute gradients of the log prob wrt the GLM parameters
self.glm_logp = self.glm.log_p
self.grads_wrt_imp = []
self.grad_lls_wrt_imp = []
self.grad_priors_wrt_imp = []
for g in self.glm.imp_model.gs:
grad,_ = grad_wrt_list(self.glm_logp, [g])
self.grads_wrt_imp.append(grad)
grad,_ = grad_wrt_list(self.glm.ll, [g])
self.grad_lls_wrt_imp.append(grad)
grad,_ = grad_wrt_list(self.glm.imp_model.log_p, [g])
self.grad_priors_wrt_imp.append(grad)
# Get the shape of the parameters from a sample of variables
# self.glm_shapes = get_shapes(self.population.extract_vars(self.population.sample(),0)['glm']['imp'],
# self.impulse_syms)
def _precompute_vars(self, x, n):
""" Precompute currents for sampling A and W
"""
nvars = self.population.extract_vars(x, n)
I_bias = seval(self.glm.bias_model.I_bias,
self.syms,
nvars)
I_stim = seval(self.glm.bkgd_model.I_stim,
self.syms,
nvars)
return I_bias, I_stim
def _glm_logp(self, n, g, x_all, I_bias, I_stim):
"""
Compute the log probability (or gradients and Hessians thereof)
of the given GLM variables. We also need the rest of the population variables,
i.e. those that are not being sampled currently, in order to evaluate the log
probability.
"""
# Extract the glm parameters
s = \
{
'I_stim' : self.glm.bkgd_model.I_stim,
'I_bias' : self.glm.bias_model.I_bias,
'n' : self.glm.n,
'W' : self.network.weights.W_flat,
'A' : self.network.graph.A
}
xv = \
{
'I_stim' : I_stim,
'I_bias' : I_bias,
'n' : x_all['glm']['n'],
'W' : x_all['net']['weights']['W'],
'A' : x_all['net']['graph']['A']
}
# Add the Dirichlet impulse response parameters
for n_pre, g_sym in enumerate(self.glm.imp_model.gs):
s[g_sym.name] = g_sym
if n_pre == n:
xv[g_sym.name] = g
else:
xv[g_sym.name] = x_all['glm']['imp'][g_sym.name]
lp = seval(self.glm.imp_model.log_p, s, xv)
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
lp += seval(self.glm.ll, s, xv)
# set_vars(self.impulse_syms, x_all['glm']['imp'], x_imp)
# x_all['glm']['imp']['g_%d' % n] = g
# lp = seval(self.glm_logp,
# self.syms,
# x_all)
return lp
def _grad_glm_logp(self, n, g, x_all, I_bias, I_stim):
"""
Compute the negative log probability (or gradients and Hessians thereof)
of the given GLM variables. We also need the rest of the population variables,
i.e. those that are not being sampled currently, in order to evaluate the log
probability.
"""
# Extract the glm parameters
# x_all['glm']['imp']['g_%d' % n] = g
# Extract the glm parameters
s = \
{
'I_stim' : self.glm.bkgd_model.I_stim,
'I_bias' : self.glm.bias_model.I_bias,
'n' : self.glm.n,
'W' : self.network.weights.W_flat,
'A' : self.network.graph.A
}
xv = \
{
'I_stim' : I_stim,
'I_bias' : I_bias,
'n' : x_all['glm']['n'],
'W' : x_all['net']['weights']['W'],
'A' : x_all['net']['graph']['A']
}
# Add the Dirichlet impulse response parameters
for n_pre, g_sym in enumerate(self.glm.imp_model.gs):
s[g_sym.name] = g_sym
if n_pre == n:
xv[g_sym.name] = g
else:
xv[g_sym.name] = x_all['glm']['imp'][g_sym.name]
glp = seval(self.grad_priors_wrt_imp[n], s, xv)
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
glp += seval(self.grad_lls_wrt_imp[n], s, xv)
# glp = seval(self.grads_wrt_imp[n],
# self.syms,
# x_all)
return glp
def update(self, x, n_post):
""" Gibbs sample the GLM parameters. These are mostly differentiable
so we use HMC wherever possible.
"""
xn = self.population.extract_vars(x, n_post)
I_bias, I_stim = self._precompute_vars(x, n_post)
A = x['net']['graph']['A']
for n_pre in range(self.population.N):
# Only sample if there is a connection from n_pre to n_post
if A[n_pre, n_post]:
# Get current g
g_0 = xn['glm']['imp']['g_%d' % n_pre]
# Create lambda functions to compute the nll and its gradient
nll = lambda g: -1.0*self._glm_logp(n_pre, g, xn, I_bias, I_stim)
grad_nll = lambda g: -1.0*self._grad_glm_logp(n_pre, g, xn, I_bias, I_stim)
# HMC with automatic parameter tuning
n_steps = 2
g_f, new_step_sz, new_accept_rate = hmc(nll,
grad_nll,
self.step_sz,
n_steps,
g_0,
adaptive_step_sz=True,
avg_accept_rate=self.avg_accept_rate)
# Update step size and accept rate
self.step_sz = new_step_sz
self.avg_accept_rate = new_accept_rate
# print "GLM step sz: %.3f\tGLM_accept rate: %.3f" % (new_step_sz, new_accept_rate)
# Unpack the optimized parameters back into the state dict
xn['glm']['imp']['g_%d' % n_pre] = g_f
else:
# No edge: Sample g from the prior
g_f = np.random.gamma(self.glm.imp_model.alpha,
np.ones(self.glm.imp_model.B))
xn['glm']['imp']['g_%d' % n_pre] = g_f
x['glms'][n_post] = xn['glm']
return x
class CollapsedGibbsNetworkColumnUpdate(ParallelMetropolisHastingsUpdate):
def __init__(self):
super(CollapsedGibbsNetworkColumnUpdate, self).__init__()
# TODO: Only use an MH proposal from the prior if you are certain
# that the prior puts mass on likely edges. Otherwise you will never
# propose to transition from no-edge to edge and mixing will be very,
# very slow.
self.propose_from_prior = False
# Define constants for Sampling
self.DEG_GAUSS_HERMITE = 10
self.GAUSS_HERMITE_ABSCISSAE, self.GAUSS_HERMITE_WEIGHTS = \
np.polynomial.hermite.hermgauss(self.DEG_GAUSS_HERMITE)
def preprocess(self, population):
""" Initialize functions that compute the gradient and Hessian of
the log probability with respect to the differentiable network
parameters, e.g. the weight matrix if it exists.
"""
self.population = population
self.network = population.network
self.glm = population.glm
self.syms = population.get_variables()
# Get the weight model
self.mu_w = self.network.weights.prior.mu.get_value()
self.sigma_w = self.network.weights.prior.sigma.get_value()
if hasattr(self.network.weights, 'refractory_prior'):
self.mu_w_ref = self.network.weights.refractory_prior.mu.get_value()
self.sigma_w_ref = self.network.weights.refractory_prior.sigma.get_value()
else:
self.mu_w_ref = self.mu_w
self.sigma_w_ref = self.sigma_w
def _precompute_vars(self, x, n_post):
""" Precompute currents for sampling A and W
"""
nvars = self.population.extract_vars(x, n_post)
I_bias = seval(self.glm.bias_model.I_bias,
self.syms,
nvars)
I_stim = seval(self.glm.bkgd_model.I_stim,
self.syms,
nvars)
I_imp = seval(self.glm.imp_model.I_imp,
self.syms,
nvars)
p_A = seval(self.network.graph.pA,
self.syms['net'],
x['net'])
return I_bias, I_stim, I_imp, p_A
def _precompute_other_current(self, x, I_imp, n_pre, n_post):
"""
Precompute the weighted currents from neurons other than n_pre
"""
# Set A[n_pre,n_post]=0 to omit this current
A = x['net']['graph']['A']
W = x['net']['weights']['W']
A_init = A[n_pre, n_post]
A[n_pre, n_post] = 0
# Get the likelihood of the GLM under A and W
s = {'A' : self.network.graph.A,
'W' : self.syms['net']['weights']['W'],
'n' :self.glm.n,
'I_imp' : self.glm.imp_model.I_imp,
'nlin' : self.syms['glm']['nlin']
}
xv = {'A' : A,
'W' : W,
'n' : n_post,
'I_imp' : I_imp,
'nlin' : x['glms'][n_post]['nlin']
}
I_net_other = seval(self.glm.I_net, s, xv)
# Reset A
A[n_pre, n_post] = A_init
return I_net_other
def _glm_ll_A_old(self, n_pre, n_post, w, x, I_bias, I_stim, I_imp):
""" Compute the log likelihood of the GLM with A=True and given W
"""
# Set A in state dict x
A = x['net']['graph']['A']
A_init = A[n_pre, n_post]
A[n_pre, n_post] = 1
# Set W in state dict x
W = x['net']['weights']['W'].reshape(A.shape)
W_init = W[n_pre, n_post]
W[n_pre, n_post] = w
# Get the likelihood of the GLM under A and W
s = {'A' : self.network.graph.A,
'W' : self.syms['net']['weights']['W'],
'n' :self.glm.n,
'I_bias' : self.glm.bias_model.I_bias,
'I_stim' : self.glm.bkgd_model.I_stim,
'I_imp' : self.glm.imp_model.I_imp,
'nlin' : self.syms['glm']['nlin']
}
xv = {'A' : A,
'W' : W.ravel(),
'n' : n_post,
'I_bias' : I_bias,
'I_stim' : I_stim,
'I_imp' : I_imp,
'nlin' : x['glms'][n_post]['nlin']
}
# Compute the log likelihood for each data sequence
ll = 0
for data in self.population.data_sequences:
self.population.set_data(data)
ll += seval(self.glm.ll, s, xv)
# Reset A and W
A[n_pre, n_post] = A_init
W[n_pre, n_post] = W_init
return ll
def _glm_ll(self, n_pre, n_post, w, x, I_bias, I_stim, I_imp, I_net_other):
""" Compute the log likelihood of the GLM with A=True and given W
"""
# Compute the weighted network current
I_net = I_net_other + w*I_imp[:,n_pre]
# Get the likelihood of the GLM under A and W
s = {'n' :self.glm.n,
'I_bias' : self.glm.bias_model.I_bias,
'I_stim' : self.glm.bkgd_model.I_stim,
'I_net' : self.glm.I_net,
'nlin' : self.syms['glm']['nlin']
}
xv = {'n' : n_post,
'I_bias' : I_bias,
'I_stim' : I_stim,
'I_net' : I_net,
'nlin' : x['glms'][n_post]['nlin']
}
# Compute the log likelihood for each data sequence
ll = 0
for data in self.population.data_sequences:
self.population.set_data(data)
ll += seval(self.glm.ll, s, xv)
return ll
def _glm_ll_noA(self, n_pre, n_post, x, I_bias, I_stim, I_imp):
""" Compute the log likelihood of the GLM with A=True and given W
"""
# Set A in state dict x
A = x['net']['graph']['A']
A_init = A[n_pre, n_post]
A[n_pre, n_post] = 0
W = x['net']['weights']['W']
# Get the likelihood of the GLM under A and W
s = {'A' : self.network.graph.A,
'W' : self.syms['net']['weights']['W'],
'n' :self.glm.n,
'I_bias' : self.glm.bias_model.I_bias,
'I_stim' : self.glm.bkgd_model.I_stim,
'I_imp' : self.glm.imp_model.I_imp,
'nlin' : self.syms['glm']['nlin']
}
xv = {'A' : A,
'W' : W.ravel(),
'n' : n_post,
'I_bias' : I_bias,
'I_stim' : I_stim,
'I_imp' : I_imp,
'nlin' : x['glms'][n_post]['nlin']
}
# Compute the log likelihood for each data sequence
ll = 0
for data in self.population.data_sequences:
self.population.set_data(data)
ll += seval(self.glm.ll, s, xv)
A[n_pre, n_post] = A_init
return ll
def _collapsed_sample_AW(self, n_pre, n_post, x,
I_bias, I_stim, I_imp, I_other, p_A):
"""
Do collapsed Gibbs sampling for an entry A_{n,n'} and W_{n,n'} where
n = n_pre and n' = n_post.
"""
# Set sigma_w and mu_w
if n_pre == n_post:
mu_w = self.mu_w_ref
sigma_w = self.sigma_w_ref
else:
mu_w = self.mu_w
sigma_w = self.sigma_w
A = x['net']['graph']['A']
W = x['net']['weights']['W'].reshape(A.shape)
# Propose from the prior and see if A would change.
prior_lp_A = np.log(p_A[n_pre, n_post])
prior_lp_noA = np.log(1.0-p_A[n_pre, n_post])
# TODO: We could make this faster by precomputing the other currents
# going into neuron n'.
# Approximate G = \int_0^\infty p({s,c} | A, W) p(W_{n,n'}) dW_{n,n'}
log_L = np.zeros(self.DEG_GAUSS_HERMITE)
weighted_log_L = np.zeros(self.DEG_GAUSS_HERMITE)
W_nns = np.sqrt(2) * sigma_w * self.GAUSS_HERMITE_ABSCISSAE + mu_w
for i in np.arange(self.DEG_GAUSS_HERMITE):
w = self.GAUSS_HERMITE_WEIGHTS[i]
W_nn = W_nns[i]
log_L[i] = self._glm_ll(n_pre, n_post, W_nn,
x, I_bias, I_stim, I_imp, I_other)
# Handle NaNs in the GLM log likelihood
if np.isnan(log_L[i]):
log_L[i] = -np.Inf
weighted_log_L[i] = log_L[i] + np.log(w/np.sqrt(np.pi))
# Handle NaNs in the GLM log likelihood
if np.isnan(weighted_log_L[i]):
weighted_log_L[i] = -np.Inf
# compute log pr(A_nn) and log pr(\neg A_nn) via log G
log_G = logsumexp(weighted_log_L)
if not np.isfinite(log_G):
print weighted_log_L
raise Exception("log_G not finie")
# Compute log Pr(A_nn=1) given prior and estimate of log lkhd after integrating out W
log_pr_A = prior_lp_A + log_G
# Compute log Pr(A_nn = 0 | {s,c}) = log Pr({s,c} | A_nn = 0) + log Pr(A_nn = 0)
log_pr_noA = prior_lp_noA + \
self._glm_ll(n_pre, n_post, 0.0, x,
I_bias, I_stim, I_imp, I_other)
if np.isnan(log_pr_noA):
log_pr_noA = -np.Inf
# Sample A
try:
A[n_pre, n_post] = log_sum_exp_sample([log_pr_noA, log_pr_A])
if np.allclose(p_A[n_pre, n_post], 1.0) and not A[n_pre, n_post]:
print log_pr_noA
print log_pr_A
raise Exception("Sampled no self edge")
except Exception as e:
raise e
# import pdb; pdb.set_trace()
set_vars('A', x['net']['graph'], A)
# Sample W from its posterior, i.e. log_L with denominator log_G
# If A_nn = 0, we don't actually need to resample W since it has no effect
if A[n_pre,n_post] == 1:
# W[n_pre, n_post] = self._inverse_cdf_sample_w(mu_w, sigma_w, W_nns, log_L)
W[n_pre, n_post] = self._adaptive_rejection_sample_w(n_pre, n_post, x, mu_w, sigma_w,
W_nns, log_L, I_bias, I_stim, I_imp, I_other)
# if not np.isfinite(self._glm_ll(n_pre, n_post, W[n_pre, n_post], x, I_bias, I_stim, I_imp)):
# raise Exception("Invalid weight sample")
# print "p_W: %.3f (v=%.3f)" % (np.interp(W[n_pre, n_post], ws, p_W) ,v)
else:
# Sample W from the prior
W[n_pre, n_post] = mu_w + sigma_w * np.random.randn()
# Set W in state dict x
x['net']['weights']['W'] = W.ravel()
def _inverse_cdf_sample_w(self, mu_w, sigma_w, W_nns, log_L):
"""
Sample weight w using inverse CDF method. We have already evaluated the
log likelihood log_L at a set of points W_nns. Use these to approximate
the probability density.
"""
log_prior_W = -0.5/sigma_w**2 * (W_nns-mu_w)**2
log_posterior_W = log_prior_W + log_L
log_p_W = log_posterior_W - logsumexp(log_posterior_W)
p_W = np.exp(log_p_W)
F_W = cumtrapz(p_W, W_nns, initial=0.0)
F_W = F_W / F_W[-1]
# Sample W_rv
v = np.random.rand()
w = np.interp(v, F_W, W_nns)
return w
def _adaptive_rejection_sample_w(self, n_pre, n_post, x, mu_w, sigma_w, ws, log_L, I_bias, I_stim, I_imp, I_other):
"""
Sample weights using adaptive rejection sampling.
This only works for log-concave distributions, which will
be the case if the nonlinearity is convex and log concave, and
when the prior on w is log concave (as it is when w~Gaussian).
"""
# import pdb; pdb.set_trace()
log_prior_W = -0.5/sigma_w**2 * (ws-mu_w)**2
log_posterior_W = log_prior_W + log_L
# Define a function to evaluate the log posterior
# For numerical stability, try to normalize
Z = np.amax(log_posterior_W)
def _log_posterior(ws_in):
ws = np.asarray(ws_in)
shape = ws.shape
ws = np.atleast_1d(ws)
lp = np.zeros_like(ws)
for (i,w) in enumerate(ws):
lp[i] = -0.5/sigma_w**2 * (w-mu_w)**2 + \
self._glm_ll(n_pre, n_post, w, x, I_bias, I_stim, I_imp, I_other) \
- Z
if isinstance(ws_in, np.ndarray):
return lp.reshape(shape)
elif isinstance(ws_in, float) or isinstance(ws_in, np.float):
return np.float(lp)
# Only use the valid ws
# valid_ws = np.arange(len(ws))[np.isfinite(log_posterior_W)]
valid_ws = np.bitwise_and(np.isfinite(log_posterior_W),
log_posterior_W > -1e8,
log_posterior_W < 1e8)
return adaptive_rejection_sample(_log_posterior,
ws[valid_ws], log_posterior_W[valid_ws] - Z,
(-np.Inf, np.Inf),
stepsz=sigma_w/2.0,
debug=False)
def _collapsed_sample_AW_with_prior(self, n_pre, n_post, x,
I_bias, I_stim, I_imp, p_A):
"""
Do collapsed Gibbs sampling for an entry A_{n,n'} and W_{n,n'} where
n = n_pre and n' = n_post.
"""
# Set sigma_w and mu_w
if n_pre == n_post:
mu_w = self.mu_w_ref
sigma_w = self.sigma_w_ref
else:
mu_w = self.mu_w
sigma_w = self.sigma_w
A = x['net']['graph']['A']
W = x['net']['weights']['W'].reshape(A.shape)
# Propose from the prior and see if A would change.
prior_lp_A = np.log(p_A[n_pre, n_post])
prop_A = np.int8(np.log(np.random.rand()) < prior_lp_A)
# We only need to compute the acceptance probability if the proposal
# would change A
A_init = A[n_pre, n_post]
W_init = W[n_pre, n_post]
if A[n_pre, n_post] != prop_A:
# Approximate G = \int_0^\infty p({s,c} | A, W) p(W_{n,n'}) dW_{n,n'}
log_L = np.zeros(self.DEG_GAUSS_HERMITE)
W_nns = np.sqrt(2) * sigma_w * self.GAUSS_HERMITE_ABSCISSAE + mu_w
for i in np.arange(self.DEG_GAUSS_HERMITE):
w = self.GAUSS_HERMITE_WEIGHTS[i]
W_nn = W_nns[i]
log_L[i] = np.log(w/np.sqrt(np.pi)) + \
self._glm_ll_A(n_pre, n_post, W_nn,
x, I_bias, I_stim, I_imp)
# Handle NaNs in the GLM log likelihood
if np.isnan(log_L[i]):
log_L[i] = -np.Inf
# compute log pr(A_nn) and log pr(\neg A_nn) via log G
from scipy.misc import logsumexp
log_G = logsumexp(log_L)
# Compute log Pr(A_nn=1) given prior and estimate of log lkhd after integrating out W
log_lkhd_A = log_G
# Compute log Pr(A_nn = 0 | {s,c}) = log Pr({s,c} | A_nn = 0) + log Pr(A_nn = 0)
log_lkhd_noA = self._glm_ll_noA(n_pre, n_post, x, I_bias, I_stim, I_imp)
# Decide whether or not to accept
log_pr_accept = log_lkhd_A - log_lkhd_noA if prop_A else log_lkhd_noA - log_lkhd_A
if np.log(np.random.rand()) < log_pr_accept:
# Update A
A[n_pre, n_post] = prop_A
# Update W if there is an edge in A
if A[n_pre, n_post]:
# Update W if there is an edge
log_p_W = log_L - log_G
# Compute the log CDF
log_F_W = [logsumexp(log_p_W[:i]) for i in range(1,self.DEG_GAUSS_HERMITE)] + [0]
# Sample via inverse CDF
W[n_pre, n_post] = np.interp(np.log(np.random.rand()),
log_F_W,
W_nns)
elif A[n_pre, n_post]:
assert A[n_pre, n_post] == A_init
# If we propose not to change A then we accept with probability 1, but we
# still need to update W
# Approximate G = \int_0^\infty p({s,c} | A, W) p(W_{n,n'}) dW_{n,n'}
log_L = np.zeros(self.DEG_GAUSS_HERMITE)
W_nns = np.sqrt(2) * sigma_w * self.GAUSS_HERMITE_ABSCISSAE + mu_w
for i in np.arange(self.DEG_GAUSS_HERMITE):
w = self.GAUSS_HERMITE_WEIGHTS[i]
W_nn = W_nns[i]
log_L[i] = np.log(w/np.sqrt(np.pi)) + \
self._glm_ll_A(n_pre, n_post, W_nn,
x, I_bias, I_stim, I_imp)
# Handle NaNs in the GLM log likelihood
if np.isnan(log_L[i]):
log_L[i] = -np.Inf
# compute log pr(A_nn) and log pr(\neg A_nn) via log G
from scipy.misc import logsumexp
log_G = logsumexp(log_L)
# Update W if there is an edge
log_p_W = log_L - log_G
# Compute the log CDF
log_F_W = [logsumexp(log_p_W[:i]) for i in range(1,self.DEG_GAUSS_HERMITE)] + [0]
# Sample via inverse CDF
W[n_pre, n_post] = np.interp(np.log(np.random.rand()),
log_F_W,
W_nns)
# Set W in state dict x
x['net']['weights']['W'] = W.ravel()
def update(self, x, n):
""" Collapsed Gibbs sample a column of A and W
"""
A = x['net']['graph']['A']
N = A.shape[0]
I_bias, I_stim, I_imp, p_A = self._precompute_vars(x, n)
order = np.arange(N)
np.random.shuffle(order)
for n_pre in order:
# Precompute the other currents
I_other = self._precompute_other_current(x, I_imp, n_pre, n)
# print "Sampling %d->%d" % (n_pre, n)
if self.propose_from_prior:
self._collapsed_sample_AW_with_prior(n_pre, n, x,
I_bias, I_stim, I_imp, p_A)
else:
self._collapsed_sample_AW(n_pre, n, x,
I_bias, I_stim, I_imp, I_other, p_A)
return x
class GibbsNetworkColumnUpdate(ParallelMetropolisHastingsUpdate):
def __init__(self):
super(GibbsNetworkColumnUpdate, self).__init__()
self.avg_accept_rate = 0.9
self.step_sz = 0.05
def preprocess(self, population):
""" Initialize functions that compute the gradient and Hessian of
the log probability with respect to the differentiable network
parameters, e.g. the weight matrix if it exists.
"""
self.N = population.model['N']
self.population = population
self.network = population.network
self.glm = population.glm
self.syms = population.get_variables()
self.g_netlp_wrt_W = T.grad(self.network.log_p, self.syms['net']['weights']['W'])
self.g_glmll_wrt_W = T.grad(self.glm.ll, self.syms['net']['weights']['W'])
def _precompute_currents(self, x, n_post):
""" Precompute currents for sampling A and W
"""
nvars = self.population.extract_vars(x, n_post)
I_bias = seval(self.glm.bias_model.I_bias,
self.syms,
nvars)
I_stim = seval(self.glm.bkgd_model.I_stim,
self.syms,
nvars)
I_imp = seval(self.glm.imp_model.I_imp,
self.syms,
nvars)
return I_bias, I_stim, I_imp
def _lp_A(self, A, x, n_post, I_bias, I_stim, I_imp):
""" Compute the log probability for a given column A[:,n_post]
"""
# Set A in state dict x
set_vars('A', x['net']['graph'], A)
# Get the prior probability of A
lp = seval(self.network.log_p,
self.syms['net'],
x['net'])
# Get the likelihood of the GLM under A
s = [self.network.graph.A] + \
_flatten(self.syms['net']['weights']) + \
[self.glm.n,
self.glm.bias_model.I_bias,
self.glm.bkgd_model.I_stim,
self.glm.imp_model.I_imp] + \
_flatten(self.syms['glm']['nlin'])
xv = [A] + \
_flatten(x['net']['weights']) + \
[n_post,
I_bias,
I_stim,
I_imp] + \
_flatten(x['glms'][n_post]['nlin'])
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
lp += self.glm.ll.eval(dict(zip(s, xv)))
return lp
# Helper functions to sample W
def _lp_W(self, W, x, n_post, I_bias, I_stim, I_imp):
""" Compute the log probability for a given column W[:,n_post]
"""
# Set A in state dict x
set_vars('W', x['net']['weights'], W)
# Get the prior probability of A
lp = seval(self.network.log_p,
self.syms['net'],
x['net'])
# Get the likelihood of the GLM under W
s = _flatten(self.syms['net']['graph']) + \
[self.network.weights.W_flat,
self.glm.n,
self.glm.bias_model.I_bias,
self.glm.bkgd_model.I_stim,
self.glm.imp_model.I_imp] + \
_flatten(self.syms['glm']['nlin'])
xv = _flatten(x['net']['graph']) + \
[W,
n_post,
I_bias,
I_stim,
I_imp] + \
_flatten(x['glms'][n_post]['nlin'])
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
lp += self.glm.ll.eval(dict(zip(s, xv)))
return lp
def _grad_lp_W(self, W, x, n_post, I_bias, I_stim, I_imp):
""" Compute the log probability for a given column W[:,n_post]
"""
# Set A in state dict x
set_vars('W', x['net']['weights'], W)
# Get the prior probability of A
g_lp = seval(self.g_netlp_wrt_W,
self.syms['net'],
x['net'])
# Get the likelihood of the GLM under W
s = _flatten(self.syms['net']['graph']) + \
[self.network.weights.W_flat,
self.glm.n,
self.glm.bias_model.I_bias,
self.glm.bkgd_model.I_stim,
self.glm.imp_model.I_imp] + \
_flatten(self.syms['glm']['nlin'])
xv = _flatten(x['net']['graph']) + \
[W,
n_post,
I_bias,
I_stim,
I_imp] + \
_flatten(x['glms'][n_post]['nlin'])
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
g_lp += seval(self.g_glmll_wrt_W,
dict(zip(range(len(s)), s)),
dict(zip(range(len(xv)),xv)))
# Ignore gradients wrt columns other than n_post
g_mask = np.zeros((self.N,self.N))
g_mask[:,n_post] = 1
g_lp *= g_mask.flatten()
return g_lp
def _sample_column_of_A(self, n_post, x, I_bias, I_stim, I_imp):
# Sample the adjacency matrix if it exists
if 'A' in x['net']['graph']:
# print "Sampling A"
A = x['net']['graph']['A']
N = A.shape[0]
# Sample coupling filters from other neurons
for n_pre in np.arange(N):
# print "Sampling A[%d,%d]" % (n_pre,n_post)
# WARNING Setting A is somewhat of a hack. It only works
# because nvars copies x's pointer to A rather than making
# a deep copy of the adjacency matrix.
A[n_pre,n_post] = 0
log_pr_noA = self._lp_A(A, x, n_post, I_bias, I_stim, I_imp)
A[n_pre,n_post] = 1
log_pr_A = self._lp_A(A, x, n_post, I_bias, I_stim, I_imp)
# Sample A[n_pre,n_post]
A[n_pre,n_post] = log_sum_exp_sample([log_pr_noA, log_pr_A])
if not np.isfinite(log_pr_noA) or not np.isfinite(log_pr_A):
import pdb; pdb.set_trace()
if n_pre == n_post and not A[n_pre, n_post]:
import pdb; pdb.set_trace()
def _sample_column_of_W(self, n_post, x, I_bias, I_stim, I_imp):
# Sample W if it exists
if 'W' in x['net']['weights']:
# print "Sampling W"
nll = lambda W: -1.0 * self._lp_W(W, x, n_post, I_bias, I_stim, I_imp)
grad_nll = lambda W: -1.0 * self._grad_lp_W(W, x, n_post, I_bias, I_stim, I_imp)
# Automatically tune these parameters
n_steps = 10
(W, new_step_sz, new_accept_rate) = hmc(nll,
grad_nll,
self.step_sz,
n_steps,
x['net']['weights']['W'],
adaptive_step_sz=True,
avg_accept_rate=self.avg_accept_rate)
# Update step size and accept rate
self.step_sz = new_step_sz
self.avg_accept_rate = new_accept_rate
# print "W step sz: %.3f\tW_accept rate: %.3f" % (new_step_sz, new_accept_rate)
# Update current W
x['net']['weights']['W'] = W
def update(self, x, n):
""" Sample a single column of the network (all the incoming
coupling filters). This is a parallelizable chunk.
"""
# Precompute the filtered currents from other GLMs
I_bias, I_stim, I_imp = self._precompute_currents(x, n)
self._sample_column_of_A(n, x, I_bias, I_stim, I_imp)
self._sample_column_of_W(n, x, I_bias, I_stim, I_imp)
return x
class LatentLocationUpdate(MetropolisHastingsUpdate):
"""
Gibbs sample the parameters of a latent distance model, namely the
latent locations (if they are not given) and the distance scale.
"""
def __init__(self):
super(LatentLocationUpdate, self).__init__()
# Use HMC if the locations are continuous
# Otherwise, use a Metropolis-Hastings update
self.avg_accept_rate = 0.9
self.step_sz = 0.001
def preprocess(self, population):
self.N = population.model['N']
# Get the location model(s)
from pyglm.components.latent import LatentLocation
self.location_models = []
self.location_updates = []
for latent_component in population.latent.latentlist:
if isinstance(latent_component, LatentLocation):
self.location_models.append(latent_component)
# Make an update for this model
if latent_component.dtype == np.int:
# update = _DiscreteLatentLocationUpdate(latent_component)
# update = _DiscreteGibbsLatentLocationUpdate(latent_component)
update = _DiscreteLocalGibbsLatentLocationUpdate(latent_component)
else:
update = _ContinuousLatentLocationUpdate(latent_component)
update.preprocess(population)
self.location_updates.append(update)
def update(self, x):
"""
Update each location update in turn
"""
for update in self.location_updates:
x = update.update(x)
return x
class _ContinuousLatentLocationUpdate(MetropolisHastingsUpdate):
"""
A special subclass to sample continuous latent locations
"""
def __init__(self, latent_location_component):
self.location = latent_location_component
def preprocess(self, population):
self.syms = population.get_variables()
# Get the shape of L
# TODO: Fix this hack!
self.L = self.location.L
self.L_shape = population.sample()['latent'][self.location.name]['L'].shape
# Compute the log probability and its gradients, taking into
# account the prior and the likelihood of any consumers of the
# location.
self.log_p = T.constant(0.)
self.log_p += self.location.log_p
self.g_log_p = T.constant(0.)
self.g_log_p += T.grad(self.location.log_p, self.L)
from pyglm.components.graph import LatentDistanceGraphModel
if isinstance(population.network.graph, LatentDistanceGraphModel):
self.log_p += population.network.graph.log_p
self.g_log_p +=T.grad(population.network.graph.log_p, self.L)
from pyglm.components.bkgd import SharedTuningCurveStimulus
if isinstance(population.glm.bkgd_model, SharedTuningCurveStimulus):
self.log_p += population.glm.bkgd.log_p
self.g_log_p +=T.grad(population.glm.bkgd.log_p, self.L)
def _lp_L(self, L, x):
# Set L in state dict x
set_vars('L', x['latent'][self.location.name], L)
lp = seval(self.log_p, self.syms, x)
assert np.all(np.isfinite(lp))
return lp
def _grad_lp_wrt_L(self, L, x):
# Set L in state dict x
set_vars('L', x['latent'][self.location.name], L)
g_lp = seval(self.g_log_p, self.syms, x)
# if not np.all(np.isfinite(g_lp)):
# import pdb; pdb.set_trace()
return g_lp
def update(self, x):
"""
Sample L using HMC given A and delta (distance scale)
"""
nll = lambda L: -1.0 * self._lp_L(L.reshape(self.L_shape), x)
grad_nll = lambda L: -1.0 * self._grad_lp_wrt_L(L.reshape(self.L_shape), x).ravel()
# Automatically tune these paramseters
n_steps = 10
(L, new_step_sz, new_accept_rate) = hmc(nll,
grad_nll,
self.step_sz,
n_steps,
x['latent'][self.location.name]['L'].ravel(),
adaptive_step_sz=True,
avg_accept_rate=self.avg_accept_rate)
# Update step size and accept rate
self.step_sz = new_step_sz
# print "Step: ", self.step_sz
self.avg_accept_rate = new_accept_rate
# print "Accept: ", self.avg_accept_rate
# Update current L
x['latent'][self.location.name]['L'] = L.reshape(self.L_shape)
return x
class _DiscreteLatentLocationUpdate(MetropolisHastingsUpdate):
"""
A special subclass to sample discrete latent locations on a grid
"""
def __init__(self, latent_location_component):
self.location = latent_location_component
def preprocess(self, population):
self.N = population.N
self.population = population
self.syms = population.get_variables()
self.L = self.location.Lmatrix
# Compute the log probability and its gradients, taking into
# account the prior and the likelihood of any consumers of the
# location.
self.log_p = self.location.log_p
self.log_lkhd = T.constant(0.)
from pyglm.components.graph import LatentDistanceGraphModel
if isinstance(population.network.graph, LatentDistanceGraphModel):
self.log_lkhd += population.network.graph.log_p
from pyglm.components.bkgd import SharedTuningCurveStimulus
if isinstance(population.glm.bkgd_model, SharedTuningCurveStimulus):
self.log_lkhd += population.glm.log_p
def _lp_L(self, L, x, n):
if not self._check_bounds(L):
return -np.Inf
# Set L in state dict x
xn = self.population.extract_vars(x, n)
set_vars('L', xn['latent'][self.location.name], L.ravel())
lp = seval(self.log_p, self.syms, xn)
lp += seval(self.log_lkhd, self.syms, xn)
return lp
def _check_bounds(self, L):
"""
Return true if locations are within the allowable range
"""
from pyglm.components.priors import Categorical, JointCategorical
prior = self.location.location_prior
if isinstance(prior, Categorical):
if np.any(L < prior.min) or np.any(L > prior.max):
return False
if isinstance(prior, JointCategorical):
if np.any(L[:,0] < prior.min0) or np.any(L[:,0] > prior.max0) or \
np.any(L[:,1] < prior.min1) or np.any(L[:,1] > prior.max1):
return False
return True
def update(self, x):
"""
Sample each entry in L using Metropolis Hastings
"""
L = seval(self.location.Lmatrix, self.syms['latent'], x['latent'])
# print "L: ", L
for n in range(self.N):
L_curr = L[n,:].copy()
lp_curr = self._lp_L(L, x, n)
# Make a symmetric proposal of \pm 1 step along each dimension independently
L_prop = L_curr + np.random.randint(-1,2,L_curr.shape)
L[n,:] = L_prop
lp_prop = self._lp_L(L, x, n)
# Accept or reject (ignoring proposal since it's symmetric)
if np.log(np.random.rand()) < lp_prop - lp_curr:
L[n,:] = L_prop
# print "%d: [%d,%d]->[%d,%d]" % (n, L_curr[0], L_curr[1], L_prop[0],L_prop[1])
else:
L[n,:] = L_curr
# print "%d: [%d,%d]->[%d,%d]" % (n, L_curr[0], L_curr[1], L_curr[0],L_curr[1])
# Update current L
if not self._check_bounds(L):
import pdb; pdb.set_trace()
x['latent'][self.location.name]['L'] = L.ravel()
return x
class _DiscreteGibbsLatentLocationUpdate(MetropolisHastingsUpdate):
"""
A special subclass to sample discrete latent locations on a grid
"""
def __init__(self, latent_location_component):
self.location = latent_location_component
def preprocess(self, population):
self.N = population.N
self.population = population
self.syms = population.get_variables()
self.L = self.location.Lmatrix
# Compute the log probability and its gradients, taking into
# account the prior and the likelihood of any consumers of the
# location.
self.log_p = self.location.log_p
self.log_lkhd = T.constant(0.)
from pyglm.components.graph import LatentDistanceGraphModel
if isinstance(population.network.graph, LatentDistanceGraphModel):
self.log_lkhd += population.network.graph.log_p
from pyglm.components.bkgd import SharedTuningCurveStimulus
if isinstance(population.glm.bkgd_model, SharedTuningCurveStimulus):
self.log_lkhd += population.glm.log_p
def _lp_L(self, L, x, n):
if not self._check_bounds(L):
return -np.Inf
# Set L in state dict x
xn = self.population.extract_vars(x, n)
set_vars('L', xn['latent'][self.location.name], L.ravel())
lp = seval(self.log_p, self.syms, xn)
lp += seval(self.log_lkhd, self.syms, xn)
return lp
def _check_bounds(self, L):
"""
Return true if locations are within the allowable range
"""
from pyglm.components.priors import Categorical, JointCategorical
prior = self.location.location_prior
if isinstance(prior, Categorical):
if np.any(L < prior.min) or np.any(L > prior.max):
return False
if isinstance(prior, JointCategorical):
if np.any(L[:,0] < prior.min0) or np.any(L[:,0] > prior.max0) or \
np.any(L[:,1] < prior.min1) or np.any(L[:,1] > prior.max1):
return False
return True
def update(self, x):
"""
Sample each entry in L using Metropolis Hastings
"""
from pyglm.components.priors import Categorical, JointCategorical
prior = self.location.location_prior
L = seval(self.location.Lmatrix, self.syms['latent'], x['latent'])
# print "L: ", L
for n in range(self.N):
# Compute the probability of each possible location
if isinstance(prior, Categorical):
lnp = np.zeros(prior.max-prior.min + 1)
for i,l in enumerate(range(prior.min, prior.max+1)):
L[n,0] = l
lnp[i] = self._lp_L(L, x, n)
L[n] = prior.min + log_sum_exp_sample(lnp)
elif isinstance(prior, JointCategorical):
d1 = prior.max0-prior.min0+1
d2 = prior.max1-prior.min1+1
lnp = np.zeros((d1,d2))
for i,l1 in enumerate(range(prior.min0, prior.max0+1)):
for j,l2 in enumerate(range(prior.min1, prior.max1+1)):
L[n,0] = l1
L[n,1] = l2
lnp[i,j] = self._lp_L(L, x, n)
# import pdb; pdb.set_trace()
# Gibbs sample from the 2d distribution
ij = log_sum_exp_sample(lnp.ravel(order='C'))
i,j = np.unravel_index(ij, (d1,d2), order='C')
L[n,0] = prior.min0 + i
L[n,1] = prior.min1 + j
else:
raise Exception('Only supporting Categorical and JointCategorical location priors')
# Update current L
if not self._check_bounds(L):
import pdb; pdb.set_trace()
x['latent'][self.location.name]['L'] = L.ravel()
return x
class _DiscreteLocalGibbsLatentLocationUpdate(MetropolisHastingsUpdate):
"""
A special subclass to sample discrete latent locations on a grid
This is a Metropolis-Hastings update that takes local steps proportional
to their relative probability.
"""
def __init__(self, latent_location_component):
self.location = latent_location_component
def preprocess(self, population):
self.N = population.N
self.population = population
self.glm = self.population.glm
self.syms = population.get_variables()
self.L = self.location.Lmatrix
self.Lflat = self.location.Lflat
# Compute the log probability and its gradients, taking into
# account the prior and the likelihood of any consumers of the
# location.
self.log_p = self.location.log_p
self.log_lkhd = T.constant(0.)
from pyglm.components.graph import LatentDistanceGraphModel
if isinstance(population.network.graph, LatentDistanceGraphModel):
self.log_lkhd += population.network.graph.log_p
from pyglm.components.bkgd import SharedTuningCurveStimulus
if isinstance(population.glm.bkgd_model, SharedTuningCurveStimulus):
self.log_lkhd += population.glm.ll
def _precompute_vars(self, x, n):
""" Precompute currents for sampling A and W
"""
nvars = self.population.extract_vars(x, n)
I_bias = seval(self.glm.bias_model.I_bias,
self.syms,
nvars)
I_stim_xt = seval(self.glm.bkgd_model.I_stim_xt,
self.syms,
nvars)
I_net = seval(self.glm.I_net,
self.syms,
nvars)
return I_bias, I_stim_xt, I_net
def _lp_L(self, L, x, n, I_bias, I_stim_xt, I_net):
if not self._check_bounds(L):
return -np.Inf
# Extract the glm parameters
s = \
{
'L' : self.Lflat,
'I_stim_xt' : self.glm.bkgd_model.I_stim_xt,
'I_bias' : self.glm.bias_model.I_bias,
'I_net' : self.glm.I_net,
'A' : self.population.network.graph.A,
'n' : self.glm.n
}
xv = \
{
'L' : L.ravel(),
'I_stim_xt' : I_stim_xt,
'I_bias' : I_bias,
'I_net' : I_net,
'A' : x['net']['graph']['A'],
'n' : n
}
lp = seval(self.log_p, s, xv)
lp += seval(self.log_lkhd, s, xv)
# # Set L in state dict x
# xn = self.population.extract_vars(x, n)
# set_vars('L', xn['latent'][self.location.name], L.ravel())
# lp = seval(self.log_p, self.syms, xn)
# lp += seval(self.log_lkhd, self.syms, xn)
return lp
def _check_bounds(self, L):
"""
Return true if locations are within the allowable range
"""
from pyglm.components.priors import Categorical, JointCategorical
prior = self.location.location_prior
if isinstance(prior, Categorical):
if np.any(L < prior.min) or np.any(L > prior.max):
return False
if isinstance(prior, JointCategorical):
if np.any(L[:,0] < prior.min0) or np.any(L[:,0] > prior.max0) or \
np.any(L[:,1] < prior.min1) or np.any(L[:,1] > prior.max1):
return False
return True
def _get_neighbors(self, L):
"""
Get valid neighbors of 2D location (l0,l1)
"""
ne = []
from pyglm.components.priors import Categorical, JointCategorical
prior = self.location.location_prior
if isinstance(prior, Categorical):
for ne0 in range(L[0]-1,L[0]+2):
if ne0 >= prior.min and ne0 <= prior.max1:
ne.append((ne0))
elif isinstance(prior, JointCategorical):
for ne0 in range(L[0]-1,L[0]+2):
for ne1 in range(L[1]-1,L[1]+2):
if ne0 >= prior.min0 and ne0 <= prior.max0:
if ne1 >= prior.min1 and ne1 <= prior.max1:
ne.append((ne0,ne1))
return ne
def update(self, x):
"""
Sample each entry in L using Metropolis Hastings
"""
prior = self.location.location_prior
L = seval(self.location.Lmatrix, self.syms['latent'], x['latent'])
# Update each of the N neuron locations serially
# import pdb; pdb.set_trace()
for n in range(self.N):
print "Sampling location of neuron ", n
# Precompute currents
I_bias, I_stim_xt, I_net = self._precompute_vars(x, n)
# Compute the probability of each neighboring location
lnp_cache = {}
curr_loc = L[n,:]
curr_neighbors = self._get_neighbors(L[n,:])
curr_lnps = []
for ne in curr_neighbors:
L[n,:] = np.array(ne)
lnp_ne = self._lp_L(L, x, n, I_bias, I_stim_xt, I_net)
lnp_cache[ne] = lnp_ne
curr_lnps.append(lnp_ne)
# Propose a neighbor according to its relative probability
prop_loc = curr_neighbors[log_sum_exp_sample(curr_lnps)]
# Compute acceptance probability
prop_neighbors = self._get_neighbors(prop_loc)
prop_lnps = []
for ne in prop_neighbors:
if ne in lnp_cache:
prop_lnps.append(lnp_cache[ne])
else:
L[n,:] = np.array(ne)
lnp_ne = self._lp_L(L, x, n, I_bias, I_stim_xt, I_net)
lnp_cache[ne] = lnp_ne
prop_lnps.append(lnp_ne)
# Acceptance probability is the ratio of normalizing constants
lnp_accept = logsumexp(curr_lnps) - logsumexp(prop_lnps)
if np.log(np.random.rand()) < lnp_accept:
L[n,:] = np.array(prop_loc)
else:
# Reject and stay in current loc
L[n,:] = np.array(curr_loc)
# Update current L
if not self._check_bounds(L):
import pdb; pdb.set_trace()
x['latent'][self.location.name]['L'] = L.ravel()
return x
class LatentTypeUpdate(MetropolisHastingsUpdate):
"""
A special subclass to sample discrete latent locations on a grid
"""
def __init__(self):
pass
def preprocess(self, population):
self.N = population.N
self.population = population
self.syms = population.get_variables()
# Get the shared tuning curve component
from pyglm.components.latent import LatentType
self.latent_types = []
for latent_component in population.latent.latentlist:
if isinstance(latent_component, LatentType):
self.latent_types.append(latent_component)
# # Compute the log probability and its gradients, taking into
# # account the prior and the likelihood of any consumers of the
# # location.
from pyglm.components.graph import StochasticBlockGraphModel
if isinstance(population.network.graph, StochasticBlockGraphModel):
self.net_log_lkhd = population.network.graph.log_p
else:
self.net_log_lkhd = T.constant(0.)
from pyglm.components.bkgd import SharedTuningCurveStimulus
if isinstance(population.glm.bkgd_model, SharedTuningCurveStimulus):
self.glm_log_lkhd = population.glm.ll
else:
self.glm_log_lkhd = T.constant(0.)
def _lp_L(self, latent_type, Y, x, n):
# Set Yin state dict x
xn = self.population.extract_vars(x, n)
set_vars('Y', xn['latent'][latent_type.name], Y.ravel())
lp = seval(latent_type.log_p, self.syms, xn)
lp += seval(self.net_log_lkhd, self.syms, xn)
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
lp += seval(self.glm_log_lkhd, self.syms, xn)
return lp
def update(self, x):
"""
Sample each entry in L using Metropolis Hastings
"""
from pyglm.inference.log_sum_exp import log_sum_exp_sample
for latent_type in self.latent_types:
# Update the latent types
R = latent_type.R
Y = x['latent'][latent_type.name]['Y']
print "Y: ", Y
for n in range(self.N):
print "Sampling latent type of neuron ", n
lpr = np.zeros(R)
for r in range(R):
Y[n] = r
lpr[r] = self._lp_L(latent_type, Y, x, n)
Y[n] = log_sum_exp_sample(lpr)
x['latent'][latent_type.name]['Y'] = Y
# Update alpha with the conjugate dirichlet prior
from pyglm.components.priors import Dirichlet
if isinstance(latent_type.alpha_prior, Dirichlet):
suffstats = latent_type.alpha_prior.alpha0.get_value()
suffstats += np.bincount(Y, minlength=R)
alpha = np.random.dirichlet(suffstats)
x['latent'][latent_type.name]['alpha'] = alpha
else:
raise Warning('Cannot update alpha prior!')
return x
class LatentLocationAndTypeUpdate(MetropolisHastingsUpdate):
"""
A special subclass to sample discrete latent locations on a grid
along with the type of the neuron
"""
def __init__(self):
raise NotImplementedError('Joint update of location and type has not yet been implemented!')
def preprocess(self, population):
self.N = population.N
self.population = population
self.syms = population.get_variables()
# Get the shared tuning curve component
from pyglm.components.latent import LatentType
self.latent_types = []
for latent_component in population.latent.latentlist:
if isinstance(latent_component, LatentType):
self.latent_types.append(latent_component)
# # Compute the log probability and its gradients, taking into
# # account the prior and the likelihood of any consumers of the
# # location.
# self.log_p = self.location.log_p
from pyglm.components.graph import StochasticBlockGraphModel
if isinstance(population.network.graph, StochasticBlockGraphModel):
self.net_log_lkhd = population.network.graph.log_p
else:
self.net_log_lkhd = T.constant(0.)
from pyglm.components.bkgd import SharedTuningCurveStimulus
if isinstance(population.glm.bkgd_model, SharedTuningCurveStimulus):
self.glm_log_lkhd = population.glm.ll
else:
self.glm_log_lkhd = T.constant(0.)
def _lp_L(self, latent_type, Y, x, n):
# Set Yin state dict x
xn = self.population.extract_vars(x, n)
set_vars('Y', xn['latent'][latent_type.name], Y.ravel())
lp = seval(latent_type.log_p, self.syms, xn)
lp += seval(latent_type.net_log_lkhd, self.syms, xn)
# Compute the log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
lp += seval(self.glm_log_lkhd, self.syms, xn)
return lp
def update(self, x):
"""
Sample each entry in L using Metropolis Hastings
"""
from pyglm.inference.log_sum_exp import log_sum_exp_sample
for latent_type in self.latent_types:
# Update the latent types
R = latent_type.R
Y = x['latent'][latent_type.name]['Y']
print "Y: ", Y
for n in range(self.N):
lpr = np.zeros(R)
for r in range(R):
Y[n] = r
lpr[r] = self._lp_L(latent_type, Y, x, n)
Y[n] = log_sum_exp_sample(lpr)
x['latent'][latent_type.name]['Y'] = Y
# Update alpha with the conjugate dirichlet prior
from pyglm.components.priors import Dirichlet
if isinstance(latent_type.alpha_prior, Dirichlet):
suffstats = latent_type.alpha_prior.alpha0.get_value()
suffstats += np.bincount(Y, minlength=R)
alpha = np.random.dirichlet(suffstats)
x['latent'][latent_type.name]['alpha'] = alpha
else:
raise Warning('Cannot update alpha prior!')
from pyglm.components.priors import Categorical, JointCategorical
prior = self.location.location_prior
L = seval(self.location.Lmatrix, self.syms['latent'], x['latent'])
# print "L: ", L
for n in range(self.N):
# Compute the probability of each possible location
if isinstance(prior, Categorical):
lnp = np.zeros(prior.max-prior.min + 1)
for i,l in enumerate(range(prior.min, prior.max+1)):
L[n,0] = l
lnp[i] = self._lp_L(L, x, n)
L[n] = prior.min + log_sum_exp_sample(lnp)
elif isinstance(prior, JointCategorical):
d1 = prior.max0-prior.min0+1
d2 = prior.max1-prior.min1+1
lnp = np.zeros((d1,d2))
for i,l1 in enumerate(range(prior.min0, prior.max0+1)):
for j,l2 in enumerate(range(prior.min1, prior.max1+1)):
L[n,0] = l1
L[n,1] = l2
lnp[i,j] = self._lp_L(L, x, n)
# import pdb; pdb.set_trace()
# Gibbs sample from the 2d distribution
ij = log_sum_exp_sample(lnp.ravel(order='C'))
i,j = np.unravel_index(ij, (d1,d2), order='C')
L[n,0] = prior.min0 + i
L[n,1] = prior.min1 + j
else:
raise Exception('Only supporting Categorical and JointCategorical location priors')
# Update current L
if not self._check_bounds(L):
import pdb; pdb.set_trace()
x['latent'][self.location.name]['L'] = L.ravel()
return x
class SharedTuningCurveUpdate(MetropolisHastingsUpdate):
"""
A special subclass to sample continuous latent locations
"""
def __init__(self):
self.n_steps = 2
self.avg_accept_rate = 0.9
self.step_sz = 0.1
def preprocess(self, population):
self.population = population
self.glm = self.population.glm
self.N = population.N
# Get the shared tuning curve component
from pyglm.components.latent import LatentTypeWithTuningCurve
self.tc_model = None
for latent_component in population.latent.latentlist:
if isinstance(latent_component, LatentTypeWithTuningCurve):
self.tc_model = latent_component
break
if self.tc_model is None:
return
self.syms = population.get_variables()
# Get the shape of w_x and w_t
self.w_x = self.tc_model.w_x
self.w_x_shape = (self.tc_model.Bx, self.tc_model.R)
self.w_t = self.tc_model.w_t
self.w_t_shape = (self.tc_model.Bt, self.tc_model.R)
# Compute the log probability and its gradients, taking into
# account the prior and the likelihood of any consumers of the
# location.
self.log_p = self.tc_model.log_p
self.g_log_p_wrt_wx = T.constant(0.)
self.g_log_p_wrt_wt = T.constant(0.)
self.g_log_p_wrt_wx += T.grad(self.tc_model.log_p, self.w_x)
self.g_log_p_wrt_wt += T.grad(self.tc_model.log_p, self.w_t)
self.log_lkhd = T.constant(0.0)
self.g_log_lkhd_wrt_wx = T.constant(0.)
self.g_log_lkhd_wrt_wt = T.constant(0.)
from pyglm.components.bkgd import SharedTuningCurveStimulus
if isinstance(population.glm.bkgd_model, SharedTuningCurveStimulus):
self.log_lkhd += population.glm.ll
self.g_log_lkhd_wrt_wx += T.grad(population.glm.ll, self.w_x)
self.g_log_lkhd_wrt_wt += T.grad(population.glm.ll, self.w_t)
def _precompute_vars(self, x):
""" Precompute currents for sampling the stimulus filters
"""
I_biases = []
I_nets = []
for n in range(self.population.N):
nvars = self.population.extract_vars(x, n)
I_biases.append(seval(self.glm.bias_model.I_bias,
self.syms,
nvars))
I_nets.append(seval(self.glm.I_net,
self.syms,
nvars))
return I_biases, I_nets
def _lp(self, x, I_biases, I_nets):
"""
Compute the log posterior of x (across all GLMs)
"""
# Set w_x in state dict x
lp = seval(self.log_p, self.syms['latent'], x['latent'])
for n in range(self.N):
# Extract the glm parameters
xn = self.population.extract_vars(x, n)
s = \
{
'I_net' : self.glm.I_net,
'I_bias' : self.glm.bias_model.I_bias,
'n' : self.glm.n,
}
s.update(self.syms['latent'])
xv = \
{
'I_net' : I_nets[n],
'I_bias' : I_biases[n],
'n' : n,
}
xv.update(xn['latent'])
# Compute the GLM log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
lp += seval(self.log_lkhd, s, xv)
return lp
def _lp_wx(self, w_x, x, I_biases, I_nets):
"""
Compute the log posterior of x (across all GLMs)
"""
# Set w_x in state dict x
set_vars('w_x', x['latent'][self.tc_model.name], w_x)
return self._lp(x, I_biases, I_nets)
def _lp_wt(self, w_t, x, I_biases, I_nets):
# Set w_t in state dict x
set_vars('w_t', x['latent'][self.tc_model.name], w_t)
return self._lp(x, I_biases, I_nets)
def _grad_lp_wrt_wx(self, w_x, x, I_biases, I_nets):
# Set L in state dict x
set_vars('w_x', x['latent'][self.tc_model.name], w_x)
g_lp = seval(self.g_log_p_wrt_wx, self.syms['latent'], x['latent'])
for n in range(self.N):
# print "Computing grad_lp_wrt_wx for neuron ", n
xn = self.population.extract_vars(x, n)
s = \
{
'I_net' : self.glm.I_net,
'I_bias' : self.glm.bias_model.I_bias,
'n' : self.glm.n,
}
s.update(self.syms['latent'])
xv = \
{
'I_net' : I_nets[n],
'I_bias' : I_biases[n],
'n' : n,
}
xv.update(xn['latent'])
# Compute the GLM log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
g_lp += seval(self.g_log_lkhd_wrt_wx, s, xv)
return g_lp
# def _grad_lp_wrt_wx(self, w_x, x):
# # Set L in state dict x
# set_vars('w_x', x['latent'][self.tc_model.name], w_x)
# g_lp = seval(self.g_log_p_wrt_wx, self.syms['latent'], x['latent'])
#
# for n in range(self.N):
# xn = self.population.extract_vars(x, n)
# g_lp += seval(self.g_log_lkhd_wrt_wx, self.syms, xn)
#
# return g_lp
def _grad_lp_wrt_wt(self, w_t, x, I_biases, I_nets):
# Set L in state dict x
set_vars('w_t', x['latent'][self.tc_model.name], w_t)
g_lp = seval(self.g_log_p_wrt_wt, self.syms['latent'], x['latent'])
for n in range(self.N):
# print "Computing grad_lp_wrt_wt for neuron ", n
xn = self.population.extract_vars(x, n)
s = \
{
'I_net' : self.glm.I_net,
'I_bias' : self.glm.bias_model.I_bias,
'n' : self.glm.n,
}
s.update(self.syms['latent'])
xv = \
{
'I_net' : I_nets[n],
'I_bias' : I_biases[n],
'n' : n,
}
xv.update(xn['latent'])
# Compute the GLM log likelihood for each data sequence
for data in self.population.data_sequences:
self.population.set_data(data)
g_lp += seval(self.g_log_lkhd_wrt_wt, s, xv)
return g_lp
def update(self, x):
"""
Sample L using HMC given A and delta (distance scale)
"""
if self.tc_model is None:
return
# Precompute other currents
I_biases, I_nets = self._precompute_vars(x)
# Update w_x
nll_wx = lambda w_x: -1.0 * self._lp_wx(w_x.reshape(self.w_x_shape), x, I_biases, I_nets)
grad_nll_wx = lambda w_x: -1.0 * self._grad_lp_wrt_wx(w_x.reshape(self.w_x_shape), x, I_biases, I_nets).ravel()
# Automatically tune these parameters
(w_x, new_step_sz, new_accept_rate) = hmc(nll_wx,
grad_nll_wx,
self.step_sz,
self.n_steps,
x['latent'][self.tc_model.name]['w_x'].ravel(),
adaptive_step_sz=True,
avg_accept_rate=self.avg_accept_rate)
# Update step size and accept rate
self.step_sz = new_step_sz
# print "Step: ", self.step_sz
self.avg_accept_rate = new_accept_rate
# print "Accept: ", self.avg_accept_rate
# Update current w_x
x['latent'][self.tc_model.name]['w_x'] = w_x.reshape(self.w_x_shape)
# Do the same for w_t
nll_wt = lambda w_t: -1.0 * self._lp_wt(w_t.reshape(self.w_t_shape), x, I_biases, I_nets)
grad_nll_wt = lambda w_t: -1.0 * self._grad_lp_wrt_wt(w_t.reshape(self.w_t_shape), x, I_biases, I_nets).ravel()
# Automatically tune these paramseters
(w_t, new_step_sz, new_accept_rate) = hmc(nll_wt,
grad_nll_wt,
self.step_sz,
self.n_steps,
x['latent'][self.tc_model.name]['w_t'].ravel(),
adaptive_step_sz=True,
avg_accept_rate=self.avg_accept_rate)
# Update step size and accept rate
self.step_sz = new_step_sz
# print "Step: ", self.step_sz
self.avg_accept_rate = new_accept_rate
# print "Accept: ", self.avg_accept_rate
# Update current w_t
x['latent'][self.tc_model.name]['w_t'] = w_t.reshape(self.w_t_shape)
return x
def initialize_updates(population):
""" Compute the set of updates required for the given population.
TODO: Figure out how to do this in a really principled way.
"""
serial_updates = []
parallel_updates = []
print "Initializing latent variable samplers"
print "Ignoring shared tuning curve update"
tc_sampler = SharedTuningCurveUpdate()
tc_sampler.preprocess(population)
serial_updates.append(tc_sampler)
loc_sampler = LatentLocationUpdate()
loc_sampler.preprocess(population)
serial_updates.append(loc_sampler)
type_sampler = LatentTypeUpdate()
type_sampler.preprocess(population)
serial_updates.append(type_sampler)
# All populations have a parallel GLM sampler
print "Initializing GLM samplers"
# glm_sampler = HmcGlmUpdate()
# glm_sampler.preprocess(population)
# parallel_updates.append(glm_sampler)
bias_sampler = HmcBiasUpdate()
bias_sampler.preprocess(population)
parallel_updates.append(bias_sampler)
bkgd_sampler = HmcBkgdUpdate()
bkgd_sampler.preprocess(population)
parallel_updates.append(bkgd_sampler)
from pyglm.components.impulse import DirichletImpulses
if isinstance(population.glm.imp_model, DirichletImpulses):
imp_sampler = HmcDirichletImpulseUpdate()
else:
imp_sampler = HmcImpulseUpdate()
imp_sampler.preprocess(population)
parallel_updates.append(imp_sampler)
# All populations have a network sampler
print "Initializing network sampler"
# net_sampler = GibbsNetworkColumnUpdate()
net_sampler = CollapsedGibbsNetworkColumnUpdate()
net_sampler.preprocess(population)
parallel_updates.append(net_sampler)
# If the graph model is a latent distance model, add its update
# from components.graph import LatentDistanceGraphModel
# if isinstance(population.network.graph, LatentDistanceGraphModel):
# print "Initializing latent location sampler"
# loc_sampler = LatentLocationUpdate()
# loc_sampler.preprocess(population)
# serial_updates.append(loc_sampler)
return serial_updates, parallel_updates
def gibbs_sample(population,
N_samples=1000,
x0=None,
init_from_mle=True,
callback=None):
"""
Sample the posterior distribution over parameters using MCMC.
"""
N = population.model['N']
dt = population.model['dt']
# Draw initial state from prior if not given
if x0 is None:
x0 = population.sample()
if init_from_mle:
print "Initializing with coordinate descent"
from pyglm.models.model_factory import make_model, convert_model
from pyglm.population import Population
mle_model = make_model('standard_glm', N=N, dt=dt)
mle_popn = Population(mle_model)
for data in population.data_sequences:
mle_popn.add_data(data)
mle_x0 = mle_popn.sample()
# Initialize with MLE under standard GLM
mle_x0 = coord_descent(mle_popn, x0=mle_x0, maxiter=1)
# Convert between inferred parameters of the standard GLM
# and the parameters of this model. Eg. Convert unweighted
# networks to weighted networks with normalized impulse responses.
x0 = convert_model(mle_popn, mle_model, mle_x0, population, population.model, x0)
# # TODO: Move this to a better place
# from pyglm.inference.smart_init import initialize_locations_by_correlation
# initialize_locations_by_correlation(population, x0)
# Create updates for this population
serial_updates, parallel_updates = initialize_updates(population)
# DEBUG Profile the Gibbs sampling loop
import cProfile, pstats, StringIO
pr = cProfile.Profile()
pr.enable()
# Alternate fitting the network and fitting the GLMs
x_smpls = [x0]
x = x0
import time
start_time = time.time()
for smpl in np.arange(N_samples):
# Call the callback
if callback is not None:
callback(x)
# Print the current log likelihood
lp = population.compute_log_p(x)
# Compute iters per second
stop_time = time.time()
if stop_time - start_time == 0:
print "Gibbs iteration %d. Iter/s exceeds time resolution. Log prob: %.3f" % (smpl, lp)
else:
print "Gibbs iteration %d. Iter/s = %f. Log prob: %.3f" % (smpl,
1.0/(stop_time-start_time),
lp)
start_time = stop_time
# Go through each parallel MH update
for parallel_update in parallel_updates:
for n in np.arange(N):
# print "Parallel update: %s for neuron %d" % (str(type(parallel_update)), n)
parallel_update.update(x, n)
# Sample the serial updates
for serial_update in serial_updates:
# print "Serial update: ", type(serial_update)
serial_update.update(x)
x_smpls.append(copy.deepcopy(x))
pr.disable()
s = StringIO.StringIO()
sortby = 'cumulative'
ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
ps.print_stats()
with open('mcmc.prof.txt', 'w') as f:
f.write(s.getvalue())
f.close()
return x_smpls
| 37.527216
| 119
| 0.554932
| 12,642
| 96,520
| 4.024996
| 0.052523
| 0.021873
| 0.006387
| 0.010436
| 0.779322
| 0.744753
| 0.712346
| 0.689608
| 0.666948
| 0.651384
| 0
| 0.005538
| 0.347109
| 96,520
| 2,571
| 120
| 37.541813
| 0.801926
| 0.176875
| 0
| 0.641967
| 0
| 0
| 0.030942
| 0
| 0
| 0
| 0
| 0.001945
| 0.001385
| 0
| null | null | 0.002078
| 0.034626
| null | null | 0.010388
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0cd2ceb88fd6ba45d68ea7c410e2eeb082a74c98
| 384
|
py
|
Python
|
src/utils/crypto.py
|
biobdeveloper/bithonledger
|
5d90aeb881d449725b4c68c6f8deb9796a94576b
|
[
"MIT"
] | 6
|
2019-11-11T23:17:00.000Z
|
2019-11-18T19:59:09.000Z
|
src/utils/crypto.py
|
biobdeveloper/bithonledger
|
5d90aeb881d449725b4c68c6f8deb9796a94576b
|
[
"MIT"
] | null | null | null |
src/utils/crypto.py
|
biobdeveloper/bithonledger
|
5d90aeb881d449725b4c68c6f8deb9796a94576b
|
[
"MIT"
] | null | null | null |
"""Cryptography module.
Encrypt and decrypt user's Bitcoin WIFs.
"""
import rncryptor
from base64 import b64encode, b64decode
def encrypt(wif, password):
return b64encode(rncryptor.RNCryptor().encrypt(data=wif, password=password)).decode('utf-8')
def decrypt(enc_wif, password):
return rncryptor.RNCryptor().decrypt(b64decode(enc_wif.encode('utf-8')), password=password)
| 25.6
| 96
| 0.760417
| 49
| 384
| 5.918367
| 0.510204
| 0.113793
| 0.117241
| 0
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| 0
| 0
| 0
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| 0
| 0
| 0.034985
| 0.106771
| 384
| 14
| 97
| 27.428571
| 0.810496
| 0.161458
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| 0.031746
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| 0.333333
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| 1
| 1
| 1
| 0
| 0
|
0
| 4
|
0ce778c5dece7d0a49de603de42d5dd930e2623d
| 98
|
py
|
Python
|
love_pet_lifetime/pet_purchase/apps.py
|
DCCop/love-pet-lifetime
|
65408e2708c471d95ed7661e287f8af2e766e6c2
|
[
"MIT"
] | null | null | null |
love_pet_lifetime/pet_purchase/apps.py
|
DCCop/love-pet-lifetime
|
65408e2708c471d95ed7661e287f8af2e766e6c2
|
[
"MIT"
] | null | null | null |
love_pet_lifetime/pet_purchase/apps.py
|
DCCop/love-pet-lifetime
|
65408e2708c471d95ed7661e287f8af2e766e6c2
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class PetPurchaseConfig(AppConfig):
name = 'pet_purchase'
| 16.333333
| 35
| 0.77551
| 11
| 98
| 6.818182
| 0.909091
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0.153061
| 98
| 5
| 36
| 19.6
| 0.903614
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| 0.122449
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| 1
| 0
| 1
| 0
|
0
| 4
|
0b61433063b8df75c7a017f885e2a92eb8c214a1
| 160
|
py
|
Python
|
test_SERVER.py
|
eelviral/RTC-Data-Streaming
|
8a139b7463477dd11021a2e7c332819ac0cc08c9
|
[
"MIT"
] | null | null | null |
test_SERVER.py
|
eelviral/RTC-Data-Streaming
|
8a139b7463477dd11021a2e7c332819ac0cc08c9
|
[
"MIT"
] | null | null | null |
test_SERVER.py
|
eelviral/RTC-Data-Streaming
|
8a139b7463477dd11021a2e7c332819ac0cc08c9
|
[
"MIT"
] | null | null | null |
import unittest
import server
class TestServer(unittest.TestCase):
def test_foo(self):
pass
if __name__ == '__main__':
unittest.main()
| 17.777778
| 37
| 0.6625
| 18
| 160
| 5.388889
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.24375
| 160
| 9
| 38
| 17.777778
| 0.801653
| 0
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| 0
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| 0.052288
| 0
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| 1
| 0.142857
| false
| 0.142857
| 0.285714
| 0
| 0.571429
| 0
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
0b651b120a52fd75620219789b420981c0a47a16
| 86
|
py
|
Python
|
expense_tracker/__init__.py
|
TClaypool00/ExpenseTrackerClient-Python
|
44f45e7e7b7434d19a20a0d1b565592a157bed88
|
[
"MIT"
] | null | null | null |
expense_tracker/__init__.py
|
TClaypool00/ExpenseTrackerClient-Python
|
44f45e7e7b7434d19a20a0d1b565592a157bed88
|
[
"MIT"
] | null | null | null |
expense_tracker/__init__.py
|
TClaypool00/ExpenseTrackerClient-Python
|
44f45e7e7b7434d19a20a0d1b565592a157bed88
|
[
"MIT"
] | null | null | null |
import pymysql
pymysql.version_info = (1,4,0, "final", 0)
pymysql.install_as_MySQLdb()
| 28.666667
| 42
| 0.767442
| 14
| 86
| 4.5
| 0.785714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.050633
| 0.081395
| 86
| 3
| 43
| 28.666667
| 0.746835
| 0
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| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
0ba45ae38dc184b1466832423691d0aeb7b099fb
| 185
|
py
|
Python
|
tests/core/test_setproctitle.py
|
inan0812/chia-blockchain
|
8de40989f56fb64d6ff1690ae0c2169cc11ad18b
|
[
"Apache-2.0"
] | 1
|
2021-09-19T18:59:19.000Z
|
2021-09-19T18:59:19.000Z
|
tests/core/test_setproctitle.py
|
inan0812/chia-blockchain
|
8de40989f56fb64d6ff1690ae0c2169cc11ad18b
|
[
"Apache-2.0"
] | null | null | null |
tests/core/test_setproctitle.py
|
inan0812/chia-blockchain
|
8de40989f56fb64d6ff1690ae0c2169cc11ad18b
|
[
"Apache-2.0"
] | 1
|
2022-02-08T19:58:12.000Z
|
2022-02-08T19:58:12.000Z
|
import unittest
from inan.util.setproctitle import setproctitle
class TestSetProcTitle(unittest.TestCase):
def test_does_not_crash(self):
setproctitle("inan test title")
| 20.555556
| 47
| 0.778378
| 22
| 185
| 6.409091
| 0.727273
| 0
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| 0
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| 0.151351
| 185
| 8
| 48
| 23.125
| 0.898089
| 0
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| 0.081081
| 0
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| 1
| 0.2
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| 0
| 0.4
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| 1
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| 1
| 0
|
0
| 4
|
0ba53c6dca51d33b3e96858b9454bfbb4d1fb098
| 112
|
py
|
Python
|
libs/python/stupendous_cow/__init__.py
|
tomault/stupendous-cow
|
95955a52fa1aa70ded564ffeb7780ada66ab3741
|
[
"Apache-2.0"
] | null | null | null |
libs/python/stupendous_cow/__init__.py
|
tomault/stupendous-cow
|
95955a52fa1aa70ded564ffeb7780ada66ab3741
|
[
"Apache-2.0"
] | null | null | null |
libs/python/stupendous_cow/__init__.py
|
tomault/stupendous-cow
|
95955a52fa1aa70ded564ffeb7780ada66ab3741
|
[
"Apache-2.0"
] | null | null | null |
"""Python packages that contain the common code for the "stupendous-cow"
article indexing and search system."""
| 37.333333
| 72
| 0.776786
| 16
| 112
| 5.4375
| 0.9375
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| 0
| 0.133929
| 112
| 2
| 73
| 56
| 0.896907
| 0.9375
| 0
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| 0
| 0
| 0
|
0
| 4
|
f03860c324dc772ec3ce22821d54ca897b8331ed
| 103
|
py
|
Python
|
start.py
|
Jajabenit250/flask-with-fairseq
|
75512f913742e29627fe4108e02fb21119086c09
|
[
"Apache-2.0"
] | null | null | null |
start.py
|
Jajabenit250/flask-with-fairseq
|
75512f913742e29627fe4108e02fb21119086c09
|
[
"Apache-2.0"
] | null | null | null |
start.py
|
Jajabenit250/flask-with-fairseq
|
75512f913742e29627fe4108e02fb21119086c09
|
[
"Apache-2.0"
] | null | null | null |
from flask import Flask
app = Flask(__name__)
@app.route('/')
def start():
return 'App Is Started'
| 17.166667
| 27
| 0.679612
| 15
| 103
| 4.4
| 0.733333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.174757
| 103
| 6
| 27
| 17.166667
| 0.776471
| 0
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| 1
| 0
|
0
| 4
|
f03d4648e4f4ff3f16e93fabcd2bb4f5ebdf5c8c
| 165
|
py
|
Python
|
API_Forms/gestionPedidos/Forms.py
|
BrianMarquez3/Python-Django
|
61f84a01b7f57254f9dcbbad86cc4c88c2acf4d7
|
[
"MIT"
] | 2
|
2020-09-28T21:23:59.000Z
|
2021-11-10T15:01:15.000Z
|
API_Forms/gestionPedidos/Forms.py
|
BrianMarquez3/Python-Django
|
61f84a01b7f57254f9dcbbad86cc4c88c2acf4d7
|
[
"MIT"
] | 21
|
2021-02-04T01:37:44.000Z
|
2022-03-12T01:00:55.000Z
|
API_Forms/gestionPedidos/Forms.py
|
BrianMarquez3/Python-Django
|
61f84a01b7f57254f9dcbbad86cc4c88c2acf4d7
|
[
"MIT"
] | null | null | null |
# API FORMS
from django import forms
class FormularioContacto(forms.Form):
asunto=forms.CharField()
email=forms.EmailField()
mensaje=forms.CharField()
| 18.333333
| 37
| 0.739394
| 19
| 165
| 6.421053
| 0.684211
| 0.229508
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157576
| 165
| 9
| 38
| 18.333333
| 0.877698
| 0.054545
| 0
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| 0
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| 0
| 1
| 0
|
0
| 4
|
f04d4f2e589fc3d46fdc0b03813fe3e2bfb0c807
| 162
|
py
|
Python
|
tests/test_encoding.py
|
mschneider/mango-explorer
|
ed50880ef80b31b679c9c89fa9bf0579391d71c9
|
[
"MIT"
] | 1
|
2021-09-09T20:49:46.000Z
|
2021-09-09T20:49:46.000Z
|
tests/test_encoding.py
|
mschneider/mango-explorer
|
ed50880ef80b31b679c9c89fa9bf0579391d71c9
|
[
"MIT"
] | null | null | null |
tests/test_encoding.py
|
mschneider/mango-explorer
|
ed50880ef80b31b679c9c89fa9bf0579391d71c9
|
[
"MIT"
] | 2
|
2021-09-09T20:49:50.000Z
|
2021-11-05T21:41:41.000Z
|
from .context import mango
def test_decode_binary():
data = mango.decode_binary(["SGVsbG8gV29ybGQ=", "base64"]) # "Hello World"
assert len(data) == 11
| 23.142857
| 79
| 0.685185
| 20
| 162
| 5.4
| 0.8
| 0.222222
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0.052239
| 0.17284
| 162
| 6
| 80
| 27
| 0.753731
| 0.080247
| 0
| 0
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| 0
| 0.14966
| 0
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| 0
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| 0
|
0
| 4
|
f04f4e33322a4f58f4878b3d21de833f5874382b
| 134
|
py
|
Python
|
zenodo_gitlab/objects.py
|
tuw-eeg/zenodo-gitlab
|
ffb6d4fd10dc9bb191c3895b66240951deddd520
|
[
"MIT"
] | null | null | null |
zenodo_gitlab/objects.py
|
tuw-eeg/zenodo-gitlab
|
ffb6d4fd10dc9bb191c3895b66240951deddd520
|
[
"MIT"
] | null | null | null |
zenodo_gitlab/objects.py
|
tuw-eeg/zenodo-gitlab
|
ffb6d4fd10dc9bb191c3895b66240951deddd520
|
[
"MIT"
] | null | null | null |
from enum import Enum
class ArchiveFormat(str, Enum):
ZIP = 'zip'
TAR_GZ = 'tar.gz'
TAR_BZ2 = 'tar.bz2'
TAR = 'tar'
| 14.888889
| 31
| 0.589552
| 20
| 134
| 3.85
| 0.5
| 0.12987
| 0.207792
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| 0.020619
| 0.276119
| 134
| 8
| 32
| 16.75
| 0.773196
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| 0
| 0
| 1
| 0
|
0
| 4
|
f06a9dbc63da38664a554f2d8b76a0954d7be7f7
| 35
|
py
|
Python
|
tests/__init__.py
|
ye-yu/aaapi
|
61470683a51b8e7bf4fd89206833e007ec584b9e
|
[
"MIT"
] | 1
|
2020-02-20T13:36:04.000Z
|
2020-02-20T13:36:04.000Z
|
tests/__init__.py
|
ye-yu/aaapi
|
61470683a51b8e7bf4fd89206833e007ec584b9e
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
ye-yu/aaapi
|
61470683a51b8e7bf4fd89206833e007ec584b9e
|
[
"MIT"
] | null | null | null |
"""Unit test package for aaapi."""
| 17.5
| 34
| 0.657143
| 5
| 35
| 4.6
| 1
| 0
| 0
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| 0.142857
| 35
| 1
| 35
| 35
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| 0.8
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| 0
| 0
|
0
| 4
|
f06f7f7f2557c8e3d54bc5ca10703997349ab3a5
| 488
|
py
|
Python
|
Heap/545.Top k Largest Numbers II/Solution.py
|
Zhenye-Na/LxxxCode
|
afd79d790d0a7495d75e6650f80adaa99bd0ff07
|
[
"MIT"
] | 12
|
2019-05-04T04:21:27.000Z
|
2022-03-02T07:06:57.000Z
|
Heap/545.Top k Largest Numbers II/Solution.py
|
Zhenye-Na/LxxxCode
|
afd79d790d0a7495d75e6650f80adaa99bd0ff07
|
[
"MIT"
] | 1
|
2019-07-24T18:43:53.000Z
|
2019-07-24T18:43:53.000Z
|
Heap/545.Top k Largest Numbers II/Solution.py
|
Zhenye-Na/LxxxCode
|
afd79d790d0a7495d75e6650f80adaa99bd0ff07
|
[
"MIT"
] | 10
|
2019-07-01T04:03:04.000Z
|
2022-03-09T03:57:37.000Z
|
import heapq
class Solution:
"""
@param: k: An integer
"""
def __init__(self, k):
# do intialization if necessary
self.pq = []
self.k = k
"""
@param: num: Number to be added
@return: nothing
"""
def add(self, num):
# write your code here
heapq.heappush(self.pq, num)
"""
@return: Top k element
"""
def topk(self):
# write your code here
return heapq.nlargest(self.k, self.pq)
| 18.074074
| 46
| 0.52459
| 60
| 488
| 4.2
| 0.55
| 0.059524
| 0.103175
| 0.134921
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.352459
| 488
| 26
| 47
| 18.769231
| 0.797468
| 0.192623
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 0
| 1
| 0.333333
| false
| 0
| 0.111111
| 0.111111
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
f08072e33f6e440479e5251f4bdd83e700bd2a76
| 35
|
py
|
Python
|
data/studio21_generated/introductory/3729/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
data/studio21_generated/introductory/3729/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
data/studio21_generated/introductory/3729/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
def count_zeros_n_double_fact(n):
| 17.5
| 33
| 0.828571
| 7
| 35
| 3.571429
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085714
| 35
| 2
| 34
| 17.5
| 0.78125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
b2e5f9b8101a6aa05fa57e499ba88645f3d2b6c5
| 45
|
py
|
Python
|
source/handlers/telegram_api/__init__.py
|
icYFTL/CBot
|
39485334feb78d3ad35c2f11c57768fd679592d6
|
[
"Apache-2.0"
] | null | null | null |
source/handlers/telegram_api/__init__.py
|
icYFTL/CBot
|
39485334feb78d3ad35c2f11c57768fd679592d6
|
[
"Apache-2.0"
] | null | null | null |
source/handlers/telegram_api/__init__.py
|
icYFTL/CBot
|
39485334feb78d3ad35c2f11c57768fd679592d6
|
[
"Apache-2.0"
] | null | null | null |
from . import *
__all__ = ['events', 'misc']
| 15
| 28
| 0.6
| 5
| 45
| 4.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177778
| 45
| 3
| 28
| 15
| 0.621622
| 0
| 0
| 0
| 0
| 0
| 0.217391
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
b2fb01fb89beede362f3bc7dc126790d47e7c341
| 43
|
py
|
Python
|
toph_copycat.py
|
novojitdas/PythonProblemsSolutions
|
99fcb6cf59f2189d8e999d639fe716bb6521bbcb
|
[
"MIT"
] | null | null | null |
toph_copycat.py
|
novojitdas/PythonProblemsSolutions
|
99fcb6cf59f2189d8e999d639fe716bb6521bbcb
|
[
"MIT"
] | null | null | null |
toph_copycat.py
|
novojitdas/PythonProblemsSolutions
|
99fcb6cf59f2189d8e999d639fe716bb6521bbcb
|
[
"MIT"
] | null | null | null |
value = input("Enter Input:")
print(value)
| 14.333333
| 29
| 0.697674
| 6
| 43
| 5
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116279
| 43
| 2
| 30
| 21.5
| 0.789474
| 0
| 0
| 0
| 0
| 0
| 0.27907
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
6511972f6a13ecfd3c63d6e8be7f27a395180650
| 43,480
|
py
|
Python
|
catboost/python-package/ut/medium/canondata/test.test_export_to_python_no_cat_features_CPU-40_/model.py
|
EkaterinaPogodina/catboost
|
4628e86e978da2ec5e4d42f6b8d05e0b5e8aab30
|
[
"Apache-2.0"
] | 2
|
2019-07-10T10:49:09.000Z
|
2020-06-19T11:40:04.000Z
|
catboost/python-package/ut/medium/canondata/test.test_export_to_python_no_cat_features_CPU-40_/model.py
|
EkaterinaPogodina/catboost
|
4628e86e978da2ec5e4d42f6b8d05e0b5e8aab30
|
[
"Apache-2.0"
] | null | null | null |
catboost/python-package/ut/medium/canondata/test.test_export_to_python_no_cat_features_CPU-40_/model.py
|
EkaterinaPogodina/catboost
|
4628e86e978da2ec5e4d42f6b8d05e0b5e8aab30
|
[
"Apache-2.0"
] | null | null | null |
### Model data
class catboost_model(object):
float_features_index = [
0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 28, 31, 32, 33, 35, 37, 38, 39, 46, 47, 48, 49,
]
float_feature_count = 50
cat_feature_count = 0
binary_feature_count = 36
tree_count = 40
float_feature_borders = [
[0.0979510546, 0.168183506, 0.175395012, 0.1838945, 0.29696101, 0.322089016, 0.356592506, 0.402612507, 0.407903492],
[0.0117153507, 0.0208603498, 0.0227272511, 0.0283820499, 0.0387445986, 0.0597131997, 0.0884035975, 0.0952057987, 0.130652487, 0.136649996, 0.159972489, 0.186926007, 0.390384018, 0.447147489, 0.474032521, 0.585997999, 0.707031012, 0.774169981],
[5.74449987e-05, 0.00453814492, 0.00574448518, 0.00947840512, 0.0641983524, 0.320514023, 0.369177997, 0.780945539, 0.817595959, 0.829216003, 0.902011514, 0.903753996, 0.937548518],
[0.5],
[0.5],
[0.5],
[0.5],
[0.5],
[0.5],
[0.5],
[0.5],
[0.5],
[0.35098052, 0.398038983, 0.417647004],
[0.0652175024, 0.183333501, 0.560386539, 0.595918536, 0.645990014, 0.652147532, 0.712215543, 0.741925001, 0.766074002, 0.825323999, 0.894724965, 0.931031466],
[0.0440538004, 0.0556640998, 0.0820585489, 0.235576987, 0.286276519, 0.291958004, 0.324301004, 0.451516002, 0.471967995, 0.488891482, 0.510249019, 0.630002975, 0.692146003, 0.795647502],
[0.119033001, 0.119927496, 0.13151899, 0.143101007, 0.182384491, 0.191996992, 0.207109511, 0.261641979, 0.262331009, 0.280564487, 0.341767013],
[0.5],
[0.5],
[0.272549003, 0.331372499, 0.347059011, 0.358823478, 0.378431499, 0.484313995, 0.503921509, 0.535293996, 0.7156865, 0.747058988],
[0.5],
[0.5],
[0.0416666493, 1.5],
[0.0225447994, 0.0437176004, 0.183991, 0.938220024, 0.938699961, 0.938757539],
[0.5],
[0.5],
[0.0243670009, 0.0521114506, 0.0697473437, 0.134183004, 0.141615003, 0.35481149, 0.449031502, 0.621537507, 0.861264467],
[0.5],
[0.0136655001, 0.0313090011, 0.0567234978, 0.116815001, 0.39230752, 0.850793004],
[0.126407504, 0.193027496, 0.318073004, 0.786653519, 0.957795024],
[0.00160040497, 0.00311657996, 0.00345350499, 0.01052895, 0.0110343499, 0.0137297995, 0.0285545997, 0.0351247005, 0.160027504],
[0.00738915009, 0.0453458503, 0.283847004, 0.392024517, 0.523900032, 0.654693007, 0.661086977, 0.66582948, 0.784554005, 0.821318984, 0.975975513],
[0.00121281995, 0.00157565507, 0.00256450498, 0.00274871988, 0.00749967527, 0.00921617076, 0.0097909905, 0.0100314207, 0.0118742995, 0.0119927004, 0.0165624507],
[0.107056499, 0.163893014, 0.241850495, 0.27432698, 0.285950482, 0.306465507, 0.337470502, 0.383904994, 0.438004494, 0.515424967, 0.521192014],
[0.387494028, 0.4393695, 0.448886007, 0.472368002, 0.502859473, 0.508242011, 0.509687006, 0.542495012, 0.566181004, 0.639330506, 0.693051457, 0.708531499, 0.7370345, 0.743195534, 0.767975509, 0.8723315, 0.88726902, 0.965276003, 0.966867507],
[0.192310005, 0.23803401, 0.244706005, 0.294632018, 0.317332506, 0.389473975, 0.40584451, 0.447151482, 0.550680041, 0.578404009, 0.631987512, 0.640262485, 0.748547494],
[0.5],
]
tree_depth = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]
tree_split_border = [2, 1, 2, 1, 2, 5, 15, 1, 10, 1, 1, 4, 3, 3, 7, 10, 12, 1, 3, 1, 5, 1, 4, 2, 7, 11, 14, 6, 7, 6, 3, 8, 1, 3, 8, 1, 6, 11, 1, 2, 1, 10, 4, 11, 10, 5, 12, 10, 7, 2, 1, 1, 11, 12, 16, 2, 3, 6, 1, 1, 1, 11, 3, 11, 10, 1, 9, 9, 8, 1, 1, 5, 2, 1, 3, 17, 11, 1, 9, 6, 7, 8, 7, 8, 5, 10, 8, 6, 5, 6, 6, 1, 17, 9, 1, 2, 4, 4, 1, 3, 14, 4, 5, 4, 10, 7, 9, 5, 6, 4, 14, 8, 1, 5, 3, 6, 9, 4, 2, 9, 2, 1, 8, 11, 1, 1, 14, 15, 3, 8, 7, 8, 4, 5, 1, 1, 8, 4, 10, 2, 9, 1, 1, 1, 1, 17, 10, 1, 3, 3, 1, 2, 5, 9, 3, 10, 2, 13, 2, 6, 1, 1, 1, 1, 4, 9, 6, 9, 11, 2, 6, 2, 4, 9, 12, 9, 9, 1, 11, 7, 14, 19, 1, 1, 3, 13, 11, 13, 1, 4, 7, 4, 7, 13, 2, 5, 1, 4, 4, 1, 1, 3, 6, 5, 6, 2, 8, 1, 5, 5, 5, 6, 1, 5, 2, 1, 4, 7, 1, 7, 1, 11, 1, 7, 8, 6, 12, 1, 11, 16, 12, 5, 4, 3, 18, 3, 13, 18, 8, 1]
tree_split_feature_index = [29, 23, 0, 18, 32, 13, 33, 24, 1, 33, 6, 30, 29, 0, 34, 31, 13, 17, 27, 26, 22, 30, 15, 18, 18, 30, 14, 25, 0, 27, 25, 29, 15, 22, 2, 8, 29, 15, 35, 21, 20, 34, 1, 34, 32, 2, 1, 14, 1, 14, 31, 26, 14, 33, 33, 2, 34, 30, 21, 0, 32, 33, 32, 32, 33, 1, 33, 0, 18, 2, 9, 27, 15, 26, 30, 33, 33, 28, 2, 0, 2, 34, 13, 32, 33, 15, 14, 18, 28, 13, 1, 26, 33, 13, 5, 28, 34, 25, 4, 14, 14, 2, 25, 13, 18, 32, 31, 29, 33, 32, 1, 30, 3, 14, 33, 31, 14, 31, 0, 29, 1, 26, 33, 1, 19, 20, 33, 1, 12, 0, 15, 31, 22, 34, 3, 29, 13, 0, 30, 34, 25, 17, 35, 6, 27, 1, 2, 4, 13, 1, 22, 27, 1, 18, 28, 13, 13, 34, 33, 34, 25, 16, 13, 34, 14, 30, 22, 32, 2, 12, 2, 31, 29, 1, 14, 34, 15, 10, 31, 30, 14, 33, 20, 23, 18, 2, 2, 1, 12, 25, 31, 22, 29, 33, 22, 0, 7, 18, 33, 17, 7, 2, 15, 32, 14, 25, 15, 16, 27, 18, 31, 32, 17, 15, 30, 11, 27, 14, 9, 33, 14, 13, 5, 25, 1, 13, 2, 4, 2, 1, 34, 30, 28, 15, 33, 31, 14, 1, 25, 20]
tree_split_xor_mask = [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, 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, 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, 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, 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, 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, 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, 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]
cat_features_index = []
one_hot_cat_feature_index = []
one_hot_hash_values = [
]
ctr_feature_borders = [
]
## Aggregated array of leaf values for trees. Each tree is represented by a separate line:
leaf_values = [
0, 0, 0.001049999981001019, 0, 0.0006999999873340129, 0.0004199999924004077, 0, 0.0003499999936670065, 0.0006999999873340129, 0, 0, 0, 0.0008999999837151595, 0.0008399999848008155, 0, 0.005499108720590722, 0, 0, 0.001310204828003209, 0, 0, 0, 0.002099799992893635, 0.003281666943095616, 0, 0, 0.001081739094670376, 0, 0, 0, 0.001261285375551461, 0.003276219466932844, 0.0001235294095295317, 0, 0.001143749976810068, 0, 0, 0, 0.002145652130229966, 0.001699999969239746, 0.0005409089896827957, 0, 0.0004052632035001316, 0, 0.001076041724695822, 0, 0.001088611397153381, 0.001412068939966888, 0, 0, 0.001978133278083802, 0.001049999981001019, 0, 0, 0.002257627220401316, 0.002847457878670443, 0, 0, 0.002380742281139534, 0, 0, 0, 0.00181075114546265, 0.003175870739941051,
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-9.16711056973941e-06, 0.0005034566287572076, 0, 0.001441386761064904, 0.0004650239878077921, 0.000486430705943646, 0, 0.0005251381598288845, 0.0001134254723145374, 0.0008620916724859883, 0, -0.0008323764273133311, 0.001453916079918413, 0.0008824879696618304, 0, -0.0001285750717252047, -0.0005069688428288851, 0, 0, 0.005816592077285318, -0.0002254920618075315, -0.0005733754752501158, 0.003707278843993314, 0.00013976365923684, 0, 0, 0, 0.0005986222024746425, 0, 0, -0.0006685565051393909, 0.0007969981287726764, 0.001040804763590685, 0.0005346885863424908, 0, 0.0007155838609951201, 0.0008941458230919449, 0.0004321542448111832, 0, 0.0001943064956706199, 0.0005784127855857222, 0.0005255190997813209, 0, 0.0009245928088131157, 0.0007190333857457896, 0.00140236367636606, 0, 0.001389858276949751, 0, 0, 0, 0, 0, 0.003671353653579372, 0, 0.00065933503866986, 0, 0.0006507363761157676, 0, 0.0006079218177244004, 0, 0.003688788915741222, 0, 0.001141351685431146,
0.0008975346700268612, 0, 0, 0, 0.001095589390694782, 0, 0, 0, -0.000398821727663964, 0, 0, 0, 0.0003960545463579374, 0.001207907130558381, 0, 0, -0.0002523844842442799, 0, 0, 0, 0.0001629665468382726, 0, 0, 0, -0.0001990460313858596, 0, 0, 0, 0.001130564575074068, 0.0003958068608952062, 0, 0, 0.0003425117019766629, -0.0002238008713817761, 0.0002829427106781958, 0, 0.001098319531893118, 0.0007711348545921387, 0, 0.005032509170381743, 0.0006772317497140267, 0.0008418024902062981, 0.001383121280801782, 0, 0.001406084535364068, 0.00377177092102054, 0.0002855971238840317, 0, 0.0009829963219868344, 0, 0.000759501127128191, 0, 0.002051457016826751, 0, -0.0003417858088937196, 0, 0.0005935258633506886, 0, -5.293601014386419e-05, 0, 0.0006672387535825124, 0.003623450780677303, -0.0001267796715909729, 0,
-0.0001120267827185123, 0, 4.593413181766548e-05, 0, 0, 0, 0.002433179729224984, 0, 0, 0, -0.0003050968826606913, 0, 0, 0, 0, 0, -0.0005743353462862718, 0.003596274900429651, 0, 0.0002932041150486089, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0002068751794016945, 0, 0.0008758398147185526, 0.000636932734205325, 0.006178877688526687, 0, 0.0008892727130054831, 0, 0, 0, 0.0006343285564268348, 0, 0, 0, -0.0005941083110153163, 0, 0, 0.001434437800170044, 0.001563699247234594, 0.001546392253934484, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]
cat_features_hashes = {
}
def hash_uint64(string):
return cat_features_hashes.get(str(string), 0x7fFFffFF)
### Applicator for the CatBoost model
def apply_catboost_model(float_features, cat_features=[], ntree_start=0, ntree_end=catboost_model.tree_count):
"""
Applies the model built by CatBoost.
Parameters
----------
float_features : list of float features
cat_features : list of categorical features
You need to pass float and categorical features separately in the same order they appeared in train dataset.
For example if you had features f1,f2,f3,f4, where f2 and f4 were considered categorical, you need to pass here float_features=f1,f3, cat_features=f2,f4
Returns
-------
prediction : formula value for the model and the features
"""
if ntree_end == 0:
ntree_end = catboost_model.tree_count
else:
ntree_end = min(ntree_end, catboost_model.tree_count)
model = catboost_model
assert len(float_features) >= model.float_feature_count
assert len(cat_features) >= model.cat_feature_count
# Binarise features
binary_features = [0] * model.binary_feature_count
binary_feature_index = 0
for i in range(len(model.float_feature_borders)):
for border in model.float_feature_borders[i]:
binary_features[binary_feature_index] += 1 if (float_features[model.float_features_index[i]] > border) else 0
binary_feature_index += 1
transposed_hash = [0] * model.cat_feature_count
for i in range(model.cat_feature_count):
transposed_hash[i] = hash_uint64(cat_features[i])
if len(model.one_hot_cat_feature_index) > 0:
cat_feature_packed_indexes = {}
for i in range(model.cat_feature_count):
cat_feature_packed_indexes[model.cat_features_index[i]] = i
for i in range(len(model.one_hot_cat_feature_index)):
cat_idx = cat_feature_packed_indexes[model.one_hot_cat_feature_index[i]]
hash = transposed_hash[cat_idx]
for border_idx in range(len(model.one_hot_hash_values[i])):
binary_features[binary_feature_index] |= (1 if hash == model.one_hot_hash_values[i][border_idx] else 0) * (border_idx + 1)
binary_feature_index += 1
if hasattr(model, 'model_ctrs') and model.model_ctrs.used_model_ctrs_count > 0:
ctrs = [0.] * model.model_ctrs.used_model_ctrs_count;
calc_ctrs(model.model_ctrs, binary_features, transposed_hash, ctrs)
for i in range(len(model.ctr_feature_borders)):
for border in model.ctr_feature_borders[i]:
binary_features[binary_feature_index] += 1 if ctrs[i] > border else 0
binary_feature_index += 1
# Extract and sum values from trees
result = 0.
tree_splits_index = 0
current_tree_leaf_values_index = 0
for tree_id in range(ntree_start, ntree_end):
current_tree_depth = model.tree_depth[tree_id]
index = 0
for depth in range(current_tree_depth):
border_val = model.tree_split_border[tree_splits_index + depth]
feature_index = model.tree_split_feature_index[tree_splits_index + depth]
xor_mask = model.tree_split_xor_mask[tree_splits_index + depth]
index |= ((binary_features[feature_index] ^ xor_mask) >= border_val) << depth
result += model.leaf_values[current_tree_leaf_values_index + index]
tree_splits_index += current_tree_depth
current_tree_leaf_values_index += (1 << current_tree_depth)
return result
| 228.842105
| 1,227
| 0.751173
| 5,723
| 43,480
| 5.671151
| 0.289883
| 0.089783
| 0.082912
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| 0.027791
| 0.02015
| 0.018764
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| 0.775809
| 0.117548
| 43,480
| 189
| 1,228
| 230.05291
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| 0.000234
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| 0.000234
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| 0.013245
| 1
| 0.013245
| false
| 0
| 0
| 0.006623
| 0.13245
| 0
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| 0
| 0
| null | 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
6522bc5c7abd6744cba75a46a735a944d3a2b574
| 93
|
py
|
Python
|
news_collector/collector/apps.py
|
orehush/channels-examples
|
5721e346c2e50381868c80c1daa8551d1d80eac5
|
[
"BSD-3-Clause"
] | 1,311
|
2016-03-23T20:04:59.000Z
|
2022-03-22T11:32:24.000Z
|
news_collector/collector/apps.py
|
orehush/channels-examples
|
5721e346c2e50381868c80c1daa8551d1d80eac5
|
[
"BSD-3-Clause"
] | 48
|
2016-12-06T06:13:55.000Z
|
2022-03-23T20:11:04.000Z
|
news_collector/collector/apps.py
|
orehush/channels-examples
|
5721e346c2e50381868c80c1daa8551d1d80eac5
|
[
"BSD-3-Clause"
] | 529
|
2016-03-23T20:19:15.000Z
|
2022-03-22T11:32:29.000Z
|
from django.apps import AppConfig
class CollectorConfig(AppConfig):
name = 'collector'
| 15.5
| 33
| 0.763441
| 10
| 93
| 7.1
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16129
| 93
| 5
| 34
| 18.6
| 0.910256
| 0
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| 0.096774
| 0
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| 1
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| false
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| 0.333333
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| 1
| 0
| 1
| 0
|
0
| 4
|
e8dd01deb2f05d551a7f88dd7fc20e3d5f3608cd
| 185
|
py
|
Python
|
packages/django-backend/notify/apps.py
|
ZechyW/cs-toolkit
|
802a28b3a4ce33754b8a986f6d2eb616aad017d4
|
[
"MIT"
] | 1
|
2021-08-02T18:32:08.000Z
|
2021-08-02T18:32:08.000Z
|
packages/django-backend/notify/apps.py
|
ZechyW/cs-toolkit
|
802a28b3a4ce33754b8a986f6d2eb616aad017d4
|
[
"MIT"
] | 5
|
2021-03-09T17:02:32.000Z
|
2022-02-26T17:28:38.000Z
|
packages/django-backend/notify/apps.py
|
ZechyW/cs-toolkit
|
802a28b3a4ce33754b8a986f6d2eb616aad017d4
|
[
"MIT"
] | 1
|
2021-08-02T18:32:09.000Z
|
2021-08-02T18:32:09.000Z
|
from django.apps import AppConfig
class NotifyConfig(AppConfig):
name = "notify"
def ready(self):
# noinspection PyUnresolvedReferences
import notify.signals
| 18.5
| 45
| 0.702703
| 18
| 185
| 7.222222
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.232432
| 185
| 9
| 46
| 20.555556
| 0.915493
| 0.189189
| 0
| 0
| 0
| 0
| 0.040541
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
e8fa23411968e14b027f71bb5a6a384e0fd3d995
| 789
|
py
|
Python
|
Module-04-Generators/py09_generator_send_example_3.py
|
CodingGearsCourses/Python-Advanced-Concepts
|
6ba1a1751fe0ee94816020a023158bc4ef13e2d3
|
[
"MIT"
] | null | null | null |
Module-04-Generators/py09_generator_send_example_3.py
|
CodingGearsCourses/Python-Advanced-Concepts
|
6ba1a1751fe0ee94816020a023158bc4ef13e2d3
|
[
"MIT"
] | null | null | null |
Module-04-Generators/py09_generator_send_example_3.py
|
CodingGearsCourses/Python-Advanced-Concepts
|
6ba1a1751fe0ee94816020a023158bc4ef13e2d3
|
[
"MIT"
] | null | null | null |
# Copyright 2020 https://www.globaletraining.com/
# Generator send method
def simple_gen(start_number=10):
i = start_number
while True:
x = (yield i * 2)
if x: # check if used send()
i += x
else:
i += 1
gen1 = simple_gen()
print(gen1.__next__())
print(gen1.send(10))
print(gen1.__next__())
print(gen1.send(20))
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
print(gen1.send(20))
print(gen1.send(20))
print(gen1.__next__())
print(gen1.__next__())
print(gen1.__next__())
| 20.763158
| 49
| 0.66033
| 108
| 789
| 4.12037
| 0.277778
| 0.444944
| 0.525843
| 0.68764
| 0.65618
| 0.65618
| 0.597753
| 0.597753
| 0.597753
| 0.555056
| 0
| 0.059002
| 0.162231
| 789
| 38
| 50
| 20.763158
| 0.614221
| 0.114068
| 0
| 0.677419
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.032258
| false
| 0
| 0
| 0
| 0.032258
| 0.709677
| 0
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| null | 1
| 1
| 1
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| 0
| 0
| 0
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| null | 0
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| 0
| 0
| 0
| 1
|
0
| 4
|
331222ae4a4ee18ea2c6aa64dfcbc198fbebdc5d
| 111
|
py
|
Python
|
src/stories/contrib/sentry/django.py
|
dargor/stories
|
550a36506c5ec0a4603c0f14a3c5fe52132ef6bf
|
[
"BSD-2-Clause"
] | 1
|
2021-07-17T01:36:41.000Z
|
2021-07-17T01:36:41.000Z
|
src/stories/contrib/sentry/django.py
|
dargor/stories
|
550a36506c5ec0a4603c0f14a3c5fe52132ef6bf
|
[
"BSD-2-Clause"
] | null | null | null |
src/stories/contrib/sentry/django.py
|
dargor/stories
|
550a36506c5ec0a4603c0f14a3c5fe52132ef6bf
|
[
"BSD-2-Clause"
] | null | null | null |
import stories.contrib.sentry.breadcrumbs # noqa
from raven.contrib.django.client import DjangoClient # noqa
| 37
| 60
| 0.81982
| 14
| 111
| 6.5
| 0.785714
| 0
| 0
| 0
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| 0.108108
| 111
| 2
| 61
| 55.5
| 0.919192
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| 1
| 0
| 0
| 0
|
0
| 4
|
335ef229f069de361d9c3d97ad5e5823e6980f59
| 237
|
py
|
Python
|
source/knowledge/python/example_python/HzuNews.py
|
Eugene-Forest/NoteBook
|
45f627c9417ce30fd301b67c83c1f82a743de687
|
[
"BSD-3-Clause"
] | 1
|
2021-12-22T09:08:26.000Z
|
2021-12-22T09:08:26.000Z
|
source/knowledge/python/example_python/HzuNews.py
|
Eugene-Forest/NoteBook
|
45f627c9417ce30fd301b67c83c1f82a743de687
|
[
"BSD-3-Clause"
] | null | null | null |
source/knowledge/python/example_python/HzuNews.py
|
Eugene-Forest/NoteBook
|
45f627c9417ce30fd301b67c83c1f82a743de687
|
[
"BSD-3-Clause"
] | null | null | null |
# 用来存储从惠州学院新闻网获取的一个新闻对象
class HzuNews:
"""一个简单的新闻信息数据结构"""
def __init__(self, title, link, time):
self.title = title
self.link = link
self.time = time
def get_title(self):
return self.title
| 18.230769
| 42
| 0.603376
| 26
| 237
| 5.307692
| 0.461538
| 0.195652
| 0
| 0
| 0
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| 0.295359
| 237
| 12
| 43
| 19.75
| 0.826347
| 0.151899
| 0
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| 0
| 0
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| 1
| 0.285714
| false
| 0
| 0
| 0.142857
| 0.571429
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
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| 0
| 0
| 0
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| null | 0
| 0
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| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
336113c5514f3b1fb67bac50810f99fdc61bfbe7
| 4,347
|
py
|
Python
|
cmsplugin_bootstrap_grid/models.py
|
movermeyer/cmsplugin-bootstrap
|
2a03a9fd609fc166a5a4e19a22fbcfbfa081fad7
|
[
"BSD-3-Clause"
] | null | null | null |
cmsplugin_bootstrap_grid/models.py
|
movermeyer/cmsplugin-bootstrap
|
2a03a9fd609fc166a5a4e19a22fbcfbfa081fad7
|
[
"BSD-3-Clause"
] | null | null | null |
cmsplugin_bootstrap_grid/models.py
|
movermeyer/cmsplugin-bootstrap
|
2a03a9fd609fc166a5a4e19a22fbcfbfa081fad7
|
[
"BSD-3-Clause"
] | null | null | null |
# coding: utf-8
from cms.models import CMSPlugin
from cmsplugin_bootstrap_grid.utils import HtmlAttributeDict
from django.conf import settings
from django.db import models
from django.utils.translation import ugettext as _
CONFIG = {'COLUMNS': 12}
CONFIG.update(getattr(settings, 'CMSPLUGIN_GRID_CONFIG', {}))
SIZE_XS_CHOICES = [('%s' % i, 'col-xs-%s' % i) for i in range(1, CONFIG['COLUMNS'] + 1)]
SIZE_SM_CHOICES = [('%s' % i, 'col-sm-%s' % i) for i in range(1, CONFIG['COLUMNS'] + 1)]
SIZE_MD_CHOICES = [('%s' % i, 'col-md-%s' % i) for i in range(1, CONFIG['COLUMNS'] + 1)]
SIZE_LG_CHOICES = [('%s' % i, 'col-lg-%s' % i) for i in range(1, CONFIG['COLUMNS'] + 1)]
SIZE_XS_OFFSET_CHOICES = [('%s' % i, 'col-xs-offset-%s' % i) for i in range(0, CONFIG['COLUMNS'] + 1)]
SIZE_SM_OFFSET_CHOICES = [('%s' % i, 'col-sm-offset-%s' % i) for i in range(0, CONFIG['COLUMNS'] + 1)]
SIZE_MD_OFFSET_CHOICES = [('%s' % i, 'col-md-offset-%s' % i) for i in range(0, CONFIG['COLUMNS'] + 1)]
SIZE_LG_OFFSET_CHOICES = [('%s' % i, 'col-lg-offset-%s' % i) for i in range(0, CONFIG['COLUMNS'] + 1)]
class Row(CMSPlugin):
css_classes = models.CharField(
_('css classes'), max_length=200, blank=True,
help_text=_("Add extra classes to bootstrap row. (Separate classes with space)"))
def _get_attrs(self):
if not hasattr(self, '_cached_attrs'):
self._cached_attrs = HtmlAttributeDict({"class": "row"})
self._cached_attrs.add_class(self.css_classes)
return self._cached_attrs
attrs = property(_get_attrs)
def __unicode__(self):
return ''
class Column(CMSPlugin):
size_xs = models.CharField(
_('Size xs'), choices=SIZE_XS_CHOICES,
default=None, max_length=50, null=True, blank=True,
help_text=_("Extra small devices Phones (<768px)"))
size_sm = models.CharField(
_('Size sm'), choices=SIZE_SM_CHOICES,
default=None, max_length=50, null=True, blank=True,
help_text=_("Small devices Tablets (≥768px)"))
size_md = models.CharField(
_('Size md'), choices=SIZE_MD_CHOICES,
default=None, max_length=50, null=True, blank=True,
help_text=_("Medium devices Desktops (≥992px)"))
size_lg = models.CharField(
_('Size lg'), choices=SIZE_LG_CHOICES,
default=None, max_length=50, null=True, blank=True,
help_text=_("Large devices Desktops (≥1200px)"))
size_offset_xs = models.CharField(
_('Offset xs'), choices=SIZE_XS_OFFSET_CHOICES,
default=None, max_length=50, null=True, blank=True,
help_text=_("Extra small devices Phones (<768px)"))
size_offset_sm = models.CharField(
_('Offset sm'), choices=SIZE_SM_OFFSET_CHOICES,
default=None, max_length=50, null=True, blank=True,
help_text=_("Small devices Tablets (≥768px)"))
size_offset_md = models.CharField(
_('Offset md'), choices=SIZE_MD_OFFSET_CHOICES,
default=None, max_length=50, null=True, blank=True,
help_text=_("Medium devices Desktops (≥992px)"))
size_offset_lg = models.CharField(
_('Offset lg'), choices=SIZE_LG_OFFSET_CHOICES,
default=None, max_length=50, null=True, blank=True,
help_text=_("Large devices Desktops (≥1200px)"))
css_classes = models.CharField(
_('css classes'), max_length=200, blank=True,
help_text=_("Add extra classes to bootstrap column. (Separate classes with space)"))
def _get_attrs(self):
if not hasattr(self, '_cached_attrs'):
self._cached_attrs = HtmlAttributeDict()
self._cached_attrs.add_class(self.css_classes)
self._cached_attrs.add_class(self.get_size_xs_display())
self._cached_attrs.add_class(self.get_size_sm_display())
self._cached_attrs.add_class(self.get_size_md_display())
self._cached_attrs.add_class(self.get_size_lg_display())
self._cached_attrs.add_class(self.get_size_offset_xs_display())
self._cached_attrs.add_class(self.get_size_offset_sm_display())
self._cached_attrs.add_class(self.get_size_offset_md_display())
self._cached_attrs.add_class(self.get_size_offset_lg_display())
return self._cached_attrs
attrs = property(_get_attrs)
def __unicode__(self):
return self.attrs['class']
| 43.909091
| 102
| 0.665286
| 611
| 4,347
| 4.436989
| 0.139116
| 0.011804
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| 0.75581
| 0.701217
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| 0
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| 0.195307
| 4,347
| 99
| 103
| 43.909091
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|
0
| 4
|
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