Parameterframe
The module provides an interface for managing solution parameters.
It allows for the structured storage and retrieval of parameter sets from a database.
import sys
import pandas as pd
import os
sys.path.append('../')
from parameterframe import ParameterFrame, MockerDatabaseConnector, SqlAlchemyDatabaseManager
Content
- Adding new solution and uploading it
- Processing new files and creating parameter set
- Adding parameter set to solution and commiting
- Uploading parameter sets
- Getting latest parameter set id for solution
- Changing parameter set status
- Pulling select parameter sets
- Reconstructing parameter se
- Structure of local commit tables
1. Adding new solution and uploading it
# - with database connector for MockerDB
pf = ParameterFrame(
database_connector = MockerDatabaseConnector(connection_details = {
'base_url' : 'http://localhost:8001'})
)
when using SqlAlchemyDatabaseManager with database for the first time, it might be useful to create tables with SqlAlchemyDatabaseManager.create_tables
and if schema of the database needs to be reset SqlAlchemyDatabaseManager.drop_tables
# - with SqlAlchemy database connector
pf = ParameterFrame(
database_connector = SqlAlchemyDatabaseManager(connection_details = {
'base_url' : 'postgresql+psycopg2://postgres:mysecretpassword@localhost:5432/mytestdb'})
)
pf.show_solutions()
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
solution_id |
solution_name |
solution_description |
deployment_date |
deprecation_date |
maintainers |
commited_parameter_sets |
pf.add_solution(
# mandatory
solution_name="new_example_solution",
# optional description
solution_description="Description of new example solution.",
deployment_date="2024-xx-xx",
deprecation_date=None,
maintainers="some text about maintainers credentials"
)
Solution id for new_example_solution: b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca
True
pf.commit_solution(
# either solution id or solution name should be provided
solution_name="new_example_solution"
)
Commited solution description for b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca
True
pf.show_solutions()
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
solution_id |
solution_name |
solution_description |
deployment_date |
deprecation_date |
maintainers |
commited_parameter_sets |
0 |
b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca |
new_example_solution |
Description of new example solution. |
2024-xx-xx |
None |
some text about maintainers credentials |
0 |
pf.push_solution(
# either solution id or solution name should be provided
solution_name = "new_example_solution"
)
HTTP Request: POST http://localhost:8001/insert "HTTP/1.1 200 OK"
True
2. Processing new files and creating parameter set
params_path = "../tests/parameterframe/example_configs"
pf = ParameterFrame(
params_path = params_path,
database_connector = MockerDatabaseConnector(connection_details = {
'base_url' : 'http://localhost:8001'})
)
pf = ParameterFrame(
params_path = params_path,
database_connector = SqlAlchemyDatabaseManager(connection_details = {
'base_url' : 'postgresql+psycopg2://postgres:mysecretpassword@localhost:5432/mytestdb'})
)
pf.process_parameters_from_files()
True
pf.show_solutions()
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
solution_id |
solution_name |
solution_description |
deployment_date |
deprecation_date |
maintainers |
commited_parameter_sets |
pf.make_parameter_set(
parameter_set_name="test_set",
parameter_set_description="example parameters for test purposes",
parameter_names=['param_1','param_2','param_10', 'param_11','param_21']
)
Parameter set id for test_set: a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5
3. Adding parameter set to solution and commiting
pf.add_parameter_set_to_solution(
solution_id = 'b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
parameter_set_name="test_set")
b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca is not in solutions saved to memory!
Name pink_happy_car_642 is assigned to b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca temporarily!
True
pf.commit_solution(solution_name="pink_happy_car_642",
parameter_set_names=["test_set"])
Commited solution tables for b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca
True
pf.show_solutions()
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
solution_id |
solution_name |
solution_description |
deployment_date |
deprecation_date |
maintainers |
commited_parameter_sets |
0 |
b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca |
None |
None |
None |
None |
None |
1 |
pf.show_parameter_sets(solution_id = 'b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca')
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
parameter_set_id |
parameter_set_name |
parameter_set_description |
deployment_status |
insertion_datetime |
commited_parameters |
0 |
a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5 |
test_set |
example parameters for test purposes |
STAGING |
2024-05-15 00:09:10 |
5 |
pf.show_parameters(solution_id = 'b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
parameter_set_id = 'a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5')
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
parameter_id |
parameter_name |
parameter_description |
file_name |
file_type |
commited_attributes |
0 |
4cea5b09e77da310c5105978f2ceea5c5d8c9c7b65d0e00b45135ea90fc011af |
param_1 |
|
param_1.yaml |
yaml |
3 |
1 |
bf11768decb1d0204e2636edd05c354573d473e67f1b048369b2ee99c865bf5f |
param_2 |
|
param_2.yaml |
yaml |
6 |
2 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
param_10 |
|
param_10.txt |
txt |
9 |
3 |
ace2f31433212fbf9e764069a30a7675ca78f496d31f061d06d0a0420fc52768 |
param_11 |
|
param_11.dill |
other |
1 |
4 |
1a4f19ee9e186ee739daecbc778501c5851d3fb5d05c4a3c1200e599855e8689 |
param_21 |
|
param_21.ipynb |
other |
2 |
4. Uploading parameter sets
pf.push_solution(solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
parameter_set_names=["test_set"])
HTTP Request: POST http://localhost:8001/insert "HTTP/1.1 200 OK"
True
5. Getting latest parameter set id for solution
# - with database connector for MockerDB
pf = ParameterFrame(
database_connector = MockerDatabaseConnector(connection_details = {
'base_url' : 'http://localhost:8001'})
)
# - with SqlAlchemy database connector
pf = ParameterFrame(
database_connector = SqlAlchemyDatabaseManager(connection_details = {
'base_url' : 'postgresql+psycopg2://postgres:mysecretpassword@localhost:5432/mytestdb'})
)
pf.get_parameter_set_id_for_solution(solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
deployment_status="STAGING")
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
['a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5']
pf.get_deployment_status(solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
parameter_set_id='a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5')
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
'STAGING'
6. Changing parameter set status
# - with database connector for MockerDB
pf = ParameterFrame(
database_connector = MockerDatabaseConnector(connection_details = {
'base_url' : 'http://localhost:8001'})
)
# - with SqlAlchemy database connector
pf = ParameterFrame(
database_connector = SqlAlchemyDatabaseManager(connection_details = {
'base_url' : 'postgresql+psycopg2://postgres:mysecretpassword@localhost:5432/mytestdb'})
)
pf.database_connector.modify_parameter_set_status(
solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
parameter_set_ids = 'a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5',
current_deployment_status = "PRODUCTION",
new_deployment_status = "STAGING"
)
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
No data was found with applied filters!
No deployed parameter_set_ids with PRODUCTION from selected!
False
pf.change_status_from_staging_to_production(
solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
parameter_set_id='a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5'
)
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8001/delete "HTTP/1.1 200 OK"
b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca + a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5 : STAGING -> PRODUCTION
HTTP Request: POST http://localhost:8001/insert "HTTP/1.1 200 OK"
pf.get_deployment_status(solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
parameter_set_id='a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5')
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
'PRODUCTION'
pf.change_status_from_production_to_archived(
solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
parameter_set_id='a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5'
)
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8001/delete "HTTP/1.1 200 OK"
b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca + a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5 : PRODUCTION -> ARCHIVED
HTTP Request: POST http://localhost:8001/insert "HTTP/1.1 200 OK"
pf.get_deployment_status(solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
parameter_set_id='a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5')
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
'ARCHIVED'
pf.change_status_from_archived_production(
solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
parameter_set_id='a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5'
)
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
No data was found with applied filters!
No deployed parameter_set_ids with PRODUCTION from selected!
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8001/delete "HTTP/1.1 200 OK"
b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca + a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5 : ARCHIVED -> PRODUCTION
HTTP Request: POST http://localhost:8001/insert "HTTP/1.1 200 OK"
pf.get_deployment_status(solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
parameter_set_id='a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5')
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
'PRODUCTION'
7. Pulling select parameter sets
params_path = "../tests/parameterframe/example_configs"
# - with database connector for MockerDB
pf2 = ParameterFrame(
params_path = params_path,
database_connector = MockerDatabaseConnector(connection_details = {
'base_url' : 'http://localhost:8001'})
)
# - with SqlAlchemy database connector
pf2 = ParameterFrame(
params_path = params_path,
database_connector = SqlAlchemyDatabaseManager(connection_details = {
'base_url' : 'postgresql+psycopg2://postgres:mysecretpassword@localhost:5432/mytestdb'})
)
pf2.show_solutions()
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
solution_id |
solution_name |
solution_description |
deployment_date |
deprecation_date |
maintainers |
commited_parameter_sets |
When pulling information with database handler, one could pull specific parameter sets, solutions and everything.
pf2.pull_solution(solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
# optionally specify parameter_set_id
parameter_set_id='a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5')
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
No data was found with applied filters!
No solutions with b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca could be pulled!
True
pf2.pull_solution(solution_id='b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca',
# optionally specify parameter_set_id
parameter_set_id=None)
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
HTTP Request: POST http://localhost:8001/search "HTTP/1.1 200 OK"
True
pf2.pull_solution(
# optional parameter to skip pull of attributes if data pulled just for show_ methods
pull_attribute_values = False
)
True
pf2.show_solutions()
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
solution_id |
solution_name |
solution_description |
deployment_date |
deprecation_date |
maintainers |
commited_parameter_sets |
0 |
cec89c4cbb8c891d388407ea93d84a5cd4f996af6d5c1b0cc5fe1cb12101acf5 |
new_example_solution |
Description of new example solution. |
2024-xx-xx |
None |
some text about maintainers credentials |
6 |
pf2.show_parameter_sets(solution_id='cec89c4cbb8c891d388407ea93d84a5cd4f996af6d5c1b0cc5fe1cb12101acf5')
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
parameter_set_id |
parameter_set_name |
parameter_set_description |
deployment_status |
insertion_datetime |
commited_parameters |
0 |
5779bbf896ebb8f09a6ea252b09f8adb1a416e8780cf1424fb9bb93dbec8deb5 |
green_tiny_car_749 |
|
STAGING |
2024-05-15 01:36:09 |
3 |
1 |
3940d6dd4c0d817625a31141874c54cf0c8d88b24994f7915deb4096b3c8d0cf |
blue_tiny_television_381 |
|
STAGING |
2024-05-15 00:37:50 |
2 |
2 |
2f3ee8e19d91a89298d40984df5e7bdd1f1a48008b2e61c88a7f6f81b4ab23f5 |
silver_happy_car_441 |
|
STAGING |
2024-05-16 00:03:25 |
1 |
3 |
82b8c5340454adf83667e59092fedbee28213475fd58ab6b3d95b4fc60f4d45f |
purple_giant_television_135 |
|
STAGING |
2024-05-16 00:05:43 |
1 |
4 |
dddc057bc151de9f8fb8caa834c8e13b789cf68cb53299b4c65c23f1e1310acd |
red_sad_scooter_769 |
|
STAGING |
2024-05-16 00:08:21 |
2 |
5 |
73ece98c90d4e0bcce8b523a8e8d2bd4290c68f2a783ea279b39fe4507e42de7 |
blue_fuzzy_refrigerator_297 |
|
STAGING |
2024-05-15 23:57:17 |
1 |
pf2.show_parameters(solution_id='cec89c4cbb8c891d388407ea93d84a5cd4f996af6d5c1b0cc5fe1cb12101acf5',
parameter_set_id='5779bbf896ebb8f09a6ea252b09f8adb1a416e8780cf1424fb9bb93dbec8deb5')
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
parameter_id |
parameter_name |
parameter_description |
file_name |
file_type |
commited_attributes |
0 |
3386ebc962b1c57745ca24320bf873df6eb84a2b9cb733607d72006347bf95b8 |
Screenshot 2024-05-04 at 02 |
|
Screenshot 2024-05-04 at 02.59.31.png |
other |
35 |
1 |
4d8ca206d9bd09296b69a95f0c3c62d233282025964c356811510cc074cc2c49 |
1 |
|
1. AF - opis projektu.pdf |
other |
34 |
2 |
5afae3951544cd3736685a3b2daa31c00106191a799b96b0c636cd35e9a416ff |
uploads |
|
uploads.zip |
other |
61 |
8. Reconstructing parameter set
os.listdir("../tests/parameterframe/reconstructed_files")
[]
pf2.reconstruct_parameter_set(
solution_name = "new_example_solution",
parameter_set_name = "test_set",
params_path = "../tests/parameterframe/reconstructed_files"
)
os.listdir("../tests/parameterframe/reconstructed_files")
['param_2.yaml',
'param_11.dill',
'param_1.yaml',
'param_10.txt',
'param_21.ipynb']
9. Structure of commit tables
solution_description
pd.DataFrame(pf2.commited_tables['b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca']['solution_description'])
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
solution_id |
solution_name |
solution_description |
deployment_date |
deprecation_date |
maintainers |
0 |
b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca |
new_example_solution |
Description of new example solution. |
2024-xx-xx |
None |
some text about maintainers credentials |
solution_parameter_set
param_set_id = 'a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5'
pd.DataFrame(pf2.commited_tables['b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca']['solution_parameter_set'][param_set_id])
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
solution_id |
parameter_set_id |
deployment_status |
insertion_datetime |
0 |
b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca |
a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5 |
PRODUCTION |
2024-05-07 19:51:13 |
parameter_set
pd.DataFrame(pf2.commited_tables['b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca']\
['parameter_set']\
[param_set_id])
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
parameter_set_id |
parameter_id |
0 |
a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5 |
4cea5b09e77da310c5105978f2ceea5c5d8c9c7b65d0e00b45135ea90fc011af |
1 |
a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5 |
bf11768decb1d0204e2636edd05c354573d473e67f1b048369b2ee99c865bf5f |
2 |
a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
3 |
a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5 |
ace2f31433212fbf9e764069a30a7675ca78f496d31f061d06d0a0420fc52768 |
4 |
a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5 |
1a4f19ee9e186ee739daecbc778501c5851d3fb5d05c4a3c1200e599855e8689 |
parameter_set_description
pd.DataFrame(pf2.commited_tables['b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca']\
['parameter_set_description']\
[param_set_id])
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
parameter_set_id |
parameter_set_name |
parameter_set_description |
0 |
a54f04d2ff154294309403206e059aec556cdcfa51120649ce663f3230a970d5 |
test_set |
example parameters for test purposes |
parameter_description
pd.DataFrame([tab for param_id, tab_list in pf2.commited_tables['b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca']\
['parameter_description']\
[param_set_id].items()\
for tab in tab_list])
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
parameter_id |
parameter_name |
parameter_description |
file_name |
file_type |
0 |
4cea5b09e77da310c5105978f2ceea5c5d8c9c7b65d0e00b45135ea90fc011af |
param_1 |
|
param_1.yaml |
yaml |
1 |
bf11768decb1d0204e2636edd05c354573d473e67f1b048369b2ee99c865bf5f |
param_2 |
|
param_2.yaml |
yaml |
2 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
param_10 |
|
param_10.txt |
txt |
3 |
ace2f31433212fbf9e764069a30a7675ca78f496d31f061d06d0a0420fc52768 |
param_11 |
|
param_11.dill |
other |
4 |
1a4f19ee9e186ee739daecbc778501c5851d3fb5d05c4a3c1200e599855e8689 |
param_21 |
|
param_21.ipynb |
other |
parameter_attribute
pd.DataFrame([tab for param_id, tab_list in pf2.commited_tables['b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca']\
['parameter_attribute']\
[param_set_id].items() \
for tab in tab_list])
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
parameter_id |
attribute_id |
previous_attribute_id |
0 |
4cea5b09e77da310c5105978f2ceea5c5d8c9c7b65d0e00b45135ea90fc011af |
ee25af17445d7622cbf61a5b9424246a1f3104704b68bd31b9b7532471d492e5 |
None |
1 |
4cea5b09e77da310c5105978f2ceea5c5d8c9c7b65d0e00b45135ea90fc011af |
8b5b2be24e60ba407b90967820da8a1385a6d67691a02bc663703160ef655101 |
None |
2 |
4cea5b09e77da310c5105978f2ceea5c5d8c9c7b65d0e00b45135ea90fc011af |
52ea872c99c586530348ba8902dcab831761673d25cf1cb0023576820289ce6b |
None |
3 |
bf11768decb1d0204e2636edd05c354573d473e67f1b048369b2ee99c865bf5f |
7d5ee0e0cd00c3703e5f346c6887baf503faaf9fe090774f6866311f4fa34179 |
None |
4 |
bf11768decb1d0204e2636edd05c354573d473e67f1b048369b2ee99c865bf5f |
ee25af17445d7622cbf61a5b9424246a1f3104704b68bd31b9b7532471d492e5 |
7d5ee0e0cd00c3703e5f346c6887baf503faaf9fe090774f6866311f4fa34179 |
5 |
bf11768decb1d0204e2636edd05c354573d473e67f1b048369b2ee99c865bf5f |
3367512147bf19ae99c986b356af11dcdc067376aa1b79eb8ba8f61324e8dc18 |
7d5ee0e0cd00c3703e5f346c6887baf503faaf9fe090774f6866311f4fa34179 |
6 |
bf11768decb1d0204e2636edd05c354573d473e67f1b048369b2ee99c865bf5f |
341769820d8937a5c9f9b980eefca37f3f37fcc6fd01c6f4c930fdb9d5dd5128 |
7d5ee0e0cd00c3703e5f346c6887baf503faaf9fe090774f6866311f4fa34179 |
7 |
bf11768decb1d0204e2636edd05c354573d473e67f1b048369b2ee99c865bf5f |
2e8b00e571f9d835d3f022a9ff49b9779034ab21bffdcde075d9d729fabeb960 |
341769820d8937a5c9f9b980eefca37f3f37fcc6fd01c6f4c930fdb9d5dd5128 |
8 |
bf11768decb1d0204e2636edd05c354573d473e67f1b048369b2ee99c865bf5f |
ecd93cf051988b23b3590415f4e7d550de264600d7d2af8704c973b9c98ca6a9 |
341769820d8937a5c9f9b980eefca37f3f37fcc6fd01c6f4c930fdb9d5dd5128 |
9 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
fa4e8d81f4dbe6d306aff59bea4693d325a203be5d5b9fde5d5f1e7cce26b861 |
None |
10 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
c26e7e96f0f3647c159b0934f4dc55207ac059abb56005d7a8acd8344ef14798 |
fa4e8d81f4dbe6d306aff59bea4693d325a203be5d5b9fde5d5f1e7cce26b861 |
11 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
f7cd339f77c1799f399d8ebcbb27f2d41a448622254d64e9270ae2316211ac1d |
c26e7e96f0f3647c159b0934f4dc55207ac059abb56005d7a8acd8344ef14798 |
12 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
15a33fe62774a1857b404f453ba1195eb4355e10bc9519f2f991dd7ba8db19b7 |
f7cd339f77c1799f399d8ebcbb27f2d41a448622254d64e9270ae2316211ac1d |
13 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
99761e3d58bc213dc3ab33f2dc8dabe5f97d3aea6b59cd367d40b76937f49aa6 |
15a33fe62774a1857b404f453ba1195eb4355e10bc9519f2f991dd7ba8db19b7 |
14 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
036a9c122c1f4c9304afa23c4d1fce5224c270a206889afa689f3efb36ff368d |
99761e3d58bc213dc3ab33f2dc8dabe5f97d3aea6b59cd367d40b76937f49aa6 |
15 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
e72aa8015688052f4e7fddbf4c74e5bf2bd74239ebf3902a5fdc008ecb03aa46 |
036a9c122c1f4c9304afa23c4d1fce5224c270a206889afa689f3efb36ff368d |
16 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
0ae8eda3dbeedbc17e27a679c5426dd3af1434f7c37b4ecd3b2fb5c492272b75 |
e72aa8015688052f4e7fddbf4c74e5bf2bd74239ebf3902a5fdc008ecb03aa46 |
17 |
9a4a3ace265c9bf2facc0044ca24260c42805c6e7b2a608dfd2f56a54d9d36be |
cedcfbb0d95798514b6aaf30118fff7b46f863f1bc8b80bb2ddd2145e5b3f318 |
0ae8eda3dbeedbc17e27a679c5426dd3af1434f7c37b4ecd3b2fb5c492272b75 |
18 |
ace2f31433212fbf9e764069a30a7675ca78f496d31f061d06d0a0420fc52768 |
ace2f31433212fbf9e764069a30a7675ca78f496d31f061d06d0a0420fc52768 |
None |
19 |
1a4f19ee9e186ee739daecbc778501c5851d3fb5d05c4a3c1200e599855e8689 |
87d93e1862f0f58199c3fcb7114b92fe59f03581804b1c8419868fb00ff8a469 |
None |
20 |
1a4f19ee9e186ee739daecbc778501c5851d3fb5d05c4a3c1200e599855e8689 |
b4a705d09aa0361f4db453da32abb05a5c4e0249d6180d2b8a58d72d08dbd6a0 |
87d93e1862f0f58199c3fcb7114b92fe59f03581804b1c8419868fb00ff8a469 |
21 |
1a4f19ee9e186ee739daecbc778501c5851d3fb5d05c4a3c1200e599855e8689 |
e4e2c33a2ea67f34bf3ac1e9d99edaad501c7dc4ea82f4b60e9d959418d8438d |
b4a705d09aa0361f4db453da32abb05a5c4e0249d6180d2b8a58d72d08dbd6a0 |
22 |
1a4f19ee9e186ee739daecbc778501c5851d3fb5d05c4a3c1200e599855e8689 |
777abf12375b7f605b21535eb0d6232ce99581c6d2b1179af976cd0708ad27ff |
e4e2c33a2ea67f34bf3ac1e9d99edaad501c7dc4ea82f4b60e9d959418d8438d |
attribute_values
pd.DataFrame([tab for param_id, tab_list in pf2.commited_tables['b5c2e4a9bdcb57cc70bdb7310c7909cc1549550add79e3fbcc8aa1cf323cd8ca']\
['attribute_values']\
[param_set_id].items() \
for tab in tab_list])
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
|
attribute_id |
previous_attribute_id |
attribute_name |
attribute_value |
attribute_value_type |
0 |
ee25af17445d7622cbf61a5b9424246a1f3104704b68bd31b9b7532471d492e5 |
None |
name |
Some name |
str |
1 |
8b5b2be24e60ba407b90967820da8a1385a6d67691a02bc663703160ef655101 |
None |
age |
111 |
int |
2 |
52ea872c99c586530348ba8902dcab831761673d25cf1cb0023576820289ce6b |
None |
country |
Some land |
str |
3 |
ee25af17445d7622cbf61a5b9424246a1f3104704b68bd31b9b7532471d492e5 |
None |
name |
Some name |
str |
4 |
7d5ee0e0cd00c3703e5f346c6887baf503faaf9fe090774f6866311f4fa34179 |
None |
employee |
{'name': 'Some name', 'id': 10293, 'contact': {'email': 'some.name... |
dict |
5 |
3367512147bf19ae99c986b356af11dcdc067376aa1b79eb8ba8f61324e8dc18 |
None |
id |
10293 |
int |
6 |
341769820d8937a5c9f9b980eefca37f3f37fcc6fd01c6f4c930fdb9d5dd5128 |
None |
contact |
{'email': 'some.name@example.com', 'phone': '+1234567890'} |
dict |
7 |
2e8b00e571f9d835d3f022a9ff49b9779034ab21bffdcde075d9d729fabeb960 |
None |
email |
some.name@example.com |
str |
8 |
ecd93cf051988b23b3590415f4e7d550de264600d7d2af8704c973b9c98ca6a9 |
None |
phone |
+1234567890 |
str |
9 |
fa4e8d81f4dbe6d306aff59bea4693d325a203be5d5b9fde5d5f1e7cce26b861 |
None |
0 |
\X/Fc7;/v`6joU5z*n{35zFB<<6BMC,}/_04],>v$Jr2&0M_7qU'IY#6uO\$kEr.)Z... |
str |
10 |
c26e7e96f0f3647c159b0934f4dc55207ac059abb56005d7a8acd8344ef14798 |
None |
1 |
A7J+1x5|?r]2zg54nxoa>W*loh8Np~*9+*KxWLuD/Z5g!=DN>}c#]Dt->tiov?|Ms.... |
str |
11 |
f7cd339f77c1799f399d8ebcbb27f2d41a448622254d64e9270ae2316211ac1d |
None |
2 |
LUs%<HRbNA_4:yYTh!!x&oFZ201sQ7;~Q_IYr"lGRMd=xx,r}|n8zHIP6%JN)",vQI... |
str |
12 |
15a33fe62774a1857b404f453ba1195eb4355e10bc9519f2f991dd7ba8db19b7 |
None |
3 |
b&z(/Z{s@U>@o!}{+(mmygo}u~AHgdu>:jz4fNBm0;Q6'o+f%H/z3^8Hh!w<#z.~21... |
str |
13 |
99761e3d58bc213dc3ab33f2dc8dabe5f97d3aea6b59cd367d40b76937f49aa6 |
None |
4 |
.#;5Cu]5~8ZmYBLI4w)|h=)C<(#`KSoM,`7n?dun7]LX>j7/U>Jf||4`AN_u*W!*3)... |
str |
14 |
036a9c122c1f4c9304afa23c4d1fce5224c270a206889afa689f3efb36ff368d |
None |
5 |
0S)*}6"i)kUg3=n:}>Ji)!"BTbzsdgps8{cR]`.41QJ<O{wr[}}gGan_O63D0WBr]<... |
str |
15 |
e72aa8015688052f4e7fddbf4c74e5bf2bd74239ebf3902a5fdc008ecb03aa46 |
None |
6 |
Xb;IgM/`T:VY*6XQ:nvB3)>@32w8H-cD"g>x`MlWp_TnuyCaz62e??md<8tR$Q=X7<... |
str |
16 |
0ae8eda3dbeedbc17e27a679c5426dd3af1434f7c37b4ecd3b2fb5c492272b75 |
None |
7 |
pq.%\nmm;M!^cyS|ApMpnjUS<#Ov?e+n"wX/to.wjifCG.fKK@6gI+Wvax&}j18R8p... |
str |
17 |
cedcfbb0d95798514b6aaf30118fff7b46f863f1bc8b80bb2ddd2145e5b3f318 |
None |
8 |
+-;Zt=ex |
str |
18 |
ace2f31433212fbf9e764069a30a7675ca78f496d31f061d06d0a0420fc52768 |
None |
0 |
b'\x80\x04\x95h\x00\x00\x00\x00\x00\x00\x00}\x94(\x8c\x07integer\x... |
bytes |
19 |
87d93e1862f0f58199c3fcb7114b92fe59f03581804b1c8419868fb00ff8a469 |
None |
0 |
b'{\n "cells": [\n {\n "cell_type": "markdown",\n "metadata":... |
bytes |
20 |
b4a705d09aa0361f4db453da32abb05a5c4e0249d6180d2b8a58d72d08dbd6a0 |
None |
1 |
xt/plain": [\n "4"\n ]\n },\n "execution_count"... |
bytes |
21 |
e4e2c33a2ea67f34bf3ac1e9d99edaad501c7dc4ea82f4b60e9d959418d8438d |
None |
2 |
"language": "python",\n "name": "python3"\n },\n "language_... |
bytes |
22 |
777abf12375b7f605b21535eb0d6232ce99581c6d2b1179af976cd0708ad27ff |
None |
3 |
r": "python",\n "pygments_lexer": "ipython3",\n "version": "3.... |
bytes |