| Name | gridlooper JSON |
| Version |
0.0.2
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| home_page | None |
| Summary | A tool to run experiments based on defined grid and function with single iteration. |
| upload_time | 2024-05-19 00:16:06 |
| maintainer | None |
| docs_url | None |
| author | Kyrylo Mordan |
| requires_python | None |
| license | None |
| keywords |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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# Grid Looper
A tool to run experiments based on defined grid and function with single iteration.
```python
import sys
sys.path.append('../')
from python_modules.gridlooper import GridLooper
```
## Usage examples
The examples contain:
1. preparing runner function
2. preparing search grid
3. running experiments
4. analysing results
### 1. Preparing runner function
Runner funtion should contain logic of experiment in a way that the parameters could be supplied with `embedder_params`
```python
def runner_function(runner_params : dict, c : int):
result = int(runner_params['a']) + runner_params['b'] + c
return result
```
### 2. Preparing search grid
Experiment combos can be defined in short form, transformed into a list and filtered with `exlusion_combos`. Some of the parameters in experiment definition could be ignored durring experiment with a use of `exclusion_keys` parameter.
```python
experiments_settings = {
'runner_params': {'a' : ['1', '2','4'],
'b' : [2, 6,10,100]},
'c' : [100, 500],#, 1000, 5000]
'name' : 'example experiment'
}
exclusion_keys = {'name'}
exclusion_combos = [{'runner_params': {'a': ['1','2'],
'b': [100, 6,10]}}]
```
```python
gl = GridLooper(
# dictionary of all possible parameter combos
experiments_settings = experiments_settings,
# keys from the experiments_settings to be ignored
exclusion_keys = exclusion_keys,
# combos from experiments_settings to be exluded
exclusion_combos = exclusion_combos,
# function that be run for each of experiment combos
runner_function = runner_function,
# optional parameter to be supplied to runner function outside of experiment settings
data = None,
# path to save experiment results
save_path = 'example_run.dill')
```
```python
gl.prepare_search_grid(
# optional if definer earlier
experiments_settings = experiments_settings,
exclusion_keys = exclusion_keys,
exclusion_combos = exclusion_combos
)
gl.experiment_configs
```
[{'runner_params': {'a': '1', 'b': 2},
'c': 100,
'config_id': '54eac3ee5ce6ae6d126502ee87dbbafce54111b346b895e1d5e29c50097fa800'},
{'runner_params': {'a': '1', 'b': 2},
'c': 500,
'config_id': 'b2ef1c49a36375e88203f9ff1f01db69457fc9eb6435333aaafee68bb871d9da'},
{'runner_params': {'a': '2', 'b': 2},
'c': 100,
'config_id': '4b1a723841dbf9f6e2a415159d0deb938373ba21506285289e46cafdcf455f05'},
{'runner_params': {'a': '2', 'b': 2},
'c': 500,
'config_id': '44a22efdfe7e385b4fbaeb84976ac0d10703a98902ce134cddd1000e09ba156a'},
{'runner_params': {'a': '4', 'b': 2},
'c': 100,
'config_id': '6dc5a94f832532513b1d739fdad694029b6b9d97cec9fa869ce3d75b822c23ce'},
{'runner_params': {'a': '4', 'b': 2},
'c': 500,
'config_id': '9e7ec3d2e9d7d5ccf4b8c05b9a7a145fc443f77cd7031dcdaf139a77f88d5944'},
{'runner_params': {'a': '4', 'b': 6},
'c': 100,
'config_id': 'dcd7c1aeb1b3c41ab924ece9ed471d682cef319304a9675dfd1f7d27f6e29c7c'},
{'runner_params': {'a': '4', 'b': 6},
'c': 500,
'config_id': 'ad38629f25dd962d157ee8b36b1fc34a54079f8b08d0d4e79fd45cecfa167d49'},
{'runner_params': {'a': '4', 'b': 10},
'c': 100,
'config_id': '6cd8cc53587798f4fc2583a122a7cad6e79cb7b6c10639e6a9714d12fa2c3092'},
{'runner_params': {'a': '4', 'b': 10},
'c': 500,
'config_id': '495efcc2399e24fed5a5dee4b3909f27688b7723eb62b28be6ff6eb74c4e8574'},
{'runner_params': {'a': '4', 'b': 100},
'c': 100,
'config_id': '2dcdd7a719ce8fa4731c8d9adefd131d809fd29e014aa9acd3be8a6538cc8765'},
{'runner_params': {'a': '4', 'b': 100},
'c': 500,
'config_id': '64f83d857c2c3a0030bd187330da30dc6d4aaf2ae1418f150b9902a269a4f3d8'}]
### 3. Running experiments
`executing_experimets` function will run `runner_function` for each set of parameters from defined `experiment_configs` for a select loop strategy.
```python
gl.executing_experimets(
# optional of defined earlier
runner_function = runner_function,
experiment_configs = gl.experiment_configs,
data = None,
loop_type= 'brute',
save_path = 'example_run.dill'
)
```
Looping: 0%| | 0/12 [00:00<?, ?item/s]
Looping: 100%|██████████| 12/12 [00:00<00:00, 156796.41item/s]
### 4. Analysing results
```python
gl.experiment_results['results']
```
{'54eac3ee5ce6ae6d126502ee87dbbafce54111b346b895e1d5e29c50097fa800': 103,
'b2ef1c49a36375e88203f9ff1f01db69457fc9eb6435333aaafee68bb871d9da': 503,
'4b1a723841dbf9f6e2a415159d0deb938373ba21506285289e46cafdcf455f05': 104,
'44a22efdfe7e385b4fbaeb84976ac0d10703a98902ce134cddd1000e09ba156a': 504,
'6dc5a94f832532513b1d739fdad694029b6b9d97cec9fa869ce3d75b822c23ce': 106,
'9e7ec3d2e9d7d5ccf4b8c05b9a7a145fc443f77cd7031dcdaf139a77f88d5944': 506,
'dcd7c1aeb1b3c41ab924ece9ed471d682cef319304a9675dfd1f7d27f6e29c7c': 110,
'ad38629f25dd962d157ee8b36b1fc34a54079f8b08d0d4e79fd45cecfa167d49': 510,
'6cd8cc53587798f4fc2583a122a7cad6e79cb7b6c10639e6a9714d12fa2c3092': 114,
'495efcc2399e24fed5a5dee4b3909f27688b7723eb62b28be6ff6eb74c4e8574': 514,
'2dcdd7a719ce8fa4731c8d9adefd131d809fd29e014aa9acd3be8a6538cc8765': 204,
'64f83d857c2c3a0030bd187330da30dc6d4aaf2ae1418f150b9902a269a4f3d8': 604}
Raw data
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"description": "# Grid Looper\n\nA tool to run experiments based on defined grid and function with single iteration.\n\n\n```python\nimport sys\nsys.path.append('../')\nfrom python_modules.gridlooper import GridLooper\n```\n\n## Usage examples\n\nThe examples contain:\n\n1. preparing runner function\n2. preparing search grid\n3. running experiments\n4. analysing results\n \n\n### 1. Preparing runner function\n\nRunner funtion should contain logic of experiment in a way that the parameters could be supplied with `embedder_params` \n\n\n```python\ndef runner_function(runner_params : dict, c : int):\n\n result = int(runner_params['a']) + runner_params['b'] + c\n\n return result\n```\n\n### 2. Preparing search grid\n\nExperiment combos can be defined in short form, transformed into a list and filtered with `exlusion_combos`. Some of the parameters in experiment definition could be ignored durring experiment with a use of `exclusion_keys` parameter.\n\n\n```python\nexperiments_settings = {\n 'runner_params': {'a' : ['1', '2','4'],\n 'b' : [2, 6,10,100]},\n\n 'c' : [100, 500],#, 1000, 5000]\n 'name' : 'example experiment'\n}\n\nexclusion_keys = {'name'}\n\nexclusion_combos = [{'runner_params': {'a': ['1','2'],\n 'b': [100, 6,10]}}]\n```\n\n\n```python\ngl = GridLooper(\n # dictionary of all possible parameter combos\n experiments_settings = experiments_settings,\n # keys from the experiments_settings to be ignored\n exclusion_keys = exclusion_keys,\n # combos from experiments_settings to be exluded\n exclusion_combos = exclusion_combos,\n # function that be run for each of experiment combos\n runner_function = runner_function,\n # optional parameter to be supplied to runner function outside of experiment settings\n data = None,\n # path to save experiment results\n save_path = 'example_run.dill')\n```\n\n\n```python\ngl.prepare_search_grid(\n # optional if definer earlier\n experiments_settings = experiments_settings,\n exclusion_keys = exclusion_keys,\n exclusion_combos = exclusion_combos\n)\n\n\ngl.experiment_configs\n```\n\n\n\n\n [{'runner_params': {'a': '1', 'b': 2},\n 'c': 100,\n 'config_id': '54eac3ee5ce6ae6d126502ee87dbbafce54111b346b895e1d5e29c50097fa800'},\n {'runner_params': {'a': '1', 'b': 2},\n 'c': 500,\n 'config_id': 'b2ef1c49a36375e88203f9ff1f01db69457fc9eb6435333aaafee68bb871d9da'},\n {'runner_params': {'a': '2', 'b': 2},\n 'c': 100,\n 'config_id': '4b1a723841dbf9f6e2a415159d0deb938373ba21506285289e46cafdcf455f05'},\n {'runner_params': {'a': '2', 'b': 2},\n 'c': 500,\n 'config_id': '44a22efdfe7e385b4fbaeb84976ac0d10703a98902ce134cddd1000e09ba156a'},\n {'runner_params': {'a': '4', 'b': 2},\n 'c': 100,\n 'config_id': '6dc5a94f832532513b1d739fdad694029b6b9d97cec9fa869ce3d75b822c23ce'},\n {'runner_params': {'a': '4', 'b': 2},\n 'c': 500,\n 'config_id': '9e7ec3d2e9d7d5ccf4b8c05b9a7a145fc443f77cd7031dcdaf139a77f88d5944'},\n {'runner_params': {'a': '4', 'b': 6},\n 'c': 100,\n 'config_id': 'dcd7c1aeb1b3c41ab924ece9ed471d682cef319304a9675dfd1f7d27f6e29c7c'},\n {'runner_params': {'a': '4', 'b': 6},\n 'c': 500,\n 'config_id': 'ad38629f25dd962d157ee8b36b1fc34a54079f8b08d0d4e79fd45cecfa167d49'},\n {'runner_params': {'a': '4', 'b': 10},\n 'c': 100,\n 'config_id': '6cd8cc53587798f4fc2583a122a7cad6e79cb7b6c10639e6a9714d12fa2c3092'},\n {'runner_params': {'a': '4', 'b': 10},\n 'c': 500,\n 'config_id': '495efcc2399e24fed5a5dee4b3909f27688b7723eb62b28be6ff6eb74c4e8574'},\n {'runner_params': {'a': '4', 'b': 100},\n 'c': 100,\n 'config_id': '2dcdd7a719ce8fa4731c8d9adefd131d809fd29e014aa9acd3be8a6538cc8765'},\n {'runner_params': {'a': '4', 'b': 100},\n 'c': 500,\n 'config_id': '64f83d857c2c3a0030bd187330da30dc6d4aaf2ae1418f150b9902a269a4f3d8'}]\n\n\n\n### 3. Running experiments\n\n`executing_experimets` function will run `runner_function` for each set of parameters from defined `experiment_configs` for a select loop strategy.\n\n\n```python\ngl.executing_experimets(\n # optional of defined earlier\n runner_function = runner_function,\n experiment_configs = gl.experiment_configs,\n data = None,\n loop_type= 'brute',\n save_path = 'example_run.dill'\n)\n```\n\n \nLooping: 0%| | 0/12 [00:00<?, ?item/s]\n\n \nLooping: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12/12 [00:00<00:00, 156796.41item/s]\n\n \n\n\n### 4. Analysing results\n\n\n```python\ngl.experiment_results['results']\n```\n\n\n\n\n {'54eac3ee5ce6ae6d126502ee87dbbafce54111b346b895e1d5e29c50097fa800': 103,\n 'b2ef1c49a36375e88203f9ff1f01db69457fc9eb6435333aaafee68bb871d9da': 503,\n '4b1a723841dbf9f6e2a415159d0deb938373ba21506285289e46cafdcf455f05': 104,\n '44a22efdfe7e385b4fbaeb84976ac0d10703a98902ce134cddd1000e09ba156a': 504,\n '6dc5a94f832532513b1d739fdad694029b6b9d97cec9fa869ce3d75b822c23ce': 106,\n '9e7ec3d2e9d7d5ccf4b8c05b9a7a145fc443f77cd7031dcdaf139a77f88d5944': 506,\n 'dcd7c1aeb1b3c41ab924ece9ed471d682cef319304a9675dfd1f7d27f6e29c7c': 110,\n 'ad38629f25dd962d157ee8b36b1fc34a54079f8b08d0d4e79fd45cecfa167d49': 510,\n '6cd8cc53587798f4fc2583a122a7cad6e79cb7b6c10639e6a9714d12fa2c3092': 114,\n '495efcc2399e24fed5a5dee4b3909f27688b7723eb62b28be6ff6eb74c4e8574': 514,\n '2dcdd7a719ce8fa4731c8d9adefd131d809fd29e014aa9acd3be8a6538cc8765': 204,\n '64f83d857c2c3a0030bd187330da30dc6d4aaf2ae1418f150b9902a269a4f3d8': 604}\n\n\n",
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