Changed slog to saver (histroically made sense but not with current d…

…esign)

.github/workflows/docs.yml

@@ -28,7 +28,7 @@ jobs:
       - run: pip install pdoc
       # ADJUST THIS: build your documentation into docs/.
       # We use a custom build script for pdoc itself, ideally you just run `pdoc -o docs/ ...` here.
-      - run: pdoc --docformat google -o docs/.html src
+      - run: pdoc --docformat google --logo "https://github.com/h-0-0/slune/slune.jpeg" -o docs/.html src
 
       - uses: actions/upload-pages-artifact@v2
         with:

CONTRIBUTING.md

@@ -0,0 +1,9 @@
+# Contributing
+If you would like to contribute to slune please first familiarize yourself with the package by taking a look at the docs. In particular please read about the class design, the base classes and take a look at the code for the helper functions in the slune module. 
+
+To contribute to the package please either submit a pull request for an open issue or open a new issue. If you are unsure about whether to open a new issue or in general have any problems please open a discussion in the discussions tab.
+
+Checklist for contributing:
+- [ ] Make sure that your code is well documented and that the documentation is clear and concise, use google style docstrings.
+- [ ] Make sure that your code is well tested and that the tests are clear and concise, want to keep coverage as high as possible! (minimum 90%) and keep tests as simple as possible!
+- [ ] Make sure that you only solve the issue that you are attempting to close, if you find other issues please open a new issue for them.

README.md

@@ -73,16 +73,16 @@ if __name__ == "__main__":
     test_y = [7, 8, 9]
     test_predictions = linear_regression_regularized(X, y, test_X, alpha)
 
-    # First let's load in a function that we can use to get a saver object that uses the default method of logging (we call this object a slog = saver + logger). The saving will be coordinated by a csv saver object which saves and reads results from csv files stored in a hierarchy of directories.
-    from slune import get_csv_slog
-    csv_slog = get_csv_slog(params = args)
+    # First let's load in a function that we can use to get a saver object that uses the default method of logging. The saving will be coordinated by a csv saver object which saves and reads results from csv files stored in a hierarchy of directories.
+    from slune import get_csv_saver
+    csv_saver = get_csv_saver(params = args)
 
     # Let's now calculate the mean squared error of our predictions and log it!
     mse = mean((test_y[i] - test_predictions[i])**2 for i in range(len(test_y)))
-    csv_slog.log({'mse': mse})
+    csv_saver.log({'mse': mse})
 
     # Let's now save our logged results!
-    slog.save_collated()
+    csv_saver.save_collated()
 ```
 Now let's write some code that will submit some jobs to train our model using different hyperparameters!!
 ```python
@@ -92,29 +92,28 @@ from slune.searchers import SearcherGrid
 grid_searcher = SearcherGrid({'alpha' : [0.25, 0.5, 0.75]}, runs = 1)
 
 # Let's now import a function which will submit a job for our model, the script_path specifies the path to the script that contains the model we want to train. The template_path specifies the path to the template script that we want to specify the job with, cargs is a list of constant arguments we want to pass to the script for each tuning. 
-# We set slog to None as we don't want to not run jobs if we have already run them before.
+# We set saver to None as we don't want to not run jobs if we have already run them before.
 from slune import sbatchit
 script_path = 'model.py'
 template_path = 'template.sh'
-sbatchit(script_path, template_path, grid_searcher, cargs=[], slog=None)
+sbatchit(script_path, template_path, grid_searcher, cargs=[], saver=None)
 ```
 Now we've submitted our jobs we will wait for them to finish 🕛🕐🕑🕒🕓🕔🕕🕖🕗🕘🕙🕚🕛, now that they are finished we can read the results!
 ```python
-from slune import get_csv_slog
-csv_slog = get_csv_slog(params = None)
-params, value = csv_slog.read(params = [], metric_name = 'mse', select_by ='min')
+from slune import get_csv_saver
+csv_saver = get_csv_saver(params = None)
+params, value = csv_saver.read(params = [], metric_name = 'mse', select_by ='min')
 print(f'Best hyperparameters: {params}')
 print(f'Their MSE: {value}')
 ```
 Amazing! 🥳 We have successfully used slune to train our model. I hope this gives you a good flavour of how you can use slune and how easy it is to use!
 
-Please check out the examples folder for notebooks detailing in more depth some potential ways you can use slune. The docs are not yet up and running 😢 but they are coming soon!
+Please check out the examples folder for notebooks detailing in more depth some potential ways you can use slune and of course please check out the docs! 
 
 ## Roadmap
 - Make package user friendly:
     - Go through automation settings.
     - Code of conduct.
-    - Contributing guidelines.
     - Add to pypi.
 Still in early stages! First thing on the horizon is better integration with SLURM:
 - Set-up notifications for job completion, failure, etc.

examples/From_README/regularized_linear_regression.ipynb

@@ -67,16 +67,16 @@
     "    test_y = [7, 8, 9]\n",
     "    test_predictions = linear_regression_regularized(X, y, test_X, alpha)\n",
     "\n",
-    "    # First let's load in a function that we can use to get a saver object that uses the default method of logging (we call this object a slog = saver + logger). The saving will be coordinated by a csv saver object which saves and reads results from csv files stored in a hierarchy of directories.\n",
-    "    from slune import get_csv_slog\n",
-    "    csv_slog = get_csv_slog(params = args)\n",
+    "    # First let's load in a function that we can use to get a saver object that uses the default method of logging. The saving will be coordinated by a csv saver object which saves and reads results from csv files stored in a hierarchy of directories.\n",
+    "    from slune import get_csv_saver\n",
+    "    csv_saver = get_csv_saver(params = args)\n",
     "\n",
     "    # Let's now calculate the mean squared error of our predictions and log it!\n",
     "    mse = mean((test_y[i] - test_predictions[i])**2 for i in range(len(test_y)))\n",
-    "    csv_slog.log({'mse': mse})\n",
+    "    csv_saver.log({'mse': mse})\n",
     "\n",
     "    # Let's now save our logged results!\n",
-    "    slog.save_collated()"
+    "    csv_saver.save_collated()"
    ]
   },
   {
@@ -100,11 +100,11 @@
     "grid_searcher = SearcherGrid({'alpha' : [0.25, 0.5, 0.75]}, runs = 1)\n",
     "\n",
     "# Let's now import a function which will submit a job for our model, the script_path specifies the path to the script that contains the model we want to train. The template_path specifies the path to the template script that we want to specify the job with, cargs is a list of constant arguments we want to pass to the script for each tuning. \n",
-    "# We set slog to None as we don't want to not run jobs if we have already run them before.\n",
+    "# We set saver to None as we don't want to not run jobs if we have already run them before.\n",
     "from slune import sbatchit\n",
     "script_path = 'model.py'\n",
     "template_path = 'template.sh'\n",
-    "sbatchit(script_path, template_path, grid_searcher, cargs=[], slog=None)"
+    "sbatchit(script_path, template_path, grid_searcher, cargs=[], saver=None)"
    ]
   },
   {
@@ -120,9 +120,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from slune import get_csv_slog\n",
-    "csv_slog = get_csv_slog(params = None)\n",
-    "params, value = csv_slog.read(params = [], metric_name = 'mse', select_by ='min')\n",
+    "from slune import get_csv_saver\n",
+    "csv_saver = get_csv_saver(params = None)\n",
+    "params, value = csv_saver.read(params = [], metric_name = 'mse', select_by ='min')\n",
     "print(f'Best hyperparameters: {params}')\n",
     "print(f'Their MSE: {value}')"
    ]

src/__init__.py

@@ -1,4 +1,5 @@
 """
 .. include:: ../README.md
 .. include:: ../CLASSDESIGN.md
+.. include:: ../CONTRIBUTING.md
 """

src/slune/__init__.py

@@ -4,10 +4,10 @@
 from .searchers import *
 from .savers import *
 from .loggers import *
-from .slune import submit_job, sbatchit, lsargs, garg, get_csv_slog
+from .slune import submit_job, sbatchit, lsargs, garg, get_csv_saver
 from . import base, utils
 
-# __all__ = ['submit_job', 'sbatchit', 'lsargs', 'garg', 'get_csv_slog',
+# __all__ = ['submit_job', 'sbatchit', 'lsargs', 'garg', 'get_csv_saver',
         #    'base', 'utils', 'default', 'grid', 'csv']
 
 import importlib.metadata

src/slune/slune.py

@@ -91,7 +91,7 @@ def single_garg(arg_name):
     else:
         return single_garg(arg_names)
 
-def get_csv_slog(params: Optional[dict]= None, root_dir: Optional[str]='slune_results') -> BaseSaver:
+def get_csv_saver(params: Optional[dict]= None, root_dir: Optional[str]='slune_results') -> BaseSaver:
     """ Returns a SaverCsv object with the given parameters and root directory.
 
     Args:

tests/test_slune.py

@@ -25,10 +25,10 @@ def test_sbatchit(self, mock_run):
         searcher = MagicMock()
         searcher.__iter__.return_value = [['arg1', 'arg2'], ['arg3', 'arg4']]
         cargs = ["carg1", "carg2"]
-        slog = None
+        saver = None
 
         # Act
-        sbatchit(script_path, template_path, searcher, cargs, slog)
+        sbatchit(script_path, template_path, searcher, cargs, saver)
 
         # Assert
         calls = [call(['sbatch', template_path, script_path, 'carg1', 'carg2', 'arg1', 'arg2'], check=True),