Package: hypernotes
hypernoteshypernotes is a lightweight Python package for taking notes on your machine learning experiments. It provides a simple way to store hyperparameters, their corresponding evaluation metrics, as well as additional information and retrieve them again later for analyzing. It is written in pure Python and requires no additional dependencies.Table of contentsInstallationBasic UsageCreate note and add to storeLoad notesUpdate notesRemove notesCreate note from another oneBonusView content of a store in your browserStore additional objectsAlternativesDevelopmentChangelog for this packageInstallationpipinstallhypernotesPython 3.6+ is requiredBasic Usagehypernotes implements aNoteand aStoreclass. ANoteis a small wrapper around Python dictionaries. This means that you can do everything with it, that you could do with a normal dictionary, but in addition, it stores:the path to your Python executable,information about the current state of your Git repository (if there is one) such as the last commit, current branch, etc.,start (upon initialization) and end datetime (call note.end() or add to store)and it provides:a useful default dictionary structureaccess to all initial dictionary keys as attributes for better auto-completion support and readability (for examplenote.parameters,note.features)If you print a note, you can see what's inside. A note right after initialization looks like this:Note(content={'text':'','model':None,'parameters':{},'features':{'identifier':[],'binary':[],'categorical':[],'numerical':[]},'target':None,'metrics':{},'info':{},'start_datetime':datetime.datetime(2019,5,21,11,3,20),'end_datetime':None,'identifier':'3228fe02-d1c8-4251-8b35-bb8ae3d5f227','python_path':'C:/example_path/python.exe','git':{'repo_name':'C:/path_to_your_repo','branch':'master','commit':'6bbdf31'}}The notes are then saved with aStoreinstance, which uses a json file. Due to this, you should only addjson-serializable objects+datetime.datetimeinstances to aNote.A note is uniquely identifiable by itsidentifierattribute.Create note and add to storefromhypernotesimportNote,Storenote=Note("Some descriptive text about your experiment")# Add name of used algorithmnote.model="randomforest"# Add hyperparameters about model training, preprocessing, etc.note.parameters["num_estimators"]=100note.parameters["impute_missings"]=True# Add the names of the features and of the target variablenote.features["identifier"]=["id"]note.features["binary"]=["bool1"]note.features["categorical"]=["cat1","cat2"]note.features["numerical"]=["num1"]note.target="target"# Some additional informationnote.info["important_stuff"]="something noteworthy"# ... Rest of your code ...# train_recall, train_precision test_recall, test_precision = train_and_evaluate_model(#                                              parameters=note.params,#                                              feature_names=note.features,#                                              target_name=note.target)# ...# Add your calculated evaluation metricsnote.metrics["train"]={"recall":train_recall,"precision":train_precision}note.metrics["test"]={"recall":test_recall,"precision":test_precision}store=Store("hyperstore.json")store.add(note)Load notesA Store instance provides theloadmethod, which can be used to retrieve the whole store. By default it returns a sorted list (most recent note first).notes=store.load()most_recent_note=notes[0]If you havepandasinstalled, you can use thereturn_dataframeargument to return a pandas dataframe.notes_df=store.load(return_dataframe=True)notes_df.head()Example of a returned pandas dataframe:start_datetimeend_datetimetextmodelidentifiermetrics.test.precisionmetrics.test.recallmetrics.train.precisionmetrics.train.recallparameters.min_sample_splitparameters.num_estimatorsparameters.sample_weightfeatures.binaryfeatures.categoricalfeatures.identifierfeatures.numericaltargetgit.branchgit.commitgit.repo_nameinfo.important_stuffpython_path02019-05-21 16:44:482019-05-21 17:05:21Another useful descriptionrandomforest0f84217d-e01b-466d-9a73-001827c605840.290.290.400.507150None[bool1][cat1, cat2][id][num1]targetmaster5e098abC:/path_to_your_reposomething noteworthyC:/example_path/python.exe12019-05-21 16:12:532019-05-21 16:30:16Useful descriptionrandomforestdd8bbc32-ff8f-433d-9eec-a24a7859622f0.820.290.910.987100balanced[bool1][cat1, cat2][id][num1]targetmaster5e098abC:/path_to_your_reposomething noteworthyC:/example_path/python.exeUpdate notesIf you want to update notes, you can do this either directly in the json file containing the notes, or load the notes as described above, change the relevant ones, and pass them to theupdatemethod.notes=store.load()updated_notes=[]fornoteinnotes[:2]:note.info["something_new"]="..."updated_notes.append(note)store.update(updated_notes)Remove notesIf you want to remove notes, you can do this either directly in the json file containing the notes, or load the notes as described above, and pass the ones which you want to remove to theremovemethod.notes=store.load()notes_to_remove=notes[:2]store.remove(notes_to_remove)Create note from another oneWhen evaluating multiple model parameters (e.g. in a grid search setup), you might find it useful to create a new note for each parameter set. To do this, you can use thefrom_notemethod to create a new note from an existing one. This takes over all existing content, but also sets a new start datetime and identifier. After creation, the notes are independent, i.e. modifying one will not affect the other.original_note=Note("Original")new_note=Note.from_note(original_note)BonusView content of a store in your browserTo get a quick glance into a store, you can use the package from the command line. It will start an http server and automatically open the relevant page in your web browser. The page contains an interactive table which shows the most relevant information of all notes in the store such as metrics and parameters. The table is similar in style to the one shown in theLoad notessection.$ python -m hypernotes hyperstore.jsonThis only requires a modern web browser as well as an internet connection to load some javascript libraries and css files.To see all available options pass the--helpargument.Store additional objectsIf you want to store larger artifacts of your experiment, such as a trained model, you could create a separate folder and use the identifier of a note as part of the name.experiment_folder=f"experiment_{note.identifier}"You can then store any additional objects into this folder and it will be very easy to lather on link them again to the hyperparameters and metrics stored using hypernotes.AlternativesCheck out tools such asMLflow,Sacred, orDVCif you need better multi-user capabilities, more advanced reproducibility features, dataset versioning, ...DevelopmentFeel free to open a GitHub issue or even better submit a pull request if you find a bug or miss a feature.Any requirements for developing the package can be installed withpip install -r requirements_dev.txtMake sure that all tests run by tox pass.