Package: climl
OverviewWhat is CLIML?CLIMLis a CLI tool that rapidly automates the creation of powerful supervised learning models:Auto-detection for regression/classification tasks.Automated hyperparameter tuning and model selection.Train models as quickly (or as extensively) as you like.Make easy predictions and output them to a CSV file.Beautifully formatted--helpif you get stuck.Who is CLIML for?Anyone who has some data and wants a quick, accessible way to apply ML.Data scientists who want to accelerate their ML prototyping workflows.Installing CLIML1. Pre-RequisitesPython >=3.11, <4.0libomp (if using MacOS)On MacOS,libompis required for LightGBM models. You can get libomp by running:brew install libomp.2. Pip InstallEnter the following into the command line in your chosen directory:$pipinstallclimlUsing CLIML1. Getting DataYou can't do machine learning without some properly formatted data.Make sure it's a rectangular dataset (like you'd get in Excel).Ensure that your dataset has column headers.Put the output you want to start predicting in the final column.Save your dataset as a CSV file. (Excel coming soon!)Navigate to where the file is saved via the command line (lsandcdare your friends).That's as complicated as it gets! Now you're ready to do some Machine Learning.2. Training a ModelYou can train a powerful ML model using justfourwords (in seconds) :$climltraindataset.csvnum_of_secondsThis will tellCLIMLto ...Detect whether the final column indataset.csvis numerical or categorical.Begin training multiple regression or classification models, respectively.Continue tuning hyperparameters for your specifiednum_of_seconds.Display the most accurate model and its hyperparameters to the terminal.Save your model as an.climlfile.If you enterlsyou'll be able to see your newly traineddataset.climlmodel!3. Inspecting a ModelForgot what type of model you trained? Interested in telling everyone about its hyperparameters? You'll only needthreewords this time:$climlinspectdataset.climlThis will output the model's type, hyperparameters, accuracy, and time taken to discover4. Making PredictionsAnnoyingly you'll needfourwhole words again.Firstly, make sure that yourthings_to_predict.csvfile is formatted exactly like the CSV file whichmodel.climlwas trained on - justwithoutthe final output column!Then run:$ climl predict things_to_predict.csv model.climlThis will:Display a prediction of the outputs for whatever inputs are inthings_to_predict.csvAppend a new column tothings_to_predict.csv, with the header "Predicted Outputs"5. Errors and--helpIf you enter something incorrect,CLIMLis pretty good at telling you why. For instance, if you enter:$climltraindata.csvYou should see something like this:It looks like you didn't specify a training time!If you're still a bit stuck, you can always enter one of the following:climl --helpcliml train --helpcliml inspect --helpcliml predict --helpTo get something like this:RoadmapHere are some things on the roadmap:Train and send predictions to an Excel fileTimeseries problemsMulti-output modelsAutomated data-cleaningPlotting functionalityIf you particularly need one, drop us an email at the bottom of the page!FAQsWhat models does CLIML train?CLIMLruns its model selection algorithm on: XGBoost, LightGBM, Random Forest, Extra_Tree, Logistic Regressions with L1 and L2 Regularization, CatBoost, KNeighboursHow long to specify for training?Depending on your dataset, start small (<20 seconds), then lengthen until you notice accuracy starts to taper off or if it's "good enough"!Do I need a super-powerful computer?No!CLIMLis suited for devices with low computational resource. But there are limits to everything!ContactAny questions are very wlecome - please send them to:hello@climl.com