Feature/experiment tracking #122
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…o feature/experiment_tracking
Fix small errors in MLExperiment
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## develop #122 +/- ##
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- Coverage 76.18% 75.66% -0.52%
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Files 73 76 +3
Lines 3388 4158 +770
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+ Hits 2581 3146 +565
- Misses 807 1012 +205
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…o feature/experiment_tracking
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We should review if we want to take this to mango-time-series as this PR is going to get obsolete |
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I guess we have to review this after we change the requirements and the refatro of the repo, right? |
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We will review it |
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Add Experiment Tracking Module
Description: This PR introduces a new module, experiment_tracking.py, to the project. This module provides functionalities for tracking and managing machine learning experiments. It includes the following classes:
MLExperiment: This class represents a machine learning experiment. It provides functionalities to initialize metrics, get feature importance, plot ROC curve, plot precision recall curve, plot feature importance, register an experiment, predict using the model, and load an experiment from a registered experiment.
MLTracker: This class manages multiple machine learning experiments. It provides functionalities to scan for existing experiments, add new experiments, compare experiments, update experiment metrics, and generate comparison dataframes and hyperparameters json.
The addition of this module will greatly enhance the ability to track and manage machine learning experiments in the project. It supports models from scikit-learn, lightgbm, and catboost libraries for both regression and classification problems.