pymlens 1.1.4

Lightweight ML experiment tracker — log, compare and visualize your ML experiments locally

PyMLens 🧪

A lightweight ML experiment tracking tool that helps data scientists log, compare, and visualize their model experiments — runs fully locally on your machine.

Installation

pip install pymlens

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Why PyMLens

Managing multiple experiments manually becomes chaotic and time-consuming. After running several models, it becomes difficult to track which model performed best and on which problem. PyMLens eliminates this problem by automatically recording all your experiments in one place.

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Features

• Easy to use — minimal code changes required
• Runs fully locally — no cloud, no data leaves your machine
• Supports both Classification and Regression problems
• Records each experiment and results automatically
• Compare model performance visually via Streamlit dashboard
• Confusion matrix tracking for classification
• MSE, MAE, RMSE, R2 tracking for regression
• Cross validation score tracking
• Copy model hyperparameters directly from dashboard
• Interactive Sunburst visualization for experiment exploration
• Dynamic themes — randomize dashboard appearance

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Supported Metrics

Classification

• Accuracy
• Precision
• Recall
• F1 Score
• Cross Validation Score
• Confusion Matrix

Regression

• MSE (Mean Squared Error)
• MAE (Mean Absolute Error)
• RMSE (Root Mean Squared Error)
• R2 Score
• Cross Validation Score

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Run Locally

Clone the project

git clone https://github.com/munishmalhotra6230/model_tracker-MLENS-.git
cd model_tracker-MLENS-

Install dependencies

pip install -r requirements.txt

Run dashboard

python -m pymlens dashboard

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Usage — Classification

from pymlens import Classification_Experiment
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC

x, y = load_iris(return_X_y=True)
xtrain, xval, ytrain, yval = train_test_split(x, y, test_size=0.2, random_state=42)

with Classification_Experiment("Iris_Classification", xtrain, ytrain, xval, yval) as exp:
    exp.Start_experiment(model=LogisticRegression())
    exp.Start_experiment(model=RandomForestClassifier())
    exp.Start_experiment(model=SVC())

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Usage — Regression

from pymlens import Regression_Experiment
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.linear_model import LinearRegression

x, y = fetch_california_housing(return_X_y=True)
xtrain, xval, ytrain, yval = train_test_split(x, y, test_size=0.2, random_state=42)

with Regression_Experiment("House_Price", xtrain, ytrain, xval, yval) as exp:
    exp.Start_experiment(model=LinearRegression())
    exp.Start_experiment(model=RandomForestRegressor())
    exp.Start_experiment(model=GradientBoostingRegressor())

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View Dashboard

python -m pymlens dashboard

Dashboard opens automatically in your browser with:

• Model leaderboard
• Metric comparison charts
• Radar view
• Precision vs Recall scatter (Classification)
• MSE vs MAE scatter (Regression)
• Cross validation stability
• Copy model hyperparameters
• Interactive Sunburst explorer

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Demo

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Screenshots

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Optimizations

Have suggestions? Join the Discord and share your ideas.

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Feedback

Join the Discord community: https://discord.gg/svx4Sfckz

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Links

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Author

• @Munish Malhotra