aimlexp - AIML & Machine Learning Lab Experiments CLI

A fast, lightweight CLI reference tool, runner, and exporter for Machine Learning and AI laboratory practical experiments. Easily view, export, or run clean, self-contained Python programs directly from your terminal using either mlexp or aimlexp.

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🚀 Installation

From PyPI:

# Lightweight CLI (zero dependencies)
pip install aimlexp

# Optional: Install with all ML lab libraries (numpy, scikit-learn, etc.)
pip install "aimlexp[lab]"

Local / Editable Mode (for development):

git clone https://github.com/example/aimlexp.git
cd aimlexp
pip install -e .

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⚡ Quick Usage

Both mlexp and aimlexp commands are available interchangeably:

1. View Experiment Catalog (--list or -l):

mlexp --list
# or
aimlexp --list

(Running mlexp without arguments also shows the catalog)

2. View Code for Any Experiment:

You can access experiments by number, canonical key, or common aliases:

# By experiment number
mlexp 4
mlexp 04

# By algorithm alias
mlexp knn
mlexp dt
mlexp svm
mlexp nb
mlexp slr
mlexp poly
mlexp kmeans
mlexp pca
mlexp lda
mlexp apriori

3. Save Code Directly to a File (-o / --output):

Save any experiment into a standalone script to work on during lab:

mlexp knn -o knn_lab.py
python knn_lab.py

4. Execute Experiment Immediately (-r / --run):

Run the experiment in Python right from your command line:

mlexp knn -r
mlexp dt --run

5. View Metadata & Overview (-i / --info):

Inspect experiment summary and aliases without printing all code:

mlexp 4 --info

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📚 Complete Experiments Catalog

#	Title	Key	Primary Aliases	Description
01	Rule-Based COVID Symptom Screening	rule_based_covid	1, rule, covid, symptom, expert_system	Rule-based symptom checking with conditional logic
02	Dataset Import, Encoding & Outliers	dataset_encoding_outliers	2, encoding, outliers, preprocessing, iqr	Median imputation, categorical encoding, and IQR outlier clipping
03	Train-Test Split, Scaling & Sampling	train_test_scaling_sampling	3, split, scaling, sampling, standard_scaler	Train/test partitioning, standard scaling, and row sampling
04	K-Nearest Neighbors (KNN) Classifier	knn_classifier	4, knn, k_nearest_neighbors	K-Nearest Neighbors (k=5) classifier on scaled Iris data
05	Decision Tree Classifier	decision_tree_classifier	5, dt, tree, decision_tree	DecisionTreeClassifier with feature threshold rules
06	Support Vector Machine (SVM) Classifier	svm_classifier	6, svm, svc, support_vector_machine	Linear Support Vector Classifier (SVC) with accuracy score
07	Naive Bayes Classifier	naive_bayes_classifier	7, nb, bayes, gaussian_nb	Gaussian Naive Bayes probabilistic classification
08	Simple Linear Regression	simple_linear_regression	8, slr, linear_regression	Single-feature regression with slope, intercept & 2D scatter plot
09	Multiple Linear Regression	multiple_linear_regression	9, mlr, multiple_regression	Multi-feature price prediction with 3D projection scatter
10	Polynomial Regression	polynomial_regression	10, poly, polynomial	Degree-2 curved polynomial regression fitting
11	Logistic Regression Classifier	logistic_regression	11, logistic, log_reg, logit	Multiclass Logistic Regression classification on Iris
12	Association Rule Learning	association_rule_learning	12, apriori, association, market_basket	Apriori algorithm discovering frequent itemsets, confidence & lift
13	K-Means Clustering	kmeans_clustering	13, kmeans, clustering	K-Means 2D clustering with marked centroid visualization
14	Hierarchical Clustering	hierarchical_clustering	14, hierarchical, agglomerative	Agglomerative hierarchical clustering dendrogram/scatter
15	Principal Component Analysis (PCA)	principal_component_analysis	15, pca, dimensionality_reduction	4D to 2D feature reduction maximizing variance
16	Linear Discriminant Analysis (LDA)	linear_discriminant_analysis	16, lda, discriminant_analysis	Supervised dimensionality reduction maximizing class separability

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📦 Building and Uploading to PyPI

Step 1: Install Build Tools

pip install --upgrade build twine

Step 2: Build Source Distribution and Wheel

From the repository root directory:

python -m build

This generates:

• dist/mlexp-0.1.0-py3-none-any.whl
• dist/mlexp-0.1.0.tar.gz

Step 3: Verify the Package Artifacts

python -m twine check dist/*

Step 4: Upload to PyPI

Option A: Test on TestPyPI first (Recommended):

python -m twine upload --repository testpypi dist/*

Option B: Upload to Production PyPI:

python -m twine upload dist/*

Enter your PyPI API token (__token__ as username and your token as password) when prompted.

Once uploaded, anyone can install and use it immediately:

pip install mlexp
mlexp --list

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📄 License

This project is licensed under the MIT License.