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aimlexp
PyPIQuick CLI reference, runner, and exporter for AIML and Machine Learning lab experiments
Versions collected1
Direct dependencies5From release 0.1.0
Last observed21 Oct, 11:20 UTC
README
Version 0.1.0 documentationaimlexp - 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.
🚀 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 .
⚡ 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
📚 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 |
📦 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.whldist/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
📄 License
This project is licensed under the MIT License.
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Versions 1
Collected releases| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.1.0 | 13 Oct 2026 | 5 |
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