dlspit 2.0.1

A multi-domain experiment package: Deep Learning (MLP, CNN, ResNet, RNN, LSTM, Transformer, GAN, VAE) and ML ESE Lab (regression, SVM, decision tree, random forest, XGBoost, k-means).

dlspit v2.0.0 — Multi-Domain Experiment Package

A pip-installable multi-domain experiment viewer and runner covering:

• DL — Deep Learning: MLP, Backpropagation, CNN, ResNet, RNN, LSTM, Transformer, GAN, VAE, Transfer Learning
• ML-ESE — Machine Learning ESE Lab: EDA, Linear Regression, Decision Tree, SVM, SVM RBF, Random Forest, XGBoost, K-Means Clustering

By default, dlspit prints the source code of the selected experiment. Use --run to actually execute it.

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Installation

# Core only (source viewing works for all experiments)
pip install dlspit

# With DL dependencies (tensorflow — required to --run DL experiments)
pip install "dlspit[dl]"

# With ML-ESE dependencies (scikit-learn, pandas, xgboost, …)
pip install "dlspit[ml-ese]"

# Everything
pip install "dlspit[all]"

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CLI Usage

List commands

# Show available domains
dlspit --list

# List all DL experiments
dlspit DL --list

# List all ML-ESE experiments
dlspit ML-ESE --list

Deep Learning experiments

# Print source code (default)
dlspit DL 1aMP_Xor
dlspit DL 4CNN 5Resnet 6RNN

# Execute an experiment
dlspit DL --run 1aMP_Xor
dlspit DL --run 4CNN

# As a module
python -m dl_experiments DL 1aMP_Xor
python -m dl_experiments DL --run 4CNN

ML-ESE experiments

# Print source code (default)
dlspit ML-ESE linear_regression
dlspit ML-ESE decision_tree

# Execute an experiment
dlspit ML-ESE --run linear_regression
dlspit ML-ESE --run kmeans_clustering

# As a module
python -m dl_experiments ML-ESE linear_regression
python -m dl_experiments ML-ESE --run random_forest

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DL Experiment Index

Key	Description
1aMP_Xor	McCulloch-Pitts neuron — XOR & XNOR gates
1bMP_Binary	MLP binary classifier (XNOR via backprop)
2aLoss_Activation	Activation & loss function comparison on MNIST
2bBPN	Backpropagation on MNIST (SGD feedforward NN)
3regularization	L2 regularisation + Dropout on MNIST
4CNN	Convolutional Neural Network on MNIST
5Resnet	ResNet50 transfer learning on CIFAR-10
6RNN	Simple RNN for IMDB sentiment analysis
7Lstm	Stacked LSTM for time-series prediction
8Transform	Transformer encoder for IMDB text classification
9aVAE	Variational Autoencoder on MNIST
9bGAN	Generative Adversarial Network on MNIST
10Transfer	MobileNetV2 transfer learning on CIFAR-10

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ML-ESE Experiment Index

Key	Description	Dataset
eda_titanic	EDA & data visualisation on the Titanic dataset	Seaborn built-in
linear_regression	Linear Regression — predicting student marks	dataset.csv
decision_tree	Decision Tree Classifier — sports match prediction	Inline
svm_classifier	SVM Classifier — student pass/fail prediction	Inline
svm_rbf_kernel	SVM with RBF Kernel — non-linear XOR-like classification	Inline
random_forest	Random Forest Classifier — loan approval prediction	loan_approval_dataset.csv
xgboost_classifier	XGBoost Classifier — loan approval prediction	loan_approval_dataset.csv
kmeans_clustering	K-Means Clustering — customer segmentation (Elbow Method)	seg.csv

Note: Experiments marked with a CSV dataset require that file to be present in your current working directory when using --run. Source code display (dlspit ML-ESE <key>) always works without any CSV.

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Python API

# DL domain
from dl_experiments.dl import list_experiments, show_source, run_experiment

print(list_experiments())          # ['1aMP_Xor', '1bMP_Binary', ...]
show_source("4CNN")                # print source, never executes
run_experiment("4CNN")             # executes (requires tensorflow)

# ML-ESE domain
from dl_experiments.ml_ese import list_experiments, show_source, run_experiment

print(list_experiments())          # ['eda_titanic', 'linear_regression', ...]
show_source("linear_regression")   # print source, never executes
run_experiment("decision_tree")    # executes (requires scikit-learn)

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Requirements

Group	Packages	Required for
core	numpy>=1.24	Always
dl	tensorflow>=2.12	Running DL experiments
ml-ese	scikit-learn, pandas, matplotlib, seaborn, xgboost	Running ML-ESE experiments

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License

MIT