process-improve 1.1.0

Designed Experiments; Latent Variables (PCA, PLS, multivariate methods with missing data); Process Monitoring; Batch data analysis.

Process Improvement using Data

A Python package for multivariate data analysis, designed experiments, and process monitoring. Companion to the online textbook Process Improvement using Data.

Installation

pip install process-improve

Quick Start

PCA — Principal Component Analysis

import pandas as pd
from process_improve.multivariate.methods import PCA, MCUVScaler

# Load and scale your data
X = pd.read_csv("your_data.csv", index_col=0)
scaler = MCUVScaler().fit(X)
X_scaled = scaler.transform(X)

# Fit a PCA model
pca = PCA(n_components=3).fit(X_scaled)

# Inspect results
print(pca.scores_)  # Score matrix (N x A)
print(pca.loadings_)  # Loading matrix (K x A)
print(pca.r2_cumulative_)  # Cumulative R² per component

# Detect outliers
outliers = pca.detect_outliers(conf_level=0.95)

# Contribution analysis
contrib = pca.score_contributions(pca.scores_.iloc[0].values)

# Select number of components via cross-validation
result = PCA.select_n_components(X_scaled, max_components=10)
print(result.n_components)

# Built-in plots
pca.score_plot()
pca.spe_plot()
pca.t2_plot()
pca.loading_plot()

PLS — Projection to Latent Structures

from process_improve.multivariate.methods import PLS, MCUVScaler

# Scale X and Y separately
scaler_x = MCUVScaler().fit(X)
scaler_y = MCUVScaler().fit(Y)

# Fit a PLS model
pls = PLS(n_components=3).fit(scaler_x.transform(X), scaler_y.transform(Y))

# Inspect results
print(pls.scores_)  # X scores (N x A)
print(pls.beta_coefficients_)  # Regression coefficients (K x M)
print(pls.r2_cumulative_)  # Cumulative R² for Y

# Predict new observations
result = pls.predict(scaler_x.transform(X_new))
print(result.y_hat)  # Predicted Y values
print(result.spe)  # SPE for new data
print(result.hotellings_t2)  # Hotelling's T² for new data

# Detect outliers and analyze contributions
outliers = pls.detect_outliers(conf_level=0.95)
contrib = pls.score_contributions(pls.scores_.iloc[0].values)

Features

• PCA with SVD, NIPALS, and missing data (TSR) algorithms
• PLS regression with sklearn-compatible API
• TPLS (Total PLS) for multi-block data
• Missing data handling via TSR and NIPALS algorithms
• Outlier detection combining Hotelling's T² and SPE with robust ESD test
• Score contributions for variable-level diagnostics
• Cross-validation for component selection (PRESS with Wold's criterion)
• Interactive plots (Plotly) for scores, loadings, SPE, and T²
• Designed experiments — full factorial, fractional factorial, response surface
• Process monitoring — Shewhart, CUSUM, EWMA control charts
• Batch data analysis — alignment, feature extraction, multivariate batch monitoring

API Design

Both PCA and PLS follow sklearn conventions:

• Fitted attributes end with _ (e.g., scores_, loadings_, spe_)
• fit() returns self
• predict() returns a Bunch object with named fields
• score() is compatible with sklearn.model_selection.cross_val_score
• Works with pandas.DataFrame inputs (preserves index and column names)

Documentation

Build the documentation locally:

cd docs
make html

License

MIT License. See LICENSE for details.