process-improve 1.40.1

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

process-improve

Multivariate analysis, designed experiments, and process monitoring for Python. Built for chemometrics, manufacturing, and pharma data - the methods that scikit-learn skips.

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New here? The architecture overview (source) is the map of the codebase - package layout, the estimator stack, and the MCP tool layer.

What it does

process-improve provides production-grade implementations of the methods practitioners actually use on real plant and lab data:

• PCA with SVD and NIPALS, plus native missing-value handling via Trimmed Score Regression
• PLS regression with a fully sklearn-compatible API, VIP scores, and cross-validated diagnostics
• TPLS - PLS for T-shaped (multi-block) data structures
• Outlier detection combining Hotelling's T² and SPE with an ESD-based test
• Designed experiments - full-factorial, fractional-factorial, and response-surface designs, plus a multi-stage DOE strategy recommender
• Process monitoring - Shewhart, CUSUM, and Holt-Winters control charts
• Batch data analysis - alignment, feature extraction, and multivariate batch monitoring (MBPCA / MBPLS)
• Interactive Plotly diagnostics bound directly to every fitted model

Outputs are pandas-native: scores, loadings, and predictions keep your row and column labels.

Scale: the estimators are in-memory - they assume the (scaled) data matrix fits in RAM, plus a couple of working copies during fit. A float64 matrix needs about rows x cols x 8 bytes (e.g. 10M x 200 is ~16 GB). For the practical limits and guidance on larger-than-RAM data, see Scaling and memory (source).

It is the companion package to the online textbook Process Improvement using Data, and powers the statistical engine behind factori.al.

Why not scikit-learn?

scikit-learn answers "what fits the data?" - process-improve answers "is this batch normal, which variable went off, and how confident am I in the prediction?" The two libraries are designed to be used together; process-improve follows sklearn conventions (fit, predict, score, the _ suffix on fitted attributes) and drops into existing pipelines.

Capability	scikit-learn	process-improve
PCA, PLS with sklearn-style API	✓	✓
Missing-data fitting (NIPALS / TSR)	-	✓
Hotelling's T² + SPE outlier limits	-	✓
Variable-level score contributions	-	✓
Cross-validated coefficient confidence intervals	-	✓
Multi-block models (TPLS)	-	✓
Designed experiments (DoE)	-	✓
Control charts (Shewhart / CUSUM / Holt-Winters)	-	✓
Batch process monitoring (MBPCA / MBPLS)	-	✓
Plotly diagnostics built in	-	✓
Labeled DataFrame outputs	partial	✓

Installation

pip install process-improve                    # core (numpy, pandas, sklearn, statsmodels, patsy, pydantic, pyyaml, tqdm)
pip install 'process-improve[plotting]'        # adds matplotlib, plotly, seaborn, ridgeplot
pip install 'process-improve[expt]'            # adds pyDOE3 (designed experiments / DOE)
pip install 'process-improve[batch]'           # adds openpyxl, scikit-image (batch process data IO)
pip install 'process-improve[mcp]'             # adds the MCP server runtime
pip install 'process-improve[fast]'            # adds numba (JIT speedups for batch alignment)
pip install 'process-improve[all]'             # everything above (the pre-1.24.11 closure)

Requires Python 3.10 or newer. The core install pulls in numpy, pandas, scipy, scikit-learn, statsmodels, patsy, pydantic, pyyaml, and tqdm. Heavier optional surfaces (plotting, designed experiments, batch IO, MCP server, numba JIT) live in extras so a caller who only needs, say, detect_multivariate_outliers does not have to install Plotly or numba.

Quick start

PCA - Principal Component Analysis

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

X = pd.read_csv("your_data.csv", index_col=0)
X_scaled = MCUVScaler().fit_transform(X)

pca = PCA(n_components=3).fit(X_scaled)
print(pca.r2_cumulative_)         # cumulative R² per component
pca.score_plot()                  # interactive Plotly figure

# Flag outliers using combined T² and SPE limits at 95% confidence
outliers = pca.detect_outliers(conf_level=0.95)

# Which variables drove the first observation off?
contrib = pca.score_contributions(pca.scores_.iloc[0].values)

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)
X_s, Y_s = scaler_x.transform(X), scaler_y.transform(Y)

pls = PLS(n_components=3).fit(X_s, Y_s)
print(pls.beta_coefficients_)     # regression coefficients (K x M)
print(pls.r2_cumulative_)         # cumulative R² for Y
print(pls.vip())                  # VIP scores per X variable

# Predict new observations (sklearn-compatible: returns just y_hat)
y_pred = pls.predict(scaler_x.transform(X_new))

# Predict with full per-row diagnostics (scores, T², SPE, plus y_hat)
result = pls.diagnose(scaler_x.transform(X_new))
result.y_hat                      # point predictions
result.spe                        # squared prediction error
result.hotellings_t2              # Hotelling's T² for new observations

# Cross-validated component selection
cv_select = PLS.select_n_components(X_s, Y_s, max_components=6)
print(cv_select.n_components)     # recommended number of components
print(cv_select.rmsecv)           # RMSECV per component count

# Cross-validation with beta-coefficient confidence intervals
cv = pls.cross_validate(X_s, Y_s, cv="loo")
print(cv.beta_ci_lower, cv.beta_ci_upper)   # 95% CI for each beta
print(cv.significant)                       # betas significantly != 0
print(cv.q_squared)                         # cross-validated R² (Q²)

DOE - multi-stage experimental strategy

from process_improve.experiments.factor import Factor, Response
from process_improve.experiments.strategy import recommend_strategy

factors = [
    Factor(name="Temperature", low=25, high=40, units="degC"),
    Factor(name="pH", low=5.0, high=7.5),
    Factor(name="Glucose", low=10, high=50, units="g/L"),
]
strategy = recommend_strategy(
    factors=factors,
    responses=[Response(name="Yield", goal="maximize", units="g/L")],
    budget=40,
    domain="fermentation",
)
for s in strategy["stages"]:
    print(s["stage_number"], s["design_type"], s["estimated_runs"])

Longer, fully-worked versions of each example live in the Quickstart guide and the process_improve/notebooks_examples/ folder.

New to designed experiments? The Applied DoE tutorial is an eight-module worked-solution series.

API design

PCA and PLS follow scikit-learn conventions: fit() returns self, fitted attributes end with a trailing underscore (scores_, loadings_, spe_, hotellings_t2_, r2_cumulative_, ...), and predict() returns an sklearn.utils.Bunch with named fields (y_hat, spe, hotellings_t2, ...). Inputs are accepted as pandas.DataFrame, and index/column labels are preserved through fit and transform.

Documentation & learning resources

• API reference & user guide: https://kgdunn.github.io/process-improve/
• Applied DoE tutorial (8 modules): https://kgdunn.github.io/process-improve/applied_doe/index.html
• Companion textbook: Process Improvement using Data
• Hosted experiment-design tool: factori.al
• Local docs build: cd docs && make html

Citing process-improve

If you use this package in academic work, please cite it:

@software{dunn_process_improve,
  author  = {Dunn, Kevin G.},
  title   = {{process-improve: Multivariate Analysis for Process Improvement}},
  year    = {2026},
  version = {v1.21.4},
  url     = {https://github.com/kgdunn/process-improve}
}

A CITATION.cff file is included, so GitHub renders a "Cite this repository" button in the sidebar.

Contributing

Bug reports, feature requests, and pull requests are welcome. See CONTRIBUTING.md for development setup, testing, and code style. Bugs and feature requests can be filed on the issue tracker.

License

MIT - see LICENSE for details.