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FAphouse provides a set of ready‑to‑use plotting utilities based on Matplotlib and Plotly: faphouse github link
# Heatmap of loadings (interactive)
model.plot_loadings(kind='heatmap', interactive=True)
# Scatter of the first two latent scores
model.plot_scores(dimensions=(0, 1), hue='group', plotly=True)
# Residual QQ‑plot
model.plot_residuals(kind='qq')
All plot functions accept a save_path argument to export static PNGs or interactive HTML files.
import numpy as np
import faphouse as fp
# Simulated data: 500 samples, 30 observed variables
np.random.seed(42)
X = np.random.randn(500, 30)
# Fit a 5‑factor model using the default EM optimizer
model = fp.FactorAnalysis(n_factors=5, method='em')
model.fit(X)
# Retrieve latent scores and loadings
scores = model.transform(X) # shape: (500, 5)
loadings = model.loadings_ # shape: (30, 5)
print("Explained variance per factor:", model.explained_variance_)
You should see a printed array of variance contributions and a convergence log in the console. The Faphouse GitHub Link: What You Need to
FactorAnalysis Class| Argument | Type | Default | Description |
|----------|------|---------|-------------|
| n_factors | int | required | Number of latent factors to infer. |
| method | 'em', 'newton', 'vi', 'mcmc' | 'em' | Optimization / inference algorithm. |
| max_iter | int | 500 | Maximum iterations. |
| tol | float | 1e-5 | Convergence tolerance on log‑likelihood. |
| rotation | 'varimax', 'promax', None | None | Post‑hoc rotation to aid interpretability. |
| regularizer | 'l1', 'l2', 'elasticnet', None | None | Penalty on loadings. |
| alpha | float | 0.0 | Strength of regularizer (if any). |
| batch_size | int | None | Mini‑batch size for stochastic EM. |
| device | 'cpu', 'cuda' | 'cpu' | Compute device (requires torch). | All plot functions accept a save_path argument to
Key Methods
| Method | Signature | Returns | Description |
|--------|-----------|---------|-------------|
| fit | fit(X, y=None) | self | Estimate model parameters from data X. |
| transform | transform(X) | np.ndarray | Project X into latent space (scores). |
| inverse_transform | inverse_transform(scores) | np.ndarray | Reconstruct observations from latent scores. |
| score | score(X) | float | Log‑likelihood of X under the fitted model. |
| rotate | rotate(method='varimax') | self | Apply rotation in‑place. |
| save | save(path) | — | Serialize model to disk. |
| load | @classmethod load(path) | FactorAnalysis | Load a previously saved model. |