Metadata-Version: 2.4
Name: cpfn
Version: 1.0.5
Summary: Conditional Push-Forward Neural Network estimator
Author-email: tedescolor <tedescolor@gmail.com>
License: Copyright (c) 2018 The Python Packaging Authority
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
Project-URL: Repository, https://github.com/tedescolor/cpfn
Project-URL: Paper, https://arxiv.org/pdf/2511.14455
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENCE
Requires-Dist: torch>=2.0
Requires-Dist: numpy>=1.20
Requires-Dist: tqdm>=4.60
Dynamic: license-file

# CPFN — Conditional Push-Forward Neural Network

Compact, importable implementation of a Conditional Push-Forward Neural Network (CPFN) estimator. 

**Paper:** https://arxiv.org/pdf/2511.14455

## Goals
- Provide a lightweight `CPFN` class for estimating conditional generators.
- Expose a simple API for training and sampling.


## Install

### From PyPI
```bash
pip install cpfn
```

## Quick Usage
```python

import random
import numpy as np
import torch
from cpfn import CPFN

# matplotlib is not a dependency of cpfn — install separately if needed:
#   pip install matplotlib
import matplotlib.pyplot as plt

# ---------------------------------------------------------------------------
# 1. Setup
# ---------------------------------------------------------------------------

SEED = 42
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)

if torch.cuda.is_available():
    device = torch.device("cuda")
elif torch.backends.mps.is_available():
    device = torch.device("mps")
else:
    device = torch.device("cpu")

print(f"Using device: {device}")

# ---------------------------------------------------------------------------
# 2. Synthetic Data — Branching Distribution
# ---------------------------------------------------------------------------
# For x < 0.5: single Gaussian branch (mu1).
# For x >= 0.5: equal-weight mixture of two Gaussian branches (mu1, mu2).

def mu1(x):
    return 10 * x * (x - 0.5) * (1.5 - x)

def mu2(x):
    return 10 * x * (x - 0.5) * (0.8 - x)

def noise_std(x):
    return 0.3 * (1.3 - x)

def sample_y(x):
    z = np.random.randn()
    if x < 0.5 or np.random.rand() < 0.5:
        return mu1(x) + z * noise_std(x)
    else:
        return mu2(x) + z * noise_std(x)

def true_conditional_pdf(y, x):
    """Analytic conditional density p(y | x)."""
    s = noise_std(x)
    def gauss(y, m): 
        return np.exp(-0.5 * ((y - m) / s) ** 2) / (np.sqrt(2 * np.pi) * s)
    if x < 0.5:
        return gauss(y, mu1(x))
    return 0.5 * gauss(y, mu1(x)) + 0.5 * gauss(y, mu2(x))


N_TRAIN = 1000
xs = np.random.rand(N_TRAIN)
ys = np.array([sample_y(x) for x in xs])

# ---------------------------------------------------------------------------
# 3. Model Training
# ---------------------------------------------------------------------------

model = CPFN(d=1, q=1, r=20, width=50, hidden_layers=3, delta=1e-15)
model.to(device)

model.fit(xs, ys, epochs=3000, lr=1e-3, m=30, h0=5e-2)
model.freeze()

# ---------------------------------------------------------------------------
# 4. Sample Comparison Plot
# ---------------------------------------------------------------------------

ys_gen = model.sample_conditional(xs, num_samples=1).flatten()

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5), sharey=True)

ax1.scatter(xs, ys, alpha=0.6, s=15, color="steelblue")
ax1.set_title("Ground Truth Samples")
ax1.set_xlabel("x")
ax1.set_ylabel("y")

ax2.scatter(xs, ys_gen, alpha=0.6, s=15, color="darkorange")
ax2.set_title("CPFN Generated Samples")
ax2.set_xlabel("x")

fig.suptitle("Training Data vs. CPFN Samples", fontsize=13, fontweight="bold")
plt.tight_layout()
plt.show()

# ---------------------------------------------------------------------------
# 5. Conditional Density Comparison
# ---------------------------------------------------------------------------

ygrid = np.linspace(-1.5, 3.0, 1000)
x_evals = [0.3, 0.7]

fig, axes = plt.subplots(1, len(x_evals), figsize=(5 * len(x_evals), 4), sharey=True)

for ax, x0 in zip(axes, x_evals):
    model_density = np.exp(model.logdensity(x0, ygrid, m=100_000))
    true_density  = true_conditional_pdf(ygrid, x0)

    ax.plot(ygrid, model_density, label="CPFN", color="darkorange", linewidth=1.8)
    ax.fill_between(ygrid, 0, model_density, alpha=0.20, color="darkorange")

    ax.plot(ygrid, true_density, label="True", color="steelblue",
            linestyle="--", linewidth=1.8)
    ax.fill_between(ygrid, 0, true_density, alpha=0.12, color="steelblue")

    ax.set_title(f"p(y | x = {x0:.1f})")
    ax.set_xlabel("y")
    ax.legend()

axes[0].set_ylabel("Density")
fig.suptitle("Conditional Density: CPFN vs. True", fontsize=13, fontweight="bold")
plt.tight_layout()
plt.show()
```
## Results

**Samples: Training Data vs. CPFN**
![Sample Comparison](https://raw.githubusercontent.com/tedescolor/cpfn/main/assets/sample_comparison.png)


**Conditional Density: CPFN vs. True**
![Conditional Density](https://raw.githubusercontent.com/tedescolor/cpfn/main/assets/conditional_density.png)

## Tests
Run the included pytest smoke test:
```bash
pytest -q
```

## Development
- Source: `src/cpfn/`
- Tests: `tests/`

## License
See `LICENCE` in the repository root.
