Initial package

.github/workflows/ci-cd.yaml

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+name: Python package
+
+on: [push, pull_request]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.12"]
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install pytest black isort flake8
+          # Install PyTorch separately as it has special requirements
+          python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
+          # Install the package in development mode with all dependencies
+          python -m pip install -e ".[dev]"
+      - name: Lint with flake8
+        run: |
+          # stop the build if there are Python syntax errors or undefined names
+          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+          # exit-zero treats all errors as warnings
+          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+      - name: Test with pytest
+        run: |
+          pytest

README.md

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 # statsmodels-sgd
+
 Reimplementation of statsmodels using stochastic gradient descent
+
+Use just like statsmodels, but with stochastic gradient descent.
+
+## Installation
+
+```bash
+pip install git+https://github.com/PolicyEngine/statsmodels-sgd.git
+```
+
+## Example
+
+```python
+import statsmodels_sgd.api as sm_sgd
+
+# Fit OLS model
+
+model = sm_sgd.OLS(n_features=X.shape[1])
+model.fit(X, y)
+
+# Fit Logit model
+
+model = sm_sgd.Logit(n_features=X.shape[1])
+model.fit(X, y)
+```

pyproject.toml

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+[project]
+name = "statsmodels-sgd"
+version = "0.1.0"
+description = "A statsmodels-like package using SGD with gradient clipping for differential privacy"
+authors = [{name = "Max Ghenis", email = "max@policyengine.org"}]
+license = {file = "LICENSE"}
+readme = "README.md"
+requires-python = ">=3.8"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+]
+dependencies = [
+    "numpy",
+    "pandas",
+    "statsmodels",
+    "torch",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest",
+    "pytest-cov",
+    "black",
+    "isort",
+    "flake8",
+]
+
+[build-system]
+requires = ["setuptools>=45", "wheel", "setuptools_scm>=6.2"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+packages = ["statsmodels_sgd"]
+
+[tool.poetry]
+name = "statsmodels-sgd"
+version = "0.1.0"
+description = "A statsmodels-like package using SGD with gradient clipping for differential privacy"
+authors = [{name = "Max Ghenis", email = "max@policyengine.org"}]
+license = "MIT"
+readme = "README.md"
+packages = [{include = "statsmodels_sgd"}]
+
+[tool.poetry.dependencies]
+python = ">=3.8"
+numpy = "*"
+pandas = "*"
+statsmodels = "*"
+torch = "*"
+
+[tool.poetry.dev-dependencies]
+pytest = "*"
+pytest-cov = "*"
+black = "*"
+isort = "*"
+flake8 = "*"

statsmodels_sgd/__init__.py

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+from . import api
+from . import regression

statsmodels_sgd/api.py

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+from .regression.linear_model import OLS
+from .regression.discrete_model import Logit
+
+# Make commonly used objects available at api level
+__all__ = ["OLS", "Logit"]

statsmodels_sgd/base_model.py

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+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+from .tools import (
+    add_constant,
+    calculate_standard_errors,
+    calculate_t_p_values,
+)
+
+import sys
+
+print(f"Python executable: {sys.executable}")
+print(f"Python version: {sys.version}")
+print(f"Python path: {sys.path}")
+
+try:
+    import torch
+
+    print(f"PyTorch version: {torch.__version__}")
+    print(f"PyTorch installation path: {torch.__file__}")
+except ImportError as e:
+    print(f"Error importing torch: {e}")
+    print("Installed packages:")
+    import subprocess
+
+    result = subprocess.run(
+        [sys.executable, "-m", "pip", "list"], capture_output=True, text=True
+    )
+    print(result.stdout)
+
+
+class BaseModel(nn.Module):
+    def __init__(
+        self,
+        n_features,
+        learning_rate=0.01,
+        epochs=1000,
+        batch_size=32,
+        clip_value=1.0,
+    ):
+        super().__init__()
+        self.linear = nn.Linear(n_features, 1)
+        self.learning_rate = learning_rate
+        self.epochs = epochs
+        self.batch_size = batch_size
+        self.clip_value = clip_value
+        self.results_ = None
+
+    def forward(self, x):
+        return self.linear(x)
+
+    def fit(self, X, y, sample_weight=None):
+        raise NotImplementedError("Subclasses must implement this method")
+
+    def predict(self, X):
+        raise NotImplementedError("Subclasses must implement this method")
+
+    def summary(self):
+        if self.results_ is None:
+            raise ValueError("Model has not been fit yet.")
+        return self.results_

statsmodels_sgd/docs/_config.yml

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+title: Statsmodels-SGD Documentation
+author: Your Name
+logo: logo.png
+execute:
+  execute_notebooks: force

statsmodels_sgd/docs/_toc.yml

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+format: jb-book
+root: index
+chapters:
+  - file: installation
+  - file: ols-example
+  - file: logit-example
+  - file