PyPI 官网下载 | glimpy-0.0.2.tar.gz

# Glimpy [](https://circleci.com/gh/KSafran/glimpy) glimpy is a Python module for fitting generalized linear models. It's based on the [scikit-learn](https://scikit-learn.org/stable/index.html) API to facilitate use with other scikit-learn tools (pipelines, cross-validation, etc.). Models are fit using the [statsmodels](https://www.statsmodels.org/stable/glm.html) package. ## Installation `pip install git+https://github.com/KSafran/glimpy` ## Getting Started Here is an example of a poisson GLM to help get you started We will simulate an experiment where we want to determine how an individual's age and weight influence the number of hospital visits they can expect to have in a given year. Start with basic imports and setup ```python >>> import numpy as np >>> from scipy.stats import poisson >>> from glimpy import GLM, Poisson >>> >>> np.random.seed(10) >>> n_samples = 1000 ``` Now we will simulate some data where observed individuals have ages ranging from 30 to 70, and weights normally distributed centered around 150 lbs. ```python >>> age = np.random.uniform(30, 70, n_samples) >>> weight = np.random.normal(150, 20, n_samples) ``` Then we will have the expected number of hospital visits vary according to the following equation. We will sample from a poisson distribution with those means to get a sample of observed hospital visits ```python >>> expected_visits = np.exp(-10 + age * 0.05 + weight * 0.08) >>> observed_visits = poisson.rvs(expected_visits) ``` Now we can fit a `GLM` object to try to recover the formula we specified above ```python >>> X = np.vstack([age, weight]).T >>> y = observed_visits >>> pglm = GLM(fit_intercept=True, family=Poisson()) >>> pglm.fit(X, y) >>> print(pglm.summary()) Generalized Linear Model Regression Results ============================================================================== Dep. Variable: y No. Observations: 1000 Model: GLM Df Residuals: 997 Model Family: Poisson Df Model: 2 Link Function: log Scale: 1.0000 Method: IRLS Log-Likelihood: -3619.1 Date: Thu, 09 Jan 2020 Deviance: 967.43 Time: 22:31:35 Pearson chi2: 961. No. Iterations: 6 Covariance Type: nonrobust ============================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------ const -10.0132 0.020 -509.601 0.000 -10.052 -9.975 x1 0.0499 0.000 301.142 0.000 0.050 0.050 x2 0.0801 0.000 800.720 0.000 0.080 0.080 ============================================================================== ``` The upshot of glimpy is that you can use easily use your favorite scikit-learn tools with glimpy GLMs. For example, you can use the scikit-learn `cross_val_score` ```python >>> from sklearn.model_selection import cross_val_score >>> print(cross_val_score(pglm, X, y, cv=4)) [263.11969239 288.58713533 205.7032204 220.68304592] ```