glum package

The two main classes in glum are GeneralizedLinearRegressor and GeneralizedLinearRegressorCV. Most users will use fit() and predict()

class glum.BinomialDistribution

Bases: ExponentialDispersionModel

A class for the Binomial distribution.

The Binomial distribution is for targets y in [0, 1].

deviance(y, mu, sample_weight=1)

Compute the deviance.

The deviance is a weighted sum of the unit deviances, \(\sum_i s_i \times d(y_i, \mu_i)\), where \(d(y, \mu)\) is the unit deviance and \(s\) are weights. In terms of the log likelihood, it is \(-2\phi \times [L(y, \mu, \phi / s) - L(y, y, \phi / s)]\).

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inversely proportional.

Return type:

float

deviance_derivative(y, mu, sample_weight=1)

Compute the derivative of the deviance with respect to mu.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,) (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

dispersion(y, mu, sample_weight=None, ddof=1, method='pearson')

Estimate the dispersion parameter \(\phi\).

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Weights or exposure to which variance is inversely proportional.
ddof (int, optional (default=1)) – Degrees of freedom consumed by the model for mu.
{'pearson' (method =) – Whether to base the estimate on the Pearson residuals or the deviance.
'deviance'} – Whether to base the estimate on the Pearson residuals or the deviance.
(default='pearson') (optional) – Whether to base the estimate on the Pearson residuals or the deviance.

Return type:

float

eta_mu_deviance(link, factor, cur_eta, X_dot_d, y, sample_weight)

Compute eta, mu and the deviance.

Compute: * the linear predictor, eta, as cur_eta + factor * X_dot_d; * the link-function-transformed prediction, mu; * the deviance.

Returns:

numpy.ndarray, shape (X.shape[0],) – The linear predictor, eta.
numpy.ndarray, shape (X.shape[0],) – The link-function-transformed prediction, mu.
float – The deviance.

Parameters:

link (Link) –
factor (float) –
cur_eta (ndarray) –
X_dot_d (ndarray) –
y (ndarray) –
sample_weight (ndarray) –

in_y_range(x)

Return True if x is in the valid range of the EDM.

Parameters:: x (array-like, shape (n_samples,)) – Target values.
Return type:: np.ndarray

log_likelihood(y, mu, sample_weight=None, dispersion=1)

Compute the log likelihood.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Sample weights.
dispersion (float, optional (default=1)) – Ignored.

Return type:

float

rowwise_gradient_hessian(link, coef, dispersion, X, y, sample_weight, eta, mu, offset=None)

Compute the gradient and negative Hessian of the log likelihood row-wise.

Returns:

numpy.ndarray, shape (X.shape[0],) – The gradient of the log likelihood, row-wise.
numpy.ndarray, shape (X.shape[0],) – The negative Hessian of the log likelihood, row-wise.

Parameters:

link (Link) –
coef (ndarray) –
X (MatrixBase | StandardizedMatrix) –
y (ndarray) –
sample_weight (ndarray) –
eta (ndarray) –
mu (ndarray) –
offset (ndarray | None) –

unit_deviance(y, mu)

Get the unit-level deviance.

See superclass documentation.

Parameters:

y (array-like) –
mu (array-like) –

Return type:

array-like

unit_deviance_derivative(y, mu)

Compute the derivative of the unit deviance with respect to mu.

The derivative of the unit deviance is given by \(-2 \times (y - \mu) / v(\mu)\), where \(v(\mu)\) is the unit variance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.

Return type:

array-like, shape (n_samples,)

unit_variance(mu)

Get the unit-level expected variance.

See superclass documentation.

Parameters:: mu (array-like) –
Return type:: array-like

unit_variance_derivative(mu)

Get the derivative of the unit variance.

See superclass documentation.

Parameters:: mu (array-like or float) –
Return type:: array-like

variance(mu, dispersion=1, sample_weight=1)

Compute the variance function.

The variance of \(Y_i \sim \mathrm{EDM}(\mu_i, \phi / s_i)\) is \(\mathrm{var}(Y_i) = (\phi / s_i) * v(\mu_i)\), with unit variance \(v(\mu)\) and weights \(s_i\).

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

variance_derivative(mu, dispersion=1, sample_weight=1)

Compute the derivative of the variance with respect to mu.

The derivative of the variance is equal to \((\phi / s_i) * v'(\mu_i)\), where \(v(\mu)\) is the unit variance and \(s_i\) are weights.

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

class glum.CloglogLink

Bases: Link

The complementary log-log link function log(-log(-p)).

derivative(mu)

Get the derivative of the cloglog link.

See superclass documentation.

Parameters:: mu (array-like) –
Return type:: array-like

inverse(lin_pred)

Get the inverse of the cloglog link.

See superclass documentation.

Note: since passing a very large value might result in an output of one, this function bounds the output to be between [50*eps, 1 - 50*eps], where eps is floating point epsilon.

Parameters:: lin_pred (array-like) –
Return type:: array-like

inverse_derivative(lin_pred)

Compute the derivative of the inverse link function h'(lin_pred).

Parameters:: lin_pred (array, shape (n_samples,)) – Usually the (fitted) linear predictor.

inverse_derivative2(lin_pred)

Compute second derivative of the inverse link function h''(lin_pred).

Parameters:: lin_pred (array, shape (n_samples,)) – Usually the (fitted) linear predictor.

link(mu)

Get the logit function of mu.

See superclass documentation.

Parameters:: mu (array-like) –
Return type:: numpy.ndarray

class glum.ExponentialDispersionModel

Bases: object

Base class for reproductive Exponential Dispersion Models (EDM).

The PDF of \(Y \sim \mathrm{EDM}(\mu, \phi)\) is given by

\[\begin{split}p(y \mid \theta, \phi) &= c(y, \phi) \exp((\theta y - A(\theta)_ / \phi) \\ &= \tilde{c}(y, \phi) \exp(-d(y, \mu) / (2\phi))\end{split}\]

with mean \(\mathrm{E}(Y) = A'(\theta) = \mu\), variance \(\mathrm{var}(Y) = \phi \cdot v(\mu)\), unit variance \(v(\mu)\) and unit deviance \(d(y, \mu)\).

Properties

lower_bound upper_bound include_lower_bound include_upper_bound

in_y_range()

Return type:: ndarray

unit_variance()

unit_variance_derivative()

variance()

Parameters:: mu (ndarray) –
Return type:: ndarray

variance_derivative()

unit_deviance()

unit_deviance_derivative()

deviance()

deviance_derivative()

starting_mu()

_mu_deviance_derivative()

eta_mu_deviance()

Parameters:

link (Link) –
factor (float) –
cur_eta (ndarray) –
X_dot_d (ndarray) –
y (ndarray) –
sample_weight (ndarray) –

gradient_hessian()

References

https://en.wikipedia.org/wiki/Exponential_dispersion_model.

deviance(y, mu, sample_weight=1)

Compute the deviance.

The deviance is a weighted sum of the unit deviances, \(\sum_i s_i \times d(y_i, \mu_i)\), where \(d(y, \mu)\) is the unit deviance and \(s\) are weights. In terms of the log likelihood, it is \(-2\phi \times [L(y, \mu, \phi / s) - L(y, y, \phi / s)]\).

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inversely proportional.

Return type:

float

deviance_derivative(y, mu, sample_weight=1)

Compute the derivative of the deviance with respect to mu.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,) (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

dispersion(y, mu, sample_weight=None, ddof=1, method='pearson')

Estimate the dispersion parameter \(\phi\).

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inversely proportional.
ddof (int, optional (default=1)) – Degrees of freedom consumed by the model for mu.
{'pearson' (method =) – Whether to base the estimate on the Pearson residuals or the deviance.
'deviance'} – Whether to base the estimate on the Pearson residuals or the deviance.
(default='pearson') (optional) – Whether to base the estimate on the Pearson residuals or the deviance.

Return type:

float

eta_mu_deviance(link, factor, cur_eta, X_dot_d, y, sample_weight)

Compute eta, mu and the deviance.

Compute: * the linear predictor, eta, as cur_eta + factor * X_dot_d; * the link-function-transformed prediction, mu; * the deviance.

Returns:

numpy.ndarray, shape (X.shape[0],) – The linear predictor, eta.
numpy.ndarray, shape (X.shape[0],) – The link-function-transformed prediction, mu.
float – The deviance.

Parameters:

link (Link) –
factor (float) –
cur_eta (ndarray) –
X_dot_d (ndarray) –
y (ndarray) –
sample_weight (ndarray) –

in_y_range(x)

Return True if x is in the valid range of the EDM.

Parameters:: x (array-like, shape (n_samples,)) – Target values.
Return type:: np.ndarray

abstract property include_lower_bound: bool: Return whether lower_bound is allowed as a value of y.

abstract property include_upper_bound: bool: Return whether upper_bound is allowed as a value of y.

abstract property lower_bound: float: Get the lower bound of values for the EDM.

rowwise_gradient_hessian(link, coef, dispersion, X, y, sample_weight, eta, mu, offset=None)

Compute the gradient and negative Hessian of the log likelihood row-wise.

Returns:

numpy.ndarray, shape (X.shape[0],) – The gradient of the log likelihood, row-wise.
numpy.ndarray, shape (X.shape[0],) – The negative Hessian of the log likelihood, row-wise.

Parameters:

link (Link) –
coef (ndarray) –
X (MatrixBase | StandardizedMatrix) –
y (ndarray) –
sample_weight (ndarray) –
eta (ndarray) –
mu (ndarray) –
offset (ndarray | None) –

abstract unit_deviance(y, mu)

Compute the unit deviance.

In terms of the log likelihood \(L\), the unit deviance is \(-2\phi\times [L(y, \mu, \phi) - L(y, y, \phi)].\)

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.

unit_deviance_derivative(y, mu)

Compute the derivative of the unit deviance with respect to mu.

The derivative of the unit deviance is given by \(-2 \times (y - \mu) / v(\mu)\), where \(v(\mu)\) is the unit variance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.

Return type:

array-like, shape (n_samples,)

abstract unit_variance(mu)

Compute the unit variance function.

The unit variance \(v(\mu)\) determines the variance as a function of the mean \(\mu\) by \(\mathrm{var}(y_i) = (\phi / s_i) \times v(\mu_i)\). It can also be derived from the unit deviance \(d(y, \mu)\) as

\[v(\mu) = \frac{2}{\frac{\partial^2 d(y, \mu)}{\partial\mu^2}}\big|_{y=\mu}.\]

Read more in background.

Parameters:

alpha ({float, array-like}, optional (default=None)) – Constant that multiplies the penalty terms and thus determines the regularization strength. If alpha_search is False (the default), then alpha must be a scalar or None (equivalent to alpha=1.0). If alpha_search is True, then alpha must be an iterable or None. See alpha_search to find how the regularization path is set if alpha is None. See the notes for the exact mathematical meaning of this parameter. alpha = 0 is equivalent to unpenalized GLMs. In this case, the design matrix X must have full column rank (no collinearities).
l1_ratio (float, optional (default=0)) – The elastic net mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0, the penalty is an L2 penalty. For l1_ratio = 1, it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
P1 ({'identity', array-like, None}, shape (n_features,), optional (default='identity')) – This array controls the strength of the regularization for each coefficient independently. A high value will lead to higher regularization while a value of zero will remove the regularization on this parameter. Note that n_features = X.shape[1]. If X is a pandas DataFrame with a categorical dtype and P1 has the same size as the number of columns, the penalty of the categorical column will be applied to all the levels of the categorical.
P2 ({'identity', array-like, sparse matrix, None}, shape (n_features,) or (n_features, n_features), optional (default='identity')) – With this option, you can set the P2 matrix in the L2 penalty w*P2*w. This gives a fine control over this penalty (Tikhonov regularization). A 2d array is directly used as the square matrix P2. A 1d array is interpreted as diagonal (square) matrix. The default 'identity' and None set the identity matrix, which gives the usual squared L2-norm. If you just want to exclude certain coefficients, pass a 1d array filled with 1 and 0 for the coefficients to be excluded. Note that P2 must be positive semi-definite. If X is a pandas DataFrame with a categorical dtype and P2 has the same size as the number of columns, the penalty of the categorical column will be applied to all the levels of the categorical. Note that if P2 is two-dimensional, its size needs to be of the same length as the expanded X matrix.
fit_intercept (bool, optional (default=True)) – Specifies if a constant (a.k.a. bias or intercept) should be added to the linear predictor (X * coef + intercept).
family (str or ExponentialDispersionModel, optional (default='normal')) – The distributional assumption of the GLM, i.e. the loss function to minimize. If a string, one of: 'binomial', 'gamma', 'gaussian', 'inverse.gaussian', 'normal', 'poisson', 'tweedie' or 'negative.binomial'. Note that 'tweedie' sets the power of the Tweedie distribution to 1.5; to use another value, specify it in parentheses (e.g., 'tweedie (1.5)'). The same applies for 'negative.binomial' and theta parameter.
link ({'auto', 'identity', 'log', 'logit', 'cloglog'} oe Link, optional (default='auto')) –
The link function of the GLM, i.e. mapping from linear predictor (X * coef) to expectation (mu). Option 'auto' sets the link depending on the chosen family as follows:
- 'identity' for family 'normal'
- 'log' for families 'poisson', 'gamma', 'inverse.gaussian' and 'negative.binomial'.
- 'logit' for family 'binomial'
solver ({'auto', 'irls-cd', 'irls-ls', 'lbfgs', 'trust-constr'}, optional (default='auto')) –
Algorithm to use in the optimization problem:
- 'auto': 'irls-ls' if l1_ratio is zero and 'irls-cd' otherwise.
- 'irls-cd': Iteratively reweighted least squares with a coordinate descent inner solver. This can deal with L1 as well as L2 penalties. Note that in order to avoid unnecessary memory duplication of X in the fit method, X should be directly passed as a Fortran-contiguous Numpy array or sparse CSC matrix.
- 'irls-ls': Iteratively reweighted least squares with a least squares inner solver. This algorithm cannot deal with L1 penalties.
- 'lbfgs': Scipy’s L-BFGS-B optimizer. It cannot deal with L1 penalties.
- 'trust-constr': Calls scipy.optimize.minimize(method='trust-constr'). It cannot deal with L1 penalties. This solver can optimize problems with inequality constraints, passed via A_ineq and b_ineq. It will be selected automatically when inequality constraints are set and solver='auto'. Note that using this method can lead to significantly increased runtimes by a factor of ten or higher.
max_iter (int, optional (default=100)) – The maximal number of iterations for solver algorithms.
gradient_tol (float, optional (default=None)) –
Stopping criterion. If None, solver-specific defaults will be used. The default value for most solvers is 1e-4, except for 'trust-constr', which requires more conservative convergence settings and has a default value of 1e-8.

For the IRLS-LS, L-BFGS and trust-constr solvers, the iteration will stop when max{|g_i|, i = 1, ..., n} <= tol, where g_i is the i-th component of the gradient (derivative) of the objective function. For the CD solver, convergence is reached when sum_i(|minimum norm of g_i|), where g_i is the subgradient of the objective and the minimum norm of g_i is the element of the subgradient with the smallest L2 norm.

If you wish to only use a step-size tolerance, set gradient_tol to a very small number.
step_size_tol (float, optional (default=None)) – Alternative stopping criterion. For the IRLS-LS and IRLS-CD solvers, the iteration will stop when the L2 norm of the step size is less than step_size_tol. This stopping criterion is disabled when step_size_tol is None.
hessian_approx (float, optional (default=0.0)) – The threshold below which data matrix rows will be ignored for updating the Hessian. See the algorithm documentation for the IRLS algorithm for further details.
warm_start (bool, optional (default=False)) – Whether to reuse the solution of the previous call to fit as initialization for coef_ and intercept_ (supersedes start_params). If False or if the attribute coef_ does not exist (first call to fit), start_params sets the start values for coef_ and intercept_.
alpha_search (bool, optional (default=False)) –
Whether to search along the regularization path for the best alpha. When set to True, alpha should either be None or an iterable. To determine the regularization path, the following sequence is used:
1. If alpha is an iterable, use it directly. All other parameters governing the regularization path are ignored.
2. If min_alpha is set, create a path from min_alpha to the lowest alpha such that all coefficients are zero.
3. If min_alpha_ratio is set, create a path where the ratio of min_alpha / max_alpha = min_alpha_ratio.
4. If none of the above parameters are set, use a min_alpha_ratio of 1e-6.
alphas (DEPRECATED. Use alpha instead.) –
n_alphas (int, optional (default=100)) – Number of alphas along the regularization path
min_alpha_ratio (float, optional (default=None)) – Length of the path. min_alpha_ratio=1e-6 means that min_alpha / max_alpha = 1e-6. If None, 1e-6 is used.
min_alpha (float, optional (default=None)) – Minimum alpha to estimate the model with. The grid will then be created over [max_alpha, min_alpha].
start_params (array-like, shape (n_features*,), optional (default=None)) – Relevant only if warm_start is False or if fit is called for the first time (so that self.coef_ does not exist yet). If None, all coefficients are set to zero and the start value for the intercept is the weighted average of y (If fit_intercept is True). If an array, used directly as start values; if fit_intercept is True, its first element is assumed to be the start value for the intercept_. Note that n_features* = X.shape[1] + fit_intercept, i.e. it includes the intercept.
selection (str, optional (default='cyclic')) – For the CD solver ‘cd’, the coordinates (features) can be updated in either cyclic or random order. If set to 'random', a random coefficient is updated every iteration rather than looping over features sequentially in the same order, which often leads to significantly faster convergence, especially when gradient_tol is higher than 1e-4.
random_state (int or RandomState, optional (default=None)) – The seed of the pseudo random number generator that selects a random feature to be updated for the CD solver. If an integer, random_state is the seed used by the random number generator; if a RandomState instance, random_state is the random number generator; if None, the random number generator is the RandomState instance used by np.random. Used when selection is 'random'.
copy_X (bool, optional (default=None)) – Whether to copy X. Since X is never modified by GeneralizedLinearRegressor, this is unlikely to be needed; this option exists mainly for compatibility with other scikit-learn estimators. If False, X will not be copied and there will be an error if you pass an X in the wrong format, such as providing integer X and float y. If None, X will not be copied unless it is in the wrong format.
check_input (bool, optional (default=True)) – Whether to bypass several checks on input: y values in range of family, sample_weight non-negative, P2 positive semi-definite. Don’t use this parameter unless you know what you are doing.
verbose (int, optional (default=0)) – For the IRLS solver, any positive number will result in a pretty progress bar showing convergence. This features requires having the tqdm package installed. For the L-BFGS and 'trust-constr' solvers, set verbose to any positive number for verbosity.
scale_predictors (bool, optional (default=False)) –
If True, estimate a scaled model where all predictors have a standard deviation of 1. This can result in better estimates if predictors are on very different scales (for example, centimeters and kilometers).

Advanced developer note: Internally, predictors are always rescaled for computational reasons, but this only affects results if scale_predictors is True.
lower_bounds (array-like, shape (n_features,), optional (default=None)) – Set a lower bound for the coefficients. Setting bounds forces the use of the coordinate descent solver ('irls-cd').
upper_bounds (array-like, shape=(n_features,), optional (default=None)) – See lower_bounds.
A_ineq (array-like, shape=(n_constraints, n_features), optional (default=None)) – Constraint matrix for linear inequality constraints of the form A_ineq w <= b_ineq. Setting inequality constraints forces the use of the local gradient-based solver 'trust-constr', which may increase runtime significantly. Note that the constraints only apply to coefficients related to features in X. If you want to constrain the intercept, add it to the feature matrix X manually and set fit_intercept==False.
b_ineq (array-like, shape=(n_constraints,), optional (default=None)) – Constraint vector for linear inequality constraints of the form A_ineq w <= b_ineq. Refer to the documentation of A_ineq for details.
drop_first (bool, optional (default = False)) – If True, drop the first column when encoding categorical variables. Set this to True when alpha=0 and solver=’auto’ to prevent an error due to a singular feature matrix.
robust (bool, optional (default = False)) – If true, then robust standard errors are computed by default.
expected_information (bool, optional (default = False)) – If true, then the expected information matrix is computed by default. Only relevant when computing robust standard errors.
force_all_finite (bool) –

coef_

Estimated coefficients for the linear predictor (X*coef_+intercept_) in the GLM.

Type:: numpy.array, shape (n_features,)

intercept_

Intercept (a.k.a. bias) added to linear predictor.

Type:: float

n_iter_

Actual number of iterations used in solver.

Type:: int

Notes

The fit itself does not need outcomes to be from an EDM, but only assumes the first two moments to be \(\mu_i \equiv \mathrm{E}(y_i) = h(x_i' w)\) and \(\mathrm{var}(y_i) = (\phi / s_i) v(\mu_i)\). The unit variance function \(v(\mu_i)\) is a property of and given by the specific EDM; see background.

The parameters \(w\) (coef_ and intercept_) are estimated by minimizing the deviance plus penalty term, which is equivalent to (penalized) maximum likelihood estimation.

For alpha > 0, the feature matrix X should be standardized in order to penalize features equally strong. Call sklearn.preprocessing.StandardScaler before calling fit.

If the target y is a ratio, appropriate sample weights s should be provided. As an example, consider Poisson distributed counts z (integers) and weights s = exposure (time, money, persons years, …). Then you fit y ≡ z/s, i.e. GeneralizedLinearModel(family='poisson').fit(X, y, sample_weight=s). The weights are necessary for the right (finite sample) mean. Consider \(\bar{y} = \sum_i s_i y_i / \sum_i s_i\): in this case, one might say that \(y\) follows a ‘scaled’ Poisson distribution. The same holds for other distributions.

References

For the coordinate descent implementation:

Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin An Improved GLMNET for L1-regularized Logistic Regression, Journal of Machine Learning Research 13 (2012) 1999-2030 https://www.csie.ntu.edu.tw/~cjlin/papers/l1_glmnet/long-glmnet.pdf

aic(X, y, sample_weight=None)

Akaike’s information criteria. Computed as: \(-2\log\hat{\mathcal{L}} + 2\hat{k}\) where \(\hat{\mathcal{L}}\) is the maximum likelihood estimate of the model, and \(\hat{k}\) is the effective number of parameters. See _compute_information_criteria for more information on the computation of \(\hat{k}\).

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features)) – Same data as used in ‘fit’
y (array-like, shape (n_samples,)) – Same data as used in ‘fit’
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Same data as used in ‘fit’

aicc(X, y, sample_weight=None)

Second-order Akaike’s information criteria (or small sample AIC). Computed as: \(-2\log\hat{\mathcal{L}} + 2\hat{k} + \frac{2k(k+1)}{n-k-1}\) where \(\hat{\mathcal{L}}\) is the maximum likelihood estimate of the model, \(n\) is the number of training instances, and \(\hat{k}\) is the effective number of parameters. See _compute_information_criteria for more information on the computation of \(\hat{k}\).

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features)) – Same data as used in ‘fit’
y (array-like, shape (n_samples,)) – Same data as used in ‘fit’
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Same data as used in ‘fit’

bic(X, y, sample_weight=None)

Bayesian information criterion. Computed as: \(-2\log\hat{\mathcal{L}} + k\log(n)\) where \(\hat{\mathcal{L}}\) is the maximum likelihood estimate of the model, \(n\) is the number of training instances, and \(\hat{k}\) is the effective number of parameters. See _compute_information_criteria for more information on the computation of \(\hat{k}\).

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features)) – Same data as used in ‘fit’
y (array-like, shape (n_samples,)) – Same data as used in ‘fit’
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Same data as used in ‘fit’

coef_table(confidence_level=0.95, X=None, y=None, mu=None, offset=None, sample_weight=None, dispersion=None, robust=None, clusters=None, expected_information=None)

Get a table of of the regression coefficients.

Includes coefficient estimates, standard errors, t-values, p-values and confidence intervals.

Parameters:

confidence_level (float, optional, default=0.95) – The confidence level for the confidence intervals.
X ({array-like, sparse matrix}, shape (n_samples, n_features), optional) – Training data. Can be omitted if a covariance matrix has already been computed or if standard errors, etc. are not desired.
y (array-like, shape (n_samples,), optional) – Target values. Can be omitted if a covariance matrix has already been computed.
mu (array-like, optional, default=None) – Array with predictions. Estimated if absent.
offset (array-like, optional, default=None) – Array with additive offsets.
sample_weight (array-like, shape (n_samples,), optional, default=None) – Individual weights for each sample.
dispersion (float, optional, default=None) – The dispersion parameter. Estimated if absent.
robust (boolean, optional, default=None) – Whether to compute robust standard errors instead of normal ones. If not specified, the model’s robust attribute is used.
clusters (array-like, optional, default=None) – Array with cluster membership. Clustered standard errors are computed if clusters is not None.
expected_information (boolean, optional, default=None) – Whether to use the expected or observed information matrix. Only relevant when computing robust standard errors. If not specified, the model’s expected_information attribute is used.

Returns:

A table of the regression results.

Return type:

pandas.DataFrame

covariance_matrix(X=None, y=None, mu=None, offset=None, sample_weight=None, dispersion=None, robust=None, clusters=None, expected_information=None, store_covariance_matrix=False, skip_checks=False)

Calculate the covariance matrix for generalized linear models.

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features), optional) – Training data. Can be omitted if a covariance matrix has already been computed.
y (array-like, shape (n_samples,), optional) – Target values. Can be omitted if a covariance matrix has already been computed.
mu (array-like, optional, default=None) – Array with predictions. Estimated if absent.
offset (array-like, optional, default=None) – Array with additive offsets.
sample_weight (array-like, shape (n_samples,), optional, default=None) – Individual weights for each sample.
dispersion (float, optional, default=None) – The dispersion parameter. Estimated if absent.
robust (boolean, optional, default=None) – Whether to compute robust standard errors instead of normal ones. If not specified, the model’s robust attribute is used.
clusters (array-like, optional, default=None) – Array with cluster membership. Clustered standard errors are computed if clusters is not None.
expected_information (boolean, optional, default=None) – Whether to use the expected or observed information matrix. Only relevant when computing robust standard errors. If not specified, the model’s expected_information attribute is used.
store_covariance_matrix (boolean, optional, default=False) – Whether to store the covariance matrix in the model instance. If a covariance matrix has already been stored, it will be overwritten.
skip_checks (boolean, optional, default=False) – Whether to skip input validation. For internal use only.

Notes

We support three types of covariance matrices:

non-robust
robust (HC-1)
clustered

For maximum-likelihood estimator, the covariance matrix takes the form \(\mathcal{H}^{-1}(\theta_0)\mathcal{I}(\theta_0) \mathcal{H}^{-1}(\theta_0)\) where \(\mathcal{H}^{-1}\) is the inverse Hessian and \(\mathcal{I}\) is the Information matrix. The different types of covariance matrices use different approximation of these quantities.

The non-robust covariance matrix is computed as the inverse of the Fisher information matrix. This assumes that the information matrix equality holds.

The robust (HC-1) covariance matrix takes the form \(\mathbf{H}^{−1} (\hat{\theta})\mathbf{G}^{T}(\hat{\theta})\mathbf{G}(\hat{\theta}) \mathbf{H}^{−1}(\hat{\theta})\) where \(\mathbf{H}\) is the empirical Hessian and \(\mathbf{G}\) is the gradient. We apply a finite-sample correction of \(\frac{N}{N-p}\).

The clustered covariance matrix uses a similar approach to the robust (HC-1) covariance matrix. However, instead of using \(\mathbf{G}^{T}(\hat{\theta} \mathbf{G}(\hat{\theta})\) directly, we first sum over all the groups first. The finite-sample correction is affected as well, becoming \(\frac{M}{M-1} \frac{N}{N-p}\) where \(M\) is the number of groups.

References

property family_instance: ExponentialDispersionModel: Return an ExponentialDispersionModel.

fit(X, y, sample_weight=None, offset=None, store_covariance_matrix=False, clusters=None, weights_sum=None)

Fit a Generalized Linear Model.

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features)) – Training data. Note that a float32 matrix is acceptable and will result in the entire algorithm being run in 32-bit precision. However, for problems that are poorly conditioned, this might result in poor convergence or flawed parameter estimates. If a Pandas data frame is provided, it may contain categorical columns. In that case, a separate coefficient will be estimated for each category. No category is omitted. This means that some regularization is required to fit models with an intercept or models with several categorical columns.
y (array-like, shape (n_samples,)) – Target values.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Individual weights w_i for each sample. Note that, for an Exponential Dispersion Model (EDM), one has \(\mathrm{var}(y_i) = \phi \times v(mu) / w_i\). If \(y_i \sim EDM(\mu, \phi / w_i)\), then \(\sum w_i y_i / \sum w_i \sim EDM(\mu, \phi / \sum w_i)\), i.e. the mean of \(y\) is a weighted average with weights equal to sample_weight.
offset (array-like, shape (n_samples,), optional (default=None)) – Added to linear predictor. An offset of 3 will increase expected y by 3 if the link is linear and will multiply expected y by 3 if the link is logarithmic.
store_covariance_matrix (bool, optional (default=False)) – Whether to estimate and store the covariance matrix of the parameter estimates. If True, the covariance matrix will be available in the covariance_matrix_ attribute after fitting.
clusters (array-like, optional, default=None) – Array with cluster membership. Clustered standard errors are computed if clusters is not None.
weights_sum (float, optional (default=None)) –

Return type:

self

get_formatted_diagnostics(full_report=False, custom_columns=None)

Get formatted diagnostics; can be printed with _report_diagnostics.

Parameters:

full_report (bool, optional (default=False)) – Print all available information. When False and custom_columns is None, a restricted set of columns is printed out.
custom_columns (iterable, optional (default=None)) – Print only the specified columns.

Return type:

str | DataFrame

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

linear_predictor(X, offset=None, alpha_index=None, alpha=None)

Compute the linear predictor, X * coef_ + intercept_.

If alpha_search is True, but alpha_index and alpha are both None, we use the last alpha value self._alphas[-1].

Parameters:

X (array-like, shape (n_samples, n_features)) – Observations. X may be a pandas data frame with categorical types. If X was also a data frame with categorical types during fitting and a category wasn’t observed at that point, the corresponding prediction will be numpy.nan.
offset (array-like, shape (n_samples,), optional (default=None)) –
alpha_index (int or list[int], optional (default=None)) – Sets the index of the alpha(s) to use in case alpha_search is True. Incompatible with alpha (see below).
alpha (float or list[float], optional (default=None)) – Sets the alpha(s) to use in case alpha_search is True. Incompatible with alpha_index (see above).

Returns:

The linear predictor.

Return type:

array, shape (n_samples, n_alphas)

property link_instance: Link: Return a Link.

predict(X, sample_weight=None, offset=None, alpha_index=None, alpha=None)

Predict using GLM with feature matrix X.

If alpha_search is True, but alpha_index and alpha are both None, we use the last alpha value self._alphas[-1].

Parameters:

X (array-like, shape (n_samples, n_features)) – Observations. X may be a pandas data frame with categorical types. If X was also a data frame with categorical types during fitting and a category wasn’t observed at that point, the corresponding prediction will be numpy.nan.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Sample weights to multiply predictions by.
offset (array-like, shape (n_samples,), optional (default=None)) –
alpha_index (int or list[int], optional (default=None)) – Sets the index of the alpha(s) to use in case alpha_search is True. Incompatible with alpha (see below).
alpha (float or list[float], optional (default=None)) – Sets the alpha(s) to use in case alpha_search is True. Incompatible with alpha_index (see above).

Returns:

Predicted values times sample_weight.

Return type:

array, shape (n_samples, n_alphas)

report_diagnostics(full_report=False, custom_columns=None)

Print diagnostics to stdout.

Parameters:

full_report (bool, optional (default=False)) – Print all available information. When False and custom_columns is None, a restricted set of columns is printed out.
custom_columns (iterable, optional (default=None)) – Print only the specified columns.

Return type:

None

score(X, y, sample_weight=None, offset=None)

Compute \(D^2\), the percentage of deviance explained.

\(D^2\) is a generalization of the coefficient of determination \(R^2\). The \(R^2\) uses the squared error and the \(D^2\), the deviance. Note that those two are equal for family='normal'.

\(D^2\) is defined as \(D^2 = 1 - \frac{D(y_{\mathrm{true}}, y_{\mathrm{pred}})}{D_{\mathrm{null}}}\), \(D_{\mathrm{null}}\) is the null deviance, i.e. the deviance of a model with intercept alone. The best possible score is one and it can be negative.

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features)) – Test samples.
y (array-like, shape (n_samples,)) – True values of target.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Sample weights.
offset (array-like, shape (n_samples,), optional (default=None)) –

Returns:

D^2 of self.predict(X) w.r.t. y.

Return type:

float

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

std_errors(X=None, y=None, mu=None, offset=None, sample_weight=None, dispersion=None, robust=None, clusters=None, expected_information=None, store_covariance_matrix=False)

Calculate standard errors for generalized linear models.

See covariance_matrix for an in-depth explanation of how the standard errors are computed.

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features), optional) – Training data. Can be omitted if a covariance matrix has already been computed.
y (array-like, shape (n_samples,), optional) – Target values. Can be omitted if a covariance matrix has already been computed.
mu (array-like, optional, default=None) – Array with predictions. Estimated if absent.
offset (array-like, optional, default=None) – Array with additive offsets.
sample_weight (array-like, shape (n_samples,), optional, default=None) – Individual weights for each sample.
dispersion (float, optional, default=None) – The dispersion parameter. Estimated if absent.
robust (boolean, optional, default=None) – Whether to compute robust standard errors instead of normal ones. If not specified, the model’s robust attribute is used.
clusters (array-like, optional, default=None) – Array with cluster membership. Clustered standard errors are computed if clusters is not None.
expected_information (boolean, optional, default=None) – Whether to use the expected or observed information matrix. Only relevant when computing robust standard errors. If not specified, the model’s expected_information attribute is used.
store_covariance_matrix (boolean, optional, default=False) – Whether to store the covariance matrix in the model instance. If a covariance matrix has already been stored, it will be overwritten.

wald_test(R=None, features=None, r=None, X=None, y=None, mu=None, offset=None, sample_weight=None, dispersion=None, robust=None, clusters=None, expected_information=None)

Compute the Wald test statistic and p-value for a linear hypothesis.

The left hand side of the hypothesis may be specified in the following ways:

R: The restriction matrix representing the linear combination of coefficients to test.
features: The name of a feature or a list of features to test.

The right hand side of the tested hypothesis is specified by r.

Parameters:

R (np.ndarray, optional, default=None) – The restriction matrix representing the linear combination of coefficients to test.
features (Union[str, list[str]], optional, default=None) – The name of a feature or a list of features to test.
r (np.ndarray, optional, default=None) – The vector representing the values of the linear combination. If None, the test is for whether the linear combinations of the coefficients are zero.
X ({array-like, sparse matrix}, shape (n_samples, n_features), optional) – Training data. Can be omitted if a covariance matrix has already been computed.
y (array-like, shape (n_samples,), optional) – Target values. Can be omitted if a covariance matrix has already been computed.
mu (array-like, optional, default=None) – Array with predictions. Estimated if absent.
offset (array-like, optional, default=None) – Array with additive offsets.
sample_weight (array-like, shape (n_samples,), optional, default=None) – Individual weights for each sample.
dispersion (float, optional, default=None) – The dispersion parameter. Estimated if absent.
robust (boolean, optional, default=None) – Whether to compute robust standard errors instead of normal ones. If not specified, the model’s robust attribute is used.
clusters (array-like, optional, default=None) – Array with cluster membership. Clustered standard errors are computed if clusters is not None.
expected_information (boolean, optional, default=None) – Whether to use the expected or observed information matrix. Only relevant when computing robust standard errors. If not specified, the model’s expected_information attribute is used.

Returns:

NamedTuple with test statistic, p-value, and degrees of freedom.

Return type:

WaldTestResult

class glum.GeneralizedLinearRegressorCV(l1_ratio=0, P1='identity', P2='identity', fit_intercept=True, family='normal', link='auto', solver='auto', max_iter=100, gradient_tol=None, step_size_tol=None, hessian_approx=0.0, warm_start=False, n_alphas=100, alphas=None, min_alpha_ratio=None, min_alpha=None, start_params=None, selection='cyclic', random_state=None, copy_X=True, check_input=True, verbose=0, scale_predictors=False, lower_bounds=None, upper_bounds=None, A_ineq=None, b_ineq=None, force_all_finite=True, cv=None, n_jobs=None, drop_first=False, robust=True, expected_information=False)

Bases: GeneralizedLinearRegressorBase

Generalized linear model with iterative fitting along a regularization path.

The best model is selected by cross-validation.

Cross-validated regression via a Generalized Linear Model (GLM) with penalties. For more on GLMs and on these parameters, see the documentation for GeneralizedLinearRegressor. CV conventions follow sklearn.linear_model.LassoCV.

Parameters:

l1_ratio (float or array of floats, optional (default=0)) – If you pass l1_ratio as an array, the fit method will choose the best value of l1_ratio and store it as self.l1_ratio.
P1 ({'identity', array-like, None}, shape (n_features,), optional (default='identity')) – This array controls the strength of the regularization for each coefficient independently. A high value will lead to higher regularization while a value of zero will remove the regularization on this parameter. Note that n_features = X.shape[1]. If X is a pandas DataFrame with a categorical dtype and P1 has the same size as the number of columns, the penalty of the categorical column will be applied to all the levels of the categorical.
P2 ({'identity', array-like, sparse matrix, None}, shape (n_features,) or (n_features, n_features), optional (default='identity')) – With this option, you can set the P2 matrix in the L2 penalty w*P2*w. This gives a fine control over this penalty (Tikhonov regularization). A 2d array is directly used as the square matrix P2. A 1d array is interpreted as diagonal (square) matrix. The default 'identity' and None set the identity matrix, which gives the usual squared L2-norm. If you just want to exclude certain coefficients, pass a 1d array filled with 1 and 0 for the coefficients to be excluded. Note that P2 must be positive semi-definite. If X is a pandas DataFrame with a categorical dtype and P2 has the same size as the number of columns, the penalty of the categorical column will be applied to all the levels of the categorical. Note that if P2 is two-dimensional, its size needs to be of the same length as the expanded X matrix.
fit_intercept (bool, optional (default=True)) – Specifies if a constant (a.k.a. bias or intercept) should be added to the linear predictor (X * coef + intercept).
family (str or ExponentialDispersionModel, optional (default='normal')) – The distributional assumption of the GLM, i.e. the loss function to minimize. If a string, one of: 'binomial', 'gamma', 'gaussian', 'inverse.gaussian', 'normal', 'poisson', 'tweedie' or 'negative.binomial'. Note that 'tweedie' sets the power of the Tweedie distribution to 1.5; to use another value, specify it in parentheses (e.g., 'tweedie (1.5)'). The same applies for 'negative.binomial' and theta parameter.
link ({'auto', 'identity', 'log', 'logit', 'cloglog'}, Link or None, optional (default='auto')) –
The link function of the GLM, i.e. mapping from linear predictor (X * coef) to expectation (mu). Option 'auto' sets the link depending on the chosen family as follows:
- 'identity' for family 'normal'
- 'log' for families 'poisson', 'gamma', 'inverse.gaussian' and 'negative.binomial'.
- 'logit' for family 'binomial'
solver ({'auto', 'irls-cd', 'irls-ls', 'lbfgs', 'trust-constr'}, optional (default='auto')) –
Algorithm to use in the optimization problem:
- 'auto': 'irls-ls' if l1_ratio is zero and 'irls-cd' otherwise.
- 'irls-cd': Iteratively reweighted least squares with a coordinate descent inner solver. This can deal with L1 as well as L2 penalties. Note that in order to avoid unnecessary memory duplication of X in the fit method, X should be directly passed as a Fortran-contiguous Numpy array or sparse CSC matrix.
- 'irls-ls': Iteratively reweighted least squares with a least squares inner solver. This algorithm cannot deal with L1 penalties.
- 'lbfgs': Scipy’s L-BFGS-B optimizer. It cannot deal with L1 penalties.
max_iter (int, optional (default=100)) – The maximal number of iterations for solver algorithms.
gradient_tol (float, optional (default=None)) –
Stopping criterion. If None, solver-specific defaults will be used. The default value for most solvers is 1e-4, except for 'trust-constr', which requires more conservative convergence settings and has a default value of 1e-8.

For the IRLS-LS, L-BFGS and trust-constr solvers, the iteration will stop when max{|g_i|, i = 1, ..., n} <= tol, where g_i is the i-th component of the gradient (derivative) of the objective function. For the CD solver, convergence is reached when sum_i(|minimum norm of g_i|), where g_i is the subgradient of the objective and the minimum norm of g_i is the element of the subgradient with the smallest L2 norm.

If you wish to only use a step-size tolerance, set gradient_tol to a very small number.
step_size_tol (float, optional (default=None)) – Alternative stopping criterion. For the IRLS-LS and IRLS-CD solvers, the iteration will stop when the L2 norm of the step size is less than step_size_tol. This stopping criterion is disabled when step_size_tol is None.
hessian_approx (float, optional (default=0.0)) – The threshold below which data matrix rows will be ignored for updating the Hessian. See the algorithm documentation for the IRLS algorithm for further details.
warm_start (bool, optional (default=False)) – Whether to reuse the solution of the previous call to fit as initialization for coef_ and intercept_ (supersedes start_params). If False or if the attribute coef_ does not exist (first call to fit), start_params sets the start values for coef_ and intercept_.
n_alphas (int, optional (default=100)) – Number of alphas along the regularization path
alphas (array-like, optional (default=None)) – List of alphas for which to compute the models. If None, the alphas are set automatically. Setting None is preferred.
min_alpha_ratio (float, optional (default=None)) – Length of the path. min_alpha_ratio=1e-6 means that min_alpha / max_alpha = 1e-6. If None, 1e-6 is used.
min_alpha (float, optional (default=None)) – Minimum alpha to estimate the model with. The grid will then be created over [max_alpha, min_alpha].
start_params (array-like, shape (n_features*,), optional (default=None)) – Relevant only if warm_start is False or if fit is called for the first time (so that self.coef_ does not exist yet). If None, all coefficients are set to zero and the start value for the intercept is the weighted average of y (If fit_intercept is True). If an array, used directly as start values; if fit_intercept is True, its first element is assumed to be the start value for the intercept_. Note that n_features* = X.shape[1] + fit_intercept, i.e. it includes the intercept.
selection (str, optional (default='cyclic')) – For the CD solver ‘cd’, the coordinates (features) can be updated in either cyclic or random order. If set to 'random', a random coefficient is updated every iteration rather than looping over features sequentially in the same order, which often leads to significantly faster convergence, especially when gradient_tol is higher than 1e-4.
random_state (int or RandomState, optional (default=None)) – The seed of the pseudo random number generator that selects a random feature to be updated for the CD solver. If an integer, random_state is the seed used by the random number generator; if a RandomState instance, random_state is the random number generator; if None, the random number generator is the RandomState instance used by np.random. Used when selection is 'random'.
copy_X (bool, optional (default=None)) – Whether to copy X. Since X is never modified by GeneralizedLinearRegressor, this is unlikely to be needed; this option exists mainly for compatibility with other scikit-learn estimators. If False, X will not be copied and there will be an error if you pass an X in the wrong format, such as providing integer X and float y. If None, X will not be copied unless it is in the wrong format.
check_input (bool, optional (default=True)) – Whether to bypass several checks on input: y values in range of family, sample_weight non-negative, P2 positive semi-definite. Don’t use this parameter unless you know what you are doing.
verbose (int, optional (default=0)) – For the IRLS solver, any positive number will result in a pretty progress bar showing convergence. This features requires having the tqdm package installed. For the L-BFGS solver, set verbose to any positive number for verbosity.
scale_predictors (bool, optional (default=False)) –
If True, estimate a scaled model where all predictors have a standard deviation of 1. This can result in better estimates if predictors are on very different scales (for example, centimeters and kilometers).

Advanced developer note: Internally, predictors are always rescaled for computational reasons, but this only affects results if scale_predictors is True.
lower_bounds (array-like, shape (n_features), optional (default=None)) – Set a lower bound for the coefficients. Setting bounds forces the use of the coordinate descent solver ('irls-cd').
upper_bounds (array-like, shape=(n_features), optional (default=None)) – See lower_bounds.
A_ineq (array-like, shape=(n_constraints, n_features), optional (default=None)) – Constraint matrix for linear inequality constraints of the form A_ineq w <= b_ineq.
b_ineq (array-like, shape=(n_constraints,), optional (default=None)) – Constraint vector for linear inequality constraints of the form A_ineq w <= b_ineq.
cv (int, cross-validation generator or Iterable, optional (default=None)) –
Determines the cross-validation splitting strategy. One of:
- None, to use the default 5-fold cross-validation,
- int, to specify the number of folds.
- Iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, KFold is used
n_jobs (int, optional (default=None)) – The maximum number of concurrently running jobs. The number of jobs that are needed is len(l1_ratio) x n_folds. -1 is the same as the number of CPU on your machine. None means 1 unless in a joblib.parallel_backend context.
drop_first (bool, optional (default = False)) – If True, drop the first column when encoding categorical variables.
force_all_finite (bool) –
robust (bool) –
expected_information (bool) –

alpha_

The amount of regularization chosen by cross validation.

Type:: float

alphas_

Alphas used by the model.

Type:: array, shape (n_l1_ratios, n_alphas)

l1_ratio_

The compromise between L1 and L2 regularization chosen by cross validation.

Type:: float

coef_

Estimated coefficients for the linear predictor in the GLM at the optimal (l1_ratio_, alpha_).

Type:: array, shape (n_features,)

intercept_

Intercept (a.k.a. bias) added to linear predictor.

Type:: float

n_iter_

The number of iterations run by the CD solver to reach the specified tolerance for the optimal alpha.

Type:: int

coef_path_

Estimated coefficients for the linear predictor in the GLM at every point along the regularization path.

Type:: array, shape (n_folds, n_l1_ratios, n_alphas, n_features)

deviance_path_

Deviance for the test set on each fold, varying alpha.

Type:: array, shape(n_folds, n_alphas)

robust

If true, then robust standard errors are computed by default.

Type:: bool, optional (default = False)

expected_information

If true, then the expected information matrix is computed by default. Only relevant when computing robust standard errors.

Type:: bool, optional (default = False)

coef_table(confidence_level=0.95, X=None, y=None, mu=None, offset=None, sample_weight=None, dispersion=None, robust=None, clusters=None, expected_information=None)

Get a table of of the regression coefficients.

Includes coefficient estimates, standard errors, t-values, p-values and confidence intervals.

Parameters:

confidence_level (float, optional, default=0.95) – The confidence level for the confidence intervals.
X ({array-like, sparse matrix}, shape (n_samples, n_features), optional) – Training data. Can be omitted if a covariance matrix has already been computed or if standard errors, etc. are not desired.
y (array-like, shape (n_samples,), optional) – Target values. Can be omitted if a covariance matrix has already been computed.
mu (array-like, optional, default=None) – Array with predictions. Estimated if absent.
offset (array-like, optional, default=None) – Array with additive offsets.
sample_weight (array-like, shape (n_samples,), optional, default=None) – Individual weights for each sample.
dispersion (float, optional, default=None) – The dispersion parameter. Estimated if absent.
robust (boolean, optional, default=None) – Whether to compute robust standard errors instead of normal ones. If not specified, the model’s robust attribute is used.
clusters (array-like, optional, default=None) – Array with cluster membership. Clustered standard errors are computed if clusters is not None.
expected_information (boolean, optional, default=None) – Whether to use the expected or observed information matrix. Only relevant when computing robust standard errors. If not specified, the model’s expected_information attribute is used.

Returns:

A table of the regression results.

Return type:

pandas.DataFrame

covariance_matrix(X=None, y=None, mu=None, offset=None, sample_weight=None, dispersion=None, robust=None, clusters=None, expected_information=None, store_covariance_matrix=False, skip_checks=False)

Calculate the covariance matrix for generalized linear models.

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features), optional) – Training data. Can be omitted if a covariance matrix has already been computed.
y (array-like, shape (n_samples,), optional) – Target values. Can be omitted if a covariance matrix has already been computed.
mu (array-like, optional, default=None) – Array with predictions. Estimated if absent.
offset (array-like, optional, default=None) – Array with additive offsets.
sample_weight (array-like, shape (n_samples,), optional, default=None) – Individual weights for each sample.
dispersion (float, optional, default=None) – The dispersion parameter. Estimated if absent.
robust (boolean, optional, default=None) – Whether to compute robust standard errors instead of normal ones. If not specified, the model’s robust attribute is used.
clusters (array-like, optional, default=None) – Array with cluster membership. Clustered standard errors are computed if clusters is not None.
expected_information (boolean, optional, default=None) – Whether to use the expected or observed information matrix. Only relevant when computing robust standard errors. If not specified, the model’s expected_information attribute is used.
store_covariance_matrix (boolean, optional, default=False) – Whether to store the covariance matrix in the model instance. If a covariance matrix has already been stored, it will be overwritten.
skip_checks (boolean, optional, default=False) – Whether to skip input validation. For internal use only.

Notes

We support three types of covariance matrices:

non-robust
robust (HC-1)
clustered

For maximum-likelihood estimator, the covariance matrix takes the form \(\mathcal{H}^{-1}(\theta_0)\mathcal{I}(\theta_0) \mathcal{H}^{-1}(\theta_0)\) where \(\mathcal{H}^{-1}\) is the inverse Hessian and \(\mathcal{I}\) is the Information matrix. The different types of covariance matrices use different approximation of these quantities.

The non-robust covariance matrix is computed as the inverse of the Fisher information matrix. This assumes that the information matrix equality holds.

The robust (HC-1) covariance matrix takes the form \(\mathbf{H}^{−1} (\hat{\theta})\mathbf{G}^{T}(\hat{\theta})\mathbf{G}(\hat{\theta}) \mathbf{H}^{−1}(\hat{\theta})\) where \(\mathbf{H}\) is the empirical Hessian and \(\mathbf{G}\) is the gradient. We apply a finite-sample correction of \(\frac{N}{N-p}\).

The clustered covariance matrix uses a similar approach to the robust (HC-1) covariance matrix. However, instead of using \(\mathbf{G}^{T}(\hat{\theta} \mathbf{G}(\hat{\theta})\) directly, we first sum over all the groups first. The finite-sample correction is affected as well, becoming \(\frac{M}{M-1} \frac{N}{N-p}\) where \(M\) is the number of groups.

References

property family_instance: ExponentialDispersionModel: Return an ExponentialDispersionModel.

fit(X, y, sample_weight=None, offset=None, store_covariance_matrix=False, clusters=None)

Choose the best model along a ‘regularization path’ by cross-validation.

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features)) – Training data. Note that a float32 matrix is acceptable and will result in the entire algorithm being run in 32-bit precision. However, for problems that are poorly conditioned, this might result in poor convergence or flawed parameter estimates. If a Pandas data frame is provided, it may contain categorical columns. In that case, a separate coefficient will be estimated for each category. No category is omitted. This means that some regularization is required to fit models with an intercept or models with several categorical columns.
y (array-like, shape (n_samples,)) – Target values.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Individual weights w_i for each sample. Note that, for an Exponential Dispersion Model (EDM), one has \(\mathrm{var}(y_i) = \phi \times v(mu) / w_i\). If \(y_i \sim EDM(\mu, \phi / w_i)\), then \(\sum w_i y_i / \sum w_i \sim EDM(\mu, \phi / \sum w_i)\), i.e. the mean of \(y\) is a weighted average with weights equal to sample_weight.
offset (array-like, shape (n_samples,), optional (default=None)) – Added to linear predictor. An offset of 3 will increase expected y by 3 if the link is linear and will multiply expected y by 3 if the link is logarithmic.
store_covariance_matrix (bool, optional (default=False)) – Whether to store the covariance matrix of the parameter estimates corresponding to the best best model.
clusters (array-like, optional, default=None) – Array with cluster membership. Clustered standard errors are computed if clusters is not None.

get_formatted_diagnostics(full_report=False, custom_columns=None)

Get formatted diagnostics; can be printed with _report_diagnostics.

Parameters:

full_report (bool, optional (default=False)) – Print all available information. When False and custom_columns is None, a restricted set of columns is printed out.
custom_columns (iterable, optional (default=None)) – Print only the specified columns.

Return type:

str | DataFrame

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

linear_predictor(X, offset=None, alpha_index=None, alpha=None)

Compute the linear predictor, X * coef_ + intercept_.

If alpha_search is True, but alpha_index and alpha are both None, we use the last alpha value self._alphas[-1].

Parameters:

X (array-like, shape (n_samples, n_features)) – Observations. X may be a pandas data frame with categorical types. If X was also a data frame with categorical types during fitting and a category wasn’t observed at that point, the corresponding prediction will be numpy.nan.
offset (array-like, shape (n_samples,), optional (default=None)) –
alpha_index (int or list[int], optional (default=None)) – Sets the index of the alpha(s) to use in case alpha_search is True. Incompatible with alpha (see below).
alpha (float or list[float], optional (default=None)) – Sets the alpha(s) to use in case alpha_search is True. Incompatible with alpha_index (see above).

Returns:

The linear predictor.

Return type:

array, shape (n_samples, n_alphas)

property link_instance: Link: Return a Link.

predict(X, sample_weight=None, offset=None, alpha_index=None, alpha=None)

Predict using GLM with feature matrix X.

If alpha_search is True, but alpha_index and alpha are both None, we use the last alpha value self._alphas[-1].

Parameters:

X (array-like, shape (n_samples, n_features)) – Observations. X may be a pandas data frame with categorical types. If X was also a data frame with categorical types during fitting and a category wasn’t observed at that point, the corresponding prediction will be numpy.nan.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Sample weights to multiply predictions by.
offset (array-like, shape (n_samples,), optional (default=None)) –
alpha_index (int or list[int], optional (default=None)) – Sets the index of the alpha(s) to use in case alpha_search is True. Incompatible with alpha (see below).
alpha (float or list[float], optional (default=None)) – Sets the alpha(s) to use in case alpha_search is True. Incompatible with alpha_index (see above).

Returns:

Predicted values times sample_weight.

Return type:

array, shape (n_samples, n_alphas)

report_diagnostics(full_report=False, custom_columns=None)

Print diagnostics to stdout.

Parameters:

full_report (bool, optional (default=False)) – Print all available information. When False and custom_columns is None, a restricted set of columns is printed out.
custom_columns (iterable, optional (default=None)) – Print only the specified columns.

Return type:

None

score(X, y, sample_weight=None, offset=None)

Compute \(D^2\), the percentage of deviance explained.

\(D^2\) is a generalization of the coefficient of determination \(R^2\). The \(R^2\) uses the squared error and the \(D^2\), the deviance. Note that those two are equal for family='normal'.

\(D^2\) is defined as \(D^2 = 1 - \frac{D(y_{\mathrm{true}}, y_{\mathrm{pred}})}{D_{\mathrm{null}}}\), \(D_{\mathrm{null}}\) is the null deviance, i.e. the deviance of a model with intercept alone. The best possible score is one and it can be negative.

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features)) – Test samples.
y (array-like, shape (n_samples,)) – True values of target.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Sample weights.
offset (array-like, shape (n_samples,), optional (default=None)) –

Returns:

D^2 of self.predict(X) w.r.t. y.

Return type:

float

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

std_errors(X=None, y=None, mu=None, offset=None, sample_weight=None, dispersion=None, robust=None, clusters=None, expected_information=None, store_covariance_matrix=False)

Calculate standard errors for generalized linear models.

See covariance_matrix for an in-depth explanation of how the standard errors are computed.

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features), optional) – Training data. Can be omitted if a covariance matrix has already been computed.
y (array-like, shape (n_samples,), optional) – Target values. Can be omitted if a covariance matrix has already been computed.
mu (array-like, optional, default=None) – Array with predictions. Estimated if absent.
offset (array-like, optional, default=None) – Array with additive offsets.
sample_weight (array-like, shape (n_samples,), optional, default=None) – Individual weights for each sample.
dispersion (float, optional, default=None) – The dispersion parameter. Estimated if absent.
robust (boolean, optional, default=None) – Whether to compute robust standard errors instead of normal ones. If not specified, the model’s robust attribute is used.
clusters (array-like, optional, default=None) – Array with cluster membership. Clustered standard errors are computed if clusters is not None.
expected_information (boolean, optional, default=None) – Whether to use the expected or observed information matrix. Only relevant when computing robust standard errors. If not specified, the model’s expected_information attribute is used.
store_covariance_matrix (boolean, optional, default=False) – Whether to store the covariance matrix in the model instance. If a covariance matrix has already been stored, it will be overwritten.

wald_test(R=None, features=None, r=None, X=None, y=None, mu=None, offset=None, sample_weight=None, dispersion=None, robust=None, clusters=None, expected_information=None)

Compute the Wald test statistic and p-value for a linear hypothesis.

The left hand side of the hypothesis may be specified in the following ways:

R: The restriction matrix representing the linear combination of coefficients to test.
features: The name of a feature or a list of features to test.

The right hand side of the tested hypothesis is specified by r.

Parameters:

R (np.ndarray, optional, default=None) – The restriction matrix representing the linear combination of coefficients to test.
features (Union[str, list[str]], optional, default=None) – The name of a feature or a list of features to test.
r (np.ndarray, optional, default=None) – The vector representing the values of the linear combination. If None, the test is for whether the linear combinations of the coefficients are zero.
X ({array-like, sparse matrix}, shape (n_samples, n_features), optional) – Training data. Can be omitted if a covariance matrix has already been computed.
y (array-like, shape (n_samples,), optional) – Target values. Can be omitted if a covariance matrix has already been computed.
mu (array-like, optional, default=None) – Array with predictions. Estimated if absent.
offset (array-like, optional, default=None) – Array with additive offsets.
sample_weight (array-like, shape (n_samples,), optional, default=None) – Individual weights for each sample.
dispersion (float, optional, default=None) – The dispersion parameter. Estimated if absent.
robust (boolean, optional, default=None) – Whether to compute robust standard errors instead of normal ones. If not specified, the model’s robust attribute is used.
clusters (array-like, optional, default=None) – Array with cluster membership. Clustered standard errors are computed if clusters is not None.
expected_information (boolean, optional, default=None) – Whether to use the expected or observed information matrix. Only relevant when computing robust standard errors. If not specified, the model’s expected_information attribute is used.

Returns:

NamedTuple with test statistic, p-value, and degrees of freedom.

Return type:

WaldTestResult

class glum.IdentityLink

Bases: Link

The identity link function g(x) = x.

derivative(mu)

Get the derivative of the identity link, a vector of ones.

See superclass documentation.

Parameters:: mu (array-like) –

inverse(lin_pred)

Compute the inverse link function h(lin_pred).

Gives the inverse relationship between linear predictor and the mean mu ≡ E(Y), i.e. h(linear predictor) = mu.

Parameters:: lin_pred (array-like, shape (n_samples,)) – Usually the (fitted) linear predictor.

inverse_derivative(lin_pred)

Compute the derivative of the inverse link function h'(lin_pred).

Parameters:: lin_pred (array-like, shape (n_samples,)) – Usually the (fitted) linear predictor.

inverse_derivative2(lin_pred)

Compute second derivative of the inverse link function h''(lin_pred).

Parameters:: lin_pred (array-like, shape (n_samples,)) – Usually the (fitted) linear predictor.

link(mu)

Return mu (identity link).

See superclass documentation.

Parameters:: mu (array-like) –

class glum.InverseGaussianDistribution

Bases: TweedieDistribution

Class for the scaled Inverse Gaussian distribution.

deviance(y, mu, sample_weight=None)

Compute the deviance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Sample weights.

Return type:

float

deviance_derivative(y, mu, sample_weight=1)

Compute the derivative of the deviance with respect to mu.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,) (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

dispersion(y, mu, sample_weight=None, ddof=1, method='pearson')

Estimate the dispersion parameter \(\phi\).

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Weights or exposure to which variance is inversely proportional.
ddof (int, optional (default=1)) – Degrees of freedom consumed by the model for mu.
{'pearson' (method =) – Whether to base the estimate on the Pearson residuals or the deviance.
'deviance'} – Whether to base the estimate on the Pearson residuals or the deviance.
(default='pearson') (optional) – Whether to base the estimate on the Pearson residuals or the deviance.

Return type:

float

eta_mu_deviance(link, factor, cur_eta, X_dot_d, y, sample_weight)

Compute eta, mu and the deviance.

Compute: * the linear predictor, eta, as cur_eta + factor * X_dot_d; * the link-function-transformed prediction, mu; * the deviance.

Returns:

numpy.ndarray, shape (X.shape[0],) – The linear predictor, eta.
numpy.ndarray, shape (X.shape[0],) – The link-function-transformed prediction, mu.
float – The deviance.

Parameters:

link (Link) –
factor (float) –
cur_eta (ndarray) –
X_dot_d (ndarray) –
y (ndarray) –
sample_weight (ndarray) –

in_y_range(x)

Return True if x is in the valid range of the EDM.

Parameters:: x (array-like, shape (n_samples,)) – Target values.
Return type:: np.ndarray

property include_lower_bound: bool: Return whether lower_bound is allowed as a value of y.

log_likelihood(y, mu, sample_weight=None, dispersion=None)

Compute the log likelihood.

For 1 < power < 2, we use the series approximation by Dunn and Smyth (2005) to compute the normalization term.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Sample weights.
dispersion (float, optional (default=None)) – Dispersion parameter \(\phi\). Estimated if None.

Return type:

float

property lower_bound: float: Return the lowest value of y allowed.

property power: float: Return the Tweedie power parameter.

rowwise_gradient_hessian(link, coef, dispersion, X, y, sample_weight, eta, mu, offset=None)

Compute the gradient and negative Hessian of the log likelihood row-wise.

Returns:

numpy.ndarray, shape (X.shape[0],) – The gradient of the log likelihood, row-wise.
numpy.ndarray, shape (X.shape[0],) – The negative Hessian of the log likelihood, row-wise.

Parameters:

link (Link) –
coef (ndarray) –
X (MatrixBase | StandardizedMatrix) –
y (ndarray) –
sample_weight (ndarray) –
eta (ndarray) –
mu (ndarray) –
offset (ndarray | None) –

unit_deviance(y, mu): Get the deviance of each observation.

unit_deviance_derivative(y, mu)

Compute the derivative of the unit deviance with respect to mu.

The derivative of the unit deviance is given by \(-2 \times (y - \mu) / v(\mu)\), where \(v(\mu)\) is the unit variance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.

Return type:

array-like, shape (n_samples,)

unit_variance(mu)

Compute the unit variance of a Tweedie distribution v(mu) = mu^power.

Parameters:: mu (array-like, shape (n_samples,)) – Predicted mean.
Return type:: numpy.ndarray, shape (n_samples,)

unit_variance_derivative(mu)

Compute the derivative of the unit variance of a Tweedie distribution.

Equation: \(v(\mu) = p \times \mu^{(p-1)}\).

Parameters:: mu (array-like, shape (n_samples,)) – Predicted mean.
Return type:: numpy.ndarray, shape (n_samples,)

variance(mu, dispersion=1, sample_weight=1)

Compute the variance function.

The variance of \(Y_i \sim \mathrm{EDM}(\mu_i, \phi / s_i)\) is \(\mathrm{var}(Y_i) = (\phi / s_i) * v(\mu_i)\), with unit variance \(v(\mu)\) and weights \(s_i\).

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

variance_derivative(mu, dispersion=1, sample_weight=1)

Compute the derivative of the variance with respect to mu.

The derivative of the variance is equal to \((\phi / s_i) * v'(\mu_i)\), where \(v(\mu)\) is the unit variance and \(s_i\) are weights.

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

class glum.Link

Bases: object

Abstract base class for Link functions.

abstract derivative(mu)

Compute the derivative of the link g'(mu).

Parameters:: mu (array-like, shape (n_samples,)) – Usually the (predicted) mean.

abstract inverse(lin_pred)

Compute the inverse link function h(lin_pred).

Gives the inverse relationship between linear predictor, lin_pred ≡ X * w, and the mean, mu ≡ E(Y), i.e. h(lin_pred) = mu.

Parameters:: lin_pred (array-like, shape (n_samples,)) – Usually the (fitted) linear predictor.

abstract inverse_derivative(lin_pred)

Compute the derivative of the inverse link function h'(lin_pred).

Parameters:: lin_pred (array-like, shape (n_samples,)) – Usually the (fitted) linear predictor.

abstract inverse_derivative2(lin_pred)

Compute second derivative of the inverse link function h''(lin_pred).

Parameters:: lin_pred (array-like, shape (n_samples,)) – Usually the (fitted) linear predictor.

abstract link(mu)

Compute the link function g(mu).

The link function links the mean, mu ≡ E(Y), to the linear predictor X * w, i.e. g(mu) is equal to the linear predictor.

Parameters:: mu (array-like, shape (n_samples,)) – Usually the (predicted) mean.

class glum.LogLink

Bases: Link

The log link function log(x).

derivative(mu)

Get the derivative of the log link, one over mu.

Parameters:: mu (array-like) –
Return type:: numpy.ndarray

inverse(lin_pred)

Get the inverse of the log link, the exponential function.

See superclass documentation.

Parameters:: lin_pred (array-like) –
Return type:: numpy.ndarray

inverse_derivative(lin_pred)

Compute the derivative of the inverse link function h'(lin_pred).

Parameters:: lin_pred (array-like, shape (n_samples,)) – Usually the (fitted) linear predictor.

inverse_derivative2(lin_pred)

Compute second derivative of the inverse link function h''(lin_pred).

Parameters:: lin_pred (array-like, shape (n_samples,)) – Usually the (fitted) linear predictor.

link(mu)

Get the log of mu.

See superclass documentation.

Parameters:: mu (array-like) –
Return type:: numpy.ndarray

class glum.LogitLink

Bases: Link

The logit link function logit(x).

derivative(mu)

Get the derivative of the logit link.

See superclass documentation.

Parameters:: mu (array-like) –
Return type:: array-like

inverse(lin_pred)

Get the inverse of the logit link.

See superclass documentation.

Note: since passing a very large value might result in an output of one, this function bounds the output to be between [50*eps, 1 - 50*eps], where eps is floating point epsilon.

Parameters:: lin_pred (array-like) –
Return type:: array-like

inverse_derivative(lin_pred)

Compute the derivative of the inverse link function h'(lin_pred).

Parameters:: lin_pred (array, shape (n_samples,)) – Usually the (fitted) linear predictor.

inverse_derivative2(lin_pred)

Compute second derivative of the inverse link function h''(lin_pred).

Parameters:: lin_pred (array, shape (n_samples,)) – Usually the (fitted) linear predictor.

link(mu)

Get the logit function of mu.

See superclass documentation.

Parameters:: mu (array-like) –
Return type:: numpy.ndarray

class glum.NegativeBinomialDistribution(theta=1.0)

Bases: ExponentialDispersionModel

A class for the Negative Binomial distribution.

A Negative Binomial distribution with mean \(\mu = \mathrm{E}(Y)\) is uniquely defined by its mean-variance relationship \(\mathrm{var}(Y) \propto \mu + \theta * \mu^2\).

Parameters:: theta (float, optional (default=1.0)) – The dispersion parameter from unit_variance \(v(\mu) = \mu + \theta * \mu^2\). For \(\theta <= 0\), no distribution exists.

References

For the log-likelihood and deviance:

M. L. Zwilling Negative Binomial Regression, The Mathematica Journal 2013. https://www.mathematica-journal.com/2013/06/27/negative-binomial-regression/

deviance(y, mu, sample_weight=None)

Compute the deviance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Sample weights.

Return type:

float

deviance_derivative(y, mu, sample_weight=1)

Compute the derivative of the deviance with respect to mu.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,) (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

dispersion(y, mu, sample_weight=None, ddof=1, method='pearson')

Estimate the dispersion parameter \(\phi\).

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Weights or exposure to which variance is inversely proportional.
ddof (int, optional (default=1)) – Degrees of freedom consumed by the model for mu.
{'pearson' (method =) – Whether to base the estimate on the Pearson residuals or the deviance.
'deviance'} – Whether to base the estimate on the Pearson residuals or the deviance.
(default='pearson') (optional) – Whether to base the estimate on the Pearson residuals or the deviance.

Return type:

float

eta_mu_deviance(link, factor, cur_eta, X_dot_d, y, sample_weight)

Compute eta, mu and the deviance.

Compute: * the linear predictor, eta, as cur_eta + factor * X_dot_d; * the link-function-transformed prediction, mu; * the deviance.

Returns:

numpy.ndarray, shape (X.shape[0],) – The linear predictor, eta.
numpy.ndarray, shape (X.shape[0],) – The link-function-transformed prediction, mu.
float – The deviance.

Parameters:

link (Link) –
factor (float) –
cur_eta (ndarray) –
X_dot_d (ndarray) –
y (ndarray) –
sample_weight (ndarray) –

in_y_range(x)

Return True if x is in the valid range of the EDM.

Parameters:: x (array-like, shape (n_samples,)) – Target values.
Return type:: np.ndarray

log_likelihood(y, mu, sample_weight=None, dispersion=1)

Compute the log likelihood.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Sample weights.
dispersion (float, optional (default=1.0)) – Ignored.

Return type:

float

rowwise_gradient_hessian(link, coef, dispersion, X, y, sample_weight, eta, mu, offset=None)

Compute the gradient and negative Hessian of the log likelihood row-wise.

Returns:

numpy.ndarray, shape (X.shape[0],) – The gradient of the log likelihood, row-wise.
numpy.ndarray, shape (X.shape[0],) – The negative Hessian of the log likelihood, row-wise.

Parameters:

link (Link) –
coef (ndarray) –
X (MatrixBase | StandardizedMatrix) –
y (ndarray) –
sample_weight (ndarray) –
eta (ndarray) –
mu (ndarray) –
offset (ndarray | None) –

property theta: float: Return the Negative Binomial theta parameter.

unit_deviance(y, mu)

Get the deviance of each observation.

Parameters:

y (ndarray) –
mu (ndarray) –

Return type:

ndarray

unit_deviance_derivative(y, mu)

Compute the derivative of the unit deviance with respect to mu.

The derivative of the unit deviance is given by \(-2 \times (y - \mu) / v(\mu)\), where \(v(\mu)\) is the unit variance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.

Return type:

array-like, shape (n_samples,)

unit_variance(mu)

Compute the unit variance of a Negative Binomial distribution v(mu) = mu + theta * mu^2.

Parameters:: mu (array-like, shape (n_samples,)) – Predicted mean.
Return type:: numpy.ndarray, shape (n_samples,)

unit_variance_derivative(mu)

Compute the derivative of the unit variance of a Negative Binomial distribution.

Equation: \(v(\mu) = 1 + 2 \times \theta \times \mu\).

Parameters:: mu (array-like, shape (n_samples,)) – Predicted mean.
Return type:: numpy.ndarray, shape (n_samples,)

variance(mu, dispersion=1, sample_weight=1)

Compute the variance function.

The variance of \(Y_i \sim \mathrm{EDM}(\mu_i, \phi / s_i)\) is \(\mathrm{var}(Y_i) = (\phi / s_i) * v(\mu_i)\), with unit variance \(v(\mu)\) and weights \(s_i\).

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

variance_derivative(mu, dispersion=1, sample_weight=1)

Compute the derivative of the variance with respect to mu.

The derivative of the variance is equal to \((\phi / s_i) * v'(\mu_i)\), where \(v(\mu)\) is the unit variance and \(s_i\) are weights.

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

class glum.NormalDistribution

Bases: TweedieDistribution

Class for the Normal (a.k.a. Gaussian) distribution.

deviance(y, mu, sample_weight=None)

Compute the deviance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Sample weights.

Return type:

float

deviance_derivative(y, mu, sample_weight=1)

Compute the derivative of the deviance with respect to mu.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,) (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

dispersion(y, mu, sample_weight=None, ddof=1, method='pearson')

Estimate the dispersion parameter \(\phi\).

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Weights or exposure to which variance is inversely proportional.
ddof (int, optional (default=1)) – Degrees of freedom consumed by the model for mu.
{'pearson' (method =) – Whether to base the estimate on the Pearson residuals or the deviance.
'deviance'} – Whether to base the estimate on the Pearson residuals or the deviance.
(default='pearson') (optional) – Whether to base the estimate on the Pearson residuals or the deviance.

Return type:

float

eta_mu_deviance(link, factor, cur_eta, X_dot_d, y, sample_weight)

Compute eta, mu and the deviance.

Compute: * the linear predictor, eta, as cur_eta + factor * X_dot_d; * the link-function-transformed prediction, mu; * the deviance.

Returns:

numpy.ndarray, shape (X.shape[0],) – The linear predictor, eta.
numpy.ndarray, shape (X.shape[0],) – The link-function-transformed prediction, mu.
float – The deviance.

Parameters:

link (Link) –
factor (float) –
cur_eta (ndarray) –
X_dot_d (ndarray) –
y (ndarray) –
sample_weight (ndarray) –

in_y_range(x)

Return True if x is in the valid range of the EDM.

Parameters:: x (array-like, shape (n_samples,)) – Target values.
Return type:: np.ndarray

property include_lower_bound: bool: Return whether lower_bound is allowed as a value of y.

log_likelihood(y, mu, sample_weight=None, dispersion=None)

Compute the log likelihood.

For 1 < power < 2, we use the series approximation by Dunn and Smyth (2005) to compute the normalization term.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Sample weights.
dispersion (float, optional (default=None)) – Dispersion parameter \(\phi\). Estimated if None.

Return type:

float

property lower_bound: float: Return the lowest value of y allowed.

property power: float: Return the Tweedie power parameter.

rowwise_gradient_hessian(link, coef, dispersion, X, y, sample_weight, eta, mu, offset=None)

Compute the gradient and negative Hessian of the log likelihood row-wise.

Returns:

numpy.ndarray, shape (X.shape[0],) – The gradient of the log likelihood, row-wise.
numpy.ndarray, shape (X.shape[0],) – The negative Hessian of the log likelihood, row-wise.

Parameters:

link (Link) –
coef (ndarray) –
X (MatrixBase | StandardizedMatrix) –
y (ndarray) –
sample_weight (ndarray) –
eta (ndarray) –
mu (ndarray) –
offset (ndarray | None) –

unit_deviance(y, mu): Get the deviance of each observation.

unit_deviance_derivative(y, mu)

Compute the derivative of the unit deviance with respect to mu.

The derivative of the unit deviance is given by \(-2 \times (y - \mu) / v(\mu)\), where \(v(\mu)\) is the unit variance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.

Return type:

array-like, shape (n_samples,)

unit_variance(mu)

Compute the unit variance of a Tweedie distribution v(mu) = mu^power.

Parameters:: mu (array-like, shape (n_samples,)) – Predicted mean.
Return type:: numpy.ndarray, shape (n_samples,)

unit_variance_derivative(mu)

Compute the derivative of the unit variance of a Tweedie distribution.

Equation: \(v(\mu) = p \times \mu^{(p-1)}\).

Parameters:: mu (array-like, shape (n_samples,)) – Predicted mean.
Return type:: numpy.ndarray, shape (n_samples,)

variance(mu, dispersion=1, sample_weight=1)

Compute the variance function.

The variance of \(Y_i \sim \mathrm{EDM}(\mu_i, \phi / s_i)\) is \(\mathrm{var}(Y_i) = (\phi / s_i) * v(\mu_i)\), with unit variance \(v(\mu)\) and weights \(s_i\).

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

variance_derivative(mu, dispersion=1, sample_weight=1)

Compute the derivative of the variance with respect to mu.

The derivative of the variance is equal to \((\phi / s_i) * v'(\mu_i)\), where \(v(\mu)\) is the unit variance and \(s_i\) are weights.

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

class glum.PoissonDistribution

Bases: TweedieDistribution

Class for the scaled Poisson distribution.

deviance(y, mu, sample_weight=None)

Compute the deviance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Sample weights.

Return type:

float

deviance_derivative(y, mu, sample_weight=1)

Compute the derivative of the deviance with respect to mu.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,) (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

dispersion(y, mu, sample_weight=None, ddof=1, method='pearson')

Estimate the dispersion parameter \(\phi\).

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Weights or exposure to which variance is inversely proportional.
ddof (int, optional (default=1)) – Degrees of freedom consumed by the model for mu.
{'pearson' (method =) – Whether to base the estimate on the Pearson residuals or the deviance.
'deviance'} – Whether to base the estimate on the Pearson residuals or the deviance.
(default='pearson') (optional) – Whether to base the estimate on the Pearson residuals or the deviance.

Return type:

float

eta_mu_deviance(link, factor, cur_eta, X_dot_d, y, sample_weight)

Compute eta, mu and the deviance.

Compute: * the linear predictor, eta, as cur_eta + factor * X_dot_d; * the link-function-transformed prediction, mu; * the deviance.

Returns:

numpy.ndarray, shape (X.shape[0],) – The linear predictor, eta.
numpy.ndarray, shape (X.shape[0],) – The link-function-transformed prediction, mu.
float – The deviance.

Parameters:

link (Link) –
factor (float) –
cur_eta (ndarray) –
X_dot_d (ndarray) –
y (ndarray) –
sample_weight (ndarray) –

in_y_range(x)

Return True if x is in the valid range of the EDM.

Parameters:: x (array-like, shape (n_samples,)) – Target values.
Return type:: np.ndarray

property include_lower_bound: bool: Return whether lower_bound is allowed as a value of y.

log_likelihood(y, mu, sample_weight=None, dispersion=None)

Compute the log likelihood.

For 1 < power < 2, we use the series approximation by Dunn and Smyth (2005) to compute the normalization term.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Sample weights.
dispersion (float, optional (default=None)) – Dispersion parameter \(\phi\). Estimated if None.

Return type:

float

property lower_bound: float: Return the lowest value of y allowed.

property power: float: Return the Tweedie power parameter.

rowwise_gradient_hessian(link, coef, dispersion, X, y, sample_weight, eta, mu, offset=None)

Compute the gradient and negative Hessian of the log likelihood row-wise.

Returns:

numpy.ndarray, shape (X.shape[0],) – The gradient of the log likelihood, row-wise.
numpy.ndarray, shape (X.shape[0],) – The negative Hessian of the log likelihood, row-wise.

Parameters:

link (Link) –
coef (ndarray) –
X (MatrixBase | StandardizedMatrix) –
y (ndarray) –
sample_weight (ndarray) –
eta (ndarray) –
mu (ndarray) –
offset (ndarray | None) –

unit_deviance(y, mu): Get the deviance of each observation.

unit_deviance_derivative(y, mu)

Compute the derivative of the unit deviance with respect to mu.

The derivative of the unit deviance is given by \(-2 \times (y - \mu) / v(\mu)\), where \(v(\mu)\) is the unit variance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.

Return type:

array-like, shape (n_samples,)

unit_variance(mu)

Compute the unit variance of a Tweedie distribution v(mu) = mu^power.

Parameters:: mu (array-like, shape (n_samples,)) – Predicted mean.
Return type:: numpy.ndarray, shape (n_samples,)

unit_variance_derivative(mu)

Compute the derivative of the unit variance of a Tweedie distribution.

Equation: \(v(\mu) = p \times \mu^{(p-1)}\).

Parameters:: mu (array-like, shape (n_samples,)) – Predicted mean.
Return type:: numpy.ndarray, shape (n_samples,)

variance(mu, dispersion=1, sample_weight=1)

Compute the variance function.

The variance of \(Y_i \sim \mathrm{EDM}(\mu_i, \phi / s_i)\) is \(\mathrm{var}(Y_i) = (\phi / s_i) * v(\mu_i)\), with unit variance \(v(\mu)\) and weights \(s_i\).

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

variance_derivative(mu, dispersion=1, sample_weight=1)

Compute the derivative of the variance with respect to mu.

The derivative of the variance is equal to \((\phi / s_i) * v'(\mu_i)\), where \(v(\mu)\) is the unit variance and \(s_i\) are weights.

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

class glum.TweedieDistribution(power=0)

Bases: ExponentialDispersionModel

A class for the Tweedie distribution.

A Tweedie distribution with mean \(\mu = \mathrm{E}(Y)\) is uniquely defined by its mean-variance relationship \(\mathrm{var}(Y) \propto \mu^{\mathrm{power}}\).

Special cases are:

Power	Distribution
0	Normal
1	Poisson
(1, 2)	Compound Poisson
2	Gamma
3	Inverse Gaussian

Parameters:: power (float, optional (default=0)) – The variance power of the unit_variance \(v(\mu) = \mu^{\mathrm{power}}\). For \(0 < \mathrm{power} < 1\), no distribution exists.

deviance(y, mu, sample_weight=None)

Compute the deviance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Sample weights.

Return type:

float

deviance_derivative(y, mu, sample_weight=1)

Compute the derivative of the deviance with respect to mu.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,) (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

dispersion(y, mu, sample_weight=None, ddof=1, method='pearson')

Estimate the dispersion parameter \(\phi\).

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=None)) – Weights or exposure to which variance is inversely proportional.
ddof (int, optional (default=1)) – Degrees of freedom consumed by the model for mu.
{'pearson' (method =) – Whether to base the estimate on the Pearson residuals or the deviance.
'deviance'} – Whether to base the estimate on the Pearson residuals or the deviance.
(default='pearson') (optional) – Whether to base the estimate on the Pearson residuals or the deviance.

Return type:

float

eta_mu_deviance(link, factor, cur_eta, X_dot_d, y, sample_weight)

Compute eta, mu and the deviance.

Compute: * the linear predictor, eta, as cur_eta + factor * X_dot_d; * the link-function-transformed prediction, mu; * the deviance.

Returns:

numpy.ndarray, shape (X.shape[0],) – The linear predictor, eta.
numpy.ndarray, shape (X.shape[0],) – The link-function-transformed prediction, mu.
float – The deviance.

Parameters:

link (Link) –
factor (float) –
cur_eta (ndarray) –
X_dot_d (ndarray) –
y (ndarray) –
sample_weight (ndarray) –

in_y_range(x)

Return True if x is in the valid range of the EDM.

Parameters:: x (array-like, shape (n_samples,)) – Target values.
Return type:: np.ndarray

property include_lower_bound: bool: Return whether lower_bound is allowed as a value of y.

log_likelihood(y, mu, sample_weight=None, dispersion=None)

Compute the log likelihood.

For 1 < power < 2, we use the series approximation by Dunn and Smyth (2005) to compute the normalization term.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Sample weights.
dispersion (float, optional (default=None)) – Dispersion parameter \(\phi\). Estimated if None.

Return type:

float

property lower_bound: float: Return the lowest value of y allowed.

property power: float: Return the Tweedie power parameter.

rowwise_gradient_hessian(link, coef, dispersion, X, y, sample_weight, eta, mu, offset=None)

Compute the gradient and negative Hessian of the log likelihood row-wise.

Returns:

numpy.ndarray, shape (X.shape[0],) – The gradient of the log likelihood, row-wise.
numpy.ndarray, shape (X.shape[0],) – The negative Hessian of the log likelihood, row-wise.

Parameters:

link (Link) –
coef (ndarray) –
X (MatrixBase | StandardizedMatrix) –
y (ndarray) –
sample_weight (ndarray) –
eta (ndarray) –
mu (ndarray) –
offset (ndarray | None) –

unit_deviance(y, mu): Get the deviance of each observation.

unit_deviance_derivative(y, mu)

Compute the derivative of the unit deviance with respect to mu.

The derivative of the unit deviance is given by \(-2 \times (y - \mu) / v(\mu)\), where \(v(\mu)\) is the unit variance.

Parameters:

y (array-like, shape (n_samples,)) – Target values.
mu (array-like, shape (n_samples,)) – Predicted mean.

Return type:

array-like, shape (n_samples,)

unit_variance(mu)

Compute the unit variance of a Tweedie distribution v(mu) = mu^power.

Parameters:: mu (array-like, shape (n_samples,)) – Predicted mean.
Return type:: numpy.ndarray, shape (n_samples,)

unit_variance_derivative(mu)

Compute the derivative of the unit variance of a Tweedie distribution.

Equation: \(v(\mu) = p \times \mu^{(p-1)}\).

Parameters:: mu (array-like, shape (n_samples,)) – Predicted mean.
Return type:: numpy.ndarray, shape (n_samples,)

variance(mu, dispersion=1, sample_weight=1)

Compute the variance function.

The variance of \(Y_i \sim \mathrm{EDM}(\mu_i, \phi / s_i)\) is \(\mathrm{var}(Y_i) = (\phi / s_i) * v(\mu_i)\), with unit variance \(v(\mu)\) and weights \(s_i\).

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

variance_derivative(mu, dispersion=1, sample_weight=1)

Compute the derivative of the variance with respect to mu.

The derivative of the variance is equal to \((\phi / s_i) * v'(\mu_i)\), where \(v(\mu)\) is the unit variance and \(s_i\) are weights.

Parameters:

mu (array-like, shape (n_samples,)) – Predicted mean.
dispersion (float, optional (default=1)) – Dispersion parameter \(\phi\).
sample_weight (array-like, shape (n_samples,), optional (default=1)) – Weights or exposure to which variance is inverse proportional.

Return type:

array-like, shape (n_samples,)

class glum.TweedieLink(p)

Bases: Link

The Tweedie link function x^(1-p) if p≠1 and log(x) if p=1.

derivative(mu)

Get the derivative of the Tweedie link.

See superclass documentation.

Parameters:: mu (array-like) –

inverse(**kwargs)

Compute the inverse link function h(lin_pred).

Gives the inverse relationship between linear predictor, lin_pred ≡ X * w, and the mean, mu ≡ E(Y), i.e. h(lin_pred) = mu.

Parameters:: lin_pred (array-like, shape (n_samples,)) – Usually the (fitted) linear predictor.

inverse_derivative(**kwargs)

Compute the derivative of the inverse link function h'(lin_pred).

Parameters:: lin_pred (array-like, shape (n_samples,)) – Usually the (fitted) linear predictor.

inverse_derivative2(**kwargs)

Compute second derivative of the inverse link function h''(lin_pred).

Parameters:: lin_pred (array-like, shape (n_samples,)) – Usually the (fitted) linear predictor.

link(mu)

Get the Tweedie canonical link.

See superclass documentation.

Parameters:: mu (array-like) –

glum.get_link(link, family)

For the Tweedie distribution, this code follows actuarial best practices regarding link functions. Note that these links are sometimes not canonical:

identity for normal (p=0);

no convention for p < 0, so let’s leave it as identity;

log otherwise.

Parameters:

link (str | Link) –
family (ExponentialDispersionModel) –

Return type:

Link