125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
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import numpy as np
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from statsmodels.tools.tools import Bunch
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class _MinimalWLS:
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"""
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Minimal implementation of WLS optimized for performance.
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Parameters
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----------
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endog : array_like
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1-d endogenous response variable. The dependent variable.
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exog : array_like
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A nobs x k array where `nobs` is the number of observations and `k`
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is the number of regressors. An intercept is not included by default
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and should be added by the user. See
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:func:`statsmodels.tools.add_constant`.
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weights : array_like, optional
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1d array of weights. If you supply 1/W then the variables are pre-
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multiplied by 1/sqrt(W). If no weights are supplied the default value
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is 1 and WLS reults are the same as OLS.
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check_endog : bool, optional
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Flag indicating whether to check for inf/nan in endog.
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If True and any are found, ValueError is raised.
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check_weights : bool, optional
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Flag indicating whether to check for inf/nan in weights.
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If True and any are found, ValueError is raised.
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Notes
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-----
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Provides only resid, scale, fittedvalues, model.weights which are used by
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methods that iteratively apply WLS.
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Does not perform any checks on the input data for type or shape
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compatibility
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"""
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msg = 'NaN, inf or invalid value detected in {0}, estimation infeasible.'
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def __init__(self, endog, exog, weights=1.0, check_endog=False,
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check_weights=False):
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self.endog = endog
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self.exog = exog
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self.weights = weights
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w_half = np.sqrt(weights)
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if check_weights:
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if not np.all(np.isfinite(w_half)):
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raise ValueError(self.msg.format('weights'))
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if check_endog:
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if not np.all(np.isfinite(endog)):
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raise ValueError(self.msg.format('endog'))
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self.wendog = w_half * endog
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if np.isscalar(weights):
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self.wexog = w_half * exog
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else:
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self.wexog = np.asarray(w_half)[:, None] * exog
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def fit(self, method='pinv'):
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"""
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Minimal implementation of WLS optimized for performance.
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Parameters
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----------
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method : str, optional
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Method to use to estimate parameters. "pinv", "qr" or "lstsq"
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* "pinv" uses the Moore-Penrose pseudoinverse
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to solve the least squares problem.
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* "qr" uses the QR factorization.
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* "lstsq" uses the least squares implementation in numpy.linalg
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Returns
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-------
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results : namedtuple
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Named tuple containing the fewest terms needed to implement
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iterative estimation in models. Currently
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* params : Estimated parameters
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* fittedvalues : Fit values using original data
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* resid : Residuals using original data
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* model : namedtuple with one field, weights
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* scale : scale computed using weighted residuals
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Notes
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-----
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Does not perform and checks on the input data
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See Also
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--------
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statsmodels.regression.linear_model.WLS
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"""
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if method == 'pinv':
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pinv_wexog = np.linalg.pinv(self.wexog)
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params = pinv_wexog.dot(self.wendog)
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elif method == 'qr':
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Q, R = np.linalg.qr(self.wexog)
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params = np.linalg.solve(R, np.dot(Q.T, self.wendog))
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else:
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params, _, _, _ = np.linalg.lstsq(self.wexog, self.wendog,
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rcond=-1)
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return self.results(params)
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def results(self, params):
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"""
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Construct results
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params : ndarray
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Model parameters
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Notes
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-----
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Allows results to be constructed from either existing parameters or
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when estimated using using ``fit``
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"""
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fitted_values = self.exog.dot(params)
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resid = self.endog - fitted_values
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wresid = self.wendog - self.wexog.dot(params)
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df_resid = self.wexog.shape[0] - self.wexog.shape[1]
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scale = np.dot(wresid, wresid) / df_resid
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return Bunch(params=params, fittedvalues=fitted_values, resid=resid,
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model=self, scale=scale)
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