# -*- coding: utf-8 -*-
"""
Created on Fri Dec 19 11:29:18 2014
Author: Josef Perktold
License: BSD-3
"""
import numpy as np
from scipy import stats
# this is similar to ContrastResults after t_test, copied and adjusted
[docs]class PredictionResults(object):
"""
Results class for predictions.
Parameters
----------
predicted_mean : ndarray
The array containing the prediction means.
var_pred_mean : ndarray
The array of the variance of the prediction means.
var_resid : ndarray
The array of residual variances.
df : int
The degree of freedom used if dist is 't'.
dist : {'norm', 't', object}
Either a string for the normal or t distribution or another object
that exposes a `ppf` method.
row_labels : list[str]
Row labels used in summary frame.
"""
def __init__(self, predicted_mean, var_pred_mean, var_resid,
df=None, dist=None, row_labels=None):
self.predicted_mean = predicted_mean
self.var_pred_mean = var_pred_mean
self.df = df
self.var_resid = var_resid
self.row_labels = row_labels
if dist is None or dist == 'norm':
self.dist = stats.norm
self.dist_args = ()
elif dist == 't':
self.dist = stats.t
self.dist_args = (self.df,)
else:
self.dist = dist
self.dist_args = ()
@property
def se_obs(self):
return np.sqrt(self.var_pred_mean + self.var_resid)
@property
def se_mean(self):
return np.sqrt(self.var_pred_mean)
[docs] def conf_int(self, obs=False, alpha=0.05):
"""
Returns the confidence interval of the value, `effect` of the
constraint.
This is currently only available for t and z tests.
Parameters
----------
alpha : float, optional
The significance level for the confidence interval.
ie., The default `alpha` = .05 returns a 95% confidence interval.
Returns
-------
ci : ndarray, (k_constraints, 2)
The array has the lower and the upper limit of the confidence
interval in the columns.
"""
se = self.se_obs if obs else self.se_mean
q = self.dist.ppf(1 - alpha / 2., *self.dist_args)
lower = self.predicted_mean - q * se
upper = self.predicted_mean + q * se
return np.column_stack((lower, upper))
[docs] def summary_frame(self, what='all', alpha=0.05):
# TODO: finish and cleanup
import pandas as pd
from collections import OrderedDict
ci_obs = self.conf_int(alpha=alpha, obs=True) # need to split
ci_mean = self.conf_int(alpha=alpha, obs=False)
to_include = OrderedDict()
to_include['mean'] = self.predicted_mean
to_include['mean_se'] = self.se_mean
to_include['mean_ci_lower'] = ci_mean[:, 0]
to_include['mean_ci_upper'] = ci_mean[:, 1]
to_include['obs_ci_lower'] = ci_obs[:, 0]
to_include['obs_ci_upper'] = ci_obs[:, 1]
self.table = to_include
# OrderedDict does not work to preserve sequence
# pandas dict does not handle 2d_array
# data = np.column_stack(list(to_include.values()))
# names = ....
res = pd.DataFrame(to_include, index=self.row_labels,
columns=to_include.keys())
return res
def get_prediction(self, exog=None, transform=True, weights=None,
row_labels=None, pred_kwds=None):
"""
Compute prediction results.
Parameters
----------
exog : array_like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a formula, do you want to pass
exog through the formula. Default is True. E.g., if you fit
a model y ~ log(x1) + log(x2), and transform is True, then
you can pass a data structure that contains x1 and x2 in
their original form. Otherwise, you'd need to log the data
first.
weights : array_like, optional
Weights interpreted as in WLS, used for the variance of the predicted
residual.
row_labels : list
A list of row labels to use. If not provided, read `exog` is
available.
**kwargs
Some models can take additional keyword arguments, see the predict
method of the model for the details.
Returns
-------
linear_model.PredictionResults
The prediction results instance contains prediction and prediction
variance and can on demand calculate confidence intervals and summary
tables for the prediction of the mean and of new observations.
"""
# prepare exog and row_labels, based on base Results.predict
if transform and hasattr(self.model, 'formula') and exog is not None:
from patsy import dmatrix
exog = dmatrix(self.model.data.design_info, exog)
if exog is not None:
if row_labels is None:
row_labels = getattr(exog, 'index', None)
if callable(row_labels):
row_labels = None
exog = np.asarray(exog)
if exog.ndim == 1 and (self.model.exog.ndim == 1 or
self.model.exog.shape[1] == 1):
exog = exog[:, None]
exog = np.atleast_2d(exog) # needed in count model shape[1]
else:
exog = self.model.exog
if weights is None:
weights = getattr(self.model, 'weights', None)
if row_labels is None:
row_labels = getattr(self.model.data, 'row_labels', None)
# need to handle other arrays, TODO: is delegating to model possible ?
if weights is not None:
weights = np.asarray(weights)
if (weights.size > 1 and
(weights.ndim != 1 or weights.shape[0] == exog.shape[1])):
raise ValueError('weights has wrong shape')
if pred_kwds is None:
pred_kwds = {}
predicted_mean = self.model.predict(self.params, exog, **pred_kwds)
covb = self.cov_params()
var_pred_mean = (exog * np.dot(covb, exog.T).T).sum(1)
var_resid = self.scale # self.mse_resid / weights
# TODO: check that we have correct scale, Refactor scale #???
# special case for now:
if self.cov_type == 'fixed scale':
var_resid = self.cov_kwds['scale']
if weights is not None:
var_resid /= weights
dist = ['norm', 't'][self.use_t]
return PredictionResults(predicted_mean, var_pred_mean, var_resid,
df=self.df_resid, dist=dist,
row_labels=row_labels)