"""
Implementation of class contaning and processing the estimation results.
:author: Michel Bierlaire
:date: Tue Mar 26 16:50:01 2019
.. todo:: rawResults should be a dict and not a class.
"""
import logging
import os
import pickle
import datetime
import glob
from typing import NamedTuple
import urllib.request as urlr
import pandas as pd
import numpy as np
from scipy import linalg
from scipy import stats
from biogeme_optimization import pareto
import biogeme.version as bv
import biogeme.filenames as bf
import biogeme.exceptions as excep
from biogeme import tools
from biogeme.parameters import biogeme_parameters
logger = logging.getLogger(__name__)
[docs]
class GeneralStatistic(NamedTuple):
value: float
format: str
[docs]
def calcPValue(t):
"""Calculates the p value of a parameter from its t-statistic.
The formula is
.. math:: 2(1-\\Phi(|t|)
where :math:`\\Phi(\\cdot)` is the CDF of a normal distribution.
:param t: t-statistics
:type t: float
:return: p-value
:rtype: float
"""
p_value = 2.0 * (1.0 - stats.norm.cdf(abs(t)))
return p_value
[docs]
class beta:
"""Class gathering the information related to the parameters
of the model
"""
[docs]
def __init__(self, name, value, bounds):
"""
Constructor
:param name: name of the parameter.
:type name: string
:param value: value of the parameter.
:type value: float
:param bounds: tuple (l,b) with lower and upper bounds
:type bounds: float,float
"""
self.name = name #: Name of the parameter
self.value = value #: Current value
self.lb = bounds[0] #: Lower bound
self.ub = bounds[1] #: Upper bound
self.stdErr = None #: Standard error
self.tTest = None #: t-test
self.pValue = None #: p-value
self.robust_stdErr = None #: Robust standard error
self.robust_tTest = None #: Robust t-test
self.robust_pValue = None #: Robust p-value
self.bootstrap_stdErr = None #: Std error calculated from bootstrap
self.bootstrap_tTest = None #: t-test calculated from bootstrap
self.bootstrap_pValue = None #: p-value calculated from bootstrap
[docs]
def is_bound_active(self, threshold=1.0e-6):
"""Check if one of the two bound is 'numerically' active. Being
numerically active means that the distance between the value of
the parameter and one of its bounds is below the threshold.
:param threshold: distance below which the bound is considered to be
active. Default: :math:`10^{-6}`
:type threshold: float
:return: True is one of the two bounds is numericall y active.
:rtype: bool
:raise BiogemeError: if ``threshold`` is negative.
"""
if threshold < 0:
raise excep.BiogemeError(f'Threshold ({threshold}) must be non negative')
if self.lb is not None and np.abs(self.value - self.lb) <= threshold:
return True
if self.ub is not None and np.abs(self.value - self.ub) <= threshold:
return True
return False
[docs]
def set_std_err(self, std_err):
"""Records the standard error, and calculates and records
the corresponding t-statistic and p-value
:param std_err: standard error.
:type std_err: float
"""
self.stdErr = std_err
if std_err == 0:
self.tTest = np.finfo(float).max
else:
self.tTest = np.nan_to_num(self.value / std_err)
self.pValue = calcPValue(self.tTest)
[docs]
def set_robust_std_err(self, std_err):
"""Records the robust standard error, and calculates and records
the corresponding t-statistic and p-value
:param std_err: robust standard error
:type std_err: float
"""
self.robust_stdErr = std_err
if std_err == 0:
self.robust_tTest = np.finfo(float).max
else:
self.robust_tTest = np.nan_to_num(self.value / std_err)
self.robust_pValue = calcPValue(self.robust_tTest)
[docs]
def set_bootstrap_std_err(self, std_err):
"""Records the robust standard error calculated by bootstrap, and
calculates and records the corresponding t-statistic and p-value
:param std_err: standard error calculated by bootstrap.
:type std_err: float
"""
self.bootstrap_stdErr = std_err
if std_err == 0:
self.bootstrap_tTest = np.finfo(float).max
else:
self.bootstrap_tTest = np.nan_to_num(self.value / std_err)
self.bootstrap_pValue = calcPValue(self.robust_tTest)
def __str__(self):
text = f'{self.name:15}: {self.value:.3g}'
if self.stdErr is not None:
text += f'[{self.stdErr:.3g} {self.tTest:.3g} {self.pValue:.3g}]'
if self.robust_stdErr is not None:
text += (
f'[{self.robust_stdErr:.3g} {self.robust_tTest:.3g} '
f'{self.robust_pValue:.3g}]'
)
if self.bootstrap_stdErr is not None:
text += (
f'[{self.bootstrap_stdErr:.3g} {self.bootstrap_tTest:.3g} '
f'{self.bootstrap_pValue:.3g}]'
)
return text
[docs]
class rawResults:
"""Class containing the raw results from the estimation"""
[docs]
def __init__(self, theModel, betaValues, fgHb, bootstrap=None):
"""
Constructor
:param theModel: object with the model
:type theModel: biogeme.BIOGEME
:param betaValues: list containing the estimated values of the
parameters
:type betaValues: list(float)
:param fgHb: tuple f,g,H,bhhh containing
- f: the value of the function,
- g: the gradient,
- H: the second derivative matrix,
- bhhh: the BHHH matrix.
:type fgHb: float,numpy.array, numpy.array, numpy.array
:param bootstrap: output of the bootstrapping. numpy array, of
size B x K, where
- B is the number of bootstrap iterations
- K is the number of parameters to estimate
Default: None.
:type bootstrap: numpy.array
"""
self.modelName = theModel.modelName #: Name of the model
self.userNotes = theModel.userNotes #: User notes
self.nparam = len(betaValues) #: Number of parameters
self.betaValues = betaValues #: Values of the parameters
self.betaNames = (
theModel.id_manager.free_betas.names
) #: Names of the parameters
self.initLogLike = theModel.initLogLike
"""Value of the likelihood function with the initial value of the
parameters
"""
self.nullLogLike = theModel.nullLogLike
"""Value of the likelihood function with equal probability model
"""
self.betas = [] #: List of objects of type results.beta
for beta_value, beta_name in zip(betaValues, self.betaNames):
bounds = theModel.getBoundsOnBeta(beta_name)
self.betas.append(beta(beta_name, beta_value, bounds))
self.logLike = fgHb[0] #: Value of the loglikelihood function
self.g = fgHb[1] #: Value of the gradient of the loglik. function
self.H = fgHb[2] #: Value of the hessian of the loglik. function
self.bhhh = fgHb[3]
"""Value of the BHHH matrix of the loglikelihood function"""
self.dataname = theModel.database.name #: Name of the database
self.sampleSize = theModel.database.getSampleSize()
"""Sample size (number of individuals if panel data)"""
self.numberOfObservations = theModel.database.getNumberOfObservations()
"""Number of observations"""
self.monteCarlo = theModel.monteCarlo
"""True if the model involved Monte Carlo integration"""
self.numberOfDraws = theModel.numberOfDraws
"""Number of draws for Monte Carlo integration"""
self.typesOfDraws = theModel.database.typesOfDraws
"""Types of draws for Monte Carlo integration"""
self.excludedData = theModel.database.excludedData
"""Number of excluded data"""
self.drawsProcessingTime = theModel.drawsProcessingTime
"""Time needed to process the draws"""
self.gradientNorm = linalg.norm(self.g) if self.g is not None else None
"""Norm of the gradient"""
self.optimizationMessages = theModel.optimizationMessages
"""Diagnostics given by the optimization algorithm"""
self.convergence = theModel.convergence
"""Success of the optimization algorithm"""
self.numberOfThreads = theModel.numberOfThreads
"""Number of threads used for parallel computing"""
self.htmlFileName = None #: Name of the HTML output file
self.F12FileName = None #: Name of the F12 output file
self.latexFileName = None #: Name of the LaTeX output file
self.pickleFileName = None #: Name of the pickle outpt file
self.bootstrap = bootstrap
"""output of the bootstrapping. numpy array, of size B x K,
where
- B is the number of bootstrap iterations
- K is the number of parameters to estimate
"""
if bootstrap is not None:
self.bootstrap_time = theModel.bootstrap_time
""" Time needed to perform the bootstrap"""
self.secondOrderTable = None #: Second order statistics
[docs]
class bioResults:
"""Class managing the estimation results"""
[docs]
def __init__(
self,
theRawResults=None,
pickleFile=None,
identification_threshold=None,
):
"""Constructor
:param theRawResults: object with the results of the estimation.
Default: None.
:type theRawResults: biogeme.results.rawResults
:param pickleFile: name of the file containing the raw results in
pickle format. It can be a URL. Default: None.
:type pickleFile: string
:param identification_threshold: if the smallest eigenvalue of
the second derivative matrix is lesser or equal to this
parameter, the model is considered not identified.
:type identification_threshold: float
:raise biogeme.exceptions.BiogemeError: if no data is provided.
"""
if identification_threshold is None:
self.identification_threshold = biogeme_parameters.get_value(
'identification_threshold'
)
else:
self.identification_threshold = identification_threshold
if theRawResults is not None:
self.data = theRawResults
"""Object of type :class:`biogeme.results.rawResults` contaning the
raw estimation results.
"""
elif pickleFile is not None:
try:
with urlr.urlopen(pickleFile) as p:
self.data = pickle.load(p)
except Exception:
pass
try:
with open(pickleFile, 'rb') as f:
self.data = pickle.load(f)
except FileNotFoundError as e:
error_msg = f'File {pickleFile} not found'
raise excep.FileNotFoundError(error_msg) from e
else:
raise excep.BiogemeError('No data provided.')
self._calculate_stats()
[docs]
def algorithm_has_converged(self):
"""Reports if the algorithm has indeed converged
:return: True if the algorithm has converged.
:rtype: bool
"""
return self.data.convergence
[docs]
def variance_covariance_missing(self):
"""Check if the variance covariance matrix is missing
:return: True if missing.
:rtype: bool
"""
return self.data.H is None
[docs]
def writePickle(self):
"""Dump the data in a file in pickle format.
:return: name of the file.
:rtype: string
"""
self.data.pickleFileName = bf.get_new_file_name(self.data.modelName, 'pickle')
with open(self.data.pickleFileName, 'wb') as f:
pickle.dump(self.data, f)
logger.info(f'Results saved in file {self.data.pickleFileName}')
return self.data.pickleFileName
def _calculate_test(self, i, j, matrix):
"""Calculates a t-test comparing two coefficients
Args:
i: index of first coefficient \f$\\beta_i\f$.
j: index of second coefficient \f$\\beta_i\f$.
matrix: estimate of the variance-covariance matrix \f$m\f$.
:return: t test
..math:: \f[\\frac{\\beta_i-\\beta_j}
{\\sqrt{m_{ii}+m_{jj} - 2 m_{ij} }}\f]
:rtype: float
"""
vi = self.data.betaValues[i]
vj = self.data.betaValues[j]
varI = matrix[i, i]
varJ = matrix[j, j]
covar = matrix[i, j]
r = varI + varJ - 2.0 * covar
if r <= 0:
test = np.finfo(float).max
else:
test = (vi - vj) / np.sqrt(r)
return test
def _calculate_stats(self):
"""Calculates the following statistics:
- likelihood ratio test between the initial and the estimated
models: :math:`-2(L_0-L^*)`
- Rho square: :math:`1 - \\frac{L^*}{L^0}`
- Rho bar square: :math:`1 - \\frac{L^* - K}{L^0}`
- AIC: :math:`2(K - L^*)`
- BIC: :math:`-2 L^* + K \\log(N)`
Estimates for the variance-covariance matrix (Rao-Cramer,
robust, and bootstrap) are also calculated, as well as t-tests and
p value for the comparison of pairs of coefficients.
"""
self.data.likelihoodRatioTestNull = (
-2.0 * (self.data.nullLogLike - self.data.logLike)
if self.data.nullLogLike is not None
else None
)
self.data.likelihoodRatioTest = (
-2.0 * (self.data.initLogLike - self.data.logLike)
if self.data.initLogLike is not None
else None
)
try:
self.data.rhoSquare = (
np.nan_to_num(1.0 - self.data.logLike / self.data.initLogLike)
if self.data.initLogLike is not None
else None
)
except ZeroDivisionError:
self.data.rhoSquare = None
try:
self.data.rhoSquareNull = (
np.nan_to_num(1.0 - self.data.logLike / self.data.nullLogLike)
if self.data.nullLogLike is not None
else None
)
except ZeroDivisionError:
self.data.rhoSquareNull = None
try:
self.data.rhoBarSquare = (
np.nan_to_num(
1.0 - (self.data.logLike - self.data.nparam) / self.data.initLogLike
)
if self.data.initLogLike is not None
else None
)
except ZeroDivisionError:
self.data.rhoBarSquare = None
try:
self.data.rhoBarSquareNull = (
np.nan_to_num(
1.0 - (self.data.logLike - self.data.nparam) / self.data.nullLogLike
)
if self.data.nullLogLike is not None
else None
)
except ZeroDivisionError:
self.data.rhoBarSquareNull = None
self.data.akaike = 2.0 * self.data.nparam - 2.0 * self.data.logLike
self.data.bayesian = -2.0 * self.data.logLike + self.data.nparam * np.log(
self.data.sampleSize
)
# We calculate the eigenstructure to report in case of singularity
if self.data.H is not None:
self.data.eigenValues, self.data.eigenVectors = linalg.eigh(
-np.nan_to_num(self.data.H)
)
_, self.data.singularValues, _ = linalg.svd(-np.nan_to_num(self.data.H))
# We use the pseudo inverse in case the matrix is singular
self.data.varCovar = -linalg.pinv(np.nan_to_num(self.data.H))
for i in range(self.data.nparam):
if self.data.varCovar[i, i] < 0:
self.data.betas[i].set_std_err(np.finfo(float).max)
else:
self.data.betas[i].set_std_err(np.sqrt(self.data.varCovar[i, i]))
d = np.diag(self.data.varCovar)
if (d > 0).all():
diag = np.diag(np.sqrt(d))
diag_inv = linalg.inv(diag)
self.data.correlation = diag_inv.dot(self.data.varCovar.dot(diag_inv))
else:
self.data.correlation = np.full_like(
self.data.varCovar, np.finfo(float).max
)
# Robust estimator
self.data.robust_varCovar = self.data.varCovar.dot(
self.data.bhhh.dot(self.data.varCovar)
)
for i in range(self.data.nparam):
if self.data.robust_varCovar[i, i] < 0:
self.data.betas[i].set_robust_std_err(np.finfo(float).max)
else:
self.data.betas[i].set_robust_std_err(
np.sqrt(self.data.robust_varCovar[i, i])
)
rd = np.diag(self.data.robust_varCovar)
if (rd > 0).all():
diag = np.diag(np.sqrt(rd))
diag_inv = linalg.inv(diag)
self.data.robust_correlation = diag_inv.dot(
self.data.robust_varCovar.dot(diag_inv)
)
else:
self.data.robust_correlation = np.full_like(
self.data.robust_varCovar, np.finfo(float).max
)
# Bootstrap
if self.data.bootstrap is not None:
self.data.bootstrap_varCovar = np.cov(self.data.bootstrap, rowvar=False)
for i in range(self.data.nparam):
if self.data.bootstrap_varCovar[i, i] < 0:
self.data.betas[i].set_bootstrap_std_err(np.finfo(float).max)
else:
self.data.betas[i].set_bootstrap_std_err(
np.sqrt(self.data.bootstrap_varCovar[i, i])
)
rd = np.diag(self.data.bootstrap_varCovar)
if (rd > 0).all():
diag = np.diag(np.sqrt(rd))
diag_inv = linalg.inv(diag)
self.data.bootstrap_correlation = diag_inv.dot(
self.data.bootstrap_varCovar.dot(diag_inv)
)
else:
self.data.bootstrap_correlation = np.full_like(
self.data.bootstrap_varCovar, np.finfo(float).max
)
self.data.secondOrderTable = {}
for i in range(self.data.nparam):
for j in range(i):
t = self._calculate_test(i, j, self.data.varCovar)
p = calcPValue(t)
trob = self._calculate_test(i, j, self.data.robust_varCovar)
prob = calcPValue(trob)
if self.data.bootstrap is not None:
tboot = self._calculate_test(i, j, self.data.bootstrap_varCovar)
pboot = calcPValue(tboot)
name = (self.data.betaNames[i], self.data.betaNames[j])
if self.data.bootstrap is not None:
self.data.secondOrderTable[name] = [
self.data.varCovar[i, j],
self.data.correlation[i, j],
t,
p,
self.data.robust_varCovar[i, j],
self.data.robust_correlation[i, j],
trob,
prob,
self.data.bootstrap_varCovar[i, j],
self.data.bootstrap_correlation[i, j],
tboot,
pboot,
]
else:
self.data.secondOrderTable[name] = [
self.data.varCovar[i, j],
self.data.correlation[i, j],
t,
p,
self.data.robust_varCovar[i, j],
self.data.robust_correlation[i, j],
trob,
prob,
]
min_eigen_index = np.argmin(self.data.eigenValues)
self.data.smallestEigenValue = self.data.eigenValues[min_eigen_index]
self.data.smallestEigenVector = self.data.eigenVectors[:, min_eigen_index]
self.data.smallestSingularValue = min(self.data.singularValues)
max_eigen_index = np.argmax(self.data.eigenValues)
self.data.largestEigenValue = self.data.eigenValues[max_eigen_index]
self.data.largestEigenVector = self.data.eigenVectors[:, max_eigen_index]
self.data.largestSingularValue = max(self.data.singularValues)
self.data.conditionNumber = (
self.data.largestEigenValue / self.data.smallestEigenValue
if self.data.smallestEigenValue
else np.finfo(np.float64).max
)
[docs]
def shortSummary(self):
"""Provides a short summary of the estimation results. Old syntax"""
logger.warning(
'The syntax "shortSummary" is deprecated and is replaced by '
'the syntax "short_summary".'
)
return self.short_summary()
[docs]
def short_summary(self):
"""Provides a short summary of the estimation results"""
text = ''
text += f'Results for model {self.data.modelName}\n'
text += f'Nbr of parameters:\t\t{self.data.nparam}\n'
text += f'Sample size:\t\t\t{self.data.sampleSize}\n'
if self.data.sampleSize != self.data.numberOfObservations:
text += f'Observations:\t\t\t{self.data.numberOfObservations}\n'
text += f'Excluded data:\t\t\t{self.data.excludedData}\n'
if self.data.nullLogLike is not None:
text += f'Null log likelihood:\t\t{self.data.nullLogLike:.7g}\n'
text += f'Final log likelihood:\t\t{self.data.logLike:.7g}\n'
if self.data.nullLogLike is not None:
text += (
f'Likelihood ratio test (null):\t\t'
f'{self.data.likelihoodRatioTestNull:.7g}\n'
)
text += f'Rho square (null):\t\t\t{self.data.rhoSquareNull:.3g}\n'
text += (
f'Rho bar square (null):\t\t\t' f'{self.data.rhoBarSquareNull:.3g}\n'
)
text += f'Akaike Information Criterion:\t{self.data.akaike:.7g}\n'
text += f'Bayesian Information Criterion:\t{self.data.bayesian:.7g}\n'
return text
def __str__(self):
text = '\n'
text += f'Results for model {self.data.modelName}\n'
if self.data.htmlFileName is not None:
text += f'Output file (HTML):\t\t\t{self.data.htmlFileName}\n'
if self.data.latexFileName is not None:
text += f'Output file (LaTeX):\t\t\t{self.data.latexFileName}\n'
text += f'Nbr of parameters:\t\t{self.data.nparam}\n'
text += f'Sample size:\t\t\t{self.data.sampleSize}\n'
if self.data.sampleSize != self.data.numberOfObservations:
text += f'Observations:\t\t\t{self.data.numberOfObservations}\n'
text += f'Excluded data:\t\t\t{self.data.excludedData}\n'
if self.data.nullLogLike is not None:
text += f'Null log likelihood:\t\t{self.data.nullLogLike:.7g}\n'
if self.data.initLogLike is not None:
text += f'Init log likelihood:\t\t{self.data.initLogLike:.7g}\n'
text += f'Final log likelihood:\t\t{self.data.logLike:.7g}\n'
if self.data.nullLogLike is not None:
text += (
f'Likelihood ratio test (null):\t\t'
f'{self.data.likelihoodRatioTestNull:.7g}\n'
)
text += f'Rho square (null):\t\t\t{self.data.rhoSquareNull:.3g}\n'
text += (
f'Rho bar square (null):\t\t\t' f'{self.data.rhoBarSquareNull:.3g}\n'
)
if self.data.initLogLike is not None:
text += (
f'Likelihood ratio test (init):\t\t'
f'{self.data.likelihoodRatioTest:.7g}\n'
)
text += f'Rho square (init):\t\t\t{self.data.rhoSquare:.3g}\n'
text += f'Rho bar square (init):\t\t\t{self.data.rhoBarSquare:.3g}\n'
text += f'Akaike Information Criterion:\t{self.data.akaike:.7g}\n'
text += f'Bayesian Information Criterion:\t{self.data.bayesian:.7g}\n'
if self.data.gradientNorm is not None:
text += f'Final gradient norm:\t\t{self.data.gradientNorm:.7g}\n'
text += '\n'.join([f'{b}' for b in self.data.betas])
text += '\n'
if self.data.secondOrderTable is not None:
for k, v in self.data.secondOrderTable.items():
text += (
'{}:\t{:.3g}\t{:.3g}\t{:.3g}\t{:.3g}\t'
'{:.3g}\t{:.3g}\t{:.3g}\t{:.3g}\n'
).format(k, *v)
return text
def _getLaTeXHeader(self):
"""Prepare the header for the LaTeX file, containing comments and the
version of Biogeme.
:return: string containing the header.
:rtype: str
"""
header = ''
header += '%% This file is designed to be included into a LaTeX document\n'
header += '%% See http://www.latex-project.org for ' 'information about LaTeX\n'
header += (
f'%% {self.data.modelName} - Report from '
f'biogeme {bv.getVersion()} '
f'[{bv.versionDate}]\n'
)
header += bv.getLaTeX()
return header
[docs]
def getLaTeX(self, onlyRobust=True):
"""Get the results coded in LaTeX
:param onlyRobust: if True, only the robust statistics are included
:type onlyRobust: bool
:return: LaTeX code
:rtype: string
"""
now = datetime.datetime.now()
latex = self._getLaTeXHeader()
if self.data.latexFileName is not None:
latex += '\n%% File ' + self.data.latexFileName + '\n'
latex += f'\n%% This file has automatically been generated on {now}</p>\n'
if self.data.dataname is not None:
latex += f'\n%%Database name: {self.data.dataname}\n'
if self.data.userNotes is not None:
# User notes
latex += f'\n%%{self.data.userNotes}\n'
latex += '\n%% General statistics\n'
latex += '\\section{General statistics}\n'
statistics = self.getGeneralStatistics()
latex += '\\begin{tabular}{ll}\n'
for name, (value, precision) in statistics.items():
if isinstance(value, bytes):
value = str(value)
if isinstance(value, str):
value = value.replace('_', '\\_')
latex += f'{name} & {value:{precision}} \\\\\n'
for key, value in self.data.optimizationMessages.items():
if key in ('Relative projected gradient', 'Relative change'):
latex += f'{key} & \\verb${value:.7g}$ \\\\\n'
else:
latex += f'{key} & \\verb${value}$ \\\\\n'
latex += '\\end{tabular}\n'
latex += '\n%%Parameter estimates\n'
latex += '\\section{Parameter estimates}\n'
table = self.getEstimatedParameters(onlyRobust)
def formatting(x):
"""Defines the formatting for the to_latex function of pandas"""
res = f'{x:.3g}'
if '.' in res:
return res
return f'{res}.0'
# Need to check for old versions of Pandas.
try:
latex += table.style.format(formatting).to_latex()
except AttributeError:
latex += table.to_latex(float_format=formatting)
latex += '\n%%Correlation\n'
latex += '\\section{Correlation}\n'
table = self.getCorrelationResults()
# Need to check for old versions of Pandas.
try:
latex += table.style.format(formatting).to_latex()
except AttributeError:
latex += table.to_latex(float_format=formatting)
return latex
[docs]
def getGeneralStatistics(self):
"""Format the results in a dict
:return: dict with the results. The keys describe each
content. Each element is a GeneralStatistic tuple,
with the value and its preferred formatting.
Example::
'Init log likelihood': (-115.30029248549191, '.7g')
:rtype: dict(string:float,string)
"""
d = {}
d['Number of estimated parameters'] = GeneralStatistic(
value=self.data.nparam, format=''
)
nf = self.numberOfFreeParameters()
if nf != self.data.nparam:
d['Number of free parameters'] = GeneralStatistic(value=nf, format='')
d['Sample size'] = GeneralStatistic(value=self.data.sampleSize, format='')
if self.data.sampleSize != self.data.numberOfObservations:
d['Observations'] = GeneralStatistic(
value=self.data.numberOfObservations, format=''
)
d['Excluded observations'] = GeneralStatistic(
value=self.data.excludedData, format=''
)
if self.data.nullLogLike is not None:
d['Null log likelihood'] = GeneralStatistic(
value=self.data.nullLogLike, format='.7g'
)
d['Init log likelihood'] = GeneralStatistic(
value=self.data.initLogLike, format='.7g'
)
d['Final log likelihood'] = GeneralStatistic(
value=self.data.logLike, format='.7g'
)
if self.data.nullLogLike is not None:
d['Likelihood ratio test for the null model'] = GeneralStatistic(
value=self.data.likelihoodRatioTestNull,
format='.7g',
)
d['Rho-square for the null model'] = GeneralStatistic(
value=self.data.rhoSquareNull, format='.3g'
)
d['Rho-square-bar for the null model'] = GeneralStatistic(
value=self.data.rhoBarSquareNull,
format='.3g',
)
d['Likelihood ratio test for the init. model'] = GeneralStatistic(
value=self.data.likelihoodRatioTest,
format='.7g',
)
d['Rho-square for the init. model'] = GeneralStatistic(
value=self.data.rhoSquare, format='.3g'
)
d['Rho-square-bar for the init. model'] = GeneralStatistic(
value=self.data.rhoBarSquare, format='.3g'
)
d['Akaike Information Criterion'] = GeneralStatistic(
value=self.data.akaike, format='.7g'
)
d['Bayesian Information Criterion'] = GeneralStatistic(
value=self.data.bayesian, format='.7g'
)
d['Final gradient norm'] = GeneralStatistic(
value=self.data.gradientNorm, format='.4E'
)
if self.data.monteCarlo:
d['Number of draws'] = GeneralStatistic(
value=self.data.numberOfDraws, format=''
)
d['Draws generation time'] = GeneralStatistic(
value=self.data.drawsProcessingTime, format=''
)
d['Types of draws'] = GeneralStatistic(
value=[f'{i}: {k}' for i, k in self.data.typesOfDraws.items()],
format='',
)
if self.data.bootstrap is not None:
d['Bootstrapping time'] = GeneralStatistic(
value=self.data.bootstrap_time, format=''
)
d['Nbr of threads'] = GeneralStatistic(
value=self.data.numberOfThreads, format=''
)
return d
[docs]
def printGeneralStatistics(self):
"""Print the general statistics of the estimation.
:return: general statistics
Example::
Number of estimated parameters: 2
Sample size: 5
Excluded observations: 0
Init log likelihood: -67.08858
Final log likelihood: -67.06549
Likelihood ratio test for the init. model: 0.04618175
Rho-square for the init. model: 0.000344
Rho-square-bar for the init. model: -0.0295
Akaike Information Criterion: 138.131
Bayesian Information Criterion: 137.3499
Final gradient norm: 3.9005E-07
Bootstrapping time: 0:00:00.042713
Nbr of threads: 16
:rtype: str
"""
statistics = self.getGeneralStatistics()
output = ''
for name, (value, precision) in statistics.items():
output += f'{name}:\t{value:{precision}}\n'
return output
[docs]
def numberOfFreeParameters(self):
"""This is the number of estimated parameters, minus those that are at
their bounds
"""
return sum(not b.is_bound_active() for b in self.data.betas)
[docs]
def getEstimatedParameters(self, onlyRobust=True):
"""Gather the estimated parameters and the corresponding statistics in
a Pandas dataframe.
:param onlyRobust: if True, only the robust statistics are included
:type onlyRobust: bool
:return: Pandas dataframe with the results
:rtype: pandas.DataFrame
"""
# There should be a more 'Pythonic' way to do this.
anyActiveBound = False
for b in self.data.betas:
if b.is_bound_active():
anyActiveBound = True
if anyActiveBound:
if onlyRobust:
columns = [
'Value',
'Active bound',
'Rob. Std err',
'Rob. t-test',
'Rob. p-value',
]
else:
columns = [
'Value',
'Active bound',
'Std err',
't-test',
'p-value',
'Rob. Std err',
'Rob. t-test',
'Rob. p-value',
]
else:
if onlyRobust:
columns = [
'Value',
'Rob. Std err',
'Rob. t-test',
'Rob. p-value',
]
else:
columns = [
'Value',
'Std err',
't-test',
'p-value',
'Rob. Std err',
'Rob. t-test',
'Rob. p-value',
]
if self.data.bootstrap is not None and not onlyRobust:
columns += [
f'Bootstrap[{len(self.data.bootstrap)}] Std err',
'Bootstrap t-test',
'Bootstrap p-value',
]
table = pd.DataFrame(columns=columns)
for b in self.data.betas:
if anyActiveBound:
if onlyRobust:
arow = {
'Value': b.value,
'Active bound': {True: 1.0, False: 0.0}[b.is_bound_active()],
'Rob. Std err': b.robust_stdErr,
'Rob. t-test': b.robust_tTest,
'Rob. p-value': b.robust_pValue,
}
else:
arow = {
'Value': b.value,
'Active bound': {True: 1.0, False: 0.0}[b.is_bound_active()],
'Std err': b.stdErr,
't-test': b.tTest,
'p-value': b.pValue,
'Rob. Std err': b.robust_stdErr,
'Rob. t-test': b.robust_tTest,
'Rob. p-value': b.robust_pValue,
}
else:
if onlyRobust:
arow = {
'Value': b.value,
'Rob. Std err': b.robust_stdErr,
'Rob. t-test': b.robust_tTest,
'Rob. p-value': b.robust_pValue,
}
else:
arow = {
'Value': b.value,
'Std err': b.stdErr,
't-test': b.tTest,
'p-value': b.pValue,
'Rob. Std err': b.robust_stdErr,
'Rob. t-test': b.robust_tTest,
'Rob. p-value': b.robust_pValue,
}
if self.data.bootstrap is not None and not onlyRobust:
arow[
f'Bootstrap[{len(self.data.bootstrap)}] Std err'
] = b.bootstrap_stdErr
arow['Bootstrap t-test'] = b.bootstrap_tTest
arow['Bootstrap p-value'] = b.bootstrap_pValue
table.loc[b.name] = pd.Series(arow)
return table
[docs]
def getCorrelationResults(self, subset=None):
"""Get the statistics about pairs of coefficients as a Pandas dataframe
:param subset: produce the results only for a subset of
parameters. If None, all the parameters are involved. Default: None
:type subset: list(str)
:return: Pandas data frame with the correlation results
:rtype: pandas.DataFrame
"""
if subset is not None:
unknown = []
for p in subset:
if p not in self.data.betaNames:
unknown.append(p)
if unknown:
logger.warning(f'Unknown parameters are ignored: {unknown}')
columns = [
'Covariance',
'Correlation',
't-test',
'p-value',
'Rob. cov.',
'Rob. corr.',
'Rob. t-test',
'Rob. p-value',
]
if self.data.bootstrap is not None:
columns += [
'Boot. cov.',
'Boot. corr.',
'Boot. t-test',
'Boot. p-value',
]
table = pd.DataFrame(columns=columns)
for k, v in self.data.secondOrderTable.items():
if subset is None:
include = True
else:
include = k[0] in subset and k[1] in subset
if include:
arow = {
'Covariance': v[0],
'Correlation': v[1],
't-test': v[2],
'p-value': v[3],
'Rob. cov.': v[4],
'Rob. corr.': v[5],
'Rob. t-test': v[6],
'Rob. p-value': v[7],
}
if self.data.bootstrap is not None:
arow['Boot. cov.'] = v[8]
arow['Boot. corr.'] = v[9]
arow['Boot. t-test'] = v[10]
arow['Boot. p-value'] = v[11]
table.loc[f'{k[0]}-{k[1]}'] = pd.Series(arow)
return table
[docs]
def getHtml(self, onlyRobust=True):
"""Get the results coded in HTML
:param onlyRobust: if True, only the robust statistics are included
:type onlyRobust: bool
:return: HTML code
:rtype: string
"""
now = datetime.datetime.now()
html = self._getHtmlHeader()
html += bv.getHtml()
html += f'<p>This file has automatically been generated on {now}</p>\n'
html += '<table>\n'
html += (
f'<tr class=biostyle><td align=right>'
f'<strong>Report file</strong>: </td>'
f'<td>{self.data.htmlFileName}</td></tr>\n'
)
html += (
f'<tr class=biostyle><td align=right>'
f'<strong>Database name</strong>: </td>'
f'<td>{self.data.dataname}</td></tr>\n'
)
html += '</table>\n'
if not self.algorithm_has_converged():
html += '<h2>Algorithm failed to converge</h2>\n'
html += (
'<p>It seems that the optimization algorithm did not converge. '
'Therefore, the results below do not correspond to the maximum '
'likelihood estimator. Check the specification of the model, '
'or the criteria for convergence of the algorithm. </p>'
)
if np.abs(self.data.smallestEigenValue) <= self.identification_threshold:
html += '<h2>Warning: identification issue</h2>\n'
html += (
f'<p>The second derivatives matrix is close to singularity. '
f'The smallest eigenvalue is '
f'{np.abs(self.data.smallestEigenValue):.3g}. This warning is '
f'triggered when it is smaller than the parameter '
f'<code>identification_threshold</code>='
f'{self.identification_threshold}.</p>'
f'<p>Variables involved:'
)
html += '<table>'
for i, ev in enumerate(self.data.smallestEigenVector):
if np.abs(ev) > self.identification_threshold:
html += (
f'<tr><td>{ev:.3g}</td>'
f'<td> *</td>'
f'<td> {self.data.betaNames[i]}</td></tr>\n'
)
html += '</table>'
html += '</p>\n'
if self.data.userNotes is not None:
# User notes
html += (
f'<blockquote style="border: 2px solid #666; '
f'padding: 10px; background-color:'
f' #ccc;">{self.data.userNotes}</blockquote>'
)
# Include here the part on statistics
html += '<h1>Estimation report</h1>\n'
html += '<table border="0">\n'
statistics = self.getGeneralStatistics()
for description, (value, precision) in statistics.items():
if value is not None:
html += (
f'<tr class=biostyle><td align=right >'
f'<strong>{description}</strong>: </td> '
f'<td>{value:{precision}}</td></tr>\n'
)
for key, value in self.data.optimizationMessages.items():
if key == 'Relative projected gradient':
html += (
f'<tr class=biostyle><td align=right >'
f'<strong>{key}</strong>: </td> '
f'<td>{value:.7g}</td></tr>\n'
)
else:
html += (
f'<tr class=biostyle><td align=right >'
f'<strong>{key}</strong>: </td> '
f'<td>{value}</td></tr>\n'
)
html += '</table>\n'
table = self.getEstimatedParameters(onlyRobust)
html += '<h1>Estimated parameters</h1>\n'
html += '<table border="1">\n'
html += '<tr class=biostyle><th>Name</th>'
for c in table.columns:
html += f'<th>{c}</th>'
html += '</tr>\n'
for name, values in table.iterrows():
html += f'<tr class=biostyle><td>{name}</td>'
for key, value in values.items():
html += f'<td>{value:.3g}</td>'
html += '</tr>\n'
html += '</table>\n'
table = self.getCorrelationResults()
html += '<h2>Correlation of coefficients</h2>\n'
html += '<table border="1">\n'
html += '<tr class=biostyle><th>Coefficient1</th><th>Coefficient2</th>'
for column in table.columns:
html += f'<th>{column}</th>'
html += '</tr>\n'
for name, values in table.iterrows():
split_name = name.split('-')
html += (
f'<tr class=biostyle><td>{split_name[0]}</td>'
f'<td>{split_name[1]}</td>'
)
for _, value in values.items():
html += f'<td>{value:.3g}</td>'
html += '</tr>\n'
html += '</table>\n'
html += f'<p>Smallest eigenvalue: ' f'{self.data.smallestEigenValue:.6g}</p>\n'
html += f'<p>Largest eigenvalue: ' f'{self.data.largestEigenValue:.6g}</p>\n'
html += f'<p>Condition number: ' f'{self.data.conditionNumber:.6g}</p>\n'
html += '</html>'
return html
[docs]
def getBetaValues(self, myBetas=None):
"""Retrieve the values of the estimated parameters, by names.
:param myBetas: names of the requested parameters. If None, all
available parameters will be reported. Default: None.
:type myBetas: list(string)
:return: dict containing the values, where the keys are the names.
:rtype: dict(string:float)
:raise biogeme.exceptions.BiogemeError: if some requested parameters
are not available.
"""
values = {}
if myBetas is None:
myBetas = self.data.betaNames
for b in myBetas:
try:
index = self.data.betaNames.index(b)
values[b] = self.data.betas[index].value
except KeyError as e:
keys = ''
for k in self.data.betaNames:
keys += f' {k}'
err = (
f'The value of {b} is not available in the results. '
f'The following parameters are available: {keys}'
)
raise excep.BiogemeError(err) from e
return values
[docs]
def getVarCovar(self):
"""Obtain the Rao-Cramer variance covariance matrix as a
Pandas data frame.
:return: Rao-Cramer variance covariance matrix
:rtype: pandas.DataFrame
"""
names = [b.name for b in self.data.betas]
vc = pd.DataFrame(index=names, columns=names)
for i, betai in enumerate(self.data.betas):
for j, betaj in enumerate(self.data.betas):
vc.at[betai.name, betaj.name] = self.data.varCovar[i, j]
return vc
[docs]
def getRobustVarCovar(self):
"""Obtain the robust variance covariance matrix as a Pandas data frame.
:return: robust variance covariance matrix
:rtype: pandas.DataFrame
"""
names = [b.name for b in self.data.betas]
vc = pd.DataFrame(index=names, columns=names)
for i, betai in enumerate(self.data.betas):
for j, betaj in enumerate(self.data.betas):
vc.at[betai.name, betaj.name] = self.data.robust_varCovar[i, j]
return vc
[docs]
def getBootstrapVarCovar(self):
"""Obtain the bootstrap variance covariance matrix as
a Pandas data frame.
:return: bootstrap variance covariance matrix, or None if not available
:rtype: pandas.DataFrame
"""
if self.data.bootstrap is None:
return None
names = [b.name for b in self.data.betas]
vc = pd.DataFrame(index=names, columns=names)
for i, betai in enumerate(self.data.betas):
for j, betaj in enumerate(self.data.betas):
vc.at[betai.name, betaj.name] = self.data.bootstrap_varCovar[i, j]
return vc
[docs]
def writeHtml(self, onlyRobust=True):
"""Write the results in an HTML file."""
self.data.htmlFileName = bf.get_new_file_name(self.data.modelName, 'html')
with open(self.data.htmlFileName, 'w', encoding='utf-8') as f:
f.write(self.getHtml(onlyRobust))
logger.info(f'Results saved in file {self.data.htmlFileName}')
[docs]
def writeLaTeX(self):
"""Write the results in a LaTeX file."""
self.data.latexFileName = bf.get_new_file_name(self.data.modelName, 'tex')
with open(self.data.latexFileName, 'w', encoding='utf-8') as f:
f.write(self.getLaTeX())
logger.info(f'Results saved in file {self.data.latexFileName}')
def _getHtmlHeader(self):
"""Prepare the header for the HTML file, containing comments and the
version of Biogeme.
:return: string containing the header.
:rtype: str
"""
html = ''
html += '<html>\n'
html += '<head>\n'
html += (
'<script src="http://transp-or.epfl.ch/biogeme/sorttable.js">' '</script>\n'
)
html += (
'<meta http-equiv="Content-Type" content="text/html; ' 'charset=utf-8" />\n'
)
html += (
f'<title>{self.data.modelName} - Report from '
f'biogeme {bv.getVersion()} '
f'[{bv.versionDate}]</title>\n'
)
html += (
'<meta name="keywords" content="biogeme, discrete choice, '
'random utility">\n'
)
html += (
f'<meta name="description" content="Report from '
f'biogeme {bv.getVersion()} '
f'[{bv.versionDate}]">\n'
)
html += '<meta name="author" content="{bv.author}">\n'
html += '<style type=text/css>\n'
html += '.biostyle\n'
html += ' {font-size:10.0pt;\n'
html += ' font-weight:400;\n'
html += ' font-style:normal;\n'
html += ' font-family:Courier;}\n'
html += '.boundstyle\n'
html += ' {font-size:10.0pt;\n'
html += ' font-weight:400;\n'
html += ' font-style:normal;\n'
html += ' font-family:Courier;\n'
html += ' color:red}\n'
html += '</style>\n'
html += '</head>\n'
html += '<body bgcolor="#ffffff">\n'
return html
[docs]
def getBetasForSensitivityAnalysis(self, myBetas, size=100, useBootstrap=True):
"""Generate draws from the distribution of the estimates, for
sensitivity analysis.
:param myBetas: names of the parameters for which draws are requested.
:type myBetas: list(string)
:param size: number of draws. If useBootstrap is True, the value is
ignored and a warning is issued. Default: 100.
:type size: int
:param useBootstrap: if True, the bootstrap estimates are
directly used. The advantage is that it does not reyl on the
assumption that the estimates follow a normal
distribution. Default: True.
:type useBootstrap: bool
:raise biogeme.exceptions.BiogemeError: if useBootstrap is True and
the bootstrap results are not available
:return: list of dict. Each dict has a many entries as parameters.
The list has as many entries as draws.
:rtype: list(dict)
"""
if useBootstrap and self.data.bootstrap is None:
err = (
'Bootstrap results are not available for simulation. '
'Use useBootstrap=False.'
)
raise excep.BiogemeError(err)
index = [self.data.betaNames.index(b) for b in myBetas]
if useBootstrap:
results = [
{myBetas[i]: value for i, value in enumerate(row)}
for row in self.data.bootstrap[:, index]
]
return results
theMatrix = (
self.data.bootstrap_varCovar if useBootstrap else self.data.robust_varCovar
)
simulatedBetas = np.random.multivariate_normal(
self.data.betaValues, theMatrix, size
)
index = [self.data.betaNames.index(b) for b in myBetas]
results = [
{myBetas[i]: value for i, value in enumerate(row)}
for row in simulatedBetas[:, index]
]
return results
[docs]
def getF12(self, robustStdErr=True):
"""F12 is a format used by the software ALOGIT to
report estimation results.
:param robustStdErr: if True, the robust standard errors are reports.
If False, the Rao-Cramer are.
:type robustStdErr: bool
:return: results in F12 format
:rtype: string
"""
# checkline1 = (
# '0000000001111111111222222222233333333334444444444'
# '5555555555666666666677777777778'
# )
# checkline2 = (
# '1234567890123456789012345678901234567890123456789'
# '0123456789012345678901234567890'
# )
results = ''
# results += f'{checkline1}\n'
# results += f'{checkline2}\n'
# Line 1, title, characters 1-79
results += f'{self.data.modelName[:79]: >79}\n'
# Line 2, subtitle, characters 1-27, and time-date, characters 57-77
t = f'From biogeme {bv.getVersion()}'
d = f'{datetime.datetime.now()}'[:19]
results += f'{t[:27]: <56}{d: <21}\n'
# Line 3, "END" (this is historical!)
results += 'END\n'
# results += f'{checkline1}\n'
# results += f'{checkline2}\n'
# Line 4-(K+3), coefficient values
# characters 1-4, " 0" (again historical)
# characters 6-15, coefficient label, suggest using first 10
# characters of label in R
# characters 16-17, " F" (this indicates whether or not the
# coefficient is constrained)
# characters 19-38, coefficient value 20 chars
# characters 39-58, standard error 20 chars
mystats = self.getGeneralStatistics()
table = self.getEstimatedParameters(onlyRobust=False)
coefNames = list(table.index.values)
for name in coefNames:
values = table.loc[name]
results += ' 0 '
results += f'{name[:10]: >10}'
if 'Active bound' in values:
if values['Active bound'] == 1:
results += ' T'
else:
results += ' F'
else:
results += ' F'
results += ' '
results += f' {values["Value"]: >+19.12e}'
if robustStdErr:
results += f' {values["Rob. Std err"]: >+19.12e}'
else:
results += f' {values["Std err"]: >+19.12e}'
results += '\n'
# Line K+4, " -1" indicates end of coefficients
results += ' -1\n'
# results += f'{checkline1}\n'
# results += f'{checkline2}\n'
# Line K+5, statistics about run
# characters 1-8, number of observations 8 chars
# characters 9-27, likelihood-with-constants 19 chars
# characters 28-47, null likelihood 20 chars
# characters 48-67, final likelihood 20 chars
results += f'{mystats["Sample size"][0]: >8}'
# The cte log likelihood is not available. We put 0 instead.
results += f' {0: >18}'
if self.data.nullLogLike is not None:
results += f' {mystats["Null log likelihood"][0]: >+19.12e}'
else:
results += f' {0: >19}'
results += f' {mystats["Final log likelihood"][0]: >+19.12e}'
results += '\n'
# results += f'{checkline1}\n'
# results += f'{checkline2}\n'
# Line K+6, more statistics
# characters 1-4, number of iterations (suggest use 0) 4 chars
# characters 5-8, error code (please use 0) 4 chars
# characters 9-29, time and date (sugg. repeat from line 2) 21 chars
if "Number of iterations" in mystats:
results += f'{mystats["Number of iterations"][0]: >4}'
else:
results += f'{0: >4}'
results += f'{0: >4}'
results += f'{d: >21}'
results += '\n'
# results += f'{checkline1}\n'
# results += f'{checkline2}\n'
# Lines (K+7)-however many we need, correlations*100000
# 10 per line, fields of width 7
# The order of these is that correlation i,j (i>j) is in position
# (i-1)*(i-2)/2+j, i.e.
# (2,1) (3,1) (3,2) (4,1) etc.
count = 0
for i, coefi in enumerate(coefNames):
for j in range(0, i):
name = (coefi, coefNames[j])
if robustStdErr:
try:
corr = int(100000 * self.data.secondOrderTable[name][5])
except OverflowError:
corr = 999999
else:
try:
corr = int(100000 * self.data.secondOrderTable[name][1])
except OverflowError:
corr = 999999
results += f'{corr:7d}'
count += 1
if count % 10 == 0:
results += '\n'
results += '\n'
return results
[docs]
def writeF12(self, robustStdErr=True):
"""Write the results in F12 file."""
self.data.F12FileName = bf.get_new_file_name(self.data.modelName, 'F12')
with open(self.data.F12FileName, 'w', encoding='utf-8') as f:
f.write(self.getF12(robustStdErr))
logger.info(f'Results saved in file {self.data.F12FileName}')
[docs]
def likelihood_ratio_test(self, other_model, significance_level=0.05):
"""This function performs a likelihood ratio test between a restricted
and an unrestricted model. The "self" model can be either the
restricted or the unrestricted.
:param other_model: other model to perform the test.
:type other_model: biogeme.results.bioResults
:param significance_level: level of significance of the
test. Default: 0.05
:type significance_level: float
:return: a tuple containing:
- a message with the outcome of the test
- the statistic, that is minus two times the difference
between the loglikelihood of the two models
- the threshold of the chi square distribution.
:rtype: LRTuple(str, float, float)
"""
LR = self.data.logLike
LU = other_model.data.logLike
KR = self.data.nparam
KU = other_model.data.nparam
return tools.likelihood_ratio_test((LU, KU), (LR, KR), significance_level)
[docs]
def compileEstimationResults(
dict_of_results,
statistics=(
'Number of estimated parameters',
'Sample size',
'Final log likelihood',
'Akaike Information Criterion',
'Bayesian Information Criterion',
),
include_parameter_estimates=True,
include_robust_stderr=False,
include_robust_ttest=True,
formatted=True,
use_short_names=False,
):
"""Compile estimation results into a common table. Old syntax."""
logger.warning(
'The syntax "compileEstimationResults" is deprecated and is replaced by '
'the syntax "compile_estimation_results".'
)
return compile_estimation_results(
dict_of_results,
statistics,
include_parameter_estimates,
include_robust_stderr,
include_robust_ttest,
formatted,
use_short_names,
)
[docs]
def compile_estimation_results(
dict_of_results,
statistics=(
'Number of estimated parameters',
'Sample size',
'Final log likelihood',
'Akaike Information Criterion',
'Bayesian Information Criterion',
),
include_parameter_estimates=True,
include_robust_stderr=False,
include_robust_ttest=True,
formatted=True,
use_short_names=False,
):
"""Compile estimation results into a common table
:param dict_of_results: dict of results, containing
for each model the name, the ID and the results, or ther name
of the pickle file containing them.
:type dict_of_results: dict(str: bioResults)
:param statistics: list of statistics to include in the summary
table
:type statistics: tuple(str)
:param include_parameter_estimates: if True, the parameter
estimates are included.
:type include_parameter_estimates: bool
:param include_robust_stderr: if True, the robust standard errors
of the parameters are included.
:type include_robust_stderr: bool
:param include_robust_ttest: if True, the t-test
of the parameters are included.
:type include_robust_ttest: bool
:param formatted: if True, a formatted string in included in the
table results. If False, the numerical values are stored. Use
"True" if you need to print the results. Use "False" if you
need to use them for further calculation.
:type formatted: bool
:param use_short_names: if True, short names, such as Model_1,
Model_2, are used to identify the model. It is nicer on for the
reporting.
:type use_short_names: bool
:return: pandas dataframe with the requested results, and the
specification of each model
:rtype: tuple(pandas.DataFrame, dict(str:dict(str:str)))
"""
model_names = tools.ModelNames()
def the_name(col):
if use_short_names:
return model_names(col)
return col
columns = [the_name(k) for k in dict_of_results.keys()]
df = pd.DataFrame(columns=columns)
configurations = {the_name(col): col for col in dict_of_results.keys()}
for model, res in dict_of_results.items():
if use_short_names:
col = model_names(model)
else:
col = model
if not isinstance(res, bioResults):
try:
res = bioResults(pickleFile=res)
except Exception:
warning = f'Impossible to access result file {res}'
logger.warning(warning)
res = None
if res is not None:
stats_results = res.getGeneralStatistics()
for s in statistics:
df.loc[s, col] = stats_results[s][0]
if include_parameter_estimates:
if formatted:
for b in res.data.betas:
std = (
(
f'({b.robust_stdErr:.3g})'
if include_robust_stderr
else ''
)
if b.robust_stdErr is not None
else '(???)'
)
ttest = (
(f'({b.robust_tTest:.3g})' if include_robust_ttest else '')
if b.robust_tTest is not None
else '(???)'
)
the_value = f'{b.value:.3g} {std} {ttest}'
row_std = ' (std)' if include_robust_stderr else ''
row_ttest = ' (t-test)' if include_robust_ttest else ''
row_title = f'{b.name}{row_std}{row_ttest}'
df.loc[row_title, col] = the_value
else:
for b in res.data.betas:
df.loc[b.name, col] = b.value
if include_robust_stderr:
df.loc[f'{b.name} (std)', col] = b.value
if include_robust_ttest:
df.loc[f'{b.name} (ttest)', col] = b.value
return df.fillna(''), configurations
[docs]
def compile_results_in_directory(
statistics=(
'Number of estimated parameters',
'Sample size',
'Final log likelihood',
'Akaike Information Criterion',
'Bayesian Information Criterion',
),
include_parameter_estimates=True,
include_robust_stderr=False,
include_robust_ttest=True,
formatted=True,
):
"""Compile estimation results found in the local directory into a
common table. The results are supposed to be in a file with
pickle extension.
:param statistics: list of statistics to include in the summary
table
:type statistics: tuple(str)
:param include_parameter_estimates: if True, the parameter
estimates are included.
:type include_parameter_estimates: bool
:param include_robust_stderr: if True, the robust standard errors
of the parameters are included.
:type include_robust_stderr: bool
:param include_robust_ttest: if True, the t-test
of the parameters are included.
:type include_robust_ttest: bool
:param formatted: if True, a formatted string in included in the
table results. If False, the numerical values are stored. Use
"True" if you need to print the results. Use "False" if you
need to use them for further calculation.
:type formatted: bool
:return: pandas dataframe with the requested results, or None if
no file was found.
:rtype: pandas.DataFrame
"""
files = glob.glob('*.pickle')
if not files:
logger.warning(f'No .pickle file found in {os.getcwd()}')
return None
the_dict = {k: k for k in files}
return compileEstimationResults(
the_dict,
statistics,
include_parameter_estimates,
include_robust_stderr,
include_robust_ttest,
formatted,
)
[docs]
def pareto_optimal(dict_of_results, a_pareto=None):
"""Identifies the non dominated models, with respect to maximum
log likelihood and minimum number of parameters
:param dict_of_results: dict of results associated with their config ID
:type dict_of_results: dict(str:bioResults)
:param a_pareto: if not None, Pareto set where the results will be inserted.
:type a_pareto: biogeme.pareto.Pareto
:return: a dict of named results with pareto optimal results
:rtype: dict(str: biogeme.results.bioResult)
"""
if a_pareto is None:
the_pareto = pareto.Pareto()
else:
the_pareto = a_pareto
for config_id, res in dict_of_results.items():
the_element = pareto.SetElement(
element_id=config_id, objectives=[-res.data.logLike, res.data.nparam]
)
the_pareto.add(the_element)
selected_results = {
element.element_id: dict_of_results[element.element_id]
for element in the_pareto.pareto
}
the_pareto.dump()
return selected_results