PosteriorErrorProbabilityModel#
- class pyopenms.PosteriorErrorProbabilityModel#
Bases:
object
Cython implementation of _PosteriorErrorProbabilityModel
- Original C++ documentation is available here
– Inherits from [‘DefaultParamHandler’]
- __init__(self) None #
Methods
__init__
(self)computeLogLikelihood
(self, ...)Computes the Maximum Likelihood with a log-likelihood function
computeProbability
(self, score)Returns the computed posterior error probability for a given score
fillDensities
(self, x_scores, ...)Writes the distributions densities into the two vectors for a set of scores.
fillLogDensities
(self, x_scores, ...)Writes the log distributions densities into the two vectors for a set of scores.
Overload:
getBothGnuplotFormula
(self, incorrect, correct)Returns the gnuplot formula of the fitted mixture distribution
Returns estimated parameters for correctly assigned sequences.
getDefaults
(self)Returns the default parameters
getGaussGnuplotFormula
(self, params)Returns the gnuplot formula of the fitted gauss distribution
getGumbelGnuplotFormula
(self, params)Returns the gnuplot formula of the fitted gumbel distribution
Returns estimated parameters for correctly assigned sequences.
getName
(self)Returns the name
getNegativePrior
(self)Returns the estimated negative prior probability
getParameters
(self)Returns the parameters
getSmallestScore
(self)Returns the smallest score used in the last fit
getSubsections
(self)initPlots
(self, x_scores)Initializes the plots
plotTargetDecoyEstimation
(self, target, decoy)Plots the estimated distribution against target and decoy hits
pos_neg_mean_weighted_posteriors
(self, ...)setName
(self, in_0)Sets the name
setParameters
(self, param)Sets the parameters
tryGnuplot
(self, gp_file)- computeLogLikelihood(self, incorrect_density: List[float], correct_density: List[float]) float #
Computes the Maximum Likelihood with a log-likelihood function
- computeProbability(self, score: float) float #
Returns the computed posterior error probability for a given score
- fillDensities(self, x_scores: List[float], incorrect_density: List[float], correct_density: List[float]) None #
Writes the distributions densities into the two vectors for a set of scores. Incorrect_densities represent the incorrectly assigned sequences
- fillLogDensities(self, x_scores: List[float], incorrect_density: List[float], correct_density: List[float]) None #
Writes the log distributions densities into the two vectors for a set of scores. Incorrect_densities represent the incorrectly assigned sequences
- fit()#
Overload:
- fit(self, search_engine_scores: List[float], outlier_handling: bytes | str | String) bool
Fits the distributions to the data points(search_engine_scores). Estimated parameters for the distributions are saved in member variables computeProbability can be used afterwards Uses two Gaussians to fit. And Gauss+Gauss or Gumbel+Gauss to plot and calculate final probabilities
- Parameters:
search_engine_scores – A vector which holds the data points
- Returns:
true if algorithm has run through. Else false will be returned. In that case no plot and no probabilities are calculated
Overload:
- fit(self, search_engine_scores: List[float], probabilities: List[float], outlier_handling: bytes | str | String) bool
Fits the distributions to the data points(search_engine_scores). Estimated parameters for the distributions are saved in member variables computeProbability can be used afterwards Uses two Gaussians to fit. And Gauss+Gauss or Gumbel+Gauss to plot and calculate final probabilities
- Parameters:
search_engine_scores – A vector which holds the data points
probabilities – A vector which holds the probability for each data point after running this function. If it has some content it will be overwritten
- Returns:
true if algorithm has run through. Else false will be returned. In that case no plot and no probabilities are calculated
- getBothGnuplotFormula(self, incorrect: GaussFitResult, correct: GaussFitResult) bytes | str | String #
Returns the gnuplot formula of the fitted mixture distribution
- getCorrectlyAssignedFitResult(self) GaussFitResult #
Returns estimated parameters for correctly assigned sequences. Fit should be used before
- getGaussGnuplotFormula(self, params: GaussFitResult) bytes | str | String #
Returns the gnuplot formula of the fitted gauss distribution
- getGumbelGnuplotFormula(self, params: GaussFitResult) bytes | str | String #
Returns the gnuplot formula of the fitted gumbel distribution
- getIncorrectlyAssignedFitResult(self) GaussFitResult #
Returns estimated parameters for correctly assigned sequences. Fit should be used before
- getNegativePrior(self) float #
Returns the estimated negative prior probability
- getSmallestScore(self) float #
Returns the smallest score used in the last fit
- getSubsections(self) List[bytes] #
- plotTargetDecoyEstimation(self, target: List[float], decoy: List[float]) None #
Plots the estimated distribution against target and decoy hits
- pos_neg_mean_weighted_posteriors(self, x_scores: List[float], incorrect_posteriors: List[float]) List[float, float] #