diff --git a/CHANGELOG.md b/CHANGELOG.md index caabc37..3b55282 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,10 +1,27 @@ # Changelog -## Master version (15/3/2016) + +## ANNZ v2.2.0 (24/5/2016) + +- Added a bias correction procedure for MLMs, which may be switched off using `glob.annz["doBiasCorMLM"] = False`. (See `README.md` and `scripts/annz_rndReg_advanced.py` for details.) + +- Added the option to generate error estimations (using the KNN method) for a general input dataset. An example script is provided as `scripts/annz_rndReg_knnErr.py`. (A detailed description is given in `README.md`.) + +- Added the `userWeights_metricPlots` job option, which can be used to set weight expressions for the performance plots of regression. (See `README.md` for details.) + +- Changed the binning scheme for the performance plots of auxiliary variables (defined using `glob.annz["addOutputVars"]`). Instead of equal-width bins, the plots now include bins which are defined as each having the same number of objects (equal-quantile binning). This e.g., reduces statistical fluctuations in computations of the bias, scatter and other parameters, as a function of the variables used for the training. + +- Changed the default number of training cycles for ANNs from `5000` to a (more reasonable) randomized choice in the range `[500,2000]` (`ANNZ::generateOptsMLM()`). The option may be set to any other value by the user, using the `NCycles` setting. E.g., during training, set: `glob.annz["userMLMopts"] = "ANNZ_MLM=ANN::HiddenLayers=N,N+3:NCycles=3500"`. - Fixed minor bug in `ANNZ::Train_binnedCls()`, which caused a mismatch of job-options for some configuration of binned classification. -## ANNZ 2.1.2 (15/3/2016) +- Added a version-tag to all intermediate option files, with a format as e.g., `[versionTag]=ANNZ_2.1.3`. + +- Minor change to the selection criteria for `ANNZ_best` in randomized regression. + +- Other minor modifications and bug fixes. + +## ANNZ v2.1.2 (15/3/2016) - Improved selection criteria for `ANNZ_best` in randomized regression. The optimization is now based on `glob.annz["optimCondReg"]="sig68"` or `"bias"`. (The `"fracSig68"` option is deprecated.) @@ -60,7 +77,7 @@ glob.annz["addMaxPDF"] = True - Other minor modifications and bug fixes. -## ANNZ 2.1.1 (15/1/2016) +## ANNZ v2.1.1 (15/1/2016) - Fixed bug in generating a name for an internal `TF1` function in `ANNZ::setupKdTreeKNN()`. @@ -98,7 +115,7 @@ glob.annz["addMaxPDF"] = True - Other minor modifications. -## ANNZ 2.1.0 (08/10/2015) +## ANNZ v2.1.0 (08/10/2015) - Removed unnecessary dictionary generation from Makefile. @@ -108,14 +125,14 @@ glob.annz["addMaxPDF"] = True - Added the option to use an entire input file as signal or background for single/randomized classification, in addition to (or instead of) defining a cut based on one of the input parameters. In order to use this option, one muse define the variables `inpFiles_sig` and `inpFiles_bck`. An example is given in `scripts/annz_rndCls_advanced.py`. -- Added a bias-correction for randomized regression PDFs. This options is now active by default, and may be turned off by setting, +- Added a bias correction for randomized regression PDFs. This options is now active by default, and may be turned off by setting, ```python glob.annz["doBiasCorPDF"] = False ``` - Other minor modifications. -## ANNZ 2.0.6 (03/8/2015) +## ANNZ v2.0.6 (03/8/2015) - Did some code optimization for tree-looping operations. @@ -139,7 +156,7 @@ Similarly, added the same transformations for the kd-tree during the `glob.annz[ - Other minor modifications. -## ANNZ 2.0.5 (17/6/2015) +## ANNZ v2.0.5 (17/6/2015) - Fixed bug in `CatFormat::addWgtKNNtoTree()`, where the weight expression for the KNN trees did not include the `ANNZ_KNN_w` weight in cases of `glob.annz["addInTrainFlag"] = True`. @@ -147,13 +164,13 @@ Similarly, added the same transformations for the kd-tree during the `glob.annz[ - Other minor modifications. -## ANNZ 2.0.4 (19/3/2015) +## ANNZ v2.0.4 (19/3/2015) - **Modified the function, `CatFormat::addWgtKNNtoTree()`, and added `CatFormat::asciiToFullTree_wgtKNN()`:** The purpose of the new features is to add an output variable, denoted as `inTrainFlag` to the output of evaluation. The new output indicates if the corresponding object is "compatible" with other objects from the training dataset. The compatibility is estimated by comparing the density of objects in the training dataset in the vicinity of the evaluated object. If the evaluated object belongs to an area of parameter-space which is not represented in the training dataset, we will get `inTrainFlag = 0`. In this case, the output of the training is probably unreliable. - Other minor modifications. -## ANNZ 2.0.3 (25/2/2015) +## ANNZ v2.0.3 (25/2/2015) - **Added *MultiClass* support to binned classification:** The new option is controlled by setting the `doMultiCls` flag. In this mode, multiple background samples can be trained simultaneously against the signal. In the context of binned classification, this means that each classification bin acts as an independent sample during the training. @@ -170,11 +187,11 @@ Similarly, added the same transformations for the kd-tree during the `glob.annz[ - Other minor modifications. -## ANNZ 2.0.2 (10/2/2015) +## ANNZ v2.0.2 (10/2/2015) Fixed bug in VarMaps::storeTreeToAscii(), where variables of type `Long64_t` were treated as `Bool_t` by mistake, causing a crash. -## ANNZ 2.0.1 (10/2/2015) +## ANNZ v2.0.1 (10/2/2015) The following changes were made: @@ -187,6 +204,6 @@ Added the variable `evalDirPostfix`, which allows to modify the name of the eval - Various small modifications. -## ANNZ 2.0.0 (26/1/2015) +## ANNZ v2.0.0 (26/1/2015) First version (v2.0.0) of the new implementation of the machine learning code, ANNz. diff --git a/README.md b/README.md index b47a05f..4571ab8 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# ANNZ 2.1.2 +# ANNZ v2.2.0 ## Introduction ANNZ uses both regression and classification techniques for estimation of single-value photo-z (or any regression problem) solutions and PDFs. In addition it is suitable for classification problems, such as star/galaxy classification. @@ -346,6 +346,34 @@ Signal and background objects may be defined by using at least one pair of varia glob.annz["inpFiles_bck"] = "sgCatalogue_star_0.txt" ``` +#### Bias correction for regression + +There are two bias correction options for regression, one for MLMs (applied during the training stage) and one for PDFs (applied during the optimization/verification phase). The former may be used for single/randomized regression, and the latter for single/randomized regression and for binned classification. +The MLM correction may be turned on/off by setting `glob.annz["doBiasCorMLM"]`, while the PDF correction my be used with `glob.annz["doBiasCorPDF"]`. An example for the relevant job options is given in `scripts/annz_rndReg_advanced.py`. + +##### MLM bias correction + +Following training of the *nominal MLM*, a second one is trained, the *bias-MLM*. +For the following, let's assume we train a single MLM, using `python scripts/annz_singleReg_quick.py --singleRegression --train` or using `python scripts/annz_rndReg_quick.py --randomRegression --train --trainIndex=0`. The output of this is a regression estimator, designated `ANNZ_0`. + +The settings of the bias-MLM are defined using the `biasCorMLMopt` parameter, which uses the same syntax as `userMLMopts`. +The inputs for the bias-MLM are the same as for the nominal, where it is also possible to add the output of the nominal MLM as an input. For instance, if the nominal MLM is trained with `glob.annz["inputVariables"] = "MAG_U;MAG_G;MAG_R"`, then the bias-MLM can be trained with either `MAG_U;MAG_G;MAG_R` or `MAG_U;MAG_G;MAG_R;ANNZ_0` as inputs. +The regression target of the bias-MLM is the difference between the target value and the regression estimator of the nominal MLM. For our example, the target of the nominal may be `glob.annz["zTrg"] = "Z"`. In this case, the regression target for training for the bias-MLM would be `(ANNZ_0 - Z)`. + +The bias correction is used by evaluating for each object the difference between the nominal MLM estimator, and the estimator of the bias correction MLM. That is, for each object we will have `de-biased estimator := MLM(nominal) - MLM(bias)`. This is applied internally in the code. The user therefore only gets the de-biased estimator as an output - this output replaces the nominal MLM solution, and has the same designation. For our example, the designation `ANNZ_0` for the output of the regression will therefore refer to the de-biased estimator. + +The MLM bias correction must be enabled during training. If using randomized regression, it is possible to use it for only a subset of the trained MLMs, by changing the value of `glob.annz["doBiasCorMLM"]` in the training loop. One should use simple MLM structures to avoid slowing down training and evaluation. For instance: `glob.annz["biasCorMLMopt"] = "ANNZ_MLM=BDT:NTrees=50"` or `glob.annz["biasCorMLMopt"] = "ANNZ_MLM=ANN::HiddenLayers=N,N+5:VarTransform=N,P:TrainingMethod=BFGS:NCycles=500:UseRegulator=True"` are reasonable baseline choices, which will not result in significant overhead - BDTs are recommended, as they are usually faster to train. + +Following derivation of the bias-MLM, a simple Kolmogorov-Smirnov test is performed, comparing the performance of the nominal MLM with the de-biased MLM. If the bias correction procedure resulted in degraded performance, it is rejected; the nominal MLM is then used as the estimator instead. + + +##### PDF bias correction + +The bias correction for PDFs may be chained after the MLM bias correction, or just used on it's own. The PDF correction is computed as part of the optimization/verification phase, and does not need to be defined during training. + +The correction is applied as a simplified unfolding of the regression solution with regards to the target. This is done by calculating the correlation for each bin of the PDF, between the target value and the position of the bin. The correlation is computed for each object in the training sample, so that the average per-bin correlation may be derived for the entire training sample. The correction is applied on the evaluated PDF. The is done by re-weighting each bin of the PDF according to the average estimated value of the regression target. The procedure is equivalent to multiplying the PDF (bin-by-bin) by the relation between the derived regression estimator (photo-z) and the target value (true redshift), as derived using the training sample. + + #### Running on a batch farm It is advisable to run ANNZ on a batch farm, especially during the training phase. An example of how this may be done is given in `scripts/annz_qsub.py`. Please note that this only serves as a guideline, and should probably be customized for a particular cluster. @@ -358,6 +386,49 @@ python scripts/annz_fits_quick.py --fitsToAscii python scripts/annz_fits_quick.py --asciiToFits ``` +#### Independent derivation of KNN error estimates + +There is the option to generate error estimation (using the KNN method) for a general input dataset. +The evaluated dataset does not need to be created using `ANNZ`. That is, one can estimate photo-zs (or any other type of regression problem) using external software. `ANNZ` can then be used to derive errors, so long as one provides the corresponding reference dataset. + +Some specifics: + + - The reference dataset must include the true value of the evaluated object, as well as a list of variables which are used for the KNN-search. + + - The errors are computed by finding the near neighbours of the evaluated object. We compute + the distribution of errors, defined for a given object as `ERR == (eval_value - true_value)`. + + - The final error is derived as the width of the distribution of `ERR`. `ANNZ` provides both a symmetric error, and error estimates in the negative and in the positive directions (towards lower and higher values of the regression variable). + +See `scripts/annz_rndReg_knnErr.py` for a complete example script. For illustration, the following job-options: + + - We define the evaluated variable and it's respective true value as e.g., + ```python + glob.annz["zReg_onlyKnnErr"] = "myPhotoZ" + glob.annz["zTrg"] = "Z" + ``` + where in this case, `myPhotoZ` is a photo-z estimator and `Z` is the true redshift. + + - The reference dataset may include the following variables: + ```python + glob.annz["inAsciiVars"] = "D:Z;F:myPhotoZ;F:MAG_U;F:MAG_G;F:MAG_R;F:MAG_I;F:MAG_Z" + ``` + where the rest of the variables, besides `myPhotoZ` and `Z`, are auxiliary variables. + + - The KNN errors will be conducted using e.g., + ```python + glob.annz["knnVars_onlyKnnErr"] = "MAG_U;MAG_G;(MAG_G-MAG_R);(MAG_R-MAG_I);(MAG_I-MAG_Z)" + ``` + or any other functional combinations of the auxiliary variables. + + - An evaluated dataset, for which we derive the KNN errors, should then, in this example, include the following parameters: + ```python + glob.annz["inAsciiVars"] = "F:MAG_U;F:MAG_G;F:MAG_R;F:MAG_I;F:MAG_Z" + ``` + That is, at the very least, the evaluated dataset must include all of the parameters listed in `glob.annz["knnVars_onlyKnnErr"]`, but it does not need to include either `myPhotoZ` or `Z`. (It is also allowed to include here other variables which are not used at all; these can then be copied over to the output catalogue using `glob.annz["addOutputVars"]`.) + + - The output catalogue will be stored in this example at `./output/test_knnErr/onlyKnnErr/eval/ANNZ_onlyKnnErr_0000.csv`. It will contain the variables, `F:myPhotoZ_err;F:myPhotoZ_errN;F:myPhotoZ_errP`, as well as any requested observer variables from the original dataset. Here `myPhotoZ_err` is the symmetric error estimated for `myPhotoZ`, and `myPhotoZ_errN`/`myPhotoZ_errP` the estimates for errors in the negative/positive directions, respectively. + ## The outputs of ANNZ @@ -428,8 +499,6 @@ glob.annz["addInTrainFlag"] = True - It is recommended to first generate a floating-point estimate of `inTrainFlag` and to study the distribution. One could e.g., decide to discard objects with a low value of `inTrainFlag`, based on how the bias or scatter increase as `inTrainFlag` decreases. Them, once a proper cut value for `maxRelRatioInRef_inTrain` is determined, `inTrainFlag` may be set to produce a binary decision. -If the regression target is detected as part of the evaluated dataset, performance plots are created as well, found e.g., at `output/test_randReg_quick/regres/eval/plots/`. This is useful if, for instance, one wants to easily check the performance on a dataset which was not used for training/testing. -The plots will also include the dependence of the performance on any added variable in the evaluated dataset. For instance, if one sets `glob.annz["addOutputVars"] = "MAG_Z"`, then the dependence of the bias, scatter etc. on `MAG_Z` wil be plotted. This is particularly useful for assessing the dependence of the performance on the `inTrainFlag` parameter, in order to decide on a cut value for the latter. ### Single regression @@ -460,6 +529,24 @@ It is possible to also generate an uncertainty estimator for the classification, glob.annz["addClsKNNerr"] = True ``` +### Performance plots for regression + +Performance plots are created during optimization/validation (possibly also during evaluation). + +The plots can take into account weights, if the `userWeights_metricPlots` is set. Numerical and logical expressions are allowed. As an example, let's assume we set the following during evaluation: +```python +glob.annz["userWeights_metricPlots"] = "((MAGERR_U <= 0.2) && (MAGERR_R < 1)) * ( (MAGERR_G<0.5)*6 + (MAGERR_G>=0.5)*1 )" +``` +The result would be that all objects with `(MAGERR_U > 0.2)` or `(MAGERR_R >= 1)` will not be included in the plots (zero weight), and that all objects with `(MAGERR_G<0.5)` will have 6 times the weight compared to objects with `(MAGERR_G>=0.5)`. +The weights thus derived will also be included in the output of the evaluation (i.e., as part of `output/test_singleReg_quick/regres/optim/eval/ANNZ_singleReg_0000.csv` for single regression), under the corresponding variable-weight name (`ANNZ_best_wgt` for single regression, or the other `*_wgt` variables for MLMs and PDFs). +The content of `userWeights_metricPlots` can only include mathematical expressions using the variables defined in `inAsciiVars` for the corresponding evaluated sample. In addition, one may use the `inTrainFlag` parameter as a weight (if the user chooses to generate it as part of the pipeline). + +For optimization and validation, the `userWeights_train` and/or `userWeights_valid` weight expressions are also included by default (assuming these were defined during training). In addition, the KNN-weights (if `useWgtKNN` was set) are also automatically taken into account. (The combination of all of these are used in addition to `userWeights_metricPlots`.) + +If the regression target is detected as part of the evaluated dataset, plots are created. These are found e.g., at `output/test_randReg_quick/regres/eval/plots/`. This is useful if, for instance, one wants to easily check the performance on a dataset which was not used for training/testing. + +The plots will also include the dependence of the performance on any added variable in the evaluated dataset. For instance, if one sets `glob.annz["addOutputVars"] = "MAG_Z"`, then the dependence of the bias, scatter etc. on `MAG_Z` will be plotted. This is particularly useful for assessing the dependence of the performance on the `inTrainFlag` parameter, in order to decide on a cut value for the latter. + ### Interpretation of estimator-weights The weights for the different estimators which were mentioned above (`ANNZ_8_wgt`, `ANNZ_best_wgt`, `ANNZ_MLM_avg_0_wgt` etc.) serve two purposes: @@ -481,6 +568,7 @@ A few notes: - The plots provided following optimization or verification all take into account the respective weights of the different estimators. + ## General comments - If ANNZ does not compile, check the definition of `$ROOTSYS` and `$LD_LIBRARY_PATH`. 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+21.317378997802734,0.32417500019073486,20.818862915039062,0.07150900363922119,19.27964210510254,0.02157299965620041,18.680456161499023,0.0150950001552701,18.267297744750977,0.015685999765992165 diff --git a/examples/scripts/annz_binCls_advanced.py b/examples/scripts/annz_binCls_advanced.py index 72dbd36..9d9e130 100644 --- a/examples/scripts/annz_binCls_advanced.py +++ b/examples/scripts/annz_binCls_advanced.py @@ -486,5 +486,5 @@ # run ANNZ with the current settings runANNZ() -log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished runing ANNZ !")) +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished running ANNZ !")) diff --git a/examples/scripts/annz_binCls_quick.py b/examples/scripts/annz_binCls_quick.py index f0d326a..7befa78 100644 --- a/examples/scripts/annz_binCls_quick.py +++ b/examples/scripts/annz_binCls_quick.py @@ -142,5 +142,5 @@ # run ANNZ with the current settings runANNZ() -log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished runing ANNZ !")) +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished running ANNZ !")) diff --git a/examples/scripts/annz_fits_quick.py b/examples/scripts/annz_fits_quick.py index 2fbb84b..86b972f 100644 --- a/examples/scripts/annz_fits_quick.py +++ b/examples/scripts/annz_fits_quick.py @@ -61,5 +61,5 @@ asciiToFits() -log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished runing ANNZ !")) +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished running ANNZ !")) diff --git a/examples/scripts/annz_rndCls_advanced.py b/examples/scripts/annz_rndCls_advanced.py index ef450b3..7750d1b 100644 --- a/examples/scripts/annz_rndCls_advanced.py +++ b/examples/scripts/annz_rndCls_advanced.py @@ -332,5 +332,5 @@ # run ANNZ with the current settings runANNZ() -log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished runing ANNZ !")) +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished running ANNZ !")) diff --git a/examples/scripts/annz_rndCls_quick.py b/examples/scripts/annz_rndCls_quick.py index 01fa406..475395b 100644 --- a/examples/scripts/annz_rndCls_quick.py +++ b/examples/scripts/annz_rndCls_quick.py @@ -117,5 +117,5 @@ # run ANNZ with the current settings runANNZ() -log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished runing ANNZ !")) +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished running ANNZ !")) diff --git a/examples/scripts/annz_rndReg_advanced.py b/examples/scripts/annz_rndReg_advanced.py index 6455794..9fd7b64 100644 --- a/examples/scripts/annz_rndReg_advanced.py +++ b/examples/scripts/annz_rndReg_advanced.py @@ -136,9 +136,17 @@ useWgtKNN = True if useWgtKNN: glob.annz["useWgtKNN"] = True - glob.annz["minNobjInVol_wgtKNN"] = 50 - glob.annz["inAsciiFiles_wgtKNN"] = "boss_dr10_colorCuts.csv" - glob.annz["inAsciiVars_wgtKNN"] = glob.annz["inAsciiVars"] + glob.annz["minNobjInVol_wgtKNN"] = 100 + # glob.annz["inAsciiFiles_wgtKNN"] = "boss_dr10_colorCuts.csv" + # glob.annz["inAsciiVars_wgtKNN"] = "F:MAG_U;F:MAGERR_U;F:MAG_G;F:MAGERR_G;F:MAG_R;F:MAGERR_R;F:MAG_I;F:MAGERR_I;F:MAG_Z;F:MAGERR_Z;D:Z" + glob.annz["inAsciiFiles_wgtKNN"] = "boss_dr10_0_large_magCut_noZ.csv" + glob.annz["inAsciiVars_wgtKNN"] = "F:MAG_U;F:MAGERR_U;F:MAG_G;F:MAGERR_G;F:MAG_R;F:MAGERR_R;F:MAG_I;F:MAGERR_I;F:MAG_Z;F:MAGERR_Z" + + # some random weird choice for [weightInp_wgtKNN, weightRef_wgtKNN] in this example, just to get different + # distributions for the input and reference samples, so that we have something to calculate weights for... + glob.annz["weightInp_wgtKNN"] = "1/pow(MAG_G*MAG_U*MAG_R*MAG_I, 5)" + glob.annz["weightRef_wgtKNN"] = "1/MAGERR_G" + glob.annz["weightVarNames_wgtKNN"] = "MAG_U;MAG_G;MAG_R;MAG_I;MAG_Z" # optional parameters (may leave empty as default value): @@ -147,6 +155,7 @@ glob.annz["outAsciiVars_wgtKNN"] = "MAG_U;MAG_G;MAGERR_U" # write out two additional variables to the output file glob.annz["weightRef_wgtKNN"] = "(MAGERR_R<0.7)*1 + (MAGERR_R>=0.7)/MAGERR_R" # down-weight objects with high MAGERR_R glob.annz["cutRef_wgtKNN"] = "MAGERR_U<200" # only use objects which have small MAGERR_U + glob.annz["doWidthRescale_wgtKNN"] = True # - trainTestTogether_wgtKNN # by default, the weights are computed for the entire sample [trainTestTogether_wgtKNN = True]. @@ -156,7 +165,7 @@ # with regards to [ref sample], and to separately get [test sample] with regards to [ref sample]. The latter # is only recommended if the training and testing samples have different inpput-variable distributions. glob.annz["trainTestTogether_wgtKNN"] = False - + # example for using a root file as input, instead of an ascii input: useRootInputFile = False if useRootInputFile: @@ -288,6 +297,37 @@ glob.annz["userCuts_train"] = glob.annz["userCuts_valid"] = "" glob.annz["userWeights_train"] = glob.annz["userWeights_valid"] = "" + # -------------------------------------------------------------------------------------------------- + # bias-correction procedure on MLMs - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # - doBiasCorMLM - whether or not to perform the correction for MLMs (during training) + # - biasCorMLMopt - MLM configuration options for the bias-correction for MLMs - simple structures are recommended !!! + # - can take the same format as userMLMopts (e.g., [biasCorMLMopt="ANNZ_MLM=BDT:VarTransform=N:NTrees=100"]) + # - can be empty (then the job options will be automatically generated, same as is setting [userMLMopts=""]) + # - can be set as [biasCorMLMopt="same"], then the same configuration options as for the nominal MLM + # for which the bias-correction is applied are used + # - simple MLMs are recommended, e.g.: + # - BDT with around 50-100 trees: + # "ANNZ_MLM=BDT:VarTransform=N:NTrees=100:BoostType=AdaBoost" + # - ANN with a simple layer structure, not too many NCycles etc.: + # "ANNZ_MLM=ANN::HiddenLayers=N,N+5:VarTransform=N,P:TrainingMethod=BFGS:TestRate=5:NCycles=500:UseRegulator=True" + # - biasCorMLMwithInp - add the nominal MLM as an input variable for the new MLM of the bias-correction (not necessary, as it + # may just add noise) + # - alwaysKeepBiasCor - whether or not to not check the KS-test and N_poiss metrics for improvement in order to + # possibly reject the bias correction (check performed if [alwaysKeepBiasCor] is set to True) + # - example use: + # ----------------------------------------------------------------------------------------------------------- + doBiasCorMLM = True + if doBiasCorMLM: + glob.annz["doBiasCorMLM"] = True + glob.annz["biasCorMLMwithInp"] = False + glob.annz["alwaysKeepBiasCor"] = False + # as an example, a couple of choices of MLM options (this shouldn't matter much though...) + if nMLMnow % 2 == 0: + glob.annz["biasCorMLMopt"] = "ANNZ_MLM=BDT:VarTransform=N:NTrees=50:BoostType=AdaBoost" + else: + glob.annz["biasCorMLMopt"] = "ANNZ_MLM=ANN:HiddenLayers=N+5:VarTransform=N,P:TrainingMethod=BFGS:NCycles=500:UseRegulator=True" + # run ANNZ with the current settings runANNZ() @@ -375,7 +415,7 @@ # -------------------------------------------------------------------------------------------------- # max_sigma68_PDF, max_bias_PDF, max_frac68_PDF - # - if max_sigma68_PDF,max_bias_PDF are positive, they put thresholds on the maximal value of + # - if max_sigma68_PDF, max_bias_PDF are positive, they put thresholds on the maximal value of # the scatter (max_sigma68_PDF), bias (max_bias_PDF) or outlier-fraction (max_frac68_PDF) of an # MLM which may be included in the PDF created in randomized regression # -------------------------------------------------------------------------------------------------- @@ -439,5 +479,5 @@ # run ANNZ with the current settings runANNZ() -log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished runing ANNZ !")) +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished running ANNZ !")) diff --git a/examples/scripts/annz_rndReg_knnErr.py b/examples/scripts/annz_rndReg_knnErr.py new file mode 100644 index 0000000..c2b191c --- /dev/null +++ b/examples/scripts/annz_rndReg_knnErr.py @@ -0,0 +1,117 @@ +# -------------------------------------------------------------------------------------------------- +# Use this script to generate knn-error estimates for a given observable (evaluated object). +# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +# - We use a reference dataset for which the true value of the evaluated object is know. +# - The errors are computed by finding the near neighbours of the evaluated object. We compute +# the distribution of errors, defined for a given object as [eval_value - true_value]. +# - The final error is then derived as the width of the distribution of errors. +# -------------------------------------------------------------------------------------------------- +# - The code is run in two stages (see details below): +# 1. genInputTrees: Here we do pre-processing of the reference dataset (done once). +# 2. evaluate: Here we evaluate the errors on a given dataset. This can be repeated +# on different samples without the need to repeat the [genInputTrees] stage. +# -------------------------------------------------------------------------------------------------- + +from helperFuncs import * + +# command line arguments and basic settings +# -------------------------------------------------------------------------------------------------- +init() + +# ================================================================================================== +# The main code - +# -------------------------------------------------------------------------------------------------- +# - run the following: +# python annz_rndReg_knnErr.py --onlyKnnErr --genInputTrees +# python annz_rndReg_knnErr.py --onlyKnnErr --evaluate +# -------------------------------------------------------------------------------------------------- +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - starting ANNZ")) + +if glob.annz["doOnlyKnnErr"]: + glob.annz["logLevel"] = "DEBUG_1" + + # outDirName - set output directory name + glob.annz["outDirName"] = "test_knnErr" + + # -------------------------------------------------------------------------------------------------- + # - zReg_onlyKnnErr, zTrg: + # The names of the evaluated object and of the true value of the latter. + # For instance, [zReg_onlyKnnErr] could be the photo-z of a galaxy, which we evaluate with some + # external code. [zTrg] would in this case be the corresponding true redshift of the galaxy. + # -------------------------------------------------------------------------------------------------- + # the name of the evaluated objects + glob.annz["zReg_onlyKnnErr"] = "myPhotoZ" + # the name of the true value of the evaluated objects + glob.annz["zTrg"] = "Z" + + # -------------------------------------------------------------------------------------------------- + # pre-processing of the input dataset + # -------------------------------------------------------------------------------------------------- + if glob.annz["doGenInputTrees"]: + # inDirName - directory in which input files are stored + glob.annz["inDirName"] = "examples/data/photoZ/knnErr" + + # inAsciiVars - list of parameter types and parameter names, corresponding to columns in the input + # file, e.g., [TYPE:NAME] may be [F:MAG_U], with 'F' standing for float. (see advanced example for detailed explanation) + glob.annz["inAsciiVars"] = "D:Z;F:myPhotoZ;F:MAG_U;F:MAG_G;F:MAG_R;F:MAG_I;F:MAG_Z" + # input dataset + glob.annz["inAsciiFiles"] = "boss_dr10_refForKnnErr.csv" + + # -------------------------------------------------------------------------------------------------- + # possibly also use a reference dataset to derive weights which will be used as part of the + # knn error estimation + # -------------------------------------------------------------------------------------------------- + # - If [useWgtKNN==True] use the reference dataset to derive weights + # - see scripts/annz_rndReg_weights.py for detailed job options for [useWgtKNN==True]. + # -------------------------------------------------------------------------------------------------- + useWgtKNN = False + if useWgtKNN: + glob.annz["useWgtKNN"] = True + glob.annz["inAsciiFiles_wgtKNN"] = "boss_dr10_refForReWeight.csv" + glob.annz["inAsciiVars_wgtKNN"] = "F:MAG_U;F:MAGERR_U;F:MAG_G;F:MAGERR_G;" + \ + "F:MAG_R;F:MAGERR_R;F:MAG_I;F:MAGERR_I;F:MAG_Z;F:MAGERR_Z" + glob.annz["weightInp_wgtKNN"] = "1/pow(MAG_G*MAG_U*MAG_R*MAG_I, 5)" + glob.annz["weightRef_wgtKNN"] = "1" + glob.annz["weightVarNames_wgtKNN"] = "MAG_U;MAG_G;MAG_R;MAG_I;MAG_Z" + + # run ANNZ with the current settings + runANNZ() + + + # -------------------------------------------------------------------------------------------------- + # - generate the knn-errors for a given dataset + # -------------------------------------------------------------------------------------------------- + if glob.annz["doEval"]: + # inDirName,inAsciiFiles - directory with files to make the calculations from, and list of input files + glob.annz["inDirName"] = "examples/data/photoZ/knnErr" + glob.annz["inAsciiFiles"] = "boss_dr10_evalForKnnErr.csv" + + # inAsciiVars - list of parameters in the input files (doesnt need to be exactly the same as + # in doGenInputTrees, but must contain all of the parameters which were used for training) + glob.annz["inAsciiVars"] = "F:MAG_U;F:MAG_G;F:MAG_R;F:MAG_I;F:MAG_Z" + + # the variables used for the knn-search (can be functional expressions containing any of the input variables) + # does not need to be the same observables which were used to derive the original [zReg_onlyKnnErr], though + # that would certainly be recommended + glob.annz["knnVars_onlyKnnErr"] = "MAG_U;MAG_G;(MAG_G-MAG_R);(MAG_R-MAG_I);(MAG_I-MAG_Z)" + + # set the number of near-neighbours used to compute the KNN error estimator + glob.annz["nErrKNN"] = 100 # should be around ~100 + + useMoreOpts = False + if useMoreOpts: + # optional cut on the reference sample for the knn-calculation + glob.annz["cuts_onlyKnnErr"] = "MAG_I<23" + # optional weights applied on the reference sample for the knn-calculation + glob.annz["weights_onlyKnnErr"] = "MAG_G" + # add parameters included in the evaluated dataset to the output + glob.annz["addOutputVars"] = "MAG_U;MAG_G;" + # add a postfix to the name of the output directory (so that we may e.g., evaluate + # multiple input datasets without overriding the directories) + glob.annz["evalDirPostfix"] = "_myPostfix" + + # run ANNZ with the current settings + runANNZ() + +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished running ANNZ !")) + diff --git a/examples/scripts/annz_rndReg_quick.py b/examples/scripts/annz_rndReg_quick.py index 41d4f86..a94e580 100644 --- a/examples/scripts/annz_rndReg_quick.py +++ b/examples/scripts/annz_rndReg_quick.py @@ -117,5 +117,5 @@ # run ANNZ with the current settings runANNZ() -log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished runing ANNZ !")) +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished running ANNZ !")) diff --git a/examples/scripts/annz_rndReg_weights.py b/examples/scripts/annz_rndReg_weights.py index 339108c..ad72a37 100644 --- a/examples/scripts/annz_rndReg_weights.py +++ b/examples/scripts/annz_rndReg_weights.py @@ -40,7 +40,7 @@ # -------------------------------------------------------------------------------------------------- log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - starting ANNZ")) -# glob.annz["logLevel"] = "DEBUG_2" +# glob.annz["logLevel"] = "DEBUG_2" # -------------------------------------------------------------------------------------------------- # general options which are the same for all stages @@ -113,14 +113,17 @@ # glob.annz["inAsciiVars_wgtKNN"] = "F:MAG_U;F:MAGERR_U;F:MAG_G;F:MAGERR_G;F:MAG_R;F:MAGERR_R;F:MAG_I;F:MAGERR_I;F:MAG_Z;F:MAGERR_Z;D:Z" glob.annz["inAsciiFiles_wgtKNN"] = glob.annz["inAsciiFiles"] glob.annz["inAsciiVars_wgtKNN"] = glob.annz["inAsciiVars"] + + # some random weird choice for [weightInp_wgtKNN, weightRef_wgtKNN] in this example, just to get different + # distributions for the input and reference samples, so that we have something to calculate weights for... glob.annz["weightInp_wgtKNN"] = "1/pow(MAG_G*MAG_U*MAG_R*MAG_I, 5)" glob.annz["weightRef_wgtKNN"] = "1/MAGERR_G" glob.annz["weightVarNames_wgtKNN"] = "MAG_U;MAG_G;MAG_R;MAG_I;MAG_Z" # optional parameters (may leave empty as default value): - glob.annz["sampleFracInp_wgtKNN"] = 0.85 # fraction of dataset to use (positive number, smaller or equal to 1) - glob.annz["sampleFracRef_wgtKNN"] = 0.90 # fraction of dataset to use (positive number, smaller or equal to 1) + glob.annz["sampleFracInp_wgtKNN"] = 0.385 # fraction of dataset to use (positive number, smaller or equal to 1) + glob.annz["sampleFracRef_wgtKNN"] = 0.390 # fraction of dataset to use (positive number, smaller or equal to 1) glob.annz["outAsciiVars_wgtKNN"] = "MAG_U;MAG_G;MAGERR_U" # write out two additional variables to the output file glob.annz["weightRef_wgtKNN"] = "(MAGERR_R<0.7)*1 + (MAGERR_R>=0.7)/MAGERR_R" # down-weight objects with high MAGERR_R glob.annz["cutRef_wgtKNN"] = "MAGERR_U<200" # only use objects which have small MAGERR_U @@ -174,7 +177,7 @@ # -------------------------------------------------------------------------------------------------- glob.annz["addInTrainFlag"] = True glob.annz["minNobjInVol_inTrain"] = 100 - glob.annz["maxRelRatioInRef_inTrain"] = 0.1 + glob.annz["maxRelRatioInRef_inTrain"] = -1 glob.annz["weightVarNames_inTrain"] = "MAG_U;MAG_G;MAG_R;MAG_I;MAG_Z" glob.annz["outAsciiVars_inTrain"] = "MAG_G;MAG_R" glob.annz["doWidthRescale_inTrain"] = True @@ -182,5 +185,5 @@ # run ANNZ with the current settings runANNZ() -log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished runing ANNZ !")) +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished running ANNZ !")) diff --git a/examples/scripts/annz_singleCls_quick.py b/examples/scripts/annz_singleCls_quick.py index 7dce872..2892fdd 100644 --- a/examples/scripts/annz_singleCls_quick.py +++ b/examples/scripts/annz_singleCls_quick.py @@ -114,5 +114,5 @@ # run ANNZ with the current settings runANNZ() -log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished runing ANNZ !")) +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished running ANNZ !")) diff --git a/examples/scripts/annz_singleReg_quick.py b/examples/scripts/annz_singleReg_quick.py index e7db75f..6667b1f 100644 --- a/examples/scripts/annz_singleReg_quick.py +++ b/examples/scripts/annz_singleReg_quick.py @@ -106,5 +106,5 @@ # run ANNZ with the current settings runANNZ() -log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished runing ANNZ !")) +log.info(whtOnBlck(" - "+time.strftime("%d/%m/%y %H:%M:%S")+" - finished running ANNZ !")) diff --git a/examples/scripts/generalSettings.py b/examples/scripts/generalSettings.py index 42c60ed..dd3acd5 100644 --- a/examples/scripts/generalSettings.py +++ b/examples/scripts/generalSettings.py @@ -74,6 +74,10 @@ def generalSettings(): # procedure in randomized regression. The default value is False. # glob.annz["optimWithMAD"] = True + # turn on or off the PDF bias correction, which is applied during optimization/verification + # during single/randomized regression and binned classification (set to True by default) + # glob.annz["doBiasCorPDF"] = False + # number of random MLM weighting schemes to generate as part of getRndMethodBestPDF() # glob.annz["nRndPdfWeightTries"] = 50 @@ -108,6 +112,14 @@ def generalSettings(): # maximal number of objects in a tree/output ascii file # glob.annz["nObjectsToWrite"] = 1e6 + # possible list of variables which will always be plotted (that is, no safety checks + # on variable type will be performed) + # glob.annz["alwaysPlotVars"] = "(MAG_R-MAG_I);(MAG_I-MAG_Z)" + + # possible list of variables for which we will NOT use quantile bins for plotting (i.e., performance plots will + # contain equal-width bins for these, and not bins defined such that the number of objects/bin is constant) + # glob.annz["noQuantileBinsPlots"] = "inTrainFlag;MAG_U" + return # -------------------------------------------------------------------------------------------------- diff --git a/examples/scripts/helperFuncs.py b/examples/scripts/helperFuncs.py index 4f2dc5d..8d1ca9f 100644 --- a/examples/scripts/helperFuncs.py +++ b/examples/scripts/helperFuncs.py @@ -35,7 +35,8 @@ def initParse(): parser.add_argument("--binnedClassification", action='store_true') parser.add_argument("--singleClassification", action='store_true') parser.add_argument("--randomClassification", action='store_true') - parser.add_argument("--inTrainFlag" , action='store_true') + parser.add_argument("--onlyKnnErr", action='store_true') + parser.add_argument("--inTrainFlag", action='store_true') parser.add_argument("--truncateLog", action='store_true') parser.add_argument("--isBatch", action='store_true') parser.add_argument("--fitsToAscii", action='store_true') @@ -63,13 +64,15 @@ def initParse(): if glob.pars["singleRegression"]: nSetups += 1 if glob.pars["randomRegression"]: nSetups += 1 if glob.pars["binnedClassification"]: nSetups += 1 + if glob.pars["onlyKnnErr"]: nSetups += 1 if glob.pars["inTrainFlag"]: nSetups += 1 if glob.pars["fitsToAscii"]: nSetups += 1 if glob.pars["asciiToFits"]: nSetups += 1 if not ((nSetups == 1) or (nSetups == 0 and (glob.pars["genInputTrees"] or hasMake or glob.pars["qsub"]))): - log.warning("Should define exactly one of --singleClassification --randomClassification , --singleRegression " \ - +"--randomRegression, --binnedClassification, --fitsToAscii, --asciiToFits !") + log.warning("Possibly missing/conflicting job-options... Did you define: --singleClassification" \ + + " --randomClassification , --singleRegression --randomRegression, --binnedClassification, " \ + +"--onlyKnnErr, --inTrainFlag, --fitsToAscii, --asciiToFits .... ?") if nSetups == 1: if glob.pars["genInputTrees"]: nModes += 1 @@ -77,12 +80,13 @@ def initParse(): if glob.pars["optimize"]: nModes += 1 if glob.pars["verify"]: nModes += 1 if glob.pars["evaluate"]: nModes += 1 + if glob.pars["onlyKnnErr"]: nModes += 1 if glob.pars["inTrainFlag"]: nModes += 1 if glob.pars["fitsToAscii"]: nModes += 1 if glob.pars["asciiToFits"]: nModes += 1 if not (nModes == 1 or hasMake): - log.warning("Should define exactly one of --genInputTrees --train , --optimize --verify, --evaluate, --inTrainFlag, --fitsToAscii, --asciiToFits !") + log.warning("Should define exactly one of --genInputTrees --train , --optimize --verify, --evaluate, --onlyKnnErr, --inTrainFlag, --fitsToAscii, --asciiToFits !") glob.pars["onlyMake"] = (((nSetups == 0) or (nModes == 0)) and hasMake and (not glob.pars["genInputTrees"])) @@ -104,6 +108,7 @@ def initParse(): glob.annz["doOptim"] = glob.pars["optimize"] glob.annz["doVerif"] = glob.pars["verify"] glob.annz["doEval"] = glob.pars["evaluate"] + glob.annz["doOnlyKnnErr"] = glob.pars["onlyKnnErr"] glob.annz["doInTrainFlag"] = glob.pars["inTrainFlag"] glob.annz["doRegression"] = glob.annz["doSingleReg"] or glob.annz["doRandomReg"] or glob.annz["doBinnedCls"] diff --git a/include/ANNZ.hpp b/include/ANNZ.hpp index 947be98..f83c033 100644 --- a/include/ANNZ.hpp +++ b/include/ANNZ.hpp @@ -59,6 +59,7 @@ class ANNZ : public BaseClass { void Train(); void Optim(); void Eval(); + void KnnErr(); private: @@ -69,9 +70,11 @@ class ANNZ : public BaseClass { // ----------------------------------------------------------------------------------------------------------- void Init(); void setTags(); + TString getBaseTagName(TString MLMname = DefOpts::NullC); TString getTagName(int nMLMnow = -1); TString getTagError(int nMLMnow = -1, TString errType = ""); TString getTagWeight(int nMLMnow = -1); + TString getTagBias(int nMLMnow = -1); TString getTagClsVal(int nMLMnow = -1); TString getTagIndex(int nMLMnow = -1); TString getTagInVarErr(int nMLMnow = -1, int nInErrNow = -1); @@ -101,13 +104,14 @@ class ANNZ : public BaseClass { void Train_singleCls(); void Train_singleReg(); void Train_binnedCls(); + void Train_singleRegBiasCor(); void generateOptsMLM(OptMaps * optMap, TString userMLMopts); void verifTarget(TTree * aTree); // ----------------------------------------------------------------------------------------------------------- // ANNZ_TMVA.cpp : // ----------------------------------------------------------------------------------------------------------- - void prepFactory(int nMLMnow = -1, TMVA::Factory * factory = NULL); + void prepFactory(int nMLMnow = -1, TMVA::Factory * factory = NULL, bool isBiasMLM = false); void doFactoryTrain(TMVA::Factory * factory); void clearReaders(Log::LOGtypes logLevel = Log::DEBUG_1); void loadReaders(map & mlmSkipNow, bool needMcPRB = true); @@ -128,6 +132,12 @@ class ANNZ : public BaseClass { double getRegClsErrINP(VarMaps * var, bool isREG, int nMLMnow, UInt_t * seedP = NULL, vector * zErrV = NULL); + // ----------------------------------------------------------------------------------------------------------- + // ANNZ_onlyKnnErr.cpp : + // ----------------------------------------------------------------------------------------------------------- + void onlyKnnErr_createTreeErrKNN(); + void onlyKnnErr_eval(); + // ----------------------------------------------------------------------------------------------------------- // ANNZ_loopRegCls.cpp : // ----------------------------------------------------------------------------------------------------------- @@ -136,7 +146,8 @@ class ANNZ : public BaseClass { double getSeparation(TH1 * hisSig, TH1 * hisBck); void deriveHisClsPrb(int nMLMnow = -1); TChain * mergeTreeFriends(TChain * aChain = NULL, TChain * aChainFriend = NULL, vector * chainFriendFileNameV = NULL, - vector * acceptV = NULL, vector * rejectV = NULL, TCut aCut = ""); + vector * acceptV = NULL, vector * rejectV = NULL, + TCut aCut = "", vector< pair > * addFormV = NULL); void verifyIndicesMLM(TChain * aChain = NULL); // ----------------------------------------------------------------------------------------------------------- @@ -154,7 +165,7 @@ class ANNZ : public BaseClass { void setBinClsPdfBinWeights(vector < vector < pair > > & pdfBinWgt, vector & nClsBinsIn); void getBinClsBiasCorPDF(TChain * aChain, vector & hisPdfBiasCorV); void doEvalReg(TChain * inChain = NULL, TString outDirName = "", vector * selctVarV = NULL); - void doMetricPlots(TChain * inChain = NULL, vector * addPlotVarV = NULL); + void doMetricPlots(TChain * inChain = NULL, vector * addPlotVarV = NULL, TString addOutputVarsIn = ""); // ----------------------------------------------------------------------------------------------------------- // ANNZ_loopRegCls.cpp : @@ -166,19 +177,21 @@ class ANNZ : public BaseClass { // private variables // =========================================================================================================== vector < Double_t > zClos_binE, zClos_binC, zPlot_binE, zPlot_binC, zPDF_binE, zPDF_binC, zBinCls_binE, zBinCls_binC; - vector < TString > mlmTagName, mlmTagWeight, mlmTagClsVal, mlmTagIndex, mlmTagErrKNN, inputVariableV; + vector < TString > mlmTagName, mlmTagWeight, mlmTagBias, mlmTagClsVal, mlmTagIndex, mlmTagErrKNN, inputVariableV; vector < map > mlmTagErr; vector < vector > pdfBinNames, inErrTag; vector < map < TString,TString> > pdfAvgNames; map < TString,bool > mlmSkip; vector < vector > inNamesVar, inNamesErr; + vector < bool > hasBiasCorMLMinp; vector < vector > inVarsScaleFunc; map < TString,TCut > userCutsM; - map < TString,TString > userWgtsM, bestMLMname; + map < TString,TString > userWgtsM, bestMLMname, mlmBaseTag; vector < time_t > trainTimeM; vector < pair > readerInptV; vector < vector > readerInptIndexV; - vector < TMVA::Reader* > regReaders; + vector < Float_t > readerBiasInptV; + vector < TMVA::Reader* > regReaders, biasReaders; vector < TMVA::Types::EAnalysisType > anlysTypes; vector < TMVA::Types::EMVA > typeMLM, allANNZtypes; map < TMVA::Types::EMVA,TString > typeToNameMLM; diff --git a/include/CntrMap.hpp b/include/CntrMap.hpp index 226951b..43f1a7f 100644 --- a/include/CntrMap.hpp +++ b/include/CntrMap.hpp @@ -65,7 +65,8 @@ class CntrMap { // size_t pos = std::string(dateTimeNow).find("CEST"); std::string choped = std::string(dateTimeNow).substr(0,pos); // dateTimeNow = (TString)choped; dateTimeNow.ReplaceAll(" "," "); dateTimeNow.ReplaceAll("\n",""); // aLOG(logLevel)<coutPurple<<"Counters ("<coutDef<coutPurple<<"-- Counters -----------------------------------------------------------------------------------------------"<coutDef<coutPurple<<" -- COUNTERS ----------------------------------------" + <<"--------------------------------------"<coutDef<OptOrNullC("baseName") : nameTag); @@ -79,12 +80,14 @@ class CntrMap { TString cutNameNow = cutCounterItr->first; int cutCountNow = cutCounterItr->second; - aLOG(logLevel) <<" - "<coutBlue+baseName+" "+glob->coutPurple)<<"" - <coutYellow)+TString::Format(" %d ",cutCountNow)<coutGreen<coutDef<coutPurple<<" -- "<coutBlue+baseName+" "+glob->coutPurple)<<"" + <coutYellow)+TString::Format(" %d ",cutCountNow)<coutGreen<coutDef<coutPurple<<"-----------------------------------------------------------------------------------------------------------"<coutDef<coutPurple<<" ----------------------------------------------------" + <<"-------------------------------------------------------"<coutDef<::iterator TreeMapItr; private: - TString outputRootFileName, outDirName, outFileName; + TString outputRootFileName, outDirName, outPlotDirName, outFileName; }; diff --git a/include/Utils.hpp b/include/Utils.hpp index 2c73f55..d3883cd 100644 --- a/include/Utils.hpp +++ b/include/Utils.hpp @@ -89,6 +89,8 @@ class Utils { inline TString floatToStr (double input, TString format = "%f" ) { return TString::Format(format,input); }; inline TString doubleToStr(double input, TString format = "%.10g") { return TString::Format(format,input); }; + inline TString getRndStr(TString format = "%.20f") { return doubleToStr(rnd->Rndm(),format); }; + Int_t strToInt (TString input); Long64_t strToLong (TString input); UInt_t strToUint (TString input); @@ -97,6 +99,9 @@ class Utils { Double_t strToDouble(TString input); Bool_t strToBool (TString input); + void getCodeVersionV(vector & strV, TString versStrIn = ""); + int getCodeVersionDiff(TString compVers); + inline TString getFilePath(TString fileName) { return (TString)fileName(0,fileName.Last('/'))+"/"; }; vector getTreeFriends(TTree * tree); diff --git a/include/VarMaps.hpp b/include/VarMaps.hpp index c5e370f..db701af 100644 --- a/include/VarMaps.hpp +++ b/include/VarMaps.hpp @@ -90,9 +90,9 @@ public : inline void setTreeCutStatus(bool cutStatus) { areCutsEnabled = cutStatus; return; } inline bool getTreeCutStatus() { return areCutsEnabled; } void addTreeCuts(TString cutType, TCut aCut); - void setTreeCuts(TString cutType, TCut aCut); - void getTreeCutsM(map & aTreeCutsM); - void setTreeCutsM(map & aTreeCutsM); + void setTreeCuts(TString cutType, TCut aCut, bool checkTreeRead = true); + void getTreeCutsM(map & aTreeCutsM); + void setTreeCutsM(map & aTreeCutsM, bool checkTreeRead = true); TCut getTreeCuts(TString cutType = ""); void updateReaderFormulae(vector < pair > & readerInptV, bool forceUpdate = false); void addReaderFormulae(vector < pair > & readerInptV); diff --git a/include/commonInclude.hpp b/include/commonInclude.hpp index 1600084..8676205 100644 --- a/include/commonInclude.hpp +++ b/include/commonInclude.hpp @@ -215,7 +215,7 @@ namespace Log { // a version with some verbosity for debugging #define DELNULL_(loc,ptr,name,verb) \ do { \ - if(verb) { aCustomLOG("DELNULL")<(factory)) return; VERIFY(LOCATION,(TString)"Memory leak ?! ",(dynamic_cast(factory))); // since all variables are read-in from TTreeFormula, we define them as floats ("F") in the factory - for(int nVarNow=0; nVarNow<(int)inNamesVar[nMLMnow].size(); nVarNow++) { + int nVars = (int)inNamesVar[nMLMnow].size(); + for(int nVarNow=0; nVarNowAddVariable(inNamesVar[nMLMnow][nVarNow],inNamesVar[nMLMnow][nVarNow],"",'F'); + + aLOG(Log::DEBUG) <AddVariable(MLMname,MLMname,"",'F'); + + aLOG(Log::DEBUG) <GetOptB("doRegression") && !glob->GetOptB("doBinnedCls")) { - factory->AddTarget(glob->GetOptC("zTrg"), glob->GetOptC("zTrgTitle")); + if(isBiasMLM) { + factory->AddTarget(biasName, (TString)MLMname+" - "+glob->GetOptC("zTrgTitle")); + } + else { + factory->AddTarget(glob->GetOptC("zTrg"), glob->GetOptC("zTrgTitle")); + } } // let TMVA know the name of the XML file // (the directory must be re-set each time a new factory is defined, sometime before the training) - TString MLMname = getTagName(nMLMnow); - (TMVA::gConfig().GetIONames()).fWeightFileDir = getKeyWord(MLMname,"trainXML","outFileDirTrain"); + TString mlmBiasName = (TString)(isBiasMLM ? biasName : MLMname); + (TMVA::gConfig().GetIONames()).fWeightFileDir = getKeyWord(mlmBiasName,"trainXML","outFileDirTrain"); return; } @@ -90,10 +109,16 @@ void ANNZ::clearReaders(Log::LOGtypes logLevel) { DELNULL_(LOCATION,regReaders[nMLMnow],(TString)"regReaders["+utils->intToStr(nMLMnow)+"]",verb); } + for(int nMLMnow=0; nMLMnow<(int)biasReaders.size(); nMLMnow++) { + bool verb = (biasReaders[nMLMnow] && inLOG(Log::DEBUG_1)); + + DELNULL_(LOCATION,biasReaders[nMLMnow],(TString)"biasReaders["+utils->intToStr(nMLMnow)+"]",verb); + } for(int nMLMnow=0; nMLMnow<(int)hisClsPrbV.size(); nMLMnow++) DELNULL(hisClsPrbV[nMLMnow]); - regReaders.clear(); readerInptV.clear(); readerInptIndexV.clear(); anlysTypes.clear(); hisClsPrbV.clear(); + regReaders.clear(); biasReaders.clear(); hisClsPrbV.clear(); + readerInptV.clear(); readerInptIndexV.clear(); anlysTypes.clear(); readerBiasInptV.clear(); return; } @@ -115,14 +140,19 @@ void ANNZ::loadReaders(map & mlmSkipNow, bool needMcPRB) { // ====================================================================== aLOG(Log::DEBUG_1) <GetOptI("nMLMs"); - bool isBinCls = glob->GetOptB("doBinnedCls"); + int nMLMs = glob->GetOptI("nMLMs"); + bool isBinCls = glob->GetOptB("doBinnedCls"); + bool doBiasCorMLM = glob->GetOptB("doBiasCorMLM"); + int maxMsg = inLOG(Log::DEBUG_1) ? nMLMs+1 : 5; // cleanup containers before initializing new readers clearReaders(Log::DEBUG_2); - readerInptV.clear(); regReaders.resize(nMLMs,NULL); readerInptIndexV.resize(nMLMs); - anlysTypes.resize(nMLMs,TMVA::Types::kNoAnalysisType); hisClsPrbV.resize(glob->GetOptI("nMLMs"),NULL); + regReaders.resize(nMLMs,NULL); biasReaders.resize(nMLMs,NULL); hisClsPrbV.resize(nMLMs,NULL); + readerInptV.clear(); readerInptIndexV.resize(nMLMs); anlysTypes.resize(nMLMs,TMVA::Types::kNoAnalysisType); + + // for the bias correction, we only need one parameter (will be filled in manually) + readerBiasInptV.resize(nMLMs,0); // ----------------------------------------------------------------------------------------------------------- // initialize readerInptV and add all required variables by input variables (formulae) - using @@ -158,66 +188,110 @@ void ANNZ::loadReaders(map & mlmSkipNow, bool needMcPRB) { // ----------------------------------------------------------------------------------------------------------- int nReadIn(0); for(int nMLMnow=0; nMLMnow= 0)); + + aRegReader->AddVariable(inVarNameNow,&(readerInptV[readerInptIndex].second)); + readerInptIndexV[nMLMnow][nReaderInputNow] = readerInptIndex; } - VERIFY(LOCATION,(TString)"Adding reader-var which does not exist in readerInptV... Something is horribly wrong... ?!?",(readerInptIndex >= 0)); - regReaders[nMLMnow]->AddVariable(inVarNameNow,&(readerInptV[readerInptIndex].second)); - readerInptIndexV[nMLMnow][nReaderInputNow] = readerInptIndex; - } + // the last variable of the reader (the order matters!) is for the original regression target + if(nReaderType == 1 && hasBiasCorMLMinp[nMLMnow]) { + aRegReader->AddVariable(MLMname,&(readerBiasInptV[nMLMnow])); + } + + // book the reader if the xml exists + TString outXmlFileName = getKeyWord(mlmBiasName,"trainXML","outXmlFileName"); + + bool foundReader = false; + std::ifstream * testFile = new std::ifstream(outXmlFileName); + if(testFile) { + if(testFile->good()) { + cout << coutPurple; aRegReader->BookMVA(mlmBiasName,outXmlFileName); cout << coutDef; + + foundReader = (dynamic_cast(aRegReader->FindMVA(mlmBiasName))); + } + } + DELNULL(testFile); - // book the reader - TString outXmlFileName = getKeyWord(MLMname,"trainXML","outXmlFileName"); - cout << coutPurple; regReaders[nMLMnow]->BookMVA(MLMname,outXmlFileName); cout << coutDef; + if(foundReader) { + TString methodName = (dynamic_cast(aRegReader->FindMVA(mlmBiasName)))->GetMethodTypeName(); + TMVA::Types::EMVA methodType = (dynamic_cast(aRegReader->FindMVA(mlmBiasName)))->GetMethodType(); + if(nReaderType == 0) { + anlysTypes[nMLMnow] = (dynamic_cast(aRegReader->FindMVA(mlmBiasName)))->GetAnalysisType(); + } - bool foundReader = (dynamic_cast(regReaders[nMLMnow]->FindMVA(MLMname))); + TMVA::Types::EMVA typeNow = typeMLM[nMLMnow];//(nReaderType == 0) ? typeMLM[nMLMnow] : typeBiasMLM[nMLMnow]; - if(foundReader) { - TString methodName = (dynamic_cast(regReaders[nMLMnow]->FindMVA(MLMname)))->GetMethodTypeName(); - TMVA::Types::EMVA methodType = (dynamic_cast(regReaders[nMLMnow]->FindMVA(MLMname)))->GetMethodType(); - anlysTypes[nMLMnow] = (dynamic_cast(regReaders[nMLMnow]->FindMVA(MLMname)))->GetAnalysisType(); + if(nReaderType == 0) { + VERIFY(LOCATION,(TString)"Found inconsistent settings (configSave_type = \""+typeToNameMLM[typeNow] + +"\" from the settings file, but "+mlmBiasName+" is of type \""+typeToNameMLM[methodType]+"\"" + ,(typeNow == methodType)); + } - VERIFY(LOCATION,(TString)"Found inconsistent settings (configSave_type = \""+typeToNameMLM[typeMLM[nMLMnow]] - +"\" from the settings file, but "+MLMname+" is of type \""+typeToNameMLM[methodType]+"\"" - ,(typeMLM[nMLMnow] == methodType)); + // load the classification response histogram for Multiclass readers + if((nReaderType == 0) && needMcPRB && (isBinCls || anlysTypes[nMLMnow] == TMVA::Types::kMulticlass)) { + TString hisClsPrbFileName = getKeyWord(MLMname,"postTrain","hisClsPrbFile"); + TString hisName = getKeyWord(MLMname,"postTrain","hisClsPrbHis"); - // load the classification response histogram for Multiclass readers - if(needMcPRB && (isBinCls || anlysTypes[nMLMnow] == TMVA::Types::kMulticlass)) { - TString hisClsPrbFileName = getKeyWord(MLMname,"postTrain","hisClsPrbFile"); - TString hisName = getKeyWord(MLMname,"postTrain","hisClsPrbHis"); + TFile * hisClsPrbFile = new TFile(hisClsPrbFileName,"READ"); - TFile * hisClsPrbFile = new TFile(hisClsPrbFileName,"READ"); + hisClsPrbV[nMLMnow] = dynamic_cast(hisClsPrbFile->Get(hisName)); + VERIFY(LOCATION,(TString)"Could not find hisClsPrbV[nMLMnow = "+utils->intToStr(nMLMnow)+"] in " + +hisClsPrbFileName+" ?!",(dynamic_cast(hisClsPrbV[nMLMnow]))); - hisClsPrbV[nMLMnow] = dynamic_cast(hisClsPrbFile->Get(hisName)); - VERIFY(LOCATION,(TString)"Could not find hisClsPrbV[nMLMnow = "+utils->intToStr(nMLMnow)+"] in " - +hisClsPrbFileName+" ?!",(dynamic_cast(hisClsPrbV[nMLMnow]))); + hisClsPrbV[nMLMnow] = (TH1*)hisClsPrbV[nMLMnow]->Clone((TString)hisName+"_cln"); + hisClsPrbV[nMLMnow]->SetDirectory(0); - hisClsPrbV[nMLMnow] = (TH1*)hisClsPrbV[nMLMnow]->Clone((TString)hisName+"_cln"); - hisClsPrbV[nMLMnow]->SetDirectory(0); + hisClsPrbFile->Close(); DELNULL(hisClsPrbFile); + } - hisClsPrbFile->Close(); DELNULL(hisClsPrbFile); + if(nReadIn < maxMsg) { + aLOG(Log::DEBUG) <intToStr((int)readType)+"\") ...",false); } else { - if (readType == ANNZ_readType::REG) readVal = (regReaders[nMLMnow]->EvaluateRegression(MLMname))[0]; + + if(readType == ANNZ_readType::REG) { + readVal = (regReaders[nMLMnow]->EvaluateRegression(MLMname))[0]; + + if(dynamic_cast(biasReaders[nMLMnow])) { + // first update the value of the regression target in the variable which is connected to the + // reader (this is not updated as part of the nominal loop, since this variable is not in the input tree) + readerBiasInptV[nMLMnow] = readVal; + + // now evaluate the bias-correction MLM and update the output variable + readVal -= (biasReaders[nMLMnow]->EvaluateRegression(getTagBias(nMLMnow)))[0]; + } + } else if(readType == ANNZ_readType::PRB) readVal = max(min(regReaders[nMLMnow]->GetProba(MLMname),1.),0.); else if(readType == ANNZ_readType::CLS) readVal = regReaders[nMLMnow]->EvaluateMVA(MLMname); else VERIFY(LOCATION,(TString)"un-supported readType (\""+utils->intToStr((int)readType)+"\") ...",false); + } return readVal; diff --git a/src/ANNZ_err.cpp b/src/ANNZ_err.cpp index 053693b..9c0a99d 100644 --- a/src/ANNZ_err.cpp +++ b/src/ANNZ_err.cpp @@ -45,7 +45,10 @@ void ANNZ::createTreeErrKNN(int nMLMnow) { // prepare the chain and input variables TChain * aChain = new TChain(inTreeName,inTreeName); aChain->SetDirectory(0); aChain->Add(inFileName); - aLOG(Log::DEBUG) <GetEntries()<<")"<<" from "<GetEntries()<<")" + <<" from "<connectTreeBranchesForm(aChain,&readerInptV); // setup cuts for the vars we loop on for sig/bck determination in case of classification if(isCls) { @@ -60,8 +63,6 @@ void ANNZ::createTreeErrKNN(int nMLMnow) { var_0->setTreeCuts("_bck",bckCuts); } - var_0->connectTreeBranchesForm(aChain,&readerInptV); - var_1->NewVarF(MLMname); var_1->NewVarF(errKNNname); var_1->NewVarI(MLMname_i); if(isCls) var_1->NewVarI(sigBckTypeName); else var_1->NewVarF(zTrgName); @@ -97,21 +98,21 @@ void ANNZ::createTreeErrKNN(int nMLMnow) { if (!var_0->hasFailedTreeCuts("_bck")) { sigBckType = 0; errKNN = clsPrb; } else if(!var_0->hasFailedTreeCuts("_sig")) { sigBckType = 1; errKNN = 1-clsPrb; } - var_1->SetVarF(MLMname,clsPrb); - var_1->SetVarF(errKNNname,errKNN); - var_1->SetVarI(sigBckTypeName,sigBckType); + var_1->SetVarF(MLMname, clsPrb); + var_1->SetVarF(errKNNname, errKNN); + var_1->SetVarI(sigBckTypeName, sigBckType); } else { double zTrg = var_0->GetVarF(zTrgName); - double regVal = getReader(var_0,ANNZ_readType::REG,true ,nMLMnow); + double regVal = getReader(var_0,ANNZ_readType::REG,true,nMLMnow); // the error of each object wrt its own true value // see: http://arxiv.org/abs/0810.2991 - Estimating the Redshift Distribution of Photometric Galaxy... - Sect. 4.2 double errKNN = regVal - zTrg; - var_1->SetVarF(MLMname,regVal); + var_1->SetVarF(MLMname, regVal); var_1->SetVarF(errKNNname,errKNN); - var_1->SetVarF(zTrgName,zTrg); + var_1->SetVarF(zTrgName, zTrg); } var_1->fillTree(); diff --git a/src/ANNZ_loopCls.cpp b/src/ANNZ_loopCls.cpp index b32ad7b..24bbf8d 100644 --- a/src/ANNZ_loopCls.cpp +++ b/src/ANNZ_loopCls.cpp @@ -459,8 +459,10 @@ void ANNZ::optimCls() { OptMaps * optMap = new OptMaps("localOptMap"); TString saveName(""); vector optNames; - saveName = "optimMLMs_PRB"; optNames.push_back(saveName); optMap->NewOptC(saveName, orderedMLMs["PRB"]); - saveName = "optimMLMs_CLS"; optNames.push_back(saveName); optMap->NewOptC(saveName, orderedMLMs["CLS"]); + + saveName = glob->versionTag(); optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(glob->versionTag())); + saveName = "optimMLMs_PRB"; optNames.push_back(saveName); optMap->NewOptC(saveName, orderedMLMs["PRB"]); + saveName = "optimMLMs_CLS"; optNames.push_back(saveName); optMap->NewOptC(saveName, orderedMLMs["CLS"]); utils->optToFromFile(&optNames,optMap,saveFileName,"WRITE"); @@ -525,10 +527,12 @@ void ANNZ::doEvalCls() { aLOG(Log::INFO)< optNames; - saveNameP = "optimMLMs_PRB"; optNames.push_back(saveNameP); optMap->NewOptC(saveNameP); - saveNameC = "optimMLMs_CLS"; optNames.push_back(saveNameC); optMap->NewOptC(saveNameC); + + saveName = glob->versionTag(); optNames.push_back(saveName); optMap->NewOptC(saveName); + saveNameP = "optimMLMs_PRB"; optNames.push_back(saveNameP); optMap->NewOptC(saveNameP); + saveNameC = "optimMLMs_CLS"; optNames.push_back(saveNameC); optMap->NewOptC(saveNameC); utils->optToFromFile(&optNames,optMap,saveFileName,"READ","SILENT_KeepFile",inLOG(Log::DEBUG_2)); @@ -736,7 +740,7 @@ void ANNZ::doEvalCls() { int nMLMnow = getTagNow(MLMname); TString MLMname_e = getTagError(nMLMnow); TString MLMname_w = getTagWeight(nMLMnow); TString MLMname_v = getTagClsVal(nMLMnow); - double clasVal = getReader(var_0,ANNZ_readType::CLS,true ,nMLMnow); + double clasVal = getReader(var_0,ANNZ_readType::CLS,true,nMLMnow); double clsPrb = getReader(var_0,ANNZ_readType::PRB,false,nMLMnow); double clsWgt = var_0->GetForm(MLMname_w); diff --git a/src/ANNZ_loopReg.cpp b/src/ANNZ_loopReg.cpp index 70c1745..7ed11ba 100644 --- a/src/ANNZ_loopReg.cpp +++ b/src/ANNZ_loopReg.cpp @@ -123,7 +123,7 @@ void ANNZ::optimReg() { // create the merged chain // TChain * aChainMerged = mergeTreeFriends(aChain_0,aChain_1,NULL,&acceptV,NULL,aCut); // deprecated - TChain * aChainMerged = mergeTreeFriends(aChain_0,aChain_1,NULL,&acceptV); // deprecated + TChain * aChainMerged = mergeTreeFriends(aChain_0,aChain_1,NULL,&acceptV); verifyIndicesMLM(aChainMerged); @@ -209,8 +209,9 @@ void ANNZ::optimReg() { TString saveName(""); vector optNames; - saveName = "bestMLM"; optNames.push_back(saveName); optMap->NewOptI(saveName, bestANNZindex); - saveName = "userPdfBins"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC("userPdfBins")); + saveName = glob->versionTag(); optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(glob->versionTag())); + saveName = "bestMLM"; optNames.push_back(saveName); optMap->NewOptI(saveName, bestANNZindex); + saveName = "userPdfBins"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC("userPdfBins")); for(int nPDFnow=0; nPDFnowNewOptC(saveName, pdfWeightList[nPDFnow]); @@ -409,10 +410,10 @@ void ANNZ::fillColosureV( map < int,vector > & zRegQnt_nANNZ, map < i utils->param->clearAll(); utils->param->NewOptB("doFracLargerSigma" , true); utils->param->NewOptB("getMAD" , optimWithMAD); - int hasQuantStats = utils->getInterQuantileStats(closH[nMLMnow][nBinNow]); + int hasQnt = utils->getInterQuantileStats(closH[nMLMnow][nBinNow]); // if the calculation disnt go through (no entries) then the currect metrics are all negative - if(hasQuantStats) { + if(hasQnt) { quant_mean = fabs(utils->param->GetOptF("quant_mean")); // all values must be positive for the optimization to work quant_sigma_68 = utils->param->GetOptF(quantScatterName); quant_fracSig68_2 = utils->param->GetOptF("quant_fracSig68_2"); @@ -494,24 +495,33 @@ void ANNZ::getBestANNZ( map < int,vector > & zRegQnt_nANNZ, map < int bestMLMsV.clear(); // before anything, clear the ouput vector - int minAcptANNZs = glob->GetOptI("minAcptMLMsForPDFs");; - TString optimCondReg = glob->GetOptC("optimCondReg"); - TString optimCondTitle = glob->GetOptB("optimWithMAD") ? (TString)"MAD" : (TString)optimCondReg; - TString biasTitle = glob->GetOptB("optimWithScaledBias") ? (TString)"bias/(1+"+glob->GetOptC("zTrgTitle")+")" : (TString)"bias"; + int minAcptANNZs = glob->GetOptI("minAcptMLMsForPDFs"); + TString optimCondReg = glob->GetOptC("optimCondReg"); + bool optimWithFracSig68 = glob->GetOptB("optimWithFracSig68"); + TString zTrgTitle = glob->GetOptC("zTrgTitle"); + TString optimCondTitle = glob->GetOptB("optimWithMAD") ? (TString)"MAD" : (TString)optimCondReg; + TString biasTitle = glob->GetOptB("optimWithScaledBias") ? (TString)"bias/(1+"+zTrgTitle+")" : (TString)"bias"; + + // if not choosing to optimize by the outlier fraction, don't use it at all (nMetrics set to 2) + int nMetrics = (optimCondReg == "fracSig68" || optimWithFracSig68) ? 3 : 2; + if(nMetrics == 2) { + aLOG(Log::INFO) < > nMLMmetricPairV; - for(int nAcptANNZNow=0; nAcptANNZNow<(int)zRegQnt_nANNZ[-1].size(); nAcptANNZNow++) { - if(zRegQnt_sigma68[-1][nAcptANNZNow] < EPS) continue; // has been initialized, such that no entries in this bin + for(int nAcptANNZnow=0; nAcptANNZnow<(int)zRegQnt_nANNZ[-1].size(); nAcptANNZnow++) { + if(zRegQnt_sigma68[-1][nAcptANNZnow] < EPS) continue; // has been initialized, such that no entries in this bin - int nMLMnow = zRegQnt_nANNZ [-1][nAcptANNZNow]; + int nMLMnow = zRegQnt_nANNZ [-1][nAcptANNZnow]; double metricNow = 0; - if (optimCondReg == "bias") metricNow = zRegQnt_bias [-1][nAcptANNZNow]; - else if(optimCondReg == "sig68") metricNow = zRegQnt_sigma68 [-1][nAcptANNZNow]; - else if(optimCondReg == "fracSig68") metricNow = zRegQnt_fracSig68[-1][nAcptANNZNow]; + if (optimCondReg == "bias") metricNow = zRegQnt_bias [-1][nAcptANNZnow]; + else if(optimCondReg == "sig68") metricNow = zRegQnt_sigma68 [-1][nAcptANNZnow]; + else if(optimCondReg == "fracSig68") metricNow = zRegQnt_fracSig68[-1][nAcptANNZnow]; else VERIFY(LOCATION,(TString)"Configuration problem... \"optimCondReg\" should have one of the "+ "following values: \"sig68\", \"bias\" or \"fracSig68\" options ...",false); @@ -521,8 +531,8 @@ void ANNZ::getBestANNZ( map < int,vector > & zRegQnt_nANNZ, map < int // sort so that the smallest element is first sort(nMLMmetricPairV.begin(),nMLMmetricPairV.end(),sortFunctors::pairIntDouble_descendSecond); - for(int nAcptANNZNow=0; nAcptANNZNow<(int)nMLMmetricPairV.size(); nAcptANNZNow++) { - bestMLMsV.push_back(nMLMmetricPairV[nAcptANNZNow].first); + for(int nAcptANNZnow=0; nAcptANNZnow<(int)nMLMmetricPairV.size(); nAcptANNZnow++) { + bestMLMsV.push_back(nMLMmetricPairV[nAcptANNZnow].first); } } nMLMmetricPairV.clear(); @@ -550,19 +560,19 @@ void ANNZ::getBestANNZ( map < int,vector > & zRegQnt_nANNZ, map < int double fracLimNow1 = fracLimNow0 + 0.1 * (1 + nfracLimNow1); fracLimNow1 = min(fracLimNow1,0.8); aLOG(Log::INFO) < > & zRegQnt_nANNZ, map < int for(int nBinNow=-1; nBinNowGetOptI("minEleForQuantile")) continue; - map < TString, vector > metricLowQuants; - for(int nMetricNow=0; nMetricNow<3; nMetricNow++) { + for(int nMetricNow=0; nMetricNow fracV, quantV(2,-1); - fracV.push_back(fracLimNow0); fracV.push_back(fracLimNow1); //fracV.push_back(0.16); fracV.push_back(0.84); + fracV.push_back(fracLimNow0); fracV.push_back(fracLimNow1); - int hasQuants(0); + int hasQnt(0); utils->param->clearAll(); - if (nMetricNow == 0) { hasQuants = utils->getQuantileV(fracV,quantV,biasV [nBinNow]); metricName = "bias"; } - else if(nMetricNow == 1) { hasQuants = utils->getQuantileV(fracV,quantV,sigma68V [nBinNow]); metricName = "sig68"; } - else if(nMetricNow == 2) { hasQuants = utils->getQuantileV(fracV,quantV,fracSig68V[nBinNow]); metricName = "fracSig68"; } - else assert(false); + if (nMetricNow == 0) { hasQnt = utils->getQuantileV(fracV,quantV,biasV [nBinNow]); metricName = "bias"; } + else if(nMetricNow == 1) { hasQnt = utils->getQuantileV(fracV,quantV,sigma68V [nBinNow]); metricName = "sig68"; } + else if(nMetricNow == 2) { hasQnt = utils->getQuantileV(fracV,quantV,fracSig68V[nBinNow]); metricName = "fracSig68"; } - double quantLow = hasQuants ? quantV[0] : -1; - double quantDif = hasQuants ? quantV[1] : -1; - // double quantDif = hasQuants ? quantLow + (quantV[2] - quantV[1])/2. : -1; + double quantLow = hasQnt ? quantV[0] : -1; + double quantDif = hasQnt ? quantV[1] : -1; + // double quantDif = hasQnt ? quantLow + (quantV[2] - quantV[1])/2. : -1; metricLowQuants[metricName].push_back(quantLow); metricLowQuants[metricName].push_back(quantDif); @@ -600,49 +606,67 @@ void ANNZ::getBestANNZ( map < int,vector > & zRegQnt_nANNZ, map < int fracV.clear(); fracV.clear(); } - for(int nAcptANNZNow=0; nAcptANNZNow hasMetric; - double frac_bias(0),frac_sig68(0),frac_fracSig68(0); - - int nMLMnow = nANNZv [nBinNow][nAcptANNZNow]; - double mean_bias = biasV [nBinNow][nAcptANNZNow]; - double mean_sigma68 = sigma68V [nBinNow][nAcptANNZNow]; - double mean_fracSig68 = fracSig68V[nBinNow][nAcptANNZNow]; + int nAcptANNZs = (int)nANNZv[nBinNow].size(); + for(int nAcptANNZnow=0; nAcptANNZnow hasMetric; + // check if "bias" is within the top fracLimNow0 percentile, as well as that "sig68","fracSig68" are within fracLimNow1 // ----------------------------------------------------------------------------------------------------------- - frac_bias = metricLowQuants["bias"][0]; frac_sig68 = metricLowQuants["sig68"][1]; frac_fracSig68 = metricLowQuants["fracSig68"][1]; + frac_bias = metricLowQuants["bias"] [0]; + frac_sig68 = metricLowQuants["sig68"][1]; + if(nMetrics == 3) frac_fracSig68 = metricLowQuants["fracSig68"][1]; + else frac_fracSig68 = mean_fracSig68+1; + if(mean_bias < frac_bias && mean_sigma68 < frac_sig68 && mean_fracSig68 < frac_fracSig68) { hasMetric["bias"] = true; bestMLMsPairs[nBinNow]["bias"].push_back(pair(nMLMnow,mean_bias)); } - // ----------------------------------------------------------------------------------------------------------- + // check if "sig68" is within the top fracLimNow0 percentile, as well as that "bias","fracSig68" are within fracLimNow1 // ----------------------------------------------------------------------------------------------------------- - frac_bias = metricLowQuants["bias"][1]; frac_sig68 = metricLowQuants["sig68"][0]; frac_fracSig68 = metricLowQuants["fracSig68"][1]; + frac_bias = metricLowQuants["bias"] [1]; + frac_sig68 = metricLowQuants["sig68"][0]; + if(nMetrics == 3) frac_fracSig68 = metricLowQuants["fracSig68"][1]; + else frac_fracSig68 = mean_fracSig68+1; + if(mean_bias < frac_bias && mean_sigma68 < frac_sig68 && mean_fracSig68 < frac_fracSig68) { hasMetric["sig68"] = true; bestMLMsPairs[nBinNow]["sig68"].push_back(pair(nMLMnow,mean_sigma68)); } - // ----------------------------------------------------------------------------------------------------------- + // check if "fracSig68" is within the top fracLimNow0 percentile, as well as that "bias","sig68" are within fracLimNow1 // ----------------------------------------------------------------------------------------------------------- - frac_bias = metricLowQuants["bias"][1]; frac_sig68 = metricLowQuants["sig68"][1]; frac_fracSig68 = metricLowQuants["fracSig68"][0]; - if(mean_bias < frac_bias && mean_sigma68 < frac_sig68 && mean_fracSig68 < frac_fracSig68) { - hasMetric["fracSig68"] = true; - bestMLMsPairs[nBinNow]["fracSig68"].push_back(pair(nMLMnow,mean_fracSig68)); + if(nMetrics == 3) { + frac_bias = metricLowQuants["bias"] [1]; + frac_sig68 = metricLowQuants["sig68"] [1]; + frac_fracSig68 = metricLowQuants["fracSig68"][0]; + if(mean_bias < frac_bias && mean_sigma68 < frac_sig68 && mean_fracSig68 < frac_fracSig68) { + hasMetric["fracSig68"] = true; + bestMLMsPairs[nBinNow]["fracSig68"].push_back(pair(nMLMnow,mean_fracSig68)); + } } if(nBinNow == -1) { if(hasMetric[optimCondReg]) nAcptBestMLMs++; } @@ -662,18 +686,24 @@ void ANNZ::getBestANNZ( map < int,vector > & zRegQnt_nANNZ, map < int // ----------------------------------------------------------------------------------------------------------- bestMLMs.clear(); for(int nBinNow=-1; nBinNowGetXaxis()->GetBinCenter(hisIntgrZregV[0]->GetXaxis()->FindBin(1-glob->GetOptF("excludeRangePdfModelFit"))); int doConstFit(0), doMeanFit(1); - assert(doConstFit+doMeanFit == 1); // consistency check - can only set one of the two options + VERIFY(LOCATION,(TString)"can only set one of the two options",(doConstFit+doMeanFit == 1)); // consistency check aLOG(Log::DEBUG) < * s TString outDirNameFull = glob->GetOptC("outDirNameFull"); TString postTrainDirName = glob->GetOptC("postTrainDirNameFull"); TString addOutputVars = glob->GetOptC("addOutputVars"); + TString userWgtPlots = glob->GetOptC("userWeights_metricPlots"); int maxNobj = glob->GetOptI("maxNobj"); TString indexName = glob->GetOptC("indexName"); TString treeName = glob->GetOptC("treeName"); @@ -2117,6 +2148,8 @@ void ANNZ::doEvalReg(TChain * inChain, TString outDirName, vector * s TString saveName_best(""); vector optNames, saveName_pdf(nPDFs); + optNames.push_back(glob->versionTag()); optMap->NewOptC(glob->versionTag(), glob->GetOptC(glob->versionTag())); + for(int nPDFnow=0; nPDFnow * s if(var_0->HasVar(zTrg) && find(addVarV.begin(),addVarV.end(),zTrg) == addVarV.end()) { addVarV.insert(addVarV.begin(),zTrg); } + + // check if any user-requested weight variables for plotting (used in doMetricPlots()) are needed, but not already included + if(userWgtPlots != "" && userWgtPlots != "1") { + vector inBranchNameV; + utils->getTreeBranchNames(aChain,inBranchNameV); + + for(int nVarNow=0; nVarNow<(int)inBranchNameV.size(); nVarNow++) { + TString varNameNow = inBranchNameV[nVarNow]; + + if(userWgtPlots.Contains(varNameNow) && (find(addVarV.begin(),addVarV.end(),varNameNow) == addVarV.end())) { + addVarV.push_back(varNameNow); + } + } + inBranchNameV.clear(); + } + for(int nVarsInNow=0; nVarsInNow<(int)addVarV.size(); nVarsInNow++) { TString addVarName = addVarV[nVarsInNow]; VERIFY(LOCATION,(TString)"from addOutputVars - trying to use undefined variable (\""+addVarName+"\") ...",var_0->HasVar(addVarName)); @@ -2704,7 +2753,7 @@ void ANNZ::doEvalReg(TChain * inChain, TString outDirName, vector * s val /= nSmearUnf; for(int nSmearUnfNow=0; nSmearUnfNowGetRandom(); - rndVal = min(max(rndVal,minValZ+EPS),maxValZ-EPS); + // rndVal = min(max(rndVal,minValZ+EPS),maxValZ-EPS); hisPDF_w[nPDFnow]->Fill(rndVal,val); } @@ -2943,8 +2992,8 @@ void ANNZ::doEvalReg(TChain * inChain, TString outDirName, vector * s * for which plots are generated. */ // =========================================================================================================== -void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { -// ========================================================================= +void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV, TString addOutputVarsIn) { +// ================================================================================================== if(!glob->GetOptB("doPlots")) { aLOG(Log::DEBUG) < * addPlotVarV) { aLOG(Log::INFO) <GetOptC("outDirNameFull"); - TString addOutputVars = glob->GetOptC("addOutputVars"); TString plotExt = glob->GetOptC("printPlotExtension"); TString basePrefix = glob->GetOptC("basePrefix"); + TString addOutputVars = glob->GetOptC("addOutputVars"); + TString alwaysPlotVars = glob->GetOptC("alwaysPlotVars"); TString baseName_regMLM_avg = glob->GetOptC("baseName_regMLM_avg"); TString baseName_regPDF_max = glob->GetOptC("baseName_regPDF_max"); TString baseName_regPDF_avg = glob->GetOptC("baseName_regPDF_avg"); @@ -2970,6 +3020,8 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { TString baseTag_v = glob->GetOptC("baseTag_v"); TString baseTag_e = glob->GetOptC("baseTag_e"); TString baseTag_w = glob->GetOptC("baseTag_w"); + TString userWgtPlots = glob->GetOptC("userWeights_metricPlots"); + TString noQuantileBinsStr = glob->GetOptC("noQuantileBinsPlots"); bool defErrBySigma68 = glob->GetOptB("defErrBySigma68"); bool doGausSigmaRelErr = glob->GetOptB("doGausSigmaRelErr"); int nPDFs = glob->GetOptI("nPDFs"); @@ -2983,6 +3035,29 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { bool plotWithSclBias = glob->GetOptB("plotWithScaledBias"); TString biasTitle = plotWithSclBias ? (TString)"#delta/(1+"+glob->GetOptC("zTrgTitle")+")" : (TString)"#delta"; + // added variables are plotted with equal-quintile binning instead of equal-width + // bins, if they are NOT included in this list + vector noQuantileBinsV = utils->splitStringByChar(noQuantileBinsStr, ';'); + + // combine a possible list of addOutputVars with that defined in the global variable + if(addOutputVarsIn != "") { + addOutputVarsIn.ReplaceAll(" ","").ReplaceAll(";;",";"); + + if(addOutputVars != "") addOutputVars += ";"; + addOutputVars += addOutputVarsIn; + } + if(alwaysPlotVars != "") { + alwaysPlotVars.ReplaceAll(" ","").ReplaceAll(";;",";"); + + if(addOutputVars != "") addOutputVars += ";"; + addOutputVars += alwaysPlotVars; + } + addOutputVars.ReplaceAll(" ","").ReplaceAll(";;",";"); + + // used for formula of user defined weights for the plots + bool hasUserWgt(userWgtPlots != "" && userWgtPlots != "1"); + TString userWgtFrm("userWgtFrm"); // some internal unique name + TString hisName(""); int nBinsZ((int)zPlot_binC.size()), maxSigmaRelErrToPlot(10); int closHisN(glob->GetOptI("closHisN")), nDrawBins_zTrg(glob->GetOptI("nDrawBins_zTrg")), hisBufSize(glob->GetOptI("hisBufSize")); @@ -3143,24 +3218,39 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { VarMaps * var = new VarMaps(glob,utils,"treePlotVar"); var->connectTreeBranches(aChain); - vector plotVars, plotVarForms, plotVarNames; - plotVarNames = utils->splitStringByChar(addOutputVars,';'); + vector plotVars, plotVarForms, plotVarNames, addVarInV, alwaysPlotV; + plotVarNames = utils->splitStringByChar(addOutputVars, ';'); + addVarInV = utils->splitStringByChar(addOutputVarsIn,';'); + alwaysPlotV = utils->splitStringByChar(alwaysPlotVars, ';'); for(int nNameNow=0; nNameNow<(int)plotVarNames.size(); nNameNow++) { TString plotVarNameNow = (TString)plotVarNames[nNameNow]; TString plotVarFormNameNow = (TString)"form_"+plotVarNameNow; + // avoid empties + if(plotVarNameNow == "") continue; + // avoid double counting + if(find(plotVars.begin(),plotVars.end(),plotVarNameNow) != plotVars.end()) continue; + + bool doPlotVar = (find(addVarInV .begin(),addVarInV .end(),plotVarNameNow) != addVarInV .end()) + || (find(alwaysPlotV.begin(),alwaysPlotV.end(),plotVarNameNow) != alwaysPlotV.end()); + if(var->HasVarF(plotVarNameNow) || var->HasVarI(plotVarNameNow)) { plotVars .push_back(plotVarNameNow); plotVarForms.push_back(plotVarFormNameNow); } + else if(doPlotVar) { + plotVars .push_back(plotVarNameNow); + plotVarForms.push_back(utils->regularizeName(plotVarFormNameNow)); + + aLOG(Log::DEBUG_1)< * addPlotVarV) { var->NewForm(plotVarFormNameNow,plotVarNameNow); } - var->connectTreeBranches(aChain); + // add formula for user defined weights for the plots + if(hasUserWgt) { + aLOG(Log::INFO)<NewForm(userWgtFrm,getRegularStrForm(userWgtPlots,var)); + } + var->connectTreeBranches(aChain); int nTypeBins = 2 + (int)plotVars.size(); @@ -3185,48 +3282,93 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { hisName = (TString)"his1_TMP"; - // get the cuts (assume here that this function is used for "_valid" only, otherwise, would need to add a flag...) - TString treeCuts = (TString)getTrainTestCuts((TString)"_comn"+";"+getTagName(0)+"_valid",0,0,0,var); + // get the cuts + TString cutStr("_comn"), treeNamePostfix(""); + + if(((TString)aChain->GetName()).EndsWith("_valid")) treeNamePostfix = "_valid"; + if(((TString)aChain->GetName()).EndsWith("_train")) treeNamePostfix = "_train"; + + int nMLMnowTag = max(0,glob->GetOptI("nMLMnow")); // needed for e.g., single-regression + if(treeNamePostfix != "") cutStr += ";"+getTagName(nMLMnowTag)+treeNamePostfix; + + TString treeCuts = (TString)getTrainTestCuts(cutStr,nMLMnowTag,0,0,var); for(int nTypeBinNow=0; nTypeBinNow>"+hisName; + bool useQuantileBins = (find(noQuantileBinsV.begin(),noQuantileBinsV.end(), plotVars[nTypeBinNow]) == noQuantileBinsV.end()); + TCanvas * tmpCnvs = new TCanvas("tmpCnvs","tmpCnvs"); - int nEvtPass = aChain->Draw(drawExprs,treeCuts); - + int nEvtPass = aChain->Draw(drawExprs,treeCuts); DELNULL(tmpCnvs); + double minVal(1), maxVal(-1); if(nEvtPass > 0) { TH1 * his1 = (TH1F*)gDirectory->Get(hisName); if(dynamic_cast(his1)) { his1->BufferEmpty(); - minVal = his1->GetXaxis()->GetBinLowEdge(his1->GetXaxis()->GetFirst()); - maxVal = his1->GetXaxis()->GetBinUpEdge (his1->GetXaxis()->GetLast() ); + if(useQuantileBins) { + vector fracV; + + fracV.resize(nBinsZ+1,1); + for(int nBinZnow=0; nBinZnowgetQuantileV(fracV,varPlot_binE[nTypeBinNow],his1))); + fracV.clear(); + + + fracV.resize(nBinsZ,1); + for(int nBinZnow=0; nBinZnowgetQuantileV(fracV,varPlot_binC[nTypeBinNow],his1))); + fracV.clear(); + + // just in case, if the quantile search failes for some reason (!?!), try to use equal-spaced bins + if(!useQuantileBins) { + varPlot_binE[nTypeBinNow].resize(nBinsZ+1,0); + varPlot_binC[nTypeBinNow].resize(nBinsZ ,0); + } + } + + if(!useQuantileBins) { + minVal = his1->GetXaxis()->GetBinLowEdge(his1->GetXaxis()->GetFirst()); + maxVal = his1->GetXaxis()->GetBinUpEdge (his1->GetXaxis()->GetLast() ); + } DELNULL(his1); } } - if(maxVal <= minVal) { minVal = 0; maxVal = 1; } - DELNULL(tmpCnvs); - - // double minVal = aChain->GetMinimum(plotVars[nTypeBinNow]); - // double maxVal = aChain->GetMaximum(plotVars[nTypeBinNow]); - // VERIFY(LOCATION,(TString)"Something is horribly wrong ?!?! ",(maxVal > minVal)); + if(useQuantileBins) { + aLOG(Log::DEBUG) < * addPlotVarV) { if((var->GetCntr("nObj")+1 % nObjectsToPrint == 0) || breakLoop) { var->printCntr(aChainName,Log::DEBUG); } if(breakLoop) break; - - double zTrg = var->GetVarF(zTrgName); + + double usrWgt = hasUserWgt ? var->GetForm(userWgtFrm) : 1; + double zTrg = var->GetVarF(zTrgName); // the only cut which is applied here... all the rest should meanifest themselves as [weight==0] if(zTrg < minValZ) { var->IncCntr((TString)zTrgName+" < "+utils->doubleToStr(minValZ)); continue; } @@ -3305,7 +3448,7 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { TString typeName = nameV_MLM_v[nMLMinNow]; double zRegV = var->GetVarF(nameV_MLM_v[nMLMinNow]); double zRegE = var->GetVarF(nameV_MLM_e[nMLMinNow]); - double zRegW = var->GetVarF(nameV_MLM_w[nMLMinNow]); if(zRegW < EPS) continue; + double zRegW = var->GetVarF(nameV_MLM_w[nMLMinNow]) * usrWgt; if(zRegW < EPS) continue; // if(zRegV < minValZ || zRegV > maxValZ) continue; zRegV = min(max(zRegV,minValZ),maxValZ); @@ -3343,7 +3486,7 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { // ----------------------------------------------------------------------------------------------------------- for(int nPDFinNow=0; nPDFinNowGetVarF(pdfTagWgtV[nPDFinNow]); if(pdfWgt < EPS) continue; + double pdfWgt = var->GetVarF(pdfTagWgtV[nPDFinNow]) * usrWgt; if(pdfWgt < EPS) continue; double pdfErr = var->GetVarF(pdfTagErrV[nPDFinNow]); his_regTrgZ[typeName][0]->Fill(zTrg,pdfWgt); @@ -3411,7 +3554,6 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { else if(nMetricNow == 7) { metricNameV[nMetricNow] = "sigmaRelErr"; metricTitleV[nMetricNow] = "#sigma_{68}(#delta/#sigma)"; } else if(nMetricNow == 8) { metricNameV[nMetricNow] = "N_pois"; metricTitleV[nMetricNow] = "N_{pois}"; } else if(nMetricNow == 9) { metricNameV[nMetricNow] = "KS_test"; metricTitleV[nMetricNow] = "KS-test"; } - else assert(0); } for(int nPlotType=0; nPlotType<2; nPlotType++) { @@ -3433,7 +3575,7 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { TString xTitle(""); if (nTypeBinNow == 0) xTitle = zTrgTitle; else if(nTypeBinNow == 1) xTitle = zRegTitle; - else xTitle = plotVars[nTypeBinNow-2]; + else xTitle = utils->regularizeName(plotVars[nTypeBinNow-2]); TString tagNow0(""), tagNow1(""); tagNow0 = (TString)"all_"+xTitle; tagNow1 = (TString)tagNow0+metricNameV[nMetricNow]; @@ -3458,7 +3600,7 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { mltGrphM[tagNow1] = new TMultiGraph(); mltGrphV[tagNow0].push_back(mltGrphM[tagNow1]); for(int nTypeBinNow=0; nTypeBinNowregularizeName(plotVars[nTypeBinNow]); + TString plotVarName = (TString)typeName+" , "+utils->regularizeName(plotVars[nTypeBinNow]); TString tagNow0(""), tagNow1(""); tagNow0 = (TString)plotVarName+"plotVars"; tagNow1 = (TString)tagNow0+"_mean"; tagNow1.ReplaceAll(" , ",""); @@ -3500,7 +3642,6 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { TH1 * his1(NULL); if (nPlotType == 0) his1 = his_clos [typeName][nTypeBinNow][nBinZnow]; else if(nPlotType == 1) his1 = his_relErr[typeName][nTypeBinNow][nBinZnow]; - else assert(0); if(hisNameNow == "") { hisNameNow = his1->GetName(); hisTitleNow = his1->GetTitle(); } @@ -3521,7 +3662,6 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { TH1 * hisSum(NULL); if (nPlotType == 0) hisSum = his_clos [typeName][nTypeBinNow][nBinsZ]; else if(nPlotType == 1) hisSum = his_relErr[typeName][nTypeBinNow][nBinsZ]; - else assert(0); utils->param->clearAll(); utils->param->NewOptB("doFracLargerSigma" , true); @@ -3568,7 +3708,7 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { // store it in the sigmaRelErr position int nMetricNow = nMetrics; - assert(metricNameV[nMetricNow] == "sigmaRelErr"); // sanity check that the nMetricNow is correct + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "sigmaRelErr")); // sanity check that the nMetricNow is correct graphAvg_Xv[nMetricNow][nTypesSoFar] = nTypesSoFar; graphAvg_Xe[nMetricNow][nTypesSoFar] = EPS; graphAvg_Yv[nMetricNow][nTypesSoFar] = yVal; graphAvg_Ye[nMetricNow][nTypesSoFar] = yErr; @@ -3602,7 +3742,7 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { } else { plotVarName = plotVars[nTypeBinNow-2]; - tagNow0 = (TString)plotVarName+"plotVars_"; + tagNow0 = (TString)utils->regularizeName(plotVarName)+"plotVars_"; grph->GetXaxis()->SetTitle(plotVarName); grph->SetTitle((TString)metricTitlesNow[nMetricNow]); @@ -3616,7 +3756,7 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { grph->GetXaxis()->SetTitle(xTitle); grph->SetTitle(hisTitleNow); - mltGrphM[(TString)"all_"+xTitle+metricNameV[nMetricNow]]->Add(grph); + mltGrphM[(TString)"all_"+utils->regularizeName(xTitle)+metricNameV[nMetricNow]]->Add(grph); xV.clear(); yV.clear(); xE.clear(); yE.clear(); } @@ -3649,7 +3789,7 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { // ----------------------------------------------------------------------------------------------------------- - // calculate the nPoisson metric and the Kolmogorovt test of the distributions + // calculate the nPoisson metric and the Kolmogorov test of the distributions // ----------------------------------------------------------------------------------------------------------- for(int nTypeMLMnow=0; nTypeMLMnow<2; nTypeMLMnow++) { int nTypeIn = (nTypeMLMnow == 0) ? nMLMsIn : nPDFsIn; @@ -3668,12 +3808,12 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { double yVal = utils->param->GetOptF("nPoisson"); int nMetricNow = nMetrics+1; - assert(metricNameV[nMetricNow] == "N_pois"); // sanity check that the nMetricNow is correct + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "N_pois")); // sanity check that the nMetricNow is correct graphAvg_Xv[nMetricNow][nTypesSoFar] = nTypesSoFar; graphAvg_Xe[nMetricNow][nTypesSoFar] = EPS; graphAvg_Yv[nMetricNow][nTypesSoFar] = yVal; graphAvg_Ye[nMetricNow][nTypesSoFar] = EPS; - // Kolmogorovt test + // Kolmogorov test // ----------------------------------------------------------------------------------------------------------- utils->param->clearAll(); utils->param->NewOptC("Kolmogorov_opt" , "prob_dist"); @@ -3681,7 +3821,7 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { yVal = utils->param->GetOptF("Kolmogorov_dist"); nMetricNow++; - assert(metricNameV[nMetricNow] == "KS_test"); // sanity check that the nMetricNow is correct + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "KS_test")); // sanity check that the nMetricNow is correct graphAvg_Xv[nMetricNow][nTypesSoFar] = nTypesSoFar; graphAvg_Xe[nMetricNow][nTypesSoFar] = EPS; graphAvg_Yv[nMetricNow][nTypesSoFar] = yVal; graphAvg_Ye[nMetricNow][nTypesSoFar] = EPS; @@ -3699,31 +3839,41 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { vector nGrphV; if (nMultGrphNow == 0) { - nMetricNow = 0; nGrphV.push_back(nMetricNow); assert(metricNameV[nMetricNow] == "mean"); // sanity check that the nMetricNow is correct + nMetricNow = 0; nGrphV.push_back(nMetricNow); + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "mean")); // sanity check that the nMetricNow is correct yAxisTitle = (TString)"< "+biasTitle+" >"; } else if(nMultGrphNow == 1) { - nMetricNow = 1; nGrphV.push_back(nMetricNow); assert(metricNameV[nMetricNow] == "sigma"); // sanity check that the nMetricNow is correct - nMetricNow = 2; nGrphV.push_back(nMetricNow); assert(metricNameV[nMetricNow] == "sigma_68"); // sanity check that the nMetricNow is correct + nMetricNow = 1; nGrphV.push_back(nMetricNow); + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "sigma")); // sanity check that the nMetricNow is correct + nMetricNow = 2; nGrphV.push_back(nMetricNow); + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "sigma_68")); // sanity check that the nMetricNow is correct yAxisTitle = "< #sigma >"; } else if(nMultGrphNow == 2) { - nMetricNow = 3; nGrphV.push_back(nMetricNow); assert(metricNameV[nMetricNow] == "fracSigma_2"); // sanity check that the nMetricNow is correct - nMetricNow = 4; nGrphV.push_back(nMetricNow); assert(metricNameV[nMetricNow] == "fracSigma_3"); // sanity check that the nMetricNow is correct - nMetricNow = 5; nGrphV.push_back(nMetricNow); assert(metricNameV[nMetricNow] == "fracSig68_2"); // sanity check that the nMetricNow is correct - nMetricNow = 6; nGrphV.push_back(nMetricNow); assert(metricNameV[nMetricNow] == "fracSig68_3"); // sanity check that the nMetricNow is correct + nMetricNow = 3; nGrphV.push_back(nMetricNow); + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "fracSigma_2")); // sanity check that the nMetricNow is correct + nMetricNow = 4; nGrphV.push_back(nMetricNow); + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "fracSigma_3")); // sanity check that the nMetricNow is correct + nMetricNow = 5; nGrphV.push_back(nMetricNow); + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "fracSig68_2")); // sanity check that the nMetricNow is correct + nMetricNow = 6; nGrphV.push_back(nMetricNow); + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "fracSig68_3")); // sanity check that the nMetricNow is correct yAxisTitle = "< f >"; } else if(nMultGrphNow == 3) { - nMetricNow = 7; nGrphV.push_back(nMetricNow); assert(metricNameV[nMetricNow] == "sigmaRelErr"); // sanity check that the nMetricNow is correct + nMetricNow = 7; nGrphV.push_back(nMetricNow); + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "sigmaRelErr")); // sanity check that the nMetricNow is correct yAxisTitle = "#sigma(#delta/#sigma)"; } else if(nMultGrphNow == 4) { - nMetricNow = 8; nGrphV.push_back(nMetricNow); assert(metricNameV[nMetricNow] == "N_pois"); // sanity check that the nMetricNow is correct + nMetricNow = 8; nGrphV.push_back(nMetricNow); + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "N_pois")); // sanity check that the nMetricNow is correct yAxisTitle = metricTitleV[nMetricNow]; } else if(nMultGrphNow == 5) { - nMetricNow = 9; nGrphV.push_back(nMetricNow); assert(metricNameV[nMetricNow] == "KS_test"); // sanity check that the nMetricNow is correct + nMetricNow = 9; nGrphV.push_back(nMetricNow); + VERIFY(LOCATION,(TString)"Sanity check",(metricNameV[nMetricNow] == "KS_test")); // sanity check that the nMetricNow is correct yAxisTitle = metricTitleV[nMetricNow]; } else break; @@ -3767,6 +3917,7 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { // create a plot of the distribution of the regression target, compared to all of the different solutions // first draw only the various PDF metrics vs the target distribution (nDrawNow hisToDelV; for(int nDrawNow=0; nDrawNowintToStr(nDrawNow); if(nPDFs == 1 && nDrawNow > 0) continue; @@ -3818,6 +3969,8 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { outputs->draw->NewOptC("axisTitleY" , hisRegTrgV[0]->GetYaxis()->GetTitle()); outputs->drawHis1dV(hisRegTrgV); } + + for(int nHisNow=0; nHisNow<(int)hisRegTrgV.size(); nHisNow++) { hisToDelV.push_back(hisRegTrgV[nHisNow]); } hisRegTrgV.clear(); } @@ -3866,14 +4019,41 @@ void ANNZ::doMetricPlots(TChain * aChain, vector * addPlotVarV) { // TString mvCmnd = (TString)"mv "+outDirNameNow+"*.C "+(hasPlotExt ? (TString)outDirNameNow+"*."+plotExt : "")+" "+plotDirName; // utils->exeShellCmndOutput(mvCmnd,true,true); + + // ----------------------------------------------------------------------------------------------------------- // cleanup + // ----------------------------------------------------------------------------------------------------------- + for(map ::iterator itr = his_corRegTrgZ.begin(); itr!=his_corRegTrgZ.end(); ++itr) { + TString typeName = itr->first; + + for(int nTypeBinNow=0; nTypeBinNow<(int)his_clos[typeName].size(); nTypeBinNow++) { + for(int nBinZnow=0; nBinZnow<(int)his_clos[typeName][nTypeBinNow].size(); nBinZnow++) { + DELNULL(his_clos[typeName][nTypeBinNow][nBinZnow]); + } + } + + for(int nTypeBinNow=0; nTypeBinNow<(int)his_relErr[typeName].size(); nTypeBinNow++) { + for(int nBinZnow=0; nBinZnow<(int)his_relErr[typeName][nTypeBinNow].size(); nBinZnow++) { + DELNULL(his_relErr[typeName][nTypeBinNow][nBinZnow]); + } + } + + for(int nTypeBinNow=0; nTypeBinNow<(int)his_regTrgZ[typeName].size(); nTypeBinNow++) { + DELNULL(his_regTrgZ[typeName][nTypeBinNow]); + } + + DELNULL(his_corRegTrgZ[typeName]); + } + + for(int nHisNow=0; nHisNow<(int)hisToDelV.size(); nHisNow++) { DELNULL(hisToDelV[nHisNow]); } + DELNULL(var); nameV_MLM_v.clear(); nameV_MLM_e.clear(); nameV_MLM_w.clear(); titleV_MLM.clear(); titleV_PDF.clear(); nameV_PDF.clear(); nameV_PDF_b.clear(); tagNameV.clear(); pdfTagWgtV.clear(); pdfTagErrV.clear(); plotVars.clear(); plotVarForms.clear(); varPlot_binE.clear(); varPlot_binC.clear(); typeTitleV.clear(); metricNameV.clear(); metricTitleV.clear(); - graphAvg_Xv.clear(); graphAvg_Xe.clear(); graphAvg_Yv.clear(); graphAvg_Ye.clear(); + graphAvg_Xv.clear(); graphAvg_Xe.clear(); graphAvg_Yv.clear(); graphAvg_Ye.clear(); hisToDelV.clear(); mltGrphAvgV.clear(); his_regTrgZ.clear(); his_corRegTrgZ.clear(); his_clos.clear(); his_relErr.clear(); return; diff --git a/src/ANNZ_loopRegCls.cpp b/src/ANNZ_loopRegCls.cpp index e9f2651..fb143b9 100644 --- a/src/ANNZ_loopRegCls.cpp +++ b/src/ANNZ_loopRegCls.cpp @@ -626,13 +626,12 @@ void ANNZ::makeTreeRegClsOneMLM(int nMLMnow) { if(isCls) { var_1->NewVarF(MLMname_v); var_1->NewVarI(sigBckTypeName); } if(!isCls || needBinClsErr) { var_1->NewVarF(MLMname_eN); var_1->NewVarF(MLMname_e); var_1->NewVarF(MLMname_eP);} - // setup cuts for the vars we loop on for sig/bck determination in case of classification - setMethodCuts(var_0,nMLMnow); - // connect the input vars to the tree before looping // ----------------------------------------------------------------------------------------------------------- var_0->connectTreeBranchesForm(aChain,&readerInptV); + setMethodCuts(var_0,nMLMnow); + // create the output tree and connect it to the output vars // ----------------------------------------------------------------------------------------------------------- inTreeName = (TString)glob->GetOptC("treeName")+treeNamePostfix; @@ -686,7 +685,7 @@ void ANNZ::makeTreeRegClsOneMLM(int nMLMnow) { // fill the output tree if(isCls) { - double clsVal = getReader(var_0,ANNZ_readType::CLS,true ,nMLMnow); + double clsVal = getReader(var_0,ANNZ_readType::CLS,true,nMLMnow); double clsPrb = getReader(var_0,ANNZ_readType::PRB,false,nMLMnow); double clsWgt = var_0->GetForm(MLMname_w) * (passCuts?1:0); @@ -696,7 +695,7 @@ void ANNZ::makeTreeRegClsOneMLM(int nMLMnow) { var_1->SetVarF(MLMname_v,clsVal); var_1->SetVarF(MLMname,clsPrb); var_1->SetVarF(MLMname_w,clsWgt); } else { - double regVal = getReader(var_0,ANNZ_readType::REG,true ,nMLMnow); + double regVal = getReader(var_0,ANNZ_readType::REG,true,nMLMnow); double regWgt = var_0->GetForm(MLMname_w) * (passCuts?1:0); // sanity check that weights are properly defined @@ -709,7 +708,8 @@ void ANNZ::makeTreeRegClsOneMLM(int nMLMnow) { if (isErrKNNnow) getRegClsErrKNN(var_0,knnErrModule,trgIndexV,nMLMv,!isCls,regErrV); else if(isErrINPnow) getRegClsErrINP(var_0,!isCls,nMLMnow,&seed,&(regErrV[nMLMnow])); - var_1->SetVarF(MLMname_eN,regErrV[nMLMnow][0]); var_1->SetVarF(MLMname_e,regErrV[nMLMnow][1]); var_1->SetVarF(MLMname_eP,regErrV[nMLMnow][2]); + var_1->SetVarF(MLMname_eN,regErrV[nMLMnow][0]); var_1->SetVarF(MLMname_e,regErrV[nMLMnow][1]); + var_1->SetVarF(MLMname_eP,regErrV[nMLMnow][2]); } var_1->fillTree(); @@ -758,7 +758,7 @@ void ANNZ::makeTreeRegClsOneMLM(int nMLMnow) { // if(trainCut != "") cutExprs += (TString)" && ("+trainCut+")"; // deprecated int nEvtPass = aChainOut->Draw(drawExprs,cutExprs); - if(nEvtPass > 0) his_all = (TH1F*)gDirectory->Get(hisName); his_all->BufferEmpty(); + if(nEvtPass > 0) { his_all = (TH1F*)gDirectory->Get(hisName); his_all->BufferEmpty(); } } if(!his_all) continue; @@ -822,6 +822,7 @@ void ANNZ::makeTreeRegClsOneMLM(int nMLMnow) { TString cut_valid = (TString)(getTrainTestCuts((TString)"_comn"+";"+MLMname+"_valid",nMLMnow)); vector optNames; + saveName = glob->versionTag(); optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(glob->versionTag())); saveName = "userCuts_train"; optNames.push_back(saveName); optMap->NewOptC(saveName, cut_train); saveName = "userCuts_valid"; optNames.push_back(saveName); optMap->NewOptC(saveName, cut_valid); saveName = "userWeights_train"; optNames.push_back(saveName); optMap->NewOptC(saveName, userWgtsM[MLMname+"_train"]); @@ -950,32 +951,36 @@ void ANNZ::deriveHisClsPrb(int nMLMnow) { TChain * aChain = new TChain(inTreeName,inTreeName); aChain->SetDirectory(0); aChain->Add(inFileName); aLOG(Log::DEBUG) <GetEntries()<<")"<<" from "<NewForm(MLMname_w,wgtStr); + + var_0->connectTreeBranchesForm(aChain,&readerInptV); + // setup cuts for the vars we loop on for sig/bck determination setMethodCuts(var_0,nMLMnow); - // override signal/background cuts, just in case... - TCut sigCuts = (userCutsM["_sig"] != "") ? userCutsM["_sig"] : userCutsM[MLMname+"_sig"]; - TCut bckCuts = (userCutsM["_bck"] != "") ? userCutsM["_bck"] : userCutsM[MLMname+"_bck"]; + // override signal/background cuts, just in case for classification; required for + // binned-classification where userCutsM[MLMname+"_sig"] and userCutsM[MLMname+"_bck"] hold the bin-cuts + TCut sigCuts = userCutsM["_sig"] + userCutsM[MLMname+"_sig"]; + TCut bckCuts = userCutsM["_bck"] + userCutsM[MLMname+"_bck"]; - VERIFY(LOCATION,(TString)"Could not determine the signal-cut ... Something is horribly wrong !!!",(sigCuts != "")); - VERIFY(LOCATION,(TString)"Could not determine the signal-cut ... Something is horribly wrong !!!",(bckCuts != "")); + VERIFY(LOCATION,(TString)"Could not determine the signal-cut ... Something is horribly wrong !!!", (sigCuts != "")); + VERIFY(LOCATION,(TString)"Could not determine the background-cut ... Something is horribly wrong !!!",(bckCuts != "")); var_0->setTreeCuts("_sig",sigCuts); var_0->setTreeCuts("_bck",bckCuts); - // create MLM-weight formulae for the input variables - TString wgtStr = getRegularStrForm(userWgtsM[MLMname+"_train"],var_0); - var_0->NewForm(MLMname_w,wgtStr); - - var_0->connectTreeBranchesForm(aChain,&readerInptV); - // create signla/background histograms TString hisName(""); vector < TMVA::PDF* > pdfV(2); map < TString, TH1* > hisM; - hisName = "sig_0"; hisM[hisName] = new TH1F(hisName,hisName,nBinsHis,1,-1); hisM[hisName]->SetDefaultBufferSize(bufSize); hisM[hisName]->SetDirectory(0); - hisName = "bck_0"; hisM[hisName] = (TH1*)hisM["sig_0"]->Clone(hisName); hisM[hisName]->SetDefaultBufferSize(bufSize); hisM[hisName]->SetDirectory(0); + hisName = "sig_0"; hisM[hisName] = new TH1F(hisName,hisName,nBinsHis,1,-1); + hisM[hisName]->SetDefaultBufferSize(bufSize); hisM[hisName]->SetDirectory(0); + + hisName = "bck_0"; hisM[hisName] = (TH1*)hisM["sig_0"]->Clone(hisName); + hisM[hisName]->SetDefaultBufferSize(bufSize); hisM[hisName]->SetDirectory(0); // ----------------------------------------------------------------------------------------------------------- // loop over the tree @@ -996,6 +1001,9 @@ void ANNZ::deriveHisClsPrb(int nMLMnow) { bool sigBckType(-1); if (!var_0->hasFailedTreeCuts("_bck")) sigBckType = 0; else if(!var_0->hasFailedTreeCuts("_sig")) sigBckType = 1; + + if (sigBckType == 0) var_0->IncCntr("nObj_bck"); + else if(sigBckType == 1) var_0->IncCntr("nObj_sig"); else continue; double clsVal = getReader(var_0,ANNZ_readType::CLS,true,nMLMnow); @@ -1007,6 +1015,9 @@ void ANNZ::deriveHisClsPrb(int nMLMnow) { } if(!breakLoop) { var_0->printCntr(inTreeName,Log::DEBUG); } + VERIFY(LOCATION,(TString)"Found no background objects... Something is horribly wrong ?!?", (var_0->GetCntr("nObj_bck") > 0)); + VERIFY(LOCATION,(TString)"Found no signal objects... Something is horribly wrong ?!?", (var_0->GetCntr("nObj_sig") > 0)); + DELNULL(var_0); DELNULL(aChain); @@ -1044,8 +1055,9 @@ void ANNZ::deriveHisClsPrb(int nMLMnow) { hisM[tag_1]->SetBinError(nBin1,err01); } - double intgr = hisM[tag_1]->Integral(); VERIFY(LOCATION,(TString)"Got empty "+tag_1+" histogram ... Something is horribly wrong ?!?", (intgr > 0)); - hisM[tag_1]->Scale(1/intgr); intgrSigBck[nHisNow] = intgr; + double intgr = hisM[tag_1]->Integral(); + VERIFY(LOCATION,(TString)"Got empty "+tag_1+" histogram ... Something is horribly wrong ?!?", (intgr > 0)); + hisM[tag_1]->Scale(1/intgr); intgrSigBck[nHisNow] = intgr; } intgrSigBck[2] = intgrSigBck[0] / (intgrSigBck[0] + intgrSigBck[1]); @@ -1124,13 +1136,15 @@ void ANNZ::deriveHisClsPrb(int nMLMnow) { * @param acceptV - If [acceptV != NULL], it will act as a list of variables which will be added to the merged tree. * @param rejectV - If [rejectV != NULL], it will act as a list of variables which will be excluded from the merged tree. * @param aCut - If [aCut != ""], it will serve as a cut on objects which will be excluded from the merged tree. + * @param addFormV - If [addFormV != NULL], it contain a list of formulae which will be added as variables to the merged tree. * * @return - The newly-created merged chain. */ // =========================================================================================================== TChain * ANNZ::mergeTreeFriends(TChain * aChain, TChain * aChainFriend, vector * chainFriendFileNameV, - vector * acceptV, vector * rejectV, TCut aCut) { -// ================================================================================================= + vector * acceptV, vector * rejectV, TCut aCut, + vector< pair > * addFormV) { +// ========================================================================== aLOG(Log::INFO) <(aChain))); @@ -1162,14 +1176,43 @@ TChain * ANNZ::mergeTreeFriends(TChain * aChain, TChain * aChainFriend, vectorSetDirectory(0); outputs->TreeMap[inTreeName] = mergedTree; - vector < pair > varTypeNameV; + // if defined, setup formula from var_0 (will be unpdated every time a new tree entry is loaded) + // and then setup a new variable for var_1, the output VarMaps + // ----------------------------------------------------------------------------------------------------------- + int nForms(0); + vector < vector > formNameV; + if(dynamic_cast >*>(addFormV)) { + nForms = (int)addFormV->size(); + formNameV.resize(nForms,vector(2,"")); + + for(int nFormNow=0; nFormNowat(nFormNow).first; + formNameV[nFormNow][1] = (TString)formNameV[nFormNow][0]+"_FRM"; + + VERIFY(LOCATION,(TString)"Found non-regularized variable name ["+formNameV[nFormNow][0]+"] ... something is " + +"horribly wrong ?!?",(formNameV[nFormNow][0] == utils->regularizeName(formNameV[nFormNow][0]))); + + var_0->NewForm(formNameV[nFormNow][1] , addFormV->at(nFormNow).second); + var_1->NewVarF(formNameV[nFormNow][0]); + + aLOG(Log::DEBUG) <at(nFormNow).second<connectTreeBranches(aChain); - var_1->varStruct(var_0,acceptV,rejectV,&varTypeNameV); var_1->createTreeBranches(mergedTree); + + vector < pair > varTypeNameV; + + var_1->varStruct(var_0,acceptV,rejectV,&varTypeNameV); + var_1->createTreeBranches(mergedTree); var_1->setDefaultVals(); bool hasCut = (aCut != ""); - if(hasCut) var_0->setTreeCuts("aCut",aCut); + if(hasCut) { + var_0->setTreeCuts("aCut",aCut); + aLOG(Log::DEBUG) <GetOptI("nObjectsToWrite")); @@ -1186,6 +1229,12 @@ TChain * ANNZ::mergeTreeFriends(TChain * aChain, TChain * aChainFriend, vectorhasFailedTreeCuts("aCut")) continue; } var_1->copyVarData(var_0,&varTypeNameV); + + // if defined, set the added variables from the list formulae (after the copy from var_0!) + for(int nFormNow=0; nFormNowSetVarF(formNameV[nFormNow][0] , var_0->GetForm(formNameV[nFormNow][1])); + } + var_1->fillTree(); var_0->IncCntr("nObj"); mayWriteObjects = true; @@ -1196,14 +1245,14 @@ TChain * ANNZ::mergeTreeFriends(TChain * aChain, TChain * aChainFriend, vectorTreeMap.erase(inTreeName); - aLOG(Log::DEBUG) <SetDirectory(0); aChainOut->Add(inFileName); - return aChainOut; } diff --git a/src/ANNZ_onlyKnnErr.cpp b/src/ANNZ_onlyKnnErr.cpp new file mode 100644 index 0000000..6a2d961 --- /dev/null +++ b/src/ANNZ_onlyKnnErr.cpp @@ -0,0 +1,342 @@ +// =========================================================================================================== +// Copyright (C) 2015, Iftach Sadeh +// +// This file is part of ANNZ. +// ANNZ is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . +// =========================================================================================================== + +// =========================================================================================================== +/** + * @brief - create and evaluate a KNN error estimator for a general dataset + */ +// =========================================================================================================== +void ANNZ::KnnErr() { +// ================== + aLOG(Log::INFO) <GetOptC("zReg_onlyKnnErr") != "")); + + if (glob->GetOptB("doGenInputTrees")) onlyKnnErr_createTreeErrKNN(); + else if(glob->GetOptB("doEval")) onlyKnnErr_eval(); + + return; +} + + +// =========================================================================================================== +/** + * @brief - create requred a KNN error reference trees for later use + */ +// =========================================================================================================== +void ANNZ::onlyKnnErr_createTreeErrKNN() { +// ======================================= + aLOG(Log::INFO) <GetOptI("nMLMnow"); + TString zTrgName = glob->GetOptC("zTrg"); + TString zRegName = glob->GetOptC("zReg_onlyKnnErr"); + TString indexName = glob->GetOptC("indexName"); + + TString MLMname = getTagName(nMLMnow); + TString errKNNname = getErrKNNname(nMLMnow); + TString MLMname_i = getTagIndex(nMLMnow); + + // ----------------------------------------------------------------------------------------------------------- + // create a dedicated tree for the error + // ----------------------------------------------------------------------------------------------------------- + VarMaps * var_0 = new VarMaps(glob,utils,"treeErrKNN_0"); + VarMaps * var_1 = new VarMaps(glob,utils,"treeErrKNN_1"); + + TString inTreeName = (TString)glob->GetOptC("treeName")+"_full"; + TString inFileName = (TString)glob->GetOptC("inputTreeDirName")+inTreeName+"*.root"; + + // prepare the chain and input variables + TChain * aChain = new TChain(inTreeName,inTreeName); aChain->SetDirectory(0); aChain->Add(inFileName); + aLOG(Log::DEBUG) <GetEntries()<<")" + <<" from "<connectTreeBranches(aChain); + + var_1->NewVarF(MLMname); var_1->NewVarF(errKNNname); var_1->NewVarI(MLMname_i); var_1->NewVarF(zTrgName); + + TString outTreeName = getKeyWord("","treeErrKNN","treeErrKNNname"); + TTree * outTree = new TTree(outTreeName,outTreeName); outTree->SetDirectory(0); outputs->TreeMap[outTreeName] = outTree; + + var_1->createTreeBranches(outTree); + var_1->setDefaultVals(); + + // loop on the input tree and create the output + // ----------------------------------------------------------------------------------------------------------- + bool breakLoop(false), mayWriteObjects(false); + int nObjectsToWrite(glob->GetOptI("nObjectsToWrite")), nObjectsToPrint(glob->GetOptI("nObjectsToPrint")); + var_0->clearCntr(); + for(Long64_t loopEntry=0; true; loopEntry++) { + if(!var_0->getTreeEntry(loopEntry)) breakLoop = true; + + if((var_0->GetCntr("nObj") % nObjectsToPrint == 0 && var_0->GetCntr("nObj") > 0) || breakLoop) { var_0->printCntr(inTreeName,Log::DEBUG); } + if((mayWriteObjects && var_0->GetCntr("nObj") % nObjectsToWrite == 0) || breakLoop) { + outputs->WriteOutObjects(false,true); outputs->ResetObjects(); mayWriteObjects = false; + } + if(breakLoop) break; + + var_1->SetVarI(MLMname_i,var_0->GetVarI(indexName)); + + // compute the KNN error for this object + // see: http://arxiv.org/abs/0810.2991 - Estimating the Redshift Distribution of Photometric Galaxy... - Sect. 4.2 + double zTrg = var_0->GetVarF(zTrgName); + double regVal = var_0->GetVarF(zRegName); + double errKNN = regVal - zTrg; + + var_1->SetVarF(zTrgName, zTrg); + var_1->SetVarF(MLMname, regVal); + var_1->SetVarF(errKNNname,errKNN); + + var_1->fillTree(); + + var_0->IncCntr("nObj"); mayWriteObjects = true; + } + if(!breakLoop) { var_0->printCntr(inTreeName,Log::DEBUG); outputs->WriteOutObjects(false,true); outputs->ResetObjects(); } + + // cleanup + DELNULL(var_0); DELNULL(var_1); DELNULL(aChain); DELNULL(outTree); outputs->TreeMap.erase(outTreeName); + + return; +} + + +// =========================================================================================================== +/** + * @brief - evaluate a KNN error estimator for a general dataset + */ +// =========================================================================================================== +void ANNZ::onlyKnnErr_eval() { +// =========================== + aLOG(Log::INFO) <GetOptI("nMLMs"); + int nMLMnow = glob->GetOptI("nMLMnow"); + TString zRegName = glob->GetOptC("zReg_onlyKnnErr"); + TString indexName = glob->GetOptC("indexName"); + TString knnErrCut = glob->GetOptC("cuts_onlyKnnErr"); + TString knnErrWgt = glob->GetOptC("weights_onlyKnnErr"); + TString knnVars = glob->GetOptC("knnVars_onlyKnnErr"); + TString asciiPostfix = glob->GetOptC("baseName_onlyKnnErr"); + bool doStoreToAscii = glob->GetOptB("doStoreToAscii"); + TString weightKNN = glob->GetOptC("baseName_wgtKNN"); + + TString MLMname = getTagName(nMLMnow); + TString errKNNname = getErrKNNname(nMLMnow); + TString MLMname_i = getTagIndex(nMLMnow); + TString MLMname_eN = getTagError(nMLMnow,"N"); MLMname_eN.ReplaceAll(MLMname,zRegName); + TString MLMname_e = getTagError(nMLMnow,""); MLMname_e .ReplaceAll(MLMname,zRegName); + TString MLMname_eP = getTagError(nMLMnow,"P"); MLMname_eP.ReplaceAll(MLMname,zRegName); + + // list of variables from the original tree to copy to the output + vector addVarV = utils->splitStringByChar(glob->GetOptC("addOutputVars"),';'); + + // ----------------------------------------------------------------------------------------------------------- + // setup the variables used for the knn search + // ----------------------------------------------------------------------------------------------------------- + inNamesVar.resize(nMLMs); + inNamesVar[nMLMnow] = utils->splitStringByChar(knnVars,';'); + + int nKnnVars = (int)inNamesVar[nMLMnow].size(); + VERIFY(LOCATION,(TString)"No input variables defined for "+MLMname+" . Something is horribly wrong ?!?",(nKnnVars > 0)); + + readerInptV.clear(); + for(int nVarNow=0; nVarNow(inVarNameNow,0)); + + aLOG(Log::DEBUG) <(nKnnVars,0)); + for(int nReaderInputNow=0; nReaderInputNow= 0)); + + readerInptIndexV[nMLMnow][nReaderInputNow] = readerInptIndex; + } + + // ----------------------------------------------------------------------------------------------------------- + // setup the kd-tree and associated objects + // ----------------------------------------------------------------------------------------------------------- + VarMaps * varKNN(NULL); vector aChainKnn(2,NULL); vector trgIndexV; + TFile * knnErrOutFile(NULL); TMVA::Factory * knnErrFactory(NULL); TMVA::kNN::ModulekNN * knnErrModule(NULL); + + TString inTreeNameKnn = getKeyWord("","treeErrKNN","treeErrKNNname"); + TString inFileNameKnn = glob->GetOptC("inputTreeDirName")+inTreeNameKnn+"*.root"; + + aChainKnn[0] = new TChain(inTreeNameKnn,inTreeNameKnn); aChainKnn[0]->SetDirectory(0); aChainKnn[0]->Add(inFileNameKnn); + + TString inTreeKnnFrnd = (TString)glob->GetOptC("treeName")+"_full"; + TString inFileKnnFrnd = (TString)glob->GetOptC("inputTreeDirName")+inTreeKnnFrnd+"*.root"; + aChainKnn[1] = new TChain(inTreeKnnFrnd,inTreeKnnFrnd); aChainKnn[1]->SetDirectory(0); aChainKnn[1]->Add(inFileKnnFrnd); + + aChainKnn[0]->AddFriend(aChainKnn[1],utils->nextTreeFriendName(aChainKnn[0])); + + int nEntriesChainKnn = aChainKnn[0]->GetEntries(); + aLOG(Log::DEBUG) <connectTreeBranches(aChainKnn[0]); // connect the tree so as to allocate memory for cut variables + + TCut cutsNow = (TCut)knnErrCut; + TString wgtReg = (TString)((knnErrWgt == "") ? weightKNN : (TString)"("+weightKNN+")*("+knnErrWgt+")"); + wgtReg = getRegularStrForm(wgtReg,varKNN); + + setupKdTreeKNN(aChainKnn[0],knnErrOutFile,knnErrFactory,knnErrModule,trgIndexV,nMLMnow,cutsNow,wgtReg); + + // ----------------------------------------------------------------------------------------------------------- + // define VarMaps to access the evaluated dataset and for writing the output + // ----------------------------------------------------------------------------------------------------------- + VarMaps * var_0 = new VarMaps(glob,utils,"treeErrKNN_0"); + VarMaps * var_1 = new VarMaps(glob,utils,"treeErrKNN_1"); + + TString inTreeName = (TString)glob->GetOptC("treeName")+"_full"; + TString inFileName = (TString)glob->GetOptC("outDirNameFull")+inTreeName+"*.root"; + + // prepare the chain and input variables + TChain * aChain = new TChain(inTreeName,inTreeName); aChain->SetDirectory(0); aChain->Add(inFileName); + aLOG(Log::DEBUG) <GetEntries()<<")" + <<" from "<connectTreeBranchesForm(aChain,&readerInptV); + + var_1->NewVarI(MLMname_i); var_1->NewVarF(MLMname_e); var_1->NewVarF(MLMname_eN); var_1->NewVarF(MLMname_eP); + + + // possible additional variables added to the output (do once after connectTreeBranchesForm of the input tree) + // ----------------------------------------------------------------------------------------------------------- + for(int nVarsInNow=0; nVarsInNow<(int)addVarV.size(); nVarsInNow++) { + VERIFY(LOCATION,(TString)"from addOutputVars - trying to use undefined variable (\"" + +addVarV[nVarsInNow]+"\") ...",var_0->HasVar(addVarV[nVarsInNow])); + } + + vector < pair > varTypeNameV; + var_1->varStruct(var_0,&addVarV,NULL,&varTypeNameV); + + TString outTreeName = (TString)glob->GetOptC("treeName")+glob->GetOptC("baseName_onlyKnnErr"); + TString outFileName = (TString)glob->GetOptC("outDirNameFull")+outTreeName+"*.root"; // to be used later on... + TTree * outTree = new TTree(outTreeName,outTreeName); outTree->SetDirectory(0); outputs->TreeMap[outTreeName] = outTree; + + var_1->createTreeBranches(outTree); + var_1->setDefaultVals(); + + // ----------------------------------------------------------------------------------------------------------- + // loop over the evaluated chain and derive the errors + // ----------------------------------------------------------------------------------------------------------- + vector nMLMv(1,nMLMnow); + vector < vector > regErrV(nMLMs,vector(3,0)); + + bool breakLoop(false), mayWriteObjects(false); + int nObjectsToWrite(glob->GetOptI("nObjectsToWrite")), nObjectsToPrint(glob->GetOptI("nObjectsToPrint")); + var_0->clearCntr(); + for(Long64_t loopEntry=0; true; loopEntry++) { + if(!var_0->getTreeEntry(loopEntry)) breakLoop = true; + + if((var_0->GetCntr("nObj") % nObjectsToPrint == 0 && var_0->GetCntr("nObj") > 0) || breakLoop) { var_0->printCntr(inTreeName,Log::DEBUG); } + if((mayWriteObjects && var_0->GetCntr("nObj") % nObjectsToWrite == 0) || breakLoop) { + outputs->WriteOutObjects(false,true); outputs->ResetObjects(); mayWriteObjects = false; + } + if(breakLoop) break; + + // copy current content of all common variables (index + content of addVarV) + var_1->copyVarData(var_0,&varTypeNameV); + + // the index variable + var_1->SetVarI(MLMname_i,var_0->GetVarI(indexName)); + + // derive the knn-errors and copy to the output tree + getRegClsErrKNN(var_0,knnErrModule,trgIndexV,nMLMv,true,regErrV); + + var_1->SetVarF(MLMname_eN,regErrV[nMLMnow][0]); + var_1->SetVarF(MLMname_e, regErrV[nMLMnow][1]); + var_1->SetVarF(MLMname_eP,regErrV[nMLMnow][2]); + + // fill the tree + var_1->fillTree(); + + var_0->IncCntr("nObj"); mayWriteObjects = true; + } + if(!breakLoop) { var_0->printCntr(inTreeName,Log::DEBUG); outputs->WriteOutObjects(false,true); outputs->ResetObjects(); } + + // cleanup + DELNULL(var_0); DELNULL(var_1); DELNULL(outTree); outputs->TreeMap.erase(outTreeName); + + + // ----------------------------------------------------------------------------------------------------------- + // write the results to an ascii file + // ----------------------------------------------------------------------------------------------------------- + if(doStoreToAscii) { + TChain * aChainAsc = new TChain(outTreeName,outTreeName); aChainAsc->SetDirectory(0); aChainAsc->Add(outFileName); + int nEntriesChain = aChainAsc->GetEntries(); + aLOG(Log::DEBUG) <Clone(); + aChain_toFriend->AddFriend(aChainAsc,utils->nextTreeFriendName(aChain_toFriend)); + + VarMaps * var_2 = new VarMaps(glob,utils,"aChainAsc"); + + vector varInV; + varInV.push_back(MLMname_e); varInV.push_back(MLMname_eN); varInV.push_back(MLMname_eP); + + // copy the content of addVarV to a new ovctor (will now be ordered such that the knn-error variables come first) + for(int nVarsInNow=0; nVarsInNow<(int)addVarV.size(); nVarsInNow++) { + if(find(varInV.begin(),varInV.end(), addVarV[nVarsInNow]) == varInV.end()) { varInV.push_back(addVarV[nVarsInNow]); } + } + + var_2->connectTreeBranches(aChain_toFriend); + + var_2->storeTreeToAscii((TString)"ANNZ"+asciiPostfix,"",0,glob->GetOptI("nObjectsToWrite"),"",&varInV,NULL); + + // cleanup + DELNULL(var_2); varInV.clear(); + aChain_toFriend->RemoveFriend(aChainAsc); DELNULL(aChain_toFriend); DELNULL(aChainAsc); + } + + // cleanup + addVarV.clear(); varTypeNameV.clear(); nMLMv.clear(); regErrV.clear(); + + DELNULL(aChain); DELNULL(varKNN); cleanupKdTreeKNN(knnErrOutFile,knnErrFactory); + + aChainKnn[0]->RemoveFriend(aChainKnn[1]); DELNULL(aChainKnn[0]); DELNULL(aChainKnn[1]); + + utils->safeRM(getKeyWord(MLMname,"knnErrXML","outFileDirKnnErr"), inLOG(Log::DEBUG)); + utils->safeRM(getKeyWord(MLMname,"knnErrXML","outFileNameKnnErr"),inLOG(Log::DEBUG)); + + aChainKnn.clear(); trgIndexV.clear(); + + return; +} + diff --git a/src/ANNZ_train.cpp b/src/ANNZ_train.cpp index 015cf75..de1150b 100644 --- a/src/ANNZ_train.cpp +++ b/src/ANNZ_train.cpp @@ -150,8 +150,8 @@ void ANNZ::Train_singleCls() { aLOG(Log::INFO) < optNames; + saveName = glob->versionTag(); optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(glob->versionTag())); saveName = "configSave_name"; optNames.push_back(saveName); optMap->NewOptC(saveName, MLMname); saveName = "configSave_type"; optNames.push_back(saveName); optMap->NewOptC(saveName, mlmType); saveName = "configSave_opts"; optNames.push_back(saveName); optMap->NewOptC(saveName, mlmOpt); @@ -190,8 +191,8 @@ void ANNZ::Train_singleCls() { // cleanup // ----------------------------------------------------------------------------------------------------------- - aLOG(Log::DEBUG)<GetOptB("keepTrainingTrees_factory")) utils->safeRM(outFileNameTrain,inLOG(Log::DEBUG)); @@ -199,7 +200,7 @@ void ANNZ::Train_singleCls() { if(!itr->second) continue; TString chainName = itr->second->GetName(); - aLOG(Log::DEBUG)<second<second<GetOptB("keepTrainingTrees_sigBckCut")) { TString sysCmnd = (TString)glob->GetOptC("trainDirNameFull")+chainName+"*.root"; @@ -233,11 +234,12 @@ void ANNZ::Train_singleReg() { // =========================== aLOG(Log::INFO) <GetOptB("doBiasCorMLM"); int minObjTrainTest = glob->GetOptI("minObjTrainTest"); int nMLMnow = glob->GetOptI("nMLMnow"); - TString MLMname = getTagName(nMLMnow); TString zTrgName = glob->GetOptC("zTrg"); - int maxNobj = glob->GetOptI("maxNobj"); + TString MLMname = getTagName(nMLMnow); + // int maxNobj = glob->GetOptI("maxNobj"); // deprecated setNominalParams(nMLMnow,glob->GetOptC("inputVariables"),glob->GetOptC("inputVarErrors")); @@ -326,17 +328,20 @@ void ANNZ::Train_singleReg() { // set the sample-weights factory->SetWeightExpression(wgtTrain,"Regression"); + // deprecated TCanvas * tmpCnvs = new TCanvas("tmpCnvs","tmpCnvs"); - int nTrain = chainM["_train_cut"]->Draw(zTrgName,cutM["_combined"]); if(maxNobj > 0 && maxNobj < nTrain) nTrain = maxNobj; - int nValid = chainM["_valid_cut"]->Draw(zTrgName,cutM["_combined"]); if(maxNobj > 0 && maxNobj < nValid) nValid = maxNobj; + int nTrain = chainM["_train_cut"]->Draw(zTrgName,cutM["_combined"]); // if(maxNobj > 0 && maxNobj < nTrain) nTrain = maxNobj; + int nValid = chainM["_valid_cut"]->Draw(zTrgName,cutM["_combined"]); // if(maxNobj > 0 && maxNobj < nValid) nValid = maxNobj; DELNULL(tmpCnvs); + VERIFY(LOCATION,(TString)"Got the following [nTrain, nValid = "+TString::Format("%d, %d",nTrain,nValid) + +"] , where all should be larger than "+utils->intToStr(minObjTrainTest) + +" ... Something is horribly wrong ?!?!" ,(nTrain >= minObjTrainTest && nValid >= minObjTrainTest)); + + nTrain = nValid = 0; TString trainValidStr = TString::Format( (TString)"nTrain_Regression=%d:nTest_Regression=%d:SplitMode=Random:",nTrain,nValid ) + factoryNorm; - VERIFY(LOCATION,(TString)"Got the following ["+trainValidStr+"] , where all should be larger than "+utils->intToStr(minObjTrainTest) - +" ... Something is horribly wrong ?!?!" ,(nTrain >= minObjTrainTest && nValid >= minObjTrainTest)); - aLOG(Log::INFO) <PrepareTrainingAndTestTree(cutM["_combined"],trainValidStr); @@ -366,6 +370,7 @@ void ANNZ::Train_singleReg() { TString saveName(""); vector optNames; + saveName = glob->versionTag(); optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(glob->versionTag())); saveName = "configSave_name"; optNames.push_back(saveName); optMap->NewOptC(saveName, MLMname); saveName = "configSave_type"; optNames.push_back(saveName); optMap->NewOptC(saveName, mlmType); saveName = "configSave_opts"; optNames.push_back(saveName); optMap->NewOptC(saveName, mlmOpt); @@ -375,6 +380,7 @@ void ANNZ::Train_singleReg() { saveName = "userWeights_valid"; optNames.push_back(saveName); optMap->NewOptC(saveName, userWgtsM[MLMname+"_valid"]); saveName = "userCuts_train"; optNames.push_back(saveName); optMap->NewOptC(saveName, (TString)userCutsM[MLMname+"_train"]); saveName = "userCuts_valid"; optNames.push_back(saveName); optMap->NewOptC(saveName, (TString)userCutsM[MLMname+"_valid"]); + saveName = "biasCorMLMwithInp"; optNames.push_back(saveName); optMap->NewOptB(saveName, hasBiasCorMLMinp[nMLMnow]); saveName = "zTrg"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(saveName)); utils->optToFromFile(&optNames,optMap,saveFileName,"WRITE"); @@ -383,35 +389,466 @@ void ANNZ::Train_singleReg() { // cleanup // ----------------------------------------------------------------------------------------------------------- - aLOG(Log::DEBUG)<GetOptB("keepTrainingTrees_factory")) utils->safeRM(outFileNameTrain,inLOG(Log::DEBUG)); for(map ::iterator itr = chainM.begin(); itr!=chainM.end(); ++itr) { if(!dynamic_cast(itr->second)) continue; - aLOG(Log::DEBUG)<second->GetName()<<" " <second<second->GetName()<<" " <second<second); } chainM.clear(); cutM.clear(); + DELNULL(optMap); + + + // ----------------------------------------------------------------------------------------------------------- + // generate result-trees from the trained MLM + // ----------------------------------------------------------------------------------------------------------- + makeTreeRegClsOneMLM(nMLMnow); + + + // ----------------------------------------------------------------------------------------------------------- + // create an aditional MLM which will be used to perform a bias-correction on the nominal MLM + // ----------------------------------------------------------------------------------------------------------- + if(doBiasCorMLM) { + // first check that the nominal MLM training was succesfull by getting a reader + // ----------------------------------------------------------------------------------------------------------- + aLOG(Log::INFO) < mlmSkipNow; + for(int nMLMnow0=0; nMLMnow0GetOptI("nMLMs"); nMLMnow0++) { + mlmSkipNow[getTagName(nMLMnow0)] = (nMLMnow0 != nMLMnow); + } + loadReaders(mlmSkipNow,false); + mlmSkipNow.clear(); + + bool isGood = dynamic_cast(regReaders[nMLMnow]); + + clearReaders(); + + // now check that the derived MLM solution is not constant (a failed training) + // ----------------------------------------------------------------------------------------------------------- + if(isGood) { + isGood = false; + + aLOG(Log::INFO) <GetOptC("treeName")+"_train"; + TString inFileName = (TString)postTrainDirName+inTreeName+"*.root"; + + TString cutExprs = (TString)getTagWeight(nMLMnow); + TString cutWgtExprs = utils->cleanWeightExpr((TString)"("+cutExprs+")*("+wgtTrain+")"); + TString hisName = "tmpHisCheck"; + TString drawExprs = (TString)MLMname+">>"+hisName; + + TChain * aChain_0 = new TChain(inTreeName,inTreeName); aChain_0->SetDirectory(0); aChain_0->Add(inFileName); + aLOG(Log::DEBUG) <GetEntries() + <<")"<<" from "<GetOptC("inputTreeDirName")+inTreeName+"*.root"; + TChain * aChain_1 = new TChain(inTreeName,inTreeName); aChain_1->SetDirectory(0); aChain_1->Add(inFileName); + + aChain_0->AddFriend(aChain_1,utils->nextTreeFriendName(aChain_0)); + aLOG(Log::DEBUG) <Draw(drawExprs,cutWgtExprs); + + TH1 * his1 = (nEvtPass == 0) ? NULL : (TH1F*)gDirectory->Get(hisName);; + if(his1) { + his1->SetDirectory(0); his1->BufferEmpty(); + + vector fracV(2,0), quantV(2,-1); + fracV[0] = 0.0001; fracV[1] = 0.9999; + utils->param->clearAll(); + if(utils->getQuantileV(fracV,quantV,his1)) { + // mimic the condition from TMVA::DataSetFactory::CalcMinMax() + isGood = (fabs(quantV[1] - quantV[0]) > 2*FLT_MIN); + + aLOG(Log::DEBUG) < isGood = "<safeRM(mlmXmlFile, inLOG(Log::DEBUG)); + } + + aChain_0->RemoveFriend(aChain_1); DELNULL(aChain_0); DELNULL(aChain_1); + DELNULL(tmpCnvs); DELNULL(his1); + } + + // if all went well, go ahead with the bias-correction + // ----------------------------------------------------------------------------------------------------------- + if(isGood) { + aLOG(Log::INFO) <GetOptB("keepTrainingTrees_sigBckCut")) { utils->safeRM((TString)glob->GetOptC("trainDirNameFull")+glob->GetOptC("treeName")+"_train"+"*.root",inLOG(Log::DEBUG)); utils->safeRM((TString)glob->GetOptC("trainDirNameFull")+glob->GetOptC("treeName")+"_valid"+"*.root",inLOG(Log::DEBUG)); } + + + return; +} + +// =========================================================================================================== +/** + * @brief - Training for an MLM used together with Train_singleReg() to perform a bias-correction. + */ +// =========================================================================================================== +void ANNZ::Train_singleRegBiasCor() { +// ================================== + aLOG(Log::INFO) <GetOptI("nMLMnow"); + TString zTrgName = glob->GetOptC("zTrg"); + double minValZ = glob->GetOptF("minValZ"); + double maxValZ = glob->GetOptF("maxValZ"); + TString userMLMopts = glob->GetOptC("userMLMopts"); + TString biasCorMLMopt = glob->GetOptC("biasCorMLMopt"); + bool alwaysKeepBiasCor = glob->GetOptB("alwaysKeepBiasCor"); + TString addOutputVars = glob->GetOptC("addOutputVars"); + bool doPlots = glob->GetOptB("doPlots"); + + TString MLMname = getTagName(nMLMnow); + TString biasName = getTagBias(nMLMnow); + TString postTrainDirName = getKeyWord(MLMname,"postTrain","postTrainDirName"); + TString trainDirName = getKeyWord(biasName,"trainXML","outFileDirTrain"); + + // number of bins for KN-test and N_poiss test histograms + int nBinsTest = 100; + + // temporary dir names (remove trailing slash and add tmp-postfix) + TString tmpDirName_0(postTrainDirName), tmpDirName_1(postTrainDirName); + tmpDirName_0 = (TString)tmpDirName_0(0,tmpDirName_0.Length()-1) + "_TMPDIR_0/"; + tmpDirName_1 = (TString)tmpDirName_1(0,tmpDirName_1.Length()-1) + "_TMPDIR_1/"; + + // the order matters here... do tmpDirName_0 last so that the new trees will be stored there... + outputs->InitializeDir(trainDirName, glob->GetOptC("baseName")); + outputs->InitializeDir(tmpDirName_0, glob->GetOptC("baseName")); + + TString wgtTrain(""); + map < TString,TChain* > chainM; + map < TString,TCut > cutM; + + // ----------------------------------------------------------------------------------------------------------- + // merge chains - needed for training of the bias-correction MLM + // ----------------------------------------------------------------------------------------------------------- + for(int nTrainValidNow=0; nTrainValidNow<2; nTrainValidNow++) { + TString treeNamePostfix = (TString)((nTrainValidNow == 0) ? "_train" : "_valid"); + + // first chain, the original input with all the traiing variables + TString inTreeName = (TString)glob->GetOptC("treeName")+treeNamePostfix; + TString inFileName = (TString)glob->GetOptC("inputTreeDirName")+inTreeName+"*.root"; + + TChain * aChain_0 = new TChain(inTreeName,inTreeName); aChain_0->SetDirectory(0); aChain_0->Add(inFileName); + aLOG(Log::DEBUG) <GetEntries()<<")"<<" from "<SetDirectory(0); aChain_1->Add(inFileName); + aLOG(Log::DEBUG) <GetEntries()<<")"<<" from "<connectTreeBranches(aChain_0); // connect the tree so as to allocate memory for cut variables + + setMethodCuts(var,nMLMnow); + + cutM["_comn"] = var->getTreeCuts("_comn"); + cutM["_train"] = var->getTreeCuts(MLMname+"_train"); + cutM["_valid"] = var->getTreeCuts(MLMname+"_valid"); + + wgtTrain = getRegularStrForm(userWgtsM[MLMname+"_train"],var); + + DELNULL(var); + } + TCut cutsNow = cutM["_comn"] + cutM[treeNamePostfix]; + + vector < pair > addFormV(1); + addFormV[0] = pair(biasName , (TString)MLMname+" - "+zTrgName); + + chainM[treeNamePostfix] = mergeTreeFriends(aChain_0,aChain_1,NULL,NULL,NULL,cutsNow,&addFormV); + + aLOG(Log::DEBUG) <GetEntries()<<")"<NewOptI("nRnd0",nMLMnow); + optMap->NewOptI("nRnd1",0); + + if(biasCorMLMopt.EqualTo("same", TString::kIgnoreCase)) { biasCorMLMopt = userMLMopts; } + else { optMap->SetOptI("nRnd1",17632029); } + + generateOptsMLM(optMap,biasCorMLMopt); + + TString factoryNorm = optMap->GetOptC("factoryNorm"); + TString mlmType = optMap->GetOptC("type"); + TString mlmOpt = optMap->GetOptC("opt"); DELNULL(optMap); // ----------------------------------------------------------------------------------------------------------- - // generate result-trees from the trained MLM + // define basic TMVA setup, create a new root output file and a factory + // ----------------------------------------------------------------------------------------------------------- + TString outFileNameTrain = getKeyWord(biasName,"trainXML","outFileNameTrain"); + TFile * outputFile = new TFile(outFileNameTrain,"RECREATE"); + TMVA::Factory * factory = new TMVA::Factory(glob->GetOptC("typeANNZ"), outputFile, glob->GetOptC("factoryFlags")); + + // prepare the factory, setting [isBiasMLM=true] to use the bias-correction dir structure + prepFactory(nMLMnow,factory,true); + + double regWeight(1.0); // weight for the entire sample + factory->AddRegressionTree(chainM["_train"], regWeight, TMVA::Types::kTraining); + factory->AddRegressionTree(chainM["_valid"], regWeight, TMVA::Types::kTesting ); + + factory->SetWeightExpression(wgtTrain,"Regression"); + + TString trainValidStr = TString::Format("nTrain_Regression=%d:nTest_Regression=%d:SplitMode=Random:",0,0)+factoryNorm; + + aLOG(Log::INFO) <PrepareTrainingAndTestTree("",trainValidStr); + + TMVA::Types::EMVA typeNow = getTypeMLMbyName(mlmType); + factory->BookMethod(typeNow,biasName,mlmOpt+glob->GetOptC("trainFlagsMLM")); + + // train the factory + // ----------------------------------------------------------------------------------------------------------- + doFactoryTrain(factory); + + // ----------------------------------------------------------------------------------------------------------- + // cleanup before performance check + // ----------------------------------------------------------------------------------------------------------- + DELNULL_(LOCATION,outputFile,(TString)"outputFile",inLOG(Log::DEBUG)); + DELNULL_(LOCATION,factory, (TString)"factory", inLOG(Log::DEBUG)); + + for(map ::iterator itr = chainM.begin(); itr!=chainM.end(); ++itr) { + if(!dynamic_cast(itr->second)) continue; + + aLOG(Log::DEBUG)<second->GetName()<<" " <second<second); + } + chainM.clear(); cutM.clear(); + + // ----------------------------------------------------------------------------------------------------------- + // check that the bias-correction actually improved the results + // ----------------------------------------------------------------------------------------------------------- + // first move the previous results to a temporary directory + TString mvCmnd = (TString)"mv "+postTrainDirName+" "+tmpDirName_1; + utils->exeShellCmndOutput(mvCmnd,inLOG(Log::DEBUG),true); + + // create the new trees (this will recreate postTrainDirName and put the trees there) // ----------------------------------------------------------------------------------------------------------- makeTreeRegClsOneMLM(nMLMnow); + // now check the results + // ----------------------------------------------------------------------------------------------------------- + aLOG(Log::INFO) < chainV(3,NULL); + vector < TH1* > hisV(3,NULL); + + TCanvas * tmpCnvs = new TCanvas("tmpCnvs","tmpCnvs"); + for(int nChekcNow=0; nChekcNow<3; nChekcNow++) { + TString inputTreeDirName(""); + if (nChekcNow == 0) inputTreeDirName = glob->GetOptC("inputTreeDirName"); // contains target variable + else if(nChekcNow == 1) inputTreeDirName = tmpDirName_1; // contains MLM without bias-correction + else if(nChekcNow == 2) inputTreeDirName = postTrainDirName; // contains MLM with bias-correction + + TString inTreeName = (TString)glob->GetOptC("treeName")+"_train"; + TString inFileName = (TString)inputTreeDirName+inTreeName+"*.root"; + + TString cutExprs = (TString)(cutM["_comn"] + cutM["_train"]); + TString cutWgtExprs = utils->cleanWeightExpr((TString)"("+cutExprs+")*("+wgtTrain+")"); + TString hisName = TString::Format("tmpHisCheck_%d",nChekcNow); + TString hisBins = TString::Format("(%d,%f,%f)",nBinsTest,minValZ,maxValZ); + TString drawExprs = (TString)((nChekcNow == 0) ? zTrgName : MLMname)+">>"+hisName+hisBins; + + chainV[nChekcNow] = new TChain(inTreeName,inTreeName); + chainV[nChekcNow]->SetDirectory(0); chainV[nChekcNow]->Add(inFileName); + aLOG(Log::DEBUG) <GetEntries() + <<")"<<" from "< 0) { + chainV[nChekcNow]->AddFriend(chainV[0],utils->nextTreeFriendName(chainV[nChekcNow])); + aLOG(Log::DEBUG) <Draw(drawExprs,cutWgtExprs); if(nEvtPass < 1) continue; + hisV[nChekcNow] = (TH1F*)gDirectory->Get(hisName); if(!hisV[nChekcNow]) continue; + + hisV[nChekcNow]->SetDirectory(0); hisV[nChekcNow]->BufferEmpty(); + } + + // cleanup + DELNULL(tmpCnvs); + + // derive the bias and scatter as a function of each of the training variables, to be + // able to compare between the original and the bias-corrected results + if(doPlots) { + TString inNamesVarAll(""); + for(int nVarNow=0; nVarNow<(int)inNamesVar[nMLMnow].size(); nVarNow++) { + if(addOutputVars.Contains(inNamesVar[nMLMnow][nVarNow])) continue; + + if(inNamesVarAll != "") inNamesVarAll += ";"; + inNamesVarAll += inNamesVar[nMLMnow][nVarNow]; + } + + for(int nChekcNow=0; nChekcNow<2; nChekcNow++) { + doMetricPlots(chainV[nChekcNow+1],NULL,inNamesVarAll); + + // move the plots to a new sub-dir + TString plotDirName_0 = outputs->GetOutPlotDirName(); + if(plotDirName_0.EndsWith("/")) { + plotDirName_0 = plotDirName_0(0,plotDirName_0.Length()-1); + } + TString plotDirName_1 = (TString)plotDirName_0+"_biasCor_"+(TString)((nChekcNow == 0) ? "before" : "after"); + + TString mvdirCmnd = (TString)"mv "+plotDirName_0+" "+plotDirName_1; + utils->exeShellCmndOutput(mvdirCmnd,true,true); + } + } + + for(int nClearNow=0; nClearNow<2; nClearNow++) { + for(int nChekcNow=0; nChekcNow<3; nChekcNow++) { + if(nClearNow == 0) { if(nChekcNow > 0) chainV[nChekcNow]->RemoveFriend(chainV[0]); } + else { DELNULL(chainV[nChekcNow]); } + } + } + chainV.clear(); + + // perform the Kolmogorov tests (NULL histogram results in [-1] as output) + utils->param->clearAll(); + utils->param->NewOptC("Kolmogorov_opt" , "prob_dist"); + utils->getKolmogorov(hisV[0],hisV[1]); + double KStestOrig = utils->param->GetOptF("Kolmogorov_dist"); + + utils->param->clearAll(); + utils->param->NewOptC("Kolmogorov_opt" , "prob_dist"); + utils->getKolmogorov(hisV[0],hisV[2]); + double KStestNew = utils->param->GetOptF("Kolmogorov_dist"); + + // calculat the N_poiss metric (NULL histogram results in [-1] as output) + utils->param->clearAll(); + utils->getNpoisson(hisV[0],hisV[1]); + double nPoissOrig = utils->param->GetOptF("nPoisson"); + + utils->param->clearAll(); + utils->getNpoisson(hisV[0],hisV[2]); + double nPoissNew = utils->param->GetOptF("nPoisson"); + + // now decide what to do (if either KS-test of N_poiss calculations failed, something is wrong...) + bool origMLMfail = ((KStestOrig < -0.5) || (nPoissOrig < -0.5)); + bool newMLMfail = ((KStestNew < -0.5) || (nPoissNew < -0.5)); + + bool newMLMbetter = ( (KStestNew < KStestOrig * 1.25) || (nPoissNew < nPoissOrig * 1.25) ) + && ( (KStestNew < KStestOrig * 1.50) && (nPoissNew < nPoissOrig * 1.50) ); + + // bool origMLMfail(KStestOrig < -0.5), newMLMfail(KStestNew < -0.5), newMLMbetter(KStestNew < KStestOrig); + + // if the original MLM could not ferform the KS test, it should be discarded alltogether + if(origMLMfail) { + aLOG(Log::WARNING) <safeRM(mlmXmlFile, inLOG(Log::DEBUG)); + } + else { + bool doKeepBiasCor = ((newMLMbetter && !newMLMfail) || alwaysKeepBiasCor); + + aLOG(Log::INFO) < will " + <<(TString)(doKeepBiasCor ? (TString)coutWhiteOnGreen+"KEEP" : (TString)coutWhiteOnRed+"DISCARD") + <safeRM(tmpDirName_1, inLOG(Log::DEBUG)); + } + else { + // restore the backup dir of the original trees without the bias-correction + utils->safeRM(postTrainDirName, inLOG(Log::DEBUG)); + + TString mvCmnd = (TString)"mv "+tmpDirName_1+" "+postTrainDirName; + utils->exeShellCmndOutput(mvCmnd,inLOG(Log::DEBUG),true); + + // remove the XML trainig file, so that the bias-correction MLM is not used + TString biasXmlFile = getKeyWord(getTagBias(nMLMnow),"trainXML","outXmlFileName"); + utils->safeRM(biasXmlFile, inLOG(Log::DEBUG)); + } + } + + // after clearing the chains, delete the their directory and the useless training directory + if(!glob->GetOptB("keepTrainingTrees_factory")) { + utils->safeRM(outFileNameTrain,inLOG(Log::DEBUG)); + utils->safeRM(tmpDirName_0, inLOG(Log::DEBUG)); + } + + for(int nHisNow=0; nHisNow<3; nHisNow++) { DELNULL(hisV[nHisNow]); } + hisV.clear(); + + // if(doPlots) { + // outputs->WriteOutObjects(true,true); outputs->ResetObjects(); + // } + return; } + + // =========================================================================================================== /** * @brief - Training for a single binned-classification MLM. @@ -773,8 +1210,8 @@ void ANNZ::Train_binnedCls() { if(!doMultiCls) { aLOG(Log::INFO) < optNames; + saveName = glob->versionTag(); optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(glob->versionTag())); saveName = "configSave_name"; optNames.push_back(saveName); optMap->NewOptC(saveName, MLMname); saveName = "configSave_type"; optNames.push_back(saveName); optMap->NewOptC(saveName, mlmType); saveName = "configSave_opts"; optNames.push_back(saveName); optMap->NewOptC(saveName, mlmOpt); @@ -818,8 +1256,8 @@ void ANNZ::Train_binnedCls() { // cleanup // ----------------------------------------------------------------------------------------------------------- - aLOG(Log::DEBUG)<GetOptB("keepTrainingTrees_factory")) utils->safeRM(outFileNameTrain,inLOG(Log::DEBUG)); @@ -828,7 +1266,7 @@ void ANNZ::Train_binnedCls() { if(!itr->second) continue; TString chainName = itr->second->GetName(); - aLOG(Log::DEBUG)<second<second<GetOptB("keepTrainingTrees_sigBckCut")) { sysCmnd = (TString)trainDirNameFull+chainName+"*.root"; @@ -863,7 +1301,8 @@ void ANNZ::Train_binnedCls() { optMap = new OptMaps("localOptMap"); TString saveName(""), allSeps(""); vector optNames; - saveName = "separation"; optNames.push_back(saveName); optMap->NewOptF(saveName); + + saveName = "separation"; optNames.push_back(saveName); optMap->NewOptF(saveName); double maxSep(-1); int maxSepIndex(0); @@ -966,8 +1405,10 @@ void ANNZ::generateOptsMLM(OptMaps * optMap, TString userMLMopts) { // ================================================================ TString type(""), opt(""); - int seed = glob->GetOptI("initSeedRnd"); if(seed > 0) seed += (optMap->GetOptI("nRnd0")+1) * 1e5 + optMap->GetOptI("nRnd1"); - int nRnd = 14; + int seed = glob->GetOptI("initSeedRnd"); + if(seed > 0) { seed += ((optMap->GetOptI("nRnd0")+1) * 1e5) + optMap->GetOptI("nRnd1"); } + + int nRnd = 15; double * rndAr = new double[nRnd]; TRandom * rnd = new TRandom3(seed); rnd->RndmArray(nRnd,rndAr); TString RandomSeed = TString::Format(":RandomSeed=%d",seed); // doArbitraryRandomization is just for RandomSeed of ANN @@ -1001,7 +1442,7 @@ void ANNZ::generateOptsMLM(OptMaps * optMap, TString userMLMopts) { TString neuronInput = "sum"; TString neuronType = (TString)((rndAr[5] < 0.5) ? "tanh" : "sigmoid"); - TString useReg = (TString)((rndAr[6] < 0.5) ? "True" : "False"); + TString useReg = (TString)((rndAr[6] < 0.8) ? "True" : "False"); TString resetStep(""), convTests(""), testRate(""); if(rndAr[7] < 0.3) resetStep = "100"; else if(rndAr[7] < 0.65) resetStep = "250"; else resetStep = "500"; @@ -1016,9 +1457,13 @@ void ANNZ::generateOptsMLM(OptMaps * optMap, TString userMLMopts) { if(rndAr[12] < 0.5) varTrans += "P"; varTrans.ReplaceAll("NP","N,P"); if(varTrans != "") varTrans = (TString)":VarTransform="+varTrans; + TString cycles(""); + if (rndAr[13] < 0.3) cycles = "500"; else if(rndAr[13] < 0.6) cycles = "800"; + else if(rndAr[13] < 0.8) cycles = "1000"; else cycles = "2000"; + opt = (TString)":HiddenLayers="+layerArc+varTrans+":NeuronType="+neuronType+":NeuronInputType="+neuronInput - +":TrainingMethod="+trainMethod+":TestRate="+testRate+":NCycles=5000" - +":UseRegulator="+useReg+":ConvergenceTests="+convTests+":ConvergenceImprove=1e-30" + +":TrainingMethod="+trainMethod+":TestRate="+testRate+":NCycles="+cycles + +":UseRegulator="+useReg+":ConvergenceTests="+convTests//+":ConvergenceImprove=1e-30" +":SamplingTraining=False:SamplingTesting=False"+":ResetStep="+resetStep+RandomSeed; } @@ -1052,7 +1497,7 @@ void ANNZ::generateOptsMLM(OptMaps * optMap, TString userMLMopts) { } // factory normalization - TString normMode = (TString)((rndAr[13] < 0.5) ? "EqualNumEvents" : "NumEvents"); + TString normMode = (TString)((rndAr[14] < 0.5) ? "EqualNumEvents" : "NumEvents"); optMap->NewOptC("factoryNorm",(TString)":NormMode="+normMode); } // ----------------------------------------------------------------------------------------------------------- @@ -1070,7 +1515,8 @@ void ANNZ::generateOptsMLM(OptMaps * optMap, TString userMLMopts) { type = Itr->first; opt = userMLMopts; opt.ReplaceAll(mlmTypePattern,""); - if(type == "ANN" && !opt.Contains("RandomSeed")) opt += RandomSeed; + if(type == "ANN" && !opt.Contains("RandomSeed",TString::kIgnoreCase)) opt += RandomSeed; + if(type == "BDT" && !opt.Contains("BoostType", TString::kIgnoreCase)) opt += ":BoostType=AdaBoost"; } if(opt == "NULL") { TString allTypes(""); diff --git a/src/ANNZ_utils.cpp b/src/ANNZ_utils.cpp index 4041c47..e583658 100644 --- a/src/ANNZ_utils.cpp +++ b/src/ANNZ_utils.cpp @@ -34,7 +34,7 @@ void ANNZ::Init() { // special case where we just create KNN weight trees // ----------------------------------------------------------------------------------------------------------- - if(glob->GetOptB("doGenInputTrees")) return; + if(glob->GetOptB("doGenInputTrees") && !glob->GetOptB("doOnlyKnnErr")) return; // ----------------------------------------------------------------------------------------------------------- // TMVA stuff @@ -81,10 +81,11 @@ void ANNZ::Init() { factoryFlags += (TString)((glob->GetOptC("transANNZ") == "") ? "" : (TString)":Transformations="+glob->GetOptC("transANNZ")); factoryFlags += (TString)( glob->GetOptB("useCoutCol") ? ":Color" : ":!Color"); factoryFlags += (TString)( glob->GetOptB("isBatch") ? ":!DrawProgressBar" : ":DrawProgressBar"); - factoryFlags += (TString)( inLOG(Log::DEBUG_1) ? ":!Silent" : ":Silent"); + factoryFlags += (TString)( inLOG(Log::DEBUG_1) ? ":!Silent:V" : ":Silent:!V"); glob->NewOptC("factoryFlags" ,factoryFlags); - glob->NewOptC("trainFlagsMLM",(TString)(inLOG(Log::DEBUG_2) ? ":!H:V": ":!H:!V")); + glob->NewOptC("trainFlagsMLM",""); + // glob->NewOptC("trainFlagsMLM",(TString)(inLOG(Log::DEBUG_2) ? ":!H": ":!H")); if(glob->GetOptB("doTrain") && !glob->GetOptB("isBatch")) { aLOG(Log::WARNING) <versionTag()); optMap->NewOptC(glob->versionTag(), glob->GetOptC(glob->versionTag())); + + utils->optToFromFile(&optNames,optMap,glob->GetOptC("userOptsFile_genInputTrees"),"READ","SILENT_KeepFile",inLOG(Log::DEBUG)); + + if(utils->getCodeVersionDiff(optMap->GetOptC(glob->versionTag())) != 0) { + aLOG(Log::WARNING) <GetOptC("userOptsFile_genInputTrees") + <GetOptC(glob->versionTag())<GetOptI("nSplit")); - if(!glob->GetOptB("doEval")) { + if(!glob->GetOptB("doEval") && !glob->GetOptB("doOnlyKnnErr")) { VERIFY(LOCATION,(TString)"Cant run with [\"nSplit\" = "+utils->intToStr(nSplit)+"] ..." +" Must set to 2 when generating the input trees !",(nSplit == 2)); } @@ -178,7 +200,7 @@ void ANNZ::Init() { VERIFY(LOCATION,(TString)"Must set (nMLMs>0)" ,(glob->GetOptI("nMLMs") > 0)); VERIFY(LOCATION,(TString)"Must set (nMLMnowGetOptI("nMLMs") > glob->GetOptI("nMLMnow"))); - if(glob->GetOptB("doRegression")) { + if(glob->GetOptB("doRegression") && !glob->GetOptB("doOnlyKnnErr")) { VERIFY(LOCATION,(TString)"Must set (minValZ < maxValZ)",(glob->GetOptF("minValZ") < glob->GetOptF("maxValZ"))); } @@ -270,8 +292,12 @@ void ANNZ::Init() { // no PDFs currently defined for classification else { glob->SetOptI("nPDFs", 0 ); } - if(glob->GetOptB("doRegression") && !glob->GetOptB("doTrain")) { + if(glob->GetOptB("doRegression") && !glob->GetOptB("doTrain") && !glob->GetOptB("doOnlyKnnErr")) { aLOG(Log::INFO) <GetOptI("nPDFs")<SetOptB("doBiasCorMLM",true); } if(glob->GetOptB("doRegression")) { @@ -375,9 +401,16 @@ void ANNZ::Init() { if(nErrINP % 2 == 1) nErrINP++; glob->SetOptI("nErrINP",nErrINP); - // set the size of the vector which holds the MLM types + // set the size of the vectors which holds the MLM types typeMLM.resize(glob->GetOptI("nMLMs"),TMVA::Types::kVariable); + // set the size of the vector which holds the option for each MLM in single/randomized + // regression to include the nominal MLM as an input parameter for the MLM bias correction + // (for training, we initialize here from the global variable. otherwise, we load this + // option per-MLM as part of loadOptsMLM()) + // ----------------------------------------------------------------------------------------------------------- + hasBiasCorMLMinp.resize(glob->GetOptI("nMLMs"), glob->GetOptB("biasCorMLMwithInp")); + // internal base-names for PDFs generated by TMVA - These should not be changed unless some convention // changes in TMVA - These are not to be confused with the PDFs which are computed by ANNZ !!! // ----------------------------------------------------------------------------------------------------------- @@ -455,7 +488,7 @@ void ANNZ::Init() { aLOG(Log::INFO) <GetOptB("doTrain")) { + if(!glob->GetOptB("doTrain") && !glob->GetOptB("doOnlyKnnErr")) { aLOG(Log::INFO) < 0)); } + if(glob->GetOptB("doOnlyKnnErr")) trainingNeeded = false; + // initialize the outputs with the training directory and reset it if(trainingNeeded) { outputs->InitializeDir(glob->GetOptC("outDirNameFull"),glob->GetOptC("baseName")); @@ -516,24 +551,27 @@ void ANNZ::setTags() { TString baseTag_PDF_a = "_PDF_a"; glob->NewOptC("baseTag_PDF_avg",baseTag_PDF_a); TString baseTag_PDF_m = "_PDF_m"; glob->NewOptC("baseTag_PDF_max",baseTag_PDF_m); TString baseTag_errKNN = "_dzTrg"; glob->NewOptC("baseTag_errKNN", baseTag_errKNN); - + + mlmBaseTag .clear(); mlmTagName .resize(nMLMs); mlmTagErr .resize(nMLMs); mlmTagErrKNN.resize(nMLMs); mlmTagWeight.resize(nMLMs); mlmTagClsVal.resize(nMLMs); mlmTagIndex .resize(nMLMs); + mlmTagBias .resize(nMLMs); inErrTag.resize(nMLMs,vector(0,"")); // the internal length is set in setNominalParams() for(int nMLMnow=0; nMLMnowGetOptC("basePrefix")+utils->intToStr(nMLMnow); // format of "ANNZ_0" - - mlmTagName [nMLMnow] = (TString)MLMname; - mlmTagWeight[nMLMnow] = (TString)MLMname+baseTag_w; - mlmTagClsVal[nMLMnow] = (TString)MLMname+"_clsVal"; - mlmTagIndex [nMLMnow] = (TString)MLMname+"_index"; - mlmTagErrKNN[nMLMnow] = (TString)MLMname+baseTag_errKNN; - - mlmTagErr[nMLMnow]["N"] = (TString)MLMname+baseTag_e+"N"; - mlmTagErr[nMLMnow][""] = (TString)MLMname+baseTag_e ; - mlmTagErr[nMLMnow]["P"] = (TString)MLMname+baseTag_e+"P"; + TString MLMname = (TString)glob->GetOptC("basePrefix")+utils->intToStr(nMLMnow); // format of "ANNZ_0" + + mlmTagName [nMLMnow] = (TString)MLMname; mlmBaseTag[mlmTagName [nMLMnow]] = MLMname; + mlmTagWeight[nMLMnow] = (TString)MLMname+baseTag_w; mlmBaseTag[mlmTagWeight[nMLMnow]] = MLMname; + mlmTagBias [nMLMnow] = (TString)MLMname+"_bias"; mlmBaseTag[mlmTagBias [nMLMnow]] = MLMname; + mlmTagClsVal[nMLMnow] = (TString)MLMname+"_clsVal"; mlmBaseTag[mlmTagClsVal[nMLMnow]] = MLMname; + mlmTagIndex [nMLMnow] = (TString)MLMname+"_index"; mlmBaseTag[mlmTagIndex [nMLMnow]] = MLMname; + mlmTagErrKNN[nMLMnow] = (TString)MLMname+baseTag_errKNN; mlmBaseTag[mlmTagErrKNN[nMLMnow]] = MLMname; + + mlmTagErr[nMLMnow]["N"] = (TString)MLMname+baseTag_e+"N"; mlmBaseTag[mlmTagErr[nMLMnow]["N"]] = MLMname; + mlmTagErr[nMLMnow][""] = (TString)MLMname+baseTag_e ; mlmBaseTag[mlmTagErr[nMLMnow][""] ] = MLMname; + mlmTagErr[nMLMnow]["P"] = (TString)MLMname+baseTag_e+"P"; mlmBaseTag[mlmTagErr[nMLMnow]["P"]] = MLMname; } if(nPDFs > 0) { @@ -567,6 +605,13 @@ void ANNZ::setTags() { return; } + +// =========================================================================================================== +TString ANNZ::getBaseTagName(TString MLMname) { +// ============================================ + VERIFY(LOCATION,(TString)"(MLMname = \""+MLMname+"\") has unsupported format",(mlmBaseTag.find(MLMname) != mlmBaseTag.end())); + return mlmBaseTag[MLMname]; +} // =========================================================================================================== TString ANNZ::getTagName(int nMLMnow) { // ==================================== @@ -587,6 +632,12 @@ TString ANNZ::getTagWeight(int nMLMnow) { return mlmTagWeight[nMLMnow]; } // =========================================================================================================== +TString ANNZ::getTagBias(int nMLMnow) { +// ==================================== + VERIFY(LOCATION,(TString)"(nMLMnow = "+utils->intToStr(nMLMnow)+") out of range ?!?",(nMLMnow >= 0 && nMLMnow < glob->GetOptI("nMLMs"))); + return mlmTagBias[nMLMnow]; +} +// =========================================================================================================== TString ANNZ::getTagClsVal(int nMLMnow) { // ====================================== VERIFY(LOCATION,(TString)"(nMLMnow = "+utils->intToStr(nMLMnow)+") out of range ?!?",(nMLMnow >= 0 && nMLMnow < glob->GetOptI("nMLMs"))); @@ -671,7 +722,9 @@ TString ANNZ::getKeyWord(TString MLMname, TString sequence, TString key) { // ----------------------------------------------------------------------------------------------------------- if(sequence == "trainXML") { // ----------------------------------------------------------------------------------------------------------- - TString trainDirNameFullNow = glob->GetOptC("trainDirNameFull") + (TString)((glob->GetOptB("doTrain")) ? "" : MLMname+"/" ); + TString trainDirNameFullNow = glob->GetOptC("trainDirNameFull"); + if(!glob->GetOptB("doTrain")) trainDirNameFullNow += (TString)getBaseTagName(MLMname)+"/"; + TString outFileDirTrain = trainDirNameFullNow+MLMname+"_weights/"; TString outXmlFileName = outFileDirTrain+glob->GetOptC("typeANNZ")+"_"+MLMname+".weights.xml"; TString configSaveFileName = outFileDirTrain+"saveTrainOpt_"+MLMname+".txt"; @@ -787,15 +840,28 @@ TString ANNZ::getRegularStrForm(TString strIn, VarMaps * var, TChain * aChain) { * cuts for binned-classification etc. */ // =========================================================================================================== -void ANNZ::loadOptsMLM() { -// ======================== +void ANNZ::loadOptsMLM() { +// ======================= aLOG(Log::DEBUG_1) <GetOptI("nMLMs"); TString weightKNN = glob->GetOptC("baseName_wgtKNN"); + // general initializations inNamesVar.resize(nMLMs); inNamesErr.resize(nMLMs); inVarsScaleFunc.resize(nMLMs); + for(int nMLMnow=0; nMLMnowGetOptB("doTrain")) { int nMLMnow = glob->GetOptI("nMLMnow"); TString MLMname = getTagName(nMLMnow); @@ -817,12 +883,15 @@ void ANNZ::loadOptsMLM() { userWgtsM[MLM_trainValid] = weightKNN; } else { - userWgtsM[MLM_trainValid] = (TString)weightKNN+"*("+userWgtsM[MLM_trainValid]+")"; + userWgtsM[MLM_trainValid] = (TString)"("+weightKNN+")*("+userWgtsM[MLM_trainValid]+")"; } } } } + // ----------------------------------------------------------------------------------------------------------- + // optimization, validation and evaluation + // ----------------------------------------------------------------------------------------------------------- else { map > modCW; // ----------------------------------------------------------------------------------------------------------- @@ -873,6 +942,7 @@ void ANNZ::loadOptsMLM() { OptMaps * optMap = new OptMaps("localOptMap"); TString saveName = ""; vector optNames; + saveName = glob->versionTag(); optNames.push_back(saveName); optMap->NewOptC(saveName); saveName = "configSave_name"; optNames.push_back(saveName); optMap->NewOptC(saveName); saveName = "configSave_type"; optNames.push_back(saveName); optMap->NewOptC(saveName); saveName = "inputVariables"; optNames.push_back(saveName); optMap->NewOptC(saveName); @@ -890,6 +960,10 @@ void ANNZ::loadOptsMLM() { saveName = "binCls_clsBins"; optNames.push_back(saveName); optMap->NewOptC(saveName); saveName = "binCls_maxBinW"; optNames.push_back(saveName); optMap->NewOptF(saveName); + if(glob->GetOptB("doRegression") && !glob->GetOptB("doBinnedCls")) { + saveName = "biasCorMLMwithInp"; optNames.push_back(saveName); optMap->NewOptB(saveName); + } + // store general information // ----------------------------------------------------------------------------------------------------------- // for classification @@ -924,6 +998,14 @@ void ANNZ::loadOptsMLM() { "\", but expected to be \""+MLMname+"\") from file ("+configSaveFileName+")", (optMap->GetOptC("configSave_name") == MLMname)); + if(utils->getCodeVersionDiff(optMap->GetOptC(glob->versionTag())) != 0) { + aLOG(Log::WARNING) <GetOptC(glob->versionTag())<GetOptB("doBinnedCls")) { userCutsM[MLMname+"_sig"] = (TCut)optMap->GetOptC(MLMname+"_sig"); @@ -933,6 +1015,9 @@ void ANNZ::loadOptsMLM() { optNames.erase(std::remove(optNames.begin(), optNames.end(), MLMname+"_bck"), optNames.end()); optMap->DelOptC(MLMname+"_bck"); } + if(glob->GetOptB("doRegression") && !glob->GetOptB("doBinnedCls")) { + hasBiasCorMLMinp[nMLMnow] = optMap->GetOptB("biasCorMLMwithInp"); + } // isDynBinCls - avoid direct comparison of the binCls_clsBins string, since different machines // may have slightly different quantile/floating point calculation, and so while the derived bins may @@ -1044,7 +1129,7 @@ void ANNZ::loadOptsMLM() { wgtFinal_valid = weightKNN; } else if(!(wgtFinal_valid.Contains(weightKNN))) { - wgtFinal_valid = (TString)weightKNN+"*("+wgtFinal_valid+")"; + wgtFinal_valid = (TString)"("+weightKNN+")*("+wgtFinal_valid+")"; } } userWgtsM[MLMname+"_valid"] = utils->cleanWeightExpr(wgtFinal_valid); @@ -1147,8 +1232,8 @@ void ANNZ::setMethodCuts(VarMaps * var, int nMLMnow, bool verbose) { TString MLMname = getTagName(nMLMnow); VERIFY(LOCATION,(TString)"Memory leak ?! ",(dynamic_cast(var))); - bool debug = inLOG(Log::DEBUG_1) && verbose; - if(debug) aLOG(Log::DEBUG_1) <printCut("<printCut("<GetOptB("hasTruth") && !glob->GetOptB("doClassification")) { if(glob->GetOptB("useCutsMinMaxZ")) { - treeCuts += (TCut)(glob->GetOptC("zTrg")+TString::Format(" > %f",glob->GetOptF("minValZ"))); - treeCuts += (TCut)(glob->GetOptC("zTrg")+TString::Format(" < %f",glob->GetOptF("maxValZ"))); + treeCuts += (TCut)(glob->GetOptC("zTrg")+TString::Format(" >= %f",glob->GetOptF("minValZ"))); + treeCuts += (TCut)(glob->GetOptC("zTrg")+TString::Format(" <= %f",glob->GetOptF("maxValZ"))); } } } diff --git a/src/CatFormat_asciiToTree.cpp b/src/CatFormat_asciiToTree.cpp index 00833a2..f95a49f 100644 --- a/src/CatFormat_asciiToTree.cpp +++ b/src/CatFormat_asciiToTree.cpp @@ -706,12 +706,14 @@ void CatFormat::inputToSplitTree(TString inAsciiFiles, TString inAsciiVars) { OptMaps * optMap = new OptMaps("localOptMap"); TString saveName = ""; vector optNames; - saveName = "nSplit"; optNames.push_back(saveName); optMap->NewOptI(saveName, glob->GetOptI(saveName)); - saveName = "treeName"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(saveName)); - saveName = "indexName"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(saveName)); - // saveName = "splitName"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(saveName)); // deprecated - // saveName = "testValidType"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(saveName)); // deprecated - saveName = "useWgtKNN"; optNames.push_back(saveName); optMap->NewOptB(saveName, glob->GetOptB(saveName)); + + saveName = glob->versionTag(); optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(glob->versionTag())); + saveName = "nSplit"; optNames.push_back(saveName); optMap->NewOptI(saveName, glob->GetOptI(saveName)); + saveName = "treeName"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(saveName)); + saveName = "indexName"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(saveName)); + // saveName = "splitName"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(saveName)); // deprecated + // saveName = "testValidType"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(saveName)); // deprecated + saveName = "useWgtKNN"; optNames.push_back(saveName); optMap->NewOptB(saveName, glob->GetOptB(saveName)); if(storeOrigFileName) { saveName = "origFileName"; optNames.push_back(saveName); optMap->NewOptC(saveName, glob->GetOptC(saveName)); } diff --git a/src/CatFormat_wgtKNN.cpp b/src/CatFormat_wgtKNN.cpp index 210f241..6317246 100644 --- a/src/CatFormat_wgtKNN.cpp +++ b/src/CatFormat_wgtKNN.cpp @@ -718,14 +718,15 @@ void CatFormat::addWgtKNNtoTree(TChain * aChainInp, TChain * aChainRef, TChain * else { var_0->IncCntr("Did not find good weight"); } } // ----------------------------------------------------------------------------------------------------------- - // derive the weight from the approximated density estimation of near objects from the reference sample, if needed + // derive the weight from the approximated density estimation of near objects + // from the reference sample, if needed // ----------------------------------------------------------------------------------------------------------- else { // find the closest object in the reference chain knnErrModule[1][0]->Find(evtNow,1); const TMVA::kNN::List & knnListInp = knnErrModule[1][0]->GetkNNList(); - // must make a sanith check before using the pointer to GetEvent() + // must make a sanity check before using the pointer to GetEvent() VERIFY(LOCATION,(TString)"could not find any near neighbours for objects ... Something is horribly wrong ?!?",(knnListInp.size() > 0)); const TMVA::kNN::Event evtRef(knnListInp.back().first->GetEvent()); @@ -738,8 +739,7 @@ void CatFormat::addWgtKNNtoTree(TChain * aChainInp, TChain * aChainRef, TChain * // find the minNobjInVol near objects in the reference chain, compared to the initial reference object. The number // of objects is estimated as the sum of their weights, scaled by the weight of the original refrence object. If weighs - // are not defined, then the sum of weights will be exactly minNobjInVol. Otherwise, several searches with increasing - // NN numbers may be needed... + // are not defined, then the sum of weights will be exactly minNobjInVol. Otherwise, several searches may be needed... // ----------------------------------------------------------------------------------------------------------- bool foundDist(false); double dist_Ref0_RefNear(0); @@ -966,8 +966,9 @@ void CatFormat::addWgtKNNtoTree(TChain * aChainInp, TChain * aChainRef, TChain * TString weightNow("1"), hisTitle(""); TChain * aChain(NULL); if (nChainNow == 0) { aChain = aChainRef; hisTitle = "Reference"; } - else if(nChainNow == 1) { aChain = aChainInpEvl; hisTitle = "Original"; } - else if(nChainNow == 2) { aChain = aChainOut; hisTitle = "Weighted"; } + else if(nChainNow == 1) { aChain = aChainInpEvl; hisTitle = "Original"; } + else if(nChainNow == 2) { aChain = aChainOut; hisTitle = "Weighted ("+typePostfix+")"; } + hisTitle.ReplaceAll("_",""); if(nChainNow == 2 && wgtKNNname != weightName) weightNow = wgtKNNname; diff --git a/src/OutMngr_utils.cpp b/src/OutMngr_utils.cpp index 20bdb63..a1bb71e 100644 --- a/src/OutMngr_utils.cpp +++ b/src/OutMngr_utils.cpp @@ -20,7 +20,10 @@ void OutMngr::SetOutDirName(TString outDirNameNow) { if(!outDirNameNow.EndsWith("/")) outDirNameNow += "/"; - outDirName = outDirNameNow; + outDirName = outDirNameNow; + outPlotDirName = (TString)outDirName+"plots/"; + if(outPlotDirName.BeginsWith("./")) outPlotDirName = outPlotDirName(2,outPlotDirName.Length()); + utils->checkPathPrefix(outDirName); utils->validDirExists(outDirName); @@ -36,6 +39,10 @@ TString OutMngr::GetOutDirName() { return outDirName; } // =========================================================================================================== +TString OutMngr::GetOutPlotDirName() { + return outPlotDirName; +} +// =========================================================================================================== TString OutMngr::GetOutFileName() { return outFileName; } @@ -86,13 +93,10 @@ void OutMngr::WriteOutObjects(bool writePdfScripts, bool dontWriteHis) { } if(writePdfScripts) { - TString outDirNamePlot = (TString)outDirName+"plots/"; - if(outDirNamePlot.BeginsWith("./")) outDirNamePlot = outDirNamePlot(2,outDirNamePlot.Length()); + utils->exeShellCmndOutput((TString)"mkdir -p "+outPlotDirName,false,true); + aLOG(Log::INFO) << coutCyan<<" - Writing to plotting directory "<exeShellCmndOutput((TString)"mkdir -p "+outDirNamePlot,false,true); - aLOG(Log::INFO) << coutCyan<<" - Writing to plotting directory "<GetOptC("printPlotExtension")); @@ -106,7 +110,7 @@ void OutMngr::WriteOutObjects(bool writePdfScripts, bool dontWriteHis) { if(dynamic_cast(CanvasMap[hisName])) { // CanvasMap[hisName]->Write(); - TString printName = outDirNamePlot+hisName; + TString printName = outPlotDirName+hisName; TString printFile = (TString)printName+".C"; CanvasMap[hisName]->Print(printFile); @@ -116,13 +120,16 @@ void OutMngr::WriteOutObjects(bool writePdfScripts, bool dontWriteHis) { fixCmnd = (TString)"sed -e 's/"+fixCmnd+"/"+hisName+"/g' < "+printFile+" > "+tmpFile+" ; mv "+tmpFile+" "+printFile; utils->exeShellCmndOutput(fixCmnd); - if(printPlotExtension != "") CanvasMap[hisName]->SaveAs((TString)outDirNamePlot+hisName+"."+printPlotExtension); + if(printPlotExtension != "") CanvasMap[hisName]->SaveAs((TString)outPlotDirName+hisName+"."+printPlotExtension); eraseKeys.push_back(hisName); } } - for(int eraseKeyNow=0; eraseKeyNowClose(); DELNULL(OutputRootFile); // utils->exeShellCmndOutput((TString)"rm -rf "+outputRootFileName); diff --git a/src/Utils.cpp b/src/Utils.cpp index 3901f44..f488307 100644 --- a/src/Utils.cpp +++ b/src/Utils.cpp @@ -458,7 +458,12 @@ void Utils::optToFromFile(vector * optNames, OptMaps * optMap, TString VERIFY(LOCATION,(TString)"Trying to use optToFromFile() with invalid pointer to optNames" ,dynamic_cast*>(optNames)); - TString timeStrPrefix = "# time("; + TString timeStrPrefix = "# time("; + + // + vector < TString > alwaysOverWriteV; + alwaysOverWriteV.push_back(glob->versionTag()); + // ----------------------------------------------------------------------------------------------------------- // write the options given in optNames to fileName in a format like: // nSplit="3";splitType="serial";maxValZ="-1";copyCodeToOutDir="TRUE" @@ -543,6 +548,8 @@ void Utils::optToFromFile(vector * optNames, OptMaps * optMap, TString int nOpts = (int)inputLineV.size(); for(int nOptNow=0; nOptNow * optNames, OptMaps * optMap, TString } } - for(int nOptNameNow=0; nOptNameNow<(int)optNames->size(); nOptNameNow++) { - if(optNames->at(nOptNameNow) == subStr0) { isInOptNames = true; break; } + if(find(alwaysOverWriteV.begin(),alwaysOverWriteV.end(),subStr0) != alwaysOverWriteV.end()) { + overWriteNow = true; } + isInOptNames = (find(optNames->begin(),optNames->end(),subStr0) != optNames->end()); + if(!isInOptNames) { if(debug) aCustomLOG("optFile") < did not find \""< * optNames, OptMaps * optMap, TString if(debug) aCustomLOG("optFile") < \""<GetOptB(optNameNow)<<"\" --> isSame = "<SetOptB(optNameNow,valBol); + if(overWriteNow) optMap->SetOptB(optNameNow,valBol); VERIFY(LOCATION,(TString)"Found wrong format of option: "+optNameNow+"=\""+optValStrNow+"\" , should be \"TRUE\" or \"FALSE\" ...", (optValStrNow == "TRUE" || optValStrNow == "FALSE")); @@ -596,7 +605,7 @@ void Utils::optToFromFile(vector * optNames, OptMaps * optMap, TString if(debug) aCustomLOG("optFile") < \""<GetOptI(optNameNow)<<"\" --> isSame = "<SetOptI(optNameNow,valInt); + if(overWriteNow) optMap->SetOptI(optNameNow,valInt); } else if(optTypeNow == "F") { double valDbl = strToDouble(optValStrNow); @@ -611,7 +620,7 @@ void Utils::optToFromFile(vector * optNames, OptMaps * optMap, TString <GetOptF(optNameNow)<<"\" --> isSame = "<SetOptF(optNameNow,valDbl); + if(overWriteNow) optMap->SetOptF(optNameNow,valDbl); } else if(optTypeNow == "C") { isSameOpt = ( optValStrNow == optMap->GetOptC(optNameNow) ); @@ -620,7 +629,7 @@ void Utils::optToFromFile(vector * optNames, OptMaps * optMap, TString if(debug) aCustomLOG("optFile") < \""<GetOptC(optNameNow)<<"\" --> isSame = "<SetOptC(optNameNow,optValStrNow); + if(overWriteNow) optMap->SetOptC(optNameNow,optValStrNow); } // ----------------------------------------------------------------------------------------------------------- @@ -634,8 +643,8 @@ void Utils::optToFromFile(vector * optNames, OptMaps * optMap, TString if(assertSame) { aLOG(Log::ERROR) < * optNames, OptMaps * optMap, TString inputLineV.clear(); } + alwaysOverWriteV.clear(); return; } @@ -696,6 +706,54 @@ void Utils::addTCuts(TCut & aCut0, TCut & aCut1) { return; } +// =========================================================================================================== +void Utils::getCodeVersionV(vector & strV, TString versStrIn) { +// ================================================================== + strV.clear(); + + TString versStr((TString)((versStrIn == "") ? glob->GetOptC(glob->versionTag()) : versStrIn)); + versStr.ReplaceAll(glob->basePrefix(),""); + + vector versV = splitStringByChar(versStr,'.'); + for(int nVersOrder=0; nVersOrder<(int)versV.size(); nVersOrder++) { + strV.push_back( strToUlong(versV[nVersOrder]) ); + } + versV.clear(); + + return; +} + +// =========================================================================================================== +// return [1] if compVers is of a higher version, [0] if the same and [-1] if of a lower version +// =========================================================================================================== +int Utils::getCodeVersionDiff(TString compVers) { +// ============================================== + vector currentVersV, compVersV; + getCodeVersionV(currentVersV); + getCodeVersionV(compVersV,compVers); + + VERIFY(LOCATION,(TString)"Found incompatible version numbers - Current version is [" + +(glob->GetOptC(glob->versionTag())).ReplaceAll(glob->basePrefix(),"") + +"] and compared version is [" + +compVers.ReplaceAll(glob->basePrefix(),"")+"] ..." + ,(currentVersV.size() == compVersV.size())); + + int versDiff(0); + for(int nVersOrder=0; nVersOrder<(int)currentVersV.size(); nVersOrder++) { + if(compVersV[nVersOrder] > currentVersV[nVersOrder]) { + versDiff = 1; + break; + } + else if(compVersV[nVersOrder] < currentVersV[nVersOrder]) { + versDiff = -1; + break; + } + } + + currentVersV.clear(); compVersV.clear(); + return versDiff; +} + // =========================================================================================================== vector Utils::getTreeFriends(TTree * tree) { // ================================================== @@ -1314,8 +1372,12 @@ void Utils::getKolmogorov(double * data0, double * data1) { } double prob(0), dist(0); - if(param->GetOptC("Kolmogorov_opt").Contains("prob")) prob = TMath::KolmogorovTest(param->GetOptI("nArrEntries"),sortedData0,param->GetOptI("nArrEntries"),sortedData1,""); - if(param->GetOptC("Kolmogorov_opt").Contains("dist")) dist = TMath::KolmogorovTest(param->GetOptI("nArrEntries"),sortedData0,param->GetOptI("nArrEntries"),sortedData1,"M"); + if(param->GetOptC("Kolmogorov_opt").Contains("prob")) { + prob = TMath::KolmogorovTest(param->GetOptI("nArrEntries"),sortedData0,param->GetOptI("nArrEntries"),sortedData1,""); + } + if(param->GetOptC("Kolmogorov_opt").Contains("dist")) { + dist = TMath::KolmogorovTest(param->GetOptI("nArrEntries"),sortedData0,param->GetOptI("nArrEntries"),sortedData1,"M"); + } param->NewOptF("Kolmogorov_prob",prob); param->NewOptF("Kolmogorov_dist",dist); diff --git a/src/VarMaps.cpp b/src/VarMaps.cpp index de166b9..626556b 100644 --- a/src/VarMaps.cpp +++ b/src/VarMaps.cpp @@ -639,7 +639,9 @@ void VarMaps::eraseTreeCutsPattern(TString cutPattern, bool ignorCase) { if(hasEle || cutPattern == "") eraseEle.push_back(itr->first); } - for(int nEraseEleNow=0; nEraseEleNow<(int)eraseEle.size(); nEraseEleNow++) { treeCutsM.erase(eraseEle[nEraseEleNow]); } + for(int nEraseEleNow=0; nEraseEleNow<(int)eraseEle.size(); nEraseEleNow++) { + treeCutsM.erase(eraseEle[nEraseEleNow]); + } eraseEle.clear(); return; @@ -670,15 +672,19 @@ void VarMaps::addTreeCuts(TString cutType, TCut aCut) { TString cutStr1 = (TString)aCut; cutStr1 = (regularizeStringForm(cutStr1)).ReplaceAll(" ",""); if(cutStr1 != "" && !cutStr0.Contains(cutStr1)) { - treeCutsM[cutType] = (TString)( (TCut)(treeCutsM[cutType]) + (TCut)(aCut) ); + treeCutsM[cutType] = regularizeStringForm((TString)((TCut)(treeCutsM[cutType]) + (TCut)(aCut))); } printCut(cutType); return; } // =========================================================================================================== -void VarMaps::setTreeCuts(TString cutType, TCut aCut) { -// ==================================================== +void VarMaps::setTreeCuts(TString cutType, TCut aCut, bool checkTreeRead) { +// ======================================================================== + if(checkTreeRead) { + VERIFY(LOCATION,(TString)"Trying to set tree-cuts with no read-tree defined !!!", (getTreeRead() != NULL)); + } + treeCutsM[cutType] = regularizeStringForm((TString)aCut); printCut(cutType); return; @@ -693,8 +699,12 @@ void VarMaps::getTreeCutsM(map & aTreeCutsM) { return; } // =========================================================================================================== -void VarMaps::setTreeCutsM(map & aTreeCutsM) { -// ========================================================== +void VarMaps::setTreeCutsM(map & aTreeCutsM, bool checkTreeRead) { +// ============================================================================== + if(checkTreeRead) { + VERIFY(LOCATION,(TString)"Trying to set tree-cuts with no read-tree defined !!!", (getTreeRead() != NULL)); + } + treeCutsM.clear(); for(map ::iterator itr=aTreeCutsM.begin(); itr!=aTreeCutsM.end(); ++itr) { treeCutsM[itr->first] = regularizeStringForm((TString)itr->second); @@ -705,9 +715,16 @@ void VarMaps::setTreeCutsM(map & aTreeCutsM) { TCut VarMaps::getTreeCuts(TString cutType) { // ========================================= TCut aCut(""); - if(cutType == "") { for(Map ::iterator itr=treeCutsM.begin(); itr!=treeCutsM.end(); ++itr) aCut += (TCut)itr->second; } - else { assert(treeCutsM.find(cutType) != treeCutsM.end()); aCut = (TCut)treeCutsM[cutType]; } - return aCut; + if(cutType == "") { + for(Map ::iterator itr=treeCutsM.begin(); itr!=treeCutsM.end(); ++itr) { + aCut += (TCut)itr->second; + } + } + else { + VERIFY(LOCATION,(TString)"Has not setup treeCutsFormM (\""+cutType+"\") ...",(treeCutsM.find(cutType) != treeCutsM.end())); + aCut = (TCut)treeCutsM[cutType]; + } + return (TCut)regularizeStringForm((TString)aCut); } // =========================================================================================================== bool VarMaps::hasFailedTreeCuts(vector & cutTypeV) { @@ -794,7 +811,8 @@ void VarMaps::printCut(TString cutType, bool debug) { aCustomLOG("")<GenerateInputTrees(); // create catalogue trees else if(aMyMain->glob->GetOptB("doInTrainFlag")) aMyMain->doInTrainFlag(); // inTrainFlag calculation, without the need for MLMs - else aMyMain->DoANNZ(); // training, validation and evaluation modes + else if(!aMyMain->glob->GetOptB("doOnlyKnnErr")) aMyMain->DoANNZ(); // training, validation and evaluation modes + + // knn-error calculation, without the need for MLMs + if(aMyMain->glob->GetOptB("doOnlyKnnErr")) aMyMain->doOnlyKnnErr(); DELNULL(aMyMain); cout<NewOptB("doOptim" ,false); // run ANNZ in validation mode glob->NewOptB("doVerif" ,false); // run ANNZ in binned classification mode and check that training is complete glob->NewOptB("doEval" ,false); // run ANNZ in evaluation mode + glob->NewOptB("doOnlyKnnErr" ,false); // run only the KNN error estimator glob->NewOptB("doInTrainFlag" ,false); // run only the KNN weight flag @@ -160,7 +164,7 @@ myANNZ::myANNZ() { glob->NewOptC("inAsciiFiles" ,""); // list of input files (if no seperate inputs for training,testing,validting) glob->NewOptC("inAsciiVars" ,""); // list of input variables and variable-types as they appear in inAsciiFiles glob->NewOptC("addOutputVars",""); // list of input variables which will be added to the ascii output - glob->NewOptB("storeOrigFileName",false); // wether to store the name of the original file for each object + glob->NewOptB("storeOrigFileName",false); // whether to store the name of the original file for each object // inpFiles_sig, inpFiles_bck - // optional lists of input files defining if an object is of type signal or background glob->NewOptC("inpFiles_sig",""); @@ -201,9 +205,9 @@ myANNZ::myANNZ() { glob->NewOptI("knnFracFact_wgtKNN" ,3); // by default, the weights are computed for the entire sample. That is, the training and the testing samples // are used together - we calculate the difference between the distribution of input-variables between [train+test samples] - // and [ref sample]. However, it is possible to decide to comput the weights for each separately. That is, to calculate - // wegiths for [train sample] with regards to [ref sample], and to separately get [test sample] with regards to [ref sample]. The - // latter is only recommended if the training and testing samples have different inpput-variable distributions. + // and [ref sample]. However, it is possible to decide to compute the weights for each separately. That is, to calculate + // weights for [train sample] with regards to [ref sample], and to separately get [test sample] with regards to [ref sample]. The + // latter is only recommended if the training and testing samples have different input-variable distributions. glob->NewOptB("trainTestTogether_wgtKNN",true); // input files (given by splitTypeTrain, splitTypeTest and inAsciiFiles) may also be root files, containing @@ -211,7 +215,13 @@ myANNZ::myANNZ() { glob->NewOptC("inTreeName" ,""); // if root input is given in inAsciiFiles_wgtKNN, the corresponding tree name is defined in treeName_wgtKNN glob->NewOptC("inTreeName_wgtKNN" ,""); - + + // independent KNN error estimation for an external dataset + // ----------------------------------------------------------------------------------------------------------- + glob->NewOptC("zReg_onlyKnnErr" ,""); // name of variable for which the error is estimated + glob->NewOptC("knnVars_onlyKnnErr" ,""); // name of variable which corresponds to the true value of "zReg_onlyKnnErr" + glob->NewOptC("cuts_onlyKnnErr" ,""); // possible cut expression applied to the KNN error estimator + glob->NewOptC("weights_onlyKnnErr" ,""); // possible weight expression applied to the KNN error estimator // addInTrainFlag, minNobjInVol_inTrain, maxRelRatioInRef_inTrain - // ----------------------------------------------------------------------------------------------------------- @@ -382,6 +392,9 @@ myANNZ::myANNZ() { // ----------------------------------------------------------------------------------------------------------- glob->NewOptC("optimCondReg","sig68"); + // force the MLM ranking alg to take the outlier fraction into account (not recommended) + glob->NewOptB("optimWithFracSig68",false); + // use MAD (median absolute deviation) instead of sigma_86 for randomized regression optimization glob->NewOptB("optimWithMAD",false); @@ -397,13 +410,14 @@ myANNZ::myANNZ() { // ----------------------------------------------------------------------------------------------------------- // user-defined cuts and weights // ----------------------------------------------------------------------------------------------------------- - glob->NewOptB("useCutsMinMaxZ" ,true); // reject objects with regression target outside of range in training/optimization - glob->NewOptC("userCuts_train" ,""); // cuts applied only on training sample - glob->NewOptC("userCuts_valid" ,""); // cuts applied only on validation sample - glob->NewOptC("userCuts_sig" ,""); // expression defining the signal sample for classification - glob->NewOptC("userCuts_bck" ,""); // expression defining the background sample for classification - glob->NewOptC("userWeights_train","1"); // weights applied on training sample - glob->NewOptC("userWeights_valid","1"); // weights applied on validation sample + glob->NewOptB("useCutsMinMaxZ" ,true); // reject objects with regression target outside of range in training/optimization + glob->NewOptC("userCuts_train" ,""); // cuts applied only on training sample + glob->NewOptC("userCuts_valid" ,""); // cuts applied only on validation sample + glob->NewOptC("userCuts_sig" ,""); // expression defining the signal sample for classification + glob->NewOptC("userCuts_bck" ,""); // expression defining the background sample for classification + glob->NewOptC("userWeights_train" ,"1"); // weights applied on training sample + glob->NewOptC("userWeights_valid" ,"1"); // weights applied on validation sample + glob->NewOptC("userWeights_metricPlots","1"); // weights applied on plotting during doMetricPlots() // if userCuts_valid (userWeights_valid) is set to a different value during optimization compared to its // value during training, then modify_userCuts_valid (modify_userWeights_valid) will determine what is @@ -429,8 +443,6 @@ myANNZ::myANNZ() { // a lower acceptance bound to check if too few MLMs are trained or if something went wrong with the optimization procedure // (e.g., not enough trained MLMs have 'good' combinations of scatter, bias and outlier-fraction metrics). glob->NewOptI("minAcptMLMsForPDFs",5); - // wether or not to perform a bias-correction on PDFs - glob->NewOptB("doBiasCorPDF" ,true); // number of random smearing to perform for the PDF bias-correction glob->NewOptI("nSmearUnf" ,100); // add calculation of maximum of PDF to output @@ -442,6 +454,29 @@ myANNZ::myANNZ() { glob->NewOptF("max_bias_PDF" ,-1); // maximal value of the bias of an MLM included in the PDF glob->NewOptF("max_frac68_PDF" ,-1); // maximal value of the outlier-fraction of an MLM included in the PDF + // bias-correction procedure on MLMs and/or PDFs + // doBiasCorPDF - whether or not to perform the correction for PDFs (during optimization) + // doBiasCorMLM - whether or not to perform the correction for MLMs (during training) + // biasCorMLMopt - MLM configuration options for the bias-correction for MLMs + // - can take the same format as userMLMopts (e.g., [biasCorMLMopt="ANNZ_MLM=BDT:VarTransform=N:NTrees=100"]) + // - can be empty (then the job options will be automatically generated, same as is setting [userMLMopts=""]) + // - can be set as [biasCorMLMopt="same"], then the same configuration options as for the nominal MLM + // for which the bias-correction is applied are used + // - simple MLMs are recommended, e.g.: + // - BDT with around 50-100 trees: + // "ANNZ_MLM=BDT:VarTransform=N:NTrees=100:BoostType=AdaBoost" + // - ANN with a simple layer structure, not too many NCycles etc.: + // "ANNZ_MLM=ANN::HiddenLayers=N,N+5:VarTransform=N,P:TrainingMethod=BFGS:NCycles=500:UseRegulator=True" + // biasCorMLMwithInp - add the nominal MLM as an input variable for the new MLM of the bias-correction (not strictly necessary) + // alwaysKeepBiasCor - whether or not to not check the KS-test and N_poiss metrics for improvement in order to + // possibly reject the bias correction (check performed if [alwaysKeepBiasCor] is set to true) + // ----------------------------------------------------------------------------------------------------------- + glob->NewOptB("doBiasCorPDF" ,true); + glob->NewOptB("doBiasCorMLM" ,true); + glob->NewOptB("biasCorMLMwithInp" ,false); + glob->NewOptB("alwaysKeepBiasCor" ,false); + glob->NewOptC("biasCorMLMopt" ,"ANNZ_MLM=BDT:VarTransform=N:NTrees=50:BoostType=AdaBoost"); + // ----------------------------------------------------------------------------------------------------------- // general stuff // ----------------------------------------------------------------------------------------------------------- @@ -454,13 +489,17 @@ myANNZ::myANNZ() { glob->NewOptC("evalDirPostfix" ,""); // add this to the name of the evaluation directory glob->NewOptI("nObjectsToWrite" ,(int)5e5); // maximal number of objects in a tree/output ascii file glob->NewOptI("nObjectsToPrint" ,(int)1e4); // frequency for printing loop counter status - glob->NewOptB("doPlots" ,true); // wether or not to generate performance and control plots + glob->NewOptB("doPlots" ,true); // whether or not to generate performance and control plots glob->NewOptC("zTrgTitle" ,"Z_{trg}"); // title of regression target (for plots) glob->NewOptC("zRegTitle" ,"Z_{reg}"); // title of regression variable (for plots) glob->NewOptF("zPlotBinWidth" ,-1); // width of bins to perform the plotting glob->NewOptI("nDrawBins_zTrg" ,-1); // number of bins in zTrg for plotting // typical width in the regression variable for plotting, e.g., for 10 plotting bins, set zClosBinWidth = (maxValZ-minValZ)/10. glob->NewOptF("zClosBinWidth" ,-1); + // possible list of variables which will always be plotted (that is, no safety checks on variable type will be performed) + glob->NewOptC("alwaysPlotVars" ,""); + // possible list of variables for which we do not use quantile bins for plotting - e.g., set as "inTrainFlag;MAG_U" + glob->NewOptC("noQuantileBinsPlots" ,"inTrainFlag"); // format for plotting (in addition to generated root scripts, which may be run with [root -l script.C] // availabe formats (leave empty [glob->NewOptC("printPlotExtension","")] to prevent plotting): @@ -483,7 +522,7 @@ myANNZ::myANNZ() { glob->NewOptB("doGausSigmaRelErr",true); glob->NewOptI("nErrKNN",100); // number of near-neighbours to use for the KNN error estimation, see setupKdTreeKNN(). - glob->NewOptB("doWidthRescale_errKNN",true); // wether or not to rescale the input parameters of the knn-err search to the range [-1,1] + glob->NewOptB("doWidthRescale_errKNN",true); // whether or not to rescale the input parameters of the knn-err search to the range [-1,1] // if propagating input-errors - nErrINP is the number of randomly generated MLM values used to propagate // the uncertainty on the input parameters to the MLM-estimator. See getRegClsErrINP() @@ -552,6 +591,11 @@ myANNZ::myANNZ() { // =========================================================================================================== void myANNZ::Init() { // ================== + // ----------------------------------------------------------------------------------------------------------- + // current version-tag for the code (always go one tag up for Master version) + // ----------------------------------------------------------------------------------------------------------- + TString basePrefix("ANNZ_"), versionTag("2.2.0"); + // ----------------------------------------------------------------------------------------------------------- // check user string-input vector optNames; @@ -566,8 +610,8 @@ void myANNZ::Init() { // ----------------------------------------------------------------------------------------------------------- // names of index parameters in trees // ----------------------------------------------------------------------------------------------------------- - TString basePrefix("ANNZ_"); glob->NewOptC("basePrefix" ,basePrefix); // base tag for all MLM names + glob->NewOptC(glob->versionTag() ,basePrefix+versionTag); // version-tag glob->NewOptC("baseName_inVarErr" ,basePrefix+"inVarErr_"); // base tag for all PDF names glob->NewOptC("baseName_nPDF" ,basePrefix+"PDF_"); // base tag for all PDF names glob->NewOptC("baseName_wgtKNN" ,basePrefix+"KNN_w"); // KNN weight variable @@ -580,6 +624,8 @@ void myANNZ::Init() { glob->NewOptC("baseName_regPDF_max" ,basePrefix+"PDF_max_"); // base-name for the peak of the pdf solution (and its error) in randomized regression glob->NewOptC("baseName_regPDF_avg" ,basePrefix+"PDF_avg_"); // base-name for the average pdf solution (and its error) in randomized regression glob->NewOptC("baseName_knnErr" ,"_knnErr"); // name-postfix of error variable derived with the KNN estimator + // internal name for the onlyKnnErr option + glob->NewOptC("baseName_onlyKnnErr" ,"_onlyKnnErr"); // name of an optional output parameter in evaluation (is it "safe" to use the result for an evaluated object) glob->NewOptC("baseName_inTrain" ,"inTrainFlag"); // optional parameter to mark if an object is of type signal (1), background (0) or undefined (-1), based on the name of the original input file @@ -597,6 +643,17 @@ void myANNZ::Init() { glob->NewOptC("evalTreePostfix" ,(TString)"_"+glob->GetOptC("evalDirName")); // postfix for evaluation trees glob->NewOptC("inTrainFlagDirName","inTrainFlag"); // name of sub-dir for the inTrainFlag glob->NewOptC("postTrainName" ,"postTrain"); // name of sub-dir for MLM input trees + + // ----------------------------------------------------------------------------------------------------------- + // special setup for the doOnlyKnnErr mode so that we can safely run ANNZ + // ----------------------------------------------------------------------------------------------------------- + if(glob->GetOptB("doOnlyKnnErr")) { + glob->SetOptB("doRegression", true); + glob->SetOptB("doSingleReg", true); + glob->SetOptI("nSplit", 1); + glob->SetOptI("nMLMs", 1); + glob->SetOptI("nMLMnow", 0); + } // ----------------------------------------------------------------------------------------------------------- // update/finalize glob-> paramters after user inputs and initialize utils and outputs @@ -607,13 +664,19 @@ void myANNZ::Init() { // initialize the logger Log::theLog::ReportingLevel() = Log::theLog::FromString((std::string)glob->GetOptC("logLevel")); - aLOG(Log::INFO)<coutWhiteOnBlack<coutBlue<<" - starting myANNZ::Init() ..."<coutDef<coutGreen<<" ----------------------------------------------" <coutDef<coutGreen<<" - Welcome to ANNZ v"<coutDef<coutGreen<<" -----------------------------------------------------" + <<"------------------------------------------------------"<coutDef<GetOptB("doRegression")) { + if (glob->GetOptB("doOnlyKnnErr")) { + analysisPrefix = "onlyKnnErr"; + } + else if(glob->GetOptB("doRegression")) { if(glob->GetOptB("doBinnedCls")) analysisPrefix = "binCls"; else analysisPrefix = "regres"; } @@ -657,7 +720,6 @@ void myANNZ::Init() { glob->NewOptC("evalDirNameFull", (TString)glob->GetOptC("outDirNamePath")+glob->GetOptC("evalDirName")); glob->NewOptC("inTrainFlagDirNameFull",(TString)glob->GetOptC("outDirNamePath")+glob->GetOptC("inTrainFlagDirName")); - if (glob->GetOptB("doGenInputTrees")) glob->NewOptC("outDirNameFull", glob->GetOptC("inputTreeDirName")); else if(glob->GetOptB("doTrain")) glob->NewOptC("outDirNameFull", glob->GetOptC("trainDirNameFull")); else if(glob->GetOptB("doOptim")) glob->NewOptC("outDirNameFull", glob->GetOptC("optimDirNameFull")); @@ -709,15 +771,29 @@ void myANNZ::Init() { bool hasGoodOpt = glob->GetOptB("doGenInputTrees") || ( !glob->GetOptB("doGenInputTrees") && ( glob->GetOptB("doTrain") || glob->GetOptB("doOptim") || glob->GetOptB("doVerif") || glob->GetOptB("doEval") - || glob->GetOptB("doInTrainFlag") ) ); - if(!hasGoodOpt) VERIFY(LOCATION,(TString)"Configuration problem... Did not find \"doGenInputTrees\", \"doTrain\", "+ - "\"doOptim\", \"doVerif\", \"doEval\" or \"doInTrainFlag\" options ...",false); + || glob->GetOptB("doInTrainFlag") ) ); + VERIFY(LOCATION,(TString)"Configuration problem... Did not find \"doGenInputTrees\", \"doTrain\", "+ + "\"doOptim\", \"doVerif\", \"doEval\" or \"doInTrainFlag\" options ..." + ,hasGoodOpt); if(glob->GetOptB("useWgtKNN") && glob->GetOptB("doGenInputTrees")) { VERIFY(LOCATION,(TString)"Configuration problem... Can not set \"useWgtKNN\"" +" without setting corresponding input files in \"inAsciiFiles_wgtKNN\"",(glob->GetOptC("inAsciiFiles_wgtKNN") != "")); } + if(glob->GetOptB("doOnlyKnnErr")) { + bool hasGoodOpt = ( glob->GetOptB("doGenInputTrees") || glob->GetOptB("doEval") ); + + VERIFY(LOCATION,(TString)"Configuration problem... Did not find \"doGenInputTrees\" or \"doEval\" options ...", hasGoodOpt); + + bool hasOtherOpt = ( glob->GetOptB("doTrain") || glob->GetOptB("doOptim") + || glob->GetOptB("doVerif") || glob->GetOptB("doInTrainFlag") ); + + VERIFY(LOCATION,(TString)"Configuration problem... \"doOnlyKnnErr\" can not be run with any of the following options: \"doTrain\", "+ + "\"doOptim\", \"doVerif\" or \"doInTrainFlag\" !" + ,!hasOtherOpt); + } + // check the tadaset division (full, or split into training/testing). make sure that the // deprecated split to 3 samples (training/testing/validation) is not requested by mistake // ----------------------------------------------------------------------------------------------------------- @@ -750,7 +826,6 @@ void myANNZ::Init() { } - if(glob->GetOptI("initSeedRnd") < 0) glob->SetOptI("initSeedRnd",0); if(glob->GetOptI("maxNobj") < 0) glob->SetOptI("maxNobj" ,0); @@ -790,7 +865,7 @@ void myANNZ::Init() { // print out final init parameters if(inLOG(Log::DEBUG)) { glob->printOpts(2,30); cout<GetOptB("doGenInputTrees") && glob->GetOptB("doEval"))); + + // ----------------------------------------------------------------------------------------------------------- + // create trees from the evaluated dataset + // ----------------------------------------------------------------------------------------------------------- + if(glob->GetOptB("doEval")) { + // initialize the inTrainFlag directory + outputs->InitializeDir(glob->GetOptC("outDirNameFull"),glob->GetOptC("baseName")); + + // create the trees + CatFormat * aCatFormat = new CatFormat("aCatFormat",utils,glob,outputs); + + aCatFormat->inputToFullTree(glob->GetOptC("inAsciiFiles"),glob->GetOptC("inAsciiVars"),"_full"); + + DELNULL(aCatFormat); + } + + // ----------------------------------------------------------------------------------------------------------- + // calculate the knn errors + // ----------------------------------------------------------------------------------------------------------- + ANNZ * aANNZ = new ANNZ("aANNZ",utils,glob,outputs); + + aANNZ->KnnErr(); + + DELNULL(aANNZ); + + return; +} + + + // =========================================================================================================== /** * @brief - Calculate the inTrainFlag quality-flag, without the need to train/optimize/evaluate MLMs.