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Diffstat (limited to 'README.md')
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@@ -55,11 +55,11 @@ Least-Squares Regression is essentially a subset of RMT with just a single tree LMT and RMT differ significantly, as LMT uses a learning algorithm that starts out with a DECART and uses bottom-up pruning to turn it into an LMT, whereas RMT build a DECART that only considers parameters that are not suitable for least-squares regression and then uses least-squares regression to find and fit leaf functions. By default, dfatool uses heuristics to determine whether it should generate a simple least-squares regression function or a fully-fledged RMT. -Arguments such as `--force-tree` and environment variables (below) can be used to generate a different flavour of performance model; see [Modeling Method Selection](doc/modeling-method.md). +Arguments such as `--force-tree` and environment variables (below) can be used to generate a different flavour of performance model; see [Modelling Method Selection](doc/modeling-method.md). Again, most of the options and methods documented here work for all three scripts: analyze-archive, analyze-kconfig, and analyze-log. * [Model Visualization and Export](doc/model-visual.md) -* [Modeling Method Selection](doc/modeling-method.md) +* [Modelling Method Selection](doc/modeling-method.md) * [Assessing Model Quality](doc/model-assessment.md) ## Model Application @@ -112,9 +112,9 @@ The following variables may be set to alter the behaviour of dfatool components. | `DFATOOL_KCONF_WITH_CHOICE_NODES` | 0, **1** | Treat kconfig choices (e.g. "choice Model → MobileNet / ResNet / Inception") as enum parameters. If enabled, the corresponding boolean kconfig variables (e.g. "Model\_MobileNet") are not converted to parameters. If disabled, all (and only) boolean kconfig variables are treated as parameters. Mostly relevant for analyze-kconfig, eval-kconfig | | `DFATOOL_COMPENSATE_DRIFT` | **0**, 1 | Perform drift compensation for loaders without sync input (e.g. EnergyTrace or Keysight) | | `DFATOOL_DRIFT_COMPENSATION_PENALTY` | 0 .. 100 (default: majority vote over several penalties) | Specify penalty for ruptures.py PELT changepoint petection | -| `DFATOOL_MODEL` | cart, decart, fol, lgbm, lmt, **rmt**, symreg, uls, xgb | Modeling method. See below for method-specific configuration options. | +| `DFATOOL_MODEL` | cart, decart, fol, lgbm, lmt, **rmt**, symreg, uls, xgb | Modelling method. See below for method-specific configuration options. | | `DFATOOL_RMT_MAX_DEPTH` | **0** .. *n* | Maximum depth for RMT. Default (0): unlimited. | -| `DFATOOL_RMT_SUBMODEL` | cart, fol, static, symreg, **uls** | Modeling method for RMT leaf functions. | +| `DFATOOL_RMT_SUBMODEL` | cart, fol, static, symreg, **uls** | Modelling method for RMT leaf functions. | | `DFATOOL_PREPROCESSING_RELEVANCE_METHOD` | **none**, mi | Ignore parameters deemed irrelevant by the specified heuristic before passing them on to `DFATOOL_MODEL`. | | `DFATOOL_PREPROCESSING_RELEVANCE_THRESHOLD` | .. **0.1** .. | Threshold for relevance heuristic. | | `DFATOOL_CART_MAX_DEPTH` | **0** .. *n* | maximum depth for sklearn CART. Default (0): unlimited. | @@ -133,8 +133,9 @@ The following variables may be set to alter the behaviour of dfatool components. | `DFATOOL_LMT_MIN_SAMPLES_LEAF` | 0.0 .. **0.1** .. 1.0, 3 .. *n* | Minimum samples that each leaf of a split candidate must contain. A value below 1.0 specifies a ratio of the total number of training samples. A value above 1 specifies an absolute number of samples. | | `DFATOOL_LMT_MAX_BINS` | 10 .. **120** | Number of bins used to determine optimal split. LMT default: 25. | | `DFATOOL_LMT_CRITERION` | **mse**, rmse, mae, poisson | Error metric to use when selecting best split. | -| `DFATOOL_ULS_ERROR_METRIC` | **ssr**, rmsd, mae, … | Error metric to use when selecting best-fitting function during unsupervised least squares (ULS) regression. Least squares regression itself minimzes root mean square deviation (rmsd), hence the equivalent (but partitioning-compatible) sum of squared residuals (ssr) is the default. Supports all metrics accepted by `--error-metric`. | +| `DFATOOL_ULS_ERROR_METRIC` | **ssr**, rmsd, **mae**, … | Error metric to use when selecting best-fitting function during unsupervised least squares (ULS) regression. By default, least squares regression minimzes root mean square deviation (rmsd), hence the equivalent (but partitioning-compatible) sum of squared residuals (ssr) is the default. If `DFATOOL_ULS_LOSS_FUNCTION` is set to another value than linear, the default is mean absolute error (mae). Supports all metrics accepted by `--error-metric`. | | `DFATOOL_ULS_FUNCTIONS` | a,b,… | List of function templates to use in ULS. Default: all supported functions. | +| `DFATOOL_ULS_LOSS_FUNCTION` | **linear**', soft\_l1, … | Loss function for least squares fitting, see `scipy.optimize.least_squares#loss` documentation. | | `DFATOOL_ULS_MIN_DISTINCT_VALUES` | 2 .. **3** .. *n* | Minimum number of unique values a parameter must take to be eligible for ULS | | `DFATOOL_ULS_SKIP_CODEPENDENT_CHECK` | **0**, 1 | Do not detect and remove co-dependent features in ULS. | | `DFATOOL_ULS_MIN_BOUND` | **-∞** .. *n* | Lower bound for ULS regression variables. Setting it to 0 can often be beneficial. | |