diff options
| -rw-r--r-- | README.md | 3 | ||||
| -rw-r--r-- | lib/parameters.py | 26 |
2 files changed, 28 insertions, 1 deletions
@@ -145,7 +145,8 @@ The following variables may be set to alter the behaviour of dfatool components. | `DFATOOL_KCONF_IGNORE_STRING` | 0, **1** | Ignore string configuration options. These often hold compiler paths and other not really helpful information. | | `DFATOOL_REGRESSION_SAFE_FUNCTIONS` | **0**, 1 | Use safe functions only (e.g. 1/x returnning 1 for x==0) | | `DFATOOL_RMT_NONBINARY_NODES` | 0, **1** | Enable non-binary nodes (i.e., nodes with more than two children corresponding to enum variables) in decision trees | -| `DFATOOL_RMT_RELEVANCE_METHOD` | **none**, std\_by\_param | Ignore parameters deemed irrelevant by the specified heuristic during regression tree generation. Use with caution. | +| `DFATOOL_RMT_RELEVANCE_METHOD` | **none**, mi, std\_by\_param | Ignore parameters deemed irrelevant by the specified heuristic during regression tree generation. mi := [Mutual Information Regression](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_regression.html). Use with caution. | +| `DFATOOL_RMT_RELEVANCE_THRESHOLD` | .. **0.5** .. | Threshold for relevance checks. | | `DFATOOL_PARAM_RELEVANCE_THRESHOLD` | 0 .. **0.5** .. 1 | Threshold for relevant parameter detection: parameter *i* is relevant if mean standard deviation (data partitioned by all parameters) / mean standard deviation (data partition by all parameters but *i*) is less than threshold | | `DFATOOL_RMT_LOSS_IGNORE_SCALAR` | **0**, 1 | Ignore scalar parameters when computing the loss for split node candidates. Instead of computing the loss of a single partition for each `x_i == j`, compute the loss of partitions for `x_i == j` in which non-scalar parameters vary and scalar parameters are constant. This way, scalar parameters do not affect the decision about which non-scalar parameter to use for splitting. | | `DFATOOL_PARAM_CATEGORICAL_TO_SCALAR` | **0**, 1 | Some models (e.g. FOL, sklearn CART, XGBoost) do not support categorical parameters. Ignore them (0) or convert them to scalar indexes (1). Conversion uses lexical order. | diff --git a/lib/parameters.py b/lib/parameters.py index a154918..ac69075 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -18,6 +18,12 @@ logger = logging.getLogger(__name__) dfatool_fol_relevance_method = os.getenv("DFATOOL_FOL_RELEVANCE_METHOD", None) dfatool_symreg_relevance_method = os.getenv("DFATOOL_SYMREG_RELEVANCE_METHOD", None) dfatool_rmt_relevance_method = os.getenv("DFATOOL_RMT_RELEVANCE_METHOD", None) +dfatool_rmt_relevance_threshold = float( + os.getenv("DFATOOL_RMT_RELEVANCE_THRESHOLD", "0.5") +) + +if dfatool_rmt_relevance_method == "mi": + import sklearn.feature_selection def distinct_param_values(param_tuples): @@ -1124,6 +1130,19 @@ class ModelAttribute: by_param = partition_by_param(data, parameters) distinct_values_by_param_index = distinct_param_values(parameters) std_lut = np.mean([np.std(v) for v in by_param.values()]) + elif dfatool_rmt_relevance_method == "mi": + fit_parameters, _, ignore_index = param_to_ndarray( + parameters, with_nan=False, categorical_to_scalar=True + ) + param_to_fit_param = dict() + j = 0 + for i in range(param_count): + if not ignore_index[i]: + param_to_fit_param[i] = j + j += 1 + mutual_information = sklearn.feature_selection.mutual_info_regression( + fit_parameters, data + ) if loss_ignore_scalar: ffs_eligible_params = list() @@ -1188,6 +1207,13 @@ class ModelAttribute: irrelevant_params.append(param_index) loss.append(np.inf) continue + elif dfatool_rmt_relevance_method == "mi": + if ( + mutual_information[param_to_fit_param[param_index]] + < dfatool_rmt_relevance_threshold + ): + loss.append(np.inf) + continue child_indexes = list() for value in unique_values: |