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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 79 |
1 files changed, 49 insertions, 30 deletions
@@ -9,44 +9,58 @@ OpenTox Algorithm REST operations --------------- - Get a list of all algorithms GET / - URIs of algorithms 200 - Get a representation of the GET /fminer/ - fminer representation 200,404 + Get a list of all algorithms GET / - URIs of algorithms 200 + Get a representation of the GET /fminer/ - fminer representation 200,404 fminer algorithms - Get a representation of the GET /fminer/bbrc - bbrc representation 200,404 + Get a representation of the GET /fminer/bbrc - bbrc representation 200,404 bbrc algorithm - Get a representation of the GET /fminer/last - last representation 200,404 + Get a representation of the GET /fminer/last - last representation 200,404 last algorithm - Get a representation of the GET /lazar - lazar representation 200,404 + Get a representation of the GET /lazar - lazar representation 200,404 lazar algorithm - Create bbrc features POST /fminer/bbrc dataset_uri, URI for feature dataset 200,400,404,500 - feature_uri, - [min_frequency=5 per-mil], - [feature_type=trees], - [backbone=true], - [min_chisq_significance=0.95], - [nr_hits=false] - Create last features POST /fminer/last dataset_uri, URI for feature dataset 200,400,404,500 - feature_uri, - [min_frequency=8 %], - [feature_type=trees], - [nr_hits=false] - Create lazar model POST /lazar dataset_uri, URI for lazar model 200,400,404,500 - prediction_feature, - feature_generation_uri - prediction_algorithm - [local_svm_kernel=weighted_tanimoto] - [min_sim=0.3] - [nr_hits=false] - [conf_stdev=false] + Get a representation of the GET /feature_selection - feature selection representation 200,404 + feature selection algorithms + Get a representation of the GET /feature_selection/rfe - rfe representation 200,404 + rfe algorithm + + + Create bbrc features POST /fminer/bbrc dataset_uri, URI for feature dataset 200,400,404,500 + feature_uri, + [min_frequency=5 per-mil], + [feature_type=trees], + [backbone=true], + [min_chisq_significance=0.95], + [nr_hits=false] + Create last features POST /fminer/last dataset_uri, URI for feature dataset 200,400,404,500 + feature_uri, + [min_frequency=8 %], + [feature_type=trees], + [nr_hits=false] + Create lazar model POST /lazar dataset_uri, URI for lazar model 200,400,404,500 + [prediction_feature], + [feature_generation_uri], + [prediction_algorithm], + [feature_dataset_uri], + [pc_type=null], + [nr_hits=false (class. using wt. maj. vote), true (else)], + [min_sim=0.3 (nominal), 0.4 (numeric features)] + [min_train_performance=0.1] + + Create selected features POST /feature_selection/rfe dataset_uri, URI for dataset 200,400,404,500 + prediction_feature, + feature_dataset_uri, + [del_missing=false] + Synopsis -------- -- prediction\_algorithm: One of "weighted\_majority\_vote" (default for classification), "local\_svm\_classification", "local\_svm\_regression (default for regression)", "local\_mlr\_prop". "weighted\_majority\_vote" is not applicable for regression. "local\_mlr\_prop" is not applicable for classification. -- local\_svm\_kernel: One of "weighted\_tanimoto", "propositionalized". local\_svm\_kernel is not appplicable when prediction\_algorithm="weighted\_majority\_vote". -- min_sim: The minimum similarity threshold for neighbors. Numeric value in [0,1]. -- nr_hits: Whether for instantiated models (local\_svm\_kernel = "propositionalized" for prediction_algorithm="local\_svm\_classification" or "local\_svm\_regression", or for prediction_algorithm="local\_mlr\_prop") nominal features should be instantiated with their occurrence counts in the instances. For non-instantiated models (local\_svm\_kernel = "weighted\_tanimoto" for prediction_algorithm="local\_svm\_classification" or "local\_svm\_regression", or for prediction_algorithm="weighted\_majority\_vote") the neighbor-to-neighbor and neighbor-to-query similarity also integrates these counts, when the parameter is set. One of "true", "false". -- conf_stdev: Whether confidence integrates distribution of neighbor activity values. When "true", the exp(-1.0*(standard deviation of neighbor activities)) is multiplied on the similarity. One of "true", "false". +- prediction\_algorithm: One of "weighted\_majority\_vote" (default for classification), "local\_svm\_classification", "local\_svm\_regression" (default for regression). "weighted\_majority\_vote" is not applicable for regression. +- pc_type: Mandatory for feature dataset, one of [geometrical, topological, electronic, constitutional, hybrid, cpsa]. +- nr_hits: Whether nominal features should be instantiated with their occurrence counts in the instances. One of "true", "false". +- min_sim: The minimum similarity threshold for neighbors. Numeric value in [0,1]. +- min_train_performance. The minimum training performance for "local\_svm\_classification" (Accuracy) and "local\_svm\_regression" (R-squared). Numeric value in [0,1]. +- del_missing: one of true, false See http://www.maunz.de/wordpress/opentox/2011/lazar-models-and-how-to-trigger-them for a graphical overview. @@ -108,4 +122,9 @@ Creates a standard Lazar model. [API documentation](http://rdoc.info/github/opentox/algorithm) -------------------------------------------------------------- +* * * + +### Create a feature dataset of selected features + curl -X POST -d dataset_uri={dataset_uri} -d prediction_feature_uri={prediction_feature_uri} -d feature_dataset_uri={feature_dataset_uri} -d del_missing=true http://webservices.in-silico.ch/test/algorithm/feature_selection/rfe + Copyright (c) 2009-2011 Christoph Helma, Martin Guetlein, Micha Rautenberg, Andreas Maunz, David Vorgrimmler, Denis Gebele. See LICENSE for details. |