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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 81 |
1 files changed, 49 insertions, 32 deletions
@@ -9,46 +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] - [activity_transform=<Log10 (regression),NOP (classification)>] - [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". -- activity_transform: Normalizing transformations of the y-values (activities), applicable only to regression problems. One of "Log10", "Inverter", "NOP". "Log10" moves all values above zero and takes the log to base 10. "Inverter" moves all values above 1.0 and takes the inverted value. "NOP" is the identity transformation, which does nothing. Model predictions are output with reverse transformation applied. -- 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. @@ -110,4 +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. |