Python: class p_con

p_con.p_con = class p_con
`` | `Class to create Models to classify Molecules active or inactive using threshold for value in training-data`
| Methods defined here: __init__(self, acc_id=None, proxy={}) `Constructor to initialize Object, use proxy if necessary` __str__(self) `String-Representation for Object` load_models(self, model_files) `load model or list of models into self.model` load_mols(self, sd_file) `load SD-File from .sdf, .sdf.gz or .sd.gz` predict(self, model_number) `try to predict activity of compounds using giving model-Number` save_model(self, outfile, model_number=0) `save Model to file using cPickle.dump` save_model_info(self, outfile, mode='html') `create html- or csv-File for models according to mode (default: "html")` save_mols(self, outfile, gzip=True) `create SD-File of current molecules in self.sd_entries` step_0_get_chembl_data(self) `Download Compound-Data for self.acc_id, these are available in self.sd_entries afterwards` step_1_keeplargestfrag(self) `remove all smaller Fragments per compound, just keep the largest` step_2_remove_dupl(self) `remove duplicates from self.sd_entries` step_3_merge_IC50(self) `merge IC50 of duplicates into one compound using mean of all values if: min(IC50) => IC50_avg-3*IC50_stddev && max(IC50) <= IC50_avg+3*IC50_stddev && IC50_stddev <= IC50_avg` step_4_set_TL(self, threshold, ic50_tag='value') `set Property "TL"(TrafficLight) for each compound: if ic50_tag (default:"value") > threshold: TL = 0, else 1` step_5_remove_descriptors(self) `remove list of Properties from each compound (hardcoded) which would corrupt process of creating Prediction-Models` step_6_calc_descriptors(self) `calculate descriptors for each compound, according to Descriptors._descList` step_7_train_models(self) `train models according to trafficlight using sklearn.ensamble.RandomForestClassifier self.model contains up to 10 models afterwards, use save_model_info(type) to create csv or html containing data for each model`