コード例 #1
0
def run_tsm_unittests(X,y,column_headers,verbose=True,logfile=None):
    tsm = TriggeredSeriesModel(column_headers, logfile=logfile, verbose=verbose,
        on_disk=False, debug=True)

    # ptf('Try set-up', logfile)
    # tsm.setup(X,y)
    # print X.head()
    # print X.iloc[0][0:15,0:4]
    # print X.iloc[0].shape
    # tsm._check_trial_integrity()
    #
    # print 'Preprocess'
    # Xpp = tsm.preprocess(X)
    # # print Xpp.iloc[0][0:15,0:4]
    # print Xpp.iloc[0].shape
    # print 'Prune'
    # Xp = tsm.prune_spots(Xpp, ['26B', '11R', '45B', '36B', '30R', '11B'], column_headers)
    # # print Xp.iloc[0][0:15]
    # print Xp.iloc[0].shape
    # print tsm.trigger_indexes, tsm.trigger_spots
    #
    # print 'Featurize'
    # Xtrig = tsm.featurize_triggers(Xp,30)
    # print Xtrig.shape
    # print tsm.trigger_feature_times
    # print tsm.trigger_features
    # print tsm.trigger_featurizers

    # print 'Now try doing all of these steps with fit'
    # tsm = TriggeredSeriesModel(column_headers, logfile=logfile, verbose=verbose,
    #     on_disk=False, debug=True, detection_featurizer_arguments = {'order':2, 'dx':20.0, 'maxmin':True},
    #     detection_reducer_arguments={'n_components':3})
    # Xf = tsm.fit(X,y, verbose=verbose, debug=True)

    print 'Now with a stacked yp and ypp classifier'
    tsm = TriggeredSeriesModel(column_headers, logfile=logfile, verbose=verbose,
        on_disk=False, debug=True, color_vector_type='DI',
        detection_featurizer_arguments = {'order':2, 'dx':20.0, 'maxmin':True, 'gauss':True, 'stacked':True, 'sigma':1},
        detection_reducer_arguments={'n_components':3},
        resample_method='over')
    Xf = tsm.fit(X,y, verbose=verbose, debug=True)
    return tsm
コード例 #2
0
ファイル: capstone.py プロジェクト: nhu2000/smelling_sepsis
            end = time.time()
            ptf( 'Data unpickled in %d seconds (%d total trials)' % ((end-start), len(X)), LOGFILE)

        run_params['logfile'] = LOGFILE
        run_params['runid'] = RUNID

        # create model
        if RUNTYPE == 'trigger':
            sm = TriggeredSeriesModel(used_column_headers.values, **run_params)
        elif RUNTYPE == 'series':
            sm = SeriesModel(**run_params)

        # Altogether now
        print ('** DOING THE FIT **')
        sm.fit(X, y, verbose=verbose, debug=debug)

        bigend = time.time()

        ptf('====> %d seconds (%0.1f mins)' % ((bigend-bigstart), (bigend-bigstart)/60.0), LOGFILE)
        print_job_info(run_params, n_jobs, n_cpus, RUNID, START_DT_STR, LOGFILE=LOGFILE,
            debug=debug, profile=PROFILE, verbose=verbose, start=False)

        print_run_details(X, sm, LOGFILE)

        save_model(sm, RUNID, MODELFILENAME, LOGFILE=LOGFILE)

        ## VIEW RESULTS
        if RUNTYPE == 'trigger':
            make_trigger_plots(sm, y, RUNID, debug=debug)
        elif RUNTYPE == 'series':