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How to use get_group method in localstack

Best Python code snippet using localstack_python

agreement_score.py

Source:agreement_score.py

...101            total_agreement = 0102            total_messages = 0103            skipped = 0104            try:105                annot1threads = annotationDataset.groupby("annotator1Name").get_group(annotatorname).thread.unique()106            except:107                annot1threads = []108            try:109                annot2threads = annotationDataset.groupby("annotator2Name").get_group(annotatorname).thread.unique()110            except:111                annot2threads = []112            try:113                annot3threads = annotationDataset.groupby("annotator3Name").get_group(annotatorname).thread.unique()114            except:115                annot3threads =[]116            for thread in annot1threads:117                if not annotationDataset.groupby('thread').get_group(thread).annotator2Name.unique()[0] in removedAnnotators \118                        and type(annotationDataset.groupby('thread').get_group(thread).annotator2Name.unique()[0]) == str:119                    comp12emp = annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy == \120                                annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy121                    comp12askemp = annotationDataset.groupby('thread').get_group(thread).annotator1AskEmpathy == \122                                   annotationDataset.groupby('thread').get_group(thread).annotator2AskEmpathy123                    comp12cta = annotationDataset.groupby('thread').get_group(thread).annotator1CallToAction == \124                                annotationDataset.groupby('thread').get_group(thread).annotator2CallToAction125                    comp12isans = annotationDataset.groupby('thread').get_group(thread).annotator1IsAnswer == \126                                  annotationDataset.groupby('thread').get_group(thread).annotator2IsAnswer127                    comp12isq = annotationDataset.groupby('thread').get_group(thread).annotator1IsQuestion == \128                                annotationDataset.groupby('thread').get_group(thread).annotator2IsQuestion129                    multiplier12 = scoredict[annotationDataset.groupby('thread').get_group(thread).annotator2Name.unique()[0]]130                    for i, _ in enumerate(comp12emp):131                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator1AskEmpathy)[i]) == float:132                            comp12askemp[i] = False # don't count nan as agreeing133                            total_messages -= 1134                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator1CallToAction)[i]) == float:135                            comp12cta[i] = False136                            total_messages -= 1137                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator1IsAnswer)[i]) == float:138                            comp12isans[i] = False139                            total_messages -= 1140                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator1IsQuestion)[i]) == float:141                            comp12isq[i] = False142                            total_messages -= 1143                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy)[i]) == float:144                            comp12emp[i] = False145                            total_messages -= 1146                    total_agreement += sum([sum(x) * multiplier12 for x in [comp12askemp, comp12cta, comp12isans, comp12isq, comp12emp]])147                    total_messages += sum([x.__len__() for x in [comp12askemp, comp12cta, comp12isans, comp12isq, comp12emp]])148                if not annotationDataset.groupby('thread').get_group(thread).annotator3Name.unique()[0] in removedAnnotators \149                        and type(annotationDataset.groupby('thread').get_group(thread).annotator3Name.unique()[0]) == str:150                    comp13emp = annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy == \151                                annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy152                    comp13askemp = annotationDataset.groupby('thread').get_group(thread).annotator1AskEmpathy == \153                                   annotationDataset.groupby('thread').get_group(thread).annotator3AskEmpathy154                    comp13cta = annotationDataset.groupby('thread').get_group(thread).annotator1CallToAction == \155                                annotationDataset.groupby('thread').get_group(thread).annotator3CallToAction156                    comp13isans = annotationDataset.groupby('thread').get_group(thread).annotator1IsAnswer == \157                                  annotationDataset.groupby('thread').get_group(thread).annotator3IsAnswer158                    comp13isq = annotationDataset.groupby('thread').get_group(thread).annotator1IsQuestion == \159                                annotationDataset.groupby('thread').get_group(thread).annotator3IsQuestion160                    # comp13 = annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy == \161                    # annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy162                    multiplier13 = scoredict[annotationDataset.groupby('thread').get_group(thread).annotator3Name.unique()[0]]163                    for i, _ in enumerate(comp13emp):164                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator1AskEmpathy)[i]) == float:165                            comp13askemp[i] = False  # don't count nan as agreeing166                            total_messages -= 1167                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator1CallToAction)[i]) == float:168                            comp13cta[i] = False169                            total_messages -= 1170                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator1IsAnswer)[i]) == float:171                            comp13isans[i] = False172                            total_messages -= 1173                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator1IsQuestion)[i]) == float:174                            comp13isq[i] = False175                            total_messages -= 1176                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy)[i]) == float:177                            comp13emp[i] = False178                            total_messages -= 1179                    total_agreement += sum([sum(x) * multiplier13 for x in [comp13askemp, comp13cta, comp13isans, comp13isq, comp13emp]])180                    total_messages += sum([x.__len__() for x in [comp13askemp, comp13cta, comp13isans, comp13isq, comp13emp]])181            for thread in annot2threads:182                if not annotationDataset.groupby('thread').get_group(thread).annotator1Name.unique()[0] in removedAnnotators \183                        and type(annotationDataset.groupby('thread').get_group(thread).annotator1Name.unique()[0]) == str:184                    comp21emp = annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy == \185                                annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy186                    comp21askemp = annotationDataset.groupby('thread').get_group(thread).annotator2AskEmpathy == \187                                   annotationDataset.groupby('thread').get_group(thread).annotator1AskEmpathy188                    comp21cta = annotationDataset.groupby('thread').get_group(thread).annotator2CallToAction == \189                                annotationDataset.groupby('thread').get_group(thread).annotator1CallToAction190                    comp21isans = annotationDataset.groupby('thread').get_group(thread).annotator2IsAnswer == \191                                  annotationDataset.groupby('thread').get_group(thread).annotator1IsAnswer192                    comp21isq = annotationDataset.groupby('thread').get_group(thread).annotator2IsQuestion == \193                                annotationDataset.groupby('thread').get_group(thread).annotator1IsQuestion194                    # comp21 = annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy == annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy195                    multiplier21 = scoredict[annotationDataset.groupby('thread').get_group(thread).annotator1Name.unique()[0]]196                    for i, _ in enumerate(comp21emp):197                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator2AskEmpathy)[i]) == float:198                            comp21askemp[i] = False  # don't count nan as agreeing199                            total_messages -= 1200                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator2CallToAction)[i]) == float:201                            comp21cta[i] = False202                            total_messages -= 1203                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator2IsAnswer)[i]) == float:204                            comp21isans[i] = False205                            total_messages -= 1206                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator2IsQuestion)[i]) == float:207                            comp21isq[i] = False208                            total_messages -= 1209                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy)[i]) == float:210                            comp21emp[i] = False211                            total_messages -= 1212                            # comp21[i] = False213                    total_agreement += sum([sum(x) * multiplier21 for x in [comp21askemp, comp21cta, comp21isans, comp21isq, comp21emp]])214                    total_messages += sum([x.__len__() for x in [comp21askemp, comp21cta, comp21isans, comp21isq, comp21emp]])215                if not annotationDataset.groupby('thread').get_group(thread).annotator3Name.unique()[0] in removedAnnotators \216                        and type(annotationDataset.groupby('thread').get_group(thread).annotator3Name.unique()[0]) == str:217                    comp23emp = annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy == \218                                annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy219                    comp23askemp = annotationDataset.groupby('thread').get_group(thread).annotator2AskEmpathy == \220                                   annotationDataset.groupby('thread').get_group(thread).annotator3AskEmpathy221                    comp23cta = annotationDataset.groupby('thread').get_group(thread).annotator2CallToAction == \222                                annotationDataset.groupby('thread').get_group(thread).annotator3CallToAction223                    comp23isans = annotationDataset.groupby('thread').get_group(thread).annotator2IsAnswer == \224                                  annotationDataset.groupby('thread').get_group(thread).annotator3IsAnswer225                    comp23isq = annotationDataset.groupby('thread').get_group(thread).annotator2IsQuestion == \226                                annotationDataset.groupby('thread').get_group(thread).annotator3IsQuestion227                    # comp23 = annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy == annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy228                    multiplier23 = scoredict[annotationDataset.groupby('thread').get_group(thread).annotator3Name.unique()[0]]229                    for i, _ in enumerate(comp23emp):230                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator2AskEmpathy)[i]) == float:231                            comp23askemp[i] = False  # don't count nan as agreeing232                            total_messages -= 1233                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator2CallToAction)[i]) == float:234                            comp23cta[i] = False235                            total_messages -= 1236                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator2IsAnswer)[i]) == float:237                            comp23isans[i] = False238                            total_messages -= 1239                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator2IsQuestion)[i]) == float:240                            comp23isq[i] = False241                            total_messages -= 1242                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy)[i]) == float:243                            comp23emp[i] = False244                            total_messages -= 1245                    total_agreement += sum([sum(x) * multiplier23 for x in [comp23askemp, comp23cta, comp23isans, comp23isq, comp23emp]])246                    total_messages += sum([x.__len__() for x in [comp23askemp, comp23cta, comp23isans, comp23isq, comp23emp]])247            for thread in annot3threads:248                if not annotationDataset.groupby('thread').get_group(thread).annotator1Name.unique()[0] in removedAnnotators \249                        and type(annotationDataset.groupby('thread').get_group(thread).annotator1Name.unique()[0]) == str:250                    comp31emp = annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy == \251                                annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy252                    comp31askemp = annotationDataset.groupby('thread').get_group(thread).annotator3AskEmpathy == \253                                   annotationDataset.groupby('thread').get_group(thread).annotator1AskEmpathy254                    comp31cta = annotationDataset.groupby('thread').get_group(thread).annotator3CallToAction == \255                                annotationDataset.groupby('thread').get_group(thread).annotator1CallToAction256                    comp31isans = annotationDataset.groupby('thread').get_group(thread).annotator3IsAnswer == \257                                  annotationDataset.groupby('thread').get_group(thread).annotator1IsAnswer258                    comp31isq = annotationDataset.groupby('thread').get_group(thread).annotator3IsQuestion == \259                                annotationDataset.groupby('thread').get_group(thread).annotator1IsQuestion260                    # comp31 = annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy == annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy261                    multiplier31 = scoredict[annotationDataset.groupby('thread').get_group(thread).annotator1Name.unique()[0]]262                    for i, _ in enumerate(comp31emp):263                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator3AskEmpathy)[i]) == float:264                            comp31askemp[i] = False  # don't count nan as agreeing265                            total_messages -= 1266                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator3CallToAction)[i]) == float:267                            comp31cta[i] = False268                            total_messages -= 1269                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator3IsAnswer)[i]) == float:270                            comp31isans[i] = False271                            total_messages -= 1272                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator3IsQuestion)[i]) == float:273                            comp31isq[i] = False274                            total_messages -= 1275                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy)[i]) == float:276                            comp31emp[i] = False277                            total_messages -= 1278                            # comp31[i] = False279                    total_agreement += sum([sum(x) * multiplier31 for x in [comp31askemp, comp31cta, comp31isans, comp31isq, comp31emp]])280                    total_messages += sum([x.__len__() for x in [comp31askemp, comp31cta, comp31isans, comp31isq, comp31emp]])281                if not annotationDataset.groupby('thread').get_group(thread).annotator2Name.unique()[0] in removedAnnotators \282                        and type(annotationDataset.groupby('thread').get_group(thread).annotator2Name.unique()[0]) == str:283                    comp32emp = annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy == \284                                annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy285                    comp32askemp = annotationDataset.groupby('thread').get_group(thread).annotator3AskEmpathy == \286                                   annotationDataset.groupby('thread').get_group(thread).annotator2AskEmpathy287                    comp32cta = annotationDataset.groupby('thread').get_group(thread).annotator3CallToAction == \288                                annotationDataset.groupby('thread').get_group(thread).annotator2CallToAction289                    comp32isans = annotationDataset.groupby('thread').get_group(thread).annotator3IsAnswer == \290                                  annotationDataset.groupby('thread').get_group(thread).annotator2IsAnswer291                    comp32isq = annotationDataset.groupby('thread').get_group(thread).annotator3IsQuestion == \292                                annotationDataset.groupby('thread').get_group(thread).annotator2IsQuestion293                    # comp32 = annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy == annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy294                    multiplier32 = scoredict[annotationDataset.groupby('thread').get_group(thread).annotator2Name.unique()[0]]295                    for i, _ in enumerate(comp32emp):296                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator3AskEmpathy)[i]) == float:297                            comp32askemp[i] = False  # don't count nan as agreeing298                            total_messages -= 1299                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator3CallToAction)[i]) == float:300                            comp32cta[i] = False301                            total_messages -= 1302                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator3IsAnswer)[i]) == float:303                            comp32isans[i] = False304                            total_messages -= 1305                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator3IsQuestion)[i]) == float:306                            comp32isq[i] = False307                            total_messages -= 1308                        if type(list(annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy)[i]) == float:309                            comp32emp[i] = False310                            total_messages -= 1311                    total_agreement += sum([sum(x) * multiplier32 for x in [comp32askemp, comp32cta, comp32isans, comp32isq, comp32emp]])312                    total_messages += sum([x.__len__() for x in [comp32askemp, comp32cta, comp32isans, comp32isq, comp32emp]])313            if total_messages == 0:314                agreement_factor = scoredict[annotatorname]315                print('annotator {} has no co annotators left!'.format(annotatorname))316            else:317                agreement_factor =(total_agreement/total_messages)318            with open('data\\agreementscore_with_nan_correction_6.csv', 'a') as file:319                writer = csv.writer(file)320                writer.writerow([iteration, annotatorname, agreement_factor])321            nextiterationscoredict[annotatorname] = 2* agreement_factor322            print(nextiterationscoredict[annotatorname])

test_core.py

Source:test_core.py

...66def test_no_mags():67    rat = '1'68    for cue in ['light', 'sound']:69        for trial in [1, 2, 3, 4]:70            this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).71                       groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])72            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))73            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))74            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))75            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))76def test_all_mags():77    rat = '2'78    for cue in ['light', 'sound']:79        for trial in [1, 2, 3, 4]:80            this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).81                       groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])82            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))83            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))84            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))85            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))86def test_sound_only():87    rat = '3'88    cue = 'sound'89    for trial in [1, 2, 3, 4]:90        this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).91                   groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])92        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))93        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))94        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))95        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))96    cue = 'light'97    for trial in [1, 2, 3, 4]:98        this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).99                   groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])100        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))101        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))102        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))103        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))104def test_light_only():105    rat = '4'106    cue = 'sound'107    for trial in [1, 2, 3, 4]:108        this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).109                   groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])110        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))111        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))112        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))113        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))114    cue = 'light'115    for trial in [1, 2, 3, 4]:116        this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).117                   groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])118        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))119        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))120        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))121        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))122def test_rewarded_sound():123    rat = '5'124    cue = 'light'125    for trial in [1, 2, 3, 4]:126        this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).127                   groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])128        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))129        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))130        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))131        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))132    cue = 'sound'133    for trial in [2, 3]:134        this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).135                   groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])136        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))137        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))138        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))139        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))140    for trial in [1, 4]:141        this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).142                   groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])143        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))144        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))145        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))146        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))147def test_iti_only():148    rat = '6'149    for cue in ['light', 'sound']:150        for trial in [1, 2, 3, 4]:151            this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).152                       groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])153            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))154            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))155            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))156            assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))157def test_half_light():158    rat = '7'159    cue = 'sound'160    for trial in [1, 2, 3, 4]:161        this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).162                   groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])163        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))164        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))165        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))166        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))167    cue = 'light'168    for trial in [1, 4]:169        this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).170                   groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])171        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 5.0))172        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))173        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 5.0))174        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))175    for trial in [2, 3]:176        this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).177                   groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])178        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 5.0))179        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))180        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))181        assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))182def test_complex():183    rat = '8'184    trial = 1185    cue = 'light'186    this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).187               groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])188    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 1.0))189    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))190    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 9.0))191    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))192    cue = 'sound'193    this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).194               groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])195    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 1.98))196    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))197    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))198    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))199    trial = 2200    cue = 'light'201    this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).202               groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])203    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))204    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))205    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))206    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))207    cue = 'sound'208    this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).209               groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])210    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))211    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))212    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))213    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))214    trial = 3215    cue = 'light'216    this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).217               groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])218    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 2.5))219    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 2.0))220    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))221    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))222    cue = 'sound'223    this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).224               groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])225    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))226    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))227    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))228    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))229    trial = 4230    cue = 'light'231    this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).232               groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])233    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))234    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))235    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))236    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))237    cue = 'sound'238    this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).239               groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])240    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))241    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))242    assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))...

manual_annotator_compare.py

Source:manual_annotator_compare.py

...14        total_agreement = 015        total_messages = 016        skipped = 017        try:18            annot1threads = annotationDataset.groupby("annotator1Name").get_group(annotatorname).thread.unique()19        except:20            annot1threads = []21        try:22            annot2threads = annotationDataset.groupby("annotator2Name").get_group(annotatorname).thread.unique()23        except:24            annot2threads = []25        try:26            annot3threads = annotationDataset.groupby("annotator3Name").get_group(annotatorname).thread.unique()27        except:28            annot3threads =[]29        for thread in annot1threads:30            if annotationDataset.groupby('thread').get_group(thread).annotator2Name.unique().__len__()>0 and \31                    annotationDataset.groupby('thread').get_group(thread).annotator3Name.unique().__len__()>0:32                if not annotationDataset.groupby('thread').get_group(thread).annotator2Name.unique()[0] in removedAnnotators and \33                    not annotationDataset.groupby('thread').get_group(thread).annotator3Name.unique()[0] in removedAnnotators:34                    otherscompEmph = annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy == \35                                     annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy36                    annotcompEmph = annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy == \37                                    [x if list(otherscompEmph)[i] else list(annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy)[i] for i, x in enumerate(annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy)]38                    for i, text in enumerate(annotationDataset.groupby('thread').get_group(thread).text):39                        if not type(list(annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy)[i]) == float:40                            if agreemode:41                                if not list(annotcompEmph)[i]:42                                    print(text, annotatorname,43                                          list(annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy)[i])44                                    if list(annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy)[i] == True:45                                        print(list(annotationDataset.groupby('thread').get_group(thread).annotator1EmpathyType)[46                                                  i],47                                              list(annotationDataset.groupby('thread').get_group(48                                                  thread).annotator1EmpathyValence)[i])49                                    # else:50                                    #     continue51                                    inp = input('1 to agree, 0 to disagree, 2 to switch agreemode')52                                    if int(inp) == 2:53                                        agreemode = not agreemode54                                        continue55                                    total_messages += 156                                    total_agreement += int(inp)57                                else:58                                    skipped+=159                            # if not list(annotcompEmph)[i]:60                            else:61                                print(text, annotatorname, list(annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy)[i])62                                if list(annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy)[i] == True:63                                    print(list(annotationDataset.groupby('thread').get_group(thread).annotator1EmpathyType)[i],64                                          list(annotationDataset.groupby('thread').get_group(thread).annotator1EmpathyValence)[i])65                                else:66                                    continue67                                inp = input('1 to agree, 0 to disagree, 2 to switch agreemode')68                                if int(inp) == 2:69                                    agreemode = not agreemode70                                    continue71                                total_messages += 172                                total_agreement += int(inp)73                                # else:74                                #     skipped+=175        print("intermediary result: {} agreed out of {} for annotator {}".format(total_agreement, total_messages,76                                                                                 annotatorname))77        for thread in annot2threads:78            if annotationDataset.groupby('thread').get_group(thread).annotator1Name.unique().__len__()>0 and \79                    annotationDataset.groupby('thread').get_group(thread).annotator3Name.unique().__len__()>0:80                if not annotationDataset.groupby('thread').get_group(thread).annotator1Name.unique()[0] in removedAnnotators and \81                    not annotationDataset.groupby('thread').get_group(thread).annotator3Name.unique()[0] in removedAnnotators:82                    otherscompEmph = annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy == annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy83                    annotcompEmph = annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy == \84                                    [x if list(otherscompEmph)[i] else list(annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy)[i] for i, x in enumerate(annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy)]85                    for i, text in enumerate(annotationDataset.groupby('thread').get_group(thread).text):86                        if not type(list(annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy)[i]) == float:87                            if agreemode:88                                if not list(annotcompEmph)[i]:89                                    print(text, annotatorname, list(annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy)[i])90                                    if list(annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy)[i] == True:91                                        print(list(annotationDataset.groupby('thread').get_group(thread).annotator2EmpathyType)[i],92                                              list(annotationDataset.groupby('thread').get_group(thread).annotator2EmpathyValence)[i])93                                    # else:94                                    #     continue95                                    inp = input('1 to agree, 0 to disagree, 2 to switch agreemode')96                                    if int(inp) == 2:97                                        agreemode = not agreemode98                                        continue99                                    total_messages += 1100                                    total_agreement += int(inp)101                                else:102                                    skipped+=1103                            else:104                                print(text, annotatorname,105                                      list(annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy)[i])106                                if list(annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy)[i] == True:107                                    print(list(annotationDataset.groupby('thread').get_group(thread).annotator2EmpathyType)[108                                              i],109                                          list(annotationDataset.groupby('thread').get_group(110                                              thread).annotator2EmpathyValence)[i])111                                else:112                                    continue113                                inp = input('1 to agree, 0 to disagree, 2 to switch agreemode')114                                if int(inp) == 2:115                                    agreemode = not agreemode116                                    continue117                                total_messages += 1118                                total_agreement += int(inp)119        print("intermediary result: {} agreed out of {} for annotator {}".format(total_agreement, total_messages, annotatorname))120        for thread in annot3threads:121            if annotationDataset.groupby('thread').get_group(thread).annotator1Name.unique().__len__()>0 and \122                    annotationDataset.groupby('thread').get_group(thread).annotator2Name.unique().__len__()>0:123                if not annotationDataset.groupby('thread').get_group(thread).annotator1Name.unique()[0] in removedAnnotators and \124                    not annotationDataset.groupby('thread').get_group(thread).annotator2Name.unique()[0] in removedAnnotators:125                    otherscompEmph = annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy == annotationDataset.groupby('thread').get_group(thread).annotator2IsEmpathy126                    annotcompEmph = annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy == \127                                    [x if list(otherscompEmph)[i] else list(annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy)[i] for i, x in enumerate(annotationDataset.groupby('thread').get_group(thread).annotator1IsEmpathy)]128                    for i, text in enumerate(annotationDataset.groupby('thread').get_group(thread).text):129                        if not type(list(annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy)[i]) == float:130                            if agreemode:131                                if not list(annotcompEmph)[i]:132                                    print(text, annotatorname,  list(annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy)[i])133                                    if list(annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy)[i] == True:134                                        print(list(annotationDataset.groupby('thread').get_group(thread).annotator3EmpathyType)[i],135                                              list(annotationDataset.groupby('thread').get_group(thread).annotator3EmpathyValence)[i])136                                    # else:137                                    #     continue138                                    inp = input('1 to agree, 0 to disagree, 2 to switch agreemode')139                                    if int(inp) == 2:140                                        agreemode = not agreemode141                                        continue142                                    total_messages += 1143                                    total_agreement += int(inp)144                                else:145                                    skipped += 1146                            else:147                                print(text, annotatorname,148                                      list(annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy)[i])149                                if list(annotationDataset.groupby('thread').get_group(thread).annotator3IsEmpathy)[i] == True:150                                    print(list(annotationDataset.groupby('thread').get_group(thread).annotator3EmpathyType)[151                                              i],152                                          list(annotationDataset.groupby('thread').get_group(153                                              thread).annotator3EmpathyValence)[i])154                                else:155                                    continue156                                inp = input('1 to agree, 0 to disagree, 2 to switch agreemode')157                                if int(inp) == 2:158                                    agreemode = not agreemode159                                    continue160                                total_messages += 1161                                total_agreement += int(inp)162        print("{} agreed out of {} for annotator {}".format(total_agreement,total_messages, annotatorname))163        print("{} skipped".format(skipped))164        with open('manual_annotator_compare.csv', 'a') as file:165            writer = csv.writer(file)166            writer.writerow([annotatorname,total_agreement,total_messages])

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