<jupyter_start><jupyter_text>Feature list* PSD_raw* PSD_den* PSD_bssm* PSD_denbssm* ICA_demix* MEL_raw* MEL_den* MEL_bssm* MEL_denbssm  Low noise ID00<jupyter_code>## Meta
machines = ['pump']
SNRs = ['6dB']
IDs = ['00']
feat_discfile = '/dataset/extdia_v1/pump6dB00_EDiaV1HPaug0_pandaDisc.pkl'<jupyter_output><empty_output><jupyter_text>PSD_raw<jupyter_code>df, tasks, npart = Make_TTsplit_and_task(feat_discfile,machines, SNRs,IDs,
                        features = [ 'PSD_bssm'],
                        feat = {'function':'flat'})

preprocessing = [
    (StandardScaler, {}),
    #(PolynomialFeatures, {'degree': 2})
]

#modeling = (uni_LogisticRegression, {'C': 0.5, 'max_iter': 600})
modeling = (uni_svm, {'C': 0.1, 'degree':3,'kernel':'rbf'})

pipe = Pipe(preprocessing, modeling, True)
pipe.run_pipe(tasks[0])

name_of_test = pipe.model.name + 'n' +'_' + npart

pipetask_to_metrics(pipe,tasks[0],df_summery,name_of_test,0.5)
print(name_of_test)<jupyter_output>../..//dataset/extdia_v1/pump6dB00_EDiaV1HPaug0_pandaDisc.pkl --> Done
...loading data
data loading completed

...preprocessing data
data preprocessing finished

...fitting the model
0.9973226597777298
model fitted successfully

...evaluating model
evaluation successfull, roc_auc: 0.7698175949924201
pipe saved to pickle<jupyter_text>High noise ID00<jupyter_code>## Meta
machines = ['pump']
SNRs = ['min6dB']
IDs = ['00']
feat_discfile = '/dataset/extdia_v1/pumpmin6dB00_EDiaV1HPaug0_pandaDisc.pkl'<jupyter_output><empty_output><jupyter_text>PSD_raw<jupyter_code>df, tasks, npart = Make_TTsplit_and_task(feat_discfile,machines, SNRs,IDs,
                        features = [ 'PSD_bssm'],
                        feat = {'function':'flat'})

preprocessing = [
    (MinMaxScaler,{}),
    #(StandardScaler, {}),
    #(PolynomialFeatures, {'degree': 2})
]

#modeling = (uni_LogisticRegression, {'C': 0.5, 'max_iter': 600})
modeling = (uni_svm, {'C': 0.1, 'degree':3,'kernel':'rbf'})

pipe = Pipe(preprocessing, modeling, True)
pipe.run_pipe(tasks[0])

name_of_test = pipe.model.name + 'n' +'_' + npart

pipetask_to_metrics(pipe,tasks[0],df_summery,name_of_test,0.5)
print(name_of_test)<jupyter_output>../..//dataset/extdia_v1/pumpmin6dB00_EDiaV1HPaug0_pandaDisc.pkl --> Done
...loading data
data loading completed

...preprocessing data
data preprocessing finished

...fitting the model
0.9793224476314132
model fitted successfully

...evaluating model
evaluation successfull, roc_auc: 0.6411560467504522
pipe saved to pickle<jupyter_text>High noise ID02<jupyter_code>## Meta
machines = ['pump']
SNRs = ['min6dB']
IDs = ['02']
feat_discfile = '/dataset/extdia_v1/pumpmin6dB02_EDiaV1HPaug0_pandaDisc.pkl'<jupyter_output><empty_output><jupyter_text>PSD_raw<jupyter_code>df, tasks, npart = Make_TTsplit_and_task(feat_discfile,machines, SNRs,IDs,
                        features = [ 'PSD_bssm'],
                        feat = {'function':'flat'})

preprocessing = [
    (StandardScaler, {}),
    #(PolynomialFeatures, {'degree': 2})
]

#modeling = (uni_LogisticRegression, {'C': 0.5, 'max_iter': 600})
modeling = (uni_svm, {'C': 0.01, 'degree':2,'kernel':'rbf', 'tol':0.1})

pipe = Pipe(preprocessing, modeling, True)
pipe.run_pipe(tasks[0])

name_of_test = pipe.model.name + 'n' +'_' + npart

pipetask_to_metrics(pipe,tasks[0],df_summery,name_of_test,0.5)
print(name_of_test)<jupyter_output>../..//dataset/extdia_v1/pumpmin6dB02_EDiaV1HPaug0_pandaDisc.pkl --> Done
...loading data
data loading completed

...preprocessing data
data preprocessing finished

...fitting the model
0.9430931539620337
model fitted successfully

...evaluating model
evaluation successfull, roc_auc: 0.6968590211833454
pipe saved to pickle<jupyter_text>Summery<jupyter_code>df_summery.to_pickle('S01_summerys/S01_SVM_pump')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1473767466385447, 'fraction_numerical': 0.052465067229106245, 'mean_word_length': 5.281456953642384, 'pattern_counts': {'":': 0, '<': 26, '<?xml version=': 0, '>': 29, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '309013', 'license': 'MIT', 'n_tokens_mistral': 1644, 'n_tokens_neox': 1490, 'n_words': 236, 'path': 'modeling/pseudo_supervised/S01_pseudo_supervsed_model_preselect_Test_SVM_on_PUMP.ipynb', 'repo_name': 'BA-HanseML/NF_Prj_MIMII_Dataset', 'size': '3793'}