

In [1]:

    
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

from evaluation import *
from filters import *
from utility import *
from features import *

Öffnen von Hdf mittels pandas



In [ ]:

    
# TODO hdf =

Beispiel Erkenner

datensätze vorbereiten



In [ ]:

    
# generate datasets
tst = ['1','2','3']
tst_ds = []

for t in tst:

    df_tst = hdf.get('/x1/t'+t+'/trx_1_4')
    lst = df_tst.columns[df_tst.columns.str.contains('_ifft_')]
    
    #df_tst_cl,_ = distortion_filter(df_tst_cl)
    
    groups = get_trx_groups(df_tst)
    df_std = rf_grouped(df_tst, groups=groups, fn=rf_std_single, label='target')
    df_mean = rf_grouped(df_tst, groups=groups, fn=rf_mean_single)
    
    df_all = pd.concat( [df_std, df_mean], axis=1 )
    
    df_all = cf_std_window(df_all, window=4, label='target')
    
    df_tst_sum = generate_class_label_presence(df_all, state_variable='target')
    
    # remove index column
    df_tst_sum = df_tst_sum[df_tst_sum.columns.values[~df_tst_sum.columns.str.contains('index')].tolist()]
    print('Columns in Dataset:',t)
    print(df_tst_sum.columns)
    

    tst_ds.append(df_tst_sum.copy())

validierung hold-out



In [ ]:

    
# holdout validation
print(hold_out_val(tst_ds, target='target', include_self=False, cl='rf', verbose=False, random_state=1))

Schließen von HDF Store



In [ ]:

    
# TODO