#!/usr/bin/env python3 import numpy as np from utils import niceNameModel, PHNALL from load import load_all_p1, load_all_p2 import pandas as pd import matplotlib.pyplot as plt if False: dataP1 = load_all_p1(add_bleu=True)[[ 'model', 'lang', 'fluency', 'adequacy', 'bleu' ]] dataP1['mult'] = dataP1['fluency'] * dataP1['adequacy'] # data = data.drop(['fluency', 'adequacy'], axis=1) for model in dataP1['model'].unique(): diff = dataP1[dataP1['model'] == model].groupby('lang').mean() print(model) print(diff) print() else: dataP2 = load_all_p2(add_bleu=False) def comp_occ(df, phnName): return df[phnName].count() / df.shape[0] def comp_sev(df, phnName): tmp = df[phnName].dropna() if tmp.shape[0] == 0: return 0 else:
#!/usr/bin/env python3 from load import load_all_p1 from utils import niceNameDocArrow import numpy as np import re import matplotlib.pyplot as plt data = load_all_p1(add_bleu=True) print('\n%%%' * 4) for docName in sorted( data['doc'].unique(), key=lambda docName: data[data['doc'] == docName].mean()['mult'], reverse=True): dfDoc = data[data['doc'] == docName] multAvg = dfDoc.mean()['mult'] errsAvg = dfDoc['errors'].mean() bleuAvg = dfDoc['bleu'].mean() bleuStd = np.sqrt(dfDoc['bleu'].std()) print( f'{niceNameDocArrow(docName):<9} & {multAvg:10.2f} & {errsAvg:10.2f} & {bleuAvg:10.2f}', '\\pmsmall{', f'{bleuStd:4.2f}', '} \\\\') print('\\hline') multAvg = data.mean()['mult'] errsAvg = data['errors'].mean() bleuAvg = data['bleu'].mean() bleuStd = np.sqrt(dfDoc['bleu'].std()) print(f'Average & {multAvg:10.2f} & {errsAvg:10.2f} & {bleuAvg:10.2f}',
#!/usr/bin/env python3 import numpy as np from utils import niceNameModel, niceNamePhn, PHNALL from load import load_all_p1, load_all_p2 import pandas as pd pd.options.display.max_rows = 70 pd.options.display.max_colwidth = 50 data = load_all_p2(clear_badlines=True, add_bleu=True) dataP1 = load_all_p1() data['sev'] = data.apply(lambda row: sum( [0 if np.isnan(row[phnName]) else row[phnName] for phnName in PHNALL]), axis=1)