/
sliced_back
35 lines (25 loc) 路 1.2 KB
/
sliced_back
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import pandas as pd
import seaborn as sns
import numpy as np
import math
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
df = pd.read_csv('/Users/noorsajid/Desktop/128_100_output.csv').set_index('Unnamed: 0')
df32 = pd.read_csv('/Users/noorsajid/Desktop/output_scores32.csv').set_index('Unnamed: 0')
df64 = pd.read_csv('/Users/noorsajid/Desktop/output64.csv').set_index('Unnamed: 0')
df256 = pd.read_csv('/Users/noorsajid/Desktop/512.csv').set_index('Unnamed: 0')
df256 = pd.read_csv('/Users/noorsajid/Desktop/output-pne256.csv').set_index('Unnamed: 0')
#df = df.fillna(0)
def change(df, num):
df['id_resuffixed'] = df.id.apply(lambda x: x[1:-10])
df['id_resuffixed'] = df.id
df['an_score_binary'] = np.where(df['an'] >= num, 1, 0)
df_id = pd.DataFrame(df.groupby(['id_resuffixed'])['an_score_binary'].sum())
gt = df.groupby(['id_resuffixed'])['gt'].max()
df_id = df_id.join(gt)
df_id = df_id.reset_index()
df_id['normal_an'] = df_id['an_score_binary']/9
fpr, tpr, _ = roc_curve(df_id['gt'], df_id['normal_an'])
roc_auc = auc(fpr, tpr)
return df, fpr, tpr, roc_auc, df_id
df256, fpr256, tpr256, roc_auc256, df_id256 = change(df64, 0.18)