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clean_dataset.py
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#!/usr/bin/env python
import pandas as pd
import numpy as np
from reproduce_ann2 import (
ann_2_1,
ann_2_2,
)
from settings import (
LR1_FEATURES,
LR2_FEATURES,
TIM_FEATURES,
SM_FEATURES,
ANN1_FEATURES,
ANN2_1_FEATURES,
ANN2_2_FEATURES,
ANN3_FEATURES,
OTHER_MODELS_RESUTLS,
Y_NAME,
)
bad_data_ids = [
'Sms100',
'Sms212',
'Sms098',
'Sms160',
'Sms131',
'Sms101',
'Sms210',
'Sms213',
'Sms061',
'Sms118',
'Sms152',
'Sms156',
'Sms136',
'Sms146',
'Sms220',
'Sms192',
'Sms107',
'Sms126',
'Sms171',
'Sms151',
'Sms148',
'Sms219',
'Sms204',
'Sms095',
'Sms137',
'Sms166',
'Sms105',
'Sms208',
'Sms201',
'Sms141',
'Sms097',
'Sms183',
'Sms089',
'Sms119',
]
df = pd.read_excel('./data/sms-export.xlsx')
clean_df = df[~df['Name'].isin(bad_data_ids)].reset_index()
borderline = clean_df[clean_df['MalignancyCharacter'] == 2]
malignant = clean_df[clean_df['MalignancyCharacter'] == 1]
benign = clean_df[clean_df['MalignancyCharacter'] == 0]
borderline.describe().to_csv("./data/stats-borderline.csv")
malignant.describe().to_csv("./data/stats-malignant.csv")
benign.describe().to_csv("./data/stats-benign.csv")
stats = clean_df.describe().to_csv("./data/stats-all.csv")
clean_df.to_csv("./data/cleaned.csv", index=False)
# new features & preprocess
clean_df.loc[:, 'Menopause'] = pd.notnull(clean_df['MenopauseAge'])
all_dims = ['ADimension', 'BDimension', 'CDimension']
clean_df.loc[:, 'MaxDimension'] = clean_df[all_dims].max(axis=1)
idx = (pd.isnull(clean_df['PapBloodFlow']) & clean_df['Pap'] == 0)
clean_df.loc[idx, 'PapBloodFlow'] = 0
idx = (pd.isnull(clean_df['APapDimension']) & clean_df['Pap'] == 0)
clean_df.loc[idx, 'APapDimension'] = 0
idx = (pd.isnull(clean_df['SeptumThickness']) & clean_df['Septum'] == 0)
clean_df.loc[idx, 'SeptumThickness'] = 0
Papillarities = (
(clean_df[['APapDimension', 'BPapDimension']].max(axis=1) > 3) | clean_df['Pap']
)
clean_df.loc[:, 'ANN2_Papillarities'] = Papillarities
clean_df.loc[:, 'ANN2_Bilateral'] = 0
clean_df.loc[:, 'ANN2_Smooth'] = (clean_df['InnerWall'] == 0).astype(np.int)
clean_df.loc[:, 'log_Ca125'] = np.log(clean_df['Ca125'])
ultrasound = np.empty(len(clean_df))
ultrasound[(
(clean_df['InnerWall'] == 0) | (clean_df['Shadow'] == 1) |
(clean_df['SeptumThickness'] < 3) | (clean_df['Echo'] < 3)
).values] = 0
ultrasound[(
(clean_df['Shadow'] == 0) | (clean_df['SeptumThickness'] >= 3)
).values] = 1
ultrasound[(clean_df['Solid'] == 1).values] = 2
ultrasound[(
(clean_df[['APapDimension', 'BPapDimension']].max(axis=1) > 3) |
(clean_df['Echo'] >= 3)
).values] = 3
clean_df.loc[:, 'Ultrasound'] = ultrasound
Unilocular = (
(clean_df['Echo'] == 1) | (clean_df['Echo'] == 2) |
(clean_df['LoculesCount'] == 0) | (clean_df['LoculesCount'] == 1) |
(clean_df['Solid'] == 1)
)
clean_df.loc[:, 'ANN2_Unilocular'] = Unilocular
X_features = list(
set(LR1_FEATURES + LR2_FEATURES + TIM_FEATURES + SM_FEATURES +
ANN1_FEATURES + ANN2_1_FEATURES + ANN2_2_FEATURES + ANN3_FEATURES)
)
clean_df['ann_2_1_Bin'] = ann_2_1(clean_df)
clean_df['ann_2_2_Bin'] = ann_2_2(clean_df)
non_empty = clean_df[X_features + [Y_NAME] + OTHER_MODELS_RESUTLS].dropna()
non_empty.to_csv('./data/dataset.csv', index=False)