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util.py
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util.py
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import numpy as np
from statistics import mode
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
import pandas as pd
from sklearn.preprocessing import StandardScaler
def split_train_test_breast_cancer():
handle = open('breast-cancer-wisconsin.data', 'r')
contents = handle.read()
handle.close()
rows = contents.split('\n')
out = np.array([[i for i in r.split(',')] for r in rows if r])
out=out[:,1:]
for col in range(9):
out[:,col][out[:,col]=='?']=mode(out[:,col])
np.random.shuffle(out)
test_data=out[:199,:]
train_data=out[199:,:]
train_features=np.array(train_data[:,:-1],dtype=int)
train_labels=np.array(train_data[:,-1],dtype=int)
train_labels[train_labels==4]=1
train_labels[train_labels==2]=0
test_features=np.array(test_data[:,:-1],dtype=int)
test_labels=np.array(test_data[:,-1],dtype=int)
test_labels[test_labels==4]=1
test_labels[test_labels==2]=0
return (train_features,train_labels,test_features,test_labels)
def split_train_test_spam():
handle = open('spambase.data', 'r')
contents = handle.read()
handle.close()
rows = contents.split('\n')
out = np.array([[i for i in r.split(',')] for r in rows if r])
np.random.shuffle(out)
test_data=out[:601,:]
train_data=out[601:,:]
train_features=np.array(train_data[:,:-1],dtype=float)
train_labels=np.array(train_data[:,-1],dtype=int)
test_features=np.array(test_data[:,:-1],dtype=float)
test_labels=np.array(test_data[:,-1],dtype=int)
scaler=StandardScaler()
scaler.fit(train_features)
train_features_spam_norm=scaler.transform(train_features)
test_features_spam_norm=scaler.transform(test_features)
return (train_features_spam_norm,train_labels,test_features_spam_norm,test_labels)