machine_learn/logistic/cancer_get_data.py

# -*- encoding:utf-8 -*-

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_predict
from numpy import shape
from sklearn import metrics


def array_to_list(data):
    data = data.tolist()
    return data


loaded_data = datasets.load_breast_cancer()
# loaded_data = datasets.load_boston()
data_X = loaded_data.data
data_y = loaded_data.target
X_train, X_test, y_train, y_test = train_test_split(data_X, data_y, test_size=0.2)
X_train = array_to_list(X_train)
X_test = array_to_list(X_test)
y_train = array_to_list(y_train)
y_test = array_to_list(y_test)

train_data = zip(X_train, y_train)
train_data = [str(list(s)) for s in train_data]
test_data = zip(X_test, y_test)
test_data = [str(list(s)) for s in test_data]
with open("cancer_train_data", "w") as f:
    f.writelines("\n".join(train_data))
with open("cancer_test_data", "w") as f:
    f.writelines("\n".join(test_data))