Summarize the code below:
import numpy as np #
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
import logging
import shap

logging.basicConfig(filename = 'log_file.log',
level = logging.INFO,
format = '%(asctime)s:%(levelname)s:%(name)s:%(message)s')

parent_logger = logging.getLogger("parent")
parent_logger.setLevel(logging.DEBUG)
parent_shandler = logging.StreamHandler()
parent_logger.addHandler(parent_shandler)

parent_logger.info('Reading NAME_1 dataset.')
NAME_1 = datasets.load_NAME_1()
columns=NAME_1['feature_names']
NAME_1 = pd.DataFrame(
data= np.c_[NAME_1['data'], NAME_1['target']],
columns= NAME_1['feature_names'] + ['target']
)
species = []

for i in range(len(NAME_1['target'])):
if NAME_1['target'][i] == 0:
species.append("setosa")
elif NAME_1['target'][i] == 1:
species.append('versicolor')
else:
species.append('virginica')

NAME_1['species'] = species
setosa = NAME_1[NAME_1.species == "setosa"]
versicolor = NAME_1[NAME_1.species=='versicolor']
virginica = NAME_1[NAME_1.species=='virginica']

parent_logger.info('Visualizing the NAME_1 dataset.')
fig, ax = plt.subplots()
fig.set_size_inches(13, 7)
ax.scatter(setosa['petal length (cm)'], setosa['petal width (cm)'], label="Setosa", facecolor="blue")
ax.scatter(versicolor['petal length (cm)'], versicolor['petal width (cm)'], label="Versicolor", facecolor="green")
ax.scatter(virginica['petal length (cm)'], virginica['petal width (cm)'], label="Virginica", facecolor="red")
ax.set_xlabel("petal length (cm)")
ax.set_ylabel("petal width (cm)")
ax.grid()
ax.set_title("NAME_1 petals")
ax.legend()

X = NAME_1.drop(['target','species'], axis=1)
bg=X.sample(50)
X = X.to_numpy()[:, (2,3)]
y = NAME_1['target']
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.5, random_state=42)
parent_logger.info('Training a model')
log_reg = LogisticRegression()
log_reg.fit(X_train,y_train)
training_prediction = log_reg.predict(X_train)
test_prediction = log_reg.predict(X_test)

print("Precision, Recall, Confusion matrix, in training\n")
print(metrics.classification_report(y_train, training_prediction, digits=3))
print(metrics.confusion_matrix(y_train, training_prediction))
print("Precision, Recall, Confusion matrix, in testing\n")
print(metrics.classification_report(y_test, test_prediction, digits=3))
print(metrics.confusion_matrix(y_te