安装
pip install catboost数据集
分类：MNIST（60000条数据784个特征），已上传CSDN
代码
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from catboost import CatBoostClassifier
from sklearn.model_selection import train_test_splittrain = pd.read_csv('./input/mnist/train.csv')
train.head()
X = train.iloc[:, 1:] # 训练数据
y = train['label'] #标签X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 划分训练、测试集def plot_digits(instances, images_per_row=10):
'''绘制数据集
:param instances: 部分数据集
:type instances: numpy.ndarray
:param images_per_row: 每一行显示图片数
'''
size = 28
images_per_row = min(len(instances), images_per_row)
images = [instance.reshape(size, size) for instance in instances]
n_rows = (len(instances) - 1) // images_per_row + 1
row_images = []
n_empty = n_rows * images_per_row - len(instances)
images.append(np.zeros((size, size * n_empty)))
for row in range(n_rows):
rimages = images[row * images_per_row: (row + 1) * images_per_row]
row_images.append(np.concatenate(rimages, axis=1))
image = np.concatenate(row_images, axis=0)
plt.imshow(image, cmap='gray_r')
plt.axis("off")
plt.figure()
plot_digits(X_train[:100].values, images_per_row=10)
plt.show()
# 定义模型
clf = CatBoostClassifier()# 训练
model = clf.fit(X_train, y_train)0: learn: 2.2139620 total: 975ms remaining: 16m 13s
1: learn: 2.1344069 total: 1.95s remaining: 16m 15s
2: learn: 2.0559619 total: 2.92s remaining: 16m 10s
3: learn: 1.9850790 total: 3.89s remaining: 16m 7s
......
996: learn: 0.1231917 total: 16m 35s remaining: 3s
997: learn: 0.1231500 total: 16m 36s remaining: 2s
998: learn: 0.1231068 total: 16m 37s remaining: 999ms
999: learn: 0.1230654 total: 16m 38s remaining: 0us# 评估
print('accuracy:', model.score(X_test, y_test))# 保存
model.save_model('mnist.model')# 加载
ccc = CatBoostClassifier()
ccc.load_model('mnist.model')# 预测
index = random.randint(0, len(X_test)) # 随机挑一个
_X = X_test.values[index]
_y = y_test.values[index] # 真值
predict = ccc.predict(_X)[0] # 预测值
_X = _X.reshape(28, 28)
plt.imshow(_X, cmap='gray_r')
plt.title('original {}'.format(_y))
plt.show()
print('index:', index)
print('original:', _y)
print('predicted:', predict)
index: 7534
original: 6
predicted: 6
index: 6510
original: 4
predicted: 4
index: 7311
original: 6
predicted: 6