python-base--/tmp1.py

import pandas as pd
from catboost import CatBoostClassifier, Pool
from sklearn.model_selection import train_test_split


def train_model(data_path, model_path):
    # 加载数据集
    data = pd.read_csv(data_path)
    print(data)
    X = pd.get_dummies(data.drop('class', axis=1))
    y = data['class']

    # 划分训练集和测试集
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # 定义CatBoost分类器并训练模型
    model = CatBoostClassifier(iterations=100, depth=2, learning_rate=0.1, loss_function='Logloss')
    model.fit(X_train, y_train, verbose=False)

    # 保存模型
    model.save_model(model_path)

    # 返回训练好的模型
    return model, list(X.columns)


def predict(model_path, input_data, input_columns):
    # 加载模型
    loaded_model = CatBoostClassifier()
    loaded_model.load_model(model_path)

    # 将输入数据转换为DataFrame格式
    input_df = pd.DataFrame(input_data, columns=input_columns)
    input_df = pd.get_dummies(input_df)

    # 使用模型进行预测
    preds_class = loaded_model.predict(input_df)
    preds_proba = loaded_model.predict_proba(input_df)

    # 返回预测结果
    return preds_class, preds_proba


data_path = 'mushroom.csv'
model_path = 'catboost_model.bin'

# 训练模型
trained_model, input_columns = train_model(data_path, model_path)

# 输入数据示例
input_data = [['x', 's', 'n', 't', 'p', 'f', 'c', 'n', 'k', 'e', 'e', 's', 's', 'w', 'w', 'p', 'w', 'o', 'p', 'k', 's', 'u']]

# 进行预测
preds_class, preds_proba = predict(model_path, input_data, input_columns)

# 输出预测结果
print('Predicted classes:', preds_class)
print('Predicted probabilities:', preds_proba)