{"id":1044376,"date":"2024-12-31T13:13:56","date_gmt":"2024-12-31T05:13:56","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1044376.html"},"modified":"2024-12-31T13:13:58","modified_gmt":"2024-12-31T05:13:58","slug":"python%e5%a6%82%e4%bd%95%e8%bf%9b%e8%a1%8c%e6%8e%92%e5%88%97%e4%ba%94%e9%a2%84%e6%b5%8b","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1044376.html","title":{"rendered":"python\u5982\u4f55\u8fdb\u884c\u6392\u5217\u4e94\u9884\u6d4b"},"content":{"rendered":"

\"python\u5982\u4f55\u8fdb\u884c\u6392\u5217\u4e94\u9884\u6d4b\"<\/p>\n

Python\u8fdb\u884c\u6392\u5217\u4e94\u9884\u6d4b\u7684\u4e3b\u8981\u65b9\u6cd5\u5305\u62ec\uff1a\u6570\u636e\u6536\u96c6\u4e0e\u6e05\u6d17\u3001\u7279\u5f81\u5de5\u7a0b\u3001\u6784\u5efa\u9884\u6d4b\u6a21\u578b\u3001\u6a21\u578b\u8bc4\u4f30\u4e0e\u4f18\u5316\u3001\u9884\u6d4b\u4e0e\u7ed3\u679c\u5206\u6790<\/strong>\u3002\u5176\u4e2d\uff0c\u6784\u5efa\u9884\u6d4b\u6a21\u578b\u662f\u6700\u5173\u952e\u7684\u4e00\u6b65\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u6392\u5217\u4e94\u9884\u6d4b\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u6536\u96c6\u4e0e\u6e05\u6d17<\/h3>\n<\/p>\n

\u8981\u8fdb\u884c\u6392\u5217\u4e94\u9884\u6d4b\uff0c\u9996\u5148\u9700\u8981\u6536\u96c6\u5386\u53f2\u5f00\u5956\u6570\u636e\u3002\u8fd9\u4e9b\u6570\u636e\u53ef\u4ee5\u4ece\u5f69\u7968\u5b98\u65b9\u7f51\u7ad9\u6216\u7b2c\u4e09\u65b9\u6570\u636e\u63d0\u4f9b\u5546\u5904\u83b7\u53d6\u3002\u6536\u96c6\u7684\u6570\u636e\u901a\u5e38\u5305\u62ec\u5f00\u5956\u65e5\u671f\u3001\u5f00\u5956\u53f7\u7801\u548c\u5176\u4ed6\u76f8\u5173\u4fe1\u606f\u3002<\/p>\n<\/p>\n

1.1\u3001\u6570\u636e\u6536\u96c6<\/h4>\n<\/p>\n

\u53ef\u4ee5\u4f7f\u7528Python\u7684\u722c\u866b\u5e93\u5982requests<\/code>\u3001BeautifulSoup<\/code>\u7b49\u4ece\u7f51\u9875\u4e0a\u722c\u53d6\u6570\u636e\uff0c\u6216\u8005\u76f4\u63a5\u4e0b\u8f7d\u5df2\u6709\u7684\u5386\u53f2\u6570\u636e\u6587\u4ef6\uff08\u5982CSV\u683c\u5f0f\uff09\uff0c\u7136\u540e\u7528pandas<\/code>\u5e93\u8fdb\u884c\u8bfb\u53d6\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u5386\u53f2\u6570\u636e<\/strong><\/h2>\n
data = pd.read_csv('lottery_data.csv')<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2\u3001\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u5728\u6536\u96c6\u5230\u6570\u636e\u540e\uff0c\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u3002\u6e05\u6d17\u6b65\u9aa4\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u53bb\u9664\u91cd\u590d\u503c\u3001\u683c\u5f0f\u8f6c\u6362\u7b49\u3002\u4f7f\u7528pandas<\/code>\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3002<\/p>\n<\/p>\n
# \u68c0\u67e5\u662f\u5426\u6709\u7f3a\u5931\u503c<\/p>\n
print(data.isnull().sum())<\/p>\n
\u53bb\u9664\u91cd\u590d\u503c<\/strong><\/h2>\n
data.drop_duplicates(inplace=True)<\/p>\n
\u8f6c\u6362\u65e5\u671f\u683c\u5f0f<\/strong><\/h2>\n
data['date'] = pd.to_datetime(data['date'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u7279\u5f81\u5de5\u7a0b<\/h3>\n<\/p>\n
\u7279\u5f81\u5de5\u7a0b\u662f\u4ece\u539f\u59cb\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u7528\u7279\u5f81\u7684\u8fc7\u7a0b\u3002\u8fd9\u4e00\u6b65\u5bf9\u4e8e\u6a21\u578b\u7684\u9884\u6d4b\u6027\u80fd\u6709\u5f88\u5927\u7684\u5f71\u54cd\u3002\u53ef\u4ee5\u4ece\u5386\u53f2\u6570\u636e\u4e2d\u63d0\u53d6\u51fa\u591a\u79cd\u7279\u5f81\uff0c\u5982\u53f7\u7801\u51fa\u73b0\u7684\u9891\u7387\u3001\u53f7\u7801\u7684\u548c\u503c\u3001\u5947\u5076\u6bd4\u3001\u5927\u5c0f\u6bd4\u7b49\u3002<\/p>\n<\/p>\n
2.1\u3001\u63d0\u53d6\u57fa\u672c\u7279\u5f81<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u53ef\u4ee5\u63d0\u53d6\u51fa\u57fa\u672c\u7684\u7279\u5f81\uff0c\u5982\u6bcf\u4e2a\u53f7\u7801\u7684\u51fa\u73b0\u9891\u7387\u3001\u548c\u503c\u3001\u5947\u5076\u6bd4\u7b49\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u548c\u503c<\/p>\n
data['sum'] = data[['num1', 'num2', 'num3', 'num4', 'num5']].sum(axis=1)<\/p>\n
\u8ba1\u7b97\u5947\u5076\u6bd4<\/strong><\/h2>\n
data['odd_count'] = data[['num1', 'num2', 'num3', 'num4', 'num5']].apply(lambda x: sum([1 for i in x if i % 2 != 0]), axis=1)<\/p>\n
data['even_count'] = 5 - data['odd_count']<\/p>\n
data['odd_even_ratio'] = data['odd_count'] \/ data['even_count']<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2\u3001\u63d0\u53d6\u9ad8\u7ea7\u7279\u5f81<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u6ed1\u52a8\u7a97\u53e3\u3001\u65f6\u95f4\u5e8f\u5217\u7b49\u65b9\u6cd5\u63d0\u53d6\u66f4\u9ad8\u7ea7\u7684\u7279\u5f81\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u6ed1\u52a8\u7a97\u53e3\u65b9\u6cd5\u63d0\u53d6\u524d\u51e0\u671f\u7684\u548c\u503c\u3001\u5947\u5076\u6bd4\u7b49\u7279\u5f81\u4f5c\u4e3a\u5f53\u524d\u671f\u7684\u8f93\u5165\u7279\u5f81\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u6ed1\u52a8\u7a97\u53e3\u63d0\u53d6\u7279\u5f81<\/p>\n
window_size = 5<\/p>\n
for i in range(1, window_size+1):<\/p>\n
    data[f'sum_lag_{i}'] = data['sum'].shift(i)<\/p>\n
    data[f'odd_even_ratio_lag_{i}'] = data['odd_even_ratio'].shift(i)<\/p>\n
\u53bb\u9664\u7f3a\u5931\u503c<\/strong><\/h2>\n
data.dropna(inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6784\u5efa\u9884\u6d4b\u6a21\u578b<\/h3>\n<\/p>\n
\u5728\u5b8c\u6210\u7279\u5f81\u5de5\u7a0b\u540e\uff0c\u53ef\u4ee5\u9009\u62e9\u9002\u5408\u7684\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u6765\u6784\u5efa\u9884\u6d4b\u6a21\u578b\u3002\u5e38\u7528\u7684\u7b97\u6cd5\u6709\u56de\u5f52\u6a21\u578b\u3001\u51b3\u7b56\u6811\u3001\u968f\u673a\u68ee\u6797\u3001XGBoost\u3001\u6df1\u5ea6\u5b66\u4e60\u7b49\u3002<\/p>\n<\/p>\n
3.1\u3001\u6570\u636e\u96c6\u5212\u5206<\/h4>\n<\/p>\n
\u5c06\u6570\u636e\u96c6\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u4ee5\u4fbf\u4e8e\u6a21\u578b\u7684\u8bad\u7ec3\u548c\u8bc4\u4f30\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
\u7279\u5f81\u548c\u6807\u7b7e<\/strong><\/h2>\n
features = data.drop(columns=['num1', 'num2', 'num3', 'num4', 'num5', 'date'])<\/p>\n
labels = data[['num1', 'num2', 'num3', 'num4', 'num5']]<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2\u3001\u6a21\u578b\u9009\u62e9\u4e0e\u8bad\u7ec3<\/h4>\n<\/p>\n
\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\uff0c\u5e76\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
from sklearn.ensemble import RandomForestRegressor<\/p>\n
from sklearn.model_selection import cross_val_score<\/p>\n
\u521b\u5efa\u968f\u673a\u68ee\u6797\u6a21\u578b<\/strong><\/h2>\n
model = RandomForestRegressor(n_estimators=100, random_state=42)<\/p>\n
\u4ea4\u53c9\u9a8c\u8bc1\u8bc4\u4f30\u6a21\u578b\u6027\u80fd<\/strong><\/h2>\n
scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')<\/p>\n
print(f'Cross-validation MSE: {(-scores.mean()):.4f}')<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(X_train, y_train)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6a21\u578b\u8bc4\u4f30\u4e0e\u4f18\u5316<\/h3>\n<\/p>\n
\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\uff0c\u5e76\u6839\u636e\u8bc4\u4f30\u7ed3\u679c\u5bf9\u6a21\u578b\u8fdb\u884c\u4f18\u5316\u3002\u5e38\u7528\u7684\u8bc4\u4f30\u6307\u6807\u6709\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u3001\u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\u7b49\u3002<\/p>\n<\/p>\n
4.1\u3001\u6a21\u578b\u8bc4\u4f30<\/h4>\n<\/p>\n
\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
from sklearn.metrics import mean_squared_error<\/p>\n
\u9884\u6d4b\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u8ba1\u7b97\u5747\u65b9\u8bef\u5dee<\/strong><\/h2>\n
mse = mean_squared_error(y_test, y_pred)<\/p>\n
print(f'Test MSE: {mse:.4f}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2\u3001\u6a21\u578b\u4f18\u5316<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u6a21\u578b\u53c2\u6570\u3001\u9009\u62e9\u4e0d\u540c\u7684\u7279\u5f81\u3001\u4f7f\u7528\u66f4\u590d\u6742\u7684\u6a21\u578b\u7b49\u65b9\u6cd5\u6765\u4f18\u5316\u6a21\u578b\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528\u7f51\u683c\u641c\u7d22\u6765\u8c03\u6574\u968f\u673a\u68ee\u6797\u7684\u53c2\u6570\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import GridSearchCV<\/p>\n
\u5b9a\u4e49\u53c2\u6570\u7f51\u683c<\/strong><\/h2>\n
param_grid = {<\/p>\n
    'n_estimators': [50, 100, 200],<\/p>\n
    'max_depth': [None, 10, 20, 30]<\/p>\n
}<\/p>\n
\u7f51\u683c\u641c\u7d22<\/strong><\/h2>\n
grid_search = GridSearchCV(model, param_grid, cv=5, scoring='neg_mean_squared_error')<\/p>\n
grid_search.fit(X_train, y_train)<\/p>\n
\u6700\u4f18\u53c2\u6570<\/strong><\/h2>\n
best_params = grid_search.best_params_<\/p>\n
print(f'Best parameters: {best_params}')<\/p>\n
\u4f7f\u7528\u6700\u4f18\u53c2\u6570\u91cd\u65b0\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
best_model = RandomForestRegressor(best_params, random_state=42)<\/p>\n
best_model.fit(X_train, y_train)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u9884\u6d4b\u4e0e\u7ed3\u679c\u5206\u6790<\/h3>\n<\/p>\n
\u5728\u6a21\u578b\u8bad\u7ec3\u548c\u4f18\u5316\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u6a21\u578b\u5bf9\u672a\u6765\u7684\u5f00\u5956\u6570\u636e\u8fdb\u884c\u9884\u6d4b\uff0c\u5e76\u5bf9\u9884\u6d4b\u7ed3\u679c\u8fdb\u884c\u5206\u6790\u3002<\/p>\n<\/p>\n
5.1\u3001\u8fdb\u884c\u9884\u6d4b<\/h4>\n<\/p>\n
\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5bf9\u672a\u6765\u7684\u5f00\u5956\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
# \u9884\u6d4b\u672a\u6765\u4e00\u671f\u7684\u5f00\u5956\u53f7\u7801<\/p>\n
future_features = ...  # \u9700\u8981\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u6784\u9020\u672a\u6765\u4e00\u671f\u7684\u7279\u5f81<\/p>\n
future_pred = best_model.predict(future_features)<\/p>\n
print(f'Predicted numbers: {future_pred}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.2\u3001\u7ed3\u679c\u5206\u6790<\/h4>\n<\/p>\n
\u5bf9\u9884\u6d4b\u7ed3\u679c\u8fdb\u884c\u5206\u6790\uff0c\u8bc4\u4f30\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u5b9e\u7528\u6027\u3002\u53ef\u4ee5\u901a\u8fc7\u4e0e\u5b9e\u9645\u5f00\u5956\u6570\u636e\u7684\u5bf9\u6bd4\u6765\u8bc4\u4f30\u9884\u6d4b\u7ed3\u679c\u3002<\/p>\n<\/p>\n
# \u4e0e\u5b9e\u9645\u5f00\u5956\u6570\u636e\u5bf9\u6bd4<\/p>\n
actual_numbers = ...  # \u5b9e\u9645\u5f00\u5956\u6570\u636e<\/p>\n
print(f'Actual numbers: {actual_numbers}')<\/p>\n
print(f'Predicted numbers: {future_pred}')<\/p>\n
\u8ba1\u7b97\u9884\u6d4b\u51c6\u786e\u7387\u7b49\u6307\u6807<\/strong><\/h2>\n
accuracy = ...  # \u6839\u636e\u5b9e\u9645\u60c5\u51b5\u8ba1\u7b97\u51c6\u786e\u7387<\/p>\n
print(f'Prediction accuracy: {accuracy:.2f}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u8fdb\u884c\u6392\u5217\u4e94\u7684\u9884\u6d4b\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u5f69\u7968\u7684\u5f00\u5956\u5177\u6709\u5f88\u5927\u7684\u968f\u673a\u6027\uff0c\u9884\u6d4b\u7ed3\u679c\u53ef\u80fd\u5e76\u4e0d\u51c6\u786e\u3002\u5728\u8fdb\u884c\u9884\u6d4b\u65f6\uff0c\u5e94\u8be5\u4fdd\u6301\u7406\u6027\uff0c\u907f\u514d\u8fc7\u5ea6\u4f9d\u8d56\u9884\u6d4b\u7ed3\u679c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u6392\u5217\u4e94\u7684\u6570\u5b57\u9884\u6d4b\uff1f<\/strong>
Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5e93\u548c\u5de5\u5177\uff0c\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u8fdb\u884c\u6392\u5217\u4e94\u6570\u5b57\u7684\u9884\u6d4b\u3002\u901a\u5e38\uff0c\u7528\u6237\u53ef\u4ee5\u5229\u7528\u6570\u636e\u5206\u6790\u5e93\uff08\u5982Pandas\uff09\u6765\u5904\u7406\u5386\u53f2\u6570\u636e\uff0c\u5e76\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u5e93\uff08\u5982Scikit-learn\uff09\u6765\u8bad\u7ec3\u6a21\u578b\u3002\u901a\u8fc7\u63d0\u53d6\u7279\u5f81\u548c\u6a21\u5f0f\uff0c\u6a21\u578b\u53ef\u4ee5\u9884\u6d4b\u53ef\u80fd\u51fa\u73b0\u7684\u6570\u5b57\u7ec4\u5408\u3002<\/p>\n
\u6709\u54ea\u4e9bPython\u5e93\u9002\u5408\u7528\u4e8e\u6392\u5217\u4e94\u9884\u6d4b\uff1f<\/strong>
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