Datawhale AI Summer Camp - New User Prediction Challenge | Study Notes

Enterprise 2023-09-15 22:39:50 views: null

Task 1: Run through the Baseline

# 1. 导入需要用到的相关库
# 导入 pandas 库，用于数据处理和分析
import pandas as pd
# 导入 numpy 库，用于科学计算和多维数组操作
import numpy as np
# 从 sklearn.tree 模块中导入 DecisionTreeClassifier 类
# DecisionTreeClassifier 用于构建决策树分类模型
from sklearn.tree import DecisionTreeClassifier

# 2. 读取训练集和测试集
# 使用 read_csv() 函数从文件中读取训练集数据，文件名为 'train.csv'
train_data = pd.read_csv('train.csv')
# 使用 read_csv() 函数从文件中读取测试集数据，文件名为 'test.csv'
test_data = pd.read_csv('test.csv')

 

train_data.head()

# 3. 将 'udmap' 列进行 One-Hot 编码 
# 数据样例：
#                    udmap  key1  key2  key3  key4  key5  key6  key7  key8  key9
# 0           {'key1': 2}     2     0     0     0     0     0     0     0     0
# 1           {'key2': 1}     0     1     0     0     0     0     0     0     0
# 2  {'key1': 3, 'key2': 2}   3     2     0     0     0     0     0     0     0

# 在 python 中, 形如 {'key1': 3, 'key2': 2} 格式的为字典类型对象, 通过key-value键值对的方式存储
# 而在本数据集中, udmap实际是以字符的形式存储, 所以处理时需要先用eval 函数将'udmap' 解析为字典

# 具体实现代码：
# 定义函数 udmap_onethot，用于将 'udmap' 列进行 One-Hot 编码
def udmap_onethot(d):
    v = np.zeros(9)  # 创建一个长度为 9 的零数组
    if d == 'unknown':  # 如果 'udmap' 的值是 'unknown'
        return v  # 返回零数组
    d = eval(d)  # 将 'udmap' 的值解析为一个字典
    for i in range(1, 10):  # 遍历 'key1' 到 'key9', 注意, 这里不包括10本身
        if 'key' + str(i) in d:  # 如果当前键存在于字典中
            v[i-1] = d['key' + str(i)]  # 将字典中的值存储在对应的索引位置上
            
    return v  # 返回 One-Hot 编码后的数组

# 注: 对于不理解的步骤, 可以逐行 print 内容查看
# 使用 apply() 方法将 udmap_onethot 函数应用于每个样本的 'udmap' 列
# np.vstack() 用于将结果堆叠成一个数组
train_udmap_df = pd.DataFrame(np.vstack(train_data['udmap'].apply(udmap_onethot)))
test_udmap_df = pd.DataFrame(np.vstack(test_data['udmap'].apply(udmap_onethot)))
# 为新的特征 DataFrame 命名列名
train_udmap_df.columns = ['key' + str(i) for i in range(1, 10)]
test_udmap_df.columns = ['key' + str(i) for i in range(1, 10)]
# 将编码后的 udmap 特征与原始数据进行拼接，沿着列方向拼接
train_data = pd.concat([train_data, train_udmap_df], axis=1)
test_data = pd.concat([test_data, test_udmap_df], axis=1)

# 4. 编码 udmap 是否为空
# 使用比较运算符将每个样本的 'udmap' 列与字符串 'unknown' 进行比较，返回一个布尔值的 Series
# 使用 astype(int) 将布尔值转换为整数（0 或 1），以便进行后续的数值计算和分析
train_data['udmap_isunknown'] = (train_data['udmap'] == 'unknown').astype(int)
test_data['udmap_isunknown'] = (test_data['udmap'] == 'unknown').astype(int)


# 5. 提取 eid 的频次特征
# 使用 map() 方法将每个样本的 eid 映射到训练数据中 eid 的频次计数
# train_data['eid'].value_counts() 返回每个 eid 出现的频次计数
train_data['eid_freq'] = train_data['eid'].map(train_data['eid'].value_counts())
test_data['eid_freq'] = test_data['eid'].map(train_data['eid'].value_counts())


# 6. 提取 eid 的标签特征（不同访问行为的人有不同的target标签）
# 使用 groupby() 方法按照 eid 进行分组，然后计算每个 eid 分组的目标值均值
# train_data.groupby('eid')['target'].mean() 返回每个 eid 分组的目标值均值
train_data['eid_mean'] = train_data['eid'].map(train_data.groupby('eid')['target'].mean())
test_data['eid_mean'] = test_data['eid'].map(train_data.groupby('eid')['target'].mean())


# 7. 提取时间戳
# 使用 pd.to_datetime() 函数将时间戳列转换为 datetime 类型
# 样例：1678932546000->2023-03-15 15:14:16
# 注: 需要注意时间戳的长度, 如果是13位则unit 为 毫秒, 如果是10位则为 秒, 这是转时间戳时容易踩的坑
# 具体实现代码：
train_data['common_ts'] = pd.to_datetime(train_data['common_ts'], unit='ms')
test_data['common_ts'] = pd.to_datetime(test_data['common_ts'], unit='ms')

# 使用 dt.hour 属性从 datetime 列中提取小时信息，并将提取的小时信息存储在新的列 'common_ts_hour'
train_data['common_ts_hour'] = train_data['common_ts'].dt.hour
test_data['common_ts_hour'] = test_data['common_ts'].dt.hour


# 8. 加载决策树模型进行训练(直接使用sklearn中导入的包进行模型建立)
clf = DecisionTreeClassifier()
# 使用 fit 方法训练模型
# train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1) 从训练数据集中移除列 'udmap', 'common_ts', 'uuid', 'target'
# 这些列可能是特征或标签，取决于数据集的设置
# train_data['target'] 是训练数据集中的标签列，它包含了每个样本的目标值
clf.fit(
    train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1),  # 特征数据：移除指定的列作为特征
    train_data['target']  # 目标数据：将 'target' 列作为模型的目标进行训练
)


# 9. 对测试集进行预测，并保存结果到result_df中
# 创建一个DataFrame来存储预测结果，其中包括两列：'uuid' 和 'target'
# 'uuid' 列来自测试数据集中的 'uuid' 列，'target' 列将用来存储模型的预测结果
result_df = pd.DataFrame({
    'uuid': test_data['uuid'],  # 使用测试数据集中的 'uuid' 列作为 'uuid' 列的值
    'target': clf.predict(test_data.drop(['udmap', 'common_ts', 'uuid'], axis=1))  # 使用模型 clf 对测试数据集进行预测，并将预测结果存储在 'target' 列中
})


# 10. 保存结果文件到本地
# 将结果DataFrame保存为一个CSV文件，文件名为 'submit.csv'
# 参数 index=None 表示不将DataFrame的索引写入文件中
result_df.to_csv('submit.csv', index=None)

Practice and answer the following questions:

If submit.csv is submitted to the iFlytek competition page, how many points will there be?

How to perform manual onehot on udmp in the code?

1：0.62710

2: Get the value of the dictionary element in the umap column by key, which is initially a nine-dimensional vector, and overwrite the value corresponding to the key in the dictionary to the corresponding position in the vector.

Task 2.1: Data Analysis and Visualization

# 导入库
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# 读取训练集和测试集文件
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

# 相关性热力图
sns.heatmap(train_data.corr().abs(), cmap='YlOrRd')

 

# x7分组下标签均值
sns.barplot(x='x7', y='target', data=train_data)

Write code to answer the following questions:

  • Fields x1 to x8 are user-related attributes and are anonymous processing fields. Add code to analyze the values ​​of these data fields. Which fields are of numeric type? Which fields are category types?
  • For numeric fields, consider plotting a boxplot grouped under labels.
  • Extract hours from common_ts and plot the change in label distribution hourly.
  • Perform onehot on udmap, count the mean value of labels corresponding to each key, and draw a histogram.

Question 1: Fields x1 to x8 are user-related attributes and are anonymous processing fields. Add code to analyze the values ​​of these data fields. Which fields are of numeric type? Which fields are category types? 

x1, x2, x3, x4, x5, x6, x7, x8 are categorical types, and x3, x4, x5 are numeric types.

#1：
import pandas as pd

# 假设您的数据已经加载到名为 'data' 的 DataFrame 中
train_data[['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8']].head(5)

Question 2: For numeric fields, consider drawing a box plot under label grouping.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 6))
for i,y in enumerate(['x3', 'x4', 'x5']):
    sns.boxplot(x="target", y=y, data=train_data, width=0.5, showfliers=False,ax=axes[i])
    ax.set_xlabel('Label')
    ax.set_ylabel(f'Feature {col}')
    ax.set_title(f'Box Plot of Feature {col}')
    ax.yaxis.grid(True)

 Question 3: Extract hours from common_ts and plot the changes in label distribution per hour.

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 6),dpi=80)

train_data['common_ts'] = pd.to_datetime(train_data['common_ts'], unit='ms')
# 使用 dt.hour 属性从 datetime 列中提取小时信息，并将提取的小时信息存储在新的列 'common_ts_hour'
train_data['common_ts_hour'] = train_data['common_ts'].dt.hour

sns.countplot(x="common_ts_hour",hue='target', data=train_data,ax = axes[0])

new_df = (train_data.groupby('common_ts_hour')['target']
            .value_counts(normalize=True)
            .sort_index()
            .unstack()
         )
new_df.plot.bar(stacked=True,ax = axes[1])

Question 4: Perform onehot on udmap, count the mean value of labels corresponding to each key, and draw a histogram.

train_data = pd.read_csv('train.csv')
def udmap_onethot(d):
    v = np.zeros(9)  # 创建一个长度为 9 的零数组
    if d == 'unknown':  # 如果 'udmap' 的值是 'unknown'
        return v  # 返回零数组
    d = eval(d)  # 将 'udmap' 的值解析为一个字典
    for i in range(1, 10):  # 遍历 'key1' 到 'key9', 注意, 这里不包括10本身
        if 'key' + str(i) in d:  # 如果当前键存在于字典中
            v[i-1] = d['key' + str(i)]  # 将字典中的值存储在对应的索引位置上
            
    return v  # 返回 One-Hot 编码后的数组

# 注: 对于不理解的步骤, 可以逐行 print 内容查看
# 使用 apply() 方法将 udmap_onethot 函数应用于每个样本的 'udmap' 列
# np.vstack() 用于将结果堆叠成一个数组
train_udmap_df = pd.DataFrame(np.vstack(train_data['udmap'].apply(udmap_onethot)))
# 为新的特征 DataFrame 命名列名
train_udmap_df.columns = ['key' + str(i) for i in range(1, 10)]
# 将编码后的 udmap 特征与原始数据进行拼接，沿着列方向拼接
train_data = pd.concat([train_data, train_udmap_df], axis=1)

key_means = {}
for key in ["key"+str(i) for i in range(1,10)]:
    key_mean = train_data[train_data[key]!=0]["target"].mean()
    key_means[key] = key_mean
key_means

key_means_df = pd.DataFrame(key_means, index = [0])
plt.figure(figsize=(12, 6))
sns.barplot(data=key_means_df, palette="Set3")
plt.xlabel('Keys')
plt.ylabel('Mean Target')
plt.title('Stacked Bar Plot of Mean Target by Keys')
plt.legend(title='Keys', loc='upper right')
plt.show()

 

Task 2.2: Model cross-validation

# 导入库
import pandas as pd
import numpy as np

# 读取训练集和测试集文件
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

# 提取udmap特征，人工进行onehot
def udmap_onethot(d):
    v = np.zeros(9)
    if d == 'unknown':
        return v
    d = eval(d)
    for i in range(1, 10):
        if 'key' + str(i) in d:
            v[i-1] = d['key' + str(i)]
            
    return v
train_udmap_df = pd.DataFrame(np.vstack(train_data['udmap'].apply(udmap_onethot)))
test_udmap_df = pd.DataFrame(np.vstack(test_data['udmap'].apply(udmap_onethot)))
train_udmap_df.columns = ['key' + str(i) for i in range(1, 10)]
test_udmap_df.columns = ['key' + str(i) for i in range(1, 10)]

# 编码udmap是否为空
train_data['udmap_isunknown'] = (train_data['udmap'] == 'unknown').astype(int)
test_data['udmap_isunknown'] = (test_data['udmap'] == 'unknown').astype(int)

# udmap特征和原始数据拼接
train_data = pd.concat([train_data, train_udmap_df], axis=1)
test_data = pd.concat([test_data, test_udmap_df], axis=1)

# 提取eid的频次特征
train_data['eid_freq'] = train_data['eid'].map(train_data['eid'].value_counts())
test_data['eid_freq'] = test_data['eid'].map(train_data['eid'].value_counts())

# 提取eid的标签特征
train_data['eid_mean'] = train_data['eid'].map(train_data.groupby('eid')['target'].mean())
test_data['eid_mean'] = test_data['eid'].map(train_data.groupby('eid')['target'].mean())

# 提取时间戳
train_data['common_ts'] = pd.to_datetime(train_data['common_ts'], unit='ms')
test_data['common_ts'] = pd.to_datetime(test_data['common_ts'], unit='ms')
train_data['common_ts_hour'] = train_data['common_ts'].dt.hour
test_data['common_ts_hour'] = test_data['common_ts'].dt.hour

# 导入模型
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.ensemble import RandomForestClassifier

# 导入交叉验证和评价指标
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import classification_report

# 训练并验证SGDClassifier
pred = cross_val_predict(
    SGDClassifier(max_iter=20),
    train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1),
    train_data['target'], 
)
print(classification_report(train_data['target'], pred, digits=3))

# 训练并验证DecisionTreeClassifier
pred = cross_val_predict(
    DecisionTreeClassifier(),
    train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1),
    train_data['target'], 
    cv=5
)
print(classification_report(train_data['target'], pred, digits=3))

# 训练并验证MultinomialNB
pred = cross_val_predict(
    MultinomialNB(),
    train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1),
    train_data['target']
)
print(classification_report(train_data['target'], pred, digits=3))

# 训练并验证RandomForestClassifier
pred = cross_val_predict(
    RandomForestClassifier(n_estimators=5),
    train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1),
    train_data['target']
)
print(classification_report(train_data['target'], pred, digits=3))

Write code to answer the following questions:

  • Which of the above models has the best macro F1 effect, and why does this model have the best effect?
  • Use a tree model to train and then visualize feature importance;
  • Add 3 more model trainings to compare the model accuracy;

Question 1: Which of the above models has the best macro F1 effect, and why does this model have the best effect?

  The decision tree has the best F1 score because its modeling power and interpretability make it suitable for many problems.

 Question 2: Use tree model training and then visualize feature importance;

from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier()
# 使用 fit 方法训练模型
# train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1) 从训练数据集中移除列 'udmap', 'common_ts', 'uuid', 'target'
# 这些列可能是特征或标签，取决于数据集的设置
# train_data['target'] 是训练数据集中的标签列，它包含了每个样本的目标值
clf.fit(
    train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1),  # 特征数据：移除指定的列作为特征
    train_data['target']  # 目标数据：将 'target' 列作为模型的目标进行训练
)

# s树的特征重要性可视化
def plot_feature_importances_cancer(model):
    n_features = train_data.shape[1]-4
    plt.barh(range(n_features),model.feature_importances_,align = 'center')
    plt.yticks(np.arange(n_features),train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1).columns)
    plt.xlabel("Feature importance")
    plt.ylabel("Feature")
    
plot_feature_importances_cancer(clf)

 

Question 3: Add 3 more models to train and compare the model accuracy;

  Here, the ridge regression classifier, extreme random tree and gradient boosting tree with default parameters are used for training to obtain the results.

from sklearn.linear_model import RidgeClassifier
from sklearn.tree import ExtraTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier

pred = cross_val_predict(
    RidgeClassifier(),
    train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1),
    train_data['target']
)
print(classification_report(train_data['target'], pred, digits=3))

pred = cross_val_predict(
    ExtraTreeClassifier(),
    train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1),
    train_data['target']
)
print(classification_report(train_data['target'], pred, digits=3))

pred = cross_val_predict(
    BaggingClassifier(),
    train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1),
    train_data['target']
)
print(classification_report(train_data['target'], pred, digits=3))

Task 2.3: Feature Engineering

  We experimented with the feature engineering method given in the tutorial and found that x3_freq and x4_freq in the test set have nan values. We considered removing these two features.

train_data['common_ts_day'] = train_data['common_ts'].dt.day
test_data['common_ts_day'] = test_data['common_ts'].dt.day

train_data['x1_freq'] = train_data['x1'].map(train_data['x1'].value_counts())
test_data['x1_freq'] = test_data['x1'].map(train_data['x1'].value_counts())
train_data['x1_mean'] = train_data['x1'].map(train_data.groupby('x1')['target'].mean())
test_data['x1_mean'] = test_data['x1'].map(train_data.groupby('x1')['target'].mean())

train_data['x2_freq'] = train_data['x2'].map(train_data['x2'].value_counts())
test_data['x2_freq'] = test_data['x2'].map(train_data['x2'].value_counts())
train_data['x2_mean'] = train_data['x2'].map(train_data.groupby('x2')['target'].mean())
test_data['x2_mean'] = test_data['x2'].map(train_data.groupby('x2')['target'].mean())

##x3_freq和x4_freq存在为nan的值
#train_data['x3_freq'] = train_data['x3'].map(train_data['x3'].value_counts())
#test_data['x3_freq'] = test_data['x3'].map(train_data['x3'].value_counts())

#train_data['x4_freq'] = train_data['x4'].map(train_data['x4'].value_counts())
#test_data['x4_freq'] = test_data['x4'].map(train_data['x4'].value_counts())

train_data['x6_freq'] = train_data['x6'].map(train_data['x6'].value_counts())
test_data['x6_freq'] = test_data['x6'].map(train_data['x6'].value_counts())
train_data['x6_mean'] = train_data['x6'].map(train_data.groupby('x6')['target'].mean())
test_data['x6_mean'] = test_data['x6'].map(train_data.groupby('x6')['target'].mean())

train_data['x7_freq'] = train_data['x7'].map(train_data['x7'].value_counts())
test_data['x7_freq'] = test_data['x7'].map(train_data['x7'].value_counts())
train_data['x7_mean'] = train_data['x7'].map(train_data.groupby('x7')['target'].mean())
test_data['x7_mean'] = test_data['x7'].map(train_data.groupby('x7')['target'].mean())

train_data['x8_freq'] = train_data['x8'].map(train_data['x8'].value_counts())
test_data['x8_freq'] = test_data['x8'].map(train_data['x8'].value_counts())
train_data['x8_mean'] = train_data['x8'].map(train_data.groupby('x8')['target'].mean())
test_data['x8_mean'] = test_data['x8'].map(train_data.groupby('x8')['target'].mean())

train_data.head()

# 训练并验证DecisionTreeClassifier
pred = cross_val_predict(
    DecisionTreeClassifier(),
    train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1),
    train_data['target']
)
print(classification_report(train_data['target'], pred, digits=3))

# 8. 加载决策树模型进行训练(直接使用sklearn中导入的包进行模型建立)
clf = DecisionTreeClassifier()
# 使用 fit 方法训练模型
# train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1) 从训练数据集中移除列 'udmap', 'common_ts', 'uuid', 'target'
# 这些列可能是特征或标签，取决于数据集的设置
# train_data['target'] 是训练数据集中的标签列，它包含了每个样本的目标值
clf.fit(
    train_data.drop(['udmap', 'common_ts', 'uuid', 'target'], axis=1),  # 特征数据：移除指定的列作为特征
    train_data['target']  # 目标数据：将 'target' 列作为模型的目标进行训练
)


# 9. 对测试集进行预测，并保存结果到result_df中
# 创建一个DataFrame来存储预测结果，其中包括两列：'uuid' 和 'target'
# 'uuid' 列来自测试数据集中的 'uuid' 列，'target' 列将用来存储模型的预测结果
result_df = pd.DataFrame({
    'uuid': test_data['uuid'],  # 使用测试数据集中的 'uuid' 列作为 'uuid' 列的值
    'target': clf.predict(test_data.drop(['udmap', 'common_ts', 'uuid'], axis=1))  # 使用模型 clf 对测试数据集进行预测，并将预测结果存储在 'target' 列中
})
#由于这里进行特征工程后存在为0的值表示为nan，我们将nan值填充为0

Improve

Common features for recommender systems

train_data["common_ts_month"] = train_data["common_ts"].dt.month # 添加新特征“month”，代表”当前月份“。
test_data["common_ts_month"] = test_data["common_ts"].dt.month

train_data["common_ts_day"] = train_data["common_ts"].dt.day # 添加新特征“day”，代表”当前日期“。
test_data["common_ts_day"] = test_data["common_ts"].dt.day

train_data["common_ts_hour"] = train_data["common_ts"].dt.hour # 添加新特征“hour”，代表”当前小时“。
test_data["common_ts_hour"] = test_data["common_ts"].dt.hour

train_data["common_ts_minute"] = train_data["common_ts"].dt.minute # 添加新特征“minute”，代表”当前分钟“。
test_data["common_ts_minute"] = test_data["common_ts"].dt.minute

train_data["common_ts_weekofyear"] = train_data["common_ts"].dt.isocalendar().week.astype(int) # 添加新特征“weekofyear”，代表”当年第几周“，并转换成 int，否则 LightGBM 无法处理。
test_data["common_ts_weekofyear"] = test_data["common_ts"].dt.isocalendar().week.astype(int)

train_data["common_ts_dayofyear"] = train_data["common_ts"].dt.dayofyear # 添加新特征“dayofyear”，代表”当年第几日“。
test_data["common_ts_dayofyear"] = test_data["common_ts"].dt.dayofyear

train_data["common_ts_dayofweek"] = train_data["common_ts"].dt.dayofweek # 添加新特征“dayofweek”，代表”当周第几日“。
test_data["common_ts_dayofweek"] = test_data["common_ts"].dt.dayofweek

train_data["common_ts_is_weekend"] = train_data["common_ts"].dt.dayofweek // 6 # 添加新特征“is_weekend”，代表”是否是周末“，1 代表是周末，0 代表不是周末。
test_data["common_ts_is_weekend"] = test_data["common_ts"].dt.dayofweek // 6

By observing the distribution map of x7, we found that when the category of x7 is 1, the labels are almost all 1, so we construct this feature

train_data["x7_01"] = train_data["x7"] == 1
train_data["x7_01"] = train_data["x7_01"].astype(int)

test_data["x7_01"] = test_data["x7"]==1
test_data["x7_01"] = test_data["x7_01"].astype(int)

Construct features by observing the label distribution within a day

train_data["common_ts_hoursofday"] = (train_data["common_ts_hour"] < 2) | (train_data["common_ts_hour"] > 13)
train_data["common_ts_hoursofday"] = train_data["common_ts_hoursofday"].astype(int)

test_data["common_ts_hoursofday"] = (test_data["common_ts_hour"] < 2) | (train_data["common_ts_hour"] > 13)
test_data["common_ts_hoursofday"] = test_data["common_ts_hoursofday"].astype(int)

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Origin blog.csdn.net/timberman666/article/details/132494371
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