数据挖掘--数据预处理(1)

﹏ヽ暗。殇╰゛Y 2022-02-09 13:34 520阅读 0赞

这篇博客总结在数据挖掘，数据预处理阶段常用的方法和技巧，对于kaggle和天池的比赛和适用

import pandas as pd
    
    train_pd = pd.read_csv(path+'training.csv') #读取数据
    test_pd = pd.read_csv(path+'sorted_test.csv')
    
    train_pd.info()#查看数据类型
    train_pd.describe()#数值型变量的统计信息
    train_pd.describe(include=['O'])#String, object类型变量的统计信息
    
    train_pd[['PIDN', 'Depth']]#获取dataframe里的特定列
    
    X_train = train_pd.drop(['PIDN','SOC','pH','Ca','P','Sand'],axis=1)#删除dataframe特定列
    
    X_train['Depth'] = X_train['Depth'].map({'Topsoil':0.0, 'Subsoil':1.0}).astype(float)#把某列里的string类型转为float
    
    train_pd.isnull().sum()#查看每一列，值为空的数据的数量 
    
    Y_submit = pd.DataFrame(Y_pred, columns=['Ca','P','pH','SOC','Sand'])#numpy array转成dataframe并且加上对应的列名
    
    Y_submit.insert(loc=0, column='PIDN', value=sample[['PIDN']])#dataframe 按列号插入列（第一列）
    
    Y_submit.to_csv(path+'submission.csv', index=False)#dataframe转excel

#SVR模型的输出只能是一维的，使用MultiOutputRegressor能处理多维输出
    from sklearn.svm import SVR
    from sklearn.multioutput import MultiOutputRegressor
    
    clf = SVR(gamma='scale', C=1.0, epsilon=0.2)
    
    multi_clf = MultiOutputRegressor(clf)

#划分验证数据集
    x_train, x_valid, y_train, y_valid = train_test_split(X_train, Y_train, test_size=0.33, random_state=123)

#按分组以平均值替换缺失值
    processed_df['Age'] = processed_df.groupby(['Pclass','Sex','Parch','SibSp'])['Age'].transform(lambda x: x.fillna(x.mean()))
    
    #对title列里的Lady...值全部替换成Rare
    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\
     	'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
    
    #把Title列里的String类型替换为对应的数值型变量
    title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
    for dataset in combine:
        dataset['Title'] = dataset['Title'].map(title_mapping)
        dataset['Title'] = dataset['Title'].fillna(0)
    
    
    #将某列的连续性数值转换为离散型数值
    for dataset in combine:    
        dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0
        dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1
        dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2
        dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3
        dataset.loc[ dataset['Age'] > 64, 'Age']

#评估模型质量
    def plot_confusion_matrix(y_true, y_pred, classes,
                              normalize=False,
                              title=None,
                              cmap=plt.cm.Blues):
        """
        This function prints and plots the confusion matrix.
        Normalization can be applied by setting `normalize=True`.
        """
        if not title:
            if normalize:
                title = 'Normalized confusion matrix'
            else:
                title = 'Confusion matrix, without normalization'
    
        # Compute confusion matrix
        cm = confusion_matrix(y_true, y_pred)
        # Only use the labels that appear in the data
        #classes = classes[unique_labels(y_true, y_pred)]
        if normalize:
            cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
            print("Normalized confusion matrix")
        else:
            print('Confusion matrix, without normalization')
    
        print(cm)
    
        fig, ax = plt.subplots()
        im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
        ax.figure.colorbar(im, ax=ax)
        # We want to show all ticks...
        ax.set(xticks=np.arange(cm.shape[1]),
               yticks=np.arange(cm.shape[0]),
               # ... and label them with the respective list entries
               xticklabels=classes, yticklabels=classes,
               title=title,
               ylabel='True label',
               xlabel='Predicted label')
    
        # Rotate the tick labels and set their alignment.
        plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
                 rotation_mode="anchor")
    
        # Loop over data dimensions and create text annotations.
        fmt = '.2f' if normalize else 'd'
        thresh = cm.max() / 2.
        for i in range(cm.shape[0]):
            for j in range(cm.shape[1]):
                ax.text(j, i, format(cm[i, j], fmt),
                        ha="center", va="center",
                        color="white" if cm[i, j] > thresh else "black")
        fig.tight_layout()
        return ax
    
    from sklearn.metrics import confusion_matrix
    from sklearn.metrics import accuracy_score
    
    lg = LogisticRegression(solver='lbfgs', random_state=42)
    lg.fit(X_train, Y_train)
    
    logistic_prediction = lg.predict(X_test)
    
    score = metrics.accuracy_score(Y_test, logistic_prediction)
    #display_confusion_matrix(Y_test, logistic_prediction, score=score)
    
    confusion_matrix(Y_test, logistic_prediction)#绘制混淆矩阵
    acc_score = accuracy_score(Y_test, logistic_prediction)
    title = 'Accuracy Score: %f' % acc_score
    
    plot_confusion_matrix(Y_test, logistic_prediction, classes=['0','1'] ,title=title)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3p6bGRt_size_16_color_FFFFFF_t_70]

#K折交叉验证评估模型
    from sklearn.model_selection import cross_val_score
    
    n_folds = 10
    cv_score_lg = cross_val_score(estimator=lg, X=X_train, y=Y_train, cv=n_folds, n_jobs=-1)
    cv_score_dt = cross_val_score(estimator=dt, X=X_train, y=Y_train, cv=n_folds, n_jobs=-1)
    cv_score_svm = cross_val_score(estimator=svm, X=X_train, y=Y_train, cv=n_folds, n_jobs=-1)
    cv_score_rf = cross_val_score(estimator=rf, X=X_train, y=Y_train, cv=n_folds, n_jobs=-1)
    
    cv_score_ann = cross_val_score(estimator=KerasClassifier(build_fn=build_ann,batch_size=16,epochs=20,verbose=0),
                                  X=X_train, y=Y_train, cv=n_folds, n_jobs=-1)
    
    cv_result = {'lg':cv_score_lg, 'dt':cv_score_dt, 'svm':cv_score_svm, 'rf':cv_score_rf,
                'ann':cv_score_ann}
    cv_data = {model: [score.mean(), score.std()] for model, score in cv_result.items()}
    cv_df = pd.DataFrame(cv_data, index=['Mean_accuracy', 'Variance'])
    cv_df

![在这里插入图片描述][2019050311545833.png]

#若处理的是二元分类问题，可以这样来判断某个特征与label之间的相关性
    train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)

![在这里插入图片描述][20190503115809484.png]

from sklearn.metrics import classification_report
    classification_report(y_test, predictions)#查看模型训练效果

![在这里插入图片描述][20190512195631989.png]

# Function to calculate correlations with the target for a dataframe
    def target_corrs(df):
    
        # List of correlations
        corrs = []
    
        # Iterate through the columns 
        for col in df.columns:
            print(col)
            # Skip the target column
            if col != 'TARGET':
                # Calculate correlation with the target
                corr = df['TARGET'].corr(df[col])
    
                # Append the list as a tuple
                corrs.append((col, corr))
                
        # Sort by absolute magnitude of correlations
        corrs = sorted(corrs, key = lambda x: abs(x[1]), reverse = True)
        
        return corrs

def count_categorical(df, group_var, df_name): #处理离散特征值，获取其counts and normalized counts
        """Computes counts and normalized counts for each observation
        of `group_var` of each unique category in every categorical variable
        
        Parameters
        --------
        df : dataframe 
            The dataframe to calculate the value counts for.
            
        group_var : string
            The variable by which to group the dataframe. For each unique
            value of this variable, the final dataframe will have one row
            
        df_name : string
            Variable added to the front of column names to keep track of columns
    
        
        Return
        --------
        categorical : dataframe
            A dataframe with counts and normalized counts of each unique category in every categorical variable
            with one row for every unique value of the `group_var`.
            
        """
        
        # Select the categorical columns
        categorical = pd.get_dummies(df.select_dtypes('object'))
    
        # Make sure to put the identifying id on the column
        categorical[group_var] = df[group_var]
    
        # Groupby the group var and calculate the sum and mean
        categorical = categorical.groupby(group_var).agg(['sum', 'mean'])
        
        column_names = []
        
        # Iterate through the columns in level 0
        for var in categorical.columns.levels[0]:
            # Iterate through the stats in level 1
            for stat in ['count', 'count_norm']:
                # Make a new column name
                column_names.append('%s_%s_%s' % (df_name, var, stat))
        
        categorical.columns = column_names
        
        return categorical

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3p6bGRt_size_16_color_FFFFFF_t_70]: /images/20220206/c471cb0f7f4c49a99d7cf1bfac4f69b4.png
[2019050311545833.png]: /images/20220206/92febb9e6b1949a0ba0979cf08ab3ef7.png
[20190503115809484.png]: /images/20220206/23e6dd8cf06d487d86b45aab134befb9.png
[20190512195631989.png]: /images/20220206/ec007dd5864e4b51bbe998d915dc5812.png