Pandas多维特征数据预处理及sklearn数据不均衡处理相关技术实践-大数据ML样本集案例实战

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1 机器学习调优步骤(第一行不平衡问题处理)

Pandas多维特征数据预处理及sklearn数据不均衡处理相关技术实践-大数据ML样本集案例实战_第1张图片

2 Pandas多维特征数据预处理

  • 数据初始化展示

      import pandas as pd
      
      取出第一行
      loans_2007 = pd.read_csv('C:\\ML\\MLData\\filtered_loans_2007.csv', skiprows=1)
      print(len(loans_2007))
      half_count = len(loans_2007) / 2
      loans_2007 = loans_2007.dropna(thresh=half_count, axis=1)
      #loans_2007 = loans_2007.drop(['desc', 'url'],axis=1)
      loans_2007.to_csv('loans_2007.csv', index=False)
      
      loans_2007.head(3)
    

Pandas多维特征数据预处理及sklearn数据不均衡处理相关技术实践-大数据ML样本集案例实战_第2张图片

Pandas多维特征数据预处理及sklearn数据不均衡处理相关技术实践-大数据ML样本集案例实战_第3张图片

  • 显示第0行数据

      import pandas as pd
      loans_2007 = pd.read_csv("loans_2007.csv")
      #loans_2007.drop_duplicates()
      print(loans_2007.iloc[0])
      print(loans_2007.shape[1])
      
      id                                1077501
      member_id                      1.2966e+06
      loan_amnt                            5000
      funded_amnt                          5000
      funded_amnt_inv                      4975
      term                            36 months
      int_rate                           10.65%
      installment                        162.87
      grade                                   B
      sub_grade                              B2
      emp_title                             NaN
      emp_length                      10+ years
      home_ownership                       RENT
      annual_inc                          24000
      verification_status              Verified
      issue_d                          Dec-2011
      loan_status                    Fully Paid
      pymnt_plan                              n
      purpose                       credit_card
      title                            Computer
      zip_code                            860xx
      addr_state                             AZ
      dti                                 27.65
      delinq_2yrs                             0
      earliest_cr_line                 Jan-1985
      inq_last_6mths                          1
      open_acc                                3
      pub_rec                                 0
      revol_bal                           13648
      revol_util                          83.7%
      total_acc                               9
      initial_list_status                     f
      out_prncp                               0
      out_prncp_inv                           0
      total_pymnt                       5863.16
      total_pymnt_inv                   5833.84
      total_rec_prncp                      5000
      total_rec_int                      863.16
      total_rec_late_fee                      0
      recoveries                              0
      collection_recovery_fee                 0
      last_pymnt_d                     Jan-2015
      last_pymnt_amnt                    171.62
      last_credit_pull_d               Nov-2016
      collections_12_mths_ex_med              0
      policy_code                             1
      application_type               INDIVIDUAL
      acc_now_delinq                          0
      chargeoff_within_12_mths                0
      delinq_amnt                             0
      pub_rec_bankruptcies                    0
      tax_liens                               0
      Name: 0, dtype: object
      52
    
  • 删除无意义列

      loans_2007 = loans_2007.drop(["id", "member_id", "funded_amnt", "funded_amnt_inv", "grade", "sub_grade", "emp_title", "issue_d"], axis=1)
      loans_2007 = loans_2007.drop(["zip_code", "out_prncp", "out_prncp_inv", "total_pymnt", "total_pymnt_inv", "total_rec_prncp"], axis=1)
      loans_2007 = loans_2007.drop(["total_rec_int", "total_rec_late_fee", "recoveries", "collection_recovery_fee", "last_pymnt_d", "last_pymnt_amnt"], axis=1)
      print(loans_2007.iloc[0])
      print(loans_2007.shape[1])
    
  • 查看预测值的状态类型

      print(loans_2007['loan_status'].value_counts())
      
      Fully Paid                                             33902
      Charged Off                                             5658
      Does not meet the credit policy. Status:Fully Paid      1988
      Does not meet the credit policy. Status:Charged Off      761
      Current                                                  201
      Late (31-120 days)                                        10
      In Grace Period                                            9
      Late (16-30 days)                                          5
      Default                                                    1
      Name: loan_status, dtype: int64
    
  • 根据贷款状态,舍弃部分不清晰结论,给出明确分类0和1,进行替换

      loans_2007 = loans_2007[(loans_2007['loan_status'] == "Fully Paid") | (loans_2007['loan_status'] == "Charged Off")]
      
      status_replace = {
          "loan_status" : {
              "Fully Paid": 1,
              "Charged Off": 0,
          }
      }
      
      loans_2007 = loans_2007.replace(status_replace)
    
  • 去除每一列值都相同的列

      #let's look for any columns that contain only one unique value and remove them
      
      orig_columns = loans_2007.columns
      drop_columns = []
      for col in orig_columns:
          col_series = loans_2007[col].dropna().unique()
          if len(col_series) == 1:
              drop_columns.append(col)
      loans_2007 = loans_2007.drop(drop_columns, axis=1)
      print(drop_columns)
      print loans_2007.shape
      loans_2007.to_csv('filtered_loans_2007.csv', index=False)
    
      ['initial_list_status', 'collections_12_mths_ex_med', 'policy_code', 'application_type', 'acc_now_delinq', 'chargeoff_within_12_mths', 'delinq_amnt', 'tax_liens']
      (39560, 24)
    
  • 空值处理

      import pandas as pd
      loans = pd.read_csv('filtered_loans_2007.csv')
      null_counts = loans.isnull().sum()
      print(null_counts)
      
      loan_amnt                 0
      term                      0
      int_rate                  0
      installment               0
      emp_length                0
      home_ownership            0
      annual_inc                0
      verification_status       0
      loan_status               0
      pymnt_plan                0
      purpose                   0
      title                    10
      addr_state                0
      dti                       0
      delinq_2yrs               0
      earliest_cr_line          0
      inq_last_6mths            0
      open_acc                  0
      pub_rec                   0
      revol_bal                 0
      revol_util               50
      total_acc                 0
      last_credit_pull_d        2
      pub_rec_bankruptcies    697
      dtype: int64
      
      loans = loans.drop("pub_rec_bankruptcies", axis=1)
      loans = loans.dropna(axis=0)
    
  • String类型分布

      print(loans.dtypes.value_counts())
      object     12
      float64    10
      int64       1
      dtype: int64
      
      object_columns_df = loans.select_dtypes(include=["object"])
      print(object_columns_df.iloc[0])
      
      term                     36 months
      int_rate                    10.65%
      emp_length               10+ years
      home_ownership                RENT
      verification_status       Verified
      pymnt_plan                       n
      purpose                credit_card
      title                     Computer
      addr_state                      AZ
      earliest_cr_line          Jan-1985
      revol_util                   83.7%
      last_credit_pull_d        Nov-2016
      Name: 0, dtype: object
      
      cols = ['home_ownership', 'verification_status', 'emp_length', 'term', 'addr_state']
      for c in cols:
          print(loans[c].value_counts())
          
      RENT        18780
      MORTGAGE    17574
      OWN          3045
      OTHER          96
      NONE            3
      Name: home_ownership, dtype: int64
      
      Not Verified       16856
      Verified           12705
      Source Verified     9937
      Name: verification_status, dtype: int64
      
      10+ years    8821
      < 1 year     4563
      2 years      4371
      3 years      4074
      4 years      3409
      5 years      3270
      1 year       3227
      6 years      2212
      7 years      1756
      8 years      1472
      9 years      1254
      n/a          1069
      Name: emp_length, dtype: int64
      
       36 months    29041
       60 months    10457
      Name: term, dtype: int64
      
      CA    7070
      NY    3788
      FL    2856
      TX    2714
      NJ    1838
      IL    1517
      PA    1504
      VA    1400
      GA    1393
      MA    1336
      OH    1208
      MD    1049
      AZ     874
      WA     834
      CO     786
      NC     780
      CT     747
      MI     722
      MO     682
      MN     611
      NV     492
      SC     470
      WI     453
      AL     446
      OR     445
      LA     435
      KY     325
      OK     298
      KS     269
      UT     256
      AR     243
      DC     211
      RI     198
      NM     188
      WV     176
      HI     172
      NH     172
      DE     113
      MT      84
      WY      83
      AK      79
      SD      63
      VT      54
      MS      19
      TN      17
      IN       9
      ID       6
      IA       5
      NE       5
      ME       3
      Name: addr_state, dtype: int64   
    
  • String类型分布2

    print(loans["purpose"].value_counts())
    print(loans["title"].value_counts())
    
    debt_consolidation    18533
    credit_card            5099
    other                  3963
    home_improvement       2965
    major_purchase         2181
    small_business         1815
    car                    1544
    wedding                 945
    medical                 692
    moving                  581
    vacation                379
    house                   378
    educational             320
    renewable_energy        103
    Name: purpose, dtype: int64
    
    Debt Consolidation                         2168
    Debt Consolidation Loan                    1706
    Personal Loan                               658
    Consolidation                               509
    debt consolidation                          502
    Credit Card Consolidation                   356
    Home Improvement                            354
    Debt consolidation                          333
    Small Business Loan                         322
    Credit Card Loan                            313
    Personal                                    308
    Consolidation Loan                          255
    Home Improvement Loan                       246
    personal loan                               234
    personal                                    220
    Loan                                        212
    Wedding Loan                                209
    consolidation                               200
    Car Loan                                    200
    Other Loan                                  190
    Credit Card Payoff                          155
    Wedding                                     152
    Major Purchase Loan                         144
    Credit Card Refinance                       143
    Consolidate                                 127
    Medical                                     122
    Credit Card                                 117
    home improvement                            111
    My Loan                                      94
    Credit Cards                                 93
                                               ... 
    DebtConsolidationn                            1
     Freedom                                      1
    Credit Card Consolidation Loan - SEG          1
    SOLAR PV                                      1
    Pay on Credit card                            1
    To pay off balloon payments due               1
    Paying off the debt                           1
    Payoff ING PLOC                               1
    Josh CC Loan                                  1
    House payoff                                  1
    Taking care of Business                       1
    Gluten Free Bakery in ideal town for it       1
    Startup Money for Small Business              1
    FundToFinanceCar                              1
    getting ready for Baby                        1
    Dougs Wedding Loan                            1
    d rock                                        1
    LC Loan 2                                     1
    swimming pool repair                          1
    engagement                                    1
    Cut the credit cards Loan                     1
    vinman                                        1
    working hard to get out of debt               1
    consolidate the rest of my debt               1
    Medical/Vacation                              1
    2BDebtFree                                    1
    Paying Off High Interest Credit Cards!        1
    Baby on the way!                              1
    cart loan                                     1
    Consolidaton                                  1
    Name: title, dtype: int64
    
  • 类型转换

      mapping_dict = {
          "emp_length": {
              "10+ years": 10,
              "9 years": 9,
              "8 years": 8,
              "7 years": 7,
              "6 years": 6,
              "5 years": 5,
              "4 years": 4,
              "3 years": 3,
              "2 years": 2,
              "1 year": 1,
              "< 1 year": 0,
              "n/a": 0
          }
      }
      loans = loans.drop(["last_credit_pull_d", "earliest_cr_line", "addr_state", "title"], axis=1)
      loans["int_rate"] = loans["int_rate"].str.rstrip("%").astype("float")
      loans["revol_util"] = loans["revol_util"].str.rstrip("%").astype("float")
      loans = loans.replace(mapping_dict)
    
  • 独热编码

      cat_columns = ["home_ownership", "verification_status", "emp_length", "purpose", "term"]
      dummy_df = pd.get_dummies(loans[cat_columns])
      loans = pd.concat([loans, dummy_df], axis=1)
      loans = loans.drop(cat_columns, axis=1)
      loans = loans.drop("pymnt_plan", axis=1)
    
  • 查看转换类型

      import pandas as pd
      loans = pd.read_csv("cleaned_loans2007.csv")
      print(loans.info())
      
      
      RangeIndex: 39498 entries, 0 to 39497
      Data columns (total 37 columns):
      loan_amnt                              39498 non-null float64
      int_rate                               39498 non-null float64
      installment                            39498 non-null float64
      annual_inc                             39498 non-null float64
      loan_status                            39498 non-null int64
      dti                                    39498 non-null float64
      delinq_2yrs                            39498 non-null float64
      inq_last_6mths                         39498 non-null float64
      open_acc                               39498 non-null float64
      pub_rec                                39498 non-null float64
      revol_bal                              39498 non-null float64
      revol_util                             39498 non-null float64
      total_acc                              39498 non-null float64
      home_ownership_MORTGAGE                39498 non-null int64
      home_ownership_NONE                    39498 non-null int64
      home_ownership_OTHER                   39498 non-null int64
      home_ownership_OWN                     39498 non-null int64
      home_ownership_RENT                    39498 non-null int64
      verification_status_Not Verified       39498 non-null int64
      verification_status_Source Verified    39498 non-null int64
      verification_status_Verified           39498 non-null int64
      purpose_car                            39498 non-null int64
      purpose_credit_card                    39498 non-null int64
      purpose_debt_consolidation             39498 non-null int64
      purpose_educational                    39498 non-null int64
      purpose_home_improvement               39498 non-null int64
      purpose_house                          39498 non-null int64
      purpose_major_purchase                 39498 non-null int64
      purpose_medical                        39498 non-null int64
      purpose_moving                         39498 non-null int64
      purpose_other                          39498 non-null int64
      purpose_renewable_energy               39498 non-null int64
      purpose_small_business                 39498 non-null int64
      purpose_vacation                       39498 non-null int64
      purpose_wedding                        39498 non-null int64
      term_ 36 months                        39498 non-null int64
      term_ 60 months                        39498 non-null int64
      dtypes: float64(12), int64(25)
      memory usage: 11.1 MB
    

3 准确率理论及不均衡处理

  • 初始定义

      import pandas as pd
      # False positives.
      fp_filter = (predictions == 1) & (loans["loan_status"] == 0)
      fp = len(predictions[fp_filter])
      
      # True positives.
      tp_filter = (predictions == 1) & (loans["loan_status"] == 1)
      tp = len(predictions[tp_filter])
      
      # False negatives.
      fn_filter = (predictions == 0) & (loans["loan_status"] == 1)
      fn = len(predictions[fn_filter])
      
      # True negatives
      tn_filter = (predictions == 0) & (loans["loan_status"] == 0)
      tn = len(predictions[tn_filter])
    
  • 逻辑回归不处理不均衡

          from sklearn.linear_model import LogisticRegression
          lr = LogisticRegression()
          cols = loans.columns
          train_cols = cols.drop("loan_status")
          features = loans[train_cols]
          target = loans["loan_status"]
          lr.fit(features, target)
          predictions = lr.predict(features)
          
          from sklearn.linear_model import LogisticRegression
          from sklearn.cross_validation import cross_val_predict, KFold
          lr = LogisticRegression()
          kf = KFold(features.shape[0], random_state=1)
          predictions = cross_val_predict(lr, features, target, cv=kf)
          predictions = pd.Series(predictions)
          
          # False positives.
          fp_filter = (predictions == 1) & (loans["loan_status"] == 0)
          fp = len(predictions[fp_filter])
          
          # True positives.
          tp_filter = (predictions == 1) & (loans["loan_status"] == 1)
          tp = len(predictions[tp_filter])
          
          # False negatives.
          fn_filter = (predictions == 0) & (loans["loan_status"] == 1)
          fn = len(predictions[fn_filter])
          
          # True negatives
          tn_filter = (predictions == 0) & (loans["loan_status"] == 0)
          tn = len(predictions[tn_filter])
          
          # Rates
          tpr = tp / float((tp + fn))
          fpr = fp / float((fp + tn))
          
          print(tpr)
          print(fpr)
          print predictions[:20]
          
          0.999084438406
          0.998049299521
          0     1
          1     1
          2     1
          3     1
          4     1
          5     1
          6     1
          7     1
          8     1
          9     1
          10    1
          11    1
          12    1
          13    1
          14    1
          15    1
          16    1
          17    1
          18    1
          19    1
          dtype: int64
    
  • 逻辑回归balanced处理不均衡

      from sklearn.linear_model import LogisticRegression
      from sklearn.cross_validation import cross_val_predict
      lr = LogisticRegression(class_weight="balanced")
      kf = KFold(features.shape[0], random_state=1)
      predictions = cross_val_predict(lr, features, target, cv=kf)
      predictions = pd.Series(predictions)
      
      # False positives.
      fp_filter = (predictions == 1) & (loans["loan_status"] == 0)
      fp = len(predictions[fp_filter])
      
      # True positives.
      tp_filter = (predictions == 1) & (loans["loan_status"] == 1)
      tp = len(predictions[tp_filter])
      
      # False negatives.
      fn_filter = (predictions == 0) & (loans["loan_status"] == 1)
      fn = len(predictions[fn_filter])
      
      # True negatives
      tn_filter = (predictions == 0) & (loans["loan_status"] == 0)
      tn = len(predictions[tn_filter])
      
      # Rates
      tpr = tp / float((tp + fn))
      fpr = fp / float((fp + tn))
      
      print(tpr)
      print(fpr)
      print predictions[:20]
      
      0.670781771464
      0.400780280192
      0     1
      1     0
      2     0
      3     1
      4     1
      5     0
      6     0
      7     0
      8     0
      9     0
      10    1
      11    0
      12    1
      13    1
      14    0
      15    0
      16    1
      17    1
      18    1
      19    0
      dtype: int64
    
  • 逻辑回归penalty处理不均衡

      from sklearn.linear_model import LogisticRegression
      from sklearn.cross_validation import cross_val_predict
      penalty = {
          0: 5,
          1: 1
      }
      
      lr = LogisticRegression(class_weight=penalty)
      kf = KFold(features.shape[0], random_state=1)
      predictions = cross_val_predict(lr, features, target, cv=kf)
      predictions = pd.Series(predictions)
      
      # False positives.
      fp_filter = (predictions == 1) & (loans["loan_status"] == 0)
      fp = len(predictions[fp_filter])
      
      # True positives.
      tp_filter = (predictions == 1) & (loans["loan_status"] == 1)
      tp = len(predictions[tp_filter])
      
      # False negatives.
      fn_filter = (predictions == 0) & (loans["loan_status"] == 1)
      fn = len(predictions[fn_filter])
      
      # True negatives
      tn_filter = (predictions == 0) & (loans["loan_status"] == 0)
      tn = len(predictions[tn_filter])
      
      # Rates
      tpr = tp / float((tp + fn))
      fpr = fp / float((fp + tn))
      
      print(tpr)
      print(fpr)
    
      0.731799521545
      0.478985635751
    
  • 随机森林balanced处理不均衡

      from sklearn.ensemble import RandomForestClassifier
      from sklearn.cross_validation import cross_val_predict
      rf = RandomForestClassifier(n_estimators=10,class_weight="balanced", random_state=1)
      #print help(RandomForestClassifier)
      kf = KFold(features.shape[0], random_state=1)
      predictions = cross_val_predict(rf, features, target, cv=kf)
      predictions = pd.Series(predictions)
      
      # False positives.
      fp_filter = (predictions == 1) & (loans["loan_status"] == 0)
      fp = len(predictions[fp_filter])
      
      # True positives.
      tp_filter = (predictions == 1) & (loans["loan_status"] == 1)
      tp = len(predictions[tp_filter])
      
      # False negatives.
      fn_filter = (predictions == 0) & (loans["loan_status"] == 1)
      fn = len(predictions[fn_filter])
      
      # True negatives
      tn_filter = (predictions == 0) & (loans["loan_status"] == 0)
      tn = len(predictions[tn_filter])
      
      # Rates
      tpr = tp / float((tp + fn))
      fpr = fp / float((fp + tn))
    

4 总结

通过本文,对于数据特征工程,具有非常重要的意义。

秦凯新 于深圳 20181220

版权声明:本套技术专栏是作者(秦凯新)平时工作的总结和升华,通过从真实商业环境抽取案例进行总结和分享,并给出商业应用的调优建议和集群环境容量规划等内容,请持续关注本套博客。QQ邮箱地址:[email protected],如有任何学术交流,可随时联系。

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