ML之FE：基于FE特征工程對RentListingInquries數據集進行預處理并導出為三種格式文件(csv格式/txt格式/libsvm稀疏txt格式)

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目錄

輸出結果

設計思路

核心代碼

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輸出結果

1.1、RentListingInquries_FE_train.csv

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1.2、RentListingInquries_FE_test.csv

2.1、RentListingInquries_FE_train.txt

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2.2、RentListingInquries_FE_test.txt

代碼輸出

y_train初步處理：? ?10 ? ? ? ?1 10000 ? ? 2 100004 ? ?0 100007 ? ?2 100013 ? ?2 100014 ? ?1 100016 ? ?2 100020 ? ?2 100026 ? ?1 100027 ? ?2 100030 ? ?2 10004 ? ? 2 100044 ? ?0 100048 ? ?2 10005 ? ? 2 100051 ? ?1 100052 ? ?2 100053 ? ?2 100055 ? ?2 100058 ? ?2 100062 ? ?2 100063 ? ?1 100065 ? ?2 100066 ? ?2 10007 ? ? 1 100071 ? ?2 100075 ? ?1 100076 ? ?2 100079 ? ?0 100081 ? ?2 ? ? ? ? ?.. 99956 ? ? 2 99960 ? ? 1 99961 ? ? 2 99964 ? ? 1 99965 ? ? 2 99966 ? ? 2 99979 ? ? 2 99980 ? ? 2 99982 ? ? 0 99984 ? ? 2 99986 ? ? 2 99987 ? ? 2 99988 ? ? 1 9999 ? ? ?1 99991 ? ? 2 99992 ? ? 2 99993 ? ? 2 99994 ? ? 2 Name: interest_level, Length: 49352, dtype: int64

train_test_sparse為最終處理：? ? ?(0, 0)?? ?1.5 ? (0, 1)?? ?3.0 ? (0, 2)?? ?40.7145 ? (0, 3)?? ?-73.9425 ? (0, 4)?? ?3000.0 ? (0, 5)?? ?1200.0 ? (0, 6)?? ?750.0 ? (0, 7)?? ?-1.5 ? (0, 8)?? ?4.5 ? (0, 9)?? ?2016.0 ? (0, 10)?? ?6.0 ? (0, 11)?? ?24.0 ? (0, 12)?? ?4.0 ? (0, 13)?? ?176.0 ? (0, 14)?? ?7.0 ? (0, 15)?? ?95.0 ? (0, 17)?? ?1.0 ? (0, 18)?? ?1.0 ? (0, 19)?? ?1.0 ? (0, 20)?? ?1.0 ? (0, 21)?? ?1.0 ? (0, 22)?? ?1.0 ? (0, 23)?? ?1.0 ? (0, 24)?? ?1.0 ? (0, 32)?? ?1.0 ? :?? ?: ? (124010, 29)?? ?1.0 ? (124010, 30)?? ?1.0 ? (124010, 31)?? ?1.0 ? (124010, 32)?? ?1.0 ? (124010, 33)?? ?1.0 ? (124010, 34)?? ?2.0 ? (124010, 35)?? ?12.0 ? (124010, 36)?? ?0.04446034405901145 ? (124010, 37)?? ?0.010558720013101165 ? (124010, 38)?? ?0.030099750139483926 ? (124010, 39)?? ?0.9593415298474148 ? (124010, 40)?? ?0.21662478672029833 ? (124010, 41)?? ?0.0020547768050611895 ? (124010, 42)?? ?0.7813204364746404 ? (124010, 43)?? ?0.12333335451008201 ? (124010, 44)?? ?1.2281905750572492e-07 ? (124010, 45)?? ?0.8766665226708605 ? (124010, 46)?? ?0.0004487893042226658 ? (124010, 47)?? ?0.001303620464837077 ? (124010, 48)?? ?0.9982475902309401 ? (124010, 49)?? ?3.0 ? (124010, 58)?? ?2.0 ? (124010, 83)?? ?1.0 ? (124010, 107)?? ?1.0 ? (124010, 114)?? ?1.0

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設計思路

正在更新……

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核心代碼

正在更新……

train_test['features_count'] = train_test['features'].apply(lambda x: len(x)) train_test['features2'] = train_test['features'] train_test['features2'] = train_test['features2'].apply(lambda x: ' '.join(x))c_vect = CountVectorizer(stop_words='english', max_features=200, ngram_range=(1, 1)) c_vect_sparse = c_vect.fit_transform(train_test['features2']) c_vect_sparse_cols = c_vect.get_feature_names()train_test.drop(['features', 'features2'], axis=1, inplace=True)from sklearn.datasets import dump_svmlight_file dump_svmlight_file(y_train, dpath + 'RentListingInquries_FE_train_libsvm.txt',X_train_sparse) # dump_svmlight_file(X_train_sparse, dpath + 'RentListingInquries_FE_train_libsvm.txt') dump_svmlight_file(X_test_sparse, dpath + 'RentListingInquries_FE_test_libsvm.txt')

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