5-2 feature_column

    Feature column is used to converting category features into one-hot encoding, or creating bucketing feature from continuous feature, or generating cross features from multiple features, etc.

    Before creating feature column, please call the functions in the module . The nine most frequently used functions in this module are shown in the figure below. All these functions will return a Categorical-Column or a Dense-Column object, but will not return bucketized_column, since the last class is inhereted from the first two classes.

    • numeric_column, the most frequently used function.
    • bucketized_column, generated from numerical column, listing multiple features from a numerical clumn; it is one-hot encoded.
    • categorical_column_with_identity, one-hot encoded, identical to the case that each bucket is one interger.
    • categorical_column_with_vocabulary_list, one-hot encoded; the dictionary is specified by the list.
    • categorical_column_with_vocabulary_file， one-hot encoded; the dictionary is specified by the file.
    • categorical_column_with_hash_bucket, used in the case with a large interger or a large dictionary.
    • indicator_column, generated by Categorical-Column; one-hot encoded.
    • embedding_column, generated by Categorical Column; the embedded vector distributed parameter needs learning/training. The recommended dimension of the embedded vector is the fourth root to the number of categories.
    • crossed_column, consists of arbitrary category column except for categorical_column_with_hash_bucket

    2. Demonstration of feature column

    Here is a complete example that solves Titanic survival problmen using feature column.

    1. #================================================================================
    2. # 1. Constructing data pipeline
    3. #================================================================================
    4. printlog("step1: prepare dataset...")
    7. dftrain_raw = pd.read_csv("../data/titanic/train.csv")
    8. dftest_raw = pd.read_csv("../data/titanic/test.csv")
    10. dfraw = pd.concat([dftrain_raw,dftest_raw])
    12. def prepare_dfdata(dfraw):
    13.     dfdata = dfraw.copy()
    14.     dfdata.columns = [x.lower() for x in dfdata.columns]
    15.     dfdata = dfdata.rename(columns={'survived':'label'})
    16.     dfdata = dfdata.drop(['passengerid','name'],axis = 1)
    17.     for col,dtype in dict(dfdata.dtypes).items():
    18.         # See if there are missing values.
    19.         if dfdata[col].hasnans:
    20.             # Adding signs to the missing columns
    21.             dfdata[col + '_nan'] = pd.isna(dfdata[col]).astype('int32')
    23.                 dfdata[col].fillna(dfdata[col].mean(),inplace = True)
    24.             else:
    25.                 dfdata[col].fillna('',inplace = True)
    26.     return(dfdata)
    28. dfdata = prepare_dfdata(dfraw)
    29. dftrain = dfdata.iloc[0:len(dftrain_raw),:]
    30. dftest = dfdata.iloc[len(dftrain_raw):,:]
    34. # Importing data from dataframe
    35. def df_to_dataset(df, shuffle=True, batch_size=32):
    36.     dfdata = df.copy()
    37.     if 'label' not in dfdata.columns:
    38.         ds = tf.data.Dataset.from_tensor_slices(dfdata.to_dict(orient = 'list'))
    39.     else: 
    40.         labels = dfdata.pop('label')
    41.         ds = tf.data.Dataset.from_tensor_slices((dfdata.to_dict(orient = 'list'), labels))  
    42.     if shuffle:
    43.         ds = ds.shuffle(buffer_size=len(dfdata))
    44.     ds = ds.batch(batch_size)
    45.     return ds
    47. ds_train = df_to_dataset(dftrain)
    48. ds_test = df_to_dataset(dftest)
    1. #================================================================================
    2. # 3. Defining the model
    3. #================================================================================
    4. printlog("step3: define model...")
    7.   layers.DenseFeatures(feature_columns), # Placing the feature into tf.keras.layers.DenseFeatures
    8.   layers.Dense(64, activation='relu'),
    9.   layers.Dense(64, activation='relu'),
    10.   layers.Dense(1, activation='sigmoid')
    11. ])
    1. #================================================================================
    2. # 5. Evaluating the model
    3. #================================================================================
    4. printlog("step5: eval model...")
    6. model.summary()
    9. %matplotlib inline
    10. %config InlineBackend.figure_format = 'svg'
    12. import matplotlib.pyplot as plt
    14. def plot_metric(history, metric):
    15.     train_metrics = history.history[metric]
    16.     val_metrics = history.history['val_'+metric]
    17.     epochs = range(1, len(train_metrics) + 1)
    18.     plt.plot(epochs, train_metrics, 'bo--')
    19.     plt.plot(epochs, val_metrics, 'ro-')
    20.     plt.title('Training and validation '+ metric)
    21.     plt.xlabel("Epochs")
    22.     plt.ylabel(metric)
    23.     plt.legend(["train_"+metric, 'val_'+metric])
    24.     plt.show()
    26. plot_metric(history,"accuracy")

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