如何使用张量流进行打印预测

Question

我建立了一个简单的预测模型（我必须预测“目标”，它可以是0或1），我希望以以下格式打印并保存到文件数据：

id_of_prediction, prediction
0, 1
1, 0
2, 1
3, 1
... 

你知道怎么做吗？现在的打印值是：

<generator object Estimator.predict at 0x7f718d5621a8>

我的代码：

def dl_model(deep_df): # deep_df is a dataframe
    deep_feat = deep_df.drop(columns=["target"], axis=1)
    deep_label = deep_df["target"]
    categorical_columns = [col for col in deep_feat.columns if len(deep_feat[col].unique())==2 or deep_feat[col].dtype=='O']
    continuous_columns = [col for col in deep_feat.columns if len(deep_feat[col].unique()) > 2 and (
                deep_feat[col].dtype == 'int64' or deep_feat[col].dtype == 'float64')]
    X_T, X_t, y_T, y_t = train_test_split(deep_feat, deep_label, test_size=0.3)
    cols_to_scale = continuous_columns[:]
    cols_to_scale.remove("age")
    scaler = StandardScaler()
    X_T.loc[:, cols_to_scale] = scaler.fit_transform(X_T.loc[:, cols_to_scale])
    X_t.loc[:, cols_to_scale] = scaler.fit_transform(X_t.loc[:, cols_to_scale])
    categorical_object_feat_cols = [tf.feature_column.embedding_column(
        tf.feature_column.categorical_column_with_hash_bucket(key=col, hash_bucket_size=1000),
        dimension=len(deep_df[col].unique())) for col in categorical_columns if deep_df[col].dtype == 'O']
    categorical_integer_feat_cols = [
        tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_identity(key=col, num_buckets=2),
                                           dimension=len(deep_df[col].unique())) for col in categorical_columns if deep_df[col].dtype == 'int64']
    continuous_feat_cols = [tf.feature_column.numeric_column(key=col) for col in continuous_columns if col != "age"]
    age_bucket = tf.feature_column.bucketized_column(tf.feature_column.numeric_column(key="age"),
                                                     boundaries=[20, 30, 40, 50, 60, 70, 80, 90])
    feat_cols = categorical_object_feat_cols + \
                categorical_integer_feat_cols + \
                continuous_feat_cols + \
                [age_bucket]

    input_fun = tf.compat.v1.estimator.inputs.pandas_input_fn(X_T, y_T, batch_size=50, num_epochs=1000, shuffle=True)
    pred_input_fun = tf.compat.v1.estimator.inputs.pandas_input_fn(X_t, batch_size=50, shuffle=False)
    DNN_model = tf.estimator.DNNClassifier(hidden_units=[10, 10, 10], feature_columns=feat_cols, n_classes=2)
    DNN_model.train(input_fn=input_fun, steps=5000)
    predictions = DNN_model.predict(pred_input_fun)
    res_pred = list(predictions)
    y_pred = []
    for i in range(len(res_pred)):
        y_pred.append(res_pred[i]["class_ids"][0])
    rep = classification_report(y_t, y_pred)
    print(rep)

    new_data = pd.read_csv("test.csv")
    predictions = DNN_model.predict(new_data) # my predictions

    print(predictions)

Answer 1

您拥有的对象是一个生成器。它的目的是迭代地返回数据，您可以在此处了解更多信息：https://wiki.python.org/moin/Generators

要展开生成器，可以将其传递给list功能。它使用生成器的每个“元素”，并将其逐个放入新列表。

因此，您可以做的是展开生成器，预览其内容并将其转储为CSV或类似格式。

Answer 2

您可以通过以下方式打印预测：

print(next(predictions))

OR

while(True):
    try:
        print(next(predictions))
    except:
        break

next()方法在python中用于迭代生成器。