Question

我正在使用 django-python 开发一个项目。已安装“ oaisearch ”以从不同网站检索元数据，并安装了“ django_elasticsearch_dsl ”以对其进行索引。证实“ oaisearch”运行没有问题。当运行“ {python3 manage.py search_index --create -f”以开始建立索引时，会发生此问题。具有您要索引的配置的文件“ documents.py ”如下。

from elasticsearch_dsl import analyzer

from django_elasticsearch_dsl import DocType, Index

from oaisearch.models import Digital_Resources

digital_resources = Index('digital_resources')

digital_resources.settings(
    number_of_shards=1,
    number_of_replicas=0
)

html_strip = analyzer(
    'html_strip',
    tokenizer="standard",
    filter=["standard", "lowercase", "stop", "snowball"],
    char_filter=["html_strip"]
)

@digital_resources.doc_type

class Resources(DocType):
    class Meta:
        model = Digital_Resources
        fields = [
            #'oai_server',
            'creator',
            'title',
            'subject',
            'description',
            'identifier',
        ]

执行上述命令后，控制台失败消息如下

Creating index 'digital_resources'
PUT http://localhost:9200/digital_resources [status:400 request:0.014s]
Traceback (most recent call last):
  File "manage.py", line 22, in <module>
    execute_from_command_line(sys.argv)
  File "/usr/local/lib/python3.5/dist-packages/django/core/management/__init__.py", line 363, in execute_from_command_line
    utility.execute()
  File "/usr/local/lib/python3.5/dist-packages/django/core/management/__init__.py", line 355, in execute
    self.fetch_command(subcommand).run_from_argv(self.argv)
  File "/usr/local/lib/python3.5/dist-packages/django/core/management/base.py", line 283, in run_from_argv
    self.execute(*args, **cmd_options)
  File "/usr/local/lib/python3.5/dist-packages/django/core/management/base.py", line 330, in execute
    output = self.handle(*args, **options)
  File "/usr/local/lib/python3.5/dist-packages/django_elasticsearch_dsl/management/commands/search_index.py", line 128, in handle
    self._create(models, options)
  File "/usr/local/lib/python3.5/dist-packages/django_elasticsearch_dsl/management/commands/search_index.py", line 84, in _create
    index.create()
  File "/usr/local/lib/python3.5/dist-packages/elasticsearch_dsl/index.py", line 203, in create
    self.connection.indices.create(index=self._name, body=self.to_dict(), **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/elasticsearch/client/utils.py", line 76, in _wrapped
    return func(*args, params=params, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/elasticsearch/client/indices.py", line 88, in create
    params=params, body=body)
  File "/usr/local/lib/python3.5/dist-packages/elasticsearch/transport.py", line 318, in perform_request
    status, headers_response, data = connection.perform_request(method, url, params, body, headers=headers, ignore=ignore, timeout=timeout)
  File "/usr/local/lib/python3.5/dist-packages/elasticsearch/connection/http_urllib3.py", line 186, in perform_request
    self._raise_error(response.status, raw_data)
  File "/usr/local/lib/python3.5/dist-packages/elasticsearch/connection/base.py", line 125, in _raise_error
    raise HTTP_EXCEPTIONS.get(status_code, TransportError)(status_code, error_message, additional_info)
elasticsearch.exceptions.RequestError: RequestError(400, 'mapper_parsing_exception', 'Root mapping definition has unsupported parameters:  [doc : {properties={identifier={type=text}, creator={type=text}, subject={type=text}, description={type=text}, title={type=text}}}]')

Answer 1

您正在运行什么版本的elasticsearch？看来您正在运行的是import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # Import Data data = np.loadtxt('./my_data.csv') # Reshape data into batches of 500 timesteps - 1 dim per timestep # For now, we do not split into testing/training sets # Assume all data is for training data_pre = data.reshape(-1, 500, 1)[:,:-1,:] # Shift input data backward by 1 data_post = data.reshape(-1, 500, 1)[:,1:,:] # Shift input data forward by 1 # Build LSTM Model for Training: # Allow flexible number of timesteps per input (shape=(None,1)) inputs = tf.keras.layers.Input(shape=(None,1)) lstm_1 = tf.keras.layers.CuDNNLSTM(units=512, return_sequences=True)(inputs) lstm_2 = tf.keras.layers.CuDNNLSTM(units=256, return_sequences=True)(lstm_1) # Activate dense layer with linear activation func for regression outputs = tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(units=1, activation='linear'))(lstm_2) lstm_model = tf.keras.Model(inputs=inputs, outputs=outputs) lstm_model.compile('adam', loss='mae', metrics=['mae','mse']) lstm_model.fit(x=data_pre, y = data_post, epochs=100, batch_size=16, shuffle=False) # I have trained up to 500 epochs and while the loss decreases there is no increase in prediction performance. # Build Stateful LSTM Model for Sample-by-Sample Prediction # Assume 1 timestep per input of dim=1 inputs = tf.keras.layers.Inputs(shape=(1,1,1)) lstm_1 = tf.keras.layers.CuDNNLSTM(units=512, return_sequences=True, stateful=True)(inputs) lstm_2 = tf.keras.layers.CuDNNLSTM(units=256, return_sequences=True, stateful=True)(lstm_1) outputs = tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(units=1, activation='linear')) prediction_model = tf.keras.Model(inputs=inputs, outputs=outputs) # Copy weights from trained, non-stateful model: prediction_model.set_weights(lstm_model.get_weights()) #Reset network state prediction_model.reset_states() #Initialize model internal state with a single sample from the input data shifted by 1 unit backwards seed = prediction_model.predict(data_pre[0][0][None, None, :]) # Predict 20secs of data output_array = np.zeros((10000,1,1)) # Allocate Memory for i in range(0,10000): temp = prediction_model.predict(seed) # Iteratively predict next sample value output_array[i] = temp seed = temp版本不支持的Elasticsearch7.x。尝试寻找更新的版本或使用elasticsearch 6.x。

使用django_elasticsearch_dsl与django-python索引的问题

1 个答案: