Example #1
0
                  }
              },
              "mappings": {
                  "diary": {
                      "properties": {
                          "content": {
                              "term_vector": "yes",
                              "type": "text",
                              "analyzer": "morfologik"
                          }
                      }
                  }
              }
          })

list_of_files = glob.glob('../ustawy/*.txt')  # create the list of file

print("loading files....")

for file_name in list_of_files:
    with open(file_name, 'r') as myfile:
        data = myfile.read()
        es.index(index=INDEX,
                 doc_type=TYPE,
                 id=file_name,
                 body={
                     "content": data,
                 })

print(es.mtermvectors(index=INDEX, doc_type=TYPE))
Example #2
0
class BaseElasticsearchBackend(Base):
    """Base connection wrapper based on the ElasticSearch official library.

    It uses two entry points to configure the underlying connection:

    * ``transport_class``: the transport class from ``elasticsearch``. By
      default ``elasticsearch.transport.Transport``.
    * ``connection_class``: the connection class used by the transport class.
      It's undefined by default, as it is on the subclasses to provide one.

    If any of these elements is not defined, an ``ImproperlyConfigured`` error
    will be raised when the backend will try to configure the client.
    """
    #: ElasticSearch transport class used by the client class to perform
    #: requests.
    transport_class = Transport
    #: ElasticSearch connection class used by the transport class to perform
    #: requests.
    connection_class = None

    def configure_client(self):
        """Instantiate and configure the ElasticSearch client.

        It simply takes the given HOSTS list and uses PARAMS as the keyword
        arguments of the ElasticSearch class.

        The client's transport_class is given by the class attribute
        ``transport_class``, and the connection class used by the transport
        class is given by the class attribute ``connection_class``.

        An ``ImproperlyConfigured`` exception is raised if any of these
        elements is undefined.
        """
        hosts = self.server['HOSTS']
        params = self.server['PARAMS']

        if not self.transport_class:
            raise ImproperlyConfigured(
                'Djangoes backend %r is not properly configured: '
                'no transport class provided' % self.__class__)

        if not self.connection_class:
            raise ImproperlyConfigured(
                'Djangoes backend %r is not properly configured: '
                'no connection class provided' % self.__class__)

        #pylint: disable=star-args
        self.client = Elasticsearch(hosts,
                                    transport_class=self.transport_class,
                                    connection_class=self.connection_class,
                                    **params)

    # Server methods
    # ==============
    # The underlying client does not require index names to perform server
    # related queries, such as "ping" or "info". The connection wrapper act
    # for them as a proxy.

    def ping(self, **kwargs):
        return self.client.ping(**kwargs)

    def info(self, **kwargs):
        return self.client.info(**kwargs)

    def put_script(self, lang, script_id, body, **kwargs):
        return self.client.put_script(lang, script_id, body, **kwargs)

    def get_script(self, lang, script_id, **kwargs):
        return self.client.get_script(lang, script_id, **kwargs)

    def delete_script(self, lang, script_id, **kwargs):
        return self.client.delete_script(lang, script_id, **kwargs)

    def put_template(self, template_id, body, **kwargs):
        return self.client.put_template(template_id, body, **kwargs)

    def get_template(self, template_id, body=None, **kwargs):
        return self.client.get_template(template_id, body, **kwargs)

    def delete_template(self, template_id=None, **kwargs):
        return self.client.delete_template(template_id, **kwargs)

    # Bulk methods
    # ============
    # The underlying client does not require index names, but it can be used.
    # As it makes sense to not give an index, developers are free to use these
    # as they want, as long as they are careful.

    def mget(self, body, index=None, doc_type=None, **kwargs):
        return self.client.mget(body, index, doc_type, **kwargs)

    def bulk(self, body, index=None, doc_type=None, **kwargs):
        return self.client.bulk(body, index, doc_type, **kwargs)

    def msearch(self, body, index=None, doc_type=None, **kwargs):
        return self.client.msearch(body, index, doc_type, **kwargs)

    def mpercolate(self, body, index=None, doc_type=None, **kwargs):
        return self.client.mpercolate(body, index, doc_type, **kwargs)

    # Scroll methods
    # ==============
    # The underlying client does not require an index to perform scroll.

    def scroll(self, scroll_id, **kwargs):
        return self.client.scroll(scroll_id, **kwargs)

    def clear_scroll(self, scroll_id, body=None, **kwargs):
        return self.client.clear_scroll(scroll_id, body, **kwargs)

    # Query methods
    # =============
    # The underlying client requires index names (or alias names) to perform
    # queries. The connection wrapper overrides these client methods to
    # automatically uses the configured names (indices and/or aliases).

    def create(self, doc_type, body, doc_id=None, **kwargs):
        return self.client.create(self.indices, doc_type, body, doc_id,
                                  **kwargs)

    def index(self, doc_type, body, doc_id=None, **kwargs):
        return self.client.index(self.indices, doc_type, body, doc_id,
                                 **kwargs)

    def exists(self, doc_id, doc_type='_all', **kwargs):
        return self.client.exists(self.indices, doc_id, doc_type, **kwargs)

    def get(self, doc_id, doc_type='_all', **kwargs):
        return self.client.get(self.indices, doc_id, doc_type, **kwargs)

    def get_source(self, doc_id, doc_type='_all', **kwargs):
        return self.client.get_source(self.indices, doc_id, doc_type, **kwargs)

    def update(self, doc_type, doc_id, body=None, **kwargs):
        return self.client.update(self.indices, doc_type, doc_id, body,
                                  **kwargs)

    def search(self, doc_type=None, body=None, **kwargs):
        return self.client.search(self.indices, doc_type, body, **kwargs)

    def search_shards(self, doc_type=None, **kwargs):
        return self.client.search_shards(self.indices, doc_type, **kwargs)

    def search_template(self, doc_type=None, body=None, **kwargs):
        return self.client.search_template(self.indices, doc_type, body,
                                           **kwargs)

    def explain(self, doc_type, doc_id, body=None, **kwargs):
        return self.client.explain(self.indices, doc_type, doc_id, body,
                                   **kwargs)

    def delete(self, doc_type, doc_id, **kwargs):
        return self.client.delete(self.indices, doc_type, doc_id, **kwargs)

    def count(self, doc_type=None, body=None, **kwargs):
        return self.client.count(self.indices, doc_type, body, **kwargs)

    def delete_by_query(self, doc_type=None, body=None, **kwargs):
        return self.client.delete_by_query(self.indices, doc_type, body,
                                           **kwargs)

    def suggest(self, body, **kwargs):
        return self.client.suggest(body, self.indices, **kwargs)

    def percolate(self, doc_type, doc_id=None, body=None, **kwargs):
        return self.client.percolate(self.indices, doc_type, doc_id, body,
                                     **kwargs)

    def count_percolate(self, doc_type, doc_id=None, body=None, **kwargs):
        return self.client.count_percolate(self.indices, doc_type, doc_id,
                                           body, **kwargs)

    def mlt(self, doc_type, doc_id, body=None, **kwargs):
        return self.client.mlt(self.indices, doc_type, doc_id, body, **kwargs)

    def termvector(self, doc_type, doc_id, body=None, **kwargs):
        return self.client.termvector(self.indices, doc_type, doc_id, body,
                                      **kwargs)

    def mtermvectors(self, doc_type=None, body=None, **kwargs):
        return self.client.mtermvectors(self.indices, doc_type, body, **kwargs)

    def benchmark(self, doc_type=None, body=None, **kwargs):
        return self.client.benchmark(self.indices, doc_type, body, **kwargs)

    def abort_benchmark(self, name=None, **kwargs):
        return self.client.abort_benchmark(name, **kwargs)

    def list_benchmarks(self, doc_type=None, **kwargs):
        return self.client.list_benchmarks(self.indices, doc_type, **kwargs)
Example #3
0
class BaseElasticsearchBackend(Base):
    """Base connection wrapper based on the ElasticSearch official library.

    It uses two entry points to configure the underlying connection:

    * ``transport_class``: the transport class from ``elasticsearch``. By
      default ``elasticsearch.transport.Transport``.
    * ``connection_class``: the connection class used by the transport class.
      It's undefined by default, as it is on the subclasses to provide one.

    If any of these elements is not defined, an ``ImproperlyConfigured`` error
    will be raised when the backend will try to configure the client.
    """
    #: ElasticSearch transport class used by the client class to perform
    #: requests.
    transport_class = Transport
    #: ElasticSearch connection class used by the transport class to perform
    #: requests.
    connection_class = None

    def configure_client(self):
        """Instantiate and configure the ElasticSearch client.

        It simply takes the given HOSTS list and uses PARAMS as the keyword
        arguments of the ElasticSearch class.

        The client's transport_class is given by the class attribute
        ``transport_class``, and the connection class used by the transport
        class is given by the class attribute ``connection_class``.

        An ``ImproperlyConfigured`` exception is raised if any of these
        elements is undefined.
        """
        hosts = self.server['HOSTS']
        params = self.server['PARAMS']

        if not self.transport_class:
            raise ImproperlyConfigured(
                'Djangoes backend %r is not properly configured: '
                'no transport class provided' % self.__class__)

        if not self.connection_class:
            raise ImproperlyConfigured(
                'Djangoes backend %r is not properly configured: '
                'no connection class provided' % self.__class__)

        #pylint: disable=star-args
        self.client = Elasticsearch(hosts,
                                    transport_class=self.transport_class,
                                    connection_class=self.connection_class,
                                    **params)

    # Server methods
    # ==============
    # The underlying client does not require index names to perform server
    # related queries, such as "ping" or "info". The connection wrapper act
    # for them as a proxy.

    def ping(self, **kwargs):
        return self.client.ping(**kwargs)

    def info(self, **kwargs):
        return self.client.info(**kwargs)

    def put_script(self, lang, script_id, body, **kwargs):
        return self.client.put_script(lang, script_id, body, **kwargs)

    def get_script(self, lang, script_id, **kwargs):
        return self.client.get_script(lang, script_id, **kwargs)

    def delete_script(self, lang, script_id, **kwargs):
        return self.client.delete_script(lang, script_id, **kwargs)

    def put_template(self, template_id, body, **kwargs):
        return self.client.put_template(template_id, body, **kwargs)

    def get_template(self, template_id, body=None, **kwargs):
        return self.client.get_template(template_id, body, **kwargs)

    def delete_template(self, template_id=None, **kwargs):
        return self.client.delete_template(template_id, **kwargs)

    # Bulk methods
    # ============
    # The underlying client does not require index names, but it can be used.
    # As it makes sense to not give an index, developers are free to use these
    # as they want, as long as they are careful.

    def mget(self, body, index=None, doc_type=None, **kwargs):
        return self.client.mget(body, index, doc_type, **kwargs)

    def bulk(self, body, index=None, doc_type=None, **kwargs):
        return self.client.bulk(body, index, doc_type, **kwargs)

    def msearch(self, body, index=None, doc_type=None, **kwargs):
        return self.client.msearch(body, index, doc_type, **kwargs)

    def mpercolate(self, body, index=None, doc_type=None, **kwargs):
        return self.client.mpercolate(body, index, doc_type, **kwargs)

    # Scroll methods
    # ==============
    # The underlying client does not require an index to perform scroll.

    def scroll(self, scroll_id, **kwargs):
        return self.client.scroll(scroll_id, **kwargs)

    def clear_scroll(self, scroll_id, body=None, **kwargs):
        return self.client.clear_scroll(scroll_id, body, **kwargs)

    # Query methods
    # =============
    # The underlying client requires index names (or alias names) to perform
    # queries. The connection wrapper overrides these client methods to
    # automatically uses the configured names (indices and/or aliases).

    def create(self, doc_type, body, doc_id=None, **kwargs):
        return self.client.create(
            self.indices, doc_type, body, doc_id, **kwargs)

    def index(self, doc_type, body, doc_id=None, **kwargs):
        return self.client.index(
            self.indices, doc_type, body, doc_id, **kwargs)

    def exists(self, doc_id, doc_type='_all', **kwargs):
        return self.client.exists(self.indices, doc_id, doc_type, **kwargs)

    def get(self, doc_id, doc_type='_all', **kwargs):
        return self.client.get(self.indices, doc_id, doc_type, **kwargs)

    def get_source(self, doc_id, doc_type='_all', **kwargs):
        return self.client.get_source(self.indices, doc_id, doc_type, **kwargs)

    def update(self, doc_type, doc_id, body=None, **kwargs):
        return self.client.update(
            self.indices, doc_type, doc_id, body, **kwargs)

    def search(self, doc_type=None, body=None, **kwargs):
        return self.client.search(self.indices, doc_type, body, **kwargs)

    def search_shards(self, doc_type=None, **kwargs):
        return self.client.search_shards(self.indices, doc_type, **kwargs)

    def search_template(self, doc_type=None, body=None, **kwargs):
        return self.client.search_template(
            self.indices, doc_type, body, **kwargs)

    def explain(self, doc_type, doc_id, body=None, **kwargs):
        return self.client.explain(
            self.indices, doc_type, doc_id, body, **kwargs)

    def delete(self, doc_type, doc_id, **kwargs):
        return self.client.delete(self.indices, doc_type, doc_id, **kwargs)

    def count(self, doc_type=None, body=None, **kwargs):
        return self.client.count(self.indices, doc_type, body, **kwargs)

    def delete_by_query(self, doc_type=None, body=None, **kwargs):
        return self.client.delete_by_query(
            self.indices, doc_type, body, **kwargs)

    def suggest(self, body, **kwargs):
        return self.client.suggest(body, self.indices, **kwargs)

    def percolate(self, doc_type, doc_id=None, body=None, **kwargs):
        return self.client.percolate(
            self.indices, doc_type, doc_id, body, **kwargs)

    def count_percolate(self, doc_type, doc_id=None, body=None, **kwargs):
        return self.client.count_percolate(
            self.indices, doc_type, doc_id, body, **kwargs)

    def mlt(self, doc_type, doc_id, body=None, **kwargs):
        return self.client.mlt(self.indices, doc_type, doc_id, body, **kwargs)

    def termvector(self, doc_type, doc_id, body=None, **kwargs):
        return self.client.termvector(
            self.indices, doc_type, doc_id, body, **kwargs)

    def mtermvectors(self, doc_type=None, body=None, **kwargs):
        return self.client.mtermvectors(self.indices, doc_type, body, **kwargs)

    def benchmark(self, doc_type=None, body=None, **kwargs):
        return self.client.benchmark(self.indices, doc_type, body, **kwargs)

    def abort_benchmark(self, name=None, **kwargs):
        return self.client.abort_benchmark(name, **kwargs)

    def list_benchmarks(self, doc_type=None, **kwargs):
        return self.client.list_benchmarks(self.indices, doc_type, **kwargs)
Example #4
0
list_of_files = glob.glob('../ustawy/*.txt')  # create the list of file

print("loading files....")

for file_name in list_of_files:
    with open(file_name, 'r') as myfile:
        data = myfile.read()
        es.index(index=INDEX, doc_type=TYPE, id=file_name, body={
            "content": data,
        })

vec_list = []

for file_name in list_of_files:
    vec_list.append(es.mtermvectors(index=INDEX, doc_type=TYPE, ids=file_name))

frequencies = {}
for vec in vec_list:
    for diary in vec['docs']:
        items = diary['term_vectors']['content']['terms'].items()

        for key, value in items:
            frequencies[key] = value['term_freq'] if key not in frequencies else frequencies[key] + value['term_freq']

# print(frequencies)

unigrams = {k: v for k, v in frequencies.items() if len(k.split()) == 1}
bigrams = {k: v for k, v in frequencies.items() if len(k.split()) == 2}

unigrams_sum = sum(unigrams.values())