python类load()的实例源码

def restart_required(self):
        """Indicates whether splunkd is in a state that requires a restart.

        :return: A ``boolean`` that indicates whether a restart is required.

        """
        response = self.get("messages").body.read()
        messages = data.load(response)['feed']
        if 'entry' not in messages:
            result = False
        else:
            if isinstance(messages['entry'], dict):
                titles = [messages['entry']['title']]
            else:
                titles = [x['title'] for x in messages['entry']]
            result = 'restart_required' in titles
        return result

def restart_required(self):
        """Indicates whether splunkd is in a state that requires a restart.

        :return: A ``boolean`` that indicates whether a restart is required.

        """
        response = self.get("messages").body.read()
        messages = data.load(response)['feed']
        if 'entry' not in messages:
            result = False
        else:
            if isinstance(messages['entry'], dict):
                titles = [messages['entry']['title']]
            else:
                titles = [x['title'] for x in messages['entry']]
            result = 'restart_required' in titles
        return result

def restart_required(self):
        """Indicates whether splunkd is in a state that requires a restart.

        :return: A ``boolean`` that indicates whether a restart is required.

        """
        response = self.get("messages").body.read()
        messages = data.load(response)['feed']
        if 'entry' not in messages:
            result = False
        else:
            if isinstance(messages['entry'], dict):
                titles = [messages['entry']['title']]
            else:
                titles = [x['title'] for x in messages['entry']]
            result = 'restart_required' in titles
        return result

def restart_required(self):
        """Indicates whether splunkd is in a state that requires a restart.

        :return: A ``boolean`` that indicates whether a restart is required.

        """
        response = self.get("messages").body.read()
        messages = data.load(response)['feed']
        if 'entry' not in messages:
            result = False
        else:
            if isinstance(messages['entry'], dict):
                titles = [messages['entry']['title']]
            else:
                titles = [x['title'] for x in messages['entry']]
            result = 'restart_required' in titles
        return result

def restart_required(self):
        """Indicates whether splunkd is in a state that requires a restart.

        :return: A ``boolean`` that indicates whether a restart is required.

        """
        response = self.get("messages").body.read()
        messages = data.load(response)['feed']
        if 'entry' not in messages:
            result = False
        else:
            if isinstance(messages['entry'], dict):
                titles = [messages['entry']['title']]
            else:
                titles = [x['title'] for x in messages['entry']]
            result = 'restart_required' in titles
        return result

def restart_required(self):
        """Indicates whether splunkd is in a state that requires a restart.

        :return: A ``boolean`` that indicates whether a restart is required.

        """
        response = self.get("messages").body.read()
        messages = data.load(response)['feed']
        if 'entry' not in messages:
            result = False
        else:
            if isinstance(messages['entry'], dict):
                titles = [messages['entry']['title']]
            else:
                titles = [x['title'] for x in messages['entry']]
            result = 'restart_required' in titles
        return result

def _load_atom(response, match=None):
    return data.load(response.body.read(), match)


# Load an array of atom entries from the body of the given response

def refresh(self, state=None):
        """Refreshes the state of this entity.

        If *state* is provided, load it as the new state for this
        entity. Otherwise, make a roundtrip to the server (by calling
        the :meth:`read` method of ``self``) to fetch an updated state,
        plus at most two additional round trips if
        the ``autologin`` field of :func:`connect` is set to ``True``.

        :param state: Entity-specific arguments (optional).
        :type state: ``dict``
        :raises EntityDeletedException: Raised if the entity no longer exists on
            the server.

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            search = s.apps['search']
            search.refresh()
        """
        if state is not None:
            self._state = state
        else:
            self._state = self.read(self.get())
        return self

def _load_atom(response, match=None):
    return data.load(response.body.read(), match)


# Load an array of atom entries from the body of the given response

def refresh(self, state=None):
        """Refreshes the state of this entity.

        If *state* is provided, load it as the new state for this
        entity. Otherwise, make a roundtrip to the server (by calling
        the :meth:`read` method of ``self``) to fetch an updated state,
        plus at most two additional round trips if
        the ``autologin`` field of :func:`connect` is set to ``True``.

        :param state: Entity-specific arguments (optional).
        :type state: ``dict``
        :raises EntityDeletedException: Raised if the entity no longer exists on
            the server.

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            search = s.apps['search']
            search.refresh()
        """
        if state is not None:
            self._state = state
        else:
            self._state = self.read(self.get())
        return self

def __init__(self, mode=DatasetMode.small):
        self.optimizer = \
            Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
        self.model = Sequential()
        self.activation = 'softmax'
        self.loss = 'categorical_crossentropy'
        self.metrics = top_k_accuracy_func_list([50, 100, 200, 300, 400, 500])

        early_stopping = EarlyStopping(monitor='val_loss', patience=3)
        self.callbacks = [early_stopping]

        '''
        Index of songs in x_train(or test) starts from 1 because of zero padding.
        Index of songs in y_train(or test) starts from zero like song hash.
        For instance:
        In dataset, index of songA is 21.
        songA's index is 22 in x_train(or test)
        songA's index is 21 in y_train(or test).
        The goal is the neural network having the ability to ignore zero-paddings
        '''
        (x_train, y_train), (x_test, y_test), songs = load(mode)

        self.max_length = max([len(playlist) for playlist in x_train])
        self.song_hash = songs

        self.x_train = np.asarray(sequence.pad_sequences(x_train, maxlen=self.max_length), dtype="int64")
        self.y_train = to_categorical(y_train, len(self.song_hash) + 1)  # Zero is included

        self.x_test = np.asarray(sequence.pad_sequences(x_test, maxlen=self.max_length), dtype="int64")
        self.y_test = to_categorical(y_test, len(self.song_hash) + 1)  # Zero is included

def _load_atom(response, match=None):
    return data.load(response.body.read(), match)


# Load an array of atom entries from the body of the given response

def refresh(self, state=None):
        """Refreshes the state of this entity.

        If *state* is provided, load it as the new state for this
        entity. Otherwise, make a roundtrip to the server (by calling
        the :meth:`read` method of ``self``) to fetch an updated state,
        plus at most two additional round trips if
        the ``autologin`` field of :func:`connect` is set to ``True``.

        :param state: Entity-specific arguments (optional).
        :type state: ``dict``
        :raises EntityDeletedException: Raised if the entity no longer exists on
            the server.

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            search = s.apps['search']
            search.refresh()
        """
        if state is not None:
            self._state = state
        else:
            self._state = self.read(self.get())
        return self

def _load_atom(response, match=None):
    return data.load(response.body.read(), match)


# Load an array of atom entries from the body of the given response

def refresh(self, state=None):
        """Refreshes the state of this entity.

        If *state* is provided, load it as the new state for this
        entity. Otherwise, make a roundtrip to the server (by calling
        the :meth:`read` method of ``self``) to fetch an updated state,
        plus at most two additional round trips if
        the ``autologin`` field of :func:`connect` is set to ``True``.

        :param state: Entity-specific arguments (optional).
        :type state: ``dict``
        :raises EntityDeletedException: Raised if the entity no longer exists on
            the server.

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            search = s.apps['search']
            search.refresh()
        """
        if state is not None:
            self._state = state
        else:
            self._state = self.read(self.get())
        return self

def _load_atom(response, match=None):
    return data.load(response.body.read(), match)


# Load an array of atom entries from the body of the given response

def refresh(self, state=None):
        """Refreshes the state of this entity.

        If *state* is provided, load it as the new state for this
        entity. Otherwise, make a roundtrip to the server (by calling
        the :meth:`read` method of ``self``) to fetch an updated state,
        plus at most two additional round trips if
        the ``autologin`` field of :func:`connect` is set to ``True``.

        :param state: Entity-specific arguments (optional).
        :type state: ``dict``
        :raises EntityDeletedException: Raised if the entity no longer exists on
            the server.

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            search = s.apps['search']
            search.refresh()
        """
        if state is not None:
            self._state = state
        else:
            self._state = self.read(self.get())
        return self

def _load_atom(response, match=None):
    return data.load(response.body.read(), match)


# Load an array of atom entries from the body of the given response

def refresh(self, state=None):
        """Refreshes the state of this entity.

        If *state* is provided, load it as the new state for this
        entity. Otherwise, make a roundtrip to the server (by calling
        the :meth:`read` method of ``self``) to fetch an updated state,
        plus at most two additional round trips if
        the ``autologin`` field of :func:`connect` is set to ``True``.

        :param state: Entity-specific arguments (optional).
        :type state: ``dict``
        :raises EntityDeletedException: Raised if the entity no longer exists on
            the server.

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            search = s.apps['search']
            search.refresh()
        """
        if state is not None:
            self._state = state
        else:
            self._state = self.read(self.get())
        return self

def iter(self, offset=0, count=None, pagesize=None, **kwargs):
        """Iterates over the collection.

        This method is equivalent to the :meth:`list` method, but
        it returns an iterator and can load a certain number of entities at a
        time from the server.

        :param offset: The index of the first entity to return (optional).
        :type offset: ``integer``
        :param count: The maximum number of entities to return (optional).
        :type count: ``integer``
        :param pagesize: The number of entities to load (optional).
        :type pagesize: ``integer``
        :param kwargs: Additional arguments (optional):

            - "search" (``string``): The search query to filter responses.

            - "sort_dir" (``string``): The direction to sort returned items:
              "asc" or "desc".

            - "sort_key" (``string``): The field to use for sorting (optional).

            - "sort_mode" (``string``): The collating sequence for sorting
              returned items: "auto", "alpha", "alpha_case", or "num".

        :type kwargs: ``dict``

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            for saved_search in s.saved_searches.iter(pagesize=10):
                # Loads 10 saved searches at a time from the
                # server.
                ...
        """
        assert pagesize is None or pagesize > 0
        if count is None:
            count = self.null_count
        fetched = 0
        while count == self.null_count or fetched < count:
            response = self.get(count=pagesize or count, offset=offset, **kwargs)
            items = self._load_list(response)
            N = len(items)
            fetched += N
            for item in items:
                yield item
            if pagesize is None or N < pagesize:
                break
            offset += N
            logging.debug("pagesize=%d, fetched=%d, offset=%d, N=%d, kwargs=%s", pagesize, fetched, offset, N, kwargs)

    # kwargs: count, offset, search, sort_dir, sort_key, sort_mode

def iter(self, offset=0, count=None, pagesize=None, **kwargs):
        """Iterates over the collection.

        This method is equivalent to the :meth:`list` method, but
        it returns an iterator and can load a certain number of entities at a
        time from the server.

        :param offset: The index of the first entity to return (optional).
        :type offset: ``integer``
        :param count: The maximum number of entities to return (optional).
        :type count: ``integer``
        :param pagesize: The number of entities to load (optional).
        :type pagesize: ``integer``
        :param kwargs: Additional arguments (optional):

            - "search" (``string``): The search query to filter responses.

            - "sort_dir" (``string``): The direction to sort returned items:
              "asc" or "desc".

            - "sort_key" (``string``): The field to use for sorting (optional).

            - "sort_mode" (``string``): The collating sequence for sorting
              returned items: "auto", "alpha", "alpha_case", or "num".

        :type kwargs: ``dict``

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            for saved_search in s.saved_searches.iter(pagesize=10):
                # Loads 10 saved searches at a time from the
                # server.
                ...
        """
        assert pagesize is None or pagesize > 0
        if count is None:
            count = self.null_count
        fetched = 0
        while count == self.null_count or fetched < count:
            response = self.get(count=pagesize or count, offset=offset, **kwargs)
            items = self._load_list(response)
            N = len(items)
            fetched += N
            for item in items:
                yield item
            if pagesize is None or N < pagesize:
                break
            offset += N
            logging.debug("pagesize=%d, fetched=%d, offset=%d, N=%d, kwargs=%s", pagesize, fetched, offset, N, kwargs)

    # kwargs: count, offset, search, sort_dir, sort_key, sort_mode

def get_minibatch(file_name, batch_size, shuffle, with_pauses=False):

    dataset = data.load(file_name)

    if shuffle:
        np.random.shuffle(dataset)

    X_batch = []
    Y_batch = []
    if with_pauses:
        P_batch = []

    if len(dataset) < batch_size:
        print "WARNING: Not enough samples in '%s'. Reduce mini-batch size to %d or use a dataset with at least %d words." % (
            file_name,
            len(dataset),
            MINIBATCH_SIZE * data.MAX_SEQUENCE_LEN)

    for subsequence in dataset:

        X_batch.append(subsequence[0])
        Y_batch.append(subsequence[1])
        if with_pauses:
            P_batch.append(subsequence[2])

        if len(X_batch) == batch_size:

            # Transpose, because the model assumes the first axis is time
            X = np.array(X_batch, dtype=np.int32).T
            Y = np.array(Y_batch, dtype=np.int32).T
            if with_pauses:
                P = np.array(P_batch, dtype=theano.config.floatX).T

            if with_pauses:
                yield X, Y, P
            else:
                yield X, Y

            X_batch = []
            Y_batch = []
            if with_pauses:
                P_batch = []

def iter(self, offset=0, count=None, pagesize=None, **kwargs):
        """Iterates over the collection.

        This method is equivalent to the :meth:`list` method, but
        it returns an iterator and can load a certain number of entities at a
        time from the server.

        :param offset: The index of the first entity to return (optional).
        :type offset: ``integer``
        :param count: The maximum number of entities to return (optional).
        :type count: ``integer``
        :param pagesize: The number of entities to load (optional).
        :type pagesize: ``integer``
        :param kwargs: Additional arguments (optional):

            - "search" (``string``): The search query to filter responses.

            - "sort_dir" (``string``): The direction to sort returned items:
              "asc" or "desc".

            - "sort_key" (``string``): The field to use for sorting (optional).

            - "sort_mode" (``string``): The collating sequence for sorting
              returned items: "auto", "alpha", "alpha_case", or "num".

        :type kwargs: ``dict``

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            for saved_search in s.saved_searches.iter(pagesize=10):
                # Loads 10 saved searches at a time from the
                # server.
                ...
        """
        assert pagesize is None or pagesize > 0
        if count is None:
            count = self.null_count
        fetched = 0
        while count == self.null_count or fetched < count:
            response = self.get(count=pagesize or count, offset=offset, **kwargs)
            items = self._load_list(response)
            N = len(items)
            fetched += N
            for item in items:
                yield item
            if pagesize is None or N < pagesize:
                break
            offset += N
            logging.debug("pagesize=%d, fetched=%d, offset=%d, N=%d, kwargs=%s", pagesize, fetched, offset, N, kwargs)

    # kwargs: count, offset, search, sort_dir, sort_key, sort_mode

def iter(self, offset=0, count=None, pagesize=None, **kwargs):
        """Iterates over the collection.

        This method is equivalent to the :meth:`list` method, but
        it returns an iterator and can load a certain number of entities at a
        time from the server.

        :param offset: The index of the first entity to return (optional).
        :type offset: ``integer``
        :param count: The maximum number of entities to return (optional).
        :type count: ``integer``
        :param pagesize: The number of entities to load (optional).
        :type pagesize: ``integer``
        :param kwargs: Additional arguments (optional):

            - "search" (``string``): The search query to filter responses.

            - "sort_dir" (``string``): The direction to sort returned items:
              "asc" or "desc".

            - "sort_key" (``string``): The field to use for sorting (optional).

            - "sort_mode" (``string``): The collating sequence for sorting
              returned items: "auto", "alpha", "alpha_case", or "num".

        :type kwargs: ``dict``

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            for saved_search in s.saved_searches.iter(pagesize=10):
                # Loads 10 saved searches at a time from the
                # server.
                ...
        """
        assert pagesize is None or pagesize > 0
        if count is None:
            count = self.null_count
        fetched = 0
        while count == self.null_count or fetched < count:
            response = self.get(count=pagesize or count, offset=offset, **kwargs)
            items = self._load_list(response)
            N = len(items)
            fetched += N
            for item in items:
                yield item
            if pagesize is None or N < pagesize:
                break
            offset += N
            logging.debug("pagesize=%d, fetched=%d, offset=%d, N=%d, kwargs=%s", pagesize, fetched, offset, N, kwargs)

    # kwargs: count, offset, search, sort_dir, sort_key, sort_mode

def iter(self, offset=0, count=None, pagesize=None, **kwargs):
        """Iterates over the collection.

        This method is equivalent to the :meth:`list` method, but
        it returns an iterator and can load a certain number of entities at a
        time from the server.

        :param offset: The index of the first entity to return (optional).
        :type offset: ``integer``
        :param count: The maximum number of entities to return (optional).
        :type count: ``integer``
        :param pagesize: The number of entities to load (optional).
        :type pagesize: ``integer``
        :param kwargs: Additional arguments (optional):

            - "search" (``string``): The search query to filter responses.

            - "sort_dir" (``string``): The direction to sort returned items:
              "asc" or "desc".

            - "sort_key" (``string``): The field to use for sorting (optional).

            - "sort_mode" (``string``): The collating sequence for sorting
              returned items: "auto", "alpha", "alpha_case", or "num".

        :type kwargs: ``dict``

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            for saved_search in s.saved_searches.iter(pagesize=10):
                # Loads 10 saved searches at a time from the
                # server.
                ...
        """
        assert pagesize is None or pagesize > 0
        if count is None:
            count = self.null_count
        fetched = 0
        while count == self.null_count or fetched < count:
            response = self.get(count=pagesize or count, offset=offset, **kwargs)
            items = self._load_list(response)
            N = len(items)
            fetched += N
            for item in items:
                yield item
            if pagesize is None or N < pagesize:
                break
            offset += N
            logging.debug("pagesize=%d, fetched=%d, offset=%d, N=%d, kwargs=%s", pagesize, fetched, offset, N, kwargs)

    # kwargs: count, offset, search, sort_dir, sort_key, sort_mode

def iter(self, offset=0, count=None, pagesize=None, **kwargs):
        """Iterates over the collection.

        This method is equivalent to the :meth:`list` method, but
        it returns an iterator and can load a certain number of entities at a
        time from the server.

        :param offset: The index of the first entity to return (optional).
        :type offset: ``integer``
        :param count: The maximum number of entities to return (optional).
        :type count: ``integer``
        :param pagesize: The number of entities to load (optional).
        :type pagesize: ``integer``
        :param kwargs: Additional arguments (optional):

            - "search" (``string``): The search query to filter responses.

            - "sort_dir" (``string``): The direction to sort returned items:
              "asc" or "desc".

            - "sort_key" (``string``): The field to use for sorting (optional).

            - "sort_mode" (``string``): The collating sequence for sorting
              returned items: "auto", "alpha", "alpha_case", or "num".

        :type kwargs: ``dict``

        **Example**::

            import splunklib.client as client
            s = client.connect(...)
            for saved_search in s.saved_searches.iter(pagesize=10):
                # Loads 10 saved searches at a time from the
                # server.
                ...
        """
        assert pagesize is None or pagesize > 0
        if count is None:
            count = self.null_count
        fetched = 0
        while count == self.null_count or fetched < count:
            response = self.get(count=pagesize or count, offset=offset, **kwargs)
            items = self._load_list(response)
            N = len(items)
            fetched += N
            for item in items:
                yield item
            if pagesize is None or N < pagesize:
                break
            offset += N
            logging.debug("pagesize=%d, fetched=%d, offset=%d, N=%d, kwargs=%s", pagesize, fetched, offset, N, kwargs)

    # kwargs: count, offset, search, sort_dir, sort_key, sort_mode