Esempio n. 1
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    def __init__(self, lower, upper):
        """
        Initialise the Sampler class.

        .. note::

            Currently only supports rectangular type restrictions on the
            parameter space

        Parameters
        ----------
        lower : array_like
            Lower or minimum bounds for the parameter space
        upper : array_like
            Upper or maximum bounds for the parameter space
        """
        self.lower = np.array(lower)
        self.upper = np.array(upper)
        self.dims = self.upper.shape[0]
        assert (self.lower.ndim == 1) and (self.upper.ndim == 1)
        assert self.lower.shape[0] == self.dims
        self.X = ArrayBuffer()
        self.y = ArrayBuffer()
        self.virtual_flag = ArrayBuffer()
        self.pending_results = {}
        self.n_tasks = None
Esempio n. 2
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    def __init__(self, lower, upper):
        """
        Initialise the Sampler class.

        .. note::

            Currently only supports rectangular type restrictions on the
            parameter space

        Parameters
        ----------
        lower : array_like
            Lower or minimum bounds for the parameter space
        upper : array_like
            Upper or maximum bounds for the parameter space
        """
        self.lower = np.array(lower)
        self.upper = np.array(upper)
        self.dims = self.upper.shape[0]
        assert (self.lower.ndim == 1) and (self.upper.ndim == 1)
        assert self.lower.shape[0] == self.dims
        self.X = ArrayBuffer()
        self.y = ArrayBuffer()
        self.virtual_flag = ArrayBuffer()
        self.pending_results = {}
        self.n_tasks = None
Esempio n. 3
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def main():

    d = 20
    n_stack = 10000

    buf = ArrayBuffer()

    st = time.time()
    for i in range(n_stack):
        # buf.append(np.random.random())
        buf.append(np.random.random(d))  # NOQA we dont do anything with a

    ft = time.time()
    print('Efficient buffer took {0:.5f} seconds'.format(ft - st))

    a = buf()

    import IPython
    IPython.embed()
    import sys
    sys.exit()
    print(a)
    exit()

    st = time.time()
    b = np.zeros((0, d))
    for i in range(n_stack):
        b = np.vstack((b, np.random.random(d)))
    ft = time.time()
    print('Repeated Vstack took {0:.5f} seconds'.format(ft - st))

    st = time.time()
    b_buf = []
    for i in range(n_stack):
        b_buf.append(np.random.random(d))
        b = np.array(b_buf)
    ft = time.time()
    print('List buffering and casting took {0:.5f} seconds'.format(ft - st))
Esempio n. 4
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    def _update(self, uid, y_true):
        """
        Update a job with its observed value.

        Parameters
        ----------
        uid : str
            A hexadecimal ID that identifies the job to be updated
        y_true : float
            The observed value corresponding to the job identified by 'uid'

        Returns
        -------
        int
            Index location in the data lists 'Sampler.X' and
            'Sampler.y' corresponding to the job being updated
        """
        # Make sure the job uid given is valid
        if uid not in self.pending_results:
            warnings.warn('Result was not pending!')
        assert uid in self.pending_results

        # Kill the job and update collected data with true observation
        ind = self.pending_results.pop(uid)

        # If the user has been pushing Nones until now, we will init properly
        if self.n_tasks is None:
            self.n_tasks = len(np.atleast_1d(y_true))
            pending_count = len(self.y)
            self.y = ArrayBuffer()
            for _ in range(pending_count):
                self.y.append(np.zeros(self.n_tasks))

        self.y()[ind] = y_true
        self.virtual_flag()[ind] = False

        return ind
Esempio n. 5
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    def _update(self, uid, y_true):
        """
        Update a job with its observed value.

        Parameters
        ----------
        uid : str
            A hexadecimal ID that identifies the job to be updated
        y_true : float
            The observed value corresponding to the job identified by 'uid'

        Returns
        -------
        int
            Index location in the data lists 'Sampler.X' and
            'Sampler.y' corresponding to the job being updated
        """
        # Make sure the job uid given is valid
        if uid not in self.pending_results:
            warnings.warn('Result was not pending!')
        assert uid in self.pending_results

        # Kill the job and update collected data with true observation
        ind = self.pending_results.pop(uid)

        # If the user has been pushing Nones until now, we will init properly
        if self.n_tasks is None:
            self.n_tasks = len(np.atleast_1d(y_true))
            pending_count = len(self.y)
            self.y = ArrayBuffer()
            for _ in range(pending_count):
                self.y.append(np.zeros(self.n_tasks))

        self.y()[ind] = y_true
        self.virtual_flag()[ind] = False

        return ind
Esempio n. 6
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class Sampler:
    """
    Provide a basic template and interface to specific Sampler subclasses.

    Attributes
    ----------
    lower : numpy.ndarray
        Lower bounds for each parameter in the parameter space
    upper : numpy.ndarray
        Upper bounds for each parameter in the parameter space
    dims : int
        Dimension of the parameter space (number of parameters)
    X : ArrayBuffer
        Contiguous Buffer of feature vectors representing observed locations
        in the parameter space
    y : ArrayBuffer
        Contiguous Buffer of target outputs or expected (virtual) target
        outputs corresponding to the feature vectors 'X'
    virtual_flag : ArrayBuffer
        A contiguous array of boolean flags indicating virtual elements of 'y'

            True: Corresponding target output is virtual

            False: Corresponding target output is observed
    pending_results : dict
        A dictionary that maps the job ID to the corresponding index in both
        the 'X' and 'y' buffers.
    """

    def __init__(self, lower, upper):
        """
        Initialise the Sampler class.

        .. note::

            Currently only supports rectangular type restrictions on the
            parameter space

        Parameters
        ----------
        lower : array_like
            Lower or minimum bounds for the parameter space
        upper : array_like
            Upper or maximum bounds for the parameter space
        """
        self.lower = np.array(lower)
        self.upper = np.array(upper)
        self.dims = self.upper.shape[0]
        assert (self.lower.ndim == 1) and (self.upper.ndim == 1)
        assert self.lower.shape[0] == self.dims
        self.X = ArrayBuffer()
        self.y = ArrayBuffer()
        self.virtual_flag = ArrayBuffer()
        self.pending_results = {}
        self.n_tasks = None

    def pick(self):
        """
        Pick the next feature location for the next observation to be taken.

        .. note::

            Currently only supports rectangular type restrictions on the
            parameter space

        Returns
        -------
        numpy.ndarray
            Location in the parameter space for the next observation to be
            taken
        str
            A random hexadecimal ID to identify the corresponding job

        Raises
        ------
        AssertionError
            Under all circumstances. See note above.
        """
        assert False

    def update(self, uid, y_true):
        """
        Update a job with its observed value.

        .. note::

            Currently a dummy function whose functionality will be
            filled by subclasses of the Sampler class

        Parameters
        ----------
        uid : str
            A hexadecimal ID that identifies the job to be updated
        y_true : float
            The observed value corresponding to the job identified by 'uid'

        Returns
        -------
        int
            Index location in the data lists 'Sampler.X' and
            'Sampler.y' corresponding to the job being updated

        Raises
        ------
        AssertionError
            Under all circumstances. See note above.
        """
        assert False

    def _assign(self, xq, yq_exp):
        """
        Assign a pair (location in parameter space, virtual target) a job ID.

        Parameters
        ----------
        xq : numpy.ndarray
            Location in the parameter space for the next observation to be
            taken
        yq_exp : float
            The virtual target output at that parameter location

        Returns
        -------
        str
            A random hexadecimal ID to identify the corresponding job
        """
        # Place a virtual observation onto the collected data
        n = len(self.X)
        self.X.append(xq)
        self.virtual_flag.append(True)

        # If we get a None, insert zeros instead
        if yq_exp is None and self.n_tasks is not None:
            self.y.append(np.zeros(self.n_tasks))
        else:
            self.y.append(yq_exp)  # then add the real one

        # Create an uid for this observation
        # m = hashlib.md5()
        # m.update(np.array(np.random.random()))
        # uid = m.hexdigest()
        uid = uuid.uuid4().hex  # "%032x" % random.getrandbits(128)

        # Note the index of corresponding to this picked location
        self.pending_results[uid] = n

        return uid

    def _update(self, uid, y_true):
        """
        Update a job with its observed value.

        Parameters
        ----------
        uid : str
            A hexadecimal ID that identifies the job to be updated
        y_true : float
            The observed value corresponding to the job identified by 'uid'

        Returns
        -------
        int
            Index location in the data lists 'Sampler.X' and
            'Sampler.y' corresponding to the job being updated
        """
        # Make sure the job uid given is valid
        if uid not in self.pending_results:
            warnings.warn('Result was not pending!')
        assert uid in self.pending_results

        # Kill the job and update collected data with true observation
        ind = self.pending_results.pop(uid)

        # If the user has been pushing Nones until now, we will init properly
        if self.n_tasks is None:
            self.n_tasks = len(np.atleast_1d(y_true))
            pending_count = len(self.y)
            self.y = ArrayBuffer()
            for _ in range(pending_count):
                self.y.append(np.zeros(self.n_tasks))

        self.y()[ind] = y_true
        self.virtual_flag()[ind] = False

        return ind
Esempio n. 7
0
class Sampler:
    """
    Provide a basic template and interface to specific Sampler subclasses.

    Attributes
    ----------
    lower : numpy.ndarray
        Lower bounds for each parameter in the parameter space
    upper : numpy.ndarray
        Upper bounds for each parameter in the parameter space
    dims : int
        Dimension of the parameter space (number of parameters)
    X : ArrayBuffer
        Contiguous Buffer of feature vectors representing observed locations
        in the parameter space
    y : ArrayBuffer
        Contiguous Buffer of target outputs or expected (virtual) target
        outputs corresponding to the feature vectors 'X'
    virtual_flag : ArrayBuffer
        A contiguous array of boolean flags indicating virtual elements of 'y'

            True: Corresponding target output is virtual

            False: Corresponding target output is observed
    pending_results : dict
        A dictionary that maps the job ID to the corresponding index in both
        the 'X' and 'y' buffers.
    """
    def __init__(self, lower, upper):
        """
        Initialise the Sampler class.

        .. note::

            Currently only supports rectangular type restrictions on the
            parameter space

        Parameters
        ----------
        lower : array_like
            Lower or minimum bounds for the parameter space
        upper : array_like
            Upper or maximum bounds for the parameter space
        """
        self.lower = np.array(lower)
        self.upper = np.array(upper)
        self.dims = self.upper.shape[0]
        assert (self.lower.ndim == 1) and (self.upper.ndim == 1)
        assert self.lower.shape[0] == self.dims
        self.X = ArrayBuffer()
        self.y = ArrayBuffer()
        self.virtual_flag = ArrayBuffer()
        self.pending_results = {}
        self.n_tasks = None

    def pick(self):
        """
        Pick the next feature location for the next observation to be taken.

        .. note::

            Currently only supports rectangular type restrictions on the
            parameter space

        Returns
        -------
        numpy.ndarray
            Location in the parameter space for the next observation to be
            taken
        str
            A random hexadecimal ID to identify the corresponding job

        Raises
        ------
        AssertionError
            Under all circumstances. See note above.
        """
        assert False

    def update(self, uid, y_true):
        """
        Update a job with its observed value.

        .. note::

            Currently a dummy function whose functionality will be
            filled by subclasses of the Sampler class

        Parameters
        ----------
        uid : str
            A hexadecimal ID that identifies the job to be updated
        y_true : float
            The observed value corresponding to the job identified by 'uid'

        Returns
        -------
        int
            Index location in the data lists 'Sampler.X' and
            'Sampler.y' corresponding to the job being updated

        Raises
        ------
        AssertionError
            Under all circumstances. See note above.
        """
        assert False

    def _assign(self, xq, yq_exp):
        """
        Assign a pair (location in parameter space, virtual target) a job ID.

        Parameters
        ----------
        xq : numpy.ndarray
            Location in the parameter space for the next observation to be
            taken
        yq_exp : float
            The virtual target output at that parameter location

        Returns
        -------
        str
            A random hexadecimal ID to identify the corresponding job
        """
        # Place a virtual observation onto the collected data
        n = len(self.X)
        self.X.append(xq)
        self.virtual_flag.append(True)

        # If we get a None, insert zeros instead
        if yq_exp is None and self.n_tasks is not None:
            self.y.append(np.zeros(self.n_tasks))
        else:
            self.y.append(yq_exp)  # then add the real one

        # Create an uid for this observation
        # m = hashlib.md5()
        # m.update(np.array(np.random.random()))
        # uid = m.hexdigest()
        uid = uuid.uuid4().hex  # "%032x" % random.getrandbits(128)

        # Note the index of corresponding to this picked location
        self.pending_results[uid] = n

        return uid

    def _update(self, uid, y_true):
        """
        Update a job with its observed value.

        Parameters
        ----------
        uid : str
            A hexadecimal ID that identifies the job to be updated
        y_true : float
            The observed value corresponding to the job identified by 'uid'

        Returns
        -------
        int
            Index location in the data lists 'Sampler.X' and
            'Sampler.y' corresponding to the job being updated
        """
        # Make sure the job uid given is valid
        if uid not in self.pending_results:
            warnings.warn('Result was not pending!')
        assert uid in self.pending_results

        # Kill the job and update collected data with true observation
        ind = self.pending_results.pop(uid)

        # If the user has been pushing Nones until now, we will init properly
        if self.n_tasks is None:
            self.n_tasks = len(np.atleast_1d(y_true))
            pending_count = len(self.y)
            self.y = ArrayBuffer()
            for _ in range(pending_count):
                self.y.append(np.zeros(self.n_tasks))

        self.y()[ind] = y_true
        self.virtual_flag()[ind] = False

        return ind