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https://github.com/SheffieldML/GPy.git
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replace np.int by int
This commit is contained in:
Martin Bubel
parent
a6d78d79aa
commit
65af6ee35e
15 changed files with 3889 additions and 2375 deletions
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@ -2,7 +2,8 @@
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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def conf_matrix(p,labels,names=['1','0'],threshold=.5,show=True):
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def conf_matrix(p, labels, names=["1", "0"], threshold=0.5, show=True):
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"""
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Returns error rate and true/false positives in a binary classification problem
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- Actual classes are displayed by column.
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@ -16,18 +17,18 @@ def conf_matrix(p,labels,names=['1','0'],threshold=.5,show=True):
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:type show: False|True
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"""
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assert p.size == labels.size, "Arrays p and labels have different dimensions."
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decision = np.ones((labels.size,1))
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decision[p<threshold] = 0
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decision = np.ones((labels.size, 1))
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decision[p < threshold] = 0
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diff = decision - labels
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false_0 = diff[diff == -1].size
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false_1 = diff[diff == 1].size
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true_1 = np.sum(decision[diff ==0])
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true_1 = np.sum(decision[diff == 0])
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true_0 = labels.size - true_1 - false_0 - false_1
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error = (false_1 + false_0)/np.float(labels.size)
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error = (false_1 + false_0) / float(labels.size)
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if show:
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print(100. - error * 100,'% instances correctly classified')
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print('%-10s| %-10s| %-10s| ' % ('',names[0],names[1]))
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print('----------|------------|------------|')
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print('%-10s| %-10s| %-10s| ' % (names[0],true_1,false_0))
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print('%-10s| %-10s| %-10s| ' % (names[1],false_1,true_0))
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return error,true_1, false_1, true_0, false_0
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print(100.0 - error * 100, "% instances correctly classified")
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print("%-10s| %-10s| %-10s| " % ("", names[0], names[1]))
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print("----------|------------|------------|")
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print("%-10s| %-10s| %-10s| " % (names[0], true_1, false_0))
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print("%-10s| %-10s| %-10s| " % (names[1], false_1, true_0))
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return error, true_1, false_1, true_0, false_0
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@ -2,6 +2,7 @@ import numpy as np
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import warnings
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import GPy
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def index_to_slices(index):
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"""
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take a numpy array of integers (index) and return a nested list of slices such that the slices describe the start, stop points for each integer in the index.
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@ -16,28 +17,35 @@ def index_to_slices(index):
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returns
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>>> [[slice(0,2,None),slice(4,5,None)],[slice(2,4,None),slice(8,10,None)],[slice(5,8,None)]]
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"""
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if len(index)==0:
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return[]
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if len(index) == 0:
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return []
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#contruct the return structure
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ind = np.asarray(index,dtype=np.int)
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ret = [[] for i in range(ind.max()+1)]
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# contruct the return structure
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ind = np.asarray(index, dtype=int)
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ret = [[] for i in range(ind.max() + 1)]
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#find the switchpoints
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ind_ = np.hstack((ind,ind[0]+ind[-1]+1))
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switchpoints = np.nonzero(ind_ - np.roll(ind_,+1))[0]
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# find the switchpoints
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ind_ = np.hstack((ind, ind[0] + ind[-1] + 1))
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switchpoints = np.nonzero(ind_ - np.roll(ind_, +1))[0]
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[ret[ind_i].append(slice(*indexes_i)) for ind_i,indexes_i in zip(ind[switchpoints[:-1]],zip(switchpoints,switchpoints[1:]))]
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[
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ret[ind_i].append(slice(*indexes_i))
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for ind_i, indexes_i in zip(
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ind[switchpoints[:-1]], zip(switchpoints, switchpoints[1:])
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)
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]
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return ret
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def get_slices(input_list):
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num_outputs = len(input_list)
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_s = [0] + [ _x.shape[0] for _x in input_list ]
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_s = [0] + [_x.shape[0] for _x in input_list]
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_s = np.cumsum(_s)
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slices = [slice(a,b) for a,b in zip(_s[:-1],_s[1:])]
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slices = [slice(a, b) for a, b in zip(_s[:-1], _s[1:])]
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return slices
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def build_XY(input_list,output_list=None,index=None):
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def build_XY(input_list, output_list=None, index=None):
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num_outputs = len(input_list)
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if output_list is not None:
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assert num_outputs == len(output_list)
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@ -47,27 +55,35 @@ def build_XY(input_list,output_list=None,index=None):
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if index is not None:
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assert len(index) == num_outputs
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I = np.hstack( [np.repeat(j,_x.shape[0]) for _x,j in zip(input_list,index)] )
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I = np.hstack([np.repeat(j, _x.shape[0]) for _x, j in zip(input_list, index)])
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else:
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I = np.hstack( [np.repeat(j,_x.shape[0]) for _x,j in zip(input_list,range(num_outputs))] )
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I = np.hstack(
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[np.repeat(j, _x.shape[0]) for _x, j in zip(input_list, range(num_outputs))]
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)
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X = np.vstack(input_list)
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X = np.hstack([X,I[:,None]])
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X = np.hstack([X, I[:, None]])
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return X,Y,I[:,None]#slices
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return X, Y, I[:, None] # slices
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def build_likelihood(Y_list,noise_index,likelihoods_list=None):
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def build_likelihood(Y_list, noise_index, likelihoods_list=None):
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Ny = len(Y_list)
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if likelihoods_list is None:
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likelihoods_list = [GPy.likelihoods.Gaussian(name="Gaussian_noise_%s" %j) for y,j in zip(Y_list,range(Ny))]
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likelihoods_list = [
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GPy.likelihoods.Gaussian(name="Gaussian_noise_%s" % j)
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for y, j in zip(Y_list, range(Ny))
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]
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else:
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assert len(likelihoods_list) == Ny
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#likelihood = GPy.likelihoods.mixed_noise.MixedNoise(likelihoods_list=likelihoods_list, noise_index=noise_index)
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likelihood = GPy.likelihoods.mixed_noise.MixedNoise(likelihoods_list=likelihoods_list)
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# likelihood = GPy.likelihoods.mixed_noise.MixedNoise(likelihoods_list=likelihoods_list, noise_index=noise_index)
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likelihood = GPy.likelihoods.mixed_noise.MixedNoise(
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likelihoods_list=likelihoods_list
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)
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return likelihood
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def ICM(input_dim, num_outputs, kernel, W_rank=1,W=None,kappa=None,name='ICM'):
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def ICM(input_dim, num_outputs, kernel, W_rank=1, W=None, kappa=None, name="ICM"):
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"""
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Builds a kernel for an Intrinsic Coregionalization Model
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@ -80,13 +96,26 @@ def ICM(input_dim, num_outputs, kernel, W_rank=1,W=None,kappa=None,name='ICM'):
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"""
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if kernel.input_dim != input_dim:
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kernel.input_dim = input_dim
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warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
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warnings.warn(
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"kernel's input dimension overwritten to fit input_dim parameter."
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)
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K = kernel.prod(GPy.kern.Coregionalize(1, num_outputs, active_dims=[input_dim], rank=W_rank,W=W,kappa=kappa,name='B'),name=name)
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K = kernel.prod(
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GPy.kern.Coregionalize(
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1,
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num_outputs,
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active_dims=[input_dim],
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rank=W_rank,
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W=W,
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kappa=kappa,
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name="B",
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),
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name=name,
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)
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return K
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def LCM(input_dim, num_outputs, kernels_list, W_rank=1,name='ICM'):
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def LCM(input_dim, num_outputs, kernels_list, W_rank=1, name="ICM"):
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"""
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Builds a kernel for an Linear Coregionalization Model
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@ -98,15 +127,15 @@ def LCM(input_dim, num_outputs, kernels_list, W_rank=1,name='ICM'):
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:type W_rank: integer
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"""
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Nk = len(kernels_list)
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K = ICM(input_dim,num_outputs,kernels_list[0],W_rank,name='%s%s' %(name,0))
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K = ICM(input_dim, num_outputs, kernels_list[0], W_rank, name="%s%s" % (name, 0))
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j = 1
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for kernel in kernels_list[1:]:
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K += ICM(input_dim,num_outputs,kernel,W_rank,name='%s%s' %(name,j))
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K += ICM(input_dim, num_outputs, kernel, W_rank, name="%s%s" % (name, j))
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j += 1
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return K
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def Private(input_dim, num_outputs, kernel, output, kappa=None,name='X'):
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def Private(input_dim, num_outputs, kernel, output, kappa=None, name="X"):
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"""
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Builds a kernel for an Intrinsic Coregionalization Model
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@ -117,7 +146,7 @@ def Private(input_dim, num_outputs, kernel, output, kappa=None,name='X'):
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:param W_rank: number tuples of the corregionalization parameters 'W'
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:type W_rank: integer
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"""
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K = ICM(input_dim,num_outputs,kernel,W_rank=1,kappa=kappa,name=name)
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K = ICM(input_dim, num_outputs, kernel, W_rank=1, kappa=kappa, name=name)
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K.B.W.fix(0)
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_range = range(num_outputs)
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_range.pop(output)
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