def test_onedim_sampling(interpolation):
# there is a sampling shortcut, so we test if it also works without the shortcut
obs_kernel = zfit.Space("obs1", limits=(-3, 1))
obs = zfit.Space("obs1", limits=(5, 15))
func1 = zfit.pdf.Uniform(6, 12, obs=obs)
func2 = zfit.pdf.Uniform(11, 11.5, obs=obs)
func = zfit.pdf.SumPDF([func1, func2], 0.5)
func1k = zfit.pdf.Uniform(-2, 1, obs=obs_kernel)
func2k = zfit.pdf.Uniform(-0.5, 1.0, obs=obs_kernel)
funck = zfit.pdf.SumPDF([func1k, func2k], 0.5)
conv = zfit.pdf.FFTConvPDFV1(
func=func, kernel=funck, n=200, interpolation=interpolation
)
conv_nosample = FFTConvPDFV1NoSampling(
func=func, kernel=funck, n=200, interpolation=interpolation
)
npoints_sample = 10000
sample = conv.sample(npoints_sample)
sample_nosample = conv_nosample.sample(npoints_sample)
x = z.unstack_x(sample)
xns = z.unstack_x(sample_nosample)
assert (
scipy.stats.ks_2samp(x, xns).pvalue > 1e-3
) # can vary a lot, but still means close
def _analytic_integrate(self, limits, norm_range):
lower, upper = limits._rect_limits_tf
lower = z.unstack_x(lower)
upper = z.unstack_x(upper)
tf.debugging.assert_all_finite(
(lower, upper),
"Are infinite limits needed? Causes troubles with NaNs")
return self.distribution.cdf(upper) - self.distribution.cdf(lower)
def func4_3deps(x):
if isinstance(x, np.ndarray):
a, b, c = x
else:
a, b, c = z.unstack_x(x)
return a**2 + b**3 + 0.5 * c
def _unnormalized_pdf(self, x):
FL = self.parameters["FL"]
AT2 = self.parameters["AT2"]
P4p = self.parameters["P4p"]
costheta_k, costheta_l, phi = z.unstack_x(x)
sintheta_k = tf.sqrt(1.0 - costheta_k * costheta_k)
sintheta_l = tf.sqrt(1.0 - costheta_l * costheta_l)
sintheta_2k = 1.0 - costheta_k * costheta_k
sintheta_2l = 1.0 - costheta_l * costheta_l
sin2theta_k = 2.0 * sintheta_k * costheta_k
cos2theta_l = 2.0 * costheta_l * costheta_l - 1.0
pdf = (
(3.0 / 4.0) * (1.0 - FL) * sintheta_2k
+ FL * costheta_k * costheta_k
+ (1.0 / 4.0) * (1.0 - FL) * sintheta_2k * cos2theta_l
+ -1.0 * FL * costheta_k * costheta_k * cos2theta_l
+ (1.0 / 2.0)
* (1.0 - FL)
* AT2
* sintheta_2k
* sintheta_2l
* tf.cos(2.0 * phi)
+ tf.sqrt(FL * (1 - FL)) * P4p * sin2theta_k * sin2theta_l * tf.cos(phi)
)
return pdf
def _unnormalized_pdf(self, x, norm_range=False):
x = z.unstack_x(x)
x_min = tf.reduce_min(self._grid)
x_max = tf.reduce_max(self._grid)
return tfp.math.interp_regular_1d_grid(x, x_min, x_max,
self._grid_estimations)
def __init__(self,
obs: ztyping.ObsTypeInput,
data: ztyping.ParamTypeInput,
num_grid_points=1024,
binning_method='linear',
weights: Union[None, np.ndarray, tf.Tensor] = None,
name: str = "KernelDensityEstimationISJ"):
r"""
Kernel Density Estimation is a non-parametric method to approximate the density of given points.
.. math::
f_h(x) = \frac{1}{nh} \sum_{i=1}^n K\Big(\frac{x-x_i}{h}\Big)
It is computed by using a trick described in a paper by Botev et al. that uses the fact, that the Kernel Density Estimation
with a Gaussian Kernel is a solution to the Heat Euqation.
Args:
data: 1-D Tensor-like.
bandwidth: Bandwidth of the kernel. Valid options are {'silverman', 'scott', 'adaptiveV1'} or a numerical.
If a numerical is given, it as to be broadcastable to the batch and event shape of the distribution.
A scalar or a `zfit.Parameter` will simply broadcast to `data` for a 1-D distribution.
obs: Observables
weights: Weights of each `data`, can be None or Tensor-like with shape compatible with `data`
name: Name of the PDF
"""
if isinstance(data, ZfitData):
if data.weights is not None:
if weights is not None:
raise OverdefinedError(
"Cannot specify weights and use a `ZfitData` with weights."
)
else:
weights = data.weights
if data.n_obs > 1:
raise ShapeIncompatibleError(
f"KDE is 1 dimensional, but data {data} has {data.n_obs} observables."
)
data = z.unstack_x(data)
shape_data = tf.shape(data)
size = tf.cast(shape_data[0], ztypes.float)
self._num_grid_points = tf.minimum(
tf.cast(size, ztypes.int), tf.constant(num_grid_points,
ztypes.int))
self._binning_method = binning_method
self._data = tf.convert_to_tensor(data, ztypes.float)
self._weights = weights
self._grid = None
self._grid_data = None
self._bandwidth, self._grid_estimations, self._grid = isj_helper.calculate_bandwidth_and_density(
self._data, self._num_grid_points, self._binning_method,
self._weights)
params = {}
super().__init__(obs=obs, name=name, params=params)
def test_onedim_sampling():
obs_kernel = zfit.Space("obs1", limits=(-3, 3))
obs = zfit.Space("obs1", limits=(5, 15))
func1 = zfit.pdf.Uniform(6, 12, obs=obs)
func2 = zfit.pdf.Uniform(11, 11.5, obs=obs)
func = zfit.pdf.SumPDF([func1, func2], 0.5)
func1k = zfit.pdf.Uniform(-2, 1, obs=obs_kernel)
func2k = zfit.pdf.Uniform(-0.5, 1., obs=obs_kernel)
funck = zfit.pdf.SumPDF([func1k, func2k], 0.5)
conv = zfit.pdf.FFTConvPDFV1(func=func, kernel=funck, n=200)
conv_nosample = FFTConvPDFV1NoSampling(func=func, kernel=funck, n=200)
npoints_sample = 10000
sample = conv.sample(npoints_sample)
sample_nosample = conv_nosample.sample(npoints_sample)
x = z.unstack_x(sample)
xns = z.unstack_x(sample_nosample)
assert scipy.stats.ks_2samp(x, xns).pvalue > 1e-6 # can vary a lot, but still means close
def unstack_x(self,
obs: ztyping.ObsTypeInput = None,
always_list: bool = False):
"""Return the unstacked data: a list of tensors or a single Tensor.
Args:
obs: which observables to return
always_list: If True, always return a list (also if length 1)
Returns:
List(tf.Tensor)
"""
return z.unstack_x(self.value(obs=obs))
def _unnormalized_pdf(self, x):
data = z.unstack_x(x)
mu = self.params['mu']
sigma = self.params['sigma']
theta = self.params['theta']
cond = tf.less_equal(data, -theta)
exp_power = -(znp.power(znp.log(-data - theta) - mu, 2) / (2 * znp.power(sigma, 2)))
outer_factor = (-data - theta) * sigma * znp.sqrt(2 * math.pi)
func = tf.where(cond,
np.exp(exp_power) / outer_factor,
0.)
return func
def gradients(self,
x: ztyping.XType,
norm_range: ztyping.LimitsType,
params: ztyping.ParamsTypeOpt = None):
warnings.warn(
"Taking the gradient *this way* in TensorFlow is inefficient! Consider taking it with"
"respect to the loss function.")
if params is not None:
params = convert_to_container(params)
if params is None or isinstance(params[0], str):
params = self.get_params(only_floating=False, names=params)
probs = self.pdf(x, norm_range=norm_range)
gradients = [
tf.gradients(ys=prob, xs=params)
for prob in z.unstack_x(probs, always_list=True)
]
return tf.stack(gradients)
def _sort_value(self, value, obs: Tuple[str]):
obs = convert_to_container(value=obs, container=tuple)
perm_indices = self.space.axes if self.space.axes != tuple(
range(value.shape[-1])) else False
# permutate = perm_indices is not None
if obs:
if not frozenset(obs) <= frozenset(self.obs):
raise ValueError(
"The observable(s) {} are not contained in the dataset. "
"Only the following are: {}".format(
frozenset(obs) - frozenset(self.obs), self.obs))
perm_indices = self.space.get_reorder_indices(obs=obs)
# values = list(values[self.obs.index(o)] for o in obs if o in self.obs)
if perm_indices:
value = z.unstack_x(value, always_list=True)
value = [value[i] for i in perm_indices]
value = z.stack_x(value)
return value
def exp_icdf(x, params, model):
lambd = params['lambda']
x = z.unstack_x(x)
x = model._shift_x(x)
return zfit.z.math.log(lambd * x) / lambd
def exp_icdf(x, params, model):
lambd = params["lambda"]
x = z.unstack_x(x)
x = model._shift_x(x)
return znp.log(lambd * x) / lambd
def __init__(self,
obs: ztyping.ObsTypeInput,
data: ztyping.ParamTypeInput,
bandwidth: ztyping.ParamTypeInput = None,
num_grid_points=1024,
binning_method='linear',
implementation='cpp',
weights: Union[None, np.ndarray, tf.Tensor] = None,
betas=[0.25, 0.25],
name: str = "KernelDensityEstimationK1"):
r"""
Kernel Density Estimation is a non-parametric method to approximate the density of given points.
.. math::
f_h(x) = \frac{1}{nh} \sum_{i=1}^n K\Big(\frac{x-x_i}{h}\Big)
It is computed by using a convolution of the data with the kernels evaluated at fixed grid points and then
interpolating between this points to get an estimate for x.
Args:
data: 1-D Tensor-like.
bandwidth: Bandwidth of the kernel. Valid options are {'silverman', 'scott', 'adaptiveV1'} or a numerical.
If a numerical is given, it as to be broadcastable to the batch and event shape of the distribution.
A scalar or a `zfit.Parameter` will simply broadcast to `data` for a 1-D distribution.
obs: Observables
weights: Weights of each `data`, can be None or Tensor-like with shape compatible with `data`
name: Name of the PDF
"""
if isinstance(data, ZfitData):
if data.weights is not None:
if weights is not None:
raise OverdefinedError(
"Cannot specify weights and use a `ZfitData` with weights."
)
else:
weights = data.weights
if data.n_obs > 1:
raise ShapeIncompatibleError(
f"KDE is 1 dimensional, but data {data} has {data.n_obs} observables."
)
data = z.unstack_x(data)
shape_data = tf.shape(data)
size = tf.cast(shape_data[0], ztypes.float)
self._num_grid_points = tf.minimum(
tf.cast(size, ztypes.int), tf.constant(num_grid_points,
ztypes.int))
self._binning_method = binning_method
self._data = tf.convert_to_tensor(data, ztypes.float)
self._bandwidth = tf.convert_to_tensor(bandwidth, ztypes.float)
self._weights = weights
self._betas = tf.convert_to_tensor(betas, ztypes.float)
self._implementation = implementation
self._grid = None
self._grid_data = None
self._grid = binning_helper.generate_grid(
self._data, num_grid_points=self._num_grid_points)
self._grid_data = binning_helper.bin(self._binning_method, self._data,
self._grid, self._weights)
if self._implementation == 'numpy':
self._grid_estimations = hofmeyr_helper.calculate_estimate_numpy(
self._grid, self._grid_data, self._betas, self._bandwidth)
elif self._implementation == 'cpp':
self._grid_estimations = hofmeyr_helper.calculate_estimate_cpp(
self._grid, self._grid_data, self._betas, self._bandwidth)
else:
self._grid_estimations = hofmeyr_helper.calculate_estimate(
self._grid, self._grid_data, self._betas, self._bandwidth)
params = {'bandwidth': self._bandwidth}
super().__init__(obs=obs, name=name, params=params)
def _unnormalized_pdf(self, x: "zfit.Data", norm_range=False):
value = z.unstack_x(x) # TODO: use this? change shaping below?
return self.distribution.prob(value=value, name="unnormalized_pdf")
def test_conv_2D_simple():
zfit.run.set_graph_mode(False) # TODO: remove, just for debugging
raise WorkInProgressError("2D convolution not yet implemented, re-activate if so")
n_points = 1000
obs1 = zfit.Space("obs1", limits=(-2, 4))
obs2 = zfit.Space("obs2", limits=(-6, 4))
obskernel = obs1 * obs2
param2 = zfit.Parameter('param2', 0.4)
gauss1 = zfit.pdf.Gauss(1., 0.5, obs=obs1)
gauss22 = zfit.pdf.CrystalBall(0.0, param2, -0.2, 3, obs=obs2)
obs1func = zfit.Space("obs1", limits=(4, 10))
obs2func = zfit.Space("obs2", limits=(-6, 4))
obs_func = obs1func * obs2func
gauss21 = zfit.pdf.Gauss(-0.5, param2, obs=obs2func)
func1 = zfit.pdf.Uniform(5, 8, obs=obs1func)
func2 = zfit.pdf.Uniform(6, 7, obs=obs1func)
func = zfit.pdf.SumPDF([func1, func2], 0.5)
func = func * gauss21
gauss = gauss1 * gauss22
conv = zfit.pdf.FFTConvPDFV1(func=func, kernel=gauss)
start = obs_func.rect_lower
stop = obs_func.rect_upper
x_tensor = tf.random.uniform((n_points, 2), start, stop)
x_tensor = tf.reshape(x_tensor, (-1, 2))
linspace = tf.linspace(start, stop, num=n_points)
linspace = tf.transpose(tf.meshgrid(*tf.unstack(linspace, axis=-1)))
linspace_func = tf.reshape(linspace, (-1, 2))
# linspace_full = tf.linspace((-8, -8), (12, 12), num=n_points)
# linspace_full = tf.transpose(tf.meshgrid(*tf.unstack(linspace_full, axis=-1)))
# linspace_full = tf.reshape(linspace_full, (-1, 2))
linspace_kernel = tf.linspace(obskernel.rect_lower,
obskernel.rect_upper, num=n_points)
linspace_kernel = tf.transpose(tf.meshgrid(*tf.unstack(linspace_kernel, axis=-1)))
linspace_kernel = tf.reshape(linspace_kernel, (-1, 2))
# linspace_kernel = obskernel.filter(linspace_full)
# linspace_func = obs_func.filter(linspace_full)
x = zfit.Data.from_tensor(obs=obs_func, tensor=x_tensor)
linspace_data = zfit.Data.from_tensor(obs=obs_func, tensor=linspace)
probs_rnd = conv.pdf(x=x)
probs = conv.pdf(x=linspace_data)
# Numpy doesn't support ndim convolution?
true_probs = true_conv_2d_np(func, gauss, obsfunc=obs_func,
xfunc=linspace_func, xkernel=linspace_kernel)
import matplotlib.pyplot as plt
# np.testing.assert_allclose(probs, true_probs, rtol=0.2, atol=0.1)
integral = conv.integrate(limits=obs_func)
assert pytest.approx(1, rel=1e-3) == integral.numpy()
probs_np = probs_rnd.numpy()
assert len(probs_np) == n_points
# probs_plot = np.reshape(probs_np, (-1, n_points))
# x_plot = linspace[0:, ]
# probs_plot_projx = np.sum(probs_plot, axis=0)
# plt.plot(x_plot, probs_np)
# probs_plot = np.reshape(probs_np, (n_points, n_points))
# plt.imshow(probs_plot)
# plt.show()
true_probsr = tf.reshape(true_probs, (n_points, n_points))
probsr = tf.reshape(probs, (n_points, n_points))
plt.figure()
plt.imshow(true_probsr, label='true probs')
plt.title('true probs')
plt.figure()
plt.imshow(probsr, label='zfit conv')
plt.title('zfit conv')
plt.show(block=False)
# test the sampling
conv_nosample = FFTConvPDFV1NoSampling(func=func, kernel=gauss)
npoints_sample = 10000
sample = conv.sample(npoints_sample)
sample_nosample = conv_nosample.sample(npoints_sample)
x, y = z.unstack_x(sample)
xns, yns = z.unstack_x(sample_nosample)
plt.figure()
plt.hist2d(x, y, bins=30)
plt.title('custom sampling, addition')
plt.figure()
plt.hist2d(xns, yns, bins=30)
plt.title('fallback sampling, accept-reject')
plt.figure()
plt.hist(x.numpy(), bins=50, label='custom', alpha=0.5)
plt.hist(xns.numpy(), bins=50, label='fallback', alpha=0.5)
plt.legend()
plt.title('x')
plt.show()
plt.figure()
plt.hist(y.numpy(), bins=50, label='custom', alpha=0.5)
plt.hist(yns.numpy(), bins=50, label='fallback', alpha=0.5)
plt.title('y')
plt.legend()
plt.show()
def __init__(self,
obs: ztyping.ObsTypeInput,
data: ztyping.ParamTypeInput,
bandwidth: ztyping.ParamTypeInput = None,
kernel=tfd.Normal,
support=None,
use_grid=False,
num_grid_points=1024,
binning_method='linear',
weights: Union[None, np.ndarray, tf.Tensor] = None,
name: str = "KernelDensityEstimation"):
r"""
Kernel Density Estimation is a non-parametric method to approximate the density of given points.
.. math::
f_h(x) = \frac{1}{nh} \sum_{i=1}^n K\Big(\frac{x-x_i}{h}\Big)
Args:
data: 1-D Tensor-like.
bandwidth: Bandwidth of the kernel. Valid options are {'silverman', 'scott', 'adaptiveV1'} or a numerical.
If a numerical is given, it as to be broadcastable to the batch and event shape of the distribution.
A scalar or a `zfit.Parameter` will simply broadcast to `data` for a 1-D distribution.
obs: Observables
weights: Weights of each `data`, can be None or Tensor-like with shape compatible with `data`
name: Name of the PDF
"""
if isinstance(data, ZfitData):
if data.weights is not None:
if weights is not None:
raise OverdefinedError(
"Cannot specify weights and use a `ZfitData` with weights."
)
else:
weights = data.weights
if data.n_obs > 1:
raise ShapeIncompatibleError(
f"KDE is 1 dimensional, but data {data} has {data.n_obs} observables."
)
data = z.unstack_x(data)
shape_data = tf.shape(data)
size = tf.cast(shape_data[0], ztypes.float)
components_distribution_generator = lambda loc, scale: tfd.Independent(
kernel(loc=loc, scale=scale))
self._num_grid_points = tf.minimum(
tf.cast(size, ztypes.int), tf.constant(num_grid_points,
ztypes.int))
self._binning_method = binning_method
self._data = tf.convert_to_tensor(data, ztypes.float)
self._bandwidth = tf.convert_to_tensor(bandwidth, ztypes.float)
self._kernel = kernel
self._weights = weights
self._grid = None
self._grid_data = None
if use_grid:
self._grid = binning_helper.generate_grid(
self._data, num_grid_points=self._num_grid_points)
self._grid_data = binning_helper.bin(self._binning_method,
self._data, self._grid,
self._weights)
mixture_distribution = tfd.Categorical(probs=self._grid_data)
components_distribution = components_distribution_generator(
loc=self._grid, scale=self._bandwidth)
else:
if weights is not None:
probs = weights / tf.reduce_sum(weights)
else:
probs = tf.broadcast_to(1 / size,
shape=(tf.cast(size, ztypes.int), ))
mixture_distribution = tfd.Categorical(probs=probs)
components_distribution = components_distribution_generator(
loc=self._data, scale=self._bandwidth)
dist_kwargs = lambda: dict(mixture_distribution=mixture_distribution,
components_distribution=
components_distribution)
distribution = tfd.MixtureSameFamily
params = {'bandwidth': self._bandwidth}
super().__init__(obs=obs,
params=params,
dist_params={},
dist_kwargs=dist_kwargs,
distribution=distribution,
name=name)
def func2_pure(self, x):
x = z.unstack_x(x)
return param2 * x + param3
def func1_pure(self, x):
x = z.unstack_x(x)
return param1 * x
def _func(self, x):
mu = self.params["mu"]
sigma = self.params["sigma"]
x = z.unstack_x(x)
return z.exp(-z.square((x - mu) / sigma))
def func3_2deps(x):
a, b = z.unstack_x(x)
return a**2 + b**2
def _unnormalized_pdf(self, x):
mu = self.params['mu']
sigma = self.params['sigma']
x = z.unstack_x(x)
return z.exp(-z.square((x - mu) / sigma))
def func1_5deps(x):
a, b, c, d, e = z.unstack_x(x)
return a + b * c**2 + d**2 * e**3
def _func(self, x):
mu = self.params['mu']
sigma = self.params['sigma']
x = z.unstack_x(x)
return z.exp(-z.square((x - mu) / sigma))
def _unnormalized_pdf(self, x): # implement function
data = z.unstack_x(x)
alpha = self.params['alpha']
return z.exp(alpha * data)
def _unnormalized_pdf(self, x):
params = [self.params[param] for param in self._PARAMS]
x = z.unstack_x(x)
args = {name: para for name, para in zip(self._PARAMS, params)}
return func(x, **args)