jonas-eschle on fix_binned_space
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fix: warning when non-extended … fix: warning when non-extended … Merge pull request #379 from zf… (compare)
jonas-eschle on fix_binned_space
fix: warning when non-extended … (compare)
jonas-eschle on fix_binned_space
fix: warning when non-extended … (compare)
jonas-eschle on set_saved_params
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We've released the 0.6 series of zfit! Major addition is a lot of new minimizers that all support uncertainty estimations the same way as used now.
They can now be invoked independent of zfit models at all and used with pure Python functions
The main changes (full changelog here
Added many new minimizers. A full list can be found in :ref:minimize_user_api
.
IpyoptV1
that wraps the powerful Ipopt large scale minimization libraryScipyLBFGSBV1
, or ScipySLSQPV1
NLoptLBFGSV1
, NLoptTruncNewtonV1
orNLoptMMAV1
but also includes more global minimizers such asNLoptMLSLV1
and NLoptESCHV1
.Completely new and overhauled minimizers design, including:
init
to minimize
Major overhaul of the FitResult
, including:
zfit_error
(equivalent of MINOS
)minuit_hesse
and minuit_minos
are now available with all minimizers as well thanks to an greatapprox
hesse that returns the approximate hessian (if available, otherwise empty)Hey @mayou36! :)
I wrote to you regarding GSoC 2021 (via aman.goel185@gmail.com) , and have a doubt regarding the same in the evaluation task.
Can I contact you over private chat?
We released multiple small releases up to 0.6.3 with a few minor improvements and bugfixes. Make sure to upgrade to the latest version using
pip install -U zfit
Thanks to the finders of the bugs. We appreciate any kind of (informal) feedback, ideas or bugs, feel free to reach out to us anytime with anything
Hi. I am intensively using zfit in notebooks, and I have been running into the well-known NameAlreadyTakenErrors. I have found workarounds that work for me, but I just wanted to say that it seems to me the example presented in here does not work. Like, if you try to use this you will get an error when minimising:
~/.local/lib/python3.6/site-packages/zfit/minimizers/minimizer_minuit.py in <listcomp>(.0)
76 errors = tuple(param.step_size for param in params)
77 start_values = [p.numpy() for p in params]
---> 78 limits = [(low.numpy(), up.numpy()) for low, up in limits]
79 errors = [err.numpy() for err in errors]
80
AttributeError: 'int' object has no attribute 'numpy'
I guess the problem is that the limits are supposed to be tf.Tensor
, but if we simply assign a float
or int
via param.lower
or param.upper
that breaks the code later?
Couldn't there be some sort of a method, such as set_limit_lower(value)
? Or am I misusing zfit somehow?
Hi, to overload the parameters in Jupyter, does the following way work?
iCell = get_ipython().execution_count #get the current cell number
par1 = zfit.Parameter("par1"+str(iCell), 8., 0., 20.)
par2 = zfit.Parameter("par2"+str(iCell), -20., -50., 50.)
par3 = zfit.Parameter("par3_"+str(iCell), 10., 0., 20.)
Hi again :) I was wondering if there is a way to have a parameter which has its upper limit depending on another parameter. As a naive illustration, imagine you are fitting a quadratic parabola ax**2+bx+c, and you want the peak of the parabola to be between 0 and 5. The would mean 0<-b/2a<5.
I naively tried something similar to this:
a = zfit.Parameter("a", 5, floating=True)
b = zfit.Parameter("b", 0, lower = -10*a, floating=True)
However, it seems that all this does is sets the upper limit to be at -10 * initial value of a. Is there a way to somehow change limit to the value as a is changing?
A new zfit version, 0.8x series is available, with bugfixes, improved numerical integration and different Kernel Density Estimations (also for large sample sizes).
The tutorials also improved in the style and have now their own site. They can be run interactively, or downloaded, or be viewed.
Hi experts.
I had a question regarding errors. I have made a quick example to highlight my question, hopefully it makes sense.
I have noticed that if I fit my pdf, and then refit that same pdf again and again in a loop, the error I get out is not the same but keeps changing. Also the errors calculated using different methods are not consistent, at least not always, even after the initial fit (e.g. 0th iteration).
I have to say I don't quite understand this. I have to say I am not an expert in fitting, so maybe this is expected, but I find it very weird.
I'll try to illustrate this with an example:
from zfit.pdf import Gauss, Exponential
obs = zfit.Space("x", limits=(-5, 5))
minimizer = zfit.minimize.Minuit(use_minuit_grad=True)
mu = zfit.Parameter("muu", 0, step_size=0.01)
sigma = zfit.Parameter("sigma", 1,step_size=0.01)
gauss = zfit.pdf.Gauss(mu=mu, sigma=sigma, obs=obs)
gauss_yield = zfit.Parameter("g_yield", 100, step_size=0.1)
gauss_ext = gauss.create_extended(gauss_yield)
lam = zfit.Parameter("lam", -1,step_size=0.01)
expo = zfit.pdf.Exponential(lam=lam, obs=obs)
expo_yield = zfit.Parameter("e_yield", 500, step_size=0.1)
expo_ext = expo.create_extended(expo_yield)
gauss_expo = zfit.pdf.SumPDF([expo_ext, gauss_ext])
random_gauss = np.random.normal(size=500)+1
random_exp = np.random.exponential(scale = 5, size=1000)-5
random_data = np.append(random_gauss, random_exp)
# then for each different error method I run this:
lam.set_value(-1)
mu.set_value(1)
sigma.set_value(1)
frac.set_value(0.5)
expo_yield.set_value(100)
gauss_yield.set_value(100)
data = zfit.Data.from_numpy(obs=obs, array=random_data)
nll = zfit.loss.UnbinnedNLL(model=gauss_expo, data=data)
iterations = np.arange(0,20)
yield_error = []
for it in iterations:
result = minimizer.minimize(nll)
result.errors() # here I also try result.hesse() with #method='hesse_np', 'approx', 'minuit_hesse'
yield_error.append(result.params[gauss_yield]['minuit_minos']['upper'])
Each of these iterations produces a different error, particularly when using minuit_hesse
and hesse_np
:
#minuit_minos upper (lower is almost the same with a negative sign)
array([118.3930117 , 118.13616212, 118.17518686, 118.1589878 ,
118.17934101, 118.17029047, 118.18437411, 118.17827166,
118.18786236, 118.1837249 , 118.19027599, 118.1874818 ,
118.19182178, 118.1899428 , 118.19290011, 118.19164637,
118.19360303, 118.19276492, 118.19412068, 118.19355127])
#minuit_hesse
array([264.56868112, 263.99099257, 264.03559365, 263.99005815,
264.0375983 , 264.01347448, 590.9170118 , 264.02200987,
590.84657398, 264.03923864, 592.87290404, 264.05010254,
589.78390794, 426.94055222, 588.76391966, 585.73316667,
591.81967819, 587.7401319 , 591.81833332, 592.84125751])
#hesse_np
array([205.6559601 , 207.41405669, nan, nan,
nan, 737.84227104, nan, 70.71791776,
314.54393221, nan, 110.97088789, 237.15737011,
nan, nan, nan, nan,
nan, 149.10569392, 300.7499339 , nan])
#approx
array([118.39301118, 118.1361616 , 118.17518634, 118.15898728,
118.17934049, 118.17028995, 118.18437359, 118.17827114,
118.18786184, 118.18372437, 118.19027547, 118.18748128,
118.19182126, 118.18994228, 118.19289958, 118.19164585,
118.19360251, 118.1927644 , 118.19412016, 118.19355074])
I understand that I am generally speaking not supposed to loop an already converged fit again, but what is puzzling me that even if I only look at the very first element in each of these lists they are not at all consistent. I noticed this in a more complicated fit that I am doing in an analysis and I am a bit puzzled. I prepared this simple mock example to make it easier to reproduce.
Is this expected? Am I doing something crazy here?
Sorry for the long question, and thanks a lot.
Hi, first of all, thanks a lot for bringing it up and making such a good reproducible examlpe. You are also welcome to opend an issue. The problem is that you create an unbinned likelihood (=> create an ExtendedUnbinnedNLL
instead, this works for me), so you are not constraining the sum of yields to be the (poisson distributied) number of events. There should be a warning displayed like
AdvancedFeatureWarning: Either you're using an advanced feature OR causing unwanted behavior. To turn this warning off, use `zfit.settings.advanced_warnings['extended_in_UnbinnedNLL']` = False` or 'all' (use with care) with `zfit.settings.advanced_warnings['all'] = False
Extended PDFs are given to a normal UnbinnedNLL. This won't take the yield into account and simply treat the PDFs as non-extended PDFs. To create an extended NLL, use the `ExtendedUnbinnedNLL`.
warn_advanced_feature("Extended PDFs are given to a normal UnbinnedNLL. This won't take the yield "
So the fit you are doing is equal to defining a sum of two pdfs using two free parameters: we end up with a degree of freedom too much. This is what causes the error to vary each time (at least I suspect it).
To explain the errors: 'minuit_minos' is the builtin minuit error (from iminuit, the minos method). minuit_hesse
is the hesse algorithm of iminuit. approx is the minimizers approximation of the hesse (and is maybe not available or completely off. It's just a "better than nothing", but often accurate enough for some usecases such as getting the order of magnitude). hesse_np
is zfits implementation of Hesse and the NaNs are probably pretty accurate: it can't determine the hession because it fails for the good reason that it's an underconstraint problem.
Just to mention, one method you didn't try is zfit_error
, zfits own implementation of "minos". In my test it gives a comparable error (42 vs 39 from minos) using the ExtendedUnbinnedNLL
UnbinnedNLL
seemed to be the culprit. I still need to investigate why the fit where I initially spotted this was misbehaving as there I was using the correct ExtendedUnbinnedNLL
. But your answer proposes some hints so I will try them. It also brings a bit more clarity about zift overall, thanks :)
strict=True
. If that doesn't work, feel also free to open an issue
Dear Experts,
I am trying to perform a fit to some data using a sum of two crystal balls. Here is a minimal version of the fit:
import zfit
from root_pandas import read_root
path = "data_path"
tree = "ntuple_tree"
dataframe = read_root(f"{path}/data.root", tree)
low_B_M = 5175.0
high_B_M = 5425.0
obs = zfit.Space("B0_M", limits=(low_B_M, high_B_M))
#B0 -> D* 3pi
mu = zfit.Parameter("mu", 5279., low_B_M, high_B_M)
sigma = zfit.Parameter("sigma", 10., 0., 50.)
alpha_L = zfit.Parameter("alpha_L", 1.43, 0., 5.)
n_L = zfit.Parameter("n_L", 1.96, 0., 100.)
alpha_R = zfit.Parameter("alpha_R", -1, -5., 0.)
n_R = zfit.Parameter("n_R", 1., 0., 100.)
frac = zfit.Parameter("frac", 0.5, 0., 1.)
n_sig = zfit.Parameter("n_sig", 0.75 * len(dataframe['B0_M']), 0., 1.1 * len(dataframe['B0_M']))
pdf_sig_L = zfit.pdf.CrystalBall(obs=obs, mu=mu, sigma=sigma, alpha=alpha_L, n=n_L)
pdf_sig_R = zfit.pdf.CrystalBall(obs=obs, mu=mu, sigma=sigma, alpha=alpha_R, n=n_R)
pdf_sig = zfit.pdf.SumPDF([pdf_sig_L, pdf_sig_R], frac).create_extended(n_sig)
# Combinatorial
lamda = zfit.Parameter("lamda", -1e-3, -1., -1e-10)
n_bkg = zfit.Parameter("n_bkg", 0.25 * len(dataframe['B0_M']), 0., 1.1 * len(dataframe['B0_M']))
pdf_bkg = zfit.pdf.Exponential(obs=obs, lam=lamda).create_extended(n_bkg)
pdf = zfit.pdf.SumPDF([pdf_sig, pdf_bkg])
data = zfit.Data.from_pandas(dataframe['B0_M'], obs=obs, weights=dataframe["n_sig_sw"])
nll = zfit.loss.ExtendedUnbinnedNLL(model=pdf, data=data)
minimizer = zfit.minimize.Minuit(tol=1e-5)
result = minimizer.minimize(nll)
print(result.info['original'])
param_hesse = result.hesse(method="minuit_hesse") # Computation of the errors
corr = result.correlation(method="minuit_hesse")
cov = result.covariance(method="minuit_hesse")
print("Fit function minimum:", result.fmin)
print("Fit converged:", result.converged)
print("Fit full minimizer information:", result.info)
params = result.params
print(params)
I am getting the following error message (I can post the full log if needed)
NotImplementedError: Cannot convert a symbolic Tensor (gradient_tape/gradient_tape/sub:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported
I am using the following versions of various packages:
tensorboard 2.5.0
tensorboard-data-server 0.6.0
tensorboard-plugin-wit 1.8.0
tensorflow 2.5.0
tensorflow-addons 0.14.0
tensorflow-estimator 2.5.0
tensorflow-probability 0.13.0
zfit 0.8.2
A colleague is able to run this code without error with the following versions:
tensorboard 2.6.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.0
tensorflow 2.5.0
tensorflow-addons 0.13.0
tensorflow-estimator 2.5.0
tensorflow-probability 0.13.0
zfit 0.8.2
Is there a known issue with the versions of the packages I am using or is there perhaps something else wrong?
!CALL FOR BINNED FIT TESTER!
After a long time of development, binned fits are finally in the alpha stage! We added the concept of binned data, PDFs and loss functions to zfit.
You can try out the unstable preview online.
An overview over the implemented binned models
While the implementation is not fully finalized and the concepts maybe also need some polishing, we're looking for feedback from the community.
An alpha version has been published and can be installed with
pip install -U zfit --pre
(the pre
flag specifies that also pre-versions will be installed)
What to expect:
There are rough edges and cornercases as well as not yet implemented methods. What we would like to hear is mostly feedback on the design. There is an zfit/zfit-development#80.
But any way of feedback (issue, Gitter, e-mail, Skype, Mattermost,... is appreciated)
clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/miniconda3/envs/zfit/include -arch x86_64 -I/opt/miniconda3/envs/zfit/include -fPIC -O2 -isystem /opt/miniconda3/envs/zfit/include -arch x86_64 -I/private/var/folders/zh/kd49jtns3237x60psw6kqy0r0000gn/T/pip-build-env-e3rpegq3/overlay/lib/python3.9/site-packages/numpy/core/include -I/opt/miniconda3/envs/zfit/include/python3.9 -c src/callback.c -o build/temp.macosx-10.9-x86_64-3.9/src/callback.o
In file included from src/callback.c:35:
src/callback.h:32:10: fatal error: 'IpStdCInterface.h' file not found
#include "IpStdCInterface.h"
^~~~~~~~~~~~~~~~~~~
1 error generated.
error: command '/usr/bin/clang' failed with exit code 1
----------------------------------------
ERROR: Failed building wheel for ipyopt
pip install zfit
as per instructions in a clean conda environment (bare Python 3.9 installed)
class HistPDF(zfit.pdf.BasePDF):
def __init__(self, data, gain, obs, name='HistPDF'):
histo = np.histogram(data/gain, bins=40, range=(0,20))
self.rv_hist = scipy.stats.rv_histogram(histo)
super().__init__(obs=obs, name=name)
def _normalized_pdf(self, x):
x = z.unstack_x(x)
probs = z.py_function(func=self.rv_hist.pdf, inp=[x], Tout=tf.float64)
probs.set_shape(x.shape)
return probs
I'm trying to include a zfit example in a Software Carpentries session, and I'm finding that zfit has a lot of difficult-to-satisfy dependencies. It makes my conda install
churn for a long time and then it wants to downgrade my OS kernels and CUDA installation. (Of course, I said, "No.")
I'll have to make an isolated environment for this, but I'm just saying, this goes against the "small, focused packages" philosophy—it's difficult to "mix" zfit in with the other packages (i.e. not have to make an isolated environment that downloads the world just to use zfit).
Quite likely, a lot of things in this list:
https://github.com/zfit/zfit/blob/develop/requirements.txt
could be optional/runtime dependencies, like this:
https://github.com/scikit-hep/uproot4/blob/main/src/uproot/extras.py
Like, for example, curve-fitting surely doesn't need Uproot: curve-fitting shouldn't care where the data come from, whether it's a ROOT file or something else. A user should install both zfit and Uproot and send data between them as NumPy arrays, Awkward Arrays, Boost/Hist histograms, etc. With zfit's explicit dependence on Uproot, I can't use it with my developer copy because it has a weird version number made by pip install -e .
, which doesn't satisfy zfit's uproot<5
constraint. But surely I should be able to mix a wide range of versions of what ought to be loosely coupled things like curve-fitting and file-reading.
Three of the dependencies are about putting colors in terminals.
mamba create --name zfitenv python ipython matplotlib xrootd jupyterlab zfit
?import zfit
in the zfitenv I get(zfitenv) C-LOSX2NHP3Y2:~ a05842ns$ python
Python 3.9.9 | packaged by conda-forge | (main, Dec 20 2021, 02:38:53)
[Clang 11.1.0 ] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import zfit
/Users/user/mambaforge/envs/zfit/lib/python3.9/site-packages/zfit/__init__.py:28: UserWarning: zfit has moved from TensorFlow 1.x to 2.x, which has some profound implications behind the scenes of zfit
and minor ones on the user side. Be sure to read the upgrade guide (can be found in the README at the top)
to have a seemless transition. If this is currently not doable (upgrading is highly recommended though)
you can downgrade zfit to <0.4. Feel free to contact us in case of problems in order to fix them ASAP.
warnings.warn(
/Users/user/mambaforge/envs/zfit/lib/python3.9/site-packages/zfit/util/execution.py:62: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`
warnings.warn("Not running on Linux. Determining available cpus for thread can fail"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/user/mambaforge/envs/zfit/lib/python3.9/site-packages/zfit/__init__.py", line 63, in <module>
from . import z
File "/Users/user/mambaforge/envs/zfit/lib/python3.9/site-packages/zfit/z/__init__.py", line 49, in <module>
from . import random, math
File "/Users/user/mambaforge/envs/zfit/lib/python3.9/site-packages/zfit/z/random.py", line 5, in <module>
import tensorflow_probability as tfp
File "/Users/user/mambaforge/envs/zfit/lib/python3.9/site-packages/tensorflow_probability/__init__.py", line 20, in <module>
from tensorflow_probability import substrates
File "/Users/user/mambaforge/envs/zfit/lib/python3.9/site-packages/tensorflow_probability/substrates/__init__.py", line 21, in <module>
from tensorflow_probability.python.internal import all_util
File "/Users/user/mambaforge/envs/zfit/lib/python3.9/site-packages/tensorflow_probability/python/__init__.py", line 142, in <module>
dir(globals()[pkg_name]) # Forces loading the package from its lazy loader.
File "/Users/user/mambaforge/envs/zfit/lib/python3.9/site-packages/tensorflow_probability/python/internal/lazy_loader.py", line 61, in __dir__
module = self._load()
File "/Users/user/mambaforge/envs/zfit/lib/python3.9/site-packages/tensorflow_probability/python/internal/lazy_loader.py", line 41, in _load
self._on_first_access()
File "/Users/user/mambaforge/envs/zfit/lib/python3.9/site-packages/tensorflow_probability/python/__init__.py", line 63, in _validate_tf_environment
raise ImportError(
ImportError: This version of TensorFlow Probability requires TensorFlow version >= 2.6; Detected an installation of version 2.4.3. Please upgrade TensorFlow to proceed.
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/zfit/__init__.py:37: UserWarning: TensorFlow warnings are by default suppressed by zfit. In order to show them, set the environment variable ZFIT_DISABLE_TF_WARNINGS=0. In order to suppress the TensorFlow warnings AND this warning, set ZFIT_DISABLE_TF_WARNINGS=1.
warnings.warn("TensorFlow warnings are by default suppressed by zfit."
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
RuntimeError: module compiled against API version 0xe but this version of numpy is 0xd
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
RuntimeError: module compiled against API version 0xe but this version of numpy is 0xd
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
ImportError: numpy.core._multiarray_umath failed to import
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
ImportError: numpy.core.umath failed to import
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_8153/2220551001.py in <module>
----> 1 import zfit
2 get_ipython().run_line_magic('pinfo', 'zfit.pdf.HistogramPDF')
3
4 print(zfit.__version__)
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/zfit/__init__.py in <module>
51
52
---> 53 _maybe_disable_warnings()
54
55 import tensorflow as tf
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/zfit/__init__.py in _maybe_disable_warnings()
46 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
47
---> 48 import tensorflow as tf
49
50 tf.get_logger().setLevel('ERROR')
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/__init__.py in <module>
39 import sys as _sys
40
---> 41 from tensorflow.python.tools import module_util as _module_util
42 from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
43
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/__init__.py in <module>
44
45 # Bring in subpackages.
---> 46 from tensorflow.python import data
47 from tensorflow.python import distribute
48 from tensorflow.python import keras
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/data/__init__.py in <module>
23
24 # pylint: disable=unused-import
---> 25 from tensorflow.python.data import experimental
26 from tensorflow.python.data.ops.dataset_ops import AUTOTUNE
27 from tensorflow.python.data.ops.dataset_ops import Dataset
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/data/experimental/__init__.py in <module>
97
98 # pylint: disable=unused-import
---> 99 from tensorflow.python.data.experimental import service
100 from tensorflow.python.data.experimental.ops.batching import dense_to_ragged_batch
101 from tensorflow.python.data.experimental.ops.batching import dense_to_sparse_batch
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/data/experimental/service/__init__.py in <module>
138 from __future__ import print_function
139
--> 140 from tensorflow.python.data.experimental.ops.data_service_ops import distribute
141 from tensorflow.python.data.experimental.ops.data_service_ops import from_dataset_id
142 from tensorflow.python.data.experimental.ops.data_service_ops import register_dataset
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/data/experimental/ops/data_service_ops.py in <module>
23
24 from tensorflow.python import tf2
---> 25 from tensorflow.python.data.experimental.ops import compression_ops
26 from tensorflow.python.data.experimental.ops.distribute_options import AutoShardPolicy
27 from tensorflow.python.data.experimental.ops.distribute_options import ExternalStatePolicy
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/data/experimental/ops/compression_ops.py in <module>
18 from __future__ import print_function
19
---> 20 from tensorflow.python.data.util import structure
21 from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
22
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/data/util/structure.py in <module>
24 import wrapt
25
---> 26 from tensorflow.python.data.util import nest
27 from tensorflow.python.framework import composite_tensor
28 from tensorflow.python.framework import ops
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/data/util/nest.py in <module>
38 import six as _six
39
---> 40 from tensorflow.python.framework import sparse_tensor as _sparse_tensor
41 from tensorflow.python.util import _pywrap_utils
42 from tensorflow.python.util import nest
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/framework/sparse_tensor.py in <module>
26 from tensorflow.python import tf2
27 from tensorflow.python.framework import composite_tensor
---> 28 from tensorflow.python.framework import constant_op
29 from tensorflow.python.framework import dtypes
30 from tensorflow.python.framework import ops
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/framework/constant_op.py in <module>
27 from tensorflow.core.framework import types_pb2
28 from tensorflow.python.eager import context
---> 29 from tensorflow.python.eager import execute
30 from tensorflow.python.framework import dtypes
31 from tensorflow.python.framework import op_callbacks
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/eager/execute.py in <module>
25 from tensorflow.python import pywrap_tfe
26 from tensorflow.python.eager import core
---> 27 from tensorflow.python.framework import dtypes
28 from tensorflow.python.framework import ops
29 from tensorflow.python.framework import tensor_shape
/disk/groups/atp/anaconda3_new/envs/yanina/lib/python3.9/site-packages/tensorflow/python/framework/dtypes.py in <module>
30 from tensorflow.python.util.tf_export import tf_export
31
---> 32 _np_bfloat16 = _pywrap_bfloat16.TF_bfloat16_type()
33
34
TypeError: Unable to convert function return value to a Python type! The signature was
() -> handle
ERROR in cling::CIFactory::createCI(): cannot extract standard library include paths!
Invoking:
LC_ALL=C x86_64-conda-linux-gnu-c++ -DNDEBUG -xc++ -E -v /dev/null 2>&1 | sed -n -e '/^.include/,${' -e '/^ \/.*++/p' -e '}'
Results was:
With exit code 0