I could not understand how numpy.testing.assert_allclose method is calculating relative difference between two arrays. Is it calculating in percentage or without taking percentage? For example, If I have two arrays
import numpy as np
gfg1 = [1, 2, 3]
gfg2 = np.array([4, 8, 9])
np.testing.assert_allclose(gfg1, gfg2)
the following error occurs:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/anaconda3/lib/python3.7/site-packages/numpy/testing/_private/utils.py", line 1515, in assert_allclose
verbose=verbose, header=header, equal_nan=equal_nan)
File "/home/anaconda3/lib/python3.7/site-packages/numpy/testing/_private/utils.py", line 841, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=1e-07, atol=0
Mismatch: 100%
Max absolute difference: 6
Max relative difference: 0.75
Max absolute difference is understood but what about relative difference?
If you go to the source code of assert_allclose you will see that it calls assert_array_compare. And inside the assert_array_compare you can see that the maximum relative difference is calculated as max(error[nonzero] / abs(y[nonzero])) where the error is abs(x - y).
So, in your case, for x = np.array([1, 2, 3]) and y = np.array([4, 8, 9]), you get
max_rel_error == max(|1-4|/|4|, |2-8|/|8|, |3-9|/|9|) == 0.75
Related
I used broyden1 in Python resolver. The question was answered here, I need to use a bit larger numbers, but not newton_krylov. If I use numbers over 100, then it starts throwing errors.
The code is here:
import numpy as np
import scipy.optimize
from scipy.optimize import fsolve
from functools import partial
from itertools import repeat
small_data=[100,220,350,480]
def G(small_data, x):
return np.cos(x) +x[::-1] - small_data
G_partial = partial(G, small_data)
approximate=list(repeat(1,period))
y = scipy.optimize.broyden1(G_partial, approximate, f_tol=1e-14)
print(y)
The error is:
Warning (from warnings module):
File "C:\Python\Python38\lib\site-packages\scipy\optimize\nonlin.py", line 1004
d = v / vdot(df, v)
RuntimeWarning: invalid value encountered in true_divide
Traceback (most recent call last):
File "read_data.py", line 176, in <module>
y = scipy.optimize.broyden1(G_partial, approximate, f_tol=1e-14)
File "<string>", line 6, in broyden1
File "C:\Python\Python38\lib\site-
packages\scipy\optimize\nonlin.py", line 350, in nonlin_solve
raise NoConvergence(_array_like(x, x0))
scipy.optimize.nonlin.NoConvergence: [ 99.49247662 219.22593164 350.14354166 480.95722345]
I found that the best method is changing the equation in Boryden1 to :
y = scipy.optimize.broyden1(G_partial, approximate, f_tol=5000e-14)
instead of:
f_tot=1e-14
so larger values will be accepted with a good accuracy
I'm trying to take the gradient of a function in which I assign numpy array elements individually (assigning local forces to a global force vector in an FEA), but this appears to break Autograd -- if I use np.zeros for the global array I get ValueError: setting an array element with a sequence, while if I use np.empty I get NotImplementedError: VJP of empty_like wrt argnums (0,) not defined.
Example:
import autograd.numpy as np
from autograd import jacobian, grad
def test(input):
a = np.empty_like(input)
a[:] = input[:]
grad(test)(np.array([0.]))
Gives the error:
C:\Miniconda3\python.exe C:/Users/JoshuaF/Desktop/gripper/softDrone/bug_test.py
Traceback (most recent call last):
File "C:\Miniconda3\lib\site-packages\autograd\core.py", line 31, in __init__
vjpmaker = primitive_vjps[fun]
KeyError: <function primitive.<locals>.f_wrapped at 0x000001AB1D0AA8C8>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:/Users/JoshuaF/Desktop/gripper/softDrone/bug_test.py", line 8, in <module>
grad(test)(np.array([0.]))
File "C:\Miniconda3\lib\site-packages\autograd\wrap_util.py", line 20, in nary_f
return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
File "C:\Miniconda3\lib\site-packages\autograd\differential_operators.py", line 25, in grad
vjp, ans = _make_vjp(fun, x)
File "C:\Miniconda3\lib\site-packages\autograd\core.py", line 10, in make_vjp
end_value, end_node = trace(start_node, fun, x)
File "C:\Miniconda3\lib\site-packages\autograd\tracer.py", line 10, in trace
end_box = fun(start_box)
File "C:\Miniconda3\lib\site-packages\autograd\wrap_util.py", line 15, in unary_f
return fun(*subargs, **kwargs)
File "C:/Users/JoshuaF/Desktop/gripper/softDrone/bug_test.py", line 5, in test
a = np.empty_like(input)
File "C:\Miniconda3\lib\site-packages\autograd\tracer.py", line 45, in f_wrapped
node = node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents)
File "C:\Miniconda3\lib\site-packages\autograd\core.py", line 35, in __init__
.format(fun_name, parent_argnums))
NotImplementedError: VJP of empty_like wrt argnums (0,) not defined
Is there any way to use Autograd on a numpy array which is assembled element-wise?
Based on the tutorial https://github.com/HIPS/autograd/blob/master/docs/tutorial.md, it looks like array assignment is unfortunately not supported in autograd functions.
I have constructed two matrices. For one I calculate matrix exponential, but for the other one I can not. They are similarly constructed and have the same structure and dimension. I really don't know why one can work but the other can not. I put my code below.
import numpy as np
import math as math
from scipy.sparse import csc_matrix
from scipy.sparse.linalg import *
sigmax = [[0, 1], [1, 0]]
sigmay = [[0, -1j], [1j, 0]]
sigmaz = [[1, 0], [0, -1]]
sigmaxx = np.kron(sigmax,sigmax)
sigmayy = np.kron(sigmay,sigmay)
sigmazz = np.kron(sigmaz,sigmaz)
sigmaxxyy = np.mat(sigmaxx) + np.mat(sigmayy)
N = 6
Hxxyy = 0
for i in range (0,N-2+1):
Hxxyy = np.mat(Hxxyy) + np.mat(np.kron(np.kron(np.identity(2**i),2*np.mat(sigmaxxyy)),np.identity(2**(N-i-2)) ))
Hxxyy = np.mat(Hxxyy) + np.mat(np.kron(np.kron(2*np.mat(sigmax),np.identity(2**(N-2))),sigmax))+np.mat(np.kron(np.kron(2*np.mat(sigmay),np.identity(2**(N-2))),sigmay))
print(expm(Hxxyy))
Hhi = 0
for j in range (0,N-1+1):
Hhi = np.mat(Hhi) + np.mat(np.kron( np.kron(np.identity(2**j),3*np.mat(sigmaz)),np.identity(2**(N-1-j))) )
print(expm(Hhi))
The error message is:
Traceback (most recent call last):
File "new test.py", line 20, in <module>
print(expm(Hhi))
File "/Users/sherlock/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/sparse/linalg/matfuncs.py", line 582, in expm
return _expm(A, use_exact_onenorm='auto')
File "/Users/sherlock/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/sparse/linalg/matfuncs.py", line 637, in _expm
X = _fragment_2_1(X, h.A, s)
File "/Users/sherlock/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/sparse/linalg/matfuncs.py", line 755, in _fragment_2_1
X[k, k] = exp_diag[k]
ValueError: setting an array element with a sequence.
Your code works in Python3 (Python 3.4.5) but fails in Python2 (Python 2.7.12).
There were a few changes in scipy/sparse/linalg/matfuncs.py between these two versions that cleaned all code paths to support both dense and sparse matrices.
Since the dimensions are not very big, a quick fix would be to
replace
expm(Hhi)
with
expm(np.array(Hhi))
I am running Python 3, and when I attempt to run this code:
from sklearn.preprocessing import LabelEncoder
cv=train.dtypes.loc[train.dtypes=='object'].index
print (cv)
le=LabelEncoder()
for i in cv:
train[i]=le.fit_transform(train[i])
test[i]=le.fit_transform(test[i])
However, i get this error:
le=LabelEncoder()
for i in cv:
train[i]=le.fit_transform(train[i])
test[i]=le.fit_transform(test[i])
Traceback (most recent call last):
File "<ipython-input-5-8739984f61b2>", line 3, in <module>
train[i]=le.fit_transform(train[i])
File "C:\Users\myname\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py", line 127, in fit_transform
self.classes_, y = np.unique(y, return_inverse=True)
File "C:\Users\myname\Anaconda3\lib\site-packages\numpy\lib\arraysetops.py", line 195, in unique
perm = ar.argsort(kind='mergesort' if return_index else 'quicksort')
TypeError: unorderable types: str() > float()
Oddly enough, if I call the encoder on a specified column in my data, the output is successful. For instance:
le.fit_transform(test['Race'])
Results in:
le.fit_transform(test['Race'])
Out[7]: array([2, 4, 4, ..., 4, 1, 4], dtype=int64)
I've tried:
float(le.fit_transform(train[i]))
str(le.fit_transform(train[i]))
Both have not worked.
Could someone please provide help me out?
I'm trying to code a multiple linear regression. Here's the line of code where my program raises an error:
least = optimize.minimize(residsq(xmat, ylist, coeff), coeff, constraints = ({'type': 'eq', 'fun': sum(resid(xmat, ylist, coeff))}), method = 'BFGS') # Choose the coefficients that minimize the sum of the residuals squared subject to keeping the sum of the residuals equal to 0.
xmat is a list of vectors: [[3,5,2],[3,1,6],[7,2,3], [9,-2,0]]. ylist is a list of the same length as xmat: [5,2,7,7]. coeff is the coefficient list, initially [mean(ylist), 0, 0, 0] ([constant, b_0, b_1, b_2]). resid is the list of residuals for each point, and residsq is the N2 norm of the residuals (sqrt of sum of squares).
Traceback (most recent call last):
File "<pyshell#3>", line 1, in <module>
import linregtest
File "C:\Python33\lib\site-packages\linregtest.py", line 4, in <module>
out = linreg.multilinreg(xmat, ylist, True)
File "C:\Python33\lib\site-packages\linreg.py", line 120, in multilinreg
least = optimize.minimize(residsq(xmat, ylist, coeff), coeff, constraints = ({'type': 'eq', 'fun': sum(resid(xmat, ylist, coeff))}), method = 'BFGS') # Choose the coefficients that minimize the sum of the residuals squared subject to keeping the sum of the residuals equal to 0.
File "C:\Python33\lib\site-packages\scipy\optimize\_minimize.py", line 302, in minimize
RuntimeWarning)
File "C:\Python33\lib\idlelib\PyShell.py", line 60, in idle_showwarning
file.write(warnings.formatwarning(message, category, filename,
AttributeError: 'NoneType' object has no attribute 'write'
Where does file come from, and how do I suppress this error?
EDIT: Solve one problem, find another. Maybe you can help me determine where SciPy is calling a float?
Traceback (most recent call last):
File "<pyshell#0>", line 1, in <module>
import linregtest
File "C:\Python33\lib\site-packages\linregtest.py", line 4, in <module>
out = linreg.multilinreg(xmat, ylist, True)
File "C:\Python33\lib\site-packages\linreg.py", line 123, in multilinreg
least = optimize.minimize(residsq(xmat, ylist, coeff), coeff, constraints = ({'type': 'eq', 'fun': sumresid(xmat, ylist, coeff)}), method = 'SLSQP') # Choose the coefficients that minimize the sum of the residuals squared subject to keeping the sum of the residuals equal to 0.
File "C:\Python33\lib\site-packages\scipy\optimize\_minimize.py", line 364, in minimize
constraints, **options)
File "C:\Python33\lib\site-packages\scipy\optimize\slsqp.py", line 301, in _minimize_slsqp
meq = sum(map(len, [atleast_1d(c['fun'](x, *c['args'])) for c in cons['eq']]))
File "C:\Python33\lib\site-packages\scipy\optimize\slsqp.py", line 301, in <listcomp>
meq = sum(map(len, [atleast_1d(c['fun'](x, *c['args'])) for c in cons['eq']]))
TypeError: 'float' object is not callable
I just edited my python 3.2 IDLE, PyShell.py (fixing lines 59 and 62)
def idle_showwarning(message, category, filename, lineno,
file=None, line=None):
if file is None:
file = sys.stderr #warning_stream
try:
file.write(warnings.formatwarning(message, category, filename,
lineno, line=line))
use sys.stderr instead of the global warning_stream which uses sys.__stderr__. sys.__stderr__ is None in my case. I don't know why a global is used.
the call to warnings.formatwarning had an extra invalid file keyword.
Now, I get the warning printed, for example
>>> import numpy as np
>>> np.uint(1) - np.uint(2)
Warning (from warnings module):
File "C:\Programs\Python32\Lib\idlelib\idle.pyw", line 1
try:
RuntimeWarning: overflow encountered in ulong_scalars
>>> 4294967295
>>>
edit:
searching for python bug reports
http://bugs.python.org/issue12438 wrong file argument has been fixed
http://bugs.python.org/issue13582 problems with sys.__stderr__ is None is open