Related
TypeError Traceback (most recent call last)
d:\website\SpeechProcessForMachineLearning-master\SpeechProcessForMachineLearning-master\speech_process.ipynb Cell 15' in <cell line: 1>()
-->1 plot_freq(signal, sample_rate)
d:\website\SpeechProcessForMachineLearning-master\SpeechProcessForMachineLearning-master\speech_process.ipynb Cell 10' in plot_freq(signal, sample_rate, fft_size)
2 def plot_freq(signal, sample_rate, fft_size=512):
3 xf = np.fft.rfft(signal, fft_size) / fft_size
----> 4 freq = np.linspace(0, sample_rate/2, fft_size/2 + 1)
5 xfp = 20 * np.log10(np.clip(np.abs(xf), 1e-20, 1e100))
6 plt.figure(figsize=(20, 5))
File <__array_function__ internals>:5, in linspace(*args, **kwargs)
File ~\AppData\Local\Programs\Python\Python39\lib\site-packages\numpy\core\function_base.py:120, in linspace(start, stop, num, endpoint, retstep, dtype, axis)
23 #array_function_dispatch(_linspace_dispatcher)
24 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None,
25 axis=0):
26 """
27 Return evenly spaced numbers over a specified interval.
28
(...)
118
119 """
--> 120 num = operator.index(num)
121 if num < 0:
122 raise ValueError("Number of samples, %s, must be non-negative." % num)
TypeError: 'float' object cannot be interpreted as an integer
What solution about this problem?
I am using a custom-defined metric in SKlearn's KNeighborsClassifier. Here's my code:
def chi_squared(x,y):
return np.divide(np.square(np.subtract(x,y)), np.sum(x,y))
Above function implementation of chi squared distance function. I have used NumPy functions because according to scikit-learn docs, metric function takes two one-dimensional numpy arrays.
I have passed the chi_squared function as an argument to KNeighborsClassifier().
knn = KNeighborsClassifier(algorithm='ball_tree', metric=chi_squared)
However, I keep getting following error:
TypeError Traceback (most recent call last)
<ipython-input-29-d2a365ebb538> in <module>
4
5 knn = KNeighborsClassifier(algorithm='ball_tree', metric=chi_squared)
----> 6 knn.fit(X_train, Y_train)
7 predictions = knn.predict(X_test)
8 print(accuracy_score(Y_test, predictions))
~/.local/lib/python3.8/site-packages/sklearn/neighbors/_classification.py in fit(self, X, y)
177 The fitted k-nearest neighbors classifier.
178 """
--> 179 return self._fit(X, y)
180
181 def predict(self, X):
~/.local/lib/python3.8/site-packages/sklearn/neighbors/_base.py in _fit(self, X, y)
497
498 if self._fit_method == 'ball_tree':
--> 499 self._tree = BallTree(X, self.leaf_size,
500 metric=self.effective_metric_,
501 **self.effective_metric_params_)
sklearn/neighbors/_binary_tree.pxi in sklearn.neighbors._ball_tree.BinaryTree.__init__()
sklearn/neighbors/_binary_tree.pxi in sklearn.neighbors._ball_tree.BinaryTree._recursive_build()
sklearn/neighbors/_ball_tree.pyx in sklearn.neighbors._ball_tree.init_node()
sklearn/neighbors/_binary_tree.pxi in sklearn.neighbors._ball_tree.BinaryTree.rdist()
sklearn/neighbors/_dist_metrics.pyx in sklearn.neighbors._dist_metrics.DistanceMetric.rdist()
sklearn/neighbors/_dist_metrics.pyx in sklearn.neighbors._dist_metrics.PyFuncDistance.dist()
sklearn/neighbors/_dist_metrics.pyx in sklearn.neighbors._dist_metrics.PyFuncDistance._dist()
<ipython-input-29-d2a365ebb538> in chi_squared(x, y)
1 def chi_squared(x,y):
----> 2 return np.divide(np.square(np.subtract(x,y)), np.sum(x,y))
3
4
5 knn = KNeighborsClassifier(algorithm='ball_tree', metric=chi_squared)
<__array_function__ internals> in sum(*args, **kwargs)
~/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py in sum(a, axis, dtype, out, keepdims, initial, where)
2239 return res
2240
-> 2241 return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,
2242 initial=initial, where=where)
2243
~/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
85 return reduction(axis=axis, out=out, **passkwargs)
86
---> 87 return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
88
89
TypeError: only integer scalar arrays can be converted to a scalar index
I can reproduce your error message with:
In [173]: x=np.arange(3); y=np.array([2,3,4])
In [174]: np.sum(x,y)
Traceback (most recent call last):
File "<ipython-input-174-1a1a267ebd82>", line 1, in <module>
np.sum(x,y)
File "<__array_function__ internals>", line 5, in sum
File "/usr/local/lib/python3.8/dist-packages/numpy/core/fromnumeric.py", line 2247, in sum
return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,
File "/usr/local/lib/python3.8/dist-packages/numpy/core/fromnumeric.py", line 87, in _wrapreduction
return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
TypeError: only integer scalar arrays can be converted to a scalar index
Correct use(s) of np.sum:
In [175]: np.sum(x)
Out[175]: 3
In [177]: np.sum(np.arange(6).reshape(2,3), axis=0)
Out[177]: array([3, 5, 7])
In [178]: np.sum(np.arange(6).reshape(2,3), 0)
Out[178]: array([3, 5, 7])
(re)read the np.sum docs if necessary!
Using np.add instead of np.sum:
In [179]: np.add(x,y)
Out[179]: array([2, 4, 6])
In [180]: x+y
Out[180]: array([2, 4, 6])
The following should be equivalent:
np.divide(np.square(np.subtract(x,y)), np.add(x,y))
(x-y)**2/(x+y)
Following is the function I want to implement in python. I am getting Type Errors when defining a function. I tried defining using numpy.piecewise function object and also using just elif commands as a definition. I want to be able to then evaluate this function at different points as well as expressions like f(X-1) etc
This is my code:
from numpy import piecewise
from scipy import *
from sympy.abc import x
from sympy.utilities.lambdify import lambdify, implemented_function
from sympy import Function
from sympy import *
h = 0.5
a = -1
n = 2
x = Symbol('x')
expr = piecewise((0, x-a <= -2*h), ((1/6)*(2*h+(x-a))**3, -2*h<=x-a<=-h), (2*h**3/3-0.5*(x-a)**2*(2*h+(x-a)), -h<= x-a<= 0), (2*(h**3/3)-0.5*(x-a)**2*(2*h+(x-a)), 0<=x-a<=2*h), ((1/6)*(2*h-(x-a))**3, h<=x-a<=2*h), (0, x-a<=2*h))
p = lambdify((x, a,b,h), expr)
def basis(x,a,b, h):
if x <= a-2*h:
return 0;
elif (x<=a-h) or (x >=2*h):
return (1/6)*(2*h+(x-a))**3
elif (x-a<= 0) or (x-a >= -h):
return (2*h**3/3-0.5*(x-a)**2*(2*h+(x-a)));
elif (x<=2*h+a) or (x >= 0):
return (2*(h**3/3)-0.5*(x-a)**2*(2*h+(x-a)));
elif (x<=a+2*h) or (x >= h):
return (1/6)*(2*h-(x-a))**3;
elif x-a<=2*h:
return 0
basis(x, -1,0.5,0)
Both ways I get this :
raise TypeError("cannot determine truth value of Relational")
TypeError: cannot determine truth value of Relational
You can use sympy's lambdify function to generate the numpy piecewise function. This is a simpler example but shows the general idea:
In [15]: from sympy import symbols, Piecewise
In [16]: x, a = symbols('x, a')
In [17]: expr = Piecewise((x, x>a), (0, True))
In [18]: expr
Out[18]:
⎧x for a < x
⎨
⎩0 otherwise
In [19]: from sympy import lambdify
In [20]: fun = lambdify((x, a), expr)
In [21]: fun([1, 3], [4, 2])
Out[21]: array([0., 3.])
In [22]: import inspect
In [23]: print(inspect.getsource(fun))
def _lambdifygenerated(x, a):
return (select([less(a, x),True], [x,0], default=nan))
Sorry about the length of this answer, but I think you need to see the full debugging process. I had to look at the tracebacks and test small pieces of your code to identify the exact problem. I've seen a lot of the numpy ambiguity error, but not this sympy relational error.
===
Lets look at the whole traceback, not just one line of it. At the very least we need to identify which line of your code is producing the problem.
In [4]: expr = np.piecewise((0, x-a <= -2*h), ((1/6)*(2*h+(x-a))**3, -2*h<=x-a<
...: =-h), (2*h**3/3-0.5*(x-a)**2*(2*h+(x-a)), -h<= x-a<= 0), (2*(h**3/3)-0.5
...: *(x-a)**2*(2*h+(x-a)), 0<=x-a<=2*h), ((1/6)*(2*h-(x-a))**3, h<=x-a<=2*h)
...: , (0, x-a<=2*h))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-893bb4b36321> in <module>
----> 1 expr = np.piecewise((0, x-a <= -2*h), ((1/6)*(2*h+(x-a))**3, -2*h<=x-a<=-h), (2*h**3/3-0.5*(x-a)**2*(2*h+(x-a)), -h<= x-a<= 0), (2*(h**3/3)-0.5*(x-a)**2*(2*h+(x-a)), 0<=x-a<=2*h), ((1/6)*(2*h-(x-a))**3, h<=x-a<=2*h), (0, x-a<=2*h))
/usr/local/lib/python3.8/dist-packages/sympy/core/relational.py in __nonzero__(self)
382
383 def __nonzero__(self):
--> 384 raise TypeError("cannot determine truth value of Relational")
385
386 __bool__ = __nonzero__
TypeError: cannot determine truth value of Relational
While np.piecewise is a numpy function, because x is a sympy.Symbol, the equations are sympy expressions. numpy and sympy are not well integrated. Somethings work, many others don't.
Did you try a small expression? Good programming practice is to start with small pieces, making sure those work first.
Let's try something smaller:
In [8]: expr = np.piecewise((0, x-a <= -2*h),
...: ((1/6)*(2*h+(x-a))**3, -2*h<=x-a<=-h))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-37ff62e49efb> in <module>
1 expr = np.piecewise((0, x-a <= -2*h),
----> 2 ((1/6)*(2*h+(x-a))**3, -2*h<=x-a<=-h))
/usr/local/lib/python3.8/dist-packages/sympy/core/relational.py in __nonzero__(self)
382
383 def __nonzero__(self):
--> 384 raise TypeError("cannot determine truth value of Relational")
385
386 __bool__ = __nonzero__
TypeError: cannot determine truth value of Relational
and smaller pieces:
In [10]: (0, x-a <= -2*h)
Out[10]: (0, x + 1 ≤ -1.0)
In [11]: ((1/6)*(2*h+(x-a))**3, -2*h<=x-a<=-h)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-11-7bd9f95d077d> in <module>
----> 1 ((1/6)*(2*h+(x-a))**3, -2*h<=x-a<=-h)
/usr/local/lib/python3.8/dist-packages/sympy/core/relational.py in __nonzero__(self)
382
383 def __nonzero__(self):
--> 384 raise TypeError("cannot determine truth value of Relational")
385
386 __bool__ = __nonzero__
TypeError: cannot determine truth value of Relational
In [12]: (1/6)*(2*h+(x-a))**3
Out[12]:
3
1.33333333333333⋅(0.5⋅x + 1)
But:
In [13]: -2*h<=x-a<=-h
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-13-5ffb419cd443> in <module>
----> 1 -2*h<=x-a<=-h
/usr/local/lib/python3.8/dist-packages/sympy/core/relational.py in __nonzero__(self)
382
383 def __nonzero__(self):
--> 384 raise TypeError("cannot determine truth value of Relational")
385
386 __bool__ = __nonzero__
TypeError: cannot determine truth value of Relational
Simplify further:
In [14]: 0 < x < 3
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-14-59ba4ce00627> in <module>
----> 1 0 < x < 3
/usr/local/lib/python3.8/dist-packages/sympy/core/relational.py in __nonzero__(self)
382
383 def __nonzero__(self):
--> 384 raise TypeError("cannot determine truth value of Relational")
385
386 __bool__ = __nonzero__
TypeError: cannot determine truth value of Relational
While a < b < c is allowed for regular Python variables and scalars, it does not work for numpy arrays, and evidently doesn't work for sympy variables either.
So the immediate problem has nothing to do with numpy. You are using invalid sympy expressions!
===
Your basis function reveals an aspect of the same problem. Again we need to look at the FULL traceback, and then test portions to identify the exact problem expression.
In [16]: basis(x, -1,0.5,0)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-16-b328f95b3c79> in <module>
----> 1 basis(x, -1,0.5,0)
<ipython-input-15-c6436540e3f3> in basis(x, a, b, h)
1 def basis(x,a,b, h):
----> 2 if x <= a-2*h:
3 return 0;
4 elif (x<=a-h) or (x >=2*h):
5 return (1/6)*(2*h+(x-a))**3
/usr/local/lib/python3.8/dist-packages/sympy/core/relational.py in __nonzero__(self)
382
383 def __nonzero__(self):
--> 384 raise TypeError("cannot determine truth value of Relational")
385
386 __bool__ = __nonzero__
TypeError: cannot determine truth value of Relational
This expression is a sympy relational:
In [17]: x <= -1
Out[17]: x ≤ -1
But we can't use such a relational in a Python if statement.
In [18]: if x <= -1: pass
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-b56148a48367> in <module>
----> 1 if x <= -1: pass
/usr/local/lib/python3.8/dist-packages/sympy/core/relational.py in __nonzero__(self)
382
383 def __nonzero__(self):
--> 384 raise TypeError("cannot determine truth value of Relational")
385
386 __bool__ = __nonzero__
TypeError: cannot determine truth value of Relational
Python if is simple True/False switch; its argument must evaluate to one or the other. The error is telling us that a sympy.Relational does not work. 0 < x < 1 is variation on that basic Python if (it tests 0<x and x<1 and performs a and).
A variation on this that we often see in numpy (and pandas) is:
In [20]: 0 < np.array([0,1,2])
Out[20]: array([False, True, True])
In [21]: 0 < np.array([0,1,2])<1
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-21-bc1039cec1fc> in <module>
----> 1 0 < np.array([0,1,2])<1
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
The numpy expression has multiple True/False values, and can't be used im a Python expression that requires a simple True/False.
edit
Correctly expanding the two sided tests:
In [23]: expr = np.piecewise((0, x-a <= -2*h),
...: ((1/6)*(2*h+(x-a))**3, (-2*h<=x-a)&(x-a<=-h)),
...: (2*h**3/3-0.5*(x-a)**2*(2*h+(x-a)), (-h<= x-a)&(x-a<= 0)),
...: (2*(h**3/3)-0.5*(x-a)**2*(2*h+(x-a)), (0<=x-a)&(x-a<=2*h)),
...: ((1/6)*(2*h-(x-a))**3, (h<=x-a)&(x-a<=2*h)), (0, x-a<=2*h))
In [24]: expr
Out[24]:
array([-0.5*(x + 1)**2*(x + 2.0) + 0.0833333333333333,
-0.5*(x + 1)**2*(x + 2.0) + 0.0833333333333333], dtype=object)
In [26]: p = lambdify((x,), expr)
x is the only sympy symbol in expr.
Looking at the resulting function:
In [27]: print(p.__doc__)
Created with lambdify. Signature:
func(x)
Expression:
[-0.5*(x + 1)**2*(x + 2.0) + 0.0833333333333333 -0.5*(x + 1)**2*(x + 2.0)...
Source code:
def _lambdifygenerated(x):
return ([-0.5*(x + 1)**2*(x + 2.0) + 0.0833333333333333, -0.5*(x + 1)**2*(x + 2.0) + 0.0833333333333333])
I'm trying to create quartile groups of a variable in a new variable. I'm getting an error message and I'm not sure why.
I wrote:
df.describe().popularity
count 10865.000000
mean 0.646446
std 1.000231
min 0.000065
25% 0.207575
50% 0.383831
75% 0.713857
max 32.985763
Name: popularity, dtype: float64
Then:
bin_edges = ['0.000065', '0.207575','0.383831','0.713857','32.985763']
bin_names = ['low','mod_low','medium','high']
df['popularity_levels']= pd.cut(df['popularity'], bin_edges, labels=bin_names)
df.head()
I'm getting the following error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-49-b6e8c834de1b> in <module>()
----> 1 df['popularity_levels']= pd.cut(df['popularity'], bin_edges, labels=bin_names)
2 df.head()
/opt/conda/lib/python3.6/site-packages/pandas/core/reshape/tile.py in cut(x, bins, right, labels, retbins, precision, include_lowest)
128 bins = np.asarray(bins)
129 bins = _convert_bin_to_numeric_type(bins, dtype)
--> 130 if (np.diff(bins) < 0).any():
131 raise ValueError('bins must increase monotonically.')
132
/opt/conda/lib/python3.6/site-packages/numpy/lib/function_base.py in diff(a, n, axis)
1766 return diff(a[slice1]-a[slice2], n-1, axis=axis)
1767 else:
-> 1768 return a[slice1]-a[slice2]
1769
1770
TypeError: ufunc 'subtract' did not contain a loop with signature matching types dtype('<U9') dtype('<U9') dtype('<U9')
What does the error mean? I think it may have to do with defining the data type of the new variable as a float... Is that right How can I fix it?
The bin_edges should be floats:
bin_edges = ['0.000065', '0.207575','0.383831','0.713857','32.985763']
# should instead be
bin_edges = [0.000065, 0.207575, 0.383831, 0.713857, 32.985763]
The error occurs since this list is converted to a numpy array:
In [11]: np.array(['0.000065', '0.207575','0.383831','0.713857','32.985763'])
Out[11]:
array(['0.000065', '0.207575', '0.383831', '0.713857', '32.985763'],
dtype='<U9')
(Here dtype='<U9' means 9 character unicode.)
In [12]: np.array(['0.000065', '0.207575','0.383831','0.713857','32.985763']) - 1
TypeError: ufunc 'subtract' did not contain a loop with signature matching types dtype('<U9') dtype('<U9') dtype('<U9')
Trying to store an array in numpy file however, while trying to extract it, and use it, getting an error message as trying to apply array to a sequence.
These are the two arrays, unsure which one is causing the issue.
X = [[1,2,3],[4,5,6],[7,8,9]]
y = [0,1,2,3,4,5,6....]
while trying to retrieve it and use it getting the values as:
X: array(list[1,2,3],list[4,5,6],list[7,8,9])
y = array([0,1,2,3,4,5...])
Here is the code:
vectors = np.array(X)
labels = np.array(y)
While retrieving working on t-sne
visualisations = TSNE(n_components=2).fit_transform(X,y)
I get the following error:
ValueError Traceback (most recent call last)
<ipython-input-11-244f99341167> in <module>()
----> 1 visualisations = TSNE(n_components=2).fit_transform(X,y)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\manifold\t_sne.py in fit_transform(self, X, y)
856 Embedding of the training data in low-dimensional space.
857 """
--> 858 embedding = self._fit(X)
859 self.embedding_ = embedding
860 return self.embedding_
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\manifold\t_sne.py in _fit(self, X, skip_num_points)
658 else:
659 X = check_array(X, accept_sparse=['csr', 'csc', 'coo'],
--> 660 dtype=[np.float32, np.float64])
661 if self.method == 'barnes_hut' and self.n_components > 3:
662 raise ValueError("'n_components' should be inferior to 4 for the "
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
431 force_all_finite)
432 else:
--> 433 array = np.array(array, dtype=dtype, order=order, copy=copy)
434
435 if ensure_2d:
ValueError: setting an array element with a sequence.
Assuming I understand you correctly you need to package the first group in a list; something like this:
import numpy as np
#X = [[1,2,3],[4,5,6],[7,8,9]]
#y = [0,1,2,3,4,5,6, 7, 8, 9]
X = np.array([[1,2,3],[4,5,6],[7,8,9]])
y = np.array([0,1,2,3,4,5, 6, 7, 8, 9])
array(list[1,2,3],list[4,5,6],list[7,8,9])
is a 1d object dtype array. To get that from
[[1,2,3],[4,5,6],[7,8,9]]
requires more than np.array([[1,2,3],[4,5,6],[7,8,9]]); either the list elements have to vary in size, or you have to initialize an object array and copy the list values to it.
In any case fit_transform cannot handle that kind of array. It expects a 2d numeric dtype. Notice the parameters to the check_array function.
If all the list elements of X are the same size, then
X = np.stack(X)
should turn it into a 2d numeric array.
I suspect X was that 1d object array type before saving. By itself save/load should not turn a 2d numeric array into an object one.