Strange error from numpy via matplotlib when trying to get a histogram of a tiny toy dataset. I'm just not sure how to interpret the error, which makes it hard to see what to do next.
Didn't find much related, though this nltk question and this gdsCAD question are superficially similar.
I intend the debugging info at bottom to be more helpful than the driver code, but if I've missed something, please ask. This is reproducible as part of an existing test suite.
if n > 1:
return diff(a[slice1]-a[slice2], n-1, axis=axis)
else:
> return a[slice1]-a[slice2]
E TypeError: ufunc 'subtract' did not contain a loop with signature matching types dtype('<U1') dtype('<U1') dtype('<U1')
../py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py:1567: TypeError
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py(1567)diff()
-> return a[slice1]-a[slice2]
(Pdb) bt
[...]
py2.7.11-venv/lib/python2.7/site-packages/matplotlib/axes/_axes.py(5678)hist()
-> m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py(606)histogram()
-> if (np.diff(bins) < 0).any():
> py2.7.11-venv/lib/python2.7/site-packages/numpy/lib/function_base.py(1567)diff()
-> return a[slice1]-a[slice2]
(Pdb) p numpy.__version__
'1.11.0'
(Pdb) p matplotlib.__version__
'1.4.3'
(Pdb) a
a = [u'A' u'B' u'C' u'D' u'E']
n = 1
axis = -1
(Pdb) p slice1
(slice(1, None, None),)
(Pdb) p slice2
(slice(None, -1, None),)
(Pdb)
I got the same error, but in my case I am subtracting dict.key from dict.value. I have fixed this by subtracting dict.value for corresponding key from other dict.value.
cosine_sim = cosine_similarity(e_b-e_a, w-e_c)
here I got error because e_b, e_a and e_c are embedding vector for word a,b,c respectively. I didn't know that 'w' is string, when I sought out w is string then I fix this by following line:
cosine_sim = cosine_similarity(e_b-e_a, word_to_vec_map[w]-e_c)
Instead of subtracting dict.key, now I have subtracted corresponding value for key
I had a similar issue where an integer in a row of a DataFrame I was iterating over was of type numpy.int64. I got the
TypeError: ufunc 'subtract' did not contain a loop with signature matching types dtype('<U1') dtype('<U1') dtype('<U1')
error when trying to subtract a float from it.
The easiest fix for me was to convert the row using pd.to_numeric(row).
Why is it applying diff to an array of strings.
I get an error at the same point, though with a different message
In [23]: a=np.array([u'A' u'B' u'C' u'D' u'E'])
In [24]: np.diff(a)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-24-9d5a62fc3ff0> in <module>()
----> 1 np.diff(a)
C:\Users\paul\AppData\Local\Enthought\Canopy\User\lib\site-packages\numpy\lib\function_base.pyc in diff(a, n, axis)
1112 return diff(a[slice1]-a[slice2], n-1, axis=axis)
1113 else:
-> 1114 return a[slice1]-a[slice2]
1115
1116
TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'numpy.ndarray'
Is this a array the bins parameter? What does the docs say bins should be?
I am fairly new to this myself, but I had a similar error and found that it is due to a type casting issue. I was trying to concatenate rather than take the difference but I think the principle is the same here. I provided a similar answer on another question so I hope that is OK.
In essence you need to use a different data type cast, in my case I needed str not float, I suspect yours is the same so my suggested solution is. I am sorry I cannot test it before suggesting but I am unclear from your example what you were doing.
return diff(str(a[slice1])-str(a[slice2]), n-1, axis=axis)
Please see my example code below for the fix to my code, the change occurs on the third to last line. The code is to produce a basic random forest model.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
This leads to an error of;
Traceback (most recent call last):
File "min_example.py", line 40, in <module>
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
The solution is to make each variable a str() type on the third to last line then write to file. No other changes to then code have been made from the above.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(str(RFpreds[i])+",,"+str(yTest[i])+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
These examples are from a larger code so I hope the examples are clear enough.
I think #James is right. I got stuck by same error while working on Polyval(). And yeah solution is to use the same type of variabes. You can use typecast to cast all variables in the same type.
BELOW IS A EXAMPLE CODE
import numpy
P = numpy.array(input().split(), float)
x = float(input())
print(numpy.polyval(P,x))
here I used float as an output type. so even the user inputs the INT value (whole number). the final answer will be typecasted to float.
I ran into the same issue, but in my case it was just a Python list instead of a Numpy array used. Using two Numpy arrays solved the issue for me.
Related
I'm new to numpy, and I have an interesting observation on the broadcasting. When I'm adding a 3x5 array directly to a 3x1 array, and update the original 3x1 array with the result, there is no broadcasting issue.
import numpy as np
total = np.random.uniform(-1,1, size=(3))[:,np.newaxis]
print(f'init = \n {total}')
for i in range(3):
total = total + np.ones(shape=(3,5))
print(f'total_{i} = \n {total}')
However, if i'm using '+=' operator to increment the 3x1 array with the value of 3x5 array, there is a broadcasting issue. May I know which rule of numpy broadcasting did I violate in the latter case?
total = np.random.uniform(-1,1, size=(3))[:,np.newaxis]
print(f'init = \n {total}')
for i in range(3):
total += np.ones(shape=(3,5))
print(f'total_{i} = \n {total}')
Thank you!
hawkoli1987
according to add function overridden in numpy array,
def add(x1, x2, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__
"""
add(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])
Add arguments element-wise.
Parameters
----------
x1, x2 : array_like
The arrays to be added.
If ``x1.shape != x2.shape``, they must be broadcastable to a common
shape (which becomes the shape of the output).
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If provided, it must have
a shape that the inputs broadcast to. If not provided or None,
a freshly-allocated array is returned. A tuple (possible only as a
keyword argument) must have length equal to the number of outputs.
add function returns a freshly-allocated array when dimensions of arrays are different.
In python, a=a+b and a+=b aren't absolutly same. + calls __add__ function and += calls __iadd__.
a = np.array([1, 2])
b = np.array([3, 4])
first_id = id(a)
a = a + b
second_id = id(a)
assert first_id == second_id # False
a = np.array([1, 2])
b = np.array([3, 4])
first_id = id(a)
a += b
second_id = id(a)
assert first_id == second_id # True
+= function does not create new objects and updates the value to the same address.
numpy add function updates an existing instance when adding an array of the same dimensions, but returns a new object when adding arrays of different dimensions. So when use += functions, the two functions must have the same dimension because the results of the add function must be updated on the same object.
For example,
a = np.array()
total = np.random.uniform(-1,1, size=(3))[:,np.newaxis]
print(id(total))
for i in range(3):
total += np.ones(shape=(3,1))
print(id(total))
id(total) are all same because add function just updates the instance in same address because dimmension of two arrays are same.
In [29]: arr = np.zeros((1,3))
The actual error message is:
In [30]: arr += np.ones((2,3))
Traceback (most recent call last):
Input In [30] in <cell line: 1>
arr += np.ones((2,3))
ValueError: non-broadcastable output operand with shape (1,3) doesn't match the broadcast shape (2,3)
I read that as say that arr on the left is "non-broadcastable", where as arr+np.ones((2,3)) is the result of broadcasting. The wording may be awkward; it's probably produced in some deep compiled function where it makes more sense.
We get a variant on this when we try to assign an array to a slice of an array:
In [31]: temp = arr + np.ones((2,3))
In [32]: temp.shape
Out[32]: (2, 3)
In [33]: arr[:] = temp
Traceback (most recent call last):
Input In [33] in <cell line: 1>
arr[:] = temp
ValueError: could not broadcast input array from shape (2,3) into shape (1,3)
This is clearer, saying that the RHS (2,3) cannot be put into the LHS (1,3) slot.
Or trying to put the (2,3) into one "row" of arr:
In [35]: arr[0] = temp
Traceback (most recent call last):
Input In [35] in <cell line: 1>
arr[0] = temp
ValueError: could not broadcast input array from shape (2,3) into shape (3,)
arr[0] = arr works because it tries to put a (1,3) into a (3,) shape - that's a workable broadcasting combination.
arr[0] = arr.T tries to put a (3,1) into a (3,), and fails.
In my code, I am trying to extract data from csv file to use in the function, but it doesnt output anything, and gives no error. My code works because I tried it with just numpy array as inputs. not sure why it doesnt work with panda.
import numpy as np
import pandas as pd
import os
# change the current directory to the directory where the running script file is
os.chdir(os.path.dirname(os.path.abspath(__file__)))
# finding best fit line for y=mx+b by iteration
def gradient_descent(x,y):
m_iter = b_iter = 1 #starting point
iteration = 10000
n = len(x)
learning_rate = 0.05
last_mse = 10000
#take baby steps to reach global minima
for i in range(iteration):
y_predicted = m_iter*x + b_iter
#mse = 1/n*sum([value**2 for value in (y-y_predicted)]) # cost function to minimize
mse = 1/n*sum((y-y_predicted)**2) # cost function to minimize
if (last_mse - mse)/mse < 0.001:
break
# recall MSE formula is 1/n*sum((yi-y_predicted)^2), where y_predicted = m*x+b
# using partial deriv of MSE formula, d/dm and d/db
dm = -(2/n)*sum(x*(y-y_predicted))
db = -(2/n)*sum((y-y_predicted))
# use current predicted value to get the next value for prediction
# by using learning rate
m_iter = m_iter - learning_rate*dm
b_iter = b_iter - learning_rate*db
print('m is {}, b is {}, cost is {}, iteration {}'.format(m_iter,b_iter,mse,i))
last_mse = mse
#x = np.array([1,2,3,4,5])
#y = np.array([5,7,8,10,13])
#gradient_descent(x,y)
df = pd.read_csv('Linear_Data.csv')
x = df['Area']
y = df['Price']
gradient_descent(x,y)
My code works because I tried it with just numpy array as inputs. not sure why it doesnt work with panda.
Well no, your code also works with pandas dataframes:
df = pd.DataFrame({'Area': [1,2,3,4,5], 'Price': [5,7,8,10,13]})
x = df['Area']
y = df['Price']
gradient_descent(x,y)
Above will give you the same output as with numpy arrays.
Try to check what's in Linear_Data.csv and/or add some print statements in the gradient_descent function just to check your assumptions. I would suggest to first of all add a print statement before the condition with the break statement:
print(last_mse, mse)
if (last_mse - mse)/mse < 0.001:
break
For each gene, I want to perform McNemar's test and then evaluate if the p-value > 0.05. I want to calculate the number of genes that pass the test.
My code raised IndexError: invalid index to scalar variable.
import pandas as pd
from statsmodels.stats.contingency_tables import mcnemar
from statsmodels.stats.gof import chisquare_effectsize
from statsmodels.stats.power import GofChisquarePower
def generate_gene_df(gene, n):
df = pd.DataFrame.from_dict(
{"Gene" : gene,
"Cells": (f'Cell{x}' for x in range(1, n+1)),
"Control": np.random.choice([1,0], p=[0.1, 0.9], size=n),
"Experimental": np.random.choice([1,0], p=[0.1+0.1, 0.9-0.1], size=n)},
orient='columns'
)
df = df.set_index(["Gene","Cells"])
return df
table = pd.crosstab([df["Gene"], df["Cells"]],
[df["Control"], df["Experimental"]]).to_numpy()
# List of simulated genes
gene_df_list = [generate_gene_df(gene, n) for gene in gene_list]
df = pd.concat(gene_df_list)
df = df.reset_index()
alpha=0.05
p_adjusted=[]
pass_test = []
# McNemar's test
result = mcnemar(table, exact=True)
# Bonferroni correction
p_adjusted = multipletests(result.pvalue, alpha=alpha, method="bonferroni")
for index, value in np.ndenumerate(table):
if result.pvalue > alpha:
np.append(pass_test, result.pvalue[index])
num_passed = len(pass_test)
print("Number of genes that failed to reject H0 is: %i" %num_passed)
Traceback:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/tmp/ipykernel_593/1521754442.py in <module>
11 for index, value in np.ndenumerate(table):
12 if result.pvalue > alpha:
---> 13 np.append(pass_test, result.pvalue[index])
14
15
IndexError: invalid index to scalar variable.
I haven't looked at mcnemar, so don't know what it produces. But the error tells us/youthat result.pvalue is a scalar value, and not indexable.
if result.pvalue > alpha:
np.append(pass_test, result.pvalue[index])
I also can deduce that from the fact that the if line works. It would return an ambiguity error if result.pvalue was an array.
Besides rereading the mcnemar docs, I'd suggest rereading the np.append docs (assuming you even did that!).
Generally we discourage use of np.append in a loop. np.append is not a list append clone
I am trying to find the main 200 components of a datasets of 846 images (2048x2048x3 RGB) with sklearn.decomposition.IncrementalPCA.
Data are read by cv2 and reshaped into a 2d np array ([846,2048x2048x3] size, float16)
To ensure a smaller memory cost, I used partial_fit() and divide the original data into smaller chunks (batches) in both partial_fit() and transform() steps.
just like the way in this problem's solution:
Python PCA on Matrix too large to fit into memory
Now my code works well for relative smaller size computations, like computing 20 components for 200 images in the datasets. It outputs right outcomes.
However, the tasks demands me to compute 200 components, which leads to the limit that my batch's size should be larger or at least equal to 200. (according to sklearn's document and the information in the terminal when running the code)
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.IncrementalPCA.html
With such big chunk size,I can finish the IPCA model set, but always face MemoryError when doing partial_fit()
What's more, another problem is:
I need to use inverse_transform later, I am not sure if I can use chunk-style compute in this step or not. (In the code below I did not use it.)
What can I do to avoid this MemoryError? Or should I replace IncrementalPCA with some other method instead ? (these alternatives should have some method like inverse_transform())
The all memory I can access to is 131661572 kB(~127GB)
My code:
from sklearn.decomposition import PCA, IncrementalPCA
import numpy as np
import cv2
import os
folder_path = "./output_img"
input=[]
for i in range(1, 847):
if i%10 == 0: print("loading",i,"th image")
# if i == 60: continue #special case, should be skipped
image_path = folder_path+f"/{i}neutral.jpg"
img = cv2.imread(image_path)
input.append(img.reshape(-1))
print("Loaded all",i,"images")
# change into numpy matrix
all_image = np.stack(input,axis=0)
# trans to 0-1 format float64
all_image = (all_image.astype(np.float16))
### shape: #_of_imag x image_pixel_num (50331648 for img_normals case)
# print(all_image)
# print(all_image.shape)
# PCA, keeps 200 features
COM_NUM=200
pca=IncrementalPCA(n_components = COM_NUM)
print("finished IPCA model set")
saving_path = "./principle847"
element_num = all_image.shape[0] # how many elements(rows) we have in the dataset
chunk_size = 220 # how many elements we feed to IPCA at a time
for i in range(0, element_num//chunk_size):
pca.partial_fit(all_image[i*chunk_size : (i+1)*chunk_size])
print("finished PCA fit:",i*chunk_size,"to",(i+1)*chunk_size)
pca.partial_fit(all_image[(i+1)*chunk_size : element_num]) #tail
print("finished PCA fit:",(i+1)*chunk_size,"to",element_num)
for i in range(0, element_num//chunk_size):
if i==0:
result = pca.transform(all_image[i*chunk_size : (i+1)*chunk_size])
else:
tmp = pca.transform(all_image[i*chunk_size : (i+1)*chunk_size])
result = np.concatenate((result, tmp), axis=0)
print("finished PCA transform:",i*chunk_size,"to",(i+1)*chunk_size)
tmp = pca.transform(all_image[(i+1)*chunk_size : element_num]) #tail
result = np.concatenate((result, tmp), axis=0)
print("finished PCA transform:",(i+1)*chunk_size,"to",element_num)
result = pca.inverse_transform(result)
print("PCA mean:",pca.mean_)
mean_img = pca.mean_
mean_img = mean_img.reshape(2048,2048,3)
mean_img = mean_img.astype(np.uint8)
cv2.imwrite(os.path.join(saving_path,("mean.png")),mean_img)
result=result.reshape(-1,2048,2048,3)
# result shape: #_of_componets * 2048 * 2048 * 3
dst = result
# dst=result/np.linalg.norm(result,axis=(3),keepdims=True)
for j in range(0,COM_NUM):
reconImage = (dst)[j]
# reconImage = reconImage.reshape(4096,4096,3)
reconImage = np.clip(reconImage,0,255)
reconImage = reconImage.astype(np.uint8)
cv2.imwrite(os.path.join(saving_path,("p"+str(j)+".png")),reconImage)
print("Saved",j+1,"principle imgs")
The error goes like:
File "model_generate.py", line 36, in <module>
pca.partial_fit(all_image[i*chunk_size : (i+1)*chunk_size])
File "/root/anaconda3/envs/PCA/lib/python3.8/site-packages/sklearn/decomposition/_incremental_pca.py", line 299, in partial_fit
U, V = svd_flip(U, V, u_based_decision=False)
File "/root/anaconda3/envs/PCA/lib/python3.8/site-packages/sklearn/utils/extmath.py", line 538, in svd_flip
max_abs_rows = np.argmax(np.abs(v), axis=1)
File "/root/anaconda3/envs/PCA/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 1103, in argmax
return _wrapfunc(a, 'argmax', axis=axis, out=out)
File "/root/anaconda3/envs/PCA/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 56, in _wrapfunc
return getattr(obj, method)(*args, **kwds)
MemoryError
The numpy polynomial fit function for masked arrays, ma.polyfit, crashes on integer iput:
import numpy.ma as ma
x = ma.arange(2)
y = ma.arange(2)
p1 = ma.polyfit(np.float32(x), y, deg=1)
p2 = ma.polyfit( x , y, deg=1)
The last line results in an error:
ValueError: data type <type 'numpy.int64'> not inexact
Why can't I fit data with integer x-values (it's no problem with the normal numpy.polyfit function), is this a (known) bug?
It is indeed a bug from numpy.ma : the rcond (a parameter to exclude some values ) takes len(x)*np.finfo(x.dtypes).eps as a default value, and np.int32 does not have any epsfield (because an int does not have a relative precision).
import numpy.ma as ma
eps = np.finfo(np.float32).eps
x = ma.arange(2)
y = ma.arange(2)
p1 = ma.polyfit(np.float32(x), y, deg=1, rcond = len(x)*eps)
p2 = ma.polyfit( x , y, deg=1, rcond = len(x)*eps)
I've looked quickly into numpy's issues, and this bug does not seems to figured there. It might be a good idea to raise a new issue : New Issue