RuntimeWarning: invalid value encountered in double_scalars
The RuntimeWarning: invalid value encountered in double_scalars arises when a mathematical operation encounters invalid values, such as division by zero or computations with NaN (Not a Number) values. Likewise, if the calculation involves extremely large or small numbers that certain libraries cannot handle, they generate this error. In such cases, these numbers are converted to null or NaN, which leads to an error in the operation.
Understanding the RuntimeWarning
The occurrence of the RuntimeWarning: invalid value encountered in double_scalars error can be attributed to various factors. By examining two distinct examples, we can gain insights into the reasons behind this warning.
Example 1
#Importing numpy libraryimport numpy as np#This will print all types of warnings.np.seterr(all='print')array1 = [1, 2, 4, 7, 8]# array2 is an empty arrayarray2 = []meanArray1 = np.mean(array1)meanArray2 = np.mean(array2) # problem in finding mean of an empty list.print(meanArray1)print(meanArray2)
Lines 2–7: We imported the NumPy library and enabled the printing of all types of warnings. We have two arrays,
array1andarray2.array1contains elements, butarray2is an empty array.Lines 8–9: We calculated the mean of
array2usingnp.mean(). Sincearray2has no elements, the mean calculation encounters invalid values, resulting inNaN. This triggers theWarning: invalid value encountered in double_scalars.
The warning message is raised because the NumPy library attempts to perform a mathematical operation that involves dividing by the number of elements in the array. Since array2 has no elements, the division results in an invalid value (NaN), and the warning is displayed to indicate this issue.
To avoid this warning, ensure that the input arrays used in mathematical operations are valid and contain at least one element.
Example 2
#Importing numpy libraryimport numpy as np#This will print all types of warnings.np.seterr(all='print')array1 = np.array([[800, 850]])array2 = np.array([[799, 1000]])#perform complex mathematical operationnumerator = np.exp(-array1).sum()denominator = np.exp(-array2).sum()print("Numerator: ",numerator,"Denominator:",denominator)answer = numerator/denominatorprint("Answer: ",answer)
Line 10–11: The
np.exp(x)function computes. After the computation of np.exp()function, the denominator is significantly closer to zero.Line 13: The answer to the division problem will be extremely large. Python will be unable to handle this large value so it will throw an error.
Methods used to prevent warning
We can rely on the effectiveness of built-in functions and conditional statements, which can help reduce such warnings. These approaches provide reliable mechanisms to handle various scenarios and ensure accurate computation.
Built-in functions: The most convenient way to prevent this error is to use built-in functions which can handle very large or small numbers so that the operation doesn't throw an error. You can use SciPy, a powerful Python library specifically designed to handle complex mathematical operations and computations involving extreme values.
Use conditional statements: If you're performing calculations where invalid values are expected, you can use conditional statements to handle those cases. For example, you can use an
ifstatement to check if a value is zero before dividing by it, or using functions likenp.isfinite()to check if a value is finite before performing computations.Validity of data: Ensure that your input data is valid and doesn't contain any unexpected or error-prone values. Validate the data sources and consider adding input checks or data cleaning steps to ensure the integrity of the data.
Example 1: fixed code – conditional statements
#Importing numpy libraryimport numpy as np#This will print all types of warnings.np.seterr(all='print')array1 = [1, 2, 4, 7, 8]# array2 is an empty arrayarray2 = []# Check if both arrays are non-emptyif array1 and array2:# Compute and print the mean of array1mean_array1 = np.mean(array1)print("Mean of array1 is:", mean_array1)# Compute and print the mean of array2mean_array2 = np.mean(array2)print("Mean of array2 is:", mean_array2)else:print("Please enter valid array")
In this updated version, the code structure is slightly modified using a conditional if statement.
Line 9–17: It checks if both
array1andarray2are non-empty before computing their respective means. If either array is empty, it prints a message asking the user to enter a valid array.
The conditional statements help in error prevention by verifying the validity of the input before performing mathematical operations.
Example 2: fixed code – built-in functions
#Importing numpy and scipy libraries.import numpy as npfrom scipy.special import logsumexp#This will print all types of warnings.np.seterr(all='print')array1 = np.array([[800, 850]])array2 = np.array([[799, 1000]])#perform complex mathematical operationanswer = np.exp(logsumexp(-array1) - logsumexp(-array2))print("Answer",answer)
Line 3: This code segment imports the necessary libraries, NumPy, and specifically the
logsumexpfunction from thescipy.specialmodule. Thelogsumexp()function is used to calculate the logarithm of the sum of exponentiated values, which helps avoid numerical underflow or overflow issues.Line 11: In this line, the
logsumexp()function is applied to the negative values ofarray1andarray2. The log rules are applied to split the original fractionnumerator/denominatorintolog(numerator) - log(denominator).
With the help of logsumexp(), we can deal with complex mathematical expressions.
Summary
The RuntimeWarning: invalid value encountered in double_scalars error occurs when a mathematical operation encounters invalid or erroneous values. To prevent this type of warning, we can employ several techniques such as using conditional statements and built-in functions to check the validity of the data before performing mathematical operations.
Free Resources
