I am trying to run below code on Jupyter Notebook: Typeerror: ‘numpy.float64’ Object Is Not Callable – f1 Score While Printing.
lr = LogisticRegression(class_weight = ‘balanced’)
lr.fit(X_train, y_train)
y_pred = lr.predict(X_train)
acc_log = round(lr.score(X_train, y_train) * 100, 2)
prec_log = round(precision_score(y_train, y_pred) * 100, 2)
recall_log = round(recall_score(y_train, y_pred) * 100, 2)
f1_log = round(f1_score(y_train, y_pred) * 100, 2)
roc_auc_log = roc_auc_score(y_train, y_pred
When trying to execute this, I am getting the below error:
—————————————————————————
TypeError Traceback (most recent call last)
<ipython-input-46-bcb2d9729eb6> in <module>
6 prec_log = round(precision_score(y_train,y_pred) * 100,2)
7 recall_log = round(recall_score(y_train,y_pred) * 100,2)
—-> 8 f1_log = round(f1_score(y_train,y_pred) * 100,2)
9 roc_auc_log = roc_auc_score(y_train,y_pred)
TypeError: ‘numpy.float64’ object is not callable
How to Fix: Columns overlap but no suffix is specified How to Fix: if you use all values, you must pass an index.
One error you may encounter when using Python is:
TypeError: ‘numpy.float64’
object is not callable
Suppose we attempt to multiply two NumPy arrays without using a multiplication sign (*) as follows:
import numpy as np
#define arrays
x = np.array([1, 2, 3, 4, 5])
y = np.array([12, 14, 14, 19, 22])
#attempt to multiply two arrays together
combo = (x)(y)
#view result
print(combo)
TypeError: ‘numpy.float64’
object is not callable
The way to avoid this error is to make sure we used the multiplication sign:
import numpy as np
#define arrays
x = np.array([1, 2, 3, 4, 5])
y = np.array([12, 14, 14, 19, 22])
#multiply two arrays together
combo = (x) * (y)
#view result
print(combo)
[12 28 42 76 110]
Instead, we need to use np.min () as follows:
import numpy as np
#define array of data
data = np.array([3.3, 4.1, 4, 5.6, 8.1, 9.9, 9.7, 10.2])
#attempt to find minimum value of array
min_val = np.min(data)
#view minimum value
print(min_val)
3.3
There’s a line in your code that says f1_score and it’s written in a type called anycodings_python. Hence the anycodings_scikit-learn error message). So rename the second one to anycodings_scikit-learn and try again. If it works, then you have to delete this one and create a new one, because the old one won’t be available for download from the Play Store anymore.
I am trying to run below code on Jupyter anycodings_scikit-learn Notebook:
lr = LogisticRegression(class_weight = ‘balanced’)
lr.fit(X_train, y_train)
y_pred = lr.predict(X_train)
acc_log = round(lr.score(X_train, y_train) * 100, 2)
prec_log = round(precision_score(y_train, y_pred) * 100, 2)
recall_log = round(recall_score(y_train, y_pred) * 100, 2)
f1_log = round(f1_score(y_train, y_pred) * 100, 2)
roc_auc_log = roc_auc_score(y_train, y_pred
When trying to execute this, I am getting anycodings_scikit-learn the below error:
—————————————————————————
TypeError Traceback (most recent call last)
<ipython-input-46-bcb2d9729eb6> in <module>
6 prec_log = round(precision_score(y_train,y_pred) * 100,2)
7 recall_log = round(recall_score(y_train,y_pred) * 100,2)
—-> 8 f1_log = round(f1_score(y_train,y_pred) * 100,2)
9 roc_auc_log = roc_auc_score(y_train,y_pred)
TypeError: ‘numpy.float64’ object is not callable
Last Updated : 28 Nov, 2021,GATE CS 2021 Syllabus
Output:
TypeError: ‘numpy.ndarray’
object is not callable
The min and max are calculated from two random distributions. For some reason, the min and max of data_men work, while line 12 gives me the error code “TypeError: ‘numpy.float64’ object is not callable” I’ve researched.
But previous errors seem to be when people missed an implicit multiplication sign
The second line will try to call the original function instead of using the numpy dtypes, which pythons own calculation methods can’t handle.
min = min(data_men)
…
w_min = min(data_women)
There is an error on the plt.axhline line. I have tried to run my code with the answer and the error continues. It seems like the library method is being redefined or reassigned by mistake.
I was able to run your code and it was accepted, you can reload module by that point. Are you willing to refresh your screen?
My Code:
import pandas as pd
import matplotlib.pyplot as plt
wnba = pd.read_csv(‘wnba.csv’)
sample_means = []
population_mean = wnba[‘PTS’].mean()
for i in range(100):
sample = wnba[‘PTS’].sample(10, random_state = i)
sample_means.append(sample.mean())
plt.scatter(range(1, 101), sample_means)
plt.axhline(population_mean)
What actually happened:
TypeError: ‘numpy.float64’
object is not callable
It seems you have redefined or reassign library method by mistake. You can reload module again by.
import matplotlib.pyplot as plt
from importlib
import reload
reload(plt)
The error happens because we don’t have the multiplication operator between the two terms. The Python interpreter thinks this is a call to (a + b) with parameters.
The code we are looking at is the revised one. The float object is not callable if you try to call it as a function. If you use mathematical operations, make sure they have all of the operators in place.
There needs to be a multiplication operator between the values. You should name your float objects after their purpose in the program.
The function is called if the Python interpreter executes the code inside it. Functions are the only ones that we can call in Python.
We can call functions by specifying the name of the function we want to use followed by a set of parentheses, for example. An example of a working function that returns a string is presented.
# Declare
function
def simple_function():
print(“Hello World!”)
# Call
function
simple_function()
# Declare function
def simple_function():
print(“Hello World!”)
# Call function
simple_function()
Hello World!
If we try to call a floating-point number, we will raise the TypeError:
number = 5.6
number()
a = 3.0
b = 4.0
lhs = a ** 2 + b ** 2
rhs = (a + b)(a + b) – 2 * a * b
print(lhs == rhs)
Let’s run the code to see what happens:
— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — –
TypeError Traceback(most recent call last)
2 b = 4.0
3 lhs = a ** 2 + b ** 2
— –≻4 rhs = (a + b)(a + b) – 2 * a * b
5 print(lhs == rhs)
TypeError: ‘float’
object is not callable
We need to put a multiplication operator between the two (a + b) terms to solve the error. Let’s look at the revised code:
a = 3.0
b = 4.0
lhs = a ** 2 + b ** 2
rhs = (a + b) * (a + b) – 2 * a * b
print(lhs == rhs)
True
float = 2.205
weight = float(input(“Enter weight in kilograms: “))
weight_in_lbs = weight * float
print(f ‘{weight} kg is equivalent to {round(weight_in_lbs, 1)} lbs
We can name our conversion variable something more meaningful to solve this error. Let’s call it “conversion”. Then we can call the float() method safely. Let’s look at the revised code:
conversion = 2.205
weight = float(input(“Enter weight in kilograms: “))
weight_in_lbs = weight * conversion
print(f ‘{weight} kg is equivalent to {round(weight_in_lbs,1)} lbs’)
Enter weight in kilograms: 85
85.0 kg is equivalent to 187.4 lbs
When you try to call numpy as a function, the numpy ndarray object is not callable. As you already know.
The best python module for array creation and manipulation is the Numpy module. It is not a callable error when we make silly mistakes in the code that leads to the ndarray object.
We have already seen that certain python object is only callable, therefore I came with this article on all the general mistakes done by the programmers.
Let’s create a simple ndarray with sample elements if we try to invoke a non callable object. The same is written in the code here.
array = np.array([
[1, 2, 3],
[5, 6, 7],
[7, 8, 9]
])()
array
Coders made a mistake while using the pandas read_csv method. I want to get some specific column values from the data that I have.
I will use that column name inside of the square brackets. The values are used instead of values by the coders. The error can be traced back to it. We are going to look at the below code.
data = pd.read_csv(“cars.csv”)
data.head()
mpg = data[“mpg”].values()
mpg
Lets create simple numpy ndarray with sample elements. Here is the code for the same
import numpy as np
sample_ndarray = np.array([1, 2, 3, 4, 5, 6, 7, 7])
last_ele = sample_ndarray(-1)
