![]() However, I want to know why it is not working when I use the same strategy as the tutorial and how to get it to work in a similar fashion.Īny help would be much appreciated. When I replace the line clf.fit(train, labels) with clf.fit(vectors_train, labels), the error goes away. labels is an array, and the error specifically points to the first parameter being the problem, so is there a data type conversion I have to do? What array element is being set to a sequence, and where is this sequence? I'm also aware that train is a DataFrame object, and that the fit() function takes in two parameters, both of which must be array-like. I am confused by what the error actually means. Labels_train, uniques = pd.factorize(train, sort = True)Ĭlf.fit(train, labels) # Value error occurs here ![]() Train, test = dataframe=True], dataframe=False] ![]() TestingData =, 0.77],, 30],, 0.77],, 0.77]]ĭataframe_training = pd.DataFrame(trainingData)ĭataframe_testing = pd.DataFrame(testingData)įrames = ĭataframe.rename(index = str, columns = ) ValueError: setting an array element with a sequence.įrom sklearn.ensemble import RandomForestClassifierįrom trics import confusion_matrix When I call the fit() function, I get the following error: In my code, I have 1 feature (1 column in the data table), and each entry in a column is a numpy array. In the tutorial, there are 4 features (4 columns in the data table), and each entry in a column is a number. My code follows this tutorial line by line, but the only major difference is the structure of the data. ![]() I have been using this tutorial as a guide. If you have any queries then you can contact us for more help.As part of a project, I am trying to use the random forest classifier from Python's SKLearn library. The above is the solutions for both cases. Valueerror: Setting an Array Element with a Sequence error generally comes when you are creating a NumPy array using a different multi-dimensional array and different types of elements of the array. The other solution for this error is that you should define the type of the NumPy array of the object type. You should make sure that you should use elements of the same type. The solution for this case is also very simple. Print(numpy_array) Valueerror when creating an array with different types of elements For example, mixing string with int or float with int e.t.c. ValueError: setting an array element with a sequence. The other cause for getting Valueerror is you are using different datatype elements for the NumPy array. Just use the array of the same dimensions in a sequence. The solution for this error is very simple. Value error when creating a multi-dimensional array When you will run the code you will get the value error. One is a 2D array and the other is a 3D array. ![]() For example, if you will create a NumPy array of multi-dimension. The first case when you will get Valueerror: Setting an Array Element with a Sequence is creating an array with different dimensions or shapes. Cause 1: Mixing with different Array dimensions import cvxpy as cp import numpy as np Problem data. I tried to solve a convex problem with cvxpy as below. You will know how to solve this error in a simple way. ValueError: setting an array element with a sequence in CVXPY minimize function. In addition, you are mixing with different dimensions. The other case when you will get this error is when you are creating a multiple-dimensional NumPy array. For example, mixing int with float or int or float with string. In python Valueerror: Setting an Array Element with a Sequence means you are creating a NumPy array of different types of elements in it. What does setting an array element with a sequence mean in Python? In this tutorial, you will know all the causes that lead to this error and how to solve this error. And when you are creating multi-dimensional NumPy array then you will mostly get the Valueerror: Setting an Array Element with a Sequence error. In python, you must be familiar with the NumPy package. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |