Valueerror: X and Y Must Be the Same Size
Import Numpy as Np Import Pandas as Pd Import Matplotlib. Pyplot as Pt Data1 = Pd. Read_Csv('Stage1_Labels. Csv') X = Data1. Iloc[: ,: -1]. Values Y = Data1...
import numpy as np
import pandas as pd
import matplotlib.pyplot as pt
data1 = pd.read_csv('stage1_labels.csv')
X = data1.iloc[:, :-1].values
y = data1.iloc[:, 1].values
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
label_X = LabelEncoder()
X[:,0] = label_X.fit_transform(X[:,0])
encoder = OneHotEncoder(categorical_features = [0])
X = encoder.fit_transform(X).toarray()
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train,y_test = train_test_split(X, y, test_size = 0.4, random_state = 0)
#fitting Simple Regression to training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
#predecting the test set results
y_pred = regressor.predict(X_test)
#Visualization of the training set results
pt.scatter(X_train, y_train, color = 'red')
pt.plot(X_train, regressor.predict(X_train), color = 'green')
pt.title('salary vs yearExp (Training set)')
pt.xlabel('years of experience')
pt.ylabel('salary')
pt.show()
I need a help understanding the error in while executing the above code. Below is the error:
"raise ValueError("x and y must be the same size")"
I have .csv file with 1398 rows and 2 column. I have taken 40% as y_test set, as it is visible in the above code.
3 Answers
Print X_train shape. What do you see? I'd bet X_train is 2d (matrix with a single column), while y_train 1d (vector). In turn you get different sizes.
I think using X_train[:,0] for plotting (which is from where the error originates) should solve the problem
Slicing with [:, :-1] will give you a 2-dimensional array (including all rows and all columns excluding the last column).
Slicing with [:, 1] will give you a 1-dimensional array (including all rows from the second column). To make this array also 2-dimensional use [:, 1:2] or [:, 1].reshape(-1, 1) or [:, 1][:, None] instead of [:, 1]. This will make x and y comparable.
An alternative to making both arrays 2-dimensional is making them both one dimensional. For this one would do [:, 0] (instead of [:, :1]) for selecting the first column and [:, 1] for selecting the second column.
Try this:
x_train=np.arange(0,len(x_train),1)
It will make an evenly spaced array and your error will be gone permanently.
David Miller brings 15 years of experience in global economics, personal finance strategy, and market dynamics. He specializes in turning complex economic trends into actionable insights for everyday readers.
