How to resolve the error- found input variables with inconsistent numbers of samples?
Fairly new to Python but building out my first RF model based on some classification data. I've converted all of the labels into int64 numerical data and loaded them into X and Y as a numpy array, but I am hitting an error when I am trying to train the models.
Here is what my arrays look like:
>>> X = np.array([[df.tran_cityname, df.tran_signupos, df.tran_signupchannel, df.tran_vmake, df.tran_vmodel, df.tran_vyear]])
>>> Y = np.array(df['completed_trip_status'].values.tolist())
>>> X
array([[[ 1, 1, 2, 3, 1, 1, 1, 1, 1, 3, 1,
3, 1, 1, 1, 1, 2, 1, 3, 1, 3, 3,
2, 3, 3, 1, 1, 1, 1],
[ 0, 5, 5, 1, 1, 1, 2, 2, 0, 2, 2,
3, 1, 2, 5, 5, 2, 1, 2, 2, 2, 2,
2, 4, 3, 5, 1, 0, 1],
[ 2, 2, 1, 3, 3, 3, 2, 3, 3, 2, 3,
2, 3, 2, 2, 3, 2, 2, 1, 1, 2, 1,
2, 2, 1, 2, 3, 1, 1],
[ 0, 0, 0, 42, 17, 8, 42, 0, 0, 0, 22,
0, 22, 0, 0, 42, 0, 0, 0, 0, 11, 0,
0, 0, 0, 0, 28, 17, 18],
[ 0, 0, 0, 70, 291, 88, 234, 0, 0, 0, 222,
0, 222, 0, 0, 234, 0, 0, 0, 0, 89, 0,
0, 0, 0, 0, 40, 291, 131],
[ 0, 0, 0, 2016, 2016, 2006, 2014, 0, 0, 0, 2015,
0, 2015, 0, 0, 2015, 0, 0, 0, 0, 2015, 0,
0, 0, 0, 0, 2016, 2016, 2010]]])
>>> Y
array(['NO', 'NO', 'NO', 'YES', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO', 'NO',
'NO', 'YES', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO', 'NO', 'NO', 'NO',
'NO', 'NO', 'NO', 'NO', 'NO', 'NO', 'NO'],
dtype='|S3')
>>> X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)
Traceback (most recent call last):
File "", line 1, in File "/Library/Python/2.7/site-packages/sklearn/cross_validation.py", line
2039, in train_test_split arrays = indexable(*arrays) File "/Library/Python/2.7/site-packages/sklearn/utils/validation.py", line 206, in indexable check_consistent_length(*result) File "/Library/Python/2.7/site-packages/sklearn/utils/validation.py", line 181, in check_consistent_length " samples: %r" % [int(l) for l in lengths])
ValueError: Found input variables with inconsistent numbers of samples: [1, 29]
To resolve the error- found input variables with inconsistent numbers of samples, you must understand that - You are running into that error because your X and Y don't have the same length (which is what train_test_split requires), i.e., X.shape[0] != Y.shape[0]. Given your current code:
>>> X.shape
(1, 6, 29)
>>> Y.shape
(29,)
To fix this error:Remove the extra list from inside of np.array() when defining X or remove the extra dimension afterwards with the following command: X = X.reshape(X.shape[1:]). Now, the shape of X will be (6, 29). Transpose X by running X = X.transpose() to get an equal number of samples in X and Y. Now, the shape of X will be (29, 6) and the shape of Y will be (29,).
Answer (1)
The error message "Found input variables with inconsistent numbers of samples" typically occurs when you're working with datasets that have mismatched dimensions. This could happen, for example, when you're trying to perform operations on two arrays or dataframes where the number of rows (samples) don't match.
Here are some steps you can take to resolve this error:
Check the dimensions of your data: Make sure that the arrays or dataframes you're working with have the same number of samples. You can use the shape attribute in Python or similar methods depending on the data structure you're using.
Inspect the data: Sometimes, the error might arise from missing values or incorrectly loaded data. Inspect your datasets to ensure they contain the expected information and that there are no missing values causing misalignment.
Merge or align datasets properly: If you're working with multiple datasets, ensure that they are properly aligned before performing any operations. You might need to merge datasets based on a common key or index.
Handle missing values: If your datasets contain missing values, decide on an appropriate strategy to handle them. You might choose to remove rows with missing values, impute them with a specific value, or use more sophisticated techniques like interpolation.
Check your code: Review your code to ensure that you're performing operations correctly and that there are no logical errors causing the mismatch in sample sizes.
Use debugging tools: If you're having trouble identifying the source of the error, consider using debugging tools or printing intermediate results to help diagnose the issue.
Consult documentation or seek help: If you're using a specific library or framework and encountering this error, check the documentation or seek help from the community to understand common causes and solutions.
By following these steps and carefully examining your data and code, you should be able to resolve the "inconsistent numbers of samples" error.