Menu
I am learning machine learning using Titanic dataset from Kaggle. I am using LabelEncoder of sklearn to transform text data to numeric labels. The following code works fine for "Sex" but not for "Embarked".
encoder = preprocessing.LabelEncoder()
features["Sex"] = encoder.fit_transform(features["Sex"])
features["Embarked"] = encoder.fit_transform(features["Embarked"])
This is the error I got
Traceback (most recent call last):
File "../src/script.py", line 20, in <module>
features["Embarked"] = encoder.fit_transform(features["Embarked"])
File "/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/label.py", line 131, in fit_transform
self.classes_, y = np.unique(y, return_inverse=True)
File "/opt/conda/lib/python3.6/site-packages/numpy/lib/arraysetops.py", line 211, in unique
perm = ar.argsort(kind='mergesort' if return_index else 'quicksort')
TypeError: '>' not supported between instances of 'str' and 'float'
944
LabelEncoder can be used to normalize labels. It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. Fit label encoder. Fit label encoder and return encoded labels.
Label Encoder: Sklearn provides a very efficient tool for encoding the levels of categorical features into numeric values. LabelEncoder encode labels with a value between 0 and n_classes-1 where n is the number of distinct labels. If a label repeats it assigns the same value to as assigned earlier.
LabelEncoder should be used to encode target values, i.e. y, and not the input X. Ordinal encoding should be used for ordinal variables (where order matters, like cold , warm , hot ); vs Label encoding should be used for non-ordinal (aka nominal) variables (where order doesn't matter, like blonde , brunette )
To reverse the process of LabelEncoder , it has a function provided specifically for the task called inverse_transform.
I solved it myself. The problem was that the particular feature had NaN values. Replacing it with a numerical value it will still throw an error since it is of different datatypes. So I replaced it with a character value
features["Embarked"] = encoder.fit_transform(features["Embarked"].fillna('0'))
Try this function, you’ll need to pass a Pandas Dataframe. It will look at the type of your column and encode. So you won’t need to even bother checking the types yourself.
def encoder(data):
'''Map the categorical variables to numbers to work with scikit learn'''
for col in data.columns:
if data.dtypes[col] == "object":
le = preprocessing.LabelEncoder()
le.fit(data[col])
data[col] = le.transform(data[col])
return data
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
