Here is the sample data I have:

Tag 1(Val: X), Tag 2(Val: Y), Tag 3(Val: Z), Label (Val: P)

Tag 1(Val: A), Tag 2(Val: B), Tag 3(Val: C), Label (Val: Q)

Tag 1(Val: D), Tag 2(Val: E), Tag 3(Val: F), Label (Val: R)

Tag 1(Val: G), Tag 2(Val: H), Tag 3(Val: I), Label (Val: S)

I started by putting the Tags into a dataframe df and the Label into a separate dataframe df_label. Then used a HashingVectorizer to prepare the text for processing by ML models (I want to hash the strings into a unique numerical value so that the ML Models can train on it)

vectorizer = HashingVectorizer()

X_train = vectorizer.transform(df)

y_train = vectorizer.transform(df_label)

clf = RandomForestClassifier(n_jobs=2, random_state=0)

clf.fit(X_train, y_train)

When I execute this, I get: ValueError: Unknown label type: 'unknown' on y_train.

I am new to both Python and ML and I am not sure whether the problem is with my basic logic or whether it is a trivial implementation issue. Appreciate your insight and support.


I was recently checking some things out. Thought would leave a working code here, in-case its helpful.

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction import FeatureHasher
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
import numpy as np

categories = [
newsgroups_train = fetch_20newsgroups(subset='train', shuffle=True, 
categories=categories, random_state=91)
newsgroups_test = fetch_20newsgroups(subset='test', shuffle=True, 
categories=categories, random_state=91)

vectorizer = FeatureHasher(input_type='string')
X_train = vectorizer.fit_transform(newsgroups_train.data)
X_test = vectorizer.fit_transform(newsgroups_test.data)

Y_train = newsgroups_train.target
Y_test = newsgroups_test.target

rf = RandomForestClassifier(n_jobs=-1, n_estimators=100)
rf.fit(X_train, Y_train)
pred = rf.predict(X_test)

score = metrics.accuracy_score(Y_test, pred)
print("accuracy: {:.3f}".format(score))

Although it is very difficult to understand your data sample i will try to correct you from what i have understood from your question.

Whenever You use any vectorizer make sure you first apply fit on your corpus/data and then transform it.

In your case you have apply transform() without applying fit on your X_train.

Important note : You don't have to perform HashingVectorizer on your Label . correct your code:

   vectorizer = HashingVectorizer()
   X_train = vectorizer.fit_transform(df)
   clf = RandomForestClassifier(n_jobs=2, random_state=0)
   clf.fit(X_train, df_label)

I would suggest to use TfidfVectorizer() instead if HashingVectorizer() but before that do some research on this.

Always refer sklearn documentation so it will help you

Hope it helps!


From ValueError: Unknown label type: 'unknown' on y_train I guess you have some unsupported/invalid data type in your y_train or the type is indeed valid but does not match with the RandomForestClassifier expectation.

Please do the followings:

  1. print/plot the y_train and make sure all values make sense. There is a fair chance that HashingVectorizer could not hash some special cases (eg foreign language characters or missing values).
  2. use type or dtype to find out the v_train data type. Make sure it is consistent with RandomForestClassifier expectation.

p.s. as @outlier mentioned, we usually do not transform our labels (y_train in your case). Many classifiers can handle strings/letters as class labels. In case they cannot, we can use a simple mapping such as P -> 0, Q -> 1, etc. The following does the job for you:

from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
encoded_y = encoder.transform(y)

You're performing a transform on your Label dataset. You dont have to do that. the vectorizer is only for the text dataset. Because youre performing a transform on your labels, it will return a matrix sparse. The fit method of your ML algorithm just asks for an Array shaped labels. Read the documentation on scikit-learn.

I hope you understand what you did wrong.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.