CODEWITHSUNDEEP
scikit-learnknntf-idfoversamplingimblearn
Dbercules
Dbercules

Reputation: 719

SMOTE initialisation expects n_neighbors <= n_samples, but n_samples < n_neighbors

I have already pre-cleaned the data, and below shows the format of the top 4 rows:

     [IN] df.head()

    [OUT]   Year    cleaned
         0  1909    acquaint hous receiv follow letter clerk crown...
         1  1909    ask secretari state war whether issu statement...
         2  1909    i beg present petit sign upward motor car driv...
         3  1909    i desir ask secretari state war second lieuten...
         4  1909    ask secretari state war whether would introduc...

I have called train_test_split() as follows:

     [IN] X_train, X_test, y_train, y_test = train_test_split(df['cleaned'], df['Year'], random_state=2)
   [Note*] `X_train` and `y_train` are now Pandas.core.series.Series of shape (1785,) and `X_test` and `y_test` are also Pandas.core.series.Series of shape (595,)

I have then vectorized the X training and testing data using the following TfidfVectorizer and fit/transform procedures:

     [IN] v = TfidfVectorizer(decode_error='replace', encoding='utf-8', stop_words='english', ngram_range=(1, 1), sublinear_tf=True)
          X_train = v.fit_transform(X_train)
          X_test = v.transform(X_test)

I'm now at the stage where I would normally apply a classifier, etc (if this were a balanced set of data). However, I initialize imblearn's SMOTE() class (to perform over-sampling)...

     [IN] smote_pipeline = make_pipeline_imb(SMOTE(), classifier(random_state=42))
          smote_model = smote_pipeline.fit(X_train, y_train)
          smote_prediction = smote_model.predict(X_test)

... but this results in:

     [OUT] ValueError: "Expected n_neighbors <= n_samples, but n_samples = 5, n_neighbors = 6.

I've attempted to whittle down the number of n_neighbors but to no avail, any tips or advice would be much appreciated. Thanks for reading.

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EDIT:

Full Traceback

The dataset/dataframe (df) contains 2380 rows across two columns, as shown in df.head() above. X_train contains 1785 of these rows in the format of a list of strings (df['cleaned']) and y_train also contains 1785 rows in the format of strings (df['Year']).

Post-vectorization using TfidfVectorizer(): X_train and X_test are converted from pandas.core.series.Series of shape '(1785,)' and '(595,)' respectively, to scipy.sparse.csr.csr_matrix of shape '(1785, 126459)' and '(595, 126459)' respectively.

As for the number of classes: using Counter(), I've calculated that there are 199 classes (Years), each instance of a class is attached to one element of aforementioned df['cleaned'] data which contains a list of strings extracted from a textual corpus.

The objective of this process is to automatically determine/guess the year, decade or century (any degree of classification will do!) of input textual data based on vocabularly present.

Upvotes: 17

Views: 58142

Answers (5)

Shonubi Korede
Shonubi Korede

Reputation: 591

I was able to solve this issue following number 1 of this answer.

from collections import Counter

Count(df) # get the classes

# drop the classes with 1 as their value because it's lower than k_neighbors which has 2 as minimum value in my case

X_res, y_res = SMOTE(k_neighbors = 2).fit_resample(X, y)

Upvotes: 0

Hisan
Hisan

Reputation: 2655

WHY IT OCCURS:

In my case it was occurring because i had as few samples as 1 for some of the values/categories. Since SMOTE is based on KNN concept, it's not possible to apply SMOTE on 1 sampled values.

HOW I SOLVED IT:

Since those 1 sampled values/categories were equivalent to outliers, i removed them from the dataset and then applied SMOTE and it worked.

Also try decreasing the k_neighbors parameter to make it work

xr, yr = SMOTE(k_neighbors=3).fit_resample(x, y)

Upvotes: 4

KARMA
KARMA

Reputation: 1

I think that's possible to use the code:

sampler = SMOTE(ratio={1: 1927, 0: 300},random_state=0)

Upvotes: 0

Remi_TRish
Remi_TRish

Reputation: 213

Try to do the below code for SMOTE

oversampler=SMOTE(kind='regular',k_neighbors=2)

This worked for me.

Upvotes: 7

user9389968
user9389968

Reputation:

Since there are approximately 200 classes and 1800 samples in the training set, you have on average 9 samples per class. The reason for the error message is that a) probably the data are not perfectly balanced and there are classes with less than 6 samples and b) the number of neighbors is 6. A few solutions for your problem:

  1. Calculate the minimum number of samples (n_samples) among the 199 classes and select n_neighbors parameter of SMOTE class less or equal to n_samples.

  2. Exclude from oversampling the classes with n_samples < n_neighbors using the ratio parameter of SMOTE class.

  3. Use RandomOverSampler class which does not have a similar restriction.

  4. Combine 3 and 4 solutions: Create a pipeline that is using SMOTE and RandomOversampler in a way that satisfies the condition n_neighbors <= n_samples for smoted classes and uses random oversampling when the condition is not satisfied.

Upvotes: 26

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