I am trying to get the tf-idf vector for a single document using Sklearn's TfidfVectorizer object. I create a vocabulary based on some training documents and use fit_transform to train the TfidfVectorizer. Then, I want to find the tf-idf vectors for any given testing document.
from sklearn.feature_extraction.text import TfidfVectorizer
self.vocabulary = "a list of words I want to look for in the documents".split()
self.vect = TfidfVectorizer(sublinear_tf=True, max_df=0.5, analyzer='word',
stop_words='english')
self.vect.fit_transform(self.vocabulary)
...
doc = "some string I want to get tf-idf vector for"
tfidf = self.vect.transform(doc)
The problem is that this returns a matrix with n rows where n is the size of my doc string. I want it to return just a single vector representing the tf-idf for the entire string. How can I make this see the string as a single document, rather than each character being a document? Also, I am very new to text mining so if I am doing something wrong conceptually, that would be great to know. Any help is appreciated.
If you want to compute tf-idf only for a given vocabulary, use vocabulary argument to TfidfVectorizer constructor,
vocabulary = "a list of words I want to look for in the documents".split()
vect = TfidfVectorizer(sublinear_tf=True, max_df=0.5, analyzer='word',
stop_words='english', vocabulary=vocabulary)
Then, to fit, i.e. calculate counts, with a given corpus, i.e. an iterable of documents, use fit:
vect.fit(corpus)
Method fit_transform is a shortening for
vect.fit(corpus)
corpus_tf_idf = vect.transform(corpus)
Last, transform method accepts a corpus, so for a single document, you should pass it as list, or it is treated as iterable of symbols, each symbol being a document.
doc_tfidf = vect.transform([doc])
Related
I want to use the TFIDFVectorizer (or CountVectorizer followed by TFIDFTransformer) to get a vector representation of my terms. That means, I want a vector for a term where the documents are the features. That's simply the transpose of a TF-IDF matrix created by the TFIDFVectorizer.
>>> vectorizer = TfidfVectorizer()
>>> model = vectorizer.fit_transform(corpus)
>>> model.transpose()
However, I have 800k documents which mean my term vectors are very sparse and very large (800k dimensions). The flag max_features in the CountVectorizer would do exactly what I'm looking for. I can specify a dimension and the CountVectorizer tries to fit all information into this dimension. Unfortunately, this option is for the document vectors rather than the terms in the vocabulary. Hence, it reduces the size of my vocabulary because the terms are the features.
Is there any way to do the opposite? Like, perform a transpose on the TFIDFVectorizer object before it starts cutting and normalizing everything? And if such an approach exists, how can I do that? Something like this:
>>> countVectorizer = CountVectorizer(input='filename', max_features=300, transpose=True)
I was looking for such an approach for a while now but every guide, code example, whatever is talking about the document TF-IDF vectors rather than the term vectors.
Thank you so much in advance!
I am not aware of any straight forward way to do this but let me propose a way how this could be achieved.
You are trying to represent each term in your corpus as a vector that uses the documents in your corpus as its component features. Because the number of documents (which are the features in your case) is very large, you would like to limit them in a way similar to what max_features does.
According to CountVectorizer user guide (same for the TfidfVectorizer):
max_features int, default=None
If not None, build a vocabulary that only consider the top
max_features ordered by term frequency across the corpus.
In a similar way, you want to keep the top documents ordered by their "frequency across the terms", as confusing as this may sound. This could be rephrased simplistically as "keep those documents that contain the most unique terms".
One way I can think of doing that is by using the inverse_transform performing the following steps:
vectorizer = TfidfVectorizer()
model = vectorizer.fit_transform(corpus)
# We use the inverse_transform which returns the
# terms per document with nonzero entries
inverse_model = vectorizer.inverse_transform(model)
# Each line in the inverse model corresponds to a document
# and contains a list of feature names (the terms).
# As we want to rank the documents we tranform the list
# of feature names to a number of features
# that each document is represented by.
inverse_model_count = list(map(lambda doc_vec: len(doc_vec), inverse_model))
# As we are going to sort the list, we need to keep track of the
# document id (its index in the corpus), so we create tuples with
# the list index of each item before we sort the list.
inverse_model_count_tuples = list(zip(range(len(inverse_model_count)),
inverse_model_count))
# Then we sort the list by the count of terms
# in each document (the second component)
max_features = 100
top_documents_tuples = sorted(inverse_model_count_tuples,
key=lambda item: item[1],
reverse=True)[:max_features]
# We are interested only in the document ids (the first tuple component)
top_documents, _ = zip(*top_documents_tuples)
# Having the top_documents ids we can slice the initial model
# to keep only the documents indicated by the top_documents list
reduced_model = model[top_documents]
Please note that this approach only takes into account the number of terms per document, no matter what is their count (CountVectorizer) or weight (TfidfVectorizer).
If the direction of this approach is acceptable for you then with some more code it could be possible to also take into account the count or weight of the terms.
I hope this helps!
here is my code
from sklearn.feature_extraction.text import TfidfVectorizer
corpus = [
"this is first document ","this is second document","this is third","which document is first", ]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
X.toarray()
now
this is what i want to do?
when i search document it should give me [ 1,2,4]documents(sentence)
when i search first document it should give me [1]documents(sentence)
when i search second it should give me [2]documents(sentence)
i want to do this with TfIdf (i can't do normal searching )
how can i do that?
First of all, you have to ask yourself the question: what does the TfidfVectorizer do? The answer is: it transforms your documents into vectors. How can you proceed further? One solution is to transform your query also into a vector by using the vectorizer. Then, you can compare the cosine similarity between the transformed query vector and each of the vectors of the documents in your database. The document with the highest cosine similarity to your query vector is the most relevant one (at least according to the Vector space model).
Here https://towardsdatascience.com/tf-idf-for-document-ranking-from-scratch-in-python-on-real-world-dataset-796d339a4089 is an example implementation.
I am in doubt as to whether I use my TD-IDF calculations correctly. I have a large corpus of different documents, each document is stored in it's own row using pandas dataframe. I feed each row to TD-IDF in scikit-learn and store feature_names (words) in a list.
I am using following code:
term_tdidf = []
def tdidf_f(vec, matrix):
f_array = np.array(vec.get_feature_names())
t_sort = np.argsort(matrix.toarray()).flatten()[::-1]
n = 100
top_term = f_array[t_sort][:n]
term_tdidf.append(set(top_term))
for row in df.document:
x = TfidfVectorizer(stop_words='english')
tfidf_matrix = x.fit_transform(row)
terms = x.get_feature_names()
tdidf_f(x, tfidf_matrix)
After that I create new dataframe where each set of tdidf from each document is stored in a separate column.
Is that correct use of TD-IDF? I am running it only on single document, so terms I am getting are only calculated within this one document, correct? As I understand td-idf should be used across all documents to find one set of frequent terms, not multiple sets. Are there any consequences of such application?
My manual review of extracted features from each document indicates that terms I am getting are fitting. Afterwards I am using those terms to calculate similarity between documents and it seems to be correct.
To compute the IDF part of the weighting, you need to count the number of times the term occurs in the whole corpus, so your code is incorrect.
Here is a minimal example of how to use it:
from sklearn.feature_extraction.text import TfidfVectorizer
corpus = ["My first document", "My second document"]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
X is then a matrix where each row is a document and each column represents a term.
I am trying to cluster documents by keywords. I'm using the following code to make a tdidf-matrix:
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf_vectorizer = TfidfVectorizer(max_df=.8, max_features=1000,
min_df=0.07, stop_words='english',
use_idf=True, tokenizer=tokenize_and_stem,
ngram_range=(1,2))
tfidf_matrix = tfidf_vectorizer.fit_transform(documents)
print(tfidf_matrix.shape)
returns (567, 209), meaning there are 567 documents, each of which has some mixture of the 209 feature words detected by the scikit-learn TdidfVectorizer.
Now, I used terms = tfidf_vectorizer.get_feature_names() to get a list of the terms. Running print(len(terms)) gives 209
Many of these words are unnecessary for the task, and they add noise to the clustering. I have went through the list by hand and extracted the meaningful feature names, resulting in a new terms list. Now, running print(len(terms)) gives 67
However, running tfidf_vectorizer.fit_transform(documents) still gives a shape of (567, 209), which means the fit_transform(documents) function is still using the noisy list of 209 terms rather than the hand-selected list of 67 terms.
How can I get the tfidf_vectorizer.fit_transform(documents) function to run using the list of 67 hand-selected terms? I'm thinking that perhaps this will require me to add at least one function to the Scikit-Learn package on my machine, correct?
Any help is greatly appreciated. Thanks!
There are two ways:
If you have identified a list of stopwords (you called them "unnecessary for the task"), just put them into the stop_words parameter of the TfidfVectorizer to ignore them in the creation of the bag of words.Note however that the predefined english stopwords won't be used any more if you set the stop_words parameter to your custom list. If you want to combine the predefined english list with your additional stopwords, just add the two lists:
from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS
stop_words = list(ENGLISH_STOP_WORDS) + ['your','additional', 'stopwords']
tfidf_vectorizer = TfidfVectorizer(stop_words=stop_words) # add your other params here
If you have a fixed vocabulary and only want these words to be counted (i.e. your terms list), just set the vocabulary parameter of TfidfVectorizer:
tfidf_vectorizer = TfidfVectorizer(vocabulary=terms) # add your other params here
I did not figure out how to solve the problem on the level I requested in the questions. However, I figured out a hacky solution that works for now.
I was able to use my hand-crafted set of terms by doing the following:
1) From terms = tfidf_vectorizer.get_feature_names(), print out terms.
2) Make a list called unwanted_terms and filling it by hand with unwanted terms from step 1.
3) Towards the top of my document, where I import stopwords:
stopwords = nltk.corpus.stopwords.words('english')
Add my list of unwanted terms to stopwords:
for item in not_needed_words_list:
stopwords.append(item)
I am attempting to remove words that occur once in my vocabulary to reduce my vocabulary size. I am using the sklearn TfidfVectorizer() and then the fit_transform function on my data frame.
tfidf = TfidfVectorizer()
tfs = tfidf.fit_transform(df['original_post'].values.astype('U'))
My first thought is the preprocessor field in the tfidf vectorizer or using the preprocessing package before machine learning.
Any tips or links to further implementation?
you are looking for min_df param (minimum frequency), from the documentation of scikit-learn TfidfVectorizer:
min_df : float in range [0.0, 1.0] or int, default=1
When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also
called cut-off in the literature. If float, the parameter represents a
proportion of documents, integer absolute counts. This parameter is
ignored if vocabulary is not None.
# remove words occuring less than 5 times
tfidf = TfidfVectorizer(min_df=5)
you can also remove common words:
# remove words occuring in more than half the documents
tfidf = TfidfVectorizer(max_df=0.5)
you can also remove stopwords like this:
tfidf = TfidfVectorizer(stop_words='english')
ShmulikA's answer will most likely work well but will remove words based on document frequency. Thus, if the specific word occurs 200 times in only 1 document, it will be removed. TF-IDF vectorizer does not provide exactly what you want. You would have to:
Fit the vectorizer to your corpus. Extract the complete vocabulary from the vectorizer
Take the words as keys in a new dictionary.
count every word occurrence:
for every document in corpus: for word in document: vocabulary[word] += 1
Now, find out if there are values = 1, drop these entries from the dictionary. Put the keys into a list and pass the list as parameter to the TF-IDF vectorizer.
It will need a lot of looping, maybe just use min_df, which works well in practice.