bag-of-words | Python Implementation of Bag of Words for Image Recognition | Computer Vision library
kandi X-RAY | bag-of-words Summary
kandi X-RAY | bag-of-words Summary
Python Implementation of Bag of Words for Image Recognition using OpenCV and sklearn
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bag-of-words Key Features
bag-of-words Examples and Code Snippets
Lines of Code : 181License : Non-SPDX (Apache License 2.0)
Copydef shared_embedding_columns(categorical_columns,
dimension,
combiner='mean',
initializer=None,
shared_embedding_collection_name=None, def shared_embedding_columns_v2(categorical_columns,
dimension,
combiner='mean',
initializer=None,
shared_embedding_collec def linear_model(features,
feature_columns,
units=1,
sparse_combiner='sum',
weight_collections=None,
trainable=True,
cols_to_vars=None):
"""Return Community Discussions
Trending Discussions on bag-of-words
QUESTION
I have converted my corpus (2 million documents) into a Bag-of-Words sparse matrix using sklearn's CountVectorizer. The shape of the sparse matrix is around 2000000 x 170000 (ie: 170k words in the corpus vocabulary).
I'm inexperienced with working on sparse matrices but have managed to perform simple calculations on it like calculating the variance of each word in the whole corpus since it involves simple mean and square operations matrices.
The issue that I am having now is that I do not know how to efficiently calculate the column wise entropy of the sparse matrix. Currently, I'm looping through each column and providing the word occurance probabilities as a list to scipy.stats.entropy which is taking very long due to the size of the sparse matrix.
An example for clarity:
...ANSWER
Answered 2021-May-07 at 17:12Using the axis parameter, it's possible to calculate the column-wise entropy for a whole array:
QUESTION
I am using a Counter() for counting words in the excel file. My goal is to acquire the most frequent words from the document. The problem that Counter() does not work properly with my file. Here is the code:
...ANSWER
Answered 2021-Feb-21 at 18:10The problem is that the bow_simple value is a counter, which you further process. This means that all items will appear only once in the list, the end result is merely counting how many variations of the words appear in the counter when lowered and processed with nltk. The solution is to create a flattened wordlist and feed that into alpha_only:
QUESTION
I would like to store vector features, like Bag-of-Words or Word-Embedding vectors of a large number of texts, in a dataset, stored in a SQL Database. What're the data structures and the best practices to save and retrieve these features?
...ANSWER
Answered 2020-Sep-29 at 14:08This would depend on a number of factors, such as the precise SQL DB you intend to use and how you store this embedding. For instance, PostgreSQL allows to store query and retrieve JSON variables ( https://www.postgresqltutorial.com/postgresql-json/ ) ; Other options as SQLite would allow to store string representations of JSONs or pickle objects - that would be OK for storing, but would make querying the elements inside the vector impossible.
QUESTION
I am new to sklearn. I want my code to group data with k-means clustering based on a text column and some additional categorical variables. CountVectorizer transforms the text to a bag-of-words and OneHotEncoder transforms the categorical variables to sets of dummies.
...ANSWER
Answered 2020-May-28 at 08:33For the record, I was able to solve the problem after reading this post.
Modified get_X function:
QUESTION
I am using scikit-learn for text processing, but my CountVectorizer isn't giving the output I expect.
My CSV file looks like:
...ANSWER
Answered 2017-May-20 at 08:58The problem is in count_vect.fit_transform(data). The function expects an iterable that yields strings. Unfortunately, these are the wrong strings, which can be verified with a simple example.
QUESTION
In a binary text classification with scikit-learn with a SGDClassifier linear model on a TF-IDF representation of a bag-of-words, I want to obtain feature importances per class through the models coefficients. I heard diverging opinions if the columns (features) should be scaled with a StandardScaler(with_mean=False) or not for this case.
With sparse data, centering of the data before scaling cannot be done anyway (the with_mean=False part). The TfidfVectorizer by default also L2 row normalizes each instance already. Based on empirical results such as the self-contained example below, it seems the top features per class make intuitively more sense when not using StandardScaler. For example 'nasa' and 'space' are top tokens for sci.space, and 'god' and 'christians' for talk.religion.misc etc.
Am I missing something? Should StandardScaler(with_mean=False) still be used for obtaining feature importances from a linear model coefficients in such NLP cases?
Are these feature importances without StandardScaler(with_mean=False) in cases like this still somehow unreliable from a theoretical point?
...ANSWER
Answered 2019-Nov-01 at 08:06I do not have a theoretical basis on this, but scaling features after TfidfVectorizer() gets me a little bit nervous since that seems to damage the idf part. My understanding of TfidfVectorizer() is that in a sense, it scales across documents and features. I cannot think of any reason to scale if your estimation method with penalization works well without scaling.
QUESTION
The question title says it all: How can I make a bag-of-words model smaller? I use a Random Forest and a bag-of-words feature set. My model reaches 30 GB in size and I am sure that most words in the feature set do not contribute to the overall performance.
How to shrink a big bag-of-words model without losing (too much) performance?
...ANSWER
Answered 2019-Oct-02 at 08:42If you don't want to change the architecture of your neural network and you are only trying to reduce the memory footprint, a tweak that can be made is to reduce the terms annotated by the CountVectorizer.
From the scikit-learn documentation, we have (at least) three parameter for reduce the vocabulary size.
max_df : float in range [0.0, 1.0] or int, default=1.0
When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words). If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.
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.
max_features : int or None, default=None
If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus. This parameter is ignored if vocabulary is not None.
In first instance, try to play with max_df and min_df. If the size is still not suitable with your requirements, you can drop the size as you like using the max_features.
NOTE:
The max_features tuning can drop your classification accuracy by an higher ratio than the other parameters
QUESTION
I'm working on goodbooks-10k dataset to make a recommender system. I want to use the tags of the books to make recommendations. Tags of the books come in an aggrageted way - for every book and every tag, there is a row with the name of the book, the name of the tag, and the number of the times this tag occurred for this book. The dataset looks like this:
I want to use this information to build a bag-of-words representation of the tags, where for every tag I have a column with the number of times this tag occurs for the book given.
What is the proper way to implement this with pandas?
Thanks in advance!
...ANSWER
Answered 2019-Aug-12 at 10:06You can use pandas.pivot_table
Sample dataframe:
QUESTION
Well, I am making a sentiment analysis classifier and I have three classes/labels, positive, neutral and negative. The Shape of my training data is (14640, 15), where
...ANSWER
Answered 2019-Aug-08 at 20:04the problem is that the predict_proba method you are using is for binary classification. In a multi classification it gives the probability for each class.
You cannot use this command:
QUESTION
I am making Sentiment Analysis Classification and I am doing it with Scikit-learn. This has 3 labels, positive, neutral and negative. The Shape of my training data is (14640, 15), where
ANSWER
Answered 2019-Aug-07 at 17:10As the warning explains:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install bag-of-words
You can use bag-of-words like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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