# Basic operations¶

## Supported VSM formats¶

At the moment the following data formats are supported:

• .bin format of word2vec (the file has to be called “vectors.bin”)
• .npy arrays with separate vocab files
• .txt plain-text vectors
• sparse vectors in hp5 format

## Importing vectors¶

VSMlib assumes a one-folder-per-vsm folder structure. All files related to the same vsm - the metadata, vectors, vocab files, etc. - must all be stored in one directory. If the vector files has the correct extension (.npy, .txt, .bin, .hp5), the library will attempt to “guess” the correct module to load it with.

>>> import vsmlib
>>> path_to_vsm = "/path/to/your/model"


The name of the model is the name directory in which the vector files are stored. For models generated with VSMlib, interpretable folder names with parameters are generated automatically.

>>> print(my_vsm.name)
w2v_comb2_w8_n25_i6_d300_skip_300


You can access the VSM metadata (recorded in metadata.json file located in the same directory as the VSM) as a Python dictionary:

>>> print(my_vsm.metadata)
{'size_dimensions': 300, 'dimensions': 300, 'size_window': '8'}


## Getting top similar neighbors of a word¶

>>> my_vsm.get_most_similar_words("apple", cnt=5)
[['apple', 1.0000000999898755],
['fruit', 0.61400752577032369],
['banana', 0.58657183882050712],
['plum', 0.5850951585421692],
['apples', 0.58464719369713347]]


This method takes an optional cnt argument specifying how many top similar neighbors to output (the default is 10). Note that the top similar vector is always the target word itself.

If you need to compute nearest neighbors for many words, this function works faster if the VSM is normalized. If it was generated with vsmlib, the normalization will be recorded in metadata, and can be checked with .normalized. VSMlib will automatically check for normalization and use the faster routine if possible. If not, you can first normalize your VSM with .normalized() method.

If for whatever reasons you need your VSM to not be normalized, you can use .cache_normalized_copy() method to cache normalized copy of embeddings. Please note that latter will consume additional memory.

.get_most_similar_vectors() enables you to do the same as .get_most_similar_words(), but searching the top neighbors by the vector representation rather than its label.

## Words to vectors and back¶

First, you need to import your model from a directory that holds only that model (.npy, .bin, .hp5 or .txt formats) and any associated files.

getting the vector representation of a word

>>> my_vsm.get_row("apple")
array([-0.17980662,  0.27027196, -0.33250481,  ... -0.22577444], dtype=float32)


You can use the above top-similar function to get the label of the vector most corresponding to your vector in your VSM vocabulary:

>>> vsm.get_most_similar_vectors(vsm.get_row("apple"))


## Filtering the vocabulary of a VSM¶

In certain cases it may be useful to filter the vocabulary of a pre-trained VSM, e.g. to ensure that two models you are comparing have the same vocabulary. VSMlib provides a .filter_by_vocab() method that returns a new model instance, the vocabulary of which contains only the words in the provided Python list of words. The list can be empty.

>>> my_vsm.get_most_similar_words("cat", cnt=5)
[['cat', 1.0],
['monkey', 0.95726192],
['dog', 0.95372206],
['koala', 0.94773519],
['puppy', 0.94360757]]
>>> my_new_vsm = my_vsm.filter_by_vocab(["dog", "hotdog", "zoo", "hammer", "cat"])
>>> my_new_vsm.get_most_similar_words("cat", cnt=5)
[['cat', 1.0],
['dog', 0.95372206],
['hotdog', 0.84262532],
['hammer', 0.80627602],
['zoo', 0.7463485]]