whatlies.embeddingset.EmbeddingSet

This object represents a set of Embeddings. You can use the same operations as an Embedding but here we apply it to the entire set instead of a single Embedding.

Parameters

  • embeddings: list of Embedding, or a single dictionary containing name:Embedding pairs
  • name: custom name of embeddingset

Usage:

from whatlies.embedding import Embedding
from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3])
bar = Embedding("bar", [0.7, 0.2])
emb = EmbeddingSet(foo, bar)
emb = EmbeddingSet({'foo': foo, 'bar': bar)

ndim: (property, readonly)

Return dimension of embedding vectors in embeddingset.

__add__(self, other)

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    def __add__(self, other):
        """
        Adds an embedding to each element in the embeddingset.

        Usage:

        ```python
        from whatlies.embedding import Embedding
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3])
        bar = Embedding("bar", [0.7, 0.2])
        buz = Embedding("buz", [0.1, 0.9])
        emb = EmbeddingSet(foo, bar)

        (emb).plot(kind="arrow")
        (emb + buz).plot(kind="arrow")
        ```
        """
        new_embeddings = {k: emb + other for k, emb in self.embeddings.items()}
        return EmbeddingSet(new_embeddings, name=f"({self.name} + {other.name})")

Adds an embedding to each element in the embeddingset.

Usage:

from whatlies.embedding import Embedding
from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3])
bar = Embedding("bar", [0.7, 0.2])
buz = Embedding("buz", [0.1, 0.9])
emb = EmbeddingSet(foo, bar)

(emb).plot(kind="arrow")
(emb + buz).plot(kind="arrow")

__contains__(self, item)

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    def __contains__(self, item):
        """
        Checks if an item is in the embeddingset.

        Usage:

        ```python
        from whatlies.embedding import Embedding
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3])
        bar = Embedding("bar", [0.7, 0.2])
        buz = Embedding("buz", [0.1, 0.9])
        emb = EmbeddingSet(foo, bar)

        "foo" in emb # True
        "dinosaur" in emb # False
        ```
        """
        return item in self.embeddings.keys()

Checks if an item is in the embeddingset.

Usage:

from whatlies.embedding import Embedding
from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3])
bar = Embedding("bar", [0.7, 0.2])
buz = Embedding("buz", [0.1, 0.9])
emb = EmbeddingSet(foo, bar)

"foo" in emb # True
"dinosaur" in emb # False

__getitem__(self, thing)

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    def __getitem__(self, thing):
        """
        Retreive a single embedding from the embeddingset.

        Usage:
        ```python
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3, 0.10])
        bar = Embedding("bar", [0.7, 0.2, 0.11])
        buz = Embedding("buz", [0.1, 0.9, 0.12])
        emb = EmbeddingSet(foo, bar, buz)

        emb["buz"]
        ```
        """
        if isinstance(thing, str):
            return self.embeddings[thing]
        new_embeddings = {t: self[t] for t in thing}
        names = ",".join(thing)
        return EmbeddingSet(new_embeddings, name=f"{self.name}.subset({names})")

Retreive a single embedding from the embeddingset.

Usage:

from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3, 0.10])
bar = Embedding("bar", [0.7, 0.2, 0.11])
buz = Embedding("buz", [0.1, 0.9, 0.12])
emb = EmbeddingSet(foo, bar, buz)

emb["buz"]

__iter__(self)

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    def __iter__(self):
        """
        Iterate over all the embeddings in the embeddingset.

        Usage:

        ```python
        from whatlies.embedding import Embedding
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3])
        bar = Embedding("bar", [0.7, 0.2])
        buz = Embedding("buz", [0.1, 0.9])
        emb = EmbeddingSet(foo, bar)

        [e for e in emb]
        ```
        """
        return self.embeddings.values().__iter__()

Iterate over all the embeddings in the embeddingset.

Usage:

from whatlies.embedding import Embedding
from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3])
bar = Embedding("bar", [0.7, 0.2])
buz = Embedding("buz", [0.1, 0.9])
emb = EmbeddingSet(foo, bar)

[e for e in emb]

__or__(self, other)

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    def __or__(self, other):
        """
        Makes every element in the embeddingset othogonal to the passed embedding.

        Usage:

        ```python
        from whatlies.embedding import Embedding
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3])
        bar = Embedding("bar", [0.7, 0.2])
        buz = Embedding("buz", [0.1, 0.9])
        emb = EmbeddingSet(foo, bar)

        (emb).plot(kind="arrow")
        (emb | buz).plot(kind="arrow")
        ```
        """
        new_embeddings = {k: emb | other for k, emb in self.embeddings.items()}
        return EmbeddingSet(new_embeddings, name=f"({self.name} | {other.name})")

Makes every element in the embeddingset othogonal to the passed embedding.

Usage:

from whatlies.embedding import Embedding
from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3])
bar = Embedding("bar", [0.7, 0.2])
buz = Embedding("buz", [0.1, 0.9])
emb = EmbeddingSet(foo, bar)

(emb).plot(kind="arrow")
(emb | buz).plot(kind="arrow")

__rshift__(self, other)

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    def __rshift__(self, other):
        """
        Maps every embedding in the embedding set unto the passed embedding.

        Usage:

        ```python
        from whatlies.embedding import Embedding
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3])
        bar = Embedding("bar", [0.7, 0.2])
        buz = Embedding("buz", [0.1, 0.9])
        emb = EmbeddingSet(foo, bar)

        (emb).plot(kind="arrow")
        (emb >> buz).plot(kind="arrow")
        ```
        """
        new_embeddings = {k: emb >> other for k, emb in self.embeddings.items()}
        return EmbeddingSet(new_embeddings, name=f"({self.name} >> {other.name})")

Maps every embedding in the embedding set unto the passed embedding.

Usage:

from whatlies.embedding import Embedding
from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3])
bar = Embedding("bar", [0.7, 0.2])
buz = Embedding("buz", [0.1, 0.9])
emb = EmbeddingSet(foo, bar)

(emb).plot(kind="arrow")
(emb >> buz).plot(kind="arrow")

__sub__(self, other)

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    def __sub__(self, other):
        """
        Subtracts an embedding from each element in the embeddingset.

        Usage:

        ```python
        from whatlies.embedding import Embedding
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3])
        bar = Embedding("bar", [0.7, 0.2])
        buz = Embedding("buz", [0.1, 0.9])
        emb = EmbeddingSet(foo, bar)

        (emb).plot(kind="arrow")
        (emb - buz).plot(kind="arrow")
        ```
        """
        new_embeddings = {k: emb - other for k, emb in self.embeddings.items()}
        return EmbeddingSet(new_embeddings, name=f"({self.name} - {other.name})")

Subtracts an embedding from each element in the embeddingset.

Usage:

from whatlies.embedding import Embedding
from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3])
bar = Embedding("bar", [0.7, 0.2])
buz = Embedding("buz", [0.1, 0.9])
emb = EmbeddingSet(foo, bar)

(emb).plot(kind="arrow")
(emb - buz).plot(kind="arrow")

add_property(self, name, func)

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    def add_property(self, name, func):
        """
        Adds a property to every embedding in the set. Very useful for plotting because
        a property can be used to assign colors.

        Arguments:
            name: name of the property to add
            func: function that receives an embedding and needs to output the property value

        Usage:

        ```python
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3, 0.10])
        bar = Embedding("bar", [0.7, 0.2, 0.11])
        emb = EmbeddingSet(foo, bar)
        emb_with_property = emb.add_property('example', lambda d: 'group-one')
        ```
        """
        return EmbeddingSet(
            {k: e.add_property(name, func) for k, e in self.embeddings.items()}
        )

Adds a property to every embedding in the set. Very useful for plotting because a property can be used to assign colors.

Parameters

Name Type Description Default
name name of the property to add required
func function that receives an embedding and needs to output the property value required

Usage:

from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3, 0.10])
bar = Embedding("bar", [0.7, 0.2, 0.11])
emb = EmbeddingSet(foo, bar)
emb_with_property = emb.add_property('example', lambda d: 'group-one')

assign(self, **kwargs)

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    def assign(self, **kwargs):
        """
        Adds properties to every embedding in the set based on the keyword arguments.

        This is very useful for plotting because a property can be used to assign colors. This method is very
        similar to `.add_property` but it might be more convenient when you want to assign multiple properties
        in one single statement.

        Arguments:
            kwargs: (name, func)-pairs that describe the name of the property as well as a value to assign.
                The value can be a single value, iterable or a function. The function expects an `Embedding` object as input.

        Usage:

        ```python
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3, 0.10])
        bar = Embedding("bar", [0.7, 0.2, 0.11])
        emb = EmbeddingSet(foo, bar)
        emb_with_property1 = emb.assign(dim0=lambda d: d.vector[0],
                                        dim1=lambda d: d.vector[1],
                                        dim2=lambda d: d.vector[2])

        emb_with_property2 = emb.assign(group=["foo_grp", "bar_grp"])

        emb_with_property3 = emb.assign(constant=1)
        ```
        """
        new_set = {}
        for idx, (k, e) in enumerate(self.embeddings.items()):
            new_emb = e
            for name, val in kwargs.items():
                if callable(val):
                    new_emb = new_emb.add_property(name, val)
                elif hasattr(val, "__iter__") and not isinstance(val, str):
                    # We want to support lists, tuples, numpy arrays but not strings
                    # those need to be handle as if they're literals.
                    if len(val) != len(self):
                        raise ValueError(
                            f"If you're passing an iterable to `.assign` then it must have the same length as the `EmbeddingSet`.\nGot: {len(val)}. Expected: {len(self)}."
                        )
                    new_emb = new_emb.add_property(name, lambda d: val[idx])
                else:
                    new_emb = new_emb.add_property(name, lambda d: val)
            new_set[k] = new_emb
        return EmbeddingSet(new_set)

Adds properties to every embedding in the set based on the keyword arguments.

This is very useful for plotting because a property can be used to assign colors. This method is very similar to .add_property but it might be more convenient when you want to assign multiple properties in one single statement.

Parameters

Name Type Description Default
**kwargs (name, func)-pairs that describe the name of the property as well as a value to assign. The value can be a single value, iterable or a function. The function expects an Embedding object as input. {}

Usage:

from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3, 0.10])
bar = Embedding("bar", [0.7, 0.2, 0.11])
emb = EmbeddingSet(foo, bar)
emb_with_property1 = emb.assign(dim0=lambda d: d.vector[0],
                                dim1=lambda d: d.vector[1],
                                dim2=lambda d: d.vector[2])

emb_with_property2 = emb.assign(group=["foo_grp", "bar_grp"])

emb_with_property3 = emb.assign(constant=1)

average(self, name=None)

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    def average(self, name=None):
        """
        Takes the average over all the embedding vectors in the embeddingset. Turns it into
        a new `Embedding`.

        Arguments:
            name: manually specify the name of the average embedding

        Usage:

        ```python
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [1.0, 0.0])
        bar = Embedding("bar", [0.0, 1.0])
        emb = EmbeddingSet(foo, bar)

        emb.average().vector                   # [0.5, 0,5]
        emb.average(name="the-average").vector # [0.5, 0.5]
        ```
        """
        name = f"{self.name}.average()" if not name else name
        x = self.to_X()
        return Embedding(name, np.mean(x, axis=0))

Takes the average over all the embedding vectors in the embeddingset. Turns it into a new Embedding.

Parameters

Name Type Description Default
name manually specify the name of the average embedding None

Usage:

from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [1.0, 0.0])
bar = Embedding("bar", [0.0, 1.0])
emb = EmbeddingSet(foo, bar)

emb.average().vector                   # [0.5, 0,5]
emb.average(name="the-average").vector # [0.5, 0.5]

compare_against(self, other, mapping=None)

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    def compare_against(
        self, other: Union[str, Embedding], mapping: Optional[Callable] = None
    ) -> List:
        """
        Compare (or map) the embeddigns in the embeddingset to a given embedding, optionally using
        a custom mapping function.

        Arguments:
            other: an `Embedding` instance, or name of an existing embedding; it is used for
                comparison with each embedding in the embeddingset.
            mapping: an optional callable used for for comparison that takes two 1D vector arrays as
                input; if not given, the normalized scalar projection (i.e. `>` operator) is used.
        """
        if isinstance(other, str):
            other = self[other]
        if mapping is None:
            return [v > other for v in self.embeddings.values()]
        elif callable(mapping):
            return [mapping(v.vector, other.vector) for v in self.embeddings.values()]
        else:
            raise ValueError(f"Unrecognized mapping value/type, got: {mapping}")

Compare (or map) the embeddigns in the embeddingset to a given embedding, optionally using a custom mapping function.

Parameters

Name Type Description Default
other Union[str, whatlies.embedding.Embedding] an Embedding instance, or name of an existing embedding; it is used for comparison with each embedding in the embeddingset. required
mapping Optional[Callable] an optional callable used for for comparison that takes two 1D vector arrays as input; if not given, the normalized scalar projection (i.e. > operator) is used. None

embset_similar(self, emb, n=10, metric='cosine')

Show source code in whatlies/embeddingset.py
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    def embset_similar(self, emb: Union[str, Embedding], n: int = 10, metric="cosine"):
        """
        Retreive an [EmbeddingSet][whatlies.embeddingset.EmbeddingSet] that are the most simmilar to the passed query.

        Arguments:
            emb: query to use
            n: the number of items you'd like to see returned
            metric: metric to use to calculate distance, must be scipy or sklearn compatible

        Returns:
            An [EmbeddingSet][whatlies.embeddingset.EmbeddingSet] containing the similar embeddings.
        """
        embs = [w[0] for w in self.score_similar(emb, n, metric)]
        return EmbeddingSet({w.name: w for w in embs})

Retreive an EmbeddingSet that are the most simmilar to the passed query.

Parameters

Name Type Description Default
emb Union[str, whatlies.embedding.Embedding] query to use required
n int the number of items you'd like to see returned 10
metric metric to use to calculate distance, must be scipy or sklearn compatible 'cosine'

Returns

Type Description
`` An EmbeddingSet containing the similar embeddings.

filter(self, func)

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    def filter(self, func):
        """
        Filters the collection of embeddings based on a predicate function.

        Arguments:
             func: callable that accepts a single embedding and outputs a boolean

        ```python
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3, 0.10])
        bar = Embedding("bar", [0.7, 0.2, 0.11])
        buz = Embedding("buz", [0.1, 0.9, 0.12])
        xyz = Embedding("xyz", [0.1, 0.9, 0.12])
        emb = EmbeddingSet(foo, bar, buz, xyz)
        emb.filter(lambda e: "foo" not in e.name)
        ```
        """
        return EmbeddingSet({k: v for k, v in self.embeddings.items() if func(v)})

Filters the collection of embeddings based on a predicate function.

Parameters

Name Type Description Default
func callable that accepts a single embedding and outputs a boolean required
from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3, 0.10])
bar = Embedding("bar", [0.7, 0.2, 0.11])
buz = Embedding("buz", [0.1, 0.9, 0.12])
xyz = Embedding("xyz", [0.1, 0.9, 0.12])
emb = EmbeddingSet(foo, bar, buz, xyz)
emb.filter(lambda e: "foo" not in e.name)

from_names_X(names, X) (classmethod)

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    @classmethod
    def from_names_X(cls, names, X):
        """
        Constructs an `EmbeddingSet` instance from the given embedding names and vectors.

        Arguments:
            names: an iterable containing the names of embeddings
            X: an iterable of 1D vectors, or a 2D numpy array; it should have the same length as `names`

        Usage:

        ```python
        from whatlies.embeddingset import EmbeddingSet

        names = ["foo", "bar", "buz"]
        vecs = [
            [0.1, 0.3],
            [0.7, 0.2],
            [0.1, 0.9],
        ]

        emb = EmbeddingSet.from_names_X(names, vecs)
        ```
        """
        X = np.array(X)
        if len(X) != len(names):
            raise ValueError(
                f"The number of given names ({len(names)}) and vectors ({len(X)}) should be the same."
            )
        return cls({n: Embedding(n, v) for n, v in zip(names, X)})

Constructs an EmbeddingSet instance from the given embedding names and vectors.

Parameters

Name Type Description Default
names an iterable containing the names of embeddings required
X an iterable of 1D vectors, or a 2D numpy array; it should have the same length as names required

Usage:

from whatlies.embeddingset import EmbeddingSet

names = ["foo", "bar", "buz"]
vecs = [
    [0.1, 0.3],
    [0.7, 0.2],
    [0.1, 0.9],
]

emb = EmbeddingSet.from_names_X(names, vecs)

merge(self, other)

Show source code in whatlies/embeddingset.py
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    def merge(self, other):
        """
        Concatenates two embeddingssets together

        Arguments:
            other: another embeddingset

        Usage:

        ```python
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3, 0.10])
        bar = Embedding("bar", [0.7, 0.2, 0.11])
        buz = Embedding("buz", [0.1, 0.9, 0.12])
        xyz = Embedding("xyz", [0.1, 0.9, 0.12])
        emb1 = EmbeddingSet(foo, bar)
        emb2 = EmbeddingSet(xyz, buz)

        both = emb1.merge(emb2)
        ```
        """
        return EmbeddingSet({**self.embeddings, **other.embeddings})

Concatenates two embeddingssets together

Parameters

Name Type Description Default
other another embeddingset required

Usage:

from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3, 0.10])
bar = Embedding("bar", [0.7, 0.2, 0.11])
buz = Embedding("buz", [0.1, 0.9, 0.12])
xyz = Embedding("xyz", [0.1, 0.9, 0.12])
emb1 = EmbeddingSet(foo, bar)
emb2 = EmbeddingSet(xyz, buz)

both = emb1.merge(emb2)

movement_df(self, other, metric='euclidean')

Show source code in whatlies/embeddingset.py
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    def movement_df(self, other, metric="euclidean"):
        """
        Creates a dataframe that shows the movement from one embeddingset to another one.

        Arguments:
            other: the other embeddingset to compare against, will only keep the overlap
            metric: metric to use to calculate movement, must be scipy or sklearn compatible

        Usage:

        ```python
        from whatlies.language import SpacyLanguage

        lang = SpacyLanguage("en_core_web_sm")

        names = ['red', 'blue', 'green', 'yellow', 'cat', 'dog', 'mouse', 'rat', 'bike', 'car']
        emb = lang[names]
        emb_ort = lang[names] | lang['cat']
        emb.movement_df(emb_ort)
        ```
        """
        overlap = list(
            set(self.embeddings.keys()).intersection(set(other.embeddings.keys()))
        )
        mat1 = np.array([w.vector for w in self[overlap]])
        mat2 = np.array([w.vector for w in other[overlap]])
        return (
            pd.DataFrame(
                {
                    "name": overlap,
                    "movement": paired_distances(mat1, mat2, metric=metric),
                }
            )
            .sort_values(["movement"], ascending=False)
            .reset_index()
        )

Creates a dataframe that shows the movement from one embeddingset to another one.

Parameters

Name Type Description Default
other the other embeddingset to compare against, will only keep the overlap required
metric metric to use to calculate movement, must be scipy or sklearn compatible 'euclidean'

Usage:

from whatlies.language import SpacyLanguage

lang = SpacyLanguage("en_core_web_sm")

names = ['red', 'blue', 'green', 'yellow', 'cat', 'dog', 'mouse', 'rat', 'bike', 'car']
emb = lang[names]
emb_ort = lang[names] | lang['cat']
emb.movement_df(emb_ort)

pipe(self, func, *args, **kwargs)

Show source code in whatlies/embeddingset.py
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    def pipe(self, func, *args, **kwargs):
        """
        Applies a function to the embedding set. Useful for method chaining and
        chunks of code that repeat.

        Arguments:
             func: callable that accepts an `EmbeddingSet` set as its first argument
             args: arguments to also pass to the function
             kwargs: keyword arguments to also pass to the function

        ```python
        from whatlies.language import SpacyLanguage, BytePairLanguage

        lang_sp = SpacyLanguage("en_core_web_sm")
        lang_bp = BytePairLanguage("en", dim=25, vs=1000)

        text = ["cat", "dog", "rat", "blue", "red", "yellow"]

        def make_plot(embset):
            return (embset
                    .plot_interactive("dog", "blue")
                    .properties(height=200, width=200))

        p1 = lang_sp[text].pipe(make_plot)
        p2 = lang_bp[text].pipe(make_plot)
        p1 | p2
        ```
        """
        return func(self, *args, **kwargs)

Applies a function to the embedding set. Useful for method chaining and chunks of code that repeat.

Parameters

Name Type Description Default
func callable that accepts an EmbeddingSet set as its first argument required
*args arguments to also pass to the function ()
**kwargs keyword arguments to also pass to the function {}
from whatlies.language import SpacyLanguage, BytePairLanguage

lang_sp = SpacyLanguage("en_core_web_sm")
lang_bp = BytePairLanguage("en", dim=25, vs=1000)

text = ["cat", "dog", "rat", "blue", "red", "yellow"]

def make_plot(embset):
    return (embset
            .plot_interactive("dog", "blue")
            .properties(height=200, width=200))

p1 = lang_sp[text].pipe(make_plot)
p2 = lang_bp[text].pipe(make_plot)
p1 | p2

plot(self, kind='arrow', x_axis=0, y_axis=1, axis_metric=None, x_label=None, y_label=None, title=None, color=None, show_ops=False, annot=True, axis_option=None)

Show source code in whatlies/embeddingset.py
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    def plot(
        self,
        kind: str = "arrow",
        x_axis: Union[int, str, Embedding] = 0,
        y_axis: Union[int, str, Embedding] = 1,
        axis_metric: Optional[Union[str, Callable, Sequence]] = None,
        x_label: Optional[str] = None,
        y_label: Optional[str] = None,
        title: Optional[str] = None,
        color: str = None,
        show_ops: bool = False,
        annot: bool = True,
        axis_option: Optional[str] = None,
    ):
        """
        Makes (perhaps inferior) matplotlib plot. Consider using `plot_interactive` instead.

        Arguments:
            kind: what kind of plot to make, can be `scatter`, `arrow` or `text`
            x_axis: the x-axis to be used, must be given when dim > 2; if an integer, the corresponding
                dimension of embedding is used.
            y_axis: the y-axis to be used, must be given when dim > 2; if an integer, the corresponding
                dimension of embedding is used.
            axis_metric: the metric used to project each embedding on the axes; only used when the corresponding
                axis (i.e. `x_axis` or `y_axis`) is a string or an `Embedding` instance. It could be a string
                (`'cosine_similarity'`, `'cosine_distance'` or `'euclidean'`), or a callable that takes two vectors as input
                and returns a scalar value as output. To set different metrics for x- and y-axis, a list or a tuple of
                two elements could be given. By default (`None`), normalized scalar projection (i.e. `>` operator) is used.
            x_label: an optional label used for x-axis; if not given, it is set based on value of `x_axis`.
            y_label: an optional label used for y-axis; if not given, it is set based on value of `y_axis`.
            title: an optional title for the plot.
            color: the color of the dots
            show_ops: setting to also show the applied operations, only works for `text`
            annot: should the points be annotated
            axis_option: a string which is passed as `option` argument to `matplotlib.pyplot.axis` in order to control
                axis properties (e.g. using `'equal'` make circles shown circular in the plot). This might be useful
                for preserving geometric relationships (e.g. orthogonality) in the generated plot. See `matplotlib.pyplot.axis`
                [documentation](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.axis.html#matplotlib-pyplot-axis)
                for possible values and their description.
        """
        if isinstance(x_axis, str):
            x_axis = self[x_axis]
        if isinstance(y_axis, str):
            y_axis = self[y_axis]

        if isinstance(axis_metric, (list, tuple)):
            x_axis_metric = axis_metric[0]
            y_axis_metric = axis_metric[1]
        else:
            x_axis_metric = axis_metric
            y_axis_metric = axis_metric

        embeddings = []
        for emb in self.embeddings.values():
            x_val, x_lab = emb._get_plot_axis_value_and_label(
                x_axis, x_axis_metric, dir="x"
            )
            y_val, y_lab = emb._get_plot_axis_value_and_label(
                y_axis, y_axis_metric, dir="y"
            )
            emb_plot = Embedding(name=emb.name, vector=[x_val, y_val], orig=emb.orig)
            embeddings.append(emb_plot)
        x_label = x_lab if x_label is None else x_label
        y_label = y_lab if y_label is None else y_label
        handle_2d_plot(
            embeddings,
            kind=kind,
            color=color,
            xlabel=x_label,
            ylabel=y_label,
            title=title,
            show_operations=show_ops,
            annot=annot,
            axis_option=axis_option,
        )
        return self

Makes (perhaps inferior) matplotlib plot. Consider using plot_interactive instead.

Parameters

Name Type Description Default
kind str what kind of plot to make, can be scatter, arrow or text 'arrow'
x_axis Union[int, str, whatlies.embedding.Embedding] the x-axis to be used, must be given when dim > 2; if an integer, the corresponding dimension of embedding is used. 0
y_axis Union[int, str, whatlies.embedding.Embedding] the y-axis to be used, must be given when dim > 2; if an integer, the corresponding dimension of embedding is used. 1
axis_metric Optional[Union[str, Callable, Sequence]] the metric used to project each embedding on the axes; only used when the corresponding axis (i.e. x_axis or y_axis) is a string or an Embedding instance. It could be a string ('cosine_similarity', 'cosine_distance' or 'euclidean'), or a callable that takes two vectors as input and returns a scalar value as output. To set different metrics for x- and y-axis, a list or a tuple of two elements could be given. By default (None), normalized scalar projection (i.e. > operator) is used. None
x_label Optional[str] an optional label used for x-axis; if not given, it is set based on value of x_axis. None
y_label Optional[str] an optional label used for y-axis; if not given, it is set based on value of y_axis. None
title Optional[str] an optional title for the plot. None
color str the color of the dots None
show_ops bool setting to also show the applied operations, only works for text False
annot bool should the points be annotated True
axis_option Optional[str] a string which is passed as option argument to matplotlib.pyplot.axis in order to control axis properties (e.g. using 'equal' make circles shown circular in the plot). This might be useful for preserving geometric relationships (e.g. orthogonality) in the generated plot. See matplotlib.pyplot.axis documentation for possible values and their description. None

plot_3d(self, x_axis=0, y_axis=1, z_axis=2, x_label=None, y_label=None, z_label=None, title=None, color=None, axis_metric=None, annot=True)

Show source code in whatlies/embeddingset.py
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    def plot_3d(
        self,
        x_axis: Union[int, str, Embedding] = 0,
        y_axis: Union[int, str, Embedding] = 1,
        z_axis: Union[int, str, Embedding] = 2,
        x_label: Optional[str] = None,
        y_label: Optional[str] = None,
        z_label: Optional[str] = None,
        title: Optional[str] = None,
        color: str = None,
        axis_metric: Optional[Union[str, Callable, Sequence]] = None,
        annot: bool = True,
    ):
        """
        Creates a 3d visualisation of the embedding.

        Arguments:
            x_axis: the x-axis to be used, must be given when dim > 3; if an integer, the corresponding
                dimension of embedding is used.
            y_axis: the y-axis to be used, must be given when dim > 3; if an integer, the corresponding
                dimension of embedding is used.
            z_axis: the z-axis to be used, must be given when dim > 3; if an integer, the corresponding
                dimension of embedding is used.
            x_label: an optional label used for x-axis; if not given, it is set based on value of `x_axis`.
            y_label: an optional label used for y-axis; if not given, it is set based on value of `y_axis`.
            z_label: an optional label used for z-axis; if not given, it is set based on value of `z_axis`.
            title: an optional title for the plot.
            color: the property to user for the color
            axis_metric: the metric used to project each embedding on the axes; only used when the corresponding
                axis is a string or an `Embedding` instance. It could be a string (`'cosine_similarity'`,
                `'cosine_distance'` or `'euclidean'`), or a callable that takes two vectors as input and
                returns a scalar value as output. To set different metrics of the three different axes,
                you can pass a list/tuple of size three that describes the metrics you're interested in.
                By default (`None`), normalized scalar projection (i.e. `>` operator) is used.
            annot: drawn points should be annotated

        **Usage**

        ```python
        from whatlies.language import SpacyLanguage
        from whatlies.transformers import Pca

        words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
                 "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
                 "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
                 "person", "family", "brother", "sister"]

        lang = SpacyLanguage("en_core_web_sm")
        emb = lang[words]

        emb.transform(Pca(3)).plot_3d(annot=True)
        emb.transform(Pca(3)).plot_3d("king", "dog", "red")
        emb.transform(Pca(3)).plot_3d("king", "dog", "red", axis_metric="cosine_distance")
        ```
        """
        if isinstance(x_axis, str):
            x_axis = self[x_axis]
        if isinstance(y_axis, str):
            y_axis = self[y_axis]
        if isinstance(z_axis, str):
            z_axis = self[z_axis]

        if isinstance(axis_metric, (list, tuple)):
            x_axis_metric = axis_metric[0]
            y_axis_metric = axis_metric[1]
            z_axis_metric = axis_metric[2]
        else:
            x_axis_metric = axis_metric
            y_axis_metric = axis_metric
            z_axis_metric = axis_metric

        # Determine axes values and labels
        if isinstance(x_axis, int):
            x_val = self.to_X()[:, x_axis]
            x_lab = "Dimension " + str(x_axis)
        else:
            x_axis_metric = Embedding._get_plot_axis_metric_callable(x_axis_metric)
            x_val = self.compare_against(x_axis, mapping=x_axis_metric)
            x_lab = x_axis.name
        x_lab = x_label if x_label is not None else x_lab

        if isinstance(y_axis, int):
            y_val = self.to_X()[:, y_axis]
            y_lab = "Dimension " + str(y_axis)
        else:
            y_axis_metric = Embedding._get_plot_axis_metric_callable(y_axis_metric)
            y_val = self.compare_against(y_axis, mapping=y_axis_metric)
            y_lab = y_axis.name
        y_lab = y_label if y_label is not None else y_lab

        if isinstance(z_axis, int):
            z_val = self.to_X()[:, z_axis]
            z_lab = "Dimension " + str(z_axis)
        else:
            z_axis_metric = Embedding._get_plot_axis_metric_callable(z_axis_metric)
            z_val = self.compare_against(z_axis, mapping=z_axis_metric)
            z_lab = z_axis.name
        z_lab = z_label if z_label is not None else z_lab

        # Save relevant information in a dataframe for plotting later.
        plot_df = pd.DataFrame(
            {
                "x_axis": x_val,
                "y_axis": y_val,
                "z_axis": z_val,
                "name": [v.name for v in self.embeddings.values()],
                "original": [v.orig for v in self.embeddings.values()],
            }
        )

        # Deal with the colors of the dots.
        if color:
            plot_df["color"] = [
                getattr(v, color) if hasattr(v, color) else ""
                for v in self.embeddings.values()
            ]

            color_map = {k: v for v, k in enumerate(set(plot_df["color"]))}
            color_val = [
                color_map[k] if not isinstance(k, float) else k
                for k in plot_df["color"]
            ]
        else:
            color_val = None

        ax = plt.axes(projection="3d")
        ax.scatter3D(
            plot_df["x_axis"], plot_df["y_axis"], plot_df["z_axis"], c=color_val, s=25
        )

        # Set the labels, titles, text annotations.
        ax.set_xlabel(x_lab)
        ax.set_ylabel(y_lab)
        ax.set_zlabel(z_lab)

        if annot:
            for i, row in plot_df.iterrows():
                ax.text(
                    row["x_axis"], row["y_axis"], row["z_axis"] + 0.05, row["original"]
                )
        if title:
            ax.set_title(label=title)
        return ax

Creates a 3d visualisation of the embedding.

Parameters

Name Type Description Default
x_axis Union[int, str, whatlies.embedding.Embedding] the x-axis to be used, must be given when dim > 3; if an integer, the corresponding dimension of embedding is used. 0
y_axis Union[int, str, whatlies.embedding.Embedding] the y-axis to be used, must be given when dim > 3; if an integer, the corresponding dimension of embedding is used. 1
z_axis Union[int, str, whatlies.embedding.Embedding] the z-axis to be used, must be given when dim > 3; if an integer, the corresponding dimension of embedding is used. 2
x_label Optional[str] an optional label used for x-axis; if not given, it is set based on value of x_axis. None
y_label Optional[str] an optional label used for y-axis; if not given, it is set based on value of y_axis. None
z_label Optional[str] an optional label used for z-axis; if not given, it is set based on value of z_axis. None
title Optional[str] an optional title for the plot. None
color str the property to user for the color None
axis_metric Optional[Union[str, Callable, Sequence]] the metric used to project each embedding on the axes; only used when the corresponding axis is a string or an Embedding instance. It could be a string ('cosine_similarity', 'cosine_distance' or 'euclidean'), or a callable that takes two vectors as input and returns a scalar value as output. To set different metrics of the three different axes, you can pass a list/tuple of size three that describes the metrics you're interested in. By default (None), normalized scalar projection (i.e. > operator) is used. None
annot bool drawn points should be annotated True

Usage

from whatlies.language import SpacyLanguage
from whatlies.transformers import Pca

words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
         "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
         "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
         "person", "family", "brother", "sister"]

lang = SpacyLanguage("en_core_web_sm")
emb = lang[words]

emb.transform(Pca(3)).plot_3d(annot=True)
emb.transform(Pca(3)).plot_3d("king", "dog", "red")
emb.transform(Pca(3)).plot_3d("king", "dog", "red", axis_metric="cosine_distance")

plot_brush(self, x_axis=0, y_axis=1, axis_metric=None, x_label=None, y_label=None, title=None, annot=False, color=None, n_show=15, interactive=False)

Show source code in whatlies/embeddingset.py
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    def plot_brush(
        self,
        x_axis: Union[int, str, Embedding] = 0,
        y_axis: Union[int, str, Embedding] = 1,
        axis_metric: Optional[Union[str, Callable, Sequence]] = None,
        x_label: Optional[str] = None,
        y_label: Optional[str] = None,
        title: Optional[str] = None,
        annot: bool = False,
        color: Union[None, str] = None,
        n_show: int = 15,
        interactive: bool = False,
    ):
        """
        Makes an interactive plot with a brush element.

        Arguments:
            x_axis: the x-axis to be used, must be given when dim > 2; if an integer, the corresponding
                dimension of embedding is used.
            y_axis: the y-axis to be used, must be given when dim > 2; if an integer, the corresponding
                dimension of embedding is used.
            axis_metric: the metric used to project each embedding on the axes; only used when the corresponding
                axis (i.e. `x_axis` or `y_axis`) is a string or an `Embedding` instance. It could be a string
                (`'cosine_similarity'`, `'cosine_distance'` or `'euclidean'`), or a callable that takes two vectors as input
                and returns a scalar value as output. To set different metrics for x- and y-axis, a list or a tuple of
                two elements could be given. By default (`None`), normalized scalar projection (i.e. `>` operator) is used.
            x_label: an optional label used for x-axis; if not given, it is set based on `x_axis` value.
            y_label: an optional label used for y-axis; if not given, it is set based on `y_axis` value.
            title: an optional title for the plot; if not given, it is set based on `x_axis` and `y_axis` values.
            annot: drawn points should be annotated
            color: a property that will be used for plotting
            n_show: number of points to show in text selection
            interactive: turn on/off the zoom/panning feature; if turned on, zoom/pan can be triggered when shift key is held

        **Usage**

        ```python
        from whatlies.language import SpacyLanguage
        from whatlies.transformers import Pca

        words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
                 "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
                 "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
                 "person", "family", "brother", "sister"]

        lang = SpacyLanguage("en_core_web_sm")
        emb = lang[words].transform(Pca(2))

        emb.plot_brush()
        ```
        """
        if isinstance(x_axis, str):
            x_axis = self[x_axis]
        if isinstance(y_axis, str):
            y_axis = self[y_axis]

        if isinstance(axis_metric, (list, tuple)):
            x_axis_metric = axis_metric[0]
            y_axis_metric = axis_metric[1]
        else:
            x_axis_metric = axis_metric
            y_axis_metric = axis_metric

        # Determine axes values and labels
        if isinstance(x_axis, int):
            x_val = self.to_X()[:, x_axis]
            x_lab = "Dimension " + str(x_axis)
        else:
            x_axis_metric = Embedding._get_plot_axis_metric_callable(x_axis_metric)
            x_val = self.compare_against(x_axis, mapping=x_axis_metric)
            x_lab = x_axis.name

        if isinstance(y_axis, int):
            y_val = self.to_X()[:, y_axis]
            y_lab = "Dimension " + str(y_axis)
        else:
            y_axis_metric = Embedding._get_plot_axis_metric_callable(y_axis_metric)
            y_val = self.compare_against(y_axis, mapping=y_axis_metric)
            y_lab = y_axis.name
        x_label = x_label if x_label is not None else x_lab
        y_label = y_label if y_label is not None else y_lab
        title = title if title is not None else "Click and Drag Here"

        plot_df = pd.DataFrame(
            {
                "x_axis": x_val,
                "y_axis": y_val,
                "name": [v.name for v in self.embeddings.values()],
                "original": [v.orig for v in self.embeddings.values()],
            }
        )

        if color:
            plot_df[color] = [
                getattr(v, color) if hasattr(v, color) else ""
                for v in self.embeddings.values()
            ]

        result = (
            alt.Chart(plot_df)
            .mark_circle(size=60)
            .encode(
                x=alt.X("x_axis", axis=alt.Axis(title=x_label)),
                y=alt.X("y_axis", axis=alt.Axis(title=y_label)),
                tooltip=["name", "original"],
                color=alt.Color(":N", legend=None) if not color else alt.Color(color),
            )
            .properties(title=title)
        )

        if annot:
            text = (
                alt.Chart(plot_df)
                .mark_text(dx=-15, dy=3, color="black")
                .encode(
                    x="x_axis",
                    y="y_axis",
                    text="original",
                )
            )
            result = result + text

        brush = alt.selection_interval(
            on="[mousedown[!event.shiftKey], mouseup] > mousemove",
            translate="[mousedown[!event.shiftKey], mouseup] > mousemove!",
        )

        ranked_text = (
            alt.Chart(plot_df)
            .mark_text()
            .encode(
                y=alt.Y("row_number:O", axis=None),
                color=alt.Color(":N", legend=None) if not color else alt.Color(color),
            )
            .transform_window(row_number="row_number()")
            .transform_filter(brush)
            .transform_window(rank="rank(row_number)")
            .transform_filter(alt.datum.rank < n_show)
        )

        text_plt = ranked_text.encode(text="original:N").properties(
            width=250, title="Text Selection"
        )

        if interactive:
            zoom = alt.selection_interval(
                bind="scales",
                on="[mousedown[event.shiftKey], mouseup] > mousemove",
                translate="[mousedown[event.shiftKey], mouseup] > mousemove!",
            )
            result = result.add_selection(zoom, brush)
        else:
            result = result.add_selection(brush)

        return result | text_plt

Makes an interactive plot with a brush element.

Parameters

Name Type Description Default
x_axis Union[int, str, whatlies.embedding.Embedding] the x-axis to be used, must be given when dim > 2; if an integer, the corresponding dimension of embedding is used. 0
y_axis Union[int, str, whatlies.embedding.Embedding] the y-axis to be used, must be given when dim > 2; if an integer, the corresponding dimension of embedding is used. 1
axis_metric Optional[Union[str, Callable, Sequence]] the metric used to project each embedding on the axes; only used when the corresponding axis (i.e. x_axis or y_axis) is a string or an Embedding instance. It could be a string ('cosine_similarity', 'cosine_distance' or 'euclidean'), or a callable that takes two vectors as input and returns a scalar value as output. To set different metrics for x- and y-axis, a list or a tuple of two elements could be given. By default (None), normalized scalar projection (i.e. > operator) is used. None
x_label Optional[str] an optional label used for x-axis; if not given, it is set based on x_axis value. None
y_label Optional[str] an optional label used for y-axis; if not given, it is set based on y_axis value. None
title Optional[str] an optional title for the plot; if not given, it is set based on x_axis and y_axis values. None
annot bool drawn points should be annotated False
color Union[NoneType, str] a property that will be used for plotting None
n_show int number of points to show in text selection 15
interactive bool turn on/off the zoom/panning feature; if turned on, zoom/pan can be triggered when shift key is held False

Usage

from whatlies.language import SpacyLanguage
from whatlies.transformers import Pca

words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
         "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
         "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
         "person", "family", "brother", "sister"]

lang = SpacyLanguage("en_core_web_sm")
emb = lang[words].transform(Pca(2))

emb.plot_brush()

plot_distance(self, metric='cosine', norm=False)

Show source code in whatlies/embeddingset.py
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    def plot_distance(self, metric="cosine", norm=False):
        """
        Make a distance plot. Shows you the distance between all the word embeddings in the set.

        Arguments:
            metric: `'cosine'`, `'correlation'` or `'euclidean'`
            norm: normalise the vectors before calculating the distances

        Usage:

        ```python
        from whatlies.language import SpacyLanguage
        lang = SpacyLanguage("en_core_web_sm")

        names = ['red', 'blue', 'green', 'yellow', 'cat', 'dog', 'mouse', 'rat', 'bike', 'car']
        emb = lang[names]
        emb.plot_distance(metric='cosine')
        emb.plot_distance(metric='euclidean')
        emb.plot_distance(metric='correlation')
        ```
        """
        allowed_metrics = ["cosine", "correlation", "euclidean"]
        if metric not in allowed_metrics:
            raise ValueError(
                f"The `metric` argument must be in {allowed_metrics}, got: {metric}."
            )

        vmin, vmax = 0, 1
        X = self.to_X(norm=norm)
        if metric == "cosine":
            distances = cosine_distances(X)
        if metric == "correlation":
            distances = 1 - np.corrcoef(X)
            vmin, vmax = -1, 1
        if metric == "euclidean":
            distances = euclidean_distances(X)
            vmin, vmax = 0, np.max(distances)

        fig, ax = plt.subplots()
        plt.imshow(distances, cmap=plt.cm.get_cmap().reversed(), vmin=vmin, vmax=vmax)
        plt.xticks(range(len(self)), self.embeddings.keys())
        plt.yticks(range(len(self)), self.embeddings.keys())
        plt.colorbar()

        # Rotate the tick labels and set their alignment.
        plt.setp(ax.get_xticklabels(), rotation=90, ha="right", rotation_mode="anchor")

Make a distance plot. Shows you the distance between all the word embeddings in the set.

Parameters

Name Type Description Default
metric 'cosine', 'correlation' or 'euclidean' 'cosine'
norm normalise the vectors before calculating the distances False

Usage:

from whatlies.language import SpacyLanguage
lang = SpacyLanguage("en_core_web_sm")

names = ['red', 'blue', 'green', 'yellow', 'cat', 'dog', 'mouse', 'rat', 'bike', 'car']
emb = lang[names]
emb.plot_distance(metric='cosine')
emb.plot_distance(metric='euclidean')
emb.plot_distance(metric='correlation')

plot_interactive(self, x_axis=0, y_axis=1, axis_metric=None, x_label=None, y_label=None, title=None, annot=True, color=None, interactive=True)

Show source code in whatlies/embeddingset.py
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    def plot_interactive(
        self,
        x_axis: Union[int, str, Embedding] = 0,
        y_axis: Union[int, str, Embedding] = 1,
        axis_metric: Optional[Union[str, Callable, Sequence]] = None,
        x_label: Optional[str] = None,
        y_label: Optional[str] = None,
        title: Optional[str] = None,
        annot: bool = True,
        color: Union[None, str] = None,
        interactive: bool = True,
    ):
        """
        Makes highly interactive plot of the set of embeddings.

        Arguments:
            x_axis: the x-axis to be used, must be given when dim > 2; if an integer, the corresponding
                dimension of embedding is used.
            y_axis: the y-axis to be used, must be given when dim > 2; if an integer, the corresponding
                dimension of embedding is used.
            axis_metric: the metric used to project each embedding on the axes; only used when the corresponding
                axis (i.e. `x_axis` or `y_axis`) is a string or an `Embedding` instance. It could be a string
                (`'cosine_similarity'`, `'cosine_distance'` or `'euclidean'`), or a callable that takes two vectors as input
                and returns a scalar value as output. To set different metrics for x- and y-axis, a list or a tuple of
                two elements could be given. By default (`None`), normalized scalar projection (i.e. `>` operator) is used.
            x_label: an optional label used for x-axis; if not given, it is set based on `x_axis` value.
            y_label: an optional label used for y-axis; if not given, it is set based on `y_axis` value.
            title: an optional title for the plot; if not given, it is set based on `x_axis` and `y_axis` values.
            annot: drawn points should be annotated
            color: a property that will be used for plotting
            interactive: turn on/off the zoom/panning feature

        **Usage**

        ```python
        from whatlies.language import SpacyLanguage

        words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
                 "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
                 "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
                 "person", "family", "brother", "sister"]

        lang = SpacyLanguage("en_core_web_sm")
        emb = lang[words]

        emb.plot_interactive('man', 'woman')
        ```
        """
        if isinstance(x_axis, str):
            x_axis = self[x_axis]
        if isinstance(y_axis, str):
            y_axis = self[y_axis]

        if isinstance(axis_metric, (list, tuple)):
            x_axis_metric = axis_metric[0]
            y_axis_metric = axis_metric[1]
        else:
            x_axis_metric = axis_metric
            y_axis_metric = axis_metric

        # Determine axes values and labels
        if isinstance(x_axis, int):
            x_val = self.to_X()[:, x_axis]
            x_lab = "Dimension " + str(x_axis)
        else:
            x_axis_metric = Embedding._get_plot_axis_metric_callable(x_axis_metric)
            x_val = self.compare_against(x_axis, mapping=x_axis_metric)
            x_lab = x_axis.name

        if isinstance(y_axis, int):
            y_val = self.to_X()[:, y_axis]
            y_lab = "Dimension " + str(y_axis)
        else:
            y_axis_metric = Embedding._get_plot_axis_metric_callable(y_axis_metric)
            y_val = self.compare_against(y_axis, mapping=y_axis_metric)
            y_lab = y_axis.name
        x_label = x_label if x_label is not None else x_lab
        y_label = y_label if y_label is not None else y_lab
        title = title if title is not None else f"{x_lab} vs. {y_lab}"

        plot_df = pd.DataFrame(
            {
                "x_axis": x_val,
                "y_axis": y_val,
                "name": [v.name for v in self.embeddings.values()],
                "original": [v.orig for v in self.embeddings.values()],
            }
        )

        if color:
            plot_df[color] = [
                getattr(v, color) if hasattr(v, color) else ""
                for v in self.embeddings.values()
            ]

        result = (
            alt.Chart(plot_df)
            .mark_circle(size=60)
            .encode(
                x=alt.X("x_axis", axis=alt.Axis(title=x_label)),
                y=alt.X("y_axis", axis=alt.Axis(title=y_label)),
                tooltip=["name", "original"],
                color=alt.Color(":N", legend=None) if not color else alt.Color(color),
            )
            .properties(title=title)
        )
        if interactive:
            result = result.interactive()

        if annot:
            text = (
                alt.Chart(plot_df)
                .mark_text(dx=-15, dy=3, color="black")
                .encode(
                    x="x_axis",
                    y="y_axis",
                    text="original",
                )
            )
            result = result + text
        return result

Makes highly interactive plot of the set of embeddings.

Parameters

Name Type Description Default
x_axis Union[int, str, whatlies.embedding.Embedding] the x-axis to be used, must be given when dim > 2; if an integer, the corresponding dimension of embedding is used. 0
y_axis Union[int, str, whatlies.embedding.Embedding] the y-axis to be used, must be given when dim > 2; if an integer, the corresponding dimension of embedding is used. 1
axis_metric Optional[Union[str, Callable, Sequence]] the metric used to project each embedding on the axes; only used when the corresponding axis (i.e. x_axis or y_axis) is a string or an Embedding instance. It could be a string ('cosine_similarity', 'cosine_distance' or 'euclidean'), or a callable that takes two vectors as input and returns a scalar value as output. To set different metrics for x- and y-axis, a list or a tuple of two elements could be given. By default (None), normalized scalar projection (i.e. > operator) is used. None
x_label Optional[str] an optional label used for x-axis; if not given, it is set based on x_axis value. None
y_label Optional[str] an optional label used for y-axis; if not given, it is set based on y_axis value. None
title Optional[str] an optional title for the plot; if not given, it is set based on x_axis and y_axis values. None
annot bool drawn points should be annotated True
color Union[NoneType, str] a property that will be used for plotting None
interactive bool turn on/off the zoom/panning feature True

Usage

from whatlies.language import SpacyLanguage

words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
         "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
         "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
         "person", "family", "brother", "sister"]

lang = SpacyLanguage("en_core_web_sm")
emb = lang[words]

emb.plot_interactive('man', 'woman')

plot_interactive_matrix(self, *axes, axes_metric=None, annot=True, width=200, height=200)

Show source code in whatlies/embeddingset.py
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    def plot_interactive_matrix(
        self,
        *axes: Union[int, str, Embedding],
        axes_metric: Optional[Union[str, Callable, Sequence]] = None,
        annot: bool = True,
        width: int = 200,
        height: int = 200,
    ):
        """
        Makes highly interactive plot of the set of embeddings.

        Arguments:
            axes: the axes that we wish to plot; each could be either an integer, the name of
                an existing embedding, or an `Embedding` instance (default: `0, 1`).
            axes_metric: the metric used to project each embedding on the axes; only used when the corresponding
                axis is a string or an `Embedding` instance. It could be a string (`'cosine_similarity'`,
                `'cosine_distance'` or `'euclidean'`), or a callable that takes two vectors as input and
                returns a scalar value as output. To set different metrics for different axes, a list or a tuple of
                the same length as `axes` could be given. By default (`None`), normalized scalar projection (i.e. `>` operator) is used.
            annot: drawn points should be annotated
            width: width of the visual
            height: height of the visual

        **Usage**

        ```python
        from whatlies.language import SpacyLanguage
        from whatlies.transformers import Pca

        words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
                 "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
                 "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
                 "person", "family", "brother", "sister"]

        lang = SpacyLanguage("en_core_web_sm")
        emb = lang[words]

        emb.transform(Pca(3)).plot_interactive_matrix(0, 1, 2)
        ```
        """
        # Set default value of axes, if not given.
        if len(axes) == 0:
            axes = [0, 1]

        if isinstance(axes_metric, (list, tuple)) and len(axes_metric) != len(axes):
            raise ValueError(
                f"The number of given axes metrics should be the same as the number of given axes. Got {len(axes)} axes vs. {len(axes_metric)} metrics."
            )
        if not isinstance(axes_metric, (list, tuple)):
            axes_metric = [axes_metric] * len(axes)

        # Get values of each axis according to their type.
        axes_vals = {}
        X = self.to_X()
        for axis, metric in zip(axes, axes_metric):
            if isinstance(axis, int):
                vals = X[:, axis]
                axes_vals["Dimension " + str(axis)] = vals
            else:
                if isinstance(axis, str):
                    axis = self[axis]
                metric = Embedding._get_plot_axis_metric_callable(metric)
                vals = self.compare_against(axis, mapping=metric)
                axes_vals[axis.name] = vals

        plot_df = pd.DataFrame(axes_vals)
        plot_df["name"] = [v.name for v in self.embeddings.values()]
        plot_df["original"] = [v.orig for v in self.embeddings.values()]
        axes_names = list(axes_vals.keys())

        result = (
            alt.Chart(plot_df)
            .mark_circle()
            .encode(
                x=alt.X(alt.repeat("column"), type="quantitative"),
                y=alt.Y(alt.repeat("row"), type="quantitative"),
                tooltip=["name", "original"],
            )
        )
        if annot:
            text_stuff = result.mark_text(dx=-15, dy=3, color="black").encode(
                text="original",
            )
            result = result + text_stuff

        result = (
            result.properties(width=width, height=height)
            .repeat(row=axes_names[::-1], column=axes_names)
            .interactive()
        )

        return result

Makes highly interactive plot of the set of embeddings.

Parameters

Name Type Description Default
*axes Union[int, str, whatlies.embedding.Embedding] the axes that we wish to plot; each could be either an integer, the name of an existing embedding, or an Embedding instance (default: 0, 1). ()
axes_metric Optional[Union[str, Callable, Sequence]] the metric used to project each embedding on the axes; only used when the corresponding axis is a string or an Embedding instance. It could be a string ('cosine_similarity', 'cosine_distance' or 'euclidean'), or a callable that takes two vectors as input and returns a scalar value as output. To set different metrics for different axes, a list or a tuple of the same length as axes could be given. By default (None), normalized scalar projection (i.e. > operator) is used. None
annot bool drawn points should be annotated True
width int width of the visual 200
height int height of the visual 200

Usage

from whatlies.language import SpacyLanguage
from whatlies.transformers import Pca

words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
         "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
         "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
         "person", "family", "brother", "sister"]

lang = SpacyLanguage("en_core_web_sm")
emb = lang[words]

emb.transform(Pca(3)).plot_interactive_matrix(0, 1, 2)

plot_movement(self, other, x_axis, y_axis, first_group_name='before', second_group_name='after', annot=True)

Show source code in whatlies/embeddingset.py
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    def plot_movement(
        self,
        other,
        x_axis: Union[str, Embedding],
        y_axis: Union[str, Embedding],
        first_group_name="before",
        second_group_name="after",
        annot: bool = True,
    ):
        """
        Makes highly interactive plot of the movement of embeddings
        between two sets of embeddings.

        Arguments:
            other: the other embeddingset
            x_axis: the x-axis to be used, must be given when dim > 2
            y_axis: the y-axis to be used, must be given when dim > 2
            first_group_name: the name to give to the first set of embeddings (default: "before")
            second_group_name: the name to give to the second set of embeddings (default: "after")
            annot: drawn points should be annotated

        **Usage**

        ```python
        from whatlies.language import SpacyLanguage

        words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
                 "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
                 "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
                 "person", "family", "brother", "sister"]

        lang = SpacyLanguage("en_core_web_sm")
        emb = lang[words]
        emb_new = emb - emb['king']

        emb.plot_movement(emb_new, 'man', 'woman')
        ```
        """
        if isinstance(x_axis, str):
            x_axis = self[x_axis]
        if isinstance(y_axis, str):
            y_axis = self[y_axis]

        df1 = (
            self.to_axis_df(x_axis, y_axis).set_index("original").drop(columns=["name"])
        )
        df2 = (
            other.to_axis_df(x_axis, y_axis)
            .set_index("original")
            .drop(columns=["name"])
            .loc[lambda d: d.index.isin(df1.index)]
        )
        df_draw = (
            pd.concat([df1, df2])
            .reset_index()
            .sort_values(["original"])
            .assign(constant=1)
        )

        plots = []
        for idx, grp_df in df_draw.groupby("original"):
            _ = (
                alt.Chart(grp_df)
                .mark_line(color="gray", strokeDash=[2, 1])
                .encode(x="x_axis:Q", y="y_axis:Q")
            )
            plots.append(_)
        p0 = reduce(lambda x, y: x + y, plots)

        p1 = (
            deepcopy(self)
            .add_property("group", lambda d: first_group_name)
            .plot_interactive(x_axis, y_axis, annot=annot, color="group")
        )
        p2 = (
            deepcopy(other)
            .add_property("group", lambda d: second_group_name)
            .plot_interactive(x_axis, y_axis, annot=annot, color="group")
        )
        return p0 + p1 + p2

Makes highly interactive plot of the movement of embeddings between two sets of embeddings.

Parameters

Name Type Description Default
other the other embeddingset required
x_axis Union[str, whatlies.embedding.Embedding] the x-axis to be used, must be given when dim > 2 required
y_axis Union[str, whatlies.embedding.Embedding] the y-axis to be used, must be given when dim > 2 required
first_group_name the name to give to the first set of embeddings (default: "before") 'before'
second_group_name the name to give to the second set of embeddings (default: "after") 'after'
annot bool drawn points should be annotated True

Usage

from whatlies.language import SpacyLanguage

words = ["prince", "princess", "nurse", "doctor", "banker", "man", "woman",
         "cousin", "neice", "king", "queen", "dude", "guy", "gal", "fire",
         "dog", "cat", "mouse", "red", "blue", "green", "yellow", "water",
         "person", "family", "brother", "sister"]

lang = SpacyLanguage("en_core_web_sm")
emb = lang[words]
emb_new = emb - emb['king']

emb.plot_movement(emb_new, 'man', 'woman')

plot_pixels(self)

Show source code in whatlies/embeddingset.py
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    def plot_pixels(self):
        """
        Makes a pixelchart of every embedding in the set.

        Usage:

        ```python
        from whatlies.language import SpacyLanguage
        from whatlies.transformers import Pca

        lang = SpacyLanguage("en_core_web_sm")

        names = ['red', 'blue', 'green', 'yellow',
                 'cat', 'dog', 'mouse', 'rat',
                 'bike', 'car', 'motor', 'cycle',
                 'firehydrant', 'japan', 'germany', 'belgium']
        emb = lang[names].transform(Pca(12)).filter(lambda e: 'pca' not in e.name)
        emb.plot_pixels()
        ```

        ![](https://rasahq.github.io/whatlies/images/pixels.png)
        """
        names = self.embeddings.keys()
        df = self.to_dataframe()
        plt.matshow(df)
        plt.yticks(range(len(names)), names)

Makes a pixelchart of every embedding in the set.

Usage:

from whatlies.language import SpacyLanguage
from whatlies.transformers import Pca

lang = SpacyLanguage("en_core_web_sm")

names = ['red', 'blue', 'green', 'yellow',
         'cat', 'dog', 'mouse', 'rat',
         'bike', 'car', 'motor', 'cycle',
         'firehydrant', 'japan', 'germany', 'belgium']
emb = lang[names].transform(Pca(12)).filter(lambda e: 'pca' not in e.name)
emb.plot_pixels()

plot_similarity(self, metric='cosine', norm=False)

Show source code in whatlies/embeddingset.py
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    def plot_similarity(self, metric="cosine", norm=False):
        """
        Make a similarity plot. Shows you the similarity between all the word embeddings in the set.

        Arguments:
            metric: `'cosine'` or `'correlation'`
            norm: normalise the embeddings before calculating the similarity

        Usage:

        ```python
        from whatlies.language import SpacyLanguage
        lang = SpacyLanguage("en_core_web_sm")

        names = ['red', 'blue', 'green', 'yellow', 'cat', 'dog', 'mouse', 'rat', 'bike', 'car']
        emb = lang[names]
        emb.plot_similarity()
        emb.plot_similarity(metric='correlation')
        ```
        """
        allowed_metrics = ["cosine", "correlation"]
        if metric not in allowed_metrics:
            raise ValueError(
                f"The `metric` argument must be in {allowed_metrics}, got: {metric}."
            )

        vmin, vmax = 0, 1
        X = self.to_X(norm=norm)
        if metric == "cosine":
            similarity = cosine_similarity(X)
        if metric == "correlation":
            similarity = np.corrcoef(X)
            vmin, vmax = -1, 1

        fig, ax = plt.subplots()
        plt.imshow(similarity, cmap=plt.cm.get_cmap(), vmin=-vmin, vmax=vmax)
        plt.xticks(range(len(self)), self.embeddings.keys())
        plt.yticks(range(len(self)), self.embeddings.keys())
        plt.colorbar()

        # Rotate the tick labels and set their alignment.
        plt.setp(ax.get_xticklabels(), rotation=90, ha="right", rotation_mode="anchor")

Make a similarity plot. Shows you the similarity between all the word embeddings in the set.

Parameters

Name Type Description Default
metric 'cosine' or 'correlation' 'cosine'
norm normalise the embeddings before calculating the similarity False

Usage:

from whatlies.language import SpacyLanguage
lang = SpacyLanguage("en_core_web_sm")

names = ['red', 'blue', 'green', 'yellow', 'cat', 'dog', 'mouse', 'rat', 'bike', 'car']
emb = lang[names]
emb.plot_similarity()
emb.plot_similarity(metric='correlation')

score_similar(self, emb, n=10, metric='cosine')

Show source code in whatlies/embeddingset.py
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    def score_similar(self, emb: Union[str, Embedding], n: int = 10, metric="cosine"):
        """
        Retreive a list of (Embedding, score) tuples that are the most similar to the passed query.

        Arguments:
            emb: query to use
            n: the number of items you'd like to see returned
            metric: metric to use to calculate distance, must be scipy or sklearn compatible

        Returns:
            An list of ([Embedding][whatlies.embedding.Embedding], score) tuples.
        """
        if n > len(self):
            raise ValueError(
                f"You cannot retreive (n={n}) more items than exist in the Embeddingset (len={len(self)})"
            )

        if isinstance(emb, str):
            if emb not in self.embeddings.keys():
                raise ValueError(
                    f"Embedding for `{emb}` does not exist in this EmbeddingSet"
                )
            emb = self[emb]

        vec = emb.vector
        queries = [w for w in self.embeddings.keys()]
        vector_matrix = self.to_X()
        distances = pairwise_distances(vector_matrix, vec.reshape(1, -1), metric=metric)
        by_similarity = sorted(zip(queries, distances), key=lambda z: z[1])
        return [(self[q], float(d)) for q, d in by_similarity[:n]]

Retreive a list of (Embedding, score) tuples that are the most similar to the passed query.

Parameters

Name Type Description Default
emb Union[str, whatlies.embedding.Embedding] query to use required
n int the number of items you'd like to see returned 10
metric metric to use to calculate distance, must be scipy or sklearn compatible 'cosine'

Returns

Type Description
`` An list of (Embedding, score) tuples.

to_dataframe(self)

Show source code in whatlies/embeddingset.py
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    def to_dataframe(self):
        """
        Turns the embeddingset into a pandas dataframe.
        """
        mat = self.to_matrix()
        return pd.DataFrame(mat, index=list(self.embeddings.keys()))

Turns the embeddingset into a pandas dataframe.

to_matrix(self)

Show source code in whatlies/embeddingset.py
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    def to_matrix(self):
        """
        Does exactly the same as `.to_X`. It takes the embedding vectors and turns it into a numpy array.
        """
        return self.to_X()

Does exactly the same as .to_X. It takes the embedding vectors and turns it into a numpy array.

to_names_X(self)

Show source code in whatlies/embeddingset.py
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    def to_names_X(self):
        """
        Get the list of names as well as an array of vectors of all embeddings.

        Usage:

        ```python
        from whatlies.embedding import Embedding
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3])
        bar = Embedding("bar", [0.7, 0.2])
        buz = Embedding("buz", [0.1, 0.9])
        emb = EmbeddingSet(foo, bar, buz)

        names, X = emb.to_names_X()
        ```
        """
        return list(self.embeddings.keys()), self.to_X()

Get the list of names as well as an array of vectors of all embeddings.

Usage:

from whatlies.embedding import Embedding
from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3])
bar = Embedding("bar", [0.7, 0.2])
buz = Embedding("buz", [0.1, 0.9])
emb = EmbeddingSet(foo, bar, buz)

names, X = emb.to_names_X()

to_X(self, norm=False)

Show source code in whatlies/embeddingset.py
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    def to_X(self, norm=False):
        """
        Takes every vector in each embedding and turns it into a scikit-learn compatible `X` matrix.

        Usage:

        ```python
        from whatlies.embedding import Embedding
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3])
        bar = Embedding("bar", [0.7, 0.2])
        buz = Embedding("buz", [0.1, 0.9])
        emb = EmbeddingSet(foo, bar, buz)

        X = emb.to_X()
        ```
        """
        X = np.array([i.vector for i in self.embeddings.values()])
        X = normalize(X) if norm else X
        return X

Takes every vector in each embedding and turns it into a scikit-learn compatible X matrix.

Usage:

from whatlies.embedding import Embedding
from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3])
bar = Embedding("bar", [0.7, 0.2])
buz = Embedding("buz", [0.1, 0.9])
emb = EmbeddingSet(foo, bar, buz)

X = emb.to_X()

to_X_y(self, y_label)

Show source code in whatlies/embeddingset.py
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    def to_X_y(self, y_label):
        """
        Takes every vector in each embedding and turns it into a scikit-learn compatible `X` matrix.
        Also retreives an array with potential labels.

        Usage:

        ```python
        from whatlies.embedding import Embedding
        from whatlies.embeddingset import EmbeddingSet

        foo = Embedding("foo", [0.1, 0.3])
        bar = Embedding("bar", [0.7, 0.2])
        buz = Embedding("buz", [0.1, 0.9])
        bla = Embedding("bla", [0.2, 0.8])

        emb1 = EmbeddingSet(foo, bar).add_property("label", lambda d: 'group-one')
        emb2 = EmbeddingSet(buz, bla).add_property("label", lambda d: 'group-two')
        emb = emb1.merge(emb2)

        X, y = emb.to_X_y(y_label='label')
        ```
        """
        X = self.to_X()
        y = np.array([getattr(e, y_label) for e in self.embeddings.values()])
        return X, y

Takes every vector in each embedding and turns it into a scikit-learn compatible X matrix. Also retreives an array with potential labels.

Usage:

from whatlies.embedding import Embedding
from whatlies.embeddingset import EmbeddingSet

foo = Embedding("foo", [0.1, 0.3])
bar = Embedding("bar", [0.7, 0.2])
buz = Embedding("buz", [0.1, 0.9])
bla = Embedding("bla", [0.2, 0.8])

emb1 = EmbeddingSet(foo, bar).add_property("label", lambda d: 'group-one')
emb2 = EmbeddingSet(buz, bla).add_property("label", lambda d: 'group-two')
emb = emb1.merge(emb2)

X, y = emb.to_X_y(y_label='label')

transform(self, transformer)

Show source code in whatlies/embeddingset.py
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    def transform(self, transformer):
        """
        Applies a transformation on the entire set.

        Usage:

        ```python
        from whatlies.embeddingset import EmbeddingSet
        from whatlies.transformers import Pca

        foo = Embedding("foo", [0.1, 0.3, 0.10])
        bar = Embedding("bar", [0.7, 0.2, 0.11])
        buz = Embedding("buz", [0.1, 0.9, 0.12])
        emb = EmbeddingSet(foo, bar, buz).transform(Pca(2))
        ```
        """
        return transformer(self)

Applies a transformation on the entire set.

Usage:

from whatlies.embeddingset import EmbeddingSet
from whatlies.transformers import Pca

foo = Embedding("foo", [0.1, 0.3, 0.10])
bar = Embedding("bar", [0.7, 0.2, 0.11])
buz = Embedding("buz", [0.1, 0.9, 0.12])
emb = EmbeddingSet(foo, bar, buz).transform(Pca(2))