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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137 | 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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95 | 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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395 | 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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115 | 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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181 | 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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203 | 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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159 | 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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520 | 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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496 | 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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545 | 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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225 | 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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560 | 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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424 | 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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355 | @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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448 | 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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641 | 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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255 | 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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732 | 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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876 | 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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1352 | 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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966 | 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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1196 | 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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1445 | 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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1074 | 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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993 | 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()
```
"""
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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919 | 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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591 | 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
| 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
| 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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324 | 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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277 | 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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304 | 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')
Show source code in whatlies/embeddingset.py
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373 | 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))