# Source code for yellowbrick.text.tsne

```
# yellowbrick.text.tsne
# Implements TSNE visualizations of documents in 2D space.
#
# Author: Benjamin Bengfort
# Author: Rebecca Bilbro
# Created: Mon Feb 20 06:33:29 2017 -0500
#
# Copyright (C) 2016 The scikit-yb developers
# For license information, see LICENSE.txt
#
# ID: tsne.py [6aa9198] benjamin@bengfort.com $
"""
Implements TSNE visualizations of documents in 2D space.
"""
##########################################################################
## Imports
##########################################################################
import numpy as np
from collections import defaultdict
from yellowbrick.draw import manual_legend
from yellowbrick.text.base import TextVisualizer
from yellowbrick.style.colors import resolve_colors
from yellowbrick.exceptions import YellowbrickValueError
from sklearn.manifold import TSNE
from sklearn.pipeline import Pipeline
from sklearn.decomposition import TruncatedSVD, PCA
##########################################################################
## Quick Methods
##########################################################################
[docs]def tsne(
X,
y=None,
ax=None,
decompose="svd",
decompose_by=50,
labels=None,
colors=None,
colormap=None,
alpha=0.7,
show=True,
**kwargs
):
"""
Display a projection of a vectorized corpus in two dimensions using TSNE,
a nonlinear dimensionality reduction method that is particularly well
suited to embedding in two or three dimensions for visualization as a
scatter plot. TSNE is widely used in text analysis to show clusters or
groups of documents or utterances and their relative proximities.
Parameters
----------
X : ndarray or DataFrame of shape n x m
A matrix of n instances with m features representing the corpus of
vectorized documents to visualize with tsne.
y : ndarray or Series of length n
An optional array or series of target or class values for instances.
If this is specified, then the points will be colored according to
their class. Often cluster labels are passed in to color the documents
in cluster space, so this method is used both for classification and
clustering methods.
ax : matplotlib axes
The axes to plot the figure on.
decompose : string or None
A preliminary decomposition is often used prior to TSNE to make the
projection faster. Specify `"svd"` for sparse data or `"pca"` for
dense data. If decompose is None, the original data set will be used.
decompose_by : int
Specify the number of components for preliminary decomposition, by
default this is 50; the more components, the slower TSNE will be.
labels : list of strings
The names of the classes in the target, used to create a legend.
colors : list or tuple of colors
Specify the colors for each individual class
colormap : string or matplotlib cmap
Sequential colormap for continuous target
alpha : float, default: 0.7
Specify a transparency where 1 is completely opaque and 0 is completely
transparent. This property makes densely clustered points more visible.
show : bool, default: True
If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot
call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply
calls ``finalize()``
kwargs : dict
Pass any additional keyword arguments to the TSNE transformer.
Example
--------
>>> from yellowbrick.text.tsne import tsne
>>> from sklearn.feature_extraction.text import TfidfVectorizer
>>> from yellowbrick.datasets import load_hobbies
>>> corpus = load_hobbies()
>>> tfidf = TfidfVectorizer()
>>> X = tfidf.fit_transform(corpus.data)
>>> y = corpus.target
>>> tsne(X, y)
Returns
-------
visualizer: TSNEVisualizer
Returns the fitted, finalized visualizer
"""
# Instantiate the visualizer
visualizer = TSNEVisualizer(
ax=ax,
decompose=decompose,
decompose_by=decompose_by,
labels=labels,
colors=colors,
colormap=colormap,
alpha=alpha,
**kwargs
)
# Fit and transform the visualizer (calls draw)
visualizer.fit(X, y, **kwargs)
if show:
visualizer.show()
else:
visualizer.finalize()
# Return the visualizer object
return visualizer
##########################################################################
## TSNEVisualizer
##########################################################################
[docs]class TSNEVisualizer(TextVisualizer):
"""
Display a projection of a vectorized corpus in two dimensions using TSNE,
a nonlinear dimensionality reduction method that is particularly well
suited to embedding in two or three dimensions for visualization as a
scatter plot. TSNE is widely used in text analysis to show clusters or
groups of documents or utterances and their relative proximities.
TSNE will return a scatter plot of the vectorized corpus, such that each
point represents a document or utterance. The distance between two points
in the visual space is embedded using the probability distribution of
pairwise similarities in the higher dimensionality; thus TSNE shows
clusters of similar documents and the relationships between groups of
documents as a scatter plot.
TSNE can be used with either clustering or classification; by specifying
the ``classes`` argument, points will be colored based on their similar
traits. For example, by passing ``cluster.labels_`` as ``y`` in ``fit()``, all
points in the same cluster will be grouped together. This extends the
neighbor embedding with more information about similarity, and can allow
better interpretation of both clusters and classes.
For more, see https://lvdmaaten.github.io/tsne/
Parameters
----------
ax : matplotlib axes
The axes to plot the figure on.
decompose : string or None, default: ``'svd'``
A preliminary decomposition is often used prior to TSNE to make the
projection faster. Specify ``"svd"`` for sparse data or ``"pca"`` for
dense data. If None, the original data set will be used.
decompose_by : int, default: 50
Specify the number of components for preliminary decomposition, by
default this is 50; the more components, the slower TSNE will be.
labels : list of strings
The names of the classes in the target, used to create a legend.
Labels must match names of classes in sorted order.
colors : list or tuple of colors
Specify the colors for each individual class
colormap : string or matplotlib cmap
Sequential colormap for continuous target
random_state : int, RandomState instance or None, optional, default: None
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by np.random. The random state is applied to the preliminary
decomposition as well as tSNE.
alpha : float, default: 0.7
Specify a transparency where 1 is completely opaque and 0 is completely
transparent. This property makes densely clustered points more visible.
kwargs : dict
Pass any additional keyword arguments to the TSNE transformer.
"""
# NOTE: cannot be np.nan
NULL_CLASS = None
def __init__(
self,
ax=None,
decompose="svd",
decompose_by=50,
labels=None,
classes=None,
colors=None,
colormap=None,
random_state=None,
alpha=0.7,
**kwargs
):
# Visual Parameters
self.alpha = alpha
self.labels = labels
self.colors = colors
self.colormap = colormap
self.random_state = random_state
# Fetch TSNE kwargs from kwargs by popping only keys belonging to TSNE params
tsne_kwargs = {
key: kwargs.pop(key) for key in TSNE().get_params() if key in kwargs
}
self.transformer_ = self.make_transformer(decompose, decompose_by, tsne_kwargs)
# Call super at the end so that size and title are set correctly
super(TSNEVisualizer, self).__init__(ax=ax, **kwargs)
[docs] def make_transformer(self, decompose="svd", decompose_by=50, tsne_kwargs={}):
"""
Creates an internal transformer pipeline to project the data set into
2D space using TSNE, applying an pre-decomposition technique ahead of
embedding if necessary. This method will reset the transformer on the
class, and can be used to explore different decompositions.
Parameters
----------
decompose : string or None, default: ``'svd'``
A preliminary decomposition is often used prior to TSNE to make
the projection faster. Specify ``"svd"`` for sparse data or ``"pca"``
for dense data. If decompose is None, the original data set will
be used.
decompose_by : int, default: 50
Specify the number of components for preliminary decomposition, by
default this is 50; the more components, the slower TSNE will be.
Returns
-------
transformer : Pipeline
Pipelined transformer for TSNE projections
"""
# TODO: detect decompose by inferring from sparse matrix or dense or
# If number of features > 50 etc.
decompositions = {"svd": TruncatedSVD, "pca": PCA}
if decompose and decompose.lower() not in decompositions:
raise YellowbrickValueError(
"'{}' is not a valid decomposition, use {}, or None".format(
decompose, ", ".join(decompositions.keys())
)
)
# Create the pipeline steps
steps = []
# Add the pre-decomposition
if decompose:
klass = decompositions[decompose]
steps.append(
(
decompose,
klass(n_components=decompose_by, random_state=self.random_state),
)
)
# Add the TSNE manifold
steps.append(
(
"tsne",
TSNE(n_components=2, random_state=self.random_state, **tsne_kwargs),
)
)
# return the pipeline
return Pipeline(steps)
[docs] def fit(self, X, y=None, **kwargs):
"""
The fit method is the primary drawing input for the TSNE projection
since the visualization requires both X and an optional y value. The
fit method expects an array of numeric vectors, so text documents must
be vectorized before passing them to this method.
Parameters
----------
X : ndarray or DataFrame of shape n x m
A matrix of n instances with m features representing the corpus of
vectorized documents to visualize with tsne.
y : ndarray or Series of length n
An optional array or series of target or class values for
instances. If this is specified, then the points will be colored
according to their class. Often cluster labels are passed in to
color the documents in cluster space, so this method is used both
for classification and clustering methods.
kwargs : dict
Pass generic arguments to the drawing method
Returns
-------
self : instance
Returns the instance of the transformer/visualizer
"""
# Store the classes we observed in y
if y is not None:
self.classes_ = np.unique(y)
elif y is None and self.labels is not None:
self.classes_ = np.array([self.labels[0]])
else:
self.classes_ = np.array([self.NULL_CLASS])
# Fit our internal transformer and transform the data.
vecs = self.transformer_.fit_transform(X)
self.n_instances_ = vecs.shape[0]
# Draw the vectors
self.draw(vecs, y, **kwargs)
# Fit always returns self.
return self
[docs] def draw(self, points, target=None, **kwargs):
"""
Called from the fit method, this method draws the TSNE scatter plot,
from a set of decomposed points in 2 dimensions. This method also
accepts a third dimension, target, which is used to specify the colors
of each of the points. If the target is not specified, then the points
are plotted as a single cloud to show similar documents.
"""
# Resolve the labels with the classes
labels = self.labels if self.labels is not None else self.classes_
if len(labels) != len(self.classes_):
raise YellowbrickValueError(
(
"number of supplied labels ({}) does not "
"match the number of classes ({})"
).format(len(labels), len(self.classes_))
)
# Create the color mapping for the labels.
self.color_values_ = resolve_colors(
n_colors=len(labels), colormap=self.colormap, colors=self.colors
)
colors = dict(zip(labels, self.color_values_))
# Transform labels into a map of class to label
labels = dict(zip(self.classes_, labels))
# Expand the points into vectors of x and y for scatter plotting,
# assigning them to their label if the label has been passed in.
# Additionally, filter classes not specified directly by the user.
series = defaultdict(lambda: {"x": [], "y": []})
if target is not None:
for t, point in zip(target, points):
label = labels[t]
series[label]["x"].append(point[0])
series[label]["y"].append(point[1])
else:
label = self.classes_[0]
for x, y in points:
series[label]["x"].append(x)
series[label]["y"].append(y)
# Plot the points
for label, points in series.items():
self.ax.scatter(
points["x"], points["y"], c=colors[label], alpha=self.alpha, label=label
)
return self.ax
[docs] def finalize(self, **kwargs):
"""
Finalize the drawing by adding a title and legend, and removing the
axes objects that do not convey information about TNSE.
"""
self.set_title("TSNE Projection of {} Documents".format(self.n_instances_))
# Remove the ticks
self.ax.set_yticks([])
self.ax.set_xticks([])
# Add the legend outside of the figure box.
if not all(self.classes_ == np.array([self.NULL_CLASS])):
box = self.ax.get_position()
self.ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
manual_legend(
self,
self.classes_,
self.color_values_,
loc="center left",
bbox_to_anchor=(1, 0.5),
)
```