# -*- coding: utf-8 -*-
#
# This file is part of Sequana software
#
# Copyright (c) 2016-2020 - Sequana Development Team
#
# File author(s):
# Thomas Cokelaer <thomas.cokelaer@pasteur.fr>
#
# Distributed under the terms of the 3-clause BSD license.
# The full license is in the LICENSE file, distributed with this software.
#
# website: https://github.com/sequana/sequana
# documentation: http://sequana.readthedocs.io
#
##############################################################################
import colorlog
from sequana.lazy import pylab
logger = colorlog.getLogger(__name__)
from sequana.viz import clusterisation
__all__ = ["TSNE"]
[docs]
class TSNE(clusterisation.Cluster):
"""t-SNE non-linear dimensionality reduction wrapper.
.. plot::
:include-source:
from sequana.viz.tsne import TSNE
from sequana import sequana_data
import pandas as pd
data = sequana_data("test_pca.csv")
df = pd.read_csv(data)
df = df.set_index("Id")
p = TSNE(df, colors={
"A1": 'r', "A2": 'r', 'A3': 'r',
"B1": 'b', "B2": 'b', 'B3': 'b'})
p.plot(perplexity=2)
"""
def __init__(self, data, colors={}):
super(TSNE, self).__init__(data, colors)
[docs]
def plot(
self,
n_components=2,
perplexity=30,
n_iter=1000,
init="random",
random_state=0,
transform="log",
switch_x=False,
switch_y=False,
switch_z=False,
colors=None,
max_features=500,
show_plot=True,
show_labels=False,
):
"""
:param n_components: at number starting at 2 or a value below 1
e.g. 0.95 means select automatically the number of components to
capture 95% of the variance
:param transform: can be 'log' or 'anscombe', log is just log10. count
with zeros, are set to 1
"""
import numpy as np
from sklearn.manifold import TSNE
pylab.clf()
data = self.scale_data(transform_method=transform)
# keep only top variable features
tokeep = data.std(axis=1).sort_values(ascending=False).index[0:max_features]
data = data.loc[tokeep]
tsne = TSNE(
perplexity=perplexity, n_components=n_components, max_iter=n_iter, random_state=random_state, init=init
)
Xr = tsne.fit_transform(data.T)
self.Xr = Xr
if switch_x:
Xr[:, 0] *= -1
if switch_y:
Xr[:, 1] *= -1
if switch_z:
Xr[:, 2] *= -1
# PC1 vs PC2
if show_plot:
pylab.figure(1)
self._plot(Xr, pca=None, pc1=0, pc2=1, colors=colors, show_labels=show_labels)
if n_components >= 3:
if show_plot:
pylab.figure(2)
self._plot(Xr, pca=None, pc1=0, pc2=2, colors=colors, show_labels=show_labels)
pylab.figure(3)
self._plot(Xr, pca=None, pc1=1, pc2=2, colors=colors, show_labels=show_labels)
return tsne
