In [1]:
from __future__ import unicode_literals, division, print_function, absolute_import
from builtins import range
import numpy as np
np.random.seed(28)
import matplotlib.pyplot as plt
from sklearn.manifold import Isomap
from sklearn.decomposition import KernelPCA
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import fetch_mldata, fetch_20newsgroups
import tensorflow as tf
tf.set_random_seed(28)
import keras
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Activation
# https://github.com/cod3licious/nlputils
from nlputils.features import FeatureTransform, features2mat
from simec import SimilarityEncoder
from utils import center_K, check_embed_match, check_similarity_match
from utils_plotting import plot_mnist, plot_20news
%matplotlib inline
%load_ext autoreload
%autoreload 2
In [2]:
# load digits
mnist = fetch_mldata('MNIST original', data_home='data')
X = mnist.data/255. # normalize to 0-1
y = np.array(mnist.target, dtype=int)
# subsample 10000 random data points
np.random.seed(42)
n_samples = 10000
n_test = 2000
rnd_idx = np.random.permutation(X.shape[0])[:n_samples]
X_test, y_test = X[rnd_idx[:n_test],:], y[rnd_idx[:n_test]]
X, y = X[rnd_idx[n_test:],:], y[rnd_idx[n_test:]]
ss = StandardScaler(with_std=False)
X = ss.fit_transform(X)
X_test = ss.transform(X_test)
n_train, n_features = X.shape
In [3]:
# centered linear kernel matrix
K_lin = center_K(np.dot(X, X.T))
In [4]:
# linear kPCA
kpca = KernelPCA(n_components=2, kernel='linear')
X_embed = kpca.fit_transform(X)
X_embed_test = kpca.transform(X_test)
plot_mnist(X_embed, y, X_embed_test, y_test, title='MNIST - linear Kernel PCA')
print("error similarity match: msqe: %.10f ; r^2: %.10f ; rho: %.10f" % check_similarity_match(X_embed, K_lin))
In [5]:
# on how many target similarities you want to train - faster and works equally well than training on all
n_targets = 1000 # K_lin.shape[1]
# initialize the model
simec = SimilarityEncoder(X.shape[1], 2, n_targets, s_ll_reg=0.5, S_ll=K_lin[:n_targets,:n_targets])
# train the model to get an embedding with which the target similarities
# can be linearly approximated
simec.fit(X, K_lin[:,:n_targets], epochs=25)
# get the embeddings
X_embeds = simec.transform(X)
X_embed_tests = simec.transform(X_test)
plot_mnist(X_embeds, y, X_embed_tests, y_test, title='MNIST - SimEc (lin. kernel, linear)')
# correlation with the embedding produced by the spectral method should be high
print("correlation with lin kPCA : %f" % check_embed_match(X_embed, X_embeds)[1])
print("correlation with lin kPCA (test): %f" % check_embed_match(X_embed_test, X_embed_tests)[1])
# similarity match error should be similar to the one from kpca
print("error similarity match: msqe: %.10f ; r^2: %.10f ; rho: %.10f" % check_similarity_match(X_embeds, K_lin))
In [6]:
# isomap
isomap = Isomap(n_neighbors=10, n_components=2)
X_embed = isomap.fit_transform(X)
X_embed_test = isomap.transform(X_test)
plot_mnist(X_embed, y, X_embed_test, y_test, title='MNIST - isomap')
In [7]:
# non-linear SimEc to approximate isomap solution
K_geod = center_K(-0.5*(isomap.dist_matrix_**2))
n_targets = 1000
# initialize the model
simec = SimilarityEncoder(X.shape[1], 2, n_targets, hidden_layers=[(20, 'tanh')], s_ll_reg=0.5,
S_ll=K_geod[:n_targets,:n_targets], opt=keras.optimizers.Adamax(lr=0.01))
# train the model to get an embedding with which the target similarities
# can be linearly approximated
simec.fit(X, K_geod[:,:n_targets], epochs=25)
# get the embeddings
X_embeds = simec.transform(X)
X_embed_tests = simec.transform(X_test)
plot_mnist(X_embeds, y, X_embed_tests, y_test, title='MNIST - SimEc (isomap, 1 h.l.)')
print("correlation with isomap : %f" % check_embed_match(X_embed, X_embeds)[1])
print("correlation with isomap (test): %f" % check_embed_match(X_embed_test, X_embed_tests)[1])
In [8]:
## load the data and transform it into a tf-idf representation
categories = [
"comp.graphics",
"rec.autos",
"rec.sport.baseball",
"sci.med",
"sci.space",
"soc.religion.christian",
"talk.politics.guns"
]
newsgroups_train = fetch_20newsgroups(subset='train', remove=(
'headers', 'footers', 'quotes'), data_home='data', categories=categories, random_state=42)
newsgroups_test = fetch_20newsgroups(subset='test', remove=(
'headers', 'footers', 'quotes'), data_home='data', categories=categories, random_state=42)
# store in dicts (if the text contains more than 3 words)
textdict = {i: t for i, t in enumerate(newsgroups_train.data) if len(t.split()) > 3}
textdict.update({i: t for i, t in enumerate(newsgroups_test.data, len(newsgroups_train.data)) if len(t.split()) > 3})
train_ids = [i for i in range(len(newsgroups_train.data)) if i in textdict]
test_ids = [i for i in range(len(newsgroups_train.data), len(textdict)) if i in textdict]
print("%i training and %i test samples" % (len(train_ids), len(test_ids)))
# transform into tf-idf features
ft = FeatureTransform(norm='max', weight=True, renorm='max')
docfeats = ft.texts2features(textdict, fit_ids=train_ids)
# organize in feature matrix
X, featurenames = features2mat(docfeats, train_ids)
X_test, _ = features2mat(docfeats, test_ids, featurenames)
print("%i features" % len(featurenames))
targets = np.hstack([newsgroups_train.target,newsgroups_test.target])
y = targets[train_ids]
y_test = targets[test_ids]
target_names = newsgroups_train.target_names
n_targets = 1000
In [9]:
# linear kPCA
kpca = KernelPCA(n_components=2, kernel='linear')
X_embed = kpca.fit_transform(X)
X_embed_test = kpca.transform(X_test)
plot_20news(X_embed, y, target_names, X_embed_test, y_test,
title='20newsgroups - linear Kernel PCA', legend=True)
# compute linear kernel and center
K_lin = center_K(X.dot(X.T).A)
K_lin_test = center_K(X_test.dot(X_test.T).A)
print("similarity approximation : msqe: %.10f ; r^2: %.10f ; rho: %.10f" % check_similarity_match(X_embed, K_lin))
print("similarity approximation (test): msqe: %.10f ; r^2: %.10f ; rho: %.10f" % check_similarity_match(X_embed_test, K_lin_test))
In [10]:
# project to 2d with linear similarity encoder
# careful: our input is sparse!!!
simec = SimilarityEncoder(X.shape[1], 2, n_targets, sparse_inputs=True, opt=keras.optimizers.SGD(lr=50.))
# train the model to get an embedding with which the target similarities
# can be linearly approximated
simec.fit(X, K_lin[:,:n_targets], epochs=25)
# get the embeddings
X_embeds = simec.transform(X)
X_embed_tests = simec.transform(X_test)
plot_20news(X_embeds, y, target_names, X_embed_tests, y_test,
title='20 newsgroups - SimEc (lin. kernel, linear)', legend=True)
print("correlation with lin kPCA : %f" % check_embed_match(X_embed, X_embeds)[1])
print("correlation with lin kPCA (test): %f" % check_embed_match(X_embed_test, X_embed_tests)[1])
print("similarity approximation : msqe: %.10f ; r^2: %.10f ; rho: %.10f" % check_similarity_match(X_embeds, K_lin))
print("similarity approximation (test): msqe: %.10f ; r^2: %.10f ; rho: %.10f" % check_similarity_match(X_embed_tests, K_lin_test))