/examples/pytorch/lda/example_20newsgroups.py

# Copyright 2021 Yifei Ma
# Modified from scikit-learn example "plot_topics_extraction_with_nmf_lda.py"
# with the following original authors with BSD 3-Clause:
# * Olivier Grisel <olivier.grisel@ensta.org>
# * Lars Buitinck
# * Chyi-Kwei Yau <chyikwei.yau@gmail.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import warnings
from time import time

import matplotlib.pyplot as plt
import numpy as np
import scipy.sparse as ss
import torch
from lda_model import LatentDirichletAllocation as LDAModel
from sklearn.datasets import fetch_20newsgroups
from sklearn.decomposition import NMF, LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

import dgl
from dgl import function as fn

n_samples = 2000
n_features = 1000
n_components = 10
n_top_words = 20
device = "cuda"


def plot_top_words(model, feature_names, n_top_words, title):
    fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True)
    axes = axes.flatten()
    for topic_idx, topic in enumerate(model.components_):
        top_features_ind = topic.argsort()[: -n_top_words - 1 : -1]
        top_features = [feature_names[i] for i in top_features_ind]
        weights = topic[top_features_ind]

        ax = axes[topic_idx]
        ax.barh(top_features, weights, height=0.7)
        ax.set_title(f"Topic {topic_idx +1}", fontdict={"fontsize": 30})
        ax.invert_yaxis()
        ax.tick_params(axis="both", which="major", labelsize=20)
        for i in "top right left".split():
            ax.spines[i].set_visible(False)
        fig.suptitle(title, fontsize=40)

    plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3)
    plt.show()


# Load the 20 newsgroups dataset and vectorize it. We use a few heuristics
# to filter out useless terms early on: the posts are stripped of headers,
# footers and quoted replies, and common English words, words occurring in
# only one document or in at least 95% of the documents are removed.

print("Loading dataset...")
t0 = time()
data, _ = fetch_20newsgroups(
    shuffle=True,
    random_state=1,
    remove=("headers", "footers", "quotes"),
    return_X_y=True,
)
data_samples = data[:n_samples]
data_test = data[n_samples : 2 * n_samples]
print("done in %0.3fs." % (time() - t0))

# Use tf (raw term count) features for LDA.
print("Extracting tf features for LDA...")
tf_vectorizer = CountVectorizer(
    max_df=0.95, min_df=2, max_features=n_features, stop_words="english"
)
t0 = time()
tf_vectorizer.fit(data)
tf = tf_vectorizer.transform(data_samples)
tt = tf_vectorizer.transform(data_test)

tf_feature_names = tf_vectorizer.get_feature_names()
tf_uv = [
    (u, v)
    for u, v, e in zip(tf.tocoo().row, tf.tocoo().col, tf.tocoo().data)
    for _ in range(e)
]
tt_uv = [
    (u, v)
    for u, v, e in zip(tt.tocoo().row, tt.tocoo().col, tt.tocoo().data)
    for _ in range(e)
]
print("done in %0.3fs." % (time() - t0))
print()

print("Preparing dgl graphs...")
t0 = time()
G = dgl.heterograph({("doc", "topic", "word"): tf_uv}, device=device)
Gt = dgl.heterograph({("doc", "topic", "word"): tt_uv}, device=device)
print("done in %0.3fs." % (time() - t0))
print()

print("Training dgl-lda model...")
t0 = time()
model = LDAModel(G.num_nodes("word"), n_components)
model.fit(G)
print("done in %0.3fs." % (time() - t0))
print()

print(f"dgl-lda training perplexity {model.perplexity(G):.3f}")
print(f"dgl-lda testing perplexity {model.perplexity(Gt):.3f}")

word_nphi = np.vstack([nphi.tolist() for nphi in model.word_data.nphi])
plot_top_words(
    type("dummy", (object,), {"components_": word_nphi}),
    tf_feature_names,
    n_top_words,
    "Topics in LDA model",
)

print("Training scikit-learn model...")

print(
    "\n" * 2,
    "Fitting LDA models with tf features, "
    "n_samples=%d and n_features=%d..." % (n_samples, n_features),
)
lda = LatentDirichletAllocation(
    n_components=n_components,
    max_iter=5,
    learning_method="online",
    learning_offset=50.0,
    random_state=0,
    verbose=1,
)
t0 = time()
lda.fit(tf)
print("done in %0.3fs." % (time() - t0))
print()

print(f"scikit-learn training perplexity {lda.perplexity(tf):.3f}")
print(f"scikit-learn testing perplexity {lda.perplexity(tt):.3f}")