为 Gensim 模型绘制 3D 图
Draw 3D Plot for Gensim model
我已经使用 Gensim 训练了我的模型。我使用 PCA 绘制了一个二维图，但不太清楚。我想把它改成可以缩放的3D。我的结果太密集了。
from sklearn.decomposition import PCA
from matplotlib import pyplot
X=model[model.wv.vocab]
pca=PCA(n_components=2)
result=pca.fit_transform(X)
pyplot.scatter(result[:,0],result[:,1])
word=list(model.wv.most_similar('eden_lake'))
for i, word in enumerate(words):
pyplot.annotate(word, xy=(result[i, 0], result[i, 1]))
pyplot.show()
结果：
可以吗？
是的，原则上可以对 LDA 模型结果进行 3D 可视化。 Here 是关于为此使用 T-SNE 的更多信息。
以下函数使用 t-SNE 而不是 PCA 进行降维，但会生成两个、三个或两个和三个维度的图（使用子图）。此外，它会为您的主题着色，以便更容易区分它们。从 anaconda 添加 %matplotlib notebook 到 Jupyter notebook 环境的开头将允许旋转 3d 图和缩放 2d 图（不要使用 [=12 同时执行两个版本=]).
该函数很长，大部分代码用于绘图格式化，但生成专业的输出。
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import seaborn as sns
from gensim.models import LdaModel
from gensim import corpora
from sklearn.manifold import TSNE
# %matplotlib notebook # if in Jupyter for rotating and zooming
def LDA_tSNE_topics_vis(dimension='both',
corpus=None,
num_topics=10,
remove_3d_outliers=False,
save_png=False):
"""
Returns the outputs of an LDA model plotted using t-SNE (t-distributed Stochastic Neighbor Embedding)
Note: t-SNE reduces the dimensionality of a space such that similar points will be closer and dissimilar points farther
Parameters
----------
dimension : str (default=both)
The dimension that t-SNE should reduce the data to for visualization
Options: 2d, 3d, and both (a plot with two subplots)
corpus : list, list of lists
The tokenized and cleaned text corpus over which analysis should be done
num_topics : int (default=10)
The number of categories for LDA based approaches
remove_3d_outliers : bool (default=False)
Whether to remove outliers from a 3d plot
save_png : bool (default=False)
Whether to save the figure as a png
Returns
-------
A t-SNE lower dimensional representation of an LDA model's topics and their constituent members
"""
dirichlet_dict = corpora.Dictionary(corpus)
bow_corpus = [dirichlet_dict.doc2bow(text) for text in corpus]
dirichlet_model = LdaModel(corpus=bow_corpus,
id2word=dirichlet_dict,
num_topics=num_topics,
update_every=1,
chunksize=len(bow_corpus),
passes=10,
alpha='auto',
random_state=42) # set for testing
df_topic_coherences = pd.DataFrame(columns = ['topic_{}'.format(i) for i in range(num_topics)])
for i in range(len(bow_corpus)):
df_topic_coherences.loc[i] = [0] * num_topics
output = dirichlet_model.__getitem__(bow=bow_corpus[i], eps=0)
for j in range(len(output)):
topic_num = output[j][0]
coherence = output[j][1]
df_topic_coherences.iloc[i, topic_num] = coherence
for i in range(num_topics):
df_topic_coherences.iloc[:, i] = df_topic_coherences.iloc[:, i].astype('float64', copy=False)
df_topic_coherences['main_topic'] = df_topic_coherences.iloc[:, :num_topics].idxmax(axis=1)
if num_topics > 10:
# cubehelix better for more than 10 colors
colors = sns.color_palette("cubehelix", num_topics)
else:
# The default sns color palette
colors = sns.color_palette('deep', num_topics)
tsne_2 = None
tsne_3 = None
if dimension == 'both':
tsne_2 = TSNE(n_components=2, perplexity=40, n_iter=300)
tsne_3 = TSNE(n_components=3, perplexity=40, n_iter=300)
elif dimension == '2d':
tsne_2 = TSNE(n_components=2, perplexity=40, n_iter=300)
elif dimension == '3d':
tsne_3 = TSNE(n_components=3, perplexity=40, n_iter=300)
else:
ValueError("An invalid value has been passed to the 'dimension' argument - choose from 2d, 3d, or both.")
if tsne_2 is not None:
tsne_results_2 = tsne_2.fit_transform(df_topic_coherences.iloc[:, :num_topics])
df_tsne_2 = pd.DataFrame()
df_tsne_2['tsne-2d-d1'] = tsne_results_2[:,0]
df_tsne_2['tsne-2d-d2'] = tsne_results_2[:,1]
df_tsne_2['main_topic'] = df_topic_coherences.iloc[:, num_topics]
df_tsne_2['color'] = [colors[int(t.split('_')[1])] for t in df_tsne_2['main_topic']]
df_tsne_2['topic_num'] = [int(i.split('_')[1]) for i in df_tsne_2['main_topic']]
df_tsne_2 = df_tsne_2.sort_values(['topic_num'], ascending = True).drop('topic_num', axis=1)
if tsne_3 is not None:
colors = [c for c in sns.color_palette()]
tsne_results_3 = tsne_3.fit_transform(df_topic_coherences.iloc[:, :num_topics])
df_tsne_3 = pd.DataFrame()
df_tsne_3['tsne-3d-d1'] = tsne_results_3[:,0]
df_tsne_3['tsne-3d-d2'] = tsne_results_3[:,1]
df_tsne_3['tsne-3d-d3'] = tsne_results_3[:,2]
df_tsne_3['main_topic'] = df_topic_coherences.iloc[:, num_topics]
df_tsne_3['color'] = [colors[int(t.split('_')[1])] for t in df_tsne_3['main_topic']]
df_tsne_3['topic_num'] = [int(i.split('_')[1]) for i in df_tsne_3['main_topic']]
df_tsne_3 = df_tsne_3.sort_values(['topic_num'], ascending = True).drop('topic_num', axis=1)
if remove_3d_outliers:
# Remove those rows with values that are more than three standard deviations from the column mean
for col in ['tsne-3d-d1', 'tsne-3d-d2', 'tsne-3d-d3']:
df_tsne_3 = df_tsne_3[np.abs(df_tsne_3[col] - df_tsne_3[col].mean()) <= (3 * df_tsne_3[col].std())]
if tsne_2 is not None and tsne_3 is not None:
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, # pylint: disable=unused-variable
figsize=(20,10))
ax1.axis('off')
else:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20,10))
if tsne_2 is not None and tsne_3 is not None:
# Plot tsne_2, with tsne_3 being added later
ax1 = sns.scatterplot(data=df_tsne_2, x="tsne-2d-d1", y="tsne-2d-d2",
hue=df_topic_coherences.iloc[:, num_topics], alpha=0.3)
light_grey_tup = (242/256, 242/256, 242/256)
ax1.set_facecolor(light_grey_tup)
ax1.axes.set_title('t-SNE 2-Dimensional Representation', fontsize=25)
ax1.set_xlabel('tsne-d1', fontsize=20)
ax1.set_ylabel('tsne-d2', fontsize=20)
handles, labels = ax1.get_legend_handles_labels()
legend_order = list(np.argsort([i.split('_')[1] for i in labels]))
ax1.legend([handles[i] for i in legend_order], [labels[i] for i in legend_order],
facecolor=light_grey_tup)
elif tsne_2 is not None:
# Plot just tsne_2
ax = sns.scatterplot(data=df_tsne_2, x="tsne-2d-d1", y="tsne-2d-d2",
hue=df_topic_coherences.iloc[:, num_topics], alpha=0.3)
ax.set_facecolor(light_grey_tup)
ax.axes.set_title('t-SNE 2-Dimensional Representation', fontsize=25)
ax.set_xlabel('tsne-d1', fontsize=20)
ax.set_ylabel('tsne-d2', fontsize=20)
handles, labels = ax.get_legend_handles_labels()
legend_order = list(np.argsort([i.split('_')[1] for i in labels]))
ax.legend([handles[i] for i in legend_order], [labels[i] for i in legend_order],
facecolor=light_grey_tup)
if tsne_2 is not None and tsne_3 is not None:
# tsne_2 has been plotted, so add tsne_3
ax2 = fig.add_subplot(121, projection='3d')
ax2.scatter(xs=df_tsne_3['tsne-3d-d1'],
ys=df_tsne_3['tsne-3d-d2'],
zs=df_tsne_3['tsne-3d-d3'],
c=df_tsne_3['color'],
alpha=0.3)
ax2.set_facecolor('white')
ax2.axes.set_title('t-SNE 3-Dimensional Representation', fontsize=25)
ax2.set_xlabel('tsne-d1', fontsize=20)
ax2.set_ylabel('tsne-d2', fontsize=20)
ax2.set_zlabel('tsne-d3', fontsize=20)
with plt.rc_context({"lines.markeredgewidth" : 0}):
# Add handles via blank lines and order their colors to match tsne_2
proxy_handles = [Line2D([0], [0], linestyle="none", marker='o', markersize=8,
markerfacecolor=colors[i]) for i in legend_order]
ax2.legend(proxy_handles, ['topic_{}'.format(i) for i in range(num_topics)],
loc='upper left', facecolor=(light_grey_tup))
elif tsne_3 is not None:
# Plot just tsne_3
ax.axis('off')
ax.set_facecolor('white')
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xs=df_tsne_3['tsne-3d-d1'],
ys=df_tsne_3['tsne-3d-d2'],
zs=df_tsne_3['tsne-3d-d3'],
c=df_tsne_3['color'],
alpha=0.3)
ax.set_facecolor('white')
ax.axes.set_title('t-SNE 3-Dimensional Representation', fontsize=25)
ax.set_xlabel('tsne-d1', fontsize=20)
ax.set_ylabel('tsne-d2', fontsize=20)
ax.set_zlabel('tsne-d3', fontsize=20)
with plt.rc_context({"lines.markeredgewidth" : 0}):
# Add handles via blank lines
proxy_handles = [Line2D([0], [0], linestyle="none", marker='o', markersize=8,
markerfacecolor=colors[i]) for i in range(len(colors))]
ax.legend(proxy_handles, ['topic_{}'.format(i) for i in range(num_topics)],
loc='upper left', facecolor=light_grey_tup)
if save_png:
plt.savefig('LDA_tSNE_{}.png'.format(time.strftime("%Y%m%d-%H%M%S")), bbox_inches='tight', dpi=500)
plt.show()
10 个主题 gensim LDA 模型在子图上的 2d 和 3d（移除异常值）表示的示例图为：
我已经使用 Gensim 训练了我的模型。我使用 PCA 绘制了一个二维图，但不太清楚。我想把它改成可以缩放的3D。我的结果太密集了。
from sklearn.decomposition import PCA
from matplotlib import pyplot
X=model[model.wv.vocab]
pca=PCA(n_components=2)
result=pca.fit_transform(X)
pyplot.scatter(result[:,0],result[:,1])
word=list(model.wv.most_similar('eden_lake'))
for i, word in enumerate(words):
pyplot.annotate(word, xy=(result[i, 0], result[i, 1]))
pyplot.show()
结果：
可以吗？
是的，原则上可以对 LDA 模型结果进行 3D 可视化。 Here 是关于为此使用 T-SNE 的更多信息。
以下函数使用 t-SNE 而不是 PCA 进行降维，但会生成两个、三个或两个和三个维度的图（使用子图）。此外，它会为您的主题着色，以便更容易区分它们。从 anaconda 添加 %matplotlib notebook 到 Jupyter notebook 环境的开头将允许旋转 3d 图和缩放 2d 图（不要使用 [=12 同时执行两个版本=]).
该函数很长，大部分代码用于绘图格式化，但生成专业的输出。
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import seaborn as sns
from gensim.models import LdaModel
from gensim import corpora
from sklearn.manifold import TSNE
# %matplotlib notebook # if in Jupyter for rotating and zooming
def LDA_tSNE_topics_vis(dimension='both',
corpus=None,
num_topics=10,
remove_3d_outliers=False,
save_png=False):
"""
Returns the outputs of an LDA model plotted using t-SNE (t-distributed Stochastic Neighbor Embedding)
Note: t-SNE reduces the dimensionality of a space such that similar points will be closer and dissimilar points farther
Parameters
----------
dimension : str (default=both)
The dimension that t-SNE should reduce the data to for visualization
Options: 2d, 3d, and both (a plot with two subplots)
corpus : list, list of lists
The tokenized and cleaned text corpus over which analysis should be done
num_topics : int (default=10)
The number of categories for LDA based approaches
remove_3d_outliers : bool (default=False)
Whether to remove outliers from a 3d plot
save_png : bool (default=False)
Whether to save the figure as a png
Returns
-------
A t-SNE lower dimensional representation of an LDA model's topics and their constituent members
"""
dirichlet_dict = corpora.Dictionary(corpus)
bow_corpus = [dirichlet_dict.doc2bow(text) for text in corpus]
dirichlet_model = LdaModel(corpus=bow_corpus,
id2word=dirichlet_dict,
num_topics=num_topics,
update_every=1,
chunksize=len(bow_corpus),
passes=10,
alpha='auto',
random_state=42) # set for testing
df_topic_coherences = pd.DataFrame(columns = ['topic_{}'.format(i) for i in range(num_topics)])
for i in range(len(bow_corpus)):
df_topic_coherences.loc[i] = [0] * num_topics
output = dirichlet_model.__getitem__(bow=bow_corpus[i], eps=0)
for j in range(len(output)):
topic_num = output[j][0]
coherence = output[j][1]
df_topic_coherences.iloc[i, topic_num] = coherence
for i in range(num_topics):
df_topic_coherences.iloc[:, i] = df_topic_coherences.iloc[:, i].astype('float64', copy=False)
df_topic_coherences['main_topic'] = df_topic_coherences.iloc[:, :num_topics].idxmax(axis=1)
if num_topics > 10:
# cubehelix better for more than 10 colors
colors = sns.color_palette("cubehelix", num_topics)
else:
# The default sns color palette
colors = sns.color_palette('deep', num_topics)
tsne_2 = None
tsne_3 = None
if dimension == 'both':
tsne_2 = TSNE(n_components=2, perplexity=40, n_iter=300)
tsne_3 = TSNE(n_components=3, perplexity=40, n_iter=300)
elif dimension == '2d':
tsne_2 = TSNE(n_components=2, perplexity=40, n_iter=300)
elif dimension == '3d':
tsne_3 = TSNE(n_components=3, perplexity=40, n_iter=300)
else:
ValueError("An invalid value has been passed to the 'dimension' argument - choose from 2d, 3d, or both.")
if tsne_2 is not None:
tsne_results_2 = tsne_2.fit_transform(df_topic_coherences.iloc[:, :num_topics])
df_tsne_2 = pd.DataFrame()
df_tsne_2['tsne-2d-d1'] = tsne_results_2[:,0]
df_tsne_2['tsne-2d-d2'] = tsne_results_2[:,1]
df_tsne_2['main_topic'] = df_topic_coherences.iloc[:, num_topics]
df_tsne_2['color'] = [colors[int(t.split('_')[1])] for t in df_tsne_2['main_topic']]
df_tsne_2['topic_num'] = [int(i.split('_')[1]) for i in df_tsne_2['main_topic']]
df_tsne_2 = df_tsne_2.sort_values(['topic_num'], ascending = True).drop('topic_num', axis=1)
if tsne_3 is not None:
colors = [c for c in sns.color_palette()]
tsne_results_3 = tsne_3.fit_transform(df_topic_coherences.iloc[:, :num_topics])
df_tsne_3 = pd.DataFrame()
df_tsne_3['tsne-3d-d1'] = tsne_results_3[:,0]
df_tsne_3['tsne-3d-d2'] = tsne_results_3[:,1]
df_tsne_3['tsne-3d-d3'] = tsne_results_3[:,2]
df_tsne_3['main_topic'] = df_topic_coherences.iloc[:, num_topics]
df_tsne_3['color'] = [colors[int(t.split('_')[1])] for t in df_tsne_3['main_topic']]
df_tsne_3['topic_num'] = [int(i.split('_')[1]) for i in df_tsne_3['main_topic']]
df_tsne_3 = df_tsne_3.sort_values(['topic_num'], ascending = True).drop('topic_num', axis=1)
if remove_3d_outliers:
# Remove those rows with values that are more than three standard deviations from the column mean
for col in ['tsne-3d-d1', 'tsne-3d-d2', 'tsne-3d-d3']:
df_tsne_3 = df_tsne_3[np.abs(df_tsne_3[col] - df_tsne_3[col].mean()) <= (3 * df_tsne_3[col].std())]
if tsne_2 is not None and tsne_3 is not None:
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, # pylint: disable=unused-variable
figsize=(20,10))
ax1.axis('off')
else:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20,10))
if tsne_2 is not None and tsne_3 is not None:
# Plot tsne_2, with tsne_3 being added later
ax1 = sns.scatterplot(data=df_tsne_2, x="tsne-2d-d1", y="tsne-2d-d2",
hue=df_topic_coherences.iloc[:, num_topics], alpha=0.3)
light_grey_tup = (242/256, 242/256, 242/256)
ax1.set_facecolor(light_grey_tup)
ax1.axes.set_title('t-SNE 2-Dimensional Representation', fontsize=25)
ax1.set_xlabel('tsne-d1', fontsize=20)
ax1.set_ylabel('tsne-d2', fontsize=20)
handles, labels = ax1.get_legend_handles_labels()
legend_order = list(np.argsort([i.split('_')[1] for i in labels]))
ax1.legend([handles[i] for i in legend_order], [labels[i] for i in legend_order],
facecolor=light_grey_tup)
elif tsne_2 is not None:
# Plot just tsne_2
ax = sns.scatterplot(data=df_tsne_2, x="tsne-2d-d1", y="tsne-2d-d2",
hue=df_topic_coherences.iloc[:, num_topics], alpha=0.3)
ax.set_facecolor(light_grey_tup)
ax.axes.set_title('t-SNE 2-Dimensional Representation', fontsize=25)
ax.set_xlabel('tsne-d1', fontsize=20)
ax.set_ylabel('tsne-d2', fontsize=20)
handles, labels = ax.get_legend_handles_labels()
legend_order = list(np.argsort([i.split('_')[1] for i in labels]))
ax.legend([handles[i] for i in legend_order], [labels[i] for i in legend_order],
facecolor=light_grey_tup)
if tsne_2 is not None and tsne_3 is not None:
# tsne_2 has been plotted, so add tsne_3
ax2 = fig.add_subplot(121, projection='3d')
ax2.scatter(xs=df_tsne_3['tsne-3d-d1'],
ys=df_tsne_3['tsne-3d-d2'],
zs=df_tsne_3['tsne-3d-d3'],
c=df_tsne_3['color'],
alpha=0.3)
ax2.set_facecolor('white')
ax2.axes.set_title('t-SNE 3-Dimensional Representation', fontsize=25)
ax2.set_xlabel('tsne-d1', fontsize=20)
ax2.set_ylabel('tsne-d2', fontsize=20)
ax2.set_zlabel('tsne-d3', fontsize=20)
with plt.rc_context({"lines.markeredgewidth" : 0}):
# Add handles via blank lines and order their colors to match tsne_2
proxy_handles = [Line2D([0], [0], linestyle="none", marker='o', markersize=8,
markerfacecolor=colors[i]) for i in legend_order]
ax2.legend(proxy_handles, ['topic_{}'.format(i) for i in range(num_topics)],
loc='upper left', facecolor=(light_grey_tup))
elif tsne_3 is not None:
# Plot just tsne_3
ax.axis('off')
ax.set_facecolor('white')
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xs=df_tsne_3['tsne-3d-d1'],
ys=df_tsne_3['tsne-3d-d2'],
zs=df_tsne_3['tsne-3d-d3'],
c=df_tsne_3['color'],
alpha=0.3)
ax.set_facecolor('white')
ax.axes.set_title('t-SNE 3-Dimensional Representation', fontsize=25)
ax.set_xlabel('tsne-d1', fontsize=20)
ax.set_ylabel('tsne-d2', fontsize=20)
ax.set_zlabel('tsne-d3', fontsize=20)
with plt.rc_context({"lines.markeredgewidth" : 0}):
# Add handles via blank lines
proxy_handles = [Line2D([0], [0], linestyle="none", marker='o', markersize=8,
markerfacecolor=colors[i]) for i in range(len(colors))]
ax.legend(proxy_handles, ['topic_{}'.format(i) for i in range(num_topics)],
loc='upper left', facecolor=light_grey_tup)
if save_png:
plt.savefig('LDA_tSNE_{}.png'.format(time.strftime("%Y%m%d-%H%M%S")), bbox_inches='tight', dpi=500)
plt.show()
10 个主题 gensim LDA 模型在子图上的 2d 和 3d（移除异常值）表示的示例图为：