保存 Graphlab LDA 模型会将主题变成乱码？
Saving Graphlab LDA model turns topics into gibberish?
好吧，这很古怪。我认为这个问题可能是由最近的 graphlab 更新引入的，因为我以前从未见过这个问题，但我不确定）。不管怎样，看看这个：
import graphlab as gl
corpus = gl.SArray('path/to/corpus_data')
lda_model = gl.topic_model.create(dataset=corpus,num_topics=10,num_iterations=50,alpha=1.0,beta=0.1)
lda_model.get_topics(num_words=3).print_rows(30)
+-------+---------------+------------------+
| topic | word | score |
+-------+---------------+------------------+
| 0 | Music | 0.0195325651638 |
| 0 | Love | 0.0120906781994 |
| 0 | Photography | 0.00936914065591 |
| 1 | Recipe | 0.0205673829742 |
| 1 | Food | 0.0202932111556 |
| 1 | Sugar | 0.0162560126511 |
| 2 | Business | 0.0223993672813 |
| 2 | Science | 0.0164027313084 |
| 2 | Education | 0.0139221301443 |
| 3 | Science | 0.0134658216431 |
| 3 | Video_game | 0.0113924173881 |
| 3 | NASA | 0.0112188654905 |
| 4 | United_States | 0.0127908290673 |
| 4 | Automobile | 0.00888669047383 |
| 4 | Australia | 0.00854809547772 |
| 5 | Disease | 0.00704245203928 |
| 5 | Earth | 0.00693360028027 |
| 5 | Species | 0.00648700544757 |
| 6 | Religion | 0.0142311765509 |
| 6 | God | 0.0139990904439 |
| 6 | Human | 0.00765681454222 |
| 7 | Google | 0.0198547267697 |
| 7 | Internet | 0.0191105480317 |
| 7 | Computer | 0.0179914269911 |
| 8 | Art | 0.0378733245262 |
| 8 | Design | 0.0223646138082 |
| 8 | Artist | 0.0142755732766 |
| 9 | Film | 0.0205971724156 |
| 9 | Earth | 0.0125386246077 |
| 9 | Television | 0.0102082224947 |
+-------+---------------+------------------+
好吧，即使对我的语料库一无所知，这些主题至少还是可以理解的，因为每个主题的热门术语或多或少是相关的。
但现在如果简单地保存并重新加载模型，主题就会完全改变（据我所知是废话）：
lda_model.save('test')
lda_model = gl.load_model('test')
lda_model.get_topics(num_words=3).print_rows(30)
+-------+-----------------------+-------------------+
| topic | word | score |
+-------+-----------------------+-------------------+
| 0 | Cleanliness | 0.00468171463384 |
| 0 | Chicken_soup | 0.00326753275774 |
| 0 | The_Language_Instinct | 0.00314506174959 |
| 1 | Equalization | 0.0015724652078 |
| 1 | Financial_crisis | 0.00132675410371 |
| 1 | Tulsa,_Oklahoma | 0.00118899041288 |
| 2 | Batoidea | 0.00142300468887 |
| 2 | Abbottabad | 0.0013474225953 |
| 2 | Migration_humaine | 0.00124284781396 |
| 3 | Gewürztraminer | 0.00147470845039 |
| 3 | Indore | 0.00107223358321 |
| 3 | White_wedding | 0.00104791136102 |
| 4 | Bregenz | 0.00130871351963 |
| 4 | Carl_Jung | 0.000879345016186 |
| 4 | ภ | 0.000855001542873 |
| 5 | 18e_eeuw | 0.000950866105797 |
| 5 | Vesuvianite | 0.000832367570269 |
| 5 | Gary_Kirsten | 0.000806410748201 |
| 6 | Sunday_Bloody_Sunday | 0.000828552346797 |
| 6 | Linear_cryptanalysis | 0.000681188343324 |
| 6 | Clothing_sizes | 0.00066708652481 |
| 7 | Mile | 0.000759081990574 |
| 7 | Pinwheel_calculator | 0.000721971708181 |
| 7 | Third_Age | 0.000623010955132 |
| 8 | Tennessee_Williams | 0.000597449568381 |
| 8 | Levite | 0.000551338743949 |
| 8 | Time_Out_(company) | 0.000536667117994 |
| 9 | David_Deutsch | 0.000543813843275 |
| 9 | Honing_(metalworking) | 0.00044496051774 |
| 9 | Clearing_(finance) | 0.000431699705779 |
+-------+-----------------------+-------------------+
知道这里可能发生什么吗？ save 应该只是 pickle 模型，所以我看不出奇怪的地方发生了，但是主题分布以某种不明显的方式完全改变了。我已经在两台不同的机器上验证了这一点（Linux 和 Mac）。有类似的奇怪结果。
编辑
将 Graphlab 从 1.7.1 降级到 1.6.1 似乎可以解决此问题，但这不是真正的解决方案。我在 1.7.1 发行说明中没有看到任何明显的内容来解释发生了什么，如果可能的话，我希望它能在 1.7.1 中工作...
这是 Graphlab create 1.7.1 中的错误。它现在已在 Graphlab Create 1.8 中修复。
好吧，这很古怪。我认为这个问题可能是由最近的 graphlab 更新引入的，因为我以前从未见过这个问题，但我不确定）。不管怎样，看看这个：
import graphlab as gl
corpus = gl.SArray('path/to/corpus_data')
lda_model = gl.topic_model.create(dataset=corpus,num_topics=10,num_iterations=50,alpha=1.0,beta=0.1)
lda_model.get_topics(num_words=3).print_rows(30)
+-------+---------------+------------------+
| topic | word | score |
+-------+---------------+------------------+
| 0 | Music | 0.0195325651638 |
| 0 | Love | 0.0120906781994 |
| 0 | Photography | 0.00936914065591 |
| 1 | Recipe | 0.0205673829742 |
| 1 | Food | 0.0202932111556 |
| 1 | Sugar | 0.0162560126511 |
| 2 | Business | 0.0223993672813 |
| 2 | Science | 0.0164027313084 |
| 2 | Education | 0.0139221301443 |
| 3 | Science | 0.0134658216431 |
| 3 | Video_game | 0.0113924173881 |
| 3 | NASA | 0.0112188654905 |
| 4 | United_States | 0.0127908290673 |
| 4 | Automobile | 0.00888669047383 |
| 4 | Australia | 0.00854809547772 |
| 5 | Disease | 0.00704245203928 |
| 5 | Earth | 0.00693360028027 |
| 5 | Species | 0.00648700544757 |
| 6 | Religion | 0.0142311765509 |
| 6 | God | 0.0139990904439 |
| 6 | Human | 0.00765681454222 |
| 7 | Google | 0.0198547267697 |
| 7 | Internet | 0.0191105480317 |
| 7 | Computer | 0.0179914269911 |
| 8 | Art | 0.0378733245262 |
| 8 | Design | 0.0223646138082 |
| 8 | Artist | 0.0142755732766 |
| 9 | Film | 0.0205971724156 |
| 9 | Earth | 0.0125386246077 |
| 9 | Television | 0.0102082224947 |
+-------+---------------+------------------+
好吧，即使对我的语料库一无所知，这些主题至少还是可以理解的，因为每个主题的热门术语或多或少是相关的。
但现在如果简单地保存并重新加载模型，主题就会完全改变（据我所知是废话）：
lda_model.save('test')
lda_model = gl.load_model('test')
lda_model.get_topics(num_words=3).print_rows(30)
+-------+-----------------------+-------------------+
| topic | word | score |
+-------+-----------------------+-------------------+
| 0 | Cleanliness | 0.00468171463384 |
| 0 | Chicken_soup | 0.00326753275774 |
| 0 | The_Language_Instinct | 0.00314506174959 |
| 1 | Equalization | 0.0015724652078 |
| 1 | Financial_crisis | 0.00132675410371 |
| 1 | Tulsa,_Oklahoma | 0.00118899041288 |
| 2 | Batoidea | 0.00142300468887 |
| 2 | Abbottabad | 0.0013474225953 |
| 2 | Migration_humaine | 0.00124284781396 |
| 3 | Gewürztraminer | 0.00147470845039 |
| 3 | Indore | 0.00107223358321 |
| 3 | White_wedding | 0.00104791136102 |
| 4 | Bregenz | 0.00130871351963 |
| 4 | Carl_Jung | 0.000879345016186 |
| 4 | ภ | 0.000855001542873 |
| 5 | 18e_eeuw | 0.000950866105797 |
| 5 | Vesuvianite | 0.000832367570269 |
| 5 | Gary_Kirsten | 0.000806410748201 |
| 6 | Sunday_Bloody_Sunday | 0.000828552346797 |
| 6 | Linear_cryptanalysis | 0.000681188343324 |
| 6 | Clothing_sizes | 0.00066708652481 |
| 7 | Mile | 0.000759081990574 |
| 7 | Pinwheel_calculator | 0.000721971708181 |
| 7 | Third_Age | 0.000623010955132 |
| 8 | Tennessee_Williams | 0.000597449568381 |
| 8 | Levite | 0.000551338743949 |
| 8 | Time_Out_(company) | 0.000536667117994 |
| 9 | David_Deutsch | 0.000543813843275 |
| 9 | Honing_(metalworking) | 0.00044496051774 |
| 9 | Clearing_(finance) | 0.000431699705779 |
+-------+-----------------------+-------------------+
知道这里可能发生什么吗？ save 应该只是 pickle 模型，所以我看不出奇怪的地方发生了，但是主题分布以某种不明显的方式完全改变了。我已经在两台不同的机器上验证了这一点（Linux 和 Mac）。有类似的奇怪结果。
编辑
将 Graphlab 从 1.7.1 降级到 1.6.1 似乎可以解决此问题，但这不是真正的解决方案。我在 1.7.1 发行说明中没有看到任何明显的内容来解释发生了什么，如果可能的话，我希望它能在 1.7.1 中工作...
这是 Graphlab create 1.7.1 中的错误。它现在已在 Graphlab Create 1.8 中修复。