GH-61: Wrap embedding visualiation into class. Fix requirement issues.

flair/visual/__init__.py

@@ -1,2 +1,2 @@
-from .manifold import tSNE, visualize, prepare_word_embeddings, prepare_char_embeddings, word_contexts, char_contexts
+from .manifold import Visualizer
 from .activations import Highlighter

flair/visual/activations.py

@@ -1,7 +1,7 @@
 import numpy
 
 
-class Highlighter:
+class Highlighter(object):
 
     def __init__(self):
         self.color_map = [

flair/visual/manifold.py

@@ -3,112 +3,132 @@
 import numpy
 
 
-def prepare_word_embeddings(embeddings, sentences):
-    X = []
+class _Transform:
+    def __init__(self):
+        pass
 
-    print('computing embeddings')
-    for sentence in tqdm.tqdm(sentences):
-        embeddings.embed(sentence)
+    def fit(self, X):
+        return self.transform.fit_transform(X)
 
-        for i, token in enumerate(sentence):
-            X.append(token.embedding.detach().numpy()[None, :])
 
-    X = numpy.concatenate(X, 0)
+class tSNE(_Transform):
+    def __init__(self):
+        super().__init__()
 
-    return X
+        self.transform = \
+            TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)
 
 
-def word_contexts(sentences):
-    contexts = []
+class Visualizer(object):
 
-    for sentence in sentences:
+    def visualize_word_emeddings(self, embeddings, sentences, output_file):
+        X = self.prepare_word_embeddings(embeddings, sentences)
+        contexts = self.word_contexts(sentences)
 
-        strs = [x.text for x in sentence.tokens]
+        trans_ = tSNE()
+        reduced = trans_.fit(X)
 
-        for i, token in enumerate(strs):
-            prop = '<b><font color="red"> {token} </font></b>'.format(
-                token=token)
+        self.visualize(reduced, contexts, output_file)
 
-            prop = ' '.join(strs[max(i - 4, 0):i]) + prop
-            prop = prop + ' '.join(strs[i + 1:min(len(strs), i + 5)])
+    def visualize_char_emeddings(self, embeddings, sentences, output_file):
+        X = self.prepare_char_embeddings(embeddings, sentences)
+        contexts = self.char_contexts(sentences)
 
-            contexts.append('<p>' + prop + '</p>')
+        trans_ = tSNE()
+        reduced = trans_.fit(X)
 
-    return contexts
+        self.visualize(reduced, contexts, output_file)
 
+    @staticmethod
+    def prepare_word_embeddings(embeddings, sentences):
+        X = []
 
-def prepare_char_embeddings(embeddings, sentences):
-    X = []
+        print('computing embeddings')
+        for sentence in tqdm.tqdm(sentences):
+            embeddings.embed(sentence)
 
-    print('computing embeddings')
-    for sentence in tqdm.tqdm(sentences):
+            for i, token in enumerate(sentence):
+                X.append(token.embedding.detach().numpy()[None, :])
 
-        sentence = ' '.join([x.text for x in sentence])
+        X = numpy.concatenate(X, 0)
 
-        hidden = embeddings.lm.get_representation([sentence])
-        X.append(hidden.squeeze().detach().numpy())
+        return X
 
-    X = numpy.concatenate(X, 0)
+    @staticmethod
+    def word_contexts(sentences):
+        contexts = []
 
-    return X
+        for sentence in sentences:
 
+            strs = [x.text for x in sentence.tokens]
 
-def char_contexts(sentences):
-    contexts = []
+            for i, token in enumerate(strs):
+                prop = '<b><font color="red"> {token} </font></b>'.format(
+                    token=token)
 
-    for sentence in sentences:
-        sentence = ' '.join([token.text for token in sentence])
+                prop = ' '.join(strs[max(i - 4, 0):i]) + prop
+                prop = prop + ' '.join(strs[i + 1:min(len(strs), i + 5)])
 
-        for i, char in enumerate(sentence):
+                contexts.append('<p>' + prop + '</p>')
 
-            context = '<span style="background-color: yellow"><b>{}</b></span>'.format(char)
-            context = ''.join(sentence[max(i - 30, 0):i]) + context
-            context = context + ''.join(sentence[i + 1:min(len(sentence), i + 30)])
+        return contexts
 
-            contexts.append(context)
+    @staticmethod
+    def prepare_char_embeddings(embeddings, sentences):
+        X = []
 
-    return contexts
+        print('computing embeddings')
+        for sentence in tqdm.tqdm(sentences):
 
+            sentence = ' '.join([x.text for x in sentence])
 
-class _Transform:
-    def __init__(self):
-        pass
+            hidden = embeddings.lm.get_representation([sentence])
+            X.append(hidden.squeeze().detach().numpy())
 
-    def fit(self, X):
-        return self.transform.fit_transform(X)
+        X = numpy.concatenate(X, 0)
 
+        return X
 
-class tSNE(_Transform):
-    def __init__(self):
+    @staticmethod
+    def char_contexts(sentences):
+        contexts = []
 
-        super().__init__()
+        for sentence in sentences:
+            sentence = ' '.join([token.text for token in sentence])
 
-        self.transform = \
-            TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)
+            for i, char in enumerate(sentence):
+
+                context = '<span style="background-color: yellow"><b>{}</b></span>'.format(char)
+                context = ''.join(sentence[max(i - 30, 0):i]) + context
+                context = context + ''.join(sentence[i + 1:min(len(sentence), i + 30)])
+
+                contexts.append(context)
 
+        return contexts
 
-def visualize(X, contexts, file):
-    import matplotlib.pyplot
-    import mpld3
+    @staticmethod
+    def visualize(X, contexts, file):
+        import matplotlib.pyplot
+        import mpld3
 
-    fig, ax = matplotlib.pyplot.subplots()
+        fig, ax = matplotlib.pyplot.subplots()
 
-    ax.grid(True, alpha=0.3)
+        ax.grid(True, alpha=0.3)
 
-    points = ax.plot(X[:, 0], X[:, 1], 'o', color='b',
-                     mec='k', ms=5, mew=1, alpha=.6)
+        points = ax.plot(X[:, 0], X[:, 1], 'o', color='b',
+                         mec='k', ms=5, mew=1, alpha=.6)
 
-    ax.set_xlabel('x')
-    ax.set_ylabel('y')
-    ax.set_title('Hover mouse to reveal context', size=20)
+        ax.set_xlabel('x')
+        ax.set_ylabel('y')
+        ax.set_title('Hover mouse to reveal context', size=20)
 
-    tooltip = mpld3.plugins.PointHTMLTooltip(
-        points[0],
-        contexts,
-        voffset=10,
-        hoffset=10
-    )
+        tooltip = mpld3.plugins.PointHTMLTooltip(
+            points[0],
+            contexts,
+            voffset=10,
+            hoffset=10
+        )
 
-    mpld3.plugins.connect(fig, tooltip)
+        mpld3.plugins.connect(fig, tooltip)
 
-    mpld3.save_html(fig, file)
+        mpld3.save_html(fig, file)

requirements.txt

@@ -3,8 +3,9 @@ awscli==1.14.32
 gensim==3.4.0
 typing==3.6.4
 pytest==3.6.4
-tqdm
-segtok
-matplotlib
-mpld3
-sklearn
+tqdm==4.26.0
+segtok==1.5.7
+matplotlib==3.0.0
+mpld3==0.3
+jinja2==2.10
+sklearn==0.0

tests/test_visual.py

@@ -8,73 +8,49 @@
 from flair.embeddings import CharLMEmbeddings, StackedEmbeddings
 import numpy
 
+from flair.visual.manifold import Visualizer, tSNE
 from flair.visual.training_curves import Plotter
 
 
-@pytest.mark.skip(reason='Skipping test by default due to long execution time.')
-def test_benchmark():
-    import time
+@pytest.mark.skipif("TRAVIS" in os.environ and os.environ["TRAVIS"] == "true", reason="Skipping this test on Travis CI.")
+def test_visualize_word_emeddings():
 
     with open('./resources/visual/snippet.txt') as f:
         sentences = [x for x in f.read().split('\n') if x]
 
-    sentences = [Sentence(x) for x in sentences[:10]]
+    sentences = [Sentence(x) for x in sentences]
 
     charlm_embedding_forward = CharLMEmbeddings('news-forward')
     charlm_embedding_backward = CharLMEmbeddings('news-backward')
 
-    embeddings = StackedEmbeddings(
-        [charlm_embedding_backward, charlm_embedding_forward]
-    )
-
-    tic = time.time()
-
-    prepare_word_embeddings(embeddings, sentences)
-
-    current_elaped = time.time() - tic
-
-    print('current implementation: {} sec/ sentence'.format(current_elaped / 10))
-
-    embeddings_f = CharLMEmbeddings('news-forward')
-    embeddings_b = CharLMEmbeddings('news-backward')
-
-    tic = time.time()
-
-    prepare_char_embeddings(embeddings_f, sentences)
-    prepare_char_embeddings(embeddings_b, sentences)
+    embeddings = StackedEmbeddings([charlm_embedding_backward, charlm_embedding_forward])
 
-    current_elaped = time.time() - tic
+    visualizer = Visualizer()
+    visualizer.visualize_word_emeddings(embeddings, sentences, './resources/visual/sentence_embeddings.html')
 
-    print('pytorch implementation: {} sec/ sentence'.format(current_elaped / 10))
+    # clean up directory
+    os.remove('./resources/visual/sentence_embeddings.html')
 
 
 @pytest.mark.skipif("TRAVIS" in os.environ and os.environ["TRAVIS"] == "true", reason="Skipping this test on Travis CI.")
-def test_show_word_embeddings():
+def test_visualize_word_emeddings():
 
     with open('./resources/visual/snippet.txt') as f:
         sentences = [x for x in f.read().split('\n') if x]
 
     sentences = [Sentence(x) for x in sentences]
 
     charlm_embedding_forward = CharLMEmbeddings('news-forward')
-    charlm_embedding_backward = CharLMEmbeddings('news-backward')
-
-    embeddings = StackedEmbeddings([charlm_embedding_backward, charlm_embedding_forward])
 
-    X = prepare_word_embeddings(embeddings, sentences)
-    contexts = word_contexts(sentences)
-
-    trans_ = tSNE()
-    reduced = trans_.fit(X)
-
-    visualize(reduced, contexts, './resources/visual/sentence_embeddings.html')
+    visualizer = Visualizer()
+    visualizer.visualize_char_emeddings(charlm_embedding_forward, sentences, './resources/visual/sentence_embeddings.html')
 
     # clean up directory
     os.remove('./resources/visual/sentence_embeddings.html')
 
 
 @pytest.mark.skipif("TRAVIS" in os.environ and os.environ["TRAVIS"] == "true", reason="Skipping this test on Travis CI.")
-def test_show_char_embeddings():
+def test_visualize():
 
     with open('./resources/visual/snippet.txt') as f:
         sentences = [x for x in f.read().split('\n') if x]
@@ -83,50 +59,27 @@ def test_show_char_embeddings():
 
     embeddings = CharLMEmbeddings('news-forward')
 
-    X_forward = prepare_char_embeddings(embeddings, sentences)
+    visualizer = Visualizer()
+
+    X_forward = visualizer.prepare_char_embeddings(embeddings, sentences)
 
     embeddings = CharLMEmbeddings('news-backward')
 
-    X_backward = prepare_char_embeddings(embeddings, sentences)
+    X_backward = visualizer.prepare_char_embeddings(embeddings, sentences)
 
     X = numpy.concatenate([X_forward, X_backward], axis=1)
 
-    contexts = char_contexts(sentences)
+    contexts = visualizer.char_contexts(sentences)
 
     trans_ = tSNE()
     reduced = trans_.fit(X)
 
-    visualize(reduced, contexts, './resources/visual/char_embeddings.html')
+    visualizer.visualize(reduced, contexts, './resources/visual/char_embeddings.html')
 
     # clean up directory
     os.remove('./resources/visual/char_embeddings.html')
 
 
-@pytest.mark.skipif("TRAVIS" in os.environ and os.environ["TRAVIS"] == "true", reason="Skipping this test on Travis CI.")
-def test_show_uni_sentence_embeddings():
-
-    with open('./resources/visual/snippet.txt') as f:
-        sentences = [x for x in f.read().split('\n') if x]
-
-    sentences = [Sentence(x) for x in sentences]
-
-    embeddings = CharLMEmbeddings('news-forward')
-
-    X = prepare_char_embeddings(embeddings, sentences)
-
-    trans_ = tSNE()
-    reduced = trans_.fit(X)
-
-    l = len(sentences[0])
-
-    contexts = char_contexts(sentences)
-
-    visualize(reduced[:l], contexts[:l], './resources/visual/uni_sentence_embeddings.html')
-
-    # clean up directory
-    os.remove('./resources/visual/uni_sentence_embeddings.html')
-
-
 def test_highlighter():
     with open('./resources/visual/snippet.txt') as f:
         sentences = [x for x in f.read().split('\n') if x]