CRF for keras 2.x (#76)

* implemented CRF

* added test for CRF

* added a chunking example for CRF

* changed to use up2date tensorflow

* added  conll2000 data

* minimize package dependency

* using logsumexp in keras instead

examples/conll2000_chunking_crf.py

@@ -0,0 +1,94 @@
+"""Train CRF and BiLSTM-CRF on CONLL2000 chunking data, similar to https://arxiv.org/pdf/1508.01991v1.pdf.
+"""
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import numpy
+from collections import Counter
+
+from keras.models import Sequential
+from keras.layers import Embedding, Bidirectional, LSTM
+from keras.preprocessing.sequence import pad_sequences
+from keras_contrib.layers import CRF
+from keras_contrib.datasets import conll2000
+
+EPOCHS = 10
+EMBED_DIM = 200
+BiRNN_UNITS = 200
+
+
+def classification_report(y_true, y_pred, labels):
+    '''Similar to the one in sklearn.metrics, reports per classs recall, precision and F1 score'''
+    y_true = numpy.asarray(y_true).ravel()
+    y_pred = numpy.asarray(y_pred).ravel()
+    corrects = Counter(yt for yt, yp in zip(y_true, y_pred) if yt == yp)
+    y_true_counts = Counter(y_true)
+    y_pred_counts = Counter(y_pred)
+    report = ((lab,  # label
+               corrects[i] / max(1, y_true_counts[i]),  # recall
+               corrects[i] / max(1, y_pred_counts[i]),  # precision
+               y_true_counts[i]  # support
+               ) for i, lab in enumerate(labels))
+    report = [(l, r, p, 2 * r * p / max(1e-9, r + p), s) for l, r, p, s in report]
+
+    print('{:<15}{:>10}{:>10}{:>10}{:>10}\n'.format('', 'recall', 'precision', 'f1-score', 'support'))
+    formatter = '{:<15}{:>10.2f}{:>10.2f}{:>10.2f}{:>10d}'.format
+    for r in report:
+        print(formatter(*r))
+    print('')
+    report2 = zip(*[(r * s, p * s, f1 * s) for l, r, p, f1, s in report])
+    N = len(y_true)
+    print(formatter('avg / total', sum(report2[0]) / N, sum(report2[1]) / N, sum(report2[2]) / N, N) + '\n')
+
+
+# ------
+# Data
+# -----
+
+# conll200 has two different targets, here will only use IBO like chunking as an example
+(train_x, _, train_y), (test_x, _, test_y), (vocab, _, class_labels) = conll2000.load_data()
+
+
+# --------------
+# 1. Regular CRF
+# --------------
+
+print('==== training CRF ====')
+
+model = Sequential()
+model.add(Embedding(len(vocab), EMBED_DIM, mask_zero=True))  # Random embedding
+crf = CRF(len(class_labels), sparse_target=True)
+model.add(crf)
+model.summary()
+
+model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
+model.fit(train_x, train_y, epochs=EPOCHS, validation_data=[test_x, test_y])
+
+test_y_pred = model.predict(test_x).argmax(-1)[test_x > 0]
+test_y_true = test_y[test_x > 0]
+
+print('\n---- Result of CRF ----\n')
+classification_report(test_y_true, test_y_pred, class_labels)
+
+# -------------
+# 2. BiLSTM-CRF
+# -------------
+
+print('==== training BiLSTM-CRF ====')
+
+model = Sequential()
+model.add(Embedding(len(vocab), EMBED_DIM, mask_zero=True))  # Random embedding
+model.add(Bidirectional(LSTM(BiRNN_UNITS // 2, return_sequences=True)))
+crf = CRF(len(class_labels), sparse_target=True)
+model.add(crf)
+model.summary()
+
+model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
+model.fit(train_x, train_y, epochs=EPOCHS, validation_data=[test_x, test_y])
+
+test_y_pred = model.predict(test_x).argmax(-1)[test_x > 0]
+test_y_true = test_y[test_x > 0]
+
+print('\n---- Result of BiLSTM-CRF ----\n')
+classification_report(test_y_true, test_y_pred, class_labels)