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diff basic/cli.py ../omerlevy-bidaf_no_answer-2e9868b224e4/basic/cli.py
32,34d31
< flags.DEFINE_bool("wy", False, "Use wy for loss / eval? [False]")
< flags.DEFINE_bool("na", False, "Enable no answer strategy and learn bias? [False]")
< flags.DEFINE_float("th", 0.5, "Threshold [0.5]")
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< flags.DEFINE_integer("batch_size", 30, "Batch size [60]")
---
> flags.DEFINE_integer("batch_size", 60, "Batch size [60]")
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< flags.DEFINE_integer("num_steps", 10000, "Number of steps [20000]")
---
> flags.DEFINE_integer("num_steps", 20000, "Number of steps [20000]")
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< flags.DEFINE_float("init_lr", 0.001, "Initial learning rate [0.001]")
---
> flags.DEFINE_float("init_lr", 0.5, "Initial learning rate [0.5]")
57a55
> flags.DEFINE_float("word_keep_prob", 1.0, "Keep prob for the dropout of words [1.0]")
diff basic/ensemble.py ../omerlevy-bidaf_no_answer-2e9868b224e4/basic/ensemble.py
10c10
< from squad.utils import get_phrase, get_best_span, get_span_score_pairs
---
> from squad.utils import get_phrase, get_best_span
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< answer = ensemble4(context, wordss, yp_list, yp2_list)
---
> answer = ensemble3(context, wordss, yp_list, yp2_list)
87,96d86
<
<
< def ensemble4(context, wordss, y1_list, y2_list):
< d = defaultdict(lambda: 0.0)
< for y1, y2 in zip(y1_list, y2_list):
< for span, score in get_span_score_pairs(y1, y2):
< d[span] += score
< span = max(d.items(), key=lambda pair: pair[1])[0]
< phrase = get_phrase(context, wordss, span)
< return phrase
diff basic/evaluator.py ../omerlevy-bidaf_no_answer-2e9868b224e4/basic/evaluator.py
8c8
< from squad.utils import get_phrase, get_best_span, get_best_span_wy
---
> from squad.utils import get_phrase, get_best_span
228a229
> new_id2na_dict = dict(list(self.id2answer_dict['na'].items()) + list(other.id2answer_dict['na'].items()))
230,237c231,232
< if 'na' in self.id2answer_dict:
< new_id2na_dict = dict(list(self.id2answer_dict['na'].items()) + list(other.id2answer_dict['na'].items()))
< new_id2answer_dict['na'] = new_id2na_dict
< e = F1Evaluation(self.data_type, self.global_step, new_idxs, new_yp, new_yp2, new_y, new_correct, new_loss, new_f1s, new_id2answer_dict)
< if 'wyp' in self.dict:
< new_wyp = self.dict['wyp'] + other.dict['wyp']
< e.dict['wyp'] = new_wyp
< return e
---
> new_id2answer_dict['na'] = new_id2na_dict
> return F1Evaluation(self.data_type, self.global_step, new_idxs, new_yp, new_yp2, new_y, new_correct, new_loss, new_f1s, new_id2answer_dict)
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< self.wyp = model.wyp
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< if config.na:
< self.na = model.na_prob
---
> self.na = model.na_prob
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< if self.config.na:
< global_step, yp, yp2, wyp, loss, na, vals = sess.run([self.global_step, self.yp, self.yp2, self.wyp, self.loss, self.na, list(self.tensor_dict.values())], feed_dict=feed_dict)
< else:
< global_step, yp, yp2, wyp, loss, vals = sess.run([self.global_step, self.yp, self.yp2, self.wyp, self.loss, list(self.tensor_dict.values())], feed_dict=feed_dict)
---
> global_step, yp, yp2, loss, vals, na = sess.run([self.global_step, self.yp, self.yp2, self.loss, list(self.tensor_dict.values()), self.na], feed_dict=feed_dict)
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< yp, yp2, wyp = yp[:data_set.num_examples], yp2[:data_set.num_examples], wyp[:data_set.num_examples]
< if self.config.wy:
< spans, scores = zip(*[get_best_span_wy(wypi, self.config.th) for wypi in wyp])
< else:
< spans, scores = zip(*[get_best_span(ypi, yp2i) for ypi, yp2i in zip(yp, yp2)])
---
> yp, yp2 = yp[:data_set.num_examples], yp2[:data_set.num_examples]
> spans, scores = zip(*[get_best_span(ypi, yp2i) for ypi, yp2i in zip(yp, yp2)])
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> id2na_dict = {id_: float(each) for id_, each in zip(data_set.data['ids'], na)}
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< if self.config.na:
< id2na_dict = {id_: float(each) for id_, each in zip(data_set.data['ids'], na)}
< id2answer_dict['na'] = id2na_dict
---
> id2answer_dict['na'] = id2na_dict
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< if self.config.wy:
< e.dict['wyp'] = wyp.tolist()
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< self.yp = tf.concat(axis=0, values=[padded_reshape(model.yp, [N, M, JX]) for model in models])
< self.yp2 = tf.concat(axis=0, values=[padded_reshape(model.yp2, [N, M, JX]) for model in models])
< self.wy = tf.concat(axis=0, values=[padded_reshape(model.wy, [N, M, JX]) for model in models])
---
> self.yp = tf.concat(0, [padded_reshape(model.yp, [N, M, JX]) for model in models])
> self.yp2 = tf.concat(0, [padded_reshape(model.yp2, [N, M, JX]) for model in models])
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< if config.na:
< self.na = model.na_prob
---
> self.na = model.na_prob
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< if self.config.na:
< global_step, yp, yp2, loss, na, vals = sess.run([self.global_step, self.yp, self.yp2, self.loss, self.na, list(self.tensor_dict.values())], feed_dict=feed_dict)
< else:
< global_step, yp, yp2, loss, vals = sess.run([self.global_step, self.yp, self.yp2, self.loss, list(self.tensor_dict.values())], feed_dict=feed_dict)
---
> global_step, yp, yp2, loss, vals, na = sess.run([self.global_step, self.yp, self.yp2, self.loss, list(self.tensor_dict.values()), self.na], feed_dict=feed_dict)
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< if self.config.na:
< id2na_dict = {id_: float(each) for id_, each in zip(data_set.data['ids'], na)}
< id2answer_dict['na'] = id2na_dict
---
> id2na_dict = {id_: float(each) for id_, each in zip(data_set.data['ids'], na)}
> id2answer_dict['na'] = id2na_dict
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< # TODO : wy support
Only in basic/: evaluator.pyc
Only in basic/: get_pr.py
diff basic/graph_handler.py ../omerlevy-bidaf_no_answer-2e9868b224e4/basic/graph_handler.py
23c23
< sess.run(tf.global_variables_initializer())
---
> sess.run(tf.initialize_all_variables())
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< self.writer = tf.summary.FileWriter(self.config.log_dir, graph=tf.get_default_graph())
---
> self.writer = tf.train.SummaryWriter(self.config.log_dir, graph=tf.get_default_graph())
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< vars_ = {var.name.split(":")[0]: var for var in tf.global_variables()}
---
> vars_ = {var.name.split(":")[0]: var for var in tf.all_variables()}
Only in basic/: __init__.pyc
diff basic/main.py ../omerlevy-bidaf_no_answer-2e9868b224e4/basic/main.py
17d16
< from my.tensorflow import get_num_params
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< print("num params: {}".format(get_num_params()))
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< print("num params: {}".format(get_num_params()))
Only in basic/: main.pyc
diff basic/model.py ../omerlevy-bidaf_no_answer-2e9868b224e4/basic/model.py
6c6
< from tensorflow.contrib.rnn import BasicLSTMCell
---
> from tensorflow.python.ops.rnn_cell import BasicLSTMCell
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< if gpu_idx > 0:
< tf.get_variable_scope().reuse_variables()
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> tf.get_variable_scope().reuse_variables()
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< self.wy = tf.placeholder('bool', [N, None, None], name='wy')
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< self.na_prob = None
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< self.summary = tf.summary.merge_all()
< self.summary = tf.summary.merge(tf.get_collection("summaries", scope=self.scope))
---
> self.summary = tf.merge_all_summaries()
> self.summary = tf.merge_summary(tf.get_collection("summaries", scope=self.scope))
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< word_emb_mat = tf.concat(axis=0, values=[word_emb_mat, self.new_emb_mat])
---
> word_emb_mat = tf.concat(0, [word_emb_mat, self.new_emb_mat])
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< xx = tf.concat(axis=3, values=[xx, Ax]) # [N, M, JX, di]
< qq = tf.concat(axis=2, values=[qq, Aq]) # [N, JQ, di]
---
> xx = tf.concat(3, [xx, Ax]) # [N, M, JX, di]
> qq = tf.concat(2, [qq, Aq]) # [N, JQ, di]
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< cell_fw = BasicLSTMCell(d, state_is_tuple=True)
< cell_bw = BasicLSTMCell(d, state_is_tuple=True)
< d_cell_fw = SwitchableDropoutWrapper(cell_fw, self.is_train, input_keep_prob=config.input_keep_prob)
< d_cell_bw = SwitchableDropoutWrapper(cell_bw, self.is_train, input_keep_prob=config.input_keep_prob)
< cell2_fw = BasicLSTMCell(d, state_is_tuple=True)
< cell2_bw = BasicLSTMCell(d, state_is_tuple=True)
< d_cell2_fw = SwitchableDropoutWrapper(cell2_fw, self.is_train, input_keep_prob=config.input_keep_prob)
< d_cell2_bw = SwitchableDropoutWrapper(cell2_bw, self.is_train, input_keep_prob=config.input_keep_prob)
< cell3_fw = BasicLSTMCell(d, state_is_tuple=True)
< cell3_bw = BasicLSTMCell(d, state_is_tuple=True)
< d_cell3_fw = SwitchableDropoutWrapper(cell3_fw, self.is_train, input_keep_prob=config.input_keep_prob)
< d_cell3_bw = SwitchableDropoutWrapper(cell3_bw, self.is_train, input_keep_prob=config.input_keep_prob)
< cell4_fw = BasicLSTMCell(d, state_is_tuple=True)
< cell4_bw = BasicLSTMCell(d, state_is_tuple=True)
< d_cell4_fw = SwitchableDropoutWrapper(cell4_fw, self.is_train, input_keep_prob=config.input_keep_prob)
< d_cell4_bw = SwitchableDropoutWrapper(cell4_bw, self.is_train, input_keep_prob=config.input_keep_prob)
---
> cell = BasicLSTMCell(d, state_is_tuple=True)
> d_cell = SwitchableDropoutWrapper(cell, self.is_train, input_keep_prob=config.input_keep_prob)
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< (fw_u, bw_u), ((_, fw_u_f), (_, bw_u_f)) = bidirectional_dynamic_rnn(d_cell_fw, d_cell_bw, qq, q_len, dtype='float', scope='u1') # [N, J, d], [N, d]
< u = tf.concat(axis=2, values=[fw_u, bw_u])
---
> (fw_u, bw_u), ((_, fw_u_f), (_, bw_u_f)) = bidirectional_dynamic_rnn(d_cell, d_cell, qq, q_len, dtype='float', scope='u1') # [N, J, d], [N, d]
> u = tf.concat(2, [fw_u, bw_u])
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< (fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, xx, x_len, dtype='float', scope='u1') # [N, M, JX, 2d]
< h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
---
> (fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell, cell, xx, x_len, dtype='float', scope='u1') # [N, M, JX, 2d]
> h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
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< (fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, xx, x_len, dtype='float', scope='h1') # [N, M, JX, 2d]
< h = tf.concat(axis=3, values=[fw_h, bw_h]) # [N, M, JX, 2d]
---
> (fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell, cell, xx, x_len, dtype='float', scope='h1') # [N, M, JX, 2d]
> h = tf.concat(3, [fw_h, bw_h]) # [N, M, JX, 2d]
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< first_cell_fw = AttentionCell(cell2_fw, u, mask=q_mask, mapper='sim',
< input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
< first_cell_bw = AttentionCell(cell2_bw, u, mask=q_mask, mapper='sim',
< input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
< second_cell_fw = AttentionCell(cell3_fw, u, mask=q_mask, mapper='sim',
< input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
< second_cell_bw = AttentionCell(cell3_bw, u, mask=q_mask, mapper='sim',
< input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
---
> first_cell = AttentionCell(cell, u, mask=q_mask, mapper='sim',
> input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
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< first_cell_fw = d_cell2_fw
< second_cell_fw = d_cell3_fw
< first_cell_bw = d_cell2_bw
< second_cell_bw = d_cell3_bw
<
< (fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(first_cell_fw, first_cell_bw, p0, x_len, dtype='float', scope='g0') # [N, M, JX, 2d]
< g0 = tf.concat(axis=3, values=[fw_g0, bw_g0])
< (fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(second_cell_fw, second_cell_bw, g0, x_len, dtype='float', scope='g1') # [N, M, JX, 2d]
< g1 = tf.concat(axis=3, values=[fw_g1, bw_g1])
---
> first_cell = d_cell
>
> (fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(first_cell, first_cell, p0, x_len, dtype='float', scope='g0') # [N, M, JX, 2d]
> g0 = tf.concat(3, [fw_g0, bw_g0])
> (fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(first_cell, first_cell, g0, x_len, dtype='float', scope='g1') # [N, M, JX, 2d]
> g1 = tf.concat(3, [fw_g1, bw_g1])
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< (fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(d_cell4_fw, d_cell4_bw, tf.concat(axis=3, values=[p0, g1, a1i, g1 * a1i]),
---
> (fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(d_cell, d_cell, tf.concat(3, [p0, g1, a1i, g1 * a1i]),
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< g2 = tf.concat(axis=3, values=[fw_g2, bw_g2])
---
> g2 = tf.concat(3, [fw_g2, bw_g2])
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< flat_logits = tf.reshape(logits, [-1, M * JX])
< flat_yp = tf.nn.softmax(flat_logits) # [-1, M*JX]
< flat_logits2 = tf.reshape(logits2, [-1, M * JX])
< flat_yp2 = tf.nn.softmax(flat_logits2)
<
< if config.na:
< na_bias = tf.get_variable("na_bias", shape=[], dtype='float')
< na_bias_tiled = tf.tile(tf.reshape(na_bias, [1, 1]), [N, 1]) # [N, 1]
< concat_flat_logits = tf.concat(axis=1, values=[na_bias_tiled, flat_logits])
< concat_flat_yp = tf.nn.softmax(concat_flat_logits)
< na_prob = tf.squeeze(tf.slice(concat_flat_yp, [0, 0], [-1, 1]), [1])
< flat_yp = tf.slice(concat_flat_yp, [0, 1], [-1, -1])
<
< concat_flat_logits2 = tf.concat(axis=1, values=[na_bias_tiled, flat_logits2])
< concat_flat_yp2 = tf.nn.softmax(concat_flat_logits2)
< na_prob2 = tf.squeeze(tf.slice(concat_flat_yp2, [0, 0], [-1, 1]), [1]) # [N]
< flat_yp2 = tf.slice(concat_flat_yp2, [0, 1], [-1, -1])
<
< self.concat_logits = concat_flat_logits
< self.concat_logits2 = concat_flat_logits2
< self.na_prob = na_prob * na_prob2
<
---
> na_bias = tf.get_variable("na_bias", shape=[1], dtype='float')
> na_bias_tiled = tf.tile(tf.reshape(na_bias, [1, 1]), [N, 1]) # [N, 1]
> flat_logits = tf.reshape(logits, [-1, M * JX]) # [N, M*JX]
> concat_flat_logits = tf.concat(1, [na_bias_tiled, flat_logits])
> concat_flat_yp = tf.nn.softmax(concat_flat_logits) # [-1, M*JX+1]
> na_prob = tf.squeeze(tf.slice(concat_flat_yp, [0, 0], [-1, 1]), [1]) # [N]
> flat_yp = tf.slice(concat_flat_yp, [0, 1], [-1, -1])
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>
> flat_logits2 = tf.reshape(logits2, [-1, M * JX])
> concat_flat_logits2 = tf.concat(1, [na_bias_tiled, flat_logits2])
> concat_flat_yp2 = tf.nn.softmax(concat_flat_logits2)
> na_prob2 = tf.squeeze(tf.slice(concat_flat_yp2, [0, 0], [-1, 1]), [1]) # [N]
> flat_yp2 = tf.slice(concat_flat_yp2, [0, 1], [-1, -1])
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< wyp = tf.nn.sigmoid(logits2)
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< self.yp = yp
< self.yp2 = yp2
< self.wyp = wyp
---
> self.concat_logits = concat_flat_logits
> self.concat_logits2 = concat_flat_logits2
> self.yp = yp # start prob dist
> self.yp2 = yp2 # end prob dist
> self.na_prob = na_prob * na_prob2
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<
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< if config.wy:
< losses = tf.nn.sigmoid_cross_entropy_with_logits(
< logits=tf.reshape(self.logits2, [-1, M, JX]), labels=tf.cast(self.wy, 'float')) # [N, M, JX]
< num_pos = tf.reduce_sum(tf.cast(self.wy, 'float'))
< num_neg = tf.reduce_sum(tf.cast(self.x_mask, 'float')) - num_pos
< damp_ratio = num_pos / num_neg
< dampened_losses = losses * (
< (tf.cast(self.x_mask, 'float') - tf.cast(self.wy, 'float')) * damp_ratio + tf.cast(self.wy, 'float'))
< new_losses = tf.reduce_sum(dampened_losses, [1, 2])
< ce_loss = tf.reduce_mean(loss_mask * new_losses)
< """
< if config.na:
< na = tf.reshape(self.na, [-1, 1])
< concat_y = tf.concat(1, [na, tf.reshape(self.wy, [-1, M * JX])])
< losses = tf.nn.softmax_cross_entropy_with_logits(
< self.concat_logits, tf.cast(concat_y, 'float') / tf.reduce_sum(tf.cast(self.wy, 'float')))
< else:
< losses = tf.nn.softmax_cross_entropy_with_logits(
< self.logits2, tf.cast(tf.reshape(self.wy, [-1, M * JX]), 'float') / tf.reduce_sum(tf.cast(self.wy, 'float')))
< ce_loss = tf.reduce_mean(loss_mask * losses)
< """
< tf.add_to_collection('losses', ce_loss)
<
< else:
< if config.na:
< na = tf.reshape(self.na, [-1, 1])
< concat_y = tf.concat(axis=1, values=[na, tf.reshape(self.y, [-1, M * JX])])
< losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.concat_logits, labels=tf.cast(concat_y, 'float'))
< concat_y2 = tf.concat(axis=1, values=[na, tf.reshape(self.y2, [-1, M * JX])])
< losses2 = tf.nn.softmax_cross_entropy_with_logits(logits=self.concat_logits2, labels=tf.cast(concat_y2, 'float'))
< else:
< losses = tf.nn.softmax_cross_entropy_with_logits(
< logits=self.logits, labels=tf.cast(tf.reshape(self.y, [-1, M * JX]), 'float'))
< losses2 = tf.nn.softmax_cross_entropy_with_logits(
< logits=self.logits2, labels=tf.cast(tf.reshape(self.y2, [-1, M * JX]), 'float'))
< ce_loss = tf.reduce_mean(loss_mask * losses)
< ce_loss2 = tf.reduce_mean(loss_mask * losses2)
< tf.add_to_collection('losses', ce_loss)
< tf.add_to_collection("losses", ce_loss2)
---
> na = tf.reshape(self.na, [-1, 1])
> concat_y = tf.concat(1, [na, tf.reshape(self.y, [-1, M * JX])])
> losses = tf.nn.softmax_cross_entropy_with_logits(
> self.concat_logits, tf.cast(concat_y, 'float'))
> ce_loss = tf.reduce_mean(loss_mask * losses)
> tf.add_to_collection('losses', ce_loss)
> concat_y2 = tf.concat(1, [na, tf.reshape(self.y2, [-1, M * JX])])
> ce_loss2 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
> self.concat_logits2, tf.cast(concat_y2, 'float')))
> tf.add_to_collection("losses", ce_loss2)
291c227
< tf.summary.scalar(self.loss.op.name, self.loss)
---
> tf.scalar_summary(self.loss.op.name, self.loss)
301c237
< tf.summary.scalar(ema_var.op.name, ema_var)
---
> tf.scalar_summary(ema_var.op.name, ema_var)
304c240
< tf.summary.histogram(ema_var.op.name, ema_var)
---
> tf.histogram_summary(ema_var.op.name, ema_var)
380d315
< wy = np.zeros([N, M, JX], dtype='bool')
384d318
< feed_dict[self.wy] = wy
390,406d323
< continue
< start_idx, stop_idx = random.choice(yi)
< j, k = start_idx
< j2, k2 = stop_idx
< if config.single:
< X[i] = [xi[j]]
< CX[i] = [cxi[j]]
< j, j2 = 0, 0
< if config.squash:
< offset = sum(map(len, xi[:j]))
< j, k = 0, k + offset
< offset = sum(map(len, xi[:j2]))
< j2, k2 = 0, k2 + offset
< y[i, j, k] = True
< y2[i, j2, k2-1] = True
< if j == j2:
< wy[i, j, k:k2] = True
408,409c325,339
< wy[i, j, k:len(batch.data['x'][i][j])] = True
< wy[i, j2, :k2] = True
---
> start_idx, stop_idx = random.choice(yi)
> j, k = start_idx
> j2, k2 = stop_idx
> if config.single:
> X[i] = [xi[j]]
> CX[i] = [cxi[j]]
> j, j2 = 0, 0
> if config.squash:
> offset = sum(map(len, xi[:j]))
> j, k = 0, k + offset
> offset = sum(map(len, xi[:j2]))
> j2, k2 = 0, k2 + offset
> y[i, j, k] = True
> y2[i, j2, k2-1] = True
>
440c370
< x[i, j, k] = each
---
> x[i, j, k] = each if random.random() <= config.word_keep_prob else 1 # 1 is UNK
469,471d398
< if supervised:
< assert np.sum(~(x_mask | ~wy)) == 0
<
500c427
< variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name)
---
> variables = tf.get_collection(tf.GraphKeys.VARIABLES, scope=tf.get_variable_scope().name)
517c444
< p0 = tf.concat(axis=3, values=[h, u_a, h * u_a, h * h_a])
---
> p0 = tf.concat(3, [h, u_a, h * u_a, h * h_a])
519c446
< p0 = tf.concat(axis=3, values=[h, u_a, h * u_a])
---
> p0 = tf.concat(3, [h, u_a, h * u_a])
Only in basic/: __pycache__
Only in basic/: read_data.pyc
diff basic/run_ensemble.sh ../omerlevy-bidaf_no_answer-2e9868b224e4/basic/run_ensemble.sh
22c22
< eval_path="$inter_dir/eval-$num.pklz"
---
> eval_path="$inter_dir/eval-$num.json"
diff basic/run_single.sh ../omerlevy-bidaf_no_answer-2e9868b224e4/basic/run_single.sh
21c21
< eval_path="$inter_dir/eval.pklz"
---
> eval_path="$inter_dir/eval.json"
Common subdirectories: basic/templates and ../omerlevy-bidaf_no_answer-2e9868b224e4/basic/templates
diff basic/trainer.py ../omerlevy-bidaf_no_answer-2e9868b224e4/basic/trainer.py
12c12
< self.opt = tf.train.AdamOptimizer(config.init_lr)
---
> self.opt = tf.train.AdadeltaOptimizer(config.init_lr)
42c42
< self.opt = tf.train.AdamOptimizer(config.init_lr)
---
> self.opt = tf.train.AdadeltaOptimizer(config.init_lr)
diff basic/visualizer.py ../omerlevy-bidaf_no_answer-2e9868b224e4/basic/visualizer.py
10,11d9
< import pickle
< import gzip
15c13,14
< from squad.utils import get_best_span, get_best_span_wy
---
> from basic.evaluator import get_span_score_pairs
> from squad.utils import get_best_span, get_span_score_pairs
38d36
< parser.add_argument("-w", "--wy", action='store_true')
56c54
< eval_path =os.path.join("out", model_name, run_id, "eval", "{}-{}.pklz".format(data_type, str(step).zfill(6)))
---
> eval_path =os.path.join("out", model_name, run_id, "eval", "{}-{}.json".format(data_type, str(step).zfill(6)))
58c56
< eval_ = pickle.load(gzip.open(eval_path, 'r'))
---
> eval_ = json.load(open(eval_path, 'r'))
84c82
< for i, (idx, yi, ypi, yp2i, wypi) in tqdm(enumerate(zip(*[eval_[key] for key in ('idxs', 'y', 'yp', 'yp2', 'wyp')])), total=len(eval_['idxs'])):
---
> for i, (idx, yi, ypi, yp2i) in tqdm(enumerate(zip(*[eval_[key] for key in ('idxs', 'y', 'yp', 'yp2')])), total=len(eval_['idxs'])):
89c87
< span, score = get_best_span_wy(wypi, 0.5) if args.wy else get_best_span(ypi, yp2i)
---
> span = get_best_span(ypi, yp2i)
91c89
< # score = "{:.3f}".format(ypi[span[0][0]][span[0][1]] * yp2i[span[1][0]][span[1][1]-1])
---
> score = "{:.3f}".format(ypi[span[0][0]][span[0][1]] * yp2i[span[1][0]][span[1][1]-1])
100,101c98,99
< 'yp': wypi if args.wy else ypi,
< 'yp2': wypi if args.wy else yp2i,
---
> 'yp': ypi,
> 'yp2': yp2i,