Merge pull request #4 from midas-research/ad_dev

KP extraction

dlkp/kp_metrics/metrics.py

@@ -0,0 +1,54 @@
+from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
+from seqeval.scheme import IOB2, IOB1
+import numpy as np
+
+
+def compute_metrics(p):
+    return_entity_level_metrics = False
+    ignore_value = -100
+    predictions, labels = p
+    label_to_id = {"B": 0, "I": 1, "O": 2}
+    id_to_label = ["B", "I", "O"]
+    # if model_args.use_CRF is False:
+    predictions = np.argmax(predictions, axis=2)
+    # print(predictions.shape, labels.shape)
+
+    # Remove ignored index (special tokens)
+    true_predictions = [
+        [id_to_label[p] for (p, l) in zip(prediction, label) if l != ignore_value]
+        for prediction, label in zip(predictions, labels)
+    ]
+    true_labels = [
+        [id_to_label[l] for (p, l) in zip(prediction, label) if l != ignore_value]
+        for prediction, label in zip(predictions, labels)
+    ]
+
+    # results = metric.compute(predictions=true_predictions, references=true_labels)
+    results = {}
+    # print("cal precisi")
+    # mode="strict"
+    results["overall_precision"] = precision_score(
+        true_labels, true_predictions, scheme=IOB2
+    )
+    results["overall_recall"] = recall_score(true_labels, true_predictions, scheme=IOB2)
+    # print("cal f1")
+    results["overall_f1"] = f1_score(true_labels, true_predictions, scheme=IOB2)
+    results["overall_accuracy"] = accuracy_score(true_labels, true_predictions)
+    if return_entity_level_metrics:
+        # Unpack nested dictionaries
+        final_results = {}
+        # print("cal entity level mat")
+        for key, value in results.items():
+            if isinstance(value, dict):
+                for n, v in value.items():
+                    final_results[f"{key}_{n}"] = v
+            else:
+                final_results[key] = value
+        return final_results
+    else:
+        return {
+            "precision": results["overall_precision"],
+            "recall": results["overall_recall"],
+            "f1": results["overall_f1"],
+            "accuracy": results["overall_accuracy"],
+        }

dlkp/models/ke/crf/crf.py

@@ -0,0 +1,291 @@
+# add models having crf classification layer with option of bilstm layers
+
+from .crf_utils import *
+from typing import List, Tuple, Dict, Union
+
+import torch
+
+VITERBI_DECODING = Tuple[List[int], float]
+
+
+class ConditionalRandomField(torch.nn.Module):
+    """
+    This module uses the "forward-backward" algorithm to compute
+    the log-likelihood of its inputs assuming a conditional random field model.
+    See, e.g. http://www.cs.columbia.edu/~mcollins/fb.pdf
+    # Parameters
+    num_tags : `int`, required
+        The number of tags.
+    constraints : `List[Tuple[int, int]]`, optional (default = `None`)
+        An optional list of allowed transitions (from_tag_id, to_tag_id).
+        These are applied to `viterbi_tags()` but do not affect `forward()`.
+        These should be derived from `allowed_transitions` so that the
+        start and end transitions are handled correctly for your tag type.
+    include_start_end_transitions : `bool`, optional (default = `True`)
+        Whether to include the start and end transition parameters.
+    """
+
+    def __init__(
+        self,
+        num_tags: int,
+        label_encoding,
+        idx2tag,
+        include_start_end_transitions: bool = True,
+    ) -> None:
+        super().__init__()
+        self.num_tags = num_tags
+        constraints = allowed_transitions(label_encoding, idx2tag)
+        # transitions[i, j] is the logit for transitioning from state i to state j.
+        self.transitions = torch.nn.Parameter(torch.Tensor(num_tags, num_tags))
+
+        # _constraint_mask indicates valid transitions (based on supplied constraints).
+        # Include special start of sequence (num_tags + 1) and end of sequence tags (num_tags + 2)
+        if constraints is None:
+            # All transitions are valid.
+            constraint_mask = torch.Tensor(num_tags + 2, num_tags + 2).fill_(1.0)
+        else:
+            constraint_mask = torch.Tensor(num_tags + 2, num_tags + 2).fill_(0.0)
+            for i, j in constraints:
+                constraint_mask[i, j] = 1.0
+
+        self._constraint_mask = torch.nn.Parameter(constraint_mask, requires_grad=False)
+
+        # Also need logits for transitioning from "start" state and to "end" state.
+        self.include_start_end_transitions = include_start_end_transitions
+        if include_start_end_transitions:
+            self.start_transitions = torch.nn.Parameter(torch.Tensor(num_tags))
+            self.end_transitions = torch.nn.Parameter(torch.Tensor(num_tags))
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        torch.nn.init.xavier_normal_(self.transitions)
+        if self.include_start_end_transitions:
+            torch.nn.init.normal_(self.start_transitions)
+            torch.nn.init.normal_(self.end_transitions)
+
+    def _input_likelihood(
+        self, logits: torch.Tensor, mask: torch.BoolTensor
+    ) -> torch.Tensor:
+        """
+        Computes the (batch_size,) denominator term for the log-likelihood, which is the
+        sum of the likelihoods across all possible state sequences.
+        """
+        batch_size, sequence_length, num_tags = logits.size()
+
+        # Transpose batch size and sequence dimensions
+        mask = mask.transpose(0, 1).contiguous()
+        logits = logits.transpose(0, 1).contiguous()
+
+        # Initial alpha is the (batch_size, num_tags) tensor of likelihoods combining the
+        # transitions to the initial states and the logits for the first timestep.
+        if self.include_start_end_transitions:
+            alpha = self.start_transitions.view(1, num_tags) + logits[0]
+        else:
+            alpha = logits[0]
+
+        # For each i we compute logits for the transitions from timestep i-1 to timestep i.
+        # We do so in a (batch_size, num_tags, num_tags) tensor where the axes are
+        # (instance, current_tag, next_tag)
+        for i in range(1, sequence_length):
+            # The emit scores are for time i ("next_tag") so we broadcast along the current_tag axis.
+            emit_scores = logits[i].view(batch_size, 1, num_tags)
+            # Transition scores are (current_tag, next_tag) so we broadcast along the instance axis.
+            transition_scores = self.transitions.view(1, num_tags, num_tags)
+            # Alpha is for the current_tag, so we broadcast along the next_tag axis.
+            broadcast_alpha = alpha.view(batch_size, num_tags, 1)
+
+            # Add all the scores together and logexp over the current_tag axis.
+            inner = broadcast_alpha + emit_scores + transition_scores
+
+            # In valid positions (mask == True) we want to take the logsumexp over the current_tag dimension
+            # of `inner`. Otherwise (mask == False) we want to retain the previous alpha.
+            alpha = logsumexp(inner, 1) * mask[i].view(batch_size, 1) + alpha * (
+                ~mask[i]
+            ).view(batch_size, 1)
+
+        # Every sequence needs to end with a transition to the stop_tag.
+        if self.include_start_end_transitions:
+            stops = alpha + self.end_transitions.view(1, num_tags)
+        else:
+            stops = alpha
+
+        # Finally we log_sum_exp along the num_tags dim, result is (batch_size,)
+        return logsumexp(stops)
+
+    def _joint_likelihood(
+        self, logits: torch.Tensor, tags: torch.Tensor, mask: torch.BoolTensor
+    ) -> torch.Tensor:
+        """
+        Computes the numerator term for the log-likelihood, which is just score(inputs, tags)
+        """
+        batch_size, sequence_length, _ = logits.data.shape
+
+        # Transpose batch size and sequence dimensions:
+        logits = logits.transpose(0, 1).contiguous()
+        mask = mask.transpose(0, 1).contiguous()
+        tags = tags.transpose(0, 1).contiguous()
+
+        # Start with the transition scores from start_tag to the first tag in each input
+        if self.include_start_end_transitions:
+            score = self.start_transitions.index_select(0, tags[0])
+        else:
+            score = 0.0
+
+        # Add up the scores for the observed transitions and all the inputs but the last
+        # print(mask.shape, tags.shape, logits.shape, sequence_length)
+        for i in range(sequence_length - 1):
+            # Each is shape (batch_size,)
+            current_tag, next_tag = tags[i], tags[i + 1]
+            # print(current_tag, next_tag)
+            # print("tags printiiinggggg")
+            # print(current_tag, next_tag)
+            # The scores for transitioning from current_tag to next_tag
+            transition_score = self.transitions[current_tag.view(-1), next_tag.view(-1)]
+
+            # The score for using current_tag
+            emit_score = logits[i].gather(1, current_tag.view(batch_size, 1)).squeeze(1)
+            # emit_score= 0
+            # Include transition score if next element is unmasked,
+            # input_score if this element is unmasked.
+            score = score + transition_score * mask[i + 1] + emit_score * mask[i]
+
+        # Transition from last state to "stop" state. To start with, we need to find the last tag
+        # for each instance.
+        last_tag_index = mask.sum(0).long() - 1
+        last_tags = tags.gather(0, last_tag_index.view(1, batch_size)).squeeze(0)
+
+        # Compute score of transitioning to `stop_tag` from each "last tag".
+        if self.include_start_end_transitions:
+            last_transition_score = self.end_transitions.index_select(0, last_tags)
+        else:
+            last_transition_score = 0.0
+
+        # Add the last input if it's not masked.
+        last_inputs = logits[-1]  # (batch_size, num_tags)
+        last_input_score = last_inputs.gather(
+            1, last_tags.view(-1, 1)
+        )  # (batch_size, 1)
+        last_input_score = last_input_score.squeeze()  # (batch_size,)
+
+        score = score + last_transition_score + last_input_score * mask[-1]
+
+        return score
+
+    def forward(
+        self, inputs: torch.Tensor, tags: torch.Tensor, mask: torch.BoolTensor = None
+    ) -> torch.Tensor:
+        """
+        Computes the log likelihood.
+        """
+        # mask[tags==-100]=0
+        if mask is None:
+            mask = torch.ones(*tags.size(), dtype=torch.bool)
+        else:
+            # The code below fails in weird ways if this isn't a bool tensor, so we make sure.
+            mask = mask.to(torch.bool)
+        # print("forward",inputs.shape, tags.shape, mask.shape)
+
+        log_denominator = self._input_likelihood(inputs, mask)
+        # temp_tags= tags
+        # tags[tags==-100]=2
+        # print(tags[0])
+        log_numerator = self._joint_likelihood(inputs, tags, mask)
+        # tags[mask==0]=-100
+        return torch.sum(log_numerator - log_denominator)
+
+    def viterbi_tags(
+        self, logits: torch.Tensor, mask: torch.BoolTensor = None, top_k: int = None
+    ) -> Union[List[VITERBI_DECODING], List[List[VITERBI_DECODING]]]:
+        """
+        Uses viterbi algorithm to find most likely tags for the given inputs.
+        If constraints are applied, disallows all other transitions.
+        Returns a list of results, of the same size as the batch (one result per batch member)
+        Each result is a List of length top_k, containing the top K viterbi decodings
+        Each decoding is a tuple  (tag_sequence, viterbi_score)
+        For backwards compatibility, if top_k is None, then instead returns a flat list of
+        tag sequences (the top tag sequence for each batch item).
+        """
+        if mask is None:
+            mask = torch.ones(*logits.shape[:2], dtype=torch.bool, device=logits.device)
+
+        if top_k is None:
+            top_k = 1
+            flatten_output = True
+        else:
+            flatten_output = False
+
+        _, max_seq_length, num_tags = logits.size()
+
+        # Get the tensors out of the variables
+        logits, mask = logits.data, mask.data
+
+        # Augment transitions matrix with start and end transitions
+        start_tag = num_tags
+        end_tag = num_tags + 1
+        transitions = torch.Tensor(num_tags + 2, num_tags + 2).fill_(-10000.0)
+
+        # Apply transition constraints
+        constrained_transitions = self.transitions * self._constraint_mask[
+            :num_tags, :num_tags
+        ] + -10000.0 * (1 - self._constraint_mask[:num_tags, :num_tags])
+        transitions[:num_tags, :num_tags] = constrained_transitions.data
+
+        if self.include_start_end_transitions:
+            transitions[
+                start_tag, :num_tags
+            ] = self.start_transitions.detach() * self._constraint_mask[
+                start_tag, :num_tags
+            ].data + -10000.0 * (
+                1 - self._constraint_mask[start_tag, :num_tags].detach()
+            )
+            transitions[
+                :num_tags, end_tag
+            ] = self.end_transitions.detach() * self._constraint_mask[
+                :num_tags, end_tag
+            ].data + -10000.0 * (
+                1 - self._constraint_mask[:num_tags, end_tag].detach()
+            )
+        else:
+            transitions[start_tag, :num_tags] = -10000.0 * (
+                1 - self._constraint_mask[start_tag, :num_tags].detach()
+            )
+            transitions[:num_tags, end_tag] = -10000.0 * (
+                1 - self._constraint_mask[:num_tags, end_tag].detach()
+            )
+
+        best_paths = []
+        # Pad the max sequence length by 2 to account for start_tag + end_tag.
+        tag_sequence = torch.Tensor(max_seq_length + 2, num_tags + 2)
+
+        for prediction, prediction_mask in zip(logits, mask):
+            mask_indices = prediction_mask.nonzero(as_tuple=False).squeeze()
+            masked_prediction = torch.index_select(prediction, 0, mask_indices)
+            sequence_length = masked_prediction.shape[0]
+
+            # Start with everything totally unlikely
+            tag_sequence.fill_(-10000.0)
+            # At timestep 0 we must have the START_TAG
+            tag_sequence[0, start_tag] = 0.0
+            # At steps 1, ..., sequence_length we just use the incoming prediction
+            tag_sequence[1 : (sequence_length + 1), :num_tags] = masked_prediction
+            # And at the last timestep we must have the END_TAG
+            tag_sequence[sequence_length + 1, end_tag] = 0.0
+
+            # We pass the tags and the transitions to `viterbi_decode`.
+            viterbi_paths, viterbi_scores = viterbi_decode(
+                tag_sequence=tag_sequence[: (sequence_length + 2)],
+                transition_matrix=transitions,
+                top_k=top_k,
+            )
+            top_k_paths = []
+            for viterbi_path, viterbi_score in zip(viterbi_paths, viterbi_scores):
+                # Get rid of START and END sentinels and append.
+                viterbi_path = viterbi_path[1:-1]
+                top_k_paths.append((viterbi_path, viterbi_score.item()))
+            best_paths.append(top_k_paths)
+
+        if flatten_output:
+            return [top_k_paths[0] for top_k_paths in best_paths]
+
+        return best_paths