bs_sent np optimization and more confs

bs_sent: mnli allow specify classifier, cos_sim allow specify embedder
fix: #5, #10 direction 1

README.md

@@ -58,8 +58,10 @@ Implemented in `/bertscore_sentence`
 Usage:
 ```python
 import bertscore_sentence.eval as bertscore_sentence
+import dar_env
 metrics = {
-    "bertscore-sentence": functools.partial(bertscore_sentence.compute),
+    "bertscore-sentence-cos-mpnet": functools.partial(bertscore_sentence.compute, embedder=dar_env.sent_embedder_mpnet),
+    "bertscore-sentence-cos-roberta": functools.partial(bertscore_sentence.compute, embedder=dar_env.sent_embedder_roberta),
 }
 ```
 
@@ -72,8 +74,10 @@ Implemented in `/mnli`
 Usage:
 ```python
 import mnli.eval as mnli
+import dar_env
 metrics = {
-    "bertscore-sentence-mnli": functools.partial(mnli.bertscore_sentence_compute),
+    "bertscore-sentence-mnli-roberta": functools.partial(mnli.bertscore_sentence_compute, classifier=dar_env.mnli_classifier_roberta),
+    "bertscore-sentence-mnli-bart": functools.partial(mnli.bertscore_sentence_compute, classifier=dar_env.mnli_classifier_bart),
 }
 ```
 

bertscore_sentence/eval.py

@@ -1,72 +1,66 @@
 import sys
 from os import path
-
 file_path = path.abspath(__file__)
 sys.path.append(path.dirname(path.dirname(file_path)))
 
 import typing
 import numpy as np
 import torch
 from tqdm.auto import trange
-from dar_env import nlp
-from dar_env import sent_embedder as embedder
+from dar_env import nlp_spacy
+import functools
+import sentence_transformers
 
 
-def cos_sim_mat_f(cand, ref) -> np.ndarray:
+def cos_sim_mat_f(cand, ref, embedder) -> np.ndarray:
     def bert_encode(piece: str):
         sentence_emb = list()
-        doc = nlp(piece)
+        doc = nlp_spacy(piece)
         doc_sents = [sent.text for sent in doc.sents]
         for sentence in doc_sents:
             with torch.no_grad():
                 sentence_emb.append(embedder.encode(sentence, convert_to_numpy=True))
         return sentence_emb, doc_sents
 
-    cand_sentence_emb, cand_sentences = bert_encode(cand)
-    ref_sentence_emb, ref_sentences = bert_encode(ref)
-    sim_mat = np.zeros((len(ref_sentence_emb), len(cand_sentence_emb)))
-    for i in range(len(ref_sentence_emb)):
-        for j in range(len(cand_sentence_emb)):
-            numerator = np.dot(ref_sentence_emb[i], cand_sentence_emb[j])  # float32
-            denominator = np.dot(np.linalg.norm(ref_sentence_emb[i]),
-                                    np.linalg.norm(cand_sentence_emb[j]))  # float32
-            cos_sim = np.divide(numerator, denominator)  # float32
-            sim_mat[i][j] = cos_sim
-            del numerator, denominator, cos_sim
-    return sim_mat, cand_sentences, ref_sentences
+    ref_sent_emb_list, ref_sents = bert_encode(ref)
+    cand_sent_emb_list, cand_sents = bert_encode(cand)
+    ref_sent_emb = np.stack(ref_sent_emb_list, axis=0)
+    cand_sent_emb = np.stack(cand_sent_emb_list, axis=0)
+    numerators = np.inner(ref_sent_emb, cand_sent_emb)
+    ref_sent_emb_norms = np.linalg.norm(ref_sent_emb, axis=1)
+    cand_sent_emb_norms = np.linalg.norm(cand_sent_emb, axis=1)
+    denominators = np.outer(ref_sent_emb_norms, cand_sent_emb_norms)
+    sim_mat = np.divide(numerators, denominators)
+    return sim_mat, cand_sents, ref_sents
 
 
 def score_np(predictions: typing.List[str], references: typing.List[str], sim_mat_f: typing.Callable) -> np.ndarray:
     cands, refs = predictions, references # simple renaming. 
+    all_scores = np.empty((len(cands), 3))
 
-    all_scores = np.zeros((len(cands), 3))
-
-    for index in trange(len(cands), desc="bertscore-sentence cands {}".format(sim_mat_f.__name__), leave=False):  # all pieces, len(cands) == len(refs)
-        sim_mat, cand_sentences, ref_sentences = sim_mat_f(cand=cands[index], ref=refs[index])
+    for index in trange(len(cands), desc="bertscore-sentence {}".format(sim_mat_f.__name__), leave=False):  # all pieces, len(cands) == len(refs)
+        sim_mat, cand_sents, ref_sents = sim_mat_f(cand=cands[index], ref=refs[index])
 
         def sum_max(is_r: bool) -> float:
-            sum_result = 0.0
             if is_r:
-                for i in range(len(ref_sentences)):
-                    sum_result += sim_mat[i].max()
+                return np.sum(np.max(sim_mat, axis=1))
             else:
-                sim_mat_t = sim_mat.transpose()
-                for j in range(len(cand_sentences)):
-                    sum_result += sim_mat_t[j].max()
-                del sim_mat_t
-            return sum_result
+                return np.sum(np.max(sim_mat, axis=0))  # equals to np.sum(np.max(sim_mat.T, axis=1))
 
-        R = (1 / len(ref_sentences)) * sum_max(True)
-        P = (1 / len(cand_sentences)) * sum_max(False)
+        R = (1 / len(ref_sents)) * sum_max(True)
+        P = (1 / len(cand_sents)) * sum_max(False)
         F = 2 * ((P * R) / (P + R))
         all_scores[index, :] = np.array([P, R, F])
         del sim_mat
 
     return all_scores
 
 
-def compute(predictions: typing.List[str], references: typing.List[str], sim_mat_f: typing.Callable = cos_sim_mat_f) -> typing.Dict:
-    cands, refs = predictions, references # simple renaming. 
+def compute(predictions: typing.List[str], references: typing.List[str], sim_mat_f: typing.Optional[typing.Callable] = None, embedder: typing.Optional[sentence_transformers.SentenceTransformer] = None) -> typing.Dict:
+    cands, refs = predictions, references # simple renaming
+    if sim_mat_f is None:  # cosine similarity by default
+        sim_mat_f = functools.partial(cos_sim_mat_f, embedder=embedder)
+        sim_mat_f.__name__ = " ".join(["cos", embedder.__name__])
     score_arr = score_np(predictions=cands, references=refs, sim_mat_f=sim_mat_f)
     return {
         "P": score_arr[:, 0].tolist(),

dar_env.py

@@ -4,10 +4,15 @@
 import evaluate
 
 
-nlp = spacy.load("en_core_web_lg")
-mnli_classifier = pipeline("text-classification",
-                           model="roberta-large-mnli", top_k=None)
-sent_embedder = sentence_transformers.SentenceTransformer("all-MiniLM-L6-v2")
+nlp_spacy = spacy.load("en_core_web_lg")
+mnli_classifier_roberta = pipeline("text-classification", model="roberta-large-mnli", top_k=None)
+mnli_classifier_roberta.__name__ = "roberta-large-mnli"
+mnli_classifier_bart = pipeline("text-classification", model="facebook/bart-large-mnli", top_k=None)
+mnli_classifier_bart.__name__ = "bart-large-mnli"
+sent_embedder_mpnet = sentence_transformers.SentenceTransformer("all-mpnet-base-v2")
+sent_embedder_mpnet.__name__ = "all-mpnet-base-v2"
+sent_embedder_roberta = sentence_transformers.SentenceTransformer("all-roberta-large-v1")
+sent_embedder_roberta.__name__ = "all-roberta-large-v1"
 bertscore = evaluate.load("bertscore")
 rouge = evaluate.load("rouge")
 bertscore = evaluate.load("bertscore")

mnli/eval.py

@@ -1,6 +1,5 @@
 import sys
 from os import path
-
 file_path = path.abspath(__file__)
 sys.path.append(path.dirname(path.dirname(file_path)))
 
@@ -9,22 +8,25 @@
 import numpy as np
 from mnli.sim import similarity
 from dar_env import nlp
+import functools
+import transformers
 
 
-def mnli_sim_mat(cand, ref) -> np.ndarray:
+def mnli_sim_mat(cand: str, ref: str, classifier: transformers.Pipeline) -> np.ndarray:
     def segmentation(piece: str):
-        doc = nlp(piece)
+        doc = nlp_spacy(piece)
         doc_sents = [sent.text for sent in doc.sents]
         return doc_sents
 
-    cand_sentences = segmentation(cand)
-    ref_sentences = segmentation(ref)
-    sim_mat = np.zeros((len(ref_sentences), len(cand_sentences)))
-    for i in range(len(ref_sentences)):
-        for j in range(len(cand_sentences)):
-            sim_mat[i][j] = similarity(ref_sentences[i], cand_sentences[j])
-    return sim_mat, cand_sentences, ref_sentences
+    cand_sents = segmentation(cand)
+    ref_sents = segmentation(ref)
+    sent_pairs = [" ".join([x, y]) for x in ref_sents for y in cand_sents]
+    sim_mat = np.empty((len(ref_sents), len(cand_sents)))
+    sim_mat.flat = similarity(sent_pairs, classifier)
+    return sim_mat, cand_sents, ref_sents
 
 
-def bertscore_sentence_compute(predictions: typing.List[str], references: typing.List[str]) -> typing.Dict:
-    return eval.compute(predictions=predictions, references=references, sim_mat_f=mnli_sim_mat)
+def bertscore_sentence_compute(predictions: typing.List[str], references: typing.List[str], classifier: transformers.Pipeline) -> typing.Dict:
+    sim_mat_f = functools.partial(mnli_sim_mat, classifier=classifier)
+    sim_mat_f.__name__ = " ".join(["mnli", classifier.__name__])
+    return eval.compute(predictions=predictions, references=references, sim_mat_f=sim_mat_f)

mnli/sim.py

@@ -1,21 +1,24 @@
 import sys
 from os import path
-
 file_path = path.abspath(__file__)
 sys.path.append(path.dirname(path.dirname(file_path)))
 
-from dar_env import mnli_classifier
+import typing
+import transformers
 
 
-def similarity(sentence_a: str, sentence_b: str):
-    sequence = " ".join([sentence_a, sentence_b])
-    classes = mnli_classifier(sequence)
-    for c in classes[0]:
-        if c["label"] == "NEUTRAL":
-            return 1 - c["score"]
-    raise Exception("Not found NEUTRAL class")
+def similarity(sent_pairs: typing.List[str], classifier: transformers.Pipeline):
+    classes = classifier(sent_pairs)
+    scores = []
+    for c in classes:
+        for category in c:
+            if category["label"] == "NEUTRAL":
+                scores.append(1 - category["score"])
+                break
+    return scores
 
 
 if __name__ == "__main__":
-    print(similarity("Each computer program uses a region of memory called the stack to enable functions to work properly.",
-          "From the outside, Les 4G, a Lyonnais bouchon (traditional restaurant), looked much like the nondescript cafe-cum-tobacco shops that can be found in most small French towns, but inside the decor was as warm and inviting as a country pub."))
+    sample_a = "Each computer program uses a region of memory called the stack to enable functions to work properly."
+    sample_b = "From the outside, Les 4G, a Lyonnais bouchon (traditional restaurant), looked much like the nondescript cafe-cum-tobacco shops that can be found in most small French towns, but inside the decor was as warm and inviting as a country pub."
+    print(similarity([" ".join([sample_a, sample_b])]))

topk/eval.py

@@ -1,15 +1,14 @@
 import sys
 from os import path
-
 file_path = path.abspath(__file__)
 sys.path.append(path.dirname(path.dirname(file_path)))
 
 import typing
-from dar_env import nlp, bertscore, rouge, bleurt
+from dar_env import nlp_spacy, bertscore, rouge, bleurt
 
 
 def extract_topk_doc(ref: str, topk: int) -> str:
-    doc = nlp(ref)
+    doc = nlp_spacy(ref)
     doc_sents = [sent.text for sent in doc.sents]
     topk_sents = doc_sents[0:topk]
     return " ".join(topk_sents)