Prompt updates for forward

continuations.py

@@ -2,12 +2,12 @@
 
 def continue_story_svo_customprompt_past_tense(model, story, question, verbose = False, width = 10):
   instructions = "The following are results from a children's reasoning test.  \nThe child will be read a short story and then answer a question about what happened before the story.  \nThe answer cannot contradict the story.  \nWe have collected the results below:\n"
-  story1="Story 1: Jim sat by the swings as Tom slowly approached. Tom gave the book to Jim during recess.\n"
-  question1="Question: Why did Jim receive the book?\n"
-  answer1="Answers:\n1. Tom desired to return the book to his friend.\n2. Jim bought the book from Tom.\n3. Tom noticed Jim dropped his book.\n4. Jim needed a book to study for his exam.\n5. Tom wanted to give Jim his favorite book.\n\n"
-  story2 = "Story 2: Mary was happy to finally cross the street.\n"
-  question2 = "Question: Why was Mary running?\n"
-  answer2="Answers:\n1. Mary was running from a monster.\n2. Mary was running a marathon.\n3. Mary wanted to get away from her parents.\n\n" 
+  story1="Story 1: Jane sat by the swings as John slowly approached. John gave the book to Jane during recess.\n"
+  question1="Question: Why did Jane receive the book?\n"
+  answer1="Answers:\n1. John desired to return the book to his friend.\n2. Jane bought the book from John.\n3. John noticed Jane dropped his book.\n4. Jane needed a book to study for his exam.\n5. John wanted to give Jane his favorite book.\n\n"
+  story2 = "Story 2: Jane was happy to finally cross the street.\n"
+  question2 = "Question: Why was Jane running?\n"
+  answer2="Answers:\n1. Jane was running from a monster.\n2. Jane was running a marathon.\n3. Jane wanted to get away from her parents.\n\n" 
   story3 = "Story 3: " + story + "\n"
   question3 = "Question: " + question +"\n"
   inp = instructions + story1 + question1 +  answer1 + story2 + question2 + answer2 + story3 + question3
@@ -18,7 +18,7 @@ def continue_story_svo_customprompt_past_tense(model, story, question, verbose =
 
   #con = construct(inp, " A.")
   #print("construct is ", con, "\n"*2)
-  out = generate(model, con, max_length=850, horizon=story, horizon_penalty=1.8, beams=5, repetition_penalty=2.8, do_beams=True)
+  out = generate(model, con, max_length=50, horizon=story, horizon_penalty=1.8, beams=5, repetition_penalty=2.8, do_beams=True)
 
   #return out
   out = out.split("Story 3")[1]
@@ -60,15 +60,23 @@ def continue_story_svo_customprompt_past_tense(model, story, question, verbose =
     except:
       continue
   return responses
-
+
+continuations_bad_words_future_tense =\
+  ["Why",  "Story", "You", "I",
+  "Think", "Erica", "Answer-", "A-", "A", "1", "2", "3", "4", "Answers",
+  "\"","\'"]
+continuations_bad_words_future_tense = sum(list(map(permute_string, continuations_bad_words_future_tense)), [])
+continuations_bad_words_ids_future_tense = list(map(lambda x: tokenizer(x)['input_ids'], continuations_bad_words_future_tense))
+
+
 def continue_story_svo_customprompt_future_tense(model, story, question, verbose = False, width = 10):
-  instructions = "The following are results from a children's reasoning test.  \nThe child will be read a short story and then answer a question about what happened before the story.  \nThe answer cannot contradict the story.  \nWe have collected the results below:\n"
-  story1="Story 1: Jim sat by the swings as Tom slowly approached. Tom gave the book to Jim during recess.\n"
-  question1="Question: What did Jim do after recieving the book?\n"
-  answer1="Answers:\n1. Jim jumped for joy!\n2. Jim threw the book away.\n3. Tom noticed that Jim looked unhappy.\n\n"
-  story2 = "Story 2: Mary was happy to finally cross the street.\n"
-  question2 = "Question: What happened after Mary crossed the street?\n"
-  answer2="Answers:\n1. She sighed in relief.\n2. Her smile quickly faded when she remembered she left her wallet at the restaurant.\n3. She looked over her shoulder to make sure she was not being followed.\n\n" 
+  instructions = "Please continue the stories below.  \nThe answer cannot contradict the story.  \nWe have collected the results below:\n"
+  story1="Story 1: John looked up from his lap and saw his friend in the distance. Jane sat by the swings as John slowly approached. John gave the book to Jane during recess.\n"
+  question1="Question: What did Jane do after recieving the book?\n"
+  answer1="Answers:\n1. Jane jumped for joy!\n2. Jane threw the book away.\n3. John noticed that Jane looked unhappy.\n\n"
+  story2 = "Story 2: Jane was happy to finally cross the street.\n"
+  question2 = "Question: What happened after Jane crossed the street?\n"
+  answer2="Answers:\n1. She sighed in relief and brushed the dust off her pants. \n2. Her smile quickly faded when she remembered she left her wallet at the restaurant.\n3. She looked over her shoulder to make sure she was not being followed.\n\n" 
   story3 = "Story 3: " + story + "\n"
   question3 = "Question: " + question +"\n"
   inp = instructions + story1 + question1 +  answer1 + story2 + question2 + answer2 + story3 + question3
@@ -79,7 +87,8 @@ def continue_story_svo_customprompt_future_tense(model, story, question, verbose
 
   #con = construct(inp, " A.")
   #print("construct is ", con, "\n"*2)
-  out = generate(model, con, max_length=850, horizon=story, horizon_penalty=1.8, beams=5, repetition_penalty=1.8, do_beams=True)
+  out = generate(model, con, max_length=50, horizon=story, horizon_penalty=1.4, beams=5,
+  repetition_penalty=1.8, do_sample=False, extra_bad_words=continuations_bad_words_ids_future_tense)
 
   #return out
   out = out.split("Story 3")[1]
@@ -105,6 +114,7 @@ def continue_story_svo_customprompt_future_tense(model, story, question, verbose
       break
   #Take responses that are long enough
   responses = list(filter(lambda x: len(x) > 5, responses))
+
   #print("og responses are ", responses)
   #Remove first space
   for i in range(len(responses)):
@@ -120,5 +130,13 @@ def continue_story_svo_customprompt_future_tense(model, story, question, verbose
       responses[i] = clean_story(responses[i])
     except:
       continue
-  return responses
+  # take only the first sentence
+
+  def prune_to_first_k_sentence(story, k = 1):
+    if k > 1:
+      return " ".join(sent_tokenize(story)[:k])
+    else:
+      return sent_tokenize(story)[0]
+
+  return list(map(lambda string: prune_to_first_k_sentence(string, 2), responses))
 

main.py

@@ -32,33 +32,6 @@ def hasSVOExpand(sent):
 total_sentences_checked = 0 
 num_stories = 5
 
-#for inp_story_full in tqdm(stories[:num_stories], position = 0, leave = True):
-for i in tqdm(r[:num_stories]):
-  inp_story_full = stories[i]
-
-  inp_story = inp_story_full[-1]
-  if backwards:
-    q = get_questions_past_tense(model, inp_story)[0]
-    print(q)
-    starts = continue_story_svo_customprompt_past_tense(model, inp_story, q)
-
-  else:
-    q = get_questions_future_tense(model, inp_story)[0]
-    print(q)
-    starts = continue_story_svo_customprompt_future_tense(model, inp_story, q)
-
-
-
-  svoability = ['x'for start in starts if hasSVOExpand(start)]
-
-  if svoability:
-    at_least_one_svoable_output += 1  
-  total_svoable_percents += len(svoability) 
-  total_sentences_checked += len(starts) 
-
-print("at least one output ", at_least_one_svoable_output/num_stories)
-print("total svoable percentages ", total_svoable_percents/total_sentences_checked) 
-
 
 import json 
 expansionfilenames = [
@@ -128,6 +101,8 @@ def not_contradict(sentA, sentB):
   choice = np.argmax(outputs)
   if choice == 0:
     return -100
+  print(outputs[2])
+  return 100
   return outputs[2]
 
 print(not_contradict("The sky is blue.", "The sky is not blue.")) #scores w/ BERT
@@ -147,7 +122,7 @@ def beam_search(beams, width=20, diversity_width=2, graph=None, reverse_rank=Tru
     if backwards:
       questions = get_questions_past_tense(model, " ".join(story_sents[:min(len(story_sents),3)]), q_prev)
     else:
-      questions = get_questions_future_tense(model, " ".join(story_sents[min(len(story_sents),3):]), q_prev)
+      questions = get_questions_future_tense(model, " ".join(story_sents[max(0, len(story_sents) - 3):]), q_prev)
 
     print("accumulated questions are ", questions)
     extensions = list()
@@ -156,7 +131,7 @@ def beam_search(beams, width=20, diversity_width=2, graph=None, reverse_rank=Tru
         continuation = continue_story_svo_customprompt_past_tense(model, story, q, width=-1)
       else:
         continuation = continue_story_svo_customprompt_future_tense(model, story, q, width=-1)
-
+      print(continuation)
       if q_prev != ['']:
         q_cur = [[q] + q_prev]*len(continuation)
       else:
@@ -166,12 +141,12 @@ def beam_search(beams, width=20, diversity_width=2, graph=None, reverse_rank=Tru
     if backwards:
       implication_story = " ".join(story_sents[0:min(len(story_sents), 1)]) #The first k sentences sliding window
     else:
-      implication_story = " ".join(story_sents[min(len(story_sents), 1):]) #The first k sentences sliding window
+      #" ".join(story_sents[max(0, len(story_sents) - 3):])
+      implication_story = " ".join(story_sents[-3:]) #The last k sentences sliding window
     extensions = list(filter(lambda x: hasSVOExpand(x[0]), extensions)) #Filter on if there is an SVO tuple
     extensions_ranks = list(map(lambda x: not_contradict(x[0], implication_story) + score, extensions)) #Rank. Include prior score via sum. This keeps track of beam scores over time.
     extensions_zip = list(filter(lambda x: x[1] > -100, zip(extensions, extensions_ranks))) #Zip
     extensions_zip = sorted(extensions_zip, key=lambda x: x[1], reverse=reverse_rank)
-
     extensions_zip = extensions_zip[:min(diversity_width, len(extensions_zip))] #Take top k
     if backwards:
       extensions = list(map(lambda x: (x[0][0]+" "+story, x[0][1], x[1]), extensions_zip)) #Sort
@@ -205,41 +180,39 @@ def remove_duplicates(beams):
   return to_ret
 
 reverse = True
-inp_story="The battle had been raging on for hours. William set his phasers to kill, he knew he had to make amends."
+inp_story="The battle had been raging on for hours. John set his phasers to kill, he knew he had to make amends."
 f=None
 
-beams, f = beam_search([(inp_story, [""], 0.0)], width=10, diversity_width=2, graph=f, reverse_rank=reverse)
-print("beams are ", beams)
-print("graph is ", f)
-
-for i in range(3):  
-  beams = remove_duplicates(beams)
-  print("beams are ", beams)
-  print("\nSTEP: " + str(i) + "\n\n")
-  print("\n".join(list(map(lambda x: x[0], beams))))
-  beams, f = beam_search(beams, width=5, diversity_width=10,graph=f, reverse_rank=reverse)
+#beams, f = beam_search([(inp_story, [""], 0.0)], width=10, diversity_width=2, graph=f, reverse_rank=reverse)
+#print("beams are ", beams)
+#print("graph is ", f)
 
+#for i in range(3):  
+#  beams = remove_duplicates(beams)
+#  print("beams are ", beams)
+#  print("\nSTEP: " + str(i) + "\n\n")
+#  print("\n".join(list(map(lambda x: x[0], beams))))
+#  beams, f = beam_search(beams, width=5, diversity_width=10,graph=f, reverse_rank=reverse)
 
-def gen_story_from_last_sentence(last_sentence, story_length=10):
-    reverse = True
-    inp_story=last_sentence
-    f=None
 
-    beams, f = beam_search([(inp_story, [""], 0.0)], width=10, diversity_width=2, graph=f, reverse_rank=reverse)
+def gen_story_from_last_sentence(last_sentence, story_length=3):
+  reverse = True
+  inp_story=last_sentence
+  f=None
+  beams, f = beam_search([(inp_story, [""], 0.0)], width=10, diversity_width=2, graph=f, reverse_rank=reverse)
+  print("beams are ", beams)
+  print("graph is ", f)
+  for i in range(story_length - 1):
+    beams = remove_duplicates(beams)
     print("beams are ", beams)
-    print("graph is ", f)
-
-    for i in range(story_length - 1):
-      beams = remove_duplicates(beams)
-      print("beams are ", beams)
-      print("\nSTEP: " + str(i) + "\n\n")
-      print("\n".join(list(map(lambda x: x[0], beams))))
-      beams, f = beam_search(beams, width=5, diversity_width=10,graph=f, reverse_rank=reverse)
-
-    return beams
+    print("\nSTEP: " + str(i) + "\n\n")
+    print("\n".join(list(map(lambda x: x[0], beams))))
+    beams, f = beam_search(beams, width=5, diversity_width=10,graph=f, reverse_rank=reverse)
+
+  return beams
 
 
-story1 = "Hansel's hand still trembles as he pushes open the twice-cooked door. \n The last time he saw the house he was glancing back over his shoulder as he and his sister fled into the trees. \n"
+story1 = "John's hand still trembles as he pushes open the twice-cooked door. \n The last time he saw the house he was glancing back over his shoulder as he and his sister fled into the trees. \n"
 story2 = "Stella loves to eat peanuts! She goes to the market every day, and is best friends with the owner."
 story3 = "There was a princess with long hair locked in a tower far away. \n A prince came to save her by climbing her hair and taking her out of the tower."
 story4 = "I woke up realizing that I became a cat. \n I caught a mouse and at the next moment I realized that I'm a human again!"
@@ -251,7 +224,7 @@ def gen_story_from_last_sentence(last_sentence, story_length=10):
 
 import pickle 
 for idx, sentence in enumerate(last_sentences):
-    story_beams = gen_story_from_last_sentence(sentence, story_length = 10)
+    story_beams = gen_story_from_last_sentence(sentence, story_length = 3)
 
     with open('storypickle' + str(idx) + '.pickle', 'wb') as handle:
         pickle.dump(story_beams, handle, protocol=pickle.HIGHEST_PROTOCOL)