fixing the csvlogger missing DF issues

.gitignore

@@ -48,3 +48,4 @@ checkpoints/
 .vscode
 *.csv
 !tests/sample_outputs/csv_attack_log.csv
+tests/test_command_line/attack_log.txt

docs/2notebook/1_Introduction_and_Transformations.ipynb

@@ -77,19 +77,19 @@
  "source": [
  "from textattack.transformations import WordSwap\n",
  "\n",
+ "\n",
  "class BananaWordSwap(WordSwap):\n",
- " \"\"\" Transforms an input by replacing any word with 'banana'.\n",
- " \"\"\"\n",
- " \n",
+ " \"\"\"Transforms an input by replacing any word with 'banana'.\"\"\"\n",
+ "\n",
  " # We don't need a constructor, since our class doesn't require any parameters.\n",
  "\n",
  " def _get_replacement_words(self, word):\n",
- " \"\"\" Returns 'banana', no matter what 'word' was originally.\n",
- " \n",
- "  Returns a list with one item, since `_get_replacement_words` is intended to\n",
- "  return a list of candidate replacement words.\n",
+ " \"\"\"Returns 'banana', no matter what 'word' was originally.\n",
+ "\n",
+ " Returns a list with one item, since `_get_replacement_words` is intended to\n",
+ " return a list of candidate replacement words.\n",
  " \"\"\"\n",
- " return ['banana']"
+ " return [\"banana\"]"
  ]
  },
  {
@@ -133,17 +133,23 @@
  "import transformers\n",
  "from textattack.models.wrappers import HuggingFaceModelWrapper\n",
  "\n",
- "model = transformers.AutoModelForSequenceClassification.from_pretrained(\"textattack/bert-base-uncased-ag-news\")\n",
- "tokenizer = transformers.AutoTokenizer.from_pretrained(\"textattack/bert-base-uncased-ag-news\")\n",
+ "model = transformers.AutoModelForSequenceClassification.from_pretrained(\n",
+ " \"textattack/bert-base-uncased-ag-news\"\n",
+ ")\n",
+ "tokenizer = transformers.AutoTokenizer.from_pretrained(\n",
+ " \"textattack/bert-base-uncased-ag-news\"\n",
+ ")\n",
  "\n",
  "model_wrapper = HuggingFaceModelWrapper(model, tokenizer)\n",
  "\n",
  "# Create the goal function using the model\n",
  "from textattack.goal_functions import UntargetedClassification\n",
+ "\n",
  "goal_function = UntargetedClassification(model_wrapper)\n",
  "\n",
  "# Import the dataset\n",
  "from textattack.datasets import HuggingFaceDataset\n",
+ "\n",
  "dataset = HuggingFaceDataset(\"ag_news\", None, \"test\")"
  ]
  },
@@ -166,14 +172,16 @@
  "outputs": [],
  "source": [
  "from textattack.search_methods import GreedySearch\n",
- "from textattack.constraints.pre_transformation import RepeatModification, StopwordModification\n",
+ "from textattack.constraints.pre_transformation import (\n",
+ " RepeatModification,\n",
+ " StopwordModification,\n",
+ ")\n",
  "from textattack import Attack\n",
  "\n",
  "# We're going to use our Banana word swap class as the attack transformation.\n",
- "transformation = BananaWordSwap() \n",
+ "transformation = BananaWordSwap()\n",
  "# We'll constrain modification of already modified indices and stopwords\n",
- "constraints = [RepeatModification(),\n",
- " StopwordModification()]\n",
+ "constraints = [RepeatModification(), StopwordModification()]\n",
  "# We'll use the Greedy search method\n",
  "search_method = GreedySearch()\n",
  "# Now, let's make the attack from the 4 components:\n",
@@ -517,8 +525,8 @@
  }
  ],
  "source": [
- "from tqdm import tqdm # tqdm provides us a nice progress bar.\n",
- "from textattack.loggers import CSVLogger # tracks a dataframe for us.\n",
+ "from tqdm import tqdm  # tqdm provides us a nice progress bar.\n",
+ "from textattack.loggers import CSVLogger  # tracks a dataframe for us.\n",
  "from textattack.attack_results import SuccessfulAttackResult\n",
  "from textattack import Attacker\n",
  "from textattack import AttackArgs\n",
@@ -530,14 +538,14 @@
  "\n",
  "attack_results = attacker.attack_dataset()\n",
  "\n",
- "#The following legacy tutorial code shows how the Attack API works in detail.\n",
+ "# The following legacy tutorial code shows how the Attack API works in detail.\n",
  "\n",
- "#logger = CSVLogger(color_method='html')\n",
+ "# logger = CSVLogger(color_method='html')\n",
  "\n",
- "#num_successes = 0\n",
- "#i = 0\n",
- "#while num_successes < 10:\n",
- " #result = next(results_iterable)\n",
+ "# num_successes = 0\n",
+ "# i = 0\n",
+ "# while num_successes < 10:\n",
+ "# result = next(results_iterable)\n",
  "# example, ground_truth_output = dataset[i]\n",
  "# i += 1\n",
  "# result = attack.attack(example, ground_truth_output)\n",
@@ -652,15 +660,19 @@
  ],
  "source": [
  "import pandas as pd\n",
- "pd.options.display.max_colwidth = 480 # increase colum width so we can actually read the examples\n",
  "\n",
- "logger = CSVLogger(color_method='html')\n",
+ "pd.options.display.max_colwidth = (\n",
+ " 480 # increase colum width so we can actually read the examples\n",
+ ")\n",
+ "\n",
+ "logger = CSVLogger(color_method=\"html\")\n",
  "\n",
  "for result in attack_results:\n",
  " logger.log_attack_result(result)\n",
  "\n",
  "from IPython.core.display import display, HTML\n",
- "display(HTML(logger.df[['original_text', 'perturbed_text']].to_html(escape=False)))"
+ "\n",
+ "display(HTML(logger.df[[\"original_text\", \"perturbed_text\"]].to_html(escape=False)))"
  ]
  },
  {
@@ -867,10 +879,10 @@
  "# For AG News, labels are 0: World, 1: Sports, 2: Business, 3: Sci/Tech\n",
  "\n",
  "custom_dataset = [\n",
- " ('Malaria deaths in Africa fall by 5% from last year', 0),\n",
- " ('Washington Nationals defeat the Houston Astros to win the World Series', 1),\n",
- " ('Exxon Mobil hires a new CEO', 2),\n",
- " ('Microsoft invests $1 billion in OpenAI', 3),\n",
+ " (\"Malaria deaths in Africa fall by 5% from last year\", 0),\n",
+ " (\"Washington Nationals defeat the Houston Astros to win the World Series\", 1),\n",
+ " (\"Exxon Mobil hires a new CEO\", 2),\n",
+ " (\"Microsoft invests $1 billion in OpenAI\", 3),\n",
  "]\n",
  "\n",
  "attack_args = AttackArgs(num_examples=4)\n",
@@ -881,14 +893,14 @@
  "\n",
  "results_iterable = attacker.attack_dataset()\n",
  "\n",
- "logger = CSVLogger(color_method='html')\n",
+ "logger = CSVLogger(color_method=\"html\")\n",
  "\n",
  "for result in results_iterable:\n",
  " logger.log_attack_result(result)\n",
  "\n",
  "from IPython.core.display import display, HTML\n",
- " \n",
- "display(HTML(logger.df[['original_text', 'perturbed_text']].to_html(escape=False)))"
+ "\n",
+ "display(HTML(logger.df[[\"original_text\", \"perturbed_text\"]].to_html(escape=False)))"
  ]
  }
  ],

docs/2notebook/2_Constraints.ipynb

@@ -100,6 +100,7 @@
  ],
  "source": [
  "import tensorflow as tf\n",
+ "\n",
  "print(tf.__version__)"
  ]
  },
@@ -149,10 +150,11 @@
  "!pip3 install .\n",
  "\n",
  "import nltk\n",
- "nltk.download('punkt') # The NLTK tokenizer\n",
- "nltk.download('maxent_ne_chunker') # NLTK named-entity chunker\n",
- "nltk.download('words') # NLTK list of words\n",
- "nltk.download('averaged_perceptron_tagger')"
+ "\n",
+ "nltk.download(\"punkt\") # The NLTK tokenizer\n",
+ "nltk.download(\"maxent_ne_chunker\") # NLTK named-entity chunker\n",
+ "nltk.download(\"words\") # NLTK list of words\n",
+ "nltk.download(\"averaged_perceptron_tagger\")"
  ]
  },
  {
@@ -205,8 +207,10 @@
  }
  ],
  "source": [
- "sentence = ('In 2017, star quarterback Tom Brady led the Patriots to the Super Bowl, '\n",
- " 'but lost to the Philadelphia Eagles.')\n",
+ "sentence = (\n",
+ " \"In 2017, star quarterback Tom Brady led the Patriots to the Super Bowl, \"\n",
+ " \"but lost to the Philadelphia Eagles.\"\n",
+ ")\n",
  "\n",
  "# 1. Tokenize using the NLTK tokenizer.\n",
  "tokens = nltk.word_tokenize(sentence)\n",
@@ -285,6 +289,7 @@
  "source": [
  "import functools\n",
  "\n",
+ "\n",
  "@functools.lru_cache(maxsize=2**14)\n",
  "def get_entities(sentence):\n",
  " tokens = nltk.word_tokenize(sentence)\n",
@@ -379,9 +384,10 @@
  "source": [
  "from textattack.constraints import Constraint\n",
  "\n",
+ "\n",
  "class NamedEntityConstraint(Constraint):\n",
- " \"\"\" A constraint that ensures `transformed_text` only substitutes named entities from `current_text` with other named entities.\n",
- " \"\"\"\n",
+ " \"\"\"A constraint that ensures `transformed_text` only substitutes named entities from `current_text` with other named entities.\"\"\"\n",
+ "\n",
  " def _check_constraint(self, transformed_text, current_text):\n",
  " transformed_entities = get_entities(transformed_text.text)\n",
  " current_entities = get_entities(current_text.text)\n",
@@ -390,26 +396,27 @@
  " if len(current_entities) == 0:\n",
  " return False\n",
  " if len(current_entities) != len(transformed_entities):\n",
- " # If the two sentences have a different number of entities, then \n",
- " # they definitely don't have the same labels. In this case, the \n",
+ " # If the two sentences have a different number of entities, then\n",
+ " # they definitely don't have the same labels. In this case, the\n",
  " # constraint is violated, and we return False.\n",
  " return False\n",
  " else:\n",
  " # Here we compare all of the words, in order, to make sure that they match.\n",
- " # If we find two words that don't match, this means a word was swapped \n",
+ " # If we find two words that don't match, this means a word was swapped\n",
  " # between `current_text` and `transformed_text`. That word must be a named entity to fulfill our\n",
  " # constraint.\n",
  " current_word_label = None\n",
  " transformed_word_label = None\n",
- " for (word_1, label_1), (word_2, label_2) in zip(current_entities, transformed_entities):\n",
+ " for (word_1, label_1), (word_2, label_2) in zip(\n",
+ " current_entities, transformed_entities\n",
+ " ):\n",
  " if word_1 != word_2:\n",
- " # Finally, make sure that words swapped between `x` and `x_adv` are named entities. If \n",
+ " # Finally, make sure that words swapped between `x` and `x_adv` are named entities. If\n",
  " # they're not, then we also return False.\n",
- " if (label_1 not in ['NNP', 'NE']) or (label_2 not in ['NNP', 'NE']):\n",
- " return False \n",
+ " if (label_1 not in [\"NNP\", \"NE\"]) or (label_2 not in [\"NNP\", \"NE\"]):\n",
+ " return False\n",
  " # If we get here, all of the labels match up. Return True!\n",
- " return True\n",
- " "
+ " return True"
  ]
  },
  {
@@ -638,17 +645,23 @@
  "import transformers\n",
  "from textattack.models.wrappers import HuggingFaceModelWrapper\n",
  "\n",
- "model = transformers.AutoModelForSequenceClassification.from_pretrained(\"textattack/albert-base-v2-ag-news\")\n",
- "tokenizer = transformers.AutoTokenizer.from_pretrained(\"textattack/albert-base-v2-ag-news\")\n",
+ "model = transformers.AutoModelForSequenceClassification.from_pretrained(\n",
+ " \"textattack/albert-base-v2-ag-news\"\n",
+ ")\n",
+ "tokenizer = transformers.AutoTokenizer.from_pretrained(\n",
+ " \"textattack/albert-base-v2-ag-news\"\n",
+ ")\n",
  "\n",
  "model_wrapper = HuggingFaceModelWrapper(model, tokenizer)\n",
  "\n",
  "# Create the goal function using the model\n",
  "from textattack.goal_functions import UntargetedClassification\n",
+ "\n",
  "goal_function = UntargetedClassification(model_wrapper)\n",
  "\n",
  "# Import the dataset\n",
  "from textattack.datasets import HuggingFaceDataset\n",
+ "\n",
  "dataset = HuggingFaceDataset(\"ag_news\", None, \"test\")"
  ]
  },
@@ -663,23 +676,27 @@
  "from textattack.transformations import WordSwapEmbedding\n",
  "from textattack.search_methods import GreedyWordSwapWIR\n",
  "from textattack import Attack\n",
- "from textattack.constraints.pre_transformation import RepeatModification, StopwordModification\n",
+ "from textattack.constraints.pre_transformation import (\n",
+ " RepeatModification,\n",
+ " StopwordModification,\n",
+ ")\n",
  "\n",
  "# We're going to the `WordSwapEmbedding` transformation. Using the default settings, this\n",
- "# will try substituting words with their neighbors in the counter-fitted embedding space. \n",
- "transformation = WordSwapEmbedding(max_candidates=20) \n",
+ "# will try substituting words with their neighbors in the counter-fitted embedding space.\n",
+ "transformation = WordSwapEmbedding(max_candidates=20)\n",
  "\n",
  "# We'll use the greedy search with word importance ranking method again\n",
  "search_method = GreedyWordSwapWIR()\n",
  "\n",
  "# Our constraints will be the same as Tutorial 1, plus the named entity constraint\n",
- "constraints = [RepeatModification(),\n",
- " StopwordModification(),\n",
- " NamedEntityConstraint(False)]\n",
+ "constraints = [\n",
+ " RepeatModification(),\n",
+ " StopwordModification(),\n",
+ " NamedEntityConstraint(False),\n",
+ "]\n",
  "\n",
- "# Now, let's make the attack using these parameters. \n",
- "attack = Attack(goal_function, constraints, transformation, search_method)\n",
- "\n"
+ "# Now, let's make the attack using these parameters.\n",
+ "attack = Attack(goal_function, constraints, transformation, search_method)"
  ]
  },
  {
@@ -800,11 +817,13 @@
  }
  ],
  "source": [
- "from textattack.loggers import CSVLogger # tracks a dataframe for us.\n",
+ "from textattack.loggers import CSVLogger  # tracks a dataframe for us.\n",
  "from textattack.attack_results import SuccessfulAttackResult\n",
  "from textattack import Attacker, AttackArgs\n",
  "\n",
- "attack_args = AttackArgs(num_successful_examples=5, log_to_csv=\"results.csv\", csv_coloring_style=\"html\")\n",
+ "attack_args = AttackArgs(\n",
+ " num_successful_examples=5, log_to_csv=\"results.csv\", csv_coloring_style=\"html\"\n",
+ ")\n",
  "attacker = Attacker(attack, dataset, attack_args)\n",
  "\n",
  "attacker.attack_dataset()"
@@ -833,13 +852,16 @@
  "outputs": [],
  "source": [
  "import pandas as pd\n",
- "pd.options.display.max_colwidth = 480 # increase column width so we can actually read the examples\n",
+ "\n",
+ "pd.options.display.max_colwidth = (\n",
+ " 480 # increase column width so we can actually read the examples\n",
+ ")\n",
  "\n",
  "from IPython.core.display import display, HTML\n",
  "\n",
  "logger = attacker.attack_log_manager.loggers[0]\n",
  "successes = logger.df[logger.df[\"result_type\"] == \"Successful\"]\n",
- "display(HTML(successes[['original_text', 'perturbed_text']].to_html(escape=False)))"
+ "display(HTML(successes[[\"original_text\", \"perturbed_text\"]].to_html(escape=False)))"
  ]
  },
  {