From 34e7e8431e2520443c78ab75543f0b7d5c57a639 Mon Sep 17 00:00:00 2001 From: sundy1994 Date: Tue, 1 Oct 2024 20:42:10 -0700 Subject: [PATCH 1/7] added BYOL feature, updated some doc strings --- pyproject.toml | 1 + src/team_comm_tools/feature_builder.py | 26 ++++- .../features/lexical_features_v2.py | 19 +++- .../utils/calculate_chat_level_features.py | 26 ++++- src/team_comm_tools/utils/check_embeddings.py | 103 +++++++++++++++++- 5 files changed, 158 insertions(+), 17 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 9461e0a2..fda4b781 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,6 +14,7 @@ dependencies = [ "emoji==1.7.0", "flask==3.0.3", "gensim>=4.3.3", + "matplotlib==3.0.0", "nltk==3.9.1", "numpy<2.0.0", "pandas==2.2.2", diff --git a/src/team_comm_tools/feature_builder.py b/src/team_comm_tools/feature_builder.py index bd600064..543c2c0a 100644 --- a/src/team_comm_tools/feature_builder.py +++ b/src/team_comm_tools/feature_builder.py @@ -92,6 +92,9 @@ class FeatureBuilder: :param compute_vectors_from_preprocessed: If true, computes vectors using preprocessed text (that is, with capitalization and punctuation removed). This was the default behavior for v.0.1.3 and earlier, but we now default to computing metrics on the unpreprocessed text (which INCLUDES capitalization and punctuation). Defaults to False. :type compute_vectors_from_preprocessed: bool, optional + :param custom_liwc_dictionary_path: This is the path of the user's own LIWC dictionary file (.dic). Defaults to empty string. + :type custom_liwc_dictionary_path: str, optional + :return: The FeatureBuilder doesn't return anything; instead, it writes the generated features to files in the specified paths. It will also print out its progress, so you should see "All Done!" in the terminal, which will indicate that the features have been generated. :rtype: None @@ -117,7 +120,8 @@ def __init__( ner_training_df: pd.DataFrame = None, ner_cutoff: int = 0.9, regenerate_vectors: bool = False, - compute_vectors_from_preprocessed: bool = False + compute_vectors_from_preprocessed: bool = False, + custom_liwc_dictionary_path: str = '' ) -> None: # Defining input and output paths. @@ -128,6 +132,23 @@ def __init__( print("Initializing Featurization...") + if not custom_liwc_dictionary_path: + self.custom_liwc_dictionary = {} + else: + # Read .dic file if the path is provided + custom_liwc_dictionary_path = Path(custom_liwc_dictionary_path) + if not custom_liwc_dictionary_path.suffix == '.dic': + print(f"WARNING: The custom LIWC dictionary file is not a .dic file: {custom_liwc_dictionary_path}") + self.custom_liwc_dictionary = {} + else: + with open(custom_liwc_dictionary_path, 'r', encoding='utf-8-sig') as file: + dicText = file.read() + try: + self.custom_liwc_dictionary = load_liwc_dict(dicText) + except Exception as e: + print(f"WARNING: Failed loading custom liwc dictionary: {e}") + self.custom_liwc_dictionary = {} + # Set features to generate # TODO --- think through more carefully which ones we want to exclude and why self.feature_dict = feature_dict @@ -564,7 +585,8 @@ def chat_level_features(self) -> None: ner_cutoff = self.ner_cutoff, conversation_id_col = self.conversation_id_col, message_col = self.message_col, - timestamp_col = self.timestamp_col + timestamp_col = self.timestamp_col, + custom_liwc_dictionary = self.custom_liwc_dictionary ) # Calling the driver inside this class to create the features. self.chat_data = chat_feature_builder.calculate_chat_level_features(self.feature_methods_chat) diff --git a/src/team_comm_tools/features/lexical_features_v2.py b/src/team_comm_tools/features/lexical_features_v2.py index 1cd7ede7..2c966d0a 100644 --- a/src/team_comm_tools/features/lexical_features_v2.py +++ b/src/team_comm_tools/features/lexical_features_v2.py @@ -26,7 +26,7 @@ def get_liwc_count(regex, chat): else: return 0 -def liwc_features(chat_df: pd.DataFrame, message_col) -> pd.DataFrame: +def liwc_features(chat_df: pd.DataFrame, message_col: str, custom_liwc_dictionary: dict={}) -> pd.DataFrame: """ This function takes in the chat level input dataframe and computes lexical features (the number of words from a given lexicon, such as LIWC). @@ -34,6 +34,7 @@ def liwc_features(chat_df: pd.DataFrame, message_col) -> pd.DataFrame: Args: chat_df (pd.DataFrame): This is a pandas dataframe of the chat level features. Should contain 'message' column. message_col (str): This is a string with the name of the column containing the message / text. + custom_liwc_dictionary (dict): This is a dictionary of the user's custom LIWC dic. Returns: pd.DataFrame: Dataframe of the lexical features stacked as columns. @@ -47,12 +48,18 @@ def liwc_features(chat_df: pd.DataFrame, message_col) -> pd.DataFrame: lexicons_dict = pickle.load(lexicons_pickle_file) # Return the lexical features stacked as columns - return pd.concat( + # return pd.concat( # Finding the # of occurrences of lexicons of each type for all the messages. - [pd.DataFrame(chat_df[message_col + "_original"].apply(lambda chat: get_liwc_count(regex, chat)))\ + df_lst = [pd.DataFrame(chat_df[message_col + "_original"].apply(lambda chat: get_liwc_count(regex, chat)))\ .rename({message_col + "_original": lexicon_type + "_lexical_wordcount"}, axis=1)\ - for lexicon_type, regex in lexicons_dict.items()], - axis=1 - ) + for lexicon_type, regex in lexicons_dict.items()] + # , axis=1 + # ) + if custom_liwc_dictionary: + df_lst += [pd.DataFrame(chat_df[message_col + "_original"].apply(lambda chat: get_liwc_count(regex, chat)))\ + .rename({message_col + "_original": lexicon_type + "_lexical_wordcount_custom"}, axis=1)\ + for lexicon_type, regex in custom_liwc_dictionary.items()] + return pd.concat(df_lst, axis=1) + except: print("WARNING: Lexicons not found. Skipping feature...") diff --git a/src/team_comm_tools/utils/calculate_chat_level_features.py b/src/team_comm_tools/utils/calculate_chat_level_features.py index ef55d4be..638a8c0a 100644 --- a/src/team_comm_tools/utils/calculate_chat_level_features.py +++ b/src/team_comm_tools/utils/calculate_chat_level_features.py @@ -32,14 +32,30 @@ class ChatLevelFeaturesCalculator: :param chat_data: Pandas dataframe of chat-level features read from the input dataset :type chat_data: pd.DataFrame + :param vect_data: Pandas dataframe containing vector data :type vect_data: pd.DataFrame + :param bert_sentiment_data: Pandas dataframe containing BERT sentiment data :type bert_sentiment_data: pd.DataFrame - :param ner_training_df: This is a pandas dataframe of training data for named entity recognition feature - :type ner_training_df: pd.DataFrame + + :param ner_training: This is a pandas dataframe of training data for named entity recognition feature + :type ner_training: pd.DataFrame + :param ner_cutoff: This is the cutoff value for the confidence of prediction for each named entity :type ner_cutoff: int + + :param conversation_id_col: A string representing the column name that should be selected as the conversation ID. Defaults to "conversation_num". + :type conversation_id_col: str + + :param message_col: A string representing the column name that should be selected as the message. Defaults to "message". + :type message_col: str + + :param timestamp_col: A string representing the column name that should be selected as the message. Defaults to "timestamp". + :type timestamp_col: str + + :param custom_liwc_dictionary: This is the user's own LIWC dictionary. Defaults to empty dictionary. + :type custom_liwc_dictionary: dict """ def __init__( self, @@ -50,7 +66,8 @@ def __init__( ner_cutoff: int, conversation_id_col: str, message_col: str, - timestamp_col: str | tuple[str, str] + timestamp_col: str | tuple[str, str], + custom_liwc_dictionary: dict ) -> None: self.chat_data = chat_data @@ -61,6 +78,7 @@ def __init__( self.conversation_id_col = conversation_id_col self.timestamp_col = timestamp_col self.message_col = message_col + self.custom_liwc_dictionary = custom_liwc_dictionary self.easy_dale_chall_words = get_dale_chall_easy_words() # load easy Dale-Chall words exactly once. self.function_words = get_function_words() # load function words exactly once self.question_words = get_question_words() # load question words exactly once @@ -169,7 +187,7 @@ def lexical_features(self) -> None: :return: None :rtype: None """ - self.chat_data = pd.concat([self.chat_data, liwc_features(self.chat_data, self.message_col)], axis = 1) + self.chat_data = pd.concat([self.chat_data, liwc_features(self.chat_data, self.message_col, self.custom_liwc_dictionary)], axis = 1) def calculate_hedge_features(self) -> None: """ diff --git a/src/team_comm_tools/utils/check_embeddings.py b/src/team_comm_tools/utils/check_embeddings.py index 2c56b3b6..77db3fb8 100644 --- a/src/team_comm_tools/utils/check_embeddings.py +++ b/src/team_comm_tools/utils/check_embeddings.py @@ -24,7 +24,8 @@ os.environ["TOKENIZERS_PARALLELISM"] = "false" # Check if embeddings exist -def check_embeddings(chat_data, vect_path, bert_path, need_sentence, need_sentiment, regenerate_vectors, message_col = "message"): +def check_embeddings(chat_data: pd.DataFrame, vect_path: str, bert_path: str, need_sentence: bool, + need_sentiment: bool, regenerate_vectors: bool, message_col: str = "message"): """ Check if embeddings and required lexicons exist, and generate them if they don't. @@ -90,15 +91,19 @@ def read_in_lexicons(directory, lexicons_dict): continue lines = [] for lexicon in lexicons: - # get rid of parentheses lexicon = lexicon.strip() - lexicon = lexicon.replace('(', '') - lexicon = lexicon.replace(')', '') + # get rid of parentheses; comment out to keep the emojis like :) + # TODO: compare the difference if we keep () + # lexicon = lexicon.replace('(', '') + # lexicon = lexicon.replace(')', '') if '*' not in lexicon: lines.append(r"\b" + lexicon.replace("\n", "") + r"\b") else: # get rid of any cases of multiple repeat -- e.g., '**' - lexicon = lexicon.replace('\**', '\*') + # lexicon = lexicon.replace('\**', '\*'); this will throw Invalid syntax error + pattern = re.compile(r'\*+') + lexicon = pattern.sub('*', lexicon) + lexicon = r"\b" + lexicon.replace("\n", "").replace("*", "") + r"\S*\b" # build the final lexicon lines.append(r"\b" + lexicon.replace("\n", "").replace("*", "") + r"\S*\b") @@ -134,6 +139,94 @@ def generate_lexicon_pkl(): except: print("WARNING: Lexicons not found. Skipping pickle generation...") +def fix_abbreviations(dicTerm: str) -> str: + """ + Helper function to fix abbreviations with punctuations. + src: https://github.com/ryanboyd/ContentCoder-Py/blob/main/ContentCodingDictionary.py#L714 + + This function goes over a list of hardcoded exceptions for the tokenizer / sentence parser + built into LIWC so that it doesn't convert them into separate strings + (e.g., we want "i.e." to not be seen as two words and two sentences [i, e]). + + :param dicTerm: The lexicon term + :type dicTerm: str + + :return: dicTerm + :rtype: str + """ + + AbbreviationList = ['ie.', 'i.e.', 'eg.', 'e.g.', 'vs.', 'ph.d.', 'phd.', 'm.d.', 'd.d.s.', 'b.a.', + 'b.s.', 'm.s.', 'u.s.a.', 'u.s.', 'u.t.', 'attn.', 'prof.', 'mr.', 'dr.', 'mrs.', + 'ms.', 'a.i.', 'a.g.i.', 'tl;dr', 't.t', 't_t'] + AbbreviationDict = {} + for item in AbbreviationList: + itemClean = item.replace('.', '-').replace(';', '-').replace('_', '-') + + if len(itemClean) > 2 and itemClean.endswith('-'): + numTrailers = len(itemClean) + itemClean = itemClean.strip('-') + numTrailers = numTrailers - len(itemClean) + itemClean = itemClean[:-1] + ''.join(['-'] * numTrailers) + itemClean[-1:] + + AbbreviationDict[item] = itemClean + AbbreviationDict[item + ','] = itemClean + + if dicTerm in AbbreviationDict.keys(): + return AbbreviationDict[dicTerm] + else: + return dicTerm + +def load_liwc_dict(dicText: str) -> dict: + """ + Loads up a dictionary that is in the LIWC 2007/2015 format. + src: https://github.com/ryanboyd/ContentCoder-Py/blob/main/ContentCodingDictionary.py#L81 + + This functions reads the content of a LIWC dictionary file in the official format, + and convert it to a dictionary with lexicon: regular expression format. + We assume the dicText has two parts: the header, which maps numbers to "category names," + and the body, which maps words in the lexicon to different category numbers, separated by a '%' sign. + + :param dicText: The content of a .dic file + :type dicText: str + + :return: dicCategories + :rtype: dict + """ + dicSplit = dicText.split('%', 2) + dicHeader, dicBody = dicSplit[1], dicSplit[2] + # read headers + catNameNumberMap = {} + for line in dicHeader.splitlines(): + if line.strip() == '': + continue + lineSplit = line.strip().split('\t') + catNameNumberMap[lineSplit[0]] = lineSplit[1] + # read body + dicCategories = {} + for line in dicBody.splitlines(): + lineSplit = line.strip().split('\t') + dicTerm, catNums = lineSplit[0], lineSplit[1:] + dicTerm = fix_abbreviations(dicTerm=' '.join(lineSplit[0].lower().strip().split())) + dicTerm = dicTerm.strip() + if dicTerm == '': + continue + if '*' not in dicTerm: + dicTerm = r"\b" + dicTerm.replace("\n", "") + r"\b" + else: + # Replace consecutive asterisks with a single asterisk -- e.g., '**'->'*' + pattern = re.compile(r'\*+') + dicTerm = pattern.sub('*', dicTerm) + dicTerm = r"\b" + dicTerm.replace("\n", "").replace("*", "") + r"\S*\b" + + for catNum in catNums: + cat = catNameNumberMap[catNum] + if cat not in dicCategories: + dicCategories[cat] = dicTerm + else: + cur_dicTerm = dicCategories[cat] + dicCategories[cat] = cur_dicTerm + "|" + dicTerm + return dicCategories + def generate_certainty_pkl(): """ Helper function for generating the pickle file containing the certainty lexicon. From 27c571e5f40f225147bb25a20e36e306eadea95c Mon Sep 17 00:00:00 2001 From: sundy1994 Date: Thu, 17 Oct 2024 10:30:35 -0700 Subject: [PATCH 2/7] add matplotlib requirements --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 3c570b21..5c86c0fb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,7 @@ convokit==3.0.0 emoji==1.7.0 flask==3.0.3 gensim>=4.3.3 +matplotlib==3.0.0 nltk==3.9.1 numpy<2.0.0 pandas==2.2.2 From 1367090d21d8acd0752b5086491730e69b112a99 Mon Sep 17 00:00:00 2001 From: sundy1994 Date: Thu, 17 Oct 2024 10:34:02 -0700 Subject: [PATCH 3/7] bug fix --- pyproject.toml | 2 +- requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index fda4b781..e23dabc2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ dependencies = [ "emoji==1.7.0", "flask==3.0.3", "gensim>=4.3.3", - "matplotlib==3.0.0", + "matplotlib>=3.0.0", "nltk==3.9.1", "numpy<2.0.0", "pandas==2.2.2", diff --git a/requirements.txt b/requirements.txt index 5c86c0fb..83fb603e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,7 +3,7 @@ convokit==3.0.0 emoji==1.7.0 flask==3.0.3 gensim>=4.3.3 -matplotlib==3.0.0 +matplotlib>=3.0.0 nltk==3.9.1 numpy<2.0.0 pandas==2.2.2 From 006ad111077aa63b7878fc29a8cdd0f7f11d871f Mon Sep 17 00:00:00 2001 From: sundy1994 Date: Thu, 17 Oct 2024 19:11:21 -0700 Subject: [PATCH 4/7] bug fixes --- src/team_comm_tools/features/lexical_features_v2.py | 5 +++-- src/team_comm_tools/utils/check_embeddings.py | 7 ++++--- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/src/team_comm_tools/features/lexical_features_v2.py b/src/team_comm_tools/features/lexical_features_v2.py index 2c966d0a..76b3330e 100644 --- a/src/team_comm_tools/features/lexical_features_v2.py +++ b/src/team_comm_tools/features/lexical_features_v2.py @@ -60,6 +60,7 @@ def liwc_features(chat_df: pd.DataFrame, message_col: str, custom_liwc_dictionar .rename({message_col + "_original": lexicon_type + "_lexical_wordcount_custom"}, axis=1)\ for lexicon_type, regex in custom_liwc_dictionary.items()] return pd.concat(df_lst, axis=1) - - except: + except FileNotFoundError: print("WARNING: Lexicons not found. Skipping feature...") + except Exception as e: + print(f'WARNING: Failed to generate lexicons due to unexpected error: {e}') diff --git a/src/team_comm_tools/utils/check_embeddings.py b/src/team_comm_tools/utils/check_embeddings.py index 77db3fb8..cb54875d 100644 --- a/src/team_comm_tools/utils/check_embeddings.py +++ b/src/team_comm_tools/utils/check_embeddings.py @@ -210,13 +210,14 @@ def load_liwc_dict(dicText: str) -> dict: dicTerm = dicTerm.strip() if dicTerm == '': continue - if '*' not in dicTerm: - dicTerm = r"\b" + dicTerm.replace("\n", "") + r"\b" - else: + + if '*' in dicTerm: # Replace consecutive asterisks with a single asterisk -- e.g., '**'->'*' pattern = re.compile(r'\*+') dicTerm = pattern.sub('*', dicTerm) dicTerm = r"\b" + dicTerm.replace("\n", "").replace("*", "") + r"\S*\b" + else: + dicTerm = r"\b" + dicTerm.replace("\n", "").replace('(', r'\(').replace(')', r'\)') + r"\b" for catNum in catNums: cat = catNameNumberMap[catNum] From 65140f1e052d78be6f5e1df810d17b6fd0cbe8d4 Mon Sep 17 00:00:00 2001 From: Xinlan Emily Hu Date: Tue, 22 Oct 2024 12:28:07 -0400 Subject: [PATCH 5/7] update documentation --- docs/build/doctrees/environment.pickle | Bin 324236 -> 326081 bytes docs/build/doctrees/feature_builder.doctree | Bin 90730 -> 93121 bytes .../features/lexical_features_v2.doctree | Bin 15414 -> 17885 bytes .../calculate_chat_level_features.doctree | Bin 87407 -> 91521 bytes .../doctrees/utils/check_embeddings.doctree | Bin 44856 -> 58554 bytes docs/build/html/feature_builder.html | 3 +- .../html/features/lexical_features_v2.html | 3 +- docs/build/html/genindex.html | 4 ++ docs/build/html/objects.inv | Bin 4365 -> 4389 bytes docs/build/html/searchindex.js | 2 +- .../utils/calculate_chat_level_features.html | 8 ++- docs/build/html/utils/check_embeddings.html | 47 +++++++++++++++++- docs/source/basics.rst | 4 +- docs/source/features_conceptual/liwc.rst | 27 ++++++++-- src/team_comm_tools/feature_builder.py | 1 + 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feature_builder module

-class feature_builder.FeatureBuilder(input_df: DataFrame, vector_directory: str = './vector_data/', output_file_base: str = 'output', output_file_path_chat_level: str = None, output_file_path_user_level: str = None, output_file_path_conv_level: str = None, custom_features: list = [], analyze_first_pct: list = [1.0], turns: bool = False, conversation_id_col: str = 'conversation_num', speaker_id_col: str = 'speaker_nickname', message_col: str = 'message', timestamp_col: str | tuple[str, str] = 'timestamp', grouping_keys: list = [], cumulative_grouping=False, within_task=False, ner_training_df: DataFrame = None, ner_cutoff: int = 0.9, regenerate_vectors: bool = False, compute_vectors_from_preprocessed: bool = False)
+class feature_builder.FeatureBuilder(input_df: DataFrame, vector_directory: str = './vector_data/', output_file_base: str = 'output', output_file_path_chat_level: str = None, output_file_path_user_level: str = None, output_file_path_conv_level: str = None, custom_features: list = [], analyze_first_pct: list = [1.0], turns: bool = False, conversation_id_col: str = 'conversation_num', speaker_id_col: str = 'speaker_nickname', message_col: str = 'message', timestamp_col: str | tuple[str, str] = 'timestamp', grouping_keys: list = [], cumulative_grouping=False, within_task=False, ner_training_df: DataFrame = None, ner_cutoff: int = 0.9, regenerate_vectors: bool = False, compute_vectors_from_preprocessed: bool = False, custom_liwc_dictionary_path: str = '')

Bases: object

The FeatureBuilder is the main engine that reads in the user’s inputs and specifications and generates conversational features. The FeatureBuilder separately calls the classes (the ChatLevelFeaturesCalculator, @@ -132,6 +132,7 @@

  • ner_cutoff (int) – This is the cutoff value for the confidence of prediction for each named entity. Defaults to 0.9.

  • regenerate_vectors (bool, optional) – If true, will regenerate vector data even if it already exists. Defaults to False.

  • compute_vectors_from_preprocessed (bool, optional) – If true, computes vectors using preprocessed text (that is, with capitalization and punctuation removed). This was the default behavior for v.0.1.3 and earlier, but we now default to computing metrics on the unpreprocessed text (which INCLUDES capitalization and punctuation). Defaults to False.

    • +

  • custom_liwc_dictionary_path (str, optional) – This is the path of the user’s own LIWC dictionary file (.dic). Defaults to empty string.

    • Returns:
    diff --git a/docs/build/html/features/lexical_features_v2.html b/docs/build/html/features/lexical_features_v2.html index 3ad8fd6b..1d0f5c84 100644 --- a/docs/build/html/features/lexical_features_v2.html +++ b/docs/build/html/features/lexical_features_v2.html @@ -136,7 +136,7 @@
    -features.lexical_features_v2.liwc_features(chat_df: DataFrame, message_col) → DataFrame
    +features.lexical_features_v2.liwc_features(chat_df: DataFrame, message_col: str, custom_liwc_dictionary: dict = {}) → DataFrame

    This function takes in the chat level input dataframe and computes lexical features (the number of words from a given lexicon, such as LIWC).

    @@ -146,6 +146,7 @@

    • chat_df (pd.DataFrame) – This is a pandas dataframe of the chat level features. Should contain ‘message’ column.

    • message_col (str) – This is a string with the name of the column containing the message / text.

      • +

    • custom_liwc_dictionary (dict) – This is a dictionary of the user’s custom LIWC dic.

    Returns:
    diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 79fe2257..137a2070 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -449,6 +449,8 @@

    F

  • featurize() (feature_builder.FeatureBuilder method) +
    • +
  • fix_abbreviations() (in module utils.check_embeddings)
  • function_mimicry_score() (in module features.word_mimicry)
    • @@ -621,6 +623,8 @@

    L

  • lexical_features() (utils.calculate_chat_level_features.ChatLevelFeaturesCalculator method)
  • liwc_features() (in module features.lexical_features_v2) +
    • +
  • load_liwc_dict() (in module utils.check_embeddings)
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46, 68], "xinlan": 62, "yashveer": 62, "ye": 19, "yeah": [31, 36], "yeoman": [18, 49], "yesno_quest": [32, 49, 54], "yet": 48, "ylatau": 12, "you": [0, 1, 2, 11, 24, 29, 31, 36, 37, 43, 47, 50, 59, 61, 62, 69], "your": [0, 29, 31, 32, 36, 37, 50, 59, 61, 62], "yourself": [31, 36, 50], "yuluan": 62, "yup": [31, 36], "yuxuan": 62, "z": [12, 39, 49, 51, 64, 73], "zero": [13, 52], "zhang": 62, "zheng": 62, "zhong": 62, "zhou": 62, "zscore": 41, "zscore_chat": 41, "zscore_chats_and_convers": 69, "zscore_convers": 41, "\u00bc": 47, "\u03c4": 55}, "titles": ["The Basics", "Worked Example", "feature_builder module", "basic_features module", "burstiness module", "certainty module", "discursive_diversity module", "fflow module", "get_all_DD_features module", "get_user_network module", "hedge module", "Features: Technical Documentation", "info_exchange_zscore module", "information_diversity module", "lexical_features_v2 module", "named_entity_recognition_features module", "other_lexical_features 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47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 69], "feature_build": 2, "featurebuild": [1, 61, 62], "fflow": 7, "file": [1, 30, 34, 35, 45, 46, 47, 51], "first": 53, "flow": 35, "forward": 35, "function": [0, 36], "gener": [1, 61, 62], "get": [1, 61, 62], "get_all_dd_featur": 8, "get_user_network": 9, "gini": 37, "gini_coeffici": 68, "hedg": [10, 38], "high": [29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60], "implement": [29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60], "import": [1, 61], "index": 59, "indic": 61, "info_exchange_zscor": 12, "inform": [1, 40, 41, 61], "information_divers": 13, "input": [1, 34], "inquiri": 42, "inspect": [1, 61], "interpret": [29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60], "introduct": 62, "intuit": [29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60], "kei": 0, "length": 43, "level": [11, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 69], "lexical_features_v2": 14, "lexicon": 42, "light": 0, "linguist": 42, "liwc": 42, "marker": 49, "messag": [43, 44], "mimicri": [45, 46], "modul": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73], "motiv": 62, "move": 46, "naiv": 54, "name": [1, 29, 47, 61], "named_entity_recognition_featur": 15, "new": 42, "note": [29, 30, 31, 33, 34, 35, 36, 38, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 55, 56, 57, 58, 59], "onlin": 48, "other": [42, 69], "other_lexical_featur": 16, "ouput": 34, "our": 62, "output": [1, 30, 35, 45, 46, 47, 51], "own": 42, "packag": [0, 1, 61], "paramet": [0, 1], "person": 53, "pip": [1, 61], "polar": 56, "polit": [49, 50], "politeness_featur": 17, "politeness_v2": 18, "politeness_v2_help": 19, "posit": 52, "preload_word_list": 70, "preprocess": 71, "pronoun": 53, "proport": 53, "quantiti": 44, "question": 54, "question_num": 20, "ratio": 60, "readabl": 21, "recept": 49, "recognit": 47, "recommend": [1, 61], "reddit_tag": 22, "relat": [29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60], "repair": 32, "roberta": 51, "run": 1, "sampl": [0, 1], "score": [33, 41, 52], "sentiment": 51, "speaker": [11, 59, 62, 69], "start": [1, 61, 62], "strategi": 50, "subject": 57, "summarize_featur": 72, "tabl": 61, "tag": 48, "take": 59, "team": [55, 61, 62], "technic": 11, "temporal_featur": 23, "textblob": [56, 57], "textblob_sentiment_analysi": 24, "time": 58, "token": 60, "toolkit": 61, "touch": 0, "train": 47, "troubleshoot": [1, 61], "turn": [1, 59], "turn_taking_featur": 25, "type": 60, "us": 61, "user": 11, "util": 69, "utter": [11, 39, 62, 69], "v": 42, "variance_in_dd": 26, "vector": 1, "virtual": [1, 61], "walkthrough": 1, "within_person_discursive_rang": 27, "word": [31, 36, 42, 60], "word_mimicri": 28, "work": 1, "your": [1, 42], "z": [41, 52], "zscore_chats_and_convers": 73}}) \ No newline at end of file diff --git a/docs/build/html/utils/calculate_chat_level_features.html b/docs/build/html/utils/calculate_chat_level_features.html index 893448e2..984730f2 100644 --- a/docs/build/html/utils/calculate_chat_level_features.html +++ b/docs/build/html/utils/calculate_chat_level_features.html @@ -96,7 +96,7 @@

      calculate_chat_level_features module

      -class utils.calculate_chat_level_features.ChatLevelFeaturesCalculator(chat_data: DataFrame, vect_data: DataFrame, bert_sentiment_data: DataFrame, ner_training: DataFrame, ner_cutoff: int, conversation_id_col: str, message_col: str, timestamp_col: str | tuple[str, str])
      +class utils.calculate_chat_level_features.ChatLevelFeaturesCalculator(chat_data: DataFrame, vect_data: DataFrame, bert_sentiment_data: DataFrame, ner_training: DataFrame, ner_cutoff: int, conversation_id_col: str, message_col: str, timestamp_col: str | tuple[str, str], custom_liwc_dictionary: dict)

      Bases: object

      Initialize variables and objects used by the ChatLevelFeaturesCalculator class.

      This class uses various feature modules to define chat-level features. It reads input data and @@ -107,8 +107,12 @@

    • chat_data (pd.DataFrame) – Pandas dataframe of chat-level features read from the input dataset

    • vect_data (pd.DataFrame) – Pandas dataframe containing vector data

    • bert_sentiment_data (pd.DataFrame) – Pandas dataframe containing BERT sentiment data

      • -

    • ner_training_df (pd.DataFrame) – This is a pandas dataframe of training data for named entity recognition feature

      • +

    • ner_training (pd.DataFrame) – This is a pandas dataframe of training data for named entity recognition feature

    • ner_cutoff (int) – This is the cutoff value for the confidence of prediction for each named entity

      • +

    • conversation_id_col (str) – A string representing the column name that should be selected as the conversation ID. Defaults to “conversation_num”.

      • +

    • message_col (str) – A string representing the column name that should be selected as the message. Defaults to “message”.

      • +

    • timestamp_col (str) – A string representing the column name that should be selected as the message. Defaults to “timestamp”.

      • +

    • custom_liwc_dictionary (dict) – This is the user’s own LIWC dictionary. Defaults to empty dictionary.

    diff --git a/docs/build/html/utils/check_embeddings.html b/docs/build/html/utils/check_embeddings.html index ba19ea73..77cfe13b 100644 --- a/docs/build/html/utils/check_embeddings.html +++ b/docs/build/html/utils/check_embeddings.html @@ -61,11 +61,13 @@
  • assign_chunk_nums module
  • check_embeddings module
    • @@ -105,7 +107,7 @@

    check_embeddings module

    -utils.check_embeddings.check_embeddings(chat_data, vect_path, bert_path, need_sentence, need_sentiment, regenerate_vectors, message_col='message')
    +utils.check_embeddings.check_embeddings(chat_data: DataFrame, vect_path: str, bert_path: str, need_sentence: bool, need_sentiment: bool, regenerate_vectors: bool, message_col: str = 'message')

    Check if embeddings and required lexicons exist, and generate them if they don’t.

    This function ensures the necessary vector and BERT embeddings are available. It also checks for the presence of certainty and lexicon files, generating them if needed.

    @@ -130,6 +132,27 @@
    +
    +
    +utils.check_embeddings.fix_abbreviations(dicTerm: str) → str
    +

    Helper function to fix abbreviations with punctuations. +src: https://github.com/ryanboyd/ContentCoder-Py/blob/main/ContentCodingDictionary.py#L714

    +

    This function goes over a list of hardcoded exceptions for the tokenizer / sentence parser +built into LIWC so that it doesn’t convert them into separate strings +(e.g., we want “i.e.” to not be seen as two words and two sentences [i, e]).

    +
    +
    Parameters:
    +

    dicTerm (str) – The lexicon term

    +
    +
    Returns:
    +

    dicTerm

    +
    +
    Return type:
    +

    str

    +
    +
    +
    +
    utils.check_embeddings.generate_bert(chat_data, output_path, message_col, batch_size=64)
    @@ -240,6 +263,28 @@
    +
    +
    +utils.check_embeddings.load_liwc_dict(dicText: str) → dict
    +

    Loads up a dictionary that is in the LIWC 2007/2015 format. +src: https://github.com/ryanboyd/ContentCoder-Py/blob/main/ContentCodingDictionary.py#L81

    +

    This functions reads the content of a LIWC dictionary file in the official format, +and convert it to a dictionary with lexicon: regular expression format. +We assume the dicText has two parts: the header, which maps numbers to “category names,” +and the body, which maps words in the lexicon to different category numbers, separated by a ‘%’ sign.

    +
    +
    Parameters:
    +

    dicText (str) – The content of a .dic file

    +
    +
    Returns:
    +

    dicCategories

    +
    +
    Return type:
    +

    dict

    +
    +
    +
    +
    utils.check_embeddings.read_in_lexicons(directory, lexicons_dict)
    diff --git a/docs/source/basics.rst b/docs/source/basics.rst index 87221375..9d7b7c4c 100644 --- a/docs/source/basics.rst +++ b/docs/source/basics.rst @@ -85,4 +85,6 @@ Here are some parameters that can be customized. For more details, refer to the 5. **regenerate_vectors**: Force-regenerate vector data even if it already exists. -6. **compute_vectors_from_preprocessed**: Computes vectors using preprocessed text (that is, with capitalization and punctuation removed). This was the default behavior for v.0.1.3 and earlier, but we now default to computing metrics on the unpreprocessed text (which INCLUDES capitalization and punctuation), and this parameter now defaults to False. \ No newline at end of file +6. **compute_vectors_from_preprocessed**: Computes vectors using preprocessed text (that is, with capitalization and punctuation removed). This was the default behavior for v.0.1.3 and earlier, but we now default to computing metrics on the unpreprocessed text (which INCLUDES capitalization and punctuation), and this parameter now defaults to False. + +7. **custom_liwc_dictionary_path**: Allows the user to "bring their own" LIWC dictionary, and thereby access more recent versions of the LIWC features. Our default version of LIWC is 2007, but users can obtain more recent versions of the lexicon by contacting `Ryan Boyd `_ and `Jamie Pennebaker `_. For more information on using the custom LIWC dictionary, please see :ref:`liwc`. \ No newline at end of file diff --git a/docs/source/features_conceptual/liwc.rst b/docs/source/features_conceptual/liwc.rst index 399cd743..2c0632f9 100644 --- a/docs/source/features_conceptual/liwc.rst +++ b/docs/source/features_conceptual/liwc.rst @@ -31,15 +31,34 @@ For each word in the LIWC lexicon, we use a regular expression to count the numb Rate of word use per 100 words = (count / utterance length) * (utterance length / 100) - **In v.1.0.4 and later:** - Lexical values are represented as a **raw count** of the number of times they appear in the utteranc + Lexical values are represented as a **raw count** of the number of times they appear in the utterance. + +New in v.1.0.5: "Bring Your Own LIWC" Custom Lexicon +****************************************************** + +v.1.0.5 introduces a new feature, **"Bring Your Own LIWC"**. We are incredibly grateful to `Ryan Boyd `_ and `Jamie Pennebaker `_ for their close support in creating this feature. + +The Team Communication Toolkit's built-in version of LIWC is the 2007 version of the lexicon. However, academic users can reach out to Ryan and Jamie for a more up-to-date version of the lexicon. The lexicon is formatted in a standard `.dic` file format. + +If you have your own local copy of the LIWC lexicon, you can use the Team Communication Toolkit to generate category wordcounts for your "custom" dictionary. We expect the format to be the LIWC standard format from Boyd and Pennebaker. + +To use "Bring Your Own LIWC," simply point the **custom_liwc_dictionary_path** in your FeatureBuilder to a local copy of the lexicon: + +.. code-block:: python + + feature_builder = FeatureBuilder( + input_df = your_data, # This is your input dataframe + output_file_base = "liwc_test_output", + custom_liwc_dictionary_path = "./LIWC2015.Dictionary.English.2024.08.27.95450.dic" # Obtain this file from Ryan and Jamie + ) + feature_builder.featurize() + Interpreting the Feature ************************* In general, the higher the value, the more that an utterance displays the concept "represented" by the lexicon. For example, a high value of the Home lexicon suggests that a speaker is discussing topics related to the home. -The scaled value is lower-bounded at 0 (if no words from the lexicon appear) and has no upper bound, as the value increases with the length of the utterance; for example, in a hypothetical utterance that is 1,000 words long, if all 1,000 words belong to a lexicon, the rate per 100 words would be (1000/1000 * 1000/100 = 10). - -We note, however, that in general, the lexicon-based approach to measuring concepts has several limitations. **Lexicons use a bag-of-words-based approach**, which means that it does not take the ordering of words or the context into account. This can be particularly signifiant for issues such as positive language; the sentence "I am not happy" contains the word "happy," which would count towards the positive lexicon --- even though the statement is negative! +We note, however, that the lexicon-based approach to measuring concepts has several limitations. **Lexicons use a bag-of-words-based approach**, which means that it does not take the ordering of words or the context into account. This can be particularly signifiant for issues such as positive language; the sentence "I am not happy" contains the word "happy," which would count towards the positive lexicon --- even though the statement is negative! Finally, **LIWC typically recommends having an utterance of at least 100 words**, as measures for shorter pieces of text may not be reliable. diff --git a/src/team_comm_tools/feature_builder.py b/src/team_comm_tools/feature_builder.py index 543c2c0a..6f370d1d 100644 --- a/src/team_comm_tools/feature_builder.py +++ b/src/team_comm_tools/feature_builder.py @@ -522,6 +522,7 @@ def featurize(self) -> None: print("All Done!") # Store column names of what we generated, so that the user can easily access them + # TODO --- this needs to be updated if the user brings their own LIWC, because the custom LIWC features are not in `self.chat_features`. self.chat_features = list(itertools.chain(*[feature_dict[feature]["columns"] for feature in self.feature_names if feature_dict[feature]["level"] == "Chat"])) self.conv_features_base = list(itertools.chain(*[feature_dict[feature]["columns"] for feature in self.feature_names if feature_dict[feature]["level"] == "Conversation"])) self.conv_features_all = [col for col in self.conv_data if col not in self.orig_data and col != 'conversation_num'] diff --git a/src/team_comm_tools/features/lexical_features_v2.py b/src/team_comm_tools/features/lexical_features_v2.py index 76b3330e..176eb306 100644 --- a/src/team_comm_tools/features/lexical_features_v2.py +++ b/src/team_comm_tools/features/lexical_features_v2.py @@ -48,13 +48,10 @@ def liwc_features(chat_df: pd.DataFrame, message_col: str, custom_liwc_dictionar lexicons_dict = pickle.load(lexicons_pickle_file) # Return the lexical features stacked as columns - # return pd.concat( - # Finding the # of occurrences of lexicons of each type for all the messages. df_lst = [pd.DataFrame(chat_df[message_col + "_original"].apply(lambda chat: get_liwc_count(regex, chat)))\ .rename({message_col + "_original": lexicon_type + "_lexical_wordcount"}, axis=1)\ for lexicon_type, regex in lexicons_dict.items()] - # , axis=1 - # ) + if custom_liwc_dictionary: df_lst += [pd.DataFrame(chat_df[message_col + "_original"].apply(lambda chat: get_liwc_count(regex, chat)))\ .rename({message_col + "_original": lexicon_type + "_lexical_wordcount_custom"}, axis=1)\ From 4909be0e708eac82d8f7097785f8a01d0ddd45f0 Mon Sep 17 00:00:00 2001 From: sundy1994 Date: Tue, 29 Oct 2024 16:18:02 -0700 Subject: [PATCH 6/7] update regex for emojis, add liwc loading checks, add columns for custom dict --- src/team_comm_tools/feature_builder.py | 4 ++ src/team_comm_tools/utils/check_embeddings.py | 39 +++++++++++++++++-- src/team_comm_tools/utils/preprocess.py | 21 +++++++++- 3 files changed, 59 insertions(+), 5 deletions(-) diff --git a/src/team_comm_tools/feature_builder.py b/src/team_comm_tools/feature_builder.py index 6f370d1d..f5e497d2 100644 --- a/src/team_comm_tools/feature_builder.py +++ b/src/team_comm_tools/feature_builder.py @@ -220,6 +220,8 @@ def __init__( # drop all columns that are in our generated feature set --- we don't want to create confusion! chat_features = list(itertools.chain(*[self.feature_dict[feature]["columns"] for feature in self.feature_dict.keys() if self.feature_dict[feature]["level"] == "Chat"])) + if self.custom_liwc_dictionary: + chat_features += [lexicon_type + "_lexical_wordcount_custom" for lexicon_type in self.custom_liwc_dictionary.keys()] columns_to_drop = [col for col in chat_features if col in self.chat_data.columns] self.chat_data = self.chat_data.drop(columns=columns_to_drop) self.orig_data = self.orig_data.drop(columns=columns_to_drop) @@ -524,6 +526,8 @@ def featurize(self) -> None: # Store column names of what we generated, so that the user can easily access them # TODO --- this needs to be updated if the user brings their own LIWC, because the custom LIWC features are not in `self.chat_features`. self.chat_features = list(itertools.chain(*[feature_dict[feature]["columns"] for feature in self.feature_names if feature_dict[feature]["level"] == "Chat"])) + if self.custom_liwc_dictionary: + self.chat_features += [lexicon_type + "_lexical_wordcount_custom" for lexicon_type in self.custom_liwc_dictionary.keys()] self.conv_features_base = list(itertools.chain(*[feature_dict[feature]["columns"] for feature in self.feature_names if feature_dict[feature]["level"] == "Conversation"])) self.conv_features_all = [col for col in self.conv_data if col not in self.orig_data and col != 'conversation_num'] diff --git a/src/team_comm_tools/utils/check_embeddings.py b/src/team_comm_tools/utils/check_embeddings.py index cb54875d..95b836d8 100644 --- a/src/team_comm_tools/utils/check_embeddings.py +++ b/src/team_comm_tools/utils/check_embeddings.py @@ -3,7 +3,7 @@ import re import os import pickle - +import warnings from tqdm import tqdm from pathlib import Path @@ -176,6 +176,36 @@ def fix_abbreviations(dicTerm: str) -> str: else: return dicTerm +def is_valid_term(dicTerm): + """ + Check if a dictionary term is valid. + + This functions returns True if the term matches the regex pattern and Flase otherwise. + The regex pattern matches: + - Alphanumeric characters (a-zA-Z0-9) + - Valid symbols: -, ', *, / + - The * symbol can only appear once at the end of a word + - Emojis are valid only when they appear alone + - The / symbol can only appear once after alphanumeric characters + - Spaces are allowed between valid words + + :param dicTerm: The dictionary term + :type dicTerm: str + + :return: True/False + :rtype: bool + """ + # List of emojis to preserve + emojis_to_preserve = { + "(:", "(;", "):", "/:", ":(", ":)", ":/", ";)" + } + emoji_pattern = '|'.join(re.escape(emoji) for emoji in emojis_to_preserve) + alphanumeric_pattern = ( + fr"^([a-zA-Z0-9\-']+(\*|\/[a-zA-Z0-9\*]*)?|({emoji_pattern})\*?)( [a-zA-Z0-9\-']+(\*|\/[a-zA-Z0-9\*]*)?)*$" + ) + + return bool(re.match(alphanumeric_pattern, dicTerm)) + def load_liwc_dict(dicText: str) -> dict: """ Loads up a dictionary that is in the LIWC 2007/2015 format. @@ -210,14 +240,17 @@ def load_liwc_dict(dicText: str) -> dict: dicTerm = dicTerm.strip() if dicTerm == '': continue - + if not is_valid_term(dicTerm): + warnings.warn(f"WARNING: invalid dict term: {dicTerm}, skipped") if '*' in dicTerm: # Replace consecutive asterisks with a single asterisk -- e.g., '**'->'*' pattern = re.compile(r'\*+') dicTerm = pattern.sub('*', dicTerm) dicTerm = r"\b" + dicTerm.replace("\n", "").replace("*", "") + r"\S*\b" + elif '(' in dicTerm or ')' in dicTerm or '/' in dicTerm: + dicTerm = dicTerm.replace("\n", "").replace('(', r'\(').replace(')', r'\)').replace('/', r'\/') else: - dicTerm = r"\b" + dicTerm.replace("\n", "").replace('(', r'\(').replace(')', r'\)') + r"\b" + dicTerm = r"\b" + dicTerm.replace("\n", "") + r"\b" for catNum in catNums: cat = catNameNumberMap[catNum] diff --git a/src/team_comm_tools/utils/preprocess.py b/src/team_comm_tools/utils/preprocess.py index 400f2fd7..d0f74149 100644 --- a/src/team_comm_tools/utils/preprocess.py +++ b/src/team_comm_tools/utils/preprocess.py @@ -91,14 +91,31 @@ def preprocess_text(text): """Preprocess text by removing non-alphanumeric characters and converting to lowercase. This function takes a string, removes any characters that are not letters, numbers, or spaces, - and converts the remaining text to lowercase. + except certain emojis, and converts the remaining text to lowercase. :param text: The input text to process. :type text: str :return: The processed text containing only alphanumeric characters and spaces in lowercase. :rtype: str """ - return(re.sub(r"[^a-zA-Z0-9 ]+", '',text).lower()) + emojis_to_preserve = { + "(:", "(;", "):", "/:", ":(", ":)", ":/", ";)" + } + + emoji_placeholders = {} + # Replace each emoji with a unique placeholder + for i, emoji in enumerate(emojis_to_preserve): + placeholder = f"EMOJI_{i}" + emoji_placeholders[placeholder] = emoji + text = text.replace(emoji, placeholder) + + # Clean the text by removing unwanted characters, except placeholders + text = re.sub(r"[^a-zA-Z0-9 EMOJI_]+", '', text) + # Restore the preserved emojis by replacing placeholders back to original emojis + for placeholder, emoji in emoji_placeholders.items(): + text = text.replace(placeholder, emoji) + + return text.lower() def preprocess_naive_turns(chat_data, column_names): """Combine adjacent rows of the same speaker in the same conversation and compress messages into a "turn". From 8e138d8e95d7a26d2ddaa2b172d59ef6bb7945fb Mon Sep 17 00:00:00 2001 From: sundy1994 Date: Wed, 30 Oct 2024 15:48:43 -0700 Subject: [PATCH 7/7] use original message col in lexical_features() to improve readability --- src/team_comm_tools/features/lexical_features_v2.py | 10 +++++----- .../utils/calculate_chat_level_features.py | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/team_comm_tools/features/lexical_features_v2.py b/src/team_comm_tools/features/lexical_features_v2.py index 176eb306..94cdf490 100644 --- a/src/team_comm_tools/features/lexical_features_v2.py +++ b/src/team_comm_tools/features/lexical_features_v2.py @@ -26,7 +26,7 @@ def get_liwc_count(regex, chat): else: return 0 -def liwc_features(chat_df: pd.DataFrame, message_col: str, custom_liwc_dictionary: dict={}) -> pd.DataFrame: +def liwc_features(chat_df: pd.DataFrame, message_col_original: str, custom_liwc_dictionary: dict={}) -> pd.DataFrame: """ This function takes in the chat level input dataframe and computes lexical features (the number of words from a given lexicon, such as LIWC). @@ -48,13 +48,13 @@ def liwc_features(chat_df: pd.DataFrame, message_col: str, custom_liwc_dictionar lexicons_dict = pickle.load(lexicons_pickle_file) # Return the lexical features stacked as columns - df_lst = [pd.DataFrame(chat_df[message_col + "_original"].apply(lambda chat: get_liwc_count(regex, chat)))\ - .rename({message_col + "_original": lexicon_type + "_lexical_wordcount"}, axis=1)\ + df_lst = [pd.DataFrame(chat_df[message_col_original].apply(lambda chat: get_liwc_count(regex, chat)))\ + .rename({message_col_original: lexicon_type + "_lexical_wordcount"}, axis=1)\ for lexicon_type, regex in lexicons_dict.items()] if custom_liwc_dictionary: - df_lst += [pd.DataFrame(chat_df[message_col + "_original"].apply(lambda chat: get_liwc_count(regex, chat)))\ - .rename({message_col + "_original": lexicon_type + "_lexical_wordcount_custom"}, axis=1)\ + df_lst += [pd.DataFrame(chat_df[message_col_original].apply(lambda chat: get_liwc_count(regex, chat)))\ + .rename({message_col_original: lexicon_type + "_lexical_wordcount_custom"}, axis=1)\ for lexicon_type, regex in custom_liwc_dictionary.items()] return pd.concat(df_lst, axis=1) except FileNotFoundError: diff --git a/src/team_comm_tools/utils/calculate_chat_level_features.py b/src/team_comm_tools/utils/calculate_chat_level_features.py index fa1b88cf..61f60fef 100644 --- a/src/team_comm_tools/utils/calculate_chat_level_features.py +++ b/src/team_comm_tools/utils/calculate_chat_level_features.py @@ -198,7 +198,7 @@ def lexical_features(self) -> None: :return: None :rtype: None """ - self.chat_data = pd.concat([self.chat_data, liwc_features(self.chat_data, self.message_col, self.custom_liwc_dictionary)], axis = 1) + self.chat_data = pd.concat([self.chat_data, liwc_features(self.chat_data, self.message_col + "_original", self.custom_liwc_dictionary)], axis = 1) def calculate_hedge_features(self) -> None: """