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finalize_gaze_data.py
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finalize_gaze_data.py
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import pandas as pd
from os import path
from utils import convert_plus_fill, create_output_dir
INPUT_DIR = "data/"
OUTPUT_DIR = "output/normalized_gaze_data/"
GAZE_DIR = "output/gaze_data/"
TEXT_DIR = "output/text_data/"
SOOD_DATASET = "sood_et_al_2020"
SARCASM_DATASET = "Mishra/Eye-tracking_and_SA-II_released_dataset"
GECO_DATASET = "GECO"
ZUCO_DATSET = "ZuCo"
PROVO_DATASET = "Provo"
FRANK_DATASET = "Frank_et_al_2013"
def modify_gaze_df(gaze_df, dataset):
if dataset == SOOD_DATASET:
gaze_df["PARAGRAPH_ID"] = gaze_df["Presented Stimulus name"].apply(lambda s: s.split(".")[0])
gaze_df["WORD_ID"] = gaze_df["PARAGRAPH_ID"] + "-" + gaze_df["word_index"].astype(str)
if dataset in [SARCASM_DATASET, ZUCO_DATSET]:
gaze_df["PARAGRAPH_ID"] = gaze_df["Text_ID"]
gaze_df["WORD_ID"] = gaze_df["PARAGRAPH_ID"].astype(str) + "-" + gaze_df["word_index"].astype(str)
if dataset == PROVO_DATASET:
gaze_df["PARAGRAPH_ID"] = gaze_df["Text_ID"]
gaze_df["WORD_ID"] = gaze_df["word_index"].astype(str)
if dataset == FRANK_DATASET:
gaze_df["PARAGRAPH_ID"] = 0
gaze_df["WORD_ID"] = gaze_df["PARAGRAPH_ID"].astype(str) + "-" + gaze_df["Text_ID"].astype(str) + "-" + \
gaze_df["word_index"].astype(str)
return gaze_df
def modify_words_df(words_df, dataset):
words_df["PARAGRAPH_ID"] = words_df["PARAGRAPH_ID"].astype(str)
if dataset == PROVO_DATASET:
words_df["INDEX"] = words_df["WORD_ID"].apply(lambda s: "".join(filter(str.isdigit, s))).astype(int)
elif dataset == FRANK_DATASET:
words_df["INDEX"] = words_df["WORD_ID"].apply(lambda s: int(s.split("-")[1])*100 + int(s.split("-")[-1].split(".")[0]))
else:
words_df["INDEX"] = words_df["WORD_ID"].apply(lambda s: s.split("-")[-1]).astype(int)
return words_df
def merge_word_data(gaze_df, words_df, dataset):
df = pd.merge(gaze_df, words_df, on="WORD_ID", how="outer")
if dataset == SOOD_DATASET:
df["Participant"] = df['Participant name'].unique()[0]
df["Gaze event duration"] = df["Gaze event duration"].fillna(0)
df = df.drop(["index", "Participant name", "Recording name", "Presented Stimulus name", "word_index", "word",
"PARAGRAPH_ID_x"], axis=1)
if dataset in [SARCASM_DATASET, ZUCO_DATSET, PROVO_DATASET, FRANK_DATASET]:
if dataset in [ZUCO_DATSET, PROVO_DATASET]:
df["Participant"] = df['Participant_ID']
else:
df["Participant"] = df['Participant_ID'].unique()[0]
df["Gaze event duration"] = df["Fixation_Duration"].fillna(0)
df = df.drop(["Participant_ID", "word_index", "PARAGRAPH_ID_x"], axis=1)
df = df.sort_values("INDEX")
if dataset in [SARCASM_DATASET, SOOD_DATASET, FRANK_DATASET]:
df = df.iloc[:words_df.shape[0]]
return df
def combine_dfs(gaze_df, words_df, dataset):
dfs = []
participant_col = ""
gaze_df = modify_gaze_df(gaze_df, dataset)
words_df = modify_words_df(words_df, dataset)
if dataset == SOOD_DATASET:
participant_col = "Participant name"
if dataset in [SARCASM_DATASET, FRANK_DATASET]:
participant_col = "Participant_ID"
for paragraph_id in words_df["PARAGRAPH_ID"].unique():
print(paragraph_id)
words_mask = words_df["PARAGRAPH_ID"] == paragraph_id
current_words = words_df[words_mask]
if dataset in [SARCASM_DATASET, FRANK_DATASET]:
paragraph_id = int(paragraph_id)
for name in gaze_df[participant_col].unique():
gaze_mask = (gaze_df[participant_col] == name) & (gaze_df["PARAGRAPH_ID"] == paragraph_id)
if gaze_mask.sum() == 0:
continue
current_gaze = gaze_df[gaze_mask]
dfs.append(merge_word_data(current_gaze, current_words, dataset))
df = pd.concat(dfs)
return df
def normalize_gaze_data(df, dataset, paragraph=False):
normalised_dfs = []
normal_col = "SENTENCE_ID"
if dataset == GECO_DATASET:
participant_col = "PP_NR"
if dataset in [SARCASM_DATASET, SOOD_DATASET, ZUCO_DATSET, PROVO_DATASET, FRANK_DATASET]:
participant_col = "Participant"
if paragraph:
normal_col = "PARAGRAPH_ID"
if dataset in [SARCASM_DATASET, SOOD_DATASET, ZUCO_DATSET, PROVO_DATASET, FRANK_DATASET]:
normal_col = "PARAGRAPH_ID_y"
for participant in df[participant_col].unique():
print(participant)
mask = df[participant_col] == participant
person_df = df[mask]
norm_data_id = df[mask][normal_col].unique()
pp_ids = df[mask][normal_col]
for data_id in norm_data_id:
data_mask = pp_ids == data_id
current_df = person_df[data_mask].set_index(["WORD_ID", participant_col]).select_dtypes(exclude="object")
print(data_id)
normalised_dfs.append(current_df / current_df.sum())
df = pd.concat(normalised_dfs)
return df
def create_normalized_files(df, dataset, sentence_output_file, paragraph_output_file):
sentence_df = normalize_gaze_data(df, dataset)
sentence_df.to_csv(sentence_output_file)
print(f"{sentence_output_file} done")
paragraph_df = normalize_gaze_data(df, dataset, paragraph=True)
paragraph_df.to_csv(paragraph_output_file)
print(f"{paragraph_output_file} done")
def normalize_sood_et_al_gaze_data(dataset):
output_path = create_output_dir(dataset, OUTPUT_DIR)
sentence_output_file = f"{output_path}/normed_study1_sentences.csv"
paragraph_output_file = f"{output_path}/normed_study1_paragraphs.csv"
if path.isfile(sentence_output_file) and path.isfile(paragraph_output_file):
print(f"{output_path} files already exist - skipping creation")
else:
study_df = pd.read_csv(f"{GAZE_DIR}{dataset}/study1_gaze_durations.csv")
study_words_df = pd.read_csv(f"{TEXT_DIR}{dataset}/study1_words.csv")
df = combine_dfs(study_df, study_words_df, dataset)
create_normalized_files(df, dataset, sentence_output_file, paragraph_output_file)
sentence_output_file = f"{output_path}/normed_study2_sentences.csv"
paragraph_output_file = f"{output_path}/normed_study2_paragraphs.csv"
if path.isfile(sentence_output_file) and path.isfile(paragraph_output_file):
print(f"{output_path} files already exist - skipping creation")
else:
study_df = pd.read_csv(f"{GAZE_DIR}{dataset}/study2_gaze_durations.csv")
study_words_df = pd.read_csv(f"{TEXT_DIR}{dataset}/study2_words.csv")
df = combine_dfs(study_df, study_words_df, dataset)
create_normalized_files(df, dataset, sentence_output_file, paragraph_output_file)
def normalize_mishra_sarcasm_gaze_data(dataset):
output_path = create_output_dir(dataset, OUTPUT_DIR)
sentence_output_file = f"{output_path}/normed_sentences.csv"
paragraph_output_file = f"{output_path}/normed_paragraphs.csv"
if path.isfile(sentence_output_file) and path.isfile(paragraph_output_file):
print(f"{output_path} files already exist - skipping creation")
else:
study_df = pd.read_csv(f"{GAZE_DIR}{dataset}/gaze_durations.csv")
study_words_df = pd.read_csv(f"{TEXT_DIR}{dataset}/words.csv")
df = combine_dfs(study_df, study_words_df, dataset)
create_normalized_files(df, dataset, sentence_output_file, paragraph_output_file)
def normalize_geco_gaze_data(dataset):
output_path = create_output_dir(dataset, OUTPUT_DIR)
sentence_output_file = f"{output_path}/normed_sentences.csv"
paragraph_output_file = f"{output_path}/normed_paragraphs.csv"
if path.isfile(sentence_output_file) and path.isfile(paragraph_output_file):
print(f"{output_path} files already exist - skipping creation")
else:
mono_df = pd.read_csv(f"{GAZE_DIR}{dataset}/MonolingualReadingData.csv")
bilingual_df = pd.read_csv(f"{GAZE_DIR}{dataset}/L2ReadingData.csv")
df = pd.concat([bilingual_df, mono_df])
keep_cols = [col for col in df.columns[11:] if
any(word in col for word in ["%", "COUNT", "DURATION", "AVERAGE", "SKIP"])]
keep_cols = list(df.columns[:11]) + keep_cols
df = df[keep_cols]
for col in df.columns[11:]:
df[col] = convert_plus_fill(df[col])
df.loc[df["WORD"].isnull(), "WORD"] = "null"
sentence_df = pd.read_csv(f"{TEXT_DIR}{dataset}/EnglishMaterialALL.csv",
usecols=['WORD_ID', "SENTENCE_ID"])
df = pd.merge(df, sentence_df, on="WORD_ID").sort_values(["PP_NR", "SENTENCE_ID"])
df["PARAGRAPH_ID"] = df["WORD_ID"].apply(lambda s: "-".join(s.split("-")[:2]))
create_normalized_files(df, dataset, sentence_output_file, paragraph_output_file)
def normalize_zuco_gaze_data(dataset):
output_path = create_output_dir(dataset, OUTPUT_DIR)
sentence_output_file = f"{output_path}/normed_task1_sentences.csv"
paragraph_output_file = f"{output_path}/normed_task1_paragraphs.csv"
if path.isfile(sentence_output_file) and path.isfile(paragraph_output_file):
print(f"{output_path} files already exist - skipping creation")
else:
gaze_df = pd.read_csv(f"{GAZE_DIR}{dataset}/t1_gaze_duration.csv")
gaze_df = modify_gaze_df(gaze_df, dataset)
words_df = pd.read_csv(f"{TEXT_DIR}{dataset}/t1_words.csv")
words_df = modify_words_df(words_df, dataset)
df = merge_word_data(gaze_df, words_df, dataset)
create_normalized_files(df, dataset, sentence_output_file, paragraph_output_file)
sentence_output_file = f"{output_path}/normed_task2_sentences.csv"
paragraph_output_file = f"{output_path}/normed_task2_paragraphs.csv"
if path.isfile(sentence_output_file) and path.isfile(paragraph_output_file):
print(f"{output_path} files already exist - skipping creation")
else:
gaze_df = pd.read_csv(f"{GAZE_DIR}{dataset}/t2_gaze_duration.csv")
gaze_df = modify_gaze_df(gaze_df, dataset)
words_df = pd.read_csv(f"{TEXT_DIR}{dataset}/t2_words.csv")
words_df = modify_words_df(words_df, dataset)
df = merge_word_data(gaze_df, words_df, dataset)
create_normalized_files(df, dataset, sentence_output_file, paragraph_output_file)
sentence_output_file = f"{output_path}/normed_task3_sentences.csv"
paragraph_output_file = f"{output_path}/normed_task3_paragraphs.csv"
if path.isfile(sentence_output_file) and path.isfile(paragraph_output_file):
print(f"{output_path} files already exist - skipping creation")
else:
gaze_df = pd.read_csv(f"{GAZE_DIR}{dataset}/t3_gaze_duration.csv")
gaze_df = modify_gaze_df(gaze_df, dataset)
words_df = pd.read_csv(f"{TEXT_DIR}{dataset}/t3_words.csv")
words_df = modify_words_df(words_df, dataset)
df = merge_word_data(gaze_df, words_df, dataset)
create_normalized_files(df, dataset, sentence_output_file, paragraph_output_file)
def normalize_provo_gaze_data(dataset):
output_path = create_output_dir(dataset, OUTPUT_DIR)
sentence_output_file = f"{output_path}/normed_sentences.csv"
paragraph_output_file = f"{output_path}/normed_paragraphs.csv"
if path.isfile(sentence_output_file) and path.isfile(paragraph_output_file):
print(f"{output_path} files already exist - skipping creation")
else:
gaze_df = pd.read_csv(f"{GAZE_DIR}{dataset}/gaze_durations.csv")
gaze_df = modify_gaze_df(gaze_df, dataset)
words_df = pd.read_csv(f"{TEXT_DIR}{dataset}/words.csv")
words_df = modify_words_df(words_df, dataset)
df = merge_word_data(gaze_df, words_df, dataset)
create_normalized_files(df, dataset, sentence_output_file, paragraph_output_file)
def normalize_frank_gaze_data(dataset):
output_path = create_output_dir(dataset, OUTPUT_DIR)
sentence_output_file = f"{output_path}/normed_sentences.csv"
paragraph_output_file = f"{output_path}/normed_paragraphs.csv"
if path.isfile(sentence_output_file) and path.isfile(paragraph_output_file):
print(f"{output_path} files already exist - skipping creation")
else:
study_df = pd.read_csv(f"{GAZE_DIR}{dataset}/gaze_durations.csv")
study_words_df = pd.read_csv(f"{TEXT_DIR}{dataset}/words.csv")
df = combine_dfs(study_df, study_words_df, dataset)
create_normalized_files(df, dataset, sentence_output_file, paragraph_output_file)
def method_chooser(dataset):
if dataset == SOOD_DATASET:
normalize_sood_et_al_gaze_data(dataset)
elif dataset == SARCASM_DATASET:
normalize_mishra_sarcasm_gaze_data(dataset)
elif dataset == GECO_DATASET:
normalize_geco_gaze_data(dataset)
elif dataset == ZUCO_DATSET:
normalize_zuco_gaze_data(dataset)
elif dataset == PROVO_DATASET:
normalize_provo_gaze_data(dataset)
elif dataset == FRANK_DATASET:
normalize_frank_gaze_data(dataset)
def main():
for dataset in [SOOD_DATASET, SARCASM_DATASET, GECO_DATASET, ZUCO_DATSET, PROVO_DATASET, FRANK_DATASET]:
if not path.isdir(path.join(INPUT_DIR, dataset)):
print(f"Cannot find {dataset} - skipping creation")
else:
method_chooser(dataset)
if __name__ == "__main__":
main()