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764
765 | class ChandraBot(object):
"""
A ChandraBot object that stores research paper details, review information, and authors.
Typical usage:
bot = ChandraBot.create_bot(
paper_file=os.path.join(PAPER_FILE),
review_file=os.path.join(REVIEW_FILE),
human_file=os.path.join(HUMAN_FILE),
)
bot.assemble_paper_book()
bot.compute_normalized_scores(dataframe_only=True)
bot.write_paper_book(output_file=book_file)
Attributes:
PAPER_DICT (dict): dictionary of attributes for the
PAPER_FILE input
REVIEW_DICT (dict): dictionary of attributs for the
REVIEW_FILE input
HUMAN_DICT (dict): dictionary of attributes for the
HUMAN_FILE input
paper_df (DataFame): paper data with the attributes
defined in PAPER_DICT
review_df (DataFrame): review data with the attributes
defined in REVIEW_DICT
human_df (DataFrame): human data with the attributes
defined in HUMAN_DICT
paper_book (PaperBook): a serialized data representation
of the paper, review, and human data. See the ProtoBuf
file for details.
"""
PAPER_DICT = {
"paper_id": pd.StringDtype(),
"authors": pd.StringDtype(),
"author_ids": pd.StringDtype(),
"title": pd.StringDtype(),
"year": np.int32,
"committee_publication_decision": pd.StringDtype(),
"committee_presentation_decision": pd.StringDtype(),
"abstract": pd.StringDtype(),
"body": pd.StringDtype(),
}
REVIEW_DICT = {
"paper_id": pd.StringDtype(),
"presentation_score": np.float32,
"commentary_to_author": pd.StringDtype(),
"commentary_to_chair": pd.StringDtype(),
"reviewer_human_hash_id": pd.StringDtype(),
"presentation_recommend": pd.StringDtype(),
"publication_recommend": pd.StringDtype(),
}
HUMAN_DICT = {
"name": pd.StringDtype(),
"aliases": pd.StringDtype(),
"hash_id": pd.StringDtype(),
"current_affiliation": pd.StringDtype(),
"previous_affiliation": pd.StringDtype(),
"last_degree_affiliation": pd.StringDtype(),
"orcid_url": pd.StringDtype(),
"orcid": pd.StringDtype(),
"author_id": pd.StringDtype(),
"verified": "bool",
}
def __init__(
self,
paper_df: pd.DataFrame = None,
review_df: pd.DataFrame = None,
human_df: pd.DataFrame = None,
input_paper_book: dm.PaperBook = None,
):
"""
Constructor
"""
if input_paper_book is None:
self.paper_df: pd.DataFrame = paper_df
self.review_df: pd.DataFrame = review_df
self.human_df: pd.DataFrame = human_df
self.paper_book = dm.PaperBook()
else:
self.paper_book: dm.PaperBook = input_paper_book
def _attribute_paper(self, paper: dm.Paper, row: list) -> None:
paper.title = row["title"]
paper.year = int(row["year"])
if row["committee_presentation_decision"].lower() == "reject":
paper.committee_presentation_decision = dm.PRESENTATION_REC_REJECT
elif row["committee_presentation_decision"].lower() == "accept":
paper.committee_presentation_decision = dm.PRESENTATION_REC_ACCEPT
else:
paper.committee_presentation_decision = dm.PRESENTATION_REC_NONE
if row["committee_publication_decision"].lower() == "reject":
paper.committee_publication_decision = dm.PUBLICATION_REC_REJECT
elif row["committee_publication_decision"].lower() == "accept":
paper.committee_publication_decision = dm.PUBLICATION_REC_ACCEPT
elif row["committee_publication_decision"].lower() == "accept_correct":
paper.committee_publication_decision = dm.PUBLICATION_REC_ACCEPT_CORRECT
else:
paper.committee_publication_decision = dm.PUBLICATION_REC_NONE
if "abstract" in row:
paper.abstract.text = row["abstract"]
else:
paper.abstract.text = "Missing"
if "body" in row:
paper.body.text = str(row["body"])
else:
paper.body.text = "Missing"
def _attribute_author(self, author: dm.Author, row: list):
author.human.name = row["name"].values[0]
if not pd.isnull(row["aliases"].values[0]):
for alias in row["aliases"].values[0].split(","):
author.human.aliases.append(alias)
author.human.hash_id = row["hash_id"].values[0]
if not pd.isnull(row["current_affiliation"].values[0]):
author.human.current_affiliation.name = row["current_affiliation"].values[0]
else:
author.human.current_affiliation.name = ""
author.human.last_degree_affiliation.name = str(
row["last_degree_affiliation"].values[0]
)
if not pd.isnull(row["previous_affiliation"].values[0]):
affil_list = row["previous_affiliation"].values[0].split(",")
if len(affil_list) > 0:
for affil in affil_list:
affiliation = author.human.previous_affiliation.add()
affiliation.name = affil
if not pd.isnull(row["orcid_url"].values[0]):
author.human.orcid_url = str(row["orcid_url"].values[0])
else:
author.human.orcid_url = ""
if not pd.isnull(row["orcid"].values[0]):
author.human.orcid = row["orcid"].values[0]
else:
author.human.orcid = ""
def _attribute_review(self, review: dm.Review, row: list):
review.presentation_score = row["presentation_score"]
if not pd.isnull(row["commentary_to_author"]):
review.commentary_to_author.text = row["commentary_to_author"]
else:
review.commentary_to_author.text = ""
if not pd.isnull(row["commentary_to_chair"]):
review.commentary_to_chair.text = row["commentary_to_chair"]
else:
review.commentary_to_chair.text = ""
if row["presentation_recommendation"].lower() == "reject":
review.presentation_recommend = dm.PRESENTATION_REC_REJECT
elif row["presentation_recommendation"].lower() == "accept":
review.presentation_recommend = dm.PRESENTATION_REC_ACCEPT
else:
review.presentation_recommend = dm.PRESENTATION_REC_NONE
if row["publication_recommendation"].lower() == "reject":
review.publication_recommend = dm.PUBLICATION_REC_REJECT
elif row["publication_recommendation"].lower() == "accept":
review.publication_recommend = dm.PUBLICATION_REC_ACCEPT
else:
review.publication_recommend = dm.PRESENTATION_REC_NONE
def _attribute_reviewer(self, review: dm.Review, row: list):
if row.empty:
return
if not pd.isnull(row["name"].values[0]):
review.reviewer.human.name = row["name"].values[0]
else:
review.reviewer.human.name = ""
if not pd.isnull(row["aliases"].values[0]):
for alias in row["aliases"].values[0].split(","):
if alias != "NA":
review.reviewer.human.aliases.append(alias)
if not pd.isnull(row["hash_id"].values[0]):
review.reviewer.human.hash_id = row["hash_id"].values[0]
else:
review.reviewer.human.hash_id = ""
if not pd.isnull(row["current_affiliation"].values[0]):
review.reviewer.human.current_affiliation.name = row[
"current_affiliation"
].values[0]
else:
review.reviewer.human.current_affiliation.name = ""
if not pd.isnull(row["last_degree_affiliation"].values[0]):
review.reviewer.human.last_degree_affiliation.name = str(
row["last_degree_affiliation"].values[0]
)
else:
review.reviewer.human.last_degree_affiliation.name = ""
if not pd.isnull(row["previous_affiliation"].values[0]):
for affil_name in row["previous_affiliation"].values[0].split(","):
affiliation = review.reviewer.human.previous_affiliation.add()
affiliation.name = affil_name
if not pd.isnull(row["orcid_url"].values[0]):
review.reviewer.human.orcid_url = str(row["orcid_url"].values[0])
else:
review.reviewer.human.orcid_url = ""
if not pd.isnull(row["orcid"].values[0]):
review.reviewer.human.orcid = str(row["orcid"].values[0])
else:
review.reviewer.human.orcid = ""
if not pd.isnull(row["verified"].values[0]):
review.reviewer.verified = bool(row["verified"].values[0])
else:
review.reviewer.verified = False
return
def assemble_paper_book(self):
"""
Assemble the input databases into the serialized data
object defined in the protobuffer. Calling this method
allows the user to navigate the data using the serialized
data objects rather than via DataFrames.
args:
None
"""
for paper_id in self.paper_df.index:
paper = self.paper_book.paper.add()
paper.number = paper_id
paper_row = self.paper_df.loc[paper_id]
self._attribute_paper(paper, paper_row)
if "author_ids" in self.paper_df.columns:
if not pd.isnull(paper_row.author_ids):
for author_id in paper_row.author_ids.split(","):
if self.human_df["author_id"].eq(author_id).any():
human_row = self.human_df.loc[
self.human_df["author_id"] == author_id
]
self._attribute_author(paper.authors.add(), human_row)
paper_review_df = self.review_df.loc[self.review_df["paper_id"] == paper_id]
paper_review_df.set_index("reviewer_human_hash_id")
for hash_id in paper_review_df.index:
review_row = paper_review_df.loc[hash_id]
reviewer_hash = review_row["reviewer_human_hash_id"]
human_row = self.human_df.loc[self.human_df["hash_id"] == reviewer_hash]
review = paper.reviews.add()
self._attribute_review(review, review_row)
self._attribute_reviewer(review, human_row)
@staticmethod
def create_bot(paper_file: str, review_file: str, human_file: str):
"""
Create a ChandraBot object from separate paper, review, and
human CSV files.
args:
paper_file: input CSV file consistent with the PAPER_DICT
definition
review_file: input CSV file consistent with the REVIEW_DICT
definition
human_file: input CSV file consistent wit the HUMAN_DICT
definition
returns: a Chandra Bot example
"""
paper_df = pd.read_csv(
paper_file, dtype=ChandraBot.PAPER_DICT, index_col="paper_id"
)
review_df = pd.read_csv(review_file, dtype=ChandraBot.REVIEW_DICT)
human_df = pd.read_csv(human_file, dtype=ChandraBot.HUMAN_DICT)
bot = ChandraBot(paper_df=paper_df, review_df=review_df, human_df=human_df)
return bot
@staticmethod
def read_paper_book(input_file: str):
"""
read_paper_book
"""
paper_book = dm.PaperBook()
try:
with open(input_file, "rb") as file_pointer:
paper_book.ParseFromString(file_pointer.read())
except IOError:
print(input_file + ": File not found.")
bot = ChandraBot(input_paper_book=paper_book)
bot.paper_df = bot.make_dataframe(dataframe_name="paper")
bot.review_df = bot.make_dataframe(dataframe_name="review")
bot.human_df = bot.make_dataframe(dataframe_name="human")
return bot
def write_paper_book(self, output_file: str):
"""
write_paper_book
"""
with open(output_file, "wb") as file_pointer:
file_pointer.write(self.paper_book.SerializeToString())
def _compute_normalized_scores(self, min_number_reviews: int):
scores_df = pd.DataFrame()
for paper in self.paper_book.paper:
for review in paper.reviews:
row_series = pd.Series(
{
"paper_id": paper.number,
"reviewer_id": review.reviewer.human.hash_id,
"score": review.presentation_score,
}
)
row_df = pd.DataFrame([row_series])
scores_df = pd.concat([scores_df, row_df], ignore_index=True)
mean_df = (
scores_df.groupby("reviewer_id")
.mean(numeric_only=True)[["score"]]
.rename(columns={"score": "mean"})
)
std_df = (
scores_df.groupby("reviewer_id")
.std(numeric_only=True)[["score"]]
.rename(columns={"score": "std"})
)
count_df = (
scores_df.groupby("reviewer_id")
.count()[["score"]]
.rename(columns={"score": "count"})
)
normalized_df = mean_df.join(std_df, on="reviewer_id").join(
count_df, on="reviewer_id"
)
matched_reviewer = []
for paper in self.paper_book.paper:
for review in paper.reviews:
hash_id = review.reviewer.human.hash_id
if hash_id not in matched_reviewer:
matched_reviewer.append(hash_id)
if hash_id in normalized_df.index:
row = normalized_df.loc[hash_id]
review.reviewer.mean_present_score = row["mean"]
review.reviewer.std_dev_present_score = row["std"]
review.reviewer.number_of_reviews = int(row["count"])
if row["count"] >= min_number_reviews:
review.normalized_present_score = (
review.presentation_score - row["mean"]
) / row["std"]
else:
review.normalized_present_score = None
def compute_normalized_scores(
self, min_number_reviews: int = 10, dataframe_only: bool = False
):
"""
compute_normalized_scores
"""
if dataframe_only:
temp_df = self.review_df.copy()
mean_df = (
temp_df.groupby("reviewer_human_hash_id")
.mean(numeric_only=True)[["presentation_score"]]
.rename(columns={"presentation_score": "mean"})
)
std_df = (
temp_df.groupby("reviewer_human_hash_id")
.std(numeric_only=True)[["presentation_score"]]
.rename(columns={"presentation_score": "std"})
)
count_df = (
temp_df.groupby("reviewer_human_hash_id")
.count()[["presentation_score"]]
.rename(columns={"presentation_score": "count"})
)
normalized_df = mean_df.join(std_df, on="reviewer_human_hash_id").join(
count_df, on="reviewer_human_hash_id"
)
temp_df = temp_df.join(normalized_df, on="reviewer_human_hash_id")
temp_df["normalized_present_score"] = (
temp_df["presentation_score"] - temp_df["mean"]
) / temp_df["std"]
temp_df = temp_df.rename(
columns={
"mean": "mean_present_score",
"std": "std_dev_present_score",
"count": "number_of_reviews",
}
)
self.review_df = temp_df.copy()
else:
self._compute_normalized_scores(min_number_reviews)
def make_dataframe(self, dataframe_name: str):
"""
make_dataframe
"""
output_df = pd.DataFrame()
if dataframe_name == "paper":
author_id_df = self._make_author_id_df()
for paper in self.paper_book.paper:
authors = []
author_ids = []
for author in paper.authors:
authors.append(author.human.name)
author_ids.append(
str(
author_id_df.loc[
author_id_df["hash_id"] == author.human.hash_id
]["author_id"].values[0]
)
)
authors_string = ",".join(authors)
authors_id_string = ",".join(author_ids)
row_series = pd.Series(
{
"paper_id": paper.number,
"authors": authors_string,
"author_ids": authors_id_string,
"title": paper.title,
"year": paper.year,
"committee_presentation_decision": paper.committee_presentation_decision,
"committee_publication_decision": paper.committee_publication_decision,
"abstract": paper.abstract.text,
"body": paper.body.text,
}
)
row_df = pd.DataFrame([row_series])
output_df = pd.concat([output_df, row_df], ignore_index=True)
elif dataframe_name == "review":
for paper in self.paper_book.paper:
for review in paper.reviews:
reviewer = review.reviewer
row_series = pd.Series(
{
"paper_id": paper.number,
"presentation_score": review.presentation_score,
"commentary_to_author": review.commentary_to_author.text,
"commentary_to_chair": review.commentary_to_chair.text,
"reviewer_human_hash_id": review.reviewer.human.hash_id,
"presentation_recommendation": review.presentation_recommend,
"publication_recommendation": review.publication_recommend,
"normalized_present_score": review.normalized_present_score,
}
)
row_df = pd.DataFrame([row_series])
output_df = pd.concat([output_df, row_df], ignore_index=True)
elif dataframe_name == "human":
author_id_df = self._make_author_id_df()
for paper in self.paper_book.paper:
authors_df = pd.DataFrame()
for author in paper.authors:
author_id = author_id_df.loc[
author_id_df["hash_id"] == author.human.hash_id
]["author_id"].values[0]
alias_str = ",".join(author.human.aliases)
affil_list = []
for affil in author.human.previous_affiliation:
affil_list.append(affil)
row_series = pd.Series(
{
"name": author.human.name,
"aliases": alias_str,
"hash_id": author.human.hash_id,
"current_affiliation": author.human.current_affiliation.name,
"previous_affiliation": ",".join(affil_list),
"last_degree_affiliation": author.human.last_degree_affiliation.name,
"orcid_url": author.human.orcid_url,
"orcid": author.human.orcid,
"author_id": author_id,
}
)
row_df = pd.DataFrame([row_series])
authors_df = pd.concat([authors_df, row_df], ignore_index=True)
reviewers_df = pd.DataFrame()
for review in paper.reviews:
reviewer = review.reviewer
alias_str = ",".join(reviewer.human.aliases)
affil_list = []
for affil in reviewer.human.previous_affiliation:
affil_list.append(affil.name)
row_series = pd.Series(
{
"name": reviewer.human.name,
"aliases": alias_str,
"hash_id": reviewer.human.hash_id,
"current_affiliation": reviewer.human.current_affiliation.name,
"previous_affiliation": ",".join(affil_list),
"last_degree_affiliation": reviewer.human.last_degree_affiliation.name,
"orcid_url": reviewer.human.orcid_url,
"orcid": reviewer.human.orcid,
"verified": reviewer.verified,
}
)
row_df = pd.DataFrame([row_series])
reviewers_df = pd.concat([reviewers_df, row_df], ignore_index=True)
r_join_df = reviewers_df[["hash_id", "verified"]].set_index("hash_id")
a_df = authors_df.set_index("hash_id").join(r_join_df, on="hash_id")
a_join_df = authors_df[["hash_id", "author_id"]].set_index("hash_id")
b_df = reviewers_df.set_index("hash_id").join(a_join_df, on="hash_id")
a_b_df = pd.concat([a_df, b_df])
output_df = pd.concat([output_df, a_b_df])
output_df = (
output_df.drop_duplicates().groupby("hash_id").first().reset_index()
)
else:
print("dataframe_name must be 'paper', 'review', or 'human'")
return output_df
def _make_author_id_df(self):
author_list = []
for paper in self.paper_book.paper:
for author in paper.authors:
if author.human.hash_id not in author_list:
author_list.append(author.human.hash_id)
return_df = pd.DataFrame({"hash_id": author_list})
return_df["author_id"] = np.arange(len(return_df)) + 1
return return_df
def count_former_coauthors(self, dataframe_only: bool = False):
"""
count former coauthors
"""
if dataframe_only:
temp_df = self.paper_df[["paper_id", "author_ids"]].copy()
temp_df = (
pd.concat(
[
temp_df["paper_id"].reset_index(drop=True),
temp_df.author_ids.str.split(",", expand=True),
],
axis=1,
)
.set_index("paper_id")
.stack()
.reset_index(level=[0, 1])
.rename(columns={0: "author_id"})
.drop(columns=["level_1"])
.set_index("paper_id")
.join(self.paper_df[["paper_id", "year"]].set_index("paper_id"))
)
h_df = self.human_df.reset_index()[["hash_id", "author_id"]].astype(
{"author_id": "int64"}
)
auth_df = (
temp_df.astype({"author_id": "int64"})
.reset_index()
.merge(h_df, on="author_id", how="left")
)
r_df = self.review_df[["paper_id", "reviewer_human_hash_id"]]
a_r_pairs_df = (
auth_df.merge(r_df, on="paper_id", how="left")
.groupby(["hash_id", "reviewer_human_hash_id"])
.size()
.reset_index(name="count")
)
temp_df = auth_df.merge(
auth_df, how="outer", on=["paper_id", "year"], suffixes=("_01", "_02")
)
gb_df = temp_df[["paper_id", "year", "hash_id_01", "hash_id_02"]].groupby(
["hash_id_01", "hash_id_02"]
)
a_a_pairs_df = (
gb_df.size()
.to_frame(name="papers_written_with_authors")
.join(
gb_df.agg({"year": "min"}).rename(
columns={"year": "year_of_first_collab"}
)
)
.reset_index()
)
conflict_r_df = a_r_pairs_df.merge(
a_a_pairs_df,
how="left",
left_on=["hash_id", "reviewer_human_hash_id"],
right_on=["hash_id_01", "hash_id_02"],
).dropna()
temp_df = auth_df.merge(r_df, how="left", on="paper_id")
df_b = temp_df.merge(
conflict_r_df, how="left", on=["hash_id", "reviewer_human_hash_id"]
).dropna()
df_c = df_b[df_b["year_of_first_collab"] <= df_b["year"]]
review_count_df = df_c[
["paper_id", "reviewer_human_hash_id", "papers_written_with_authors"]
].reset_index(drop=True)
self.review_df = self.review_df.merge(
review_count_df, how="left", on=["paper_id", "reviewer_human_hash_id"]
).fillna(0)
else:
pairs_df = pd.DataFrame()
for paper in self.paper_book.paper:
a_list = []
for author in paper.authors:
a_list.append(author.human.hash_id)
pairs = [a_list, a_list]
data = list(itertools.product(*pairs))
idx = [f"{i}" for i in range(1, len(data) + 1)]
temp_df = pd.DataFrame(data, index=idx, columns=list("ab"))
temp_df = temp_df[temp_df.a != temp_df.b].copy()
temp_df["year"] = paper.year
pairs_df = pd.concat([pairs_df, temp_df])
count_df = (
pairs_df.groupby(["a", "b"]).size().reset_index(name="paper_count")
)
first_df = (
pairs_df.groupby(["a", "b"])
.min()[["year"]]
.rename(columns={"year": "year_first_collab"})
)
pairs_df = count_df.join(first_df, on=["a", "b"])
for paper in self.paper_book.paper:
a_list = []
for author in paper.authors:
a_list.append(author.human.hash_id)
for review in paper.reviews:
r_hash_id = review.reviewer.human.hash_id
temp_df = pairs_df[pairs_df["a"] == r_hash_id]
for auth in a_list:
a_df = temp_df[
(temp_df.b == auth) & temp_df.year_first_collab
<= paper.year
].copy()
review.papers_written_with_authors += sum(a_df["paper_count"])
@staticmethod
def _count_words_in_text(key_words, output_col_name, input_df, input_col_name):
look_for = "|".join(key_words)
input_df[output_col_name] = input_df[input_col_name].str.count(look_for)
return input_df
def count_words_in_paper_abstract(
self, key_words, column_name: str, dataframe_only: bool = True
):
"""
count words in paper abstract
"""
if dataframe_only:
self.paper_df = ChandraBot._count_words_in_text(
key_words, column_name, self.paper_df, "abstract"
)
else:
print("dataframe_only must be True")
def count_words_in_review_commentary(
self, key_words, column_name: str, dataframe_only: bool = True
):
"""
count words in review commentary
"""
if dataframe_only:
self.review_df = ChandraBot._count_words_in_text(
key_words, column_name, self.review_df, "commentary_to_author"
)
else:
print("dataframe_only must be True")
def append_verified_reviewer(self, min_count: int, dataframe_only: bool = False):
"""
append verified reviewer
"""
if dataframe_only:
temp_df = pd.merge(
self.review_df,
self.human_df[["hash_id", "verified"]],
how="left",
left_on=["reviewer_human_hash_id"],
right_on=["hash_id"],
)
temp_df["verified"] = temp_df["verified"].fillna(False)
temp_df = temp_df.loc[temp_df.verified][
["paper_id", "presentation_score"]
].copy()
temp_df = temp_df.groupby("paper_id").agg(
n=("presentation_score", "size"),
mean_verified_score=("presentation_score", "mean"),
)
temp_df = temp_df.loc[temp_df["n"] >= min_count].copy().reset_index()
self.review_df = pd.merge(
self.review_df,
temp_df[["paper_id", "mean_verified_score"]],
how="left",
on=["paper_id"],
)
else:
for paper in self.paper_book.paper:
v_list = np.empty((0))
for review in paper.reviews:
if review.reviewer.verified:
v_list = np.append(v_list, review.normalized_present_score)
if len(v_list) > min_count:
paper.mean_verified_score = np.mean(v_list)
else:
paper.mean_verified_score = np.nan
return
|