Create assign_reviews.py

Co-authored by @mdhaber

assign_reviews.py

@@ -0,0 +1,132 @@
+# %%
+####################
+## ASSIGN REVIEWS ##
+####################
+# Imports
+import json
+import pandas as pd
+import numpy as np
+from scipy.optimize import milp, Bounds, LinearConstraint
+
+MIN_REVIEWS_PER_PERSON = 5
+MAX_REVIEWS_PER_PERSON = 9
+MIN_REVIEWERS_PER_SUBMISSION = 4
+MAX_REVIEWERS_PER_SUBMISSION = 4
+ASSIGN_TUTORIALS_TO_ANYONE = True
+TUTORIAL_COEFF = .8
+
+DEBUG = True
+
+df_submissions = pd.read_csv("2023_submissions_to_assign.csv")
+df_reviewers = pd.read_csv("2023_reviewers_to_assign.csv")
+
+df_submissions = df_submissions.assign(assigned_reviewer_ids=[[]]*len(df_submissions))
+df_reviewers = df_reviewers.assign(assigned_submission_ids=[[]]*len(df_reviewers))
+
+reviewers = df_reviewers.to_dict("records")
+submissions = df_submissions.to_dict("records")
+
+n_reviewers = len(reviewers)
+n_submissions = len(submissions)
+
+# Maximize the total number of reviews
+objective_fun = -np.ones((n_reviewers, n_submissions))
+
+if ASSIGN_TUTORIALS_TO_ANYONE:
+    # Make tutorials more expensive to review
+    for n, reviewer in enumerate(reviewers):
+        objective_fun[n][df_submissions.track == "TUT"] *= TUTORIAL_COEFF
+
+objective_fun = objective_fun.flatten()
+
+# both zero if reviewer cannot review submission, both one if reviewer is assigned to submission
+lb = np.zeros((n_reviewers, n_submissions)) # lower bound
+ub = np.zeros((n_reviewers, n_submissions)) # upper bound
+
+submission_constraints = np.zeros((n_submissions, n_reviewers, n_submissions)) # constraints on reviews per submission
+reviewer_constraints = np.zeros((n_reviewers, n_reviewers, n_submissions)) # constraints on submissions per reviewer. 1 in all the places where reviewer could be assigned
+
+# Establish lower and upper bounds
+# reviewer cannot be assigned out of domain
+# reviewer must be re-assigned previous assignments
+for i, reviewer in enumerate(reviewers):
+    for j, submission in enumerate(submissions):
+        # each variable is assignment of a submission j to a reviewer i
+        # everyone can be assigned a tutorial because we're short on tutorial reviewers
+        in_domain = submission["track"] in reviewer["tracks"] or (ASSIGN_TUTORIALS_TO_ANYONE and submission["track"] == "TUT")
+        no_conflict = submission["submission_id"] not in reviewer["conflicts_submission_ids"]
+        ub[i, j] = in_domain and no_conflict
+
+        already_assigned = submission["submission_id"] in reviewer["assigned_submission_ids"]
+        lb[i, j] = already_assigned
+
+# Constraints
+# each reviewer assigned < max reviews
+for i, reviewer in enumerate(reviewers):
+    reviewer_constraints[i, i, :] = 1
+reviewer_constraints = reviewer_constraints.reshape(n_reviewers, -1)
+
+# each paper assigned to 4 reviewers
+for j, _ in enumerate(submissions):
+    submission_constraints[j, :, j] = 1
+submission_constraints = submission_constraints.reshape(n_submissions, -1)
+
+bounds = Bounds(lb.ravel(), ub.ravel())
+submission_per_reviewer_constraint = LinearConstraint(reviewer_constraints,
+                                                      MIN_REVIEWS_PER_PERSON,
+                                                      MAX_REVIEWS_PER_PERSON)
+reviews_per_paper_constraint = LinearConstraint(submission_constraints,
+                                                MIN_REVIEWERS_PER_SUBMISSION,
+                                                MAX_REVIEWERS_PER_SUBMISSION)
+constraints = [submission_per_reviewer_constraint,
+               reviews_per_paper_constraint]
+
+# Run MILP
+res = milp(objective_fun, integrality=True, bounds=bounds, constraints=constraints)
+print(res)
+
+# %%
+x = np.round(res.x).astype(bool)
+solution = x.reshape(n_reviewers, n_submissions)
+
+# %%
+############################
+## FORMAT AND OUTPUT DATA ##
+############################
+for reviewer, assignments in zip(reviewers, solution):
+    reviewer["assigned_submission_ids"] = df_submissions.submission_id[assignments].values.tolist()
+    if DEBUG:
+        # Check how many tutorials everyone got
+        reviewer["is_tutorial"] = [t == "TUT" for t in df_submissions.track[assignments]]
+        # Check that each reviewer actually was assigned a submission in their domain
+        reviewer["track_in_domain"] = [t in reviewer["tracks"] for t in df_submissions.track[assignments]]
+
+if DEBUG:
+    result = {
+        reviewer["reviewer_id"]: sorted(reviewer["is_tutorial"])
+        for reviewer in reviewers
+    }
+
+    with open("review-assignments-debug.json", "w") as fp:
+        fp.write(json.dumps(result, indent=4))
+
+result = {
+    reviewer["reviewer_id"]: reviewer["assigned_submission_ids"]
+    for reviewer in reviewers
+}
+
+with open("review-assignments.json", "w") as fp:
+    fp.write(json.dumps(result, indent=4))
+
+for submission, assignments in zip(submissions, solution.T):
+    submission["assigned_reviewer_ids"] = df_reviewers.reviewer_id[assignments].values.tolist()
+
+result = {
+    submission["submission_id"]: submission["assigned_reviewer_ids"]
+    for submission in submissions
+}
+
+with open("submission-assignments.json", "w") as fp:
+    fp.write(json.dumps(result, indent=4))
+
+# %%