Add missing requirement

_scripts/run_all_study.py

@@ -50,14 +50,14 @@ def generate_results(lifters, seed=0):
 
 def run_all(recompute=RECOMPUTE):
     lifters = [
-        # (RangeOnlyLocLifter, dict(n_positions=3, n_landmarks=10, d=3, level="no")),
-        # (RangeOnlyLocLifter, dict(n_positions=3, n_landmarks=10, d=3, level="quad")),
+        (RangeOnlyLocLifter, dict(n_positions=3, n_landmarks=10, d=3, level="no")),
+        (RangeOnlyLocLifter, dict(n_positions=3, n_landmarks=10, d=3, level="quad")),
         (Stereo2DLifter, dict(n_landmarks=3, param_level="ppT", level="urT")),
-        # (Stereo3DLifter, dict(n_landmarks=4, param_level="ppT", level="urT")),
-        # (WahbaLifter, dict(n_landmarks=5, d=3, robust=True, level="xwT", n_outliers=1)),
-        # (MonoLifter, dict(n_landmarks=6, d=3, robust=True, level="xwT", n_outliers=1)),
-        # (WahbaLifter, dict(n_landmarks=4, d=3, robust=False, level="no", n_outliers=0)),
-        # (MonoLifter, dict(n_landmarks=5, d=3, robust=False, level="no", n_outliers=0)),
+        (Stereo3DLifter, dict(n_landmarks=4, param_level="ppT", level="urT")),
+        (WahbaLifter, dict(n_landmarks=5, d=3, robust=True, level="xwT", n_outliers=1)),
+        (MonoLifter, dict(n_landmarks=6, d=3, robust=True, level="xwT", n_outliers=1)),
+        (WahbaLifter, dict(n_landmarks=4, d=3, robust=False, level="no", n_outliers=0)),
+        (MonoLifter, dict(n_landmarks=5, d=3, robust=False, level="no", n_outliers=0)),
     ]
 
     fname = f"{RESULTS_DIR}/all_df_new.pkl"

_scripts/run_datasets_ro.py

@@ -5,7 +5,7 @@
 
 try:
     matplotlib.use("TkAgg")  # non-interactive
-except Exception as e:
+except:
     pass
 
 import pandas as pd

_scripts/run_other_study.py

@@ -112,18 +112,18 @@ def run_wahba(n_seeds, recompute, tightness=True, scalability=True):
 
     print("================= Wahba study ==================")
 
-    # if tightness:
-    #    lifter_tightness(WahbaLifter, d=d, n_landmarks=4, robust=False)
+    if tightness:
+        lifter_tightness(WahbaLifter, d=d, n_landmarks=4, robust=False)
     if scalability:
-        # lifter_scalability_new(
-        #    WahbaLifter,
-        #    d=d,
-        #    n_landmarks=4,
-        #    robust=False,
-        #    n_outliers=0,
-        #    n_seeds=n_seeds,
-        #    recompute=recompute,
-        # )
+        lifter_scalability_new(
+            WahbaLifter,
+            d=d,
+            n_landmarks=4,
+            robust=False,
+            n_outliers=0,
+            n_seeds=n_seeds,
+            recompute=recompute,
+        )
         lifter_scalability_new(
             WahbaLifter,
             d=d,
@@ -172,6 +172,6 @@ def run_all(n_seeds, recompute, tightness=True, scalability=True):
 
 
 if __name__ == "__main__":
-    # run_all(n_seeds=1, recompute=True)
+    run_all(n_seeds=1, recompute=True)
     # run_mono(n_seeds=1, recompute=True)
-    run_wahba(n_seeds=1, recompute=True)
+    # run_wahba(n_seeds=1, recompute=True)

_scripts/run_stereo_study.py

@@ -96,10 +96,10 @@ def stereo_scalability_new(n_seeds, recompute, d=2):
 
     learner = Learner(lifter=lifter, variable_list=lifter.variable_list)
 
-    # if lifter.d == 2:
-    #    fname_root = f"{RESULTS_DIR}/scalability_{learner.lifter}"
-    #    learner = Learner(lifter=lifter, variable_list=lifter.variable_list)
-    #    run_scalability_plot(learner, recompute=recompute, fname_root=fname_root)
+    if lifter.d == 2:
+        fname_root = f"{RESULTS_DIR}/scalability_{learner.lifter}"
+        learner = Learner(lifter=lifter, variable_list=lifter.variable_list)
+        run_scalability_plot(learner, recompute=recompute, fname_root=fname_root)
 
     df = run_scalability_new(
         learner,

environment.yml

@@ -19,16 +19,9 @@ dependencies:
     - pytest>=7.2.2
     - jupyter>=1.0.0
     - black>=23.1.0
-    - autograd>=1.2.0
     - mosek
     - tbb=2020.2 # sparseqr conflict
     - autograd>=1.6.2
 
     - pip:
-      - chompack>=2.3.1
-      #- sparseqr
-      - pymanopt>=2.1.1
-      - asrl-pylgmath>=1.0.3
-      - -e poly_matrix # build local poly_matrix submodule
-      - -e certifiable-tools
-      - -e .  # build local lifting package
+      - -r requirements.txt

requirements.txt

@@ -15,4 +15,5 @@ sparseqr
 asrl-pylgmath>=1.0.3
 -e poly_matrix/
 -e certifiable-tools/
+-e mat_weight_certs/
 -e .