correlation plot update

code/plots/correlations.py

@@ -30,10 +30,10 @@ args = parser.parse_args()
 
 data = {
     "truth": "output/aggregates_lena_age24_human.pickle",
-    "lena_raw": "output/aggregates_lena_age24_algo.pickle",
-    "vtc_raw": "output/aggregates_vtc_age24_algo.pickle",
-    "lena_calibrated": "output/aggregates_lena_age24_dev_siblings_binomial_hurdle_fast.pickle",    
-    "vtc_calibrated": "output/aggregates_vtc_age24_dev_siblings_binomial_hurdle_fast.pickle",    
+    "lena_raw": "output/aggregates_lena_age24_algo_only.pickle",
+    "vtc_raw": "output/aggregates_vtc_age24_algo_only.pickle",
+    "lena_calibrated": "output/aggregates_lena_dev.pickle",    
+    "vtc_calibrated": "output/aggregates_vtc_dev.pickle",    
 }
 
 for key in data:
@@ -42,12 +42,12 @@ for key in data:
 
 samples = {
     "truth": np.load("output/aggregates_lena_age24_human.npz"),
-    "lena_raw": np.load("output/aggregates_lena_age24_algo.npz"),
-    "vtc_raw": np.load("output/aggregates_vtc_age24_algo.npz"),
+    "lena_raw": np.load("output/aggregates_lena_age24_algo_only.npz"),
+    "vtc_raw": np.load("output/aggregates_vtc_age24_algo_only.npz"),
     # "lena_calibrated": np.load("output/aggregates_lena_dev_siblings_effect.npz"),
     # "vtc_calibrated": np.load("output/aggregates_vtc_dev_siblings_effect.npz"),
-    "lena_calibrated": np.load("output/aggregates_lena_age24_dev_siblings_binomial_hurdle_fast.npz"),
-    "vtc_calibrated": np.load("output/aggregates_vtc_age24_dev_siblings_binomial_hurdle_fast.npz")
+    "lena_calibrated": np.load("output/aggregates_lena_dev.npz"),
+    "vtc_calibrated": np.load("output/aggregates_vtc_dev.npz")
 }
 
 labels = {

code/plots/correlations_child_level.py

@@ -182,10 +182,10 @@ def correlations(data, samples):
 
 data = {
     "truth": "output/aggregates_lena_age24_human.pickle",
-    "lena_raw": "output/aggregates_lena_age24_algo.pickle",
-    "vtc_raw": "output/aggregates_vtc_age24_algo.pickle",
-    "lena_calibrated": "output/aggregates_lena_age24_dev_siblings_binomial_hurdle_fast.pickle",    
-    "vtc_calibrated": "output/aggregates_vtc_age24_dev_siblings_binomial_hurdle_fast.pickle",    
+    "lena_raw": "output/aggregates_lena_age24_algo_only.pickle",
+    "vtc_raw": "output/aggregates_vtc_age24_algo_only.pickle",
+    "lena_calibrated": "output/aggregates_lena_dev.pickle",    
+    "vtc_calibrated": "output/aggregates_vtc_dev.pickle",    
 }
 
 for key in data:
@@ -194,12 +194,12 @@ for key in data:
 
 samples = {
     "truth": np.load("output/aggregates_lena_age24_human.npz"),
-    "lena_raw": np.load("output/aggregates_lena_age24_algo.npz"),
-    "vtc_raw": np.load("output/aggregates_vtc_age24_algo.npz"),
+    "lena_raw": np.load("output/aggregates_lena_age24_algo_only.npz"),
+    "vtc_raw": np.load("output/aggregates_vtc_age24_algo_only.npz"),
     # "lena_calibrated": np.load("output/aggregates_lena_dev_siblings_effect.npz"),
     # "vtc_calibrated": np.load("output/aggregates_vtc_dev_siblings_effect.npz"),
-    "lena_calibrated": np.load("output/aggregates_lena_age24_dev_siblings_binomial_hurdle_fast.npz"),
-    "vtc_calibrated": np.load("output/aggregates_vtc_age24_dev_siblings_binomial_hurdle_fast.npz")
+    "lena_calibrated": np.load("output/aggregates_lena_dev.npz"),
+    "vtc_calibrated": np.load("output/aggregates_vtc_dev.npz")
 }
 
 labels = {