Merge branch 'analysis-example' of gin.g-node.org:/LAAC-LSCP/speaker-confusion-model into analysis-example

code/plots/correlations.py

@@ -32,8 +32,8 @@ 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_voc_small_dev_siblings_binomial_hurdle_fast.pickle",    
-    "vtc_calibrated": "output/aggregates_vtc_age24_voc_small_dev_siblings_binomial_hurdle_fast.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",    
 }
 
 for key in data:
@@ -46,8 +46,8 @@ samples = {
     "vtc_raw": np.load("output/aggregates_vtc_age24_algo.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_voc_small_dev_siblings_binomial_hurdle_fast.npz"),
-    "vtc_calibrated": np.load("output/aggregates_vtc_age24_voc_small_dev_siblings_binomial_hurdle_fast.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")
 }
 
 labels = {

code/plots/correlations_child_level.py

@@ -184,8 +184,8 @@ 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_voc_small_dev_siblings_binomial_hurdle_fast.pickle",    
-    "vtc_calibrated": "output/aggregates_vtc_age24_voc_small_dev_siblings_binomial_hurdle_fast.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",    
 }
 
 for key in data:
@@ -198,8 +198,8 @@ samples = {
     "vtc_raw": np.load("output/aggregates_vtc_age24_algo.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_voc_small_dev_siblings_binomial_hurdle_fast.npz"),
-    "vtc_calibrated": np.load("output/aggregates_vtc_age24_voc_small_dev_siblings_binomial_hurdle_fast.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")
 }
 
 labels = {

code/plots/effects_comparison.py

@@ -47,13 +47,13 @@ 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_calibrated": np.load("output/aggregates_lena_dev_siblings_effect.npz"),
-    # "vtc_calibrated": np.load("output/aggregates_vtc_dev_siblings_effect.npz"),
+    "lena_raw_algo_only": np.load("output/aggregates_lena_age24_algo_only.npz"),
+    "vtc_raw_algo_only": np.load("output/aggregates_vtc_age24_algo_only.npz"),
     "lena_calibrated": np.load(
-        "output/aggregates_lena_age24_voc_small_dev_siblings_binomial_hurdle_fast.npz"
+        "output/aggregates_lena_age24_dev_siblings_binomial_hurdle_fast.npz"
     ),
     "vtc_calibrated": np.load(
-        "output/aggregates_vtc_age24_voc_small_dev_siblings_binomial_hurdle_fast.npz"
+        "output/aggregates_vtc_age24_dev_siblings_binomial_hurdle_fast.npz"
     ),
     "prior": prior_distribution,
 }
@@ -64,6 +64,8 @@ labels = {
     "truth": "Manual annotations",
     "lena_raw": "LENA (uncalibrated)",
     "vtc_raw": "VTC (uncalibrated)",
+    "lena_raw_algo_only": "LENA (uncalibrated, algo only)",
+    "vtc_raw_algo_only": "VTC (uncalibrated, algo only)",
     "lena_calibrated": "LENA (calibrated)",
     "vtc_calibrated": "VTC (calibrated)",
 }