from XFitting to PCAFit, PreFit & FullFit

datajoint · Mar 20, 2024 · 142ad44 · 142ad44
1 parent d201bc4
commit 142ad44
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Showing 2 changed files with 18 additions and 16 deletions.
diff --git a/element_moseq/moseq_infer.py b/element_moseq/moseq_infer.py
@@ -17,7 +17,8 @@
 )
 
 from element_interface.utils import find_full_path
-from .readers.kpms_reader import load_kpms_dj_config, generate_kpms_dj_config
+from .readers.kpms_reader import load_kpms_dj_config
+
 from keypoint_moseq import (
     load_checkpoint,
     load_keypoints,

diff --git a/element_moseq/moseq_train.py b/element_moseq/moseq_train.py
@@ -12,6 +12,7 @@
 
 from element_interface.utils import find_full_path
 from .readers.kpms_reader import generate_kpms_dj_config, load_kpms_dj_config
+
 from keypoint_moseq import (
     setup_project, 
     load_config, 
@@ -339,7 +340,7 @@ def make(self, key):
 
 
 @schema
-class PCAFitting(dj.Computed):
+class PCAFit(dj.Computed):
     """Automated fitting of the PCA model.
 
     Attributes:
@@ -402,14 +403,14 @@ class LatentDimension(dj.Imported):
     The analysis aims to select each of the components that explain the 90% of variance (fixed threshold).
 
     Attributes:
-        PCAFitting (foreign key)           : PCAFitting Key.
+        PCAFit (foreign key)               : PCAFit Key.
         variance_percentage (float)        : Variance threshold. Fixed value to 90%.
         latent_dimension (int)             : Number of principal components required to explain the specified variance.
         latent_dim_desc (varchar)          : Automated description of the computation result.
     """
 
     definition = """
-    -> PCAFitting                                   # PCAFitting Key
+    -> PCAFit                                   # PCAFit Key
     ---
     variance_percentage      : float            # Variance threshold. Fixed value to 0.9
     latent_dimension         : int              # Number of principal components required to explain the specified variance.
@@ -421,7 +422,7 @@ def make(self, key):
         Make function to compute and store the latent dimensions that explain a 90% variance threshold.
 
         Args:
-            key (dict): PCAFitting Key.
+            key (dict): PCAFit Key.
 
         Raises:
 
@@ -474,15 +475,15 @@ class PreFittingTask(dj.Manual):
     """Table to specify the parameters for the pre-fitting (AR-HMM) of the model.
 
     Attributes:
-        PCAFitting (foreign key)   : PCA fitting task.
+        PCAFit (foreign key)                : PCAFit task.
         pre_latent_dim (int)                : Number of latent dimensions to use for the model pre-fitting.
         pre_kappa (int)                     : Kappa value to use for the model pre-fitting.
         pre_num_iterations (int)            : Number of Gibbs sampling iterations to run in the model pre-fitting.
         pre_fitting_desc(varchar)           : User-defined description of the pre-fitting task.
     """
 
     definition = """
-    -> PCAFitting                      # PCAFitting Key
+    -> PCAFit                           # PCAFit Key
     pre_latent_dim               : int # Number of latent dimensions to use for the model pre-fitting
     pre_kappa                    : int # Kappa value to use for the model pre-fitting
     pre_num_iterations           : int # Number of Gibbs sampling iterations to run in the model pre-fitting.
@@ -492,7 +493,7 @@ class PreFittingTask(dj.Manual):
 
 
 @schema
-class PreFitting(dj.Computed):
+class PreFit(dj.Computed):
     """Automated computation to fit a AR-HMM model.
 
     Attributes:
@@ -527,7 +528,7 @@ def make(self, key):
         5. Initialize the model that create a `model` dict containing states, parameters, hyperparameters, noise prior, and random seed.
         6. Update the model dict with the selected kappa for the AR-HMM fitting
         7. Fit the AR-HMM model using the `pre_num_iterations` and create a subdirectory in `kpms_project_output_dir` with the model's latest checkpoint
-        8. Calculate the duration of the model fitting computation and insert it in the `PreFitting` table
+        8. Calculate the duration of the model fitting computation and insert it in the `PreFit` table
         """
 
         kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
@@ -590,7 +591,7 @@ class FullFittingTask(dj.Manual):
     """Table to specify the parameters for the full fitting of the model. The full model will generally require a lower value of kappa to yield the same target syllable durations.
 
     Attributes:
-        PCAFitting (foreign key)    : PCAFitting Key.
+        PCAFit (foreign key)                 : PCAFit Key.
         full_latent_dim (int)                : Number of latent dimensions to use for the model full fitting.
         full_kappa (int)                     : Kappa value to use for the model full fitting.
         full_num_iterations (int)            : Number of Gibbs sampling iterations to run in the model full fitting.
@@ -599,7 +600,7 @@ class FullFittingTask(dj.Manual):
     """
 
     definition = """
-    -> PCAFitting                          # PCAFitting Key
+    -> PCAFit                                       # PCAFit Key
     full_latent_dim              : int              # Number of latent dimensions to use for the model full fitting
     full_kappa                   : int              # Kappa value to use for the model full fitting
     full_num_iterations          : int              # Number of Gibbs sampling iterations to run in the model full fitting.
@@ -609,20 +610,20 @@ class FullFittingTask(dj.Manual):
 
 
 @schema
-class FullFitting(dj.Computed):
+class FullFit(dj.Computed):
     """Automated computation to fit the full model.
 
     Attributes:
         FullFittingTask (foreign key)        : FullFittingTask Key.
         model_name                           : varchar(100) # Name of the full-fitted model (output_dir/model_name)
-        full_fitting_duration (float)        : Time duration (seconds) of the full fitting model
+        full_fit_duration (float)            : Time duration (seconds) of the full fitting model
     """
 
     definition = """
     -> FullFittingTask                           # FullFittingTask Key
     ---
     model_name                    : varchar(100) # Name of the full-fitted model (output_dir/model_name)
-    full_fitting_duration=NULL    : float        # Time duration (seconds) of the full fitting model 
+    full_fit_duration=NULL        : float        # Time duration (seconds) of the full fitting model 
     """
 
     def make(self, key):
@@ -645,7 +646,7 @@ def make(self, key):
             8. Reindex syllable labels by their frequency in the most recent model snapshot in a checkpoint file. \
                 This function permutes the states and parameters of a saved checkpoint so that syllables are labeled \
                 in order of frequency (i.e. so that 0 is the most frequent, 1 is the second most, and so on).
-            8. Calculate the duration of the model fitting computation and insert it in the `PreFitting` table
+            8. Calculate the duration of the model fitting computation and insert it in the `PreFit` table
         """
 
         kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
@@ -698,6 +699,6 @@ def make(self, key):
                     kpms_project_output_dir.relative_to(get_kpms_processed_data_dir())
                     / model_name
                 ).as_posix(),
-                "full_fitting_duration": duration_seconds,
+                "full_fit_duration": duration_seconds,
             }
         )