add and plot vector norms

Author: hsanchez

Diff for: ml.py

@@ -7,7 +7,7 @@
 import pandas as pd
 from pandas.io.formats.style import Styler
 
-from .arrays import ArrayLike
+from .arrays import ArrayLike, normalize_vector_norms
 from .common import with_status
 from .console import new_quiet_console, stderr
 from .data import (_check_input_dataframe, build_multi_index_dataframe,
@@ -371,6 +371,32 @@ def intra_cluster_variation(
   return wss
 
 
+def get_vector_norms(
+  target_activities: ArrayLike, 
+  timeline_slice_aligned_models: list, 
+  timeline_slices: ArrayLike, 
+  act2idx: dict) -> ArrayLike:
+  
+  # NOTE: timeline_slice_aligned_models is
+  # [dynamic_model.embeddings[t] for t in range(len(dynamic_model.embeddings))]
+  
+  aligned_activity_norms = []
+  for act in target_activities:
+    # norms = []
+    embeddings = []
+    for time_slice in timeline_slices:
+      act_emb_at_week = timeline_slice_aligned_models[timeline_slices.index(time_slice)][act2idx[act], :]
+      embeddings.append(act_emb_at_week)
+      # norm = np.linalg.norm(act_emb_at_week)
+      # norms.append(norm)
+    
+    # norms = np.array(norms)
+    # norms = norms / sum(norms)
+    aligned_activity_norms.append(normalize_vector_norms(embeddings))
+  return aligned_activity_norms
+
+
+
 def build_tsne_projection(
   activity: str,
   time_slices: ArrayLike,

Diff for: plots.py

@@ -303,9 +303,7 @@ def make_dendrogram(*args, **kwargs):
   return ddata
 
 
-def plot_dynamic_activity_embeddings(
-  annotated_coordinates: ArrayLike,
-  **kwargs) -> None:
+def plot_dynamic_activity_embeddings(annotated_coordinates: ArrayLike, **kwargs) -> None:
   pyplot_module = kwargs.get('pyplot_module', None)
   if pyplot_module is None:
     plt = import_module('matplotlib.pyplot', 'matplotlib')
@@ -329,6 +327,38 @@ def plot_dynamic_activity_embeddings(
       ha=ha,
       va=va)
   plt.show()
+  
+  
+def plot_embedding_changes_in_vector_space(
+  target_activities: ArrayLike, 
+  aligned_activity_norms: ArrayLike, 
+  timeline_slices: ArrayLike, **kwargs) -> None:
+  
+  pyplot_module = kwargs.get('pyplot_module', None)
+  if pyplot_module is None:
+    plt = import_module('matplotlib.pyplot', 'matplotlib')
+  else:
+    plt = pyplot_module
+  
+  if isinstance(timeline_slices, np.ndarray):
+    timeline_slices = timeline_slices.tolist()
+  
+  figsize = kwargs.get('figsize', (15,10))
+  markersize = kwargs.get('markersize', 7)
+  plt.figure(figsize=figsize)
+  time_frames = [week for week in timeline_slices]
+  markers = ['+', 'o', 'x']
+  plt.clf()
+
+  for idx in range(len(aligned_activity_norms)):
+    norms = aligned_activity_norms[idx]
+    plt.plot(time_frames, norms, marker=markers[idx], markersize=markersize)
+
+  plt.legend(target_activities)
+  plt.xlabel('week')
+  plt.ylabel('activity norm')
+  plt.show()
+
 
 
 if __name__ == "__main__":