version bump v0.0.1.dev8 - renamed dual flatness to flatness components

README.md

@@ -1,7 +1,6 @@
 
 <p align="center">
     <h1 align="center">🧶 Disent</h1>
-    <p align="center">⚠️ W.I.P</p>
     <p align="center">
         <i>A modular disentangled representation learning framework for pytorch</i>
     </p>
@@ -29,9 +28,13 @@
 </p>
 
 <p align="center">
+    <p align="center">⚠️ API is not yet stable</p>
     <p align="center">
         Visit the <a href="https://disent.dontpanic.sh/">docs</a> for more info, or browse the  <a href="https://github.com/nmichlo/disent/releases">releases</a>.
     </p>
+    <p align="center">
+        <a href="https://github.com/nmichlo/disent/issues/new/choose">Contributions</a> are welcome!
+    </p>
 </p>
 
 ----------------------
@@ -133,10 +136,10 @@ submit an issue if you have a request for an additional framework.
   + [SAP](https://arxiv.org/abs/1711.00848)
   + [Unsupervised Scores](https://github.com/google-research/disentanglement_lib)
   + 🧵 Flatness Score
-    - Measures max width over path length of factor traversal embeddings, a combined measure of linearity and ordering.
-  + 🧵 Dual Flatness - Linearity & Ordering
+    - Measures max width (furthest two points) over path length (sum of distances between consecutive points) of factor traversal embeddings. A combined measure of linearity, monotonicity and ordering.
+  + 🧵 Flatness Components - Linearity, Monotonicity & Ordering
     - Measure **linearity** of factor traversal embeddings using average Pearson's correlation matrices
-    - Measure **ordering** of factor traversal embedding using average Spearman's rank correlation matrices
+    - Measure **monotonicity** of factor traversal embedding using average Spearman's rank correlation matrices
     - Measure **ordering** of embeddings by checking anchor-positive and anchor-negative distances correspond to ground-truth factors
 
 Some popular metrics still need to be added, please submit an issue if you wish to

disent/metrics/__init__.py

@@ -31,7 +31,7 @@
 
 # Nathan Michlo et. al
 from ._flatness import metric_flatness
-from ._dual_flatness import metric_dual_flatness
+from ._flatness_components import metric_flatness_components
 
 
 # ========================================================================= #
@@ -43,21 +43,21 @@
 
 
 FAST_METRICS = {
-    'dci':           _wrapped_partial(metric_dci,           num_train=1000, num_test=500, boost_mode='sklearn'),  # takes
-    'factor_vae':    _wrapped_partial(metric_factor_vae,    num_train=700, num_eval=350, num_variance_estimate=1000),  # may not be accurate, but it just takes waay too long otherwise 20+ seconds
-    'flatness':      _wrapped_partial(metric_flatness,      factor_repeats=128),
-    'dual_flatness': _wrapped_partial(metric_dual_flatness, factor_repeats=128),
-    'mig':           _wrapped_partial(metric_mig,           num_train=2000),
-    'sap':           _wrapped_partial(metric_sap,           num_train=2000, num_test=1000),
-    'unsupervised':  _wrapped_partial(metric_unsupervised,  num_train=2000),
+    'dci':                 _wrapped_partial(metric_dci,           num_train=1000, num_test=500, boost_mode='sklearn'),  # takes
+    'factor_vae':          _wrapped_partial(metric_factor_vae,    num_train=700, num_eval=350, num_variance_estimate=1000),  # may not be accurate, but it just takes waay too long otherwise 20+ seconds
+    'flatness':            _wrapped_partial(metric_flatness,      factor_repeats=128),
+    'flatness_components': _wrapped_partial(metric_flatness_components, factor_repeats=128),
+    'mig':                 _wrapped_partial(metric_mig,           num_train=2000),
+    'sap':                 _wrapped_partial(metric_sap,           num_train=2000, num_test=1000),
+    'unsupervised':        _wrapped_partial(metric_unsupervised,  num_train=2000),
 }
 
 DEFAULT_METRICS = {
-    'dci':           metric_dci,
-    'factor_vae':    metric_factor_vae,
-    'flatness':      metric_flatness,
-    'dual_flatness': metric_dual_flatness,
-    'mig':           metric_mig,
-    'sap':           metric_sap,
-    'unsupervised':  metric_unsupervised,
+    'dci':                 metric_dci,
+    'factor_vae':          metric_factor_vae,
+    'flatness':            metric_flatness,
+    'flatness_components': metric_flatness_components,
+    'mig':                 metric_mig,
+    'sap':                 metric_sap,
+    'unsupervised':        metric_unsupervised,
 }

disent/metrics/_dual_flatness.py → disent/metrics/_flatness_components.py

@@ -50,7 +50,7 @@
 # ========================================================================= #
 
 
-def metric_dual_flatness(
+def metric_flatness_components(
         ground_truth_dataset: GroundTruthDataset,
         representation_function: callable,
         factor_repeats: int = 1024,
@@ -211,7 +211,7 @@ def print_r(name, steps, result, clr=colors.lYLW, t: Timer = None):
     def calculate(name, steps, dataset, get_repr):
         global aggregate_measure_distances_along_factor
         with Timer() as t:
-            r = metric_dual_flatness(dataset, get_repr, factor_repeats=64, batch_size=64)
+            r = metric_flatness_components(dataset, get_repr, factor_repeats=64, batch_size=64)
         results.append((name, steps, r))
         print_r(name, steps, r, colors.lRED, t=t)
         print(colors.GRY, '='*100, colors.RST, sep='')

experiment/config/metrics/all.yaml

@@ -1,7 +1,7 @@
 # @package _group_
 metric_list:
   - flatness:
-  - dual_flatness:
+  - flatness_components:
   - mig:
   - sap:
   - unsupervised:

setup.py

@@ -48,7 +48,7 @@
     author="Nathan Juraj Michlo",
     author_email="[email protected]",
 
-    version="0.0.1.dev7",
+    version="0.0.1.dev8",
     python_requires="==3.8",
     packages=setuptools.find_packages(),