Merge pull request #67 from artefactory/coeff-name

ENH: coeff name in .report + some cLogit interpretation
artefactory · Apr 18, 2024 · 4616a49 · 4616a49
2 parents 2ce0c24 + 1695dfd
commit 4616a49
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Showing 4 changed files with 76 additions and 48 deletions.
diff --git a/README.md b/README.md
@@ -25,7 +25,7 @@ This repository contains a private version of the package.
 - [choice-learn-private](#choice-learn-private)
   - [Introduction - Discrete Choice Modelling](#introduction---discrete-choice-modelling)
   - [What's in there ?](#whats-in-there)
-  - [Getting Started](#getting-started---fast-track)
+  - [Getting Started](#getting-started)
   - [Installation](#installation)
   - [Usage](#usage)
   - [Documentation](#documentation)

diff --git a/choice_learn/models/conditional_logit.py b/choice_learn/models/conditional_logit.py
@@ -651,7 +651,10 @@ def compute_report(self, dataset):
         i = 0
         for weight in self.trainable_weights:
             for j in range(weight.shape[1]):
-                names.append(f"{weight.name}_{j}")
+                if weight.shape[1] > 1:
+                    names.append(f"{weight.name[:-2]}_{j}")
+                else:
+                    names.append(f"{weight.name[:-2]}")
                 estimations.append(weight.numpy()[0][j])
                 z_values.append(weight.numpy()[0][j] / weights_std[i].numpy())
                 p_z.append(2 * (1 - dist.cdf(tf.math.abs(z_values[-1])).numpy()))

diff --git a/choice_learn/models/simple_mnl.py b/choice_learn/models/simple_mnl.py
@@ -255,10 +255,13 @@ def compute_report(self, dataset):
         p_z = []
         i = 0
         for weight in self.trainable_weights:
-            for j in range(weight.shape[0]):
-                names.append(f"{weight.name}_{j}")
-                estimations.append(weight.numpy()[j])
-                z_values.append(weight.numpy()[j] / weights_std[i].numpy())
+            for j in range(weight.shape[1]):
+                if weight.shape[1] > 1:
+                    names.append(f"{weight.name[:-2]}_{j}")
+                else:
+                    names.append(f"{weight.name[:-2]}")
+                estimations.append(weight.numpy()[0][j])
+                z_values.append(weight.numpy()[0][j] / weights_std[i].numpy())
                 p_z.append(2 * (1 - dist.cdf(tf.math.abs(z_values[-1])).numpy()))
                 i += 1
 

diff --git a/notebooks/introduction/3_model_clogit.ipynb b/notebooks/introduction/3_model_clogit.ipynb