ADD: few simple LC model tests (#191)

* ADD: basic LC model tests

* ADD: eagerly exec for SimpleMNL

* ADD: call to compute_report()

* ENH: remove dependecy to tfp for Normal cdf

* ENH: make latent class tests faster

choice_learn/models/conditional_logit.py

@@ -1,6 +1,7 @@
 """Conditional MNL model."""
 
 import logging
+import math
 
 import numpy as np
 import pandas as pd
@@ -645,10 +646,12 @@ def compute_report(self, choice_dataset):
         pandas.DataFrame
             A DF with estimation, Std Err, z_value and p_value for each coefficient.
         """
-        import tensorflow_probability as tfp
+
+        def phi(x):
+            """Cumulative distribution function for the standard normal distribution."""
+            return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
 
         weights_std = self.get_weights_std(choice_dataset)
-        dist = tfp.distributions.Normal(loc=0.0, scale=1.0)
 
         names = []
         z_values = []
@@ -663,7 +666,7 @@ def compute_report(self, choice_dataset):
                     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()))
+                p_z.append(2 * (1 - phi(tf.math.abs(z_values[-1]).numpy())))
                 i += 1
 
         return pd.DataFrame(

choice_learn/models/latent_class_base_model.py

@@ -107,6 +107,8 @@ def instantiate(self, **kwargs):
         for model in self.models:
             model.instantiate(**kwargs)
 
+        self.instantiated = True
+
     # @tf.function
     def batch_predict(
         self,

choice_learn/models/simple_mnl.py

@@ -4,6 +4,7 @@
 """
 
 import logging
+import math
 
 import pandas as pd
 import tensorflow as tf
@@ -254,10 +255,12 @@ def compute_report(self, choice_dataset):
         pandas.DataFrame
             A DF with estimation, Std Err, z_value and p_value for each coefficient.
         """
-        import tensorflow_probability as tfp
+
+        def phi(x):
+            """Cumulative distribution function for the standard normal distribution."""
+            return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
 
         weights_std = self.get_weights_std(choice_dataset)
-        dist = tfp.distributions.Normal(loc=0.0, scale=1.0)
 
         names = []
         z_values = []
@@ -272,7 +275,7 @@ def compute_report(self, choice_dataset):
                     names.append(f"{weight.name[:-2]}")
                 estimations.append(weight.numpy()[j])
                 z_values.append(weight.numpy()[j] / weights_std[i].numpy())
-                p_z.append(2 * (1 - dist.cdf(tf.math.abs(z_values[-1])).numpy()))
+                p_z.append(2 * (1 - phi(tf.math.abs(z_values[-1]).numpy())))
                 i += 1
 
         return pd.DataFrame(

notebooks/introduction/3_model_clogit.ipynb

@@ -1361,7 +1361,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "tf_env",
    "language": "python",
    "name": "python3"
   },
@@ -1375,7 +1375,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

tests/integration_tests/models/test_conditional_logit.py

@@ -55,7 +55,8 @@ def test_mode_canada_fit():
     canada_dataset = load_modecanada(as_frame=False, preprocessing="tutorial")
 
     model = ConditionalLogit(coefficients=coefficients)
-    model.fit(canada_dataset, get_report=True)
+    model.fit(canada_dataset)
+    model.compute_report(canada_dataset)
 
     total_nll = model.evaluate(canada_dataset) * len(canada_dataset)
     assert total_nll <= 1874.4, f"Got NLL: {total_nll}"

tests/integration_tests/models/test_latent_class.py

@@ -14,7 +14,7 @@ def test_latent_simple_mnl():
     """Test the simple latent class model fit() method."""
     tf.config.run_functions_eagerly(True)
     lc_model = LatentClassSimpleMNL(
-        n_latent_classes=3, fit_method="mle", optimizer="lbfgs", epochs=1000, lbfgs_tolerance=1e-20
+        n_latent_classes=3, fit_method="mle", optimizer="lbfgs", epochs=1000, lbfgs_tolerance=1e-8
     )
     _, _ = lc_model.fit(elec_dataset)
 
@@ -25,7 +25,7 @@ def test_latent_clogit():
     """Test the conditional logit latent class model fit() method."""
     tf.config.run_functions_eagerly(True)
     lc_model = LatentClassConditionalLogit(
-        n_latent_classes=3, fit_method="mle", optimizer="lbfgs", epochs=1000, lbfgs_tolerance=1e-12
+        n_latent_classes=3, fit_method="mle", optimizer="lbfgs", epochs=40, lbfgs_tolerance=1e-8
     )
     lc_model.add_shared_coefficient(
         coefficient_name="pf", feature_name="pf", items_indexes=[0, 1, 2, 3]
@@ -58,11 +58,28 @@ def test_manual_lc():
         model_parameters={"add_exit_choice": False},
         n_latent_classes=3,
         fit_method="mle",
-        epochs=1000,
+        epochs=40,
         optimizer="lbfgs",
-        lbfgs_tolerance=1e-12,
+        lbfgs_tolerance=1e-8,
     )
 
     manual_lc.instantiate(n_items=4, n_shared_features=0, n_items_features=6)
     _ = manual_lc.fit(elec_dataset)
     assert manual_lc.evaluate(elec_dataset) < 1.15
+
+
+def test_manual_lc_gd():
+    """Test manual specification of Latent Class Simple MNL model with gradient descent."""
+    tf.config.run_functions_eagerly(True)
+    manual_lc = BaseLatentClassModel(
+        model_class=SimpleMNL,
+        model_parameters={"add_exit_choice": False},
+        n_latent_classes=3,
+        fit_method="mle",
+        epochs=10,
+        optimizer="Adam",
+    )
+    manual_lc.instantiate(n_items=4, n_shared_features=0, n_items_features=6)
+    nll_before = manual_lc.evaluate(elec_dataset)
+    _ = manual_lc.fit(elec_dataset)
+    assert manual_lc.evaluate(elec_dataset) < nll_before

tests/integration_tests/models/test_simple_mnl.py

@@ -8,13 +8,14 @@
 dataset = load_swissmetro()
 
 
-def test_simple_mnl_lbfgs_fit_with_lbfgs():
+def test_simple_mnl_fit_with_lbfgs():
     """Tests that SimpleMNL can fit with LBFGS."""
+    tf.config.run_functions_eagerly(True)
     global dataset
 
     model = SimpleMNL(epochs=20)
-    model.fit(dataset)
-    model.evaluate(dataset)
+    _ = model.fit(dataset, get_report=True)
+    _ = model.evaluate(dataset)
     assert model.evaluate(dataset) < 1.0
 
 
@@ -24,7 +25,7 @@ def test_simple_mnl_lbfgs_fit_with_adam():
     global dataset
 
     model = SimpleMNL(epochs=20, optimizer="adam", batch_size=256)
-    model.fit(dataset, get_report=True)
+    model.fit(dataset)
     model.evaluate(dataset)
     assert model.evaluate(dataset) < 1.0
 
@@ -34,10 +35,12 @@ def test_that_endpoints_run():
 
     No verification of results.
     """
+    tf.config.run_functions_eagerly(True)
     global dataset
 
     model = SimpleMNL(epochs=20)
-    model.fit(dataset)
+    _ = model.fit(dataset)
+    _ = model.compute_report(dataset)
     model.evaluate(dataset)
     model.predict_probas(dataset)
     assert True