Fixes for Windows.

sandialabs · Aug 15, 2024 · 504fe33 · 504fe33
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commit 504fe33
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Showing 9 changed files with 10 additions and 10 deletions.
diff --git a/.github/workflows/build-and-run-tests.yml b/.github/workflows/build-and-run-tests.yml
@@ -7,7 +7,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: ["3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
         os: [ubuntu-latest, windows-latest, macos-latest]
     runs-on: ${{ matrix.os }}
     steps:

diff --git a/.github/workflows/run-examples.yml b/.github/workflows/run-examples.yml
@@ -7,7 +7,7 @@ jobs:
   build:
     strategy:
       matrix:
-        python-version: ["3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
         os: [ubuntu-latest, windows-latest, macos-latest]
     runs-on: ${{ matrix.os }}
     steps:

diff --git a/examples/modeling/arad.py b/examples/modeling/arad.py
@@ -14,7 +14,7 @@
 # Config
 rng = np.random.default_rng(42)
 OOD_QUANTILE = 0.99
-VERBOSE = True
+VERBOSE = False
 # Some of the following parameters are set low because this example runs on GitHub Actions and
 #   we don't want it taking a bunch of time.
 # When running this locally, change the values per their corresponding comment, otherwise
@@ -54,7 +54,7 @@
     arad.predict(gross_train_ss)
     ood_threshold = np.quantile(gross_train_ss.info.recon_error, OOD_QUANTILE)
 
-    reconstructions = arad.predict(test_ss, verbose=True)
+    reconstructions = arad.predict(test_ss, verbose=VERBOSE)
     ood = test_ss.info.recon_error.values > ood_threshold
     false_positive_rate = ood.mean()
     mean_recon_error = test_ss.info.recon_error.values.mean()

diff --git a/examples/modeling/arad_latent_prediction.py b/examples/modeling/arad_latent_prediction.py
@@ -15,7 +15,7 @@
 
 # Config
 rng = np.random.default_rng(42)
-VERBOSE = True
+VERBOSE = False
 # Some of the following parameters are set low because this example runs on GitHub Actions and
 #   we don't want it taking a bunch of time.
 # When running this locally, change the values per their corresponding comment, otherwise

diff --git a/examples/modeling/classifier_comparison.py b/examples/modeling/classifier_comparison.py
@@ -37,7 +37,7 @@
 train_fg_ss.normalize()
 
 model_nn = MLPClassifier()
-model_nn.fit(train_fg_ss, epochs=10, patience=5, verbose=1)
+model_nn.fit(train_fg_ss, epochs=10, patience=5)
 
 # Create PB model
 model_pb = PoissonBayesClassifier()

diff --git a/examples/modeling/label_proportion_estimation.py b/examples/modeling/label_proportion_estimation.py
@@ -55,7 +55,6 @@
     batch_size=10,
     epochs=2,
     validation_split=0.2,
-    verbose=True,
     bg_cps=300
 )
 

diff --git a/examples/modeling/neural_network_classifier.py b/examples/modeling/neural_network_classifier.py
@@ -24,7 +24,7 @@
 train_ss.normalize()
 
 model = MLPClassifier()
-model.fit(train_ss, epochs=10, patience=5, verbose=1)
+model.fit(train_ss, epochs=10, patience=5)
 
 # Generate some test data
 static_synth.samples_per_seed = 50

diff --git a/pyproject.toml b/pyproject.toml
@@ -63,7 +63,8 @@ dependencies = [
     "seaborn                        ==0.13.*",  # >= 3.8
     "tensorflow                     ==2.16.*",  # 3.9 - 3.12
     "tensorflow-model-optimization  ==0.8.*",   # 3.7 - 3.12
-    "tf2onnx                        ==1.16.*",  # 3.7 - 3.10
+    "onnx                           ==1.16.1",  # 3.7 - 3.10
+    "tf2onnx                        ==1.16.1",  # 3.7 - 3.10
     "tqdm                           ==4.66.*",  # >= 3.7
     "typeguard                      ==4.3.*",   # 3.9 - 3.12
 ]

diff --git a/riid/models/neural_nets/__init__.py b/riid/models/neural_nets/__init__.py
@@ -592,7 +592,7 @@ def fit(self, seeds_ss: SampleSet, ss: SampleSet, bg_cps: int = 300, is_gross: b
             if verbose:
                 print("Finding OOD detection threshold function...")
 
-            train_logits = self.model.predict(spectra)
+            train_logits = self.model.predict(spectra, verbose=0)
             train_lpes = self.activation(tf.convert_to_tensor(train_logits, dtype=tf.float32))
             self.spline_recon_errors = reconstruction_error(
                 tf.convert_to_tensor(spectra, dtype=tf.float32),