Neural Network submission to the challenge including 3x2 and FoM or SNR

.gitignore

@@ -0,0 +1,132 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
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+
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
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+.coverage.*
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+coverage.xml
+*.cover
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+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
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+# Django stuff:
+*.log
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+*.hdf5
+notebooks/dev/

README.md

@@ -40,6 +40,13 @@ You will get two datasets, based on two different simulations, which will allow
 The first dataset, which can be found under `data` after download is based on the CosmoDC2 simulation. The second dataset, found under
 `data_buzzard` is based on the Buzzard simulations.
 
+## Installing dependencies
+
+From the root folder:
+```
+$ pip install -r requirements.txt
+```
+
 
 ## Metric
 

bin/challenge.py

@@ -107,9 +107,7 @@ def run_one(classifier_name, bands, settings, train_data, train_z, valid_data,
     name = str(classifier.__name__)
     code = abs(hash(str(settings)))
     tc.metrics.plot_distributions(valid_z, results, f"plots/{name}_{code}_{bands}.png", metadata=settings)
-
     return scores
 
-
 if __name__=="__main__":
     main()

example/example_output.txt

@@ -0,0 +1,5 @@
+RandomForest run_1 {'bins': 1} {'SNR_ww': 134.80948870306673, 'SNR_gg': 555.4535070472674, 'SNR_3x2': 562.3045275125236, 'FOM_3x2': 11406.381379130828} 
+RandomForest run_2 {'bins': 2} {'SNR_ww': 153.37996427859213, 'SNR_gg': 749.4259413373646, 'SNR_3x2': 752.8800006487635, 'FOM_3x2': 11420.79691061082} 
+RandomForest run_3 {'bins': 3} {'SNR_ww': 159.18538698343457, 'SNR_gg': 922.7346985760454, 'SNR_3x2': 925.4853956272517, 'FOM_3x2': 43768.68018337518} 
+RandomForest run_4 {'bins': 4} {'SNR_ww': 160.84108815042694, 'SNR_gg': 1037.3860103346599, 'SNR_3x2': 1039.8374894959643, 'FOM_3x2': 13800.43390167526} 
+IBandOnly run_3 {'bins': 3} {'SNR_ww': 135.13888426367922, 'SNR_gg': 565.866023397332, 'SNR_3x2': 572.2806557629426, 'FOM_3x2': 25548.34390335924} 

example/neural_network_buzzard_griz.yaml

@@ -0,0 +1,34 @@
+metrics: [SNR_ww, SNR_gg, SNR_3x2, FOM_3x2, FOM_DETF_3x2]
+bands: griz
+training_file: data_buzzard/training.hdf5
+validation_file: data_buzzard/validation.hdf5
+output_file: example/neural_output_buzzard_griz.txt
+# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo"
+metrics_impl: jax-cosmo
+
+run:
+  # hello
+  NeuralNetwork:
+    {% for nbins in [5, 10] %}
+    run_{{ nbins}}:
+      bins: {{ nbins }}
+      metric: FOM_DETF
+      output_dir: models_buzzard
+      # These special settings decide whether the
+      # color and error colums are passed to the classifier
+      # as well as the magnitudes
+      colors: True
+      errors: True
+    {% endfor %}
+
+    {% for nbins in [5, 10] %}
+    run_{{ nbins}}:
+      bins: {{ nbins }}
+      metric: SNR
+      output_dir: models_buzzard
+      # These special settings decide whether the
+      # color and error colums are passed to the classifier
+      # as well as the magnitudes
+      colors: True
+      errors: True
+    {% endfor %}

example/neural_network_buzzard_riz.yaml

@@ -0,0 +1,34 @@
+metrics: [SNR_ww, SNR_gg, SNR_3x2, FOM_3x2, FOM_DETF_3x2]
+bands: riz
+training_file: data_buzzard/training.hdf5
+validation_file: data_buzzard/validation.hdf5
+output_file: example/neural_output_buzzard_riz.txt
+# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo"
+metrics_impl: jax-cosmo
+
+run:
+  # hello
+  NeuralNetwork:
+    {% for nbins in [5, 10] %}
+    run_{{ nbins}}:
+      bins: {{ nbins }}
+      metric: FOM_DETF
+      output_dir: models_buzzard
+      # These special settings decide whether the
+      # color and error colums are passed to the classifier
+      # as well as the magnitudes
+      colors: True
+      errors: True
+    {% endfor %}
+
+    {% for nbins in [5, 10] %}
+    run_{{ nbins}}:
+      bins: {{ nbins }}
+      metric: SNR
+      output_dir: models_buzzard
+      # These special settings decide whether the
+      # color and error colums are passed to the classifier
+      # as well as the magnitudes
+      colors: True
+      errors: True
+    {% endfor %}

example/neural_network_dc2_griz.yaml

@@ -0,0 +1,34 @@
+metrics: [SNR_ww, SNR_gg, SNR_3x2, FOM_3x2, FOM_DETF_3x2]
+bands: griz
+training_file: data/training.hdf5
+validation_file: data/validation.hdf5
+output_file: example/neural_output_griz.txt
+# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo"
+metrics_impl: jax-cosmo
+
+run:
+  # hello
+  NeuralNetwork:
+    {% for nbins in [5, 10] %}
+    run_{{ nbins}}:
+      bins: {{ nbins }}
+      metric: FOM_DETF
+      output_dir: models
+      # These special settings decide whether the
+      # color and error colums are passed to the classifier
+      # as well as the magnitudes
+      colors: True
+      errors: True
+    {% endfor %}
+
+    {% for nbins in [5, 10] %}
+    run_{{ nbins}}:
+      bins: {{ nbins }}
+      metric: SNR
+      output_dir: models
+      # These special settings decide whether the
+      # color and error colums are passed to the classifier
+      # as well as the magnitudes
+      colors: True
+      errors: True
+    {% endfor %}

example/neural_network_dc2_riz.yaml

@@ -0,0 +1,34 @@
+metrics: [SNR_ww, SNR_gg, SNR_3x2, FOM_3x2, FOM_DETF_3x2]
+bands: riz
+training_file: data/training.hdf5
+validation_file: data/validation.hdf5
+output_file: example/neural_output_riz.txt
+# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo"
+metrics_impl: jax-cosmo
+
+run:
+  # hello
+  NeuralNetwork:
+    {% for nbins in [5, 10] %}
+    run_{{ nbins}}:
+      bins: {{ nbins }}
+      metric: FOM_DETF
+      output_dir: models
+      # These special settings decide whether the
+      # color and error colums are passed to the classifier
+      # as well as the magnitudes
+      colors: True
+      errors: True
+    {% endfor %}
+
+    {% for nbins in [5, 10] %}
+    run_{{ nbins}}:
+      bins: {{ nbins }}
+      metric: SNR
+      output_dir: models
+      # These special settings decide whether the
+      # color and error colums are passed to the classifier
+      # as well as the magnitudes
+      colors: True
+      errors: True
+    {% endfor %}

example/neural_output_fom.txt

@@ -0,0 +1,3 @@
+NeuralNetwork run_2 {'bins': 2, 'metric': 'FOM'} {'SNR_ww': 135.7762701025696, 'SNR_gg': 747.3564650562411, 'SNR_3x2': 752.2536087103781, 'FOM_3x2': 9051.18761144165} 
+NeuralNetwork run_3 {'bins': 3, 'metric': 'FOM'} {'SNR_ww': 138.1739975730084, 'SNR_gg': 843.9211384344102, 'SNR_3x2': 847.0072653794633, 'FOM_3x2': 84547.97008004926} 
+NeuralNetwork run_4 {'bins': 4, 'metric': 'FOM'} {'SNR_ww': 143.93512392186238, 'SNR_gg': 937.789093247378, 'SNR_3x2': 940.4664612599967, 'FOM_3x2': 14978.628112642044} 

example/neural_output_snr.txt

@@ -0,0 +1,3 @@
+NeuralNetwork run_2 {'bins': 2, 'metric': 'SNR'} {'SNR_ww': 135.8368248575087, 'SNR_gg': 770.3102116003334, 'SNR_3x2': 775.1787513453132, 'FOM_3x2': 19945.877149713706} 
+NeuralNetwork run_3 {'bins': 3, 'metric': 'SNR'} {'SNR_ww': 145.57034579817267, 'SNR_gg': 919.1359275700552, 'SNR_3x2': 921.8455311553354, 'FOM_3x2': 19930.736921522202} 
+NeuralNetwork run_4 {'bins': 4, 'metric': 'SNR'} {'SNR_ww': 159.623483697185, 'SNR_gg': 987.2312600491794, 'SNR_3x2': 989.8354725475687, 'FOM_3x2': 45587.37245610679} 

models/README.md

@@ -0,0 +1 @@
+# Folder for storing trained models