Move common imports to top-level; fix model saving bugs.

examples/courses/Primer 1/Primer1.ipynb

@@ -155,15 +155,15 @@
  "from riid.gadras.api import GADRAS_API_SEEMINGLY_AVAILABLE\n",
  "\n",
  "if GADRAS_API_SEEMINGLY_AVAILABLE:\n",
- " from riid.data.synthetic.seed import SeedSynthesizer\n",
+ " from riid import SeedSynthesizer\n",
  " seed_syn = SeedSynthesizer()\n",
  " # The YAML file defining the seed synthesis specification is ultimately parsed into a dictionary.\n",
  " # You can also load it yourself and pass in the dictionary instead - this is useful for varying detector parameters!\n",
  " seeds_ss = seed_syn.generate(\"./spec_nai_few_sources.yaml\")\n",
  "else:\n",
  " # If you don't have Windows with GADRAS installed, this will use the dummy seeds below which are not actual gamma spectra.\n",
  " # Another option would be to load a seeds file obtained elsewhere.\n",
- " from riid.data.synthetic import get_dummy_seeds\n",
+ " from riid import get_dummy_seeds\n",
  " seeds_ss = get_dummy_seeds()"
  ]
  },
@@ -251,7 +251,7 @@
  "outputs": [],
  "source": [
  "\"\"\"Seed mixing\"\"\"\n",
- "from riid.data.synthetic.seed import SeedMixer\n",
+ "from riid import SeedMixer\n",
  "\n",
  "mixed_bg_seeds_ss = SeedMixer(\n",
  " bg_seeds_ss,\n",
@@ -278,7 +278,7 @@
  "outputs": [],
  "source": [
  "\"\"\"Combining SampleSets\"\"\"\n",
- "from riid.data.sampleset import SampleSet\n",
+ "from riid import SampleSet\n",
  "\n",
  "combined_ss = SampleSet()\n",
  "combined_ss.concat([fg_seeds_ss, mixed_bg_seeds_ss])\n",
@@ -318,14 +318,14 @@
  "outputs": [],
  "source": [
  "\"\"\"Static Synthesis\"\"\"\n",
- "from riid.data.synthetic.static import StaticSynthesizer\n",
+ "from riid import StaticSynthesizer\n",
  "\n",
  "static_syn = StaticSynthesizer(\n",
  " samples_per_seed=100,\n",
  " bg_cps=300,\n",
  " live_time_function=\"uniform\",\n",
  " live_time_function_args=(0.25, 8),\n",
- " snr_function=\"uniform\",\n",
+ " snr_function=\"log10\",\n",
  " snr_function_args=(0.1, 100),\n",
  " apply_poisson_noise=True,\n",
  " return_fg=True,\n",
@@ -348,8 +348,7 @@
  "outputs": [],
  "source": [
  "\"\"\"Normalization\"\"\"\n",
- "gross_ss.normalize()\n",
- "bg_ss.normalize()"
+ "fg_ss.normalize()"
  ]
  },
  {
@@ -371,16 +370,10 @@
  "outputs": [],
  "source": [
  "\"\"\"Model fitting\"\"\"\n",
- "from riid.models.neural_nets import MLPClassifier\n",
- "from riid.metrics import single_f1\n",
+ "from riid.models import MLPClassifier\n",
  "\n",
- "model = MLPClassifier(\n",
- " hidden_layers=(256,),\n",
- " learning_rate=4e-3,\n",
- " metrics=[single_f1]\n",
- ")\n",
- "\n",
- "history = model.fit(gross_ss, bg_ss, epochs=25, patience=5, verbose=True)"
+ "model = MLPClassifier()\n",
+ "history = model.fit(fg_ss, epochs=10, verbose=True)"
  ]
  },
  {
@@ -402,12 +395,9 @@
  "outputs": [],
  "source": [
  "\"\"\"Generate some in-distribution data the model has not seen.\"\"\"\n",
- "test_bg_ss, test_gross_ss = static_syn.generate(fg_seeds_ss, bg_seeds_ss)\n",
- "test_bg_ss.normalize()\n",
- "test_gross_ss.normalize()\n",
- "# Adjust ground truth\n",
- "#test_gross_ss.sources.drop(test_bg_ss.sources.columns, axis=1, inplace=True)\n",
- "#test_gross_ss.normalize_sources()"
+ "test_fg_ss, test_gross_ss = static_syn.generate(fg_seeds_ss, bg_seeds_ss)\n",
+ "test_fg_ss.normalize()\n",
+ "test_gross_ss.normalize()"
  ]
  },
  {
@@ -417,7 +407,7 @@
  "outputs": [],
  "source": [
  "\"\"\"Use the model!\"\"\"\n",
- "model.predict(test_gross_ss, test_bg_ss) # Saved in your SampleSet containing non-background sources (the gross spectra)"
+ "model.predict(test_fg_ss) # Results are saved in the SampleSet's prediction_probas DataFrame"
  ]
  },
  {
@@ -429,8 +419,8 @@
  "\"\"\"Calculate performance metric\"\"\"\n",
  "from sklearn.metrics import f1_score\n",
  "\n",
- "labels = test_gross_ss.get_labels()\n",
- "predictions = test_gross_ss.get_predictions()\n",
+ "labels = test_fg_ss.get_labels()\n",
+ "predictions = test_fg_ss.get_predictions()\n",
  "f1_score(labels, predictions, average=\"micro\")"
  ]
  },
@@ -443,7 +433,7 @@
  "\"\"\"Confusion Matrix\"\"\"\n",
  "from riid.visualize import confusion_matrix\n",
  "\n",
- "_ = confusion_matrix(test_gross_ss)"
+ "_ = confusion_matrix(test_fg_ss)"
  ]
  },
  {
@@ -455,7 +445,7 @@
  "\"\"\"SNR vs. Model Score\"\"\"\n",
  "from riid.visualize import plot_snr_vs_score\n",
  "\n",
- "_ = plot_snr_vs_score(test_gross_ss, xscale=\"log\")"
+ "_ = plot_snr_vs_score(test_fg_ss, xscale=\"log\")"
  ]
  },
  {
@@ -465,13 +455,23 @@
  "outputs": [],
  "source": [
  "\"\"\"Save model\"\"\"\n",
- "import os\n",
+ "from pathlib import Path\n",
+ "\n",
+ "\n",
+ "def _delete_if_exists(path: Path):\n",
+ " if path.exists():\n",
+ " path.unlink()\n",
  "\n",
- "model_path = \"./model.h5\"\n",
- "if os.path.exists(model_path):\n",
- " os.remove(model_path)\n",
+ "model_path_json = Path(\"./model.json\")\n",
+ "model_path_tflite = model_path_json.with_suffix(\".tflite\")\n",
+ "model_path_onnx = model_path_json.with_suffix(\".onnx\")\n",
+ "_delete_if_exists(model_path_json)\n",
+ "_delete_if_exists(model_path_tflite)\n",
+ "_delete_if_exists(model_path_onnx)\n",
  "\n",
- "model.save(model_path)"
+ "model.save(str(model_path_json))\n",
+ "model.to_tflite(str(model_path_tflite))\n",
+ "model.to_onnx(str(model_path_onnx))"
  ]
  },
  {
@@ -571,7 +571,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.10.11"
+ "version": "3.12.4"
  },
  "orig_nbformat": 4,
  "vscode": {

examples/data/conversion/pcf_to_ss.py

@@ -12,10 +12,9 @@
 import os
 from pathlib import Path
 
-from riid import SAMPLESET_HDF_FILE_EXTENSION
+from riid import SAMPLESET_HDF_FILE_EXTENSION, read_pcf
 from riid.data.converters import (_validate_and_create_output_dir,
  convert_directory)
-from riid.data.sampleset import read_pcf
 
 
 def convert_and_save(input_file_path: str, output_dir: str = None,

examples/data/difficulty_score.py

@@ -2,9 +2,7 @@
 # Under the terms of Contract DE-NA0003525 with NTESS,
 # the U.S. Government retains certain rights in this software.
 """This example demonstrates how to compute the difficulty of a given SampleSet."""
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.seed import SeedMixer
-from riid.data.synthetic.static import StaticSynthesizer
+from riid import SeedMixer, StaticSynthesizer, get_dummy_seeds
 
 fg_seeds_ss, bg_seeds_ss = get_dummy_seeds().split_fg_and_bg()
 mixed_bg_seed_ss = SeedMixer(bg_seeds_ss, mixture_size=3)\

examples/data/preprocessing/energy_calibration.py

@@ -8,9 +8,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.seed import SeedMixer
-from riid.data.synthetic.static import StaticSynthesizer
+from riid import SeedMixer, StaticSynthesizer, get_dummy_seeds
 
 SYNTHETIC_DATA_CONFIG = {
  "samples_per_seed": 10,

examples/data/synthesis/mix_seeds.py

@@ -3,8 +3,8 @@
 # the U.S. Government retains certain rights in this software.
 """This example demonstrates how to generate synthetic gamma spectra from seeds."""
 import numpy as np
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.seed import SeedMixer
+
+from riid import SeedMixer, get_dummy_seeds
 
 fg_seeds_ss, bg_seeds_ss = get_dummy_seeds().split_fg_and_bg()
 

examples/data/synthesis/synthesize_passbys.py

@@ -7,8 +7,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.passby import PassbySynthesizer
+from riid import PassbySynthesizer, get_dummy_seeds
 
 if len(sys.argv) == 2:
  import matplotlib

examples/data/synthesis/synthesize_seeds.py

@@ -4,7 +4,7 @@
 """This example demonstrates how to generate synthetic seeds from GADRAS."""
 import yaml
 
-from riid.data.synthetic.seed import SeedSynthesizer
+from riid import SeedSynthesizer
 
 seed_synth_config = """
 ---

examples/data/synthesis/synthesize_seeds_advanced.py

@@ -5,7 +5,7 @@
 configuration expansion features."""
 import yaml
 
-from riid.data.synthetic.seed import SeedSynthesizer
+from riid import SeedSynthesizer
 
 seed_synth_config = """
 ---

examples/data/synthesis/synthesize_spectra.py

@@ -2,9 +2,7 @@
 # Under the terms of Contract DE-NA0003525 with NTESS,
 # the U.S. Government retains certain rights in this software.
 """This example demonstrates how to generate synthetic gamma spectra from seeds."""
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.seed import SeedMixer
-from riid.data.synthetic.static import StaticSynthesizer
+from riid import SeedMixer, StaticSynthesizer, get_dummy_seeds
 
 SYNTHETIC_DATA_CONFIG = {
  "samples_per_seed": 10000,

examples/modeling/anomaly_detection.py

@@ -10,10 +10,8 @@
 import numpy as np
 from matplotlib import cm
 
+from riid import PassbySynthesizer, SeedMixer, get_dummy_seeds
 from riid.anomaly import PoissonNChannelEventDetector
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.passby import PassbySynthesizer
-from riid.data.synthetic.seed import SeedMixer
 
 if len(sys.argv) == 2:
  import matplotlib

examples/modeling/arad.py

@@ -6,10 +6,8 @@
 import numpy as np
 import pandas as pd
 
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.seed import SeedMixer
-from riid.data.synthetic.static import StaticSynthesizer
-from riid.models.neural_nets.arad import ARADv1, ARADv2
+from riid import SeedMixer, StaticSynthesizer, get_dummy_seeds
+from riid.models import ARADv1, ARADv2
 
 # Config
 rng = np.random.default_rng(42)

examples/modeling/arad_latent_prediction.py

@@ -8,10 +8,8 @@
 from keras.api.metrics import Accuracy, CategoricalCrossentropy
 from sklearn.metrics import f1_score, mean_squared_error
 
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.seed import SeedMixer
-from riid.data.synthetic.static import StaticSynthesizer
-from riid.models.neural_nets.arad import ARADLatentPredictor, ARADv2
+from riid import SeedMixer, StaticSynthesizer, get_dummy_seeds
+from riid.models import ARADLatentPredictor, ARADv2
 
 # Config
 rng = np.random.default_rng(42)

examples/modeling/classifier_comparison.py

@@ -7,12 +7,9 @@
 import matplotlib.pyplot as plt
 from sklearn.metrics import f1_score
 
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.seed import SeedMixer
-from riid.data.synthetic.static import StaticSynthesizer
-from riid.models.bayes import PoissonBayesClassifier
+from riid import SeedMixer, StaticSynthesizer, get_dummy_seeds
 from riid.metrics import precision_recall_curve
-from riid.models.neural_nets import MLPClassifier
+from riid.models import MLPClassifier, PoissonBayesClassifier
 from riid.visualize import plot_precision_recall
 
 if len(sys.argv) == 2:

examples/modeling/label_proportion_estimation.py

@@ -4,10 +4,8 @@
 
 from sklearn.metrics import mean_absolute_error
 
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.seed import SeedMixer
-from riid.data.synthetic.static import StaticSynthesizer
-from riid.models.neural_nets import LabelProportionEstimator
+from riid import SeedMixer, StaticSynthesizer, get_dummy_seeds
+from riid.models import LabelProportionEstimator
 
 # Generate some mixture training data.
 fg_seeds_ss, bg_seeds_ss = get_dummy_seeds().split_fg_and_bg()

examples/modeling/neural_network_classifier.py

@@ -5,10 +5,8 @@
 import numpy as np
 from sklearn.metrics import f1_score
 
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.seed import SeedMixer
-from riid.data.synthetic.static import StaticSynthesizer
-from riid.models.neural_nets import MLPClassifier
+from riid import SeedMixer, StaticSynthesizer, get_dummy_seeds
+from riid.models import MLPClassifier
 
 # Generate some training data
 fg_seeds_ss, bg_seeds_ss = get_dummy_seeds().split_fg_and_bg()

examples/run_examples.py

@@ -6,6 +6,7 @@
 import subprocess
 import sys
 from pathlib import Path
+
 import pandas as pd
 from tabulate import tabulate
 

examples/visualization/confusion_matrix.py

@@ -4,10 +4,8 @@
 """This example demonstrates how to obtain confusion matrices."""
 import sys
 
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.seed import SeedMixer
-from riid.data.synthetic.static import StaticSynthesizer
-from riid.models.neural_nets import MLPClassifier
+from riid import SeedMixer, StaticSynthesizer, get_dummy_seeds
+from riid.models import MLPClassifier
 from riid.visualize import confusion_matrix
 
 if len(sys.argv) == 2:

examples/visualization/distance_matrix.py

@@ -9,7 +9,7 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
 
-from riid.data.synthetic import get_dummy_seeds
+from riid import get_dummy_seeds
 
 if len(sys.argv) == 2:
  import matplotlib

examples/visualization/plot_sampleset_compare_to.py

@@ -4,9 +4,7 @@
 """This example demonstrates how to compare sample sets."""
 import sys
 
-from riid.data.synthetic import get_dummy_seeds
-from riid.data.synthetic.seed import SeedMixer
-from riid.data.synthetic.static import StaticSynthesizer
+from riid import SeedMixer, StaticSynthesizer, get_dummy_seeds
 from riid.visualize import plot_ss_comparison
 
 if len(sys.argv) == 2:

examples/visualization/plot_spectra.py

@@ -4,7 +4,7 @@
 """This example demonstrates how to plot gamma spectra."""
 import sys
 
-from riid.data.synthetic import get_dummy_seeds
+from riid import get_dummy_seeds
 from riid.visualize import plot_spectra
 
 if len(sys.argv) == 2: