Merge to main

.gitignore

@@ -0,0 +1,6 @@
+undodir/
+*npy
+*png
+*dat
+*ipynb
+__pycache__

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.2.0
+    hooks:
+    - id: trailing-whitespace
+    - id: requirements-txt-fixer
+    - id: end-of-file-fixer
+    - id: check-yaml
+    - id: check-added-large-files
+    - id: debug-statements
+    - id: check-merge-conflict
+
+-   repo: https://github.com/ambv/black
+    rev: 22.10.0
+    hooks:
+    - id: black
+      language: python
+      types: [python]
+      args: ["--line-length=88"]
+
+-   repo: https://github.com/PyCQA/flake8
+    rev: 5.0.4
+    hooks:
+    - id: flake8
+      args: ["--max-line-length=88", "--extend-ignore=E203, E402", "--doctests"]
+
+-   repo: https://github.com/pre-commit/mirrors-clang-format
+    rev: v14.0.6
+    hooks:
+    -   id: clang-format

examples/appendix_examples/forward_model/linear_gaussian.py

@@ -0,0 +1,48 @@
+import numpy as np
+import os
+import shutil
+
+
+class linear_gaussian:
+    def __init__(self, true_theta=np.arange(1, 4), spatial_res=100):
+        self.spatial_res = spatial_res
+        self.xtrain = np.linspace(-1, 1, self.spatial_res)
+        self.true_theta = true_theta
+        self.num_parameters = self.true_theta.shape[0]
+        self.vm = self.compute_vm()
+
+    def compute_vm(self):
+        vm = np.tile(self.xtrain[:, None], (1, self.num_parameters))
+        vm = np.cumprod(vm, axis=1)
+        return vm
+
+    def compute_prediction(self, theta):
+        return self.vm @ theta.reshape(-1, 1)
+
+
+def main():
+    # Begin User Input
+    true_theta = np.array([1, 2, 3])
+    spatial_res = 100
+    # End User Input
+    model = linear_gaussian(true_theta=true_theta, spatial_res=spatial_res)
+    prediction = model.compute_prediction(true_theta)
+
+    data = np.zeros((spatial_res, 2))
+    data[:, 0] = model.xtrain
+    data[:, 1] = prediction[:, 0]
+
+    # Create the folders
+    save_path = "./Output"
+
+    if os.path.exists(save_path) is True:
+        shutil.rmtree(save_path)
+
+    os.mkdir(save_path)
+
+    data_save_path = os.path.join(save_path, "true_output.npy")
+    np.save(data_save_path, data)
+
+
+if __name__ == "__main__":
+    main()

examples/appendix_examples/learning_model/config.yaml

@@ -0,0 +1,19 @@
+campaign_id: 1
+
+total_samples: 100
+num_samples: 100
+
+model_noise_cov: 1.0e-1
+objective_scaling: 1.0e-10
+
+#
+compute_map: False
+compute_mle: False
+compute_post: False
+compute_true_post: True
+
+#
+compute_identifiability: True
+global_num_outer_samples: 10000
+global_num_inner_samples: 5000
+restart_identifiability: False

examples/appendix_examples/learning_model/create_new_campaign.sh

@@ -0,0 +1,44 @@
+#!/bin/bash
+
+campaign_results_path=$PWD/campaign_results
+dname=$campaign_results_path/campaign_${1}
+
+if [[ -d $dname ]]; then
+
+    read -p "Campaign already exists! Recreate it? [y/yes/Y] : " flag
+
+    if [[ $flag == "y" || $flag == "yes" || $flag == "Y" ]]; then
+
+        rm -rf $dname
+        mkdir -p $dname
+        fname=$dname/readme.txt
+        fig_file_name=$dname/Figures
+        mkdir -p $fig_file_name
+        echo "Campaign description" >> $fname
+        cp config.yaml $dname
+        echo "Campaign "${1}" (Re)created using config.yaml"
+
+        # Generating the data from using config file
+        python generate_training_data.py ${1}
+
+    else
+
+        echo "Campaign "${1}" was not removed"
+
+    fi
+
+else
+
+    mkdir -p $dname
+    fname=$dname/readme.txt
+    fig_file_name=$dname/Figures
+    mkdir -p $fig_file_name
+    echo "Campaign description" >> $fname
+    cp config.yaml $dname
+    echo "Campaign "${1}" created using config.yaml"
+
+    # Generating the data from using config file
+    python generate_training_data.py ${1}
+
+
+fi

examples/appendix_examples/learning_model/generate_training_data.py

@@ -0,0 +1,63 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+import sys
+import yaml
+
+sys.path.append("../forward_model/")
+from linear_gaussian import linear_gaussian
+
+
+plt.rc("text", usetex=True)
+plt.rc("font", family="serif", size=25)
+plt.rc("lines", linewidth=3)
+plt.rc("axes", labelpad=18, titlepad=20)
+
+with open("./config.yaml", "r") as config_file:
+    config_data = yaml.safe_load(config_file)
+
+campaign_id = config_data["campaign_id"]
+
+assert campaign_id == int(sys.argv[1]), "Make sure the campaign id match"
+
+total_samples = config_data["total_samples"]
+true_model = linear_gaussian(spatial_res=total_samples)
+true_prediction = true_model.compute_prediction(theta=true_model.true_theta)
+
+# Subsampling
+num_sub_samples = config_data["num_samples"]
+assert num_sub_samples <= total_samples
+sample_idx = np.arange(total_samples)
+np.random.shuffle(sample_idx)
+sample_idx = sample_idx[:num_sub_samples]
+
+
+# Adding noise
+model_noise_cov = config_data["model_noise_cov"]
+noise = np.sqrt(model_noise_cov) * (np.random.randn(num_sub_samples))
+training_data = np.zeros((num_sub_samples, 2))
+training_data[:, 0] = true_model.xtrain[sample_idx]
+training_data[:, 1] = true_prediction[sample_idx, 0] + noise
+
+
+# Saving data
+data_save_path = os.path.join(
+    "./campaign_results", "campaign_{0:d}/training_data.npy".format(campaign_id)
+)
+sample_idx_save_path = os.path.join(
+    "./campaign_results", "campaign_{0:d}/sample_idx.npy".format(campaign_id)
+)
+np.save(data_save_path, training_data)
+np.save(sample_idx_save_path, sample_idx)
+
+
+fig_save_path = os.path.join(
+    "./campaign_results", "campaign_{0:d}/Figures/training_data.png".format(campaign_id)
+)
+plt.figure(figsize=(10, 5))
+plt.plot(true_model.xtrain, true_prediction, label="True", c="r")
+plt.scatter(training_data[:, 0], training_data[:, 1], label="Data", c="k")
+plt.legend(loc="upper left")
+plt.tight_layout()
+plt.savefig(fig_save_path)
+plt.close()