Add ci (ready to go)

.github/workflows/run_ci.yml

@@ -0,0 +1,63 @@
+name: PolyODENet_CI
+
+on:
+  pull_request:
+    branches:
+      - master
+  push:
+    branches:
+      - '**'
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: checkout
+        uses: actions/checkout@v3
+      - name: get-python
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.9'
+      - name: upgrade-pip
+        run: |
+              python -m pip install --upgrade pip
+              python -m pip install wheel
+      - name: get-numpy
+        run: |
+              pip install numpy
+      - name: get-pytorch
+        run: |
+              pip install torch==1.11.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: get-torchdiffeq
+        run: |
+              git clone https://github.com/rtqichen/torchdiffeq
+              cd torchdiffeq
+              python setup.py install
+      - name: get-pytest
+        run: |
+              pip install -U pytest
+              pip install -U pytest-cov
+              pytest --version
+      - name: install-PolyODENet
+        run: |
+              python setup.py install
+      - name: run-tests
+        run: |
+              cd Tests
+              pytest --cov=. --cov-report=xml
+              pwd
+              ls -l
+              cd ..
+              pwd
+              ls -l
+      # - name: publish-coverage
+      #   uses: codecov/codecov-action@v2
+      #   with:
+      #     token: ${{ secrets.CODECOV_TOKEN }}
+      #     directory: ./Tests
+      #     env_vars: OS,PYTHON
+      #     fail_ci_if_error: true
+      #     files: ./Tests/coverage.xml
+      #     flags: unittests
+      #     name: PolyODENet
+      #     verbose: true

Source/main.py

@@ -3,12 +3,27 @@
 from Source.visualize import plot_write_ode
 import numpy as np
 import os
+import sys
 import torch
 import datetime
 import torch.multiprocessing as mp
 
 
 def main():
+    '''
+    Main and Do_it construct.
+
+    Introducing this two level approach allows the code to be run in two
+    different ways:
+    - From the command line when the main function will pass the system
+      command line arguments.
+    - From inside another Python script when the do_it function can have an
+      artificial set of command line arguments passed to it.
+    The latter approach is a way to run test cases with, e.g. pytest.
+    '''
+    do_it(sys.argv[1:])
+
+def do_it(arguments):
     parser = argparse.ArgumentParser()
     parser.add_argument('-c', "--conserve", type=int, default=0,
                         help="Number of conservation constraints")
@@ -48,7 +63,7 @@ def main():
                         help="Working directory")
     parser.add_argument("--zeroth", action='store_true',
                         help="Add 0th order terms")
-    args = parser.parse_args()
+    args = parser.parse_args(arguments)
 
     world_size = args.jobs
     os.chdir(os.path.expandvars(os.path.expanduser(args.work_dir)))
@@ -116,6 +131,7 @@ def main():
     print(trainer.ode.basis_weights.data.numpy())
     torch.save(trainer.ode, args.name+'.pt')
     plot_write_ode(trainer.ode, concentrations, timestamps, args.name, trainer.device)
+    return trainer.ode.basis_weights.data.numpy()
 
 
 if __name__ == '__main__':

Source/visualize.py

@@ -13,7 +13,7 @@ def plot_write_ode(ode, real_concentrations, real_timestamps, name, device):
     tensor_concentrations.to(device)
     tensor_timestamps.to(device)
 
-    with open(f"Rate_equations_of_{name}.txt", "w") as f:
+    with open(f"Rate_equations_of_{name.replace('/','_')}.txt", "w") as f:
         f.write(str(ode))
 
     for i in range(len(real_timestamps)):
@@ -36,10 +36,10 @@ def plot_write_ode(ode, real_concentrations, real_timestamps, name, device):
         plt.ylabel("Concentration")
         plt.ylim(bottom=-0.1)
         plt.legend()
-        title = f'Plot_of_{name}_set_{i}'
+        title = f"Plot_of_{name.replace('/','_')}_set_{i}"
         plt.title(title)
         plt.savefig(f"{title}.jpg", dpi=300)
 
         pred_df = pd.DataFrame(data=np.hstack([timestamps[:, np.newaxis], pred_conc]),
                                columns=["## Time"] + [f'Set {j + 1} ' for j in range(pred_conc.shape[1])])
-        pred_df.to_csv(f"Predicted_Concentrations_of_{name}_set_{i}.csv", sep='\t', index=False, float_format='%8.6f')
+        pred_df.to_csv(f"Predicted_Concentrations_of_{name.replace('/','_')}_set_{i}.csv", sep='\t', index=False, float_format='%8.6f')

Tests/test_basic/README.md

@@ -0,0 +1,49 @@
+## Test constructed from Example 1
+### Consecutive reactions with 1 unknown intermediate
+
+This example has consecutive reactions with species C being the intermediate.
+
+A <--> C --> B
+
+The dimensionless reaction constants are:
+* k_1+ = 2 (first reaction forward)
+* k_1- = 1 (first reaction reverse)
+* k_2  = 3 (second reaction)
+
+The corresponding ODEs are:
+
+d[A]/dt = -2[A] + 1[C] 
+
+d[B]/dt = 3[C] 
+
+d[C]/dt = 2[A] - 4[C]
+
+The original data are generated using scipy.integrate.odeint.
+The initial condition is A0=2, B0=0, C0=0.
+
+In the 'ex1.txt' file, concentration data for C are replaced with -1,
+meaning missing profile. Only the initial condition C0=0 is kept.
+
+The 'ex1.guess' file gives almost-optimized ODE coefficients so the 
+test can be fast.
+
+The 'ex1.ind' and 'ex1.scale' files control the polynomial model.
+
+Use the following command for a test run. 
+
+```train_poly -f ex1.txt -m 1000 -N ex1 -igs```
+
+Your 'Rate_equations_of_ex1.txt' should look like
+
+```
+PolynomialODE():
+
+d[A]/dt = -1.84[A] + 0.682[C]
+
+d[B]/dt = 2.73[C]
+
+d[C]/dt = 1.84[A] - 3.32[C]
+```
+
+The test case is executed by the ```test_basic.py``` script. This script has
+to be invoked by `pytest` from one directory up.

Tests/test_basic/ex1.guess

@@ -0,0 +1,3 @@
+-1.0  0.0  1.0
+ 0.0  0.0  0.0
+ 2.0  2.0 -4.0

Tests/test_basic/ex1.ind

@@ -0,0 +1,3 @@
+0 0  0 1  0 0
+1 0  1 1  1 1
+2 0  2 1  2 2

Tests/test_basic/ex1.scale

@@ -0,0 +1,3 @@
+1.  0. -1.
+0.  0. 0.        
+1.  1. 1.

Tests/test_basic/ex1.txt

@@ -0,0 +1,40 @@
+0.00000000e+00 2.00000000e+00 0.00000000e+00 0.00000000e+00
+5.00000000e-02 1.81405372e+00 1.35894200e-02 -1
+1.00000000e-01 1.65282218e+00 4.93666785e-02 -1
+1.50000000e-01 1.51198657e+00 1.01118013e-01 -1
+2.00000000e-01 1.38808346e+00 1.64033431e-01 -1
+2.50000000e-01 1.27832791e+00 2.34405081e-01 -1
+3.00000000e-01 1.18047370e+00 3.09389644e-01 -1
+3.50000000e-01 1.09270275e+00 3.86821013e-01 -1
+4.00000000e-01 1.01353765e+00 4.65062829e-01 -1
+4.50000000e-01 9.41772570e-01 5.42892366e-01 -1
+5.00000000e-01 8.76418400e-01 6.19409136e-01 -1
+5.50000000e-01 8.16659425e-01 6.93963034e-01 -1
+6.00000000e-01 7.61818928e-01 7.66097856e-01 -1
+6.50000000e-01 7.11331926e-01 8.35506946e-01 -1
+7.00000000e-01 6.64723524e-01 9.01998415e-01 -1
+7.50000000e-01 6.21591704e-01 9.65467851e-01 -1
+8.00000000e-01 5.81593652e-01 1.02587704e+00 -1
+8.50000000e-01 5.44434844e-01 1.08323726e+00 -1
+9.00000000e-01 5.09860345e-01 1.13759634e+00 -1
+9.50000000e-01 4.77647843e-01 1.18902857e+00 -1
+1.00000000e+00 4.47602076e-01 1.23762684e+00 -1
+1.05000000e+00 4.19550345e-01 1.28349666e+00 -1
+1.10000000e+00 3.93338904e-01 1.32675151e+00 -1
+1.15000000e+00 3.68830036e-01 1.36750929e+00 -1
+1.20000000e+00 3.45899681e-01 1.40588967e+00 -1
+1.25000000e+00 3.24435504e-01 1.44201209e+00 -1
+1.30000000e+00 3.04335307e-01 1.47599422e+00 -1
+1.35000000e+00 2.85505726e-01 1.50795097e+00 -1
+1.40000000e+00 2.67861151e-01 1.53799367e+00 -1
+1.45000000e+00 2.51322822e-01 1.56622956e+00 -1
+1.50000000e+00 2.35818075e-01 1.59276147e+00 -1
+1.55000000e+00 2.21279701e-01 1.61768766e+00 -1
+1.60000000e+00 2.07645403e-01 1.64110167e+00 -1
+1.65000000e+00 1.94857328e-01 1.66309239e+00 -1
+1.70000000e+00 1.82861665e-01 1.68374408e+00 -1
+1.75000000e+00 1.71608296e-01 1.70313651e+00 -1
+1.80000000e+00 1.61050482e-01 1.72134506e+00 -1
+1.85000000e+00 1.51144597e-01 1.73844089e+00 -1
+1.90000000e+00 1.41849884e-01 1.75449113e+00 -1
+1.95000000e+00 1.33128239e-01 1.76955904e+00 -1

Tests/test_basic/test_basic.py

@@ -0,0 +1,13 @@
+from Source.main import do_it
+import numpy
+
+def train_basic():
+    args = ['-f', 'test_basic/ex1.txt', '-m', '1000', '-N', 'test_basic/ex1', '-igs']
+    return do_it(args)
+
+def test_basic():
+    answer = numpy.array([[-1.8376, 0., 1.8376], [ 0., 0., 0. ], [ 0.6819, 2.7304, -3.3204]])
+    result = train_basic()
+    diff   = result - answer
+    vector = diff.reshape((9))
+    assert numpy.linalg.norm(vector,numpy.inf) < 1.0e-3