iit

.github/workflows/pylinter.yml

@@ -13,14 +13,13 @@ jobs:
       # - name: Checkout repo
       #   uses: actions/checkout@v3
       - uses: actions/checkout@v4
+      - name: Install flake8
+        run: pip install flake8
       - name: Python Linter
-        uses: sunnysid3up/python-linter@master
+        uses: suo/flake8-github-action@releases/v1
         with:
-          source: "src"
-          mypy-options: "--ignore-missing-imports --show-error-codes"
-          black-options: "--exclude **/*"
-          #pylint-options: "--rcfile=setup.cfg"
-          isort-options: "-w 100"
-          django: true
+          checkName: 'flake8_py3'
+          path: "src"
+          args: "--max-line-length=120 --ignore=E402,E731,F541,W291,E122,E127,F401,E266,E241,C901,E741,W293,F811,W503,E203"
       #- name: GitHub Action for pylint
       #  uses: marian-code/python-lint-annotate@v3

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.defaultInterpreterPath": "/Users/sophiasun/miniforge3/envs/py3/bin/python"
+}

src/experiments/covid_exp.py

@@ -28,7 +28,7 @@ def experiment(train, valid, test, target_len=50, name="exp"):
     encdec_model = lstm.lstm_seq2seq(
         input_size=1, output_size=1, embedding_size=128, target_len=target_len
     )
-    models = [rnn_model]  # , encdec_model]
+    models = [rnn_model, encdec_model]
 
     x_train = train.X
     y_train = train.Y
@@ -82,7 +82,7 @@ def experiment(train, valid, test, target_len=50, name="exp"):
 
     epsilon_ls = np.linspace(0.05, 0.50, 10)
 
-    for k, uqmethodß in UQ.items():
+    for k, uqmethod in UQ.items():
         print(k)
         area = []
         coverage = []

src/experiments/drone_exp.py

@@ -1,4 +1,3 @@
-
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -7,78 +6,98 @@
 from src.models import rnn, lstm, bjrnn, cfrnn, copulaCPTS, dplstm, vanila_copula
 
 
-datapath = '../../robotics_project/PythonRobotics/AerialNavigation/drone_3d_trajectory_following/data/dataset_smallnoise.npy'
+datapath = "../../robotics_project/PythonRobotics/AerialNavigation/\
+    drone_3d_trajectory_following/data/dataset_smallnoise.npy"
 
 data = np.load(datapath, allow_pickle=True)
-x_downsample = data[:,::3,:].astype(np.float32)
-
-train_len=60
-pred_len=10
-
-x_train = torch.tensor(x_downsample[:600,:train_len,:], dtype=torch.float)
-y_train = torch.tensor(x_downsample[:600,train_len:train_len+pred_len,:3], dtype=torch.float)
-
-x_cali = torch.tensor(x_downsample[600:800,:train_len,:], dtype=torch.float)
-y_cali = torch.tensor(x_downsample[600:800,train_len:train_len+pred_len,:3], dtype=torch.float)
-
-x_test = torch.tensor(x_downsample[800:,:train_len,:], dtype=torch.float)
-y_test = torch.tensor(x_downsample[800:, train_len:train_len+pred_len,:3], dtype=torch.float)
-
-def experiment(x_downsample, train_len=60, pred_len = 10, name='exp', 
-            x_train=x_train,
-            y_train=y_train,
-            x_cali=x_cali,
-            y_cali=y_cali,
-            x_test=x_test,
-            y_test=y_test):
-
-    rnn_model = rnn.rnn(embedding_size=128, input_size=6, output_size=3, horizon=pred_len)
-    encdec_model = lstm.lstm_seq2seq(input_size=6, output_size=3, embedding_size=128, target_len = pred_len)
-    models = [rnn_model,encdec_model]
-
-    models[0].train_model(x_train,y_train, n_epochs=500, batch_size=150)
-    #models[1].train_model(x_train,y_train, n_epochs=500, batch_size=150)
-
-    with open('./trained/models_%s.pkl'%name, 'wb') as f:
-        pickle.dump(models , f)
-
-
+x_downsample = data[:, ::3, :].astype(np.float32)
+
+train_len = 60
+pred_len = 10
+
+x_train = torch.tensor(x_downsample[:600, :train_len, :], dtype=torch.float)
+y_train = torch.tensor(
+    x_downsample[:600, train_len : train_len + pred_len, :3], dtype=torch.float
+)
+
+x_cali = torch.tensor(x_downsample[600:800, :train_len, :], dtype=torch.float)
+y_cali = torch.tensor(
+    x_downsample[600:800, train_len : train_len + pred_len, :3], dtype=torch.float
+)
+
+x_test = torch.tensor(x_downsample[800:, :train_len, :], dtype=torch.float)
+y_test = torch.tensor(
+    x_downsample[800:, train_len : train_len + pred_len, :3], dtype=torch.float
+)
+
+
+def experiment(
+    x_downsample,
+    train_len=60,
+    pred_len=10,
+    name="exp",
+    x_train=x_train,
+    y_train=y_train,
+    x_cali=x_cali,
+    y_cali=y_cali,
+    x_test=x_test,
+    y_test=y_test,
+):
+
+    rnn_model = rnn.rnn(
+        embedding_size=128, input_size=6, output_size=3, horizon=pred_len
+    )
+    encdec_model = lstm.lstm_seq2seq(
+        input_size=6, output_size=3, embedding_size=128, target_len=pred_len
+    )
+    models = [rnn_model, encdec_model]
+
+    models[0].train_model(x_train, y_train, n_epochs=500, batch_size=150)
+    # models[1].train_model(x_train,y_train, n_epochs=500, batch_size=150)
+
+    with open("./trained/models_%s.pkl" % name, "wb") as f:
+        pickle.dump(models, f)
+
     UQ = {}
 
-
     bj_class = bjrnn.bj_rnn(models[0], recursion_depth=10)
-    UQ['bjrnn'] = bj_class   
-
-    dprnn = dplstm.DPRNN(epochs=150,  input_size=6, output_size=3, n_steps=pred_len, batch_size=64, dropout_prob=0.1)
+    UQ["bjrnn"] = bj_class
+
+    dprnn = dplstm.DPRNN(
+        epochs=150,
+        input_size=6,
+        output_size=3,
+        n_steps=pred_len,
+        batch_size=64,
+        dropout_prob=0.1,
+    )
     dprnn.fit(x_train, y_train)
-    UQ['dprnn'] = dprnn
-    
+    UQ["dprnn"] = dprnn
+
     cf = cfrnn.CFRNN(models[0], x_cali, y_cali)
     cf.calibrate()
-    UQ['cfrnn'] = cf
-    
+    UQ["cfrnn"] = cf
+
     cf = cfrnn.CFRNN(models[1], x_cali, y_cali)
     cf.calibrate()
-    UQ['cf-EncDec'] = cf
+    UQ["cf-EncDec"] = cf
 
     copula = copulaCPTS.copulaCPTS(models[0], x_cali, y_cali)
     copula.calibrate()
-    UQ['copula-rnn'] = copula
-    
+    UQ["copula-rnn"] = copula
+
     copula = copulaCPTS.copulaCPTS(models[1], x_cali, y_cali)
     copula.calibrate()
-    UQ['copula-EncDec'] = copula
-
-
-
+    UQ["copula-EncDec"] = copula
+
     areas = {}
     coverages = {}
 
     epsilon_ls = np.linspace(0.05, 0.50, 10)
 
     for k, uqmethod in UQ.items():
         print(k)
-       
+
         area = []
         coverage = []
         for eps in epsilon_ls:
@@ -88,46 +107,55 @@ def experiment(x_downsample, train_len=60, pred_len = 10, name='exp',
             coverage.append(uqmethod.calc_coverage_3d(box, pred, y_test))
         areas[k] = area
         coverages[k] = coverage
-    
-    with open('./trained/uq_%s.pkl'%name, 'wb') as f:
-        pickle.dump(UQ , f)
-    with open('./trained/results_%s.pkl'%name, 'wb') as f:
-        pickle.dump((areas, coverages) , f)
-        
+
+    with open("./trained/uq_%s.pkl" % name, "wb") as f:
+        pickle.dump(UQ, f)
+    with open("./trained/results_%s.pkl" % name, "wb") as f:
+        pickle.dump((areas, coverages), f)
+
     return areas, coverages, (models, UQ)
 
+
 def main():
     lens = [1, 5, 10, 15]
     for pred_len in lens:
-        x_train = torch.tensor(x_downsample[:600,:train_len,:], dtype=torch.float)
-        y_train = torch.tensor(x_downsample[:600,train_len:train_len+pred_len,:3], dtype=torch.float)
-
-        x_cali = torch.tensor(x_downsample[600:800,:train_len,:], dtype=torch.float)
-        y_cali = torch.tensor(x_downsample[600:800,train_len:train_len+pred_len,:3], dtype=torch.float)
-
-        x_test = torch.tensor(x_downsample[800:,:train_len,:], dtype=torch.float)
-        y_test = torch.tensor(x_downsample[800:, train_len:train_len+pred_len,:3], dtype=torch.float)
+        x_train = torch.tensor(x_downsample[:600, :train_len, :], dtype=torch.float)
+        y_train = torch.tensor(
+            x_downsample[:600, train_len : train_len + pred_len, :3], dtype=torch.float
+        )
+
+        x_cali = torch.tensor(x_downsample[600:800, :train_len, :], dtype=torch.float)
+        y_cali = torch.tensor(
+            x_downsample[600:800, train_len : train_len + pred_len, :3],
+            dtype=torch.float,
+        )
+
+        x_test = torch.tensor(x_downsample[800:, :train_len, :], dtype=torch.float)
+        y_test = torch.tensor(
+            x_downsample[800:, train_len : train_len + pred_len, :3], dtype=torch.float
+        )
 
         for i in range(3):
-            res = experiment(x_downsample, pred_len= pred_len, 
-                name='drone_split_len_'+str(pred_len)+'_run_'+str(i),
+            res = experiment(
+                x_downsample,
+                pred_len=pred_len,
+                name="drone_split_len_" + str(pred_len) + "_run_" + str(i),
                 x_train=x_train,
                 y_train=y_train,
                 x_cali=x_cali,
                 y_cali=y_cali,
                 x_test=x_test,
-                y_test=y_test
-                )
-            print('finished run ' + str(pred_len))
-            del res 
+                y_test=y_test,
+            )
+            print("finished run " + str(pred_len))
+            del res
 
-
     for i in range(3):
-        res = experiment(x_downsample, name='drone_vanila_'+str(i))
-        print('finished run ' + str(i))
-        del res 
+        res = experiment(x_downsample, name="drone_vanila_" + str(i))
+        print("finished run " + str(i))
+        del res
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
     main()