test codecov report

.github/workflows/codecov.yml

@@ -22,6 +22,6 @@ jobs:
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v1
         with:
-          flags: unittests, uitests
+          flags: uitests
           fail_ci_if_error: false
           verbose: true

My_AutoML/_utils/_file.py

@@ -11,7 +11,7 @@
 Author: Panyi Dong (panyid2@illinois.edu)
 
 -----
-Last Modified: Friday, 8th April 2022 10:27:24 pm
+Last Modified: Sunday, 17th April 2022 1:02:29 am
 Modified By: Panyi Dong (panyid2@illinois.edu)
 
 -----
@@ -38,6 +38,9 @@
 SOFTWARE.
 """
 
+import os
+import pickle
+
 # save model
 def save_model(
     encoder,
@@ -67,3 +70,49 @@ def save_model(
         print(feature_selection_hyperparameters, file=f, end="\n")
         f.write("{}\n".format(model))
         print(model_hyperparameters, file=f, end="\n")
+
+
+# save list of methods
+def save_methods(file_name, methods):
+
+    """
+    Parameters
+    ----------
+    file_name: path of the file to save
+
+    methods: list of methods objects to save
+    """
+
+    with open(file_name, "wb") as out_f:
+        for method in methods:
+            pickle.dump(method, out_f)
+
+
+# load methods
+def load_methods(file_name):
+
+    """
+    Parameters
+    ----------
+    file_name: path of the file to load
+    """
+
+    with open(file_name, "rb") as in_f:
+        results = []
+
+        # load all methods
+        while True:
+            try:
+                results.append(pickle.load(in_f))
+            except EOFError:
+                break
+
+    return results
+
+# find exact folder path
+def find_exact_path(path, spec_str) :
+
+    for folder in os.listdir(path):
+
+        if spec_str in os.path.join(path, folder):
+            return os.path.join(path, folder)

codecov.yml

@@ -1,4 +1,5 @@
 codecov:
+  bot: "codecov-io"
   notify:
     require_ci_to_pass: yes
 
@@ -10,18 +11,19 @@ coverage:
     # https://codecov.readme.io/v1.0/docs/commit-status
     project:
       default:
-        against: auto
+        base: auto
         target: 70% # specify the target coverage for each commit status
         threshold: 40% # allow this little decrease on project
         # https://github.com/codecov/support/wiki/Filtering-Branches
         # branches: master
-        if_ci_failed: success
+        if_ci_failed: error
     # https://github.com/codecov/support/wiki/Patch-Status
     patch:
       default:
-        against: auto
-        target: 30% # specify the target "X%" coverage to hit
+        base: auto
+        target: 80% # specify the target "X%" coverage to hit
         threshold: 50% # allow this much decrease on patch
+        if_ci_failed: error
     changes: false
 
 parsers:

requirements.txt

@@ -8,8 +8,8 @@ matplotlib
 ray
 ray[tune]
 ray[rllib]
-# tqdm==4.62.3
-# mlflow==1.21.0
+tqdm==4.62.3
+mlflow==1.21.0
 tensorboardX
 hyperopt==0.2.5
 auto-sklearn==0.14.6

requirements_nn.txt

@@ -8,8 +8,8 @@ matplotlib
 ray
 ray[tune]
 ray[rllib]
-# tqdm==4.62.3
-# mlflow==1.21.0
+tqdm==4.62.3
+mlflow==1.21.0
 tensorboardX
 hyperopt==0.2.5
 auto-sklearn==0.14.6

tests/test_imputer/test_imputer.py

@@ -11,7 +11,7 @@
 Author: Panyi Dong (panyid2@illinois.edu)
 
 -----
-Last Modified: Saturday, 16th April 2022 8:42:36 pm
+Last Modified: Saturday, 16th April 2022 11:46:30 pm
 Modified By: Panyi Dong (panyid2@illinois.edu)
 
 -----
@@ -80,31 +80,31 @@
 #             )
 
 
-# def test_imputer():
+def test_imputer():
 
-#     from My_AutoML._imputation import imputers
-#     from My_AutoML._utils import formatting
+    from My_AutoML._imputation import imputers
+    from My_AutoML._utils import formatting
 
-#     for method_name, method_object in zip(imputers.keys(), imputers.values()):
+    for method_name, method_object in zip(imputers.keys(), imputers.values()):
 
-#         imputer = method_object()
-#         if method_name != "KNNImputer":
+        imputer = method_object()
+        if method_name != "KNNImputer":
 
-#             data = pd.read_csv("Appendix/healthcare-dataset-stroke-data.csv")
+            data = pd.read_csv("Appendix/healthcare-dataset-stroke-data.csv")
 
-#             encoder = formatting()
-#             encoder.fit(data)
+            encoder = formatting()
+            encoder.fit(data)
 
-#             data = imputer.fill(data)
+            data = imputer.fill(data)
 
-#             assert (
-#                 imputer._fitted == True
-#             ), "The method {} is not correctly fitted.".format(method_name)
-#             assert (
-#                 data.isnull().any().any() == False
-#             ), "The imputation method {} fail to impute all missings.".format(
-#                 method_name
-#             )
+            assert (
+                imputer._fitted == True
+            ), "The method {} is not correctly fitted.".format(method_name)
+            assert (
+                data.isnull().any().any() == False
+            ), "The imputation method {} fail to impute all missings.".format(
+                method_name
+            )
 
 
 def test_DummyImputer():