Merge pull request #19 from spirosmaggioros/algorithms_optimization

Codecov 80+% check | Workflows upadte to build the whl file for spare_scores installation | More test cases and fixes

.github/workflows/macos_test_cases.yml

@@ -23,7 +23,12 @@ jobs:
         - name: Install pip
           run: conda run -n spare conda install pip
         - name: Install spare scores
-          run: conda run -n spare pip install spare_scores
+          run: |
+           pip install setuptools twine wheel
+           python setup.py bdist_wheel
+           cd dist 
+           WHEEL_FILE=$(ls spare_scores*)
+           pip install "$WHEEL_FILE"
         - name: Download dependencies
           run: pip install setuptools && pip install . 
         - name: Run unit tests

.github/workflows/ubuntu_test_cases.yml

@@ -23,20 +23,16 @@ jobs:
         - name: Install pip
           run: conda run -n spare conda install pip
         - name: Install spare scores
-          run: conda run -n spare pip install spare_scores
+          run: |
+           pip install setuptools twine wheel
+           python setup.py bdist_wheel
+           cd dist 
+           WHEEL_FILE=$(ls spare_scores*)
+           pip install "$WHEEL_FILE"
         - name: Download dependencies
           run:  pip install setuptools && pip install .
         - name: Run unit tests
           run: |
             cd tests/unit && python -m unittest discover -s . -p "*.py"
-        - name: Generate Coverage Report
-          run: |
-            pip install pytest-cov
-            cd tests/unit && pytest --cov=../../ --cov-report=xml
-        - name: Upload Coverage to Codecov
-          uses: codecov/[email protected]
-          with:
-            token: ${{ secrets.CODECOV_TOKEN }}
-            slug: CBICA/spare_score
           
 

codecov.yml

@@ -18,3 +18,9 @@ comment:
   layout: "reach,diff,flags,tree"
   behavior: default
   require_changes: no
+
+ignore:
+  - "merge_ROI_demo_and_test.py"
+  - "setup.py"
+  - "spare_scores/cli.py"
+  - "tests/conftest.py"

spare_scores/classes.py

@@ -2,10 +2,11 @@
 from typing import Any
 
 import pandas as pd
-from data_prep import logging_basic_config
-from mlp import MLPModel
-from mlp_torch import MLPTorchModel
-from svm import SVMModel
+
+from .data_prep import logging_basic_config
+from .mlp import MLPModel
+from .mlp_torch import MLPTorchModel
+from .svm import SVMModel
 
 
 class SpareModel:
@@ -77,7 +78,7 @@ def __init__(
                 predictors, target, key_var, verbose, **parameters, **kwargs
             )
         else:
-            logger.err(f"Model type {self.model_type} not supported.")
+            logger.error(f"Model type {self.model_type} not supported.")
             raise NotImplementedError("Only SVM is supported currently.")
 
     def set_parameters(self, **parameters: Any) -> None:

spare_scores/cli.py

@@ -1,7 +1,8 @@
 import argparse
 
 import pkg_resources  # type: ignore
-from spare import spare_test, spare_train
+
+from .spare import spare_test, spare_train
 
 VERSION = pkg_resources.require("spare_scores")[0].version
 

spare_scores/data_prep.py

@@ -6,7 +6,8 @@
 import numpy as np
 import pandas as pd
 from scipy import stats
-from util import convert_to_number_if_possible
+
+from .util import convert_to_number_if_possible
 
 
 def check_train(

spare_scores/mlp.py

@@ -4,7 +4,6 @@
 
 import numpy as np
 import pandas as pd
-from data_prep import logging_basic_config
 from sklearn import metrics
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.model_selection import GridSearchCV, KFold
@@ -13,6 +12,8 @@
 from sklearn.preprocessing import StandardScaler
 from sklearn.utils._testing import ignore_warnings
 
+from .data_prep import logging_basic_config
+
 
 class MLPModel:
     """

spare_scores/mlp_torch.py

@@ -9,7 +9,6 @@
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from data_prep import logging_basic_config
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.metrics import (
     accuracy_score,
@@ -29,6 +28,8 @@
 from sklearn.utils._testing import ignore_warnings
 from torch.utils.data import DataLoader, Dataset
 
+from .data_prep import logging_basic_config
+
 os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"  # for MPS backend
 device = (
     "cuda"

spare_scores/spare.py

@@ -3,14 +3,21 @@
 
 import numpy as np
 import pandas as pd
-from classes import MetaData, SpareModel
-from data_prep import (
+
+from .classes import MetaData, SpareModel
+from .data_prep import (
     check_test,
     check_train,
     convert_cat_variables,
     logging_basic_config,
 )
-from util import check_file_exists, is_unique_identifier, load_df, load_model, save_file
+from .util import (
+    check_file_exists,
+    is_unique_identifier,
+    load_df,
+    load_model,
+    save_file,
+)
 
 
 def spare_train(
@@ -105,7 +112,7 @@ def spare_train(
 
     # Check if it contains any errors.
     try:
-        df, predictors, mdl_task = check_train(
+        df, predictors, mdl_task = check_train(  # type: ignore
             df, predictors, to_predict, verbose, pos_group
         )
     except Exception as e:
@@ -200,9 +207,6 @@ def spare_train(
     if output != "" and output is not None:
         save_file(result, output, "train", logger)
 
-    print("###### PRINTING ########")
-    print(result)
-    print("####### END ###########")
     res["status"] = "OK"
     res["data"] = result
     res["status_code"] = 0

spare_scores/svm.py

@@ -4,12 +4,13 @@
 
 import numpy as np
 import pandas as pd
-from data_prep import logging_basic_config
 from sklearn import metrics
 from sklearn.model_selection import GridSearchCV, RepeatedKFold
 from sklearn.preprocessing import StandardScaler
 from sklearn.svm import SVC, LinearSVC, LinearSVR
-from util import expspace
+
+from .data_prep import logging_basic_config
+from .util import expspace
 
 
 class SVMModel:

tests/unit/test_data_prep.py

@@ -1,21 +1,17 @@
 import logging
 import os
-import sys
 import unittest
 
 import pandas as pd
 
-sys.path.append(
-    "../../spare_scores/"
-)  # check_test and check_train were imported from the build, but now they are updated
-from data_prep import (  # If updates go through, it can be updated to spare_scores.data_prep
+from spare_scores.data_prep import (  # If updates go through, it can be updated to spare_scores.data_prep
     age_sex_match,
     check_test,
     check_train,
     logging_basic_config,
     smart_unique,
 )
-from util import load_df
+from spare_scores.util import load_df
 
 
 class CheckDataPrep(unittest.TestCase):