"Fixed" imports for CLI tools

README.md

@@ -84,15 +84,30 @@ $ python ./src/suite.py
 1. Open CVSuite and select the desired training set
 2. Press 'Run analysis for entire dataset(!)', this will export a CSV file with all preprocessed data in the `./out` directory
     - Based on your system configuration, this might take a while
-3. Run the CVSuiteTestKNN CLI tool:
+3. Run the CVSuiteTestKNN CLI tool, the following arguments are required:
+    - `-i` Input CSV file
+    - `-o` Output folder, likely `./out/models`
 ```sh
 $ python ./src/helpers/test/knn.py -i ./out/result-(date/time).csv -o ./out/models/ 
 ```
-4. Edit your `config.json` to include the newly created model
+4. The script generates two files; A fitted scaler to use with other models (`.pkl` file) and the model itself (`.yaml` file)
+5. Edit your `config.json` to include the newly created model
 
+### Train and export a Decision tree model
 > :memo: **Please note:**<br>
 > The KNN Training script also generates the scaler required to make the decision tree model
 
+1. Run the CVSuiteTestTree CLI Tool using the following arguments:
+    - `-i` Input CSV file
+    - `-o` Output folder, likely `./out/models`
+    - `-m` Model to train; `dectree`, `randforest` or `extratree`
+    - `-s` Scaler file to use (`.pkl` file)
+```sh
+python ./src/helpers/test/decision_tree.py -i ./out/result-(date/time).csv -o ./out/models/ -m 'dectree' -s ./out/models/scale_(date/time).pkl
+```
+2. The script generates one `.pkl` file based on the chosen model
+3. Edit your `config.json` to include the newly created model
+
 ---
 
 Arne van Iterson<br>

src/helpers/test/__init__.py

@@ -0,0 +1,2 @@
+from .decision_tree import *
+from .knn import *

src/helpers/test/decision_tree.py

@@ -6,14 +6,24 @@ import numpy as np
 import csv
 import argparse
 import os
+import sys
 
-from ..tags import Tree
+try:
+    # Perform relative import if included from CVSuite
+    from ..tags import Tree
+    from ..logger import C_DONE
+except ImportError:
+    # This solution is hot garbage but I refuse to spend any more time on it
+    directory = os.path.dirname(os.path.realpath(__file__))
+    sys.path.append(os.path.join(directory, '..'))
+    from tags import Tree
+    from logger import C_DONE
 
 parser = argparse.ArgumentParser(prog='DecisionTree CLI')
 parser.add_argument('-i', '--input', help='Input CSV file', required=True)
 parser.add_argument('-o', '--output', help='Output model folder', required=True)
 parser.add_argument('-m', '--model', help='Chosen model (\'dectree\', \'randforest\' or \'extratree\')', required=True)
-parser.add_argument('-s', '--scaler', help='Scaler preprocesser', required=True)
+parser.add_argument('-s', '--scaler', help='Scaler preprocessor', required=True)
 
 class CVSuiteTestTree:
     def __init__(self, model_path = None):
@@ -83,7 +93,7 @@ class CVSuiteTestDecisionTree(CVSuiteTestTree):
                 ccp_alpha=0 
             )
         self.model.fit(data, labels)
-        self.save(output, 'decisiontree.joblib')
+        self.save(output, 'decisiontree.pkl')
 
 class CVSuiteTestRandomForest(CVSuiteTestTree):
     def train(self, data, labels, output) -> None:
@@ -92,13 +102,13 @@ class CVSuiteTestRandomForest(CVSuiteTestTree):
             criterion='gini', 
         )
         self.model.fit(data, labels)
-        self.save(output, 'randomforest.joblib')
+        self.save(output, 'randomforest.pkl')
 
 class CVSuiteTestExtraTrees(CVSuiteTestTree):
     def train(self, data, labels, output) -> None:
         self.model = tree.ExtraTreeClassifier()
         self.model.fit(data, labels)
-        self.save(output, 'extratrees.joblib')
+        self.save(output, 'extratrees.pkl')
 
 if __name__ == "__main__":
     args = parser.parse_args()
@@ -114,4 +124,5 @@ if __name__ == "__main__":
         exit()
     
     test.addScaler(args.scaler)
-    test.trainCSV(args.input, args.output)
+    test.trainCSV(args.input, args.output)
+    print(C_DONE + "Model trained successfully!")

src/helpers/test/knn.py

@@ -8,9 +8,18 @@ import yaml
 import joblib
 import datetime
 import os
+import sys
 
-from ..logger import C_DBUG
-from ..tags import Tree
+try:
+    # Perform relative import if included from CVSuite
+    from ..tags import Tree
+    from ..logger import C_DONE, C_DBUG
+except ImportError:
+    # This solution is hot garbage but I refuse to spend any more time on it
+    directory = os.path.dirname(os.path.realpath(__file__))
+    sys.path.append(os.path.join(directory, '..'))
+    from tags import Tree
+    from logger import C_DONE, C_DBUG
 
 parser = argparse.ArgumentParser(prog='KNN Train CLI')
 parser.add_argument('-i', '--input', help='Input CSV file', required=True)
@@ -38,7 +47,7 @@ class CVSuiteTestKNN:
         file.close()        
         
         header = data.pop(0)
-        print("CSV tags: ", header)
+        # print("CSV tags: ", header)
         
         # Get classifier tags
         tags_int = []
@@ -97,8 +106,8 @@ class CVSuiteTestKNN:
         if self.trained:
             raise EnvironmentError("Model already trained!")
         else:
-            print(data)
-            print(data.shape)
+            # print(data)
+            # print(data.shape)
             self.knn.train(data, cv.ml.ROW_SAMPLE, tags)
             
             # Save it
@@ -116,4 +125,5 @@ class CVSuiteTestKNN:
 if __name__ == "__main__":
     args = parser.parse_args()
     test = CVSuiteTestKNN()
-    test.trainCSV(args.input, args.output)
+    test.trainCSV(args.input, args.output)
+    print(C_DONE + "Model trained successfully!")