Merge branch 'main' of https://arnweb.nl/gitea/arne/EV5_Beeldherk_Bomen

src/helpers/test/decision_tree.py

@@ -0,0 +1,127 @@
+from enum import Enum
+from sklearn.preprocessing import maxabs_scale, MaxAbsScaler
+from sklearn.ensemble import RandomForestClassifier
+from joblib import dump, load
+from sklearn import tree
+import csv
+import argparse
+import os
+
+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 file', required=True)
+
+class Tree(Enum):
+    ACCASIA = 0
+    BERK = 1
+    EIK = 2
+    ELS = 3
+    ESDOORN = 4
+    ES = 5
+    LINDE = 6
+    PLATAAN = 7
+
+class CVSuiteTestTree:
+    def __init__(self, model_path = None):
+        self.model_path = model_path
+        if self.model_path is not None:
+            self.model = load(self.model_path)
+        self.scaler = None
+
+    def trainCSV(self, path, output) -> None:
+        with open(path, 'r') as file:
+            reader = csv.reader(file, delimiter= ',')
+            matrix = list(reader)
+
+            i = 0
+            labels = []
+            data = [[] for x in range(len(matrix)-1)]
+            
+            # Load all but the headers
+            for row in matrix[1:]:
+                ## append data to lists
+                label = row.pop(0).upper()
+                labels.append(Tree[label].value)
+                
+                # remove ID
+                row.pop(0)
+                
+                # append all but ID and tree
+                for element in row:
+                    data[i].append(float(element))
+                i += 1
+        
+        # normalize data
+        if self.scaler is not None:
+            norm = self.scaler.fit(data)
+            for row in norm:
+                print(len(row))
+        else:
+            raise EnvironmentError("No scaler found")
+
+        # train model
+        self.train(norm, labels, output)
+
+    def addScaler(self, path) -> None:
+        self.scaler = load(path)
+
+    def train(self, data, labels, output) -> None:
+        print("You called the parent class, doofus")
+
+    def save(self, output, name) -> None:
+        path = os.path.join(output, name)
+        dump(self.model, path)
+        pass
+
+    def predict(self, data) -> None | int:
+        if self.model is not None:
+            return self.model.predict([data])
+        else:
+            return None
+
+class CVSuiteTestDecisionTree(CVSuiteTestTree):
+    def train(self, data, labels, output) -> None:
+        self.model = tree.DecisionTreeClassifier(
+                class_weight=None,
+                min_samples_leaf=2,
+                max_depth=None, 
+                random_state=False, 
+                criterion='gini', 
+                splitter='best',
+                ccp_alpha=0 
+            )
+        self.model.fit(data, labels)
+        self.save(output, 'decisiontree.joblib')
+
+class CVSuiteTestRandomForest(CVSuiteTestTree):
+    def train(self, data, labels, output) -> None:
+        self.model = RandomForestClassifier(
+            n_estimators=150,
+            criterion='gini', 
+        )
+        self.model.fit(data, labels)
+        self.save(output, 'randomforest.joblib')
+
+class CVSuiteTestExtraTrees(CVSuiteTestTree):
+    def train(self, data, labels, output) -> None:
+        self.model = tree.ExtraTreeClassifier()
+        self.model.fit(data, labels)
+        self.save(output, 'extratrees.joblib')
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    test = CVSuiteTestRandomForest()
+    test.trainCSV(args.input, args.output)
+    test = CVSuiteTestDecisionTree(
+        "C:\\Users\\Tom\\Desktop\\Files\\Repositories\\EV5_Beeldherk_Bomen\\models\\randomforest.joblib"
+    )
+    path = "C:\\Users\\Tom\\Desktop\\Files\\Repositories\\EV5_Beeldherk_Bomen\\dataset\\csv\\result-2023-10-21T09.59.24.csv"
+    file = open(path, 'r')
+    reader = csv.reader(file, delimiter=',')
+    matrix = list(reader)
+    file.close()
+
+    data = [float(x) for x in matrix[2][2:]]
+    norm = maxabs_scale(data)
+
+    print(test.predict(norm))