src/helpers/test/decision_tree.py

@@ -1,127 +0,0 @@
-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))