from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
import numpy as np
import pandas as pd
import pickle as pi

class Classifier:
    def __init__(self):
        #Array für alle Ergebnisse
        self.ergebnis = []
    
    def train_models(self, X_train, X_test, y_train, y_test, models):
        for self.model in models:
            #-----------------------
            #Knn-Classifier
            #-----------------------
            if self.model == 'knn':
                #Optimalen Knn-Classifier bestimmen
                error = []
                for i in range(1, 40):
                    knn = KNeighborsClassifier(n_neighbors=i)
                    knn.fit(X_train, y_train)
                    pred_i = knn.predict(X_test)
                    error.append(np.mean(pred_i != y_test))

                #Knn-Classifier trainieren
                knnclf = KNeighborsClassifier(n_neighbors=7)
                knnclf.fit(X_train, y_train)

                #Knn-Classifier Akkuranz bestimmen
                score = knnclf.score(X_test,y_test)
                self.ergebnis.append(['knn-classifier', score, knnclf])
            #-----------------------
                
            #-----------------------
            #Decision Tree
            #-----------------------
            elif self.model == 'dt':
                #class_weight gebrauchen für DT und RF

                #Optimalen Decision Tree bestimmen
                #Zu testende Decision Tree Parameter
                dt = DecisionTreeClassifier()
                tree_para = {'criterion':['gini','entropy'],'max_depth':[i for i in range(1,20)], 'min_samples_split':[i for i in range (2,20)]}

                #GridSearchCV 
                grd_clf = GridSearchCV(dt, tree_para, cv=5)
                grd_clf.fit(X_train, y_train)

                #Besten gefundenen Decision Tree übergeben
                dt_clf = grd_clf.best_estimator_

                score = dt_clf.score(X_test,y_test)
                self.ergebnis.append(['decision tree', score, dt_clf])
            #-----------------------

            #-----------------------
            #Random Forest
            #-----------------------
            elif self.model == 'rf':
                #rf = RandomForestClassifier(max_depth=8, criterion="entropy", min_samples_split=9)
                rf = RandomForestClassifier(n_estimators=100)
                rf.fit(X_train,y_train)
                score = rf.score(X_test,y_test)
                self.ergebnis.append(['random forest', score, rf])
            #-----------------------

            #-----------------------
            #Support Vector Machine
            #-----------------------
            elif self.model == 'svm':
                svm = SVC(kernel = 'poly')
                svm.fit(X_train, y_train)
                score = svm.score(X_test,y_test)
                self.ergebnis.append(['support vector machine', score, svm])

            #-----------------------
            #MLP
            #-----------------------
            elif self.model == 'mlp':
                mlp = MLPClassifier(hidden_layer_sizes=[100,100], max_iter=5000, solver='sgd'
                , learning_rate='adaptive', learning_rate_init=0.01, n_iter_no_change=200, early_stopping=True)
                mlp.fit(X_train, y_train)
                score = mlp.score(X_test,y_test)
                self.ergebnis.append(['multi-layer perceptron', score, mlp])
                print("iterations: {}; layers: {}; loss: {}".format(mlp.n_iter_, mlp.n_layers_, mlp.loss_))
                epochs = np.linspace(1,mlp.n_iter_, mlp.n_iter_)
                
                #plt.plot(epochs, mlp.loss_curve_, label="Fehlerfunktion")
                #plt.plot(weight,2* weight,label="Ableitung")
                #plt.show()


        return self.ergebnis
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
import numpy as np
import pandas as pd
import pickle as pi

class Classifier:
    def __init__(self):
        #Array für alle Ergebnisse
        self.ergebnis = []
    
    def train_models(self, X_train, X_test, y_train, y_test, models):
        for self.model in models:
            #-----------------------
            #Knn-Classifier
            #-----------------------
            if self.model == 'knn':
                #Optimalen Knn-Classifier bestimmen
                error = []
                for i in range(1, 40):
                    knn = KNeighborsClassifier(n_neighbors=i)
                    knn.fit(X_train, y_train)
                    pred_i = knn.predict(X_test)
                    error.append(np.mean(pred_i != y_test))

                #Knn-Classifier trainieren
                knnclf = KNeighborsClassifier(n_neighbors=7)
                knnclf.fit(X_train, y_train)

                #Knn-Classifier Akkuranz bestimmen
                score = knnclf.score(X_test,y_test)
                self.ergebnis.append(['knn-classifier', score, knnclf])
            #-----------------------
                
            #-----------------------
            #Decision Tree
            #-----------------------
            elif self.model == 'dt':
                #class_weight gebrauchen für DT und RF

                #Optimalen Decision Tree bestimmen
                #Zu testende Decision Tree Parameter
                dt = DecisionTreeClassifier()
                tree_para = {'criterion':['gini','entropy'],'max_depth':[i for i in range(1,20)], 'min_samples_split':[i for i in range (2,20)]}

                #GridSearchCV 
                grd_clf = GridSearchCV(dt, tree_para, cv=5)
                grd_clf.fit(X_train, y_train)

                #Besten gefundenen Decision Tree übergeben
                dt_clf = grd_clf.best_estimator_

                score = dt_clf.score(X_test,y_test)
                self.ergebnis.append(['decision tree', score, dt_clf])
            #-----------------------

            #-----------------------
            #Random Forest
            #-----------------------
            elif self.model == 'rf':
                #rf = RandomForestClassifier(max_depth=8, criterion="entropy", min_samples_split=9)
                rf = RandomForestClassifier(n_estimators=100)
                rf.fit(X_train,y_train)
                score = rf.score(X_test,y_test)
                self.ergebnis.append(['random forest', score, rf])
            #-----------------------

            #-----------------------
            #Support Vector Machine
            #-----------------------
            elif self.model == 'svm':
                svm = SVC(kernel = 'poly')
                svm.fit(X_train, y_train)
                score = svm.score(X_test,y_test)
                self.ergebnis.append(['support vector machine', score, svm])

            #-----------------------
            #MLP
            #-----------------------
            elif self.model == 'mlp':
                mlp = MLPClassifier(hidden_layer_sizes=[100,100], max_iter=5000, solver='sgd'
                , learning_rate='adaptive', learning_rate_init=0.01, n_iter_no_change=200, early_stopping=True)
                mlp.fit(X_train, y_train)
                score = mlp.score(X_test,y_test)
                self.ergebnis.append(['multi-layer perceptron', score, mlp])
                print("iterations: {}; layers: {}; loss: {}".format(mlp.n_iter_, mlp.n_layers_, mlp.loss_))
                epochs = np.linspace(1,mlp.n_iter_, mlp.n_iter_)
                
                #plt.plot(epochs, mlp.loss_curve_, label="Fehlerfunktion")
                #plt.plot(weight,2* weight,label="Ableitung")
                #plt.show()


        return self.ergebnis
#Array für alle Ergebnisse #----------------------- #Knn-Classifier #----------------------- #Optimalen Knn-Classifier bestimmen #Knn-Classifier trainieren #Knn-Classifier Akkuranz bestimmen #----------------------- #----------------------- #Decision Tree #----------------------- #class_weight gebrauchen für DT und RF #Optimalen Decision Tree bestimmen #Zu testende Decision Tree Parameter #GridSearchCV #Besten gefundenen Decision Tree übergeben #----------------------- #----------------------- #Random Forest #----------------------- #rf = RandomForestClassifier(max_depth=8, criterion="entropy", min_samples_split=9) #----------------------- #----------------------- #Support Vector Machine #----------------------- #----------------------- #MLP #----------------------- #plt.plot(epochs, mlp.loss_curve_, label="Fehlerfunktion") #plt.plot(weight,2* weight,label="Ableitung") #plt.show()