import Classification
import NNC
import Plotting
import pandas as pd
#%%
Plotting.Plot()
#%%
print ("1 feature")
l1 = Classification.LogReg(2,3)
l2 = Classification.SVM(2,3)
l3 = Classification.DTC(2,3)
l4 = Classification.KNC(2,3)
l5 = Classification.RFC(2,3)
l6 = Classification.MLP(2,3)
l7 = Classification.ABC(2,3)
l8 = Classification.GNB(2,3)
l9 = Classification.QDA(2,3)
l10 = Classification.SGD(2,3)
l11= NNC.NNC(2,3)
df1 = pd.DataFrame(data = {"LogReg": l1, "SVM": l2, "DTC": l3, "KNC": l4, "RFC": l5,"MLP": l6,
"ABC": l7, "GNB": l8, "QDA": l9, "SGD": l10, "NNC":l11}, index = [".9", ".8", ".5", ".25"])
print(df1)
#%%
print ("9 features")
m1 = Classification.LogReg(2,11)
m2 = Classification.SVM(2,11)
m3 = Classification.DTC(2,11)
m4 = Classification.KNC(2,11)
def main_dico(datadict, datadict_pca, datadict_tsne):
"""
Author: Guillaume S\n
Try every classification algorithm on datadict, and print resulting accuracies.
Args:
datadict: dictionary returned by function "preprocessing_main" in Preprocessing.py
"""
accuracies = {}
parameters = {}
print("\n--------------For "+ datadict.get("dataset_name") +" dataset--------------")
print("Run neural networks...")
param_grid = {"nbneurons" : [[4,12,8], [16,32,24,12], [32,64,16,8]], "epochs" : [100,200,300]}
_, neural_network_param, neural_network_acc = neuralNetwork.neuralNetworkGridSearch(datadict, param_grid)
_, neural_network_param_pca, neural_network_acc_pca = neuralNetwork.neuralNetworkGridSearch(datadict_pca, param_grid)
_, neural_network_param_tsne, neural_network_acc_tsne = neuralNetwork.neuralNetworkGridSearch(datadict_tsne, param_grid)
accuracies["neural_network"] = neural_network_acc
accuracies["neural_network_pca"] = neural_network_acc_pca
accuracies["neural_network_tsne"] = neural_network_acc_tsne
parameters["neural_network"] = neural_network_param
parameters["neural_network_pca"] = neural_network_param_pca
parameters["neural_network_tsne"] = neural_network_param_tsne
print("Run kNN...")
knn_acc, knn_param = Classification.knn(datadict)
knn_acc_pca, knn_param_pca = Classification.knn(datadict_pca)
knn_acc_tsne, knn_param_tsne = Classification.knn(datadict_tsne)
accuracies["knn"] = knn_acc
accuracies["knn_pca"] = knn_acc_pca
accuracies["knn_tsne"] = knn_acc_tsne
parameters["knn"] = knn_param
parameters["knn_pca"] = knn_param_pca
parameters["knn_tsne"] = knn_param_tsne
print("Run logistic regression...")
logreg_acc = Classification.log_reg(datadict)
logreg_acc_pca = Classification.log_reg(datadict_pca)
logreg_acc_tsne = Classification.log_reg(datadict_tsne)
accuracies["logreg"] = logreg_acc
accuracies["logreg_pca"] = logreg_acc_pca
accuracies["logreg_tsne"] = logreg_acc_tsne
parameters["logreg"] = "No parameters for this algorithm"
parameters["logreg_pca"] = "No parameters for this algorithm"
parameters["logreg_tsne"] = "No parameters for this algorithm"
print("Run SVM...")
svm_acc, svm_param = Classification.SVM(datadict)
svm_acc_pca, svm_param_pca = Classification.SVM(datadict_pca)
svm_acc_tsne, svm_param_tsne = Classification.SVM(datadict_tsne)
accuracies["svm"] = svm_acc
accuracies["svm_pca"] = svm_acc_pca
accuracies["svm_tsne"] = svm_acc_tsne
parameters["svm"] = svm_param
parameters["svm_pca"] = svm_param_pca
parameters["svm_tsne"] = svm_param_tsne
print("Run stochastic gradient...")
gradient_acc, gradient_param = Classification.gradient(datadict)
gradient_acc_pca, gradient_param_pca = Classification.gradient(datadict_pca)
gradient_acc_tsne, gradient_param_tsne = Classification.gradient(datadict_tsne)
accuracies["stochastic_gradient"] = gradient_acc
accuracies["stochastic_gradient_pca"] = gradient_acc_pca
accuracies["stochastic_gradient_tsne"] = gradient_acc_tsne
parameters["stochastic_gradient"] = gradient_param
parameters["stochastic_gradient_pca"] = gradient_param_pca
parameters["stochastic_gradient_tsne"] = gradient_param_tsne
print("Run random forest...")
randomforest_acc, randomforest_param = random_forest.rdforest(datadict)
randomforest_acc_pca, randomforest_param_pca = random_forest.rdforest(datadict_pca)
randomforest_acc_tsne, randomforest_param_tsne = random_forest.rdforest(datadict_tsne)
accuracies["randomforest"] = randomforest_acc
accuracies["randomforest_pca"] = randomforest_acc_pca
accuracies["randomforest_tsne"] = randomforest_acc_tsne
parameters["randomforest"] = randomforest_param
parameters["randomforest_pca"] = randomforest_param_pca
parameters["randomforest_tsne"] = randomforest_param_tsne
print("Run decision tree...")
_, _, decision_tree_acc_list, _ = DecisionTree.decisiontree_main(datadict)
accuracies["decision_tree"] = max(decision_tree_acc_list)
parameters["decision_tree"] = "No parameters for this algorithm"
print("Done.\n")
#Take the biggest accuracy, algorithm name and parameters
best_accuracy = max(accuracies.values())
best_accuracy_name = max(accuracies, key=accuracies.get)
best_param = parameters.get(best_accuracy_name)
print("---Results for "+datadict.get("dataset_name")+" dataset---")
print("Best accuracy : "+str(best_accuracy))
print("Obtained with : "+str(best_accuracy_name))
print("Best parameters : "+str(best_param))
print("Every accuracy : ")
for name in accuracies.keys():
print(" "+ name + " : " + str(accuracies.get(name)) + " ; with parameters : "+str(parameters.get(name)))
print(" ")
__author__ = 'Prateek'
import Classification
print("1 feature")
Classification.LogReg(2, 3)
Classification.SVM(2, 3)
Classification.DTC(2, 3)
print("9 features")
Classification.LogReg(2, 11)
Classification.SVM(2, 11)
Classification.DTC(2, 11)
print("16 features")
Classification.LogReg(2, 18)
Classification.SVM(2, 18)
Classification.DTC(2, 18)
print("30 features")
Classification.LogReg(2, 32)
Classification.SVM(2, 32)
Classification.DTC(2, 32)