Delete Volumetric.py

Volumetric.py

-# Importing the libraries
-
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-from sklearn.impute import SimpleImputer
-from sklearn.metrics import accuracy_score
-from sklearn.metrics import f1_score
-from sklearn.metrics import confusion_matrix
-from sklearn.metrics import precision_score
-from sklearn.metrics import recall_score
-from sklearn.model_selection import train_test_split
-from sklearn.tree import DecisionTreeClassifier
-
-
-# Importing the dataset
-
-train = pd.read_csv("/Users/ASHEN/Education/SLIIT/Research/Wanheda_Server/MobileBotNet/packet_data.csv",index_col=0,low_memory=False)
-
-train.shape
-
-
-# Feature Scaling
-
-from sklearn import preprocessing 
-for f in train.columns: 
-    if train[f].dtype=='object': 
-        label = preprocessing.LabelEncoder() 
-        label.fit(list(train[f].values)) 
-        train[f] = lbl.transform(list(train[f].values))
-train.fillna((-999), inplace=True) 
-train=np.array(train) 
-train = train.astype(float)
-
-Y = train['Label']
-X = train.drop("Label",axis=1)
-
-print(train.shape)
-print(X.shape)
-print(Y.shape)
-
-
-# Spliting the dataset into the training set and test set
-
-seed = 7
-test_size = 0.33
-X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)
-
-print(X_train.shape)
-print(y_train.shape)
-print(X_test.shape)
-print(y_test.shape)
-
-
-# Fitting Decision Tree to the training set
-
-model=DecisionTreeClassifier(max_depth=5,random_state=0)
-model.fit(X_train, y_train)
-y_pred = model.predict(X_test)
-
-print(model)
-
-
-# Plotting the tree, here it should access graph's first element
-
-from sklearn import tree
-from sklearn.externals.six import StringIO  
-import pydot 
-
-dot_data = StringIO() 
-tree.export_graphviz(model, out_file=dot_data) 
-graph = pydot.graph_from_dot_data(dot_data.getvalue()) 
-
-graph[0].write_pdf("tree.pdf")
-
-
-# Predicting the data[round(value) for value in y_pred]
-
-y_pred = model.predict(X_test)
-
-
-# Creating confusion metrics to check the performance of algorithm
-
-cm=confusion_matrix(y_test, y_pred)
-print("confusion matrix:\n",cm)
-
-
-# Accuracy
-
-accuracy = accuracy_score(y_test, y_pred)
-print("accuracy:",accuracy)
-
-
-# F1 Score
-
-f1score=f1_score(y_test, y_pred)
-print("f1_score:",f1score)
-
-
-# Precision Score
-
-pr=precision_score(y_test,y_pred)
-print("Precision:",pr)
-
-
-# Recall Score
-
-rs=recall_score(y_test,y_pred)
-print("Recall_score:",rs)
-
-
-# Misclassified Samples
-
-misclassified_samples = X_test[y_test != y_pred]
-mc=misclassified_samples.shape[0]
-print("Misclassified :",mc)
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