{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# We import all libraries that we use\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn import metrics, datasets\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import KFold\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn import tree\n", "from sklearn.tree import export_graphviz\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import matthews_corrcoef\n", "from sklearn.metrics import precision_score\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.metrics import recall_score\n", "from sklearn.metrics import f1_score" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The size of the database is: (165, 14)\n" ] } ], "source": [ "#route = 'C:/Users/Documents/Database/' # This is an example of Database´s route, can be local or online. You can put here your local direction\n", "#https://nimbus.cicese.mx/public.php?service=files&t=454bf55581f6dc4a3b893b9967805f96 # or a web direction\n", "\n", "# We salve the database in df´s variable and use the method of Pandas read_CSV to reading CSV files\n", "df = pd.read_csv(route + 'database.csv') # Put here your local direction where you have the database \n", "print(\"The size of the database is:\", df.shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Show the type of the data\n", " Latency (ms) float64\n", " Jitter (ms) float64\n", " Bit Rate (Mbps) float64\n", " Packet Loss Rate (%) float64\n", " Peak Data Rate DL (Gbps) float64\n", " Peak Data Rate UL (Gbps) float64\n", " Mobility (km/h) float64\n", " Reliability (%) float64\n", " Service Availability (%) float64\n", " Survival Time (ms) float64\n", " Experienced Data Rate DL (Mbps) float64\n", " Experienced Data Rate UL (Mbps) float64\n", " Interruption Time (ms) float64\n", " Service object\n", "dtype: object\n" ] } ], "source": [ "# Pandas assign automatically a type of data but we need to check that the type of data be numeric\n", "# because the mathematic functions of Machine Learning (ML) don´t permit text-type of data\n", "# and as we can see, all type of data are float.\n", "\n", "print(\"Show the type of the data\\n\", df.dtypes) " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Latency (ms)Jitter (ms)Bit Rate (Mbps)Packet Loss Rate (%)Peak Data Rate DL (Gbps)Peak Data Rate UL (Gbps)Mobility (km/h)Reliability (%)Service Availability (%)Survival Time (ms)Experienced Data Rate DL (Mbps)Experienced Data Rate UL (Mbps)Interruption Time (ms)Service
015.05.0011.01.000000e-0118.07.000260.095.0000099.00008.01000.0500.01000.0UHD_Video_Streaming
15.05.5010.01.000000e+0020.010.00020.095.0000099.20009.0990.0440.02000.0UHD_Video_Streaming
28.010.0050.03.800000e+0015.07.00015.097.0000099.900010.01000.050.00.2Immerse_Experience
340.01.000.51.000000e-0518.09.0000.099.9200099.999010.05.08.00.0Smart_Grid
490.018.000.28.000000e-0213.02.000480.099.9995099.9999100.010.010.01000.0ITS
5130.05.008.09.000000e-0114.06.000400.099.9400095.0000100.050.025.00.0Vo5G
610.019.0032.04.700000e+0013.05.00026.095.6000099.92008.9900.040.00.1Immerse_Experience
72.03.0015.08.000000e-090.20.200100.099.9999699.00001.010.0100.00.0e_Health
85.00.5010.07.500000e-040.80.02480.099.9992099.00001.050.025.00.0Connected_Vehicles
91.00.050.61.000000e-0715.06.00028.099.9990099.99990.01.010.0100.0Industry_Automation
\n", "
" ], "text/plain": [ " Latency (ms) Jitter (ms) Bit Rate (Mbps) Packet Loss Rate (%) \\\n", "0 15.0 5.00 11.0 1.000000e-01 \n", "1 5.0 5.50 10.0 1.000000e+00 \n", "2 8.0 10.00 50.0 3.800000e+00 \n", "3 40.0 1.00 0.5 1.000000e-05 \n", "4 90.0 18.00 0.2 8.000000e-02 \n", "5 130.0 5.00 8.0 9.000000e-01 \n", "6 10.0 19.00 32.0 4.700000e+00 \n", "7 2.0 3.00 15.0 8.000000e-09 \n", "8 5.0 0.50 10.0 7.500000e-04 \n", "9 1.0 0.05 0.6 1.000000e-07 \n", "\n", " Peak Data Rate DL (Gbps) Peak Data Rate UL (Gbps) Mobility (km/h) \\\n", "0 18.0 7.000 260.0 \n", "1 20.0 10.000 20.0 \n", "2 15.0 7.000 15.0 \n", "3 18.0 9.000 0.0 \n", "4 13.0 2.000 480.0 \n", "5 14.0 6.000 400.0 \n", "6 13.0 5.000 26.0 \n", "7 0.2 0.200 100.0 \n", "8 0.8 0.024 80.0 \n", "9 15.0 6.000 28.0 \n", "\n", " Reliability (%) Service Availability (%) Survival Time (ms) \\\n", "0 95.00000 99.0000 8.0 \n", "1 95.00000 99.2000 9.0 \n", "2 97.00000 99.9000 10.0 \n", "3 99.92000 99.9990 10.0 \n", "4 99.99950 99.9999 100.0 \n", "5 99.94000 95.0000 100.0 \n", "6 95.60000 99.9200 8.9 \n", "7 99.99996 99.0000 1.0 \n", "8 99.99920 99.0000 1.0 \n", "9 99.99900 99.9999 0.0 \n", "\n", " Experienced Data Rate DL (Mbps) Experienced Data Rate UL (Mbps) \\\n", "0 1000.0 500.0 \n", "1 990.0 440.0 \n", "2 1000.0 50.0 \n", "3 5.0 8.0 \n", "4 10.0 10.0 \n", "5 50.0 25.0 \n", "6 900.0 40.0 \n", "7 10.0 100.0 \n", "8 50.0 25.0 \n", "9 1.0 10.0 \n", "\n", " Interruption Time (ms) Service \n", "0 1000.0 UHD_Video_Streaming \n", "1 2000.0 UHD_Video_Streaming \n", "2 0.2 Immerse_Experience \n", "3 0.0 Smart_Grid \n", "4 1000.0 ITS \n", "5 0.0 Vo5G \n", "6 0.1 Immerse_Experience \n", "7 0.0 e_Health \n", "8 0.0 Connected_Vehicles \n", "9 100.0 Industry_Automation " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This shows the first five rows contained in the dataset\n", "df.head(10)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [ { "ename": "TypeError", "evalue": "describe() got an unexpected keyword argument 'columns'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Now, we are going to see some characteristics of the dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m#df.describe(include='all')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: describe() got an unexpected keyword argument 'columns'" ] } ], "source": [ "# Now, we are going to see some characteristics of the dataset\n", "#df.describe(include='all')\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The size of the independent variable is: (165, 8)\n", "The size of the dependent variable is: (165,)\n" ] } ], "source": [ "# ------------------------------------------ 1st SIMULATION ---------------------------------------------------\n", "\n", "# Data Labeling block\n", "\n", "X = df.iloc[:,:8].values # Independent Variable corresponds to the KPI parameters\n", "Y = df.iloc[:,-1].values # Dependent Variable corresponds to 5G services’ labels\n", "\n", "print(\"The size of the independent variable is:\",X.shape)\n", "print(\"The size of the dependent variable is:\",Y.shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The size of the test data is: (33, 8)\n", "The size of the train data is: (132, 8)\n" ] } ], "source": [ "# Data split block\n", "# The data is divided such that 80% to train the ML algorithm\n", "# and the remaining 20% to test using the train_test_split function\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)\n", "print(\"The size of the test data is:\", X_test.shape)\n", "print(\"The size of the train data is:\", X_train.shape)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# In this cell, we are going to create several Supervised Machine Learning (SML) algorithms \n", "# to check which one works best as a classifier in this simulation. For example:\n", "\n", "# With the function DecisionTreeClassifier we create a Decision Tree algorithm\n", "# With the function RandomForestClassifier we create a Random Forest algorithm\n", "# With the function SVC and kernel=linear we create a Linear Support Vector Classification algorithm\n", "# With the function KNeighborsClassifier we create a Classifier implementing the k-nearest neighbors vote\n", "# With the function MLPClassifier we create a Multi-layer Perceptron classifier\n", "# With the function GaussianNB we create a Gaussian Naive Bayes classifier\n", "\n", "\n", "classifiers = [DecisionTreeClassifier(criterion = 'entropy', random_state = 0), \n", " RandomForestClassifier(n_estimators = 5, criterion = 'entropy', random_state = 0), \n", " SVC(kernel = \"linear\", C = 0.025), \n", " KNeighborsClassifier(3), \n", " MLPClassifier(alpha = 1, max_iter = 1000)] \n", "\n", "# We create a tuple which contain the names of the classifiers.\n", "names = [\"Decision Tree\", \"Random Forest\", \"Linear SVM\", \"K-Nearest Neighbors\", \"Neural Net\"]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Decision Tree model metric accuracy is: 100.0 %\n", "Metrics of cross-validation of the Decision Tree [1. 1. 1. 1. 1. 1.\n", " 1. 0.92307692 1. 1. ]\n", "The Cross-validation mean of the Decision Tree is: 99.23076923076923 %\n", "------------------------------------------------------------------------------------------------\n", "The Random Forest model metric accuracy is: 100.0 %\n", "Metrics of cross-validation of the Random Forest [1. 1. 1. 1. 1. 1.\n", " 1. 0.92307692 1. 1. ]\n", "The Cross-validation mean of the Random Forest is: 99.23076923076923 %\n", "------------------------------------------------------------------------------------------------\n", "The Linear SVM model metric accuracy is: 94.6969696969697 %\n", "Metrics of cross-validation of the Linear SVM [0.92857143 0.92857143 0.84615385 0.92307692 1. 1.\n", " 0.92307692 0.84615385 0.84615385 1. ]\n", "The Cross-validation mean of the Linear SVM is: 92.41758241758242 %\n", "------------------------------------------------------------------------------------------------\n", "The K-Nearest Neighbors model metric accuracy is: 81.81818181818183 %\n", "Metrics of cross-validation of the K-Nearest Neighbors [0.42857143 0.78571429 0.46153846 0.69230769 0.61538462 0.84615385\n", " 0.53846154 0.53846154 0.38461538 0.69230769]\n", "The Cross-validation mean of the K-Nearest Neighbors is: 59.83516483516483 %\n", "------------------------------------------------------------------------------------------------\n", "The Neural Net model metric accuracy is: 99.24242424242425 %\n", "Metrics of cross-validation of the Neural Net [1. 0.85714286 0.76923077 0.92307692 0.84615385 0.92307692\n", " 0.92307692 0.76923077 0.76923077 1. ]\n", "The Cross-validation mean of the Neural Net is: 87.80219780219781 %\n", "------------------------------------------------------------------------------------------------\n" ] } ], "source": [ "# For the validation of the algorithm we use cross-validation technique with 10 division and accuracy it´s checked\n", "kf = KFold(n_splits = 10)\n", "cross_val_scores = [] # We create an empty tuple that will contain the cross-validation values means of each model\n", "\n", "for name, model in zip(names, classifiers):\n", " model.fit(X_train, y_train) # We fit each model\n", " score = model.score(X_train, y_train) # It is checked if the model learned well, testing its accuracy on the same training data\n", " print(\"The\", name,\"model metric accuracy is:\", score*100, \"%\")\n", " cros_val_scores = cross_val_score(model, X_train, y_train, cv = kf, scoring = \"accuracy\") # We use K-Fold cross-validation\n", " print(\"Metrics of cross-validation of the\", name, cros_val_scores)\n", " print(\"The Cross-validation mean of the\", name,\"is:\", cros_val_scores.mean()*100, \"%\")\n", " cross_val_scores.append(cros_val_scores.mean()) # Values of cross-validation stage are going to be inserted in the tuple\n", " print(\"------------------------------------------------------------------------------------------------\") " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is the confusion matrix result of the 1st Simulation with a Decision Tree \n", " [[3 0 0 0 0 0 0 0 0]\n", " [0 6 0 0 0 0 0 0 0]\n", " [0 0 6 0 0 0 1 0 0]\n", " [0 0 0 5 0 0 0 0 0]\n", " [1 0 0 0 2 0 0 0 0]\n", " [0 0 0 0 0 3 0 0 0]\n", " [0 0 0 0 0 0 2 0 0]\n", " [0 0 0 0 0 0 0 3 0]\n", " [0 0 0 0 0 0 0 0 1]] \n", "\n", "The Decision Tree model has an accuracy of: 93.93939393939394 %\n", "The Decision Tree model has a Matthews coeficient of: 93.1991983989024 %\n", "--------------------------------------------------------------------------\n", "\n", "This is the confusion matrix result of the 1st Simulation with a Random Forest \n", " [[3 0 0 0 0 0 0 0 0]\n", " [0 6 0 0 0 0 0 0 0]\n", " [0 0 6 0 0 0 1 0 0]\n", " [0 0 0 5 0 0 0 0 0]\n", " [0 0 0 0 3 0 0 0 0]\n", " [0 0 0 0 0 3 0 0 0]\n", " [0 0 0 0 0 0 2 0 0]\n", " [0 1 0 0 0 0 0 2 0]\n", " [0 0 0 0 0 0 0 0 1]] \n", "\n", "The Random Forest model has an accuracy of: 93.93939393939394 %\n", "The Random Forest model has a Matthews coeficient of: 93.17697228144989 %\n", "--------------------------------------------------------------------------\n", "\n", "This is the confusion matrix result of the 1st Simulation with a Linear SVM \n", " [[3 0 0 0 0 0 0 0 0]\n", " [0 6 0 0 0 0 0 0 0]\n", " [0 0 6 0 0 0 1 0 0]\n", " [0 0 0 5 0 0 0 0 0]\n", " [0 0 0 0 3 0 0 0 0]\n", " [0 0 0 0 0 3 0 0 0]\n", " [0 0 0 0 0 0 2 0 0]\n", " [0 0 0 0 0 0 0 3 0]\n", " [0 0 0 0 0 0 0 0 1]] \n", "\n", "The Linear SVM model has an accuracy of: 96.96969696969697 %\n", "The Linear SVM model has a Matthews coeficient of: 96.60384333174481 %\n", "--------------------------------------------------------------------------\n", "\n", "This is the confusion matrix result of the 1st Simulation with a K-Nearest Neighbors \n", " [[2 0 0 0 0 0 1 0 0]\n", " [0 4 0 0 0 0 1 0 1]\n", " [0 0 6 0 0 0 1 0 0]\n", " [0 0 0 5 0 0 0 0 0]\n", " [0 0 0 0 3 0 0 0 0]\n", " [3 0 0 0 0 0 0 0 0]\n", " [0 0 0 0 0 0 2 0 0]\n", " [0 0 0 0 0 0 0 3 0]\n", " [0 0 0 0 0 0 0 0 1]] \n", "\n", "The K-Nearest Neighbors model has an accuracy of: 78.78787878787878 %\n", "The K-Nearest Neighbors model has a Matthews coeficient of: 76.89035244163077 %\n", "--------------------------------------------------------------------------\n", "\n", "This is the confusion matrix result of the 1st Simulation with a Neural Net \n", " [[2 0 0 0 0 1 0 0 0]\n", " [0 6 0 0 0 0 0 0 0]\n", " [0 0 6 0 0 0 1 0 0]\n", " [0 0 0 4 1 0 0 0 0]\n", " [0 0 0 0 3 0 0 0 0]\n", " [1 0 0 0 0 2 0 0 0]\n", " [0 0 0 0 0 0 2 0 0]\n", " [0 0 0 0 0 0 0 3 0]\n", " [0 0 0 0 0 0 0 0 1]] \n", "\n", "The Neural Net model has an accuracy of: 87.87878787878788 %\n", "The Neural Net model has a Matthews coeficient of: 86.21542189009848 %\n", "--------------------------------------------------------------------------\n", "\n" ] } ], "source": [ "# Model Testing block.\n", "\n", "y_predic_models = [] # We create an empty tuple that will contain the values of the predictions made by each model.\n", "accuracy_models = [] # We create an empty tuple that will contain the values of the accuracy made by each model.\n", "mcc_models = [] # We create an empty tuple that will contain the values of the Matthews coefcient of each model.\n", "\n", "for name, model in zip(names, classifiers):\n", " y_predic = model.predict(X_test) # We predict the test data (Xtest)\n", " y_predic_models.append(model.predict(X_test)) # The predictions are going to be inserted in the tuple \n", " matrix = confusion_matrix(y_test, y_predic) # Confusion Matrix of each model\n", " print(\"This is the confusion matrix result of the 1st Simulation with a\", name,\"\\n\", matrix, \"\\n\")\n", " # Accuracy and Matthews coeficient of each model\n", " accuracy = accuracy_score(y_test, y_predic) # Model accuracy of each model \n", " accuracy_models.append(accuracy) # The values of accuracy are going to be inserted in tuple\n", " print(\"The\", name, \"model has an accuracy of:\", accuracy*100, \"%\")\n", " mcc = matthews_corrcoef(y_test, y_predic) # Matthews coeficient\n", " mcc_models.append(mcc) # The values of Matthews coeficient are going to be inserted in tuple\n", " print(\"The\", name, \"model has a Matthews coeficient of:\", mcc*100, \"%\")\n", " print(\"--------------------------------------------------------------------------\\n\")" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The metrics of the Decision Tree model is:\n", "\n", " precision recall f1-score support\n", "\n", " Connected_Vehicles 0.750 1.000 0.857 3\n", " ITS 1.000 1.000 1.000 6\n", " Immerse_Experience 1.000 0.857 0.923 7\n", " Industry_Automation 1.000 1.000 1.000 5\n", " Smart_Grid 1.000 0.667 0.800 3\n", " Surveillance 1.000 1.000 1.000 3\n", " UHD_Video_Streaming 0.667 1.000 0.800 2\n", " Vo5G 1.000 1.000 1.000 3\n", " e_Health 1.000 1.000 1.000 1\n", "\n", " accuracy 0.939 33\n", " macro avg 0.935 0.947 0.931 33\n", " weighted avg 0.957 0.939 0.940 33\n", "\n", "The metrics of the Random Forest model is:\n", "\n", " precision recall f1-score support\n", "\n", " Connected_Vehicles 1.000 1.000 1.000 3\n", " ITS 0.857 1.000 0.923 6\n", " Immerse_Experience 1.000 0.857 0.923 7\n", " Industry_Automation 1.000 1.000 1.000 5\n", " Smart_Grid 1.000 1.000 1.000 3\n", " Surveillance 1.000 1.000 1.000 3\n", " UHD_Video_Streaming 0.667 1.000 0.800 2\n", " Vo5G 1.000 0.667 0.800 3\n", " e_Health 1.000 1.000 1.000 1\n", "\n", " accuracy 0.939 33\n", " macro avg 0.947 0.947 0.938 33\n", " weighted avg 0.954 0.939 0.939 33\n", "\n", "The metrics of the Linear SVM model is:\n", "\n", " precision recall f1-score support\n", "\n", " Connected_Vehicles 1.000 1.000 1.000 3\n", " ITS 1.000 1.000 1.000 6\n", " Immerse_Experience 1.000 0.857 0.923 7\n", " Industry_Automation 1.000 1.000 1.000 5\n", " Smart_Grid 1.000 1.000 1.000 3\n", " Surveillance 1.000 1.000 1.000 3\n", " UHD_Video_Streaming 0.667 1.000 0.800 2\n", " Vo5G 1.000 1.000 1.000 3\n", " e_Health 1.000 1.000 1.000 1\n", "\n", " accuracy 0.970 33\n", " macro avg 0.963 0.984 0.969 33\n", " weighted avg 0.980 0.970 0.972 33\n", "\n", "The metrics of the K-Nearest Neighbors model is:\n", "\n", " precision recall f1-score support\n", "\n", " Connected_Vehicles 0.400 0.667 0.500 3\n", " ITS 1.000 0.667 0.800 6\n", " Immerse_Experience 1.000 0.857 0.923 7\n", " Industry_Automation 1.000 1.000 1.000 5\n", " Smart_Grid 1.000 1.000 1.000 3\n", " Surveillance 0.000 0.000 0.000 3\n", " UHD_Video_Streaming 0.400 1.000 0.571 2\n", " Vo5G 1.000 1.000 1.000 3\n", " e_Health 0.500 1.000 0.667 1\n", "\n", " accuracy 0.788 33\n", " macro avg 0.700 0.799 0.718 33\n", " weighted avg 0.803 0.788 0.775 33\n", "\n", "The metrics of the Neural Net model is:\n", "\n", " precision recall f1-score support\n", "\n", " Connected_Vehicles 0.600 1.000 0.750 3\n", " ITS 1.000 1.000 1.000 6\n", " Immerse_Experience 1.000 0.857 0.923 7\n", " Industry_Automation 1.000 0.800 0.889 5\n", " Smart_Grid 1.000 1.000 1.000 3\n", " Surveillance 0.500 0.333 0.400 3\n", " UHD_Video_Streaming 0.667 1.000 0.800 2\n", " Vo5G 1.000 1.000 1.000 3\n", " e_Health 1.000 1.000 1.000 1\n", "\n", " accuracy 0.879 33\n", " macro avg 0.863 0.888 0.862 33\n", " weighted avg 0.898 0.879 0.877 33\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\David\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] } ], "source": [ "# All the metrics and their values\n", "\n", "for name, y_predic in zip(names, y_predic_models):\n", " print(\"The metrics of the\", name, \"model is:\\n\")\n", " print(metrics.classification_report(y_test, y_predic, digits = 3))" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "# ------------------------------------------ 2nd SIMULATION ------------------------------------\n", "# ------------------------------------------------------------------------------------------------\n", "# Remember that the process is almost the same, with a few diferences" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The size of the independent variable is: (165, 13)\n", "The size of the dependent variable is: (165,)\n" ] } ], "source": [ "# The first we need to change is the size of independent and dependent variables because we include KQI parameters\n", "\n", "# Data Labeling block\n", "X2 = df.iloc[:,:-1].values # Independent Variable corresponds to the KPI parameters\n", "Y2 = df.iloc[:,-1].values # Dependent Variable corresponds to the 5G services\n", "\n", "print(\"The size of the independent variable is:\",X2.shape)\n", "print(\"The size of the dependent variable is:\",Y2.shape)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The size of the test data is: (33, 13)\n", "The size of the train data is: (132, 13)\n" ] } ], "source": [ "# Data split block, The data is divided equal to the first simulation\n", "\n", "X_train2, X_test2, y_train2, y_test2 = train_test_split(X2, Y2, test_size = 0.2,random_state = 42)\n", "print(\"The size of the test data is:\", X_test2.shape)\n", "print(\"The size of the train data is:\", X_train2.shape)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Decision Tree model metric accuracy is: 100.0 %\n", "Metrics of cross-validation of the Decision Tree [1. 1. 1. 1. 1. 0.84615385\n", " 1. 0.92307692 1. 1. ]\n", "The Cross-validation mean of the Decision Tree is: 97.69230769230771 %\n", "--------------------------------------------------------------------------------------------\n", "The Random Forest model metric accuracy is: 100.0 %\n", "Metrics of cross-validation of the Random Forest [0.92857143 1. 1. 1. 0.92307692 1.\n", " 1. 1. 1. 1. ]\n", "The Cross-validation mean of the Random Forest is: 98.51648351648352 %\n", "--------------------------------------------------------------------------------------------\n", "The Linear SVM model metric accuracy is: 99.24242424242425 %\n", "Metrics of cross-validation of the Linear SVM [0.92857143 1. 0.92307692 0.92307692 1. 0.84615385\n", " 0.76923077 0.84615385 0.92307692 1. ]\n", "The Cross-validation mean of the Linear SVM is: 91.59340659340658 %\n", "--------------------------------------------------------------------------------------------\n", "The K-Nearest Neighbors model metric accuracy is: 94.6969696969697 %\n", "Metrics of cross-validation of the K-Nearest Neighbors [0.78571429 0.85714286 0.92307692 0.92307692 0.84615385 0.84615385\n", " 0.61538462 0.84615385 0.84615385 0.84615385]\n", "The Cross-validation mean of the K-Nearest Neighbors is: 83.35164835164835 %\n", "--------------------------------------------------------------------------------------------\n", "The Neural Net model metric accuracy is: 99.24242424242425 %\n", "Metrics of cross-validation of the Neural Net [0.92857143 0.92857143 0.76923077 0.92307692 1. 0.84615385\n", " 0.76923077 0.84615385 1. 0.92307692]\n", "The Cross-validation mean of the Neural Net is: 89.34065934065934 %\n", "--------------------------------------------------------------------------------------------\n" ] } ], "source": [ "# Algorithm training block\n", "# We fit the same algorithms so we don't have to create another, just trainning it\n", "cross_val_scores2 = [] # We create another empty tuple for save the values of cross-validation stage in this simulation\n", "\n", "for name, model in zip(names, classifiers):\n", " model.fit(X_train2, y_train2) # We fit each model\n", " score = model.score(X_train2, y_train2) # It is checked if the model learned well, testing its accuracy on the same training data\n", " print(\"The\", name,\"model metric accuracy is:\", score*100, \"%\")\n", " cros_val_scores = cross_val_score(model, X_train2, y_train2, cv = kf, scoring = \"accuracy\") # We use K-Fold cross-validation\n", " print(\"Metrics of cross-validation of the\", name, cros_val_scores)\n", " print(\"The Cross-validation mean of the\", name,\"is:\", cros_val_scores.mean()*100, \"%\")\n", " cross_val_scores2.append(cros_val_scores.mean()) # Values of cross-validation stage are going to be inserted in the tuple\n", " print(\"--------------------------------------------------------------------------------------------\") " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is the confusion matrix result of the 1st Simulation with a Decision Tree \n", " [[3 0 0 0 0 0 0 0 0]\n", " [0 6 0 0 0 0 0 0 0]\n", " [0 0 7 0 0 0 0 0 0]\n", " [0 0 0 5 0 0 0 0 0]\n", " [0 0 0 0 2 1 0 0 0]\n", " [0 0 0 0 0 3 0 0 0]\n", " [0 0 0 0 0 0 2 0 0]\n", " [0 0 0 0 0 0 0 3 0]\n", " [0 0 0 0 0 0 0 0 1]] \n", "\n", "The Decision Tree model has an accuracy of: 96.96969696969697 %\n", "The Decision Tree model has a Matthews coeficient of: 96.58490025554875 %\n", "--------------------------------------------------------------------------\n", "\n", "This is the confusion matrix result of the 1st Simulation with a Random Forest \n", " [[3 0 0 0 0 0 0 0 0]\n", " [0 6 0 0 0 0 0 0 0]\n", " [0 0 7 0 0 0 0 0 0]\n", " [0 0 0 5 0 0 0 0 0]\n", " [1 0 0 0 2 0 0 0 0]\n", " [0 0 0 0 0 3 0 0 0]\n", " [0 0 0 0 0 0 2 0 0]\n", " [0 0 0 0 0 0 0 3 0]\n", " [0 0 0 0 0 0 0 0 1]] \n", "\n", "The Random Forest model has an accuracy of: 96.96969696969697 %\n", "The Random Forest model has a Matthews coeficient of: 96.58490025554875 %\n", "--------------------------------------------------------------------------\n", "\n", "This is the confusion matrix result of the 1st Simulation with a Linear SVM \n", " [[3 0 0 0 0 0 0 0 0]\n", " [0 6 0 0 0 0 0 0 0]\n", " [0 0 7 0 0 0 0 0 0]\n", " [0 0 0 5 0 0 0 0 0]\n", " [0 0 0 0 3 0 0 0 0]\n", " [0 0 0 0 0 3 0 0 0]\n", " [0 0 0 0 0 0 2 0 0]\n", " [0 0 0 0 0 0 0 3 0]\n", " [0 0 0 0 0 0 0 0 1]] \n", "\n", "The Linear SVM model has an accuracy of: 100.0 %\n", "The Linear SVM model has a Matthews coeficient of: 100.0 %\n", "--------------------------------------------------------------------------\n", "\n", "This is the confusion matrix result of the 1st Simulation with a K-Nearest Neighbors \n", " [[3 0 0 0 0 0 0 0 0]\n", " [0 4 0 0 0 0 2 0 0]\n", " [0 0 7 0 0 0 0 0 0]\n", " [0 0 0 4 1 0 0 0 0]\n", " [0 0 0 0 3 0 0 0 0]\n", " [1 0 0 0 0 1 0 0 1]\n", " [0 0 0 0 0 0 2 0 0]\n", " [0 0 0 0 0 0 0 3 0]\n", " [1 0 0 0 0 0 0 0 0]] \n", "\n", "The K-Nearest Neighbors model has an accuracy of: 81.81818181818183 %\n", "The K-Nearest Neighbors model has a Matthews coeficient of: 79.87224976623695 %\n", "--------------------------------------------------------------------------\n", "\n", "This is the confusion matrix result of the 1st Simulation with a Neural Net \n", " [[3 0 0 0 0 0 0 0 0]\n", " [0 6 0 0 0 0 0 0 0]\n", " [0 0 7 0 0 0 0 0 0]\n", " [0 0 0 4 1 0 0 0 0]\n", " [0 0 0 0 3 0 0 0 0]\n", " [0 0 0 0 0 3 0 0 0]\n", " [0 0 0 0 0 0 2 0 0]\n", " [0 0 0 0 0 0 0 3 0]\n", " [0 0 0 0 0 0 0 0 1]] \n", "\n", "The Neural Net model has an accuracy of: 96.96969696969697 %\n", "The Neural Net model has a Matthews coeficient of: 96.59217405063588 %\n", "--------------------------------------------------------------------------\n", "\n" ] } ], "source": [ "# Model Testing block.\n", "\n", "y_predic_models2 = [] # We create another empty tuple that will contain the values of the predictions made by each model.\n", "accuracy_models2 = [] # We create another empty tuple that will contain the values of the accuracy made by each model.\n", "mcc_models2 = [] # We create another empty tuple that will contain the values of the Matthews coefcient of each model.\n", "\n", "for name, model in zip(names, classifiers):\n", " y_predic2 = model.predict(X_test2) # We predict the test data (Xtest)\n", " y_predic_models2.append(model.predict(X_test2)) # The predictions are going to be inserted in the tuple \n", " matrix = confusion_matrix(y_test2, y_predic2) # Confusion Matrix of each model\n", " print(\"This is the confusion matrix result of the 1st Simulation with a\", name,\"\\n\", matrix, \"\\n\")\n", " # Accuracy and Matthews coeficient of each model\n", " accuracy = accuracy_score(y_test2, y_predic2) # Model accuracy of each model \n", " accuracy_models2.append(accuracy) # The values of accuracy are going to be inserted in tuple\n", " print(\"The\", name, \"model has an accuracy of:\", accuracy*100, \"%\")\n", " mcc = matthews_corrcoef(y_test2, y_predic2) # Matthews coeficient\n", " mcc_models2.append(mcc) # The values of Matthews coeficient are going to be inserted in tuple\n", " print(\"The\", name, \"model has a Matthews coeficient of:\", mcc*100, \"%\")\n", " print(\"--------------------------------------------------------------------------\\n\")" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The metrics of the Decision Tree model is:\n", "\n", " precision recall f1-score support\n", "\n", " Connected_Vehicles 1.000 1.000 1.000 3\n", " ITS 1.000 1.000 1.000 6\n", " Immerse_Experience 1.000 1.000 1.000 7\n", " Industry_Automation 1.000 1.000 1.000 5\n", " Smart_Grid 1.000 0.667 0.800 3\n", " Surveillance 0.750 1.000 0.857 3\n", " UHD_Video_Streaming 1.000 1.000 1.000 2\n", " Vo5G 1.000 1.000 1.000 3\n", " e_Health 1.000 1.000 1.000 1\n", "\n", " accuracy 0.970 33\n", " macro avg 0.972 0.963 0.962 33\n", " weighted avg 0.977 0.970 0.969 33\n", "\n", "The metrics of the Random Forest model is:\n", "\n", " precision recall f1-score support\n", "\n", " Connected_Vehicles 0.750 1.000 0.857 3\n", " ITS 1.000 1.000 1.000 6\n", " Immerse_Experience 1.000 1.000 1.000 7\n", " Industry_Automation 1.000 1.000 1.000 5\n", " Smart_Grid 1.000 0.667 0.800 3\n", " Surveillance 1.000 1.000 1.000 3\n", " UHD_Video_Streaming 1.000 1.000 1.000 2\n", " Vo5G 1.000 1.000 1.000 3\n", " e_Health 1.000 1.000 1.000 1\n", "\n", " accuracy 0.970 33\n", " macro avg 0.972 0.963 0.962 33\n", " weighted avg 0.977 0.970 0.969 33\n", "\n", "The metrics of the Linear SVM model is:\n", "\n", " precision recall f1-score support\n", "\n", " Connected_Vehicles 1.000 1.000 1.000 3\n", " ITS 1.000 1.000 1.000 6\n", " Immerse_Experience 1.000 1.000 1.000 7\n", " Industry_Automation 1.000 1.000 1.000 5\n", " Smart_Grid 1.000 1.000 1.000 3\n", " Surveillance 1.000 1.000 1.000 3\n", " UHD_Video_Streaming 1.000 1.000 1.000 2\n", " Vo5G 1.000 1.000 1.000 3\n", " e_Health 1.000 1.000 1.000 1\n", "\n", " accuracy 1.000 33\n", " macro avg 1.000 1.000 1.000 33\n", " weighted avg 1.000 1.000 1.000 33\n", "\n", "The metrics of the K-Nearest Neighbors model is:\n", "\n", " precision recall f1-score support\n", "\n", " Connected_Vehicles 0.600 1.000 0.750 3\n", " ITS 1.000 0.667 0.800 6\n", " Immerse_Experience 1.000 1.000 1.000 7\n", " Industry_Automation 1.000 0.800 0.889 5\n", " Smart_Grid 0.750 1.000 0.857 3\n", " Surveillance 1.000 0.333 0.500 3\n", " UHD_Video_Streaming 0.500 1.000 0.667 2\n", " Vo5G 1.000 1.000 1.000 3\n", " e_Health 0.000 0.000 0.000 1\n", "\n", " accuracy 0.818 33\n", " macro avg 0.761 0.756 0.718 33\n", " weighted avg 0.880 0.818 0.815 33\n", "\n", "The metrics of the Neural Net model is:\n", "\n", " precision recall f1-score support\n", "\n", " Connected_Vehicles 1.000 1.000 1.000 3\n", " ITS 1.000 1.000 1.000 6\n", " Immerse_Experience 1.000 1.000 1.000 7\n", " Industry_Automation 1.000 1.000 1.000 5\n", " Smart_Grid 1.000 1.000 1.000 3\n", " Surveillance 0.750 1.000 0.857 3\n", " UHD_Video_Streaming 1.000 1.000 1.000 2\n", " Vo5G 1.000 0.667 0.800 3\n", " e_Health 1.000 1.000 1.000 1\n", "\n", " accuracy 0.970 33\n", " macro avg 0.972 0.963 0.962 33\n", " weighted avg 0.977 0.970 0.969 33\n", "\n" ] } ], "source": [ "# All the metrics and their values for each model\n", "\n", "for name, y_predic in zip(names, y_predic_models2):\n", " print(\"The metrics of the\", name, \"model is:\\n\")\n", " print(metrics.classification_report(y_test2, y_predic, digits = 3))" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "def calculate_difference(): # A function to calculate if the predictive model is overfitting\n", " global difference\n", " global overfitting\n", " global accuracy\n", " accuracy_cross_validation = cross_val_scores2[1]*100 # We calculate difference for the Random Forest algorithm\n", " accuracy = accuracy_models2[1]*100\n", " difference = accuracy_cross_validation - accuracy # Difference between the accuracy of the cross-validation stage and the accuracy of the predictive model\n", " \n", " if difference > 5.5 or accuracy == 100: # If difference > 5 or accuracy = 100% the model is overfitting\n", " overfitting = True\n", " print(\"It's look like the predictive model is overfitting\\n\")\n", " elif accuracy < accuracy_models[1]*100: # We compared with the accuracy of the Decision Tree in the first simulation\n", " print(\"It's look like the predictive model is not good\\n\")\n", " overfitting = True\n", " else: # else the model is not overfitting and capable for classify services\n", " overfitting = False" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "It´s looks like the predictive model is correct and ready to predict new services with a accuracy of 97 %\n" ] } ], "source": [ "#----------------------------------- Model Validation block ----------------------------------------------\n", "\n", "# If the results achieved in terms of the metrics are not as expected, the entire cycle must be repeated, \n", "# starting from the training of the ML algorithm, until a reasonable rate of success is observed, so that \n", "# the model will generate fewer mistakes in the future. In the latter case, one or more of the following actions can be taken:\n", "# •\tIncreasing the volume of data used to train the ML algorithm and test the predictive model.\n", "# •\tChoosing another ML algorithm.\n", "# •\tMaking the algorithm used in the simulation more straightforward or more complex to achieve better precision\n", "# We choose the 3rd option, and we make a new Random Forest with a max_depth of 3 causing that this new Random Forest\n", "# be more complex\n", "\n", "calculate_difference() # Call the function for calculate the difference \n", "# overfitting = True # You can erase the comment and probe the while cicle if overfitting = True\n", "\n", "while overfitting: # While the model is overfitting this cicle will be repeated\n", " print(\"The training of the model will be executed again\")\n", " X_train2, X_test2, y_train2, y_test2 = train_test_split(X2, Y2, test_size = 0.2) # Data will be slit again\n", " algorithm = RandomForestClassifier(criterion = 'entropy', random_state = 0, max_depth = 3) # the 3rd option\n", " algorithm.fit(X_train2, y_train2) # The algorithm will be trained again\n", " kf = KFold(shuffle = True) # The kf values will be shuffle in every cicle\n", " cross_val = cross_val_score(algorithm, X_train2, y_train2, cv = kf, scoring = \"accuracy\")\n", " # We need to update the values in the tuples previously created\n", " cross_val_scores2[1] = cross_val.mean() \n", " y_predic_models2[1] = algorithm.predict(X_test2) \n", " accuracy_models2[1] = accuracy_score(y_test2, y_predic_models2[1])\n", " calculate_difference() # And we called the function for verify if the model is overffiting\n", " \n", "\n", "print(\"\\nIt´s looks like the predictive model is correct and ready to predict new services with a accuracy of\",\n", " round(accuracy), \"%\")" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We show how looks like one of the trees of the Random Forest trained with KPI parameters\n", "\n", "# Feature names of the first simulation\n", "feature_names = ['Latency (ms)', 'Jitter (ms)', 'Bit Rate (Mbps)', 'Packet Loss Rate (%)', \n", " 'Peak Data Rate DL (Gbps)', 'Peak Data Rate UL (Gbps)', 'Mobility (km/h)',\n", " 'Reliability (%)']\n", "\n", "# Feature names of the second simulation\n", "feature_names2 = ['Latency (ms)', 'Jitter (ms)', 'Bit Rate (Mbps)', 'Packet Loss Rate (%)', \n", " 'Peak Data Rate DL (Gbps)', 'Peak Data Rate UL (Gbps)', 'Mobility (km/h)',\n", " 'Reliability (%)', 'Service Availability (%)', 'Survival Time (ms)', \n", " 'Experienced Data Rate DL (Mbps)', 'Experienced Data Rate UL (Mbps)',\n", " 'Interruption Time (ms)']\n", "\n", "# Class names \n", "class_names = ['UHD video streaming', 'Immerse experience', 'Connected vehicles', 'eHealth',\n", " 'Industry automation', 'Smart grid', 'Video surveillance', 'ITS', 'Vo5G']\n", "\n", "\n", "plt.figure(figsize=(22,10)) # Figure size\n", "estimator = classifiers[1] # Remember that the second value of the tuple corresponds to the Random Forest algorithm\n", "estimator.fit(X_train, y_train) # We train it again with the KPIs training data.\n", "# The forest is trained in the same way since we are indicating the parameter of random_state = 0\n", "\n", "# Now we draw the scheme\n", "tree.plot_tree(estimator[2], feature_names = feature_names, fontsize = 9, \n", " filled = True, rounded = True, class_names = class_names)\n", "\n", "plt.savefig('tree', dpi = 100) # We save the 1st scheme in .PNG format\n", "\n", "plt.figure(figsize=(22,10)) \n", "estimator = classifiers[1]\n", "estimator.fit(X_train2, y_train2) # We train it again with the KPIs and KQIs training data.\n", "tree.plot_tree(estimator[2], feature_names = feature_names2, fontsize = 9,\n", " filled = True, rounded = True, class_names = class_names)\n", "\n", "plt.savefig('tree2', dpi=100) # We save the 2nd scheme in .PNG format" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }