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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "6064e0b1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 0 1 2 3 4 5 \\\n",
+ "0 70.399324 127673.0908 -49.572308 127648.0176 -169.578319 127723.2374 \n",
+ "\n",
+ " 6 7 8 9 ... 119 120 121 122 123 \\\n",
+ "0 65.689611 605.91099 -57.003571 626.78553 ... 0 0 0 0 0 \n",
+ "\n",
+ " 124 125 126 127 128 \n",
+ "0 0 0 0 0 0 \n",
+ "\n",
+ "[1 rows x 129 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Import scikit-learn dataset library\n",
+ "#from sklearn import datasets\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "from sklearn import svm, metrics\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from joblib import dump\n",
+ "\n",
+ "\n",
+ "df = pd.read_csv('H:\\AI classification\\TrainingDataMulti.csv', header=None)\n",
+ "\n",
+ "print(df.head(1))\n",
+ "\n",
+ "df_feature = df.iloc[:, :128]\n",
+ "\n",
+ "df_label = df.iloc[:, 128]\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "#dftest = pd.read_csv('H:\\AI classification\\TestingDataBinary.csv')\n",
+ "\n",
+ "#X_test = dftest.iloc[:, :128]\n",
+ "\n",
+ "#y_test = dftest.iloc[:, 128]\n",
+ "\n",
+ "\n",
+ "#Load dataset\n",
+ "#cancer = datasets.load_breast_cancer()\n",
+ "\n",
+ "# print the names of the features\n",
+ "#print(\"Features: \", cancer.feature_names)\n",
+ "\n",
+ "# print the label type of cancer('malignant' 'benign')\n",
+ "#print(\"Labels: \", cancer.target_names)\n",
+ "\n",
+ "# print data(feature)shape\n",
+ "#print (cancer.data.shape)\n",
+ "\n",
+ "\n",
+ "# Split dataset into training set and test set\n",
+ "X_train, X_test, y_train, y_test = train_test_split(df_feature, df_label, test_size=0.2) # 80% training and 20% test\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c4bcd563",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Create a svm Classifier\n",
+ "clf = svm.SVC(kernel='linear') # Linear Kernel\n",
+ "\n",
+ "#Train the model using the training sets\n",
+ "clf.fit(X_train, y_train)\n",
+ "\n",
+ "#Predict the response for test dataset\n",
+ "y_pred = clf.predict(X_test)\n",
+ "\n",
+ "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "cdc65331",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.9558333333333333\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a rfc \n",
+ "clf1 = RandomForestClassifier(n_estimators=200, max_features=78)\n",
+ "\n",
+ "clf1.fit(X_train, y_train)\n",
+ "\n",
+ "y_pred1 = clf1.predict(X_test)\n",
+ "\n",
+ "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred1))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "ae7d5339",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Scores [0.934375 0.91979167 0.940625 0.94583333 0.93958333]\n",
+ "Mean Scores 0.9360416666666668\n"
+ ]
+ }
+ ],
+ "source": [
+ "scores1 = cross_val_score(clf1, X_train, y_train, cv=5)\n",
+ "\n",
+ "print(\"Scores\", scores1)\n",
+ "print(\"Mean Scores\", np.mean(scores1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "253d7c20",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['H:/AI classification/RFC_part2.pkl']"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dump(clf1, 'H:/AI classification/RFC_part2.pkl')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "943bcea4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load testing dataset\n",
+ "test_data=pd.read_csv('H:\\AI classification\\TestingDataMulti.csv', header=None)\n",
+ "# predict dataset\n",
+ "predictions = clf1.predict(test_data)\n",
+ "predictions_df = pd.DataFrame(predictions)\n",
+ "# write the result to dataset\n",
+ "result = pd.concat([test_data,predictions_df], axis=1)\n",
+ "#create a csv dcument\n",
+ "result.to_csv('H:/AI classification/test_pre2.csv', index = False, header = False)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "8a8a1e04",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+ " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1\n",
+ " 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(predictions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1521abed",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.9.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}