{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Z8R69UJ6cvTF" }, "source": [ "# **INPUT LIBRARY**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fni2ZayEHNBL" }, "outputs": [], "source": [ "import librosa\n", "import librosa.display\n", "import pandas as pd\n", "import os\n", "import numpy as np\n", "from scipy.fftpack import dct\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.svm import LinearSVC\n", "from sklearn import metrics\n", "from joblib import dump, load\n", "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report, confusion_matrix, f1_score\n", "# from google.colab import drive\n", "import ctypes\n", "from ctypes import util\n", "import matplotlib.pyplot as plt\n", "from openpyxl import Workbook\n", "from openpyxl.utils.dataframe import dataframe_to_rows\n", "from openpyxl.styles import Border, Side" ] }, { "cell_type": "markdown", "metadata": { "id": "QyDOnMcJgZ48" }, "source": [ "# **PROSES MFCC**" ] }, { "cell_type": "markdown", "metadata": { "id": "ebw2dT3acNyx" }, "source": [ "**Fungis Pre-Emphasis**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SigcZQ-5Hdz_" }, "outputs": [], "source": [ "# Fungsi pre-emphasis\n", "def pre_emphasis(signal, coefficient=0.97):\n", " return np.append(signal[0], signal[1:] - coefficient * signal[:-1])" ] }, { "cell_type": "markdown", "metadata": { "id": "vWao7svucTno" }, "source": [ "**Fungsi Frame-Blocking**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PhqB8zmrHhQw" }, "outputs": [], "source": [ "# Fungsi framing\n", "def framing(signal, sample_rate, frame_length=0.025,\n", " frame_step=0.010):\n", " nsamples_signal = len(signal)\n", " nsamples_frame = int(round(frame_length * sample_rate))\n", " nsamples_stride = int(round(frame_step * sample_rate))\n", " n_frames = int(np.ceil((nsamples_signal - nsamples_frame) / nsamples_stride) + 1)\n", " nsamples_padding = ((n_frames - 1) * nsamples_stride + nsamples_frame) - nsamples_signal\n", " z = np.zeros(nsamples_padding)\n", " signal = np.append(signal, z)\n", " frames = np.empty((n_frames, nsamples_frame))\n", " for i in range(n_frames):\n", " left = i * nsamples_stride\n", " right = left + nsamples_frame\n", " frame = signal[left:right]\n", " frames[i] = frame\n", " return frames" ] }, { "cell_type": "markdown", "metadata": { "id": "TSzNwvIdcaY8" }, "source": [ "**Fungsi MFCC**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "urkPLMLFHklQ" }, "outputs": [], "source": [ "# Fungsi untuk mengekstrak fitur MFCC dari file audio\n", "def extract_mfcc(audio_file, sr=44100, frame_length=0.025, frame_step=0.010, nfilt=40, num_ceps=12):\n", " y, _ = librosa.load(audio_file, sr=sr)\n", "\n", " # Normalize\n", " y_norm = librosa.util.normalize(y)\n", "\n", " # Pre-emphasis\n", " y_pre_emphasis = pre_emphasis(y_norm)\n", "\n", " # Framing\n", " frames = framing(y_pre_emphasis, sr, frame_length, frame_step)\n", "\n", " # Hamming window\n", " frames *= np.hanning(len(frames[0]))\n", "\n", " # FFT\n", " NFFT = 512\n", " mag_frames = np.absolute(np.fft.rfft(frames, NFFT))\n", " pow_frames = ((1.0 / NFFT) * ((mag_frames) ** 2))\n", "\n", " # Mel filter bank\n", " low_freq_mel = 0\n", " high_freq_mel = (2595 * np.log10(1 + (sr / 2) / 700))\n", " mel_points = np.linspace(low_freq_mel, high_freq_mel, nfilt + 2)\n", " hz_points = (700 * (10**(mel_points / 2595) - 1))\n", " bin = np.floor((NFFT + 1) * hz_points / sr)\n", "\n", " fbank = np.zeros((nfilt, int(np.floor(NFFT / 2 + 1))))\n", " for m in range(1, nfilt + 1):\n", " f_m_minus = int(bin[m - 1])\n", " f_m = int(bin[m])\n", " f_m_plus = int(bin[m + 1])\n", " for k in range(f_m_minus, f_m):\n", " fbank[m - 1, k] = (k - bin[m - 1]) / (bin[m] - bin[m - 1])\n", " for k in range(f_m, f_m_plus):\n", " fbank[m - 1, k] = (bin[m + 1] - k) / (bin[m + 1] - bin[m])\n", "\n", " filter_banks = np.dot(pow_frames, fbank.T)\n", " filter_banks = np.where(filter_banks == 0, np.finfo(float).eps, filter_banks)\n", " filter_banks = 20 * np.log10(filter_banks)\n", "\n", " # Discrete Cosine Transform (DCT) for MFCC\n", " mfcc = dct(filter_banks, type=2, axis=1, norm='ortho')[:, 1:(num_ceps + 1)]\n", "\n", " return np.mean(mfcc, axis=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "17rFJaT_gGwo" }, "source": [ "**Dataset**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CKcTFlrEHq0T" }, "outputs": [], "source": [ "# Directory tempat file audio Anda disimpan\n", "dataset = '/content/drive/MyDrive/REVISI SKRIPSI/Dataset Pertama'" ] }, { "cell_type": "code", "source": [ "dataset_kedua = '/content/drive/MyDrive/REVISI SKRIPSI/Dataset Kedua'" ], "metadata": { "id": "rXuoZlCgI73-" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "urbp24shuk3s" }, "source": [ "**Inisialisasi Dataset**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "M5kcsHfqHvL2" }, "outputs": [], "source": [ "data = []\n", "\n", "# Loop melalui setiap direktori label\n", "for label_folder in os.listdir(dataset):\n", " label_dir = os.path.join(dataset, label_folder)\n", " if os.path.isdir(label_dir):\n", " # Loop melalui file-file audio di dalam setiap label\n", " for file in os.listdir(label_dir):\n", " if file.endswith(\".wav\"):\n", " audio_file = os.path.join(label_dir, file)\n", " mfcc_features = extract_mfcc(audio_file)\n", " # Menambahkan ID ke data\n", " features_list = list(mfcc_features) + [label_folder]\n", " data.append(features_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GKuXVqAyXGa1" }, "outputs": [], "source": [ "# Inisialisasi data\n", "data = []\n", "\n", "# Loop melalui setiap direktori ID\n", "for id_folder in os.listdir(dataset):\n", " id_dir = os.path.join(dataset, id_folder)\n", " if os.path.isdir(id_dir):\n", " # Loop melalui setiap label di dalam direktori ID\n", " for label in os.listdir(id_dir):\n", " label_dir = os.path.join(id_dir, label)\n", " if os.path.isdir(label_dir):\n", " # Loop melalui file-file audio di dalam setiap label\n", " for file in os.listdir(label_dir):\n", " if file.endswith(\".wav\"):\n", " audio_file = os.path.join(label_dir, file)\n", " mfcc_features = extract_mfcc(audio_file)\n", " # Menambahkan ID ke data\n", " data.append(list(mfcc_features) + [id_folder] + [label])" ] }, { "cell_type": "markdown", "metadata": { "id": "FVsE69BGgSZi" }, "source": [ "**Menyimpan Hasil MFCC ke CSV**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "RYqBjtOxXXon" }, "outputs": [], "source": [ "# Mengubah data ke dalam DataFrame\n", "df = pd.DataFrame(data, columns=[f'mfcc_{i}' for i in range(1, 13)] + ['id', 'label'])\n", "\n", "# Menggabungkan kolom ID dan label menjadi satu kolom id_label\n", "df['id_label'] = df['id'] + '_' + df['label']\n", "\n", "# Menyimpan DataFrame yang sudah diubah ke dalam file CSV\n", "df.to_csv('/content/drive/MyDrive/Skripsi/mfcc/backup_4_label.csv', index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Lgn-kmfFCajq" }, "outputs": [], "source": [ " # Mengubah data ke dalam DataFrame\n", "df = pd.DataFrame(data, columns=[f'mfcc_{i}' for i in range(1, 13)] + ['target'])\n", "\n", "# Menyimpan DataFrame yang sudah diubah ke dalam file CSV\n", "df.to_csv('/content/drive/MyDrive/REVISI SKRIPSI/dataset_pertama.csv', index=False)" ] }, { "cell_type": "code", "source": [ "# Menyimpan DataFrame ke dalam file Excel\n", "wb = Workbook()\n", "ws = wb.active\n", "\n", "# Menulis data ke dalam worksheet\n", "for r in dataframe_to_rows(df, index=True, header=True):\n", " ws.append(r)\n", "\n", "# Menambahkan border di antara setiap sel\n", "for row in ws.iter_rows():\n", " for cell in row:\n", " cell.border = Border(left=Side(style='thin'),\n", " right=Side(style='thin'),\n", " top=Side(style='thin'),\n", " bottom=Side(style='thin'))\n", "\n", "# Menyimpan file Excel\n", "wb.save('/content/drive/MyDrive/Persiapan Sidang/inisialisasi data label.xlsx')" ], "metadata": { "id": "a2W2-nG4rZOu" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "C4VSfME2v0F9" }, "source": [ "# **KLASIFIKASI SVM**" ] }, { "cell_type": "markdown", "metadata": { "id": "9MO1GqtaCxX_" }, "source": [ "## **A. Split Data**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "M9FGECAbxYbg" }, "outputs": [], "source": [ "read_csv = pd.read_csv('/content/drive/MyDrive/REVISI SKRIPSI/dataset_pertama.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 443 }, "id": "yVh_JnDdozez", "outputId": "850babf2-eda9-4f7b-cbcf-9c1c4d81ffe8" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " mfcc_1 mfcc_2 mfcc_3 mfcc_4 mfcc_5 mfcc_6 \\\n", "0 134.007563 -10.597640 7.266465 -3.754714 -13.808333 2.155736 \n", "1 50.750191 -5.598916 32.949004 -22.501260 14.779131 -19.538301 \n", "2 122.293842 -12.814458 12.493625 -10.989077 -15.963840 1.330859 \n", "3 126.803849 -0.053941 8.270297 -6.800313 -13.392560 -0.899367 \n", "4 10.468602 9.117828 -8.683499 -1.466168 24.753077 -21.531051 \n", ".. ... ... ... ... ... ... \n", "145 36.427943 25.707284 8.212668 10.450583 -5.248485 -11.510404 \n", "146 43.954271 6.456048 2.131374 -9.353882 -17.369689 -7.557169 \n", "147 55.782108 26.974924 -5.329280 -1.423440 -9.777956 -8.758294 \n", "148 26.584968 -8.043035 -4.178721 -0.592769 -15.964644 -9.010896 \n", "149 19.589333 9.885163 -20.071891 -13.235444 -15.691194 -10.111671 \n", "\n", " mfcc_7 mfcc_8 mfcc_9 mfcc_10 mfcc_11 mfcc_12 target \n", "0 -12.878377 -4.975601 6.321278 -2.777375 -6.506576 -1.876924 Hilmi \n", "1 7.961897 -11.957439 7.594936 -14.929685 5.006317 -1.295943 Hilmi \n", "2 -17.650124 -3.014083 3.925209 -6.664020 -1.170681 -0.027291 Hilmi \n", "3 -14.874580 0.227556 7.045879 -7.464138 -5.668817 -0.148281 Hilmi \n", "4 2.124193 -6.581121 11.937312 -18.942954 5.763798 0.569468 Hilmi \n", ".. ... ... ... ... ... ... ... \n", "145 -6.744978 -9.246621 -3.982937 -10.705474 -6.795224 -6.532252 Random \n", "146 -15.326618 -16.521912 1.466365 -6.723767 1.931086 2.092220 Random \n", "147 -1.948076 -6.980490 -0.893085 -4.223947 -4.055362 -5.809257 Random \n", "148 -4.953900 -11.460907 -0.344327 -4.228080 -1.677582 -2.752977 Random \n", "149 2.390462 -5.974159 -0.621773 -2.311574 4.188963 -0.232814 Random \n", "\n", "[150 rows x 13 columns]" ], "text/html": [ "\n", "
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Data Standartlization**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "o4Enm5nm1ejn" }, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" ] }, { "cell_type": "code", "source": [ "model = SVC(kernel='rbf', C=8, gamma=0.001)\n", "# model = SVC(kernel='linear', C=0.1)" ], "metadata": { "id": "bBAFR2v2tOej" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Wrap model SVM dengan OneVsOneClassifier\n", "ovo_model = OneVsOneClassifier(model)\n", "\n", "# Latih model pada data latih\n", "ovo_model.fit(X_train, y_train)\n", "\n", "# Prediksi pada data uji\n", "y_pred_ovo = ovo_model.predict(X_test)\n", "\n", "print(\"Accuracy:\", metrics.accuracy_score(y_pred_ovo, y_test))\n", "print(\"\\nClassification Report:\\n\", classification_report(y_pred_ovo, y_test))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "A1GxW3ANoyLq", "outputId": "e7dc06bd-f5d2-4581-9f82-0ad627f7b143" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy: 0.9666666666666667\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Hilmi 1.00 0.86 0.92 7\n", " Random 1.00 1.00 1.00 7\n", " Tanti 0.83 1.00 0.91 5\n", " Vasyilla 1.00 1.00 1.00 7\n", " Yudha 1.00 1.00 1.00 4\n", "\n", " accuracy 0.97 30\n", " macro avg 0.97 0.97 0.97 30\n", "weighted avg 0.97 0.97 0.97 30\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "# Wrap model SVM dengan OneVsOneClassifier\n", "ovr_model =OneVsRestClassifier(model)\n", "\n", "# Latih model pada data latih\n", "ovr_model.fit(X_train, y_train)\n", "\n", "# Prediksi pada data uji\n", "y_pred_ovr = ovr_model.predict(X_test)\n", "\n", "print(\"Accuracy:\", metrics.accuracy_score(y_pred_ovr, y_test))\n", "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred_ovr))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0RJWyMC0uMkw", "outputId": "e2a1f8fd-5e9a-4667-b3f6-efae745555e7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy: 0.9666666666666667\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Hilmi 0.86 1.00 0.92 6\n", " Random 1.00 1.00 1.00 7\n", " Tanti 1.00 0.83 0.91 6\n", " Vasyilla 1.00 1.00 1.00 7\n", " Yudha 1.00 1.00 1.00 4\n", "\n", " accuracy 0.97 30\n", " macro avg 0.97 0.97 0.97 30\n", "weighted avg 0.97 0.97 0.97 30\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "cm = confusion_matrix(y_test, y_pred_ovo)\n", "\n", "# Hitung True Positives (TP) untuk setiap kelas\n", "TP = np.diag(cm)\n", "\n", "TN = []\n", "FP = []\n", "FN = []\n", "for i in range(len(cm)):\n", " temp = np.delete(cm, i, 0) # hapus baris ke-i\n", " temp = np.delete(temp, i, 1) # hapus kolom ke-i\n", " TN.append(np.sum(np.delete(np.delete(cm, i, 0), i, 1)))\n", " FP.append(np.sum(cm[:, i]) - cm[i, i])\n", " FN.append(np.sum(cm[i, :]) - cm[i, i])\n", "\n", "# Hitung total TP, TN, FP, FN\n", "total_TP = np.sum(TP)\n", "total_TN = np.sum(TN)\n", "total_FP = np.sum(FP)\n", "total_FN = np.sum(FN)\n", "\n", "# Cetak total\n", "print(f\"Total True Positives (TP): {total_TP}\")\n", "print(f\"Total True Negatives (TN): {total_TN}\")\n", "print(f\"Total False Positives (FP): {total_FP}\")\n", "print(f\"Total False Negatives (FN): {total_FN}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ho499J8zjT30", "outputId": "e307f777-bac9-4b78-b5c1-1468bc9da5dd" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total True Positives (TP): 29\n", "Total True Negatives (TN): 119\n", "Total False Positives (FP): 1\n", "Total False Negatives (FN): 1\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "o_vQ0aqkrZVY" }, "source": [ "## **C. Mencari kernel yang terbaik**\n", "\n", "a. Default kernel\n", "\n", "b. Melakukan validasi silang K-Fold dengan kernel yang berbeda\n", "\n", "c. Membandingkan kernel yang terbaik\n", "\n", "d. Kernel Paling Akurat (kernel='poly', C=0.1, degree=3, gamma=8)" ] }, { "cell_type": "markdown", "metadata": { "id": "bIpz46aODKV4" }, "source": [ "### ***a. Default kernel***" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vfIftRBoxgo8", "outputId": "77a76665-92a9-444c-d3e9-4a83cf4bed58" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------- Linear ---------------------------------\n", "Accuracy: 0.5787037037037037\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Depan Mati 0.62 0.70 0.66 37\n", " Depan Menyala 0.68 0.63 0.65 43\n", " Samping Mati 0.55 0.47 0.51 34\n", "Samping Menyala 0.51 0.54 0.53 39\n", " Tengah Mati 0.55 0.55 0.55 31\n", " Tengah Menyala 0.55 0.56 0.55 32\n", "\n", " accuracy 0.58 216\n", " macro avg 0.58 0.58 0.57 216\n", " weighted avg 0.58 0.58 0.58 216\n", "\n", "--------------------------------- RBF ---------------------------------\n", "Accuracy: 0.4398148148148148\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Depan Mati 0.51 0.57 0.54 37\n", " Depan Menyala 0.51 0.49 0.50 43\n", " Samping Mati 0.41 0.53 0.46 34\n", "Samping Menyala 0.33 0.36 0.35 39\n", " Tengah Mati 0.41 0.23 0.29 31\n", " Tengah Menyala 0.45 0.44 0.44 32\n", "\n", " accuracy 0.44 216\n", " macro avg 0.44 0.43 0.43 216\n", " weighted avg 0.44 0.44 0.43 216\n", "\n", "--------------------------------- Polynomial ---------------------------------\n", "Accuracy: 0.4027777777777778\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Depan Mati 0.44 0.22 0.29 37\n", " Depan Menyala 0.54 0.44 0.49 43\n", " Samping Mati 0.24 0.76 0.37 34\n", "Samping Menyala 0.50 0.28 0.36 39\n", " Tengah Mati 0.67 0.26 0.37 31\n", " Tengah Menyala 0.71 0.47 0.57 32\n", "\n", " accuracy 0.40 216\n", " macro avg 0.52 0.41 0.41 216\n", " weighted avg 0.51 0.40 0.41 216\n", "\n" ] } ], "source": [ "# Default Linear\n", "svm_default_linear = SVC(kernel='linear')\n", "svm_default_linear.fit(X_train, y_train)\n", "default_linear = svm_default_linear.predict(X_test)\n", "# dump(svm_default_linear, '/content/drive/MyDrive/Skripsi/linear/3.pkl')\n", "\n", "# # Default RBF\n", "svm_default_rbf = SVC(kernel='rbf')\n", "svm_default_rbf.fit(X_train, y_train)\n", "default_rbf = svm_default_rbf.predict(X_test)\n", "# dump(svm_default_linear, '/content/drive/MyDrive/Skripsi/rbf/4.pkl')\n", "\n", "# Default Poly\n", "svm_default_poly = SVC(kernel='poly')\n", "svm_default_poly.fit(X_train, y_train)\n", "default_poly = svm_default_poly.predict(X_test)\n", "# dump(svm_default_linear, '/content/drive/MyDrive/Skripsi/poly/poly_hilmi_3.pkl')\n", "\n", "# # Evaluasi model\n", "print('--------------------------------- Linear ---------------------------------')\n", "print(\"Accuracy:\", accuracy_score(y_test, default_linear))\n", "print(\"\\nClassification Report:\\n\", classification_report(y_test, default_linear))\n", "\n", "print('--------------------------------- RBF ---------------------------------')\n", "\n", "print(\"Accuracy:\", accuracy_score(y_test, default_rbf))\n", "print(\"\\nClassification Report:\\n\", classification_report(y_test, default_rbf))\n", "\n", "print('--------------------------------- Polynomial ---------------------------------')\n", "\n", "print(\"Accuracy:\", accuracy_score(y_test, default_poly))\n", "print(\"\\nClassification Report:\\n\", classification_report(y_test, default_poly))" ] }, { "cell_type": "markdown", "metadata": { "id": "TkKYogWSKC7m" }, "source": [ "### ***b. Melakukan validasi silang K-fold dengan kernel yang berbeda***" ] }, { "cell_type": "markdown", "metadata": { "id": "z33TWr6_jza2" }, "source": [ "Dalam K-fold cross validation, dataset dibagi menjadi K subset yang sama besar. Setiap subset digunakan satu kali sebagai data uji sementara subset lainnya digunakan sebagai data pelatihan. Ini dilakukan K kali dengan setiap subset digunakan sebagai data uji sekali. Ini memastikan bahwa semua data digunakan untuk pelatihan dan pengujian, dan hasil akhir adalah rata-rata dari kinerja model pada semua iterasi." ] }, { "cell_type": "markdown", "metadata": { "id": "CIZftb1ujXcj" }, "source": [ "#### 1. CV on Kernel" ] }, { "cell_type": "markdown", "metadata": { "id": "ByDnGhjgK1dN" }, "source": [ "##### *CV on Linear kernel*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "apSkmWJ0I3iV" }, "outputs": [], "source": [ "from sklearn.model_selection import cross_val_score" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AxNRgjWlAgTn", "outputId": "b607efc1-21f6-419c-fd29-f834e7cd1acd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.31481481 0.27777778 0.5 0.47222222 0.4537037 0.25\n", " 0.31481481 0.27777778 0.44444444 0.39814815]\n", "\n", "Rata-rata Akurasi: 0.37037037037037035\n" ] } ], "source": [ "svc=SVC(kernel='linear')\n", "scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy') #cv is cross validation\n", "print(scores)\n", "print()\n", "print(\"Rata-rata Akurasi:\", scores.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "53eHmjLgLK6L" }, "source": [ "##### *CV on Linear RBF*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "iYPULGRdLL_d", "outputId": "f6f4f50d-a92a-49db-ec01-848b066b721c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.14814815 0.36111111 0.34259259 0.39814815 0.39814815 0.26851852\n", " 0.49074074 0.33333333 0.48148148 0.46296296]\n", "\n", "Rata-rata Akurasi: 0.3685185185185185\n" ] } ], "source": [ "svc=SVC(kernel='rbf')\n", "scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy') #cv is cross validation\n", "print(scores)\n", "print()\n", "print(\"Rata-rata Akurasi:\", scores.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "9z2TeRgOLmcE" }, "source": [ "##### CV on Kernel Polynomial" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PEqeJfBbOpZw", "outputId": "0132e132-114e-428e-f384-ce19cfdd548f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.12962963 0.38888889 0.38888889 0.58333333 0.39814815 0.16666667\n", " 0.33333333 0.21296296 0.46296296 0.30555556]\n", "\n", "Rata-rata Akurasi: 0.337037037037037\n" ] } ], "source": [ "svc=SVC(kernel='poly')\n", "scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy') #cv is cross validation\n", "print(scores)\n", "print()\n", "print(\"Rata-rata Akurasi:\", scores.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "6dOzBBDti-lP" }, "source": [ "##### **Note :** CV (Cross Validation) adalah yang merupakan teknik yang digunakan untuk mengevaluasi kinerja model machine learning. Cross-validation melibatkan pembagian dataset menjadi subset yang saling eksklusif, di mana beberapa subset digunakan untuk melatih model dan subset lainnya digunakan untuk menguji model. Ini membantu menghindari overfitting dan memberikan estimasi yang lebih baik tentang kinerja model pada data yang tidak terlihat." ] }, { "cell_type": "markdown", "metadata": { "id": "BuOJaSexlifl" }, "source": [ "#### 2. Mencari Kernel, degree, C dan gamma" ] }, { "cell_type": "markdown", "metadata": { "id": "lglj8qk0PEy5" }, "source": [ "##### Mengambil kernel sebagai linear dan mengambil nilai C" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IGbkcH93PVI-" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 389 }, "id": "0Kaz_KbwPuZs", "outputId": "aacc1e63-22bc-4e0f-ee5e-dd9c917e91fd" }, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m 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\u001b[0mnew_object\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mnew_object_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 91\u001b[0m \u001b[0mparams_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/svm/_classes.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, C, kernel, degree, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, break_ties, random_state)\u001b[0m\n\u001b[1;32m 798\u001b[0m ):\n\u001b[1;32m 799\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 800\u001b[0;31m super().__init__(\n\u001b[0m\u001b[1;32m 801\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 802\u001b[0m \u001b[0mdegree\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdegree\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, kernel, degree, gamma, coef0, tol, C, nu, shrinking, probability, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state, break_ties)\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0m_parameter_constraints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munused_param\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 702\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m \u001b[0;34m@\u001b[0m\u001b[0mabstractmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 704\u001b[0m def __init__(\n\u001b[1;32m 705\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "C_range=list(range(1,5))\n", "acc_score=[]\n", "for c in C_range:\n", " svc = SVC(kernel='linear', C=c)\n", " scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n", " acc_score.append(scores.mean())\n", "print(acc_score)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6SOP5EF2QCuC" }, "outputs": [], "source": [ "C_values=list(range(1,5))\n", "# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n", "plt.plot(C_values,acc_score)\n", "plt.xticks(np.arange(0,5,1))\n", "plt.xlabel('Value of C for SVC')\n", "plt.ylabel('Cross-Validated Accuracy')" ] }, { "cell_type": "markdown", "metadata": { "id": "wG0_1_Vnjnx9" }, "source": [ "**Penjelasan :** Dari plot di atas kita dapat melihat bahwa akurasinya mendekati 86% untuk C=1.2,kemudian turun sekitar 90%." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "I9CMwDX_k1vQ" }, "outputs": [], "source": [ "C_range=list(np.arange(0.1,5))\n", "acc_score=[]\n", "for c in C_range:\n", " svc = SVC(kernel='linear', C=c)\n", " scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n", " acc_score.append(scores.mean())\n", "print(acc_score)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ocVhPSRilHwi" }, "outputs": [], "source": [ "C_values=list(np.arange(0.1,5))\n", "# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n", "plt.plot(C_values,acc_score)\n", "plt.xticks(np.arange(0.0,5,0.4))\n", "plt.xlabel('Value of C for SVC ')\n", "plt.ylabel('Cross-Validated Accuracy')" ] }, { "cell_type": "markdown", "metadata": { "id": "p6on4ZEJlqnT" }, "source": [ "**Jadi:** Akurasi tertinggi terdapat pada 1.1" ] }, { "cell_type": "markdown", "metadata": { "id": "DV3MdEU6Q-Hq" }, "source": [ "##### Mengambil kernel sebagai rbf dan mengambil nilai gamma yang berbeda" ] }, { "cell_type": "markdown", "metadata": { "id": "8k6eQ1adROYE" }, "source": [ "**Note:** Secara teknis, parameter gamma merupakan kebalikan dari standar deviasi kernel RBF (fungsi Gaussian), yang digunakan sebagai ukuran kemiripan antara dua titik. Secara intuitif, nilai gamma yang kecil menentukan fungsi Gaussian dengan varian yang besar. Dalam hal ini, dua titik dapat dianggap serupa meskipun letaknya berjauhan. Sebaliknya, nilai gamma yang besar berarti mendefinisikan fungsi Gaussian dengan varians yang kecil dan dalam hal ini, dua titik dianggap serupa hanya jika keduanya berdekatan." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zi4lL-mPRaV3" }, "outputs": [], "source": [ "gamma_range=[0.0001,0.001,0.01,0.1,1,10,100]\n", "acc_score=[]\n", "for g in gamma_range:\n", " svc = SVC(kernel='rbf', gamma=g)\n", " scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n", " acc_score.append(scores.mean())\n", "print(acc_score)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OmvFietuRgyZ" }, "outputs": [], "source": [ "gamma_range=[0.0001,0.001,0.01,0.1,1,10,100]\n", "\n", "# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n", "plt.plot(gamma_range,acc_score)\n", "plt.xlabel('Value of gamma for SVC ')\n", "plt.xticks(np.arange(0.0001,100,5))\n", "plt.ylabel('Cross-Validated Accuracy')" ] }, { "cell_type": "markdown", "metadata": { "id": "BWNG6ZB4SS8h" }, "source": [ "**Penjelasan :** Kita dapat melihat bahwa untuk gamma=0.0001,0.001,0.01, dan 10, kinerja kernel buruk. Kita juga dapat melihat sedikit penurunan dalam skor akurasi ketika gamma adalah 10. Mari kita lihat lebih detail untuk rentang 0,1 hingga 1." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7LS8V8eKSSTz" }, "outputs": [], "source": [ "gamma_range=[0.0001,0.001]\n", "acc_score=[]\n", "for g in gamma_range:\n", " svc = SVC(kernel='rbf', gamma=g)\n", " scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n", " acc_score.append(scores.mean())\n", "print(acc_score)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9KFgTAWIZvwp" }, "outputs": [], "source": [ "gamma_range=[0.0001,0.001]\n", "\n", "# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n", "plt.plot(gamma_range,acc_score)\n", "plt.xlabel('Value of gamma for SVC ')\n", "plt.ylabel('Cross-Validated Accuracy')" ] }, { "cell_type": "markdown", "metadata": { "id": "zsB13ybnalLj" }, "source": [ "**Penjelasan :** Skornya terus menurun mulai dari gamma 0.0001" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fSmV8uCAezmJ" }, "outputs": [], "source": [ "# gamma_range=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]\n", "# acc_score=[]\n", "# for g in gamma_range:\n", "# svc = SVC(kernel='rbf', gamma=g)\n", "# scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n", "# acc_score.append(scores.mean())\n", "# print(acc_score)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "z8-g053Ke8vX" }, "outputs": [], "source": [ "# gamma_range=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]\n", "\n", "# # plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n", "# plt.plot(gamma_range,acc_score)\n", "# plt.xlabel('Value of gamma for SVC ')\n", "# plt.ylabel('Cross-Validated Accuracy')" ] }, { "cell_type": "markdown", "metadata": { "id": "ZLr3F2k9npsH" }, "source": [ "**Penjelasan :** Kita dapat melihat adanya kenaikan skor akurasi yang konstan mulai 0,7 hingga 0,8. Dan penurunan skor terlihat dari gamma ke 0,81 hingga gamma ke 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9mrqmvJZoMc9" }, "outputs": [], "source": [ "# gamma_range=[0.7, 0.8]\n", "# acc_score=[]\n", "# for g in gamma_range:\n", "# svc = SVC(kernel='rbf', gamma=g)\n", "# scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n", "# acc_score.append(scores.mean())\n", "# print(acc_score)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iJdBt_OModmC" }, "outputs": [], "source": [ "# gamma_range=[0.7, 0.81]\n", "\n", "# # plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n", "# plt.plot(gamma_range,acc_score)\n", "# plt.xlabel('Value of gamma for SVC ')\n", "# plt.ylabel('Cross-Validated Accuracy')" ] }, { "cell_type": "markdown", "metadata": { "id": "re-W-Bi1o8lD" }, "source": [ "**Hasilnya :** Jadi akurasi paling tinggi di kernel rbf yaitu pada gamma 0,70" ] }, { "cell_type": "markdown", "metadata": { "id": "xzN7Ml7yfL6I" }, "source": [ "##### Mengambil kernel polinomial dengan derajat berbeda" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "D2oEWYbEfN36" }, "outputs": [], "source": [ "degree=[2,3,4,5,6]\n", "acc_score=[]\n", "for d in degree:\n", " svc = SVC(kernel='poly', degree=d)\n", " scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n", " acc_score.append(scores.mean())\n", "print(acc_score)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sNLaiJavfRcz" }, "outputs": [], "source": [ "degree=[2,3,4,5,6]\n", "\n", "# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n", "plt.plot(degree,acc_score,color='r')\n", "plt.xlabel('degrees for SVC ')\n", "plt.ylabel('Cross-Validated Accuracy')" ] }, { "cell_type": "markdown", "metadata": { "id": "oSWy_QjIhHQp" }, "source": [ "**Penjelasan :** Skor yang tinggi untuk polinomial derajat ketiga dan kemudian terjadi penurunan skor akurasi hingga ke degrees 6.0" ] }, { "cell_type": "markdown", "metadata": { "id": "aP7X0AiAl8s9" }, "source": [ "#### 3. Menjalankan SVM sesuai parameter yang terbaik dari masing-masing kernel" ] }, { "cell_type": "markdown", "metadata": { "id": "d0IyrybqiT8U" }, "source": [ "##### Sekarang jalankan SVM dengan mengambil hyperparameter C=1.1 dan kernel sebagai linier" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "askkOgBppyX7" }, "outputs": [], "source": [ "from sklearn import metrics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vHkJgr_IjLPS", "outputId": "6b278626-76b0-45c6-853d-f36b030b4057" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8555555555555555\n" ] } ], "source": [ "svc= SVC(kernel='linear',C=5)\n", "svc.fit(X_train,y_train)\n", "y_predict=svc.predict(X_test)\n", "accuracy_score= metrics.accuracy_score(y_test,y_predict)\n", "print(accuracy_score)" ] }, { "cell_type": "markdown", "metadata": { "id": "bso5az0PigSq" }, "source": [ "With K-fold cross validation(where K=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nd1uaSXpp-yU", "outputId": "de4e6fb2-8ff1-4ed1-ee16-5c2bf5738ea9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.87037037 0.91666667 0.89814815 0.89814815 0.9537037 0.87962963\n", " 0.90740741 0.91666667 0.92592593 0.85185185]\n" ] } ], "source": [ "svc=SVC(kernel='linear',C=5)\n", "scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n", "print(scores)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0V6UTMK9qENC", "outputId": "cb111ba7-7585-4b70-f926-af15e377bcbe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9018518518518517\n" ] } ], "source": [ "print(scores.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "AIDSY-Y6ijUy" }, "source": [ "##### Sekarang jalankan SVM dengan mengambil hyperparameter gamma=0.70 dan kernel sebagai rbf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tANamosIqPbY", "outputId": "8b036d36-1c7a-40d4-c7c9-61e34c686ae3" }, "outputs": [ { "data": { "text/plain": [ "0.42407407407407405" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svc= SVC(kernel='rbf', gamma=0.0001)\n", "svc.fit(X_train,y_train)\n", "y_predict=svc.predict(X_test)\n", "# dump(svc, '/content/drive/MyDrive/Skripsi/rbf/rbf_hp_g3.pkl')\n", "metrics.accuracy_score(y_test,y_predict)" ] }, { "cell_type": "markdown", "metadata": { "id": "MacT54CpimFA" }, "source": [ "With K-fold cross validation(where K=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ntkTwMi5qYOv", "outputId": "0b917e17-a5d6-46c4-ed5d-7132c1f4abb8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.77777778 0.92592593 0.83333333 0.81481481 0.91666667 0.81481481\n", " 0.82407407 0.74074074 0.77777778 0.65740741]\n" ] } ], "source": [ "svc=SVC(kernel='rbf',gamma=0.001)\n", "scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n", "print(scores)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RExu0sorricU", "outputId": "e478bbf3-2025-49be-f768-53a7644f753a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8083333333333333\n" ] } ], "source": [ "print(scores.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "NY_C0C_Di9oQ" }, "source": [ "##### Sekarang jalankan SVM dengan mengambil hyperparameter degree=2 dan kernel sebagai poly" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "71pTZUJ5qyll", "outputId": "a6639192-c098-4f11-877b-5ce6aaf4f7a8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.15925925925925927\n" ] } ], "source": [ "svc= SVC(kernel='poly',degree=6)\n", "svc.fit(X_train,y_train)\n", "y_predict=svc.predict(X_test)\n", "accuracy_score= metrics.accuracy_score(y_test,y_predict)\n", "print(accuracy_score)" ] }, { "cell_type": "markdown", "metadata": { "id": "11994jzsjDFY" }, "source": [ "With K-fold cross validation(where K=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YNClZNedq3la", "outputId": "aa40b018-bac2-443c-e553-53d8d695c809" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.21296296 0.27777778 0.26851852 0.30555556 0.38888889 0.32407407\n", " 0.37962963 0.32407407 0.35185185 0.28703704]\n" ] } ], "source": [ "svc=SVC(kernel='poly',degree=6)\n", "scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n", "print(scores)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2_21UHOqrp_Q", "outputId": "210dc429-9b3f-4b97-d2cc-9aeca0c3e147" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.3120370370370371\n" ] } ], "source": [ "print(scores.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "BaQieKcwrPaj" }, "source": [ "### ***c. Membandingkan Kernel mana yang terbaik***" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1OmCznEnsK-V" }, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "from sklearn import metrics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vTSuCuiTrU-F" }, "outputs": [], "source": [ "svm_model= SVC()\n", "tuned_parameters = {\n", " 'C': [0.1,1,8,9,10,12,13,15,20,30,40,50,60,70,80,90,100,110,120,130,140],\n", " 'gamma':[1, 0.1, 0.001, 0.001, 0.0001, 0.00001, 0.00009],\n", " 'kernel': ['rbf']\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xWLwV0CnraU9" }, "outputs": [], "source": [ "# model_svm = GridSearchCV(svm_model, tuned_parameters,cv=10,scoring='accuracy')\n", "model_svm = GridSearchCV(svm_model, tuned_parameters, refit=True, verbose=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ENUX6xhQreRU", "outputId": "80767437-2f89-4c39-c23e-b904eb00f572" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fitting 5 folds for each of 147 candidates, totalling 735 fits\n", "[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=0.1, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=0.1, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=0.1, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=0.1, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=0.1, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=0.1, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=1, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=1, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=1, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=1, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=1, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=1, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=1, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=1, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=1, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=1, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=1, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=8, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=8, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=8, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=8, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=8, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=8, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=8, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=8, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=8, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=8, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=8, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=8, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=8, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=8, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=8, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=8, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=9, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=9, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=9, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=9, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ...........................C=9, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=9, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=9, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=9, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=9, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=9, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=9, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=9, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=9, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=9, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=9, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=9, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=10, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=10, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=10, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=10, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=10, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=10, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=10, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=10, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=10, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=10, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=10, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=12, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=12, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=12, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=12, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=12, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=12, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=12, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=12, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=12, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=12, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=12, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=12, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=12, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=12, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=12, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=12, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=13, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=13, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=13, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=13, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=13, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=13, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=13, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=13, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=13, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=13, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=13, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=13, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=13, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=13, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=13, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=13, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=15, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=15, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=15, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=15, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=15, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=15, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=15, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=15, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=15, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=15, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=15, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=15, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=15, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=15, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=15, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=15, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=20, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=20, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=20, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=20, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=20, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=20, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=20, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=20, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=20, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=20, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=20, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=20, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=20, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=20, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=20, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=20, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=30, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=30, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=30, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ..........................C=30, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=30, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=30, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=30, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=30, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=30, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=30, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=30, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=30, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=30, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=30, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=30, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=30, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=30, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=40, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ..........................C=40, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=40, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=40, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ..........................C=40, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=40, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=40, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=40, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=40, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=40, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=40, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=40, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=40, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=40, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=40, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=40, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=40, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=50, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=50, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ..........................C=50, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ..........................C=50, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=50, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=50, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=50, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=50, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=50, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=50, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=50, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=50, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=50, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=50, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=50, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=50, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=50, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=50, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=60, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=60, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=60, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=60, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=60, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=60, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=60, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=60, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=60, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=60, gamma=0.1, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=60, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=60, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=60, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=60, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=60, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=60, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=70, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ..........................C=70, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=70, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=70, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ..........................C=70, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=70, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=70, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=70, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=70, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=70, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=70, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=70, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=70, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=70, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=70, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=70, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=70, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=80, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=80, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ..........................C=80, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ..........................C=80, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=80, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=80, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=80, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=80, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=80, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=80, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=80, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=80, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=80, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=80, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=80, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=80, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=80, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=90, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=90, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ..........................C=90, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END ..........................C=90, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ..........................C=90, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END ........................C=90, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=90, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=90, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=90, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ........................C=90, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=90, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=90, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=90, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=90, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=90, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=90, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=90, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=90, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=1e-05, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=90, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END ......................C=90, gamma=9e-05, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=90, gamma=9e-05, kernel=rbf; total time= 0.1s\n", "[CV] END ......................C=90, gamma=9e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=100, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=100, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=100, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=100, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=100, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time= 0.2s\n", "[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=100, gamma=0.0001, kernel=rbf; total time= 0.1s\n", "[CV] END ....................C=100, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=100, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=100, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=100, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=1e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=100, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=100, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=110, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=110, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=110, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=110, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=110, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=110, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=110, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=110, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=110, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=110, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=110, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=110, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=110, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=110, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=110, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=110, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=110, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=110, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=110, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=110, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=110, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=110, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=110, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=110, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=110, gamma=0.0001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=110, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=110, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=110, gamma=1e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=110, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=110, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=110, gamma=9e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=110, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=110, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=110, gamma=9e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=110, gamma=9e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=120, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=120, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=120, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=120, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=120, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=120, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=120, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=120, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=120, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=120, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END ....................C=120, gamma=0.0001, kernel=rbf; total time= 0.1s\n", "[CV] END ....................C=120, gamma=0.0001, kernel=rbf; total time= 0.1s\n", "[CV] END ....................C=120, gamma=0.0001, kernel=rbf; total time= 0.1s\n", "[CV] END ....................C=120, gamma=0.0001, kernel=rbf; total time= 0.1s\n", "[CV] END ....................C=120, gamma=0.0001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=1e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=1e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=1e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=1e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=120, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=120, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=120, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=120, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=120, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=120, gamma=9e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=130, gamma=1, kernel=rbf; total time= 0.2s\n", "[CV] END .........................C=130, gamma=1, kernel=rbf; total time= 0.2s\n", "[CV] END .........................C=130, gamma=1, kernel=rbf; total time= 0.2s\n", "[CV] END .........................C=130, gamma=1, kernel=rbf; total time= 0.2s\n", "[CV] END .........................C=130, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=130, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=130, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=130, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=130, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=130, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=130, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=130, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=130, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=130, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=130, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=130, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=130, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=130, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=130, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=130, gamma=0.001, kernel=rbf; total time= 0.1s\n", "[CV] END ....................C=130, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=130, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=130, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=130, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=130, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=130, gamma=1e-05, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=130, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=130, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=130, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=130, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=130, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=130, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=130, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=130, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=130, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=140, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=140, gamma=1, kernel=rbf; total time= 0.1s\n", "[CV] END .........................C=140, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=140, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .........................C=140, gamma=1, kernel=rbf; total time= 0.0s\n", "[CV] END .......................C=140, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=140, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=140, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=140, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .......................C=140, gamma=0.1, kernel=rbf; total time= 0.1s\n", "[CV] END .....................C=140, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=0.001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=140, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=140, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=140, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=140, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END ....................C=140, gamma=0.0001, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=1e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=9e-05, kernel=rbf; total time= 0.0s\n", "[CV] END .....................C=140, gamma=9e-05, kernel=rbf; total time= 0.1s\n", "0.7956039603960396\n" ] } ], "source": [ "model_svm.fit(X_train, y_train)\n", "print(model_svm.best_score_)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8NKzvgPMsMbu", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "fb3d9297-a52d-43bb-e9ee-aef0ca7421ac" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n" ] } ], "source": [ "print(model_svm.best_params_)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eSXhqW_suU5U", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a413fb0d-041b-4501-e961-63ae037fe808" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "0.5925925925925926\n" ] } ], "source": [ "y_pred= model_svm.predict(X_test)\n", "print(metrics.accuracy_score(y_pred,y_test))" ] }, { "cell_type": "markdown", "metadata": { "id": "ibo6EbWGnp_S" }, "source": [ "### ***d. Kernel Paling Akurat (kernel='poly', C=0.1, degree=3, gamma=8)***" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NXoCoBtOf3DK" }, "outputs": [], "source": [ "import numpy as np\n", "np.random.seed(0)\n", "from sklearn import metrics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "F49lZ0VyunPT", "outputId": "9582a775-ac94-444e-aaba-dd29a748131b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy: 0.6\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Depan Mati 0.43 0.67 0.52 9\n", " Depan Menyala 0.57 0.67 0.62 12\n", " Samping Mati 0.70 0.64 0.67 11\n", "Samping Menyala 0.53 0.60 0.56 15\n", " Tengah Mati 0.70 0.47 0.56 15\n", " Tengah Menyala 0.80 0.62 0.70 13\n", "\n", " accuracy 0.60 75\n", " macro avg 0.62 0.61 0.60 75\n", " weighted avg 0.63 0.60 0.60 75\n", "\n" ] } ], "source": [ "# Kernel Terbaik\n", "# model = SVC(kernel='rbf', C=10, gamma=0.001) #RBF\n", "model = SVC(kernel='linear', C=10) #Linear\n", "model.fit(X_train, y_train)\n", "y_pred = model.predict(X_test)\n", "# dump(model, '/content/drive/MyDrive/Skripsi/model 4 label/linear.pkl')\n", "\n", "print(\"Accuracy:\", metrics.accuracy_score(y_pred,y_test))\n", "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": { "id": "hopKMQh5uWIj" }, "source": [ "# **CONFUSION MATRIX**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 117 }, "id": "RGNAK2sqxHd2", "outputId": "3426e010-813e-4278-bf29-759537d1fb60" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "OneVsOneClassifier(estimator=SVC(C=8, gamma=0.001))" ], "text/html": [ "
OneVsOneClassifier(estimator=SVC(C=8, gamma=0.001))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ] }, "metadata": {}, "execution_count": 6 } ], "source": [ "model_load = load('/content/drive/MyDrive/REVISI SKRIPSI/Model Dataset Pertama/60:40/model_pertama.pkl')\n", "model_load" ] }, { "cell_type": "code", "source": [ "import seaborn as sns" ], "metadata": { "id": "xsCqGjoFV3oN" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "conf_matrix = confusion_matrix(y_test, y_pred_ovo)\n", "plt.figure(figsize=(8, 6))\n", "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Hilmi', 'Tanti', 'Vasyilla', 'Yudha', 'Random'], yticklabels=['Hilmi', 'Tanti', 'Vasyilla', 'Yudha', 'Random'])\n", "plt.xlabel('Predicted')\n", "plt.ylabel('True')\n", "plt.title('Confusion Matrix')\n", "plt.savefig('Confusion Matrix 4.png')\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 564 }, "id": "8BYSx5yEVTkV", "outputId": "c6113373-7f59-45d2-da69-40c46f2a5982" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Bhe0B1ddvyr2", "outputId": "ff191246-7a68-4344-bffe-5873608875a6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Confusion matrix\n", "\n", " [[6 0 0 0 0]\n", " [0 7 0 0 0]\n", " [1 0 5 0 0]\n", " [0 0 0 7 0]\n", " [0 0 0 0 4]]\n", "\n", "True Positives(TP) = [6 7 5 7 4]\n", "\n", "True Negatives(TN) = [23, 23, 24, 23, 26]\n", "\n", "False Positives(FP) = [1 0 0 0 0]\n", "\n", "False Negatives(FN) = [0 0 1 0 0]\n" ] } ], "source": [ "cm = confusion_matrix(y_test, y_pred_ovo)\n", "\n", "# Hitung True Negatives (TN) untuk setiap kelas\n", "TN = []\n", "for i in range(len(cm)):\n", " temp = np.delete(cm, i, 0) # hapus baris ke-i\n", " temp = np.delete(temp, i, 1) # hapus kolom ke-i\n", " TN.append(np.sum(temp))\n", "\n", "print('Confusion matrix\\n\\n', cm)\n", "\n", "print('\\nTrue Positives(TP) = ', np.diag(cm))\n", "\n", "print('\\nTrue Negatives(TN) = ', TN)\n", "\n", "print('\\nFalse Positives(FP) = ', np.sum(cm, axis=0) - np.diag(cm))\n", "\n", "print('\\nFalse Negatives(FN) = ', np.sum(cm, axis=1) - np.diag(cm))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "REEXlIrQGpCv" }, "source": [ "Classification Accuracy" ] }, { "cell_type": "code", "source": [ "cm = confusion_matrix(y_test, y_pred_ovo)\n", "\n", "# Hitung True Positives (TP) untuk setiap kelas\n", "TP = np.diag(cm)\n", "\n", "TN = []\n", "FP = []\n", "FN = []\n", "for i in range(len(cm)):\n", " temp = np.delete(cm, i, 0) # hapus baris ke-i\n", " temp = np.delete(temp, i, 1) # hapus kolom ke-i\n", " TN.append(np.sum(np.delete(np.delete(cm, i, 0), i, 1)))\n", " FP.append(np.sum(cm[:, i]) - cm[i, i])\n", " FN.append(np.sum(cm[i, :]) - cm[i, i])\n", "\n", "# Hitung total TP, TN, FP, FN\n", "total_TP = np.sum(TP)\n", "total_TN = np.sum(TN)\n", "total_FP = np.sum(FP)\n", "total_FN = np.sum(FN)\n", "\n", "# Cetak total\n", "print(f\"Total True Positives (TP): {total_TP}\")\n", "print(f\"Total True Negatives (TN): {total_TN}\")\n", "print(f\"Total False Positives (FP): {total_FP}\")\n", "print(f\"Total False Negatives (FN): {total_FN}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Ar5rYwEhYDU", "outputId": "88da9785-936f-4a49-a5d7-60051fa305d8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total True Positives (TP): 29\n", "Total True Negatives (TN): 119\n", "Total False Positives (FP): 1\n", "Total False Negatives (FN): 1\n" ] } ] }, { "cell_type": "code", "source": [ "# Hitung True Positives (TP) untuk setiap kelas\n", "TP = np.diag(cm)\n", "\n", "# Hitung True Negatives (TN), False Positives (FP), dan False Negatives (FN) untuk setiap kelas\n", "TN = []\n", "FP = []\n", "FN = []\n", "for i in range(len(cm)):\n", " temp = np.delete(cm, i, 0) # hapus baris ke-i\n", " temp = np.delete(temp, i, 1) # hapus kolom ke-i\n", " TN.append(np.sum(np.delete(np.delete(cm, i, 0), i, 1)))\n", " FP.append(np.sum(cm[:, i]) - cm[i, i])\n", " FN.append(np.sum(cm[i, :]) - cm[i, i])\n", "\n", "# Cetak hasil\n", "for i in range(len(cm)):\n", " print(f\"Kelas {i}:\")\n", " print(f\"True Negatives (TN): {TN[i]}, False Positives (FP): {FP[i]}, False Negatives (FN): {FN[i]}, True Positives (TP): {TP[i]}\")\n", " print()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pi-CXMyRfvEU", "outputId": "cd1cfd56-636d-47ce-e039-a4a70d841a47" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Kelas 0:\n", "True Negatives (TN): 31, False Positives (FP): 1, False Negatives (FN): 0, True Positives (TP): 13\n", "\n", "Kelas 1:\n", "True Negatives (TN): 36, False Positives (FP): 0, False Negatives (FN): 0, True Positives (TP): 9\n", "\n", "Kelas 2:\n", "True Negatives (TN): 36, False Positives (FP): 0, False Negatives (FN): 1, True Positives (TP): 8\n", "\n", "Kelas 3:\n", "True Negatives (TN): 35, False Positives (FP): 0, False Negatives (FN): 0, True Positives (TP): 10\n", "\n", "Kelas 4:\n", "True Negatives (TN): 41, False Positives (FP): 0, False Negatives (FN): 0, True Positives (TP): 4\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "# Hitung True Positives (TP) untuk setiap kelas\n", "TP = np.diag(cm)\n", "\n", "# Hitung True Negatives (TN), False Positives (FP), dan False Negatives (FN) untuk setiap kelas\n", "TN = []\n", "FP = []\n", "FN = []\n", "for i in range(len(cm)):\n", " temp = np.delete(cm, i, 0) # hapus baris ke-i\n", " temp = np.delete(temp, i, 1) # hapus kolom ke-i\n", " TN.append(np.sum(np.delete(np.delete(cm, i, 0), i, 1)))\n", " FP.append(np.sum(cm[:, i]) - cm[i, i])\n", " FN.append(np.sum(cm[i, :]) - cm[i, i])\n", "\n", "# Hitung classification accuracy\n", "classification_accuracy = (np.sum(TP) + np.sum(TN)) / float(np.sum(TP) + np.sum(TN) + np.sum(FP) + np.sum(FN))\n", "\n", "print('Classification accuracy : {0:0.4f}'.format(classification_accuracy))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RW4_VBTUfcyX", "outputId": "eaf3b7f5-5436-43d5-9072-124ef36511f4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Classification accuracy : 0.9867\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Ze8LQXFNGAMi", "outputId": "bbf616f6-4075-4377-fa02-53e0a8018257" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Classification accuracy : 1.0000\n" ] } ], "source": [ "TP = cm[0,0]\n", "TN = cm[1,1]\n", "FP = cm[0,1]\n", "FN = cm[1,0]\n", "\n", "classification_accuracy = (TP + TN) / float(TP + TN + FP + FN)\n", "\n", "print('Classification accuracy : {0:0.4f}'.format(classification_accuracy))" ] }, { "cell_type": "markdown", "metadata": { "id": "xSZckWMWGvf3" }, "source": [ "Classification Error" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YufxpOUHGYt3" }, "outputs": [], "source": [ "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n", "\n", "print('Classification error : {0:0.4f}'.format(classification_error))" ] }, { "cell_type": "markdown", "metadata": { "id": "ALY58D2FHCXI" }, "source": [ "Precision" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "b_fz1x1qGYhQ" }, "outputs": [], "source": [ "# print precision score\n", "\n", "precision = TP / float(TP + FP)\n", "\n", "\n", "print('Precision : {0:0.4f}'.format(precision))" ] }, { "cell_type": "markdown", "metadata": { "id": "1YzKeSa9HH0w" }, "source": [ "Recall" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yB-6_-v5HZbO", "outputId": "8c167574-70b3-44f6-c59b-d2e9daa6558d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Recall or Sensitivity : 1.0000\n" ] } ], "source": [ "recall = TP / float(TP + FN)\n", "\n", "print('Recall or Sensitivity : {0:0.4f}'.format(recall))" ] }, { "cell_type": "markdown", "metadata": { "id": "7Fc7pouCJZBS" }, "source": [ "F1-Score" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4de9T_vFHgwy", "outputId": "0b766dfe-c802-4697-9b6e-4016db2cc09d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "F1-score: 0.9832053251408089\n" ] } ], "source": [ "# F1-score untuk model dengan kernel RBF\n", "f1_score_rbf = f1_score(y_test, y_pred_ovo, average='weighted')\n", "print(\"F1-score:\", f1_score_rbf)" ] } ], "metadata": { "colab": { "collapsed_sections": [ "bIpz46aODKV4", "TkKYogWSKC7m", "CIZftb1ujXcj", "BuOJaSexlifl", "aP7X0AiAl8s9" ], "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }