mirror of
https://github.com/Andreaierardi/Master-DataScience-Notes.git
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1184 lines
80 KiB
Plaintext
1184 lines
80 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# ML - Linear regression, KNN, SVM"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Linear regression"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" school sex age address famsize Pstatus Medu Fedu Mjob Fjob ... \\\n",
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"0 GP F 18 U GT3 A 4 4 at_home teacher ... \n",
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"1 GP F 17 U GT3 T 1 1 at_home other ... \n",
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"2 GP F 15 U LE3 T 1 1 at_home other ... \n",
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"3 GP F 15 U GT3 T 4 2 health services ... \n",
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"4 GP F 16 U GT3 T 3 3 other other ... \n",
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"\n",
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" famrel freetime goout Dalc Walc health absences G1 G2 G3 \n",
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"0 4 3 4 1 1 3 6 5 6 6 \n",
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"1 5 3 3 1 1 3 4 5 5 6 \n",
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"2 4 3 2 2 3 3 10 7 8 10 \n",
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"3 3 2 2 1 1 5 2 15 14 15 \n",
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"4 4 3 2 1 2 5 4 6 10 10 \n",
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"\n",
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"[5 rows x 33 columns]\n"
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]
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}
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],
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"source": [
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"#Import Library\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn import linear_model\n",
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"import sklearn\n",
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"from sklearn.utils import shuffle\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib import style\n",
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"import pickle\n",
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"\n",
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"data = pd.read_csv(\"Datasets/Students/student-mat.csv\", sep=\";\")\n",
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"# Since our data is seperated by semicolons we need to do sep=\";\"\n",
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"print(data.head())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = data[[\"G1\", \"G2\", \"G3\", \"studytime\", \"failures\", \"absences\"]]\n",
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"predict = \"G3\"\n",
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"\n",
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"X = np.array(data.drop([predict], 1)) # Features\n",
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"y = np.array(data[predict]) # Labels"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.8521230122769358\n",
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"Coefficient: \n",
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" [ 0.15164891 0.98147393 -0.16509308 -0.32193465 0.04498984]\n",
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"Intercept: \n",
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" -1.5907529202513135\n",
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"12.279895401470538 [14 12 1 0 3] 12\n",
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"7.503962373930127 [8 8 2 0 8] 6\n",
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"15.227552394977513 [16 15 2 0 0] 15\n",
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"7.745590968178561 [ 9 8 2 0 10] 8\n",
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"16.205791131538525 [14 16 1 0 3] 16\n",
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"10.84670994730399 [13 11 2 0 0] 10\n",
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"19.45816805298856 [19 19 4 0 4] 20\n",
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"14.93912087678917 [14 15 3 0 4] 16\n",
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"3.4793613983764784 [3 5 2 1 8] 5\n",
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"7.309136703621595 [8 8 1 0 0] 11\n",
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"14.197853422314571 [14 14 1 0 2] 14\n",
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"6.199182798446923 [ 7 6 2 0 26] 6\n",
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"11.803053678911542 [10 9 2 0 75] 9\n",
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"12.093074669254824 [10 12 2 0 16] 11\n",
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"8.712198607146872 [9 9 1 0 6] 10\n",
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"9.576804496598653 [11 10 3 0 4] 10\n",
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"19.268409826386197 [17 18 2 0 21] 18\n",
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"18.09686079769726 [16 18 3 0 2] 18\n",
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"6.1157667639535385 [7 7 3 0 6] 7\n",
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"8.802178295526796 [9 9 1 0 8] 10\n",
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"9.286950862204275 [ 8 10 2 0 4] 10\n",
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"5.547010861464581 [7 7 2 2 4] 9\n",
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"12.064764198363493 [11 12 2 0 12] 11\n",
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"10.708505218778123 [11 11 1 0 0] 12\n",
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"5.479345291407545 [ 7 6 2 0 10] 6\n",
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"13.94420276692953 [15 14 4 0 4] 14\n",
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"7.234023308790361 [8 8 2 0 2] 8\n",
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"12.771106406160298 [12 13 1 1 6] 14\n",
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"-0.8459525682513952 [6 0 1 0 0] 0\n",
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"9.452043945415435 [ 8 10 1 0 4] 10\n",
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"7.8972398740470755 [10 8 2 0 10] 8\n",
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"5.140731269448818 [ 6 6 2 1 13] 8\n",
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"15.075903489109 [15 15 2 0 0] 15\n",
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"7.845337552501315 [6 9 1 1 4] 8\n",
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"6.992394714541925 [7 8 2 0 0] 0\n",
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"9.998392467606932 [12 10 3 0 10] 12\n",
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"10.633391823946887 [11 11 2 0 2] 11\n",
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"15.525801931008607 [15 15 2 0 10] 15\n",
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"9.779881684669405 [11 10 2 1 12] 10\n",
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"17.535552719982505 [16 17 1 0 4] 18\n"
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]
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}
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],
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"source": [
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"\n",
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"linear = linear_model.LinearRegression()\n",
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"\n",
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"linear.fit(x_train, y_train)\n",
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"acc = linear.score(x_test, y_test)\n",
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"print(acc)\n",
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"\n",
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"print('Coefficient: \\n', linear.coef_)\n",
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"print('Intercept: \\n', linear.intercept_)\n",
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"\n",
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"predictions = linear.predict(x_test)\n",
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"\n",
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"for x in range(len(predictions)):\n",
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" print(predictions[x], x_test[x], y_test[x])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Saving model and importing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.utils import shuffle\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib import style\n",
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"import pickle"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Saving "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"Models/studentgrades.pickle\", \"wb\") as f:\n",
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" pickle.dump(linear, f)\n",
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"\n",
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"# linear is the name of the model we created in the last tutorial\n",
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"# it should be defined above this"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Loading model "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"pickle_in = open(\"Models/studentgrades.pickle\", \"rb\")\n",
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"linear = pickle.load(pickle_in)\n",
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"\n",
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"# Now we can use linear to predict grades like before"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Training multiple model\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "Found input variables with inconsistent numbers of samples: [569, 395]",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-53-512615342312>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mbest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mlinear\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32mE:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[1;34m(*arrays, **options)\u001b[0m\n\u001b[0;32m 2094\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Invalid parameters passed: %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2095\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2096\u001b[1;33m \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2097\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2098\u001b[0m \u001b[0mn_samples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32mE:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mindexable\u001b[1;34m(*iterables)\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 229\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 230\u001b[1;33m \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 231\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32mE:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m 203\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 204\u001b[0m raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[1;32m--> 205\u001b[1;33m \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[0;32m 206\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [569, 395]"
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]
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}
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],
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"source": [
|
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"# TRAIN MODEL MULTIPLE TIMES FOR BEST SCORE\n",
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"best = 0\n",
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"for _ in range(20):\n",
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" x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.1)\n",
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"\n",
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" linear = linear_model.LinearRegression()\n",
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" \n",
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" linear.fit(x_train, y_train)\n",
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" acc = linear.score(x_test, y_test)\n",
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" print(\"Accuracy: \" + str(acc))\n",
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" \n",
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" # If the current model has a better score than one we've already trained then save it\n",
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" if acc > best:\n",
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" best = acc\n",
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" with open(\"Models/studentgrades.pickle\", \"wb\") as f:\n",
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" pickle.dump(linear, f)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Plotting data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"No handles with labels found to put in legend.\n"
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]
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},
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{
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"data": {
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"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Drawing and plotting model\n",
|
|
"plot = \"failures\" # Change this to G1, G2, studytime or absences to see other graphs\n",
|
|
"plt.scatter(data[plot], data[\"G3\"]) \n",
|
|
"plt.legend(loc=4)\n",
|
|
"plt.xlabel(plot)\n",
|
|
"plt.ylabel(\"Final Grade\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# K-Nearest Neighbors (KNN)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" buying maint door persons lug_boot safety class\n",
|
|
"0 vhigh vhigh 2 2 small low unacc\n",
|
|
"1 vhigh vhigh 2 2 small med unacc\n",
|
|
"2 vhigh vhigh 2 2 small high unacc\n",
|
|
"3 vhigh vhigh 2 2 med low unacc\n",
|
|
"4 vhigh vhigh 2 2 med med unacc\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import sklearn\n",
|
|
"from sklearn.utils import shuffle\n",
|
|
"from sklearn.neighbors import KNeighborsClassifier\n",
|
|
"import pandas as pd\n",
|
|
"import numpy as np\n",
|
|
"from sklearn import linear_model, preprocessing\n",
|
|
"\n",
|
|
"data = pd.read_csv(\"Datasets/Car Data Set/car.data\")\n",
|
|
"print(data.head())\n",
|
|
"\n",
|
|
"le = preprocessing.LabelEncoder()\n",
|
|
"buying = le.fit_transform(list(data[\"buying\"]))\n",
|
|
"maint = le.fit_transform(list(data[\"maint\"]))\n",
|
|
"door = le.fit_transform(list(data[\"door\"]))\n",
|
|
"persons = le.fit_transform(list(data[\"persons\"]))\n",
|
|
"lug_boot = le.fit_transform(list(data[\"lug_boot\"]))\n",
|
|
"safety = le.fit_transform(list(data[\"safety\"]))\n",
|
|
"cls = le.fit_transform(list(data[\"class\"]))\n",
|
|
" \n",
|
|
"predict = \"class\" #optional\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy model: 0.9479768786127167\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"X = list(zip(buying, maint, door, persons, lug_boot, safety))\n",
|
|
"y = list(cls)\n",
|
|
"\n",
|
|
"x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size = 0.1)\n",
|
|
"\n",
|
|
"model = KNeighborsClassifier(n_neighbors=9)\n",
|
|
"\n",
|
|
"model.fit(x_train, y_train)\n",
|
|
"acc = model.score(x_test, y_test)\n",
|
|
"print(\"Accuracy model: \",acc)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Predicted: good Data: (3, 3, 3, 2, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[1237, 705, 154, 339, 1066, 1092, 239, 757, 1143]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (3, 2, 2, 2, 2, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 680, 814, 913, 466, 842, 266, 927, 508, 1218]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 0, 3, 1, 0, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1115, 94, 1417, 673, 1000, 1512, 796, 11, 510]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: acc Data: (1, 2, 3, 1, 2, 0) Actual: acc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 460, 1480, 1498, 942, 1268, 78, 766, 1521, 970]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 0, 2, 0, 1, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 571, 1239, 222, 76, 31, 1546, 531, 1126, 211]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 0, 3, 1, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1161, 1285, 1110, 1290, 1347, 1189, 118, 695, 1171]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 1, 2, 2, 1, 2) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 194, 1086, 82, 1238, 1025, 998, 173, 1483, 440]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 2, 3, 1, 1, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1339, 306, 999, 663, 1524, 865, 1470, 168, 1515]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 2, 1, 0, 2, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 721, 487, 1241, 867, 261, 15, 119, 1529, 1013]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 2, 1, 1, 0, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[181, 887, 809, 238, 529, 433, 866, 736, 34]], dtype=int64))\n",
|
|
"Predicted: good Data: (0, 3, 3, 0, 2, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[ 795, 644, 979, 526, 91, 437, 1425, 1545, 619]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (2, 2, 0, 2, 0, 2) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 883, 598, 48, 52, 1294, 21, 423, 1325, 574]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 1, 2, 2, 1, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1125, 267, 548, 1029, 1372, 1473, 732, 353, 422]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 0, 3, 2, 0, 2) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1.41421356,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[ 240, 385, 1099, 1483, 445, 1115, 1177, 1301, 370]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 2, 0, 0, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 299, 368, 166, 1131, 277, 92, 164, 403, 1036]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 1, 2, 0, 0, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 337, 1348, 309, 836, 941, 928, 620, 831, 533]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 3, 0, 1, 2, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 910, 380, 593, 282, 1314, 1313, 546, 226, 495]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: vgood Data: (2, 2, 2, 1, 1, 0) Actual: vgood\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 679, 622, 754, 37, 805, 1218, 1084, 83, 431]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 1, 3, 1, 1, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 350, 586, 357, 872, 803, 1552, 142, 222, 1115]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 3, 2, 2, 1, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1021, 79, 1309, 1326, 1365, 667, 1068, 732, 475]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: acc Data: (1, 1, 1, 2, 0, 2) Actual: acc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 410, 416, 1477, 369, 969, 66, 1360, 1172, 181]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 0, 0, 0, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1020, 656, 747, 456, 1071, 1010, 605, 120, 853]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 0, 1, 1, 1, 2) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 27, 895, 930, 745, 204, 1201, 115, 1400, 1491]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 1, 3, 2, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1388, 1034, 1252, 652, 611, 1353, 1125, 548, 255]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 0, 2, 0, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[1024, 773, 1482, 1171, 901, 1394, 444, 176, 941]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 3, 2, 0, 0, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 2, 84, 1192, 1152, 1374, 159, 1081, 1532, 207]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 3, 1, 1, 2, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1122, 345, 879, 256, 1457, 1042, 701, 1410, 1170]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 1, 2, 1, 2, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 970, 1076, 1426, 512, 1548, 476, 1455, 1265, 1001]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 2, 0, 1, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1062, 529, 400, 433, 763, 1419, 647, 633, 1211]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 1, 3, 2, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 634, 30, 255, 705, 1478, 1038, 1385, 770, 201]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 1, 2, 2, 1, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 201, 770, 150, 923, 112, 1388, 748, 1353, 1308]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 0, 0, 0, 2, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 185, 456, 987, 1322, 692, 1071, 1074, 55, 774]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 3, 3, 1, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1437, 371, 242, 1451, 93, 794, 1193, 498, 332]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 3, 0, 0, 2, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 920, 380, 463, 546, 119, 693, 15, 1122, 1212]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 0, 2, 2, 1, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 214, 326, 20, 353, 573, 1308, 784, 1493, 1385]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 1, 0, 1, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1045, 1297, 1235, 919, 637, 496, 453, 1463, 412]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 1, 2, 2, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 561, 440, 811, 381, 1250, 1133, 82, 259, 411]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 3, 2, 1, 1, 2) Actual: unacc\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 894, 1324, 1021, 761, 992, 643, 830, 823, 1326]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 1, 3, 0, 0, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 645, 90, 1393, 1176, 1424, 731, 1479, 1274, 510]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 0, 1, 2, 1, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 746, 193, 407, 228, 224, 1044, 1243, 1278, 1027]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (1, 0, 0, 2, 1, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1094, 439, 676, 1416, 500, 1375, 746, 193, 819]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 0, 2, 2, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[ 893, 1483, 1234, 126, 1543, 1475, 673, 531, 194]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 0, 2, 1, 1, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 20, 326, 901, 1491, 178, 528, 444, 770, 1385]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 2, 2, 0, 2, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 867, 1407, 927, 915, 1336, 361, 1529, 300, 1035]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 1, 0, 2, 1, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 592, 558, 1525, 923, 1357, 270, 518, 206, 971]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 3, 3, 2, 1, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1188, 364, 580, 29, 626, 1466, 106, 1186, 680]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 3, 1, 2, 0, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[441, 59, 926, 559, 768, 505, 607, 316, 81]], dtype=int64))\n",
|
|
"Predicted: good Data: (0, 2, 2, 2, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[ 675, 965, 88, 1179, 57, 246, 1250, 381, 525]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 1, 2, 0, 2, 0) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 476, 952, 1442, 1181, 145, 187, 636, 970, 543]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 2, 2, 2, 1, 2) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1233, 1210, 525, 151, 538, 360, 344, 998, 82]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 0, 0, 0, 0, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 243, 1397, 954, 815, 847, 706, 848, 892, 439]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 3, 3, 2, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1022, 1231, 239, 1430, 51, 522, 26, 339, 1053]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 1, 3, 0, 2, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1448, 1061, 898, 452, 570, 367, 1069, 606, 1394]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 3, 3, 0, 1, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1192, 587, 1522, 1081, 493, 1004, 1186, 917, 334]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 3, 3, 0, 1, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 480, 591, 1206, 884, 666, 984, 669, 767, 1120]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 0, 3, 1, 0, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1078, 903, 564, 951, 1000, 1150, 430, 730, 271]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 3, 1, 0, 2, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1412, 1122, 226, 1088, 1500, 751, 1212, 1457, 1350]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 2, 3, 0, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 897, 743, 984, 236, 467, 480, 1389, 286, 452]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (1, 0, 1, 1, 0, 2) Actual: unacc\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1477, 1205, 1201, 1106, 66, 900, 1003, 1400, 599]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 2, 3, 0, 1, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[699, 858, 301, 537, 889, 917, 170, 334, 499]], dtype=int64))\n",
|
|
"Predicted: good Data: (2, 3, 3, 0, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 51, 666, 984, 504, 459, 105, 587, 509, 499]], dtype=int64))\n",
|
|
"Predicted: good Data: (2, 2, 1, 2, 1, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 659, 902, 1140, 1304, 398, 678, 712, 373, 319]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 0, 3, 2, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 711, 958, 1099, 1234, 1230, 1418, 1000, 1177, 1115]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 2, 1, 2, 1, 2) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1025, 310, 1427, 1519, 1453, 876, 948, 88, 1360]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 3, 3, 0, 1, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1437, 1425, 493, 587, 1370, 619, 276, 242, 399]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 0, 1, 1, 1, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 785, 870, 930, 1155, 137, 1102, 1491, 223, 573]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 1, 0, 0, 0, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 449, 565, 243, 97, 1196, 633, 848, 38, 420]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 3, 2, 1, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1464, 888, 563, 34, 881, 612, 1082, 232, 761]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (3, 1, 3, 1, 0, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1376, 611, 1252, 1078, 1184, 1012, 1334, 1080, 1245]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 0, 0, 1, 0, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 863, 1405, 815, 780, 845, 1397, 488, 1321, 14]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: acc Data: (1, 2, 3, 2, 0, 2) Actual: acc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 103, 448, 660, 1262, 1490, 972, 534, 184, 1229]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 3, 2, 0, 2, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 867, 50, 1223, 15, 254, 361, 560, 728, 1035]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 2, 0, 0, 1, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1469, 1169, 1209, 719, 1432, 322, 449, 554, 1196]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 3, 2, 1, 0, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1458, 159, 674, 1316, 1242, 260, 292, 2, 788]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (2, 2, 3, 1, 0, 2) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1262, 318, 596, 534, 1236, 279, 583, 741, 1537]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 0, 1, 0, 1, 0) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1072, 682, 1097, 847, 342, 819, 220, 1446, 954]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 1, 1, 0, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 635, 747, 1163, 1538, 536, 1050, 1377, 216, 1359]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 1, 0, 1, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 602, 1211, 97, 647, 1476, 742, 1167, 633, 265]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 3, 3, 2, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1414, 1528, 1365, 93, 1451, 327, 1068, 1191, 364]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 1, 3, 0, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 499, 1111, 367, 1478, 1482, 898, 444, 537, 1394]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 2, 2, 2, 2, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 816, 151, 246, 42, 1250, 198, 908, 1265, 82]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 1, 1, 2, 2, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 744, 671, 547, 1265, 885, 1382, 41, 315, 274]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 1, 3, 0, 0, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[ 11, 321, 96, 730, 594, 1298, 1000, 1512, 165]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 2, 0, 2, 2, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1516, 653, 506, 1185, 582, 5, 1006, 1517, 148]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 1, 2, 1, 2, 0) Actual: unacc\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1214, 113, 1434, 654, 145, 1426, 28, 970, 1113]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 3, 1, 0, 2, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 481, 89, 235, 1046, 976, 702, 347, 492, 966]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 1, 1, 2, 0, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1248, 351, 697, 1391, 224, 1226, 633, 602, 1006]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (1, 0, 1, 1, 2, 2) Actual: unacc\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 962, 245, 1377, 204, 1400, 434, 990, 1020, 747]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 1, 1, 2, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 602, 1211, 1543, 468, 893, 850, 1172, 265, 1025]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 3, 3, 2, 1, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 13, 1068, 717, 79, 302, 93, 242, 1138, 934]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 2, 1, 2, 2, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 209, 246, 690, 712, 1153, 198, 613, 229, 373]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 1, 1, 2, 2, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1516, 1044, 547, 1434, 315, 113, 1517, 672, 1006]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 0, 2, 2, 1, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[213, 194, 445, 407, 259, 228, 531, 82, 978]], dtype=int64))\n",
|
|
"Predicted: good Data: (1, 2, 3, 1, 2, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1435, 735, 1470, 512, 236, 1515, 1455, 743, 183]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 1, 2, 0, 1, 0) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 62, 420, 187, 340, 145, 31, 1126, 1113, 1220]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: vgood Data: (1, 1, 0, 2, 0, 0) Actual: vgood\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 676, 1321, 351, 1014, 1378, 1375, 1039, 1248, 274]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 1, 2, 0, 1, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 337, 889, 1394, 537, 748, 941, 230, 461, 176]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 0, 2, 0, 1, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 128, 298, 1031, 176, 230, 632, 149, 773, 1135]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (3, 1, 0, 2, 0, 2) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1357, 270, 317, 598, 21, 868, 1431, 186, 807]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 3, 0, 0, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 716, 1036, 40, 1551, 328, 109, 617, 33, 982]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 0, 2, 1, 2, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 839, 962, 1157, 695, 245, 39, 912, 127, 825]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 1, 1, 2, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1039, 1121, 1549, 1415, 181, 1248, 887, 1360, 647]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 3, 0, 0, 1, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1060, 36, 490, 1169, 244, 1107, 179, 1017, 449]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 3, 1, 2, 2, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1413, 253, 1516, 74, 1187, 1185, 1270, 1350, 624]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (3, 1, 2, 2, 2, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 838, 150, 700, 47, 201, 1493, 101, 1135, 1308]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (2, 3, 2, 1, 0, 2) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1180, 596, 741, 413, 232, 881, 63, 551, 292]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 2, 1, 2, 2, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 712, 814, 1304, 197, 466, 290, 1317, 487, 1307]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 0, 3, 1, 1, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 125, 370, 445, 531, 1115, 343, 94, 222, 727]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 1, 1, 2, 1, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1032, 672, 850, 1254, 1006, 274, 547, 265, 351]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 0, 3, 0, 2, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1015, 100, 199, 1161, 1285, 1002, 773, 824, 1171]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 3, 0, 0, 1, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1436, 843, 179, 102, 983, 852, 490, 1036, 322]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 3, 0, 2, 1, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 627, 1531, 140, 1427, 429, 233, 876, 355, 1439]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 1, 2, 1, 1, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 575, 754, 461, 1030, 1275, 1076, 748, 1154, 622]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 0, 3, 2, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1.41421356,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[ 796, 445, 1115, 1251, 1099, 958, 126, 1230, 240]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 2, 3, 1, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1191, 1451, 1273, 498, 1553, 1194, 1481, 698, 170]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 2, 0, 0, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 920, 1051, 75, 637, 261, 119, 919, 1178, 687]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (2, 3, 1, 1, 1, 0) Actual: unacc\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1457, 1539, 879, 394, 1489, 1190, 392, 751, 797]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 0, 2, 1, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[ 713, 1189, 1385, 444, 412, 773, 20, 72, 634]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 2, 2, 0, 0, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1532, 159, 330, 377, 1080, 260, 1174, 300, 207]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 1, 1, 1, 2, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1452, 885, 1095, 219, 933, 414, 107, 1382, 691]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 3, 3, 0, 1, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1411, 405, 1425, 428, 437, 493, 526, 168, 556]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 2, 0, 0, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 544, 449, 1456, 1017, 133, 97, 188, 763, 1503]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 1, 0, 2, 2, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[ 148, 1517, 1278, 1006, 625, 113, 349, 457, 547]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 0, 0, 0, 2, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[1016, 43, 994, 774, 248, 1071, 863, 639, 954]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 1, 3, 2, 1, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 826, 1251, 1238, 663, 82, 1256, 445, 1139, 1552]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 2, 3, 0, 0, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 911, 1224, 399, 1533, 834, 556, 1151, 1437, 96]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 0, 3, 1, 0, 2) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[1176, 1177, 731, 1475, 1115, 370, 240, 937, 1205]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 2, 0, 2, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 568, 1043, 1211, 468, 116, 710, 776, 1419, 1062]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (2, 0, 1, 2, 1, 0) Actual: unacc\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1416, 1303, 0, 1132, 294, 746, 436, 455, 1033]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: vgood Data: (1, 1, 3, 1, 0, 0) Actual: vgood\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1417, 1158, 135, 1078, 911, 703, 1000, 160, 958]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 2, 0, 0, 1, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1444, 164, 102, 567, 249, 1505, 1131, 1209, 723]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 2, 1, 1, 2, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1108, 608, 659, 712, 686, 613, 487, 721, 1440]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 2, 2, 1, 2, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 965, 352, 654, 1515, 306, 283, 512, 1426, 908]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 1, 1, 0, 1, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 320, 1207, 139, 1155, 1241, 223, 786, 553, 432]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 1, 0, 0, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 55, 762, 311, 1168, 1026, 1300, 729, 1101, 1396]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 2, 3, 0, 1, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 587, 276, 117, 1061, 570, 1481, 509, 1004, 698]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (0, 0, 3, 2, 1, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 94, 445, 796, 228, 1256, 1164, 1124, 958, 1486]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 0, 1, 2, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 434, 1381, 714, 1540, 204, 1134, 177, 115, 916]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 1, 0, 0, 1, 2) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 146, 1362, 1485, 871, 1266, 541, 1168, 46, 311]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 3, 0, 1, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 359, 827, 1291, 738, 919, 920, 546, 495, 1042]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 1, 2, 0, 0, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 111, 62, 1533, 45, 1306, 187, 1255, 160, 549]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 3, 3, 0, 1, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 29, 864, 917, 1450, 334, 1495, 254, 1336, 17]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: vgood Data: (1, 2, 0, 1, 0, 0) Actual: vgood\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1321, 221, 809, 1209, 400, 529, 1378, 322, 387]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 2, 3, 0, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 509, 1009, 570, 1199, 499, 367, 459, 858, 666]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 0, 4, 1, 1, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[ 384, 557, 313, 1264, 1356, 890, 1454, 832, 1334]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 1, 1, 0, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1130, 25, 521, 133, 565, 97, 1019, 602, 988]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 2, 3, 1, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 301, 961, 1184, 611, 609, 583, 170, 652, 330]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 0, 1, 0, 2, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1322, 479, 223, 773, 1074, 1241, 1024, 870, 813]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 0, 0, 1, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1049, 1010, 997, 1020, 524, 986, 990, 284, 799]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 3, 0, 1, 1, 2) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 907, 1305, 288, 1472, 429, 196, 233, 402, 579]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 1, 1, 2, 1, 1) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1360, 382, 373, 1029, 1502, 850, 274, 1127, 267]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 0, 3, 1, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1158, 1298, 451, 430, 1115, 343, 1177, 1000, 510]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 3, 2, 1, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[1217, 390, 1179, 486, 891, 908, 272, 965, 1429]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (3, 1, 2, 2, 0, 2) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 152, 1279, 317, 1358, 1388, 770, 831, 784, 533]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 1, 2, 0, 2, 2) Actual: good\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[1171, 1163, 367, 216, 72, 1482, 1199, 1069, 158]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 3, 0, 0, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 702, 976, 1100, 1145, 200, 1240, 1461, 1281, 1244]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (3, 1, 3, 2, 0, 2) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1.41421356,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[1494, 262, 8, 255, 152, 831, 770, 1227, 1279]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 2, 0, 0, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 403, 736, 529, 1036, 1432, 817, 322, 647, 1173]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 3, 2, 2, 2, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[1187, 806, 955, 1179, 253, 908, 1200, 485, 7]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 2, 1, 0, 1, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 446, 297, 629, 889, 210, 555, 1013, 1178, 261]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (1, 3, 2, 1, 0, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 336, 1260, 794, 949, 2, 477, 1374, 707, 612]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 0, 1, 0, 2, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 479, 1074, 1097, 1028, 664, 1446, 54, 774, 1016]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 1, 2, 0, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 111, 31, 62, 1479, 6, 1346, 1393, 549, 539]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (1, 1, 0, 0, 1, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 875, 1072, 954, 249, 1196, 1396, 848, 554, 45]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 0, 2, 0, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1.41421356, 1.41421356, 1.41421356, 1.41421356]]), array([[1130, 549, 426, 510, 878, 1255, 1126, 1239, 1475]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (2, 1, 0, 2, 1, 0) Actual: acc\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 676, 640, 1033, 835, 206, 1525, 1014, 1416, 406]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 0, 2, 1, 0, 1) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 451, 1475, 1346, 1115, 16, 531, 673, 549, 126]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (3, 1, 0, 1, 0, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1.41421356]]), array([[ 157, 425, 186, 180, 85, 780, 1405, 957, 1182]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (0, 0, 1, 2, 2, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 990, 986, 434, 811, 1133, 366, 801, 1504, 407]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (3, 3, 3, 0, 0, 2) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 17, 251, 56, 1537, 540, 1395, 858, 170, 851]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: unacc Data: (3, 1, 2, 1, 1, 2) Actual: unacc\n",
|
|
"N: (array([[1., 1., 1., 1., 1., 1., 1., 1., 1.]]), array([[ 444, 1333, 831, 141, 748, 941, 1520, 770, 575]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 2, 0, 0, 2, 0) Actual: good\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1.41421356, 1.41421356]]), array([[ 719, 1500, 1212, 1355, 994, 264, 581, 983, 1128]],\n",
|
|
" dtype=int64))\n",
|
|
"Predicted: good Data: (2, 0, 0, 2, 0, 0) Actual: unacc\n",
|
|
"N: (array([[1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1.41421356, 1.41421356, 1.41421356]]), array([[ 455, 70, 1225, 1375, 1014, 1416, 494, 1249, 780]],\n",
|
|
" dtype=int64))\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"predicted = model.predict(x_test)\n",
|
|
"names = [\"unacc\", \"acc\", \"good\", \"vgood\"]\n",
|
|
"\n",
|
|
"for x in range(len(predicted)):\n",
|
|
" print(\"Predicted: \", names[predicted[x]], \"Data: \", x_test[x], \"Actual: \", names[y_test[x]])\n",
|
|
" n = model.kneighbors([x_test[x]], 9, True)\n",
|
|
" print(\"N: \", n)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Support Vector Machines (SVM)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
|
|
" 'mean smoothness' 'mean compactness' 'mean concavity'\n",
|
|
" 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
|
|
" 'radius error' 'texture error' 'perimeter error' 'area error'\n",
|
|
" 'smoothness error' 'compactness error' 'concavity error'\n",
|
|
" 'concave points error' 'symmetry error' 'fractal dimension error'\n",
|
|
" 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
|
|
" 'worst smoothness' 'worst compactness' 'worst concavity'\n",
|
|
" 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n",
|
|
"['malignant' 'benign']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import sklearn\n",
|
|
"from sklearn import datasets\n",
|
|
"from sklearn import svm\n",
|
|
"from sklearn import metrics\n",
|
|
"from sklearn.neighbors import KNeighborsClassifier\n",
|
|
"\n",
|
|
"cancer = datasets.load_breast_cancer()\n",
|
|
"\n",
|
|
"print(cancer.feature_names)\n",
|
|
"print(cancer.target_names)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[1.095e+01 2.135e+01 7.190e+01 ... 1.424e-01 2.964e-01 9.606e-02]\n",
|
|
" [1.300e+01 2.182e+01 8.750e+01 ... 2.060e-01 4.378e-01 1.072e-01]\n",
|
|
" [1.184e+01 1.894e+01 7.551e+01 ... 6.913e-02 2.535e-01 7.993e-02]\n",
|
|
" ...\n",
|
|
" [1.187e+01 2.154e+01 7.683e+01 ... 8.750e-02 2.305e-01 9.952e-02]\n",
|
|
" [1.094e+01 1.859e+01 7.039e+01 ... 7.887e-02 2.251e-01 7.732e-02]\n",
|
|
" [1.049e+01 1.861e+01 6.686e+01 ... 6.528e-02 2.213e-01 7.842e-02]] [0 0 1 1 1 1 0 0 0 1 1 0 1 1 1 1 0 1 0 1 0 1 0 0 0 0 1 1 0 1 0 1 1 1 0 1 1\n",
|
|
" 1 0 1 1 0 1 0 0 1 1 1 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 1 1 1 0 1 0 1 0 1 1 1\n",
|
|
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 0 1 1 0 1 0 1 1 0 1 0 1 0 0 1 1 1\n",
|
|
" 1 0 0 0 1 1 0 0 1 1 1 1 1 0 0 1 1 1 0 1 1 0 0 1 0 1 0 1 0 0 0 0 1 1 1 1 1\n",
|
|
" 0 0 0 1 0 1 0 0 1 1 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 1 1 0 1 0 0 0 0 1 0 1\n",
|
|
" 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 0 0 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 0\n",
|
|
" 1 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 0 0 1 1 1 1 0 0 1 0 1 1 1 0 1 0 1 1 1 1 0\n",
|
|
" 1 1 0 1 1 0 1 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1\n",
|
|
" 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 1 0\n",
|
|
" 0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 0 0 1 1 1\n",
|
|
" 0 1 1 0 0 0 0 0 1 1 0 0 0 1 0 1 1 0 1 0 1 1 0 0 0 0 1 1 1 1 1 1 1 0 1 0 0\n",
|
|
" 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0 0 1 0 0 0 1 1 1 0 1\n",
|
|
" 0 1 1 1 0 0 1 0 1 1 1]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"x = cancer.data\n",
|
|
"y = cancer.target\n",
|
|
"\n",
|
|
"x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.2)\n",
|
|
"\n",
|
|
"print(x_train, y_train)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy: 0.9385964912280702\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"clf = svm.SVC(kernel=\"linear\")\n",
|
|
"clf.fit(x_train, y_train)\n",
|
|
"\n",
|
|
"y_pred = clf.predict(x_test)\n",
|
|
"\n",
|
|
"acc = metrics.accuracy_score(y_test, y_pred)\n",
|
|
"\n",
|
|
"print(\"Accuracy: \",acc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|