Rick van Hattem
77 lines
1.9 KiB
Plaintext
77 lines
1.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "1583265b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "ab87f56987a44663ae7c8fd3192247b6",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Optimization Progress: 0%| | 0/100 [00:00<?, ?pipeline/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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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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"\n",
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"\n",
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"TPOT closed during evaluation in one generation.\n",
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"WARNING: TPOT may not provide a good pipeline if TPOT is stopped/interrupted in a early generation.\n",
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"\n",
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"\n",
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"TPOT closed prematurely. Will use the current best pipeline.\n",
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"\n",
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"Best pipeline: MultinomialNB(VarianceThreshold(input_matrix, threshold=0.2), alpha=0.01, fit_prior=False)\n",
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"0.9473684210526315\n"
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]
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}
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],
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"source": [
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"import tpot\n",
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"iris = load_iris()\n",
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"X_train, X_test, y_train, y_test = train_test_split(\n",
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" iris.data, iris.target, train_size=0.75, test_size=0.25)\n",
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"\n",
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"tpot = tpot.TPOTClassifier(verbosity=2, max_time_mins=10)\n",
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"tpot.fit(X_train, y_train)\n",
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"print(tpot.score(X_test, y_test))\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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