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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'/Users/ssievert/Developer/stsievert/dask-ml/dask_ml/__init__.py'"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from time import time\n",
+ "\n",
+ "import numpy as np\n",
+ "import scipy.stats as stats\n",
+ "import dask.array as da\n",
+ "\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.linear_model import SGDClassifier\n",
+ "import sklearn\n",
+ "\n",
+ "import dask_ml\n",
+ "from dask_ml.datasets import make_classification\n",
+ "from dask_ml.wrappers import Incremental\n",
+ "from dask_ml.model_selection import HyperbandCV\n",
+ "import dask_searchcv\n",
+ "\n",
+ "from time import sleep\n",
+ "dask_ml.__file__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from distributed import Client, LocalCluster\n",
+ "procs, threads = 8, 8\n",
+ "num_workers = procs * threads\n",
+ "cluster = LocalCluster(n_workers=procs, threads_per_worker=threads)\n",
+ "client = Client(cluster)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "n, d = int(10e3), int(100)\n",
+ "X, y = make_classification(n_features=d, n_samples=n,\n",
+ " chunks=(n // 10, d))\n",
+ "classes = da.unique(y)\n",
+ "\n",
+ "kwargs = dict(loss='hinge', penalty='elasticnet',\n",
+ " max_iter=1.0, warm_start=True)\n",
+ "model = Incremental(SGDClassifier(), **kwargs)\n",
+ "params = {'alpha': np.logspace(-4, 0, num=1000),\n",
+ " 'l1_ratio': stats.uniform(0, 1),\n",
+ " 'average': [True, False]}\n",
+ "partial_fit_kwargs = {'classes': classes}\n",
+ "\n",
+ "from dask_ml.utils import ConstantFunction\n",
+ "model = ConstantFunction()\n",
+ "params = {'value': stats.uniform(0, 1)}\n",
+ "sleep = 0.1\n",
+ "partial_fit_kwargs = {'sleep': sleep}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fastest possible time = 2.2171875s\n"
+ ]
+ }
+ ],
+ "source": [
+ "n, d = int(1000), int(200)\n",
+ "X, y = make_classification(n_features=d, n_samples=n,\n",
+ " chunks=(n//10, d))\n",
+ "alg = HyperbandCV(model, params, max_iter=81)\n",
+ "\n",
+ "info = alg.info()\n",
+ "\n",
+ "train_time = sleep * info['total_partial_fit_calls']\n",
+ "best_time = train_time / num_workers\n",
+ "print(f\"Fastest possible time = {best_time}s\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Actual time = 9.322114944458008\n"
+ ]
+ }
+ ],
+ "source": [
+ "start = time()\n",
+ "alg.fit(X, y, **partial_fit_kwargs)\n",
+ "actual_time = time() - start\n",
+ "print(f\"Actual time = {actual_time}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
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+ " mean_test_score | \n",
+ " mean_train_score | \n",
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+ "201 0.000133 0.363630 None bracket=0-0 \n",
+ "202 0.000114 0.251782 None bracket=0-3 \n",
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+ "204 0.000240 0.703019 None bracket=1-7 \n",
+ "205 0.000208 0.942910 None bracket=1-4 \n",
+ "\n",
+ " num_models partial_fit_calls score std_fit_time std_score_time \\\n",
+ "201 5 81 0.363630 0 0 \n",
+ "202 5 81 0.251782 0 0 \n",
+ "203 5 81 0.971782 0 0 \n",
+ "204 2 81 0.703019 0 0 \n",
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+ "\n",
+ " std_test_score std_train_score \n",
+ "201 0 None \n",
+ "202 0 None \n",
+ "203 0 None \n",
+ "204 0 None \n",
+ "205 0 None "
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "df = pd.DataFrame(alg.history_)\n",
+ "print(df.bracket.max())\n",
+ "print(df.bracket_iter.unique())\n",
+ "df.tail()\n",
+ "# df[df.mean_test_score > 0.95]"
+ ]
+ },
+ {
+ "cell_type": "code",
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+ "metadata": {},
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+ "encoding": {
+ "row": {
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+ "y": {
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+ "text/plain": [
+ "\n",
+ "\n",
+ "If you see this message, it means the renderer has not been properly enabled\n",
+ "for the frontend that you are using. For more information, see\n",
+ "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from altair import Chart\n",
+ "Chart(df).mark_circle().encode(\n",
+ " x='bracket_iter', row='bracket', y='mean_test_score')"
+ ]
+ },
+ {
+ "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": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}