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Hyperband example
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Example.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"TODO: \n",
"1. Use dask.distributed joblib interface\n",
"1. Firm up cross validation.\n",
" * Use GridSearchCV for very small grids"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'../dask-ml/dask_ml/__init__.py'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sys\n",
"sys.path = ['../dask-ml/'] + sys.path\n",
"import dask_ml\n",
"\n",
"from dask_searchcv import Hyperband\n",
"\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import SGDClassifier\n",
"import sklearn\n",
"import time\n",
"import numpy as np\n",
"import dask.array as da\n",
"from dask_ml.linear_model import PartialSGDClassifier\n",
"import scipy.stats as stats\n",
"from dask_ml.datasets import make_classification\n",
"dask_ml.__file__"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1]\n"
]
}
],
"source": [
"n, d = int(1e3), int(8e3)\n",
"X, y = make_classification(chunks=(n//10, d))\n",
"classes = da.unique(y).compute().tolist()\n",
"print(classes)\n",
"\n",
"# model = PartialSGDClassifier(loss='hinge', penalty='elasticnet',\n",
"# alpha=0.0001, l1_ratio=0.5,\n",
"# max_iter=1.0, warm_start=True, average=True,\n",
"# classes=classes)\n",
"# params = {'alpha': np.logspace(-4, 0, num=1000),\n",
"# 'l1_ratio': stats.uniform(0, 1),\n",
"# 'eta0': np.logspace(-4, 1, num=1000),\n",
"# 'power_t': stats.uniform(0, 1)}\n",
"\n",
"class TestFunction:\n",
" def _fn(self):\n",
" return self.value\n",
"\n",
" def get_params(self, deep=None, **kwargs):\n",
" return {k: getattr(self, k) for k, v in kwargs.items()}\n",
"\n",
" def set_params(self, **kwargs):\n",
" for k, v in kwargs.items():\n",
" setattr(self, k, v)\n",
" return self\n",
"\n",
" def partial_fit(self, *args, **kwargs):\n",
" pass\n",
"\n",
" def score(self, *args, **kwargs):\n",
" return self._fn()\n",
" \n",
"model = TestFunction()\n",
"params = {'value': stats.uniform(0, 1)}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from distributed import Client, LocalCluster\n",
"import distributed\n",
"\n",
"kwargs = {'n_workers': 8, 'threads_per_worker': 1}\n",
"cluster = LocalCluster(**kwargs)\n",
"client = Client(cluster)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ssievert/Developer/stsievert/dask-searchcv/dask_searchcv/adaptive.py:246: UserWarning: model has no attribute warm_start. Hyperband will assume it is reusing previous calls to `partial_fit` during each call to `partial_fit`\n",
" warnings.warn('model has no attribute warm_start. Hyperband will assume it '\n"
]
},
{
"data": {
"text/plain": [
"Hyperband(eta=3, max_iter=None,\n",
" model=<__main__.TestFunction object at 0x114d55ba8>, n_jobs=-1,\n",
" params={'value': <scipy.stats._distn_infrastructure.rv_frozen object at 0x114d55c50>})"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alg = Hyperband(model, params, max_iter=81, n_jobs=-1)\n",
"\n",
"alg.fit(X, y, classes=classes)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n",
"[0 1 2 3]\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bracket</th>\n",
" <th>bracket_iter</th>\n",
" <th>model_id</th>\n",
" <th>num_models</th>\n",
" <th>partial_fit_iters</th>\n",
" <th>val_score</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>s=0-0</td>\n",
" <td>5</td>\n",
" <td>81.0</td>\n",
" <td>0.900382</td>\n",
" <td>0.900382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>s=0-1</td>\n",
" <td>5</td>\n",
" <td>81.0</td>\n",
" <td>0.107369</td>\n",
" <td>0.107369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>s=0-2</td>\n",
" <td>5</td>\n",
" <td>81.0</td>\n",
" <td>0.694792</td>\n",
" <td>0.694792</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>s=0-3</td>\n",
" <td>5</td>\n",
" <td>81.0</td>\n",
" <td>0.914636</td>\n",
" <td>0.914636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>s=0-4</td>\n",
" <td>5</td>\n",
" <td>81.0</td>\n",
" <td>0.129101</td>\n",
" <td>0.129101</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bracket bracket_iter model_id num_models partial_fit_iters val_score \\\n",
"198 0 0 s=0-0 5 81.0 0.900382 \n",
"199 0 0 s=0-1 5 81.0 0.107369 \n",
"200 0 0 s=0-2 5 81.0 0.694792 \n",
"201 0 0 s=0-3 5 81.0 0.914636 \n",
"202 0 0 s=0-4 5 81.0 0.129101 \n",
"\n",
" value \n",
"198 0.900382 \n",
"199 0.107369 \n",
"200 0.694792 \n",
"201 0.914636 \n",
"202 0.129101 "
]
},
"execution_count": 6,
"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()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fit_time</th>\n",
" <th>param_value</th>\n",
" <th>params</th>\n",
" <th>score_time</th>\n",
" <th>test_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000003</td>\n",
" <td>0.255089</td>\n",
" <td>{'value': 0.255089294638416}</td>\n",
" <td>0.000002</td>\n",
" <td>0.255089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.000004</td>\n",
" <td>0.357995</td>\n",
" <td>{'value': 0.35799455755066456}</td>\n",
" <td>0.000002</td>\n",
" <td>0.357995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.000006</td>\n",
" <td>0.306098</td>\n",
" <td>{'value': 0.30609797148984563}</td>\n",
" <td>0.000002</td>\n",
" <td>0.306098</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.000004</td>\n",
" <td>0.410605</td>\n",
" <td>{'value': 0.41060493214300653}</td>\n",
" <td>0.000002</td>\n",
" <td>0.410605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.000003</td>\n",
" <td>0.281013</td>\n",
" <td>{'value': 0.28101256339566794}</td>\n",
" <td>0.000001</td>\n",
" <td>0.281013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fit_time param_value params score_time \\\n",
"0 0.000003 0.255089 {'value': 0.255089294638416} 0.000002 \n",
"1 0.000004 0.357995 {'value': 0.35799455755066456} 0.000002 \n",
"2 0.000006 0.306098 {'value': 0.30609797148984563} 0.000002 \n",
"3 0.000004 0.410605 {'value': 0.41060493214300653} 0.000002 \n",
"4 0.000003 0.281013 {'value': 0.28101256339566794} 0.000001 \n",
"\n",
" test_score \n",
"0 0.255089 \n",
"1 0.357995 \n",
"2 0.306098 \n",
"3 0.410605 \n",
"4 0.281013 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = pd.DataFrame(alg.cv_results_)\n",
"results.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
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",
"text/plain": [
"<VegaLite 2 object>\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": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import altair as alt\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# different brackets; need a better name than \"s\"\n",
"df['-bracket'] = -1 * df['bracket']\n",
"\n",
"alt.Chart(df).mark_line().encode(\n",
" x='bracket_iter', \n",
" y=alt.Y('val_score', scale=alt.Scale(zero=False)),\n",
" color='model_id',\n",
" row='-bracket')"
]
},
{
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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