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@stsievert
Last active April 14, 2019 23:12
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Hyperparameter comparisons (with successive halving, hyperband, stop on plateau and passive random sampling)
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Adaptive hyperparameter optimization\n",
"\n",
"This notebook looks at a realistic use case of hyperparameter optimization. To do this, this notebook uses\n",
"\n",
"* a realistic deep learning model\n",
"* a realistic set of hyperparameters\n",
"\n",
"There are many hyperparameter for any model or framework. These can be specific to the model, or be related to the optimization framework used to minimize the model.\n",
"\n",
"This notebook will show\n",
"\n",
"* the model input and output (noisy and clean images respectively)\n",
"* the parameter space we are searching over\n",
"* a newly developed hyperparameter optimization algorithm and it's integration\n",
"* a comparison with 3 hyperparameter selection algorithms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from dask_ml.model_selection._successive_halving import stop_on_plateau\n",
"from dask_ml.model_selection import HyperbandCV"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Client</h3>\n",
"<ul>\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:61559\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Cluster</h3>\n",
"<ul>\n",
" <li><b>Workers: </b>8</li>\n",
" <li><b>Cores: </b>8</li>\n",
" <li><b>Memory: </b>17.18 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:61559' processes=8 cores=8>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import distributed\n",
"from distributed import Client\n",
"client = Client()\n",
"client"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data\n",
"See below for an image."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import noisy_mnist\n",
"_X, _y = noisy_mnist.dataset()#n=10 * 1024)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(dask.array<array, shape=(70000, 784), dtype=float32, chunksize=(35000, 784)>,\n",
" dask.array<array, shape=(70000, 784), dtype=float32, chunksize=(35000, 784)>)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import dask.array as da\n",
"n, d = _X.shape\n",
"X = da.from_array(_X, chunks=(n // 2, d))\n",
"y = da.from_array(_y, chunks=n // 2)\n",
"X, y"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x144 with 24 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"cols = 12\n",
"w = 1.0\n",
"fig, axs = plt.subplots(figsize=(cols*w, 2*w), ncols=cols, nrows=2)\n",
"for col, (upper, lower) in enumerate(zip(axs[0], axs[1])):\n",
" if col == 0:\n",
" upper.text(-28, 14, 'ground\\ntruth')\n",
" lower.text(-28, 14, 'output')\n",
" i = np.random.choice(len(X))\n",
" noisy = X[i].reshape(28, 28)\n",
" clean = y[i].reshape(28, 28)\n",
" kwargs = {'cbar': False, 'xticklabels': False, 'yticklabels': False, 'cmap': 'gray'}\n",
" sns.heatmap(noisy, ax=lower, **kwargs)\n",
" sns.heatmap(clean, ax=upper, **kwargs)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I use a deep learning library (PyTorch) for this model, at least through the scikit-learn interface for PyTorch, [skorch].\n",
"\n",
"[skorch]:https://github.com/dnouri/skorch"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from autoencoder import Autoencoder, NegLossScore\n",
"import torch\n",
"\n",
"model = NegLossScore(module=Autoencoder,\n",
" criterion=torch.nn.BCELoss,\n",
" warm_start=True,\n",
" train_split=None,\n",
" max_epochs=1,\n",
" callbacks=[])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I don't show it here; I'd rather concentrate on tuning hyperparameters. But briefly, it's a simple fully connected 3 hidden layer autoencoder with a latent dimension of 49."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parameters\n",
"\n",
"The parameters I am interested in tuning are\n",
"\n",
"* model\n",
" * initialization\n",
" * activation function\n",
" * weight decay (which is similar to $\\ell_2$ regularization)\n",
"* optimizer\n",
" * which optimizer to use (e.g., Adam, SGD)\n",
" * batch size used to approximate gradient\n",
" * learning rate (but not for Adam)\n",
" * momentum for SGD\n",
" \n",
"After looking at the results, I think I was too exploratory in my tuning of step size. I should have experimented with it more to determine a reasonable range."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"params = {\n",
" 'module__init': ['xavier_uniform_',\n",
" 'xavier_normal_',\n",
" 'kaiming_uniform_',\n",
" 'kaiming_normal_',\n",
" ],\n",
" 'module__activation': ['ReLU', 'LeakyReLU', 'ELU', 'PReLU'],\n",
" 'optimizer': ['SGD'] * 5 + ['Adam'] * 2,\n",
" 'batch_size': [32, 64, 128, 256, 512],\n",
" 'optimizer__lr': np.logspace(1, -1.5, num=1000),\n",
" 'optimizer__weight_decay': [0]*200 + np.logspace(-5, -3, num=1000).tolist(),\n",
" 'optimizer__nesterov': [True],\n",
" 'optimizer__momentum': np.linspace(0, 1, num=1000),\n",
" 'train_split': [None],\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import ParameterSampler\n",
"import torch\n",
"\n",
"def trim_params(**kwargs):\n",
" if kwargs['optimizer'] != 'Adam':\n",
" kwargs.pop('optimizer__amsgrad', None)\n",
" if kwargs['optimizer'] == 'Adam':\n",
" kwargs.pop('optimizer__lr', None)\n",
" if kwargs['optimizer'] != 'SGD':\n",
" kwargs.pop('optimizer__nesterov', None)\n",
" kwargs.pop('optimizer__momentum', None)\n",
" kwargs['optimizer'] = getattr(torch.optim, kwargs['optimizer'])\n",
" return kwargs\n",
"\n",
"param_list = [trim_params(**param)\n",
" for param in ParameterSampler(params, int(1e4))]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"## for debugging; ignore this cell\n",
"# from sklearn.linear_model import SGDClassifier\n",
"# from sklearn.datasets import make_classification\n",
"# from sklearn.model_selection import ParameterSampler\n",
"# import dask.array as da\n",
"# import numpy as np\n",
"# model = SGDClassifier()\n",
"# params = {'alpha': np.logspace(-7, 0, num=int(1e6))}\n",
"\n",
"# n, d = int(10e3), 700\n",
"# _X, _y = make_classification(n_samples=n, n_features=d,\n",
"# random_state=1)\n",
"# X = da.from_array(_X, chunks=(n // 10, d))\n",
"# y = da.from_array(_y, chunks=n // 10)\n",
"# X, y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hyperparameter optimization"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from dask_ml.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import SGDClassifier\n",
"\n",
"max_iter = 243\n",
"all_hist = {}"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"fit_params = {}\n",
"if isinstance(model, SGDClassifier):\n",
" fit_params = {'classes': da.unique(y).compute()}\n",
" param_list = list(ParameterSampler(params, max_iter * 100))\n",
"else:\n",
" param_list = [trim_params(**param)\n",
" for param in ParameterSampler(params, max_iter * 100)]\n",
"search = HyperbandCV(model, param_list, max_iter, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"HyperbandCV(asynchronous=True, eta=3, max_iter=243,\n",
" model=<class 'autoencoder.NegLossScore'>[uninitialized](\n",
" module=<class 'autoencoder.Autoencoder'>,\n",
"),\n",
" params=[{'train_split': None, 'optimizer__weight_decay': 1.1696827039703846e-05, 'optimizer': <class 'torch.optim.adam.Adam'>, 'module__init': 'kaiming_normal_', 'module__activation': 'ELU', 'batch_size': 512}, {'train_split': None, 'optimizer__weight_decay': 1.607704421673822e-05, 'optimizer__neste...tim.sgd.SGD'>, 'module__init': 'kaiming_uniform_', 'module__activation': 'ReLU', 'batch_size': 512}],\n",
" patience=inf, random_state=42, scoring=None, test_size=0.15,\n",
" tol=0.001)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.fit(X_train, y_train, **fit_params)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<class 'autoencoder.NegLossScore'>[initialized](\n",
" module_=Autoencoder(\n",
" (encoder): Sequential(\n",
" (0): Linear(in_features=784, out_features=196, bias=True)\n",
" (1): PReLU(num_parameters=1)\n",
" (2): Linear(in_features=196, out_features=49, bias=True)\n",
" (3): PReLU(num_parameters=1)\n",
" )\n",
" (decoder): Sequential(\n",
" (0): Linear(in_features=49, out_features=784, bias=True)\n",
" (1): Sigmoid()\n",
" )\n",
" ),\n",
")"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.best_estimator_"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.09276870638132095"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.best_score_"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'train_split': None,\n",
" 'optimizer__weight_decay': 2.071649675602067e-05,\n",
" 'optimizer__nesterov': True,\n",
" 'optimizer__momentum': 0.5655655655655656,\n",
" 'optimizer__lr': 1.3936192742241424,\n",
" 'optimizer': torch.optim.sgd.SGD,\n",
" 'module__init': 'xavier_normal_',\n",
" 'module__activation': 'PReLU',\n",
" 'batch_size': 64}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.best_params_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizing output of best estimator"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"noisy_test = X_test.compute()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(7000, 784)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clean_hat = search.best_estimator_.predict(noisy_test)\n",
"clean_hat.shape"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x216 with 36 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cols = 12\n",
"w = 1.0\n",
"fig, axs = plt.subplots(figsize=(cols*w, 3*w), ncols=cols, nrows=3)\n",
"for col, (upper, middle, lower) in enumerate(zip(axs[0], axs[1], axs[2])):\n",
" if col == 0:\n",
" upper.text(-28, 14, 'ground\\ntruth')\n",
" middle.text(-28, 14, 'input')\n",
" lower.text(-28, 14, 'output')\n",
" i = np.random.choice(len(X_test))\n",
" noisy = X_test[i].reshape(28, 28)\n",
" clean = y_test[i].reshape(28, 28)\n",
" clean_hat_i = clean_hat[i].reshape(28, 28)\n",
" kwargs = {'cbar': False, 'xticklabels': False, 'yticklabels': False, 'cmap': 'gray'}\n",
" sns.heatmap(noisy, ax=middle, **kwargs)\n",
" sns.heatmap(clean, ax=upper, **kwargs)\n",
" sns.heatmap(clean_hat_i, ax=lower, **kwargs)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['succ-halv-bracket=4', 'succ-halv-bracket=3', 'succ-halv-bracket=2', 'succ-halv-bracket=1', 'succ-halv-bracket=0'])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_history = {f'succ-halv-{bracket}': bracket_hist\n",
" for bracket, bracket_hist in search.history_.items()}\n",
"all_history.keys()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import copy\n",
"brackets = list(reversed(sorted(all_history.keys())))\n",
"assert brackets[-1] == 'succ-halv-bracket=0'\n",
"bracket_cont = copy.deepcopy([all_history[key] for key in all_history])\n",
"for bracket_id, hist in enumerate(bracket_cont):\n",
" for h in hist:\n",
" h['model_id'] = key(bracket_id, h['model_id'])\n",
"all_history['hyperband'] = sum(bracket_cont, [])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setting parameters for Hyperband\n",
"Need to know two things:\n",
"\n",
"1. how many \"epochs\" or \"passes through data\" to train model\n",
"2. how many configs to evaluate\n",
" * this is some measure of how complex the search space is\n",
" \n",
"This determines\n",
"\n",
"* The `max_iter` argument for `HyperbandCV`\n",
"* the chunks size for the array to pass in\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparison with early stopping"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(243, 19, 4743)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_calls = search.metadata_['partial_fit_calls']\n",
"num_calls = max_iter\n",
"num_models = total_calls // num_calls\n",
"num_calls, num_models, search.metadata_['partial_fit_calls']"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"from dask_ml.model_selection._successive_halving import _HistoryRecorder, stop_on_plateau\n",
"from dask_ml.model_selection._incremental import fit\n",
"from dask_ml.model_selection import train_test_split\n",
"from sklearn.model_selection import ParameterSampler\n",
"import random\n",
"\n",
"rand_search = _HistoryRecorder(stop_on_plateau, patience=10, tol=1e-3, max_iter=num_calls)\n",
"\n",
"X_train2, X_test2, y_train2, y_test2 = train_test_split(X_train, y_train, test_size=0.15, random_state=42)\n",
"if isinstance(model, SGDClassifier):\n",
" rand_params = list(ParameterSampler(params, int(num_models)))\n",
" fit_params = {'classes': da.unique(y).compute()}\n",
"else:\n",
" rand_params = [trim_params(**param)\n",
" for param in ParameterSampler(params, int(num_models))]\n",
" fit_params = {}\n",
"\n",
"# rand_params = [random.choice(param_list[-search.metadata_['models']:])\n",
"# for _ in range(num_models)]\n",
"_ = fit(\n",
" model,\n",
" rand_params,\n",
" X_train2,\n",
" y_train2,\n",
" X_test2,\n",
" y_test2,\n",
" additional_partial_fit_calls=rand_search.fit,\n",
" fit_params=fit_params,\n",
" random_state=42\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"all_history['stop_on_plateau'] = rand_search.history"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"rand_search2 = _HistoryRecorder(stop_on_plateau, patience=np.inf, tol=1e-3, max_iter=num_calls)\n",
"\n",
"X_train2, X_test2, y_train2, y_test2 = train_test_split(X_train, y_train, test_size=0.15, random_state=42)\n",
"if isinstance(model, SGDClassifier):\n",
" rand_params = list(ParameterSampler(params, int(num_models)))\n",
" fit_params = {'classes': da.unique(y).compute()}\n",
"else:\n",
" rand_params = [trim_params(**param)\n",
" for param in ParameterSampler(params, int(num_models))]\n",
" fit_params = {}\n",
"\n",
"_ = fit(\n",
" model,\n",
" rand_params,\n",
" X_train2,\n",
" y_train2,\n",
" X_test2,\n",
" y_test2,\n",
" additional_partial_fit_calls=rand_search2.fit,\n",
" fit_params=fit_params,\n",
" random_state=42\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"all_history['random-search-passive'] = rand_search2.history"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Performance\n",
"`HyperbandCV` will find close to the best possible parameters with the given computational budget.*\n",
"\n",
"<sup>* \"will\" := with high probability,\n",
"\"close\" := within log factors,\n",
"\"best possible\" in expected value.</sup>\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"from pprint import pprint\n",
"import toolz\n",
"\n",
"def shape_history(history, alg='', **kwargs):\n",
" \"\"\"\n",
" There's a bug in this code; what if model_id is repeated?\n",
" \"\"\"\n",
" history = sorted(history, key=lambda item: item['wall_time'])\n",
" \n",
" out = []\n",
" scores = {}\n",
" calls = {}\n",
" train_time = {}\n",
" \n",
" start = min(h['wall_time'] for h in history)\n",
" for h in history:\n",
" scores[h['model_id']] = h['score']\n",
" calls[h['model_id']] = h['partial_fit_calls']\n",
" train_time[h['model_id']] = h['partial_fit_time'] + h['score_time']\n",
" out += [{'wall_time': h['wall_time'] - start,\n",
" 'best_score': max(scores.values()),\n",
" 'cumulative_partial_fit_calls': sum(calls.values()),\n",
" 'alg': alg,\n",
" 'adaptive': alg not in {'succ-halv-bracket=0', 'stop_on_plateau'} and 'passive' not in alg,\n",
" 'train_time': sum(train_time.values()),\n",
" **kwargs\n",
" }]\n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [],
"source": [
"hist = {alg: shape_history(h, alg=alg)\n",
" for alg, h in all_history.items()}\n",
"hist = sum(list(hist.values()), [])"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [
{
"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>adaptive</th>\n",
" <th>alg</th>\n",
" <th>best_score</th>\n",
" <th>cumulative_partial_fit_calls</th>\n",
" <th>train_time</th>\n",
" <th>wall_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>True</td>\n",
" <td>succ-halv-bracket=4</td>\n",
" <td>-0.241370</td>\n",
" <td>1</td>\n",
" <td>5.575856</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>True</td>\n",
" <td>succ-halv-bracket=4</td>\n",
" <td>-0.127417</td>\n",
" <td>2</td>\n",
" <td>30.409564</td>\n",
" <td>9.536743e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>True</td>\n",
" <td>succ-halv-bracket=4</td>\n",
" <td>-0.127417</td>\n",
" <td>3</td>\n",
" <td>44.927191</td>\n",
" <td>9.536743e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>True</td>\n",
" <td>succ-halv-bracket=4</td>\n",
" <td>-0.127417</td>\n",
" <td>4</td>\n",
" <td>51.307614</td>\n",
" <td>9.536743e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>True</td>\n",
" <td>succ-halv-bracket=4</td>\n",
" <td>-0.127417</td>\n",
" <td>5</td>\n",
" <td>67.586778</td>\n",
" <td>2.145767e-06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" adaptive alg best_score cumulative_partial_fit_calls \\\n",
"0 True succ-halv-bracket=4 -0.241370 1 \n",
"1 True succ-halv-bracket=4 -0.127417 2 \n",
"2 True succ-halv-bracket=4 -0.127417 3 \n",
"3 True succ-halv-bracket=4 -0.127417 4 \n",
"4 True succ-halv-bracket=4 -0.127417 5 \n",
"\n",
" train_time wall_time \n",
"0 5.575856 0.000000e+00 \n",
"1 30.409564 9.536743e-07 \n",
"2 44.927191 9.536743e-07 \n",
"3 51.307614 9.536743e-07 \n",
"4 67.586778 2.145767e-06 "
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"df = pd.DataFrame(hist)\n",
"# df.to_csv('')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"w = 5\n",
"fig, axs = plt.subplots(ncols=2, figsize=(2*w, w))\n",
"\n",
"adaptiveness = sorted([a for a in df.alg.unique() if a not in ['hyperband', 'stop_on_plateau']])\n",
"adaptiveness = ['stop_on_plateau'] + adaptiveness\n",
"\n",
"for x, ax in zip(['cumulative_partial_fit_calls', 'wall_time (min)'], axs):\n",
" show = df.copy()\n",
" show['wall_time (min)'] = show['wall_time'] / 60\n",
" sns.lineplot(\n",
" x=x,\n",
" y='best_score',\n",
" hue='alg',\n",
" hue_order=adaptiveness,\n",
" style='adaptive',\n",
" style_order=[True, False],\n",
" data=show,\n",
" ax=ax,\n",
" palette='magma',\n",
" ci=None,\n",
" estimator='mean'\n",
" )\n",
" ax.grid(linestyle='--')\n",
" ax.set_ylim(-0.13, -0.09)\n",
"plt.savefig('./successive-halving-comparison.png', dpi=300, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"w = 6\n",
"fig, axs = plt.subplots(ncols=2, figsize=(2*(w + 0.0), 1.00*w))\n",
"\n",
"adaptiveness = ['random-search-passive', 'stop_on_plateau', 'hyperband']\n",
"\n",
"show = df.copy()\n",
"show['wall_time (min)'] = show['wall_time'] / 60\n",
"for x, ax in zip(['cumulative_partial_fit_calls', 'wall_time (min)'], axs):\n",
" sns.lineplot(\n",
" x=x,\n",
" y='best_score',\n",
" hue='alg',\n",
" hue_order=adaptiveness,\n",
" #style='adaptive',\n",
" style_order=[True, False],\n",
" data=show,\n",
" ax=ax,\n",
" palette='magma',\n",
" estimator='max',\n",
" ci=None,\n",
" )\n",
" ax.grid(linestyle='--')\n",
" ax.set_ylim(-0.13, -0.09)\n",
"plt.savefig('./hyperband-comparison.png', dpi=300, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parameter visualization"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"hist = pd.DataFrame(search.cv_results_)\n",
"hist['param_optimizer_'] = hist['param_optimizer'].apply(lambda opt: str(opt).replace('<class', '').strip('>'))\n",
"hist['test_loss'] = -1 * hist['test_score']\n",
"hist = hist.sort_values(by='test_loss')\n",
"hist['rank'] = np.arange(len(hist)) + 1"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"w = 5\n",
"fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(2*w, 2*w))\n",
"axs = axs.flat[:]\n",
"hues = ['param_optimizer', 'param_module__init', 'param_module__activation', 'param_batch_size']\n",
"for ax, hue in zip(axs, hues):\n",
" cmap = None\n",
" if 'batch_size' in hue:\n",
" cmap = 'viridis'\n",
" sns.barplot(\n",
" x='rank', \n",
" y='test_loss',\n",
" hue=hue,\n",
" data=hist,\n",
" ax=ax,\n",
" palette=cmap,\n",
" dodge=False,\n",
" )\n",
" ax.set_xlim(-1.5, 50)\n",
" ax.set_ylim(0, 0.14)\n",
" ax.grid(linestyle='--', which='y')\n",
" ax.legend(loc='lower right')\n",
" ax.set_title(hue.replace('param_', ''))\n",
" ax.tick_params(labelbottom=False)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"sgd_alg = [a for a in hist.param_optimizer_.unique() if 'sgd' in a.lower()][0]\n",
"sgd = hist[hist.param_optimizer_ == sgd_alg]"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\" 'torch.optim.sgd.SGD'\"]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"w = 5\n",
"fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(2*w, 2*w))\n",
"axs = axs.flat[:]\n",
"variables = ['param_optimizer__lr', 'param_optimizer__momentum', 'param_optimizer__weight_decay', 'param_batch_size']\n",
"print(sgd.param_optimizer_.unique())\n",
"for ax, var in zip(axs, variables):\n",
" show = sgd.copy()\n",
" show = show.sort_values(by=var)\n",
" if 'weight_decay' in var:\n",
" show[var] += 1e-8\n",
" show = show[show.test_loss < 0.16]\n",
" sns.scatterplot(\n",
" x=var,\n",
" y='test_loss',\n",
" data=show,\n",
" hue='test_loss',\n",
" palette='magma',\n",
" legend=False,\n",
" ax=ax,\n",
" )\n",
" if 'lr' in var:\n",
" ax.set_xscale('log', basex=10)\n",
" if 'batch_size' in var:\n",
" ax.set_xscale('log', basex=2)\n",
" if 'weight_decay' in var:\n",
" ax.set_xlim(5e-9, 1e-3)\n",
" ax.set_xscale('log', basex=10)\n",
" \n",
" ax.grid(linestyle='--')\n",
" ax.set_ylim(0.08, 0.16)\n",
"plt.show()"
]
},
{
"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
}
import skorch.utils
from skorch import NeuralNetRegressor
import torch.nn as nn
import torch
import skorch
def _initialize(method, layer, gain=1):
weight = layer.weight.data
# _before = weight.data.clone()
kwargs = {'gain': gain} if 'xavier' in str(method) else {}
method(weight.data, **kwargs)
# assert torch.all(weight.data != _before)
class Autoencoder(nn.Module):
def __init__(self, activation='ReLU', init='xavier_uniform_',
**kwargs):
super().__init__()
self.activation = activation
self.init = init
self._iters = 0
init_method = getattr(torch.nn.init, init)
act_layer = getattr(nn, activation)
act_kwargs = {'inplace': True} if self.activation != 'PReLU' else {}
gain = 1
if self.activation in ['LeakyReLU', 'ReLU']:
name = 'leaky_relu' if self.activation == 'LeakyReLU' else 'relu'
gain = torch.nn.init.calculate_gain(name)
inter_dim = 28 * 28 // 4
latent_dim = inter_dim // 4
layers = [
nn.Linear(28 * 28, inter_dim),
act_layer(**act_kwargs),
nn.Linear(inter_dim, latent_dim),
act_layer(**act_kwargs)
]
for layer in layers:
if hasattr(layer, 'weight') and layer.weight.data.dim() > 1:
_initialize(init_method, layer, gain=gain)
self.encoder = nn.Sequential(*layers)
layers = [
nn.Linear(latent_dim, inter_dim),
act_layer(**act_kwargs),
nn.Linear(inter_dim, 28 * 28),
nn.Sigmoid()
]
layers = [
nn.Linear(latent_dim, 28 * 28),
nn.Sigmoid()
]
for layer in layers:
if hasattr(layer, 'weight') and layer.weight.data.dim() > 1:
_initialize(init_method, layer, gain=gain)
self.decoder = nn.Sequential(*layers)
def forward(self, x):
self._iters += 1
shape = x.size()
x = x.view(x.shape[0], -1)
x = self.encoder(x)
x = self.decoder(x)
return x.view(shape)
class NegLossScore(NeuralNetRegressor):
steps = 0
def partial_fit(self, *args, **kwargs):
super().partial_fit(*args, **kwargs)
self.steps += 1
def score(self, X, y):
X = skorch.utils.to_tensor(X, device=self.device)
y = skorch.utils.to_tensor(y, device=self.device)
self.initialize_criterion()
y_hat = self.predict(X)
y_hat = skorch.utils.to_tensor(y_hat, device=self.device)
loss = super().get_loss(y_hat, y, X=X, training=False).item()
print(f'steps = {self.steps}, loss = {loss}')
return -1 * loss
def initialize(self, *args, **kwargs):
super().initialize(*args, **kwargs)
self.callbacks_ = []
from keras.datasets import mnist
import numpy as np
import skimage.util
import random
import skimage.filters
import skimage
import scipy.signal
def noise_img(x):
noises = [
{"mode": "s&p", "amount": np.random.uniform(0.1, 0.1)},
{"mode": "gaussian", "var": np.random.uniform(0.10, 0.15)},
]
# noise = random.choice(noises)
noise = noises[1]
return skimage.util.random_noise(x, **noise)
def train_formatting(img):
img = img.reshape(28, 28).astype("float32")
return img.flat[:]
def blur_img(img):
assert img.ndim == 1
n = int(np.sqrt(img.shape[0]))
img = img.reshape(n, n)
h = np.zeros((n, n))
angle = np.random.uniform(-5, 5)
w = random.choice(range(1, 3))
h[n // 2, n // 2 - w : n // 2 + w] = 1
h = skimage.transform.rotate(h, angle)
h /= h.sum()
y = scipy.signal.convolve(img, h, mode="same")
return y.flat[:]
def dataset(n=None):
(x_train, _), (x_test, _) = mnist.load_data()
x = np.concatenate((x_train, x_test))
if n:
x = x[:n]
else:
n = int(70e3)
x = x.astype("float32") / 255.
x = np.reshape(x, (len(x), 28 * 28))
y = np.apply_along_axis(train_formatting, 1, x)
clean = y.copy()
noisy = y.copy()
# order = [noise_img, blur_img]
# order = [blur_img]
order = [noise_img]
random.shuffle(order)
for fn in order:
noisy = np.apply_along_axis(fn, 1, noisy)
noisy = noisy.astype("float32")
clean = clean.astype("float32")
# noisy = noisy.reshape(-1, 1, 28, 28).astype("float32")
# clean = clean.reshape(-1, 1, 28, 28).astype("float32")
return noisy, clean
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