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richardliaw/torch_cnn_service_old.ipynb

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torch_cnn_service_old.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Service API on PyTorch CNN"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<script type='text/javascript'>/*\n",
" * Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n",
" */\n",
"\n",
"requirejs.config({\n",
" paths: {\n",
" plotly: ['https://cdn.plot.ly/plotly-latest.min'],\n",
" },\n",
"});\n",
"if (!window.Plotly) {\n",
" require(['plotly'], function(plotly) {\n",
" window.Plotly = plotly;\n",
" });\n",
"}\n",
"/*\n",
" * Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n",
" */\n",
"\n",
"// helper functions used across multiple plots\n",
"function rgb(rgb_array) {\n",
" return 'rgb(' + rgb_array.join() + ')';\n",
"}\n",
"\n",
"function copy_and_reverse(arr) {\n",
" const copy = arr.slice();\n",
" copy.reverse();\n",
" return copy;\n",
"}\n",
"\n",
"function axis_range(grid, is_log) {\n",
" return is_log ?\n",
" [Math.log10(Math.min(...grid)), Math.log10(Math.max(...grid))]:\n",
" [Math.min(...grid), Math.max(...grid)];\n",
"}\n",
"\n",
"function relativize_data(f, sd, rel, arm_data, metric) {\n",
" // if relative, extract status quo & compute ratio\n",
" const f_final = rel === true ? [] : f;\n",
" const sd_final = rel === true ? []: sd;\n",
"\n",
" if (rel === true) {\n",
" const f_sq = (\n",
" arm_data['in_sample'][arm_data['status_quo_name']]['y'][metric]\n",
" );\n",
" const sd_sq = (\n",
" arm_data['in_sample'][arm_data['status_quo_name']]['se'][metric]\n",
" );\n",
"\n",
" for (let i = 0; i < f.length; i++) {\n",
" res = relativize(f[i], sd[i], f_sq, sd_sq);\n",
" f_final.push(100 * res[0]);\n",
" sd_final.push(100 * res[1]);\n",
" }\n",
" }\n",
"\n",
" return [f_final, sd_final];\n",
"}\n",
"\n",
"function relativize(m_t, sem_t, m_c, sem_c) {\n",
" r_hat = (\n",
" (m_t - m_c) / Math.abs(m_c) -\n",
" Math.pow(sem_c, 2) * m_t / Math.pow(Math.abs(m_c), 3)\n",
" );\n",
" variance = (\n",
" (Math.pow(sem_t, 2) + Math.pow((m_t / m_c * sem_c), 2)) /\n",
" Math.pow(m_c, 2)\n",
" )\n",
" return [r_hat, Math.sqrt(variance)];\n",
"}\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-16 12:03:28] ipy_plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
]
}
],
"source": [
"import torch\n",
"import numpy as np\n",
"\n",
"from ax.plot.contour import plot_contour\n",
"from ax.plot.trace import optimization_trace_single_method\n",
"from ax.service.ax_client import AxClient\n",
"from ax.utils.notebook.plotting import render, init_notebook_plotting\n",
"from ax.utils.tutorials.cnn_utils import load_mnist, train, evaluate\n",
"\n",
"\n",
"init_notebook_plotting()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"dtype = torch.float\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"train_loader, valid_loader, test_loader = load_mnist()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ax = AxClient()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-16 12:03:29] ax.service.utils.dispatch: Using Bayesian Optimization generation strategy. Iterations after 5 will take longer to generate due to model-fitting.\n"
]
}
],
"source": [
"ax.create_experiment(\n",
" name=\"hartmann_test_experiment\",\n",
" parameters=[\n",
" {\"name\": \"lr\", \"type\": \"range\", \"bounds\": [1e-6, 0.4], \"log_scale\": True},\n",
" {\"name\": \"momentum\", \"type\": \"range\", \"bounds\": [0.0, 1.0]},\n",
" ],\n",
" objective_name=\"Accuracy\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def train_evaluate(parameterization):\n",
" net = train(train_loader=train_loader, parameters=parameterization, dtype=dtype, device=device)\n",
" return evaluate(\n",
" net=net,\n",
" data_loader=valid_loader,\n",
" dtype=dtype,\n",
" device=device,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running trial 1/30...\n",
"Running trial 2/30...\n",
"Running trial 3/30...\n",
"Running trial 4/30...\n",
"Running trial 5/30...\n",
"Running trial 6/30...\n",
"Running trial 7/30...\n",
"Running trial 8/30...\n",
"Running trial 9/30...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/xarfuse/uid-132168/9ce44a70-ns-4026531840/gpytorch/utils/cholesky.py:42: RuntimeWarning:\n",
"\n",
"A not p.d., added jitter of 1e-08 to the diagonal\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running trial 10/30...\n",
"Running trial 11/30...\n",
"Running trial 12/30...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/xarfuse/uid-132168/9ce44a70-ns-4026531840/gpytorch/utils/cholesky.py:42: RuntimeWarning:\n",
"\n",
"A not p.d., added jitter of 1e-08 to the diagonal\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running trial 13/30...\n",
"Running trial 14/30...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/xarfuse/uid-132168/9ce44a70-ns-4026531840/gpytorch/utils/cholesky.py:42: RuntimeWarning:\n",
"\n",
"A not p.d., added jitter of 1e-08 to the diagonal\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running trial 15/30...\n"
]
}
],
"source": [
"for i in range(15):\n",
" print(f\"Running trial {i+1}/30...\")\n",
" parameters, trial_index = ax.get_next_trial()\n",
" ax.complete_trial(trial_index=trial_index, raw_data=train_evaluate(parameters))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'lr': 0.000406586189910757, 'momentum': 0.38365804357915195}"
]
},
"execution_count": 32,
"metadata": {
"bento_obj_id": "140194491747352"
},
"output_type": "execute_result"
}
],
"source": [
"best_parameters, values = ax.get_best_parameters()\n",
"best_parameters"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Accuracy': 0.9471667003474779}"
]
},
"execution_count": 33,
"metadata": {
"bento_obj_id": "140194338208936"
},
"output_type": "execute_result"
}
],
"source": [
"means, covariances = values\n",
"means"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div id=\"b5939c1773d4411f88c58ceac909c062\" style=\"width: 100%;\" class=\"plotly-graph-div\"></div><script type='text/javascript'>/*\n",
" * Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n",
" */\n",
"\n",
"require(['plotly'], function(Plotly) {\n",
" window.PLOTLYENV = window.PLOTLYENV || {};\n",
" window.PLOTLYENV.BASE_URL = 'https://plot.ly';\n",
" /*\n",
" * Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n",
" */\n",
"\n",
"const arm_data = {\"metrics\": [\"Accuracy\"], \"in_sample\": {\"0_0\": {\"name\": \"0_0\", \"parameters\": {\"lr\": 0.000689613499131318, \"momentum\": 0.1318114995956421}, \"y\": {\"Accuracy\": 0.939}, \"y_hat\": {\"Accuracy\": 0.9389997518354475}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854438506988735}, \"context_stratum\": null}, \"1_0\": {\"name\": \"1_0\", \"parameters\": {\"lr\": 0.036271346141737555, \"momentum\": 0.7088054418563843}, \"y\": {\"Accuracy\": 0.099}, \"y_hat\": {\"Accuracy\": 0.09900007324930987}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854456531675133}, \"context_stratum\": null}, \"2_0\": {\"name\": \"2_0\", \"parameters\": {\"lr\": 8.925798988053579e-05, \"momentum\": 0.2809634804725647}, \"y\": {\"Accuracy\": 0.8978333333333334}, \"y_hat\": {\"Accuracy\": 0.8978329669723976}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854434082873402}, \"context_stratum\": null}, \"3_0\": {\"name\": \"3_0\", \"parameters\": {\"lr\": 0.0003373235790576741, \"momentum\": 0.6197535991668701}, \"y\": {\"Accuracy\": 0.9468333333333333}, \"y_hat\": {\"Accuracy\": 0.9468330260586308}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854428245893105}, \"context_stratum\": null}, \"4_0\": {\"name\": \"4_0\", \"parameters\": {\"lr\": 0.31052302454362635, \"momentum\": 0.42262789607048035}, \"y\": {\"Accuracy\": 0.09866666666666667}, \"y_hat\": {\"Accuracy\": 0.09866667576528027}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854456902729378}, \"context_stratum\": null}, \"5_0\": {\"name\": \"5_0\", \"parameters\": {\"lr\": 2.8078117931950864e-05, \"momentum\": 0.8458679644271455}, \"y\": {\"Accuracy\": 0.9046666666666666}, \"y_hat\": {\"Accuracy\": 0.9046663929853913}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854435153224027}, \"context_stratum\": null}, \"6_0\": {\"name\": \"6_0\", \"parameters\": {\"lr\": 9.289244735054885e-05, \"momentum\": 0.6072196767886054}, \"y\": {\"Accuracy\": 0.9086666666666666}, \"y_hat\": {\"Accuracy\": 0.9086670037113641}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854428545591424}, \"context_stratum\": null}, \"7_0\": {\"name\": \"7_0\", \"parameters\": {\"lr\": 1.0000000000000042e-06, \"momentum\": 0.9999999999999999}, \"y\": {\"Accuracy\": 0.7941666666666667}, \"y_hat\": {\"Accuracy\": 0.7941665870968726}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854455546954202}, \"context_stratum\": null}, \"8_0\": {\"name\": \"8_0\", \"parameters\": {\"lr\": 0.00015794962339565747, \"momentum\": 1.0}, \"y\": {\"Accuracy\": 0.7441666666666666}, \"y_hat\": {\"Accuracy\": 0.7441667907054121}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854453677411354}, \"context_stratum\": null}, \"9_0\": {\"name\": \"9_0\", \"parameters\": {\"lr\": 0.000406586189910757, \"momentum\": 0.38365804357915195}, \"y\": {\"Accuracy\": 0.9471666666666667}, \"y_hat\": {\"Accuracy\": 0.9471667003474779}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.0001285440098758816}, \"context_stratum\": null}, \"10_0\": {\"name\": \"10_0\", \"parameters\": {\"lr\": 2.2730625110446297e-06, \"momentum\": 0.3751255532608917}, \"y\": {\"Accuracy\": 0.14966666666666667}, \"y_hat\": {\"Accuracy\": 0.1496668219604495}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854456645845671}, \"context_stratum\": null}, \"11_0\": {\"name\": \"11_0\", \"parameters\": {\"lr\": 0.0002246836150902879, \"momentum\": 0.0}, \"y\": {\"Accuracy\": 0.9093333333333333}, \"y_hat\": {\"Accuracy\": 0.9093332850701927}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854444757832962}, \"context_stratum\": null}, \"12_0\": {\"name\": \"12_0\", \"parameters\": {\"lr\": 0.000285374298730309, \"momentum\": 0.2029237020625205}, \"y\": {\"Accuracy\": 0.923}, \"y_hat\": {\"Accuracy\": 0.9230005093681425}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854367949303373}, \"context_stratum\": null}, \"13_0\": {\"name\": \"13_0\", \"parameters\": {\"lr\": 8.863465742267098e-06, \"momentum\": 0.9999999999981208}, \"y\": {\"Accuracy\": 0.8815}, \"y_hat\": {\"Accuracy\": 0.8815000815395677}, \"se\": {\"Accuracy\": 0.0}, \"se_hat\": {\"Accuracy\": 0.00012854446484663823}, \"context_stratum\": null}}, \"out_of_sample\": {}, \"status_quo_name\": null};\n",
"const density = 50;\n",
"const grid_x = [1e-06, 1.3011511650442548e-06, 1.692994354296022e-06, 2.2028415765056147e-06, 2.866229883678204e-06, 3.729398352432554e-06, 4.852511011181743e-06, 6.3138503555892e-06, 8.215273746089953e-06, 1.0689313005882424e-05, 1.390841207112662e-05, 1.809694657026198e-05, 2.354686311364001e-05, 3.063802837345029e-05, 3.986470631277378e-05, 5.1870009063012666e-05, 6.749072272319499e-05, 8.781563250096393e-05, 0.00011426141253772724, 0.00014867137004306603, 0.00019344392634026088, 0.0002516997901283655, 0.0003274994751669172, 0.0004261263236648159, 0.0005544547624925005, 0.0007214294601814526, 0.000938688782612345, 0.0012213760031100258, 0.0015891948094037057, 0.002067782677737912, 0.0026904978401970136, 0.0035007443993213955, 0.004554997653699184, 0.005926740503884541, 0.007711585311544345, 0.010033938212454078, 0.013055670395116691, 0.01698740074503987, 0.02210317627048227, 0.028759573555516536, 0.03742055263793628, 0.04868979566145066, 0.06335278435066323, 0.0824315491666629, 0.10725590623460621, 0.13955614735503497, 0.18158364372009145, 0.23626776957937787, 0.3074200836506151, 0.4];\n",
"const grid_y = [0.0, 0.02040816326530612, 0.04081632653061224, 0.061224489795918366, 0.08163265306122448, 0.1020408163265306, 0.12244897959183673, 0.14285714285714285, 0.16326530612244897, 0.18367346938775508, 0.2040816326530612, 0.22448979591836732, 0.24489795918367346, 0.26530612244897955, 0.2857142857142857, 0.3061224489795918, 0.32653061224489793, 0.3469387755102041, 0.36734693877551017, 0.3877551020408163, 0.4081632653061224, 0.42857142857142855, 0.44897959183673464, 0.4693877551020408, 0.4897959183673469, 0.5102040816326531, 0.5306122448979591, 0.5510204081632653, 0.5714285714285714, 0.5918367346938775, 0.6122448979591836, 0.6326530612244897, 0.6530612244897959, 0.673469387755102, 0.6938775510204082, 0.7142857142857142, 0.7346938775510203, 0.7551020408163265, 0.7755102040816326, 0.7959183673469387, 0.8163265306122448, 0.836734693877551, 0.8571428571428571, 0.8775510204081632, 0.8979591836734693, 0.9183673469387754, 0.9387755102040816, 0.9591836734693877, 0.9795918367346939, 1.0];\n",
"const f = [0.05105142242139438, 0.07326279435488137, 0.10234609356033031, 0.1382732000796884, 0.1807496326386543, 0.22920125780313005, 0.2827826003367556, 0.3404059850418584, 0.40078756992451725, 0.46250513487454303, 0.5240633680876985, 0.5839644986652924, 0.6407843008291811, 0.6932546456266001, 0.7403528182944958, 0.7813935678571116, 0.8161114336624476, 0.8447093500429768, 0.8678403489527071, 0.8864925268578867, 0.9017722069708651, 0.9146257940904475, 0.925585489215689, 0.9345986721665711, 0.9410285342049025, 0.9438907787092483, 0.9422055929262716, 0.9352757532933, 0.922790128060456, 0.9047970850453548, 0.8816310395812752, 0.8538358144064534, 0.8220974139519108, 0.7871879418810861, 0.7499197534849961, 0.711108511565232, 0.671543895863295, 0.6319668967433909, 0.5930528089403965, 0.5553992006810706, 0.5195182509003353, 0.4858329143550807, 0.4546763942289055, 0.4262943901250468, 0.4008495717243289, 0.3784277326173378, 0.3590451245287537, 0.34265656089326596, 0.32916398939704544, 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"const lower_is_better = false;\n",
"const metric = \"Accuracy\";\n",
"const rel = false;\n",
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"const xvar = \"lr\";\n",
"const yvar = \"momentum\";\n",
"const x_is_log = true;\n",
"const y_is_log = false;\n",
"\n",
"const GREEN_SCALE = [[247, 252, 253], [229, 245, 249], [204, 236, 230], [153, 216, 201], [102, 194, 164], [65, 174, 118], [35, 139, 69], [0, 109, 44], [0, 68, 27]];\n",
"const GREEN_PINK_SCALE = [[142, 1, 82], [197, 27, 125], [222, 119, 174], [241, 182, 218], [253, 224, 239], [247, 247, 247], [230, 245, 208], [184, 225, 134], [127, 188, 65], [77, 146, 33], [39, 100, 25]];\n",
"const BLUE_SCALE = [[255, 247, 251], [236, 231, 242], [208, 209, 230], [166, 189, 219], [116, 169, 207], [54, 144, 192], [5, 112, 176], [3, 78, 123]];\n",
"\n",
"// format data\n",
"const res = relativize_data(f, sd, rel, arm_data, metric);\n",
"const f_final = res[0];\n",
"const sd_final = res[1];\n",
"\n",
"// calculate max of abs(outcome), used for colorscale\n",
"const f_absmax = Math.max(Math.abs(Math.min(...f_final)), Math.max(...f_final));\n",
"\n",
"// transform to nested array\n",
"var f_plt = [];\n",
"while(f_final.length) f_plt.push(f_final.splice(0, density));\n",
"var sd_plt = [];\n",
"while(sd_final.length) sd_plt.push(sd_final.splice(0, density));\n",
"\n",
"// create traces\n",
"const CONTOUR_CONFIG = {\n",
" autocolorscale: false,\n",
" autocontour: true,\n",
" contours: {\n",
" coloring: 'heatmap',\n",
" },\n",
" hoverinfo: 'x+y+z',\n",
" ncontours: density / 2,\n",
" type: 'contour',\n",
" x: grid_x,\n",
" y: grid_y,\n",
"};\n",
"\n",
"let f_scale;\n",
"if (rel === true) {\n",
" f_scale = lower_is_better === true\n",
" ? GREEN_PINK_SCALE.reverse()\n",
" : GREEN_PINK_SCALE;\n",
"} else {\n",
" f_scale = GREEN_SCALE;\n",
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"\n",
"const f_trace = {\n",
" colorbar: {\n",
" x: 0.45,\n",
" y: 0.5,\n",
" ticksuffix: rel === true ? '%' : '',\n",
" tickfont: {\n",
" size: 8,\n",
" },\n",
" },\n",
" colorscale: f_scale.map(\n",
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" ),\n",
" xaxis: 'x',\n",
" yaxis: 'y',\n",
" z: f_plt,\n",
" // zmax and zmin are ignored if zauto is true\n",
" zauto: !rel,\n",
" zmax: f_absmax,\n",
" zmin: -f_absmax,\n",
"};\n",
"\n",
"const sd_trace = {\n",
" colorbar: {\n",
" x: 1,\n",
" y: 0.5,\n",
" ticksuffix: rel === true ? '%' : '',\n",
" tickfont: {\n",
" size: 8,\n",
" },\n",
" },\n",
" colorscale: BLUE_SCALE.map(\n",
" (v, i) => [i / (BLUE_SCALE.length - 1), rgb(v)]\n",
" ),\n",
" xaxis: 'x2',\n",
" yaxis: 'y2',\n",
" z: sd_plt,\n",
"};\n",
"\n",
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" f_trace[key] = CONTOUR_CONFIG[key];\n",
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"});\n",
"\n",
"// get in-sample arms\n",
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"const arm_y = [];\n",
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"\n",
"Object.keys(arm_data['in_sample']).forEach(arm_name => {\n",
" arm_x.push(arm_data['in_sample'][arm_name]['parameters'][xvar]);\n",
" arm_y.push(arm_data['in_sample'][arm_name]['parameters'][yvar]);\n",
" arm_text.push(arm_name);\n",
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"\n",
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" hoverinfo: 'text',\n",
" legendgroup: 'In-sample',\n",
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" mode: 'markers',\n",
" name: 'In-sample',\n",
" text: arm_text,\n",
" type: 'scatter',\n",
" x: arm_x,\n",
" y: arm_y,\n",
"};\n",
"\n",
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" xaxis: 'x',\n",
" yaxis: 'y',\n",
"};\n",
"\n",
"const sd_in_sample_arm_trace = {\n",
" showlegend: false,\n",
" xaxis: 'x2',\n",
" yaxis: 'y2',\n",
"};\n",
"\n",
"Object.keys(base_in_sample_arm_config).forEach(key => {\n",
" f_in_sample_arm_trace[key] = base_in_sample_arm_config[key];\n",
" sd_in_sample_arm_trace[key] = base_in_sample_arm_config[key];\n",
"});\n",
"\n",
"const traces = [\n",
" f_trace,\n",
" sd_trace,\n",
" f_in_sample_arm_trace,\n",
" sd_in_sample_arm_trace,\n",
"];\n",
"\n",
"// start symbol at 2 for candidate markers\n",
"let i = 2;\n",
"\n",
"// iterate over out-of-sample arms\n",
"Object.keys(arm_data['out_of_sample']).forEach(generator_run_name => {\n",
" const ax = [];\n",
" const ay = [];\n",
" const atext = [];\n",
"\n",
" Object.keys(arm_data['out_of_sample'][generator_run_name]).forEach(arm_name => {\n",
" ax.push(\n",
" arm_data['out_of_sample'][generator_run_name][arm_name]['parameters'][xvar]\n",
" );\n",
" ay.push(\n",
" arm_data['out_of_sample'][generator_run_name][arm_name]['parameters'][yvar]\n",
" );\n",
" atext.push('<em>Candidate ' + arm_name + '</em>');\n",
" });\n",
"\n",
" traces.push({\n",
" hoverinfo: 'text',\n",
" legendgroup: generator_run_name,\n",
" marker: {color: 'black', symbol: i, opacity: 0.5},\n",
" mode: 'markers',\n",
" name: generator_run_name,\n",
" text: atext,\n",
" type: 'scatter',\n",
" xaxis: 'x',\n",
" x: ax,\n",
" yaxis: 'y',\n",
" y: ay,\n",
" });\n",
" traces.push({\n",
" hoverinfo: 'text',\n",
" legendgroup: generator_run_name,\n",
" marker: {color: 'black', symbol: i, opacity: 0.5},\n",
" mode: 'markers',\n",
" name: 'In-sample',\n",
" showlegend: false,\n",
" text: atext,\n",
" type: 'scatter',\n",
" x: ax,\n",
" xaxis: 'x2',\n",
" y: ay,\n",
" yaxis: 'y2',\n",
" });\n",
" i += 1;\n",
"});\n",
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"\n",
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"const ytype = y_is_log ? 'log' : 'linear';\n",
"\n",
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" autosize: false,\n",
" margin: {\n",
" l: 35,\n",
" r: 35,\n",
" t: 35,\n",
" b: 100,\n",
" pad: 0,\n",
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" hovermode: 'closest',\n",
" legend: {orientation: 'h', x: 0, y: -0.25},\n",
" height: 450,\n",
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"render(\n",
" plot_contour(\n",
" model=ax.generation_strategy.model, param_x='lr', param_y='momentum', metric_name='Accuracy'\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
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"source": [
"We can also plot the optimization trace, showing the progression of finding the point with the optimal objective:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
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"# `plot_single_method` expects a 2-d array of means, because it expects to average means from multiple \n",
"# optimization runs, so we wrap out best objectives array in another array.\n",
"best_objectives = np.array([[trial.objective_mean * 100 for trial in ax.experiment.trials.values()]])\n",
"best_objective_plot = optimization_trace_single_method(\n",
" y=np.maximum.accumulate(best_objectives, axis=1),\n",
" title=\"Model performance vs. # of iterations\",\n",
" ylabel=\"Accuracy\",\n",
")\n",
"render(best_objective_plot)"
]
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