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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "4cd1da0e",
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from synthetic_task.plot_results import load_results_QP, load_results_CP, plot_total_loss_vs_method_by_ydim, plot_total_time_vs_method_by_ydim, plot_total_time_vs_ydim_for_methods, plot_time_scaling_vs_ydim, load_results_across_batches, plot_time_scaling_vs_batch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6f2b2dea",
"metadata": {},
"outputs": [],
"source": [
"sns.set_theme(style=\"whitegrid\", context=\"talk\")\n",
"palette = sns.color_palette()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d24a0bbc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"method: ffocp_eq\n",
"method: ffocp_eq\n",
"method: ffocp_eq\n",
"method: ffocp_eq\n",
"method: ffocp_eq\n",
"method: ffocp_eq\n"
]
}
],
"source": [
"# PLOT BATCH ABLATION\n",
"ROOT = \"..\"\n",
"\n",
"METHODS = [\n",
" \"ffocp_eq\",\n",
"]\n",
"METHODS_LEGEND = {\n",
" \"ffocp_eq\": \"FFOCP\",\n",
"}\n",
"\n",
"method_order = [METHODS_LEGEND[m] for m in METHODS]\n",
"\n",
"df_all = load_results_across_batches(\n",
" root_dir=ROOT,\n",
" batch_sizes=None,\n",
" loader_fn=load_results_CP,\n",
" methods=METHODS,\n",
" methods_legend=METHODS_LEGEND,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "18f79b4b",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>epoch</th>\n",
" <th>train_ts_loss</th>\n",
" <th>test_ts_loss</th>\n",
" <th>train_df_loss</th>\n",
" <th>test_df_loss</th>\n",
" <th>forward_time</th>\n",
" <th>backward_time</th>\n",
" <th>method</th>\n",
" <th>seed</th>\n",
" <th>n</th>\n",
" <th>lr</th>\n",
" <th>ydim</th>\n",
" <th>batch_size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>57.452311</td>\n",
" <td>0.0</td>\n",
" <td>-0.464479</td>\n",
" <td>0.0</td>\n",
" <td>434.527348</td>\n",
" <td>428.260238</td>\n",
" <td>FFOCP</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>800</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>50.233187</td>\n",
" <td>0.0</td>\n",
" <td>-0.465843</td>\n",
" <td>0.0</td>\n",
" <td>468.582487</td>\n",
" <td>462.893587</td>\n",
" <td>FFOCP</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>800</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>58.097721</td>\n",
" <td>0.0</td>\n",
" <td>-0.464782</td>\n",
" <td>0.0</td>\n",
" <td>480.707573</td>\n",
" <td>475.793314</td>\n",
" <td>FFOCP</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>800</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>56.541165</td>\n",
" <td>0.0</td>\n",
" <td>-0.470788</td>\n",
" <td>0.0</td>\n",
" <td>478.779373</td>\n",
" <td>474.103904</td>\n",
" <td>FFOCP</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>800</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>54.126785</td>\n",
" <td>0.0</td>\n",
" <td>-0.463682</td>\n",
" <td>0.0</td>\n",
" <td>482.228468</td>\n",
" <td>477.997777</td>\n",
" <td>FFOCP</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>800</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>0</td>\n",
" <td>1.596674</td>\n",
" <td>0.0</td>\n",
" <td>-0.441706</td>\n",
" <td>0.0</td>\n",
" <td>89.305095</td>\n",
" <td>97.298301</td>\n",
" <td>FFOCP</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>800</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>0</td>\n",
" <td>0.988028</td>\n",
" <td>0.0</td>\n",
" <td>-0.389009</td>\n",
" <td>0.0</td>\n",
" <td>85.063648</td>\n",
" <td>95.986092</td>\n",
" <td>FFOCP</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>800</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>0</td>\n",
" <td>0.982131</td>\n",
" <td>0.0</td>\n",
" <td>-0.388680</td>\n",
" <td>0.0</td>\n",
" <td>82.734893</td>\n",
" <td>94.441057</td>\n",
" <td>FFOCP</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>800</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>0</td>\n",
" <td>0.987993</td>\n",
" <td>0.0</td>\n",
" <td>-0.390248</td>\n",
" <td>0.0</td>\n",
" <td>83.124762</td>\n",
" <td>93.911058</td>\n",
" <td>FFOCP</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>800</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>0</td>\n",
" <td>0.974203</td>\n",
" <td>0.0</td>\n",
" <td>-0.387242</td>\n",
" <td>0.0</td>\n",
" <td>90.870878</td>\n",
" <td>102.601617</td>\n",
" <td>FFOCP</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>800</td>\n",
" <td>32</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>74 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" epoch train_ts_loss test_ts_loss train_df_loss test_df_loss \\\n",
"0 0 57.452311 0.0 -0.464479 0.0 \n",
"1 0 50.233187 0.0 -0.465843 0.0 \n",
"2 0 58.097721 0.0 -0.464782 0.0 \n",
"3 0 56.541165 0.0 -0.470788 0.0 \n",
"4 0 54.126785 0.0 -0.463682 0.0 \n",
".. ... ... ... ... ... \n",
"69 0 1.596674 0.0 -0.441706 0.0 \n",
"70 0 0.988028 0.0 -0.389009 0.0 \n",
"71 0 0.982131 0.0 -0.388680 0.0 \n",
"72 0 0.987993 0.0 -0.390248 0.0 \n",
"73 0 0.974203 0.0 -0.387242 0.0 \n",
"\n",
" forward_time backward_time method seed n lr ydim batch_size \n",
"0 434.527348 428.260238 FFOCP 1 NaN 0.001 800 1 \n",
"1 468.582487 462.893587 FFOCP 2 NaN 0.001 800 1 \n",
"2 480.707573 475.793314 FFOCP 3 NaN 0.001 800 1 \n",
"3 478.779373 474.103904 FFOCP 4 NaN 0.001 800 1 \n",
"4 482.228468 477.997777 FFOCP 5 NaN 0.001 800 1 \n",
".. ... ... ... ... .. ... ... ... \n",
"69 89.305095 97.298301 FFOCP 5 NaN 0.001 800 16 \n",
"70 85.063648 95.986092 FFOCP 1 NaN 0.001 800 32 \n",
"71 82.734893 94.441057 FFOCP 2 NaN 0.001 800 32 \n",
"72 83.124762 93.911058 FFOCP 3 NaN 0.001 800 32 \n",
"73 90.870878 102.601617 FFOCP 5 NaN 0.001 800 32 \n",
"\n",
"[74 rows x 13 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_all"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "af45fabd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'../syn_batch_scaling_3panels.pdf'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_all[\"method\"] = pd.Categorical(df_all[\"method\"], categories=method_order, ordered=True)\n",
"\n",
"plot_time_scaling_vs_batch(\n",
" df_all,\n",
" plot_path=ROOT,\n",
" methods_order=method_order,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "334a179a",
"metadata": {},
"outputs": [],
"source": [
"# PLOT GENERAL TASK -- SOC\n",
"\n",
"batch_size = 8\n",
"SOC_DIR = f\"../synthetic_results_general_{batch_size}\"\n",
"METHODS = [\n",
" \"cvxpylayer\",\n",
" \"lpgd\",\n",
" \"bpqp\",\n",
" \"ffocp_eq\",\n",
"]\n",
"METHODS_LEGEND = {\n",
" \"cvxpylayer\": \"CvxpyLayer\",\n",
" \"lpgd\": \"LPGD\",\n",
" \"bpqp\": \"BPQP\",\n",
" \"ffocp_eq\": \"FFOCP\",\n",
"}\n",
"\n",
"METHODS_STEPS = [method+\"_steps\" for method in METHODS]\n",
"\n",
"method_order = [METHODS_LEGEND[m] for m in METHODS]\n",
"\n",
"markers = [\"o\", \"s\", \"D\", \"^\", \"v\", \"x\"]\n",
"markers_dict = {method: markers[i] for i, method in enumerate(method_order)}\n",
"\n",
"LINEWIDTH = 1.5"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4570a792",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"method: cvxpylayer\n",
"method: lpgd\n",
"method: bpqp\n",
"method: ffocp_eq\n"
]
}
],
"source": [
"df = load_results_CP(base_dir=SOC_DIR, methods=METHODS, methods_legend=METHODS_LEGEND)\n",
"df = df.rename(columns=lambda c: c.strip() if isinstance(c, str) else c)\n",
"df[\"method\"] = pd.Categorical(df[\"method\"], categories=method_order, ordered=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ddf3423e",
"metadata": {},
"outputs": [],
"source": [
"plot_total_time_vs_method_by_ydim(df, SOC_DIR, ('forward_time','backward_time'), tag=\"syn\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "e9a629f8",
"metadata": {},
"outputs": [],
"source": [
"plot_time_scaling_vs_ydim(df, plot_path=SOC_DIR, methods_order=method_order)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "70a1da48",
"metadata": {},
"outputs": [],
"source": [
"# plot all ydim\n",
"# selected_methods = [\"FFOCP\", \"BPQP\"]\n",
"# plot_total_time_vs_ydim_for_methods(\n",
"# df,\n",
"# methods_subset=selected_methods,\n",
"# time_names=('forward_time', 'backward_time'),\n",
"# plot_path=SOC_DIR,\n",
"# plot_name_tag=\"syn_steps_solve_ffocp_bpqp\"\n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "2bec5350",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"method: cvxpylayer\n",
"method: lpgd\n",
"method: bpqp\n",
"method: ffocp_eq\n"
]
}
],
"source": [
"df = load_results_CP(base_dir=SOC_DIR, methods=METHODS_STEPS, methods_legend=METHODS_LEGEND)\n",
"df = df.rename(columns=lambda c: c.strip() if isinstance(c, str) else c)\n",
"df[\"method\"] = pd.Categorical(df[\"method\"], categories=method_order, ordered=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "d6d6b860",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1000, 200, 300, 400, 500, 600, 700, 800, 900])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df[\"method\"] == \"FFOCP\"].ydim.unique()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "759d526c",
"metadata": {},
"outputs": [],
"source": [
"plot_total_loss_vs_method_by_ydim(df, SOC_DIR)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "6b090796",
"metadata": {},
"outputs": [],
"source": [
"# PLOT SYNTHETIC TASK -- QP\n",
"\n",
"batch_size = 8\n",
"QP_DIR = f\"../synthetic_results_{batch_size}\"\n",
"METHODS = [\n",
" \"cvxpylayer\",\n",
" \"lpgd\",\n",
" \"bpqp\",\n",
" \"ffocp_eq\",\n",
" \"dqp\",\n",
" \"qpth\",\n",
" \"ffoqp_eq_schur\", \n",
"]\n",
"METHODS_LEGEND = {\n",
" \"cvxpylayer\": \"CvxpyLayer\",\n",
" \"lpgd\": \"LPGD\",\n",
" \"bpqp\": \"BPQP\",\n",
" \"ffocp_eq\": \"FFOCP\",\n",
" \"dqp\": \"dQP\",\n",
" \"qpth\": \"qpth\",\n",
" \"ffoqp_eq_schur\": \"FFOQP\",\n",
"}\n",
"\n",
"METHODS_STEPS = [method+\"_steps\" for method in METHODS]\n",
"\n",
"method_order = [METHODS_LEGEND[m] for m in METHODS]\n",
"\n",
"markers = [\"o\", \"s\", \"D\", \"^\", \"v\", \"x\", \"P\", \"s\"]\n",
"markers_dict = {method: markers[i] for i, method in enumerate(method_order)}\n",
"\n",
"LINEWIDTH = 1.5"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "c814b993",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"method: cvxpylayer\n",
"method: lpgd\n",
"method: bpqp\n",
"method: ffocp_eq\n",
"method: dqp\n",
"method: qpth\n",
"method: ffoqp_eq_schur\n"
]
}
],
"source": [
"df = load_results_QP(base_dir=QP_DIR, methods=METHODS, methods_legend=METHODS_LEGEND)\n",
"df = df.rename(columns=lambda c: c.strip() if isinstance(c, str) else c)\n",
"df[\"method\"] = pd.Categorical(df[\"method\"], categories=method_order, ordered=True)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "0a794b66",
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>epoch</th>\n",
" <th>train_ts_loss</th>\n",
" <th>test_ts_loss</th>\n",
" <th>train_df_loss</th>\n",
" <th>test_df_loss</th>\n",
" <th>forward_time</th>\n",
" <th>backward_time</th>\n",
" <th>method</th>\n",
" <th>seed</th>\n",
" <th>n</th>\n",
" <th>lr</th>\n",
" <th>ydim</th>\n",
" <th>backwardTol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>436</th>\n",
" <td>0</td>\n",
" <td>2.002229</td>\n",
" <td>0.0</td>\n",
" <td>-0.468039</td>\n",
" <td>0.0</td>\n",
" <td>99.561114</td>\n",
" <td>35.265574</td>\n",
" <td>FFOQP</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>1000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>437</th>\n",
" <td>0</td>\n",
" <td>2.004048</td>\n",
" <td>0.0</td>\n",
" <td>-0.468603</td>\n",
" <td>0.0</td>\n",
" <td>93.989844</td>\n",
" <td>27.312217</td>\n",
" <td>FFOQP</td>\n",
" <td>10</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>1000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>438</th>\n",
" <td>0</td>\n",
" <td>1.981446</td>\n",
" <td>0.0</td>\n",
" <td>-0.468794</td>\n",
" <td>0.0</td>\n",
" <td>101.130303</td>\n",
" <td>36.416768</td>\n",
" <td>FFOQP</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>1000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>439</th>\n",
" <td>0</td>\n",
" <td>2.014030</td>\n",
" <td>0.0</td>\n",
" <td>-0.468329</td>\n",
" <td>0.0</td>\n",
" <td>100.519457</td>\n",
" <td>34.933484</td>\n",
" <td>FFOQP</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>1000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>440</th>\n",
" <td>0</td>\n",
" <td>1.994176</td>\n",
" <td>0.0</td>\n",
" <td>-0.468258</td>\n",
" <td>0.0</td>\n",
" <td>98.747680</td>\n",
" <td>35.228853</td>\n",
" <td>FFOQP</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>1000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>521</th>\n",
" <td>0</td>\n",
" <td>1.988679</td>\n",
" <td>0.0</td>\n",
" <td>-0.469108</td>\n",
" <td>0.0</td>\n",
" <td>80.720694</td>\n",
" <td>29.228782</td>\n",
" <td>FFOQP</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>900</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>522</th>\n",
" <td>0</td>\n",
" <td>1.990780</td>\n",
" <td>0.0</td>\n",
" <td>-0.469006</td>\n",
" <td>0.0</td>\n",
" <td>78.616584</td>\n",
" <td>20.049297</td>\n",
" <td>FFOQP</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>900</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>523</th>\n",
" <td>0</td>\n",
" <td>1.992276</td>\n",
" <td>0.0</td>\n",
" <td>-0.468968</td>\n",
" <td>0.0</td>\n",
" <td>89.442196</td>\n",
" <td>22.292826</td>\n",
" <td>FFOQP</td>\n",
" <td>7</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>900</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>524</th>\n",
" <td>0</td>\n",
" <td>1.993443</td>\n",
" <td>0.0</td>\n",
" <td>-0.469414</td>\n",
" <td>0.0</td>\n",
" <td>100.181939</td>\n",
" <td>27.915672</td>\n",
" <td>FFOQP</td>\n",
" <td>8</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>900</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>525</th>\n",
" <td>0</td>\n",
" <td>1.995657</td>\n",
" <td>0.0</td>\n",
" <td>-0.468818</td>\n",
" <td>0.0</td>\n",
" <td>84.645610</td>\n",
" <td>28.476709</td>\n",
" <td>FFOQP</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>900</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>90 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" epoch train_ts_loss test_ts_loss train_df_loss test_df_loss \\\n",
"436 0 2.002229 0.0 -0.468039 0.0 \n",
"437 0 2.004048 0.0 -0.468603 0.0 \n",
"438 0 1.981446 0.0 -0.468794 0.0 \n",
"439 0 2.014030 0.0 -0.468329 0.0 \n",
"440 0 1.994176 0.0 -0.468258 0.0 \n",
".. ... ... ... ... ... \n",
"521 0 1.988679 0.0 -0.469108 0.0 \n",
"522 0 1.990780 0.0 -0.469006 0.0 \n",
"523 0 1.992276 0.0 -0.468968 0.0 \n",
"524 0 1.993443 0.0 -0.469414 0.0 \n",
"525 0 1.995657 0.0 -0.468818 0.0 \n",
"\n",
" forward_time backward_time method seed n lr ydim backwardTol \n",
"436 99.561114 35.265574 FFOQP 1 NaN 0.001 1000 NaN \n",
"437 93.989844 27.312217 FFOQP 10 NaN 0.001 1000 NaN \n",
"438 101.130303 36.416768 FFOQP 2 NaN 0.001 1000 NaN \n",
"439 100.519457 34.933484 FFOQP 3 NaN 0.001 1000 NaN \n",
"440 98.747680 35.228853 FFOQP 4 NaN 0.001 1000 NaN \n",
".. ... ... ... ... .. ... ... ... \n",
"521 80.720694 29.228782 FFOQP 5 NaN 0.001 900 NaN \n",
"522 78.616584 20.049297 FFOQP 6 NaN 0.001 900 NaN \n",
"523 89.442196 22.292826 FFOQP 7 NaN 0.001 900 NaN \n",
"524 100.181939 27.915672 FFOQP 8 NaN 0.001 900 NaN \n",
"525 84.645610 28.476709 FFOQP 9 NaN 0.001 900 NaN \n",
"\n",
"[90 rows x 13 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df[\"method\"] == \"FFOQP\"]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "9a46b4f7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"filtered_method_order: ['BPQP', 'FFOCP']\n",
"available method: {'FFOCP', 'BPQP'}\n",
"groups: ['BPQP', 'FFOCP']\n",
"methods: ['BPQP', 'FFOCP']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"filtered_method_order: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"available method: {'dQP', 'qpth', 'LPGD', 'FFOCP', 'FFOQP', 'BPQP', 'CvxpyLayer'}\n",
"groups: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"methods: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"filtered_method_order: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"available method: {'dQP', 'qpth', 'LPGD', 'FFOCP', 'FFOQP', 'BPQP', 'CvxpyLayer'}\n",
"groups: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"methods: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"filtered_method_order: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"available method: {'dQP', 'qpth', 'LPGD', 'FFOCP', 'FFOQP', 'BPQP', 'CvxpyLayer'}\n",
"groups: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"methods: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"filtered_method_order: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"available method: {'dQP', 'qpth', 'LPGD', 'FFOCP', 'FFOQP', 'BPQP', 'CvxpyLayer'}\n",
"groups: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"methods: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"filtered_method_order: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"available method: {'dQP', 'qpth', 'LPGD', 'FFOCP', 'FFOQP', 'BPQP', 'CvxpyLayer'}\n",
"groups: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"methods: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"filtered_method_order: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"available method: {'dQP', 'qpth', 'LPGD', 'FFOCP', 'FFOQP', 'BPQP', 'CvxpyLayer'}\n",
"groups: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"methods: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"filtered_method_order: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"available method: {'dQP', 'qpth', 'LPGD', 'FFOCP', 'FFOQP', 'BPQP', 'CvxpyLayer'}\n",
"groups: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"methods: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"filtered_method_order: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"available method: {'dQP', 'qpth', 'LPGD', 'FFOCP', 'FFOQP', 'BPQP', 'CvxpyLayer'}\n",
"groups: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"methods: ['CvxpyLayer', 'qpth', 'LPGD', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"filtered_method_order: ['qpth', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"available method: {'dQP', 'qpth', 'FFOCP', 'FFOQP', 'BPQP'}\n",
"groups: ['qpth', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n",
"methods: ['qpth', 'BPQP', 'dQP', 'FFOCP', 'FFOQP']\n"
]
}
],
"source": [
"plot_total_time_vs_method_by_ydim(df, QP_DIR, ('forward_time','backward_time'), tag=\"syn\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "abc2735a",
"metadata": {},
"outputs": [],
"source": [
"plot_time_scaling_vs_ydim(df, plot_path=QP_DIR, methods_order=method_order)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "fe392971",
"metadata": {},
"outputs": [],
"source": [
"# plot all ydim\n",
"selected_methods = [\"FFOQP\", \"BPQP\"]\n",
"plot_total_time_vs_ydim_for_methods(\n",
" df,\n",
" methods_subset=selected_methods,\n",
" time_names=('forward_time', 'backward_time'),\n",
" plot_path=QP_DIR,\n",
" plot_name_tag=\"syn_steps_solve_ffocp_bpqp\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "8707e692",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"method: cvxpylayer\n",
"method: lpgd\n",
"method: bpqp\n",
"method: ffocp_eq\n",
"method: dqp\n",
"method: qpth\n",
"method: ffoqp_eq_schur\n"
]
}
],
"source": [
"df = load_results_QP(base_dir=QP_DIR, methods=METHODS_STEPS, methods_legend=METHODS_LEGEND)\n",
"df = df.rename(columns=lambda c: c.strip() if isinstance(c, str) else c)\n",
"df[\"method\"] = pd.Categorical(df[\"method\"], categories=method_order, ordered=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97c4aeb0",
"metadata": {},
"outputs": [],
"source": [
"plot_total_loss_vs_method_by_ydim(df, QP_DIR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3cb8e08c",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "rl",
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|