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lan496/plot_ase_filters.ipynb

Created October 21, 2023 03:23
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plot_ase_filters.ipynb
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"source": [
"import pandas as pd\n",
"import plotly.express as px"
]
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"source": [
"## Gradient check"
]
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" </tr>\n",
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" <th>55</th>\n",
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" filter eps diff\n",
"51 ExpCellFilterNew 0.001953 7.160000e-08\n",
"59 ExpCellFilterNew 0.500000 3.416465e+02\n",
"13 UnitCellFilter 0.007812 3.000000e-10\n",
"55 ExpCellFilterNew 0.031250 1.157502e-01"
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"source": [
"df = pd.read_json(\"filters.json\")\n",
"df.loc[df['filter'] == 'ExpCellFilter', 'filter'] = 'ExpCellFilterNew'\n",
"df.sample(4)"
]
},
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"fig = px.scatter(\n",
" df,\n",
" x='eps', y='diff', color='filter',\n",
" log_x=True, log_y=True,\n",
")\n",
"fig.update_layout({\n",
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"})\n",
"fig.write_image(\"filter_diff_check.png\")\n",
"fig"
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"source": [
"## Opt convergence"
]
},
{
"cell_type": "code",
"execution_count": 30,
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" <th>optimizer</th>\n",
" <th>filter</th>\n",
" <th>fmax</th>\n",
" <th>steps</th>\n",
" <th>force_calls</th>\n",
" <th>elapsed_time_s</th>\n",
" <th>elapsed_time_s_per_force_calls</th>\n",
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" <th>15</th>\n",
" <td>BFGSLineSearch</td>\n",
" <td>UnitCellFilter</td>\n",
" <td>0.000001</td>\n",
" <td>13</td>\n",
" <td>21</td>\n",
" <td>0.433302</td>\n",
" <td>0.020633</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>SciPyFminCG</td>\n",
" <td>ExpCellFilterNew</td>\n",
" <td>0.000001</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>0.498827</td>\n",
" <td>0.031177</td>\n",
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" <tr>\n",
" <th>24</th>\n",
" <td>BFGSLineSearch</td>\n",
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" <td>6</td>\n",
" <td>8</td>\n",
" <td>0.310646</td>\n",
" <td>0.038831</td>\n",
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" <th>53</th>\n",
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"text/plain": [
" optimizer filter fmax steps force_calls \\\n",
"15 BFGSLineSearch UnitCellFilter 0.000001 13 21 \n",
"85 SciPyFminCG ExpCellFilterNew 0.000001 16 16 \n",
"24 BFGSLineSearch ExpCellFilterOld 0.010000 6 8 \n",
"53 LBFGSLineSearch ExpCellFilterOld 0.001000 9 17 \n",
"\n",
" elapsed_time_s elapsed_time_s_per_force_calls \n",
"15 0.433302 0.020633 \n",
"85 0.498827 0.031177 \n",
"24 0.310646 0.038831 \n",
"53 0.568866 0.033463 "
]
},
"execution_count": 30,
"metadata": {},
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"df_opt = pd.read_json(\"opt.json\")\n",
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"df_opt['elapsed_time_s_per_force_calls'] = df_opt['elapsed_time_s'] / df_opt['force_calls']\n",
"df_opt.sample(4)"
]
},
{
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},
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"source": [
"## Size dependency"
]
},
{
"cell_type": "code",
"execution_count": 34,
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" <th>15</th>\n",
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" optimizer filter fmax natoms steps force_calls \\\n",
"7 LBFGSLineSearch ExpCellFilterOld 0.010 256 12 21 \n",
"10 LBFGSLineSearch ExpCellFilterNew 0.001 108 16 27 \n",
"1 LBFGSLineSearch ExpCellFilterOld 0.001 32 10 17 \n",
"15 LBFGSLineSearch ExpCellFilterNew 0.010 256 12 21 \n",
"\n",
" elapsed_time_s elapsed_time_s_per_force_calls \n",
"7 8.716559 0.415074 \n",
"10 3.885512 0.143908 \n",
"1 1.927791 0.113399 \n",
"15 6.900836 0.328611 "
]
},
"execution_count": 34,
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"df_natoms= pd.read_json(\"opt_natoms.json\")\n",
"df_natoms.loc[df_natoms['filter'] == 'ExpCellFilter', 'filter'] = 'ExpCellFilterNew'\n",
"df_natoms['elapsed_time_s_per_force_calls'] = df_natoms['elapsed_time_s'] / df_natoms['force_calls']\n",
"df_natoms.sample(4)"
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},
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"cell_type": "code",
"execution_count": 35,
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{
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