Skip to content

Instantly share code, notes, and snippets.

@JiaweiZhuang

JiaweiZhuang/KPP_ML_demo.ipynb

Last active August 8, 2018 21:03
Show Gist options
  • Save JiaweiZhuang/fec25483fe58ecaf08060e7b6c81e8bd to your computer and use it in GitHub Desktop.
Save JiaweiZhuang/fec25483fe58ecaf08060e7b6c81e8bd to your computer and use it in GitHub Desktop.
Download ZIP
A simple demo on learning KPP prediction in a global model context
Display the source blob
Display the rendered blob
Raw
KPP_ML_demo.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import xarray as xr"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"c48-ml.tavg1_3d_kpp_Nv.20130701_0400z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_0500z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_0600z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_0700z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_0800z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_0900z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_1000z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_1100z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_1200z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_1300z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_1400z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_1500z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_1600z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_1700z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_1800z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_1900z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_2000z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_2100z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_2200z.nc4\r\n",
"c48-ml.tavg1_3d_kpp_Nv.20130701_2300z.nc4\r\n"
]
}
],
"source": [
"ls ./nc/ # data location"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Frozen(SortedKeysDict(OrderedDict([('lon', 144), ('lat', 91), ('lev', 25), ('time', 20)])))"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = xr.open_mfdataset('./nc/*.nc4').drop('KPP_RFactive')\n",
"ds.dims"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['KPP_AFTER_CHEM_ACET', 'KPP_AFTER_CHEM_ALD2', 'KPP_AFTER_CHEM_ALK4', 'KPP_AFTER_CHEM_Br', 'KPP_AFTER_CHEM_Br2', 'KPP_AFTER_CHEM_BrNO2', 'KPP_AFTER_CHEM_BrNO3', 'KPP_AFTER_CHEM_BrO', 'KPP_AFTER_CHEM_C2H6', 'KPP_AFTER_CHEM_C3H8', 'KPP_AFTER_CHEM_CH2Br2', 'KPP_AFTER_CHEM_CH2O', 'KPP_AFTER_CHEM_CH3Br', 'KPP_AFTER_CHEM_CHBr3', 'KPP_AFTER_CHEM_CO', 'KPP_AFTER_CHEM_DMS', 'KPP_AFTER_CHEM_GLYC', 'KPP_AFTER_CHEM_H2O2', 'KPP_AFTER_CHEM_HAC', 'KPP_AFTER_CHEM_HBr', 'KPP_AFTER_CHEM_HNO2', 'KPP_AFTER_CHEM_HNO3', 'KPP_AFTER_CHEM_HNO4', 'KPP_AFTER_CHEM_HOBr', 'KPP_AFTER_CHEM_IEPOX', 'KPP_AFTER_CHEM_ISOP', 'KPP_AFTER_CHEM_ISOPN', 'KPP_AFTER_CHEM_MACR', 'KPP_AFTER_CHEM_MAP', 'KPP_AFTER_CHEM_MEK', 'KPP_AFTER_CHEM_MMN', 'KPP_AFTER_CHEM_MOBA', 'KPP_AFTER_CHEM_MP', 'KPP_AFTER_CHEM_MPN', 'KPP_AFTER_CHEM_MSA', 'KPP_AFTER_CHEM_MVK', 'KPP_AFTER_CHEM_N2O5', 'KPP_AFTER_CHEM_NO', 'KPP_AFTER_CHEM_NO2', 'KPP_AFTER_CHEM_NO3', 'KPP_AFTER_CHEM_O3', 'KPP_AFTER_CHEM_PAN', 'KPP_AFTER_CHEM_PMN', 'KPP_AFTER_CHEM_PPN', 'KPP_AFTER_CHEM_PROPNN', 'KPP_AFTER_CHEM_PRPE', 'KPP_AFTER_CHEM_R4N2', 'KPP_AFTER_CHEM_RCHO', 'KPP_AFTER_CHEM_RIP', 'KPP_AFTER_CHEM_SO2', 'KPP_AFTER_CHEM_SO4', 'KPP_AIRDEN', 'KPP_BEFORE_CHEM_ACET', 'KPP_BEFORE_CHEM_ALD2', 'KPP_BEFORE_CHEM_ALK4', 'KPP_BEFORE_CHEM_Br', 'KPP_BEFORE_CHEM_Br2', 'KPP_BEFORE_CHEM_BrNO2', 'KPP_BEFORE_CHEM_BrNO3', 'KPP_BEFORE_CHEM_BrO', 'KPP_BEFORE_CHEM_C2H6', 'KPP_BEFORE_CHEM_C3H8', 'KPP_BEFORE_CHEM_CH2Br2', 'KPP_BEFORE_CHEM_CH2O', 'KPP_BEFORE_CHEM_CH3Br', 'KPP_BEFORE_CHEM_CHBr3', 'KPP_BEFORE_CHEM_CO', 'KPP_BEFORE_CHEM_DMS', 'KPP_BEFORE_CHEM_GLYC', 'KPP_BEFORE_CHEM_H2O2', 'KPP_BEFORE_CHEM_HAC', 'KPP_BEFORE_CHEM_HBr', 'KPP_BEFORE_CHEM_HNO2', 'KPP_BEFORE_CHEM_HNO3', 'KPP_BEFORE_CHEM_HNO4', 'KPP_BEFORE_CHEM_HOBr', 'KPP_BEFORE_CHEM_IEPOX', 'KPP_BEFORE_CHEM_ISOP', 'KPP_BEFORE_CHEM_ISOPN', 'KPP_BEFORE_CHEM_MACR', 'KPP_BEFORE_CHEM_MAP', 'KPP_BEFORE_CHEM_MEK', 'KPP_BEFORE_CHEM_MMN', 'KPP_BEFORE_CHEM_MOBA', 'KPP_BEFORE_CHEM_MP', 'KPP_BEFORE_CHEM_MPN', 'KPP_BEFORE_CHEM_MSA', 'KPP_BEFORE_CHEM_MVK', 'KPP_BEFORE_CHEM_N2O5', 'KPP_BEFORE_CHEM_NO', 'KPP_BEFORE_CHEM_NO2', 'KPP_BEFORE_CHEM_NO3', 'KPP_BEFORE_CHEM_O3', 'KPP_BEFORE_CHEM_PAN', 'KPP_BEFORE_CHEM_PMN', 'KPP_BEFORE_CHEM_PPN', 'KPP_BEFORE_CHEM_PROPNN', 'KPP_BEFORE_CHEM_PRPE', 'KPP_BEFORE_CHEM_R4N2', 'KPP_BEFORE_CHEM_RCHO', 'KPP_BEFORE_CHEM_RIP', 'KPP_BEFORE_CHEM_SO2', 'KPP_BEFORE_CHEM_SO4', 'KPP_JVAL_001', 'KPP_JVAL_002', 'KPP_JVAL_003', 'KPP_JVAL_007', 'KPP_JVAL_008', 'KPP_JVAL_009', 'KPP_JVAL_010', 'KPP_JVAL_011', 'KPP_JVAL_012', 'KPP_JVAL_013', 'KPP_JVAL_014', 'KPP_JVAL_015', 'KPP_JVAL_016', 'KPP_JVAL_017', 'KPP_JVAL_018', 'KPP_JVAL_023', 'KPP_JVAL_028', 'KPP_JVAL_029', 'KPP_JVAL_030', 'KPP_JVAL_031', 'KPP_JVAL_032', 'KPP_JVAL_034', 'KPP_JVAL_055', 'KPP_JVAL_056', 'KPP_JVAL_057', 'KPP_JVAL_058', 'KPP_JVAL_059', 'KPP_JVAL_060', 'KPP_JVAL_061', 'KPP_JVAL_063', 'KPP_JVAL_064', 'KPP_JVAL_065', 'KPP_JVAL_066', 'KPP_JVAL_068', 'KPP_JVAL_069', 'KPP_JVAL_070', 'KPP_JVAL_071', 'KPP_JVAL_072', 'KPP_JVAL_073', 'KPP_JVAL_074', 'KPP_JVAL_075', 'KPP_JVAL_076', 'KPP_JVAL_077', 'KPP_JVAL_078', 'KPP_JVAL_079', 'KPP_JVAL_080', 'KPP_JVAL_081', 'KPP_JVAL_082', 'KPP_JVAL_083', 'KPP_JVAL_084', 'KPP_JVAL_085', 'KPP_JVAL_086', 'KPP_JVAL_087', 'KPP_JVAL_088', 'KPP_JVAL_089', 'KPP_JVAL_090', 'KPP_JVAL_091', 'KPP_JVAL_092', 'KPP_JVAL_093', 'KPP_JVAL_094', 'KPP_JVAL_095', 'KPP_JVAL_096', 'KPP_JVAL_097', 'KPP_JVAL_098', 'KPP_JVAL_099', 'KPP_JVAL_103', 'KPP_JVAL_104', 'KPP_JVAL_105', 'KPP_PRESS', 'KPP_QICE', 'KPP_QLIQ', 'KPP_RH', 'KPP_SUNCOS', 'KPP_TEMP', 'KPP_YLAT']\n"
]
}
],
"source": [
"print(list(ds.data_vars.keys())) # all variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore spatial distribution"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x3158314e0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds['KPP_BEFORE_CHEM_O3'][0,0].plot() # starts with stratosphere"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import interact, IntSlider, Dropdown"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "508b513e9e39434696f22944ce56eda2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Dropdown(description='var', options=('KPP_AFTER_CHEM_ACET', 'KPP_AFTER_CHEM_ALD2', 'KPP_…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"@interact(var=Dropdown(options=list(ds.data_vars.keys())),\n",
" l=IntSlider(min=0, max=ds['lev'].size, step=1, value=0, continuous_update=False),\n",
" t=IntSlider(min=0, max=ds['time'].size, step=1, value=0, continuous_update=False)\n",
" )\n",
"def plot_layer(var, l, t):\n",
" ds[var].isel(lev=l, time=t).plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Clean-up data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>AFTER_CHEM_ACET</th>\n",
" <th>AFTER_CHEM_ALD2</th>\n",
" <th>AFTER_CHEM_ALK4</th>\n",
" <th>AFTER_CHEM_Br</th>\n",
" <th>AFTER_CHEM_Br2</th>\n",
" <th>AFTER_CHEM_BrNO2</th>\n",
" <th>AFTER_CHEM_BrNO3</th>\n",
" <th>AFTER_CHEM_BrO</th>\n",
" <th>AFTER_CHEM_C2H6</th>\n",
" <th>AFTER_CHEM_C3H8</th>\n",
" <th>...</th>\n",
" <th>JVAL_103</th>\n",
" <th>JVAL_104</th>\n",
" <th>JVAL_105</th>\n",
" <th>PRESS</th>\n",
" <th>QICE</th>\n",
" <th>QLIQ</th>\n",
" <th>RH</th>\n",
" <th>SUNCOS</th>\n",
" <th>TEMP</th>\n",
" <th>YLAT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865113e-11</td>\n",
" <td>1.637107e-18</td>\n",
" <td>1.635292e-12</td>\n",
" <td>1.068629e-15</td>\n",
" <td>4.579582e-14</td>\n",
" <td>9.704897e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>7.523303e-08</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865113e-11</td>\n",
" <td>1.637107e-18</td>\n",
" <td>1.635292e-12</td>\n",
" <td>1.068629e-15</td>\n",
" <td>4.579582e-14</td>\n",
" <td>9.704897e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>7.523303e-08</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865113e-11</td>\n",
" <td>1.637107e-18</td>\n",
" <td>1.635292e-12</td>\n",
" <td>1.068629e-15</td>\n",
" <td>4.579582e-14</td>\n",
" <td>9.704897e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>7.523303e-08</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865113e-11</td>\n",
" <td>1.637107e-18</td>\n",
" <td>1.635292e-12</td>\n",
" <td>1.068629e-15</td>\n",
" <td>4.579582e-14</td>\n",
" <td>9.704897e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>7.523303e-08</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865113e-11</td>\n",
" <td>1.637107e-18</td>\n",
" <td>1.635292e-12</td>\n",
" <td>1.068629e-15</td>\n",
" <td>4.579582e-14</td>\n",
" <td>9.704897e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>7.523303e-08</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 178 columns</p>\n",
"</div>"
],
"text/plain": [
" AFTER_CHEM_ACET AFTER_CHEM_ALD2 AFTER_CHEM_ALK4 AFTER_CHEM_Br \\\n",
"0 2.728892e-10 1.298598e-12 2.865113e-11 1.637107e-18 \n",
"1 2.728892e-10 1.298598e-12 2.865113e-11 1.637107e-18 \n",
"2 2.728892e-10 1.298598e-12 2.865113e-11 1.637107e-18 \n",
"3 2.728892e-10 1.298598e-12 2.865113e-11 1.637107e-18 \n",
"4 2.728892e-10 1.298598e-12 2.865113e-11 1.637107e-18 \n",
"\n",
" AFTER_CHEM_Br2 AFTER_CHEM_BrNO2 AFTER_CHEM_BrNO3 AFTER_CHEM_BrO \\\n",
"0 1.635292e-12 1.068629e-15 4.579582e-14 9.704897e-14 \n",
"1 1.635292e-12 1.068629e-15 4.579582e-14 9.704897e-14 \n",
"2 1.635292e-12 1.068629e-15 4.579582e-14 9.704897e-14 \n",
"3 1.635292e-12 1.068629e-15 4.579582e-14 9.704897e-14 \n",
"4 1.635292e-12 1.068629e-15 4.579582e-14 9.704897e-14 \n",
"\n",
" AFTER_CHEM_C2H6 AFTER_CHEM_C3H8 ... JVAL_103 JVAL_104 \\\n",
"0 1.418422e-10 1.211562e-11 ... 7.523303e-08 1.854967e-09 \n",
"1 1.418422e-10 1.211562e-11 ... 7.523303e-08 1.854967e-09 \n",
"2 1.418422e-10 1.211562e-11 ... 7.523303e-08 1.854967e-09 \n",
"3 1.418422e-10 1.211562e-11 ... 7.523303e-08 1.854967e-09 \n",
"4 1.418422e-10 1.211562e-11 ... 7.523303e-08 1.854967e-09 \n",
"\n",
" JVAL_105 PRESS QICE QLIQ RH SUNCOS \\\n",
"0 3.524438e-08 336.440338 8.357382e-08 0.0 66.803986 0.0 \n",
"1 3.524438e-08 336.440338 8.357382e-08 0.0 66.803986 0.0 \n",
"2 3.524438e-08 336.440338 8.357382e-08 0.0 66.803986 0.0 \n",
"3 3.524438e-08 336.440338 8.357382e-08 0.0 66.803986 0.0 \n",
"4 3.524438e-08 336.440338 8.357382e-08 0.0 66.803986 0.0 \n",
"\n",
" TEMP YLAT \n",
"0 215.943314 -88.531097 \n",
"1 215.943314 -88.531097 \n",
"2 215.943314 -88.531097 \n",
"3 215.943314 -88.531097 \n",
"4 215.943314 -88.531097 \n",
"\n",
"[5 rows x 178 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# take a small subset to make computation faster\n",
"df = ds.isel(time=0, lev=0, drop=True).to_dataframe().reset_index(drop=True).rename(columns=lambda s: s.replace('KPP_', ''))\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>ACET</th>\n",
" <th>ALD2</th>\n",
" <th>ALK4</th>\n",
" <th>Br</th>\n",
" <th>Br2</th>\n",
" <th>BrNO2</th>\n",
" <th>BrNO3</th>\n",
" <th>BrO</th>\n",
" <th>C2H6</th>\n",
" <th>C3H8</th>\n",
" <th>...</th>\n",
" <th>PAN</th>\n",
" <th>PMN</th>\n",
" <th>PPN</th>\n",
" <th>PROPNN</th>\n",
" <th>PRPE</th>\n",
" <th>R4N2</th>\n",
" <th>RCHO</th>\n",
" <th>RIP</th>\n",
" <th>SO2</th>\n",
" <th>SO4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865113e-11</td>\n",
" <td>1.637107e-18</td>\n",
" <td>1.635292e-12</td>\n",
" <td>1.068629e-15</td>\n",
" <td>4.579582e-14</td>\n",
" <td>9.704897e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>6.646499e-11</td>\n",
" <td>3.078956e-16</td>\n",
" <td>4.255343e-12</td>\n",
" <td>1.862332e-12</td>\n",
" <td>9.602595e-15</td>\n",
" <td>1.757224e-11</td>\n",
" <td>3.626772e-13</td>\n",
" <td>5.298711e-16</td>\n",
" <td>4.431386e-11</td>\n",
" <td>4.458733e-11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865113e-11</td>\n",
" <td>1.637107e-18</td>\n",
" <td>1.635292e-12</td>\n",
" <td>1.068629e-15</td>\n",
" <td>4.579582e-14</td>\n",
" <td>9.704897e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>6.646499e-11</td>\n",
" <td>3.078956e-16</td>\n",
" <td>4.255343e-12</td>\n",
" <td>1.862332e-12</td>\n",
" <td>9.602595e-15</td>\n",
" <td>1.757224e-11</td>\n",
" <td>3.626772e-13</td>\n",
" <td>5.298711e-16</td>\n",
" <td>4.431386e-11</td>\n",
" <td>4.458733e-11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865113e-11</td>\n",
" <td>1.637107e-18</td>\n",
" <td>1.635292e-12</td>\n",
" <td>1.068629e-15</td>\n",
" <td>4.579582e-14</td>\n",
" <td>9.704897e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>6.646499e-11</td>\n",
" <td>3.078956e-16</td>\n",
" <td>4.255343e-12</td>\n",
" <td>1.862332e-12</td>\n",
" <td>9.602595e-15</td>\n",
" <td>1.757224e-11</td>\n",
" <td>3.626772e-13</td>\n",
" <td>5.298711e-16</td>\n",
" <td>4.431386e-11</td>\n",
" <td>4.458733e-11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865113e-11</td>\n",
" <td>1.637107e-18</td>\n",
" <td>1.635292e-12</td>\n",
" <td>1.068629e-15</td>\n",
" <td>4.579582e-14</td>\n",
" <td>9.704897e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>6.646499e-11</td>\n",
" <td>3.078956e-16</td>\n",
" <td>4.255343e-12</td>\n",
" <td>1.862332e-12</td>\n",
" <td>9.602595e-15</td>\n",
" <td>1.757224e-11</td>\n",
" <td>3.626772e-13</td>\n",
" <td>5.298711e-16</td>\n",
" <td>4.431386e-11</td>\n",
" <td>4.458733e-11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865113e-11</td>\n",
" <td>1.637107e-18</td>\n",
" <td>1.635292e-12</td>\n",
" <td>1.068629e-15</td>\n",
" <td>4.579582e-14</td>\n",
" <td>9.704897e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>6.646499e-11</td>\n",
" <td>3.078956e-16</td>\n",
" <td>4.255343e-12</td>\n",
" <td>1.862332e-12</td>\n",
" <td>9.602595e-15</td>\n",
" <td>1.757224e-11</td>\n",
" <td>3.626772e-13</td>\n",
" <td>5.298711e-16</td>\n",
" <td>4.431386e-11</td>\n",
" <td>4.458733e-11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 51 columns</p>\n",
"</div>"
],
"text/plain": [
" ACET ALD2 ALK4 Br Br2 \\\n",
"0 2.728892e-10 1.298598e-12 2.865113e-11 1.637107e-18 1.635292e-12 \n",
"1 2.728892e-10 1.298598e-12 2.865113e-11 1.637107e-18 1.635292e-12 \n",
"2 2.728892e-10 1.298598e-12 2.865113e-11 1.637107e-18 1.635292e-12 \n",
"3 2.728892e-10 1.298598e-12 2.865113e-11 1.637107e-18 1.635292e-12 \n",
"4 2.728892e-10 1.298598e-12 2.865113e-11 1.637107e-18 1.635292e-12 \n",
"\n",
" BrNO2 BrNO3 BrO C2H6 C3H8 \\\n",
"0 1.068629e-15 4.579582e-14 9.704897e-14 1.418422e-10 1.211562e-11 \n",
"1 1.068629e-15 4.579582e-14 9.704897e-14 1.418422e-10 1.211562e-11 \n",
"2 1.068629e-15 4.579582e-14 9.704897e-14 1.418422e-10 1.211562e-11 \n",
"3 1.068629e-15 4.579582e-14 9.704897e-14 1.418422e-10 1.211562e-11 \n",
"4 1.068629e-15 4.579582e-14 9.704897e-14 1.418422e-10 1.211562e-11 \n",
"\n",
" ... PAN PMN PPN PROPNN \\\n",
"0 ... 6.646499e-11 3.078956e-16 4.255343e-12 1.862332e-12 \n",
"1 ... 6.646499e-11 3.078956e-16 4.255343e-12 1.862332e-12 \n",
"2 ... 6.646499e-11 3.078956e-16 4.255343e-12 1.862332e-12 \n",
"3 ... 6.646499e-11 3.078956e-16 4.255343e-12 1.862332e-12 \n",
"4 ... 6.646499e-11 3.078956e-16 4.255343e-12 1.862332e-12 \n",
"\n",
" PRPE R4N2 RCHO RIP SO2 \\\n",
"0 9.602595e-15 1.757224e-11 3.626772e-13 5.298711e-16 4.431386e-11 \n",
"1 9.602595e-15 1.757224e-11 3.626772e-13 5.298711e-16 4.431386e-11 \n",
"2 9.602595e-15 1.757224e-11 3.626772e-13 5.298711e-16 4.431386e-11 \n",
"3 9.602595e-15 1.757224e-11 3.626772e-13 5.298711e-16 4.431386e-11 \n",
"4 9.602595e-15 1.757224e-11 3.626772e-13 5.298711e-16 4.431386e-11 \n",
"\n",
" SO4 \n",
"0 4.458733e-11 \n",
"1 4.458733e-11 \n",
"2 4.458733e-11 \n",
"3 4.458733e-11 \n",
"4 4.458733e-11 \n",
"\n",
"[5 rows x 51 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# chemical after reaction\n",
"# target to predict\n",
"df_y = df.iloc[:, df.columns.str.startswith('AFTER_CHEM')].rename(columns=lambda s: s.replace('AFTER_CHEM_', ''))\n",
"df_y.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>ACET</th>\n",
" <th>ALD2</th>\n",
" <th>ALK4</th>\n",
" <th>Br</th>\n",
" <th>Br2</th>\n",
" <th>BrNO2</th>\n",
" <th>BrNO3</th>\n",
" <th>BrO</th>\n",
" <th>C2H6</th>\n",
" <th>C3H8</th>\n",
" <th>...</th>\n",
" <th>PAN</th>\n",
" <th>PMN</th>\n",
" <th>PPN</th>\n",
" <th>PROPNN</th>\n",
" <th>PRPE</th>\n",
" <th>R4N2</th>\n",
" <th>RCHO</th>\n",
" <th>RIP</th>\n",
" <th>SO2</th>\n",
" <th>SO4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865114e-11</td>\n",
" <td>1.723187e-18</td>\n",
" <td>1.635037e-12</td>\n",
" <td>1.068581e-15</td>\n",
" <td>4.674042e-14</td>\n",
" <td>9.922784e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>6.646499e-11</td>\n",
" <td>3.089089e-16</td>\n",
" <td>4.255342e-12</td>\n",
" <td>1.862332e-12</td>\n",
" <td>9.606171e-15</td>\n",
" <td>1.757224e-11</td>\n",
" <td>3.626778e-13</td>\n",
" <td>5.298354e-16</td>\n",
" <td>4.431367e-11</td>\n",
" <td>4.458733e-11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865114e-11</td>\n",
" <td>1.723187e-18</td>\n",
" <td>1.635037e-12</td>\n",
" <td>1.068581e-15</td>\n",
" <td>4.674042e-14</td>\n",
" <td>9.922784e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>6.646499e-11</td>\n",
" <td>3.089089e-16</td>\n",
" <td>4.255342e-12</td>\n",
" <td>1.862332e-12</td>\n",
" <td>9.606171e-15</td>\n",
" <td>1.757224e-11</td>\n",
" <td>3.626778e-13</td>\n",
" <td>5.298354e-16</td>\n",
" <td>4.431367e-11</td>\n",
" <td>4.458733e-11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865114e-11</td>\n",
" <td>1.723187e-18</td>\n",
" <td>1.635037e-12</td>\n",
" <td>1.068581e-15</td>\n",
" <td>4.674042e-14</td>\n",
" <td>9.922784e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>6.646499e-11</td>\n",
" <td>3.089089e-16</td>\n",
" <td>4.255342e-12</td>\n",
" <td>1.862332e-12</td>\n",
" <td>9.606171e-15</td>\n",
" <td>1.757224e-11</td>\n",
" <td>3.626778e-13</td>\n",
" <td>5.298354e-16</td>\n",
" <td>4.431367e-11</td>\n",
" <td>4.458733e-11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865114e-11</td>\n",
" <td>1.723187e-18</td>\n",
" <td>1.635037e-12</td>\n",
" <td>1.068581e-15</td>\n",
" <td>4.674042e-14</td>\n",
" <td>9.922784e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>6.646499e-11</td>\n",
" <td>3.089089e-16</td>\n",
" <td>4.255342e-12</td>\n",
" <td>1.862332e-12</td>\n",
" <td>9.606171e-15</td>\n",
" <td>1.757224e-11</td>\n",
" <td>3.626778e-13</td>\n",
" <td>5.298354e-16</td>\n",
" <td>4.431367e-11</td>\n",
" <td>4.458733e-11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865114e-11</td>\n",
" <td>1.723187e-18</td>\n",
" <td>1.635037e-12</td>\n",
" <td>1.068581e-15</td>\n",
" <td>4.674042e-14</td>\n",
" <td>9.922784e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>6.646499e-11</td>\n",
" <td>3.089089e-16</td>\n",
" <td>4.255342e-12</td>\n",
" <td>1.862332e-12</td>\n",
" <td>9.606171e-15</td>\n",
" <td>1.757224e-11</td>\n",
" <td>3.626778e-13</td>\n",
" <td>5.298354e-16</td>\n",
" <td>4.431367e-11</td>\n",
" <td>4.458733e-11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 51 columns</p>\n",
"</div>"
],
"text/plain": [
" ACET ALD2 ALK4 Br Br2 \\\n",
"0 2.728892e-10 1.298598e-12 2.865114e-11 1.723187e-18 1.635037e-12 \n",
"1 2.728892e-10 1.298598e-12 2.865114e-11 1.723187e-18 1.635037e-12 \n",
"2 2.728892e-10 1.298598e-12 2.865114e-11 1.723187e-18 1.635037e-12 \n",
"3 2.728892e-10 1.298598e-12 2.865114e-11 1.723187e-18 1.635037e-12 \n",
"4 2.728892e-10 1.298598e-12 2.865114e-11 1.723187e-18 1.635037e-12 \n",
"\n",
" BrNO2 BrNO3 BrO C2H6 C3H8 \\\n",
"0 1.068581e-15 4.674042e-14 9.922784e-14 1.418422e-10 1.211562e-11 \n",
"1 1.068581e-15 4.674042e-14 9.922784e-14 1.418422e-10 1.211562e-11 \n",
"2 1.068581e-15 4.674042e-14 9.922784e-14 1.418422e-10 1.211562e-11 \n",
"3 1.068581e-15 4.674042e-14 9.922784e-14 1.418422e-10 1.211562e-11 \n",
"4 1.068581e-15 4.674042e-14 9.922784e-14 1.418422e-10 1.211562e-11 \n",
"\n",
" ... PAN PMN PPN PROPNN \\\n",
"0 ... 6.646499e-11 3.089089e-16 4.255342e-12 1.862332e-12 \n",
"1 ... 6.646499e-11 3.089089e-16 4.255342e-12 1.862332e-12 \n",
"2 ... 6.646499e-11 3.089089e-16 4.255342e-12 1.862332e-12 \n",
"3 ... 6.646499e-11 3.089089e-16 4.255342e-12 1.862332e-12 \n",
"4 ... 6.646499e-11 3.089089e-16 4.255342e-12 1.862332e-12 \n",
"\n",
" PRPE R4N2 RCHO RIP SO2 \\\n",
"0 9.606171e-15 1.757224e-11 3.626778e-13 5.298354e-16 4.431367e-11 \n",
"1 9.606171e-15 1.757224e-11 3.626778e-13 5.298354e-16 4.431367e-11 \n",
"2 9.606171e-15 1.757224e-11 3.626778e-13 5.298354e-16 4.431367e-11 \n",
"3 9.606171e-15 1.757224e-11 3.626778e-13 5.298354e-16 4.431367e-11 \n",
"4 9.606171e-15 1.757224e-11 3.626778e-13 5.298354e-16 4.431367e-11 \n",
"\n",
" SO4 \n",
"0 4.458733e-11 \n",
"1 4.458733e-11 \n",
"2 4.458733e-11 \n",
"3 4.458733e-11 \n",
"4 4.458733e-11 \n",
"\n",
"[5 rows x 51 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# chemical field before reaction\n",
"df_x = df.iloc[:, df.columns.str.startswith('BEFORE_CHEM')].rename(columns=lambda s: s.replace('BEFORE_CHEM_', ''))\n",
"df_x.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>JVAL_001</th>\n",
" <th>JVAL_002</th>\n",
" <th>JVAL_003</th>\n",
" <th>JVAL_007</th>\n",
" <th>JVAL_008</th>\n",
" <th>JVAL_009</th>\n",
" <th>JVAL_010</th>\n",
" <th>JVAL_011</th>\n",
" <th>JVAL_012</th>\n",
" <th>JVAL_013</th>\n",
" <th>...</th>\n",
" <th>JVAL_093</th>\n",
" <th>JVAL_094</th>\n",
" <th>JVAL_095</th>\n",
" <th>JVAL_096</th>\n",
" <th>JVAL_097</th>\n",
" <th>JVAL_098</th>\n",
" <th>JVAL_099</th>\n",
" <th>JVAL_103</th>\n",
" <th>JVAL_104</th>\n",
" <th>JVAL_105</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.459530e-21</td>\n",
" <td>0.000092</td>\n",
" <td>7.523303e-08</td>\n",
" <td>5.973814e-07</td>\n",
" <td>0.000001</td>\n",
" <td>1.239645e-07</td>\n",
" <td>1.445704e-07</td>\n",
" <td>0.000416</td>\n",
" <td>0.050949</td>\n",
" <td>0.006555</td>\n",
" <td>...</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>1.445704e-07</td>\n",
" <td>4.658256e-09</td>\n",
" <td>1.445704e-07</td>\n",
" <td>7.523303e-08</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.459530e-21</td>\n",
" <td>0.000092</td>\n",
" <td>7.523303e-08</td>\n",
" <td>5.973814e-07</td>\n",
" <td>0.000001</td>\n",
" <td>1.239645e-07</td>\n",
" <td>1.445704e-07</td>\n",
" <td>0.000416</td>\n",
" <td>0.050949</td>\n",
" <td>0.006555</td>\n",
" <td>...</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>1.445704e-07</td>\n",
" <td>4.658256e-09</td>\n",
" <td>1.445704e-07</td>\n",
" <td>7.523303e-08</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.459530e-21</td>\n",
" <td>0.000092</td>\n",
" <td>7.523303e-08</td>\n",
" <td>5.973814e-07</td>\n",
" <td>0.000001</td>\n",
" <td>1.239645e-07</td>\n",
" <td>1.445704e-07</td>\n",
" <td>0.000416</td>\n",
" <td>0.050949</td>\n",
" <td>0.006555</td>\n",
" <td>...</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>1.445704e-07</td>\n",
" <td>4.658256e-09</td>\n",
" <td>1.445704e-07</td>\n",
" <td>7.523303e-08</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.459530e-21</td>\n",
" <td>0.000092</td>\n",
" <td>7.523303e-08</td>\n",
" <td>5.973814e-07</td>\n",
" <td>0.000001</td>\n",
" <td>1.239645e-07</td>\n",
" <td>1.445704e-07</td>\n",
" <td>0.000416</td>\n",
" <td>0.050949</td>\n",
" <td>0.006555</td>\n",
" <td>...</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>1.445704e-07</td>\n",
" <td>4.658256e-09</td>\n",
" <td>1.445704e-07</td>\n",
" <td>7.523303e-08</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.459530e-21</td>\n",
" <td>0.000092</td>\n",
" <td>7.523303e-08</td>\n",
" <td>5.973814e-07</td>\n",
" <td>0.000001</td>\n",
" <td>1.239645e-07</td>\n",
" <td>1.445704e-07</td>\n",
" <td>0.000416</td>\n",
" <td>0.050949</td>\n",
" <td>0.006555</td>\n",
" <td>...</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>3.951851e-08</td>\n",
" <td>1.445704e-07</td>\n",
" <td>4.658256e-09</td>\n",
" <td>1.445704e-07</td>\n",
" <td>7.523303e-08</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" JVAL_001 JVAL_002 JVAL_003 JVAL_007 JVAL_008 JVAL_009 \\\n",
"0 2.459530e-21 0.000092 7.523303e-08 5.973814e-07 0.000001 1.239645e-07 \n",
"1 2.459530e-21 0.000092 7.523303e-08 5.973814e-07 0.000001 1.239645e-07 \n",
"2 2.459530e-21 0.000092 7.523303e-08 5.973814e-07 0.000001 1.239645e-07 \n",
"3 2.459530e-21 0.000092 7.523303e-08 5.973814e-07 0.000001 1.239645e-07 \n",
"4 2.459530e-21 0.000092 7.523303e-08 5.973814e-07 0.000001 1.239645e-07 \n",
"\n",
" JVAL_010 JVAL_011 JVAL_012 JVAL_013 ... JVAL_093 \\\n",
"0 1.445704e-07 0.000416 0.050949 0.006555 ... 3.951851e-08 \n",
"1 1.445704e-07 0.000416 0.050949 0.006555 ... 3.951851e-08 \n",
"2 1.445704e-07 0.000416 0.050949 0.006555 ... 3.951851e-08 \n",
"3 1.445704e-07 0.000416 0.050949 0.006555 ... 3.951851e-08 \n",
"4 1.445704e-07 0.000416 0.050949 0.006555 ... 3.951851e-08 \n",
"\n",
" JVAL_094 JVAL_095 JVAL_096 JVAL_097 JVAL_098 \\\n",
"0 3.951851e-08 3.951851e-08 3.951851e-08 1.445704e-07 4.658256e-09 \n",
"1 3.951851e-08 3.951851e-08 3.951851e-08 1.445704e-07 4.658256e-09 \n",
"2 3.951851e-08 3.951851e-08 3.951851e-08 1.445704e-07 4.658256e-09 \n",
"3 3.951851e-08 3.951851e-08 3.951851e-08 1.445704e-07 4.658256e-09 \n",
"4 3.951851e-08 3.951851e-08 3.951851e-08 1.445704e-07 4.658256e-09 \n",
"\n",
" JVAL_099 JVAL_103 JVAL_104 JVAL_105 \n",
"0 1.445704e-07 7.523303e-08 1.854967e-09 3.524438e-08 \n",
"1 1.445704e-07 7.523303e-08 1.854967e-09 3.524438e-08 \n",
"2 1.445704e-07 7.523303e-08 1.854967e-09 3.524438e-08 \n",
"3 1.445704e-07 7.523303e-08 1.854967e-09 3.524438e-08 \n",
"4 1.445704e-07 7.523303e-08 1.854967e-09 3.524438e-08 \n",
"\n",
"[5 rows x 68 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# photolysis rate\n",
"df_jv = df.iloc[:, df.columns.str.contains('JVAL')]\n",
"df_jv.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>AIRDEN</th>\n",
" <th>PRESS</th>\n",
" <th>QICE</th>\n",
" <th>QLIQ</th>\n",
" <th>RH</th>\n",
" <th>SUNCOS</th>\n",
" <th>TEMP</th>\n",
" <th>YLAT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.542535</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.542535</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.542535</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.542535</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.542535</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AIRDEN PRESS QICE QLIQ RH SUNCOS TEMP \\\n",
"0 0.542535 336.440338 8.357382e-08 0.0 66.803986 0.0 215.943314 \n",
"1 0.542535 336.440338 8.357382e-08 0.0 66.803986 0.0 215.943314 \n",
"2 0.542535 336.440338 8.357382e-08 0.0 66.803986 0.0 215.943314 \n",
"3 0.542535 336.440338 8.357382e-08 0.0 66.803986 0.0 215.943314 \n",
"4 0.542535 336.440338 8.357382e-08 0.0 66.803986 0.0 215.943314 \n",
"\n",
" YLAT \n",
"0 -88.531097 \n",
"1 -88.531097 \n",
"2 -88.531097 \n",
"3 -88.531097 \n",
"4 -88.531097 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# physical fields (the rest of variables)\n",
"df_phy = df.iloc[:, ~np.logical_or(df.columns.str.contains('CHEM'), df.columns.str.contains('JVAL'))]\n",
"df_phy.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(13104, 51)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_y.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore probablity distribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chemical fields"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>ACET</th>\n",
" <th>ALD2</th>\n",
" <th>ALK4</th>\n",
" <th>Br</th>\n",
" <th>Br2</th>\n",
" <th>BrNO2</th>\n",
" <th>BrNO3</th>\n",
" <th>BrO</th>\n",
" <th>C2H6</th>\n",
" <th>C3H8</th>\n",
" <th>...</th>\n",
" <th>PAN</th>\n",
" <th>PMN</th>\n",
" <th>PPN</th>\n",
" <th>PROPNN</th>\n",
" <th>PRPE</th>\n",
" <th>R4N2</th>\n",
" <th>RCHO</th>\n",
" <th>RIP</th>\n",
" <th>SO2</th>\n",
" <th>SO4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>...</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.401123e-10</td>\n",
" <td>3.823564e-12</td>\n",
" <td>1.947143e-11</td>\n",
" <td>6.259054e-14</td>\n",
" <td>7.553818e-13</td>\n",
" <td>9.427531e-15</td>\n",
" <td>4.624079e-13</td>\n",
" <td>9.652934e-13</td>\n",
" <td>2.210329e-10</td>\n",
" <td>1.426287e-11</td>\n",
" <td>...</td>\n",
" <td>2.956010e-10</td>\n",
" <td>8.148639e-14</td>\n",
" <td>2.014411e-11</td>\n",
" <td>4.856518e-12</td>\n",
" <td>4.343495e-13</td>\n",
" <td>2.458434e-11</td>\n",
" <td>5.797942e-13</td>\n",
" <td>1.512168e-12</td>\n",
" <td>3.216163e-11</td>\n",
" <td>2.391726e-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.634536e-10</td>\n",
" <td>7.359943e-12</td>\n",
" <td>1.874352e-11</td>\n",
" <td>7.740567e-14</td>\n",
" <td>9.880962e-13</td>\n",
" <td>9.503338e-15</td>\n",
" <td>6.402940e-13</td>\n",
" <td>1.025082e-12</td>\n",
" <td>8.741514e-11</td>\n",
" <td>7.972309e-12</td>\n",
" <td>...</td>\n",
" <td>1.925646e-10</td>\n",
" <td>5.192180e-13</td>\n",
" <td>1.409666e-11</td>\n",
" <td>4.326460e-12</td>\n",
" <td>3.777129e-12</td>\n",
" <td>1.468304e-11</td>\n",
" <td>7.648941e-13</td>\n",
" <td>1.543027e-11</td>\n",
" <td>2.667244e-11</td>\n",
" <td>2.004458e-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.930122e-10</td>\n",
" <td>5.288192e-13</td>\n",
" <td>7.462442e-14</td>\n",
" <td>7.174774e-22</td>\n",
" <td>4.105624e-17</td>\n",
" <td>8.187243e-17</td>\n",
" <td>1.050510e-18</td>\n",
" <td>1.126468e-18</td>\n",
" <td>7.589903e-11</td>\n",
" <td>6.002485e-13</td>\n",
" <td>...</td>\n",
" <td>4.725316e-12</td>\n",
" <td>2.236085e-24</td>\n",
" <td>3.650279e-13</td>\n",
" <td>5.926181e-14</td>\n",
" <td>3.809442e-23</td>\n",
" <td>3.090099e-12</td>\n",
" <td>1.704982e-14</td>\n",
" <td>4.787360e-38</td>\n",
" <td>1.432616e-13</td>\n",
" <td>9.386456e-12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.095908e-10</td>\n",
" <td>1.745301e-12</td>\n",
" <td>5.151981e-12</td>\n",
" <td>2.196718e-18</td>\n",
" <td>5.161659e-15</td>\n",
" <td>3.038655e-15</td>\n",
" <td>6.227055e-14</td>\n",
" <td>3.385416e-15</td>\n",
" <td>1.593691e-10</td>\n",
" <td>8.561455e-12</td>\n",
" <td>...</td>\n",
" <td>1.254218e-10</td>\n",
" <td>1.053754e-16</td>\n",
" <td>6.975357e-12</td>\n",
" <td>2.017778e-12</td>\n",
" <td>7.118359e-16</td>\n",
" <td>1.562448e-11</td>\n",
" <td>2.743248e-13</td>\n",
" <td>1.915482e-19</td>\n",
" <td>1.824306e-11</td>\n",
" <td>9.708499e-11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>4.011973e-10</td>\n",
" <td>2.696527e-12</td>\n",
" <td>1.715803e-11</td>\n",
" <td>4.890061e-15</td>\n",
" <td>9.002611e-14</td>\n",
" <td>6.867224e-15</td>\n",
" <td>2.066553e-13</td>\n",
" <td>7.662127e-13</td>\n",
" <td>1.979074e-10</td>\n",
" <td>1.373617e-11</td>\n",
" <td>...</td>\n",
" <td>3.004458e-10</td>\n",
" <td>1.340395e-15</td>\n",
" <td>1.717098e-11</td>\n",
" <td>3.846249e-12</td>\n",
" <td>8.724246e-15</td>\n",
" <td>2.088308e-11</td>\n",
" <td>4.634419e-13</td>\n",
" <td>1.821337e-16</td>\n",
" <td>2.686098e-11</td>\n",
" <td>2.246761e-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>5.489459e-10</td>\n",
" <td>3.791357e-12</td>\n",
" <td>2.892257e-11</td>\n",
" <td>1.281939e-13</td>\n",
" <td>1.408566e-12</td>\n",
" <td>1.270732e-14</td>\n",
" <td>5.813116e-13</td>\n",
" <td>1.673444e-12</td>\n",
" <td>2.819024e-10</td>\n",
" <td>1.852384e-11</td>\n",
" <td>...</td>\n",
" <td>4.098205e-10</td>\n",
" <td>1.163122e-14</td>\n",
" <td>3.313351e-11</td>\n",
" <td>6.026199e-12</td>\n",
" <td>3.442802e-14</td>\n",
" <td>3.223574e-11</td>\n",
" <td>6.489342e-13</td>\n",
" <td>3.170857e-15</td>\n",
" <td>4.056088e-11</td>\n",
" <td>3.102405e-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.825721e-09</td>\n",
" <td>2.155189e-10</td>\n",
" <td>2.433858e-10</td>\n",
" <td>3.456310e-13</td>\n",
" <td>7.139807e-12</td>\n",
" <td>1.548395e-13</td>\n",
" <td>4.981079e-12</td>\n",
" <td>8.417060e-12</td>\n",
" <td>9.589409e-10</td>\n",
" <td>1.091102e-10</td>\n",
" <td>...</td>\n",
" <td>1.916771e-09</td>\n",
" <td>1.065997e-11</td>\n",
" <td>8.137223e-11</td>\n",
" <td>5.085085e-11</td>\n",
" <td>1.136979e-10</td>\n",
" <td>1.727804e-10</td>\n",
" <td>1.531439e-11</td>\n",
" <td>4.541626e-10</td>\n",
" <td>5.452135e-10</td>\n",
" <td>2.207964e-09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 51 columns</p>\n",
"</div>"
],
"text/plain": [
" ACET ALD2 ALK4 Br Br2 \\\n",
"count 1.310400e+04 1.310400e+04 1.310400e+04 1.310400e+04 1.310400e+04 \n",
"mean 4.401123e-10 3.823564e-12 1.947143e-11 6.259054e-14 7.553818e-13 \n",
"std 1.634536e-10 7.359943e-12 1.874352e-11 7.740567e-14 9.880962e-13 \n",
"min 1.930122e-10 5.288192e-13 7.462442e-14 7.174774e-22 4.105624e-17 \n",
"25% 3.095908e-10 1.745301e-12 5.151981e-12 2.196718e-18 5.161659e-15 \n",
"50% 4.011973e-10 2.696527e-12 1.715803e-11 4.890061e-15 9.002611e-14 \n",
"75% 5.489459e-10 3.791357e-12 2.892257e-11 1.281939e-13 1.408566e-12 \n",
"max 1.825721e-09 2.155189e-10 2.433858e-10 3.456310e-13 7.139807e-12 \n",
"\n",
" BrNO2 BrNO3 BrO C2H6 C3H8 \\\n",
"count 1.310400e+04 1.310400e+04 1.310400e+04 1.310400e+04 1.310400e+04 \n",
"mean 9.427531e-15 4.624079e-13 9.652934e-13 2.210329e-10 1.426287e-11 \n",
"std 9.503338e-15 6.402940e-13 1.025082e-12 8.741514e-11 7.972309e-12 \n",
"min 8.187243e-17 1.050510e-18 1.126468e-18 7.589903e-11 6.002485e-13 \n",
"25% 3.038655e-15 6.227055e-14 3.385416e-15 1.593691e-10 8.561455e-12 \n",
"50% 6.867224e-15 2.066553e-13 7.662127e-13 1.979074e-10 1.373617e-11 \n",
"75% 1.270732e-14 5.813116e-13 1.673444e-12 2.819024e-10 1.852384e-11 \n",
"max 1.548395e-13 4.981079e-12 8.417060e-12 9.589409e-10 1.091102e-10 \n",
"\n",
" ... PAN PMN PPN PROPNN \\\n",
"count ... 1.310400e+04 1.310400e+04 1.310400e+04 1.310400e+04 \n",
"mean ... 2.956010e-10 8.148639e-14 2.014411e-11 4.856518e-12 \n",
"std ... 1.925646e-10 5.192180e-13 1.409666e-11 4.326460e-12 \n",
"min ... 4.725316e-12 2.236085e-24 3.650279e-13 5.926181e-14 \n",
"25% ... 1.254218e-10 1.053754e-16 6.975357e-12 2.017778e-12 \n",
"50% ... 3.004458e-10 1.340395e-15 1.717098e-11 3.846249e-12 \n",
"75% ... 4.098205e-10 1.163122e-14 3.313351e-11 6.026199e-12 \n",
"max ... 1.916771e-09 1.065997e-11 8.137223e-11 5.085085e-11 \n",
"\n",
" PRPE R4N2 RCHO RIP SO2 \\\n",
"count 1.310400e+04 1.310400e+04 1.310400e+04 1.310400e+04 1.310400e+04 \n",
"mean 4.343495e-13 2.458434e-11 5.797942e-13 1.512168e-12 3.216163e-11 \n",
"std 3.777129e-12 1.468304e-11 7.648941e-13 1.543027e-11 2.667244e-11 \n",
"min 3.809442e-23 3.090099e-12 1.704982e-14 4.787360e-38 1.432616e-13 \n",
"25% 7.118359e-16 1.562448e-11 2.743248e-13 1.915482e-19 1.824306e-11 \n",
"50% 8.724246e-15 2.088308e-11 4.634419e-13 1.821337e-16 2.686098e-11 \n",
"75% 3.442802e-14 3.223574e-11 6.489342e-13 3.170857e-15 4.056088e-11 \n",
"max 1.136979e-10 1.727804e-10 1.531439e-11 4.541626e-10 5.452135e-10 \n",
"\n",
" SO4 \n",
"count 1.310400e+04 \n",
"mean 2.391726e-10 \n",
"std 2.004458e-10 \n",
"min 9.386456e-12 \n",
"25% 9.708499e-11 \n",
"50% 2.246761e-10 \n",
"75% 3.102405e-10 \n",
"max 2.207964e-09 \n",
"\n",
"[8 rows x 51 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_x.describe()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 16 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pd.plotting.scatter_matrix(df_x[['O3', 'NO', 'NO2', 'CO']], diagonal='kde');"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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>O3</th>\n",
" <th>NO</th>\n",
" <th>NO2</th>\n",
" <th>CO</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1.310400e+04</td>\n",
" <td>13104.000000</td>\n",
" <td>13104.000000</td>\n",
" <td>1.310400e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-1.123590e-06</td>\n",
" <td>308.314362</td>\n",
" <td>0.002858</td>\n",
" <td>-9.311394e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.291296e-04</td>\n",
" <td>10163.926758</td>\n",
" <td>0.053375</td>\n",
" <td>1.762833e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-1.474141e-03</td>\n",
" <td>-1.000000</td>\n",
" <td>-0.299839</td>\n",
" <td>-2.149959e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-3.240075e-05</td>\n",
" <td>-0.706993</td>\n",
" <td>-0.011658</td>\n",
" <td>-1.197721e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-6.145305e-06</td>\n",
" <td>-0.010923</td>\n",
" <td>0.000372</td>\n",
" <td>-7.290761e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>-2.907796e-07</td>\n",
" <td>0.020855</td>\n",
" <td>0.022518</td>\n",
" <td>-5.148714e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4.760274e-03</td>\n",
" <td>653396.000000</td>\n",
" <td>0.302070</td>\n",
" <td>5.831961e-04</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" O3 NO NO2 CO\n",
"count 1.310400e+04 13104.000000 13104.000000 1.310400e+04\n",
"mean -1.123590e-06 308.314362 0.002858 -9.311394e-05\n",
"std 2.291296e-04 10163.926758 0.053375 1.762833e-04\n",
"min -1.474141e-03 -1.000000 -0.299839 -2.149959e-03\n",
"25% -3.240075e-05 -0.706993 -0.011658 -1.197721e-04\n",
"50% -6.145305e-06 -0.010923 0.000372 -7.290761e-06\n",
"75% -2.907796e-07 0.020855 0.022518 -5.148714e-07\n",
"max 4.760274e-03 653396.000000 0.302070 5.831961e-04"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# percentage change\n",
"((df_y - df_x)/df_x)[['O3', 'NO', 'NO2', 'CO']].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Photolysis rates"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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>JVAL_001</th>\n",
" <th>JVAL_002</th>\n",
" <th>JVAL_003</th>\n",
" <th>JVAL_007</th>\n",
" <th>JVAL_008</th>\n",
" <th>JVAL_009</th>\n",
" <th>JVAL_010</th>\n",
" <th>JVAL_011</th>\n",
" <th>JVAL_012</th>\n",
" <th>JVAL_013</th>\n",
" <th>...</th>\n",
" <th>JVAL_093</th>\n",
" <th>JVAL_094</th>\n",
" <th>JVAL_095</th>\n",
" <th>JVAL_096</th>\n",
" <th>JVAL_097</th>\n",
" <th>JVAL_098</th>\n",
" <th>JVAL_099</th>\n",
" <th>JVAL_103</th>\n",
" <th>JVAL_104</th>\n",
" <th>JVAL_105</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1.310400e+04</td>\n",
" <td>13104.000000</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>13104.000000</td>\n",
" <td>13104.000000</td>\n",
" <td>13104.000000</td>\n",
" <td>...</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.401664e-19</td>\n",
" <td>0.000264</td>\n",
" <td>9.872157e-06</td>\n",
" <td>1.654080e-05</td>\n",
" <td>2.681452e-05</td>\n",
" <td>3.190222e-06</td>\n",
" <td>2.769155e-06</td>\n",
" <td>0.004862</td>\n",
" <td>0.121187</td>\n",
" <td>0.015593</td>\n",
" <td>...</td>\n",
" <td>1.087513e-06</td>\n",
" <td>1.087513e-06</td>\n",
" <td>1.087513e-06</td>\n",
" <td>1.087513e-06</td>\n",
" <td>2.769155e-06</td>\n",
" <td>3.051320e-07</td>\n",
" <td>2.769155e-06</td>\n",
" <td>9.872157e-06</td>\n",
" <td>8.901995e-08</td>\n",
" <td>1.691379e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.492517e-19</td>\n",
" <td>0.000220</td>\n",
" <td>1.823171e-05</td>\n",
" <td>2.115793e-05</td>\n",
" <td>3.200162e-05</td>\n",
" <td>4.025470e-06</td>\n",
" <td>3.350382e-06</td>\n",
" <td>0.005284</td>\n",
" <td>0.094349</td>\n",
" <td>0.012140</td>\n",
" <td>...</td>\n",
" <td>1.427564e-06</td>\n",
" <td>1.427564e-06</td>\n",
" <td>1.427564e-06</td>\n",
" <td>1.427564e-06</td>\n",
" <td>3.350382e-06</td>\n",
" <td>4.552236e-07</td>\n",
" <td>3.350382e-06</td>\n",
" <td>1.823171e-05</td>\n",
" <td>1.289266e-07</td>\n",
" <td>2.449605e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2.460170e-23</td>\n",
" <td>0.000018</td>\n",
" <td>2.930058e-08</td>\n",
" <td>2.865550e-07</td>\n",
" <td>7.176703e-07</td>\n",
" <td>6.632969e-08</td>\n",
" <td>7.670415e-08</td>\n",
" <td>0.000233</td>\n",
" <td>0.009387</td>\n",
" <td>0.001208</td>\n",
" <td>...</td>\n",
" <td>1.921948e-08</td>\n",
" <td>1.921948e-08</td>\n",
" <td>1.921948e-08</td>\n",
" <td>1.921948e-08</td>\n",
" <td>7.670415e-08</td>\n",
" <td>2.536006e-09</td>\n",
" <td>7.670415e-08</td>\n",
" <td>2.930058e-08</td>\n",
" <td>8.027692e-10</td>\n",
" <td>1.525262e-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.881690e-22</td>\n",
" <td>0.000065</td>\n",
" <td>5.364220e-08</td>\n",
" <td>3.558112e-07</td>\n",
" <td>8.460636e-07</td>\n",
" <td>8.254597e-08</td>\n",
" <td>8.877939e-08</td>\n",
" <td>0.000277</td>\n",
" <td>0.034318</td>\n",
" <td>0.004416</td>\n",
" <td>...</td>\n",
" <td>2.378304e-08</td>\n",
" <td>2.378304e-08</td>\n",
" <td>2.378304e-08</td>\n",
" <td>2.378304e-08</td>\n",
" <td>8.877939e-08</td>\n",
" <td>3.940522e-09</td>\n",
" <td>8.877939e-08</td>\n",
" <td>5.364220e-08</td>\n",
" <td>1.079188e-09</td>\n",
" <td>2.050458e-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>9.802799e-22</td>\n",
" <td>0.000096</td>\n",
" <td>7.996867e-08</td>\n",
" <td>6.072950e-07</td>\n",
" <td>1.406635e-06</td>\n",
" <td>1.266792e-07</td>\n",
" <td>1.461424e-07</td>\n",
" <td>0.000420</td>\n",
" <td>0.053303</td>\n",
" <td>0.006858</td>\n",
" <td>...</td>\n",
" <td>4.014941e-08</td>\n",
" <td>4.014941e-08</td>\n",
" <td>4.014941e-08</td>\n",
" <td>4.014941e-08</td>\n",
" <td>1.461424e-07</td>\n",
" <td>5.203602e-09</td>\n",
" <td>1.461424e-07</td>\n",
" <td>7.996867e-08</td>\n",
" <td>1.898466e-09</td>\n",
" <td>3.607086e-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>5.932260e-21</td>\n",
" <td>0.000491</td>\n",
" <td>1.031567e-05</td>\n",
" <td>3.492193e-05</td>\n",
" <td>5.983091e-05</td>\n",
" <td>6.699788e-06</td>\n",
" <td>5.961356e-06</td>\n",
" <td>0.010910</td>\n",
" <td>0.212288</td>\n",
" <td>0.027315</td>\n",
" <td>...</td>\n",
" <td>2.176480e-06</td>\n",
" <td>2.176480e-06</td>\n",
" <td>2.176480e-06</td>\n",
" <td>2.176480e-06</td>\n",
" <td>5.961356e-06</td>\n",
" <td>4.972065e-07</td>\n",
" <td>5.961356e-06</td>\n",
" <td>1.031567e-05</td>\n",
" <td>1.531383e-07</td>\n",
" <td>2.909627e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.548319e-17</td>\n",
" <td>0.000743</td>\n",
" <td>9.620030e-05</td>\n",
" <td>7.009407e-05</td>\n",
" <td>1.015712e-04</td>\n",
" <td>1.351428e-05</td>\n",
" <td>1.095715e-05</td>\n",
" <td>0.016208</td>\n",
" <td>0.336001</td>\n",
" <td>0.043233</td>\n",
" <td>...</td>\n",
" <td>5.175474e-06</td>\n",
" <td>5.175474e-06</td>\n",
" <td>5.175474e-06</td>\n",
" <td>5.175474e-06</td>\n",
" <td>1.095715e-05</td>\n",
" <td>1.918909e-06</td>\n",
" <td>1.095715e-05</td>\n",
" <td>9.620030e-05</td>\n",
" <td>5.180078e-07</td>\n",
" <td>9.842149e-06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" JVAL_001 JVAL_002 JVAL_003 JVAL_007 JVAL_008 \\\n",
"count 1.310400e+04 13104.000000 1.310400e+04 1.310400e+04 1.310400e+04 \n",
"mean 1.401664e-19 0.000264 9.872157e-06 1.654080e-05 2.681452e-05 \n",
"std 7.492517e-19 0.000220 1.823171e-05 2.115793e-05 3.200162e-05 \n",
"min 2.460170e-23 0.000018 2.930058e-08 2.865550e-07 7.176703e-07 \n",
"25% 2.881690e-22 0.000065 5.364220e-08 3.558112e-07 8.460636e-07 \n",
"50% 9.802799e-22 0.000096 7.996867e-08 6.072950e-07 1.406635e-06 \n",
"75% 5.932260e-21 0.000491 1.031567e-05 3.492193e-05 5.983091e-05 \n",
"max 1.548319e-17 0.000743 9.620030e-05 7.009407e-05 1.015712e-04 \n",
"\n",
" JVAL_009 JVAL_010 JVAL_011 JVAL_012 JVAL_013 \\\n",
"count 1.310400e+04 1.310400e+04 13104.000000 13104.000000 13104.000000 \n",
"mean 3.190222e-06 2.769155e-06 0.004862 0.121187 0.015593 \n",
"std 4.025470e-06 3.350382e-06 0.005284 0.094349 0.012140 \n",
"min 6.632969e-08 7.670415e-08 0.000233 0.009387 0.001208 \n",
"25% 8.254597e-08 8.877939e-08 0.000277 0.034318 0.004416 \n",
"50% 1.266792e-07 1.461424e-07 0.000420 0.053303 0.006858 \n",
"75% 6.699788e-06 5.961356e-06 0.010910 0.212288 0.027315 \n",
"max 1.351428e-05 1.095715e-05 0.016208 0.336001 0.043233 \n",
"\n",
" ... JVAL_093 JVAL_094 JVAL_095 JVAL_096 \\\n",
"count ... 1.310400e+04 1.310400e+04 1.310400e+04 1.310400e+04 \n",
"mean ... 1.087513e-06 1.087513e-06 1.087513e-06 1.087513e-06 \n",
"std ... 1.427564e-06 1.427564e-06 1.427564e-06 1.427564e-06 \n",
"min ... 1.921948e-08 1.921948e-08 1.921948e-08 1.921948e-08 \n",
"25% ... 2.378304e-08 2.378304e-08 2.378304e-08 2.378304e-08 \n",
"50% ... 4.014941e-08 4.014941e-08 4.014941e-08 4.014941e-08 \n",
"75% ... 2.176480e-06 2.176480e-06 2.176480e-06 2.176480e-06 \n",
"max ... 5.175474e-06 5.175474e-06 5.175474e-06 5.175474e-06 \n",
"\n",
" JVAL_097 JVAL_098 JVAL_099 JVAL_103 JVAL_104 \\\n",
"count 1.310400e+04 1.310400e+04 1.310400e+04 1.310400e+04 1.310400e+04 \n",
"mean 2.769155e-06 3.051320e-07 2.769155e-06 9.872157e-06 8.901995e-08 \n",
"std 3.350382e-06 4.552236e-07 3.350382e-06 1.823171e-05 1.289266e-07 \n",
"min 7.670415e-08 2.536006e-09 7.670415e-08 2.930058e-08 8.027692e-10 \n",
"25% 8.877939e-08 3.940522e-09 8.877939e-08 5.364220e-08 1.079188e-09 \n",
"50% 1.461424e-07 5.203602e-09 1.461424e-07 7.996867e-08 1.898466e-09 \n",
"75% 5.961356e-06 4.972065e-07 5.961356e-06 1.031567e-05 1.531383e-07 \n",
"max 1.095715e-05 1.918909e-06 1.095715e-05 9.620030e-05 5.180078e-07 \n",
"\n",
" JVAL_105 \n",
"count 1.310400e+04 \n",
"mean 1.691379e-06 \n",
"std 2.449605e-06 \n",
"min 1.525262e-08 \n",
"25% 2.050458e-08 \n",
"50% 3.607086e-08 \n",
"75% 2.909627e-06 \n",
"max 9.842149e-06 \n",
"\n",
"[8 rows x 68 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_jv.describe()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 16 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pd.plotting.scatter_matrix(df_jv[['JVAL_001', 'JVAL_002', 'JVAL_003', 'JVAL_007']], diagonal='kde');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Physical fields"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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>AIRDEN</th>\n",
" <th>PRESS</th>\n",
" <th>QICE</th>\n",
" <th>QLIQ</th>\n",
" <th>RH</th>\n",
" <th>SUNCOS</th>\n",
" <th>TEMP</th>\n",
" <th>YLAT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>13104.000000</td>\n",
" <td>13104.000000</td>\n",
" <td>1.310400e+04</td>\n",
" <td>1.310400e+04</td>\n",
" <td>13104.000000</td>\n",
" <td>13104.000000</td>\n",
" <td>13104.000000</td>\n",
" <td>1.310400e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.556872</td>\n",
" <td>387.528687</td>\n",
" <td>1.827721e-06</td>\n",
" <td>1.612894e-06</td>\n",
" <td>50.977757</td>\n",
" <td>0.231971</td>\n",
" <td>242.610504</td>\n",
" <td>2.384768e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.027103</td>\n",
" <td>16.760065</td>\n",
" <td>5.296918e-06</td>\n",
" <td>4.766276e-06</td>\n",
" <td>29.972166</td>\n",
" <td>0.299548</td>\n",
" <td>13.364087</td>\n",
" <td>5.246186e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.431110</td>\n",
" <td>312.158600</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.125228</td>\n",
" <td>0.000000</td>\n",
" <td>212.088501</td>\n",
" <td>-8.853110e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.536365</td>\n",
" <td>388.627769</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>23.218089</td>\n",
" <td>0.000000</td>\n",
" <td>234.171436</td>\n",
" <td>-4.599250e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.551361</td>\n",
" <td>394.372650</td>\n",
" <td>5.482036e-10</td>\n",
" <td>0.000000e+00</td>\n",
" <td>53.661047</td>\n",
" <td>0.004706</td>\n",
" <td>245.208992</td>\n",
" <td>2.553513e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.577818</td>\n",
" <td>396.156845</td>\n",
" <td>9.587646e-07</td>\n",
" <td>1.950562e-07</td>\n",
" <td>78.559256</td>\n",
" <td>0.431489</td>\n",
" <td>254.935863</td>\n",
" <td>4.599250e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.628657</td>\n",
" <td>400.414581</td>\n",
" <td>7.072287e-05</td>\n",
" <td>7.118885e-05</td>\n",
" <td>100.000175</td>\n",
" <td>0.999599</td>\n",
" <td>262.379639</td>\n",
" <td>8.853110e+01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AIRDEN PRESS QICE QLIQ RH \\\n",
"count 13104.000000 13104.000000 1.310400e+04 1.310400e+04 13104.000000 \n",
"mean 0.556872 387.528687 1.827721e-06 1.612894e-06 50.977757 \n",
"std 0.027103 16.760065 5.296918e-06 4.766276e-06 29.972166 \n",
"min 0.431110 312.158600 0.000000e+00 0.000000e+00 0.125228 \n",
"25% 0.536365 388.627769 0.000000e+00 0.000000e+00 23.218089 \n",
"50% 0.551361 394.372650 5.482036e-10 0.000000e+00 53.661047 \n",
"75% 0.577818 396.156845 9.587646e-07 1.950562e-07 78.559256 \n",
"max 0.628657 400.414581 7.072287e-05 7.118885e-05 100.000175 \n",
"\n",
" SUNCOS TEMP YLAT \n",
"count 13104.000000 13104.000000 1.310400e+04 \n",
"mean 0.231971 242.610504 2.384768e-06 \n",
"std 0.299548 13.364087 5.246186e+01 \n",
"min 0.000000 212.088501 -8.853110e+01 \n",
"25% 0.000000 234.171436 -4.599250e+01 \n",
"50% 0.004706 245.208992 2.553513e-15 \n",
"75% 0.431489 254.935863 4.599250e+01 \n",
"max 0.999599 262.379639 8.853110e+01 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_phy.describe()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 16 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pd.plotting.scatter_matrix(df_phy[['AIRDEN', 'PRESS', 'TEMP', 'RH']], diagonal='kde');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build ML model"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from sklearn.neural_network import MLPRegressor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare training & test data"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>ACET</th>\n",
" <th>ALD2</th>\n",
" <th>ALK4</th>\n",
" <th>Br</th>\n",
" <th>Br2</th>\n",
" <th>BrNO2</th>\n",
" <th>BrNO3</th>\n",
" <th>BrO</th>\n",
" <th>C2H6</th>\n",
" <th>C3H8</th>\n",
" <th>...</th>\n",
" <th>JVAL_104</th>\n",
" <th>JVAL_105</th>\n",
" <th>AIRDEN</th>\n",
" <th>PRESS</th>\n",
" <th>QICE</th>\n",
" <th>QLIQ</th>\n",
" <th>RH</th>\n",
" <th>SUNCOS</th>\n",
" <th>TEMP</th>\n",
" <th>YLAT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865114e-11</td>\n",
" <td>1.723187e-18</td>\n",
" <td>1.635037e-12</td>\n",
" <td>1.068581e-15</td>\n",
" <td>4.674042e-14</td>\n",
" <td>9.922784e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" <td>0.542535</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.728892e-10</td>\n",
" <td>1.298598e-12</td>\n",
" <td>2.865114e-11</td>\n",
" <td>1.723187e-18</td>\n",
" <td>1.635037e-12</td>\n",
" <td>1.068581e-15</td>\n",
" <td>4.674042e-14</td>\n",
" <td>9.922784e-14</td>\n",
" <td>1.418422e-10</td>\n",
" <td>1.211562e-11</td>\n",
" <td>...</td>\n",
" <td>1.854967e-09</td>\n",
" <td>3.524438e-08</td>\n",
" <td>0.542535</td>\n",
" <td>336.440338</td>\n",
" <td>8.357382e-08</td>\n",
" <td>0.0</td>\n",
" <td>66.803986</td>\n",
" <td>0.0</td>\n",
" <td>215.943314</td>\n",
" <td>-88.531097</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 127 columns</p>\n",
"</div>"
],
"text/plain": [
" ACET ALD2 ALK4 Br Br2 \\\n",
"0 2.728892e-10 1.298598e-12 2.865114e-11 1.723187e-18 1.635037e-12 \n",
"1 2.728892e-10 1.298598e-12 2.865114e-11 1.723187e-18 1.635037e-12 \n",
"\n",
" BrNO2 BrNO3 BrO C2H6 C3H8 \\\n",
"0 1.068581e-15 4.674042e-14 9.922784e-14 1.418422e-10 1.211562e-11 \n",
"1 1.068581e-15 4.674042e-14 9.922784e-14 1.418422e-10 1.211562e-11 \n",
"\n",
" ... JVAL_104 JVAL_105 AIRDEN PRESS QICE \\\n",
"0 ... 1.854967e-09 3.524438e-08 0.542535 336.440338 8.357382e-08 \n",
"1 ... 1.854967e-09 3.524438e-08 0.542535 336.440338 8.357382e-08 \n",
"\n",
" QLIQ RH SUNCOS TEMP YLAT \n",
"0 0.0 66.803986 0.0 215.943314 -88.531097 \n",
"1 0.0 66.803986 0.0 215.943314 -88.531097 \n",
"\n",
"[2 rows x 127 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# all input variables\n",
"df_in = pd.concat([df_x, df_jv, df_phy], axis=1)\n",
"df_in.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(13104, 127)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# scaling has no effect for decision tree\n",
"# do this just for neural nets\n",
"X_all= StandardScaler().fit_transform(df_in)\n",
"X_all.shape"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(13104, 1)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# predict the difference\n",
"# pick up a single variable for now\n",
"Y_all = StandardScaler().fit_transform( (df_y-df_x)[['O3']]) \n",
"Y_all.shape"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, Y_train, Y_test = train_test_split(X_all, Y_all, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Decision tree"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"model_tree = DecisionTreeRegressor(max_leaf_nodes=1000)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.59 s, sys: 23.7 ms, total: 1.61 s\n",
"Wall time: 1.52 s\n"
]
},
{
"data": {
"text/plain": [
"DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
" max_leaf_nodes=1000, min_impurity_decrease=0.0,\n",
" min_impurity_split=None, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best')"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time model_tree.fit(X_train, Y_train)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x3198964a8>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# feature importance diagnostics\n",
"feat_importances = pd.Series(model_tree.feature_importances_, index=df_in.columns)\n",
"feat_importances.nlargest(20).plot(kind='barh')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"Y_pred_train = model_tree.predict(X_train)\n",
"Y_pred_test = model_tree.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.9974661528242696, 0.6737864050188009)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# crazily fitting\n",
"r2_score(Y_pred_train, Y_train), r2_score(Y_pred_test, Y_test)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x319f02a90>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAH0VJREFUeJzt3XuQnHWd7/H3t+eWmVwgIZNJIEACEjIRIZLZlCKruEmAREtAywu7IpzdNWwdRD2ltXpQj9Sx9qxuJV6PUo5ChaVU9BQXUxKImXhhFSTOQEhCJgZMCLkMkyG3STLJ3Pp7/ni6J52hZ6anu5/p7nk+r6qp7nn66X5+Tzrz7V9/f9/n9zN3R0RExr9YoRsgIiJjQwFfRCQiFPBFRCJCAV9EJCIU8EVEIkIBX0QkIhTwRUQiQgFfRCQiFPBFRCKivNANSDV9+nSfM2dOoZshIlJSWlpa3nD32pH2K6qAP2fOHJqbmwvdDBGRkmJmezLZTykdEZGIUMAXEYkIBXwRkYjIOeCb2YVm9lszazWzl8zsM4nt95rZfjPbnPhZkXtzRUQkW/kYtO0DPufuz5vZZKDFzDYkHvuWu6/KwzFERCRHOQd8d28D2hL3j5tZK3BBrq8rIiL5ldccvpnNAd4OPJfY9Ckz22JmD5jZ1HweS0RERidvAd/MJgGPAJ91907gPuBSYCHBN4DVQzxvpZk1m1lzR0dHvpojIiKD5CXgm1kFQbD/ibs/CuDu7e7e7+5x4EfA4nTPdfdGd29w94ba2hEvFBORMOxYB2vvDm5l3MpHlY4B9wOt7v7NlO2zUna7BdiW67FEJCQ7n4SKmuBWxq18VOm8C7gN2GpmmxPb7gFuNbOFgAOvAnfm4VgiEoZ5y4NgP295oVsiIcpHlc4fAEvzkL4bipSK+SuCHxnXdKWtiEhEKOCLiESEAr6ISEQo4IuIRIQCvohIRCjgi4hEhAK+iEhEKOCLiESEAr6ISEQo4IuIRIQCvohIRCjgi4hEhAK+iEhEKOCLiESEAr6ISEQo4IuIRIQCvohIRCjgi4hEhAK+iEhE5BzwzexCM/utmbWa2Utm9pnE9mlmtsHMXk7cTs29uSIikq189PD7gM+5ez3wDuAuM1sAfBHY6O6XARsTv4uISIHkHPDdvc3dn0/cPw60AhcANwEPJnZ7ELg512OJiEj28prDN7M5wNuB54A6d2+D4EMBmJHPY4mIyOjkLeCb2STgEeCz7t45iuetNLNmM2vu6OjIV3NERGSQvAR8M6sgCPY/cfdHE5vbzWxW4vFZwMF0z3X3RndvcPeG2trafDRHRETSyEeVjgH3A63u/s2Uh9YCtyfu3w78MtdjiYhI9srz8BrvAm4DtprZ5sS2e4CvA78ws38CXgM+nIdjiYhIlnIO+O7+B8CGeHhJrq8vIiL5oSttRUQKqGl7O194ZAtN29tDP5YCvohIAW1obaemoowNrQr4IiLj2rL6Orp6+1lWXxf6sfIxaCsiIllauqCOpQvCD/agHr6ISGjGMj+fCQV8EZGQjGV+PhMK+CIiIRnL/HwmlMMXEQnJWObnM6EevohIRCjgi4hEhAK+iEhEKOCLiESEAr6ISEQo4IuIRIQCvohIRCjgi4hEhAK+iAjFN+9NGBTwRSRcO9bB2ruD2yJWbPPehEEBX0TCtfNJqKgJbotYsc17EwbNpSMi4Zq3PAj285YXuiXDKrZ5b8KQl4BvZg8A7wcOuvsViW33Ap8EOhK73ePuxf2dTkTyb/6K4EcKLl8pnTXAjWm2f8vdFyZ+FOxFZGyUyLjBWMtLwHf3p4HD+XgtEZGclci4wVgLe9D2U2a2xcweMLOpIR9LRASAzdXv5Lmd+9hc/c5CN6WohBnw7wMuBRYCbcDqdDuZ2Uozazaz5o6OjnS7iIiMys86r+Cpuffws84rCt2UohJawHf3dnfvd/c48CNg8RD7Nbp7g7s31NbWhtUcEYmQKJRYZiO0skwzm+XubYlfbwG2hXUsEZFUUSixzEa+yjJ/BlwHTDezfcBXgevMbCHgwKvAnfk4loiIZCcvAd/db02z+f58vLaIiOSHplYQEYkIBXwRkYhQwBcRiQgFfBGRiFDAFxGJCAV8ESmoKKw0VSwU8EWkoKKw0lSxUMAXkYLSNAhjRyteiUhBaRqEsaMevoiESjn64qEevoiEas0zu2nv7Gb/kS715AtMAV9E8q5pezsbWtuVly8yCvgiknepvfo7rpmr4F8kFPBFJFRLYy0srXgSYsuBFYVuTqRp0FZE8u6Oa+Zy9cVTueOauVpQvIiohy8iWUvN1acOyJ5VahlbHgT7ecsL1EpJUsAXkaylXiU7ZAXO/BXBjxScUjoikjVdJVta1MMXkazpKtnSkpcevpk9YGYHzWxbyrZpZrbBzF5O3E7Nx7FERCQ7+UrprAFuHLTti8BGd78M2Jj4XURECiQvAd/dnwYOD9p8E/Bg4v6DwM35OJaIhEtz34xfYQ7a1rl7G0DidkaIxxKRHKQG+bzMT79jHay9O7iVolHwKh0zW2lmzWbW3NHRUejmiERSapDPS+WNLrYqSmEG/HYzmwWQuD2Ybid3b3T3BndvqK2tDbE5IjKU1CC/dEEd3/jQlblV38xbDr1dutiqyIRZlrkWuB34euL2lyEeS0RykPfySl1sVZTyVZb5M+BZ4HIz22dm/0QQ6JeZ2cvAssTvIiJSIHnp4bv7rUM8tCQfry8iIrkr+KCtiIiMDQV8EZGIUMAXKXG6UEoypYAvUuLycqGURIICvkgJa9reztZ9R3liaxu1kyoL3Rwpcgr4IiVsQ2s7vf3OOdUVdJzoKXRzpMhpPnyREjJ4ScFl9XXsP9IFoEVIZEQK+CIlZM0zu2nv7Gb/ka6Bq2O1AIlkSikdkVxkOyukZpOUAlDAF8lFtrNCZvC8dOWWd1wzl6svnsod18zNtsUSYQr4IrnIdlbIDJ6XrtwyLzNZSmSZuxe6DQMaGhq8ubm50M0Qyb8d64Le/LzlQ84i2bS9nTXP7AYY6MGnDtCKDMXMWty9YaT9NGgrMhZSUzhDBPwNre20d3YP3FdPXvJNKR2RsZBBCmdZfR11U6qom1KlEksJhVI6IgUwuJ5eJBeZpnTUwxcZQ8nKmzXP7M5u/pvB5Zwq75RRUMAXGQODAz2Q3ULhg8s5tVi4jIIGbUVCtGr9Dv5f8z5O9/bTcPFUIAj0d1wzN7tUzrzlZ6p90v0uMgwFfJE8Gpybf2Lr65zu7aenP86uQ118aUV9bjn7wYuDa7FwGQWldESyMUTu/LGHG1n4wld47OFGAN73tplMqChjznkTcw/2IjkKvYdvZq8Cx4F+oC+TkWSR0GRwAVRG0tTVN21v59p4M6eo4tp4UG32+Rvm8/kb5uej5SI5G6se/nvdfaGCvRRcvgY5E3X1j568kveu+h2r1u9gQ2s7v40voppu/hDL8r+6qm4kRMrhS7TkaZDzQ7+Zwua9N9LvUBY7yUPP7mH1Rxay5sj1PMH12U9ulsEVuSLZGouA78CvzcyBH7p74xgcUyS9HAc5m7a38z9+/jzHu+MD2+JxwMjP3PSqupEQjUXAf5e7HzCzGcAGM9vh7k8nHzSzlcBKgIsuumgMmiMyesmJzTbtOkRP/OzHZkyu4sMNs9M/cbRjBqq6kRCFHvDd/UDi9qCZPQYsBp5OebwRaIRgaoWw2yMyGskyyz++3EH78W56U4J9eQz+5T2XDj8oqxSNFJFQA76ZTQRi7n48cf964H+HeUyRfFi1fge/aN7H4ZPduEPcoboiRl+/U1Ue4+/m1/KDj2cwMKsUjRSRsHv4dcBjZpY81k/d/amQjykyvBHSLKvW7+D//vavZ21bFmvhpqot9Cy4gQ/e+snMj6UUjRSRUAO+u+8CrgrzGCLDShfch0izfOgHf6DltWMsibXw7+UtNMUXsTG+CIDbz2vl2gWXQscTsHZL7nX8IgWgK21lfEtXdz9obvrNG37Kc9/5B87d9xsAlsZaOEUVS2MtxIBPvfdSrr1qAbzSBCc7NFmZlCwFfBnf0i08Mn8FfOB7NMUXMf/LT9D6+5/zUkcvy2ItADQlLp6qeuv72fX19wWDsl0H4S1LYWJtdmvYihQBXXgl48/gNM6g1Muq9Tt46E97OHaqD4Cm2CKWxlp4ofqd1FHFgYnXUXv9nWfX1CcHXxevVCpH8itf031kQAFfxp90Ofod69j33KOsfvUSHjt1ZlhpSayFpbEgX3/rLf+Nbwx14ZQGXyUsY1i6q4Av48eOdbCpEQ7tgng3XPVxIKil7338Adq6YizmOR5LqSO4PtbCSar42JStmslSCmMMS3cV8GX82PkkHG+Drg4oq4ADLVx171McO93PktiVAz35ZK/+N97AR69rgNbHof7mQrdeomoMvz0q4Et2xjDvmHFbTnXC6aP09PXR29PH7pf/yrHefgA2ppRYfqOikZ5YNbdP2Q5d04PB2K6DxXVOIiFQlY5kpxBrqQ41dXCiLSf2vshDnQs53ldBr5czeJ6O8hjUz5xMw1vruW3KZq6teQ1qZpyputH6sDLOqYcv2SnElAHJgLyp8exjH93LoRM9/OTIlVwV+ytm0EuM6dbJklgLG+OLWHFF3ZmpEP7zm3D6WPBzoAU+8XiwfV+L0jsyringS3YKUbVSMyMIyOUToPZy2Pkkp7f9iorTh6mmnLl2NQvtr3RRyTRO8JJfzNJYC/f/n/919uuc7IC+brCys7cna+2V3pFxSimdqCulFZZSLn56Y/cWOpofp/L0YWIG1dbH38U2c5SJ1NJJHGOOtVP11ve/+XUm1sLkWTBxWlBXn5R6kZbSOzIOqYcfdWHXAA/XU860F50ot+w81EbbsdNU9Z9gph2lwvoGdnEHI06F93OqrJKqiglMm1DNB99+QfCBlnqMxSvTH3fwtxbNcinjjAJ+1OUrFz9U8N7UCJ1tcHTvm4P6psagjDLdY0lNX4Nnv0t/fw8THd7iQCxYRi05KusGGHTauZx/9YozqZ/6m9N/oGWSjtKFVjIOKeBHXb4C23DfFGyY56Vb8ib54VEzg5N//AET4j2Yg1kiuCeet9enc74dJkYcM5g5oS8I9ku/EvykvpZ66iIK+JKQ6yDlUN8UUtMng6V7bMc62PAVTpzqoerEPqqIYySCPYBDHNjhF7HVL+Hdk9u54PTLUDkFJs8I8vyp1FMXGaCAL4GdT8Lxg7Ah0TMebZBMF1iH+hBJ6cEPaPoavPgTOP46cZyaxMLgJHr2ENzvJcZhptBY9lG+e60HqZuL3w/dx4J91JMXGZKqdCQwbzkcfgWmXXp2ZUouVTxDVbokP1z+9H3Y/it45J/p/8Nq+jvbiMd9IH0z0LP3xI/BYabwbP2X+e69Xz5TtdN9DM69cPiZLEupGkkkJAr4Epi/ApZ9DSbXBT3vZHDMpDxxqGCabi765PbDr0BfL5w+TLz7JDH3N/1nTE3vH2AaxyvOo/1v//3MEoPJ14eR26gySxGldCRFMi2z9u4zwTGTKp6hBmzTpXmavhakYarPIx5/JejJpwzqpubqk9s9BrOvfj/MW87CdK+fycCsFhMXwdzTlUkURkNDgzc3Nxe6GTJS7n24nHzXwWASs4PbgrLI2YvOPHagBXb/nrjHIX52oB/ME+WXMYCaWvjXV0I6WZHSZ2Yt7t4w0n6h9/DN7EbgO0AZ8GN3/3rYx5QcDVXZMlJPfu3dcLw9WPu1fEIwCHugBfb+GXpPEIcgPw9Dlmome/cegzJiECuHmVfk/RRFoijUHL6ZlQHfB5YDC4BbzWxBmMeUEM1bDh1/CS6USjf4WTMDXt4A3g+9J6HmPDjZQbz3RHAl7AhfJh2IGcTKKygrq4SKaoiVnT39wWhpsFZkQNiDtouBV9x9l7v3AA8DN4V8TBlJJkFw8D7J1aQ6dkBZZdDLH7xP18Gzeu497a3E27aNGOghqMCJGVBWFfTqZ70dpsyCd9wV7JDvSiGRCAo74F8A7E35fV9i2wAzW2lmzWbW3NHREXJzBMgsCA7eZ+eT8MbOYErh17ecGQRN1u7vWBds8zhxghx8ebx/2Dw9BIHeDGKUwYSpwTHnvhtmXA53twRXzG5qhL2bgtvRGqpSSCSCwg746f7cz+rvuXujuze4e0NtbW3IzRlnsk1XDBUEU19v8D7zlgepmuppUDs/yNnXzIDX/gjHDsDDf0/nr75EPB6kbtxHHpSNmxHDoGJi8D/lnAugoiooDU1t28mOYD6ek1l0COavgA98T1fbihD+oO0+4MKU32cDB0I+ZnRkO9NlJoOyg4Nk8n6y4uY/bw7SO9PeAm0v4MCkzl0DQX6oYO8O8fJKyi9fAdVTzlT0VJ8Hx/YEFTmDq4Am1kJ/X3ArIlkLO+D/GbjMzOYC+4GPAX8f8jGjYzS15ZnMlZP6eqn7p64E9YHvJapx2qCvh/62bViixDKT9E2sZhqx8qrg6tirPnp2W1Lr/1O3Dzcfj4hkLNSA7+59ZvYpYD1BWeYD7v5SmMeUIWTybSC1558afPc8c6bM8uWn4PBu6O+lP96LxT2zQA9YxUToPRUk9Y63vbktQ32AaQI0kbwIvQ7f3dcBqonLh8G99NGkdEb7beDo3iBnPrE2SLd0bIcJ0+DQLuJ9XRBPVNUMl6cn2McgGJA9f2FQhfP6i8GKUwrsImNKUyuUksEBfjRBfDTBdFMjHHgxSLvMrIQTB6DqHE4ee52qeHcQxJPTHnB2zE+9QjbYXhbcmfue4PU69wf1+SIy5hTwS8ngAB9Wj/hkB5w+HNxv30oXlUzoP0m1n5m9ciDKe6LsKrE4SVlZZVCn33cqqKf3ONRdAbt/H1T5WAwmzwyqbjY1aqFwkTGkgF9KxirlkaiGcaC3r49y+s9abWpw0O+OVVBJP2VTZgVPqqwOAnp/bxD8Z70NjuwOgj0WpHOSwlxPV0TOooAfVemqdhJX077etpfaxAIkMZxuyignPtCbH5jR0oyyimqqy8rgkiVnFiE5fxE8853gU6GsMqjsSV1nNt3yg7muuCUiI9JsmVGVnOjs9S3BhVSLVwYplv0t7DtVzjl+nBrr5XWfSi9lzLIjHPUaqulhgvXSO2kmEz+wOn2QXns3tG0N5rz/mzvPBPiR2lNRE1zs9YHvhXfeIuNQ0cyWKXmUaS948H6D54tPXjy15xkgNlAi+cLeI9R2lxNzp9cqOWaVxHBe85mUE6fGuumaeCFTZl1IZXJ1qXTtSB7nPf+aeW9d89WLhE4rXpWSTCcCSzcPTvL35P2ug7DsaxyqnkPL4SrufK6W/+q6mNNeRYtfhjtUei9v+BS2XfBRZtdOY9o55zJzUnmwnOBwgTyb6Qw0BYJI6BTwS0mmE4Glmwcn+XvK/ab4Iha9dhcfOvkF1vcvotaO8bRfSb3tZQ919FPG8QuX8N//5dPB8oe184LKm6N7g5WrSmHaYU2PLDJAOfwIatrezr1rt7H/6OmzZrJbEmthWayF2JQ6PlLdEsyTM3lG0PNOpoWO7oXay4NFTt6ytPhz7hobkAhQDj/qhsj3N21v59MPv0BXT/+bnnJdzR4+ds4+qG+A2TefnVPf1BiUWpZVBCmd+puDtFCx59w1NiAyQAF/vBhm2oVVey7hoWf3cLKnj/74oPmpgXOqy7ntHRdz287N0NcDf/4hzG58c4/YgInTh+8pjzSwPNbll5quQWSAcvilanBuevBA7bzl7Dt4iNWvXsJ9v/srx0730Rc/MxVCeQxmnzuBH3+igRe/egOfv2F+0Gs/fSRI5QweGF68Ei5cPPJygyMNLGsFKpGCUQ+/VA0zr07T9nZ+/dgWrj7Vwfb4cfpTuvQGLL+ijh98PE26L1kv3/p4ULaZKtOe8kgpFKVYRApGAb8U7VgH+5+HrkNw1T8E21IC8upvP80nTj1LF1UsjbWwMb4IA6ZMKGf1RxaydEHd0K/ddTAYjO06OPSxh0vJjPTBoBSLSMEo4JeinU9CvA+qzhkIzKvW7+CBP77K6Z5+HGiKLWJprIWm+CJqKsu4+qJzueOauWeC/VBTKxxNLEE8VOom21W2RKTgFPBLSTJI18wI6uG7Dg2kXh76056zKm9+E1/E73wR8+om893rL39zrz5d4N75ZFBy2duV2apYIlJSFPBLScpVsvtq6tnd59iL2/nK5t9xqrtvYLeqcuNv5kw7u0efaqiefCbBXCkZkZKlgF9KEgH50ZNX8sRf2gZSNr1T+qmpKmdqeRnTJlbyuXQ9+lRD9eRHE8w1u6VIyVHALyFN8UVs6J3NU39p41j8fDbGFwEw6VQfd1xzcVBamYl8pGWGy+Xrw0CkKIUW8M3sXuCTQEdi0z2J9W0lC6vW7+CHT+8Cdyxl1fDyGHz7oyNU3gyWj7TMcB8aGtgVKUph9/C/5e6rQj5GJDyx9XXicSfuMGVCGYvnngMwdJ4+bMN9aGhgV6QoKaVTpFat38ETW1/nfW+byedvmM/73jaTh/60Bxxue+co0jeFkPphUOj0TqGPL1JEQpstM5HSuQPoBJqBz7n7keGeo9kyg8nN1jyzmz/tOkRleRmTqsrZ9KWlhW5W9go9W2Whjy8yBjKdLTOnuXTMrMnMtqX5uQm4D7gUWAi0AauHeI2VZtZsZs0dHR3pdomMVet38Nmfb+al/Z0siT3PV/0+VlS+UOhm5SbTOfzH6/FFisiYzIdvZnOAX7n7FcPtF8UefrJHD7Cz/QSVZcYbJ3r4Vs39WMVErphRwezbf1zgVopIMSv4fPhmNsvd2xK/3gJsC+tYpSiZo59QHqMvHnzoTp9UyaneOP/8t3NZfvE/auBTRPIqzEHb/zCzhQQz8r4K3BnisUpK0Kvfw7nV5XR193FZ3SRgcMXNfA0yikhehRbw3f22sF67VDVtb2dDazv7j3RxyfQadr3RNboLpkREcqCyzHzIsPRvQ2s7NRVlANSffw6fXjLvzTX0KiMUkZAo4OfDCFeWJnv2tZMq6TjRM/zFUrpKVURCoiUO82GE0r9kz77jRA/f+NCVw18ZqzJCEQmJevj5MMLcNMvq69jQ2s6y+gymQND0wyISEgX8PEimbJbV16XtvS9dkH67iMhYUsDPgzXP7Ka9s5v9R7oU2EWkaCngZyn1CtlDJ3oK3BoRkZEp4GdpQ2s77Z3dANRNqeKCqTWZ5ehFRApEAT9Ly+rr2H+kCyjgnPQiIqOggJ8lDcSKSKlRHb6ISEQo4IuIRIQCvohIRCjgi4hEhAK+iEhERDbgN21v5wuPbKFpe3uhmyIiMiYiG/CTM1huaFXAF5FoiGzAX1ZfR1dvv66OFZHIiOyFV7pwSkSiJrI9fBGRqMkp4JvZh83sJTOLm1nDoMf+p5m9YmZ/MbMbcmumiIjkKteUzjbgg8APUzea2QLgY8BbgfOBJjOb5+79OR5PRESylFMP391b3f0vaR66CXjY3bvdfTfwCrA4l2MNRyWWIiIjCyuHfwGwN+X3fYltb2JmK82s2cyaOzo6sjqYSixFREY2YsA3syYz25bm56bhnpZmm6fb0d0b3b3B3Rtqa2szbfdZVGIpIjKyEXP47r40i9fdB1yY8vts4EAWr5MRlViKiIwsrJTOWuBjZlZlZnOBy4BNIR1LREQykGtZ5i1mtg94J/CEma0HcPeXgF8A24GngLtUoSMiUlg5lWW6+2PAY0M89m/Av+Xy+iIikj+60lZEJCIU8EVEIkIBX0QkIhTwRUQiwtzTXg9VEGbWAezJ8unTgTfy2JxCG2/nA+PvnHQ+xW+8ndNQ53Oxu4945WpRBfxcmFmzuzeMvGdpGG/nA+PvnHQ+xW+8nVOu56OUjohIRCjgi4hExHgK+I2FbkCejbfzgfF3Tjqf4jfezimn8xk3OXwRERneeOrhi4jIMEo64I/3NXXN7F4z229mmxM/KwrdpmyY2Y2J9+EVM/tioduTKzN71cy2Jt6T5kK3Jxtm9oCZHTSzbSnbppnZBjN7OXE7tZBtHI0hzqdk/37M7EIz+62ZtSZi3GcS23N6j0o64HNmTd2nUzcOWlP3RuAHZlY29s3Li2+5+8LEz7pCN2a0Ev/u3weWAwuAWxPvT6l7b+I9KdWSvzUEfxupvghsdPfLgI2J30vFGt58PlC6fz99wOfcvR54B3BX4u8mp/eopAN+saypK8NaDLzi7rvcvQd4mOD9kQJy96eBw4M23wQ8mLj/IHDzmDYqB0OcT8ly9zZ3fz5x/zjQSrBMbE7vUUkH/GFkvKZuCfiUmW1JfGUtma/YKcbTe5HkwK/NrMXMVha6MXlU5+5tEAQcYEaB25MPpf73g5nNAd4OPEeO71HRB/yw19QttBHO7z7gUmAh0AasLmhjs1My78UovMvdryZIU91lZu8udIMkrZL/+zGzScAjwGfdvTPX18tpAZSxUApr6uYi0/Mzsx8Bvwq5OWEomfciU+5+IHF70MweI0hbPT38s0pCu5nNcvc2M5sFHCx0g3Lh7u3J+6X492NmFQTB/ifu/mhic07vUdH38LM0LtbUTbyhSbcQDFKXmj8Dl5nZXDOrJBhMX1vgNmXNzCaa2eTkfeB6SvN9SWctcHvi/u3ALwvYlpyV8t+PmRlwP9Dq7t9MeSin96ikL7wys1uA7wG1wFFgs7vfkHjsS8A/Eox2f9bdnyxYQ7NkZg8RfB114FXgzmT+rpQkyuG+DZQBDySWvyxJZnYJZ5b1LAd+WornY2Y/A64jmH2xHfgq8DjBWtQXAa8BH3b3khgIHeJ8rqNE/37M7Frgv4CtQDyx+R6CPH7W71FJB3wREcnceE3piIjIIAr4IiIRoYAvIhIRCvgiIhGhgC8iEhEK+CIiEaGALyISEQr4IiIR8f8BAC/OwdZMeM4AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(Y_pred_train, Y_train, alpha=0.5, s=4)\n",
"plt.scatter(Y_pred_test, Y_test, alpha=0.5, s=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Neural net"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"model_nn = MLPRegressor(hidden_layer_sizes=(50, ), activation='tanh', solver='adam')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.45 s, sys: 78.2 ms, total: 2.53 s\n",
"Wall time: 1.29 s\n"
]
},
{
"data": {
"text/plain": [
"MLPRegressor(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
" beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
" hidden_layer_sizes=(50,), learning_rate='constant',\n",
" learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
" nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
" shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
" verbose=False, warm_start=False)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time model_nn.fit(X_train, Y_train.ravel())"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"Y_nn_pred_train = model_nn.predict(X_train)\n",
"Y_nn_pred_test = model_nn.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.9772652522893356, 0.9258379059471054)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# much better than tree\n",
"r2_score(Y_nn_pred_train, Y_train), r2_score(Y_nn_pred_test, Y_test)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x319e57e80>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(Y_nn_pred_train, Y_train, alpha=0.5, s=4)\n",
"plt.scatter(Y_nn_pred_test, Y_test, alpha=0.5, s=4)"
]
},
{
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment