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Diss/05_Database/Python/sklearn-rerun.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup\n",
"\n",
"`TRAINING_RANGE` und `TEST_RANGE` müssen je nach Länge des Datensatzes angepasst werden.\n",
"\n",
"**Keine Anpassung erforderlich** (Siehe Datensatz herunterladen)\n",
"- 30% Training-Daten (0-30%)\n",
"- 70% Test-Daten (30%-100%)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"311"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"INPUT_FILE = 'data.csv'\n",
"TARGET_COLUMN = 'flt_obd_speed'\n",
"# Still contains positional information and acceleration; however we currently train\n",
"# sample by sample without knowledge of previous or other data, so it should not be\n",
"# possible for the Regressor to simply \"calculate\" the speed.\n",
"EXCLUDED_COLUMNS = ('flt_gps_speed', 'flt_obd_engine_load', 'flt_obd_engine_rpm',\n",
" 'flt_obd_maf', 'flt_obd_accelerator_pedal','flt_time','flt_time_system_clock',\n",
" 'flt_time_utc','flt_ax','flt_ay','flt_az','flt_gx','flt_gy','flt_gz','flt_compass',\n",
" 'flt_number_of_satelites','flt_accuracy','flt_gps_bearing','flt_calc_dist_gps',\n",
" 'flt_calc_dist_vt','flt_calc_ax_vt','flt_timeIP',\n",
" 'weat_latitude','weat_longitude','weat_distanceIP','weat_timeIP','weat_join_idx',\n",
" 'hAccel_1','hAccel_2','hAccel_3','flt_mAccel_1','flt_mAccel_2','flt_mAccel_3',\n",
" 'flt_mGier_1','flt_mGier_2','flt_mGier_3','rot_Accel_1','rot_Accel_2','rot_Accel_3',\n",
" 'rot_Gier_1','rot_Gier_2','rot_Gier_3','rot_Accel_flt_1','rot_Accel_flt_2','rot_Accel_flt_3',\n",
" 'rot_Gier_flt_1','rot_Gier_flt_2','rot_Gier_flt_3'\n",
" )\n",
"# See explanation below the feature importance plot\n",
"OVERFITTING_COLUMNS = ('weat_temperature', 'weat_humidity', 'join_idx', 'weat_windBearing', 'weat_windSpeed',\n",
" 'latitude', 'longitude', 'flt_latitude', 'flt_longitude',\n",
" 'ors_percentage_cumsum', 'flt_obd_air_temperature',\n",
" 'mb_step_weight')\n",
"# Since there are a lot of fields containing those\n",
"# Note: This breaks the map plotting\n",
"OVERFITTING_SUBWORDS = ('distance', 'remainDistance', 'remainDistanze', 'cumsumDistance', 'segDistance', 'time', 'remainTime')\n",
"\n",
"from runsql import runsql\n",
"DATA_COLUMNS = [c['Field']\n",
" for c in runsql('show columns from computeddata')\n",
" if c['Type'] == 'double'\n",
" and c['Field'] != TARGET_COLUMN\n",
" and c['Field'] not in EXCLUDED_COLUMNS\n",
" and c['Field'] not in OVERFITTING_COLUMNS\n",
" and not any([w in c['Field'] for w in OVERFITTING_SUBWORDS])]\n",
"len(DATA_COLUMNS)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Datensatz herunterladen\n",
"\n",
"`SETUP_ID` anpassen, Rest läuft automatisch.\n",
"\n",
"Einige Datensätze besitzen keinen Eintrag für `flt_obd_speed`. Für eine Liste der Datensätze *mit* diesem Wert kann beispielsweise folgende SQL-Abfrage verwendet werden (Achtung, braucht (prinzipbedingt) sehr lange!):\n",
"\n",
"```sql\n",
"select setup_id s, count(*) n\n",
"from computeddata\n",
"group by s\n",
"having not exists (\n",
" select *\n",
" from computeddata\n",
" where setup_id = s\n",
" and flt_obd_speed is null)\n",
"order by n asc\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"SETUP_ID = 868\n",
"import csv\n",
"from runsql import runsql\n",
"reader = runsql('select * from computeddata where setup_id = {} order by distance asc'.format(SETUP_ID))\n",
"reader_data = list(reader) # list(...) so that following cells can be repeated"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"data = []\n",
"target = []\n",
"for row in reader_data:\n",
" data += [[float(row[c]) if row[c] != '' else math.nan for c in DATA_COLUMNS]]\n",
" target += [float(row[TARGET_COLUMN])] # Errors if NaN in TARGET_COLUMN"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"tr_st = 0\n",
"tr_ed = math.floor(len(data)*0.3)\n",
"TRAINING_RANGE = (tr_st, tr_ed)\n",
"TEST_RANGE = (tr_ed, len(data)) # TEST_RANGE = (len(data)-tr_ed, len(data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Leere Zellen füllen\n",
"\n",
"Da nicht alle Datensätze alle Spalten haben – gäbe sicherlich bessere Strategien, aber das funktioniert erstaunlich gut (wahrscheinlich sind die \"wichtigen\" Spalten immer vorhanden)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(7228, 311)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.impute import SimpleImputer\n",
"imp = SimpleImputer(strategy='constant', fill_value=0) # Other strategies remove fully null columns\n",
"data = imp.fit_transform(data)\n",
"import numpy as np\n",
"np.shape(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyze INPUT DATA\n",
"Eingangsdaten analysieren"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": false
},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'astropy'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-fda9f06cb23c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Convert to Table\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mastropy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtable\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDATA_COLUMNS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mlat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'latitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'astropy'"
]
}
],
"source": [
"# Convert to Table\n",
"import sys\n",
"from astropy.table import Table\n",
"t = Table(data, names=DATA_COLUMNS)\n",
"lat = t['latitude']\n",
"lng = t['longitude']\n",
"# Subsampling ... use points every 50m for plotting\n",
"lat = lat[::10]\n",
"lng = lng[::10]\n",
"# determine range to print based on min, max lat and lon of the data\n",
"margin = 0 # buffer to add to the range\n",
"lat_min = min(lat) - margin\n",
"lat_max = max(lat) + margin\n",
"lon_min = min(lng) - margin\n",
"lon_max = max(lng) + margin\n",
"t"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'lon_min' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-d80dd05874af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Converts given lat/lon in WGS84 Datum to XY in Spherical Mercator EPSG:900913\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0moriginShift\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m6378137\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2.0\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0;31m# 20037508.342789244\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mxExtent_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlon_min\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0moriginShift\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m180\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0myExtent_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m90\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlat_min\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m360\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m180\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0myExtent_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myExtent_min\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0moriginShift\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m180\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'lon_min' is not defined"
]
}
],
"source": [
"# Calculation ZOOM LEVEL\n",
"width = 640\n",
"height = 640\n",
"tileSize= 256*4\n",
"\n",
"# Converts given lat/lon in WGS84 Datum to XY in Spherical Mercator EPSG:900913\"\n",
"originShift = 2 * math.pi * 6378137/2.0; # 20037508.342789244\n",
"xExtent_min = lon_min * originShift / 180;\n",
"yExtent_min = math.log(math.tan((90 + lat_min) * math.pi / 360 )) / (math.pi / 180);\n",
"yExtent_min = yExtent_min * originShift / 180;\n",
"xExtent_max = lon_max * originShift / 180;\n",
"yExtent_max = math.log(math.tan((90 + lat_max) * math.pi / 360 )) / (math.pi / 180);\n",
"yExtent_max = yExtent_max * originShift / 180;\n",
"\n",
"minResX = (xExtent_max-xExtent_min)/width;\n",
"minResY = (yExtent_max-yExtent_min)/height;\n",
"minRes = max([minResX, minResY]);\n",
"initialResolution = 2 * math.pi * 6378137 / tileSize; # 156543.03392804062 for tileSize 256 pixels\n",
"zoomlevel = math.floor(math.log2(initialResolution/minRes));\n",
"\n",
"# Enforce valid zoom levels\n",
"if zoomlevel < 0:\n",
" zoomlevel = 0\n",
"if zoomlevel > 19: \n",
" zoomlevel = 19"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'plotly'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-546f749e9267>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Analyze Data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_objs\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotly\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
]
}
],
"source": [
"# Analyze Data\n",
"import plotly\n",
"import plotly.graph_objs as go\n",
"import plotly.plotly as py\n",
"\n",
"plotly.tools.set_credentials_file(username='ziegmann', api_key='yGii8dk78Sjz7jzzad1n')\n",
"mapbox_access_token = 'pk.eyJ1Ijoiam9oYW5ubmVzLXppZWdtYW5uIiwiYSI6ImNqbDJmamo5bDFxNjQzcWxtd2IzejNhcXoifQ.iVXGH-jpe2FH3f52MM9yHQ'\n",
"\n",
"data_p = [\n",
" go.Scattermapbox(\n",
" lat=lat,\n",
" lon=lng,\n",
" mode='markers',\n",
" marker=dict(size=6))\n",
"]\n",
"\n",
"layout = go.Layout(\n",
" title='OBD-II GPS Logging',\n",
" autosize=True,\n",
" hovermode='closest',\n",
" mapbox=dict(\n",
" accesstoken=mapbox_access_token,\n",
" bearing=0,\n",
" center=dict(\n",
" lon=(lon_max-lon_min)/2+lon_min,\n",
" lat=(lat_max-lat_min)/2+lat_min,\n",
" ),\n",
" style='dark',\n",
" pitch=0,\n",
" zoom=zoomlevel\n",
" ),\n",
")\n",
"\n",
"fig = dict(data=data_p, layout=layout)\n",
"#plotly.offline.plot(fig, filename='Mapbox.html')\n",
"py.iplot(fig, filename='Mapbox.html')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 't' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-6fad4a6b6b96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtemp_d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mxaxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp_d\u001b[0m \u001b[0;31m# range(int(temp_d[0]), int(temp_d[-1]))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxvline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtemp_d\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTRAINING_RANGE\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 't' is not defined"
]
}
],
"source": [
"import matplotlib.pyplot as plot\n",
"temp_d=t['distance']\n",
"xaxis = temp_d # range(int(temp_d[0]), int(temp_d[-1]))\n",
"plot.figure(figsize=(15,10))\n",
"plot.axvline(x=temp_d[TRAINING_RANGE[0]])\n",
"plot.axvline(x=temp_d[TEST_RANGE[0]])\n",
"plot.plot(temp_d[TEST_RANGE[0]:TEST_RANGE[1]], target[TEST_RANGE[0]:TEST_RANGE[1]], 'b',\n",
" xaxis, t['hr_traficSpeed']*3.6, 'r',\n",
" xaxis, t['hr_SpeedLimit'],\n",
" )\n",
"plot.legend(['Training','Test','OBD Speed','HERE Traffic Speed', 'HERE Speed Limint'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hyperparametersuche\n",
"\n",
"Utility-Methode; wurde zum Testen verschiedener Ansätze verwendet (könnte man jetzt vermutlich inlinen)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def gridsearch(base, params, n_jobs = None, scoring = 'neg_mean_squared_error', cv = 5):\n",
" if n_jobs == None:\n",
" import os\n",
" n_jobs = os.cpu_count()\n",
" from sklearn.model_selection import GridSearchCV\n",
" return GridSearchCV(base, params, n_jobs = n_jobs, scoring = scoring, cv = cv)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training\n",
"\n",
"Bei großen Datensätzen kann es zur Fehlerausgabe \"UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\" kommen. Scheint vereinzelt am Ergebnis aber nicht viel zu ändern.\n",
"\n",
"Es werden alle gegebenen Parameterkombinationen mittels Cross-Validation getestet; die besten für die Vorhersage verwendet und dann auch ausgegeben."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'max_depth': 25, 'max_features': 'auto', 'n_estimators': 30}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import ExtraTreesRegressor\n",
"clf = gridsearch(ExtraTreesRegressor(),\n",
" [{'n_estimators': range(10, 151, 10), # range(50, 151, 25) woher kommt dieser Range ??? bzw. wie hast du diesen bestimmt\n",
" #'criterion': 'mse', # or mae\n",
" 'max_depth': [None] + list(range(5, 30, 5)), # [None] + list(range(5, 30, 5)) woher kommt die Tiefe???\n",
" 'max_features': ['auto', 'sqrt', 'log2']}])\n",
"clf.fit(data[TRAINING_RANGE[0]:TRAINING_RANGE[1]], target[TRAINING_RANGE[0]:TRAINING_RANGE[1]])\n",
"\n",
"clf.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'mean_fit_time': array([0.354249 , 0.79441934, 1.25249095, 1.49625349, 1.93027773,\n",
" 2.24387007, 2.4904139 , 2.895293 , 3.20287757, 3.50983381,\n",
" 3.97271113, 4.11586518, 4.48519735, 4.72912731, 5.14817266,\n",
" 0.04423618, 0.08621392, 0.13328133, 0.17075572, 0.21398087,\n",
" 0.2588737 , 0.30962873, 0.36070104, 0.40511727, 0.45955114,\n",
" 0.5011034 , 0.60203395, 0.67245507, 0.6569664 , 0.70136666,\n",
" 0.04226255, 0.07670875, 0.11308975, 0.14939733, 0.18552012,\n",
" 0.23937974, 0.26285305, 0.32900119, 0.3522655 , 0.39659209,\n",
" 0.40396571, 0.44964314, 0.4723722 , 0.53757739, 0.65494919,\n",
" 0.17849088, 0.34791207, 0.54411359, 0.71416249, 0.88251758,\n",
" 1.11789994, 1.29423423, 1.52374082, 1.66922779, 1.77447481,\n",
" 2.07780304, 2.24101272, 2.40471034, 2.66792517, 2.77014585,\n",
" 0.02898493, 0.05413675, 0.07995615, 0.10661769, 0.13007274,\n",
" 0.15481515, 0.18086815, 0.20769763, 0.2343183 , 0.26527953,\n",
" 0.2935153 , 0.33177338, 0.36235185, 0.37096252, 0.40656013,\n",
" 0.02325392, 0.04331279, 0.06272826, 0.08270316, 0.10971384,\n",
" 0.17232876, 0.17200007, 0.18206158, 0.17309127, 0.19521604,\n",
" 0.20993433, 0.23464007, 0.24960027, 0.26664772, 0.28945398,\n",
" 0.2912416 , 0.5676209 , 0.8318996 , 1.07622962, 1.40815511,\n",
" 1.6886867 , 1.87827697, 2.18615904, 2.50469851, 2.75788021,\n",
" 3.07619658, 3.28124547, 3.60089507, 3.88388324, 4.19362683,\n",
" 0.03184733, 0.06731343, 0.10487499, 0.12941942, 0.15784516,\n",
" 0.19582472, 0.23173294, 0.26465154, 0.30009389, 0.33053908,\n",
" 0.38766012, 0.41408119, 0.48962455, 0.49016871, 0.59197683,\n",
" 0.02888336, 0.05819721, 0.10936904, 0.10591393, 0.14810505,\n",
" 0.15613675, 0.18962402, 0.21691275, 0.23368306, 0.28471084,\n",
" 0.29033227, 0.3277741 , 0.41399932, 0.48123517, 0.51789856,\n",
" 0.44039564, 0.82520576, 1.14204006, 1.58146319, 1.94402895,\n",
" 2.19927888, 2.81907158, 3.55728283, 3.64064865, 3.62569618,\n",
" 4.31143336, 4.86302681, 5.76675272, 6.05807281, 5.91549006,\n",
" 0.05024166, 0.09979162, 0.12677641, 0.18424149, 0.24640083,\n",
" 0.27064304, 0.31262088, 0.38195 , 0.49088311, 0.48541241,\n",
" 0.53476691, 0.65155554, 0.68942699, 0.88495617, 1.04833636,\n",
" 0.0526403 , 0.09765396, 0.16419468, 0.20194225, 0.30080633,\n",
" 0.31799712, 0.31764269, 0.38253827, 0.43329287, 0.45139332,\n",
" 0.53986011, 0.53655562, 0.63648005, 0.5325912 , 0.58162036,\n",
" 0.46992674, 0.93497663, 1.2893857 , 1.56028132, 2.05937686,\n",
" 2.33360987, 2.85232253, 3.23770618, 3.5966466 , 3.88657041,\n",
" 4.37009206, 4.54625793, 4.77426357, 5.17501936, 5.79213696,\n",
" 0.04879074, 0.09079742, 0.15635724, 0.19251299, 0.22858171,\n",
" 0.28544545, 0.3199976 , 0.48000746, 0.440062 , 0.49408102,\n",
" 0.57380242, 0.60556431, 0.67271924, 0.74746256, 0.80862145,\n",
" 0.04797869, 0.09522023, 0.12276573, 0.16594014, 0.19319735,\n",
" 0.2456708 , 0.34138098, 0.35470405, 0.46128249, 0.42201519,\n",
" 0.46068392, 0.52622786, 0.55168724, 0.58463302, 0.57970524,\n",
" 0.44984665, 0.76379418, 1.26765337, 1.58913031, 1.75598593,\n",
" 2.28685694, 2.68016443, 2.9822752 , 3.46551061, 3.85301399,\n",
" 4.12816525, 4.46324148, 4.79202251, 5.28187246, 5.94874501,\n",
" 0.04591041, 0.10198936, 0.18029256, 0.19092259, 0.25483379,\n",
" 0.33952613, 0.35525656, 0.38768106, 0.4736516 , 0.5704102 ,\n",
" 0.62678609, 0.76815619, 0.70917974, 0.7630445 , 0.86493125,\n",
" 0.04619966, 0.10625024, 0.14151397, 0.19020128, 0.22798786,\n",
" 0.24330769, 0.31431155, 0.32160859, 0.38110061, 0.38920293,\n",
" 0.52858944, 0.56043167, 0.56416335, 0.57743664, 0.49332504]),\n",
" 'std_fit_time': array([2.49922948e-02, 7.74217030e-02, 1.10159001e-01, 9.47097317e-02,\n",
" 1.37255682e-01, 1.57770914e-01, 1.28389108e-01, 2.54955040e-01,\n",
" 5.29675323e-02, 2.66963366e-01, 3.37396196e-01, 2.62813472e-01,\n",
" 2.10664490e-01, 3.15564013e-01, 2.64235968e-01, 1.26316443e-03,\n",
" 2.26104934e-03, 5.55743226e-03, 3.90979381e-03, 6.09757228e-03,\n",
" 6.69685018e-03, 7.30816646e-03, 1.62094114e-02, 1.71148991e-02,\n",
" 2.23738147e-02, 1.82161353e-02, 7.36775955e-02, 3.73586890e-02,\n",
" 2.57687883e-02, 1.92760898e-02, 2.41198963e-03, 2.31979728e-03,\n",
" 3.03107307e-03, 5.61386261e-03, 1.62509662e-03, 3.60233101e-02,\n",
" 1.59116767e-02, 5.28095454e-02, 2.91726319e-02, 3.23197798e-02,\n",
" 1.70994728e-02, 2.49818397e-02, 9.49004735e-03, 2.69828366e-02,\n",
" 4.59440595e-02, 2.18513675e-02, 9.30771858e-03, 2.88863305e-02,\n",
" 3.50371979e-02, 2.29231874e-02, 3.94804209e-02, 9.57047780e-02,\n",
" 5.81704143e-02, 9.34573783e-02, 3.55130178e-02, 1.11080014e-01,\n",
" 8.84437843e-02, 7.39304513e-02, 1.50767994e-01, 1.59471999e-01,\n",
" 1.02557050e-03, 1.20651619e-03, 2.39544813e-03, 4.07513097e-03,\n",
" 2.27185695e-03, 2.16080789e-03, 3.11249499e-03, 6.72939061e-03,\n",
" 6.85563098e-03, 1.16835575e-02, 8.19969595e-03, 1.79934272e-02,\n",
" 4.98359419e-02, 8.69551984e-03, 1.88216876e-02, 2.03666848e-03,\n",
" 6.02046276e-04, 2.13604239e-04, 1.55867025e-03, 1.58254940e-02,\n",
" 2.50615883e-02, 3.03223683e-02, 2.77638883e-02, 2.73403043e-03,\n",
" 8.53699358e-03, 2.50661433e-02, 7.76877810e-03, 1.47465983e-02,\n",
" 7.81854799e-03, 1.28731444e-02, 5.55824113e-03, 3.00280873e-02,\n",
" 4.98386227e-02, 3.69187927e-02, 6.67567905e-02, 7.01783875e-02,\n",
" 2.81158663e-02, 1.10618008e-01, 8.41269159e-02, 1.45170477e-01,\n",
" 1.75592032e-01, 6.98388959e-02, 1.48146585e-01, 1.70924299e-01,\n",
" 2.54475280e-01, 6.31027991e-04, 5.02408987e-03, 1.60493631e-02,\n",
" 7.53570141e-03, 3.20558521e-03, 2.86547342e-03, 3.36387326e-03,\n",
" 4.36895760e-03, 4.63870237e-03, 7.42014215e-03, 1.60967044e-02,\n",
" 1.63048419e-02, 5.48596747e-02, 1.29173584e-02, 5.64937820e-02,\n",
" 7.43682493e-04, 6.07648627e-03, 2.70057079e-02, 3.90976835e-03,\n",
" 2.11842957e-02, 2.20039382e-03, 7.86848818e-03, 1.57586954e-02,\n",
" 7.43004488e-03, 3.04632188e-02, 1.09522803e-02, 3.21867491e-02,\n",
" 6.49132254e-02, 7.82554738e-02, 1.20992846e-01, 7.21731918e-02,\n",
" 1.80411469e-01, 8.36967206e-02, 1.23996020e-01, 1.40105860e-01,\n",
" 1.30504886e-01, 3.48487954e-01, 3.41077150e-01, 2.19523802e-01,\n",
" 3.52709198e-01, 3.56258476e-01, 2.98041693e-01, 5.51500380e-01,\n",
" 2.52120145e-01, 4.57708837e-01, 1.14586397e-03, 1.95165065e-02,\n",
" 7.35535983e-03, 1.05967837e-02, 5.26978682e-02, 2.33381507e-02,\n",
" 1.92915793e-02, 4.70084000e-02, 1.10020777e-01, 6.85507414e-02,\n",
" 3.63050905e-02, 1.25018921e-01, 1.13166629e-01, 1.34612719e-01,\n",
" 9.05039446e-02, 1.31046971e-02, 1.41024696e-02, 5.04103738e-02,\n",
" 5.24615066e-02, 4.56449264e-02, 5.22391866e-02, 2.28758036e-02,\n",
" 8.80867010e-02, 6.86626382e-02, 1.05591058e-01, 7.78094088e-02,\n",
" 1.22055635e-01, 1.01061731e-01, 4.23400114e-02, 5.02860993e-02,\n",
" 1.06977142e-01, 2.14448433e-01, 7.56308972e-02, 7.00328063e-02,\n",
" 1.41128656e-01, 2.52814003e-01, 2.90749944e-01, 3.16516656e-01,\n",
" 3.89330076e-01, 1.47226555e-01, 3.12725054e-01, 4.68040128e-01,\n",
" 3.24320790e-01, 3.26399357e-01, 1.85385433e-01, 3.47072502e-03,\n",
" 3.15348209e-03, 2.82471831e-02, 1.87104847e-02, 1.08181212e-02,\n",
" 1.57104243e-02, 2.19113889e-02, 4.49369663e-02, 3.00569197e-02,\n",
" 4.29666569e-02, 3.73194329e-02, 4.20737484e-02, 8.45556977e-02,\n",
" 5.37957666e-02, 1.24860109e-01, 3.87393437e-03, 2.43807844e-02,\n",
" 1.06078626e-02, 1.03668806e-02, 1.89383508e-02, 3.53795442e-02,\n",
" 8.79604104e-02, 8.32901129e-02, 6.15795255e-02, 4.92986001e-02,\n",
" 1.00305168e-01, 8.68614048e-02, 7.66403265e-02, 6.77128708e-02,\n",
" 6.18780312e-02, 1.08732097e-01, 8.46056714e-02, 1.71172772e-01,\n",
" 2.29152026e-01, 1.09417552e-01, 1.10380641e-01, 1.21303534e-01,\n",
" 2.89440530e-01, 3.51370530e-01, 2.07028519e-01, 2.08629082e-01,\n",
" 2.38906278e-01, 2.55932559e-01, 3.10232559e-01, 2.75303730e-01,\n",
" 1.11835508e-03, 2.06341983e-02, 5.32283448e-02, 1.48952242e-02,\n",
" 3.58759749e-02, 7.20716498e-02, 2.59290993e-02, 2.72188902e-02,\n",
" 7.05616566e-02, 1.11064430e-01, 5.50472324e-02, 6.19868732e-02,\n",
" 9.98184235e-02, 5.12603856e-02, 5.66748511e-02, 6.89798121e-03,\n",
" 3.53205983e-02, 2.67140309e-02, 4.54267763e-02, 3.88866452e-02,\n",
" 1.61985561e-02, 5.90894848e-02, 2.99348780e-02, 6.55537264e-02,\n",
" 2.51525132e-02, 1.14164430e-01, 8.27182965e-02, 1.11078387e-01,\n",
" 6.18167554e-02, 4.14701187e-02]),\n",
" 'mean_score_time': array([0.00190105, 0.00325398, 0.00427103, 0.00443282, 0.00572996,\n",
" 0.00568509, 0.00726991, 0.00973587, 0.00889282, 0.00914836,\n",
" 0.01193113, 0.010707 , 0.01107011, 0.01253595, 0.01384563,\n",
" 0.00169873, 0.00272341, 0.00349941, 0.00443468, 0.00522628,\n",
" 0.00616984, 0.00809078, 0.00892258, 0.00944285, 0.01006079,\n",
" 0.01104374, 0.01604872, 0.01342454, 0.01482773, 0.01531544,\n",
" 0.00267053, 0.00327053, 0.00423017, 0.00521798, 0.00625696,\n",
" 0.00756803, 0.01168871, 0.01629248, 0.01027703, 0.01157398,\n",
" 0.01262646, 0.01456351, 0.01448832, 0.02741027, 0.01828885,\n",
" 0.00202827, 0.00274434, 0.00363326, 0.00461264, 0.0053164 ,\n",
" 0.00594611, 0.0070951 , 0.0072309 , 0.00910692, 0.00944915,\n",
" 0.00995741, 0.01148453, 0.01224504, 0.01219096, 0.01325336,\n",
" 0.00202851, 0.00311599, 0.00353689, 0.00453248, 0.0054606 ,\n",
" 0.00660758, 0.00745807, 0.00812931, 0.00908842, 0.01014876,\n",
" 0.01169047, 0.01557865, 0.01228347, 0.01421127, 0.0164175 ,\n",
" 0.00228081, 0.00327544, 0.00412469, 0.00494499, 0.00881624,\n",
" 0.01331534, 0.00729389, 0.00835152, 0.00910068, 0.01258216,\n",
" 0.01007733, 0.01119666, 0.01325426, 0.01278877, 0.01396813,\n",
" 0.00171504, 0.00309868, 0.00327082, 0.00393744, 0.00546112,\n",
" 0.00600533, 0.00663023, 0.00756655, 0.00847631, 0.00880799,\n",
" 0.00978613, 0.0103045 , 0.01164327, 0.01927934, 0.01439037,\n",
" 0.00184617, 0.00292101, 0.00364289, 0.00445719, 0.00544825,\n",
" 0.00614548, 0.00743389, 0.00795069, 0.00915632, 0.01038027,\n",
" 0.01313705, 0.01712308, 0.01439776, 0.01410079, 0.02263441,\n",
" 0.00219383, 0.00333934, 0.00765014, 0.0073143 , 0.00703335,\n",
" 0.007129 , 0.00853424, 0.01064754, 0.0103127 , 0.01273522,\n",
" 0.012254 , 0.01984081, 0.02403951, 0.02214255, 0.01885109,\n",
" 0.0025383 , 0.00277963, 0.00410342, 0.00485315, 0.0086216 ,\n",
" 0.01028032, 0.00923896, 0.01025167, 0.00899057, 0.00971112,\n",
" 0.01611943, 0.01508698, 0.01441755, 0.01555414, 0.01432576,\n",
" 0.00183296, 0.00338836, 0.00428658, 0.00459757, 0.008007 ,\n",
" 0.00625381, 0.00823569, 0.00896454, 0.0096046 , 0.01179695,\n",
" 0.01313 , 0.01598849, 0.01822391, 0.02101655, 0.03716373,\n",
" 0.00450559, 0.00581183, 0.00868039, 0.01267571, 0.01021137,\n",
" 0.0127943 , 0.0134131 , 0.01345835, 0.01346002, 0.01780076,\n",
" 0.01938024, 0.02319527, 0.02529249, 0.02303181, 0.02419887,\n",
" 0.00435166, 0.00308976, 0.00635595, 0.00478921, 0.00628524,\n",
" 0.00793438, 0.01108131, 0.00848966, 0.01000352, 0.00953794,\n",
" 0.01097364, 0.0139215 , 0.01222644, 0.01224408, 0.01468167,\n",
" 0.00196624, 0.00301633, 0.00390964, 0.00452199, 0.00556607,\n",
" 0.006392 , 0.00768118, 0.01277604, 0.0110642 , 0.01218615,\n",
" 0.01268215, 0.01767502, 0.01393905, 0.02079268, 0.01968646,\n",
" 0.00342979, 0.00437932, 0.00548873, 0.00722423, 0.00682554,\n",
" 0.00909562, 0.01276522, 0.01279902, 0.0215415 , 0.01534147,\n",
" 0.01427946, 0.02206411, 0.01645074, 0.02000618, 0.01554608,\n",
" 0.0043427 , 0.00288467, 0.00362473, 0.00460877, 0.00753756,\n",
" 0.00685964, 0.00812874, 0.00841136, 0.00805507, 0.00941248,\n",
" 0.01202202, 0.01187034, 0.01742249, 0.01302929, 0.01959119,\n",
" 0.00178313, 0.007195 , 0.00380158, 0.00526109, 0.00555668,\n",
" 0.00702529, 0.00790472, 0.01086941, 0.01031022, 0.01246042,\n",
" 0.0132216 , 0.01713457, 0.02301383, 0.01588621, 0.02066183,\n",
" 0.00283895, 0.00377893, 0.00742383, 0.00582228, 0.00704598,\n",
" 0.00913119, 0.01392918, 0.01011014, 0.01452384, 0.01305919,\n",
" 0.02185259, 0.01802359, 0.017415 , 0.01738276, 0.01188393]),\n",
" 'std_score_time': array([1.23828064e-04, 5.19633376e-04, 1.02245492e-03, 5.04882326e-04,\n",
" 9.09417378e-04, 4.48723249e-04, 2.24736613e-03, 5.48394455e-03,\n",
" 1.77840212e-03, 5.92523041e-04, 4.26878463e-03, 8.18800112e-04,\n",
" 5.49652916e-04, 6.34092050e-04, 5.34877858e-04, 4.33623033e-05,\n",
" 1.89089617e-04, 1.94477063e-04, 4.06461532e-04, 3.76249616e-04,\n",
" 2.30774426e-04, 1.73481026e-03, 2.15156630e-03, 1.22296507e-03,\n",
" 4.11786504e-04, 2.29418761e-04, 7.07185521e-03, 6.40878713e-04,\n",
" 1.04794823e-03, 8.30861986e-04, 5.78624415e-04, 2.57425194e-04,\n",
" 2.31032283e-04, 2.54575557e-04, 7.43134457e-05, 6.52512139e-04,\n",
" 6.88084859e-03, 8.30719410e-03, 6.45615167e-04, 6.99962848e-04,\n",
" 1.04397454e-03, 1.52073271e-03, 1.01507734e-03, 1.68973948e-02,\n",
" 1.43756105e-03, 1.81444365e-04, 1.64975844e-04, 2.36277070e-04,\n",
" 4.70306387e-04, 3.81201459e-04, 7.58093830e-04, 4.82412668e-04,\n",
" 2.28569971e-04, 1.31710421e-03, 9.82972948e-04, 4.49874963e-04,\n",
" 2.34670958e-03, 1.21481446e-03, 3.79140736e-04, 1.02538085e-03,\n",
" 2.05823565e-04, 3.84757583e-04, 3.50340357e-04, 3.85571853e-04,\n",
" 2.60804234e-04, 5.91823561e-04, 4.33115776e-04, 5.98712820e-04,\n",
" 6.14224022e-04, 6.38786530e-04, 1.42314807e-03, 5.17665862e-03,\n",
" 5.60476302e-04, 1.81916048e-03, 2.24813564e-03, 1.92226336e-04,\n",
" 3.12705228e-04, 2.64756265e-04, 4.71222760e-04, 4.79788341e-03,\n",
" 5.76902692e-03, 7.12011856e-04, 4.50925198e-04, 3.21953938e-04,\n",
" 4.82601133e-03, 2.24464423e-03, 5.48174046e-04, 1.59523743e-03,\n",
" 9.89126662e-04, 3.29872069e-03, 9.08875390e-05, 7.55914193e-04,\n",
" 6.27365137e-05, 1.06237714e-04, 1.40531091e-03, 5.01862657e-04,\n",
" 4.70177044e-04, 4.83674116e-04, 1.02865934e-03, 2.44369279e-04,\n",
" 4.73406779e-04, 2.50961050e-04, 6.59830981e-04, 1.29922809e-02,\n",
" 1.34370503e-03, 1.45938160e-04, 3.93071872e-04, 3.29619617e-04,\n",
" 2.22398987e-04, 6.76647160e-04, 3.20698000e-04, 3.77951972e-04,\n",
" 4.00605384e-04, 6.45454386e-04, 7.69817684e-04, 3.56045329e-03,\n",
" 1.05040551e-02, 1.45751153e-03, 7.76012767e-04, 9.73767651e-03,\n",
" 6.02876818e-05, 1.96991374e-04, 5.94356532e-03, 4.10995471e-03,\n",
" 1.27771040e-03, 3.73271143e-04, 5.79555529e-04, 2.17216430e-03,\n",
" 1.21461111e-03, 1.86568836e-03, 7.62084437e-04, 1.08514187e-02,\n",
" 9.16176902e-03, 6.23381122e-03, 9.17839717e-03, 1.44606886e-03,\n",
" 2.77043686e-04, 7.94216598e-04, 1.64937118e-03, 4.95145329e-03,\n",
" 6.81156713e-03, 2.36312061e-03, 3.91655094e-03, 1.10412905e-03,\n",
" 1.36549522e-03, 6.20335639e-03, 5.39269016e-03, 3.39303983e-03,\n",
" 3.26271051e-03, 7.74314814e-04, 8.47943310e-05, 1.92926006e-03,\n",
" 1.00788197e-03, 2.05647964e-04, 3.59462984e-03, 3.30959842e-04,\n",
" 1.76190129e-03, 7.99754531e-04, 9.19755280e-04, 5.43292426e-04,\n",
" 8.60722710e-04, 1.99637046e-03, 5.57777434e-03, 9.31126524e-03,\n",
" 8.82420438e-03, 2.69883472e-03, 3.62273555e-03, 5.24503667e-03,\n",
" 6.32402642e-03, 5.11302993e-03, 7.74701193e-03, 4.30289763e-03,\n",
" 6.05388821e-03, 2.27724544e-03, 5.17598763e-03, 1.04347983e-02,\n",
" 7.61514508e-03, 7.37911349e-03, 1.21806886e-02, 1.09686122e-02,\n",
" 4.96289970e-03, 6.90124437e-04, 5.26642224e-03, 3.53381975e-04,\n",
" 1.61611619e-03, 3.00332070e-03, 4.28998564e-03, 2.12881715e-03,\n",
" 1.90379531e-03, 1.31317181e-03, 1.50341387e-03, 5.79492468e-03,\n",
" 9.55435588e-04, 9.05156173e-04, 2.48691409e-03, 1.54906846e-04,\n",
" 7.33438028e-04, 5.73004866e-04, 3.66900789e-04, 3.40917940e-04,\n",
" 4.27506705e-04, 5.74819380e-04, 3.42964020e-03, 1.79808359e-03,\n",
" 1.36841990e-03, 1.92754423e-03, 5.53281273e-03, 7.91753053e-04,\n",
" 7.77689986e-03, 4.76689193e-03, 1.60773759e-03, 1.39252821e-03,\n",
" 1.02757180e-03, 2.45219057e-03, 1.34592552e-03, 1.97586276e-03,\n",
" 4.88121098e-03, 4.77316175e-03, 7.79258853e-03, 1.97911568e-03,\n",
" 2.32772553e-03, 1.00801520e-02, 2.10594059e-03, 5.63136666e-03,\n",
" 6.19364829e-04, 4.91772046e-03, 6.11528874e-04, 4.15285565e-04,\n",
" 3.84033132e-04, 4.08778778e-03, 1.53273487e-03, 2.81015814e-03,\n",
" 2.25849119e-03, 5.83860349e-04, 2.97543193e-04, 1.48553833e-03,\n",
" 2.13613625e-03, 5.99377644e-03, 8.63423875e-04, 6.74788438e-03,\n",
" 7.20218291e-05, 5.21321954e-03, 6.98710336e-04, 2.11311806e-04,\n",
" 4.88354717e-04, 7.33867714e-04, 9.89362431e-04, 5.17852916e-03,\n",
" 2.16504953e-03, 1.03100139e-03, 3.91432024e-03, 4.56294117e-03,\n",
" 1.29437994e-02, 1.55387483e-03, 4.00496648e-03, 5.72763341e-04,\n",
" 4.87677356e-04, 5.48905822e-03, 5.02968246e-04, 1.20657277e-03,\n",
" 1.94411256e-03, 8.70258195e-03, 5.95366032e-04, 8.58513571e-03,\n",
" 2.07306773e-03, 9.77820917e-03, 6.33699034e-03, 4.24460549e-03,\n",
" 2.75189599e-03, 1.91999503e-03]),\n",
" 'param_max_depth': masked_array(data=[None, None, None, None, None, None, None, None, None,\n",
" None, None, None, None, None, None, None, None, None,\n",
" None, None, None, None, None, None, None, None, None,\n",
" None, None, None, None, None, None, None, None, None,\n",
" None, None, None, None, None, None, None, None, None,\n",
" 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
" 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
" 5, 5, 5, 5, 5, 5, 5, 5, 5, 10, 10, 10, 10, 10, 10, 10,\n",
" 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
" 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
" 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 15, 15, 15, 15,\n",
" 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,\n",
" 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,\n",
" 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20,\n",
" 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,\n",
" 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,\n",
" 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,\n",
" 20, 20, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,\n",
" 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,\n",
" 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,\n",
" 25, 25, 25, 25, 25],\n",
" mask=[False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False],\n",
" fill_value='?',\n",
" dtype=object),\n",
" 'param_max_features': masked_array(data=['auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
" 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
" 'auto', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt',\n",
" 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt',\n",
" 'sqrt', 'sqrt', 'log2', 'log2', 'log2', 'log2', 'log2',\n",
" 'log2', 'log2', 'log2', 'log2', 'log2', 'log2', 'log2',\n",
" 'log2', 'log2', 'log2', 'auto', 'auto', 'auto', 'auto',\n",
" 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
" 'auto', 'auto', 'auto', 'auto', 'sqrt', 'sqrt', 'sqrt',\n",
" 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt',\n",
" 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'log2', 'log2',\n",
" 'log2', 'log2', 'log2', 'log2', 'log2', 'log2', 'log2',\n",
" 'log2', 'log2', 'log2', 'log2', 'log2', 'log2', 'auto',\n",
" 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
" 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
" 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt',\n",
" 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt',\n",
" 'sqrt', 'log2', 'log2', 'log2', 'log2', 'log2', 'log2',\n",
" 'log2', 'log2', 'log2', 'log2', 'log2', 'log2', 'log2',\n",
" 'log2', 'log2', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
" 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
" 'auto', 'auto', 'auto', 'sqrt', 'sqrt', 'sqrt', 'sqrt',\n",
" 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt',\n",
" 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'log2', 'log2', 'log2',\n",
" 'log2', 'log2', 'log2', 'log2', 'log2', 'log2', 'log2',\n",
" 'log2', 'log2', 'log2', 'log2', 'log2', 'auto', 'auto',\n",
" 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
" 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'sqrt',\n",
" 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt',\n",
" 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt',\n",
" 'log2', 'log2', 'log2', 'log2', 'log2', 'log2', 'log2',\n",
" 'log2', 'log2', 'log2', 'log2', 'log2', 'log2', 'log2',\n",
" 'log2', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
" 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
" 'auto', 'auto', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt',\n",
" 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt', 'sqrt',\n",
" 'sqrt', 'sqrt', 'sqrt', 'log2', 'log2', 'log2', 'log2',\n",
" 'log2', 'log2', 'log2', 'log2', 'log2', 'log2', 'log2',\n",
" 'log2', 'log2', 'log2', 'log2'],\n",
" mask=[False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False],\n",
" fill_value='?',\n",
" dtype=object),\n",
" 'param_n_estimators': masked_array(data=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,\n",
" 140, 150, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110,\n",
" 120, 130, 140, 150, 10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
" 100, 110, 120, 130, 140, 150, 10, 20, 30, 40, 50, 60,\n",
" 70, 80, 90, 100, 110, 120, 130, 140, 150, 10, 20, 30,\n",
" 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,\n",
" 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,\n",
" 140, 150, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110,\n",
" 120, 130, 140, 150, 10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
" 100, 110, 120, 130, 140, 150, 10, 20, 30, 40, 50, 60,\n",
" 70, 80, 90, 100, 110, 120, 130, 140, 150, 10, 20, 30,\n",
" 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,\n",
" 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,\n",
" 140, 150, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110,\n",
" 120, 130, 140, 150, 10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
" 100, 110, 120, 130, 140, 150, 10, 20, 30, 40, 50, 60,\n",
" 70, 80, 90, 100, 110, 120, 130, 140, 150, 10, 20, 30,\n",
" 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,\n",
" 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,\n",
" 140, 150, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110,\n",
" 120, 130, 140, 150, 10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
" 100, 110, 120, 130, 140, 150],\n",
" mask=[False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False,\n",
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" 90.48931113, 89.37137616, 85.57474351, 90.0404037 ,\n",
" 83.47824507, 87.95777097, 92.86351327, 191.9655868 ,\n",
" 141.76518068, 91.94651667, 113.26118246, 99.80229724,\n",
" 93.93636838, 103.84944241, 130.35117713, 109.06628319,\n",
" 95.11820961, 105.36423283, 111.99525148, 123.60618005,\n",
" 95.66164483, 103.57733635]),\n",
" 'rank_test_score': array([ 80, 95, 32, 27, 88, 2, 21, 11, 98, 75, 71, 36, 57,\n",
" 73, 54, 246, 133, 207, 110, 120, 161, 101, 136, 124, 81, 137,\n",
" 123, 114, 111, 128, 139, 247, 134, 220, 226, 192, 202, 217, 180,\n",
" 178, 167, 219, 181, 216, 191, 93, 45, 64, 69, 52, 68, 41,\n",
" 14, 40, 58, 77, 70, 34, 30, 46, 261, 211, 248, 235, 233,\n",
" 240, 245, 228, 223, 199, 234, 236, 232, 190, 230, 252, 262, 257,\n",
" 270, 263, 265, 260, 258, 251, 264, 269, 266, 268, 267, 259, 42,\n",
" 23, 12, 49, 91, 72, 29, 8, 9, 16, 76, 7, 28, 38,\n",
" 19, 148, 253, 203, 94, 96, 147, 115, 154, 122, 140, 108, 221,\n",
" 116, 143, 162, 255, 229, 256, 237, 250, 241, 243, 227, 213, 218,\n",
" 244, 209, 225, 231, 239, 10, 83, 50, 44, 26, 25, 15, 5,\n",
" 24, 39, 60, 74, 18, 22, 35, 100, 118, 172, 112, 102, 144,\n",
" 125, 127, 126, 142, 145, 189, 86, 131, 151, 249, 160, 215, 187,\n",
" 157, 159, 152, 176, 166, 212, 163, 193, 179, 214, 188, 182, 92,\n",
" 43, 59, 84, 6, 53, 47, 55, 61, 62, 63, 78, 51, 65,\n",
" 204, 97, 156, 205, 194, 107, 119, 85, 141, 173, 135, 89, 113,\n",
" 90, 150, 196, 171, 170, 117, 168, 197, 177, 206, 222, 208, 153,\n",
" 169, 185, 184, 186, 79, 87, 1, 37, 48, 67, 3, 82, 56,\n",
" 13, 4, 17, 33, 66, 31, 20, 130, 165, 99, 105, 132, 121,\n",
" 138, 149, 155, 104, 103, 106, 146, 129, 254, 238, 109, 183, 200,\n",
" 164, 201, 242, 175, 158, 195, 210, 224, 174, 198], dtype=int32)}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf.cv_results_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Testen und Plotten\n",
"\n",
"Wenn mir anderem Datensatz getestet werden soll:\n",
"- Neuen Datensatz herunterladen und einlesen\n",
"- Eventuell `TEST_RANGE` anpassen\n",
"- Untere Zelle ausführen"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"xaxis = range(0, TEST_RANGE[1])\n",
"plot.figure(figsize=(15,10))\n",
"plot.axvline(x=TRAINING_RANGE[0])\n",
"plot.axvline(x=TRAINING_RANGE[1])\n",
"plot.plot(xaxis, target, 'b', xaxis, clf.predict(data), 'r')\n",
"plot.legend(['Training','Test','TARGET OBD Speed','PREDICTED OBD Speed'])\n",
"plot.xlabel('Sample #')\n",
"plot.ylabel('OBD-Geschwindigkeit / 100km/h')\n",
"plot.title(\"ExtreTreesRegressor\")\n",
"plot.savefig('plot-ExtraTreesRegressor.pdf')\n",
"plot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Anscheinend wird immer die höchste Geschwindigkeit vorrausgesagt, die der Regressor je gesehen hat. Wird er beispielsweise mit einer Landstraßenfahrt trainiert, ist auch auf der Autobahn bei 100 km/h Schluss."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x1119.6 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot.figure(figsize = (15, 0.05*len(DATA_COLUMNS)))\n",
"importances = clf.best_estimator_.feature_importances_\n",
"stddev = np.var([t.feature_importances_ for t in clf.best_estimator_.estimators_], axis = 0)\n",
"sorted_indices = np.argsort(importances)[-30:]\n",
"plot.barh(np.array(DATA_COLUMNS)[sorted_indices], importances[sorted_indices], xerr = stddev[sorted_indices])\n",
"plot.ylabel('Feature')\n",
"plot.xlabel('Gewichtung (± Standardabweichung))')\n",
"plot.title(\"Feature-Gewichtung eines ExtreTreesRegressor\")\n",
"plot.savefig('plot-ExtraTreesRegressor-features.pdf')\n",
"plot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Einige der Top-Features sprachen für starkes Overfitting:\n",
"- Temperatur und Luftfeuchtigkeit (`weat_temperature`, `weat_humidity`) bewegen kleinem Rahmen, und sollten auf das Fahrverhalten nur bedingten Einfluss haben (wenn überhaupt, nur auf längere Sicht gesehen; z.B. langsameres Fahren bei schlechtem Wetter).\n",
"- `join_idx` macht keinen Sinn.\n",
"- Position (Breiten- und Längengrad: `flt_latitude`, `latitude`, `longitude`, `flt_longitude`) funktionieren nur auf einer spezifischen Strecke. Das Lernen von Eigenheiten dieser Strecke ist nicht Aufgabe des Regressors; das sollte höchstens in anderen Datenquellen gespeichert und dann zur Berechnung hergezogen werden.\n",
"- Zeit und Entfernung (`hr_remainDistance`, `tt_calc_distance`, `ei_distance_lldist`, `hr_distance_lldist`, `gh_distance_lldist`, `time` etc.) sind schlecht auf andere Fahrten übertragbar; würde ich dieselbe Strecke von einem anderen Ausgangspunkt aus fahren, wäre die gesamte Vorhersage verschoben\n",
"- Windrichtung (`weat_windBearing`) hätte durchaus einen Einfluss (Rücken-/Gegenwind), allerdings nur, wenn die aktuelle Fahrtrichtung auch verwendet wird\n",
"\n",
"Diese Features wurden daher ausgeschlossen, was zu einer merklichen Verbesserung der Prädiktion führt (ist nun besser als HERE Maps in nahezu allen verwendeten Kriterien).\n",
"\n",
"Interessanterweise scheinen die Bäume allerdings vor allem eine Aggregation verschiedener Quellen für die Durchschnittsgeschwindigkeit zu sein …"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Gütekriterium - Prädiktion\n",
"\n",
"Berechung des Gütekritierums\n",
"- Root-mean-square deviation RMSE\n",
"- NRMSE Normalized root-mean-square deviation\n",
"- Mean absolute error MAE\n",
"- Mean absolute percentage error MAP\n",
"- Symmetric mean absolute percentage error\n",
"- https://en.wikipedia.org/wiki/Least_absolute_deviations\n",
"- https://en.wikipedia.org/wiki/Mean_signed_deviation\n",
"- Pearson Correlation Coefficient\n",
"- Accuracy (Interval of given size; absolute and relative)\n",
"- Median Absolute Deviation\n",
"\n",
"BITTE weitere Kriterien ergänzen\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RMSE = 12.43 km/h\n",
"NRMSE = 45.87 %\n",
"MAE = 9.82 km/h\n",
"MAP = 11.78 %\n",
"SMAPE = 11.69 %\n",
"MSD = 5.22 km/h\n",
"CORR = 0.88\n",
"ACC_A = 60.14 %\n",
"ACC_R = 50.57 %\n",
"MAD = 7.89 km/h\n"
]
},
{
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"text/plain": [
"<Figure size 1080x5598 with 1 Axes>"
]
},
"metadata": {
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"output_type": "display_data"
},
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"text/plain": [
"<Figure size 1080x5598 with 1 Axes>"
]
},
"metadata": {
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"output_type": "display_data"
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{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x5598 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ta = target[TEST_RANGE[0]:TEST_RANGE[1]]\n",
"pr = clf.predict(data[TEST_RANGE[0]:TEST_RANGE[1]])\n",
"RMSE = math.sqrt(sum((ta-pr)**2)/len(ta))\n",
"print(\"RMSE = %.2f km/h\" %RMSE)\n",
"NRMSE = 1-math.sqrt(sum((ta-pr)**2))/math.sqrt(sum( (ta-np.mean(ta) )**2 ))\n",
"print(\"NRMSE = %.2f %%\" %(NRMSE*100))\n",
"MAE = sum(((ta-pr)**2)**(1/2))/len(ta)\n",
"print(\"MAE = %.2f km/h\" %MAE)\n",
"with np.errstate(divide = 'ignore'): map_elements = np.abs((ta - pr) / ta)\n",
"map_elements[map_elements == np.inf] = 0\n",
"MAP = np.sum(map_elements) / len(ta)\n",
"print(\"MAP = %.2f %%\" % (MAP*100))\n",
"SMAPE = np.sum(np.abs(ta - pr) / ((ta + pr) / 2)) / len(ta)\n",
"print(\"SMAPE = %.2f %%\" % (SMAPE*100))\n",
"MSD = np.sum(ta - pr) / len(ta)\n",
"print(\"MSD = %.2f km/h\" % MSD)\n",
"CORR = np.corrcoef(ta, pr)[1][0]\n",
"print(\"CORR = %.2f\" % CORR)\n",
"ACC_A_THRESHOLD = 10\n",
"ACC_A = (np.abs(ta - pr) < ACC_A_THRESHOLD).sum() / len(ta)\n",
"print(\"ACC_A = %.2f %%\" % (ACC_A*100))\n",
"ACC_R_THRESHOLD = 0.1\n",
"ACC_R = (np.abs(ta / pr - 1) < ACC_R_THRESHOLD).sum() / len(ta)\n",
"print(\"ACC_R = %.2f %%\" % (ACC_R*100))\n",
"MAD = np.median(np.abs(ta - pr))\n",
"print(\"MAD = %.2f km/h\" % MAD)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Vergleich mit HERE Maps Trafic Speed\n",
"Kann zum Vergleich sehr gut herangezogen werden ;)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RMSE = 16.82 km/h\n",
"NRMSE = 26.73 %\n",
"MAE = 14.06 km/h\n",
"MAP = 15.87 %\n",
"SMAPE = 16.62 %\n",
"MSD = 9.82 km/h\n",
"CORR = 0.81\n",
"ACC_A = 40.18 %\n",
"ACC_R = 32.00 %\n",
"MAD = 12.84 km/h\n"
]
}
],
"source": [
"ta = target[TEST_RANGE[0]:TEST_RANGE[1]]\n",
"pr = [row['hr_traficSpeed'] for row in reader_data] #t['hr_traficSpeed']\n",
"pr = np.array([float(d) if d != '' else 0.0 for d in pr])\n",
"pr = pr[TEST_RANGE[0]:TEST_RANGE[1]] * 3.6\n",
"RMSE = math.sqrt(sum((ta-pr)**2)/len(ta))\n",
"print(\"RMSE = %.2f km/h\" %RMSE)\n",
"NRMSE = 1-math.sqrt(sum((ta-pr)**2))/math.sqrt(sum( (ta-np.mean(ta) )**2 ))\n",
"print(\"NRMSE = %.2f %%\" %(NRMSE*100))\n",
"MAE = sum(((ta-pr)**2)**(1/2))/len(ta)\n",
"print(\"MAE = %.2f km/h\" %MAE)\n",
"with np.errstate(divide = 'ignore'): map_elements = np.abs((ta - pr) / ta)\n",
"map_elements[map_elements == np.inf] = 0\n",
"MAP = np.sum(map_elements) / len(ta)\n",
"print(\"MAP = %.2f %%\" % (MAP*100))\n",
"SMAPE = np.sum(np.abs(ta - pr) / ((ta + pr) / 2)) / len(ta)\n",
"print(\"SMAPE = %.2f %%\" % (SMAPE*100))\n",
"MSD = np.sum(ta - pr) / len(ta)\n",
"print(\"MSD = %.2f km/h\" % MSD)\n",
"CORR = np.corrcoef(ta, pr)[1][0]\n",
"print(\"CORR = %.2f\" % CORR)\n",
"ACC_A_THRESHOLD = 10\n",
"ACC_A = (np.abs(ta - pr) < ACC_A_THRESHOLD).sum() / len(ta)\n",
"print(\"ACC_A = %.2f %%\" % (ACC_A*100))\n",
"ACC_R_THRESHOLD = 0.1\n",
"ACC_R = (np.abs(ta / pr - 1) < ACC_R_THRESHOLD).sum() / len(ta)\n",
"print(\"ACC_R = %.2f %%\" % (ACC_R*100))\n",
"MAD = np.median(np.abs(ta - pr))\n",
"print(\"MAD = %.2f km/h\" % MAD)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generalisierbarkeit\n",
"\n",
"Nach der abgeschlossenen Fahrt wird mit der ganzen Fahrt trainiert, und anschließend eine andere Fahrt vorhergesagt. Als Parameter werden die gefundenen Parameter der Grid Search beim ersten mal verwendet, wodurch sich der Vorgang erheblich beschleunigt."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ExtraTreesRegressor(n_estimators=40)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf2 = ExtraTreesRegressor(n_estimators = clf.best_params_['n_estimators'],\n",
" max_depth = clf.best_params_['max_depth'],\n",
" max_features = clf.best_params_['max_features'])\n",
"clf2.fit(data, target)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<BarContainer object of 311 artists>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot.figure(figsize = (15, 0.25 * len(DATA_COLUMNS)))\n",
"importances = clf2.feature_importances_\n",
"stddev = np.var([t.feature_importances_ for t in clf2.estimators_], axis = 0)\n",
"sorted_indices = np.argsort(importances)\n",
"plot.barh(np.array(DATA_COLUMNS)[sorted_indices], importances[sorted_indices], xerr = stddev[sorted_indices])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x5598 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x5598 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x5598 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"SETUP_ID_2 = 450\n",
"\n",
"reader2 = runsql('select * from computeddata where setup_id = {} order by distance asc'.format(SETUP_ID_2))\n",
"reader_data2 = list(reader2) # list(...) so that following cells can be repeated"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"data2 = []\n",
"target2 = []\n",
"for row in reader_data2:\n",
" data2 += [[float(row[c]) if row[c] != '' else math.nan for c in DATA_COLUMNS]]\n",
" target2 += [float(row[TARGET_COLUMN]) if row[TARGET_COLUMN] != '' else math.nan]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"data2 = imp.transform(data2)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('imputed-{}.csv'.format(SETUP_ID_2), data2, delimiter=',')\n",
"np.savetxt('target-{}.csv'.format(SETUP_ID_2), target2, delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f7b95e70e80>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot.figure(figsize=(15,10))\n",
"xaxis = range(0, len(target2))\n",
"plot.plot(xaxis, target2, 'b', xaxis, clf2.predict(data2), 'r')\n",
"plot.legend(['TARGET OBD Speed','PREDICTED OBD Speed'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wie bereits vorher festgestellt, scheint der Regressor mit höheren Geschwindigkeiten nicht vertraut. Trainieren wir also mit diesem Datensatz einen weiteren Regressor, und testen wieder mit einem anderen."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ExtraTreesRegressor(n_estimators=40)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf3 = ExtraTreesRegressor(n_estimators = clf.best_params_['n_estimators'],\n",
" max_depth = clf.best_params_['max_depth'],\n",
" max_features = clf.best_params_['max_features'])\n",
"clf3.fit(data2, target2)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<BarContainer object of 311 artists>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot.figure(figsize = (15, 0.25 * len(DATA_COLUMNS)))\n",
"importances = clf3.feature_importances_\n",
"stddev = np.var([t.feature_importances_ for t in clf3.estimators_], axis = 0)\n",
"sorted_indices = np.argsort(importances)\n",
"plot.barh(np.array(DATA_COLUMNS)[sorted_indices], importances[sorted_indices], xerr = stddev[sorted_indices])"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x5598 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"SETUP_ID_3 = 888\n",
"\n",
"reader3 = runsql('select * from computeddata where setup_id = {} order by distance asc'.format(SETUP_ID_3))\n",
"reader_data3 = list(reader3) # list(...) so that following cells can be repeated"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"data3 = []\n",
"target3 = []\n",
"for row in reader_data3:\n",
" data3 += [[float(row[c]) if row[c] != '' else math.nan for c in DATA_COLUMNS]]\n",
" target3 += [float(row[TARGET_COLUMN]) if row[TARGET_COLUMN] != '' else math.nan]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"data3 = imp.transform(data3)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f7b3b490a90>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot.figure(figsize=(15,10))\n",
"xaxis = range(0, len(target3))\n",
"plot.plot(xaxis, target3, 'b', xaxis, clf3.predict(data3), 'r')\n",
"plot.legend(['TARGET OBD Speed','PREDICTED OBD Speed'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wir vergleichen die Prädiktion mit HERE Maps:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RMSE = 20.79 km/h\n",
"NRMSE = 27.49 %\n",
"MAE = 16.00 km/h\n",
"MAP = 15.57 %\n",
"SMAPE = 14.19 %\n",
"MSD = -9.49 km/h\n",
"CORR = 0.84\n",
"ACC_A = 43.62 %\n",
"ACC_R = 44.37 %\n",
"MAD = 12.91 km/h\n"
]
}
],
"source": [
"ta = target3\n",
"pr = clf3.predict(data3)\n",
"RMSE = math.sqrt(sum((ta-pr)**2)/len(ta))\n",
"print(\"RMSE = %.2f km/h\" %RMSE)\n",
"NRMSE = 1-math.sqrt(sum((ta-pr)**2))/math.sqrt(sum( (ta-np.mean(ta) )**2 ))\n",
"print(\"NRMSE = %.2f %%\" %(NRMSE*100))\n",
"MAE = sum(((ta-pr)**2)**(1/2))/len(ta)\n",
"print(\"MAE = %.2f km/h\" %MAE)\n",
"with np.errstate(divide = 'ignore'): map_elements = np.abs((ta - pr) / ta)\n",
"map_elements[map_elements == np.inf] = 0\n",
"MAP = np.sum(map_elements) / len(ta)\n",
"print(\"MAP = %.2f %%\" % (MAP*100))\n",
"SMAPE = np.sum(np.abs(ta - pr) / ((ta + pr) / 2)) / len(ta)\n",
"print(\"SMAPE = %.2f %%\" % (SMAPE*100))\n",
"MSD = np.sum(ta - pr) / len(ta)\n",
"print(\"MSD = %.2f km/h\" % MSD)\n",
"CORR = np.corrcoef(ta, pr)[1][0]\n",
"print(\"CORR = %.2f\" % CORR)\n",
"ACC_A_THRESHOLD = 10\n",
"ACC_A = (np.abs(ta - pr) < ACC_A_THRESHOLD).sum() / len(ta)\n",
"print(\"ACC_A = %.2f %%\" % (ACC_A*100))\n",
"ACC_R_THRESHOLD = 0.1\n",
"ACC_R = (np.abs(ta / pr - 1) < ACC_R_THRESHOLD).sum() / len(ta)\n",
"print(\"ACC_R = %.2f %%\" % (ACC_R*100))\n",
"MAD = np.median(np.abs(ta - pr))\n",
"print(\"MAD = %.2f km/h\" % MAD)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RMSE = 21.57 km/h\n",
"NRMSE = 24.75 %\n",
"MAE = 16.79 km/h\n",
"MAP = 14.52 %\n",
"SMAPE = 15.78 %\n",
"MSD = 12.98 km/h\n",
"CORR = 0.81\n",
"ACC_A = 42.47 %\n",
"ACC_R = 35.90 %\n",
"MAD = 12.00 km/h\n"
]
}
],
"source": [
"ta = target3\n",
"pr = np.array([float(d['hr_traficSpeed']) if d['hr_traficSpeed'] != '' else 0.0 for d in reader_data3])\n",
"pr = pr * 3.6\n",
"RMSE = math.sqrt(sum((ta-pr)**2)/len(ta))\n",
"print(\"RMSE = %.2f km/h\" %RMSE)\n",
"NRMSE = 1-math.sqrt(sum((ta-pr)**2))/math.sqrt(sum( (ta-np.mean(ta) )**2 ))\n",
"print(\"NRMSE = %.2f %%\" %(NRMSE*100))\n",
"MAE = sum(((ta-pr)**2)**(1/2))/len(ta)\n",
"print(\"MAE = %.2f km/h\" %MAE)\n",
"with np.errstate(divide = 'ignore'): map_elements = np.abs((ta - pr) / ta)\n",
"map_elements[map_elements == np.inf] = 0\n",
"MAP = np.sum(map_elements) / len(ta)\n",
"print(\"MAP = %.2f %%\" % (MAP*100))\n",
"SMAPE = np.sum(np.abs(ta - pr) / ((ta + pr) / 2)) / len(ta)\n",
"print(\"SMAPE = %.2f %%\" % (SMAPE*100))\n",
"MSD = np.sum(ta - pr) / len(ta)\n",
"print(\"MSD = %.2f km/h\" % MSD)\n",
"CORR = np.corrcoef(ta, pr)[1][0]\n",
"print(\"CORR = %.2f\" % CORR)\n",
"ACC_A_THRESHOLD = 10\n",
"ACC_A = (np.abs(ta - pr) < ACC_A_THRESHOLD).sum() / len(ta)\n",
"print(\"ACC_A = %.2f %%\" % (ACC_A*100))\n",
"ACC_R_THRESHOLD = 0.1\n",
"ACC_R = (np.abs(ta / pr - 1) < ACC_R_THRESHOLD).sum() / len(ta)\n",
"print(\"ACC_R = %.2f %%\" % (ACC_R*100))\n",
"MAD = np.median(np.abs(ta - pr))\n",
"print(\"MAD = %.2f km/h\" % MAD)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"scaler = StandardScaler()\n",
"scaler.fit(data[TRAINING_RANGE[0]:TRAINING_RANGE[1]])\n",
"\n",
"scaled_training_data = scaler.transform(data[TRAINING_RANGE[0]:TRAINING_RANGE[1]])\n",
"scaled_data = scaler.transform(data)\n",
"scaled_target = np.multiply(target, 0.01)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"def gridsearch_train_and_plot(base, params = {}):\n",
" best_clf = gridsearch(base, params)\n",
" best_clf.fit(scaled_training_data, scaled_target[TRAINING_RANGE[0]:TRAINING_RANGE[1]])\n",
" xaxis = range(0, TEST_RANGE[1])\n",
" print(best_clf.best_params_)\n",
" plot.figure(figsize=(15,10))\n",
" plot.axvline(x=TRAINING_RANGE[0])\n",
" plot.axvline(x=TRAINING_RANGE[1])\n",
" plot.plot(xaxis, scaled_target, 'b', xaxis, np.maximum(-1.0, np.minimum(4.0, best_clf.predict(scaled_data))), 'rx')\n",
" plot.legend(['Training','Test','TARGET OBD Speed','PREDICTED OBD Speed'])\n",
" plot.xlabel('Sample #')\n",
" plot.ylabel('OBD-Geschwindigkeit / 100km/h')\n",
" name = type(base).__name__\n",
" plot.title(name)\n",
" plot.savefig(f'plot-{name}.pdf')\n",
" plot.show()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'gamma': 'auto', 'kernel': 'rbf'}\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn import svm\n",
"gridsearch_train_and_plot(svm.SVR(),\n",
" {'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
" 'gamma': ['scale', 'auto']})"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'gamma': 'auto', 'kernel': 'rbf'}\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gridsearch_train_and_plot(svm.NuSVR(),\n",
" {'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
" 'gamma': ['scale', 'auto']})"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'loss': 'epsilon_insensitive', 'penalty': 'l1'}\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn import linear_model\n",
"gridsearch_train_and_plot(linear_model.SGDRegressor(),\n",
" {'loss': ['squared_loss', 'huber', 'epsilon_insensitive', 'squared_epsilon_insensitive'],\n",
" 'penalty': ['l1', 'l2', 'elasticnet']})"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'n_neighbors': 10, 'weights': 'distance'}\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn import neighbors\n",
"gridsearch_train_and_plot(neighbors.KNeighborsRegressor(),\n",
" {'n_neighbors': [5, 10, 20, 50, 100, 200, 500],\n",
" 'weights': ['uniform', 'distance']})"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31m_RemoteTraceback\u001b[0m Traceback (most recent call last)",
"\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/usr/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py\", line 418, in _process_worker\n r = call_item()\n File \"/usr/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py\", line 272, in __call__\n return self.fn(*self.args, **self.kwargs)\n File \"/usr/lib/python3.8/site-packages/joblib/_parallel_backends.py\", line 608, in __call__\n return self.func(*args, **kwargs)\n File \"/usr/lib/python3.8/site-packages/joblib/parallel.py\", line 255, in __call__\n return [func(*args, **kwargs)\n File \"/usr/lib/python3.8/site-packages/joblib/parallel.py\", line 255, in <listcomp>\n return [func(*args, **kwargs)\n File \"/usr/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 560, in _fit_and_score\n test_scores = _score(estimator, X_test, y_test, scorer)\n File \"/usr/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 607, in _score\n scores = scorer(estimator, X_test, y_test)\n File \"/usr/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 87, in __call__\n score = scorer._score(cached_call, estimator,\n File \"/usr/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 212, in _score\n return self._sign * self._score_func(y_true, y_pred,\n File \"/usr/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 73, in inner_f\n return f(**kwargs)\n File \"/usr/lib/python3.8/site-packages/sklearn/metrics/_regression.py\", line 253, in mean_squared_error\n y_type, y_true, y_pred, multioutput = _check_reg_targets(\n File \"/usr/lib/python3.8/site-packages/sklearn/metrics/_regression.py\", line 86, in _check_reg_targets\n y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)\n File \"/usr/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 73, in inner_f\n return f(**kwargs)\n File \"/usr/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 645, in check_array\n _assert_all_finite(array,\n File \"/usr/lib/python3.8/site-packages/sklearn/utils/validation.py\", line 97, in _assert_all_finite\n raise ValueError(\nValueError: Input contains NaN, infinity or a value too large for dtype('float64').\n\"\"\"",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-54-4c87d5304a0a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# i think something is borked in there\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m gridsearch_train_and_plot(neighbors.RadiusNeighborsRegressor(),\n\u001b[0m\u001b[1;32m 3\u001b[0m {'radius': [0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1.0],\n\u001b[1;32m 4\u001b[0m 'weights': ['uniform', 'distance']})\n",
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"\u001b[0;32m/usr/lib/python3.8/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m 734\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 735\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 736\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 738\u001b[0m \u001b[0;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1015\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1016\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1017\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1018\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1019\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 907\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 909\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 910\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 911\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m 560\u001b[0m AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m 561\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 562\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 563\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mLokyTimeoutError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 564\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 440\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 389\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
]
}
],
"source": [
"# i think something is borked in there\n",
"gridsearch_train_and_plot(neighbors.RadiusNeighborsRegressor(),\n",
" {'radius': [0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1.0],\n",
" 'weights': ['uniform', 'distance']})"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'normalize_y': True}\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn import gaussian_process\n",
"gridsearch_train_and_plot(gaussian_process.GaussianProcessRegressor(),\n",
" {'normalize_y': [True, False]})"
]
},
{
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}