In [1]:
import sys
sys.path.append('/Users/spacecoffin/Development')
import GravelKicker as gk
import librosa
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import sklearn as sk
from datetime import datetime
from supriya.tools import nonrealtimetools
%matplotlib inline
In [2]:
this_dir = '/Users/spacecoffin/Development/GravelKicker/__gen_files'
In [ ]:
dir_list = os.listdir(path=this_dir)
_pickle_path = os.path.join(this_dir, "df.p")
if "df.p" in dir_list:
#_pickle_path = os.path.join(this_dir, "df.p")
_old_df = pd.read_pickle(_pickle_path)
_pickle_dir = make_out_dir(this_dir, "pickle_files")
dt_identifier = datetime.now().strftime("df-%Y_%m_%d_%H%M%S.p")
_old_pickle_path = os.path.join(_pickle_dir, dt_identifier)
pd.to_pickle(_old_pickle_path)
In [3]:
pmtx = gk.generator.gendy1.gen_params(dists=(0., 0.), rows=100)
In [4]:
df = gk.generator.gendy1.format_params(pmtx)
In [5]:
df
Out[5]:
adparam
ampscale
ddparam
durscale
ampdist
durdist
knum
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maxfrequency
init_cps
hash
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100 rows × 11 columns
In [30]:
pmtx
Out[30]:
array([[ 1.40566725e-01, 2.96348706e-01, 6.94119437e-01,
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1.60000000e+01],
[ 4.38835281e-01, 6.68790457e-01, 2.21934772e-01,
1.99449462e-01, 5.00000000e+00, 5.00000000e+00,
1.20000000e+01, 3.88908730e+01, 6.17354127e+01,
1.60000000e+01],
[ 5.44935871e-01, 1.17482472e-01, 4.35653652e-01,
9.00235117e-01, 2.00000000e+00, 0.00000000e+00,
1.00000000e+01, 3.08677063e+01, 2.79382585e+03,
1.60000000e+01],
[ 3.95026275e-01, 6.83074667e-01, 1.27903847e-01,
6.95616148e-01, 3.00000000e+00, 1.00000000e+00,
1.10000000e+01, 6.17354127e+01, 3.69994423e+02,
1.60000000e+01],
[ 3.43855395e-01, 7.99278448e-01, 1.31273567e-01,
2.13649375e-01, 4.00000000e+00, 4.00000000e+00,
1.20000000e+01, 3.91995436e+02, 7.39988845e+02,
1.60000000e+01],
[ 6.60814016e-01, 4.66262544e-01, 8.11170039e-01,
8.29434882e-01, 0.00000000e+00, 2.00000000e+00,
1.40000000e+01, 2.61625565e+02, 3.11126984e+02,
1.60000000e+01],
[ 9.76757669e-01, 4.37239896e-01, 3.33418054e-01,
7.90673838e-01, 0.00000000e+00, 2.00000000e+00,
9.00000000e+00, 1.15623257e+01, 2.18267645e+01,
1.60000000e+01],
[ 3.46096712e-01, 6.65650199e-03, 8.66110898e-02,
9.70710505e-01, 3.00000000e+00, 3.00000000e+00,
1.30000000e+01, 1.74614116e+02, 2.46941651e+02,
1.60000000e+01],
[ 9.01814461e-02, 2.05660623e-01, 5.37871528e-01,
7.43020144e-02, 5.00000000e+00, 3.00000000e+00,
1.40000000e+01, 5.19130872e+01, 1.16540940e+02,
1.60000000e+01],
[ 6.96076211e-01, 4.14700477e-01, 2.43478701e-01,
5.35827853e-01, 5.00000000e+00, 1.00000000e+00,
1.20000000e+01, 2.44997147e+01, 1.30812783e+02,
1.60000000e+01],
[ 4.97783418e-01, 1.15200605e-01, 6.38931637e-01,
4.46775202e-01, 5.00000000e+00, 5.00000000e+00,
1.00000000e+01, 1.83540480e+01, 3.67080960e+01,
1.60000000e+01],
[ 9.16018992e-01, 3.72392766e-01, 4.64937396e-01,
3.73454475e-01, 1.00000000e+00, 1.00000000e+00,
1.10000000e+01, 1.66121879e+03, 2.21746105e+03,
1.60000000e+01],
[ 3.59384110e-01, 2.31281577e-01, 9.80787092e-01,
6.06335052e-01, 3.00000000e+00, 2.00000000e+00,
1.40000000e+01, 1.74614116e+02, 5.54365262e+02,
1.60000000e+01],
[ 7.01058769e-01, 8.78345353e-02, 5.30614298e-01,
8.06027846e-01, 3.00000000e+00, 0.00000000e+00,
1.00000000e+01, 2.77182631e+02, 4.93883301e+02,
1.60000000e+01],
[ 8.46154512e-01, 1.87416702e-01, 2.22742871e-01,
2.55321866e-01, 2.00000000e+00, 2.00000000e+00,
1.20000000e+01, 7.77817459e+01, 1.23470825e+02,
1.60000000e+01],
[ 2.44638450e-01, 4.89615141e-03, 1.44001771e-01,
3.93863621e-01, 5.00000000e+00, 0.00000000e+00,
1.20000000e+01, 4.93883301e+02, 5.87329536e+02,
1.60000000e+01],
[ 2.64003613e-01, 9.77735276e-01, 7.19114612e-01,
9.01769181e-01, 4.00000000e+00, 2.00000000e+00,
1.20000000e+01, 1.22498574e+01, 6.92956577e+01,
1.60000000e+01],
[ 9.76232131e-01, 4.73980819e-01, 8.56676586e-01,
1.85855418e-02, 4.00000000e+00, 3.00000000e+00,
1.10000000e+01, 3.08677063e+01, 6.22253967e+02,
1.60000000e+01],
[ 6.96363877e-01, 9.01892568e-01, 8.74152129e-02,
1.40171550e-01, 1.00000000e+00, 3.00000000e+00,
1.30000000e+01, 8.30609395e+02, 1.39691293e+03,
1.60000000e+01],
[ 2.44547789e-02, 6.95094086e-01, 6.19703209e-01,
4.80139384e-01, 1.00000000e+00, 3.00000000e+00,
1.40000000e+01, 1.83540480e+01, 5.19130872e+01,
1.60000000e+01],
[ 4.99682276e-01, 8.54642128e-02, 1.16964019e-01,
8.82417334e-01, 3.00000000e+00, 5.00000000e+00,
9.00000000e+00, 1.45676175e+01, 3.88908730e+01,
1.60000000e+01],
[ 3.32999431e-01, 7.58517863e-01, 9.57923895e-01,
8.58893091e-01, 4.00000000e+00, 5.00000000e+00,
1.20000000e+01, 2.77182631e+02, 5.23251131e+02,
1.60000000e+01],
[ 1.19481319e-01, 5.16393929e-01, 8.57334431e-03,
6.21834337e-01, 5.00000000e+00, 4.00000000e+00,
1.00000000e+01, 1.47997769e+03, 2.63702046e+03,
1.60000000e+01],
[ 7.87179804e-01, 9.29303701e-01, 5.02257370e-01,
9.89073623e-01, 2.00000000e+00, 4.00000000e+00,
1.30000000e+01, 8.80000000e+02, 1.31851023e+03,
1.60000000e+01],
[ 2.71883593e-01, 3.03523995e-01, 7.76082888e-01,
2.31191883e-01, 4.00000000e+00, 1.00000000e+00,
1.10000000e+01, 6.98456463e+02, 1.17465907e+03,
1.60000000e+01],
[ 3.25999117e-01, 3.93316786e-01, 5.96222219e-01,
1.59517751e-01, 0.00000000e+00, 3.00000000e+00,
9.00000000e+00, 1.09133822e+01, 1.64813778e+02,
1.60000000e+01],
[ 1.25434927e-01, 9.15497073e-01, 9.36152996e-01,
9.46849897e-01, 1.00000000e+00, 4.00000000e+00,
1.10000000e+01, 5.50000000e+01, 1.03826174e+02,
1.60000000e+01],
[ 2.20754135e-01, 2.08976731e-01, 1.98145901e-01,
8.55898171e-01, 4.00000000e+00, 4.00000000e+00,
1.20000000e+01, 4.93883301e+02, 7.39988845e+02,
1.60000000e+01],
[ 9.17049535e-01, 6.26543410e-03, 2.08145487e-02,
5.81651906e-01, 3.00000000e+00, 4.00000000e+00,
1.10000000e+01, 3.91995436e+02, 4.93883301e+02,
1.60000000e+01],
[ 3.44972430e-01, 6.08715328e-01, 9.36192542e-02,
9.67605131e-01, 5.00000000e+00, 2.00000000e+00,
1.00000000e+01, 2.61625565e+02, 2.93664768e+02,
1.60000000e+01],
[ 2.17571318e-01, 3.97113944e-01, 9.00572365e-01,
6.46500433e-01, 5.00000000e+00, 0.00000000e+00,
1.40000000e+01, 9.72271824e+00, 1.37500000e+01,
1.60000000e+01],
[ 6.09425226e-01, 9.53499121e-01, 3.67830864e-01,
2.95025276e-01, 1.00000000e+00, 4.00000000e+00,
1.10000000e+01, 1.09133822e+01, 3.72931009e+03,
1.60000000e+01],
[ 6.03153526e-01, 6.75552074e-01, 2.27855896e-01,
2.19524198e-01, 3.00000000e+00, 2.00000000e+00,
1.20000000e+01, 1.94454365e+01, 4.89994295e+01,
1.60000000e+01],
[ 4.44281858e-01, 5.36325911e-01, 7.90351741e-01,
7.57456099e-01, 0.00000000e+00, 3.00000000e+00,
1.30000000e+01, 5.50000000e+01, 1.64813778e+02,
1.60000000e+01],
[ 6.53895144e-01, 8.87003260e-01, 9.60499685e-03,
9.62404894e-01, 5.00000000e+00, 5.00000000e+00,
1.20000000e+01, 9.72271824e+00, 1.83540480e+01,
1.60000000e+01],
[ 9.17862960e-01, 7.84819867e-01, 6.00791650e-01,
4.39622459e-01, 0.00000000e+00, 2.00000000e+00,
1.00000000e+01, 4.40000000e+02, 6.22253967e+02,
1.60000000e+01],
[ 4.59416026e-01, 9.64323383e-01, 7.38905189e-03,
3.65515440e-01, 4.00000000e+00, 0.00000000e+00,
1.10000000e+01, 1.54338532e+01, 4.36535289e+01,
1.60000000e+01],
[ 2.18429455e-01, 8.03875937e-02, 6.71319582e-01,
7.06118679e-02, 0.00000000e+00, 2.00000000e+00,
1.30000000e+01, 6.59255114e+02, 1.24450793e+03,
1.60000000e+01],
[ 8.88572972e-01, 4.25046821e-01, 4.71270212e-01,
3.01855318e-01, 0.00000000e+00, 4.00000000e+00,
1.20000000e+01, 1.95997718e+02, 4.66163762e+02,
1.60000000e+01],
[ 7.06000732e-02, 5.00510665e-01, 2.49573626e-02,
2.57327761e-01, 1.00000000e+00, 1.00000000e+00,
1.10000000e+01, 3.11126984e+02, 1.04650226e+03,
1.60000000e+01],
[ 5.59480495e-01, 3.51992463e-01, 5.07058652e-01,
5.04548853e-01, 0.00000000e+00, 0.00000000e+00,
9.00000000e+00, 1.17465907e+03, 1.86465505e+03,
1.60000000e+01],
[ 3.04304972e-01, 7.25535293e-01, 7.81526949e-03,
9.95179416e-01, 1.00000000e+00, 4.00000000e+00,
1.10000000e+01, 1.09133822e+01, 1.03826174e+02,
1.60000000e+01],
[ 3.89979482e-01, 3.04110776e-01, 5.48185920e-01,
7.46558343e-01, 1.00000000e+00, 5.00000000e+00,
1.20000000e+01, 2.59565436e+01, 4.12034446e+01,
1.60000000e+01],
[ 6.03574550e-01, 7.71480041e-01, 6.40922688e-01,
5.57669701e-01, 3.00000000e+00, 0.00000000e+00,
1.00000000e+01, 2.18267645e+01, 3.46478289e+01,
1.60000000e+01],
[ 6.33824101e-01, 1.00791411e-01, 8.86926243e-01,
5.69302888e-01, 3.00000000e+00, 3.00000000e+00,
1.00000000e+01, 1.95997718e+02, 2.07652349e+02,
1.60000000e+01],
[ 4.83270844e-01, 4.31958188e-02, 8.89111208e-01,
3.03748410e-02, 5.00000000e+00, 1.00000000e+00,
1.10000000e+01, 6.17354127e+01, 8.73070579e+01,
1.60000000e+01],
[ 4.87945587e-01, 6.90624501e-01, 4.78157023e-01,
4.23797250e-02, 1.00000000e+00, 4.00000000e+00,
8.00000000e+00, 4.36535289e+01, 1.84997211e+02,
1.60000000e+01],
[ 4.17061098e-02, 8.54476414e-01, 2.04662729e-01,
2.41624398e-01, 1.00000000e+00, 3.00000000e+00,
9.00000000e+00, 1.10873052e+03, 2.09300452e+03,
1.60000000e+01],
[ 5.93181370e-01, 2.04257731e-01, 6.25243785e-01,
1.02961477e-01, 1.00000000e+00, 5.00000000e+00,
1.20000000e+01, 3.11126984e+02, 2.21746105e+03,
1.60000000e+01],
[ 7.75826478e-01, 2.84495709e-01, 8.59169508e-01,
6.31274988e-01, 2.00000000e+00, 2.00000000e+00,
1.10000000e+01, 9.17702400e+00, 1.09133822e+01,
1.60000000e+01],
[ 1.08605262e-01, 6.09662022e-01, 5.96985380e-01,
3.49630688e-01, 2.00000000e+00, 4.00000000e+00,
1.20000000e+01, 1.30812783e+02, 2.46941651e+02,
1.60000000e+01],
[ 1.20932073e-01, 6.81907879e-01, 9.21699988e-01,
1.67986030e-01, 3.00000000e+00, 0.00000000e+00,
1.20000000e+01, 8.17579892e+00, 4.18600904e+03,
1.60000000e+01],
[ 9.43480882e-01, 1.72716304e-01, 7.31615737e-01,
2.35318539e-01, 5.00000000e+00, 3.00000000e+00,
8.00000000e+00, 1.64813778e+02, 3.29627557e+02,
1.60000000e+01],
[ 3.36802048e-01, 8.93346266e-01, 5.56808351e-01,
4.23929893e-01, 4.00000000e+00, 0.00000000e+00,
8.00000000e+00, 3.88908730e+01, 3.95106641e+03,
1.60000000e+01],
[ 3.32561690e-01, 4.32786774e-01, 1.98115883e-01,
4.70694013e-03, 2.00000000e+00, 4.00000000e+00,
1.30000000e+01, 2.20000000e+02, 3.95106641e+03,
1.60000000e+01],
[ 7.74137628e-01, 3.24171250e-01, 9.54343651e-02,
6.80617300e-01, 5.00000000e+00, 5.00000000e+00,
1.20000000e+01, 1.10873052e+03, 3.95106641e+03,
1.60000000e+01],
[ 2.00390003e-01, 4.80073455e-01, 2.03514730e-01,
8.01010403e-01, 1.00000000e+00, 0.00000000e+00,
1.40000000e+01, 4.66163762e+02, 6.22253967e+02,
1.60000000e+01],
[ 3.88211952e-01, 7.23324070e-01, 9.08966653e-01,
1.15439206e-01, 5.00000000e+00, 2.00000000e+00,
1.10000000e+01, 6.54063913e+01, 8.73070579e+01,
1.60000000e+01],
[ 2.05755206e-02, 5.84655459e-01, 6.36086635e-01,
2.77855111e-01, 2.00000000e+00, 3.00000000e+00,
1.10000000e+01, 1.95997718e+02, 2.93664768e+02,
1.60000000e+01],
[ 4.92622980e-01, 9.89929425e-01, 9.76492480e-01,
8.92141956e-03, 2.00000000e+00, 1.00000000e+00,
1.40000000e+01, 5.87329536e+02, 1.86465505e+03,
1.60000000e+01],
[ 7.59947779e-01, 5.97140933e-02, 3.37852753e-02,
8.30424594e-01, 4.00000000e+00, 0.00000000e+00,
1.20000000e+01, 9.79988590e+01, 1.30812783e+02,
1.60000000e+01],
[ 2.33052433e-01, 3.95877031e-01, 7.80625965e-01,
6.12112506e-01, 0.00000000e+00, 4.00000000e+00,
1.20000000e+01, 3.88908730e+01, 1.23470825e+02,
1.60000000e+01],
[ 6.25775589e-01, 1.73320709e-01, 7.32627992e-01,
2.32759446e-01, 0.00000000e+00, 5.00000000e+00,
1.30000000e+01, 3.29627557e+02, 7.83990872e+02,
1.60000000e+01],
[ 6.13349483e-01, 4.70304177e-01, 2.07820805e-01,
1.58491173e-01, 2.00000000e+00, 1.00000000e+00,
1.40000000e+01, 2.07652349e+02, 2.46941651e+02,
1.60000000e+01],
[ 5.88138769e-01, 9.02429914e-01, 6.52229789e-01,
5.62310140e-01, 0.00000000e+00, 5.00000000e+00,
1.10000000e+01, 1.66121879e+03, 3.95106641e+03,
1.60000000e+01],
[ 2.32791422e-01, 2.67940554e-01, 8.00947476e-01,
2.51281146e-01, 4.00000000e+00, 3.00000000e+00,
1.30000000e+01, 3.88908730e+01, 7.77817459e+01,
1.60000000e+01],
[ 6.80249933e-02, 6.42926405e-02, 8.62884989e-02,
9.11257901e-01, 0.00000000e+00, 2.00000000e+00,
1.10000000e+01, 9.79988590e+01, 1.86465505e+03,
1.60000000e+01],
[ 4.63363314e-01, 7.89818197e-01, 6.07832977e-01,
1.10045903e-01, 3.00000000e+00, 3.00000000e+00,
1.20000000e+01, 8.66195722e+00, 1.37500000e+01,
1.60000000e+01],
[ 4.40565527e-02, 2.91823856e-01, 9.46863941e-02,
8.32175186e-01, 4.00000000e+00, 1.00000000e+00,
1.10000000e+01, 1.17465907e+03, 1.86465505e+03,
1.60000000e+01],
[ 4.59862069e-01, 3.49460539e-01, 9.07124286e-01,
8.39087962e-01, 5.00000000e+00, 4.00000000e+00,
1.30000000e+01, 8.17579892e+00, 6.54063913e+01,
1.60000000e+01],
[ 5.92218547e-01, 4.65846456e-01, 9.50627801e-01,
8.14859360e-01, 5.00000000e+00, 5.00000000e+00,
1.20000000e+01, 1.63515978e+01, 8.24068892e+01,
1.60000000e+01],
[ 3.01785372e-01, 9.53624952e-01, 6.82601271e-01,
1.26688128e-01, 2.00000000e+00, 5.00000000e+00,
1.10000000e+01, 6.17354127e+01, 5.23251131e+02,
1.60000000e+01],
[ 3.87658424e-01, 4.06595450e-01, 7.97436325e-01,
9.41025691e-01, 0.00000000e+00, 0.00000000e+00,
9.00000000e+00, 1.16540940e+02, 2.20000000e+02,
1.60000000e+01],
[ 8.17272867e-01, 7.40968764e-01, 8.04849419e-01,
6.91434430e-01, 3.00000000e+00, 2.00000000e+00,
1.00000000e+01, 3.46478289e+01, 1.23470825e+02,
1.60000000e+01]])
In [8]:
%time
for i, row in df.iterrows():
session = nonrealtimetools.Session()
builder = gk.generator.gendy1.make_builder(row)
out = gk.generator.gendy1.build_out(builder)
synthdef = builder.build()
with session.at(0):
synth_a = session.add_synth(duration=10, synthdef=synthdef)
gk.util.render_session(session, this_dir, row["hash"])
1 loop, best of 3: 18.9 s per loop
In [10]:
%timeit
for i, row in df.iterrows():
y, sr = librosa.load(os.path.join(this_dir, "aif_files", row["hash"] + ".aiff"))
_y_normed = librosa.util.normalize(y)
_mfcc = librosa.feature.mfcc(y=_y_normed, sr=sr, n_mfcc=13)
_cent = np.mean(librosa.feature.spectral_centroid(y=_y_normed, sr=sr))
_mfcc_mean = gk.feature_extraction.get_stats(_mfcc)["mean"]
X_row = np.append(_mfcc_mean, _cent)
if i==0:
X_mtx = X_row
else:
X_mtx = np.vstack((X_mtx, X_row))
1 loop, best of 3: 1min 28s per loop
Thought: For feature extraction, it would probably be faster to extract all time domain vectors $y$ into a NumPy array and perform the necessary LibROSA operations across the rows of the vector, possibly leveraging under-the-hood efficiencies.
"1min 43s per loop" below
In [13]:
for i, row in df.iterrows():
session = nonrealtimetools.Session()
builder = gk.generator.gendy1.make_builder(row)
out = gk.generator.gendy1.build_out(builder)
synthdef = builder.build()
with session.at(0):
synth_a = session.add_synth(duration=10, synthdef=synthdef)
gk.util.render_session(session, this_dir, row["hash"])
y, sr = librosa.load(os.path.join(this_dir, "aif_files", row["hash"] + ".aiff"))
_y_normed = librosa.util.normalize(y)
_mfcc = librosa.feature.mfcc(y=_y_normed, sr=sr, n_mfcc=13)
_cent = np.mean(librosa.feature.spectral_centroid(y=_y_normed, sr=sr))
_mfcc_mean = gk.feature_extraction.get_stats(_mfcc)["mean"]
X_row = np.append(_mfcc_mean, _cent)
if i==0:
X_mtx = X_row
else:
X_mtx = np.vstack((X_mtx, X_row))
1 loop, best of 3: 1min 43s per loop
In [16]:
X_mtx.shape
Out[16]:
(100, 14)
In [20]:
def col_rename_4_mfcc(c):
if (c < 13):
return "mfcc_mean_{}".format(c)
else:
return "spectral_centroid"
In [21]:
pd.DataFrame(X_mtx).rename_axis(lambda c: col_rename_4_mfcc(c), axis=1)
Out[21]:
mfcc_mean_0
mfcc_mean_1
mfcc_mean_2
mfcc_mean_3
mfcc_mean_4
mfcc_mean_5
mfcc_mean_6
mfcc_mean_7
mfcc_mean_8
mfcc_mean_9
mfcc_mean_10
mfcc_mean_11
mfcc_mean_12
spectral_centroid
0
-130.944498
212.246248
22.733654
40.690030
12.067444
11.575786
-2.923093
1.963526
1.012338
5.041066
-1.778145
-0.059208
-0.053824
439.129383
1
-399.895787
110.209896
74.756361
45.275353
30.022552
24.211951
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100 rows × 14 columns
In [66]:
pmtx.shape
Out[66]:
(100, 10)
In [67]:
X_mtx.shape
Out[67]:
(100, 14)
In [68]:
X_mtx[0]
Out[68]:
array([ -1.30944498e+02, 2.12246248e+02, 2.27336540e+01,
4.06900302e+01, 1.20674442e+01, 1.15757863e+01,
-2.92309292e+00, 1.96352599e+00, 1.01233809e+00,
5.04106581e+00, -1.77814489e+00, -5.92080878e-02,
-5.38241320e-02, 4.39129383e+02])
In [31]:
X_train, X_test, y_train, y_test = sk.model_selection.train_test_split(
X_mtx, pmtx, test_size=0.4, random_state=1)
In [38]:
# Create linear regression objectc
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(X_train, y_train)
# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error
print("Mean squared error: %.2f"
% np.mean((regr.predict(X_test) - y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % regr.score(X_test, y_test))
Coefficients:
[[ -7.64326344e-04 5.43840972e-04 8.33835578e-04 -1.63296951e-03
1.84396630e-03 -1.63494109e-03 -4.13973644e-03 9.93769874e-03
-1.21937858e-02 1.07145282e-02 -3.50315085e-03 8.43229154e-03
5.97796792e-03 7.19034767e-05]
[ 4.14076160e-03 1.49650267e-03 7.37848988e-03 -5.89920432e-03
6.43978853e-03 2.56377513e-03 5.08002799e-03 6.34448830e-03
2.48995221e-03 -9.09774452e-04 3.85030377e-03 1.95772826e-03
1.02450172e-02 -8.23419500e-05]
[ -1.58993713e-03 2.26292332e-03 -2.75211523e-03 7.45264689e-03
-9.26048513e-04 -1.65945356e-03 -4.77375839e-03 1.51891806e-02
-1.34561050e-02 1.02081968e-02 -5.44665508e-03 1.38347150e-02
-1.23215340e-02 1.37929495e-04]
[ 7.85798120e-04 2.41554881e-03 1.78210384e-03 -9.60912755e-04
-5.29459040e-03 3.47813067e-03 -1.96233126e-03 2.84434039e-03
1.35618930e-03 -1.21362974e-03 2.00095401e-03 1.95225907e-03
7.85679139e-03 4.24680697e-05]
[ 7.59350445e-03 -1.94676177e-02 4.10676580e-05 -1.23607006e-02
8.17922958e-03 1.16830267e-03 2.88827243e-02 -5.40019464e-02
5.85324462e-02 -3.58495151e-02 2.00269831e-02 -5.54215649e-02
3.18534999e-02 -1.65913946e-03]
[ -7.57996905e-04 1.48878162e-02 -2.02212716e-02 -1.26859790e-02
3.28634748e-02 -1.00350966e-02 2.60161747e-02 3.03667595e-02
1.97929204e-02 -5.33650786e-03 1.85626625e-02 1.78251979e-04
-1.00894504e-02 3.90389290e-04]
[ -7.42013192e-03 1.64585909e-02 -3.66278515e-02 -1.15205242e-02
6.69830320e-03 2.32797054e-02 -1.70235246e-02 4.02606929e-02
5.74493635e-02 -5.28742501e-02 1.69008951e-02 2.07720815e-02
1.42142293e-02 6.60588879e-04]
[ -4.02348887e+00 4.78619594e+00 -5.20732849e+00 1.43378482e+00
-1.86539229e-01 -8.95742471e-01 -2.38130301e+00 -1.14239048e-01
-7.14676019e+00 6.04096194e+00 -5.28217010e+00 6.65615280e+00
-4.69621834e+00 6.35692181e-01]
[ 1.10679242e+01 -1.45142504e+01 2.78669011e+01 -1.57162603e+01
2.37260432e+01 -2.25193655e+01 1.09819093e+01 -2.93601263e+01
1.87695403e+01 -2.66324515e+01 2.66129494e+01 -3.35883337e+01
-9.61925708e+00 -2.26352472e-01]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
0.00000000e+00 0.00000000e+00]]
Mean squared error: 80571.86
Variance score: 0.27
/Users/spacecoffin/Development/sprbrg/lib/python3.5/site-packages/scipy/linalg/basic.py:884: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.
warnings.warn(mesg, RuntimeWarning)
In [42]:
# Scale data
standard_scaler = sk.preprocessing.StandardScaler()
X_scaled = standard_scaler.fit_transform(X_mtx)
#Xte_s = standard_scaler.transform(X_test)
robust_scaler = sk.preprocessing.RobustScaler()
X_rscaled = robust_scaler.fit_transform(X_mtx)
#Xte_r = robust_scaler.transform(X_test)
In [57]:
X_scaled.mean(axis=0)
Out[57]:
array([ 2.95319325e-16, 1.93178806e-16, 3.21964677e-17,
-5.66213743e-17, -6.66133815e-18, 3.96696564e-16,
5.88418203e-17, -1.86517468e-16, 7.00828284e-17,
3.03090886e-16, -1.66533454e-17, 7.10542736e-17,
-1.66533454e-16, 4.86277685e-16])
In [59]:
X_scaled.mean(axis=0).mean()
Out[59]:
1.0526797687506115e-16
In [61]:
X_scaled.std(axis=0)
Out[61]:
array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
In [43]:
X_train, X_test, y_train, y_test = sk.model_selection.train_test_split(
X_scaled, pmtx, test_size=0.4, random_state=1)
In [44]:
# Create linear regression objectc
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(X_train, y_train)
# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error
print("Mean squared error: %.2f"
% np.mean((regr.predict(X_test) - y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % regr.score(X_test, y_test))
Coefficients:
[[ -1.16321270e-01 3.82759545e-02 3.54522472e-02 -2.71379638e-02
3.49357740e-02 -1.90305103e-02 -5.18060153e-02 1.02756139e-01
-1.37969854e-01 1.01095033e-01 -3.91266879e-02 7.60528902e-02
5.51004476e-02 1.01775935e-01]
[ 6.30174076e-01 1.05325032e-01 3.13711785e-01 -9.80375889e-02
1.22008193e-01 2.98420226e-02 6.35731312e-02 6.56022225e-02
2.81732309e-02 -8.58401572e-03 4.30040385e-02 1.76572278e-02
9.44309239e-02 -1.16551095e-01]
[ -2.41969294e-01 1.59266319e-01 -1.17011881e-01 1.23853912e-01
-1.75449093e-02 -1.93158324e-02 -5.97403733e-02 1.57056638e-01
-1.52252703e-01 9.63176327e-02 -6.08336845e-02 1.24778663e-01
-1.13570706e-01 1.95232608e-01]
[ 1.19589016e-01 1.70008221e-01 7.57698369e-02 -1.59691993e-02
-1.00311277e-01 4.04850070e-02 -2.45572550e-02 2.94105753e-02
1.53449669e-02 -1.14509885e-02 2.23486532e-02 1.76078998e-02
7.24180405e-02 6.01115230e-02]
[ 1.15563998e+00 -1.37014621e+00 1.74607657e-03 -2.05419785e-01
1.54963632e-01 1.35988973e-02 3.61447856e-01 -5.58381942e-01
6.62281034e-01 -3.38251751e-01 2.23681353e-01 -4.99860587e-01
2.93601793e-01 -2.34843261e+00]
[ -1.15358005e-01 1.04781619e+00 -8.59749258e-01 -2.10825516e-01
6.22631187e-01 -1.16807272e-01 3.25574916e-01 3.13993315e-01
2.23952297e-01 -5.03516749e-02 2.07326357e-01 1.60769800e-03
-9.29970255e-02 5.52577382e-01]
[ -1.12925477e+00 1.15836854e+00 -1.55730900e+00 -1.91457077e-01
1.26906010e-01 2.70972866e-01 -2.13037953e-01 4.16296919e-01
6.50026205e-01 -4.98885623e-01 1.88766079e-01 1.87348461e-01
1.31016160e-01 9.35031986e-01]
[ -6.12326578e+02 3.36856227e+02 -2.21400360e+02 2.38277569e+01
-3.53417106e+00 -1.04263306e+01 -2.98004044e+01 -1.18123560e+00
-8.08639317e+01 5.69984265e+01 -5.89965520e+01 6.00334626e+01
-4.32862365e+01 8.99791901e+02]
[ 1.68440485e+03 -1.02152433e+03 1.18481904e+03 -2.61185099e+02
4.49513465e+02 -2.62122605e+02 1.37431204e+02 -3.03584694e+02
2.12372989e+02 -2.51285780e+02 2.97240003e+02 -3.02941359e+02
-8.86631341e+01 -3.20391106e+02]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
0.00000000e+00 0.00000000e+00]]
Mean squared error: 80571.86
Variance score: 0.27
In [64]:
y_test[0]
Out[64]:
array([ 3.32561690e-01, 4.32786774e-01, 1.98115883e-01,
4.70694013e-03, 2.00000000e+00, 4.00000000e+00,
1.30000000e+01, 2.20000000e+02, 3.95106641e+03,
1.60000000e+01])
In [71]:
X_test[0]
Out[71]:
array([ 1.01086499, -1.90257535, -0.26937053, -0.36982032, 0.42170525,
1.26344082, 0.32565618, -0.63435796, -0.34336578, 1.15169182,
0.46333517, -0.35546419, -0.22943205, 2.27780905])
In [70]:
regr.predict(X_test[0])
/Users/spacecoffin/Development/sprbrg/lib/python3.5/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
DeprecationWarning)
Out[70]:
array([[ 5.81091297e-01, 6.46632233e-01, 4.05079195e-01,
3.24762723e-01, 1.28734484e+00, 1.97150205e+00,
1.01313047e+01, 1.11999703e+03, 3.38978192e+03,
1.60000000e+01]])
In [72]:
y_test[0]
Out[72]:
array([ 3.32561690e-01, 4.32786774e-01, 1.98115883e-01,
4.70694013e-03, 2.00000000e+00, 4.00000000e+00,
1.30000000e+01, 2.20000000e+02, 3.95106641e+03,
1.60000000e+01])
In [ ]:
Content source: spacecoffin/GravelKicker
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