In [8]:
import pandas as pd
import numpy as np
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (12, 7)

import pop_rel_5_base

Populating the interactive namespace from numpy and matplotlib
In [9]:
model = pop_rel_5_base.basemodel()

Best model: Pipeline(memory=None,
     steps=[('polynomialfeatures', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('linearregression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True))])
Training error:
RMSE: 93.55149784836121
Rsq: 0.5382782732540343
Test error:
RMSE: 46.125252148291736
Rsq: 0.5344478444386802
In [10]:
pop_rel_5_base.firstNonZeroPop()

Best model: Pipeline(memory=None,
     steps=[('polynomialfeatures', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('linearregression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True))])
Training error:
RMSE: 101.45161930408847
Rsq: 0.38044818794933577
Test error:
RMSE: 51.75587810304219
Rsq: 0.4293502114515424
Out[10]:
Pipeline(memory=None,
     steps=[('polynomialfeatures', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('linearregression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True))])
In [11]:
pop_rel_5_base.nthNonZeroPop(3)

Best model: Pipeline(memory=None,
     steps=[('polynomialfeatures', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('linearregression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False))])
Training error:
RMSE: 82.73206633295496
Rsq: 0.5879903312990828
Test error:
RMSE: 42.163949016123404
Rsq: 0.6212672537146233
Out[11]:
Pipeline(memory=None,
     steps=[('polynomialfeatures', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('linearregression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False))])
In [12]:
pop_rel_5_base.nthNonZeroPop(5)

Best model: Pipeline(memory=None,
     steps=[('polynomialfeatures', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('linearregression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True))])
Training error:
RMSE: 79.90308271485766
Rsq: 0.6156855315355054
Test error:
RMSE: 41.278649987794466
Rsq: 0.637004477053178
Out[12]:
Pipeline(memory=None,
     steps=[('polynomialfeatures', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('linearregression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True))])