
from scipy import interpolate
import scipy as sp
import sklearn

import statsmodels as sm
from statsmodels.gam.api import GLMGam, BSplines
from statsmodels.gam.generalized_additive_model import LogitGam
from statsmodels.gam.tests.test_penalized import df_autos
from patsy import dmatrices

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt


data=pd.read_csv('data/heart_deasease.csv')
data.head(2)
X=data.copy()
X.drop(['chd','row.names','famhist', 'obesity'],axis=1,inplace=True)
y=data.chd


data_heart=pd.read_csv('data/heart_deasease.csv')
data_heart.drop(['row.names','famhist', 'obesity'],axis=1,inplace=True)

data_heart.columns

#'sbp', 'tobacco', 'ldl', 'adiposity', 'typea', 'alcohol', 'age'

x_spline = data_heart[['ldl','age', 'adiposity','typea']]
bs = BSplines(x_spline, df=[5,5,5,5], degree=[3, 3,3,3])
gam_bs = GLMGam.from_formula('chd ~ ldl + age + adiposity + typea', data=data_heart,
                                 smoother=bs)#, alpha=alpha)
res_bs = gam_bs.fit()
#alpha = np.array((10000000.0, 10000000.0, 215.44346900318845, 46415.88833612782))
gam_bs_poiss = GLMGam.from_formula('chd ~ ldl + age + adiposity + typea', data=data_heart,
                                 smoother=bs, #alpha=alpha,
                                 family=sm.genmod.families.family.Poisson())
#statsmodels.gam.generalized_additive_model.LogitGam
res_bs_poiss = gam_bs_poiss.fit()

#print(res_bs_poiss.summary())
#gam_bs.select_penweight()[0]
#gam_bs.select_penweight_kfold()[0]


#res_bs_poiss.__dict__['_results'].__dict__['model'].__dict__.keys()

X


alpha = np.array((1.0, 1.0, 1.0))#, 215.4446900318845, 46415.88833612782))
#gam_bs = LogitGam.from_formula('chd ~ ldl + age + adiposity + typea', data=data_heart)#, smoother=bs,alpha=alpha)
y,X=dmatrices('chd ~ ldl + age + adiposity', data=data_heart,return_type='dataframe')
x_spline2 = data_heart[['ldl','age','adiposity']]
bs2 = BSplines(x_spline2, df=[8,8,8], degree=[3, 3,3])

log_m=LogitGam(y, smoother=bs2,alpha=alpha)
#res_lm=log_m.fit()
fit=log_m.fit_regularized()

Optimization terminated successfully    (Exit mode 0)
            Current function value: 0.5426514025933599
            Iterations: 173
            Function evaluations: 173
            Gradient evaluations: 173


#fig, ax_lst = plt.subplots(2, 3)  # 3 rows, 1 column; numbered row-wise
for i in range(3):
    res_bs.plot_partial(i, cpr=True)


alp=gam_bs_poiss.select_penweight_kfold(alphas=None, cv_iterator=None, cost=None, k_folds=5, k_grid=4)
alp

((46415.88833612782, 10000000.0, 215.44346900318845, 46415.88833612782),
 <statsmodels.gam.gam_cross_validation.gam_cross_validation.MultivariateGAMCVPath at 0x19ec662f910>)

	sbp	tobacco	ldl	adiposity	typea	alcohol	age
0	160	12.00	5.73	23.11	49	97.20	52
1	144	0.01	4.41	28.61	55	2.06	63
2	118	0.08	3.48	32.28	52	3.81	46
3	170	7.50	6.41	38.03	51	24.26	58
4	134	13.60	3.50	27.78	60	57.34	49
...	...	...	...	...	...	...	...
457	214	0.40	5.98	31.72	64	0.00	58
458	182	4.20	4.41	32.10	52	18.72	52
459	108	3.00	1.59	15.23	40	26.64	55
460	118	5.40	11.61	30.79	64	23.97	40
461	132	0.00	4.82	33.41	62	0.00	46

Generalized Additive Models¶