def example1_run():
data_file = "https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/fishing_long.csv"
df = pd.read_csv(data_file)
varnames = ['price', 'catch', 'income']
X = df[varnames].values
y = df['choice'].values
transvars = ['catch']
model = MixedLogit()
model.fit(
X,
y,
# isvars=['income'],
transvars=transvars,
transformation="boxcox",
alts=['beach', 'boat', 'charter', 'pier'],
# scipy_optimisation=True,
# isvars=['income', 'price'],
randvars={'catch': 'n'},
# init_coeff=np.repeat(0, 11),
# gtol=1e-4,
grad=False,
hess=False,
# tol=1e-4,
# method="L-BFGS-B",
# fit_intercept=True,
varnames=varnames)
model.summary()
def example3_run():
df = pd.read_csv("https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/fishing_long.csv")
varnames = ['price', 'catch']
X = df[varnames].values
y = df['choice'].values
model = MixedLogit()
# initial_coeffs = [0.5, 0.5, 0.5, -0.1, 0.5, 1, 1, 1]
model.fit(X, y, varnames=varnames,
# isvars=['income'],
alts=['beach', 'boat', 'charter', 'pier'],
randvars={'price': 'n'},
transvars=['price', 'catch'],
# verbose=0,
n_draws=600,
maxiter=2000,
# grad=False,
hess=False,
halton=True,
# init_coeff=np.repeat(0.2, 5),
correlation=True,
# weights=np.ones(1182),
# method="L-BFGS-B",
fit_intercept=True
)
model.summary()
def example8_run():
df = pd.read_csv(
"https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/electricity_long.csv"
)
varnames = ['cl', 'loc', 'pf', 'wk', 'tod', 'seas']
y = df['choice'].values
# choice_var = df['choice']
df['wk'] = -df['wk']
# df['tod'] = -df['tod']
X = df[varnames].values
# isvarnames = []
randvars = {'wk': 'u', 'seas': 'n', 'tod': 'n'}
corvars = ['tod', 'seas']
alts = [1, 2, 3, 4]
np.random.seed(123)
model = MixedLogit()
# init_coeff=[]
model.fit(
X,
y,
varnames=varnames,
alts=alts,
# isvars=None,
randvars=randvars,
# ids=df['chid'],
panels=df.id.values,
isvars=[],
halton=True,
# correlation=True,
# transvars=['cl', 'loc'],
# method="L-BFGS-B",
correlation=corvars,
# tol=1e-8,
# hess=False,
# grad=False,
# weights=None,
# avail=None,
# base_alt=None,
# fit_intercept=False,
# init_coeff=None,
# maxiter=2000,
# random_state=None,
verbose=1,
n_draws=200)
model.summary()
def example12_run():
try:
df = pd.read_csv(
"examples_prit/Final_RT_Dataset_weights_corrected.csv")
except FileNotFoundError:
df = pd.read_csv(
"xlogitprit/examples_prit/Final_RT_Dataset_weights_corrected.csv")
all_vars = [
'cost', 'risk', 'seats', 'noise', 'crowdness', 'convloc', 'clientele'
]
is_vars = ['gender', 'age']
rand_vars = {'cost': 'n', 'risk': 'n'} #, 'seats': 'ln'}
choice_set = ['WaterTaxi', 'Ferry', 'Hovercraft', 'Helicopter']
choice_var = df['choice']
# alt_var = df['idx.alt']
Tol = 1e-4
# av = df['av']
choice_id = df['idx.chid']
ind_id = df['idx.id']
weights = df['weight']
R = 100
X = df[all_vars]
# X_zeros = np.zeros_like(X)
model = MixedLogit()
model.fit(
X=X,
y=choice_var,
varnames=all_vars,
alts=choice_set,
# isvars=is_vars,
ids=choice_id,
weights=weights,
# init_coeff=np.repeat(20, 10),
panels=ind_id,
randvars=rand_vars,
grad=False,
hess=False,
# n_draws=R,
# correlation=['cost', 'risk'],
# fit_intercept=True,
# avail=av,
gtol=Tol)
model.summary()
def example7_run():
df = pd.read_csv(
"https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/electricity_long.csv"
)
varnames = ["pf", "cl", "loc", "wk", "tod", "seas"]
df['tod'] = -df['tod']
df['seas'] = -df['seas']
X = df[varnames].values
y = df['choice'].values
choice_id = df['chid']
alt = [1, 2, 3, 4]
np.random.seed(123)
model = MixedLogit()
model.fit(
X,
y,
varnames,
alts=alt,
randvars={
'cl': 't',
'loc': 'n',
'wk': 'u',
'tod': 'ln',
'seas': 'ln'
},
fit_intercept=True,
isvars=['pf'],
# transformation="boxcox",
# transvars=['cl', 'loc', 'wk'],
correlation=True,
# ids=choice_id,
panels=df.id.values,
# isvars=[],
# grad=False,
# hess=False,
halton=False,
# tol=1e-4,
# method='L-BFGS-B',
n_draws=400)
model.summary()
from xlogitprit import MixedLogit
import numpy as np
import pandas as pd
data_file = "https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/fishing_long.csv"
df = pd.read_csv(data_file)
varnames = ['price', 'catch', 'income']
X = df[varnames].values
y = df['choice'].values
transvars = ['catch']
model = MixedLogit()
model.fit(
X,
y,
# isvars=['income'],
transvars=transvars,
transformation="boxcox",
alts=['beach', 'boat', 'charter', 'pier'],
# scipy_optimisation=True,
isvars=['income', 'price'],
randvars={'catch': 'n'},
weights=np.ones(4782),
# init_coeff=np.repeat(0, 11),
# gtol=1e-4,
# grad=False,
# hess=False,
# tol=1e-4,
# method="L-BFGS-B",
fit_intercept=True,
from xlogitprit import MixedLogit
df = pd.read_csv(
"https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/electricity_long.csv"
)
varnames = ["pf", "cl", "loc", "wk", "tod", "seas"]
df['tod'] = -df['tod']
df['seas'] = -df['seas']
X = df[varnames].values
y = df['choice'].values
choice_id = df['chid']
alt = [1, 2, 3, 4]
np.random.seed(123)
model = MixedLogit()
model.fit(
X,
y,
varnames,
alts=alt,
randvars={
'cl': 't',
'loc': 'n',
'wk': 'u',
'tod': 'ln',
'seas': 'ln'
},
fit_intercept=True,
isvars=['pf'],
# transformation="boxcox",
from xlogitprit import MixedLogit
df = pd.read_csv("https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/electricity_long.csv")
varnames = ["pf", "cl", "loc", "wk", "tod", "seas"]
df['tod'] = -df['tod']
df['seas'] = -df['seas']
X = df[varnames].values
y = df['choice'].values
choice_id = df['chid']
alt = [1, 2, 3, 4]
np.random.seed(123)
model = MixedLogit()
model.fit(X, y,
varnames,
alts=alt,
randvars={'cl': 'n', 'loc': 'n', 'wk': 'u', 'tod': 'ln', 'seas': 'ln'},
# fit_intercept=True,
# isvars=['pf'],
transformation="boxcox",
transvars=['pf', 'cl'],
# correlation=True,
weights=np.ones(361),
# ids=choice_id,
panels=df.id.values,
isvars=[],
# grad=False,
# hess=False,
import pandas as pd
import numpy as np
from xlogitprit import MixedLogit
df = pd.read_csv(
"https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/fishing_long.csv"
)
varnames = ['price', 'catch']
X = df[varnames].values
y = df['choice'].values
model = MixedLogit()
# initial_coeffs = [0.5, 0.5, 0.5, -0.1, 0.5, 1, 1, 1]
model.fit(
X,
y,
varnames=varnames,
# isvars=['income'],
alts=['beach', 'boat', 'charter', 'pier'],
randvars={'price': 'n'},
transvars=['price', 'catch'],
# verbose=0,
n_draws=600,
maxiter=2000,
# grad=False,
# hess=False,
halton=True,
# init_coeff=np.repeat(0.2, 5),
correlation=True,
def example2_run():
df = pd.read_csv(
"https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/electricity_long.csv"
)
varnames = ["pf", "tod", "seas", "cl", "loc", "wk"]
# tod = df['tod']
# kde = ss.gaussian_kde(tod)
# xs = np.linspace(min(tod), max(tod))
# plt.plot(xs, kde(xs))
# plt.savefig('testtod.png')
# print(1/0)
# Testing plots of tod and seas
df['tod'] = -df['tod']
df['seas'] = -df['seas']
X = df[varnames].values
y = df['choice'].values
choice_id = df['chid']
panels = df.id.values
alt = [1, 2, 3, 4]
randvars1 = {"cl": 'n', "loc": 'n', "wk": 'u', "tod": 'ln', "seas": 'ln'}
randvars2 = {"wk": 'u', "tod": 'ln', "seas": 'ln', "cl": 'n', "loc": 'n'}
randvars3 = {"wk": 'u', "tod": 'ln', "seas": 'ln', "cl": 'n', "loc": 'n'}
randvars4 = {"wk": 'u', "tod": 'ln', "seas": 'ln', "cl": 'n', "loc": 'n'}
# randvars4 = {"cl": 'n', "loc": 'n', "wk": 'n', "tod": 'ln', "seas": 'ln'}
# init_coeff = np.array([-0.93415106, 2.18043941, 2.19710561, -0.24598737, 2.55557668, 1.89018881,
# 0.40421956, 0.15024535, 0.23448914, -0.01404247, 0.06802344, 0.36800581,
# -0.21865647, 0.22454976, 0.13975384, 2.35863656, -0.1631371, 0.15818687,
# 0.10194485, 1.71571673, 0.8943477])
np.random.seed(123)
model = MixedLogit()
model.check_if_gpu_available()
N = len(np.unique(df['id'].values))
training_size = int(0.8 * N)
ids = np.random.choice(N, training_size, replace=False)
train_idx = [ii for ii, id_val in enumerate(df['id']) if id_val in ids]
test_idx = [ii for ii, id_val in enumerate(df['id']) if id_val not in ids]
X_train = X[train_idx, :]
y_train = y[train_idx]
panels_train = panels[train_idx]
X_test = X[test_idx, :]
y_test = y[test_idx]
panels_test = panels[test_idx]
model.fit(
X,
y,
varnames,
alts=alt,
randvars=randvars1,
# init_coeff=init_coeff,
# fit_intercept=True,
# isvars=['pf'],
# transformation="boxcox",
# transvars=['pf'],
# correlation=True,
# correlation=['wk', 'tod', 'loc'],
# weights=np.ones(361),
# ids=choice_id,
panels=panels,
# isvars=[],
# grad=False,
# hess=False,
# ftol=1e-7,
# gtol=1e-7,
# halton=True,
# method='L-BFGS-B',
# maxiter=100,
# n_draws=80,
# verbose=False
)
model.summary()
model.validation_loglik(X_test, y_test, panels=panels_test)
model.corr()
model.cov()
model.stddev()
from xlogitprit import MixedLogit
df = pd.read_csv(
"https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/electricity_long.csv"
)
varnames = ["pf", "cl", "loc", "wk", "tod", "seas"]
df['tod'] = -df['tod']
df['seas'] = -df['seas']
X = df[varnames].values
y = df['choice'].values
alt = [1, 2, 3, 4]
np.random.seed(123)
model = MixedLogit()
# init_coeff = np.repeat(.1, 13)
model.fit(
X,
y,
varnames,
alts=alt,
randvars={
'cl': 'n',
'loc': 'n',
'wk': 'u',
'tod': 'ln',
'seas': 'ln'
},
# fit_intercept=True,
# transformation="boxcox",
all_vars = [
'cost', 'risk', 'seats', 'noise', 'crowdness', 'convloc', 'clientele'
]
is_vars = ['gender', 'age']
rand_vars = {'cost': 'n', 'risk': 'n', 'seats': 'n'}
choice_set = ['WaterTaxi', 'Ferry', 'Hovercraft', 'Helicopter']
choice_var = df['choice']
alt_var = df['idx.alt']
Tol = 1e-4
# av = df['av']
choice_id = df['idx.chid']
ind_id = df['idx.id']
weights = df['weight']
R = 100
model = MixedLogit()
model.fit(
X=df[all_vars],
y=choice_var,
varnames=all_vars,
alts=alt_var,
# isvars=is_vars,
ids=choice_id,
weights=weights,
# panels=ind_id,
randvars=rand_vars,
# n_draws=R,
# correlation=['cost', 'risk'],
# fit_intercept=True,
# avail=av,
gtol=Tol)
"https://raw.githubusercontent.com/arteagac/xlogit/master/examples/data/electricity_long.csv"
)
varnames = ['cl', 'loc', 'pf', 'wk', 'tod', 'seas']
y = df['choice'].values
# choice_var = df['choice']
df['wk'] = -df['wk']
# df['tod'] = -df['tod']
X = df[varnames].values
# isvarnames = []
randvars = {'wk': 'u', 'seas': 'n', 'tod': 'n'}
corvars = ['tod', 'seas']
alts = [1, 2, 3, 4]
np.random.seed(123)
model = MixedLogit()
# init_coeff=[]
model.fit(
X,
y,
varnames=varnames,
alts=alts,
# isvars=None,
randvars=randvars,
# ids=df['chid'],
panels=df.id.values,
isvars=[],
halton=True,
# correlation=True,
# transvars=['cl', 'loc'],
# method="L-BFGS-B",