def test_validate_inputs():
"""
Covers potential mistakes in parameters of the fit method that xlogit
should be able to identify
"""
model = MixedLogit()
with pytest.raises(ValueError): # wrong distribution
model.fit(X,
y,
varnames=varnames,
alts=alts,
ids=ids,
n_draws=10,
maxiter=0,
verbose=0,
halton=True,
randvars={'a': 'fake'})
with pytest.raises(ValueError): # wrong var name
model.fit(X,
y,
varnames=varnames,
alts=alts,
ids=ids,
n_draws=10,
maxiter=0,
verbose=0,
halton=True,
randvars={'fake': 'n'})
def test_fit():
"""
Ensures the log-likelihood works for a single iterations with the default
initial coefficients. The value of -1.794 was computed by hand for
comparison purposes
"""
# There is no need to initialize a random seed as the halton draws produce
# reproducible results
model = MixedLogit()
model.fit(X,
y,
varnames=varnames,
alts=alts,
n_draws=10,
panels=panels,
ids=ids,
randvars=randvars,
maxiter=0,
verbose=0,
halton=True)
assert pytest.approx(model.loglikelihood, -1.79451632)
def test_fit():
"""
Ensures the log-likelihood works for multiple iterations with the default
initial coefficients. The value of -1.473423 was computed by hand for
comparison purposes
"""
# There is no need to initialize a random seed as the halton draws produce
# reproducible results
model = MixedLogit()
model.fit(X,
y,
varnames,
alts,
ids,
randvars,
n_draws=10,
panels=panels,
maxiter=0,
verbose=0,
halton=True,
init_coeff=np.repeat(.1, 4))
assert model.loglikelihood == pytest.approx(-1.473423)
panels = None
batch_size = 5000
if not use_gpu:
device.disable_gpu_acceleration()
if profile:
ini_ram = curr_ram()
profiler = Profiler().start(measure_gpu_mem=use_gpu)
np.random.seed(0)
model = MixedLogit()
model.fit(X,
y,
varnames,
alts=alts,
ids=ids,
n_draws=n_draws,
panels=panels,
verbose=0,
randvars=randvars,
batch_size=batch_size)
if profile:
ellapsed, max_ram, max_gpu = profiler.stop()
log("{:6} {:7.2f} {:11.2f} {:7.3f} {:7.3f} {}".format(
n_draws, ellapsed, model.loglikelihood, max_ram - ini_ram, max_gpu,
model.convergence))
profiler.export('xlogit' + ('_gpu' if use_gpu else ''), dataset, n_draws,
ellapsed, model.loglikelihood, max_ram - ini_ram, max_gpu,
model.convergence)
if not profile:
# ===== STEP 3. CREATE MODEL SPECIFICATION =====
df['ASC_TRAIN'] = np.ones(len(df)) * (df['alt'] == 'TRAIN')
df['ASC_CAR'] = np.ones(len(df)) * (df['alt'] == 'CAR')
df['TT'], df['CO'] = df['TT'] / 100, df['CO'] / 100 # Scale variables
annual_pass = (df['GA'] == 1) & (df['alt'].isin(['TRAIN', 'SM']))
df.loc[annual_pass, 'CO'] = 0 # Cost zero for pass holders
# ===== STEP 4. ESTIMATE MODEL PARAMETERS =====
from xlogit import MixedLogit
varnames = ['ASC_CAR', 'ASC_TRAIN', 'CO', 'TT']
model = MixedLogit()
model.fit(X=df[varnames],
y=df['CHOICE'],
varnames=varnames,
alts=df['alt'],
ids=df['custom_id'],
panels=df["ID"],
avail=df['AV'],
randvars={'TT': 'n'},
n_draws=1500)
model.summary()
"""
OUTPUT:
Estimation time= 1.3 seconds
---------------------------------------------------------------------------
Coefficient Estimate Std.Err. z-val P>|z|
---------------------------------------------------------------------------
ASC_CAR 0.2831101 0.0564250 5.0174585 3.29e-06 ***
ASC_TRAIN -0.5722759 0.0791059 -7.2343003 7.96e-12 ***
CO -1.6601672 0.0778264 -21.3316760 3.38e-78 ***
TT -3.2289890 0.1749384 -18.4578595 6.36e-62 ***
randvars = {'meals': 'n', 'petfr': 'n', 'emipp': 'n'}
alts = [1, 2, 3]
panels = None
if not use_gpu:
device.disable_gpu_acceleration()
if profile:
ini_ram = curr_ram()
profiler = Profiler().start(measure_gpu_mem=use_gpu)
np.random.seed(0)
model = MixedLogit()
model.fit(X,
y,
varnames,
alts=alts,
n_draws=n_draws,
panels=panels,
verbose=0,
randvars=randvars)
if profile:
ellapsed, max_ram, max_gpu = profiler.stop()
log("{:6} {:7.2f} {:11.2f} {:7.3f} {:7.3f} {}".format(
n_draws, ellapsed, model.loglikelihood, max_ram - ini_ram, max_gpu,
model.convergence))
profiler.export('xlogit' + ('_gpu' if use_gpu else ''), dataset, n_draws,
ellapsed, model.loglikelihood, max_ram - ini_ram, max_gpu,
model.convergence)
if not profile:
log("Variable Estimate Std.Err.")