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Lint fixes

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This commit is contained in:
Ben Letham 2017-08-28 11:38:02 -07:00
parent cc3238acb7
commit 4523315ffc
2 changed files with 41 additions and 38 deletions
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73
python/fbprophet/forecaster.py
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@ -66,7 +66,7 @@ class Prophet(object):
fluctuations, smaller values dampen the seasonality. Can be specified
for individual seasonalities using add_seasonality.
holidays_prior_scale: Parameter modulating the strength of the holiday
components model, unless overriden in the holidays input.
components model, unless overridden in the holidays input.
changepoint_prior_scale: Parameter modulating the flexibility of the
automatic changepoint selection. Large values will allow many
changepoints, small values will allow few changepoints.
@ -115,8 +115,8 @@ class Prophet(object):
if holidays is not None:
if not (
isinstance(holidays, pd.DataFrame)
and 'ds' in holidays
and 'holiday' in holidays
and 'ds' in holidays # noqa W503
and 'holiday' in holidays # noqa W503
):
raise ValueError("holidays must be a DataFrame with 'ds' and "
"'holiday' columns.")
@ -232,32 +232,7 @@ class Prophet(object):
df = df.sort_values('ds')
df.reset_index(inplace=True, drop=True)
if initialize_scales:
if self.growth == 'logistic' and 'floor' in df:
self.logistic_floor = True
floor = df['floor']
else:
floor = 0.
self.y_scale = (df['y'] - floor).abs().max()
if self.y_scale == 0:
self.y_scale = 1
self.start = df['ds'].min()
self.t_scale = df['ds'].max() - self.start
for name, props in self.extra_regressors.items():
standardize = props['standardize']
if standardize == 'auto':
if set(df[name].unique()) == set([1, 0]):
# Don't standardize binary variables.
standardize = False
else:
standardize = True
if standardize:
mu = df[name].mean()
std = df[name].std()
if std == 0:
std = mu
self.extra_regressors[name]['mu'] = mu
self.extra_regressors[name]['std'] = std
self.initialize_scales(initialize_scales, df)
if self.logistic_floor:
if 'floor' not in df:
@ -279,6 +254,35 @@ class Prophet(object):
raise ValueError('Found NaN in column ' + name)
return df
def initialize_scales(self, initialize_scales, df):
if not initialize_scales:
return
if self.growth == 'logistic' and 'floor' in df:
self.logistic_floor = True
floor = df['floor']
else:
floor = 0.
self.y_scale = (df['y'] - floor).abs().max()
if self.y_scale == 0:
self.y_scale = 1
self.start = df['ds'].min()
self.t_scale = df['ds'].max() - self.start
for name, props in self.extra_regressors.items():
standardize = props['standardize']
if standardize == 'auto':
if set(df[name].unique()) == set([1, 0]):
# Don't standardize binary variables.
standardize = False
else:
standardize = True
if standardize:
mu = df[name].mean()
std = df[name].std()
if std == 0:
std = mu
self.extra_regressors[name]['mu'] = mu
self.extra_regressors[name]['std'] = std
def set_changepoints(self):
"""Set changepoints
@ -422,7 +426,7 @@ class Prophet(object):
if ps <= 0:
raise ValueError('Prior scale must be > 0')
prior_scales[row.holiday] = ps
for offset in range(lw, uw + 1):
occurrence = dt + timedelta(days=offset)
try:
@ -918,7 +922,7 @@ class Prophet(object):
for i, t_s in enumerate(changepoint_ts):
gammas[i] = (
(t_s - m - np.sum(gammas))
* (1 - k_cum[i] / k_cum[i + 1])
* (1 - k_cum[i] / k_cum[i + 1]) # noqa W503
)
# Get cumulative rate and offset at each t
k_t = k * np.ones_like(t)
@ -997,7 +1001,7 @@ class Prophet(object):
comp_features = X[:, cols]
comp = (
np.matmul(comp_features, comp_beta.transpose())
* self.y_scale
* self.y_scale # noqa W503
)
data[component] = np.nanmean(comp, axis=1)
data[component + '_lower'] = np.nanpercentile(comp, lower_p,
@ -1025,7 +1029,6 @@ class Prophet(object):
components = components.append(new_comp)
return components
def sample_posterior_predictive(self, df):
"""Prophet posterior predictive samples.
@ -1237,7 +1240,7 @@ class Prophet(object):
ax.plot(fcst['ds'].values, fcst['yhat'], ls='-', c='#0072B2')
if 'cap' in fcst and plot_cap:
ax.plot(fcst['ds'].values, fcst['cap'], ls='--', c='k')
if self.logistic_floor and 'floor' in fcst and plot_cap :
if self.logistic_floor and 'floor' in fcst and plot_cap:
ax.plot(fcst['ds'].values, fcst['floor'], ls='--', c='k')
if uncertainty:
ax.fill_between(fcst['ds'].values, fcst['yhat_lower'],
@ -1333,7 +1336,7 @@ class Prophet(object):
artists += ax.plot(fcst['ds'].values, fcst[name], ls='-', c='#0072B2')
if 'cap' in fcst and plot_cap:
artists += ax.plot(fcst['ds'].values, fcst['cap'], ls='--', c='k')
if self.logistic_floor and 'floor' in fcst and plot_cap :
if self.logistic_floor and 'floor' in fcst and plot_cap:
ax.plot(fcst['ds'].values, fcst['floor'], ls='--', c='k')
if uncertainty:
artists += [ax.fill_between(

6
python/fbprophet/tests/test_prophet.py
View file

@ -521,15 +521,15 @@ class TestProphet(TestCase):
fcst['extra_regressors'][0],
fcst['numeric_feature'][0] + fcst['binary_feature2'][0],
)
self.assertEqual(
self.assertAlmostEqual(
fcst['seasonalities'][0],
fcst['yearly'][0] + fcst['weekly'][0],
)
self.assertEqual(
self.assertAlmostEqual(
fcst['seasonal'][0],
fcst['seasonalities'][0] + fcst['extra_regressors'][0],
)
self.assertEqual(
self.assertAlmostEqual(
fcst['yhat'][0],
fcst['trend'][0] + fcst['seasonal'][0],
)