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https://github.com/saymrwulf/prophet.git
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Compute and store binary matrix of which seasonalities/regressors correspond to which columns in the feature matrix (Py)
This commit is contained in:
Ben Letham
parent
8e8e04858a
commit
b9923fd9fd
2 changed files with 70 additions and 43 deletions
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@ -152,6 +152,7 @@ class Prophet(object):
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self.params = {}
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self.history = None
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self.history_dates = None
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self.train_component_cols = None
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self.validate_inputs()
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def validate_inputs(self):
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@ -187,7 +188,7 @@ class Prophet(object):
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if '_delim_' in name:
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raise ValueError('Name cannot contain "_delim_"')
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reserved_names = [
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'trend', 'seasonal', 'seasonalities', 'daily', 'weekly', 'yearly',
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'trend', 'additive_terms', 'daily', 'weekly', 'yearly',
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'holidays', 'zeros', 'extra_regressors', 'yhat'
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]
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rn_l = [n + '_lower' for n in reserved_names]
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@ -557,6 +558,8 @@ class Prophet(object):
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-------
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pd.DataFrame with regression features.
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list of prior scales for each column of the features dataframe.
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Dataframe with indicators for which regression components correspond to
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which columns.
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"""
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seasonal_features = []
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prior_scales = []
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@ -588,7 +591,10 @@ class Prophet(object):
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seasonal_features.append(
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pd.DataFrame({'zeros': np.zeros(df.shape[0])}))
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prior_scales.append(1.)
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return pd.concat(seasonal_features, axis=1), prior_scales
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seasonal_features = pd.concat(seasonal_features, axis=1)
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component_cols = self.regressor_column_matrix(seasonal_features)
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return seasonal_features, prior_scales, component_cols
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def parse_seasonality_args(self, name, arg, auto_disable, default_order):
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"""Get number of fourier components for built-in seasonalities.
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@ -779,8 +785,9 @@ class Prophet(object):
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history = self.setup_dataframe(history, initialize_scales=True)
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self.history = history
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self.set_auto_seasonalities()
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seasonal_features, prior_scales = (
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seasonal_features, prior_scales, component_cols = (
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self.make_all_seasonality_features(history))
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self.train_component_cols = component_cols
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self.set_changepoints()
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@ -884,7 +891,7 @@ class Prophet(object):
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cols.append('floor')
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# Add in forecast components
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df2 = pd.concat((df[cols], intervals, seasonal_components), axis=1)
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df2['yhat'] = df2['trend'] + df2['seasonal']
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df2['yhat'] = df2['trend'] + df2['additive_terms']
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return df2
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@staticmethod
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@ -984,22 +991,38 @@ class Prophet(object):
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-------
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Dataframe with seasonal components.
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"""
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seasonal_features, _ = self.make_all_seasonality_features(df)
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seasonal_features, _, component_cols = (
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self.make_all_seasonality_features(df)
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)
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lower_p = 100 * (1.0 - self.interval_width) / 2
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upper_p = 100 * (1.0 + self.interval_width) / 2
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X = seasonal_features.as_matrix()
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data = {}
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for component in component_cols.columns:
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beta_c = self.params['beta'] * component_cols[component].values
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comp = np.matmul(X, beta_c.transpose()) * self.y_scale
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data[component] = np.nanmean(comp, axis=1)
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data[component + '_lower'] = np.nanpercentile(
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comp, lower_p, axis=1,
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)
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data[component + '_upper'] = np.nanpercentile(
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comp, upper_p, axis=1,
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)
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return pd.DataFrame(data)
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def regressor_column_matrix(self, seasonal_features):
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components = pd.DataFrame({
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'col': np.arange(seasonal_features.shape[1]),
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'component': [x.split('_delim_')[0] for x in seasonal_features.columns],
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})
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# Add total for all regression components
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# Add total for all additive components
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components = components.append(pd.DataFrame({
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'col': np.arange(seasonal_features.shape[1]),
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'component': 'seasonal',
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'component': 'additive_terms',
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}))
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# Add totals for seasonality, holiday, and extra regressors
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components = self.add_group_component(
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components, 'seasonalities', self.seasonalities.keys())
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# Add totals for holidays and extra regressors
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if self.holidays is not None:
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components = self.add_group_component(
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components, 'holidays', self.holidays['holiday'].unique())
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@ -1007,23 +1030,16 @@ class Prophet(object):
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components, 'extra_regressors', self.extra_regressors.keys())
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# Remove the placeholder
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components = components[components['component'] != 'zeros']
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X = seasonal_features.as_matrix()
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data = {}
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for component, features in components.groupby('component'):
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cols = features.col.tolist()
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comp_beta = self.params['beta'][:, cols]
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comp_features = X[:, cols]
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comp = (
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np.matmul(comp_features, comp_beta.transpose())
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* self.y_scale # noqa W503
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)
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data[component] = np.nanmean(comp, axis=1)
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data[component + '_lower'] = np.nanpercentile(comp, lower_p,
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axis=1)
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data[component + '_upper'] = np.nanpercentile(comp, upper_p,
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axis=1)
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return pd.DataFrame(data)
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# Convert to a binary matrix
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component_cols = pd.crosstab(
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components['col'], components['component'],
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)
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# Compare to the training, if set.
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if self.train_component_cols is not None:
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component_cols = component_cols[self.train_component_cols.columns]
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if not component_cols.equals(self.train_component_cols):
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raise Exception('A bug occurred in constructing regressors.')
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return component_cols
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def add_group_component(self, components, name, group):
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"""Adds a component with given name that contains all of the components
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@ -1061,7 +1077,7 @@ class Prophet(object):
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)))
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# Generate seasonality features once so we can re-use them.
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seasonal_features, _ = self.make_all_seasonality_features(df)
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seasonal_features, _, _ = self.make_all_seasonality_features(df)
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sim_values = {'yhat': [], 'trend': [], 'seasonal': []}
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for i in range(n_iterations):
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@ -460,12 +460,20 @@ class TestProphet(TestCase):
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m.add_seasonality(name='monthly', period=30, fourier_order=5,
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prior_scale=2.)
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m.fit(DATA.copy())
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seasonal_features, prior_scales = m.make_all_seasonality_features(
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m.history)
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seasonal_features, prior_scales, component_cols = (
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m.make_all_seasonality_features(m.history)
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)
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self.assertEqual(sum(component_cols['monthly']), 10)
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self.assertEqual(sum(component_cols['special_day']), 1)
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self.assertEqual(sum(component_cols['weekly']), 6)
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if seasonal_features.columns[0] == 'monthly_delim_1':
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true = [2.] * 10 + [10.] * 6 + [4.]
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self.assertEqual(sum(component_cols['monthly'][:10]), 10)
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self.assertEqual(sum(component_cols['weekly'][10:16]), 6)
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else:
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true = [10.] * 6 + [2.] * 10 + [4.]
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self.assertEqual(sum(component_cols['weekly'][:6]), 6)
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self.assertEqual(sum(component_cols['monthly'][6:16]), 10)
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self.assertEqual(prior_scales, true)
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def test_added_regressors(self):
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@ -504,12 +512,19 @@ class TestProphet(TestCase):
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self.assertAlmostEqual(df2['numeric_feature'][0], -1.726962, places=4)
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self.assertAlmostEqual(df2['binary_feature2'][0], 2.022859, places=4)
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# Check that feature matrix and prior scales are correctly constructed
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seasonal_features, prior_scales = m.make_all_seasonality_features(df2)
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self.assertIn('binary_feature', seasonal_features)
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self.assertIn('numeric_feature', seasonal_features)
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self.assertIn('binary_feature2', seasonal_features)
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seasonal_features, prior_scales, component_cols = (
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m.make_all_seasonality_features(df2)
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)
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self.assertEqual(seasonal_features.shape[1], 29)
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self.assertEqual(set(prior_scales[26:]), set([0.2, 0.5, 10.]))
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names = ['binary_feature', 'numeric_feature', 'binary_feature2']
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true_priors = [0.2, 0.5, 10.]
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for i, name in enumerate(names):
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self.assertIn(name, seasonal_features)
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self.assertEqual(sum(component_cols[name]), 1)
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self.assertEqual(
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sum(np.array(prior_scales) * component_cols[name]),
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true_priors[i],
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)
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# Check that forecast components are reasonable
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future = pd.DataFrame({
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'ds': ['2014-06-01'],
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@ -520,23 +535,19 @@ class TestProphet(TestCase):
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m.predict(future)
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future['binary_feature2'] = 0
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fcst = m.predict(future)
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self.assertEqual(fcst.shape[1], 31)
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self.assertEqual(fcst.shape[1], 28)
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self.assertEqual(fcst['binary_feature'][0], 0)
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self.assertAlmostEqual(
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fcst['extra_regressors'][0],
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fcst['numeric_feature'][0] + fcst['binary_feature2'][0],
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)
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self.assertAlmostEqual(
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fcst['seasonalities'][0],
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fcst['yearly'][0] + fcst['weekly'][0],
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)
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self.assertAlmostEqual(
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fcst['seasonal'][0],
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fcst['seasonalities'][0] + fcst['extra_regressors'][0],
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fcst['additive_terms'][0],
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fcst['yearly'][0] + fcst['weekly'][0] + fcst['extra_regressors'][0]
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)
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self.assertAlmostEqual(
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fcst['yhat'][0],
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fcst['trend'][0] + fcst['seasonal'][0],
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fcst['trend'][0] + fcst['additive_terms'][0],
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)
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# Check fails if constant extra regressor
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df['constant_feature'] = 5
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