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Lint fixes

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This commit is contained in:
Ben Letham 2017-09-25 17:34:27 -07:00
parent 2be8821c95
commit 230b2ca6e0
3 changed files with 38 additions and 18 deletions
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2
python/fbprophet/models.py
View file

@ -2,7 +2,7 @@
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
from __future__ import absolute_import

48
python/fbprophet/tests/test_diagnostics.py
View file

@ -10,21 +10,28 @@ from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import numpy as np
import pandas as pd
# fb-block 1 start
import os
from unittest import TestCase
from fbprophet import Prophet
from fbprophet import diagnostics
DATA = pd.read_csv(
os.path.join(os.path.dirname(__file__), 'data.csv'), parse_dates=['ds']
).head(100)
# fb-block 1 end
# fb-block 2
class TestDiagnostics(TestCase):
def __init__(self, *args, **kwargs):
super(TestDiagnostics, self).__init__(*args, **kwargs)
# Use first 100 record in data.csv
self.__df = pd.read_csv(os.path.join(os.path.dirname(__file__), 'data.csv'), parse_dates=['ds']).head(100)
self.__df = DATA
def test_simulated_historical_forecasts(self):
m = Prophet()
@ -34,47 +41,55 @@ class TestDiagnostics(TestCase):
for h in [1, 3]:
period = '{} days'.format(p)
horizon = '{} days'.format(h)
df_shf = diagnostics.simulated_historical_forecasts(m, horizon=horizon, k=k, period=period)
df_shf = diagnostics.simulated_historical_forecasts(
m, horizon=horizon, k=k, period=period)
# All cutoff dates should be less than ds dates
self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all())
# The unique size of output cutoff should be equal to 'k'
self.assertEqual(len(np.unique(df_shf['cutoff'])), k)
self.assertEqual(max(df_shf['ds'] - df_shf['cutoff']), pd.Timedelta(horizon))
self.assertEqual(
max(df_shf['ds'] - df_shf['cutoff']),
pd.Timedelta(horizon),
)
dc = df_shf['cutoff'].diff()
dc = dc[dc > pd.Timedelta(0)].min()
self.assertTrue(dc >= pd.Timedelta(period))
# Each y in df_shf and self.__df with same ds should be equal
df_merged = pd.merge(df_shf, self.__df, 'left', on='ds')
self.assertAlmostEqual(np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
self.assertAlmostEqual(
np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
def test_simulated_historical_forecasts_logistic(self):
m = Prophet(growth='logistic')
df = self.__df.copy()
df['cap'] = 40
m.fit(df)
df_shf = diagnostics.simulated_historical_forecasts(m, horizon='3 days', k=2, period='3 days')
df_shf = diagnostics.simulated_historical_forecasts(
m, horizon='3 days', k=2, period='3 days')
# All cutoff dates should be less than ds dates
self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all())
# The unique size of output cutoff should be equal to 'k'
self.assertEqual(len(np.unique(df_shf['cutoff'])), 2)
# Each y in df_shf and self.__df with same ds should be equal
df_merged = pd.merge(df_shf, df, 'left', on='ds')
self.assertAlmostEqual(np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
self.assertAlmostEqual(
np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
def test_simulated_historical_forecasts_default_value_check(self):
m = Prophet()
m.fit(self.__df)
# Default value of period should be equal to 0.5 * horizon
df_shf1 = diagnostics.simulated_historical_forecasts(m, horizon='10 days', k=1)
df_shf2 = diagnostics.simulated_historical_forecasts(m, horizon='10 days', k=1, period='5 days')
self.assertAlmostEqual(((df_shf1 - df_shf2)**2)[['y', 'yhat']].sum().sum(), 0.0)
df_shf1 = diagnostics.simulated_historical_forecasts(
m, horizon='10 days', k=1)
df_shf2 = diagnostics.simulated_historical_forecasts(
m, horizon='10 days', k=1, period='5 days')
self.assertAlmostEqual(
((df_shf1 - df_shf2)**2)[['y', 'yhat']].sum().sum(), 0.0)
def test_cross_validation(self):
m = Prophet()
m.fit(self.__df)
# Calculate the number of cutoff points(k)
te = self.__df['ds'].max()
ts = self.__df['ds'].min()
horizon = pd.Timedelta('4 days')
period = pd.Timedelta('10 days')
k = 5
@ -91,6 +106,9 @@ class TestDiagnostics(TestCase):
m = Prophet()
m.fit(self.__df)
# Default value of initial should be equal to 3 * horizon
df_cv1 = diagnostics.cross_validation(m, horizon='32 days', period='10 days')
df_cv2 = diagnostics.cross_validation(m, horizon='32 days', period='10 days', initial='96 days')
self.assertAlmostEqual(((df_cv1 - df_cv2)**2)[['y', 'yhat']].sum().sum(), 0.0)
df_cv1 = diagnostics.cross_validation(
m, horizon='32 days', period='10 days')
df_cv2 = diagnostics.cross_validation(
m, horizon='32 days', period='10 days', initial='96 days')
self.assertAlmostEqual(
((df_cv1 - df_cv2)**2)[['y', 'yhat']].sum().sum(), 0.0)

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

@ -10,12 +10,12 @@ from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import itertools
import numpy as np
import pandas as pd
# fb-block 1 start
import os
import itertools
from unittest import TestCase
from fbprophet import Prophet
@ -551,6 +551,8 @@ class TestProphet(TestCase):
def test_copy(self):
# These values are created except for its default values
holiday = pd.DataFrame(
{'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})
products = itertools.product(
['linear', 'logistic'], # growth
[None, pd.to_datetime(['2016-12-25'])], # changepoints
@ -558,7 +560,7 @@ class TestProphet(TestCase):
[True, False], # yearly_seasonality
[True, False], # weekly_seasonality
[True, False], # daily_seasonality
[None, pd.DataFrame({'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})], # holidays
[None, holiday], # holidays
[1.1], # seasonality_prior_scale
[1.1], # holidays_prior_scale
[0.1], # changepoint_prior_scale