def fit(self,
y,
period,
x=None,
metric="smape",
val_size=None,
verbose=False):
"""
Build the model using best-tuned hyperparameter values.
:param y: pd.Series or 1-D np.array, time series to predict.
:param period: Int or Str, the number of observations per cycle: 1 or "annual" for yearly data, 4 or "quarterly"
for quarterly data, 7 or "daily" for daily data, 12 or "monthly" for monthly data, 24 or "hourly" for hourly
data, 52 or "weekly" for weekly data. First-letter abbreviations of strings work as well ("a", "q", "d", "m",
"h" and "w", respectively). Additional reference: https://robjhyndman.com/hyndsight/seasonal-periods/.
:param x: pd.DataFrame or 2-D np.array, exogeneous predictors, optional
:param metric: Str, the metric used for model selection. One of "mse" (mean squared error), "mae" (mean absolute
error).
:param val_size: Int, the number of most recent observations to use as validation set for tuning.
:param verbose: Boolean, True for printing additional info while tuning.
:return: None
"""
self.y = y
self.name = "Bayesian Dynamic Linear Model"
self.key = "bdlm"
self._tune(y=y,
period=period,
x=x,
metric=metric,
val_size=val_size,
verbose=verbose)
self.model = pydlm.dlm(y)
self.model = self.model + pydlm.trend(degree=self.params["trend"],
discount=0.5)
self.model = self.model + pydlm.seasonality(period=self.period,
discount=0.99)
if self.params["ar"] is not None:
self.model = self.model + pydlm.autoReg(degree=self.params["ar"],
discount=0.99)
if x is not None:
for variable_id, x_variable in enumerate(x.T):
self.model = self.model + pydlm.dynamic(
features=[[v] for v in x_variable],
discount=0.99,
name=str(variable_id))
with SuppressStdoutStderr():
self.model.tune()
self.model.fit()
def dlm_exogenous_r3(y, s, k, a, t, e, r):
""" One way to use dlm
:returns: x, s', w
"""
if not s:
s = dict()
s['dim'] = dimension(y)
s = dlm_set_exog_hyperparams(s=s, r=r)
y0, exog = split_exogenous(y=y)
s['n_obs'] = 0
s['model'] = quietDlm([], printInfo=False) + trend(
s['trend_degree'], s['discount']) + seasonality(
s['period'], s['discount'])
s['model'] = s['model'] + fixedAutoReg(
degree=s['auto_degree'], name='ar', w=1.0)
if exog:
exog_wrapped = [[None if np.isnan(ex0) else ex0 for ex0 in exog]]
s['model'] = s['model'] + dynamic(features=exog_wrapped,
discount=0.99,
name='exog') # Set's first exog
if y is not None:
y = wrap(y)
assert dimension(y) == s['dim'], 'Cannot change dimension of data sent'
s['n_obs'] += 1
y0, exog = split_exogenous(y=y)
y0_passed_in = None if np.isnan(
y0) else y0 # pydlm uses None for missing values
s['model'].append([y0_passed_in])
if exog:
exog_wrapped = [[None if np.isnan(ex0) else ex0 for ex0 in exog]]
if s['n_obs'] > 1:
s['model'].append(
data=exog_wrapped,
component='exog') # Don't get first exog twice
num_obs = len(s['model'].data) if s.get('model') else 0
if num_obs % s['n_fit'] == s['n_fit'] - 1:
_, _, s = dlm_exogenous_r3(y=None, s=s, k=k, a=a, t=t, e=10, r=r)
s['model'].fitForwardFilter()
return _dlm_exog_prediction_helper(s=s, k=k, y=y)
if y is None:
if dimension(y) == 1:
s['model'].tune(maxit=20)
# Don't tune if exogenous ... haven't got this to work
s['model'].fit()
return None, None, s
def SerBayes(sDay,nAhead,x0,hWeek):
dta = sDay['y']
dta.index = [pd.datetime.strptime(str(x)[0:10],'%Y-%m-%d') for x in dta.index]
t_line = [float(calendar.timegm(x.utctimetuple()))/1000000 for x in dta.index]
dta.index = t_line
model = pydlm.dlm(dta)
model = model + pydlm.trend(degree=1,discount=0.98,name='a',w=10.0)
model = model + pydlm.dynamic(features=[[v] for v in t_line],discount=1,name='b',w=10.0)
model = model + pydlm.autoReg(degree=3,data=dta.values,name='ar3',w=1.0)
allStates = model.getLatentState(filterType='forwardFilter')
model.evolveMode('independent')
model.noisePrior(2.0)
model.fit()
model.plot()
model.turnOff('predict')
model.plotCoef(name='a')
model.plotCoef(name='b')
model.plotCoef(name='ar3')
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import numpy as np
import matplotlib.pyplot as plt
import pydlm
# Simple example (random walk)
n = 100
a = 1.0 + np.random.normal(0, 5, n) # the intercept
x = np.random.normal(0, 2, n) # the control variable
b = 3.0 # the coefficient
y = a + b * x
dlm = pydlm.dlm(y)
dlm = dlm + pydlm.trend(degree=0, discount=0.98, name='a', w=10.0)
dlm = dlm + pydlm.dynamic(
features=[[v] for v in x], discount=1, name='b', w=10.0)
# randomly generate data
data = [0] * 100 + [3] * 100
# creadte model
dlm = pydlm.dlm(data)
# add components
dlm = dlm + pydlm.trend(1, name='lineTrend', w=1.0) # covariance=1
dlm = dlm + pydlm.seasonality(7, name='7day', w=1.0)
dlm = dlm + pydlm.autoReg(degree=3, data=data, name='ar3', w=1.0)
dlm.ls()
# delete unwanted component
dlm.delete('7day')
def ts_fit(self, suppress=False):
"""Fit DLM to the time series data.
Parameters:
----------
suppress: bool
Suppress or not some of the output messages
"""
self._prepare_fit()
self._model = None
self.ts_split()
ts_df = self._train_dt.copy()
# Fit
self._dlm_logger.info("Trying to fit the DLM model....")
try:
if not suppress:
self._dlm_logger.info("...via using parameters\n")
print_attributes(self)
ts_df = ts_df.reset_index()
ts_df.columns = self._ts_df_cols
self._model = dlm(ts_df['y'])
# trend
if self._dlm_trend is not None:
self._model = self._model + trend(
degree=self._dlm_trend['degree'],
discount=self._dlm_trend['discount'],
name=self._dlm_trend['name'],
w=self._dlm_trend['w'])
# seasonality
if self._dlm_seasonality is not None:
self._model = self._model + seasonality(
period=self._dlm_seasonality['period'],
discount=self._dlm_seasonality['discount'],
name=self._dlm_seasonality['name'],
w=self._dlm_seasonality['w'])
# dynamic
if self._train_dlm_dynamic is not None:
for i in range(len(self._train_dlm_dynamic['features'])):
self._model = self._model + dynamic(
features=self._train_dlm_dynamic['features'][i]
['features'],
discount=self._train_dlm_dynamic['features'][i]
['discount'],
name=self._train_dlm_dynamic['features'][i]['name'],
w=self._train_dlm_dynamic['features'][i]['w'])
# auto_reg
if self._dlm_auto_reg is not None:
self._model = self._model + autoReg(
degree=self._dlm_auto_reg['degree'],
discount=self._dlm_auto_reg['discount'],
name=self._dlm_auto_reg['name'],
w=self._dlm_auto_reg['w'])
# long_season
if self._dlm_long_season is not None:
ls = longSeason(period=self._dlm_long_season['period'],
stay=self._dlm_long_season['stay'],
data=ts_df,
name=self._dlm_long_season['name'],
w=self._dlm_long_season['w'])
self._model = self._model + ls
if not suppress:
self._dlm_logger.info("The constructed DLM model components:")
print(self._model.ls())
# tic
start = time()
if self._use_rolling_window:
self._model.fitForwardFilter(useRollingWindow=True,
windowLength=self._window_size)
self._model.fitBackwardSmoother()
else:
self._model.fit()
self.model_fit = self._model
# toc
if not suppress:
self._dlm_logger.info("Time elapsed: {} sec.".format(time() -
start))
except (Exception, ValueError) as e:
self._dlm_logger.exception("DLM error...{}".format(e))
return -1
else:
self._dlm_logger.info("Model successfully fitted to the data!")
self._dlm_logger.info("Computing fitted values and residuals...")
# Residuals
self.residuals = pd.Series(self.model_fit.getResidual(),
index=self._train_dt.index)
try:
self.lower_conf_int = pd.Series(
self.model_fit.getInterval()[1],
index=self._train_dt.index)
self.upper_conf_int = pd.Series(
self.model_fit.getInterval()[0],
index=self._train_dt.index)
except ValueError as e:
self._dlm_logger.exception(
"Something went wrong in getInterval...{}".format(e))
self.mse = self.model_fit.getMSE()
# Fitted values
# this is not elegant, but found no other way
self.fittedvalues = self._train_dt['y'] + self.residuals
return self
simple_dlm.turnOff('data points')
simple_dlm.plot()
# Plot each component (attribution)
simple_dlm.turnOff('predict plot')
simple_dlm.turnOff('filtered plot')
simple_dlm.plot('linear_trend')
simple_dlm.plot('seasonal52')
# Plot the prediction give the first 350 weeks and forcast the next 200 weeks.
simple_dlm.plotPredictN(N=200, date=350)
# Plot the prediction give the first 250 weeks and forcast the next 200 weeks.
simple_dlm.plotPredictN(N=200, date=250)
# Build a dynamic regression model
from pydlm import dynamic
regressor10 = dynamic(features=features,
discount=1.0,
name='regressor10',
w=10)
drm = dlm(time_series) + linear_trend + seasonal52 + regressor10
drm.fit()
# Plot the fitted results
drm.turnOff('data points')
drm.plot()
# Plot each component (attribution)
drm.turnOff('predict plot')
drm.turnOff('filtered plot')
drm.plot('linear_trend')
drm.plot('seasonal52')
drm.plot('regressor10')
# Plot the prediction give the first 300 weeks and forcast the next 150 weeks.
drm.plotPredictN(N=150, date=300)
# Plot the fitted results
simple_dlm.turnOff('data points')
simple_dlm.plot()
# Plot each component (attribution)
simple_dlm.turnOff('predict plot')
simple_dlm.turnOff('filtered plot')
simple_dlm.plot('linear_trend')
simple_dlm.plot('seasonal52')
# Plot the prediction give the first 350 weeks and forcast the next 200 weeks.
simple_dlm.plotPredictN(N=200, date=350)
# Plot the prediction give the first 250 weeks and forcast the next 200 weeks.
simple_dlm.plotPredictN(N=200, date=250)
# Build a dynamic regression model
from pydlm import dynamic
regressor10 = dynamic(features=features, discount=1.0, name='regressor10', w=10)
drm = dlm(time_series) + linear_trend + seasonal52 + regressor10
drm.fit()
# Plot the fitted results
drm.turnOff('data points')
drm.plot()
# Plot each component (attribution)
drm.turnOff('predict plot')
drm.turnOff('filtered plot')
drm.plot('linear_trend')
drm.plot('seasonal52')
drm.plot('regressor10')
# Plot the prediction give the first 300 weeks and forcast the next 150 weeks.
drm.plotPredictN(N=150, date=300)
# Plot the prediction give the first 250 weeks and forcast the next 200 weeks.
def estimate_and_predict_dlm_PR(calendar,
df_propor_PR_ts,
punched_df,
end_train_date,
start_test_date,
start_of_this_year,
enable_sales,
pred_weeks=8,
locality=10,
r=0.05,
missing_val=201735):
'''
accept the forecasting sales_proportion data as one regressor
df_propor_PR_test: []
return type: DataFrame with prediction result
return: columns = [wm_yr_wk_nbr,club,yhat]
'''
res = pd.DataFrame()
punched = punched_df.groupby(['club_nbr', 'posting_date'])['cost'].sum()
punched.column = ['total_punched_wg']
punched = punched.reset_index()
punched = pd.merge(left=punched,
right=calendar,
how='left',
left_on='posting_date',
right_on='calendar_date').drop('calendar_date', axis=1)
# mean wage among all clubs
punched = removehurricane('cost', punched, 201733, 201739, sales=False)
punched_mean = punched.groupby(['wm_yr_wk_nbr',
'posting_date'])['cost'].mean()
punched_mean = punched_mean.reset_index()
punched_mean.columns = ['wm_yr_wk_nbr', 'posting_date', 'cost']
punched_mean['club_nbr'] = pd.Series(np.ones([punched_mean.shape[0]]))
##########################
if missing_val not in punched_mean.wm_yr_wk_nbr.tolist():
punched_mean.loc[-1] = [
missing_val,
punched_mean.loc[punched_mean.wm_yr_wk_nbr == wm_nbr_add(
missing_val, -2)].iloc[0, 1] + timedelta(days=14),
0.5 * punched_mean.loc[punched_mean.wm_yr_wk_nbr == wm_nbr_add(
missing_val, -2)].iloc[0, 2] +
0.5 * punched_mean.loc[punched_mean.wm_yr_wk_nbr == wm_nbr_add(
missing_val, 2)].iloc[0, 2], 1
] # adding a row
punched_mean.index = punched_mean.index + 1
#########################
punched_mean1 = punched_mean.copy(deep=True)
punched_mean1['cost'] = 0.5 * punched_mean1['cost'] + 0.25 * punched_mean1[
'cost'].shift(1) + 0.25 * punched_mean1['cost'].shift(2)
ty = punched_mean1['cost'].mean()
punched_mean1[['cost']] = punched_mean1[['cost']].fillna(value=ty)
punched_mean1 = estimate_and_predict_prophet_PR(
calendar,
punched_mean1,
end_train_date,
start_test_date,
daily_view=False,
pred_days=120) #predict the mean wages.
punched_mean1 = punched_mean1.drop('club', axis=1)
punched_mean1.columns = ['posting_date', 'PR_cost']
punched_mean1 = pd.merge(left=punched_mean1,
right=calendar,
how='left',
left_on='posting_date',
right_on='calendar_date').drop('calendar_date',
axis=1)
tmp = punched.groupby(['wm_yr_wk_nbr', 'posting_date'])['cost'].mean()
tmp = tmp.reset_index()
tmp.columns = ['wm_yr_wk_nbr', 'posting_date', 'PR_cost']
tmp = tmp.loc[tmp.wm_yr_wk_nbr <= end_train_date]
tmp['PR_cost'] = 0.5 * tmp['PR_cost'] + 0.25 * tmp['PR_cost'].shift(
1) + 0.25 * tmp['PR_cost'].shift(2)
ty = tmp['PR_cost'].mean()
tmp[['PR_cost']] = tmp[['PR_cost']].fillna(value=ty)
punched_mean = pd.concat([tmp, punched_mean1], axis=0)
if missing_val not in punched_mean.wm_yr_wk_nbr.tolist():
tu = [
0.5 * punched_mean.loc[punched_mean.wm_yr_wk_nbr == wm_nbr_add(
missing_val, -2)].iloc[0, 0] +
0.5 * punched_mean.loc[punched_mean.wm_yr_wk_nbr == wm_nbr_add(
missing_val, 2)].iloc[0, 0]
]
tu.append(punched_mean.loc[punched_mean.wm_yr_wk_nbr == wm_nbr_add(
missing_val, -2)].iloc[0, 1] + timedelta(days=14))
tu.append(missing_val)
punched_mean.loc[-1] = tu # adding a row
punched_mean.index = punched_mean.index + 1 # shifting index
punched_mean = punched_mean.sort_values(
by='wm_yr_wk_nbr').reset_index().drop('index', axis=1)
punched = punched.drop('posting_date', axis=1)
punched_pro = punched_df.groupby(['club_nbr',
'posting_date'])['cost'].sum()
punched_pro.column = ['total_punched_wg']
punched_pro = punched_pro.reset_index()
punched_pro = pd.merge(left=punched_pro,
right=calendar,
how='left',
left_on='posting_date',
right_on='calendar_date').drop('calendar_date',
axis=1)
punched_pro = removehurricane('cost',
punched_pro,
201733,
201739,
sales=False)
#201735 is Maria Hurrican Missing
#201737 is the Irma Hurricane
club_ls = punched.club_nbr.unique()
for club in club_ls:
pro_club = punched_pro[punched_pro.club_nbr.isin([club])]
#########################################
# adding missing value
if missing_val not in pro_club.wm_yr_wk_nbr.tolist():
pro_club.loc[-1] = [
club, pro_club.loc[pro_club.wm_yr_wk_nbr == wm_nbr_add(
missing_val, -2)].iloc[0, 1] + timedelta(days=14),
0.5 * pro_club.loc[pro_club.wm_yr_wk_nbr == wm_nbr_add(
missing_val, -2)].iloc[0, 2] +
0.5 * pro_club.loc[pro_club.wm_yr_wk_nbr == wm_nbr_add(
missing_val, 2)].iloc[0, 2], missing_val
] # adding a row
pro_club.index = pro_club.index + 1 # shifting index
####################################################
pro_club = pro_club.sort_values(by='posting_date').reset_index().drop(
'index', axis=1)
pro_sales = df_propor_PR_ts.loc[df_propor_PR_ts.club == club].drop(
['club'], axis=1)
pro_club = pro_club.drop(['club_nbr', 'posting_date'], axis=1)
pro_club.columns = ['cost', 'wm_yr_wk_nbr']
pro_sales['total_sales'] = pro_sales['total_sales_across'] * pro_sales[
'per_nbr_fc']
pro_sales = pd.concat(
[pro_sales] +
[pro_sales.total_sales.shift(x) for x in range(1, 3)],
axis=1)
pro_sales.columns = [
'wm_yr_wk_nbr', 'per_nbr_fc', 'total_sales_across',
'total_sales_0', 'sr_1', 'sr_2'
]
#########################################
# adding missing value
if missing_val not in pro_sales.wm_yr_wk_nbr.unique().tolist():
tu = []
for k in range(len(pro_sales.columns)):
tu.append(
0.5 * pro_sales.loc[pro_sales.wm_yr_wk_nbr == wm_nbr_add(
missing_val, -2)].iloc[0, k] +
0.5 * pro_sales.loc[pro_sales.wm_yr_wk_nbr == wm_nbr_add(
missing_val, 2)].iloc[0, k])
tu[0] = int(tu[0])
pro_sales.loc[-1] = tu # adding a row
pro_sales.index = pro_sales.index + 1 # shifting index
pro_sales = pro_sales.sort_values(
by='wm_yr_wk_nbr').reset_index().drop('index', axis=1)
pro_sales = pd.merge(left=pro_sales,
right=punched_mean,
how='right',
left_on='wm_yr_wk_nbr',
right_on='wm_yr_wk_nbr',
validate='1:1')
pro_sales = pro_sales.drop(['posting_date'], axis=1)
pro_sales = pro_sales.apply(lambda x: x.fillna(x.mean()), axis=0)
pro_sales_train = pro_sales.loc[
pro_sales.wm_yr_wk_nbr <= end_train_date]
pro_sales_test = pro_sales.loc[
pro_sales.wm_yr_wk_nbr >= start_test_date]
# trend
linear_trend = trend(degree=2, discount=0.98, name='linear_trend', w=8)
# seasonality
seasonal26 = seasonality(period=26,
discount=1,
name='seasonal26',
w=12)
# control variable
sales0 = pro_sales_train['total_sales_0'].values.tolist()
s0 = [[x] for x in sales0]
sales1 = pro_sales_train['sr_1'].values.tolist()
s1 = [[x] for x in sales1]
sales2 = pro_sales_train['sr_2'].values.tolist()
s2 = [[x] for x in sales2]
macro = pro_sales_train['PR_cost'].values.tolist()
m1 = [[x] for x in macro]
#####################################
s0 = dynamic(features=s0, discount=0.99, name='sales0', w=8)
s1 = dynamic(features=s1, discount=0.99, name='sales1',
w=6) # use the actual sales and forecasting sales amount
s2 = dynamic(features=s2, discount=0.95, name='sales2', w=6)
m1 = dynamic(features=m1, discount=0.99, name='macro', w=12)
#e1 = dynamic(features=e1,discount=0.95,name='eff',w=6)
drm = dlm(pro_club['cost']) + linear_trend + seasonal26 + autoReg(
degree=locality, name='ar2', w=6) + m1 #+s0+s1+s2+m1
drm.fit()
#testset
pro_sales_test = pro_sales_test.head(pred_weeks)
sales0test = pro_sales_test['total_sales_0'].head(
pred_weeks).values.tolist()
s0test = [[x] for x in sales0test]
sales1test = pro_sales_test['sr_1'].head(pred_weeks).values.tolist()
s1test = [[x] for x in sales1test]
sales2test = pro_sales_test['sr_2'].head(pred_weeks).values.tolist()
s2test = [[x] for x in sales2test]
macrotest = pro_sales_test['PR_cost'].head(pred_weeks).values.tolist()
m1test = [[x] for x in macrotest]
#efftest = testset['eff'].head(pred_weeks).values.tolist()
#e1test = [[x] for x in efftest]
features = {
'sales0': s0test,
'sales1': s1test,
'sales2': s2test,
'macro': m1test
} #,'eff':e1test}
(predictMean, predictVar) = drm.predictN(N=pred_weeks,
date=drm.n - 1,
featureDict=features)
#locality
pro_sales = pro_sales.drop(['sr_1', 'sr_2'], axis=1)
pro_sales['ratio'] = pro_sales['total_sales_0'] / pro_sales[
'total_sales_across']
pro_sales['ratio_1'] = pro_sales['ratio'].shift(1)
pro_sales['ratio_2'] = pro_sales['ratio'].shift(2)
trainset1_year = pro_club.loc[
pro_club.wm_yr_wk_nbr <= end_train_date].loc[
pro_club.wm_yr_wk_nbr >= end_train_date - locality]
trainset_year = pro_sales.loc[
pro_sales.wm_yr_wk_nbr <= end_train_date].loc[
pro_sales.wm_yr_wk_nbr >= end_train_date - locality]
trainset_year.apply(lambda x: x.fillna(x.mean()), axis=0)
linear_trend_year = trend(degree=1,
discount=0.99,
name='linear_trend_year',
w=10)
sales0_year = trainset_year['ratio'].values.tolist()
s0_year = [[x] for x in sales0_year]
# use the forecast of the ratio of each club among total in PR area
# since this is a local model, the total amount in area can be assumed to be constant.
sales1_year = trainset_year['ratio_1'].values.tolist()
s1_year = [[x] for x in sales1_year]
sales2_year = trainset_year['ratio_2'].values.tolist()
s2_year = [[x] for x in sales2_year]
macro_year = trainset_year['PR_cost'].values.tolist()
m1_year = [[x] for x in macro_year]
#####################################
s0_year = dynamic(features=s0_year,
discount=0.99,
name='sales0_year',
w=10)
s1_year = dynamic(features=s1_year,
discount=0.99,
name='sales1_year',
w=8)
s2_year = dynamic(features=s2_year,
discount=0.95,
name='sales2_year',
w=6)
m1_year = dynamic(features=m1_year,
discount=0.99,
name='macro_year',
w=10)
#e1_year = dynamic(features=e1_year,discount=0.95,name='eff_year',w=6)
if enable_sales:
drm_year = dlm(trainset1_year['cost']) + autoReg(
degree=locality, name='ar2', w=5
) + linear_trend_year + m1_year + s0_year + s1_year + s2_year
else:
drm_year = dlm(trainset1_year['cost']) + autoReg(
degree=locality, name='ar2',
w=5) + linear_trend_year + m1_year #+s0_year+s1_year+s2_year
drm_year.fit()
testset_year = pro_sales.loc[
pro_sales.wm_yr_wk_nbr >= start_test_date].head(pred_weeks)
sales0test = testset_year['ratio'].head(pred_weeks).values.tolist()
s0test = [[x] for x in sales0test]
sales1test = testset_year['ratio_1'].head(pred_weeks).values.tolist()
s1test = [[x] for x in sales1test]
sales2test = testset_year['ratio_2'].head(pred_weeks).values.tolist()
s2test = [[x] for x in sales2test]
features_year = {
'sales0_year': s0test,
'sales1_year': s1test,
'sales2_year': s2test,
'macro_year': m1test
}
(predictMean_year,
predictVar_year) = drm_year.predictN(N=pred_weeks,
date=drm_year.n - 1,
featureDict=features_year)
weeklist = []
p1 = np.exp(-r * (abs(end_train_date - start_of_this_year - 52)))
p2 = 1 - p1
for k in range(pred_weeks):
weeklist.append(wm_nbr_add(start_test_date, 2 * k))
if res.shape[0] == 0:
res['wm_yr_wk_nbr'] = weeklist
res['club'] = pd.Series(club * np.ones(pred_weeks),
index=res.index)
res['yhat'] = pd.Series(p1 * np.asarray(predictMean) +
p2 * np.asarray(predictMean_year),
index=res.index)
else:
tmp = pd.DataFrame()
tmp['wm_yr_wk_nbr'] = weeklist
tmp['club'] = pd.Series(club * np.ones(pred_weeks),
index=tmp.index)
tmp['yhat'] = pd.Series(p1 * np.asarray(predictMean) +
p2 * np.asarray(predictMean_year),
index=tmp.index)
res = pd.concat([res, tmp], axis=0)
return res
def _tune(self,
y,
period,
x=None,
metric="smape",
val_size=None,
verbose=False):
"""
Tune hyperparameters of the model.
:param y: pd.Series or 1-D np.array, time series to predict.
:param period: Int or Str, the number of observations per cycle: 1 or "annual" for yearly data, 4 or "quarterly"
for quarterly data, 7 or "daily" for daily data, 12 or "monthly" for monthly data, 24 or "hourly" for hourly
data, 52 or "weekly" for weekly data. First-letter abbreviations of strings work as well ("a", "q", "d", "m",
"h" and "w", respectively). Additional reference: https://robjhyndman.com/hyndsight/seasonal-periods/.
:param x: pd.DataFrame or 2-D np.array, exogeneous predictors, optional
:param metric: Str, the metric used for model selection. One of "mse" (mean squared error), "mae" (mean absolute
error).
:param val_size: Int, the number of most recent observations to use as validation set for tuning.
:param verbose: Boolean, True for printing additional info while tuning.
:return: None
"""
self.period = data_utils.period_to_int(period) if type(
period) == str else period
val_size = int(len(y) * .1) if val_size is None else val_size
y_train, y_val = model_utils.train_val_split(y, val_size=val_size)
if x is not None:
x_train, x_val = model_utils.train_val_split(x, val_size=val_size)
metric_fun = get_metric(metric)
params_grid = {
"trend": [0, 1, 2, 3],
"ar": [None],
# "ar": [None, 1, 2, 3],
}
params_keys, params_values = zip(*params_grid.items())
params_permutations = [
dict(zip(params_keys, v))
for v in itertools.product(*params_values)
]
scores = []
for permutation in params_permutations:
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model = pydlm.dlm(y_train)
model = model + pydlm.trend(degree=permutation["trend"],
discount=0.5)
model = model + pydlm.seasonality(period=self.period,
discount=0.99)
if permutation["ar"] is not None:
model = model + pydlm.autoReg(degree=permutation["ar"],
discount=0.99)
if x is not None:
for variable_id, x_variable in enumerate(x_train.T):
model = model + pydlm.dynamic(
features=[[v] for v in x_variable],
discount=0.99,
name=str(variable_id))
with SuppressStdoutStderr():
model.tune()
model.fit()
if x is not None:
x_val_dict = {}
for variable_id, x_variable in enumerate(x_val.T):
x_val_dict.update(
{str(variable_id): [[v] for v in x_variable]})
else:
x_val_dict = None
y_pred = model.predictN(date=model.n - 1,
N=len(y_val),
featureDict=x_val_dict)[0]
score = metric_fun(y_val, y_pred)
scores.append(score)
except:
scores.append(np.inf)
best_params = params_permutations[np.nanargmin(scores)]
self.params.update(best_params)
self.params["tuned"] = True