Forecasting COVID-Affected Data

This notebook showcases some scalecast techniques that can be used to forecast on series heavily affected by the COVID-19 pandemic. Special thanks to Zohoor Nezhad Halafi for helping with this notebook! Connect with her on LinkedIn.

import pandas as pd
import numpy as np
from scalecast.Forecaster import Forecaster
from scalecast.MVForecaster import MVForecaster
from scalecast.SeriesTransformer import SeriesTransformer
from scalecast.AnomalyDetector import AnomalyDetector
from scalecast.ChangepointDetector import ChangepointDetector
from scalecast.util import plot_reduction_errors, break_mv_forecaster, metrics
from scalecast import GridGenerator
from scalecast.multiseries import export_model_summaries
from scalecast.auxmodels import mlp_stack, auto_arima
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt
import seaborn as sns
import os
from tqdm.notebook import tqdm
import pickle

sns.set(rc={'figure.figsize':(12,8)})

GridGenerator.get_example_grids()

airline_series = ['Hou-Dom','IAH-DOM','IAH-Int']
fcst_horizon = 4

data = {
    l:pd.read_csv(
        os.path.join('data',l+'.csv')
    ) for l in airline_series
}

fdict = {
    l:Forecaster(
        y=df['PASSENGERS'],
        current_dates=df['Date'],
        future_dates=fcst_horizon,
    ) for l,df in data.items()
}

for l, f in fdict.items():
    f.plot()
    plt.title(l,size=16)
    plt.show()

Add Anomalies to Model

# scan on whole dataset to compare
for l, f in fdict.items():
    tr = SeriesTransformer(f)
    tr.DiffTransform(1)
    tr.DiffTransform(12)
    f2 = tr.ScaleTransform()
    ad = AnomalyDetector(f2)
    ad.MonteCarloDetect_sliding(36,36)
    ad.plot_anom()
    plt.title(f'{l} (diffed series) identified anomalies - all periods',size=16)
    plt.show()

scanning from obs 36 to obs 72
scanning from obs 72 to obs 108
scanning from obs 108 to obs 144
scanning from obs 144 to obs 180
scanning from obs 180 to obs 216
scanning from obs 216 to obs 252

scanning from obs 36 to obs 72
scanning from obs 72 to obs 108
scanning from obs 108 to obs 144
scanning from obs 144 to obs 180
scanning from obs 180 to obs 216
scanning from obs 216 to obs 252

scanning from obs 36 to obs 72
scanning from obs 72 to obs 108
scanning from obs 108 to obs 144
scanning from obs 144 to obs 180
scanning from obs 180 to obs 216
scanning from obs 216 to obs 252

• Sept 11 2001

• Recession sept 2008

• Hurricane Harvey August 2017

# scan over only covid periods
for l, f in fdict.items():
    ad.MonteCarloDetect(start_at='2020-04-01',stop_at='2021-09-01')
    ad.plot_anom()
    plt.title(f'{l} (diffed series) identified anomalies - covid only',size=16)
    plt.show()
    f = ad.WriteAnomtoXvars(f=f,drop_first=True)
    f.add_other_regressor(start = '2001-09-01', end='2001-09-01', called='2001')
    f.add_other_regressor(start = '2008-09-01', end='2008-09-01', called='Great Recession')
    f.add_other_regressor(start = '2017-08-01', end='2017-08-01', called='Harvey')
    fdict[l] = f

Add Changepoints to Model

for l, f in fdict.items():
    cd = ChangepointDetector(f)
    cd.DetectCPCUSUM()
    cd.plot()
    plt.title(f'{l} identified changepoints',size=16)
    plt.show()
    f = cd.WriteCPtoXvars()
    fdict[l] = f

Choose Xvars

regressors = pd.read_csv('data/Regressors.csv',parse_dates=['Date'],index_col=0)
for dt in f.future_dates:
    regressors.loc[dt] = [0]*regressors.shape[1]

for c in regressors:
    series = regressors[c]
    if adfuller(series)[1] >= 0.01:
        regressors[c] = regressors[c].diff()
regressors = regressors.fillna(0) # just for now
regressors.head()

	Number of available seats Domestic and international level	Number of domestic flights	Number of International flights	Revenue Passenger-miles/Domestic	Revenue Passenger-miles/International	TSI/Passengers	TSI/Freight	Trade, Transportation, and Utilities	All employees Information	Employees In financial activities	...	Employees in Leisure and Hospitality	All Employees,Education and Health Services	All Employee,Mining and Logging: Oil and Gas Extraction	Employees in Other Services	Unemployement Rate in Houston Area	Texas Business CyclesIndex	PCE on Durable goods(Billions of Dollars, Monthly, Seasonally Adjusted Annual Rate)	PCE on Recreational activities	Recession	WTO Oil Price
Date
2001-01-01	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.00	...	0.00	0.00	0.00	0.00	4.1	0.0	0.0	0.0	0	0.00
2001-02-01	-7491574.0	0.0	0.0	0.0	0.0	0.0	-0.1	0.51	0.11	0.10	...	0.57	0.81	0.36	-0.13	3.9	0.3	22.4	0.0	0	0.03
2001-03-01	8507261.0	0.0	0.0	0.0	0.0	0.5	0.2	-0.12	-0.12	0.13	...	0.38	0.60	-0.02	0.33	4.1	0.1	-13.8	0.0	0	-2.37
2001-04-01	-1273374.0	0.0	0.0	0.0	0.0	0.6	-1.5	-0.23	-0.12	-0.05	...	-0.18	0.72	-0.07	0.29	4.0	0.0	-16.8	0.0	1	0.17
2001-05-01	2815795.0	0.0	0.0	0.0	0.0	0.5	1.5	0.57	-0.58	-0.01	...	0.18	1.61	0.73	-0.22	4.2	-0.2	7.7	0.0	1	1.23

5 rows × 21 columns

Variable Reduction

dropped = pd.DataFrame(columns=fdict.keys())

# first to get regressors for regular times
for l, f in fdict.items():
    print(f'reducing {l}')
    f2 = f.deepcopy()
    f2.add_ar_terms(36)
    f.add_seasonal_regressors(
        'month',
        raw=False,
        sincos=True
    )
    f2.add_time_trend()
    f2.diff()
    f2.ingest_Xvars_df(
        regressors.reset_index(),
        date_col='Date',
    )
    f2.reduce_Xvars(
        method='pfi',
        estimator='elasticnet',
        cross_validate=True,
        cvkwargs={'k':3},
        dynamic_tuning=fcst_horizon, # optimize on fcst_horizon worth of predictions
        overwrite=False,
    )
    for x in f2.pfi_dropped_vars:
        dropped.loc[x,l] = 1
    for x in f2.reduced_Xvars:
        dropped.loc[x,l] = 0

    plot_reduction_errors(f2)
    plt.title(f'{l} normal times reduction errors',size=12)
    plt.show()

reducing Hou-Dom

reducing IAH-DOM

reducing IAH-Int

Which variables were dropped most?

dropped = dropped.fillna(0).drop('total times dropped',axis=1,errors='ignore')
dropped['total times dropped'] = dropped.sum(axis=1)
dropped.sort_values(['total times dropped'],ascending=False).head(25)

	Hou-Dom	IAH-DOM	IAH-Int	total times dropped
2001	1	1	1	3
PCE on Recreational activities	1	1	1	3
AR16	1	1	1	3
AR11	1	1	1	3
Anomaly_2021-05-01	1	1	1	3
Unemployement Rate in Houston Area	1	1	1	3
AR33	0	1	1	2
AR10	0	1	1	2
AR5	0	1	1	2
Harvey	0	1	1	2
WTO Oil Price	0	1	1	2
AR13	0	1	1	2
AR15	0	1	1	2
AR21	0	1	1	2
TSI/Freight	0	1	1	2
Anomaly_2021-09-01	1	1	0	2
Anomaly_2021-03-01	0	1	1	2
AR3	0	1	1	2
All Employees,Education and Health Services	0	1	1	2
Employees In financial activities	0	1	1	2
AR34	0	1	1	2
AR2	0	1	1	2
AR22	0	1	1	2
AR27	0	1	1	2
Anomaly_2020-08-01	0	1	1	2

Which variables were dropped least?

dropped.sort_values(['total times dropped']).head(40)

	Hou-Dom	IAH-DOM	IAH-Int	total times dropped
AR36	0	0	0	0
Revenue Passenger-miles/Domestic	0	0	0	0
AR18	0	0	0	0
Number of domestic flights	0	0	0	0
Number of International flights	0	0	0	0
cp2	0	0	1	1
AR17	0	0	1	1
All employees Information	0	0	1	1
Employees in Other Services	0	0	1	1
Employees in Leisure and Hospitality	0	0	1	1
Anomaly_2020-04-01	0	0	1	1
PCE on Durable goods(Billions of Dollars, Monthly, Seasonally Adjusted Annual Rate)	0	0	1	1
AR25	0	0	1	1
AR31	0	0	1	1
AR6	0	0	1	1
Trade, Transportation, and Utilities	0	0	1	1
AR29	0	0	1	1
AR24	0	0	1	1
AR26	0	0	1	1
AR19	0	0	1	1
Revenue Passenger-miles/International	0	0	1	1
Recession	0	0	1	1
AR14	0	0	1	1
Texas Business CyclesIndex	0	0	1	1
AR12	0	0	1	1
AR1	0	0	1	1
TSI/Passengers	0	0	1	1
AR30	0	0	1	1
Number of available seats Domestic and international level	0	0	1	1
Anomaly_2021-03-01	0	1	1	2
AR22	0	1	1	2
All Employees,Education and Health Services	0	1	1	2
AR2	0	1	1	2
Employees In financial activities	0	1	1	2
TSI/Freight	0	1	1	2
AR34	0	1	1	2
Anomaly_2020-08-01	0	1	1	2
AR21	0	1	1	2
Anomaly_2021-09-01	1	1	0	2
AR9	0	1	1	2

What about AR terms?

ar_dropped = dropped.reset_index()
ar_dropped = ar_dropped.loc[ar_dropped['index'].str.contains('AR')]
ar_dropped['ar'] = ar_dropped['index'].apply(lambda x: int(x[2:]))
ar_dropped.sort_values('ar')

	index	Hou-Dom	IAH-DOM	IAH-Int	total times dropped	ar
53	AR1	0	0	1	1	1
60	AR2	0	1	1	2	2
25	AR3	0	1	1	2	3
9	AR4	0	1	1	2	4
28	AR5	0	1	1	2	5
37	AR6	0	0	1	1	6
19	AR7	0	1	1	2	7
24	AR8	0	1	1	2	8
7	AR9	0	1	1	2	9
27	AR10	0	1	1	2	10
4	AR11	1	1	1	3	11
66	AR12	0	0	1	1	12
31	AR13	0	1	1	2	13
58	AR14	0	0	1	1	14
32	AR15	0	1	1	2	15
3	AR16	1	1	1	3	16
49	AR17	0	0	1	1	17
51	AR18	0	0	0	0	18
56	AR19	0	0	1	1	19
13	AR20	0	1	1	2	20
33	AR21	0	1	1	2	21
61	AR22	0	1	1	2	22
12	AR23	0	1	1	2	23
70	AR24	0	0	1	1	24
40	AR25	0	0	1	1	25
55	AR26	0	0	1	1	26
26	AR27	0	1	1	2	27
10	AR28	0	1	1	2	28
36	AR29	0	0	1	1	29
62	AR30	0	0	1	1	30
38	AR31	0	0	1	1	31
14	AR32	0	1	1	2	32
34	AR33	0	1	1	2	33
52	AR34	0	1	1	2	34
23	AR35	0	1	1	2	35
69	AR36	0	0	0	0	36

Subjectively make selections

final_Xvars_selected = [
    x for x in dropped.loc[dropped['total times dropped'] == 0].index if x in regressors
]

final_anom_selected = [
    x for x in dropped.loc[dropped['total times dropped'] <= 1].index if x.startswith('Anomaly') or x in ('Harvey','2001','Great Recession') or x.startswith('cp')
]

lags = {
    'Hou-Dom':[1,12,24,36],
    'IAH-DOM':[1,12,24,36],
    'IAH-Int':[1,12,24,36],
}

for l, f in fdict.items():
    f.drop_Xvars(*[x for x in f.get_regressor_names() if x not in final_anom_selected])
    f.add_seasonal_regressors('month',raw=False,sincos=True)
    f.diff()

Multivariate Forecasting

mvfdict = fdict.copy()

for x in final_Xvars_selected:
    if x in regressors:
        mvfdict[x] = Forecaster(
            y=regressors[x].values[:-4],
            current_dates = regressors.index.values[:-4],
        )
        lags[x] = [1]

for l, f in fdict.items():
    f.drop_Xvars(*[x for x in f.get_regressor_names() if x in regressors])

mvf = MVForecaster(*mvfdict.values(),names=mvfdict.keys())
mvf.add_optimizer_func(lambda x: x[0]*(1/3) + x[1]*(1/3) + x[2]*(1/3),'weighted') # first three series only get weight
mvf.set_optimize_on('weighted')
mvf.set_test_length(fcst_horizon)
mvf.set_validation_metric('mae')
mvf.set_validation_length(36)
mvf

MVForecaster(
    DateStartActuals=2001-01-01T00:00:00.000000000
    DateEndActuals=2021-09-01T00:00:00.000000000
    Freq=MS
    N_actuals=249
    N_series=6
    SeriesNames=['Hou-Dom', 'IAH-DOM', 'IAH-Int', 'Number of International  flights', 'Number of domestic flights', 'Revenue Passenger-miles/Domestic']
    ForecastLength=4
    Xvars=['Anomaly_2020-04-01', 'cp2', 'monthsin', 'monthcos']
    TestLength=4
    ValidationLength=36
    ValidationMetric=mae
    ForecastsEvaluated=[]
    CILevel=0.95
    BootstrapSamples=100
    CurrentEstimator=mlr
    OptimizeOn=weighted
)

elasticnet = {
    'alpha':[i/10 for i in range(1,21)],
    'l1_ratio':[0,0.25,0.5,0.75,1],
    'normalizer':['scale','minmax',None],
    'lags':[lags],
}
knn = {
    'n_neighbors':range(2,76),
    'lags':[lags],
}

lightgbm = {
    'n_estimators':[150,200,250],
    'boosting_type':['gbdt','dart','goss'],
    'max_depth':[1,2,3],
    'learning_rate':[0.001,0.01,0.1],
    'lags':[lags],
}
mlp = {
    'activation':['relu','tanh'],
    'hidden_layer_sizes':[(25,),(25,25,)],
    'solver':['lbfgs','adam'],
    'normalizer':['minmax','scale'],
    'lags':[lags],
}
mlr = {
    'normalizer':['scale','minmax',None],
    'lags':[lags],
}
sgd={
    'penalty':['l2','l1','elasticnet'],
    'l1_ratio':[0,0.15,0.5,0.85,1],
    'learning_rate':['invscaling','constant','optimal','adaptive'],
    'lags':[lags],
}
svr={
    'kernel':['linear'],
    'C':[.5,1,2,3],
    'epsilon':[0.01,0.1,0.5],
    'lags':[lags],
}
xgboost = {
    'n_estimators':[150,200,250],
    'scale_pos_weight':[5,10],
    'learning_rate':[0.1,0.2],
    'gamma':[0,3,5],
    'subsample':[0.8,0.9],
    'lags':[lags],
}

models = (
    'knn',
    'mlr',
    'elasticnet',
    'sgd',
    'xgboost',
    'lightgbm',
    'mlp',
)

print('begining models cross validated')
for m in tqdm(models):
    mvf.set_estimator(m)
    mvf.ingest_grid(globals()[m])
    mvf.limit_grid_size(10)
    mvf.cross_validate(k=3,dynamic_tuning=fcst_horizon)
    mvf.auto_forecast(call_me=f'{m}_cv_mv')

print('begining models tuned')
for m in tqdm(models):
    mvf.set_estimator(m)
    mvf.ingest_grid(globals()[m])
    mvf.limit_grid_size(10)
    mvf.tune(dynamic_tuning=fcst_horizon)
    mvf.auto_forecast(call_me=f'{m}_tune_mv')

begining models cross validated

begining models tuned

mvf.set_best_model(determine_best_by='LevelTestSetMAPE')
results = mvf.export('model_summaries')
results_sub = results.loc[results['Series'].isin(airline_series)]
results_sub[['Series','ModelNickname','LevelTestSetR2','LevelTestSetMAPE']]

	Series	ModelNickname	LevelTestSetR2	LevelTestSetMAPE
0	Hou-Dom	elasticnet_tune_mv	-0.008851	0.092853
1	Hou-Dom	knn_cv_mv	-0.122960	0.100203
2	Hou-Dom	mlr_cv_mv	-4.865199	0.209266
3	Hou-Dom	elasticnet_cv_mv	-0.046781	0.090712
4	Hou-Dom	sgd_cv_mv	0.044149	0.086127
5	Hou-Dom	xgboost_cv_mv	-1.806345	0.130049
6	Hou-Dom	lightgbm_cv_mv	-0.020278	0.087836
7	Hou-Dom	mlp_cv_mv	-0.082069	0.087113
8	Hou-Dom	knn_tune_mv	-0.731346	0.106951
9	Hou-Dom	mlr_tune_mv	-4.865199	0.209266
10	Hou-Dom	sgd_tune_mv	0.069918	0.086680
11	Hou-Dom	xgboost_tune_mv	-1.687364	0.129505
12	Hou-Dom	lightgbm_tune_mv	0.106759	0.086545
13	Hou-Dom	mlp_tune_mv	0.140452	0.080000
14	IAH-DOM	elasticnet_tune_mv	-2.675066	0.130365
15	IAH-DOM	knn_cv_mv	-1.521107	0.102719
16	IAH-DOM	mlr_cv_mv	-0.700337	0.065628
17	IAH-DOM	elasticnet_cv_mv	-3.282911	0.143407
18	IAH-DOM	sgd_cv_mv	-3.305075	0.144816
19	IAH-DOM	xgboost_cv_mv	-1.703854	0.112821
20	IAH-DOM	lightgbm_cv_mv	-3.602143	0.150592
21	IAH-DOM	mlp_cv_mv	-3.669990	0.150982
22	IAH-DOM	knn_tune_mv	-0.765313	0.077754
23	IAH-DOM	mlr_tune_mv	-0.700337	0.065628
24	IAH-DOM	sgd_tune_mv	-3.141806	0.141990
25	IAH-DOM	xgboost_tune_mv	-2.161605	0.126577
26	IAH-DOM	lightgbm_tune_mv	-2.480593	0.125745
27	IAH-DOM	mlp_tune_mv	-2.759960	0.128641
28	IAH-Int	elasticnet_tune_mv	-0.019007	0.153717
29	IAH-Int	knn_cv_mv	-0.411933	0.196223
30	IAH-Int	mlr_cv_mv	-2.984908	0.283815
31	IAH-Int	elasticnet_cv_mv	-0.175874	0.148786
32	IAH-Int	sgd_cv_mv	-0.637143	0.207411
33	IAH-Int	xgboost_cv_mv	-1.073559	0.225976
34	IAH-Int	lightgbm_cv_mv	-0.132948	0.156983
35	IAH-Int	mlp_cv_mv	-0.302144	0.156178
36	IAH-Int	knn_tune_mv	-0.689363	0.192302
37	IAH-Int	mlr_tune_mv	-2.984908	0.283815
38	IAH-Int	sgd_tune_mv	-0.498934	0.200455
39	IAH-Int	xgboost_tune_mv	-0.300637	0.173677
40	IAH-Int	lightgbm_tune_mv	-0.715109	0.207723
41	IAH-Int	mlp_tune_mv	-0.430382	0.193184

models_to_stack = (
    'knn_cv_mv',
    'elasticnet_cv_mv',
    'xgboost_cv_mv',
    'lightgbm_cv_mv',
)
mlp_stack(
    mvf,
    model_nicknames = models_to_stack,
    call_me='stacking_mv',
)

mvf.set_best_model(determine_best_by='LevelTestSetMAPE')
results = mvf.export('model_summaries')
results_sub = results.loc[results['Series'].isin(airline_series)]
results_sub[['Series','ModelNickname','LevelTestSetR2','LevelTestSetMAPE']]

	Series	ModelNickname	LevelTestSetR2	LevelTestSetMAPE
0	Hou-Dom	stacking_mv	0.137636	0.062217
1	Hou-Dom	knn_cv_mv	-0.122960	0.100203
2	Hou-Dom	mlr_cv_mv	-4.865199	0.209266
3	Hou-Dom	elasticnet_cv_mv	-0.046781	0.090712
4	Hou-Dom	sgd_cv_mv	0.044149	0.086127
5	Hou-Dom	xgboost_cv_mv	-1.806345	0.130049
6	Hou-Dom	lightgbm_cv_mv	-0.020278	0.087836
7	Hou-Dom	mlp_cv_mv	-0.082069	0.087113
8	Hou-Dom	knn_tune_mv	-0.731346	0.106951
9	Hou-Dom	mlr_tune_mv	-4.865199	0.209266
10	Hou-Dom	elasticnet_tune_mv	-0.008851	0.092853
11	Hou-Dom	sgd_tune_mv	0.069918	0.086680
12	Hou-Dom	xgboost_tune_mv	-1.687364	0.129505
13	Hou-Dom	lightgbm_tune_mv	0.106759	0.086545
14	Hou-Dom	mlp_tune_mv	0.140452	0.080000
15	IAH-DOM	stacking_mv	-1.030366	0.097983
16	IAH-DOM	knn_cv_mv	-1.521107	0.102719
17	IAH-DOM	mlr_cv_mv	-0.700337	0.065628
18	IAH-DOM	elasticnet_cv_mv	-3.282911	0.143407
19	IAH-DOM	sgd_cv_mv	-3.305075	0.144816
20	IAH-DOM	xgboost_cv_mv	-1.703854	0.112821
21	IAH-DOM	lightgbm_cv_mv	-3.602143	0.150592
22	IAH-DOM	mlp_cv_mv	-3.669990	0.150982
23	IAH-DOM	knn_tune_mv	-0.765313	0.077754
24	IAH-DOM	mlr_tune_mv	-0.700337	0.065628
25	IAH-DOM	elasticnet_tune_mv	-2.675066	0.130365
26	IAH-DOM	sgd_tune_mv	-3.141806	0.141990
27	IAH-DOM	xgboost_tune_mv	-2.161605	0.126577
28	IAH-DOM	lightgbm_tune_mv	-2.480593	0.125745
29	IAH-DOM	mlp_tune_mv	-2.759960	0.128641
30	IAH-Int	stacking_mv	0.725500	0.062671
31	IAH-Int	knn_cv_mv	-0.411933	0.196223
32	IAH-Int	mlr_cv_mv	-2.984908	0.283815
33	IAH-Int	elasticnet_cv_mv	-0.175874	0.148786
34	IAH-Int	sgd_cv_mv	-0.637143	0.207411
35	IAH-Int	xgboost_cv_mv	-1.073559	0.225976
36	IAH-Int	lightgbm_cv_mv	-0.132948	0.156983
37	IAH-Int	mlp_cv_mv	-0.302144	0.156178
38	IAH-Int	knn_tune_mv	-0.689363	0.192302
39	IAH-Int	mlr_tune_mv	-2.984908	0.283815
40	IAH-Int	elasticnet_tune_mv	-0.019007	0.153717
41	IAH-Int	sgd_tune_mv	-0.498934	0.200455
42	IAH-Int	xgboost_tune_mv	-0.300637	0.173677
43	IAH-Int	lightgbm_tune_mv	-0.715109	0.207723
44	IAH-Int	mlp_tune_mv	-0.430382	0.193184

Univariate Forecasting

f1, f2, f3 = break_mv_forecaster(mvf)[:3]
fdict[airline_series[0]] = f1
fdict[airline_series[1]] = f2
fdict[airline_series[2]] = f3

f1

Forecaster(
    DateStartActuals=2001-02-01T00:00:00.000000000
    DateEndActuals=2021-09-01T00:00:00.000000000
    Freq=MS
    N_actuals=248
    ForecastLength=4
    Xvars=['Anomaly_2020-04-01', 'cp2', 'monthsin', 'monthcos']
    Differenced=1
    TestLength=4
    ValidationLength=36
    ValidationMetric=rmse
    ForecastsEvaluated=['knn_cv_mv', 'mlr_cv_mv', 'elasticnet_cv_mv', 'sgd_cv_mv', 'xgboost_cv_mv', 'lightgbm_cv_mv', 'mlp_cv_mv', 'knn_tune_mv', 'mlr_tune_mv', 'elasticnet_tune_mv', 'sgd_tune_mv', 'xgboost_tune_mv', 'lightgbm_tune_mv', 'mlp_tune_mv', 'stacking_mv']
    CILevel=0.95
    BootstrapSamples=100
    CurrentEstimator=None
)

models = (
    'knn',
    'mlr',
    'elasticnet',
    'sgd',
    'xgboost',
    'lightgbm',
    'mlp',
    'hwes',
    'arima',
    'prophet',
    'silverkite',
    'theta',
)

for l, f in fdict.items():
    print(f'processing {l}')
    f.add_ar_terms(1)
    f.add_AR_terms((3,6))
    f.add_AR_terms((3,12))
    auto_arima(
        f,
        m=12,
        error_action='ignore',
    )
    f.manual_forecast(
        order=f.auto_arima_params['order'],
        seasonal_order=f.auto_arima_params['seasonal_order'],
        Xvars = 'all',
        call_me='auto_arima_anom',
    )
    f.tune_test_forecast(
        models,
        limit_grid_size=10,
        cross_validate=True,
        k=3,
        dynamic_tuning=fcst_horizon,
        suffix='_cv_uv',
    )
    f.tune_test_forecast(
        models,
        limit_grid_size=10,
        cross_validate=False,
        dynamic_tuning=fcst_horizon,
        suffix='_tune_uv',
    )

processing Hou-Dom
processing IAH-DOM
processing IAH-Int

results = export_model_summaries(
    fdict,
    determine_best_by='LevelTestSetMAPE',
)
results[
    [
        'Series',
        'ModelNickname',
        'LevelTestSetMAPE',
    ]
].sort_values(
    [
        'Series',
        'LevelTestSetMAPE',
    ]
).head(25)

	Series	ModelNickname	LevelTestSetMAPE
0	Hou-Dom	prophet_cv_uv	0.024967
1	Hou-Dom	arima_tune_uv	0.031463
2	Hou-Dom	arima_cv_uv	0.031463
3	Hou-Dom	hwes_tune_uv	0.040938
4	Hou-Dom	hwes_cv_uv	0.040938
5	Hou-Dom	auto_arima_anom	0.041796
6	Hou-Dom	prophet_tune_uv	0.046722
7	Hou-Dom	sgd_cv_uv	0.054584
8	Hou-Dom	sgd_tune_uv	0.056646
9	Hou-Dom	stacking_mv	0.062217
10	Hou-Dom	auto_arima	0.071545
11	Hou-Dom	mlr_tune_uv	0.072219
12	Hou-Dom	mlr_cv_uv	0.072219
13	Hou-Dom	elasticnet_cv_uv	0.072386
14	Hou-Dom	elasticnet_tune_uv	0.073144
15	Hou-Dom	mlp_cv_uv	0.075766
16	Hou-Dom	mlp_tune_uv	0.079230
17	Hou-Dom	mlp_tune_mv	0.080000
18	Hou-Dom	lightgbm_cv_uv	0.084266
19	Hou-Dom	sgd_cv_mv	0.086127
20	Hou-Dom	lightgbm_tune_mv	0.086545
21	Hou-Dom	sgd_tune_mv	0.086680
22	Hou-Dom	mlp_cv_mv	0.087113
23	Hou-Dom	lightgbm_tune_uv	0.087297
24	Hou-Dom	lightgbm_cv_mv	0.087836

models_to_stack = (
    'knn_cv_uv',
    'sgd_cv_uv',
    'elasticnet_cv_uv',
    'xgboost_cv_uv',
)

for l, f in fdict.items():
    mlp_stack(
        f,
        model_nicknames=models_to_stack,
        call_me='stacking_uv'
    )

for l, f in fdict.items():
    # trains 120 total lstm models to get forecast intervals probabilistically
    f.set_estimator('rnn')
    f.proba_forecast(
        lags=48,
        layers_struct=[('LSTM',{'units':100,'dropout':0})]*2 + [('Dense',{'units':10})]*2,
        epochs=8,
        random_seed=42,
        validation_split=0.2,
        call_me = 'lstm',
    )

    f.set_estimator('combo')
    f.manual_forecast(how='simple',call_me='avg')
    f.manual_forecast(how='weighted',determine_best_by='LevelTestSetMAPE',call_me='weighted')

Epoch 1/8
5/5 [==============================] - 4s 220ms/step - loss: 0.4620 - val_loss: 0.2296
Epoch 2/8
5/5 [==============================] - 0s 50ms/step - loss: 0.1978 - val_loss: 0.0809
Epoch 3/8
5/5 [==============================] - 0s 50ms/step - loss: 0.1269 - val_loss: 0.0839
Epoch 4/8
5/5 [==============================] - 0s 53ms/step - loss: 0.1066 - val_loss: 0.0730
Epoch 5/8
5/5 [==============================] - 0s 55ms/step - loss: 0.1078 - val_loss: 0.0715
Epoch 6/8
5/5 [==============================] - 0s 57ms/step - loss: 0.1027 - val_loss: 0.0679
Epoch 7/8
5/5 [==============================] - 0s 57ms/step - loss: 0.1011 - val_loss: 0.0684
Epoch 8/8
5/5 [==============================] - 0s 57ms/step - loss: 0.1015 - val_loss: 0.0667
Epoch 1/8
5/5 [==============================] - 3s 216ms/step - loss: 0.4601 - val_loss: 0.3736
Epoch 2/8
5/5 [==============================] - 0s 53ms/step - loss: 0.3318 - val_loss: 0.2527
Epoch 3/8
5/5 [==============================] - 0s 53ms/step - loss: 0.2008 - val_loss: 0.1088
Epoch 4/8
5/5 [==============================] - 0s 57ms/step - loss: 0.1475 - val_loss: 0.0784
Epoch 5/8
5/5 [==============================] - 0s 58ms/step - loss: 0.1171 - val_loss: 0.0935
Epoch 6/8
5/5 [==============================] - 0s 58ms/step - loss: 0.1159 - val_loss: 0.0686
Epoch 7/8
5/5 [==============================] - 0s 59ms/step - loss: 0.1070 - val_loss: 0.0794
Epoch 8/8
5/5 [==============================] - 0s 58ms/step - loss: 0.1058 - val_loss: 0.0712
Epoch 1/8
5/5 [==============================] - 4s 212ms/step - loss: 0.4531 - val_loss: 0.3494
Epoch 2/8
5/5 [==============================] - 0s 56ms/step - loss: 0.3395 - val_loss: 0.2977
Epoch 3/8
5/5 [==============================] - 0s 62ms/step - loss: 0.2786 - val_loss: 0.2221
Epoch 4/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1931 - val_loss: 0.1028
Epoch 5/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1221 - val_loss: 0.1066
Epoch 6/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1128 - val_loss: 0.0671
Epoch 7/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1043 - val_loss: 0.0803
Epoch 8/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1072 - val_loss: 0.0695
Epoch 1/8
5/5 [==============================] - 3s 208ms/step - loss: 0.4293 - val_loss: 0.2660
Epoch 2/8
5/5 [==============================] - 0s 53ms/step - loss: 0.2586 - val_loss: 0.2127
Epoch 3/8
5/5 [==============================] - 0s 59ms/step - loss: 0.2098 - val_loss: 0.1606
Epoch 4/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1548 - val_loss: 0.0804
Epoch 5/8
5/5 [==============================] - 0s 60ms/step - loss: 0.1092 - val_loss: 0.0948
Epoch 6/8
5/5 [==============================] - 0s 62ms/step - loss: 0.1145 - val_loss: 0.0755
Epoch 7/8
5/5 [==============================] - 0s 61ms/step - loss: 0.1030 - val_loss: 0.0713
Epoch 8/8
5/5 [==============================] - 0s 60ms/step - loss: 0.1055 - val_loss: 0.0713
Epoch 1/8
5/5 [==============================] - 4s 209ms/step - loss: 0.4792 - val_loss: 0.2554
Epoch 2/8
5/5 [==============================] - 0s 61ms/step - loss: 0.2547 - val_loss: 0.2213
Epoch 3/8
5/5 [==============================] - 0s 62ms/step - loss: 0.2354 - val_loss: 0.1948
Epoch 4/8
5/5 [==============================] - 0s 63ms/step - loss: 0.2018 - val_loss: 0.1737
Epoch 5/8
5/5 [==============================] - 0s 61ms/step - loss: 0.1835 - val_loss: 0.1632
Epoch 6/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1655 - val_loss: 0.1375
Epoch 7/8
5/5 [==============================] - 0s 66ms/step - loss: 0.1479 - val_loss: 0.1149
Epoch 8/8
5/5 [==============================] - 0s 66ms/step - loss: 0.1307 - val_loss: 0.0922
Epoch 1/8
5/5 [==============================] - 3s 208ms/step - loss: 0.4198 - val_loss: 0.1965
Epoch 2/8
5/5 [==============================] - 0s 58ms/step - loss: 0.1934 - val_loss: 0.1305
Epoch 3/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1269 - val_loss: 0.1067
Epoch 4/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1175 - val_loss: 0.0894
Epoch 5/8
5/5 [==============================] - 0s 86ms/step - loss: 0.1071 - val_loss: 0.0720
Epoch 6/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1016 - val_loss: 0.0698
Epoch 7/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1033 - val_loss: 0.0673
Epoch 8/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1021 - val_loss: 0.0675
Epoch 1/8
5/5 [==============================] - 4s 212ms/step - loss: 0.3767 - val_loss: 0.2195
Epoch 2/8
5/5 [==============================] - 0s 59ms/step - loss: 0.1603 - val_loss: 0.1068
Epoch 3/8
5/5 [==============================] - 0s 66ms/step - loss: 0.1155 - val_loss: 0.0934
Epoch 4/8
5/5 [==============================] - 0s 65ms/step - loss: 0.1072 - val_loss: 0.0758
Epoch 5/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1023 - val_loss: 0.0729
Epoch 6/8
5/5 [==============================] - 0s 65ms/step - loss: 0.1031 - val_loss: 0.0683
Epoch 7/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1002 - val_loss: 0.0644
Epoch 8/8
5/5 [==============================] - 0s 65ms/step - loss: 0.1005 - val_loss: 0.0648
Epoch 1/8
5/5 [==============================] - 3s 219ms/step - loss: 0.4492 - val_loss: 0.3560
Epoch 2/8
5/5 [==============================] - 0s 60ms/step - loss: 0.3102 - val_loss: 0.2044
Epoch 3/8
5/5 [==============================] - 0s 71ms/step - loss: 0.2073 - val_loss: 0.1401
Epoch 4/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1334 - val_loss: 0.0936
Epoch 5/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1162 - val_loss: 0.0925
Epoch 6/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1104 - val_loss: 0.0837
Epoch 7/8
5/5 [==============================] - 0s 65ms/step - loss: 0.1038 - val_loss: 0.0777
Epoch 8/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1062 - val_loss: 0.0714
Epoch 1/8
5/5 [==============================] - 4s 211ms/step - loss: 0.4593 - val_loss: 0.2216
Epoch 2/8
5/5 [==============================] - 0s 60ms/step - loss: 0.1930 - val_loss: 0.0755
Epoch 3/8
5/5 [==============================] - 0s 61ms/step - loss: 0.1168 - val_loss: 0.0891
Epoch 4/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1054 - val_loss: 0.0771
Epoch 5/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1080 - val_loss: 0.0758
Epoch 6/8
5/5 [==============================] - 0s 65ms/step - loss: 0.1047 - val_loss: 0.0671
Epoch 7/8
5/5 [==============================] - 0s 70ms/step - loss: 0.0987 - val_loss: 0.0685
Epoch 8/8
5/5 [==============================] - 0s 66ms/step - loss: 0.1010 - val_loss: 0.0668
Epoch 1/8
5/5 [==============================] - 3s 211ms/step - loss: 0.3480 - val_loss: 0.1881
Epoch 2/8
5/5 [==============================] - 0s 55ms/step - loss: 0.1496 - val_loss: 0.1327
Epoch 3/8
5/5 [==============================] - 0s 58ms/step - loss: 0.1256 - val_loss: 0.0812
Epoch 4/8
5/5 [==============================] - 0s 66ms/step - loss: 0.1118 - val_loss: 0.0679
Epoch 5/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1041 - val_loss: 0.0699
Epoch 6/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1041 - val_loss: 0.0719
Epoch 7/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1011 - val_loss: 0.0718
Epoch 8/8
5/5 [==============================] - 0s 66ms/step - loss: 0.1016 - val_loss: 0.0662
Epoch 1/8
5/5 [==============================] - 4s 278ms/step - loss: 0.3927 - val_loss: 0.1465
Epoch 2/8
5/5 [==============================] - 0s 62ms/step - loss: 0.1637 - val_loss: 0.1382
Epoch 3/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1637 - val_loss: 0.0927
Epoch 4/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1179 - val_loss: 0.0952
Epoch 5/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1103 - val_loss: 0.0755
Epoch 6/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1057 - val_loss: 0.0721
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1038 - val_loss: 0.0688
Epoch 8/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1020 - val_loss: 0.0706
Epoch 1/8
5/5 [==============================] - 4s 254ms/step - loss: 0.3792 - val_loss: 0.2446
Epoch 2/8
5/5 [==============================] - 0s 69ms/step - loss: 0.2609 - val_loss: 0.2121
Epoch 3/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1907 - val_loss: 0.1374
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1291 - val_loss: 0.0814
Epoch 5/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1152 - val_loss: 0.0888
Epoch 6/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1110 - val_loss: 0.0718
Epoch 7/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1024 - val_loss: 0.0704
Epoch 8/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1031 - val_loss: 0.0699
Epoch 1/8
5/5 [==============================] - 4s 263ms/step - loss: 0.4634 - val_loss: 0.1887
Epoch 2/8
5/5 [==============================] - 0s 65ms/step - loss: 0.1608 - val_loss: 0.0798
Epoch 3/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1264 - val_loss: 0.0769
Epoch 4/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1073 - val_loss: 0.0790
Epoch 5/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1057 - val_loss: 0.0830
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1065 - val_loss: 0.0696
Epoch 7/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1033 - val_loss: 0.0670
Epoch 8/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1027 - val_loss: 0.0732
Epoch 1/8
5/5 [==============================] - 5s 364ms/step - loss: 0.4697 - val_loss: 0.3228
Epoch 2/8
5/5 [==============================] - 0s 100ms/step - loss: 0.2379 - val_loss: 0.1059
Epoch 3/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1345 - val_loss: 0.0838
Epoch 4/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1206 - val_loss: 0.0798
Epoch 5/8
5/5 [==============================] - 0s 89ms/step - loss: 0.1090 - val_loss: 0.0798
Epoch 6/8
5/5 [==============================] - 0s 88ms/step - loss: 0.1082 - val_loss: 0.0723
Epoch 7/8
5/5 [==============================] - 1s 146ms/step - loss: 0.1056 - val_loss: 0.0726
Epoch 8/8
5/5 [==============================] - 0s 100ms/step - loss: 0.1040 - val_loss: 0.0767
Epoch 1/8
5/5 [==============================] - 4s 223ms/step - loss: 0.3879 - val_loss: 0.2142
Epoch 2/8
5/5 [==============================] - 0s 62ms/step - loss: 0.1915 - val_loss: 0.1533
Epoch 3/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1613 - val_loss: 0.1085
Epoch 4/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1361 - val_loss: 0.0919
Epoch 5/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1199 - val_loss: 0.0895
Epoch 6/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1102 - val_loss: 0.0791
Epoch 7/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1039 - val_loss: 0.0719
Epoch 8/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1019 - val_loss: 0.0672
Epoch 1/8
5/5 [==============================] - 5s 252ms/step - loss: 0.4495 - val_loss: 0.2936
Epoch 2/8
5/5 [==============================] - 0s 71ms/step - loss: 0.2243 - val_loss: 0.0909
Epoch 3/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1233 - val_loss: 0.1121
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1221 - val_loss: 0.0722
Epoch 5/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1044 - val_loss: 0.0728
Epoch 6/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1050 - val_loss: 0.0705
Epoch 7/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1017 - val_loss: 0.0683
Epoch 8/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1035 - val_loss: 0.0710
Epoch 1/8
5/5 [==============================] - 3s 214ms/step - loss: 0.3382 - val_loss: 0.2025
Epoch 2/8
5/5 [==============================] - 0s 61ms/step - loss: 0.1671 - val_loss: 0.1272
Epoch 3/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1206 - val_loss: 0.0859
Epoch 4/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1111 - val_loss: 0.0765
Epoch 5/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1075 - val_loss: 0.0655
Epoch 6/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1050 - val_loss: 0.0676
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1006 - val_loss: 0.0729
Epoch 8/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1005 - val_loss: 0.0657
Epoch 1/8
5/5 [==============================] - 3s 211ms/step - loss: 0.3966 - val_loss: 0.2425
Epoch 2/8
5/5 [==============================] - 0s 59ms/step - loss: 0.1635 - val_loss: 0.1310
Epoch 3/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1218 - val_loss: 0.0867
Epoch 4/8
5/5 [==============================] - 0s 65ms/step - loss: 0.1174 - val_loss: 0.0791
Epoch 5/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1061 - val_loss: 0.0781
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1054 - val_loss: 0.0676
Epoch 7/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1003 - val_loss: 0.0674
Epoch 8/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1002 - val_loss: 0.0673
Epoch 1/8
5/5 [==============================] - 5s 273ms/step - loss: 0.4536 - val_loss: 0.2352
Epoch 2/8
5/5 [==============================] - 0s 60ms/step - loss: 0.2195 - val_loss: 0.0839
Epoch 3/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1171 - val_loss: 0.0999
Epoch 4/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1121 - val_loss: 0.0683
Epoch 5/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1038 - val_loss: 0.0733
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1033 - val_loss: 0.0707
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1004 - val_loss: 0.0659
Epoch 8/8
5/5 [==============================] - 0s 72ms/step - loss: 0.0994 - val_loss: 0.0654
Epoch 1/8
5/5 [==============================] - 5s 226ms/step - loss: 0.4240 - val_loss: 0.2890
Epoch 2/8
5/5 [==============================] - 0s 59ms/step - loss: 0.2342 - val_loss: 0.1640
Epoch 3/8
5/5 [==============================] - 0s 61ms/step - loss: 0.1377 - val_loss: 0.0749
Epoch 4/8
5/5 [==============================] - 0s 61ms/step - loss: 0.1181 - val_loss: 0.0889
Epoch 5/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1039 - val_loss: 0.0670
Epoch 6/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1030 - val_loss: 0.0728
Epoch 7/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1036 - val_loss: 0.0721
Epoch 8/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1012 - val_loss: 0.0663
Epoch 1/8
5/5 [==============================] - 4s 244ms/step - loss: 0.4628 - val_loss: 0.3032
Epoch 2/8
5/5 [==============================] - 0s 60ms/step - loss: 0.2330 - val_loss: 0.1009
Epoch 3/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1380 - val_loss: 0.1133
Epoch 4/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1185 - val_loss: 0.0787
Epoch 5/8
5/5 [==============================] - 0s 66ms/step - loss: 0.1099 - val_loss: 0.0818
Epoch 6/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1080 - val_loss: 0.0658
Epoch 7/8
5/5 [==============================] - 0s 64ms/step - loss: 0.0994 - val_loss: 0.0718
Epoch 8/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1015 - val_loss: 0.0680
Epoch 1/8
5/5 [==============================] - 3s 215ms/step - loss: 0.4780 - val_loss: 0.3311
Epoch 2/8
5/5 [==============================] - 0s 63ms/step - loss: 0.2221 - val_loss: 0.1361
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1313 - val_loss: 0.1162
Epoch 4/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1200 - val_loss: 0.0776
Epoch 5/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1116 - val_loss: 0.0669
Epoch 6/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1046 - val_loss: 0.0743
Epoch 7/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1020 - val_loss: 0.0691
Epoch 8/8
5/5 [==============================] - 0s 65ms/step - loss: 0.1035 - val_loss: 0.0699
Epoch 1/8
5/5 [==============================] - 4s 253ms/step - loss: 0.4216 - val_loss: 0.1989
Epoch 2/8
5/5 [==============================] - 0s 66ms/step - loss: 0.1909 - val_loss: 0.1219
Epoch 3/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1425 - val_loss: 0.0693
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1113 - val_loss: 0.0675
Epoch 5/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1053 - val_loss: 0.0670
Epoch 6/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1019 - val_loss: 0.0664
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1010 - val_loss: 0.0706
Epoch 8/8
5/5 [==============================] - 0s 73ms/step - loss: 0.0997 - val_loss: 0.0702
Epoch 1/8
5/5 [==============================] - 3s 218ms/step - loss: 0.4416 - val_loss: 0.2831
Epoch 2/8
5/5 [==============================] - 0s 65ms/step - loss: 0.2329 - val_loss: 0.1571
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1408 - val_loss: 0.1208
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1209 - val_loss: 0.0977
Epoch 5/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1094 - val_loss: 0.0827
Epoch 6/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1088 - val_loss: 0.0733
Epoch 7/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1029 - val_loss: 0.0725
Epoch 8/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1052 - val_loss: 0.0700
Epoch 1/8
5/5 [==============================] - 4s 238ms/step - loss: 0.4120 - val_loss: 0.2061
Epoch 2/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1739 - val_loss: 0.1037
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1385 - val_loss: 0.1082
Epoch 4/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1111 - val_loss: 0.0834
Epoch 5/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1076 - val_loss: 0.0746
Epoch 6/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1039 - val_loss: 0.0659
Epoch 7/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1000 - val_loss: 0.0690
Epoch 8/8
5/5 [==============================] - 0s 72ms/step - loss: 0.0999 - val_loss: 0.0656
Epoch 1/8
5/5 [==============================] - 4s 236ms/step - loss: 0.3936 - val_loss: 0.2807
Epoch 2/8
5/5 [==============================] - 0s 60ms/step - loss: 0.2506 - val_loss: 0.1960
Epoch 3/8
5/5 [==============================] - 0s 65ms/step - loss: 0.1686 - val_loss: 0.0824
Epoch 4/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1244 - val_loss: 0.0909
Epoch 5/8
5/5 [==============================] - 0s 84ms/step - loss: 0.1101 - val_loss: 0.0748
Epoch 6/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1070 - val_loss: 0.0713
Epoch 7/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1025 - val_loss: 0.0664
Epoch 8/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1012 - val_loss: 0.0674
Epoch 1/8
5/5 [==============================] - 4s 236ms/step - loss: 0.4210 - val_loss: 0.2066
Epoch 2/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1626 - val_loss: 0.1242
Epoch 3/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1269 - val_loss: 0.0778
Epoch 4/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1042 - val_loss: 0.0751
Epoch 5/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1032 - val_loss: 0.0694
Epoch 6/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1003 - val_loss: 0.0681
Epoch 7/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1008 - val_loss: 0.0663
Epoch 8/8
5/5 [==============================] - 0s 79ms/step - loss: 0.0997 - val_loss: 0.0655
Epoch 1/8
5/5 [==============================] - 4s 228ms/step - loss: 0.4332 - val_loss: 0.2339
Epoch 2/8
5/5 [==============================] - 0s 59ms/step - loss: 0.1974 - val_loss: 0.1167
Epoch 3/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1404 - val_loss: 0.1110
Epoch 4/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1149 - val_loss: 0.1094
Epoch 5/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1116 - val_loss: 0.0835
Epoch 6/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1125 - val_loss: 0.0689
Epoch 7/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1029 - val_loss: 0.0793
Epoch 8/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1042 - val_loss: 0.0726
Epoch 1/8
5/5 [==============================] - 3s 207ms/step - loss: 0.4108 - val_loss: 0.2654
Epoch 2/8
5/5 [==============================] - 0s 59ms/step - loss: 0.2124 - val_loss: 0.1372
Epoch 3/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1366 - val_loss: 0.0673
Epoch 4/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1074 - val_loss: 0.0849
Epoch 5/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1105 - val_loss: 0.0704
Epoch 6/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1043 - val_loss: 0.0697
Epoch 7/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1033 - val_loss: 0.0698
Epoch 8/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1030 - val_loss: 0.0681
Epoch 1/8
5/5 [==============================] - 4s 276ms/step - loss: 0.4871 - val_loss: 0.2833
Epoch 2/8
5/5 [==============================] - 0s 74ms/step - loss: 0.2129 - val_loss: 0.0989
Epoch 3/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1284 - val_loss: 0.0944
Epoch 4/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1180 - val_loss: 0.0703
Epoch 5/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1036 - val_loss: 0.0751
Epoch 6/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1037 - val_loss: 0.0702
Epoch 7/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1005 - val_loss: 0.0693
Epoch 8/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1014 - val_loss: 0.0664
Epoch 1/8
5/5 [==============================] - 4s 218ms/step - loss: 0.4543 - val_loss: 0.3302
Epoch 2/8
5/5 [==============================] - 0s 62ms/step - loss: 0.2753 - val_loss: 0.1645
Epoch 3/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1374 - val_loss: 0.1095
Epoch 4/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1161 - val_loss: 0.0818
Epoch 5/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1076 - val_loss: 0.0694
Epoch 6/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1018 - val_loss: 0.0734
Epoch 7/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1014 - val_loss: 0.0679
Epoch 8/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1014 - val_loss: 0.0704
Epoch 1/8
5/5 [==============================] - 4s 253ms/step - loss: 0.4470 - val_loss: 0.2336
Epoch 2/8
5/5 [==============================] - 0s 75ms/step - loss: 0.2225 - val_loss: 0.1406
Epoch 3/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1471 - val_loss: 0.0668
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1129 - val_loss: 0.0906
Epoch 5/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1185 - val_loss: 0.0766
Epoch 6/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1116 - val_loss: 0.0668
Epoch 7/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1067 - val_loss: 0.0732
Epoch 8/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1045 - val_loss: 0.0728
Epoch 1/8
5/5 [==============================] - 5s 345ms/step - loss: 0.3600 - val_loss: 0.1618
Epoch 2/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1455 - val_loss: 0.0905
Epoch 3/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1219 - val_loss: 0.0847
Epoch 4/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1056 - val_loss: 0.0826
Epoch 5/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1071 - val_loss: 0.0687
Epoch 6/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1035 - val_loss: 0.0654
Epoch 7/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1008 - val_loss: 0.0682
Epoch 8/8
5/5 [==============================] - 0s 83ms/step - loss: 0.1010 - val_loss: 0.0656
Epoch 1/8
5/5 [==============================] - 4s 227ms/step - loss: 0.5048 - val_loss: 0.3985
Epoch 2/8
5/5 [==============================] - 0s 67ms/step - loss: 0.3078 - val_loss: 0.2169
Epoch 3/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1968 - val_loss: 0.1200
Epoch 4/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1295 - val_loss: 0.1103
Epoch 5/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1228 - val_loss: 0.0818
Epoch 6/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1097 - val_loss: 0.0803
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1051 - val_loss: 0.0740
Epoch 8/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1092 - val_loss: 0.0728
Epoch 1/8
5/5 [==============================] - 4s 235ms/step - loss: 0.4198 - val_loss: 0.2489
Epoch 2/8
5/5 [==============================] - 0s 68ms/step - loss: 0.2026 - val_loss: 0.1621
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1482 - val_loss: 0.1045
Epoch 4/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1201 - val_loss: 0.0866
Epoch 5/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1117 - val_loss: 0.0732
Epoch 6/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1109 - val_loss: 0.0663
Epoch 7/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1006 - val_loss: 0.0688
Epoch 8/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1002 - val_loss: 0.0659
Epoch 1/8
5/5 [==============================] - 4s 218ms/step - loss: 0.4322 - val_loss: 0.2733
Epoch 2/8
5/5 [==============================] - 0s 71ms/step - loss: 0.2340 - val_loss: 0.1413
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1437 - val_loss: 0.0971
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1162 - val_loss: 0.0941
Epoch 5/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1137 - val_loss: 0.0844
Epoch 6/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1042 - val_loss: 0.0739
Epoch 7/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1054 - val_loss: 0.0710
Epoch 8/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1032 - val_loss: 0.0656
Epoch 1/8
5/5 [==============================] - 4s 283ms/step - loss: 0.4392 - val_loss: 0.1988
Epoch 2/8
5/5 [==============================] - 0s 61ms/step - loss: 0.1863 - val_loss: 0.1257
Epoch 3/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1447 - val_loss: 0.0869
Epoch 4/8
5/5 [==============================] - 0s 65ms/step - loss: 0.1090 - val_loss: 0.0933
Epoch 5/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1161 - val_loss: 0.0720
Epoch 6/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1033 - val_loss: 0.0663
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1015 - val_loss: 0.0674
Epoch 8/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1003 - val_loss: 0.0641
Epoch 1/8
5/5 [==============================] - 3s 211ms/step - loss: 0.4296 - val_loss: 0.2323
Epoch 2/8
5/5 [==============================] - 0s 60ms/step - loss: 0.1646 - val_loss: 0.1102
Epoch 3/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1151 - val_loss: 0.0960
Epoch 4/8
5/5 [==============================] - 0s 66ms/step - loss: 0.1092 - val_loss: 0.0745
Epoch 5/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1056 - val_loss: 0.0773
Epoch 6/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1055 - val_loss: 0.0713
Epoch 7/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1032 - val_loss: 0.0714
Epoch 8/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1014 - val_loss: 0.0706
Epoch 1/8
5/5 [==============================] - 4s 270ms/step - loss: 0.4104 - val_loss: 0.1197
Epoch 2/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1379 - val_loss: 0.0930
Epoch 3/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1173 - val_loss: 0.0695
Epoch 4/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1065 - val_loss: 0.0669
Epoch 5/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1066 - val_loss: 0.0765
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1069 - val_loss: 0.0753
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1034 - val_loss: 0.0696
Epoch 8/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1003 - val_loss: 0.0678
Epoch 1/8
5/5 [==============================] - 4s 233ms/step - loss: 0.3521 - val_loss: 0.1905
Epoch 2/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1519 - val_loss: 0.1034
Epoch 3/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1183 - val_loss: 0.0824
Epoch 4/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1071 - val_loss: 0.0845
Epoch 5/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1071 - val_loss: 0.0708
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1071 - val_loss: 0.0703
Epoch 7/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1056 - val_loss: 0.0693
Epoch 8/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1047 - val_loss: 0.0704
Epoch 1/8
5/5 [==============================] - 5s 292ms/step - loss: 0.5010 - val_loss: 0.3337
Epoch 2/8
5/5 [==============================] - 0s 68ms/step - loss: 0.2583 - val_loss: 0.1420
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1443 - val_loss: 0.1009
Epoch 4/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1182 - val_loss: 0.1119
Epoch 5/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1146 - val_loss: 0.0859
Epoch 6/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1122 - val_loss: 0.0973
Epoch 7/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1080 - val_loss: 0.0878
Epoch 8/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1078 - val_loss: 0.0931
Epoch 1/8
5/5 [==============================] - 5s 320ms/step - loss: 0.4257 - val_loss: 0.2465
Epoch 2/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1725 - val_loss: 0.1440
Epoch 3/8
5/5 [==============================] - 0s 94ms/step - loss: 0.1413 - val_loss: 0.1158
Epoch 4/8
5/5 [==============================] - 0s 89ms/step - loss: 0.1138 - val_loss: 0.1017
Epoch 5/8
5/5 [==============================] - 1s 108ms/step - loss: 0.1163 - val_loss: 0.0972
Epoch 6/8
5/5 [==============================] - 1s 169ms/step - loss: 0.1077 - val_loss: 0.0872
Epoch 7/8
5/5 [==============================] - 1s 109ms/step - loss: 0.1090 - val_loss: 0.0923
Epoch 8/8
5/5 [==============================] - 1s 106ms/step - loss: 0.1093 - val_loss: 0.0898
Epoch 1/8
5/5 [==============================] - 4s 229ms/step - loss: 0.4553 - val_loss: 0.1722
Epoch 2/8
5/5 [==============================] - 0s 62ms/step - loss: 0.1758 - val_loss: 0.1273
Epoch 3/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1420 - val_loss: 0.0989
Epoch 4/8
5/5 [==============================] - 1s 117ms/step - loss: 0.1249 - val_loss: 0.1008
Epoch 5/8
5/5 [==============================] - 1s 188ms/step - loss: 0.1110 - val_loss: 0.1013
Epoch 6/8
5/5 [==============================] - 0s 92ms/step - loss: 0.1116 - val_loss: 0.0906
Epoch 7/8
5/5 [==============================] - 0s 91ms/step - loss: 0.1110 - val_loss: 0.0879
Epoch 8/8
5/5 [==============================] - 0s 90ms/step - loss: 0.1095 - val_loss: 0.0937
Epoch 1/8
5/5 [==============================] - 3s 217ms/step - loss: 0.4561 - val_loss: 0.2289
Epoch 2/8
5/5 [==============================] - 0s 74ms/step - loss: 0.2222 - val_loss: 0.1831
Epoch 3/8
5/5 [==============================] - 0s 86ms/step - loss: 0.1762 - val_loss: 0.1572
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1512 - val_loss: 0.1140
Epoch 5/8
5/5 [==============================] - 0s 84ms/step - loss: 0.1244 - val_loss: 0.0985
Epoch 6/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1089 - val_loss: 0.0924
Epoch 7/8
5/5 [==============================] - 0s 84ms/step - loss: 0.1091 - val_loss: 0.0890
Epoch 8/8
5/5 [==============================] - 0s 107ms/step - loss: 0.1119 - val_loss: 0.0891
Epoch 1/8
5/5 [==============================] - 4s 220ms/step - loss: 0.4510 - val_loss: 0.2694
Epoch 2/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1826 - val_loss: 0.1577
Epoch 3/8
5/5 [==============================] - 0s 66ms/step - loss: 0.1341 - val_loss: 0.0952
Epoch 4/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1209 - val_loss: 0.1013
Epoch 5/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1107 - val_loss: 0.0908
Epoch 6/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1085 - val_loss: 0.0910
Epoch 7/8
5/5 [==============================] - 0s 64ms/step - loss: 0.1070 - val_loss: 0.0860
Epoch 8/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1061 - val_loss: 0.0876
Epoch 1/8
5/5 [==============================] - 3s 222ms/step - loss: 0.4646 - val_loss: 0.2459
Epoch 2/8
5/5 [==============================] - 0s 62ms/step - loss: 0.1769 - val_loss: 0.1648
Epoch 3/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1333 - val_loss: 0.1120
Epoch 4/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1254 - val_loss: 0.0942
Epoch 5/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1122 - val_loss: 0.1037
Epoch 6/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1151 - val_loss: 0.0904
Epoch 7/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1084 - val_loss: 0.0922
Epoch 8/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1091 - val_loss: 0.0873
Epoch 1/8
5/5 [==============================] - 4s 220ms/step - loss: 0.4846 - val_loss: 0.3335
Epoch 2/8
5/5 [==============================] - 1s 124ms/step - loss: 0.2892 - val_loss: 0.2251
Epoch 3/8
5/5 [==============================] - 0s 84ms/step - loss: 0.2149 - val_loss: 0.1717
Epoch 4/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1393 - val_loss: 0.1104
Epoch 5/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1258 - val_loss: 0.0953
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1101 - val_loss: 0.0956
Epoch 7/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1113 - val_loss: 0.0926
Epoch 8/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1102 - val_loss: 0.0887
Epoch 1/8
5/5 [==============================] - 3s 219ms/step - loss: 0.5529 - val_loss: 0.3860
Epoch 2/8
5/5 [==============================] - 0s 64ms/step - loss: 0.3421 - val_loss: 0.2278
Epoch 3/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1963 - val_loss: 0.1094
Epoch 4/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1275 - val_loss: 0.1176
Epoch 5/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1247 - val_loss: 0.0981
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1137 - val_loss: 0.0946
Epoch 7/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1116 - val_loss: 0.0886
Epoch 8/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1086 - val_loss: 0.0869
Epoch 1/8
5/5 [==============================] - 4s 220ms/step - loss: 0.4849 - val_loss: 0.3383
Epoch 2/8
5/5 [==============================] - 0s 66ms/step - loss: 0.2608 - val_loss: 0.1820
Epoch 3/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1520 - val_loss: 0.1439
Epoch 4/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1365 - val_loss: 0.1055
Epoch 5/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1168 - val_loss: 0.1007
Epoch 6/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1155 - val_loss: 0.0919
Epoch 7/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1077 - val_loss: 0.0887
Epoch 8/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1076 - val_loss: 0.0874
Epoch 1/8
5/5 [==============================] - 4s 219ms/step - loss: 0.5882 - val_loss: 0.3979
Epoch 2/8
5/5 [==============================] - 0s 70ms/step - loss: 0.3944 - val_loss: 0.3196
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.3007 - val_loss: 0.2020
Epoch 4/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1702 - val_loss: 0.1035
Epoch 5/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1305 - val_loss: 0.0974
Epoch 6/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1117 - val_loss: 0.0956
Epoch 7/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1145 - val_loss: 0.0932
Epoch 8/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1084 - val_loss: 0.0984
Epoch 1/8
5/5 [==============================] - 3s 217ms/step - loss: 0.4444 - val_loss: 0.2905
Epoch 2/8
5/5 [==============================] - 0s 65ms/step - loss: 0.2042 - val_loss: 0.1075
Epoch 3/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1278 - val_loss: 0.0964
Epoch 4/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1193 - val_loss: 0.1031
Epoch 5/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1146 - val_loss: 0.0865
Epoch 6/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1068 - val_loss: 0.0916
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1072 - val_loss: 0.0880
Epoch 8/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1075 - val_loss: 0.0876
Epoch 1/8
5/5 [==============================] - 3s 239ms/step - loss: 0.5037 - val_loss: 0.3601
Epoch 2/8
5/5 [==============================] - 0s 70ms/step - loss: 0.2783 - val_loss: 0.2187
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1971 - val_loss: 0.1513
Epoch 4/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1363 - val_loss: 0.1029
Epoch 5/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1162 - val_loss: 0.0950
Epoch 6/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1129 - val_loss: 0.0976
Epoch 7/8
5/5 [==============================] - 0s 83ms/step - loss: 0.1109 - val_loss: 0.0891
Epoch 8/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1092 - val_loss: 0.0878
Epoch 1/8
5/5 [==============================] - 4s 230ms/step - loss: 0.4347 - val_loss: 0.2433
Epoch 2/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1994 - val_loss: 0.1239
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1266 - val_loss: 0.1119
Epoch 4/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1237 - val_loss: 0.0857
Epoch 5/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1156 - val_loss: 0.1002
Epoch 6/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1092 - val_loss: 0.0965
Epoch 7/8
5/5 [==============================] - 0s 88ms/step - loss: 0.1104 - val_loss: 0.0855
Epoch 8/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1072 - val_loss: 0.0893
Epoch 1/8
5/5 [==============================] - 4s 236ms/step - loss: 0.4672 - val_loss: 0.3460
Epoch 2/8
5/5 [==============================] - 0s 73ms/step - loss: 0.2682 - val_loss: 0.2080
Epoch 3/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1880 - val_loss: 0.1477
Epoch 4/8
5/5 [==============================] - 0s 91ms/step - loss: 0.1397 - val_loss: 0.1021
Epoch 5/8
5/5 [==============================] - 0s 99ms/step - loss: 0.1178 - val_loss: 0.0896
Epoch 6/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1164 - val_loss: 0.0922
Epoch 7/8
5/5 [==============================] - 0s 102ms/step - loss: 0.1106 - val_loss: 0.0891
Epoch 8/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1082 - val_loss: 0.0941
Epoch 1/8
5/5 [==============================] - 4s 260ms/step - loss: 0.4650 - val_loss: 0.3402
Epoch 2/8
5/5 [==============================] - 0s 74ms/step - loss: 0.2561 - val_loss: 0.2101
Epoch 3/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1868 - val_loss: 0.1034
Epoch 4/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1276 - val_loss: 0.0948
Epoch 5/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1246 - val_loss: 0.0955
Epoch 6/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1088 - val_loss: 0.0968
Epoch 7/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1086 - val_loss: 0.0899
Epoch 8/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1077 - val_loss: 0.0859
Epoch 1/8
5/5 [==============================] - 4s 232ms/step - loss: 0.4851 - val_loss: 0.2090
Epoch 2/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1803 - val_loss: 0.1379
Epoch 3/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1515 - val_loss: 0.0933
Epoch 4/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1217 - val_loss: 0.1008
Epoch 5/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1137 - val_loss: 0.0982
Epoch 6/8
5/5 [==============================] - 0s 84ms/step - loss: 0.1094 - val_loss: 0.0995
Epoch 7/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1106 - val_loss: 0.0870
Epoch 8/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1071 - val_loss: 0.0898
Epoch 1/8
5/5 [==============================] - 3s 247ms/step - loss: 0.5362 - val_loss: 0.3065
Epoch 2/8
5/5 [==============================] - 0s 73ms/step - loss: 0.2498 - val_loss: 0.1286
Epoch 3/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1349 - val_loss: 0.1222
Epoch 4/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1174 - val_loss: 0.0950
Epoch 5/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1112 - val_loss: 0.0963
Epoch 6/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1128 - val_loss: 0.0901
Epoch 7/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1057 - val_loss: 0.0920
Epoch 8/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1072 - val_loss: 0.0873
Epoch 1/8
5/5 [==============================] - 3s 226ms/step - loss: 0.3716 - val_loss: 0.1545
Epoch 2/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1517 - val_loss: 0.0954
Epoch 3/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1158 - val_loss: 0.1110
Epoch 4/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1160 - val_loss: 0.0982
Epoch 5/8
5/5 [==============================] - 0s 90ms/step - loss: 0.1128 - val_loss: 0.0883
Epoch 6/8
5/5 [==============================] - 1s 112ms/step - loss: 0.1098 - val_loss: 0.0931
Epoch 7/8
5/5 [==============================] - 0s 94ms/step - loss: 0.1102 - val_loss: 0.0879
Epoch 8/8
5/5 [==============================] - 0s 89ms/step - loss: 0.1084 - val_loss: 0.0883
Epoch 1/8
5/5 [==============================] - 5s 282ms/step - loss: 0.4775 - val_loss: 0.2264
Epoch 2/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1833 - val_loss: 0.1396
Epoch 3/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1232 - val_loss: 0.1239
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1276 - val_loss: 0.0917
Epoch 5/8
5/5 [==============================] - 0s 83ms/step - loss: 0.1123 - val_loss: 0.0958
Epoch 6/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1100 - val_loss: 0.0873
Epoch 7/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1078 - val_loss: 0.0877
Epoch 8/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1061 - val_loss: 0.0881
Epoch 1/8
5/5 [==============================] - 5s 294ms/step - loss: 0.4750 - val_loss: 0.3055
Epoch 2/8
5/5 [==============================] - 0s 67ms/step - loss: 0.2100 - val_loss: 0.1387
Epoch 3/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1286 - val_loss: 0.1087
Epoch 4/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1174 - val_loss: 0.0927
Epoch 5/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1140 - val_loss: 0.0927
Epoch 6/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1076 - val_loss: 0.0908
Epoch 7/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1094 - val_loss: 0.0899
Epoch 8/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1072 - val_loss: 0.0885
Epoch 1/8
5/5 [==============================] - 5s 285ms/step - loss: 0.4185 - val_loss: 0.2925
Epoch 2/8
5/5 [==============================] - 0s 62ms/step - loss: 0.2248 - val_loss: 0.1906
Epoch 3/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1634 - val_loss: 0.1329
Epoch 4/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1273 - val_loss: 0.1117
Epoch 5/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1206 - val_loss: 0.0983
Epoch 6/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1089 - val_loss: 0.0910
Epoch 7/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1091 - val_loss: 0.0863
Epoch 8/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1057 - val_loss: 0.0870
Epoch 1/8
5/5 [==============================] - 5s 301ms/step - loss: 0.3663 - val_loss: 0.2243
Epoch 2/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1755 - val_loss: 0.1262
Epoch 3/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1199 - val_loss: 0.1040
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1199 - val_loss: 0.0972
Epoch 5/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1142 - val_loss: 0.0873
Epoch 6/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1111 - val_loss: 0.0910
Epoch 7/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1109 - val_loss: 0.0921
Epoch 8/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1094 - val_loss: 0.0893
Epoch 1/8
5/5 [==============================] - 5s 308ms/step - loss: 0.5264 - val_loss: 0.2916
Epoch 2/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1966 - val_loss: 0.1625
Epoch 3/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1344 - val_loss: 0.1269
Epoch 4/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1255 - val_loss: 0.0911
Epoch 5/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1108 - val_loss: 0.0987
Epoch 6/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1101 - val_loss: 0.0876
Epoch 7/8
5/5 [==============================] - 0s 83ms/step - loss: 0.1072 - val_loss: 0.0917
Epoch 8/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1066 - val_loss: 0.0869
Epoch 1/8
5/5 [==============================] - 4s 305ms/step - loss: 0.5563 - val_loss: 0.3354
Epoch 2/8
5/5 [==============================] - 0s 69ms/step - loss: 0.2302 - val_loss: 0.1510
Epoch 3/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1635 - val_loss: 0.1071
Epoch 4/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1216 - val_loss: 0.1167
Epoch 5/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1121 - val_loss: 0.0957
Epoch 6/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1128 - val_loss: 0.0951
Epoch 7/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1093 - val_loss: 0.0885
Epoch 8/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1080 - val_loss: 0.0922
Epoch 1/8
5/5 [==============================] - 6s 323ms/step - loss: 0.4861 - val_loss: 0.3270
Epoch 2/8
5/5 [==============================] - 0s 71ms/step - loss: 0.2357 - val_loss: 0.1664
Epoch 3/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1416 - val_loss: 0.1177
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1341 - val_loss: 0.1032
Epoch 5/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1083 - val_loss: 0.0918
Epoch 6/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1131 - val_loss: 0.0954
Epoch 7/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1096 - val_loss: 0.0893
Epoch 8/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1091 - val_loss: 0.0928
Epoch 1/8
5/5 [==============================] - 4s 260ms/step - loss: 0.5360 - val_loss: 0.3559
Epoch 2/8
5/5 [==============================] - 0s 69ms/step - loss: 0.2752 - val_loss: 0.1948
Epoch 3/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1606 - val_loss: 0.1081
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1255 - val_loss: 0.0964
Epoch 5/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1161 - val_loss: 0.1077
Epoch 6/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1115 - val_loss: 0.0965
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1142 - val_loss: 0.0916
Epoch 8/8
5/5 [==============================] - 0s 84ms/step - loss: 0.1088 - val_loss: 0.0975
Epoch 1/8
5/5 [==============================] - 4s 254ms/step - loss: 0.5516 - val_loss: 0.4349
Epoch 2/8
5/5 [==============================] - 0s 63ms/step - loss: 0.3751 - val_loss: 0.2875
Epoch 3/8
5/5 [==============================] - 0s 68ms/step - loss: 0.2946 - val_loss: 0.2508
Epoch 4/8
5/5 [==============================] - 0s 71ms/step - loss: 0.2303 - val_loss: 0.1590
Epoch 5/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1448 - val_loss: 0.1343
Epoch 6/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1412 - val_loss: 0.1232
Epoch 7/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1188 - val_loss: 0.1091
Epoch 8/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1157 - val_loss: 0.1005
Epoch 1/8
5/5 [==============================] - 4s 370ms/step - loss: 0.5056 - val_loss: 0.3230
Epoch 2/8
5/5 [==============================] - 0s 81ms/step - loss: 0.2670 - val_loss: 0.1642
Epoch 3/8
5/5 [==============================] - 0s 83ms/step - loss: 0.1688 - val_loss: 0.1187
Epoch 4/8
5/5 [==============================] - 0s 106ms/step - loss: 0.1262 - val_loss: 0.0949
Epoch 5/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1158 - val_loss: 0.1073
Epoch 6/8
5/5 [==============================] - 0s 101ms/step - loss: 0.1152 - val_loss: 0.0943
Epoch 7/8
5/5 [==============================] - 1s 128ms/step - loss: 0.1100 - val_loss: 0.0874
Epoch 8/8
5/5 [==============================] - 0s 95ms/step - loss: 0.1073 - val_loss: 0.0874
Epoch 1/8
5/5 [==============================] - 4s 221ms/step - loss: 0.4814 - val_loss: 0.2698
Epoch 2/8
5/5 [==============================] - 0s 68ms/step - loss: 0.2795 - val_loss: 0.2185
Epoch 3/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1783 - val_loss: 0.1246
Epoch 4/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1294 - val_loss: 0.1070
Epoch 5/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1281 - val_loss: 0.1037
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1070 - val_loss: 0.0908
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1095 - val_loss: 0.0894
Epoch 8/8
5/5 [==============================] - 0s 86ms/step - loss: 0.1080 - val_loss: 0.0883
Epoch 1/8
5/5 [==============================] - 5s 288ms/step - loss: 0.4528 - val_loss: 0.2696
Epoch 2/8
5/5 [==============================] - 0s 83ms/step - loss: 0.1771 - val_loss: 0.1441
Epoch 3/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1343 - val_loss: 0.1034
Epoch 4/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1194 - val_loss: 0.0997
Epoch 5/8
5/5 [==============================] - 0s 84ms/step - loss: 0.1132 - val_loss: 0.0934
Epoch 6/8
5/5 [==============================] - 0s 95ms/step - loss: 0.1091 - val_loss: 0.0922
Epoch 7/8
5/5 [==============================] - 0s 83ms/step - loss: 0.1089 - val_loss: 0.0882
Epoch 8/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1087 - val_loss: 0.0877
Epoch 1/8
5/5 [==============================] - 4s 252ms/step - loss: 0.5027 - val_loss: 0.2607
Epoch 2/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1905 - val_loss: 0.1532
Epoch 3/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1396 - val_loss: 0.1259
Epoch 4/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1193 - val_loss: 0.1017
Epoch 5/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1144 - val_loss: 0.0879
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1083 - val_loss: 0.0928
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1089 - val_loss: 0.0867
Epoch 8/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1066 - val_loss: 0.0857
Epoch 1/8
5/5 [==============================] - 4s 258ms/step - loss: 0.4938 - val_loss: 0.3788
Epoch 2/8
5/5 [==============================] - 0s 69ms/step - loss: 0.2757 - val_loss: 0.2120
Epoch 3/8
5/5 [==============================] - 0s 86ms/step - loss: 0.1612 - val_loss: 0.1489
Epoch 4/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1382 - val_loss: 0.1211
Epoch 5/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1250 - val_loss: 0.0924
Epoch 6/8
5/5 [==============================] - 0s 86ms/step - loss: 0.1146 - val_loss: 0.0951
Epoch 7/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1094 - val_loss: 0.0934
Epoch 8/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1076 - val_loss: 0.0875
Epoch 1/8
5/5 [==============================] - 4s 311ms/step - loss: 0.4695 - val_loss: 0.2814
Epoch 2/8
5/5 [==============================] - 0s 69ms/step - loss: 0.2014 - val_loss: 0.1572
Epoch 3/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1323 - val_loss: 0.1183
Epoch 4/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1199 - val_loss: 0.1050
Epoch 5/8
5/5 [==============================] - 0s 99ms/step - loss: 0.1108 - val_loss: 0.0888
Epoch 6/8
5/5 [==============================] - 0s 84ms/step - loss: 0.1113 - val_loss: 0.0903
Epoch 7/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1078 - val_loss: 0.0953
Epoch 8/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1089 - val_loss: 0.0864
Epoch 1/8
5/5 [==============================] - 6s 512ms/step - loss: 0.4966 - val_loss: 0.3159
Epoch 2/8
5/5 [==============================] - 0s 96ms/step - loss: 0.3002 - val_loss: 0.2229
Epoch 3/8
5/5 [==============================] - 1s 104ms/step - loss: 0.2252 - val_loss: 0.1459
Epoch 4/8
5/5 [==============================] - 0s 103ms/step - loss: 0.1540 - val_loss: 0.0924
Epoch 5/8
5/5 [==============================] - 0s 92ms/step - loss: 0.1209 - val_loss: 0.0979
Epoch 6/8
5/5 [==============================] - 0s 94ms/step - loss: 0.1168 - val_loss: 0.0888
Epoch 7/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1162 - val_loss: 0.0884
Epoch 8/8
5/5 [==============================] - 0s 89ms/step - loss: 0.1123 - val_loss: 0.0913
Epoch 1/8
5/5 [==============================] - 4s 244ms/step - loss: 0.4177 - val_loss: 0.2801
Epoch 2/8
5/5 [==============================] - 0s 87ms/step - loss: 0.2180 - val_loss: 0.1653
Epoch 3/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1400 - val_loss: 0.1016
Epoch 4/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1161 - val_loss: 0.0970
Epoch 5/8
5/5 [==============================] - 0s 92ms/step - loss: 0.1074 - val_loss: 0.0906
Epoch 6/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1082 - val_loss: 0.0918
Epoch 7/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1087 - val_loss: 0.0872
Epoch 8/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1080 - val_loss: 0.0859
Epoch 1/8
5/5 [==============================] - 4s 258ms/step - loss: 0.4474 - val_loss: 0.1747
Epoch 2/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1594 - val_loss: 0.1318
Epoch 3/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1330 - val_loss: 0.1019
Epoch 4/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1195 - val_loss: 0.0955
Epoch 5/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1132 - val_loss: 0.0905
Epoch 6/8
5/5 [==============================] - 0s 68ms/step - loss: 0.1106 - val_loss: 0.0971
Epoch 7/8
5/5 [==============================] - 0s 89ms/step - loss: 0.1091 - val_loss: 0.0885
Epoch 8/8
5/5 [==============================] - 0s 86ms/step - loss: 0.1085 - val_loss: 0.0869
Epoch 1/8
5/5 [==============================] - 4s 241ms/step - loss: 0.5250 - val_loss: 0.2925
Epoch 2/8
5/5 [==============================] - 0s 63ms/step - loss: 0.2537 - val_loss: 0.1388
Epoch 3/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1612 - val_loss: 0.1164
Epoch 4/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1247 - val_loss: 0.0902
Epoch 5/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1137 - val_loss: 0.0922
Epoch 6/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1119 - val_loss: 0.0883
Epoch 7/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1099 - val_loss: 0.0927
Epoch 8/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1054 - val_loss: 0.0893
Epoch 1/8
5/5 [==============================] - 4s 230ms/step - loss: 0.4727 - val_loss: 0.0974
Epoch 2/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1783 - val_loss: 0.1104
Epoch 3/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1376 - val_loss: 0.0958
Epoch 4/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1193 - val_loss: 0.1119
Epoch 5/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1121 - val_loss: 0.1027
Epoch 6/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1141 - val_loss: 0.0879
Epoch 7/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1091 - val_loss: 0.0984
Epoch 8/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1089 - val_loss: 0.0873
Epoch 1/8
5/5 [==============================] - 4s 210ms/step - loss: 0.4265 - val_loss: 0.2164
Epoch 2/8
5/5 [==============================] - 0s 60ms/step - loss: 0.1712 - val_loss: 0.1509
Epoch 3/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1374 - val_loss: 0.1068
Epoch 4/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1222 - val_loss: 0.0899
Epoch 5/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1173 - val_loss: 0.0928
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1099 - val_loss: 0.0969
Epoch 7/8
5/5 [==============================] - 0s 86ms/step - loss: 0.1072 - val_loss: 0.0899
Epoch 8/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1082 - val_loss: 0.0857
Epoch 1/8
5/5 [==============================] - 3s 221ms/step - loss: 0.5075 - val_loss: 0.3002
Epoch 2/8
5/5 [==============================] - 0s 64ms/step - loss: 0.2871 - val_loss: 0.2482
Epoch 3/8
5/5 [==============================] - 0s 82ms/step - loss: 0.2305 - val_loss: 0.1712
Epoch 4/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1598 - val_loss: 0.1141
Epoch 5/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1235 - val_loss: 0.0978
Epoch 6/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1178 - val_loss: 0.1063
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1140 - val_loss: 0.0952
Epoch 8/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1094 - val_loss: 0.0900
Epoch 1/8
5/5 [==============================] - 4s 227ms/step - loss: 0.5536 - val_loss: 0.4502
Epoch 2/8
5/5 [==============================] - 0s 72ms/step - loss: 0.3756 - val_loss: 0.1998
Epoch 3/8
5/5 [==============================] - 0s 93ms/step - loss: 0.2155 - val_loss: 0.1760
Epoch 4/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1966 - val_loss: 0.1442
Epoch 5/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1933 - val_loss: 0.1454
Epoch 6/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1865 - val_loss: 0.1267
Epoch 7/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1819 - val_loss: 0.1286
Epoch 8/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1797 - val_loss: 0.1242
Epoch 1/8
5/5 [==============================] - 4s 240ms/step - loss: 0.4798 - val_loss: 0.3670
Epoch 2/8
5/5 [==============================] - 0s 75ms/step - loss: 0.3003 - val_loss: 0.1744
Epoch 3/8
5/5 [==============================] - 0s 78ms/step - loss: 0.2073 - val_loss: 0.1636
Epoch 4/8
5/5 [==============================] - 0s 81ms/step - loss: 0.2014 - val_loss: 0.1420
Epoch 5/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1847 - val_loss: 0.1382
Epoch 6/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1846 - val_loss: 0.1315
Epoch 7/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1863 - val_loss: 0.1316
Epoch 8/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1847 - val_loss: 0.1271
Epoch 1/8
5/5 [==============================] - 4s 209ms/step - loss: 0.5232 - val_loss: 0.3507
Epoch 2/8
5/5 [==============================] - 0s 64ms/step - loss: 0.3048 - val_loss: 0.2290
Epoch 3/8
5/5 [==============================] - 0s 74ms/step - loss: 0.2401 - val_loss: 0.1498
Epoch 4/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1992 - val_loss: 0.1597
Epoch 5/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1957 - val_loss: 0.1383
Epoch 6/8
5/5 [==============================] - 1s 124ms/step - loss: 0.1862 - val_loss: 0.1296
Epoch 7/8
5/5 [==============================] - 0s 93ms/step - loss: 0.1834 - val_loss: 0.1303
Epoch 8/8
5/5 [==============================] - 0s 94ms/step - loss: 0.1810 - val_loss: 0.1267
Epoch 1/8
5/5 [==============================] - 4s 261ms/step - loss: 0.4982 - val_loss: 0.3606
Epoch 2/8
5/5 [==============================] - 0s 66ms/step - loss: 0.2965 - val_loss: 0.2275
Epoch 3/8
5/5 [==============================] - 0s 66ms/step - loss: 0.2272 - val_loss: 0.1766
Epoch 4/8
5/5 [==============================] - 0s 71ms/step - loss: 0.2007 - val_loss: 0.1383
Epoch 5/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1938 - val_loss: 0.1332
Epoch 6/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1872 - val_loss: 0.1418
Epoch 7/8
5/5 [==============================] - 0s 63ms/step - loss: 0.1820 - val_loss: 0.1313
Epoch 8/8
5/5 [==============================] - 0s 66ms/step - loss: 0.1843 - val_loss: 0.1310
Epoch 1/8
5/5 [==============================] - 4s 238ms/step - loss: 0.5299 - val_loss: 0.3215
Epoch 2/8
5/5 [==============================] - 0s 68ms/step - loss: 0.3126 - val_loss: 0.2267
Epoch 3/8
5/5 [==============================] - 0s 68ms/step - loss: 0.2429 - val_loss: 0.1552
Epoch 4/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1917 - val_loss: 0.1275
Epoch 5/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1858 - val_loss: 0.1449
Epoch 6/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1879 - val_loss: 0.1297
Epoch 7/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1859 - val_loss: 0.1307
Epoch 8/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1790 - val_loss: 0.1302
Epoch 1/8
5/5 [==============================] - 4s 301ms/step - loss: 0.4187 - val_loss: 0.3205
Epoch 2/8
5/5 [==============================] - 0s 84ms/step - loss: 0.2412 - val_loss: 0.1885
Epoch 3/8
5/5 [==============================] - 0s 91ms/step - loss: 0.2167 - val_loss: 0.1410
Epoch 4/8
5/5 [==============================] - 0s 94ms/step - loss: 0.1855 - val_loss: 0.1577
Epoch 5/8
5/5 [==============================] - 1s 116ms/step - loss: 0.1870 - val_loss: 0.1310
Epoch 6/8
5/5 [==============================] - 1s 112ms/step - loss: 0.1853 - val_loss: 0.1311
Epoch 7/8
5/5 [==============================] - 1s 117ms/step - loss: 0.1784 - val_loss: 0.1354
Epoch 8/8
5/5 [==============================] - 1s 113ms/step - loss: 0.1808 - val_loss: 0.1283
Epoch 1/8
5/5 [==============================] - 4s 245ms/step - loss: 0.5952 - val_loss: 0.4157
Epoch 2/8
5/5 [==============================] - 0s 71ms/step - loss: 0.3230 - val_loss: 0.2512
Epoch 3/8
5/5 [==============================] - 0s 73ms/step - loss: 0.2258 - val_loss: 0.1597
Epoch 4/8
5/5 [==============================] - 0s 76ms/step - loss: 0.2052 - val_loss: 0.1442
Epoch 5/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1840 - val_loss: 0.1402
Epoch 6/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1927 - val_loss: 0.1274
Epoch 7/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1841 - val_loss: 0.1380
Epoch 8/8
5/5 [==============================] - 0s 86ms/step - loss: 0.1851 - val_loss: 0.1312
Epoch 1/8
5/5 [==============================] - 4s 255ms/step - loss: 0.4880 - val_loss: 0.2641
Epoch 2/8
5/5 [==============================] - 0s 81ms/step - loss: 0.2221 - val_loss: 0.2057
Epoch 3/8
5/5 [==============================] - 0s 79ms/step - loss: 0.2058 - val_loss: 0.1359
Epoch 4/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1908 - val_loss: 0.1431
Epoch 5/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1881 - val_loss: 0.1383
Epoch 6/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1823 - val_loss: 0.1276
Epoch 7/8
5/5 [==============================] - 0s 88ms/step - loss: 0.1794 - val_loss: 0.1322
Epoch 8/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1807 - val_loss: 0.1278
Epoch 1/8
5/5 [==============================] - 4s 240ms/step - loss: 0.4971 - val_loss: 0.3298
Epoch 2/8
5/5 [==============================] - 0s 78ms/step - loss: 0.2524 - val_loss: 0.2043
Epoch 3/8
5/5 [==============================] - 0s 77ms/step - loss: 0.2239 - val_loss: 0.1314
Epoch 4/8
5/5 [==============================] - 0s 83ms/step - loss: 0.1897 - val_loss: 0.1434
Epoch 5/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1915 - val_loss: 0.1418
Epoch 6/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1849 - val_loss: 0.1382
Epoch 7/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1885 - val_loss: 0.1260
Epoch 8/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1837 - val_loss: 0.1259
Epoch 1/8
5/5 [==============================] - 4s 237ms/step - loss: 0.4911 - val_loss: 0.2808
Epoch 2/8
5/5 [==============================] - 0s 61ms/step - loss: 0.2422 - val_loss: 0.1590
Epoch 3/8
5/5 [==============================] - 0s 74ms/step - loss: 0.2079 - val_loss: 0.1519
Epoch 4/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1859 - val_loss: 0.1287
Epoch 5/8
5/5 [==============================] - 0s 83ms/step - loss: 0.1826 - val_loss: 0.1341
Epoch 6/8
5/5 [==============================] - 0s 88ms/step - loss: 0.1821 - val_loss: 0.1304
Epoch 7/8
5/5 [==============================] - 0s 91ms/step - loss: 0.1797 - val_loss: 0.1264
Epoch 8/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1798 - val_loss: 0.1324
Epoch 1/8
5/5 [==============================] - 3s 228ms/step - loss: 0.4821 - val_loss: 0.2923
Epoch 2/8
5/5 [==============================] - 0s 70ms/step - loss: 0.2435 - val_loss: 0.2100
Epoch 3/8
5/5 [==============================] - 0s 75ms/step - loss: 0.2161 - val_loss: 0.1352
Epoch 4/8
5/5 [==============================] - 0s 84ms/step - loss: 0.1860 - val_loss: 0.1377
Epoch 5/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1822 - val_loss: 0.1253
Epoch 6/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1830 - val_loss: 0.1278
Epoch 7/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1797 - val_loss: 0.1280
Epoch 8/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1791 - val_loss: 0.1238
Epoch 1/8
5/5 [==============================] - 3s 234ms/step - loss: 0.4641 - val_loss: 0.2769
Epoch 2/8
5/5 [==============================] - 0s 69ms/step - loss: 0.2632 - val_loss: 0.2183
Epoch 3/8
5/5 [==============================] - 0s 73ms/step - loss: 0.2270 - val_loss: 0.1384
Epoch 4/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1961 - val_loss: 0.1570
Epoch 5/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1949 - val_loss: 0.1515
Epoch 6/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1893 - val_loss: 0.1377
Epoch 7/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1828 - val_loss: 0.1284
Epoch 8/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1833 - val_loss: 0.1274
Epoch 1/8
5/5 [==============================] - 3s 254ms/step - loss: 0.5581 - val_loss: 0.4006
Epoch 2/8
5/5 [==============================] - 0s 66ms/step - loss: 0.3953 - val_loss: 0.3233
Epoch 3/8
5/5 [==============================] - 0s 68ms/step - loss: 0.3392 - val_loss: 0.3120
Epoch 4/8
5/5 [==============================] - 0s 69ms/step - loss: 0.3046 - val_loss: 0.2468
Epoch 5/8
5/5 [==============================] - 0s 72ms/step - loss: 0.2650 - val_loss: 0.1999
Epoch 6/8
5/5 [==============================] - 0s 67ms/step - loss: 0.2121 - val_loss: 0.1515
Epoch 7/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1900 - val_loss: 0.1345
Epoch 8/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1866 - val_loss: 0.1530
Epoch 1/8
5/5 [==============================] - 3s 219ms/step - loss: 0.5849 - val_loss: 0.4818
Epoch 2/8
5/5 [==============================] - 0s 69ms/step - loss: 0.4327 - val_loss: 0.3128
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.2740 - val_loss: 0.1485
Epoch 4/8
5/5 [==============================] - 0s 71ms/step - loss: 0.2161 - val_loss: 0.1772
Epoch 5/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1933 - val_loss: 0.1384
Epoch 6/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1879 - val_loss: 0.1389
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1815 - val_loss: 0.1426
Epoch 8/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1832 - val_loss: 0.1342
Epoch 1/8
5/5 [==============================] - 4s 271ms/step - loss: 0.5064 - val_loss: 0.2341
Epoch 2/8
5/5 [==============================] - 1s 125ms/step - loss: 0.2361 - val_loss: 0.1721
Epoch 3/8
5/5 [==============================] - 0s 100ms/step - loss: 0.2123 - val_loss: 0.1434
Epoch 4/8
5/5 [==============================] - 0s 83ms/step - loss: 0.1916 - val_loss: 0.1415
Epoch 5/8
5/5 [==============================] - 0s 91ms/step - loss: 0.1859 - val_loss: 0.1296
Epoch 6/8
5/5 [==============================] - 0s 101ms/step - loss: 0.1816 - val_loss: 0.1322
Epoch 7/8
5/5 [==============================] - 0s 96ms/step - loss: 0.1808 - val_loss: 0.1265
Epoch 8/8
5/5 [==============================] - 1s 106ms/step - loss: 0.1828 - val_loss: 0.1269
Epoch 1/8
5/5 [==============================] - 4s 238ms/step - loss: 0.5633 - val_loss: 0.3690
Epoch 2/8
5/5 [==============================] - 0s 73ms/step - loss: 0.3807 - val_loss: 0.3072
Epoch 3/8
5/5 [==============================] - 0s 93ms/step - loss: 0.2994 - val_loss: 0.1856
Epoch 4/8
5/5 [==============================] - 1s 111ms/step - loss: 0.2083 - val_loss: 0.1478
Epoch 5/8
5/5 [==============================] - 0s 103ms/step - loss: 0.1959 - val_loss: 0.1475
Epoch 6/8
5/5 [==============================] - 1s 113ms/step - loss: 0.1881 - val_loss: 0.1373
Epoch 7/8
5/5 [==============================] - 1s 117ms/step - loss: 0.1792 - val_loss: 0.1307
Epoch 8/8
5/5 [==============================] - 1s 112ms/step - loss: 0.1807 - val_loss: 0.1298
Epoch 1/8
5/5 [==============================] - 4s 212ms/step - loss: 0.4948 - val_loss: 0.2011
Epoch 2/8
5/5 [==============================] - 0s 58ms/step - loss: 0.2015 - val_loss: 0.1755
Epoch 3/8
5/5 [==============================] - 0s 61ms/step - loss: 0.2129 - val_loss: 0.1405
Epoch 4/8
5/5 [==============================] - 0s 67ms/step - loss: 0.1879 - val_loss: 0.1366
Epoch 5/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1856 - val_loss: 0.1402
Epoch 6/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1878 - val_loss: 0.1279
Epoch 7/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1803 - val_loss: 0.1265
Epoch 8/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1792 - val_loss: 0.1253
Epoch 1/8
5/5 [==============================] - 5s 262ms/step - loss: 0.4490 - val_loss: 0.1686
Epoch 2/8
5/5 [==============================] - 0s 73ms/step - loss: 0.2219 - val_loss: 0.1521
Epoch 3/8
5/5 [==============================] - 0s 95ms/step - loss: 0.2045 - val_loss: 0.1359
Epoch 4/8
5/5 [==============================] - 1s 127ms/step - loss: 0.1872 - val_loss: 0.1453
Epoch 5/8
5/5 [==============================] - 1s 112ms/step - loss: 0.1829 - val_loss: 0.1326
Epoch 6/8
5/5 [==============================] - 0s 93ms/step - loss: 0.1884 - val_loss: 0.1339
Epoch 7/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1801 - val_loss: 0.1358
Epoch 8/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1829 - val_loss: 0.1274
Epoch 1/8
5/5 [==============================] - 4s 288ms/step - loss: 0.5309 - val_loss: 0.3266
Epoch 2/8
5/5 [==============================] - 0s 79ms/step - loss: 0.2580 - val_loss: 0.1865
Epoch 3/8
5/5 [==============================] - 0s 75ms/step - loss: 0.2138 - val_loss: 0.1293
Epoch 4/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1913 - val_loss: 0.1482
Epoch 5/8
5/5 [==============================] - 0s 86ms/step - loss: 0.1944 - val_loss: 0.1318
Epoch 6/8
5/5 [==============================] - 0s 90ms/step - loss: 0.1877 - val_loss: 0.1312
Epoch 7/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1874 - val_loss: 0.1232
Epoch 8/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1818 - val_loss: 0.1266
Epoch 1/8
5/5 [==============================] - 4s 335ms/step - loss: 0.4827 - val_loss: 0.3860
Epoch 2/8
5/5 [==============================] - 0s 74ms/step - loss: 0.3317 - val_loss: 0.2813
Epoch 3/8
5/5 [==============================] - 0s 84ms/step - loss: 0.2618 - val_loss: 0.1749
Epoch 4/8
5/5 [==============================] - 0s 82ms/step - loss: 0.2005 - val_loss: 0.1354
Epoch 5/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1917 - val_loss: 0.1450
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1853 - val_loss: 0.1288
Epoch 7/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1808 - val_loss: 0.1299
Epoch 8/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1808 - val_loss: 0.1286
Epoch 1/8
5/5 [==============================] - 4s 237ms/step - loss: 0.4630 - val_loss: 0.2591
Epoch 2/8
5/5 [==============================] - 0s 67ms/step - loss: 0.2357 - val_loss: 0.1823
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.2134 - val_loss: 0.1352
Epoch 4/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1881 - val_loss: 0.1349
Epoch 5/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1885 - val_loss: 0.1388
Epoch 6/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1846 - val_loss: 0.1279
Epoch 7/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1799 - val_loss: 0.1246
Epoch 8/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1800 - val_loss: 0.1269
Epoch 1/8
5/5 [==============================] - 4s 255ms/step - loss: 0.5214 - val_loss: 0.4254
Epoch 2/8
5/5 [==============================] - 0s 73ms/step - loss: 0.3736 - val_loss: 0.3013
Epoch 3/8
5/5 [==============================] - 0s 72ms/step - loss: 0.2641 - val_loss: 0.1910
Epoch 4/8
5/5 [==============================] - 0s 76ms/step - loss: 0.2119 - val_loss: 0.1532
Epoch 5/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1940 - val_loss: 0.1392
Epoch 6/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1952 - val_loss: 0.1437
Epoch 7/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1850 - val_loss: 0.1410
Epoch 8/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1844 - val_loss: 0.1304
Epoch 1/8
5/5 [==============================] - 3s 226ms/step - loss: 0.4843 - val_loss: 0.2980
Epoch 2/8
5/5 [==============================] - 0s 69ms/step - loss: 0.2650 - val_loss: 0.1744
Epoch 3/8
5/5 [==============================] - 0s 70ms/step - loss: 0.2016 - val_loss: 0.1664
Epoch 4/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1910 - val_loss: 0.1399
Epoch 5/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1891 - val_loss: 0.1403
Epoch 6/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1834 - val_loss: 0.1377
Epoch 7/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1808 - val_loss: 0.1242
Epoch 8/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1789 - val_loss: 0.1285
Epoch 1/8
5/5 [==============================] - 5s 350ms/step - loss: 0.5210 - val_loss: 0.3093
Epoch 2/8
5/5 [==============================] - 0s 103ms/step - loss: 0.3043 - val_loss: 0.2095
Epoch 3/8
5/5 [==============================] - 0s 108ms/step - loss: 0.2177 - val_loss: 0.1496
Epoch 4/8
5/5 [==============================] - 0s 91ms/step - loss: 0.1954 - val_loss: 0.1648
Epoch 5/8
5/5 [==============================] - 0s 92ms/step - loss: 0.1959 - val_loss: 0.1374
Epoch 6/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1871 - val_loss: 0.1316
Epoch 7/8
5/5 [==============================] - 0s 93ms/step - loss: 0.1873 - val_loss: 0.1380
Epoch 8/8
5/5 [==============================] - 1s 114ms/step - loss: 0.1873 - val_loss: 0.1343
Epoch 1/8
5/5 [==============================] - 4s 246ms/step - loss: 0.5340 - val_loss: 0.3565
Epoch 2/8
5/5 [==============================] - 0s 70ms/step - loss: 0.3171 - val_loss: 0.1720
Epoch 3/8
5/5 [==============================] - 0s 76ms/step - loss: 0.2071 - val_loss: 0.1418
Epoch 4/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1883 - val_loss: 0.1413
Epoch 5/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1892 - val_loss: 0.1318
Epoch 6/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1862 - val_loss: 0.1300
Epoch 7/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1799 - val_loss: 0.1243
Epoch 8/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1772 - val_loss: 0.1259
Epoch 1/8
5/5 [==============================] - 4s 263ms/step - loss: 0.5136 - val_loss: 0.3324
Epoch 2/8
5/5 [==============================] - 0s 73ms/step - loss: 0.2833 - val_loss: 0.1539
Epoch 3/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1945 - val_loss: 0.1585
Epoch 4/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1955 - val_loss: 0.1433
Epoch 5/8
5/5 [==============================] - 0s 80ms/step - loss: 0.1866 - val_loss: 0.1302
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1874 - val_loss: 0.1364
Epoch 7/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1828 - val_loss: 0.1287
Epoch 8/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1807 - val_loss: 0.1332
Epoch 1/8
5/5 [==============================] - 3s 244ms/step - loss: 0.5185 - val_loss: 0.3172
Epoch 2/8
5/5 [==============================] - 0s 77ms/step - loss: 0.2466 - val_loss: 0.2061
Epoch 3/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1986 - val_loss: 0.1545
Epoch 4/8
5/5 [==============================] - 0s 81ms/step - loss: 0.1940 - val_loss: 0.1388
Epoch 5/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1885 - val_loss: 0.1329
Epoch 6/8
5/5 [==============================] - 0s 78ms/step - loss: 0.1835 - val_loss: 0.1288
Epoch 7/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1822 - val_loss: 0.1259
Epoch 8/8
5/5 [==============================] - 0s 79ms/step - loss: 0.1796 - val_loss: 0.1345
Epoch 1/8
5/5 [==============================] - 3s 229ms/step - loss: 0.5872 - val_loss: 0.3900
Epoch 2/8
5/5 [==============================] - 0s 75ms/step - loss: 0.3193 - val_loss: 0.2960
Epoch 3/8
5/5 [==============================] - 0s 79ms/step - loss: 0.2569 - val_loss: 0.2088
Epoch 4/8
5/5 [==============================] - 0s 82ms/step - loss: 0.2314 - val_loss: 0.1755
Epoch 5/8
5/5 [==============================] - 0s 83ms/step - loss: 0.1940 - val_loss: 0.1636
Epoch 6/8
5/5 [==============================] - 1s 121ms/step - loss: 0.1943 - val_loss: 0.1457
Epoch 7/8
5/5 [==============================] - 0s 98ms/step - loss: 0.1837 - val_loss: 0.1427
Epoch 8/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1839 - val_loss: 0.1304
Epoch 1/8
5/5 [==============================] - 4s 242ms/step - loss: 0.4376 - val_loss: 0.3040
Epoch 2/8
5/5 [==============================] - 0s 68ms/step - loss: 0.2356 - val_loss: 0.1868
Epoch 3/8
5/5 [==============================] - 0s 69ms/step - loss: 0.2124 - val_loss: 0.1483
Epoch 4/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1983 - val_loss: 0.1436
Epoch 5/8
5/5 [==============================] - 0s 71ms/step - loss: 0.1882 - val_loss: 0.1330
Epoch 6/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1837 - val_loss: 0.1369
Epoch 7/8
5/5 [==============================] - 0s 72ms/step - loss: 0.1831 - val_loss: 0.1230
Epoch 8/8
5/5 [==============================] - 0s 70ms/step - loss: 0.1795 - val_loss: 0.1289
Epoch 1/8
5/5 [==============================] - 4s 227ms/step - loss: 0.4241 - val_loss: 0.2990
Epoch 2/8
5/5 [==============================] - 0s 78ms/step - loss: 0.2557 - val_loss: 0.1962
Epoch 3/8
5/5 [==============================] - 0s 76ms/step - loss: 0.2040 - val_loss: 0.1549
Epoch 4/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1984 - val_loss: 0.1368
Epoch 5/8
5/5 [==============================] - 0s 82ms/step - loss: 0.1985 - val_loss: 0.1474
Epoch 6/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1891 - val_loss: 0.1443
Epoch 7/8
5/5 [==============================] - 0s 86ms/step - loss: 0.1866 - val_loss: 0.1277
Epoch 8/8
5/5 [==============================] - 0s 77ms/step - loss: 0.1803 - val_loss: 0.1315
Epoch 1/8
5/5 [==============================] - 4s 227ms/step - loss: 0.6345 - val_loss: 0.5964
Epoch 2/8
5/5 [==============================] - 0s 63ms/step - loss: 0.5611 - val_loss: 0.5045
Epoch 3/8
5/5 [==============================] - 0s 68ms/step - loss: 0.4514 - val_loss: 0.3171
Epoch 4/8
5/5 [==============================] - 0s 70ms/step - loss: 0.2691 - val_loss: 0.1828
Epoch 5/8
5/5 [==============================] - 0s 73ms/step - loss: 0.2009 - val_loss: 0.1522
Epoch 6/8
5/5 [==============================] - 0s 62ms/step - loss: 0.1918 - val_loss: 0.1298
Epoch 7/8
5/5 [==============================] - 0s 69ms/step - loss: 0.1816 - val_loss: 0.1314
Epoch 8/8
5/5 [==============================] - 0s 75ms/step - loss: 0.1831 - val_loss: 0.1387
Epoch 1/8
5/5 [==============================] - 4s 227ms/step - loss: 0.5176 - val_loss: 0.3632
Epoch 2/8
5/5 [==============================] - 0s 76ms/step - loss: 0.2917 - val_loss: 0.1954
Epoch 3/8
5/5 [==============================] - 0s 74ms/step - loss: 0.2084 - val_loss: 0.1636
Epoch 4/8
5/5 [==============================] - 0s 73ms/step - loss: 0.2035 - val_loss: 0.1425
Epoch 5/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1906 - val_loss: 0.1327
Epoch 6/8
5/5 [==============================] - 0s 74ms/step - loss: 0.1843 - val_loss: 0.1407
Epoch 7/8
5/5 [==============================] - 0s 76ms/step - loss: 0.1824 - val_loss: 0.1326
Epoch 8/8
5/5 [==============================] - 0s 73ms/step - loss: 0.1805 - val_loss: 0.1276
Epoch 1/8
5/5 [==============================] - 9s 619ms/step - loss: 0.5797 - val_loss: 0.3233
Epoch 2/8
5/5 [==============================] - 1s 115ms/step - loss: 0.2873 - val_loss: 0.2037
Epoch 3/8
5/5 [==============================] - 1s 110ms/step - loss: 0.2205 - val_loss: 0.1515
Epoch 4/8
5/5 [==============================] - 1s 111ms/step - loss: 0.1900 - val_loss: 0.1438
Epoch 5/8
5/5 [==============================] - 1s 112ms/step - loss: 0.1948 - val_loss: 0.1343
Epoch 6/8
5/5 [==============================] - 1s 108ms/step - loss: 0.1818 - val_loss: 0.1380
Epoch 7/8
5/5 [==============================] - 1s 138ms/step - loss: 0.1807 - val_loss: 0.1310
Epoch 8/8
5/5 [==============================] - 1s 104ms/step - loss: 0.1865 - val_loss: 0.1359
Epoch 1/8
5/5 [==============================] - 6s 347ms/step - loss: 0.4531 - val_loss: 0.2934
Epoch 2/8
5/5 [==============================] - 0s 86ms/step - loss: 0.2282 - val_loss: 0.1922
Epoch 3/8
5/5 [==============================] - 0s 97ms/step - loss: 0.2197 - val_loss: 0.1455
Epoch 4/8
5/5 [==============================] - 1s 136ms/step - loss: 0.1874 - val_loss: 0.1386
Epoch 5/8
5/5 [==============================] - 1s 107ms/step - loss: 0.1883 - val_loss: 0.1387
Epoch 6/8
5/5 [==============================] - 0s 94ms/step - loss: 0.1869 - val_loss: 0.1407
Epoch 7/8
5/5 [==============================] - 0s 99ms/step - loss: 0.1847 - val_loss: 0.1297
Epoch 8/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1815 - val_loss: 0.1287
Epoch 1/8
5/5 [==============================] - 5s 328ms/step - loss: 0.5409 - val_loss: 0.3259
Epoch 2/8
5/5 [==============================] - 0s 74ms/step - loss: 0.2661 - val_loss: 0.1302
Epoch 3/8
5/5 [==============================] - 0s 84ms/step - loss: 0.1925 - val_loss: 0.1536
Epoch 4/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1915 - val_loss: 0.1486
Epoch 5/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1858 - val_loss: 0.1414
Epoch 6/8
5/5 [==============================] - 0s 91ms/step - loss: 0.1827 - val_loss: 0.1381
Epoch 7/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1850 - val_loss: 0.1300
Epoch 8/8
5/5 [==============================] - 0s 84ms/step - loss: 0.1866 - val_loss: 0.1332
Epoch 1/8
5/5 [==============================] - 5s 294ms/step - loss: 0.5476 - val_loss: 0.2092
Epoch 2/8
5/5 [==============================] - 0s 75ms/step - loss: 0.2396 - val_loss: 0.1394
Epoch 3/8
5/5 [==============================] - 0s 83ms/step - loss: 0.2041 - val_loss: 0.1674
Epoch 4/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1932 - val_loss: 0.1419
Epoch 5/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1873 - val_loss: 0.1302
Epoch 6/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1809 - val_loss: 0.1300
Epoch 7/8
5/5 [==============================] - 0s 86ms/step - loss: 0.1819 - val_loss: 0.1334
Epoch 8/8
5/5 [==============================] - 0s 85ms/step - loss: 0.1775 - val_loss: 0.1294
Epoch 1/8
5/5 [==============================] - 5s 392ms/step - loss: 0.5127 - val_loss: 0.1769
Epoch 2/8
5/5 [==============================] - 0s 76ms/step - loss: 0.2590 - val_loss: 0.1552
Epoch 3/8
5/5 [==============================] - 0s 89ms/step - loss: 0.2232 - val_loss: 0.1886
Epoch 4/8
5/5 [==============================] - 0s 89ms/step - loss: 0.2017 - val_loss: 0.1344
Epoch 5/8
5/5 [==============================] - 0s 90ms/step - loss: 0.1878 - val_loss: 0.1282
Epoch 6/8
5/5 [==============================] - 0s 90ms/step - loss: 0.1829 - val_loss: 0.1369
Epoch 7/8
5/5 [==============================] - 0s 90ms/step - loss: 0.1868 - val_loss: 0.1273
Epoch 8/8
5/5 [==============================] - 0s 88ms/step - loss: 0.1797 - val_loss: 0.1324
Epoch 1/8
5/5 [==============================] - 5s 340ms/step - loss: 0.4706 - val_loss: 0.2935
Epoch 2/8
5/5 [==============================] - 0s 78ms/step - loss: 0.2333 - val_loss: 0.1686
Epoch 3/8
5/5 [==============================] - 0s 87ms/step - loss: 0.2142 - val_loss: 0.1574
Epoch 4/8
5/5 [==============================] - 0s 91ms/step - loss: 0.1905 - val_loss: 0.1354
Epoch 5/8
5/5 [==============================] - 0s 89ms/step - loss: 0.1826 - val_loss: 0.1339
Epoch 6/8
5/5 [==============================] - 0s 93ms/step - loss: 0.1837 - val_loss: 0.1357
Epoch 7/8
5/5 [==============================] - 0s 87ms/step - loss: 0.1836 - val_loss: 0.1285
Epoch 8/8
5/5 [==============================] - 0s 89ms/step - loss: 0.1812 - val_loss: 0.1267
Epoch 1/8
5/5 [==============================] - 5s 330ms/step - loss: 0.4341 - val_loss: 0.2047
Epoch 2/8
5/5 [==============================] - 0s 75ms/step - loss: 0.2194 - val_loss: 0.1852
Epoch 3/8
5/5 [==============================] - 0s 84ms/step - loss: 0.2155 - val_loss: 0.1368
Epoch 4/8
5/5 [==============================] - 0s 93ms/step - loss: 0.1913 - val_loss: 0.1391
Epoch 5/8
5/5 [==============================] - 0s 88ms/step - loss: 0.1832 - val_loss: 0.1303
Epoch 6/8
5/5 [==============================] - 0s 90ms/step - loss: 0.1830 - val_loss: 0.1276
Epoch 7/8
5/5 [==============================] - 0s 90ms/step - loss: 0.1808 - val_loss: 0.1262
Epoch 8/8
5/5 [==============================] - 0s 89ms/step - loss: 0.1813 - val_loss: 0.1277
Epoch 1/8
5/5 [==============================] - 5s 314ms/step - loss: 0.4232 - val_loss: 0.2485
Epoch 2/8
5/5 [==============================] - 0s 80ms/step - loss: 0.2193 - val_loss: 0.1619
Epoch 3/8
5/5 [==============================] - 0s 90ms/step - loss: 0.1991 - val_loss: 0.1393
Epoch 4/8
5/5 [==============================] - 0s 90ms/step - loss: 0.1880 - val_loss: 0.1284
Epoch 5/8
5/5 [==============================] - 0s 95ms/step - loss: 0.1798 - val_loss: 0.1299
Epoch 6/8
5/5 [==============================] - 0s 88ms/step - loss: 0.1817 - val_loss: 0.1267
Epoch 7/8
5/5 [==============================] - 0s 88ms/step - loss: 0.1809 - val_loss: 0.1282
Epoch 8/8
5/5 [==============================] - 0s 92ms/step - loss: 0.1810 - val_loss: 0.1328

results = export_model_summaries(fdict,determine_best_by='LevelTestSetMAPE')
results[['Series','ModelNickname','LevelTestSetMAPE']].sort_values(['Series','LevelTestSetMAPE']).head(25)

	Series	ModelNickname	LevelTestSetMAPE
0	Hou-Dom	prophet_cv_uv	0.024967
1	Hou-Dom	arima_tune_uv	0.031463
2	Hou-Dom	arima_cv_uv	0.031463
3	Hou-Dom	hwes_tune_uv	0.040938
4	Hou-Dom	hwes_cv_uv	0.040938
5	Hou-Dom	auto_arima_anom	0.041796
6	Hou-Dom	prophet_tune_uv	0.046722
7	Hou-Dom	sgd_cv_uv	0.054584
8	Hou-Dom	sgd_tune_uv	0.056646
9	Hou-Dom	stacking_mv	0.062217
10	Hou-Dom	auto_arima	0.071545
11	Hou-Dom	mlr_tune_uv	0.072219
12	Hou-Dom	mlr_cv_uv	0.072219
13	Hou-Dom	elasticnet_cv_uv	0.072386
14	Hou-Dom	weighted	0.072712
15	Hou-Dom	elasticnet_tune_uv	0.073144
16	Hou-Dom	mlp_cv_uv	0.075766
17	Hou-Dom	mlp_tune_uv	0.079230
18	Hou-Dom	mlp_tune_mv	0.080000
19	Hou-Dom	avg	0.081842
20	Hou-Dom	lightgbm_cv_uv	0.084266
21	Hou-Dom	sgd_cv_mv	0.086127
22	Hou-Dom	lightgbm_tune_mv	0.086545
23	Hou-Dom	sgd_tune_mv	0.086680
24	Hou-Dom	mlp_cv_mv	0.087113

test = pd.read_csv(
    'data/Future Passengers.csv',
    parse_dates=['DATE'],
    index_col=0,
)
test.head()

	HOU-Int	Hou-Dom	IAH-DOM	IAH-Int
DATE
2021-10-01	27153	498237	1375815	285498
2021-11-01	34242	495800	1356136	336156
2021-12-01	39101	477752	1331893	417561
2022-01-01	29084	387446	1036051	287998

Export Results

final_results = results.copy()
for l, f in fdict.items():
    df = pd.DataFrame()
    fcsts = f.export('lvl_fcsts').set_index('DATE')
    for c in fcsts:
        df.loc[c,'FcstMAPE' + l] = metrics.mape(test[l],fcsts[c])
    df['Series'] = l
    final_results = final_results.merge(
        df.reset_index(),
        how='left',
        left_on=['ModelNickname','Series'],
        right_on=['index','Series'],
    )
final_results['FcstMAPE'] = final_results[
    [
        c for c in final_results if c.startswith('FcstMAPE')
    ]
].apply(
    lambda x: (
        x[0]
        if not np.isnan(x[0])
        else x[1] if not np.isnan(x[1])
        else x[2] if not np.isnan(x[2])
        else x[3]
    ),
    axis=1,
)
final_results.drop(
    [
        c for c in final_results if (
            c.startswith('FcstMAPE') and c != 'FcstMAPE'
        ) or c.startswith('index')
    ],
    axis=1,
    inplace=True,
)
final_results['ObjectEstimator'] = final_results['ModelNickname'].apply(
    lambda x: 'MVForecaster' if '_mv' in x else 'Forecaster'
)
final_results['Used Anomalies'] = final_results['Xvars'].apply(
    lambda x: False if x is None else final_anom_selected[0] in x
)

final_results[['Series','ModelNickname','LevelTestSetMAPE','FcstMAPE']].sort_values(['Series','FcstMAPE']).head(10)

	Series	ModelNickname	LevelTestSetMAPE	FcstMAPE
7	Hou-Dom	sgd_cv_uv	0.054584	0.027731
8	Hou-Dom	sgd_tune_uv	0.056646	0.034350
23	Hou-Dom	sgd_tune_mv	0.086680	0.049739
36	Hou-Dom	knn_tune_mv	0.106951	0.052050
21	Hou-Dom	sgd_cv_mv	0.086127	0.054210
15	Hou-Dom	elasticnet_tune_uv	0.073144	0.058379
0	Hou-Dom	prophet_cv_uv	0.024967	0.060044
6	Hou-Dom	prophet_tune_uv	0.046722	0.060630
25	Hou-Dom	lightgbm_tune_uv	0.087297	0.060856
14	Hou-Dom	weighted	0.072712	0.061285

final_results.to_csv('final_results.csv',index=False)

pd.pivot_table(
    final_results,
    index='Estimator',
    values='FcstMAPE',
    aggfunc=np.mean,
).sort_values('FcstMAPE')

	FcstMAPE
Estimator
sgd	0.059227
hwes	0.070423
combo	0.071353
prophet	0.076977
arima	0.078932
mlr	0.082993
lightgbm	0.086944
stacking	0.093409
silverkite	0.099222
knn	0.100048
mlp	0.108065
elasticnet	0.110694
rnn	0.124109
xgboost	0.126917
theta	0.153371

pd.pivot_table(
    final_results,
    index='ObjectEstimator',
    values='FcstMAPE',
    aggfunc=np.mean,
).sort_values('FcstMAPE')

	FcstMAPE
ObjectEstimator
Forecaster	0.091701
MVForecaster	0.101191

with open('fdict.pckl', 'wb') as fl:
    pickle.dump(fdict,fl)