datacubeR

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import holidays
df_train = pd.read_csv('../inputs/train.csv', index_col=0, parse_dates=['date'])
df_test = pd.read_csv('../inputs/test.csv', index_col=0, parse_dates=['date'])

df_train.shape, df_test.shape

((26298, 5), (6570, 4))

Create Date Variables

cols = ['country', 'store','product']
combination = 1
for col in cols:
    combination *= df_train[col].nunique()
print(f'Data Change every {combination} entries')
Data Change every 18 entries
df_train.head(36)
datecountrystoreproductnum_sold
row_id
02015-01-01FinlandKaggleMartKaggle Mug329
12015-01-01FinlandKaggleMartKaggle Hat520
22015-01-01FinlandKaggleMartKaggle Sticker146
32015-01-01FinlandKaggleRamaKaggle Mug572
42015-01-01FinlandKaggleRamaKaggle Hat911
52015-01-01FinlandKaggleRamaKaggle Sticker283
62015-01-01NorwayKaggleMartKaggle Mug526
72015-01-01NorwayKaggleMartKaggle Hat906
82015-01-01NorwayKaggleMartKaggle Sticker250
92015-01-01NorwayKaggleRamaKaggle Mug1005
102015-01-01NorwayKaggleRamaKaggle Hat1461
112015-01-01NorwayKaggleRamaKaggle Sticker395
122015-01-01SwedenKaggleMartKaggle Mug440
132015-01-01SwedenKaggleMartKaggle Hat624
142015-01-01SwedenKaggleMartKaggle Sticker175
152015-01-01SwedenKaggleRamaKaggle Mug706
162015-01-01SwedenKaggleRamaKaggle Hat1195
172015-01-01SwedenKaggleRamaKaggle Sticker324
182015-01-02FinlandKaggleMartKaggle Mug318
192015-01-02FinlandKaggleMartKaggle Hat493
202015-01-02FinlandKaggleMartKaggle Sticker127
212015-01-02FinlandKaggleRamaKaggle Mug544
222015-01-02FinlandKaggleRamaKaggle Hat794
232015-01-02FinlandKaggleRamaKaggle Sticker249
242015-01-02NorwayKaggleMartKaggle Mug538
252015-01-02NorwayKaggleMartKaggle Hat845
262015-01-02NorwayKaggleMartKaggle Sticker237
272015-01-02NorwayKaggleRamaKaggle Mug949
282015-01-02NorwayKaggleRamaKaggle Hat1446
292015-01-02NorwayKaggleRamaKaggle Sticker415
302015-01-02SwedenKaggleMartKaggle Mug384
312015-01-02SwedenKaggleMartKaggle Hat572
322015-01-02SwedenKaggleMartKaggle Sticker155
332015-01-02SwedenKaggleRamaKaggle Mug653
342015-01-02SwedenKaggleRamaKaggle Hat986
352015-01-02SwedenKaggleRamaKaggle Sticker293
def create_date_features(df):
    df['day_of_year'] = df.date.dt.day_of_year
    df['day_of_month'] = df.date.dt.day
    df['day_of_week'] = df.date.dt.weekday
    df['month'] = df.date.dt.month
    df['quarter'] = df.date.dt.quarter
    df['year'] = df.date.dt.year
    df['period'] = df.date.dt.to_period('M')
    return df

df_train = create_date_features(df_train)
df_test = create_date_features(df_test)

df_train.shape, df_test.shape
    
((26298, 12), (6570, 11))

Time Range

df_train.date.agg({np.min, np.max})
amin   2015-01-01
amax   2018-12-31
Name: date, dtype: datetime64[ns]
df_test.date.agg({np.min, np.max})
amin   2019-01-01
amax   2019-12-31
Name: date, dtype: datetime64[ns]

Entries per year

df_train.period.value_counts().sort_index().plot(figsize = (12,8));

png

df_test.period.value_counts().sort_index().plot(figsize = (12,8));

png

Number of Categories

def cat_per_column(df):
    cat_cols = [col for col in df.columns if df[col].dtype == 'object']
    for col in cat_cols:
        print(df[col].value_counts(), '\n')
        

cat_per_column(df_train)
Finland    8766
Norway     8766
Sweden     8766
Name: country, dtype: int64 

KaggleMart    13149
KaggleRama    13149
Name: store, dtype: int64 

Kaggle Mug        8766
Kaggle Hat        8766
Kaggle Sticker    8766
Name: product, dtype: int64 
cat_per_column(df_test)
Finland    2190
Norway     2190
Sweden     2190
Name: country, dtype: int64 

KaggleMart    3285
KaggleRama    3285
Name: store, dtype: int64 

Kaggle Mug        2190
Kaggle Hat        2190
Kaggle Sticker    2190
Name: product, dtype: int64 

Sales Trend

df_train.set_index('period').num_sold.plot(figsize = (16,8));

png

df_train.groupby(['country', 'store','product']).num_sold.agg(['min','max','mean'])
minmaxmean
countrystoreproduct
FinlandKaggleMartKaggle Hat2101113362.479808
Kaggle Mug126774204.200548
Kaggle Sticker70326103.044490
KaggleRamaKaggle Hat3541895628.926762
Kaggle Mug2201398356.110883
Kaggle Sticker128559180.232033
NorwayKaggleMartKaggle Hat3351809594.645448
Kaggle Mug2011113334.370294
Kaggle Sticker114518169.577687
KaggleRamaKaggle Hat59628841036.357974
Kaggle Mug3661935584.297741
Kaggle Sticker214874295.607803
SwedenKaggleMartKaggle Hat2481207419.214237
Kaggle Mug149730235.885010
Kaggle Sticker86356119.613279
KaggleRamaKaggle Hat4282169731.452430
Kaggle Mug2531438411.273101
Kaggle Sticker148637208.314853
df_train.groupby(['country', 'store','product']).num_sold.mean().unstack(level = 'store').assign(ratio = lambda x: x.KaggleRama/x.KaggleMart)
storeKaggleMartKaggleRamaratio
countryproduct
FinlandKaggle Hat362.479808628.9267621.735067
Kaggle Mug204.200548356.1108831.743927
Kaggle Sticker103.044490180.2320331.749070
NorwayKaggle Hat594.6454481036.3579741.742817
Kaggle Mug334.370294584.2977411.747457
Kaggle Sticker169.577687295.6078031.743200
SwedenKaggle Hat419.214237731.4524301.744818
Kaggle Mug235.885010411.2731011.743532
Kaggle Sticker119.613279208.3148531.741570
plt.figure(figsize = (18,12))
for i, (combi, df) in enumerate(df_train.groupby(['country', 'store', 'product'])):
    ax = plt.subplot(6, 3, i+1, ymargin=0.5)
    ax.hist(df.num_sold, bins=50, color='pink')
    ax.set_title(combi)
plt.suptitle('Histograms of num_sold', y=1.03)
plt.tight_layout(h_pad=3.0)
plt.show()

png

Ventas promedio por día

df_train.query('store == "KaggleRama"').groupby(["day_of_week"]).num_sold.mean().plot(label = 'KaggleRama')
df_train.query('store == "KaggleMart"').groupby(["day_of_week"]).num_sold.mean().plot(label = 'KaggleMart')
plt.legend()
<matplotlib.legend.Legend at 0x7f5eb1627b50>

png

df_train.query('product == "Kaggle Sticker"').groupby(["day_of_week"]).num_sold.mean().plot(label = "Kaggle Sticker")
df_train.query('product == "Kaggle Hat"').groupby(["day_of_week"]).num_sold.mean().plot(label = "Kaggle Hat")
df_train.query('product == "Kaggle Mug"').groupby(["day_of_week"]).num_sold.mean().plot(label = "Kaggle Mug")
plt.legend();

png

sns.catplot(x = 'day_of_week', y = 'num_sold', data = df_train, kind = 'bar', ci = None, hue = 'country', row ='store', col = 'product');

png

df_train.groupby("day_of_month").num_sold.mean().plot();

png

df_train.query('store == "KaggleRama"').groupby(["day_of_month"]).num_sold.mean().plot(label = 'KaggleRama')
df_train.query('store == "KaggleMart"').groupby(["day_of_month"]).num_sold.mean().plot(label = 'KaggleMart')
plt.legend();

png

df_train.query('product == "Kaggle Sticker"').groupby(["day_of_month"]).num_sold.mean().plot(label = "Kaggle Sticker")
df_train.query('product == "Kaggle Hat"').groupby(["day_of_month"]).num_sold.mean().plot(label = "Kaggle Hat")
df_train.query('product == "Kaggle Mug"').groupby(["day_of_month"]).num_sold.mean().plot(label = "Kaggle Mug")
plt.legend();

png

sns.catplot(x = 'day_of_month', y = 'num_sold', data = df_train, kind = 'bar', ci = None, hue = 'country', row ='store', col = 'product');

png

df_train.groupby("day_of_year").num_sold.mean().plot();

png

df_train.query('store == "KaggleRama"').groupby(["day_of_year"]).num_sold.mean().plot(label = 'KaggleRama')
df_train.query('store == "KaggleMart"').groupby(["day_of_year"]).num_sold.mean().plot(label = 'KaggleMart')
plt.legend();

png

df_train.query('product == "Kaggle Sticker"').groupby(["day_of_year"]).num_sold.mean().plot(label = 'Kaggle Sticker')
df_train.query('product == "Kaggle Hat"').groupby(["day_of_year"]).num_sold.mean().plot(label = 'Kaggle Hat')
df_train.query('product == "Kaggle Mug"').groupby(["day_of_year"]).num_sold.mean().plot(label = 'Kaggle Mug')
plt.legend();

png

Bigger Periods

df_train.groupby("month").num_sold.mean().plot();

png

df_train.query('store == "KaggleRama"').groupby(["month"]).num_sold.mean().plot(label = 'KaggleRama')
df_train.query('store == "KaggleMart"').groupby(["month"]).num_sold.mean().plot(label = 'KaggleMart')
plt.legend();

png

df_train.query('product == "Kaggle Sticker"').groupby(["month"]).num_sold.mean().plot(label = 'Kaggle Sticker')
df_train.query('product == "Kaggle Hat"').groupby(["month"]).num_sold.mean().plot(label = 'Kaggle Hat')
df_train.query('product == "Kaggle Mug"').groupby(["month"]).num_sold.mean().plot(label = 'Kaggle Mug')
plt.legend();

png

df_train.groupby("quarter").num_sold.mean().plot();

png

df_train.query('store == "KaggleRama"').groupby(["quarter"]).num_sold.mean().plot(label = 'KaggleRama')
df_train.query('store == "KaggleMart"').groupby(["quarter"]).num_sold.mean().plot(label = 'KaggleMart')
plt.legend();

png

df_train.query('product == "Kaggle Sticker"').groupby(["quarter"]).num_sold.mean().plot(label = 'Kaggle Sticker')
df_train.query('product == "Kaggle Hat"').groupby(["quarter"]).num_sold.mean().plot(label = 'Kaggle Hat')
df_train.query('product == "Kaggle Mug"').groupby(["quarter"]).num_sold.mean().plot(label = 'Kaggle Mug')
plt.legend();

png

df_train.groupby("year").num_sold.mean().plot();

png

df_train.query('store == "KaggleRama"').groupby(["year"]).num_sold.mean().plot(label = 'KaggleRama')
df_train.query('store == "KaggleMart"').groupby(["year"]).num_sold.mean().plot(label = 'KaggleMart')
plt.legend();

png

df_train.query('product == "Kaggle Sticker"').groupby(["year"]).num_sold.mean().plot(label = 'Kaggle Sticker')
df_train.query('product == "Kaggle Hat"').groupby(["year"]).num_sold.mean().plot(label = 'Kaggle Hat')
df_train.query('product == "Kaggle Mug"').groupby(["year"]).num_sold.mean().plot(label = 'Kaggle Mug')
plt.legend();

png

plt.figure(figsize = (18,12))
df_train.set_index('date').num_sold.plot(label = 'daily')
df_train.set_index('date').num_sold.resample('W').mean().plot(label = 'weekly')
df_train.set_index('date').num_sold.resample('M').mean().plot(label = 'monthly')
plt.legend();

png

Holiday Effect

holidays.Chile(years=2022)
{datetime.date(2022, 1, 1): "Año Nuevo [New Year's Day]",
 datetime.date(2022, 4, 15): 'Semana Santa (Viernes Santo) [Good Friday)]',
 datetime.date(2022, 4, 16): 'Semana Santa (Sábado Santo) [Good Saturday)]',
 datetime.date(2022, 4, 17): 'Día de Pascuas [Easter Day]',
 datetime.date(2022, 5, 1): 'Día Nacional del Trabajo [Labour Day]',
 datetime.date(2022, 5, 21): 'Día de las Glorias Navales [Navy Day]',
 datetime.date(2022, 6, 19): 'Día Nacional de los Pueblos Indígenas',
 datetime.date(2022, 6, 27): 'San Pedro y San Pablo [Saint Peter and Saint Paul]',
 datetime.date(2022, 7, 16): 'Virgen del Carmen [Our Lady of Mount Carmel]',
 datetime.date(2022, 8, 15): 'Asunción de la Virgen [Assumption of Mary]',
 datetime.date(2022, 9, 18): 'Día de la Independencia [Independence Day]',
 datetime.date(2022, 9, 19): 'Día de las Glorias del Ejército [Army Day]',
 datetime.date(2022, 10, 10): 'Día del Descubrimiento de dos Mundos [Columbus Day]',
 datetime.date(2022, 10, 31): 'Día Nacional de las Iglesias Evangélicas y Protestantes  [Reformation Day]',
 datetime.date(2022, 11, 1): 'Día de Todos los Santos [All Saints Day]',
 datetime.date(2022, 12, 8): 'La Inmaculada Concepción [Immaculate Conception]',
 datetime.date(2022, 12, 25): 'Navidad [Christmas]'}
holidays.Turkey(years=2021)
{datetime.date(2021, 1, 1): "New Year's Day",
 datetime.date(2021, 4, 23): "National Sovereignty and Children's Day",
 datetime.date(2021, 5, 1): 'Labour Day',
 datetime.date(2021, 5, 19): 'Commemoration of Ataturk, Youth and Sports Day',
 datetime.date(2021, 7, 15): 'Democracy and National Unity Day',
 datetime.date(2021, 8, 30): 'Victory Day',
 datetime.date(2021, 10, 29): 'Republic Day',
 datetime.date(2021, 5, 13): 'Ramadan Feast',
 datetime.date(2021, 5, 14): 'Ramadan Feast Holiday',
 datetime.date(2021, 5, 15): 'Ramadan Feast Holiday',
 datetime.date(2021, 7, 20): 'Sacrifice Feast',
 datetime.date(2021, 7, 21): 'Sacrifice Feast Holiday',
 datetime.date(2021, 7, 22): 'Sacrifice Feast Holiday',
 datetime.date(2021, 7, 23): 'Sacrifice Feast Holiday'}
finland = pd.DataFrame([dict(date = date, 
                            finland_holiday = event, 
                            country= 'Finland') for date, event in holidays.Finland(years=[2015, 2016, 2017, 2018, 2019]).items()])
finland['date'] = finland['date'].astype("datetime64")

norway = pd.DataFrame([dict(date = date, 
                        norway_holiday = event, 
                        country= 'Norway') for date, event in holidays.Norway(years=[2015, 2016, 2017, 2018, 2019]).items()])
norway['date'] = norway['date'].astype("datetime64")

sweden = pd.DataFrame([dict(date = date, 
                            sweden_holiday = event.replace(", Söndag", ""), 
                            country= 'Sweden') for date, event in holidays.Sweden(years=[2015, 2016, 2017, 2018, 2019]).items() if event != 'Söndag'])
sweden['date'] = sweden['date'].astype("datetime64")

df_train = df_train.merge(finland, on = ['date', 'country'], how = 'left').merge(norway, on = ['date', 'country'], how = 'left').merge(sweden, on = ['date', 'country'], how = 'left')
df_test = df_test.merge(finland, on = ['date', 'country'], how = 'left').merge(norway, on = ['date', 'country'], how = 'left').merge(sweden, on = ['date', 'country'], how = 'left')
df_train
datecountrystoreproductnum_soldday_of_yearday_of_monthday_of_weekmonthquarteryearperiodfinland_holidaynorway_holidaysweden_holiday
02015-01-01FinlandKaggleMartKaggle Mug3291131120152015-01UudenvuodenpäiväNaNNaN
12015-01-01FinlandKaggleMartKaggle Hat5201131120152015-01UudenvuodenpäiväNaNNaN
22015-01-01FinlandKaggleMartKaggle Sticker1461131120152015-01UudenvuodenpäiväNaNNaN
32015-01-01FinlandKaggleRamaKaggle Mug5721131120152015-01UudenvuodenpäiväNaNNaN
42015-01-01FinlandKaggleRamaKaggle Hat9111131120152015-01UudenvuodenpäiväNaNNaN
................................................
262932018-12-31SwedenKaggleMartKaggle Hat82336531012420182018-12NaNNaNNyårsafton
262942018-12-31SwedenKaggleMartKaggle Sticker25036531012420182018-12NaNNaNNyårsafton
262952018-12-31SwedenKaggleRamaKaggle Mug100436531012420182018-12NaNNaNNyårsafton
262962018-12-31SwedenKaggleRamaKaggle Hat144136531012420182018-12NaNNaNNyårsafton
262972018-12-31SwedenKaggleRamaKaggle Sticker38836531012420182018-12NaNNaNNyårsafton

26298 rows × 15 columns

Fourier Effect

from numpy.fft import rfft, irfft, rfftfreq

def low_pass(s, threshold=2e4, d = None):
    if d is None:
        d = 2e-3 / s.size
    fourier = rfft(s)
    frequencies = rfftfreq(s.size, d=d)
    fourier[frequencies > threshold] = 0
    return irfft(fourier)
plt.figure(figsize = (18,12))
s = df_train.num_sold
for i, d in enumerate([1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8, 1e-9], start=1):
    ax = plt.subplot(3,3, i)
    ax.plot(low_pass(s, threshold=2e4, d = d))
    ax.set_title(f'd = {d}')

png \[f(t) \sim \frac{a_o}{2} + \sum{}_{n=1}^{\infty}\left[a_n cos\left(\frac{2n\pi}{T}t\right) + b_n sin\left(\frac{2n\pi}{T}t\right)\right]\]

\(var\_sin = sin\left(\frac{2\pi}{max\_value}t\right)\) \(var\_cos = cos\left(\frac{2\pi}{max\_value}t\right)\)

\(var\_sin = sin\left(\frac{2n\pi}{max\_value}t\right) = sin\left(\frac{2\pi}{\frac{max\_value}{n}}t\right)\) \(var\_cos = cos\left(\frac{2n\pi}{max\_value}t\right) = cos\left(\frac{2\pi}{\frac{max\_value}{n}}t\right)\)

Interactions

df_train['product']
0            Kaggle Mug
1            Kaggle Hat
2        Kaggle Sticker
3            Kaggle Mug
4            Kaggle Hat
              ...      
26293        Kaggle Hat
26294    Kaggle Sticker
26295        Kaggle Mug
26296        Kaggle Hat
26297    Kaggle Sticker
Name: product, Length: 26298, dtype: object
products = pd.get_dummies(df_train['product'])
products.index = df_train.date

fourier_variables = pd.DataFrame({'sin': np.sin(2*np.pi*1*df_train['day_of_week']/7),
                            'cos': np.cos(2*np.pi*1*df_train['day_of_week']/7),
                            'sin2': np.sin(2*np.pi*2*df_train['day_of_week']/7),
                            'cos2': np.cos(2*np.pi*2*df_train['day_of_week']/7),
                            'sin3': np.sin(2*np.pi*3*df_train['day_of_week']/7),
                            'cos3': np.cos(2*np.pi*3*df_train['day_of_week']/7)})

fourier_variables.index = df_train.date
fourier_variables[['sin','cos']].plot(figsize = (16,12));

png

ax = fourier_variables.plot.scatter('sin', 'cos', figsize = (16,12)).set_aspect('equal')

png

(products['Kaggle Hat']*fourier_variables['sin']).plot(alpha = 0.4, figsize = (14,10), label = 'Sin')
(products['Kaggle Sticker']*fourier_variables['sin2']).plot(alpha = 0.4, figsize = (14,10), label = 'Sin2')
(products['Kaggle Mug']*fourier_variables['cos2']).plot(alpha = 0.4, figsize = (14,10), label = 'Sin3')
plt.legend();

png

Automate variable creation

from feature_engine.creation import CyclicalTransformer

# New class allowing to add a suffix to differentiate different n values.
class CyclicalTransformerV2(CyclicalTransformer):
    def __init__(self, suffix = None, **kwargs):
            super().__init__(**kwargs)
            self.suffix = suffix
        
    def transform(self, X):
        X = super().transform(X)
        if self.suffix is not None:
            transformed_names = X.filter(regex = r'sin$|cos$').columns
            new_names = {name: name + self.suffix for name in transformed_names}
            X.rename(columns=new_names, inplace=True)
        return X
from feature_engine.creation import CombineWithReferenceFeature
from sklearn.pipeline import Pipeline
from feature_engine.encoding import OneHotEncoder
from feature_engine.selection import DropFeatures
from feature_engine.imputation import CategoricalImputer
from catboost import CatBoostRegressor

cyc_1 = CyclicalTransformerV2(variables = ['day_of_year','day_of_month','day_of_week'], 
                            max_values = {'day_of_year':365,'day_of_month': 30,'day_of_week': 7}, 
                            suffix = '_1')
cyc_2 = CyclicalTransformerV2(variables = ['day_of_year','day_of_month','day_of_week'], 
                            max_values = {'day_of_year':365/2,'day_of_month': 30/2,'day_of_week': 7/2}, 
                            suffix = '_2')
cyc_3 = CyclicalTransformerV2(variables = ['day_of_year','day_of_month','day_of_week'], 
                            max_values = {'day_of_year':365/3,'day_of_month': 30/3,'day_of_week': 7/3}, 
                            suffix = '_3')
cyc_4 = CyclicalTransformerV2(variables = ['day_of_year','day_of_month','day_of_week'], 
                            max_values = {'day_of_year':365/4,'day_of_month': 30/4,'day_of_week': 7/4}, 
                            suffix = '_4')

Categorical Variables Needed for Catboost

X = df_train.drop(columns='num_sold')
y = df_train.num_sold
cat_vars = [var.name for _, var in X.iteritems() if var.dtype == 'object']
cat_vars
['country',
 'store',
 'product',
 'finland_holiday',
 'norway_holiday',
 'sweden_holiday']

Evaluation Metric

def smape(y_true, y_pred):
    numerator = np.abs(y_pred - y_true)
    denominator = (np.abs(y_true) + np.abs(y_pred)) / 2
    
    return np.mean(numerator / denominator)*100

Time Series Split

from sklearn.model_selection import TimeSeriesSplit

folds = TimeSeriesSplit(n_splits=2)
for fold, (train_idx, test_idx) in enumerate(folds.split(X)):
    if fold == 1:
        X_train, y_train = X.iloc[train_idx], y.iloc[train_idx]
        X_test, y_test = X.iloc[test_idx], y.iloc[test_idx]
        
X_train.shape, y_train.shape, X_test.shape, y_test.shape
((17532, 14), (17532,), (8766, 14), (8766,))
X_train[cat_vars] = X_train[cat_vars].astype('str')
X_test[cat_vars] = X_test[cat_vars].astype('str')
/home/alfonso/miniconda3/envs/ds/lib/python3.7/site-packages/pandas/core/frame.py:3641: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self[k1] = value[k2]

First Model Loss SMAPE

model = CatBoostRegressor(iterations=15000,
                            bootstrap_type='Bayesian',
                            boosting_type='Plain',
                            loss_function='MAE',
                            eval_metric='SMAPE',
                            # task_type="GPU",
                            # devices='0:1'
                            )

prep = Pipeline(steps  =[
    ('drop',DropFeatures(features_to_drop=['date','period'])),

])

prep.fit(X_train)
X_val = prep.transform(X_test)

print('Predictor Variables: ', X_val.shape[1])

pipe = Pipeline(steps = [
    ('prep', prep),
    ('model', model)
])

pipe.fit(X_train, y_train, model__cat_features = cat_vars, 
            model__eval_set = (X_val, y_test),
            model__early_stopping_rounds = 1000)
y_pred = pipe.predict(X_test)

print('Test: ', smape(y_test, y_pred))
Predictor Variables:  12
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2998:	learn: 4.2760911	test: 9.2858071	best: 9.2820329 (1998)	total: 16.7s	remaining: 1m 6s
Stopped by overfitting detector  (1000 iterations wait)

bestTest = 9.282032873
bestIteration = 1998

Shrink model to first 1999 iterations.
Test:  9.282032873182944

Using MAE as Proxy for GPU

model_mae = CatBoostRegressor(iterations=15000,
                            bootstrap_type='Bayesian',
                            boosting_type='Plain',
                            loss_function='MAE',
                            eval_metric='MAE',
                            task_type="GPU",
                            devices='0:1')

prep = Pipeline(steps  =[
    ('cat_imputation', CategoricalImputer()),
    ('ohe',OneHotEncoder()),
    ('drop',DropFeatures(features_to_drop=['date','period'])),
    # ('model', model)

])

print()
prep.fit(X_train)
X_val = prep.transform(X_test)
X_val.columns

Index(['day_of_year', 'day_of_month', 'day_of_week', 'month', 'quarter',
       'year', 'country_Finland', 'country_Norway', 'country_Sweden',
       'store_KaggleMart', 'store_KaggleRama', 'product_Kaggle Mug',
       'product_Kaggle Hat', 'product_Kaggle Sticker',
       'finland_holiday_Uudenvuodenpäivä', 'finland_holiday_nan',
       'finland_holiday_Loppiainen', 'finland_holiday_Pitkäperjantai',
       'finland_holiday_Pääsiäispäivä', 'finland_holiday_2. pääsiäispäivä',
       'finland_holiday_Vappu', 'finland_holiday_Helatorstai',
       'finland_holiday_Helluntaipäivä', 'finland_holiday_Juhannusaatto',
       'finland_holiday_Juhannuspäivä', 'finland_holiday_Pyhäinpäivä',
       'finland_holiday_Itsenäisyyspäivä', 'finland_holiday_Jouluaatto',
       'finland_holiday_Joulupäivä', 'finland_holiday_Tapaninpäivä',
       'norway_holiday_nan', 'norway_holiday_Første nyttårsdag',
       'norway_holiday_Skjærtorsdag', 'norway_holiday_Langfredag',
       'norway_holiday_Første påskedag', 'norway_holiday_Andre påskedag',
       'norway_holiday_Arbeidernes dag',
       'norway_holiday_Kristi himmelfartsdag', 'norway_holiday_Grunnlovsdag',
       'norway_holiday_Første pinsedag', 'norway_holiday_Andre pinsedag',
       'norway_holiday_Første juledag', 'norway_holiday_Andre juledag',
       'sweden_holiday_nan', 'sweden_holiday_Nyårsdagen',
       'sweden_holiday_Trettondedag jul', 'sweden_holiday_Långfredagen',
       'sweden_holiday_Påskdagen', 'sweden_holiday_Annandag påsk',
       'sweden_holiday_Första maj', 'sweden_holiday_Kristi himmelsfärdsdag',
       'sweden_holiday_Pingstdagen', 'sweden_holiday_Sveriges nationaldag',
       'sweden_holiday_Midsommarafton', 'sweden_holiday_Midsommardagen',
       'sweden_holiday_Alla helgons dag', 'sweden_holiday_Julafton',
       'sweden_holiday_Juldagen', 'sweden_holiday_Annandag jul',
       'sweden_holiday_Nyårsafton'],
      dtype='object')
print('Predictor Variables: ', X_val.shape[1])

pipe = Pipeline(steps = [
    ('prep', prep),
    ('model', model_mae)
])

pipe.fit(X_train, y_train, 
            model__eval_set = (X_val, y_test),
            model__early_stopping_rounds = 1000)
y_pred = pipe.predict(X_test)

print('Test: ', smape(y_test, y_pred))

Adding Fourier Terms

%%time

prep = Pipeline(steps  =[
    ('cat_imputation', CategoricalImputer()),
    ('ohe',OneHotEncoder()),
    ('cyc', cyc_1),
    ('cyc2', cyc_2),
    ('combo', CombineWithReferenceFeature(variables_to_combine=['day_of_year_sin_1', 'day_of_year_cos_1',
                                                                'day_of_month_sin_1', 'day_of_month_cos_1', 'day_of_week_sin_1',
                                                                'day_of_week_cos_1', 'day_of_year_sin_2', 'day_of_year_cos_2',
                                                                'day_of_month_sin_2', 'day_of_month_cos_2', 'day_of_week_sin_2',
                                                                'day_of_week_cos_2'], 
                                        reference_variables=['product_Kaggle Mug', 'product_Kaggle Hat','product_Kaggle Sticker'],
                                        operations=['mul'])),
    ('drop',DropFeatures(features_to_drop=['date','period'])),
    # ('modelo', model)

])

prep.fit(X_train)
X_val = prep.transform(X_test)
print('Predictor Variables: ', X_val.shape[1])


pipe = Pipeline(steps = [
    ('prep', prep),
    ('model', model_mae)
])

pipe.fit(X_train, y_train, 
            model__eval_set = (X_val, y_test),
            model__early_stopping_rounds = 1000)
y_pred = pipe.predict(X_test)

print('Test: ', smape(y_test, y_pred))
Predictor Variables:  108
0:	learn: 181.2698494	test: 199.4307980	best: 199.4307980 (0)	total: 2.23ms	remaining: 33.5s
1:	learn: 176.3116016	test: 194.4883071	best: 194.4883071 (1)	total: 4.04ms	remaining: 30.3s
2:	learn: 171.4610569	test: 190.0181810	best: 190.0181810 (2)	total: 5.87ms	remaining: 29.3s
3:	learn: 166.8275724	test: 185.8308949	best: 185.8308949 (3)	total: 8.1ms	remaining: 30.4s
4:	learn: 162.2937486	test: 181.3087497	best: 181.3087497 (4)	total: 10.3ms	remaining: 30.8s
5:	learn: 157.9057580	test: 177.2733858	best: 177.2733858 (5)	total: 12.4ms	remaining: 30.9s
6:	learn: 153.5994182	test: 173.2642454	best: 173.2642454 (6)	total: 14.2ms	remaining: 30.5s
7:	learn: 149.5199492	test: 169.5257529	best: 169.5257529 (7)	total: 16.2ms	remaining: 30.3s
8:	learn: 145.4766997	test: 165.7930641	best: 165.7930641 (8)	total: 17.9ms	remaining: 29.9s
9:	learn: 141.6223049	test: 161.9694559	best: 161.9694559 (9)	total: 19.8ms	remaining: 29.7s
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bestTest = 37.60413815
bestIteration = 1954
Shrink model to first 1955 iterations.
Test:  8.367805614986699
CPU times: user 9.08 s, sys: 3.05 s, total: 12.1 s
Wall time: 6.33 s

3 Terms

prep = Pipeline(steps  =[
    ('cat_imputation', CategoricalImputer()),
    ('ohe',OneHotEncoder()),
    ('cyc', cyc_1),
    ('cyc2', cyc_2),
    ('cyc3', cyc_3),
    ('combo', CombineWithReferenceFeature(variables_to_combine=['day_of_year_sin_1', 'day_of_year_cos_1','day_of_month_sin_1', 
                                                                'day_of_month_cos_1', 'day_of_week_sin_1','day_of_week_cos_1', 
                                                                'day_of_year_sin_2', 'day_of_year_cos_2','day_of_month_sin_2', 
                                                                'day_of_month_cos_2', 'day_of_week_sin_2','day_of_week_cos_2', 
                                                                'day_of_year_sin_3', 'day_of_year_cos_3','day_of_month_sin_3', 
                                                                'day_of_month_cos_3', 'day_of_week_sin_3','day_of_week_cos_3',], 
                                        reference_variables=['product_Kaggle Mug', 'product_Kaggle Hat','product_Kaggle Sticker'],
                                        operations=['mul'])),
    ('drop',DropFeatures(features_to_drop=['date','period']))

])

prep.fit(X_train, y_train)
X_val = prep.transform(X_test)
print('Predictor Variables: ', X_val.shape[1])

pipe = Pipeline(steps = [
    ('prep', prep),
    ('model', model_mae)
])

pipe.fit(X_train, y_train, 
            model__eval_set = (X_val, y_test),
            model__early_stopping_rounds = 1000)
y_pred = pipe.predict(X_test)

print('Test: ', smape(y_test, y_pred))
Predictor Variables:  132
0:	learn: 181.2635609	test: 199.4405088	best: 199.4405088 (0)	total: 2.25ms	remaining: 33.7s
1:	learn: 176.3028462	test: 194.4956508	best: 194.4956508 (1)	total: 4ms	remaining: 30s
2:	learn: 171.4495637	test: 189.6589665	best: 189.6589665 (2)	total: 5.76ms	remaining: 28.8s
3:	learn: 166.8192163	test: 185.4854552	best: 185.4854552 (3)	total: 7.69ms	remaining: 28.8s
4:	learn: 162.2696213	test: 180.9580196	best: 180.9580196 (4)	total: 9.46ms	remaining: 28.4s
5:	learn: 157.8770106	test: 176.9122034	best: 176.9122034 (5)	total: 11.2ms	remaining: 27.9s
6:	learn: 153.5661077	test: 172.9037332	best: 172.9037332 (6)	total: 12.9ms	remaining: 27.7s
7:	learn: 149.4049738	test: 169.1205653	best: 169.1205653 (7)	total: 14.7ms	remaining: 27.6s
8:	learn: 145.3574036	test: 165.3826859	best: 165.3826859 (8)	total: 16.5ms	remaining: 27.4s
9:	learn: 141.5007558	test: 161.5552989	best: 161.5552989 (9)	total: 18.2ms	remaining: 27.3s
10:	learn: 137.7594541	test: 157.8434006	best: 157.8434006 (10)	total: 19.9ms	remaining: 27.2s
11:	learn: 134.1314454	test: 154.6008727	best: 154.6008727 (11)	total: 21.7ms	remaining: 27.1s
12:	learn: 130.5676905	test: 151.3192163	best: 151.3192163 (12)	total: 23.5ms	remaining: 27.1s
13:	learn: 127.1471310	test: 148.2315480	best: 148.2315480 (13)	total: 25.3ms	remaining: 27.1s
14:	learn: 123.8215264	test: 145.2419148	best: 145.2419148 (14)	total: 27.1ms	remaining: 27.1s
15:	learn: 120.7019022	test: 142.4747890	best: 142.4747890 (15)	total: 28.8ms	remaining: 27s
16:	learn: 117.5832906	test: 139.6803131	best: 139.6803131 (16)	total: 30.6ms	remaining: 26.9s
17:	learn: 114.5719470	test: 136.9682866	best: 136.9682866 (17)	total: 32.3ms	remaining: 26.9s
18:	learn: 111.5926877	test: 134.2898129	best: 134.2898129 (18)	total: 34.1ms	remaining: 26.9s
19:	learn: 108.8452687	test: 131.8463666	best: 131.8463666 (19)	total: 35.8ms	remaining: 26.8s
20:	learn: 106.1153391	test: 129.3982717	best: 129.3982717 (20)	total: 37.6ms	remaining: 26.8s
21:	learn: 103.4950519	test: 126.8058407	best: 126.8058407 (21)	total: 39.4ms	remaining: 26.8s
22:	learn: 100.9422342	test: 124.5032084	best: 124.5032084 (22)	total: 41.2ms	remaining: 26.8s
23:	learn: 98.4238749	test: 122.2810860	best: 122.2810860 (23)	total: 42.9ms	remaining: 26.8s
24:	learn: 96.1178559	test: 120.2848363	best: 120.2848363 (24)	total: 44.6ms	remaining: 26.7s
25:	learn: 93.8905929	test: 118.1503465	best: 118.1503465 (25)	total: 46.4ms	remaining: 26.7s
26:	learn: 91.6373916	test: 115.9531143	best: 115.9531143 (26)	total: 48.2ms	remaining: 26.7s
27:	learn: 89.4084389	test: 113.9983744	best: 113.9983744 (27)	total: 49.9ms	remaining: 26.7s
28:	learn: 87.2377225	test: 112.0671558	best: 112.0671558 (28)	total: 51.7ms	remaining: 26.7s
29:	learn: 85.1551877	test: 110.2404247	best: 110.2404247 (29)	total: 53.5ms	remaining: 26.7s
30:	learn: 83.1957207	test: 108.5405473	best: 108.5405473 (30)	total: 55.3ms	remaining: 26.7s
31:	learn: 81.2980122	test: 106.9237181	best: 106.9237181 (31)	total: 57.1ms	remaining: 26.7s
32:	learn: 79.3793706	test: 105.1526780	best: 105.1526780 (32)	total: 58.9ms	remaining: 26.7s
33:	learn: 77.6569416	test: 103.4421415	best: 103.4421415 (33)	total: 60.6ms	remaining: 26.7s
34:	learn: 75.9460344	test: 101.7393480	best: 101.7393480 (34)	total: 62.4ms	remaining: 26.7s
35:	learn: 74.2560532	test: 100.3250485	best: 100.3250485 (35)	total: 64.1ms	remaining: 26.7s
36:	learn: 72.6674866	test: 98.7491016	best: 98.7491016 (36)	total: 65.9ms	remaining: 26.7s
37:	learn: 71.0679401	test: 97.3534965	best: 97.3534965 (37)	total: 67.7ms	remaining: 26.7s
38:	learn: 69.5402906	test: 95.7937058	best: 95.7937058 (38)	total: 69.5ms	remaining: 26.7s
39:	learn: 68.0274355	test: 94.4782826	best: 94.4782826 (39)	total: 71.3ms	remaining: 26.7s
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3773:	learn: 14.0280639	test: 36.9625221	best: 36.8800729 (2773)	total: 7.16s	remaining: 21.3s
bestTest = 36.88007287
bestIteration = 2773
Shrink model to first 2774 iterations.
Test:  8.21481404038169

Four Terms

prep = Pipeline(steps  =[
    ('cat_imputation', CategoricalImputer()),
    ('ohe',OneHotEncoder()),
    ('cyc', cyc_1),
    ('cyc2', cyc_2),
    ('cyc3', cyc_3),
    ('cyc4', cyc_4),
    ('combo', CombineWithReferenceFeature(variables_to_combine=['day_of_year_sin_1', 'day_of_year_cos_1','day_of_month_sin_1', 
                                                                'day_of_month_cos_1', 'day_of_week_sin_1','day_of_week_cos_1', 
                                                                'day_of_year_sin_2', 'day_of_year_cos_2','day_of_month_sin_2', 
                                                                'day_of_month_cos_2', 'day_of_week_sin_2','day_of_week_cos_2', 
                                                                'day_of_year_sin_3', 'day_of_year_cos_3','day_of_month_sin_3', 
                                                                'day_of_month_cos_3', 'day_of_week_sin_3','day_of_week_cos_3', 
                                                                'day_of_year_sin_4', 'day_of_year_cos_4','day_of_month_sin_4', 
                                                                'day_of_month_cos_4', 'day_of_week_sin_4','day_of_week_cos_4',], 
                                        reference_variables=['product_Kaggle Mug', 'product_Kaggle Hat','product_Kaggle Sticker'],
                                        operations=['mul'])),
    ('drop',DropFeatures(features_to_drop=['date','period'])),

])

prep.fit(X_train, y_train)
X_val = prep.transform(X_test)
print('Predictor Variables: ', X_val.shape[1])

pipe = Pipeline(steps = [
    ('prep', prep),
    ('model', model_mae)
])

pipe.fit(X_train, y_train, 
            model__eval_set = (X_val, y_test),
            model__early_stopping_rounds = 1000)
y_pred = pipe.predict(X_test)

print('Test: ', smape(y_test, y_pred))
Predictor Variables:  156
0:	learn: 181.2635894	test: 199.4405088	best: 199.4405088 (0)	total: 2.34ms	remaining: 35.1s
1:	learn: 176.3028462	test: 194.4956651	best: 194.4956651 (1)	total: 4.16ms	remaining: 31.2s
2:	learn: 171.4504478	test: 190.0233573	best: 190.0233573 (2)	total: 5.99ms	remaining: 29.9s
3:	learn: 166.7272559	test: 185.6252709	best: 185.6252709 (3)	total: 7.84ms	remaining: 29.4s
4:	learn: 162.2187001	test: 181.4335786	best: 181.4335786 (4)	total: 9.69ms	remaining: 29.1s
5:	learn: 157.8388376	test: 177.4186345	best: 177.4186345 (5)	total: 11.5ms	remaining: 28.8s
6:	learn: 153.5368754	test: 173.4059292	best: 173.4059292 (6)	total: 13.4ms	remaining: 28.8s
7:	learn: 149.4168663	test: 169.3129563	best: 169.3129563 (7)	total: 15.3ms	remaining: 28.6s
8:	learn: 145.3703514	test: 165.5706993	best: 165.5706993 (8)	total: 17.1ms	remaining: 28.4s
9:	learn: 141.5253536	test: 161.7481177	best: 161.7481177 (9)	total: 19ms	remaining: 28.5s
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