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)
| date | country | store | product | num_sold | |
|---|---|---|---|---|---|
| row_id | |||||
| 0 | 2015-01-01 | Finland | KaggleMart | Kaggle Mug | 329 |
| 1 | 2015-01-01 | Finland | KaggleMart | Kaggle Hat | 520 |
| 2 | 2015-01-01 | Finland | KaggleMart | Kaggle Sticker | 146 |
| 3 | 2015-01-01 | Finland | KaggleRama | Kaggle Mug | 572 |
| 4 | 2015-01-01 | Finland | KaggleRama | Kaggle Hat | 911 |
| 5 | 2015-01-01 | Finland | KaggleRama | Kaggle Sticker | 283 |
| 6 | 2015-01-01 | Norway | KaggleMart | Kaggle Mug | 526 |
| 7 | 2015-01-01 | Norway | KaggleMart | Kaggle Hat | 906 |
| 8 | 2015-01-01 | Norway | KaggleMart | Kaggle Sticker | 250 |
| 9 | 2015-01-01 | Norway | KaggleRama | Kaggle Mug | 1005 |
| 10 | 2015-01-01 | Norway | KaggleRama | Kaggle Hat | 1461 |
| 11 | 2015-01-01 | Norway | KaggleRama | Kaggle Sticker | 395 |
| 12 | 2015-01-01 | Sweden | KaggleMart | Kaggle Mug | 440 |
| 13 | 2015-01-01 | Sweden | KaggleMart | Kaggle Hat | 624 |
| 14 | 2015-01-01 | Sweden | KaggleMart | Kaggle Sticker | 175 |
| 15 | 2015-01-01 | Sweden | KaggleRama | Kaggle Mug | 706 |
| 16 | 2015-01-01 | Sweden | KaggleRama | Kaggle Hat | 1195 |
| 17 | 2015-01-01 | Sweden | KaggleRama | Kaggle Sticker | 324 |
| 18 | 2015-01-02 | Finland | KaggleMart | Kaggle Mug | 318 |
| 19 | 2015-01-02 | Finland | KaggleMart | Kaggle Hat | 493 |
| 20 | 2015-01-02 | Finland | KaggleMart | Kaggle Sticker | 127 |
| 21 | 2015-01-02 | Finland | KaggleRama | Kaggle Mug | 544 |
| 22 | 2015-01-02 | Finland | KaggleRama | Kaggle Hat | 794 |
| 23 | 2015-01-02 | Finland | KaggleRama | Kaggle Sticker | 249 |
| 24 | 2015-01-02 | Norway | KaggleMart | Kaggle Mug | 538 |
| 25 | 2015-01-02 | Norway | KaggleMart | Kaggle Hat | 845 |
| 26 | 2015-01-02 | Norway | KaggleMart | Kaggle Sticker | 237 |
| 27 | 2015-01-02 | Norway | KaggleRama | Kaggle Mug | 949 |
| 28 | 2015-01-02 | Norway | KaggleRama | Kaggle Hat | 1446 |
| 29 | 2015-01-02 | Norway | KaggleRama | Kaggle Sticker | 415 |
| 30 | 2015-01-02 | Sweden | KaggleMart | Kaggle Mug | 384 |
| 31 | 2015-01-02 | Sweden | KaggleMart | Kaggle Hat | 572 |
| 32 | 2015-01-02 | Sweden | KaggleMart | Kaggle Sticker | 155 |
| 33 | 2015-01-02 | Sweden | KaggleRama | Kaggle Mug | 653 |
| 34 | 2015-01-02 | Sweden | KaggleRama | Kaggle Hat | 986 |
| 35 | 2015-01-02 | Sweden | KaggleRama | Kaggle Sticker | 293 |
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));
df_test.period.value_counts().sort_index().plot(figsize = (12,8));
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));
df_train.groupby(['country', 'store','product']).num_sold.agg(['min','max','mean'])
| min | max | mean | |||
|---|---|---|---|---|---|
| country | store | product | |||
| Finland | KaggleMart | Kaggle Hat | 210 | 1113 | 362.479808 |
| Kaggle Mug | 126 | 774 | 204.200548 | ||
| Kaggle Sticker | 70 | 326 | 103.044490 | ||
| KaggleRama | Kaggle Hat | 354 | 1895 | 628.926762 | |
| Kaggle Mug | 220 | 1398 | 356.110883 | ||
| Kaggle Sticker | 128 | 559 | 180.232033 | ||
| Norway | KaggleMart | Kaggle Hat | 335 | 1809 | 594.645448 |
| Kaggle Mug | 201 | 1113 | 334.370294 | ||
| Kaggle Sticker | 114 | 518 | 169.577687 | ||
| KaggleRama | Kaggle Hat | 596 | 2884 | 1036.357974 | |
| Kaggle Mug | 366 | 1935 | 584.297741 | ||
| Kaggle Sticker | 214 | 874 | 295.607803 | ||
| Sweden | KaggleMart | Kaggle Hat | 248 | 1207 | 419.214237 |
| Kaggle Mug | 149 | 730 | 235.885010 | ||
| Kaggle Sticker | 86 | 356 | 119.613279 | ||
| KaggleRama | Kaggle Hat | 428 | 2169 | 731.452430 | |
| Kaggle Mug | 253 | 1438 | 411.273101 | ||
| Kaggle Sticker | 148 | 637 | 208.314853 |
df_train.groupby(['country', 'store','product']).num_sold.mean().unstack(level = 'store').assign(ratio = lambda x: x.KaggleRama/x.KaggleMart)
| store | KaggleMart | KaggleRama | ratio | |
|---|---|---|---|---|
| country | product | |||
| Finland | Kaggle Hat | 362.479808 | 628.926762 | 1.735067 |
| Kaggle Mug | 204.200548 | 356.110883 | 1.743927 | |
| Kaggle Sticker | 103.044490 | 180.232033 | 1.749070 | |
| Norway | Kaggle Hat | 594.645448 | 1036.357974 | 1.742817 |
| Kaggle Mug | 334.370294 | 584.297741 | 1.747457 | |
| Kaggle Sticker | 169.577687 | 295.607803 | 1.743200 | |
| Sweden | Kaggle Hat | 419.214237 | 731.452430 | 1.744818 |
| Kaggle Mug | 235.885010 | 411.273101 | 1.743532 | |
| Kaggle Sticker | 119.613279 | 208.314853 | 1.741570 |
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()
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>
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();
sns.catplot(x = 'day_of_week', y = 'num_sold', data = df_train, kind = 'bar', ci = None, hue = 'country', row ='store', col = 'product');
df_train.groupby("day_of_month").num_sold.mean().plot();
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();
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();
sns.catplot(x = 'day_of_month', y = 'num_sold', data = df_train, kind = 'bar', ci = None, hue = 'country', row ='store', col = 'product');
df_train.groupby("day_of_year").num_sold.mean().plot();
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();
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();
Bigger Periods
df_train.groupby("month").num_sold.mean().plot();
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();
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();
df_train.groupby("quarter").num_sold.mean().plot();
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();
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();
df_train.groupby("year").num_sold.mean().plot();
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();
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();
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();
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
| date | country | store | product | num_sold | day_of_year | day_of_month | day_of_week | month | quarter | year | period | finland_holiday | norway_holiday | sweden_holiday | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2015-01-01 | Finland | KaggleMart | Kaggle Mug | 329 | 1 | 1 | 3 | 1 | 1 | 2015 | 2015-01 | Uudenvuodenpäivä | NaN | NaN |
| 1 | 2015-01-01 | Finland | KaggleMart | Kaggle Hat | 520 | 1 | 1 | 3 | 1 | 1 | 2015 | 2015-01 | Uudenvuodenpäivä | NaN | NaN |
| 2 | 2015-01-01 | Finland | KaggleMart | Kaggle Sticker | 146 | 1 | 1 | 3 | 1 | 1 | 2015 | 2015-01 | Uudenvuodenpäivä | NaN | NaN |
| 3 | 2015-01-01 | Finland | KaggleRama | Kaggle Mug | 572 | 1 | 1 | 3 | 1 | 1 | 2015 | 2015-01 | Uudenvuodenpäivä | NaN | NaN |
| 4 | 2015-01-01 | Finland | KaggleRama | Kaggle Hat | 911 | 1 | 1 | 3 | 1 | 1 | 2015 | 2015-01 | Uudenvuodenpäivä | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 26293 | 2018-12-31 | Sweden | KaggleMart | Kaggle Hat | 823 | 365 | 31 | 0 | 12 | 4 | 2018 | 2018-12 | NaN | NaN | Nyårsafton |
| 26294 | 2018-12-31 | Sweden | KaggleMart | Kaggle Sticker | 250 | 365 | 31 | 0 | 12 | 4 | 2018 | 2018-12 | NaN | NaN | Nyårsafton |
| 26295 | 2018-12-31 | Sweden | KaggleRama | Kaggle Mug | 1004 | 365 | 31 | 0 | 12 | 4 | 2018 | 2018-12 | NaN | NaN | Nyårsafton |
| 26296 | 2018-12-31 | Sweden | KaggleRama | Kaggle Hat | 1441 | 365 | 31 | 0 | 12 | 4 | 2018 | 2018-12 | NaN | NaN | Nyårsafton |
| 26297 | 2018-12-31 | Sweden | KaggleRama | Kaggle Sticker | 388 | 365 | 31 | 0 | 12 | 4 | 2018 | 2018-12 | NaN | NaN | Nyårsafton |
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}')
\[f(t) \sim \frac{a_o}{2} + \sum{}_{n=1}^{\infty}\left[a_n cos\left(\frac{2n\pi}{T}t\right) + b_n sin\left(\frac{2n\pi}{T}t\right)\right]\]
\(var\_sin = sin\left(\frac{2\pi}{max\_value}t\right)\) \(var\_cos = cos\left(\frac{2\pi}{max\_value}t\right)\)
\(var\_sin = sin\left(\frac{2n\pi}{max\_value}t\right) = sin\left(\frac{2\pi}{\frac{max\_value}{n}}t\right)\) \(var\_cos = cos\left(\frac{2n\pi}{max\_value}t\right) = cos\left(\frac{2\pi}{\frac{max\_value}{n}}t\right)\)
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));
ax = fourier_variables.plot.scatter('sin', 'cos', figsize = (16,12)).set_aspect('equal')
(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();
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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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
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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
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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
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bestTest = 37.24779332
bestIteration = 2541
Shrink model to first 2542 iterations.
Test: 8.329192748273288