# ts2ml
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
## Install
``` sh
pip install ts2ml
```
## How to use
``` python
import pandas as pd
from ts2ml.core import add_missing_slots
from ts2ml.core import transform_ts_data_into_features_and_target
```
``` python
df = pd.DataFrame({
'pickup_hour': ['2022-01-01 00:00:00', '2022-01-01 01:00:00', '2022-01-01 03:00:00', '2022-01-01 01:00:00', '2022-01-01 02:00:00', '2022-01-01 05:00:00'],
'pickup_location_id': [1, 1, 1, 2, 2, 2],
'rides': [2, 3, 1, 1, 2, 1]
})
df
```
<div>
<style scoped>
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</style>
| | pickup_hour | pickup_location_id | rides |
|-----|---------------------|--------------------|-------|
| 0 | 2022-01-01 00:00:00 | 1 | 2 |
| 1 | 2022-01-01 01:00:00 | 1 | 3 |
| 2 | 2022-01-01 03:00:00 | 1 | 1 |
| 3 | 2022-01-01 01:00:00 | 2 | 1 |
| 4 | 2022-01-01 02:00:00 | 2 | 2 |
| 5 | 2022-01-01 05:00:00 | 2 | 1 |
</div>
Let’s fill the missing slots with zeros
``` python
df = add_missing_slots(df, datetime_col='pickup_hour', entity_col='pickup_location_id', value_col='rides', freq='H')
df
```
100%|██████████| 2/2 [00:00<00:00, 907.86it/s]
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| | pickup_hour | pickup_location_id | rides |
|-----|---------------------|--------------------|-------|
| 0 | 2022-01-01 00:00:00 | 1 | 2 |
| 1 | 2022-01-01 01:00:00 | 1 | 3 |
| 2 | 2022-01-01 02:00:00 | 1 | 0 |
| 3 | 2022-01-01 03:00:00 | 1 | 1 |
| 4 | 2022-01-01 04:00:00 | 1 | 0 |
| 5 | 2022-01-01 05:00:00 | 1 | 0 |
| 6 | 2022-01-01 00:00:00 | 2 | 0 |
| 7 | 2022-01-01 01:00:00 | 2 | 1 |
| 8 | 2022-01-01 02:00:00 | 2 | 2 |
| 9 | 2022-01-01 03:00:00 | 2 | 0 |
| 10 | 2022-01-01 04:00:00 | 2 | 0 |
| 11 | 2022-01-01 05:00:00 | 2 | 1 |
</div>
Now, let’s build features and targets to predict the number of rides for
the next hour for each location_id, by using the historical number of
rides for the last 3 hours
``` python
features, targets = transform_ts_data_into_features_and_target(
df,
n_features=3,
datetime_col='pickup_hour',
entity_col='pickup_location_id',
value_col='rides',
n_targets=1,
step_size=1,
step_name='hour'
)
```
100%|██████████| 2/2 [00:00<00:00, 597.86it/s]
``` python
features
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
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}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| | rides_previous_3_hour | rides_previous_2_hour | rides_previous_1_hour | pickup_hour | pickup_location_id |
|-----|-----------------------|-----------------------|-----------------------|---------------------|--------------------|
| 0 | 2.0 | 3.0 | 0.0 | 2022-01-01 03:00:00 | 1 |
| 1 | 3.0 | 0.0 | 1.0 | 2022-01-01 04:00:00 | 1 |
| 2 | 0.0 | 1.0 | 2.0 | 2022-01-01 03:00:00 | 2 |
| 3 | 1.0 | 2.0 | 0.0 | 2022-01-01 04:00:00 | 2 |
</div>
``` python
targets
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| | target_rides_next_hour |
|-----|------------------------|
| 0 | 1.0 |
| 1 | 0.0 |
| 2 | 0.0 |
| 3 | 0.0 |
</div>
``` python
Xy_df = pd.concat([features, targets], axis=1)
Xy_df
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| | rides_previous_3_hour | rides_previous_2_hour | rides_previous_1_hour | pickup_hour | pickup_location_id | target_rides_next_hour |
|-----|-----------------------|-----------------------|-----------------------|---------------------|--------------------|------------------------|
| 0 | 2.0 | 3.0 | 0.0 | 2022-01-01 03:00:00 | 1 | 1.0 |
| 1 | 3.0 | 0.0 | 1.0 | 2022-01-01 04:00:00 | 1 | 0.0 |
| 2 | 0.0 | 1.0 | 2.0 | 2022-01-01 03:00:00 | 2 | 0.0 |
| 3 | 1.0 | 2.0 | 0.0 | 2022-01-01 04:00:00 | 2 | 0.0 |
</div>
# Another Example
Montly spaced time series
``` python
import pandas as pd
import numpy as np
# Generate timestamp index with monthly frequency
date_rng = pd.date_range(start='1/1/2020', end='12/1/2022', freq='MS')
# Create list of city codes
cities = ['FOR', 'SP', 'RJ']
# Create dataframe with random sales data for each city on each month
df = pd.DataFrame({
'date': date_rng,
'city': np.repeat(cities, len(date_rng)//len(cities)),
'sales': np.random.randint(1000, 5000, size=len(date_rng))
})
df
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| | date | city | sales |
|-----|------------|------|-------|
| 0 | 2020-01-01 | FOR | 4944 |
| 1 | 2020-02-01 | FOR | 3435 |
| 2 | 2020-03-01 | FOR | 4543 |
| 3 | 2020-04-01 | FOR | 3879 |
| 4 | 2020-05-01 | FOR | 2601 |
| 5 | 2020-06-01 | FOR | 2922 |
| 6 | 2020-07-01 | FOR | 4542 |
| 7 | 2020-08-01 | FOR | 1338 |
| 8 | 2020-09-01 | FOR | 2938 |
| 9 | 2020-10-01 | FOR | 2695 |
| 10 | 2020-11-01 | FOR | 4065 |
| 11 | 2020-12-01 | FOR | 3864 |
| 12 | 2021-01-01 | SP | 2652 |
| 13 | 2021-02-01 | SP | 2137 |
| 14 | 2021-03-01 | SP | 2663 |
| 15 | 2021-04-01 | SP | 1168 |
| 16 | 2021-05-01 | SP | 4523 |
| 17 | 2021-06-01 | SP | 4135 |
| 18 | 2021-07-01 | SP | 3566 |
| 19 | 2021-08-01 | SP | 2121 |
| 20 | 2021-09-01 | SP | 1070 |
| 21 | 2021-10-01 | SP | 1624 |
| 22 | 2021-11-01 | SP | 3034 |
| 23 | 2021-12-01 | SP | 4063 |
| 24 | 2022-01-01 | RJ | 2297 |
| 25 | 2022-02-01 | RJ | 3430 |
| 26 | 2022-03-01 | RJ | 2903 |
| 27 | 2022-04-01 | RJ | 4197 |
| 28 | 2022-05-01 | RJ | 4141 |
| 29 | 2022-06-01 | RJ | 2899 |
| 30 | 2022-07-01 | RJ | 4529 |
| 31 | 2022-08-01 | RJ | 3612 |
| 32 | 2022-09-01 | RJ | 1856 |
| 33 | 2022-10-01 | RJ | 4804 |
| 34 | 2022-11-01 | RJ | 1764 |
| 35 | 2022-12-01 | RJ | 4425 |
</div>
FOR city only have data for 2020 year, RJ only for 2022 and SP only for
2021. Let’s also simulate more missing slots between the years.
``` python
# Generate random indices to drop
drop_indices = np.random.choice(df.index, size=int(len(df)*0.2), replace=False)
# Drop selected rows from dataframe
df = df.drop(drop_indices)
df.reset_index(drop=True, inplace=True)
df
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| | date | city | sales |
|-----|------------|------|-------|
| 0 | 2020-01-01 | FOR | 4944 |
| 1 | 2020-02-01 | FOR | 3435 |
| 2 | 2020-03-01 | FOR | 4543 |
| 3 | 2020-04-01 | FOR | 3879 |
| 4 | 2020-05-01 | FOR | 2601 |
| 5 | 2020-06-01 | FOR | 2922 |
| 6 | 2020-07-01 | FOR | 4542 |
| 7 | 2020-08-01 | FOR | 1338 |
| 8 | 2020-09-01 | FOR | 2938 |
| 9 | 2020-11-01 | FOR | 4065 |
| 10 | 2020-12-01 | FOR | 3864 |
| 11 | 2021-01-01 | SP | 2652 |
| 12 | 2021-02-01 | SP | 2137 |
| 13 | 2021-03-01 | SP | 2663 |
| 14 | 2021-07-01 | SP | 3566 |
| 15 | 2021-08-01 | SP | 2121 |
| 16 | 2021-10-01 | SP | 1624 |
| 17 | 2021-11-01 | SP | 3034 |
| 18 | 2021-12-01 | SP | 4063 |
| 19 | 2022-01-01 | RJ | 2297 |
| 20 | 2022-02-01 | RJ | 3430 |
| 21 | 2022-03-01 | RJ | 2903 |
| 22 | 2022-04-01 | RJ | 4197 |
| 23 | 2022-05-01 | RJ | 4141 |
| 24 | 2022-06-01 | RJ | 2899 |
| 25 | 2022-09-01 | RJ | 1856 |
| 26 | 2022-10-01 | RJ | 4804 |
| 27 | 2022-11-01 | RJ | 1764 |
| 28 | 2022-12-01 | RJ | 4425 |
</div>
Now lets fill the missing slots with zero values. The function will
complete the missing slots with zeros:
``` python
df_full = add_missing_slots(df, datetime_col='date', entity_col='city', value_col='sales', freq='MS')
df_full
```
100%|██████████| 3/3 [00:00<00:00, 843.70it/s]
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| | date | city | sales |
|-----|------------|------|-------|
| 0 | 2020-01-01 | FOR | 4944 |
| 1 | 2020-02-01 | FOR | 3435 |
| 2 | 2020-03-01 | FOR | 4543 |
| 3 | 2020-04-01 | FOR | 3879 |
| 4 | 2020-05-01 | FOR | 2601 |
| ... | ... | ... | ... |
| 103 | 2022-08-01 | RJ | 0 |
| 104 | 2022-09-01 | RJ | 1856 |
| 105 | 2022-10-01 | RJ | 4804 |
| 106 | 2022-11-01 | RJ | 1764 |
| 107 | 2022-12-01 | RJ | 4425 |
<p>108 rows × 3 columns</p>
</div>
Let’s build a dataset for training a machine learning model to predict
the sales for the next 3 months, for each city, based on historical data
of sales for the previous 6 months.
``` python
features, targets = transform_ts_data_into_features_and_target(
df_full,
n_features=3,
datetime_col='date',
entity_col='city',
value_col='sales',
n_targets=1,
step_size=1,
step_name='month'
)
```
100%|██████████| 3/3 [00:00<00:00, 205.58it/s]
``` python
pd.concat([features, targets], axis=1)
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| | sales_previous_3_month | sales_previous_2_month | sales_previous_1_month | date | city | target_sales_next_month |
|-----|------------------------|------------------------|------------------------|------------|------|-------------------------|
| 0 | 4944.0 | 3435.0 | 4543.0 | 2020-04-01 | FOR | 3879.0 |
| 1 | 3435.0 | 4543.0 | 3879.0 | 2020-05-01 | FOR | 2601.0 |
| 2 | 4543.0 | 3879.0 | 2601.0 | 2020-06-01 | FOR | 2922.0 |
| 3 | 3879.0 | 2601.0 | 2922.0 | 2020-07-01 | FOR | 4542.0 |
| 4 | 2601.0 | 2922.0 | 4542.0 | 2020-08-01 | FOR | 1338.0 |
| ... | ... | ... | ... | ... | ... | ... |
| 91 | 4197.0 | 4141.0 | 2899.0 | 2022-07-01 | RJ | 0.0 |
| 92 | 4141.0 | 2899.0 | 0.0 | 2022-08-01 | RJ | 0.0 |
| 93 | 2899.0 | 0.0 | 0.0 | 2022-09-01 | RJ | 1856.0 |
| 94 | 0.0 | 0.0 | 1856.0 | 2022-10-01 | RJ | 4804.0 |
| 95 | 0.0 | 1856.0 | 4804.0 | 2022-11-01 | RJ | 1764.0 |
<p>96 rows × 6 columns</p>
</div>
# Embedding on Sklearn Pipelines
``` python
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer
```
``` python
add_missing_slots_transformer = FunctionTransformer(
add_missing_slots,
kw_args={
'datetime_col': 'date',
'entity_col': 'city',
'value_col': 'sales',
'freq': 'MS'
}
)
transform_ts_data_into_features_and_target_transformer = FunctionTransformer(
transform_ts_data_into_features_and_target,
kw_args={
'n_features': 3,
'datetime_col': 'date',
'entity_col': 'city',
'value_col': 'sales',
'n_targets': 1,
'step_size': 1,
'step_name': 'month',
'concat_Xy': True
}
)
```
``` python
ts_data_to_features_and_target_pipeline = make_pipeline(
add_missing_slots_transformer,
transform_ts_data_into_features_and_target_transformer
)
ts_data_to_features_and_target_pipeline
```
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FunctionTransformer(func=<function add_missing_slots at 0x11f8f49d0>,
kw_args={'datetime_col': 'date',
'entity_col': 'city',
'freq': 'MS',
'value_col': 'sales'})),
('functiontransformer-2',
FunctionTransformer(func=<function transform_ts_data_into_features_and_target at 0x11f925ca0>,
kw_args={'concat_Xy': True,
'datetime_col': 'date',
'entity_col': 'city',
'n_features': 3, 'n_targets': 1,
'step_name': 'month',
'step_size': 1,
'value_col': 'sales'}))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('functiontransformer-1',
FunctionTransformer(func=<function add_missing_slots at 0x11f8f49d0>,
kw_args={'datetime_col': 'date',
'entity_col': 'city',
'freq': 'MS',
'value_col': 'sales'})),
('functiontransformer-2',
FunctionTransformer(func=<function transform_ts_data_into_features_and_target at 0x11f925ca0>,
kw_args={'concat_Xy': True,
'datetime_col': 'date',
'entity_col': 'city',
'n_features': 3, 'n_targets': 1,
'step_name': 'month',
'step_size': 1,
'value_col': 'sales'}))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">FunctionTransformer</label><div class="sk-toggleable__content"><pre>FunctionTransformer(func=<function add_missing_slots at 0x11f8f49d0>,
kw_args={'datetime_col': 'date', 'entity_col': 'city',
'freq': 'MS', 'value_col': 'sales'})</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label sk-toggleable__label-arrow">FunctionTransformer</label><div class="sk-toggleable__content"><pre>FunctionTransformer(func=<function transform_ts_data_into_features_and_target at 0x11f925ca0>,
kw_args={'concat_Xy': True, 'datetime_col': 'date',
'entity_col': 'city', 'n_features': 3,
'n_targets': 1, 'step_name': 'month',
'step_size': 1, 'value_col': 'sales'})</pre></div></div></div></div></div></div></div>
``` python
Xy_df = ts_data_to_features_and_target_pipeline.fit_transform(df)
Xy_df
```
100%|██████████| 3/3 [00:00<00:00, 715.47it/s]
100%|██████████| 3/3 [00:00<00:00, 184.12it/s]
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
| | sales_previous_3_month | sales_previous_2_month | sales_previous_1_month | date | city | target_sales_next_month |
|-----|------------------------|------------------------|------------------------|------------|------|-------------------------|
| 0 | 4944.0 | 3435.0 | 4543.0 | 2020-04-01 | FOR | 3879.0 |
| 1 | 3435.0 | 4543.0 | 3879.0 | 2020-05-01 | FOR | 2601.0 |
| 2 | 4543.0 | 3879.0 | 2601.0 | 2020-06-01 | FOR | 2922.0 |
| 3 | 3879.0 | 2601.0 | 2922.0 | 2020-07-01 | FOR | 4542.0 |
| 4 | 2601.0 | 2922.0 | 4542.0 | 2020-08-01 | FOR | 1338.0 |
| ... | ... | ... | ... | ... | ... | ... |
| 91 | 4197.0 | 4141.0 | 2899.0 | 2022-07-01 | RJ | 0.0 |
| 92 | 4141.0 | 2899.0 | 0.0 | 2022-08-01 | RJ | 0.0 |
| 93 | 2899.0 | 0.0 | 0.0 | 2022-09-01 | RJ | 1856.0 |
| 94 | 0.0 | 0.0 | 1856.0 | 2022-10-01 | RJ | 4804.0 |
| 95 | 0.0 | 1856.0 | 4804.0 | 2022-11-01 | RJ | 1764.0 |
<p>96 rows × 6 columns</p>
</div>
Raw data
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"author": "Jo\u00e3o Nogueira",
"author_email": "joaopcnogueira@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/56/c0/46aa04962b31d0eb5282df70a68fbde9a29a3b5f9bf893ddf9d96f3c75a2/ts2ml-1.0.1.tar.gz",
"platform": null,
"description": "# ts2ml\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n## Install\n\n``` sh\npip install ts2ml\n```\n\n## How to use\n\n``` python\nimport pandas as pd\nfrom ts2ml.core import add_missing_slots\nfrom ts2ml.core import transform_ts_data_into_features_and_target\n```\n\n``` python\ndf = pd.DataFrame({\n 'pickup_hour': ['2022-01-01 00:00:00', '2022-01-01 01:00:00', '2022-01-01 03:00:00', '2022-01-01 01:00:00', '2022-01-01 02:00:00', '2022-01-01 05:00:00'],\n 'pickup_location_id': [1, 1, 1, 2, 2, 2],\n 'rides': [2, 3, 1, 1, 2, 1]\n})\ndf\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | pickup_hour | pickup_location_id | rides |\n|-----|---------------------|--------------------|-------|\n| 0 | 2022-01-01 00:00:00 | 1 | 2 |\n| 1 | 2022-01-01 01:00:00 | 1 | 3 |\n| 2 | 2022-01-01 03:00:00 | 1 | 1 |\n| 3 | 2022-01-01 01:00:00 | 2 | 1 |\n| 4 | 2022-01-01 02:00:00 | 2 | 2 |\n| 5 | 2022-01-01 05:00:00 | 2 | 1 |\n\n</div>\n\nLet\u2019s fill the missing slots with zeros\n\n``` python\ndf = add_missing_slots(df, datetime_col='pickup_hour', entity_col='pickup_location_id', value_col='rides', freq='H')\ndf\n```\n\n 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:00<00:00, 907.86it/s]\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | pickup_hour | pickup_location_id | rides |\n|-----|---------------------|--------------------|-------|\n| 0 | 2022-01-01 00:00:00 | 1 | 2 |\n| 1 | 2022-01-01 01:00:00 | 1 | 3 |\n| 2 | 2022-01-01 02:00:00 | 1 | 0 |\n| 3 | 2022-01-01 03:00:00 | 1 | 1 |\n| 4 | 2022-01-01 04:00:00 | 1 | 0 |\n| 5 | 2022-01-01 05:00:00 | 1 | 0 |\n| 6 | 2022-01-01 00:00:00 | 2 | 0 |\n| 7 | 2022-01-01 01:00:00 | 2 | 1 |\n| 8 | 2022-01-01 02:00:00 | 2 | 2 |\n| 9 | 2022-01-01 03:00:00 | 2 | 0 |\n| 10 | 2022-01-01 04:00:00 | 2 | 0 |\n| 11 | 2022-01-01 05:00:00 | 2 | 1 |\n\n</div>\n\nNow, let\u2019s build features and targets to predict the number of rides for\nthe next hour for each location_id, by using the historical number of\nrides for the last 3 hours\n\n``` python\nfeatures, targets = transform_ts_data_into_features_and_target(\n df,\n n_features=3,\n datetime_col='pickup_hour', \n entity_col='pickup_location_id', \n value_col='rides',\n n_targets=1,\n step_size=1,\n step_name='hour'\n)\n```\n\n 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:00<00:00, 597.86it/s]\n\n``` python\nfeatures\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | rides_previous_3_hour | rides_previous_2_hour | rides_previous_1_hour | pickup_hour | pickup_location_id |\n|-----|-----------------------|-----------------------|-----------------------|---------------------|--------------------|\n| 0 | 2.0 | 3.0 | 0.0 | 2022-01-01 03:00:00 | 1 |\n| 1 | 3.0 | 0.0 | 1.0 | 2022-01-01 04:00:00 | 1 |\n| 2 | 0.0 | 1.0 | 2.0 | 2022-01-01 03:00:00 | 2 |\n| 3 | 1.0 | 2.0 | 0.0 | 2022-01-01 04:00:00 | 2 |\n\n</div>\n\n``` python\ntargets\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | target_rides_next_hour |\n|-----|------------------------|\n| 0 | 1.0 |\n| 1 | 0.0 |\n| 2 | 0.0 |\n| 3 | 0.0 |\n\n</div>\n\n``` python\nXy_df = pd.concat([features, targets], axis=1)\nXy_df\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | rides_previous_3_hour | rides_previous_2_hour | rides_previous_1_hour | pickup_hour | pickup_location_id | target_rides_next_hour |\n|-----|-----------------------|-----------------------|-----------------------|---------------------|--------------------|------------------------|\n| 0 | 2.0 | 3.0 | 0.0 | 2022-01-01 03:00:00 | 1 | 1.0 |\n| 1 | 3.0 | 0.0 | 1.0 | 2022-01-01 04:00:00 | 1 | 0.0 |\n| 2 | 0.0 | 1.0 | 2.0 | 2022-01-01 03:00:00 | 2 | 0.0 |\n| 3 | 1.0 | 2.0 | 0.0 | 2022-01-01 04:00:00 | 2 | 0.0 |\n\n</div>\n\n# Another Example\n\nMontly spaced time series\n\n``` python\nimport pandas as pd\nimport numpy as np\n\n# Generate timestamp index with monthly frequency\ndate_rng = pd.date_range(start='1/1/2020', end='12/1/2022', freq='MS')\n\n# Create list of city codes\ncities = ['FOR', 'SP', 'RJ']\n\n# Create dataframe with random sales data for each city on each month\ndf = pd.DataFrame({\n 'date': date_rng,\n 'city': np.repeat(cities, len(date_rng)//len(cities)),\n 'sales': np.random.randint(1000, 5000, size=len(date_rng))\n})\ndf\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | date | city | sales |\n|-----|------------|------|-------|\n| 0 | 2020-01-01 | FOR | 4944 |\n| 1 | 2020-02-01 | FOR | 3435 |\n| 2 | 2020-03-01 | FOR | 4543 |\n| 3 | 2020-04-01 | FOR | 3879 |\n| 4 | 2020-05-01 | FOR | 2601 |\n| 5 | 2020-06-01 | FOR | 2922 |\n| 6 | 2020-07-01 | FOR | 4542 |\n| 7 | 2020-08-01 | FOR | 1338 |\n| 8 | 2020-09-01 | FOR | 2938 |\n| 9 | 2020-10-01 | FOR | 2695 |\n| 10 | 2020-11-01 | FOR | 4065 |\n| 11 | 2020-12-01 | FOR | 3864 |\n| 12 | 2021-01-01 | SP | 2652 |\n| 13 | 2021-02-01 | SP | 2137 |\n| 14 | 2021-03-01 | SP | 2663 |\n| 15 | 2021-04-01 | SP | 1168 |\n| 16 | 2021-05-01 | SP | 4523 |\n| 17 | 2021-06-01 | SP | 4135 |\n| 18 | 2021-07-01 | SP | 3566 |\n| 19 | 2021-08-01 | SP | 2121 |\n| 20 | 2021-09-01 | SP | 1070 |\n| 21 | 2021-10-01 | SP | 1624 |\n| 22 | 2021-11-01 | SP | 3034 |\n| 23 | 2021-12-01 | SP | 4063 |\n| 24 | 2022-01-01 | RJ | 2297 |\n| 25 | 2022-02-01 | RJ | 3430 |\n| 26 | 2022-03-01 | RJ | 2903 |\n| 27 | 2022-04-01 | RJ | 4197 |\n| 28 | 2022-05-01 | RJ | 4141 |\n| 29 | 2022-06-01 | RJ | 2899 |\n| 30 | 2022-07-01 | RJ | 4529 |\n| 31 | 2022-08-01 | RJ | 3612 |\n| 32 | 2022-09-01 | RJ | 1856 |\n| 33 | 2022-10-01 | RJ | 4804 |\n| 34 | 2022-11-01 | RJ | 1764 |\n| 35 | 2022-12-01 | RJ | 4425 |\n\n</div>\n\nFOR city only have data for 2020 year, RJ only for 2022 and SP only for\n2021. Let\u2019s also simulate more missing slots between the years.\n\n``` python\n# Generate random indices to drop\ndrop_indices = np.random.choice(df.index, size=int(len(df)*0.2), replace=False)\n\n# Drop selected rows from dataframe\ndf = df.drop(drop_indices)\ndf.reset_index(drop=True, inplace=True)\ndf\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | date | city | sales |\n|-----|------------|------|-------|\n| 0 | 2020-01-01 | FOR | 4944 |\n| 1 | 2020-02-01 | FOR | 3435 |\n| 2 | 2020-03-01 | FOR | 4543 |\n| 3 | 2020-04-01 | FOR | 3879 |\n| 4 | 2020-05-01 | FOR | 2601 |\n| 5 | 2020-06-01 | FOR | 2922 |\n| 6 | 2020-07-01 | FOR | 4542 |\n| 7 | 2020-08-01 | FOR | 1338 |\n| 8 | 2020-09-01 | FOR | 2938 |\n| 9 | 2020-11-01 | FOR | 4065 |\n| 10 | 2020-12-01 | FOR | 3864 |\n| 11 | 2021-01-01 | SP | 2652 |\n| 12 | 2021-02-01 | SP | 2137 |\n| 13 | 2021-03-01 | SP | 2663 |\n| 14 | 2021-07-01 | SP | 3566 |\n| 15 | 2021-08-01 | SP | 2121 |\n| 16 | 2021-10-01 | SP | 1624 |\n| 17 | 2021-11-01 | SP | 3034 |\n| 18 | 2021-12-01 | SP | 4063 |\n| 19 | 2022-01-01 | RJ | 2297 |\n| 20 | 2022-02-01 | RJ | 3430 |\n| 21 | 2022-03-01 | RJ | 2903 |\n| 22 | 2022-04-01 | RJ | 4197 |\n| 23 | 2022-05-01 | RJ | 4141 |\n| 24 | 2022-06-01 | RJ | 2899 |\n| 25 | 2022-09-01 | RJ | 1856 |\n| 26 | 2022-10-01 | RJ | 4804 |\n| 27 | 2022-11-01 | RJ | 1764 |\n| 28 | 2022-12-01 | RJ | 4425 |\n\n</div>\n\nNow lets fill the missing slots with zero values. The function will\ncomplete the missing slots with zeros:\n\n``` python\ndf_full = add_missing_slots(df, datetime_col='date', entity_col='city', value_col='sales', freq='MS')\ndf_full\n```\n\n 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3/3 [00:00<00:00, 843.70it/s]\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | date | city | sales |\n|-----|------------|------|-------|\n| 0 | 2020-01-01 | FOR | 4944 |\n| 1 | 2020-02-01 | FOR | 3435 |\n| 2 | 2020-03-01 | FOR | 4543 |\n| 3 | 2020-04-01 | FOR | 3879 |\n| 4 | 2020-05-01 | FOR | 2601 |\n| ... | ... | ... | ... |\n| 103 | 2022-08-01 | RJ | 0 |\n| 104 | 2022-09-01 | RJ | 1856 |\n| 105 | 2022-10-01 | RJ | 4804 |\n| 106 | 2022-11-01 | RJ | 1764 |\n| 107 | 2022-12-01 | RJ | 4425 |\n\n<p>108 rows \u00d7 3 columns</p>\n</div>\n\nLet\u2019s build a dataset for training a machine learning model to predict\nthe sales for the next 3 months, for each city, based on historical data\nof sales for the previous 6 months.\n\n``` python\nfeatures, targets = transform_ts_data_into_features_and_target(\n df_full,\n n_features=3,\n datetime_col='date',\n entity_col='city',\n value_col='sales',\n n_targets=1,\n step_size=1,\n step_name='month'\n)\n```\n\n 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3/3 [00:00<00:00, 205.58it/s]\n\n``` python\npd.concat([features, targets], axis=1)\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | sales_previous_3_month | sales_previous_2_month | sales_previous_1_month | date | city | target_sales_next_month |\n|-----|------------------------|------------------------|------------------------|------------|------|-------------------------|\n| 0 | 4944.0 | 3435.0 | 4543.0 | 2020-04-01 | FOR | 3879.0 |\n| 1 | 3435.0 | 4543.0 | 3879.0 | 2020-05-01 | FOR | 2601.0 |\n| 2 | 4543.0 | 3879.0 | 2601.0 | 2020-06-01 | FOR | 2922.0 |\n| 3 | 3879.0 | 2601.0 | 2922.0 | 2020-07-01 | FOR | 4542.0 |\n| 4 | 2601.0 | 2922.0 | 4542.0 | 2020-08-01 | FOR | 1338.0 |\n| ... | ... | ... | ... | ... | ... | ... |\n| 91 | 4197.0 | 4141.0 | 2899.0 | 2022-07-01 | RJ | 0.0 |\n| 92 | 4141.0 | 2899.0 | 0.0 | 2022-08-01 | RJ | 0.0 |\n| 93 | 2899.0 | 0.0 | 0.0 | 2022-09-01 | RJ | 1856.0 |\n| 94 | 0.0 | 0.0 | 1856.0 | 2022-10-01 | RJ | 4804.0 |\n| 95 | 0.0 | 1856.0 | 4804.0 | 2022-11-01 | RJ | 1764.0 |\n\n<p>96 rows \u00d7 6 columns</p>\n</div>\n\n# Embedding on Sklearn Pipelines\n\n``` python\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import FunctionTransformer\n```\n\n``` python\nadd_missing_slots_transformer = FunctionTransformer(\n add_missing_slots, \n kw_args={\n 'datetime_col': 'date', \n 'entity_col': 'city', \n 'value_col': 'sales', \n 'freq': 'MS'\n }\n)\n\ntransform_ts_data_into_features_and_target_transformer = FunctionTransformer(\n transform_ts_data_into_features_and_target, \n kw_args={\n 'n_features': 3, \n 'datetime_col': 'date', \n 'entity_col': 'city', \n 'value_col': 'sales', \n 'n_targets': 1, \n 'step_size': 1, \n 'step_name': 'month',\n 'concat_Xy': True\n }\n)\n```\n\n``` python\nts_data_to_features_and_target_pipeline = make_pipeline(\n add_missing_slots_transformer,\n transform_ts_data_into_features_and_target_transformer\n)\nts_data_to_features_and_target_pipeline\n```\n\n<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 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div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[('functiontransformer-1',\n FunctionTransformer(func=<function add_missing_slots at 0x11f8f49d0>,\n kw_args={'datetime_col': 'date',\n 'entity_col': 'city',\n 'freq': 'MS',\n 'value_col': 'sales'})),\n ('functiontransformer-2',\n FunctionTransformer(func=<function transform_ts_data_into_features_and_target at 0x11f925ca0>,\n kw_args={'concat_Xy': True,\n 'datetime_col': 'date',\n 'entity_col': 'city',\n 'n_features': 3, 'n_targets': 1,\n 'step_name': 'month',\n 'step_size': 1,\n 'value_col': 'sales'}))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('functiontransformer-1',\n FunctionTransformer(func=<function add_missing_slots at 0x11f8f49d0>,\n kw_args={'datetime_col': 'date',\n 'entity_col': 'city',\n 'freq': 'MS',\n 'value_col': 'sales'})),\n ('functiontransformer-2',\n FunctionTransformer(func=<function transform_ts_data_into_features_and_target at 0x11f925ca0>,\n kw_args={'concat_Xy': True,\n 'datetime_col': 'date',\n 'entity_col': 'city',\n 'n_features': 3, 'n_targets': 1,\n 'step_name': 'month',\n 'step_size': 1,\n 'value_col': 'sales'}))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=<function add_missing_slots at 0x11f8f49d0>,\n kw_args={'datetime_col': 'date', 'entity_col': 'city',\n 'freq': 'MS', 'value_col': 'sales'})</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=<function transform_ts_data_into_features_and_target at 0x11f925ca0>,\n kw_args={'concat_Xy': True, 'datetime_col': 'date',\n 'entity_col': 'city', 'n_features': 3,\n 'n_targets': 1, 'step_name': 'month',\n 'step_size': 1, 'value_col': 'sales'})</pre></div></div></div></div></div></div></div>\n\n``` python\nXy_df = ts_data_to_features_and_target_pipeline.fit_transform(df)\nXy_df\n```\n\n 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3/3 [00:00<00:00, 715.47it/s]\n 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3/3 [00:00<00:00, 184.12it/s]\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | sales_previous_3_month | sales_previous_2_month | sales_previous_1_month | date | city | target_sales_next_month |\n|-----|------------------------|------------------------|------------------------|------------|------|-------------------------|\n| 0 | 4944.0 | 3435.0 | 4543.0 | 2020-04-01 | FOR | 3879.0 |\n| 1 | 3435.0 | 4543.0 | 3879.0 | 2020-05-01 | FOR | 2601.0 |\n| 2 | 4543.0 | 3879.0 | 2601.0 | 2020-06-01 | FOR | 2922.0 |\n| 3 | 3879.0 | 2601.0 | 2922.0 | 2020-07-01 | FOR | 4542.0 |\n| 4 | 2601.0 | 2922.0 | 4542.0 | 2020-08-01 | FOR | 1338.0 |\n| ... | ... | ... | ... | ... | ... | ... |\n| 91 | 4197.0 | 4141.0 | 2899.0 | 2022-07-01 | RJ | 0.0 |\n| 92 | 4141.0 | 2899.0 | 0.0 | 2022-08-01 | RJ | 0.0 |\n| 93 | 2899.0 | 0.0 | 0.0 | 2022-09-01 | RJ | 1856.0 |\n| 94 | 0.0 | 0.0 | 1856.0 | 2022-10-01 | RJ | 4804.0 |\n| 95 | 0.0 | 1856.0 | 4804.0 | 2022-11-01 | RJ | 1764.0 |\n\n<p>96 rows \u00d7 6 columns</p>\n</div>\n\n\n",
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