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https://github.com/Garve/mamimo
A package to compute a marketing mix model.
https://github.com/Garve/mamimo
Last synced: 2 months ago
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A package to compute a marketing mix model.
- Host: GitHub
- URL: https://github.com/Garve/mamimo
- Owner: Garve
- Created: 2022-05-14T22:17:13.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-30T08:44:04.000Z (over 1 year ago)
- Last Synced: 2024-10-29T15:31:49.301Z (3 months ago)
- Language: Python
- Size: 170 KB
- Stars: 70
- Watchers: 10
- Forks: 12
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-marketing-machine-learning - mamimo
README
# MaMiMo
This is a small library that helps you with your everyday **Ma**rketing **Mi**x **Mo**delling. It contains a few saturation functions, carryovers and some utilities for creating with time features. You can also read my article about it here: [>>>Click<<<](https://towardsdatascience.com/a-small-python-library-for-marketing-mix-modeling-mamimo-100f31666e18).
Give it a try via `pip install mamimo`!
# Small Example
You can create a marketing mix model using different components from MaMiMo as well as [scikit-learn](https://scikit-learn.org/stable/). First, we can create a dataset via
```python
from mamimo.datasets import load_fake_mmm
data = load_fake_mmm()
X = data.drop(columns=['Sales'])
y = data['Sales']
```
`X` contains media spends only now, but you can enrich it with more information.
## Feature Engineering
MaMiMo lets you add time features, for example, via
```python
from mamimo.time_utils import add_time_features, add_date_indicators
X = (X
.pipe(add_time_features, month=True)
.pipe(add_date_indicators, special_date=["2020-01-05"])
.assign(trend=range(200))
)
```
This adds
- a month column (integers between 1 and 12),
- a binary column named special_date that is 1 on the 5h of January 2020 and 0 everywhere else, and
- a (so far linear) trend which is only counting up from 0 to 199.
`X` looks like this now:
## Building a Model
We can now build a final model like this:
```python
from mamimo.time_utils import PowerTrend
from mamimo.carryover import ExponentialCarryover
from mamimo.saturation import ExponentialSaturation
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
cats = [list(range(1, 13))] # different months, known beforehand
preprocess = ColumnTransformer(
[
('tv_pipe', Pipeline([
('carryover', ExponentialCarryover()),
('saturation', ExponentialSaturation())
]), ['TV']),
('radio_pipe', Pipeline([
('carryover', ExponentialCarryover()),
('saturation', ExponentialSaturation())
]), ['Radio']),
('banners_pipe', Pipeline([
('carryover', ExponentialCarryover()),
('saturation', ExponentialSaturation())
]), ['Banners']),
('month', OneHotEncoder(sparse=False, categories=cats), ['month']),
('trend', PowerTrend(), ['trend']),
('special_date', ExponentialCarryover(), ['special_date'])
]
)
model = Pipeline([
('preprocess', preprocess),
('regression', LinearRegression(
positive=True,
fit_intercept=False # no intercept because of the months
)
)
])
```
This builds a model that does the following:
- the media channels are preprocessed using the [adstock transformation](https://en.wikipedia.org/wiki/Advertising_adstock), i.e. a carryover effect and a saturation is added
- the month is one-hot (dummy) encoded
- the trend is changed from linear to something like t^a, with some exponent a to be optimized
- the special_date 2020-01-05 gets a carryover effect as well, meaning that not only on this special week there was some special effect on the sales, but also the weeks after it
## Training The Model
We can then hyperparameter tune the model via
```python
from scipy.stats import randint, uniform
from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit
tuned_model = RandomizedSearchCV(
model,
param_distributions={
'preprocess__tv_pipe__carryover__window': randint(1, 10),
'preprocess__tv_pipe__carryover__strength': uniform(0, 1),
'preprocess__tv_pipe__saturation__exponent': uniform(0, 1),
'preprocess__radio_pipe__carryover__window': randint(1, 10),
'preprocess__radio_pipe__carryover__strength': uniform(0, 1),
'preprocess__radio_pipe__saturation__exponent': uniform(0, 1),
'preprocess__banners_pipe__carryover__window': randint(1, 10),
'preprocess__banners_pipe__carryover__strength': uniform(0, 1),
'preprocess__banners_pipe__saturation__exponent': uniform(0, 1),
'preprocess__trend__power': uniform(0, 2),
'preprocess__special_date__window': randint(1, 10),
'preprocess__special_date__strength': uniform(0, 1),
},
cv=TimeSeriesSplit(),
random_state=0,
n_iter=1000, # can take some time, lower number for faster results
)
tuned_model.fit(X, y)
```
You can also use `GridSearch`, Optuna, or other hyperparameter tune methods and packages here, as long as it is compatible to scikit-learn.
## Analyzing
With `tuned_model.predict(X)` and some plotting, we get
You can get the best found hyperparameters using `print(tuned_model.best_params_)`.
### Plotting
You can compute the channel contributions via
```python
from mamimo.analysis import breakdown
contributions = breakdown(tuned_model.best_estimator_, X, y)
```
This returns a dataframe with the contributions of each channel fo each time step, summing to the historical values present in `y`. You can get a nice plot via
```python
ax = contributions.plot.area(
figsize=(16, 10),
linewidth=1,
title="Predicted Sales and Breakdown",
ylabel="Sales",
xlabel="Date",
)
handles, labels = ax.get_legend_handles_labels()
ax.legend(
handles[::-1],
labels[::-1],
title="Channels",
loc="center left",
bbox_to_anchor=(1.01, 0.5),
)
```
Wow, that's a lot of channels. Let us group some of them together.
```python
group_channels = {'Baseline': [f'month__month_{i}' for i in range(1, 13)] + ['Base', 'trend__trend']}
# read: 'Baseline consists of the months, base and trend.'
# You can add more groups!
contributions = breakdown(
tuned_model.best_estimator_,
X,
y,
group_channels
)
```
If we plot again, we get
Yay!
-----------------
[](https://ko-fi.com/G2G7EBKVH)