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https://github.com/jmwoloso/pychattr
Python Channel Attribution (pychattr) - A Python implementation of the excellent R ChannelAttribution library
https://github.com/jmwoloso/pychattr
channel-attribution data-analysis data-science machine-learning python python-channel-attribution rpy2 wrapper
Last synced: about 1 month ago
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Python Channel Attribution (pychattr) - A Python implementation of the excellent R ChannelAttribution library
- Host: GitHub
- URL: https://github.com/jmwoloso/pychattr
- Owner: jmwoloso
- License: gpl-3.0
- Created: 2018-07-11T00:01:31.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-09-05T20:29:08.000Z (over 2 years ago)
- Last Synced: 2024-09-26T21:36:26.331Z (3 months ago)
- Topics: channel-attribution, data-analysis, data-science, machine-learning, python, python-channel-attribution, rpy2, wrapper
- Language: Python
- Homepage:
- Size: 2.08 MB
- Stars: 57
- Watchers: 10
- Forks: 22
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-marketing-machine-learning - pychattr
README
# README
This is a Python implementation based on the ChannelAttribution package in R developed by Davide Altomare and David Loris.
https://cran.r-project.org/web/packages/ChannelAttribution/ChannelAttribution.pdf
**Please Note:** The authors of the original R library now also provide a python implementation of [ChannelAttribution](https://pypi.org/project/ChannelAttribution/) which you should probably use instead of my implmentation since theirs has additional features not available in this package.
# Installation
```pip install pychattr```
# Markov Model
```
import pandas as pd
from pychattr.channel_attribution import MarkovModel
data = {
"path": [
"A >>> B >>> A >>> B >>> B >>> A",
"A >>> B >>> B >>> A >>> A",
"A >>> A"
],
"conversions": [1, 1, 1],
"revenue": [1, 1, 1],
"cost": [1, 1, 1]
}
df = pd.DataFrame(data)
path_feature="path"
conversion_feature="conversions"
null_feature=None
revenue_feature="revenue"
cost_feature="cost"
separator=">>>"
k_order=1
n_simulations=10000
max_steps=None
return_transition_probs=True
random_state=26
# instantiate the model
mm = MarkovModel(path_feature=path_feature,
conversion_feature=conversion_feature,
null_feature=null_feature,
revenue_feature=revenue_feature,
cost_feature=cost_feature,
separator=separator,
k_order=k_order,
n_simulations=n_simulations,
max_steps=max_steps,
return_transition_probs=return_transition_probs,
random_state=random_state)
# fit the model
mm.fit(df)
```
```
# view the simulation results
print(mm.attribution_model_)
```
```
channel_name total_conversions
0 A 1.991106
1 B 1.008894
```
```
# view the transition matrix
print(mm.transition_matrix_)
```
```
channel_from channel_to transition_probability
0 (start) A 1.0
1 A B 0.5
2 A (conversion) 0.5
3 B A 1.0
```
```
# view the removal effects
print(mm.removal_effects_)
```
```
channel_name removal_effect
0 A 1.0000
1 B 0.5067
```
# Heuristic Model
```
import pandas as pd
from pychattr.channel_attribution import HeuristicModel
data = {
"path": [
"A >>> B >>> A >>> B >>> B >>> A",
"A >>> B >>> B >>> A >>> A",
"A >>> A"
],
"conversions": [1, 1, 1],
"revenue": [1, 1, 1],
"cost": [1, 1, 1]
}
df = pd.DataFrame(data)
path_feature="path"
conversion_feature="conversions"
null_feature=None
revenue_feature="revenue"
cost_feature="cost"
separator=">>>"
first_touch=True
last_touch=True
linear_touch=True
ensemble_results=True
# instantiate the model
hm = HeuristicModel(path_feature=path_feature,
conversion_feature=conversion_feature,
null_feature=null_feature,
revenue_feature=revenue_feature,
cost_feature=cost_feature,
separator=separator,
first_touch=first_touch,
last_touch=last_touch,
linear_touch=linear_touch,
ensemble_results=ensemble_results)
# fit the model
hm.fit(df)
```
```
# view the heuristic results
print(hm.attribution_model_)
```
```
channel first_touch_conversions ... ensemble_revenue ensemble_cost
0 A 3.0 ... 8.1 5.1
1 B 0.0 ... 0.9 0.9
[2 rows x 13 columns]
```