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import sys
# install uplift library scikit-uplift and other libraries
!{sys.executable} -m pip install scikit-uplift dill catboost
📝 Load data¶
We are going to use a Lenta dataset from the BigTarget Hackathon hosted in summer 2020 by Lenta and Microsoft.
Lenta is a russian food retailer.
Data description¶
✏️ Dataset can be loaded from sklift.datasets module using fetch_lenta function.
Read more about dataset in the api docs.
This is an uplift modeling dataset containing data about Lenta's customers grociery shopping, marketing campaigns communications as treatment and store visits as target.
✏️ Major columns:¶
group- treatment / control flagresponse_att- binary targetCardHolder- customer idgender- customer genderage- customer age
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from sklift.datasets import fetch_lenta
# returns sklearn Bunch object
# with data, target, treatment keys
# data features (pd.DataFrame), target (pd.Series), treatment (pd.Series) values
dataset = fetch_lenta()
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print(f"Dataset type: {type(dataset)}\n")
print(f"Dataset features shape: {dataset.data.shape}")
print(f"Dataset target shape: {dataset.target.shape}")
print(f"Dataset treatment shape: {dataset.treatment.shape}")
📝 EDA¶
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dataset.data.head().append(dataset.data.tail())
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🤔 target share for treatment / control¶
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import pandas as pd
pd.crosstab(dataset.treatment, dataset.target, normalize='index')
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# make treatment binary
treat_dict = {
'test': 1,
'control': 0
}
dataset.treatment = dataset.treatment.map(treat_dict)
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# fill NaNs in the categorical feature `gender`
# for CatBoostClassifier
dataset.data['gender'] = dataset.data['gender'].fillna(value='Не определен')
print(dataset.data['gender'].value_counts(dropna=False))
✂️ train test split¶
- stratify by two columns: treatment and target.
Intuition: In a binary classification problem definition we stratify train set by splitting target 0/1 column. In uplift modeling we have two columns instead of one.
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from sklearn.model_selection import train_test_split
stratify_cols = pd.concat([dataset.treatment, dataset.target], axis=1)
X_train, X_val, trmnt_train, trmnt_val, y_train, y_val = train_test_split(
dataset.data,
dataset.treatment,
dataset.target,
stratify=stratify_cols,
test_size=0.3,
random_state=42
)
print(f"Train shape: {X_train.shape}")
print(f"Validation shape: {X_val.shape}")
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from sklift.models import ClassTransformation
from catboost import CatBoostClassifier
estimator = CatBoostClassifier(verbose=100,
cat_features=['gender'],
random_state=42,
thread_count=1)
ct_model = ClassTransformation(estimator=estimator)
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ct_model.fit(
X=X_train,
y=y_train,
treatment=trmnt_train
)
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Save model¶
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import dill
with open("model.dill", 'wb') as f:
dill.dump(ct_model, f)
Uplift prediction¶
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uplift_ct = ct_model.predict(X_val)
🚀🚀🚀 Uplift metrics¶
🚀 [email protected]¶
- uplift at first k%
- usually falls between [0; 1] depending on k, model quality and data
[email protected] = target mean at k% in the treatment group - target mean at k% in the control group¶
How to count [email protected]:
- sort by predicted uplift
- select first k%
- count target mean in the treatment group
- count target mean in the control group
- substract the mean in the control group from the mean in the treatment group
Code parameter options:
strategy='overall'- sort by uplift treatment and control togetherstrategy='by_group'- sort by uplift treatment and control separately
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from sklift.metrics import uplift_at_k
# k = 10%
k = 0.1
# strategy='overall' sort by uplift treatment and control together
uplift_overall = uplift_at_k(y_val, uplift_ct, trmnt_val, strategy='overall', k=k)
# strategy='by_group' sort by uplift treatment and control separately
uplift_bygroup = uplift_at_k(y_val, uplift_ct, trmnt_val, strategy='by_group', k=k)
print(f"[email protected]{k * 100:.0f}%: {uplift_overall:.4f} (sort groups by uplift together)")
print(f"[email protected]{k * 100:.0f}%: {uplift_bygroup:.4f} (sort groups by uplift separately)")
🚀 uplift_by_percentile table¶
Count metrics for each percentile in data in descending order by uplift prediction (by rows):
n_treatment- treatment group size in the one percentilen_control- control group size in the one percentileresponse_rate_treatment- target mean in the treatment group in the one percentileresponse_rate_control- target mean in the control group in the one percentileuplift = response_rate_treatment - response_rate_controlin the one percentile
Code parameter options are:
strategy='overall'- sort by uplift treatment and control groups togetherstrategy='by_group'- sort by uplift treatment and control groups separatelytotal=True- show total metric on full datastd=True- show metrics std by row
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from sklift.metrics import uplift_by_percentile
uplift_by_percentile(y_val, uplift_ct, trmnt_val,
strategy='overall',
total=True, std=True, bins=10)
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from sklift.metrics import weighted_average_uplift
uplift_full_data = weighted_average_uplift(y_val, uplift_ct, trmnt_val, bins=10)
print(f"average uplift on full data: {uplift_full_data:.4f}")
🚀 uplift_by_percentile plot¶
- visualize results of
uplift_by_percentiletable
Two ways to plot:
- line plot
kind='line' - bar plot
kind='bar'
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from sklift.viz import plot_uplift_by_percentile
# line plot
plot_uplift_by_percentile(y_val, uplift_ct, trmnt_val, strategy='overall', kind='line');
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# bar plot
plot_uplift_by_percentile(y_val, uplift_ct, trmnt_val, strategy='overall', kind='bar');
🚀 Qini curve¶
The curve plots the absolute incremental outcome of the treated group compared to group with no treatment.
plot Qini curve:
blue lineis areal Qini curvebased on data.red lineis anideal Qini curvebased on data. Code:perfect=Truegrey lineis arandom Qini curvebased on data
🚀 AUQC (area under Qini curve or Qini coefficient)¶
Qini coefficient = light blue area between the real Qini curve and the random Qini curve normalized on area between the random and the ideal line
- metric is printed at the title of the Qini curve plot
- can be called as a separate function
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from sklift.viz import plot_qini_curve
# with ideal Qini curve (red line)
# perfect=True
plot_qini_curve(y_val, uplift_ct, trmnt_val, perfect=True);
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# no ideal Qini curve
# only real Qini curve
# perfect=False
plot_qini_curve(y_val, uplift_ct, trmnt_val, perfect=False);
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from sklift.metrics import qini_auc_score
# AUQC = area under Qini curve = Qini coefficient
auqc = qini_auc_score(y_val, uplift_ct, trmnt_val)
print(f"Qini coefficient on full data: {auqc:.4f}")
🚀 Uplift curve¶
The Uplift curve plots incremental uplift.
blue lineis areal Uplift curvebased on data.red lineis anideal Uplift curvebased on data. Code:perfect=Truegrey lineis arandom Uplift curvebased on data.
🚀 AUUQ (area under uplift curve)¶
Area under uplift curve= blue area between the real Uplift curve and the random Uplift curve- appears at the title of the Uplift curve plot
- can be called as a separate function
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from sklift.viz import plot_uplift_curve
# with ideal curve
# perfect=True
plot_uplift_curve(y_val, uplift_ct, trmnt_val, perfect=True);
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# only real
# perfect=False
plot_uplift_curve(y_val, uplift_ct, trmnt_val, perfect=False);
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from sklift.metrics import uplift_auc_score
# AUUQ = area under uplift curve
auuc = uplift_auc_score(y_val, uplift_ct, trmnt_val)
print(f"Uplift auc score on full data: {auuc:.4f}")
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