|
import pandas |
|
import datetime |
|
import numpy as np |
|
from sklearn.base import clone |
|
from sklearn.model_selection import train_test_split |
|
from sklearn.ensemble import GradientBoostingClassifier |
|
|
|
|
|
def uplift_fit_predict(model, X_train, treatment_train, target_train, X_test): |
|
""" |
|
Реализация простого способа построения uplift-модели. |
|
|
|
Обучаем два бинарных классификатора, которые оценивают вероятность target для клиента: |
|
1. с которым была произведена коммуникация (treatment=1) |
|
2. с которым не было коммуникации (treatment=0) |
|
|
|
В качестве оценки uplift для нового клиента берется разница оценок вероятностей: |
|
Predicted Uplift = P(target|treatment=1) - P(target|treatment=0) |
|
""" |
|
X_treatment, y_treatment = X_train[treatment_train == 1, :], target_train[treatment_train == 1] |
|
X_control, y_control = X_train[treatment_train == 0, :], target_train[treatment_train == 0] |
|
model_treatment = clone(model).fit(X_treatment, y_treatment) |
|
model_control = clone(model).fit(X_control, y_control) |
|
predict_treatment = model_treatment.predict_proba(X_test)[:, 1] |
|
predict_control = model_control.predict_proba(X_test)[:, 1] |
|
predict_uplift = predict_treatment - predict_control |
|
return predict_uplift |
|
|
|
|
|
def uplift_score(prediction, treatment, target, rate=0.3): |
|
""" |
|
Подсчет Uplift Score |
|
""" |
|
order = np.argsort(-prediction) |
|
treatment_n = int((treatment == 1).sum() * rate) |
|
treatment_p = target[order][treatment[order] == 1][:treatment_n].mean() |
|
control_n = int((treatment == 0).sum() * rate) |
|
control_p = target[order][treatment[order] == 0][:control_n].mean() |
|
score = treatment_p - control_p |
|
return score |
|
|
|
|
|
|
|
|
|
df_clients = pandas.read_csv('data/clients.csv', index_col='client_id') |
|
df_train = pandas.read_csv('data/uplift_train.csv', index_col='client_id') |
|
df_test = pandas.read_csv('data/uplift_test.csv', index_col='client_id') |
|
|
|
|
|
|
|
df_clients['first_issue_unixtime'] = pandas.to_datetime(df_clients['first_issue_date']).astype(int)/10**9 |
|
df_clients['first_redeem_unixtime'] = pandas.to_datetime(df_clients['first_redeem_date']).astype(int)/10**9 |
|
df_features = pandas.DataFrame({ |
|
'gender_M': (df_clients['gender'] == 'M').astype(int), |
|
'gender_F': (df_clients['gender'] == 'F').astype(int), |
|
'gender_U': (df_clients['gender'] == 'U').astype(int), |
|
'age': df_clients['age'], |
|
'first_issue_time': df_clients['first_issue_unixtime'], |
|
'first_redeem_time': df_clients['first_redeem_unixtime'], |
|
'issue_redeem_delay': df_clients['first_redeem_unixtime'] - df_clients['first_issue_unixtime'], |
|
}).fillna(0) |
|
|
|
|
|
|
|
|
|
indices_train = df_train.index |
|
indices_test = df_test.index |
|
indices_learn, indices_valid = train_test_split(df_train.index, test_size=0.3, random_state=123) |
|
|
|
valid_uplift = uplift_fit_predict( |
|
model=GradientBoostingClassifier(), |
|
X_train=df_features.loc[indices_learn, :].fillna(0).values, |
|
treatment_train=df_train.loc[indices_learn, 'treatment_flg'].values, |
|
target_train=df_train.loc[indices_learn, 'target'].values, |
|
X_test=df_features.loc[indices_valid, :].fillna(0).values, |
|
) |
|
valid_score = uplift_score( |
|
valid_uplift, |
|
treatment=df_train.loc[indices_valid, 'treatment_flg'].values, |
|
target=df_train.loc[indices_valid, 'target'].values, |
|
) |
|
print('Validation score:', valid_score) |
|
|
|
|
|
|
|
|
|
test_uplift = uplift_fit_predict( |
|
model=GradientBoostingClassifier(), |
|
X_train=df_features.loc[indices_train, :].fillna(0).values, |
|
treatment_train=df_train.loc[indices_train, 'treatment_flg'].values, |
|
target_train=df_train.loc[indices_train, 'target'].values, |
|
X_test=df_features.loc[indices_test, :].fillna(0).values, |
|
) |
|
|
|
df_submission = pandas.DataFrame({'uplift': test_uplift}, index=df_test.index) |
|
df_submission.to_csv('submission.csv') |
|
|
