In [1]:
import os
import warnings
warnings.filterwarnings('ignore')
In [2]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from causalml.inference.meta import BaseSRegressor, BaseTRegressor, BaseXRegressor, BaseRRegressor
from causalml.inference.tree import UpliftTreeClassifier, UpliftRandomForestClassifier
from causalml.dataset.regression import synthetic_data
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.tree import DecisionTreeRegressor
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
import shap
import matplotlib.pyplot as plt
import time
from sklearn.inspection import permutation_importance
from sklearn.model_selection import train_test_split
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # for lightgbm to work
%reload_ext autoreload
%autoreload 2
%matplotlib inline
In [3]:
plt.style.use('fivethirtyeight')
In [4]:
n_features = 25
n_samples = 10000
y, X, w, tau, b, e = synthetic_data(mode=1, n=n_samples, p=n_features, sigma=0.5)
In [5]:
w_multi = np.array(['treatment_A' if x==1 else 'control' for x in w])
e_multi = {'treatment_A': e}
In [6]:
feature_names = ['stars', 'tiger', 'merciful', 'quixotic', 'fireman', 'dependent',
'shelf', 'touch', 'barbarous', 'clammy', 'playground', 'rain', 'offer',
'cute', 'future', 'damp', 'nonchalant', 'change', 'rigid', 'sweltering',
'eight', 'wrap', 'lethal', 'adhesive', 'lip'] # specify feature names
model_tau = LGBMRegressor(importance_type='gain') # specify model for model_tau
S Learner¶
In [7]:
base_algo = LGBMRegressor()
# base_algo = XGBRegressor()
# base_algo = RandomForestRegressor()
# base_algo = LinearRegression()
slearner = BaseSRegressor(base_algo, control_name='control')
slearner.estimate_ate(X, w_multi, y)
Out[7]:
array([0.55124656])
In [8]:
slearner_tau = slearner.fit_predict(X, w_multi, y)
Feature Importance (method = auto)¶
In [9]:
slearner.get_importance(X=X,
tau=slearner_tau,
normalize=True,
method='auto',
features=feature_names)
Out[9]:
{'treatment_A': stars 0.486489
tiger 0.343104
quixotic 0.083821
merciful 0.045366
fireman 0.034689
touch 0.001894
sweltering 0.000652
shelf 0.000577
cute 0.000312
barbarous 0.000302
adhesive 0.000302
wrap 0.000288
lip 0.000285
playground 0.000244
rigid 0.000238
lethal 0.000203
future 0.000184
dependent 0.000177
eight 0.000155
rain 0.000152
nonchalant 0.000151
clammy 0.000118
offer 0.000103
change 0.000102
damp 0.000090
dtype: float64}
In [10]:
slearner.plot_importance(X=X,
tau=slearner_tau,
normalize=True,
method='auto',
features=feature_names)
Feature Importance (method = permutation)¶
In [11]:
slearner.get_importance(X=X,
tau=slearner_tau,
method='permutation',
features=feature_names,
random_state=42)
Out[11]:
{'treatment_A': stars 0.962595
tiger 0.741225
quixotic 0.183951
merciful 0.077581
fireman 0.075747
touch 0.004322
sweltering 0.000695
wrap 0.000621
shelf 0.000302
barbarous 0.000229
lethal 0.000107
future 0.000065
clammy 0.000056
change 0.000053
offer 0.000040
cute 0.000037
dependent 0.000023
playground -0.000002
lip -0.000022
rain -0.000032
eight -0.000044
damp -0.000054
nonchalant -0.000057
adhesive -0.000083
rigid -0.000130
dtype: float64}
In [12]:
start_time = time.time()
slearner.get_importance(X=X,
tau=slearner_tau,
method='permutation',
features=feature_names,
random_state=42)
print("Elapsed time: %s seconds" % (time.time() - start_time))
Elapsed time: 1.2573859691619873 seconds
In [13]:
slearner.plot_importance(X=X,
tau=slearner_tau,
method='permutation',
features=feature_names,
random_state=42)
Feature Importance (sklearn.inspection.permutation_importance)¶
In [14]:
start_time = time.time()
X_train, X_test, y_train, y_test = train_test_split(X, slearner_tau, test_size=0.3, random_state=42)
model_tau_fit = model_tau.fit(X_train, y_train)
perm_imp_test = permutation_importance(
estimator=model_tau_fit,
X=X_test,
y=y_test,
random_state=42).importances_mean
pd.Series(perm_imp_test, feature_names).sort_values(ascending=False)
print("Elapsed time: %s seconds" % (time.time() - start_time))
Elapsed time: 1.4103600978851318 seconds
In [15]:
pd.Series(perm_imp_test, feature_names).sort_values(ascending=False)
Out[15]:
stars 0.962595 tiger 0.741225 quixotic 0.183951 merciful 0.077581 fireman 0.075747 touch 0.004322 sweltering 0.000695 wrap 0.000621 shelf 0.000302 barbarous 0.000229 lethal 0.000107 future 0.000065 clammy 0.000056 change 0.000053 offer 0.000040 cute 0.000037 dependent 0.000023 playground -0.000002 lip -0.000022 rain -0.000032 eight -0.000044 damp -0.000054 nonchalant -0.000057 adhesive -0.000083 rigid -0.000130 dtype: float64
In [16]:
pd.Series(perm_imp_test, feature_names).sort_values().plot(kind='barh', figsize=(12, 8))
plt.title('Test Set Permutation Importances')
Out[16]:
Text(0.5, 1.0, 'Test Set Permutation Importances')
In [17]:
perm_imp_train = permutation_importance(
estimator=model_tau_fit,
X=X_train,
y=y_train,
random_state=42).importances_mean
pd.Series(perm_imp_train, feature_names).sort_values(ascending=False)
Out[17]:
stars 0.979218 tiger 0.755084 quixotic 0.178054 merciful 0.080888 fireman 0.076262 touch 0.005695 wrap 0.001916 sweltering 0.001616 adhesive 0.001341 shelf 0.001234 rigid 0.001205 barbarous 0.001200 playground 0.001101 cute 0.001020 lip 0.000883 offer 0.000883 clammy 0.000804 future 0.000763 nonchalant 0.000757 lethal 0.000701 dependent 0.000606 eight 0.000584 damp 0.000553 change 0.000413 rain 0.000339 dtype: float64
In [18]:
pd.Series(perm_imp_train, feature_names).sort_values().plot(kind='barh', figsize=(12, 8))
plt.title('Training Set Permutation Importances')
Out[18]:
Text(0.5, 1.0, 'Training Set Permutation Importances')
Shapley Values¶
In [19]:
shap_slearner = slearner.get_shap_values(X=X, tau=slearner_tau)
shap_slearner
Out[19]:
{'treatment_A': array([[ 3.94366656e-02, -2.29530311e-01, 1.42158979e-02, ...,
-1.62871545e-04, 5.82372435e-04, 2.88715214e-04],
[-7.54144307e-02, 1.85798470e-01, -1.90590808e-02, ...,
-3.17803418e-05, -3.82232450e-04, -3.76163889e-05],
[ 3.89606913e-02, 1.75083435e-01, -1.63511157e-02, ...,
-1.32024179e-03, -1.94442532e-04, 2.52281080e-04],
...,
[ 8.43145649e-02, 2.30140122e-01, 1.60212264e-02, ...,
2.85981707e-06, -1.13863828e-06, -2.36089392e-05],
[ 1.11671050e-01, -7.22417643e-02, 1.41737832e-03, ...,
-2.59095285e-05, -9.11502869e-05, 4.50328633e-05],
[-2.63074721e-02, 1.38382654e-01, -2.21219286e-02, ...,
-1.03485074e-04, -2.62784688e-04, -6.61313016e-05]])}
In [20]:
np.mean(np.abs(shap_slearner['treatment_A']),axis=0)
Out[20]:
array([1.45973193e-01, 1.22802198e-01, 1.92613910e-02, 3.90087021e-02,
2.65509008e-02, 3.57503802e-04, 7.94938074e-04, 4.61141671e-03,
9.36039134e-04, 1.25960368e-04, 4.44237157e-04, 2.37262549e-04,
2.96173809e-04, 3.57679078e-04, 7.35552208e-04, 1.42282895e-04,
2.40108166e-04, 2.84783457e-04, 4.65052691e-04, 1.54977802e-03,
1.82520000e-04, 7.36111819e-04, 4.84202462e-04, 5.03599958e-04,
2.75059742e-04])
In [21]:
# Plot shap values without specifying shap_dict
slearner.plot_shap_values(X=X, tau=slearner_tau, features=feature_names)
In [22]:
# Plot shap values WITH specifying shap_dict
slearner.plot_shap_values(shap_dict=shap_slearner)
In [23]:
# interaction_idx set to None (no color coding for interaction effects)
slearner.plot_shap_dependence(treatment_group='treatment_A',
feature_idx=1,
X=X,
tau=slearner_tau,
interaction_idx=None,
shap_dict=shap_slearner)
In [24]:
# interaction_idx set to 'auto' (searches for feature with greatest approximate interaction)
# specify feature names
slearner.plot_shap_dependence(treatment_group='treatment_A',
feature_idx='tiger',
X=X,
tau=slearner_tau,
interaction_idx='auto',
shap_dict=shap_slearner,
features=feature_names)
In [25]:
# interaction_idx set to specific index
slearner.plot_shap_dependence(treatment_group='treatment_A',
feature_idx=1,
X=X,
tau=slearner_tau,
interaction_idx=10,
shap_dict=shap_slearner,
features=feature_names)
T Learner¶
In [26]:
tlearner = BaseTRegressor(LGBMRegressor(), control_name='control')
tlearner.estimate_ate(X, w_multi, y)
Out[26]:
(array([0.5540839]), array([0.53933868]), array([0.56882913]))
In [27]:
tlearner_tau = tlearner.fit_predict(X, w_multi, y)
Feature Importance (method = auto)¶
In [28]:
tlearner.get_importance(X=X,
tau=tlearner_tau,
normalize=True,
method='auto',
features=feature_names)
Out[28]:
{'treatment_A': stars 0.384675
tiger 0.254866
quixotic 0.059036
merciful 0.053492
fireman 0.033534
future 0.017594
sweltering 0.015322
adhesive 0.014223
wrap 0.014126
clammy 0.012104
playground 0.012003
shelf 0.011924
barbarous 0.011038
rigid 0.010894
rain 0.010578
nonchalant 0.010465
change 0.010405
offer 0.010258
cute 0.009573
lethal 0.009008
touch 0.008742
lip 0.008528
dependent 0.007199
eight 0.005438
damp 0.004975
dtype: float64}
In [29]:
tlearner.plot_importance(X=X,
tau=tlearner_tau,
normalize=True,
method='auto',
features=feature_names)
Feature Importance (method = permutation)¶
In [30]:
tlearner.get_importance(X=X,
tau=tlearner_tau,
method='permutation',
features=feature_names,
random_state=42)
Out[30]:
{'treatment_A': stars 0.567860
tiger 0.404623
quixotic 0.064612
merciful 0.038721
fireman 0.032291
future 0.012999
wrap 0.011618
adhesive 0.011075
sweltering 0.006733
clammy 0.005775
change 0.005465
rain 0.005355
touch 0.005151
barbarous 0.004527
dependent 0.003598
playground 0.003543
rigid 0.003508
nonchalant 0.003497
lip 0.003109
offer 0.002861
shelf 0.002399
lethal 0.001886
cute 0.001628
eight 0.000871
damp 0.000650
dtype: float64}
In [31]:
tlearner.plot_importance(X=X,
tau=tlearner_tau,
method='permutation',
features=feature_names,
random_state=42)
Feature Importance (sklearn.inspection.permutation_importance)¶
In [32]:
start_time = time.time()
X_train, X_test, y_train, y_test = train_test_split(X, tlearner_tau, test_size=0.3, random_state=42)
model_tau_fit = model_tau.fit(X_train, y_train)
perm_imp_test = permutation_importance(
estimator=model_tau_fit,
X=X_test,
y=y_test,
random_state=42).importances_mean
pd.Series(perm_imp_test, feature_names).sort_values(ascending=False)
print("Elapsed time: %s seconds" % (time.time() - start_time))
Elapsed time: 1.0853099822998047 seconds
In [33]:
pd.Series(perm_imp_test, feature_names).sort_values(ascending=False)
Out[33]:
stars 0.567860 tiger 0.404623 quixotic 0.064612 merciful 0.038721 fireman 0.032291 future 0.012999 wrap 0.011618 adhesive 0.011075 sweltering 0.006733 clammy 0.005775 change 0.005465 rain 0.005355 touch 0.005151 barbarous 0.004527 dependent 0.003598 playground 0.003543 rigid 0.003508 nonchalant 0.003497 lip 0.003109 offer 0.002861 shelf 0.002399 lethal 0.001886 cute 0.001628 eight 0.000871 damp 0.000650 dtype: float64
In [34]:
pd.Series(perm_imp_test, feature_names).sort_values().plot(kind='barh', figsize=(12, 8))
plt.title('Test Set Permutation Importances')
Out[34]:
Text(0.5, 1.0, 'Test Set Permutation Importances')
Shapley Values¶
In [35]:
shap_tlearner = tlearner.get_shap_values(X=X, tau=tlearner_tau)
shap_tlearner
Out[35]:
{'treatment_A': array([[ 0.01983429, -0.1980894 , 0.00685216, ..., -0.00420184,
-0.00505335, 0.00887105],
[-0.12117409, 0.23882282, -0.03173552, ..., -0.00303379,
0.00405849, -0.00871143],
[ 0.0129052 , 0.21498206, -0.03055744, ..., -0.02454731,
0.01187846, -0.03121241],
...,
[ 0.02176915, 0.26183205, 0.02514154, ..., 0.02521532,
0.03471841, 0.00165231],
[ 0.09952416, -0.06701196, 0.00206727, ..., 0.0037859 ,
0.0130603 , 0.00089347],
[-0.01132435, 0.12937684, -0.02672874, ..., -0.00077457,
0.00556904, -0.01268997]])}
In [36]:
# Plot shap values without specifying shap_dict
tlearner.plot_shap_values(X=X, tau=tlearner_tau, features=feature_names)
In [37]:
# Plot shap values WITH specifying shap_dict
tlearner.plot_shap_values(shap_dict=shap_tlearner)
X Learner¶
In [38]:
xlearner = BaseXRegressor(LGBMRegressor(), control_name='control')
xlearner.estimate_ate(X, w_multi, y, p=e_multi)
Out[38]:
(array([0.49865807]), array([0.48473804]), array([0.5125781]))
In [39]:
xlearner_tau = xlearner.predict(X, w_multi, y, p=e_multi)
Feature Importance (method = auto)¶
In [40]:
xlearner.get_importance(X=X,
tau=xlearner_tau,
normalize=True,
method='auto',
features=feature_names)
Out[40]:
{'treatment_A': stars 0.451909
tiger 0.323575
quixotic 0.027299
merciful 0.023991
wrap 0.015435
rain 0.014365
sweltering 0.012960
adhesive 0.011703
fireman 0.010942
nonchalant 0.010571
lethal 0.009734
future 0.009083
shelf 0.008143
barbarous 0.007333
lip 0.007117
playground 0.007064
cute 0.006529
offer 0.006323
rigid 0.006248
change 0.005930
dependent 0.005665
eight 0.004983
clammy 0.004959
touch 0.004267
damp 0.003871
dtype: float64}
In [41]:
xlearner.plot_importance(X=X,
tau=xlearner_tau,
normalize=True,
method='auto',
features=feature_names)
Feature Importance (method = permutation)¶
In [42]:
xlearner.get_importance(X=X,
tau=xlearner_tau,
method='permutation',
features=feature_names,
random_state=42)
Out[42]:
{'treatment_A': stars 0.852106
tiger 0.591941
quixotic 0.034504
merciful 0.024898
wrap 0.018366
sweltering 0.015290
rain 0.015165
adhesive 0.014575
lethal 0.012336
future 0.011654
fireman 0.010589
nonchalant 0.009900
shelf 0.007157
barbarous 0.006110
lip 0.005784
playground 0.005575
cute 0.005101
change 0.004219
rigid 0.003825
offer 0.003547
dependent 0.003187
clammy 0.003115
touch 0.002917
eight 0.002577
damp 0.001972
dtype: float64}
In [43]:
xlearner.plot_importance(X=X,
tau=xlearner_tau,
method='permutation',
features=feature_names,
random_state=42)
Feature Importance (sklearn.inspection.permutation_importance)¶
In [44]:
start_time = time.time()
X_train, X_test, y_train, y_test = train_test_split(X, xlearner_tau, test_size=0.3, random_state=42)
model_tau_fit = model_tau.fit(X_train, y_train)
perm_imp_test = permutation_importance(
estimator=model_tau_fit,
X=X_test,
y=y_test,
random_state=42).importances_mean
pd.Series(perm_imp_test, feature_names).sort_values(ascending=False)
print("Elapsed time: %s seconds" % (time.time() - start_time))
Elapsed time: 1.1362731456756592 seconds
In [45]:
pd.Series(perm_imp_test, feature_names).sort_values(ascending=False)
Out[45]:
stars 0.852106 tiger 0.591941 quixotic 0.034504 merciful 0.024898 wrap 0.018366 sweltering 0.015290 rain 0.015165 adhesive 0.014575 lethal 0.012336 future 0.011654 fireman 0.010589 nonchalant 0.009900 shelf 0.007157 barbarous 0.006110 lip 0.005784 playground 0.005575 cute 0.005101 change 0.004219 rigid 0.003825 offer 0.003547 dependent 0.003187 clammy 0.003115 touch 0.002917 eight 0.002577 damp 0.001972 dtype: float64
In [46]:
pd.Series(perm_imp_test, feature_names).sort_values().plot(kind='barh', figsize=(12, 8))
plt.title('Test Set Permutation Importances')
Out[46]:
Text(0.5, 1.0, 'Test Set Permutation Importances')
Shapley Values¶
In [47]:
shap_xlearner = xlearner.get_shap_values(X=X, tau=xlearner_tau)
shap_xlearner
Out[47]:
{'treatment_A': array([[ 1.39587544e-02, -1.38111092e-01, -4.66281096e-04, ...,
-5.34398610e-03, -7.19564955e-03, 1.08016212e-02],
[-1.13233179e-01, 1.87891976e-01, -8.26238931e-03, ...,
-6.04099568e-03, 5.62940200e-03, -2.01524533e-03],
[-8.36121936e-02, 1.67828628e-01, -5.13376118e-03, ...,
-2.39928248e-02, 2.82413204e-02, -1.34879032e-02],
...,
[-9.21333349e-02, 1.79998101e-01, 2.97610014e-03, ...,
1.03298453e-02, 3.22332256e-02, -7.79747422e-03],
[ 8.20946132e-02, -3.97760850e-02, 1.05089795e-02, ...,
4.26645134e-03, 1.61760581e-02, 5.25206615e-03],
[-2.63176982e-02, 1.02573325e-01, -1.73067708e-04, ...,
-7.49134980e-03, 3.28218192e-02, -3.68958547e-03]])}
In [48]:
# shap_dict not specified
xlearner.plot_shap_values(X=X, tau=xlearner_tau, features=feature_names)
In [49]:
# shap_dict specified
xlearner.plot_shap_values(shap_dict=shap_xlearner)
R Learner¶
In [50]:
rlearner = BaseRRegressor(LGBMRegressor(), control_name='control')
rlearner_tau = rlearner.fit_predict(X, w_multi, y, p=e_multi)
Feature Importance (method = auto)¶
In [51]:
rlearner.get_importance(X=X,
tau=rlearner_tau,
normalize=True,
method='auto',
features=feature_names)
Out[51]:
{'treatment_A': stars 0.229371
tiger 0.182911
merciful 0.052773
rain 0.045701
quixotic 0.035532
dependent 0.034649
playground 0.029319
lip 0.029179
sweltering 0.028628
barbarous 0.026947
nonchalant 0.026473
lethal 0.025862
adhesive 0.024912
offer 0.024012
shelf 0.022196
fireman 0.020700
damp 0.020668
future 0.019985
wrap 0.019723
cute 0.018876
change 0.017793
touch 0.017618
eight 0.016975
clammy 0.016388
rigid 0.012809
dtype: float64}
In [52]:
rlearner.plot_importance(X=X,
tau=rlearner_tau,
method='auto',
features=feature_names)
Feature Importance (method = permutation)¶
In [53]:
rlearner.get_importance(X=X,
tau=rlearner_tau,
method='permutation',
features=feature_names,
random_state=42)
Out[53]:
{'treatment_A': stars 0.317007
tiger 0.259112
rain 0.043181
merciful 0.029149
quixotic 0.024564
sweltering 0.022064
future 0.017083
offer 0.016832
dependent 0.016667
wrap 0.015181
lip 0.014977
adhesive 0.013323
nonchalant 0.013155
lethal 0.011167
fireman 0.010991
barbarous 0.010426
playground 0.010022
clammy 0.009221
change 0.009154
damp 0.008008
shelf 0.007940
cute 0.005631
touch 0.005509
rigid 0.004789
eight 0.004115
dtype: float64}
In [54]:
rlearner.plot_importance(X=X,
tau=rlearner_tau,
method='permutation',
features=feature_names,
random_state=42)
Feature Importance (sklearn.inspection.permutation_importance)¶
In [55]:
start_time = time.time()
X_train, X_test, y_train, y_test = train_test_split(X, rlearner_tau, test_size=0.3, random_state=42)
model_tau_fit = model_tau.fit(X_train, y_train)
perm_imp_test = permutation_importance(
estimator=model_tau_fit,
X=X_test,
y=y_test,
random_state=42).importances_mean
pd.Series(perm_imp_test, feature_names).sort_values(ascending=False)
print("Elapsed time: %s seconds" % (time.time() - start_time))
Elapsed time: 1.259948968887329 seconds
In [56]:
pd.Series(perm_imp_test, feature_names).sort_values(ascending=False)
Out[56]:
stars 0.317007 tiger 0.259112 rain 0.043181 merciful 0.029149 quixotic 0.024564 sweltering 0.022064 future 0.017083 offer 0.016832 dependent 0.016667 wrap 0.015181 lip 0.014977 adhesive 0.013323 nonchalant 0.013155 lethal 0.011167 fireman 0.010991 barbarous 0.010426 playground 0.010022 clammy 0.009221 change 0.009154 damp 0.008008 shelf 0.007940 cute 0.005631 touch 0.005509 rigid 0.004789 eight 0.004115 dtype: float64
In [57]:
pd.Series(perm_imp_test, feature_names).sort_values().plot(kind='barh', figsize=(12, 8))
plt.title('Test Set Permutation Importances')
Out[57]:
Text(0.5, 1.0, 'Test Set Permutation Importances')
Shapley Values¶
In [58]:
shap_rlearner = rlearner.get_shap_values(X=X, tau=rlearner_tau)
shap_rlearner
Out[58]:
{'treatment_A': array([[ 0.03023169, -0.12050095, 0.00054553, ..., -0.01171146,
-0.01144764, 0.00517501],
[-0.09549191, 0.15077295, -0.00517459, ..., -0.00820947,
0.01215939, 0.00585136],
[-0.09012906, 0.14228088, -0.00819578, ..., -0.03594954,
0.01436807, -0.01736302],
...,
[-0.12475088, 0.19588879, -0.00240182, ..., 0.00711909,
0.02774982, 0.00973711],
[ 0.05356909, -0.07152747, 0.00651961, ..., 0.00789984,
0.01908949, -0.00387345],
[-0.06715088, 0.08753955, 0.00163448, ..., 0.00246522,
0.02189301, 0.01139043]])}
In [59]:
# without providing shap_dict
rlearner.plot_shap_values(X=X, tau=rlearner_tau, features=feature_names)
In [60]:
# with providing shap_dict
rlearner.plot_shap_values(shap_dict=shap_rlearner)
Uplift Tree/Forest¶
Note that uplift trees/forests are only implemented for classification at the moment, hence the following section uses a different synthetic data generation process.
UpliftTreeClassifier¶
In [61]:
from causalml.dataset import make_uplift_classification
df, x_names = make_uplift_classification()
In [62]:
uplift_tree = UpliftTreeClassifier(control_name='control')
uplift_tree.fit(X=df[x_names].values,
treatment=df['treatment_group_key'].values,
y=df['conversion'].values)
In [63]:
pd.Series(uplift_tree.feature_importances_, index=x_names).sort_values().plot(kind='barh', figsize=(12,8))
Out[63]:
<matplotlib.axes._subplots.AxesSubplot at 0x7ff8f0e13e48>
UpliftRandomForestClassifier¶
In [64]:
uplift_rf = UpliftRandomForestClassifier(control_name='control')
uplift_rf.fit(X=df[x_names].values,
treatment=df['treatment_group_key'].values,
y=df['conversion'].values)
In [65]:
pd.Series(uplift_rf.feature_importances_, index=x_names).sort_values().plot(kind='barh', figsize=(12,8))
Out[65]:
<matplotlib.axes._subplots.AxesSubplot at 0x7ff90d027358>
In [ ]: