Uplift Trees/Forests Visualization

Introduction

This example notebooks illustrates how to visualize uplift trees for interpretation and diagnosis.

Supported Models

These visualization functions work only for tree-based algorithms:

• Uplift tree/random forests on KL divergence, Euclidean Distance, and Chi-Square

• Uplift tree/random forests on Contextual Treatment Selection

Currently, they are NOT supporting Meta-learner algorithms

• S-learner

• T-learner

• X-learner

• R-learner

Supported Usage

This notebook will show how to use visualization for:

• Uplift Tree and Uplift Random Forest

  • Visualize a trained uplift classification tree model

  • Visualize an uplift tree in a trained uplift random forests

• Training and Validation Data

  • Visualize the validation tree: fill the trained uplift classification tree with validation (or testing) data, and show the statistics for both training data and validation data

• One Treatment Group and Multiple Treatment Groups

  • Visualize the case where there are one control group and one treatment group

  • Visualize the case where there are one control group and multiple treatment groups

Step 1 Load Modules

from causalml.dataset import make_uplift_classification
from causalml.inference.tree import UpliftTreeClassifier, UpliftRandomForestClassifier
from causalml.inference.tree import uplift_tree_string, uplift_tree_plot

import numpy as np
import pandas as pd
from IPython.display import Image
from sklearn.model_selection import train_test_split

One Control + One Treatment for Uplift Classification Tree

# Data generation
df, x_names = make_uplift_classification()

# Rename features for easy interpretation of visualization
x_names_new = ['feature_%s'%(i) for i in range(len(x_names))]
rename_dict = {x_names[i]:x_names_new[i] for i in range(len(x_names))}
df = df.rename(columns=rename_dict)
x_names = x_names_new

df.head()

df = df[df['treatment_group_key'].isin(['control','treatment1'])]

# Look at the conversion rate and sample size in each group
df.pivot_table(values='conversion',
               index='treatment_group_key',
               aggfunc=[np.mean, np.size],
               margins=True)

	mean	size
	conversion	conversion
treatment_group_key
control	0.5110	1000
treatment1	0.5140	1000
All	0.5125	2000

# Split data to training and testing samples for model validation (next section)
df_train, df_test = train_test_split(df, test_size=0.2, random_state=111)

# Train uplift tree
uplift_model = UpliftTreeClassifier(max_depth = 4, min_samples_leaf = 200, min_samples_treatment = 50, n_reg = 100, evaluationFunction='KL', control_name='control')

uplift_model.fit(df_train[x_names].values,
                 treatment=df_train['treatment_group_key'].values,
                 y=df_train['conversion'].values)

# Print uplift tree as a string
result = uplift_tree_string(uplift_model.fitted_uplift_tree, x_names)

feature_17 >= -0.44234212654232735?
yes -> feature_10 >= 1.020659213325515?
                yes -> [0.3813559322033898, 0.6065573770491803]
                no  -> [0.5078125, 0.5267857142857143]
no  -> feature_9 >= 0.8142773340486678?
                yes -> [0.4596774193548387, 0.61]
                no  -> feature_4 >= 0.280545459525536?
                                yes -> [0.5522875816993464, 0.4143302180685358]
                                no  -> [0.5070422535211268, 0.5748031496062992]

Read the tree

• First line: node split condition

• impurity: the value for the loss function

• total_sample: total sample size in this node

• group_sample: sample size by treatment group

• uplift score: the treatment effect between treatment and control (when there are multiple treatment groups, this is the maximum of the treatment effects)

• uplift p_value: the p_value for the treatment effect

• validation uplift score: when validation data is filled in the tree, this reflects the uplift score based on the - validation data. It can be compared with the uplift score (for training data) to check if there are over-fitting issue.

# Plot uplift tree
graph = uplift_tree_plot(uplift_model.fitted_uplift_tree,x_names)
Image(graph.create_png())

Visualize Validation Tree: One Control + One Treatment for Uplift Classification Tree

Note the validation uplift score will update.

### Fill the trained tree with testing data set
# The uplift score based on testing dataset is shown as validation uplift score in the tree nodes
uplift_model.fill(X=df_test[x_names].values, treatment=df_test['treatment_group_key'].values, y=df_test['conversion'].values)

# Plot uplift tree
graph = uplift_tree_plot(uplift_model.fitted_uplift_tree,x_names)
Image(graph.create_png())

Visualize a Tree in Random Forest

# Split data to training and testing samples for model validation (next section)
df_train, df_test = train_test_split(df, test_size=0.2, random_state=111)

# Train uplift tree
uplift_model = UpliftRandomForestClassifier(n_estimators=5, max_depth = 5, min_samples_leaf = 200, min_samples_treatment = 50, n_reg = 100, evaluationFunction='KL', control_name='control')

uplift_model.fit(df_train[x_names].values,
                 treatment=df_train['treatment_group_key'].values,
                 y=df_train['conversion'].values)

# Specify a tree in the random forest (the index can be any integer from 0 to n_estimators-1)
uplift_tree = uplift_model.uplift_forest[0]
# Print uplift tree as a string
result = uplift_tree_string(uplift_tree.fitted_uplift_tree, x_names)

feature_0 >= -0.44907381030867755?
yes -> feature_6 >= -0.0583060585067711?
                yes -> feature_9 >= 0.03401322870693866?
                                yes -> [0.4774193548387097, 0.5396825396825397]
                                no  -> [0.34615384615384615, 0.6129032258064516]
                no  -> feature_12 >= 0.4863045964698285?
                                yes -> [0.48299319727891155, 0.5714285714285714]
                                no  -> [0.582089552238806, 0.4452054794520548]
no  -> feature_10 >= 1.0043523431178796?
                yes -> [0.4807692307692308, 0.35766423357664234]
                no  -> [0.5229357798165137, 0.5426356589147286]

# Plot uplift tree
graph = uplift_tree_plot(uplift_tree.fitted_uplift_tree,x_names)
Image(graph.create_png())

Fill the tree with validation data

### Fill the trained tree with testing data set
# The uplift score based on testing dataset is shown as validation uplift score in the tree nodes
uplift_tree.fill(X=df_test[x_names].values, treatment=df_test['treatment_group_key'].values, y=df_test['conversion'].values)

# Plot uplift tree
graph = uplift_tree_plot(uplift_tree.fitted_uplift_tree,x_names)
Image(graph.create_png())

One Control + Multiple Treatments

# Data generation
df, x_names = make_uplift_classification()
# Look at the conversion rate and sample size in each group
df.pivot_table(values='conversion',
               index='treatment_group_key',
               aggfunc=[np.mean, np.size],
               margins=True)

	mean	size
	conversion	conversion
treatment_group_key
control	0.511	1000
treatment1	0.514	1000
treatment2	0.559	1000
treatment3	0.600	1000
All	0.546	4000

# Split data to training and testing samples for model validation (next section)
df_train, df_test = train_test_split(df, test_size=0.2, random_state=111)

# Train uplift tree
uplift_model = UpliftTreeClassifier(max_depth = 3, min_samples_leaf = 200, min_samples_treatment = 50, n_reg = 100, evaluationFunction='KL', control_name='control')

uplift_model.fit(df_train[x_names].values,
                 treatment=df_train['treatment_group_key'].values,
                 y=df_train['conversion'].values)

# Plot uplift tree
# The uplift score represents the best uplift score among all treatment effects
graph = uplift_tree_plot(uplift_model.fitted_uplift_tree,x_names)
Image(graph.create_png())

# Save the graph as pdf
graph.write_pdf("tbc.pdf")
# Save the graph as png
graph.write_png("tbc.png")

True