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Categorical Model Explanation using the Python Client

This is a sample code to use our EXPAI Client for a Categorical Model step by step

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Imports

Remember to install our Python client by using

`pip install -U expai`

from expai import ExpaiAccount

import numpy as np

import pandas as pd

import os

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# Trust notebook for correct plots rendering

!jupyter trust Categorical\ Model\ Explanation.ipynb

Login to your account

email = 'YOUR-EMAIL'

user_pass = 'YOUR-PASS'

expai_client = ExpaiAccount(email=email, user_pass=user_pass)

Create a Project

The first step to interact with your account is creating a Project.

expai_client.create_project("Fairness Project")

Interact with your Project

We will first obtain a Project object that will allow us to interact with its content and generate explanations.

project = expai_client.get_project(project_name = "Fairness Project")

Create a model

In this step, we use the model provided here. All parameters and their descriptions can be found in the Python client docs.

project.create_model(model_path = os.path.abspath("../models/fairness_pipeline.pkl"),

model_name="Potential model",

model_summary="Annual revenue over 100K$ model",

model_library = 'pickle',

model_objective = 'classification',

model_prediction_type = "binary",

output_classes = ["No", "Yes"])

Create a sample

In this step, we use the dataset provided here. All parameters and their descriptions can be found in the Python client docs.

# Sample - Categorical

project.create_sample(sample_path=os.path.abspath("../datasets/fairness.csv"),

sample_name="Dataset",

sample_encoding='utf-8',

sample_separator=";",

sample_target_col = "target",

protected_columns=['race', 'sex'],

drop_columns=['race', 'sex'],

is_display=False)

Explain the model

Once we have included a model and a sample to our project, we can execute all available explanations. First of all, we must generate a Model Explainer object for our model.

# Set global variables for explanation

model_name = "Potential model"

sample_name = "Dataset"

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# Get Model Explainer object

explainer = project.get_model_explainer(model_name)

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# By default, set to None. Use the following section to replace

subset_indexes = None

Defining a subgroup for explanations

This step can be skipped if you are not interested on defining a subgroup.

Sometimes, we don't want to study how our model works for the whole dataset but for a specific meaningful subgroup. In this case, we could be interested on studying how the model behaves for people under 40 years old.

# Get the stored sample for filtering

df = project.get_sample(sample_name=sample_name)

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# Filter the dataframe

subset = df[df['age']<=40]

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# Obtain the indexes for our samples of interest

subset_indexes = list(subset.index)

Generate Explanations and Plots

Once we obtained the Model Explainer, we can use it to generate all possible explanations for our model. When an explanation is generated an Explanation object will be returned.

Model Explanation

This explanation represents the importance of each variable in the predictions. It is computed as the increase in the prediction error when this variable is removed.

# Generate the explanation

exp_model = explainer.explain_model(sample_name=sample_name, subset_indexes=subset_indexes)

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# Plot

exp_model.plot_all()

Output for Model Explanation

Variable Explanation

In this case, we will explore the effect of a given variable in our predictions. In other words, we will represent the average prediction of the model in terms of the variable.

# Variables that we want to explain

variables = ['marital-status', 'capital-gain']

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# Type for the variables (categorical or numerical)

variables_type = {'marital-status': 'categorical',

'capital-gain': 'numerical'}

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# Generate explanation

exp_var = explainer.explain_variable_effect(sample_name=sample_name, variables=variables, variables_type=variables_type, subset_indexes=subset_indexes)

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# Plot all of them

exp_var.plot_all()

Output sample for Variable Explanation

Explain a unique entry

In this explanation, we will be able to understand which was the impact of each variable for the prediction of a unique entry in our dataset.

# Select the index to be explained. If we filtered, it must be within the filtered dataframe.

index_to_explain = 2

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# Generate explanation

exp_sample = explainer.explain_sample(sample_name=sample_name, index=index_to_explain, subset_indexes=subset_indexes)

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# Plot

exp_sample.plot_all()

Output sample for entry explanation

WHAT IF

This explanation will allow you to see what would happen if we change only one variable in the entry while all remaining variables are kept the same. It will plot all possible values for a variable and the prediction for them.

# Select index to analyze

index_to_explain = 2

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# Variables to analyze in this sample

variables = ['age']

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# Type for the variables

variables_type = {'age': 'numerical'}

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exp_what_if = explainer.what_if(sample_name=sample_name, index=index_to_explain, variables=variables, variables_type=variables_type, subset_indexes=subset_indexes)

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# Plot

exp_what_if.plot_all()

WHAT IF example

WHAT IF BATTLE

This explanation is similar to the previous one but now we can replace all the values we want at the same time and study how predictions will change.

# Select index to analyze

index_to_explain = 2

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# Values to be replaced

replace_dict = {'age': 35,

'capital-gain': 5000}

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exp_what_if_battle = explainer.what_if_battle(sample_name=sample_name, index=index_to_explain, replace_dict=replace_dict, subset_indexes=subset_indexes)

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# Plot

exp_what_if_battle.plot_all()

WHAT IF Battle example.

Last modified 7mo ago