Preprocessing Tabular Data

In this notebook, we focus on preprocessing tabular data. Then, we use our preprocessed tabular data to select important features and prune unimportant ones out. Finally, we use our selected features to train a machine learning model. We showcase how to preprocess 2 different tabular data sets.

Dataset and Package Dependencies

Library Dependencies:

• sagemaker>=2.84.0

• numpy

• pandas

• plotly

• sklearn

• matplotlib

• seaborn

Setting up the notebook

[ ]:

import os
import sys
import subprocess
import pkg_resources

def get_sagemaker_version():
"Return the version of 'sagemaker' in your kernel or -1 if 'sagemaker' is not installed"
for i in pkg_resources.working_set:
if i.key == "sagemaker":
return "%s==%s" % (i.key, i.version)
return -1

# Store original 'sagemaker' version
sagemaker_version = get_sagemaker_version()

# Install any missing dependencies
!{sys.executable} -m pip install -qU 'plotly' 'sagemaker>=2.84.0'

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import pyplot
import seaborn as sns
import pickle
import ast
import plotly.express as px
import plotly.offline as pyo
import plotly.graph_objs as go

from sklearn.datasets import *
import sklearn.model_selection
from sklearn.datasets import make_regression
import sklearn.model_selection
from sklearn.neighbors import KNeighborsRegressor
from sklearn.inspection import permutation_importance

!{sys.executable} -m pip install -qU 'xgboost'
import xgboost
from xgboost import XGBRegressor

# SageMaker dependencies
import sagemaker
from sagemaker import get_execution_role
from sagemaker.inputs import TrainingInput
from sagemaker.image_uris import retrieve

# This instantiates a SageMaker session that we will be operating in.
session = sagemaker.Session()

# This object represents the IAM role that we are assigned.
role = sagemaker.get_execution_role()
print(role)


This section demonstrates how to preprocess tabular data for training a machine learning model via Amazon SageMaker

Here you can select the tabular data set of your choice to preprocess.

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data_sets = {"diabetes": "load_diabetes()", "california": "fetch_california_housing()"}


To do select a particular dataset, assign choosen_data_set below to be one of ‘diabetes’, or ‘california’ where each name corresponds to the it’s respective dataset.

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# Change choosen_data_set variable to one of the data sets above.
choosen_data_set = "california"
assert choosen_data_set in data_sets.keys()
print("I selected the '{}' dataset!".format(choosen_data_set))


Step 2: Describe Feature Information

Here you can select the tabular data set of your choice to preprocess.

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data_set = eval(data_sets[choosen_data_set])

X = pd.DataFrame(data_set.data, columns=data_set.feature_names)
Y = pd.DataFrame(data_set.target)

print("Features:", list(X.columns))
print("Dataset shape:", X.shape)
print("Dataset Type:", type(X))
print("Label set shape:", Y.shape)
print("Label set Type:", type(X))


We describe both our training data inputs X and outputs Y by computing the count, mean, std, min, percentiles.

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display(X.describe())

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display(Y.describe())


Step 3: Plot on Feature Correlation

Here we show a heatmap and clustergrid across all our features. These visualizations help us analyze correlated features and are particularly important if we want to remove redundant features. The heatmap computes a similarity score across each feature and colors like features using this score. The clustergrid is similar, however it presents feature correlations hierarchically.

Note: For the purposes of this notebook we do not remove any features but by gathering the findings from these plots one may choose to and can do so at this point.

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plt.figure(figsize=(14, 12))
cor = X.corr()
sns.heatmap(cor, annot=True, cmap=sns.diverging_palette(20, 220, n=200))
plt.show()

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cluster_map = sns.clustermap(cor, cmap=sns.diverging_palette(20, 220, n=200), linewidths=0.1)
plt.setp(cluster_map.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
cluster_map


Step 4: Partition Dataset into Train, Test, Validation Splits

Here using the sklearn framework we partition our selected dataset into Train, Test and Validation splits. We choose a partition size of 1/3 and then further split the training set into 2/3 training and 1/3 validation set.

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# We partition the dataset into 2/3 training and 1/3 test set.
X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(X, Y, test_size=0.33)

# We further split the training set into a validation set i.e., 2/3 training set, and 1/3 validation set
X_train, X_val, Y_train, Y_val = sklearn.model_selection.train_test_split(
X_train, Y_train, test_size=0.33
)


Part 2: Feature Selection for Tabular Data

This section demonstrates how to select important features and prune unimportant ones prior to training our machine learning model. This is an important step that yields better prediction performance.

Step 1: Computing Feature Importance Scores to Select Features

We show two approaches for computing feature importance scores for each feature. We can rank each feature by their corresponding feature importance score in an effort to prune unimportant features which will yield a better performing model.

The first approach, uses XGBoost and the second uses permutation feature importance.

Step 1a: Ranking features by Feature Importance using XGBoost

Here we use gradient boosting to extract importance scores for each feature. The importance scores calculated for each feature inform us how useful the feature was for constructing the boosted decision tree and can be ranked and compared to one another for feature selection.

[ ]:

X_data, y_label = make_regression(
n_samples=X_train.shape[0], n_features=X_train.shape[1], n_informative=10, random_state=1
)
xgboost_model = XGBRegressor()
xgboost_model.fit(X_data, y_label)

feature_importances_xgboost = xgboost_model.feature_importances_
for index, importance_score in enumerate(feature_importances_xgboost):
print("Feature: {}, Score: {}".format(X_train.columns[index], importance_score))

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def create_bar_plot(feature_importances, X_train):
"""
Create a bar plot of features against their corresponding feature importance score.
"""
x_indices = [_ for _ in range(len(feature_importances))]
plt.figure(figsize=(15, 5))
plt.bar(x_indices, feature_importances, color="blue")
plt.xticks(x_indices, X_train.columns)
plt.xlabel("Feature", fontsize=18)
plt.ylabel("Importance Score", fontsize=18)
plt.title("Feature Importance Scores", fontsize=18)
plt.show()

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create_bar_plot(feature_importances_xgboost, X_train)


In the following cell, we rank each feature based on corresponding importance score.

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def show_ranked_feature_importance_list(scores, data):
"""
Prints the features ranked by their corresponding importance score.
"""
lst = list(zip(data.columns, scores))
ranked_lst = sorted(lst, key=lambda t: t[1], reverse=True)
print(pd.DataFrame(ranked_lst, columns=["Feature", "Importance Score"]))

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show_ranked_feature_importance_list(feature_importances_xgboost, X_train)


Step 1b: Ranking features by Permutation Feature Importance using the Scikit-learn k-NN Algorithm

This approach is commonly used for selecting features in tabular data. We first randomly shuffle a single feature value and train a model. In this example we use the k-nearest-neighbours algorithm to train our model. The permutation feature importance score is the decrease in models score when this single feature value is shuffled. The decrease in the model score is representative of how dependant the model is on the feature. This technique can be computed many times with altering permutations per feature.

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X_data, y_label = make_regression(
n_samples=X_train.shape[0], n_features=X_train.shape[1], n_informative=10, random_state=1
)
k_nn_model = KNeighborsRegressor()
k_nn_model.fit(X_data, y_label)
feature_importances_permutations = permutation_importance(
k_nn_model, X_data, y_label, scoring="neg_mean_squared_error"
).importances_mean

for index, importance_score in enumerate(feature_importances_permutations):
print("Feature: {}, Score: {}".format(X_train.columns[index], importance_score))

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create_bar_plot(feature_importances_permutations, X_train)

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show_ranked_feature_importance_list(feature_importances_permutations, X_train)


Step 2: Prune Unimportant Features

Thus far, we have discussed two common approaches for obtaining a ranked list of feature importance scores for each feature. From these lists we can infer unimportant features based on their importance scores and can eliminate them from our training, validation and test sets. For example, if feature A has a higher importance score then feature B’s importance score, then this implies that feature A is more important then feature B and vice versa. We mention that both approaches constrain the removal of features to the dataset itself which is independent of the problem domain.

After selecting your desired approach, move onto the next cell to prune features that have the importance score less than or equal to a threshold value. Depending on the approach of your choice and the distribution of scores, the threshold value may vary.

In this example, we select the first approach with XGBoost and set the threshold value to 0.01.

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threshold = 0.01

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def remove_features(lst, data, threshold):
"""
Remove features found in lst from data iff its importance score is below threshold.
"""
features_to_remove = []
for index, pair in enumerate(list(zip(data.columns, lst))):
if pair[1] <= threshold:
features_to_remove.append(pair[0])

if features_to_remove:
data.drop(features_to_remove, axis=1)


Assign lst to be feature_importances_permutations or feature_importances_xgboost if want to use the ranked list from that uses XGBoost or permutation feature importance respectively.

We remove all features that are below threshold from our training data, X_train, validation data, X_val and testing data X_test respectively.

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remove_features(lst=feature_importances_xgboost, data=X_train, threshold=threshold)
remove_features(lst=feature_importances_xgboost, data=X_val, threshold=threshold)
remove_features(lst=feature_importances_xgboost, data=X_test, threshold=threshold)


Part 3: Training a Model on Tabular Data using Amazon SageMaker

This section demonstrates how to train a machine learning model via Amazon SageMaker using tabular data. You can train either an XGBoost or Linear Learner (regression) model on tabular data in Amazon SageMaker.

Here we upload our training and validation data to an S3 bucket. This is a critical step because we will be specifying this S3 bucket’s location during the training step.

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data_dir = "../data/" + choosen_data_set
if not os.path.exists(data_dir):
os.makedirs(data_dir)

prefix = choosen_data_set + "-deploy-hl"
pd.concat([Y_train, X_train], axis=1).to_csv(
)
pd.concat([Y_val, X_val], axis=1).to_csv(
)



Here we have a pointer to our training and validation data sets stored in an S3 bucket.

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s3_input_train = TrainingInput(s3_data=train_location, content_type="text/csv")
s3_input_validation = TrainingInput(s3_data=val_location, content_type="text/csv")


Step 2: Select and Train the Model

Select between the XGBoost or Linear Learner algorithm by assigning model_selected to either ‘xgboost’ or ‘linear-learner’.

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# Select between xgboost or linear-learner (regression)
models = ["xgboost", "linear-learner"]
model_selected = "xgboost"
assert model_selected in models
print("Selected model:", model_selected)


Here we retrieve our container and instantiate our model object using the Estimator class.

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container = retrieve(framework=model_selected, region=session.boto_region_name, version="1.5-1")

model = sagemaker.estimator.Estimator(
container,
role,
instance_count=1,
instance_type="ml.m4.xlarge",
output_path="s3://{}/{}/output".format(session.default_bucket(), prefix),
sagemaker_session=session,
)


Step 3: Set hyperparameters

Thus far, we have instantiated our model with our container and uploaded our preprocessed data to our S3 bucket. Next, we set our hyperparameters for our choosen model. We note that both XGBoost and linear learner have different hyperparameters that can be set.

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if model_selected == "xgboost":
model.set_hyperparameters(
max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.8,
objective="reg:linear",
early_stopping_rounds=10,
num_round=1,
)

if model_selected == "linear-learner":
model.set_hyperparameters(
feature_dim=X_train.shape[1], predictor_type="regressor", mini_batch_size=100
)


Our estimator object is instantiated with hyperparameter settings, now it is time to train! To do this we specify our S3 bucket’s location that is storing our training data and validation data and pass it via a dictionary to the fit method.

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model.fit({"train": s3_input_train, "validation": s3_input_validation}, wait=True)


Step 4: Save Trained Model

The model has been trained. Below we select and download the model we just trained above.

To download the last trained model we assign the s3_uri parameter to be model.model_data.

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sagemaker.s3.S3Downloader.download(s3_uri=model.model_data, local_path="./")

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