Python - Random Sample Training and Test Data from dictionary

Python - Random Sample Training and Test Data from dictionary

If you have your data in a Python dictionary and you want to split it into training and test sets randomly, you can use the train_test_split function from the sklearn.model_selection module in the Scikit-learn library. This function is typically used for splitting arrays or matrices of data, but you can adapt it to work with data from a dictionary.

Here's a step-by-step guide on how to do it:

Step 1: Install Scikit-learn

First, ensure you have Scikit-learn installed. You can install it using pip if you haven't already:

pip install scikit-learn

Step 2: Prepare Your Data

Assuming you have a dictionary where each key-value pair corresponds to a feature and its values, you first need to convert this into a format that can be used with train_test_split.

Let's say you have a dictionary like this:

data = { 'feature1': [value1, value2, value3, ...], 'feature2': [value1, value2, value3, ...], ... 'target': [target1, target2, target3, ...] }

You need to separate the features from the target values.

Step 3: Split the Data into Features and Target

Extract the features and target from the dictionary:

X = [ [data['feature1'][i], data['feature2'][i], ...] for i in range(len(data['feature1'])) ] y = data['target']

Step 4: Import train_test_split

Import the train_test_split function:

from sklearn.model_selection import train_test_split

Step 5: Split the Data into Training and Test Sets

Now, use train_test_split to split your data:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

In this line, test_size=0.2 means that 20% of the data will be used for the test set, and 80% for the training set. random_state=42 is used for reproducibility of the results.

Step 6: Use the Split Data

You can now use X_train, X_test, y_train, and y_test in your machine learning model.

Full Example

Here's a full example putting all the steps together:

import sklearn from sklearn.model_selection import train_test_split # Example data data = { 'feature1': [1, 2, 3, 4, 5], 'feature2': [6, 7, 8, 9, 10], 'target': [0, 1, 0, 1, 0] } # Preparing the data X = [ [data['feature1'][i], data['feature2'][i]] for i in range(len(data['feature1'])) ] y = data['target'] # Splitting the data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Outputs print("X_train:", X_train) print("X_test:", X_test) print("y_train:", y_train) print("y_test:", y_test)

This example demonstrates a simple case with two features. In a real-world scenario, you might have more complex data and additional preprocessing steps.

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