Sequence padding and one-hot encoding of independent variables|S-Logix

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How to Do Sequence Padding and One-Hot Encoding of Independent Variables

Sequence Padding and One-Hot Encoding

Condition for Sequence Padding and One-Hot Encoding

Description:
Sequence Padding: This technique is used to adjust the length of input sequences to a fixed size by adding padding values (usually zeros) to ensure all sequences are equal in length.

One-Hot Encoding: This technique is used to convert categorical variables into a binary matrix, where each category is represented by a unique column with a 1 for presence and 0 for absence.

Step-by-Step Process

Sequence Padding:
Import libraries like keras.preprocessing.sequence to handle sequence padding.
Prepare the Sequences:
Create a list of sequences that you want to pad.
Pad Sequences:
Use the pad_sequences() function to pad the sequences to a fixed length.
One-Hot Encoding:
Use OneHotEncoder from sklearn.preprocessing or pandas.get_dummies() to convert categorical data into a binary matrix.

Sample Source Code

# Code for Padding sequence

from keras.preprocessing.sequence import pad_sequences

sequences = [
[1, 2, 3, 4, 5],
[6, 7, 8],
[9, 10, 11, 12],
[13, 14],
[15, 16, 17, 18, 19, 20]
]

# Padding sequences to have the same length
padded_sequences = pad_sequences(sequences, maxlen=6, padding='post', truncating='post')

print("Padded Sequences:")
print(padded_sequences)

# Code for One-hot encoding
import pandas as pd
from sklearn.preprocessing import OneHotEncoder

categories = ['dog', 'cat', 'dog', 'bird', 'cat', 'dog', 'bird', 'cat']

categories_reshaped = pd.DataFrame(categories, columns=['Animal'])

encoder = OneHotEncoder(sparse_output=False)

# Fit and transform the data to obtain one-hot encoded values
encoded_data = encoder.fit_transform(categories_reshaped)

df_encoded = pd.DataFrame(encoded_data, columns=encoder.categories_[0])

print("\nOne-Hot Encoded Data:")
print(df_encoded)

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