wehub-resource-sync
61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
"""Add polynomial features to the features set"""
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import numpy as np
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from .normalize import normalize
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def generate_polynomials(dataset, polynomial_degree, normalize_data=False):
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"""Extends data set with polynomial features of certain degree.
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Returns a new feature array with more features, comprising of
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x1, x2, x1^2, x2^2, x1*x2, x1*x2^2, etc.
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:param dataset: dataset that we want to generate polynomials for.
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:param polynomial_degree: the max power of new features.
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:param normalize_data: flag that indicates whether polynomials need to normalized or not.
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"""
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# Split features on two halves.
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features_split = np.array_split(dataset, 2, axis=1)
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dataset_1 = features_split[0]
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dataset_2 = features_split[1]
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# Extract sets parameters.
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(num_examples_1, num_features_1) = dataset_1.shape
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(num_examples_2, num_features_2) = dataset_2.shape
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# Check if two sets have equal amount of rows.
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if num_examples_1 != num_examples_2:
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raise ValueError('Can not generate polynomials for two sets with different number of rows')
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# Check if at list one set has features.
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if num_features_1 == 0 and num_features_2 == 0:
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raise ValueError('Can not generate polynomials for two sets with no columns')
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# Replace empty set with non-empty one.
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if num_features_1 == 0:
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dataset_1 = dataset_2
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elif num_features_2 == 0:
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dataset_2 = dataset_1
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# Make sure that sets have the same number of features in order to be able to multiply them.
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num_features = num_features_1 if num_features_1 < num_examples_2 else num_features_2
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dataset_1 = dataset_1[:, :num_features]
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dataset_2 = dataset_2[:, :num_features]
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# Create polynomials matrix.
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polynomials = np.empty((num_examples_1, 0))
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# Generate polynomial features of specified degree.
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for i in range(1, polynomial_degree + 1):
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for j in range(i + 1):
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polynomial_feature = (dataset_1 ** (i - j)) * (dataset_2 ** j)
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polynomials = np.concatenate((polynomials, polynomial_feature), axis=1)
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# Normalize polynomials if needed.
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if normalize_data:
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polynomials = normalize(polynomials)[0]
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# Return generated polynomial features.
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return polynomials
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