Source code for pyts.image.gaf

"""Code for Gramian Angular Field."""

# Author: Johann Faouzi <>
# License: BSD-3-Clause

import numpy as np
from math import ceil
from numba import njit, prange
from sklearn.base import BaseEstimator
from sklearn.utils.validation import check_array
from ..approximation import PiecewiseAggregateApproximation
from ..base import UnivariateTransformerMixin
from ..preprocessing import MinMaxScaler

def _gasf(X_cos, X_sin, n_samples, image_size):
    X_gasf = np.empty((n_samples, image_size, image_size))
    for i in prange(n_samples):
        X_gasf[i] = np.outer(X_cos[i], X_cos[i]) - np.outer(X_sin[i], X_sin[i])
    return X_gasf

def _gadf(X_cos, X_sin, n_samples, image_size):
    X_gadf = np.empty((n_samples, image_size, image_size))
    for i in prange(n_samples):
        X_gadf[i] = np.outer(X_sin[i], X_cos[i]) - np.outer(X_cos[i], X_sin[i])
    return X_gadf

[docs]class GramianAngularField(BaseEstimator, UnivariateTransformerMixin): """Gramian Angular Field. Parameters ---------- image_size : int or float (default = 1.) Shape of the output images. If float, it represents a percentage of the size of each time series and must be between 0 and 1. Output images are square, thus providing the size of one dimension is enough. sample_range : None or tuple (min, max) (default = (-1, 1)) Desired range of transformed data. If None, no scaling is performed and all the values of the input data must be between -1 and 1. If tuple, each sample is scaled between min and max; min must be greater than or equal to -1 and max must be lower than or equal to 1. method : 'summation' or 'difference' (default = 'summation') Type of Gramian Angular Field. 's' can be used for 'summation' and 'd' for 'difference'. overlapping : bool (default = False) If True, reduce the size of each time series using PAA with possible overlapping windows. flatten : bool (default = False) If True, images are flattened to be one-dimensional. References ---------- .. [1] Z. Wang and T. Oates, "Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks." AAAI Workshop (2015). Examples -------- >>> from pyts.datasets import load_gunpoint >>> from pyts.image import GramianAngularField >>> X, _, _, _ = load_gunpoint(return_X_y=True) >>> transformer = GramianAngularField() >>> X_new = transformer.transform(X) >>> X_new.shape (50, 150, 150) """
[docs] def __init__(self, image_size=1., sample_range=(-1, 1), method='summation', overlapping=False, flatten=False): self.image_size = image_size self.sample_range = sample_range self.method = method self.overlapping = overlapping self.flatten = flatten
[docs] def fit(self, X=None, y=None): """Pass. Parameters ---------- X Ignored y Ignored Returns ------- self : object """ return self
[docs] def transform(self, X): """Transform each time series into a GAF image. Parameters ---------- X : array-like, shape = (n_samples, n_timestamps) Returns ------- X_new : array-like, shape = (n_samples, image_size, image_size) Transformed data. If ``flatten=True``, the shape is `(n_samples, image_size * image_size)`. """ X = check_array(X) n_samples, n_timestamps = X.shape image_size = self._check_params(n_timestamps) paa = PiecewiseAggregateApproximation( window_size=None, output_size=image_size, overlapping=self.overlapping ) X_paa = paa.fit_transform(X) if self.sample_range is None: X_min, X_max = np.min(X_paa), np.max(X_paa) if (X_min < -1) or (X_max > 1): raise ValueError("If 'sample_range' is None, all the values " "of X must be between -1 and 1.") X_cos = X_paa else: scaler = MinMaxScaler(sample_range=self.sample_range) X_cos = scaler.fit_transform(X_paa) X_sin = np.sqrt(np.clip(1 - X_cos ** 2, 0, 1)) if self.method in ['s', 'summation']: X_new = _gasf(X_cos, X_sin, n_samples, image_size) else: X_new = _gadf(X_cos, X_sin, n_samples, image_size) if self.flatten: return X_new.reshape(n_samples, -1) return X_new
def _check_params(self, n_timestamps): if not isinstance(self.image_size, (int, np.integer, float, np.floating)): raise TypeError("'image_size' must be an integer or a float.") if isinstance(self.image_size, (int, np.integer)): if self.image_size < 1 or self.image_size > n_timestamps: raise ValueError( "If 'image_size' is an integer, it must be greater " "than or equal to 1 and lower than or equal to " "n_timestamps (got {0}).".format(self.image_size) ) image_size = self.image_size else: if not (0 < self.image_size <= 1.): raise ValueError( "If 'image_size' is a float, it must be greater " "than 0 and lower than or equal to 1 (got {0})." .format(self.image_size) ) image_size = ceil(self.image_size * n_timestamps) if not ((self.sample_range is None) or (isinstance(self.sample_range, tuple))): raise TypeError("'sample_range' must be None or a tuple.") if isinstance(self.sample_range, tuple): if len(self.sample_range) != 2: raise ValueError("If 'sample_range' is a tuple, its length " "must be equal to 2.") if not -1 <= self.sample_range[0] < self.sample_range[1] <= 1: raise ValueError( "If 'sample_range' is a tuple, it must satisfy " "-1 <= sample_range[0] < sample_range[1] <= 1." ) if self.method not in ['s', 'd', 'summation', 'difference']: raise ValueError("'method' must be either 'summation', 's', " "'difference' or 'd'.") return image_size