added the Boss-SP algorithm and its example. Tests are still missing. #156
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Not a pull request meant to be accepted, but a pull request meant to share code and get feedback |
pyts/classification/bosssp.py
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| level : integer (default = 3) | ||
| Number of times the series is being divided. Maximum of 3. |
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The order of the parameters is relevant: the parameters are usually sorted based on their relevance / importance. level is a key element of BOSSSP (compared to BOSS), so it should be higher on the list (above window_step I would say).
pyts/classification/bosssp.py
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| anova=False, drop_sum=False, norm_mean=False, norm_std=False, | ||
| strategy='quantile', alphabet=None, | ||
| numerosity_reduction=True, use_idf=True, smooth_idf=False, | ||
| sublinear_tf=True, level=3): |
pyts/classification/bosssp.py
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| self.use_idf = use_idf | ||
| self.smooth_idf = smooth_idf | ||
| self.sublinear_tf = sublinear_tf | ||
| self.level = level |
pyts/classification/bosssp.py
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| y[:length//2]) | ||
| words_3 = self._boss_word_extractor(X[length//2:], | ||
| y[:length//2:]) | ||
| words = words + words_2 + words_3 |
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What are concatenated in BOSSSP are the histograms (and not the list of words). For instance, if level=3, the final histogram is the concatenation of 7 histograms. You need to first compute the histogram for each words_i list, then concatenate the histogram, instead of concatenating first the words_i lists and then computing the histogram.
pyts/classification/bosssp.py
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| else: | ||
| X_bow = np.asarray([' '.join(X_word[i]) for i in range(n_samples)]) | ||
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| words_list = np.asarray(X_bow[0].split(' ')).tolist() |
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You only get the transform for the first time series (X_bow[0]).
pyts/classification/bosssp.py
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| def _check_params(self, n_timestamps): | ||
| if not isinstance(self.word_size, (int, np.integer)): | ||
| raise TypeError("'word_size' must be an integer.") | ||
| if not self.word_size >= 1: | ||
| raise ValueError("'word_size' must be a positive integer.") | ||
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| if not isinstance(self.window_size, | ||
| (int, np.integer, float, np.floating)): | ||
| raise TypeError("'window_size' must be an integer or a float.") | ||
| if isinstance(self.window_size, (int, np.integer)): | ||
| if self.drop_sum: | ||
| if not 1 <= self.window_size <= (n_timestamps - 1): | ||
| raise ValueError( | ||
| "If 'window_size' is an integer, it must be greater " | ||
| "than or equal to 1 and lower than or equal to " | ||
| "(n_timestamps - 1) if 'drop_sum=True'." | ||
| ) | ||
| else: | ||
| if not 1 <= self.window_size <= n_timestamps: | ||
| raise ValueError( | ||
| "If 'window_size' is an integer, it must be greater " | ||
| "than or equal to 1 and lower than or equal to " | ||
| "n_timestamps if 'drop_sum=False'." | ||
| ) | ||
| window_size = self.window_size | ||
| else: | ||
| if not 0 < self.window_size <= 1: | ||
| raise ValueError( | ||
| "If 'window_size' is a float, it must be greater " | ||
| "than 0 and lower than or equal to 1." | ||
| ) | ||
| window_size = ceil(self.window_size * n_timestamps) | ||
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| if not isinstance(self.window_step, | ||
| (int, np.integer, float, np.floating)): | ||
| raise TypeError("'window_step' must be an integer or a float.") | ||
| if isinstance(self.window_step, (int, np.integer)): | ||
| if not 1 <= self.window_step <= n_timestamps: | ||
| raise ValueError( | ||
| "If 'window_step' is an integer, it must be greater " | ||
| "than or equal to 1 and lower than or equal to " | ||
| "n_timestamps." | ||
| ) | ||
| window_step = self.window_step | ||
| else: | ||
| if not 0 < self.window_step <= 1: | ||
| raise ValueError( | ||
| "If 'window_step' is a float, it must be greater " | ||
| "than 0 and lower than or equal to 1." | ||
| ) | ||
| window_step = ceil(self.window_step * n_timestamps) | ||
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| if self.drop_sum: | ||
| if not self.word_size <= (window_size - 1): | ||
| raise ValueError( | ||
| "'word_size' must be lower than or equal to " | ||
| "(window_size - 1) if 'drop_sum=True'." | ||
| ) | ||
| else: | ||
| if not self.word_size <= window_size: | ||
| raise ValueError( | ||
| "'word_size' must be lower than or equal to " | ||
| "window_size if 'drop_sum=False'." | ||
| ) | ||
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| if not isinstance(self.level, (int, np.integer)): | ||
| raise TypeError("'level' must be an integer.") | ||
| if not self.level >= 1: | ||
| raise ValueError("'level' must be a positive integer.") | ||
| if self.level >= 4: | ||
| raise ValueError("'level' must not exceed 3.") | ||
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| return window_size, window_step |
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Likewise, it might be easier to use the corresponding of method of BOSS if the code is the same. You could just add the checks for level.
pyts/classification/bosssp.py
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| words_2 = self._boss_word_extractor(X[:length//2], | ||
| y[:length//2]) | ||
| words_3 = self._boss_word_extractor(X[length//2:], | ||
| y[:length//2:]) |
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You split your dataset in two, but on the first axis (the axis of samples) instead of the second axis (the time axis).
pyts/classification/bosssp.py
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| words_4 = self._boss_word_extractor(X[:length//4], | ||
| y[:length//4]) | ||
| words_5 = self._boss_word_extractor(X[length//4:length//2], | ||
| y[length//4:length//2]) | ||
| words_6 = self._boss_word_extractor(X[length//2:length*3//4], | ||
| y[length//2:length*3//4]) | ||
| words_7 = self._boss_word_extractor(X[length*3//4:], | ||
| y[length*3//4:]) | ||
| words = words + words_4 + words_5 + words_6 + words_7 |
| if not isinstance(self.window_size, | ||
| (int, np.integer, float, np.floating)): | ||
| raise TypeError("'window_size' must be an integer or a float.") | ||
| if isinstance(self.window_size, (int, np.integer)): | ||
| if self.drop_sum: | ||
| if not 1 <= self.window_size <= (n_timestamps - 1): | ||
| raise ValueError( | ||
| "If 'window_size' is an integer, it must be greater " | ||
| "than or equal to 1 and lower than or equal to " | ||
| "(n_timestamps - 1) if 'drop_sum=True'." | ||
| ) | ||
| else: | ||
| if not 1 <= self.window_size <= n_timestamps: | ||
| raise ValueError( | ||
| "If 'window_size' is an integer, it must be greater " | ||
| "than or equal to 1 and lower than or equal to " | ||
| "n_timestamps if 'drop_sum=False'." | ||
| ) | ||
| window_size = self.window_size | ||
| else: | ||
| if not 0 < self.window_size <= 1: | ||
| raise ValueError( | ||
| "If 'window_size' is a float, it must be greater " | ||
| "than 0 and lower than or equal to 1." | ||
| ) | ||
| window_size = ceil(self.window_size * n_timestamps) | ||
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| if not isinstance(self.window_step, | ||
| (int, np.integer, float, np.floating)): | ||
| raise TypeError("'window_step' must be an integer or a float.") | ||
| if isinstance(self.window_step, (int, np.integer)): | ||
| if not 1 <= self.window_step <= n_timestamps: | ||
| raise ValueError( | ||
| "If 'window_step' is an integer, it must be greater " | ||
| "than or equal to 1 and lower than or equal to " | ||
| "n_timestamps." | ||
| ) | ||
| window_step = self.window_step | ||
| else: | ||
| if not 0 < self.window_step <= 1: | ||
| raise ValueError( | ||
| "If 'window_step' is a float, it must be greater " | ||
| "than 0 and lower than or equal to 1." | ||
| ) | ||
| window_step = ceil(self.window_step * n_timestamps) |
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Since this method is run in the _boss_word_extractor method, if the values of window_size and window_step are floats, the proportions are computed on the split datasets, which means that the values of window_size and window_step would be different for different levels. It might not be an issue in itself, but in this case it should probably be mentioned in the documentation.
| plt.ylabel("Occurences", fontsize=14) | ||
| plt.title("Number of occurence of each word after BOSS-SP transformation", | ||
| fontsize=15) | ||
| plt.legend(loc='best') |
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You have no label in your plot, so there is no legend to display.
…t for the ones regarding code similar from other python modules as discussed orally
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Corrected the issues mentioned in the pull request feedback, except for the ones regarding code similar from other python modules as discussed orally |
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@SvenBarray, would you like to add this to Not many changes have to be made:
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