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@@ -2,6 +2,7 @@ notebooks/ | |
| plots/ | ||
| output*/ | ||
| *.csv | ||
| *.ipynb | ||
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| # Log | ||
| log.out | ||
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| import itertools | ||
| from typing import Optional, Tuple | ||
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| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
| from scipy.cluster.hierarchy import cophenet, dendrogram, fcluster, linkage | ||
| from scipy.spatial.distance import pdist | ||
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| class HierarchicalClustering: | ||
| def __init__( | ||
| self, distance: str = "average", n_cluster: int = 2, criterion: str = "maxclust" | ||
| ): | ||
| """This object is able to perform Hierarchical Clustering on a given set of points | ||
| Parameters | ||
| ---------- | ||
| distance : str, optional | ||
| Clustering distance criteria, by default "average" | ||
| n_cluster : int, optional | ||
| Number of clusters to form, by default 2 | ||
| criterion : str, optional | ||
| Criterion used to compute the clusters, by default "maxclust" | ||
| """ | ||
| self.distance = distance | ||
| self.n_cluster = n_cluster | ||
| self.criterion = criterion | ||
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| # Attributes filled with `perform_clustering` | ||
| self.x = np.empty(0) # Set of data points | ||
| self.z = np.empty(0) # The hierarchical clustering encoded as a linkage matrix | ||
| # z[i] will tell us which clusters were merged in the i-th iteration | ||
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| # Attributes filled with `plot_dendogram` | ||
| self.dendrogram = {} | ||
| # A dictionary of data structures computed to render the dendrogram | ||
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| # Attributes filled with `compute_thresholds_and_centroids` | ||
| self.thresh = np.empty(0) | ||
| self.centroids = np.empty(0) | ||
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| def perform_clustering( | ||
| self, ser: np.array, distance: Optional[str] = None | ||
| ) -> np.array: | ||
| """Performs the actual clustering, using the linkage function | ||
| Parameters | ||
| ---------- | ||
| ser : np.array | ||
| Series of points to group in clusters | ||
| distance : str, optional | ||
| Clustering distance criteria, by default None (takes the one from the class) | ||
| """ | ||
| self.distance = distance if distance is not None else self.distance | ||
| # The shape of our X matrix must be (n, m) | ||
| # n = samples, m = features | ||
| self.x = np.expand_dims(ser, axis=1) | ||
| self.z = linkage(self.x, method=self.distance) | ||
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| @property | ||
| def cophenet(self): | ||
| # Cophenet correlation coefficient | ||
| c, coph_dists = cophenet(self.z, pdist(self.x)) | ||
| return c | ||
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| def plot_dendrogram( | ||
| self, p: int = 6, max_d: Optional[float] = None, figsize: Tuple[int] = (3, 3) | ||
| ): | ||
| """Plots the dendrogram | ||
| Parameters | ||
| ---------- | ||
| p : int, optional | ||
| Last split, by default 6 | ||
| max_d : Optional[float], optional | ||
| Maximum distance between splits, by default None | ||
| figsize : Tuple[int], optional | ||
| Figure size, by default (3, 3) | ||
| """ | ||
| fig, ax = plt.subplots(figsize=figsize) | ||
| self.dendrogram = dendrogram( | ||
| self.z, | ||
| p=p, | ||
| orientation="right", | ||
| truncate_mode="lastp", | ||
| labels=self.x[:, 0], | ||
| ax=ax, | ||
| ) | ||
| if max_d is not None: | ||
| ax.axvline(x=max_d, c="k") | ||
| return fig, ax | ||
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| @property | ||
| def dendrogram_distance(self): | ||
| return sorted(set(itertools.chain(*self.dendrogram["dcoord"])), reverse=True) | ||
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| def plot_dendrogram_distance(self, figsize: Tuple[int] = (10, 3)): | ||
| """Plots the dendrogram distances | ||
| Parameters | ||
| ---------- | ||
| figsize : Tuple[int], optional | ||
| Size of the figure, by default (10, 3) | ||
| """ | ||
| # Initialize plots | ||
| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=figsize) | ||
| # Dendrogram distance | ||
| ax1.scatter( | ||
| range(2, len(self.dendrogram_distance) + 1), self.dendrogram_distance[:-1] | ||
| ) | ||
| ax1.set_ylabel("Distance") | ||
| ax1.set_xlabel("Number of clusters") | ||
| ax1.grid() | ||
| # Dendrogram distance difference | ||
| diff = np.divide( | ||
| -np.diff(self.dendrogram_distance), self.dendrogram_distance[:-1] | ||
| ) | ||
| ax2.scatter(range(3, len(self.dendrogram_distance) + 1), diff[:-1]) | ||
| ax2.set_ylabel("Gradient") | ||
| ax2.set_xlabel("Number of clusters") | ||
| ax2.grid() | ||
| return fig, (ax1, ax2) | ||
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| def compute_thresholds_and_centroids( | ||
| self, | ||
| n_cluster: Optional[int] = None, | ||
| criterion: Optional[str] = None, | ||
| centroid: str = "median", | ||
| ): | ||
| """Computes the thresholds and centroids of each group | ||
| Parameters | ||
| ---------- | ||
| n_cluster : Optional[int], optional | ||
| Number of clusters, by default None | ||
| criterion : Optional[str], optional | ||
| Criterion used to compute the clusters, by default None | ||
| centroid : str, optional | ||
| Method to compute the centroids (median or mean), by default "median" | ||
| """ | ||
| self.n_cluster = n_cluster if n_cluster is not None else self.n_cluster | ||
| self.criterion = criterion if criterion is not None else self.criterion | ||
| clusters = fcluster(self.z, self.n_cluster, self.criterion) | ||
| # Get centroids | ||
| if centroid == "median": | ||
| fun = np.median | ||
| elif centroid == "mean": | ||
| fun = np.mean | ||
| self.centroids = np.array( | ||
| sorted([fun(self.x[clusters == (c + 1)]) for c in range(self.n_cluster)]) | ||
| ) | ||
| # Sort clusters by power | ||
| x_max = sorted( | ||
| [np.max(self.x[clusters == (c + 1)]) for c in range(self.n_cluster)] | ||
| ) | ||
| x_min = sorted( | ||
| [np.min(self.x[clusters == (c + 1)]) for c in range(self.n_cluster)] | ||
| ) | ||
| thresh = np.divide(np.array(x_min[1:]) + np.array(x_max[:-1]), 2) | ||
| self.thresh = np.insert(thresh, 0, 0, axis=0) |
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