Merge pull request #395 from knaaptime/skplt

rm vendor silhouette plotting

.ci/310.yml

@@ -36,7 +36,6 @@ dependencies:
 
   - python-wget
   - contextily
-  - scikit-plot
   - python-graphviz
   - nbsphinx
   - numpydoc

.ci/311.yml

@@ -35,5 +35,4 @@ dependencies:
   - coverage
   - python-wget
   - contextily
-  - scikit-plot
   - python-graphviz

.ci/312.yml

@@ -36,7 +36,6 @@ dependencies:
 
   - python-wget
   - contextily
-  - scikit-plot
   - python-graphviz
   - sphinx>=1.4.3
   - sphinxcontrib-bibtex==1

.github/workflows/unittests.yml

@@ -40,7 +40,7 @@ jobs:
           micromamba-version: 'latest'
 
       - name: Install geosnap
-        run: pip install . ;python geosnap/tests/_dl_data.py;
+        run: pip install . --no-deps ;python geosnap/tests/_dl_data.py;
         env:
           COMBO_DATA: ${{ secrets.COMBO_DATA }}
 

environment.yml

@@ -18,7 +18,6 @@ dependencies:
   - xlrd
   - tobler >=0.8.2
   - contextily
-  - scikit-plot
   - mapclassify
   - spopt >=0.3.0
   - s3fs

geosnap/analyze/_cluster_wrappers.py

@@ -57,7 +57,7 @@ def kmeans(
     verbose=0,
     random_state=None,
     copy_x=True,
-    algorithm="auto",
+    algorithm="lloyd",
     **kwargs,
 ):
     """K-Means clustering.

geosnap/analyze/_model_results.py

@@ -14,14 +14,14 @@
 
 import esda
 import geopandas as gpd
-import scikitplot as skplt
 from sklearn.metrics import (
     calinski_harabasz_score,
     davies_bouldin_score,
     silhouette_samples,
 )
 
 from ..visualize.mapping import plot_timeseries
+from ..visualize.skplt import plot_silhouette as _plot_silhouette
 from .dynamics import predict_markov_labels as _predict_markov_labels
 from .incs import lincs_from_gdf
 
@@ -369,7 +369,7 @@ def plot_silhouette(self, metric="euclidean", title="Silhouette Score"):
             elif self.pooling == "pooled":
                 # if pooled, scale the whole series at once
                 df.loc[:, self.columns] = self.scaler.fit_transform(df.values)
-        fig = skplt.metrics.plot_silhouette(
+        fig = _plot_silhouette(
             df[self.columns].values, self.labels, metric=metric, title=title
         )
 

geosnap/analyze/_region_wrappers.py

@@ -71,7 +71,9 @@ def kmeans_spatial(data, columns, w, n_clusters=5, **kwargs):
     return model
 
 
-def spenc(data, w, columns, n_clusters=5, gamma=500, random_state=None, **kwargs):
+def spenc(
+    data, w, columns, n_clusters=5, gamma=1, random_state=None, n_jobs=-1, **kwargs
+):
     """Spatially encouraged spectral clustering.
 
     :cite:`wolf2018`
@@ -101,6 +103,7 @@ def spenc(data, w, columns, n_clusters=5, gamma=500, random_state=None, **kwargs
         attrs_name=columns,
         gamma=gamma,
         random_state=random_state,
+        n_jobs=n_jobs,
     )
 
     model.solve()
@@ -115,7 +118,7 @@ def skater(
     floor=-np.inf,
     islands="increase",
     cluster_args=None,
-    **kwargs
+    **kwargs,
 ):
     """SKATER spatial clustering algorithm.
 
@@ -193,7 +196,7 @@ def max_p(
     threshold=10,
     max_iterations_construction=99,
     top_n=2,
-    **kwargs
+    **kwargs,
 ):
     """Max-p clustering algorithm :cite:`Duque2012`.
 

geosnap/harmonize/harmonize.py

@@ -142,10 +142,7 @@ def harmonize(
         times.remove(target_year)
         target_df = dfs[dfs[temporal_index] == target_year]
 
-    if target_df.index.name:
-        unit_index = target_df.index.name
-    else:
-        unit_index = "id"
+    unit_index = target_df.index.name if target_df.index.name else "id"
     target_df[unit_index] = target_df.index.values
 
     geom_name = target_df.geometry.name
@@ -209,10 +206,11 @@ def harmonize(
                                 pixel_values=pixel_values,
                                 raster=raster,
                             )
-                except IOError:
-                    raise IOError(
-                        "Unable to locate raster. If using the `dasymetric` or model-based methods. You"
-                        "must provide a raster file and indicate which pixel values contain developed land"
+                except OSError as e:
+                    raise OSError from e(
+                        "Unable to locate raster. If using the `dasymetric` or model-based "
+                        "methods. You must provide a raster file and indicate which pixel "
+                        "values contain developed land"
                     )
             else:
                 raise ValueError('weights_method must of one of ["area", "dasymetric"]')

geosnap/visualize/skplt.py

@@ -0,0 +1,147 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.metrics import (
+    silhouette_samples,
+    silhouette_score,
+)
+from sklearn.preprocessing import LabelEncoder
+
+
+def plot_silhouette(
+    X,
+    cluster_labels,
+    title="Silhouette Analysis",
+    metric="euclidean",
+    ax=None,
+    figsize=None,
+    cmap="nipy_spectral",
+    title_fontsize="large",
+    text_fontsize="medium",
+):
+    """Plots silhouette analysis of clusters provided.
+
+    NOTE: this function is vendored from scikit-plot which is no longer maintained
+
+    Args:
+        X (array-like, shape (n_samples, n_features)):
+            Data to cluster, where n_samples is the number of samples and
+            n_features is the number of features.
+
+        cluster_labels (array-like, shape (n_samples,)):
+            Cluster label for each sample.
+
+        title (string, optional): Title of the generated plot. Defaults to
+            "Silhouette Analysis"
+
+        metric (string or callable, optional): The metric to use when
+            calculating distance between instances in a feature array.
+            If metric is a string, it must be one of the options allowed by
+            sklearn.metrics.pairwise.pairwise_distances. If X is
+            the distance array itself, use "precomputed" as the metric.
+
+        copy (boolean, optional): Determines whether ``fit`` is used on
+            **clf** or on a copy of **clf**.
+
+        ax (:class:`matplotlib.axes.Axes`, optional): The axes upon which to
+            plot the curve. If None, the plot is drawn on a new set of axes.
+
+        figsize (2-tuple, optional): Tuple denoting figure size of the plot
+            e.g. (6, 6). Defaults to ``None``.
+
+        cmap (string or :class:`matplotlib.colors.Colormap` instance, optional):
+            Colormap used for plotting the projection. View Matplotlib Colormap
+            documentation for available options.
+            https://matplotlib.org/users/colormaps.html
+
+        title_fontsize (string or int, optional): Matplotlib-style fontsizes.
+            Use e.g. "small", "medium", "large" or integer-values. Defaults to
+            "large".
+
+        text_fontsize (string or int, optional): Matplotlib-style fontsizes.
+            Use e.g. "small", "medium", "large" or integer-values. Defaults to
+            "medium".
+
+    Returns:
+        ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was
+            drawn.
+
+    Example:
+        >>> import scikitplot as skplt
+        >>> kmeans = KMeans(n_clusters=4, random_state=1)
+        >>> cluster_labels = kmeans.fit_predict(X)
+        >>> skplt.metrics.plot_silhouette(X, cluster_labels)
+        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
+        >>> plt.show()
+
+        .. image:: _static/examples/plot_silhouette.png
+           :align: center
+           :alt: Silhouette Plot
+    """
+    cluster_labels = np.asarray(cluster_labels)
+
+    le = LabelEncoder()
+    cluster_labels_encoded = le.fit_transform(cluster_labels)
+
+    n_clusters = len(np.unique(cluster_labels))
+
+    silhouette_avg = silhouette_score(X, cluster_labels, metric=metric)
+
+    sample_silhouette_values = silhouette_samples(X, cluster_labels, metric=metric)
+
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, figsize=figsize)
+
+    ax.set_title(title, fontsize=title_fontsize)
+    ax.set_xlim([-0.1, 1])
+
+    ax.set_ylim([0, len(X) + (n_clusters + 1) * 10 + 10])
+
+    ax.set_xlabel("Silhouette coefficient values", fontsize=text_fontsize)
+    ax.set_ylabel("Cluster label", fontsize=text_fontsize)
+
+    y_lower = 10
+
+    for i in range(n_clusters):
+        ith_cluster_silhouette_values = sample_silhouette_values[
+            cluster_labels_encoded == i
+        ]
+
+        ith_cluster_silhouette_values.sort()
+
+        size_cluster_i = ith_cluster_silhouette_values.shape[0]
+        y_upper = y_lower + size_cluster_i
+
+        color = plt.cm.get_cmap(cmap)(float(i) / n_clusters)
+
+        ax.fill_betweenx(
+            np.arange(y_lower, y_upper),
+            0,
+            ith_cluster_silhouette_values,
+            facecolor=color,
+            edgecolor=color,
+            alpha=0.7,
+        )
+
+        ax.text(
+            -0.05,
+            y_lower + 0.5 * size_cluster_i,
+            str(le.classes_[i]),
+            fontsize=text_fontsize,
+        )
+
+        y_lower = y_upper + 10
+
+    ax.axvline(
+        x=silhouette_avg,
+        color="red",
+        linestyle="--",
+        label="Silhouette score: {0:0.3f}".format(silhouette_avg),
+    )
+
+    ax.set_yticks([])  # Clear the y-axis labels / ticks
+    ax.set_xticks(np.arange(-0.1, 1.0, 0.2))
+
+    ax.tick_params(labelsize=text_fontsize)
+    ax.legend(loc="best", fontsize=text_fontsize)
+
+    return ax

pyproject.toml

@@ -40,7 +40,6 @@ dependencies = [
     "quilt3>=3.6",
     "pyarrow>=0.14.1",
     "contextily",
-    "scikit-plot",
     "tobler>=0.8.2",
     "spopt>=0.3.0",
     "fsspec",