saber.cluster
calc_silhouette(workdir, x, n_clusters='all', samples=75000)
Calculate the silhouette score for the given number of clusters
Parameters:
Name | Type | Description | Default |
---|---|---|---|
workdir |
str
|
path to the project directory |
required |
x |
np.ndarray
|
a numpy array of the prepared FDC data |
required |
n_clusters |
the number of clusters to calculate the silhouette score for |
'all'
|
|
samples |
int
|
the number of samples to use for the silhouette score calculation |
75000
|
Returns:
Type | Description |
---|---|
None
|
None |
Source code in saber/cluster.py
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cluster(workdir, x=None, max_clusters=13)
Trains scikit-learn MiniBatchKMeans models and saves as pickle
Parameters:
Name | Type | Description | Default |
---|---|---|---|
workdir |
str
|
path to the project directory |
required |
x |
np.ndarray
|
a numpy array of the prepared FDC data |
None
|
max_clusters |
int
|
maximum number of clusters to train |
13
|
Returns:
Type | Description |
---|---|
None
|
None |
Source code in saber/cluster.py
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plot_centers(workdir, plt_width=2, plt_height=2, max_cols=3)
Plot the cluster centers for each cluster.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
workdir |
str
|
path to the project directory |
required |
plt_width |
int
|
width of each subplot in inches |
2
|
plt_height |
int
|
height of each subplot in inches |
2
|
max_cols |
int
|
maximum number of columns of subplots in the figure |
3
|
Returns:
Type | Description |
---|---|
None
|
None |
Source code in saber/cluster.py
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plot_clusters(workdir, x=None, n_clusters='all', max_cols=3, plt_width=2, plt_height=2, n_lines=2500)
Generate figures of the clustered FDC's
Parameters:
Name | Type | Description | Default |
---|---|---|---|
workdir |
str
|
path to the project directory |
required |
x |
np.ndarray
|
a numpy array of the prepared FDC data |
None
|
n_clusters |
number of clusters to create figures for |
'all'
|
|
max_cols |
int
|
maximum number of columns (subplots) in the figure |
3
|
plt_width |
int
|
width of each subplot in inches |
2
|
plt_height |
int
|
height of each subplot in inches |
2
|
n_lines |
int
|
max number of lines to plot in each subplot |
2500
|
Returns:
Type | Description |
---|---|
None
|
None |
Source code in saber/cluster.py
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plot_fit_metrics(workdir, plt_width=5, plt_height=3)
Plot the cluster metrics, inertia and silhouette score, vs number of clusters
Parameters:
Name | Type | Description | Default |
---|---|---|---|
workdir |
str
|
path to the project directory |
required |
plt_width |
int
|
width of each subplot in inches |
5
|
plt_height |
int
|
height of each subplot in inches |
3
|
Returns:
Type | Description |
---|---|
None
|
None |
Source code in saber/cluster.py
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plot_silhouettes(workdir, plt_width=3, plt_height=3)
Plot the silhouette scores for each cluster. Based on https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html
Parameters:
Name | Type | Description | Default |
---|---|---|---|
workdir |
str
|
path to the project directory |
required |
plt_width |
int
|
width of each subplot in inches |
3
|
plt_height |
int
|
height of each subplot in inches |
3
|
Returns:
Type | Description |
---|---|
None
|
None |
Source code in saber/cluster.py
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predict_labels(workdir, n_clusters, x)
Predict the cluster labels for a set number of FDCs
Parameters:
Name | Type | Description | Default |
---|---|---|---|
workdir |
str
|
path to the project directory |
required |
n_clusters |
int
|
number of cluster model to use for prediction |
required |
x |
pd.DataFrame
|
A dataframe with 1 row per FDC (stream) and 1 column per FDC value. Index is the stream's ID. |
required |
Returns:
Type | Description |
---|---|
pd.DataFrame
|
None |
Source code in saber/cluster.py
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summarize_fit(workdir)
Generate a summary of the clustering results save the centers and labels to parquet
Parameters:
Name | Type | Description | Default |
---|---|---|---|
workdir |
str
|
path to the project directory |
required |
Returns:
Type | Description |
---|---|
None
|
None |
Source code in saber/cluster.py
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