Cluster Visualisations#
Visualisations for your clustering.
- class relevanceai.operations.viz.cluster.ClusterVizOps#
Cluster Visualisations. May contain additional visualisation dependencies.
- centroid_heatmap(metric='cosine', vmin=0, vmax=1, print_n=8, round_print_float=2)#
Heatmap visualisation of the closest clusters. Prints the ones ranked from top to bottom in terms of largest cosine similarity.
- plot_basic_distributions(numeric_field, top_indices=10, dataset_id=None)#
Plot the sentence length distributions across each cluster
Example
from relevanceai import Client client = Client() cluster_ops = client.ClusterVizOps( dataset_id="sample_dataset", vector_fields=["sample_vector_"], alias="kmeans-5" ) cluster_ops.plot_basic_distributions()
- Parameters
numeric_field (str) – The numeric field to plot
top_indices (int) – The top indices in the plotting
dataset_id (Optional[str]) – The dataset ID
- plot_distributions(numeric_field, measure_function=None, top_indices=10, dataset_id=None, asc=True, measurement_name='measurement')#
Plot the distributions across each cluster measure_function is run on each cluster and plots
Example
from scipy.stats import skew ops.plot_distributions_measure(numeric_field, skew, dataset_id=dataset_id)
- Parameters
numeric_field (str) – The numeric field to plot the distribution by
measure_function (callable) – What to measure the function
top_indices (int) – The top indices
dataset_id (str) – The dataset ID to use
asc (bool) – If True, the distributions are plotted
measurement_name (str) – The name of what should be plotted for the graphs
- plot_most_skewed(numeric_field, top_indices=10, dataset_id=None, asc=True)#
Plot the most skewed numeric fields
- show_closest(cluster_ids=None, text_fields=None, image_fields=None)#
Show the clusters with the closest.