Metadata-Version: 2.1
Name: bldg-point-clustering
Version: 0.0.3
Summary: A Python 3.5+ wrapper for clustering building point labels using KMeans, DBScan, and Agglomerative clustering
Home-page: UNKNOWN
Author: Sriharsha Guduguntla
Author-email: sguduguntla@berkeley.edu
License: UNKNOWN
Description: ### bldg_point_clustering
        
        **PyPi Package:** <https://pypi.org/project/bldg-point-clustering/>
        
        **Docs:** <https://bldg-point-clustering.readthedocs.io/en/latest/>
        
        ## Introduction
        
        A Python 3.5+ wrapper for clustering building point labels
        using KMeans, DBScan, and Agglomerative clustering.
        
        ## Installation
        
        Using pip for Python 3.5+ run:
        
        ```bash
        $ pip install bldg_point_clustering
        ```
        
        ## Quick Start
        
        Instantiate Featurizer object and get featurized Pandas DataFrame.
        
        Instantiate Cluster object and pass in featurized
        DataFrame to. Then, call a clustering method with the
        appropriate parameters.
        
        Use the plot3D function in the Plotter to create a
        3D plot of metrics returned by any of the clustering trials.
        
        ## Example Usage
        
        Running one iteration of the KMeans algorithm.
        
        ```python
        import pandas as pd
        import numpy as np
        from bldg_point_clustering.cluster import Cluster
        from bldg_point_clustering.featurizer import Featurizer
        
        filename = "GBSF"
        
        df = pd.read_csv("./datasets/" + filename + ".csv")
        
        first_column = df.iloc[:, 0]
        
        f = Featurizer(filename, corpus=first_column)
        
        featurized_df = f.bag_of_words()
        
        c = Cluster(df, featurized_df)
        
        clustered_df = c.kmeans(n_clusters=300, plot=True, to_csv=True)
        
        metrics = c.get_metrics_df()
        
        avg_levenshtein_score = np.mean(c.get_levenshtein_scores())
        ```
        
        Running several iterations of the KMeans algorithm.
        
        ```python
        from bldg_point_clustering.plotter import plot_3D
        
        c.kmeans_trials()
        
        metrics = c.get_metrics_df()
        
        plot_3D(metrics, "n_clusters", "Avg Levenshtein Score", "Silhouette Score")
        ```
        This process is similar for DBScan and Agglomerative. 
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
