Metadata-Version: 2.1
Name: clustergram
Version: 0.2.1
Summary: Clustergram - visualization and diagnostics for cluster analysis
Home-page: https://github.com/martinfleis/clustergram
Author: Martin Fleischmann
Author-email: martin@martinfleischmann.net
License: UNKNOWN
Description: # Clustergram
        
        ![logo clustergram](https://raw.githubusercontent.com/martinfleis/clustergram/master/doc/_static/logo.svg)
        
        ## Visualization and diagnostics for cluster analysis
        
        Clustergram is a diagram proposed by Matthias Schonlau in his paper *[The clustergram: A graph for visualizing hierarchical and nonhierarchical cluster analyses](https://journals.sagepub.com/doi/10.1177/1536867X0200200405)*.
        
        > In hierarchical cluster analysis, dendrograms are used to visualize how clusters are formed. I propose an alternative graph called a “clustergram” to examine how cluster members are assigned to clusters as the number of clusters increases. This graph is useful in exploratory analysis for nonhierarchical clustering algorithms such as k-means and for hierarchical cluster algorithms when the number of observations is large enough to make dendrograms impractical.
        
        The clustergram was later implemented in R by [Tal Galili](https://www.r-statistics.com/2010/06/clustergram-visualization-and-diagnostics-for-cluster-analysis-r-code/), who also gives a thorough explanation of the concept.
        
        This is a Python translation of Tal's script written for `scikit-learn` and RAPIDS `cuML` implementations of K-Means and Gaussian Mixture Model (scikit-learn only) clustering.
        
        ## Getting started
        
        You can install clustergram from `conda` or `pip`:
        
        ```shell
        conda install clustergram -c conda-forge
        ```
        
        ```shell
        pip install clustergram
        ```
        
        In any case, you still need to install your selected backend 
        (`scikit-learn` or `cuML`).
        
        The example of clustergram on Palmer penguins dataset:
        
        ```python
        import seaborn
        df = seaborn.load_dataset('penguins')
        ```
        
        First we have to select numerical data and scale them.
        
        ```python
        from sklearn.preprocessing import scale
        data = scale(df.drop(columns=['species', 'island', 'sex']).dropna())
        ```
        
        And then we can simply pass the data to `clustergram`.
        
        ```python
        from clustergram import Clustergram
        
        cgram = Clustergram(range(1, 8))
        cgram.fit(data)
        cgram.plot()
        ```
        
        ![Default clustergram](https://raw.githubusercontent.com/martinfleis/clustergram/master/doc/_static/default.png)
        
        ## Styling
        
        `Clustergram.plot()` returns matplotlib axis and can be fully customised as any other matplotlib plot.
        
        ```python
        seaborn.set(style='whitegrid')
        
        cgram.plot(
            ax=ax,
            size=0.5,
            linewidth=0.5,
            cluster_style={"color": "lightblue", "edgecolor": "black"},
            line_style={"color": "red", "linestyle": "-."},
            figsize=(12, 8)
        )
        ```
        ![Colored clustergram](https://raw.githubusercontent.com/martinfleis/clustergram/master/doc/_static/colors.png)
        
        ## Mean options
        
        On the `y` axis, a clustergram can use mean values as in the original paper by Matthias Schonlau or PCA weighted mean values as in the implementation by Tal Galili.
        
        ```python
        cgram = Clustergram(range(1, 8), pca_weighted=True)
        cgram.fit(data)
        cgram.plot(figsize=(12, 8))
        ```
        ![Default clustergram](https://raw.githubusercontent.com/martinfleis/clustergram/master/doc/_static/pca_true.png)
        
        ```python
        cgram = Clustergram(range(1, 8), pca_weighted=False)
        cgram.fit(data)
        cgram.plot(figsize=(12, 8))
        ```
        ![Default clustergram](https://raw.githubusercontent.com/martinfleis/clustergram/master/doc/_static/pca_false.png)
        
        ## Scikit-learn and RAPIDS cuML backends
        
        Clustergram offers two backends for the computation - `scikit-learn` which uses CPU and RAPIDS.AI `cuML`, which uses GPU. Note that both are optional dependencies, but you will need at least one of them to generate clustergram.
        
        Using scikit-learn (default):
        
        ```python
        cgram = Clustergram(range(1, 8), backend='sklearn')
        cgram.fit(data)
        cgram.plot()
        ```
        
        Using cuML:
        
        ```python
        cgram = Clustergram(range(1, 8), backend='cuML')
        cgram.fit(data)
        cgram.plot()
        ```
        
        `data` can be all data types supported by the selected backend (including `cudf.DataFrame` with `cuML` backend).
        
        ## Supported methods
        
        Clustergram currently supports K-Means and Gaussian Mixture Model clustering methods. Note tha GMM is supported only for `scikit-learn` backend.
        
        Using K-Means (default):
        
        ```python
        cgram = Clustergram(range(1, 8), method='kmeans')
        cgram.fit(data)
        cgram.plot()
        ```
        
        Using Gaussian Mixture Model:
        
        ```python
        cgram = Clustergram(range(1, 8), method='gmm')
        cgram.fit(data)
        cgram.plot()
        ```
        
        ## Partial plot
        
        `Clustergram.plot()` can also plot only a part of the diagram, if you want to focus on a limited range of `k`.
        
        ```python
        cgram = Clustergram(range(1, 20))
        cgram.fit(data)
        cgram.plot(figsize=(12, 8))
        ```
        ![Long clustergram](https://raw.githubusercontent.com/martinfleis/clustergram/master/doc/_static/20_clusters.png)
        
        ```python
        cgram.plot(k_range=range(3, 10), figsize=(12, 8))
        ```
        ![Limited clustergram](https://raw.githubusercontent.com/martinfleis/clustergram/master/doc/_static/limited_plot.png)
        
        ## Saving clustergram
        
        You can save both plot and `clustergram.Clustergram` to a disk.
        
        ### Saving plot
        
        `Clustergram.plot()` returns matplotlib axis object and as such can be saved as any other plot:
        
        ```python
        import matplotlib.pyplot as plt
        
        cgram.plot()
        plt.savefig('clustergram.svg')
        ```
        
        ### Saving object
        
        If you want to save your computed `clustergram.Clustergram` object to a disk, you can use `pickle` library:
        
        ```python
        import pickle
        
        with open('clustergram.pickle','wb') as f:
            pickle.dump(cgram, f)
        ```
        
        Then loading is equally simple:
        
        ```python
        with open('clustergram.pickle','rb') as f:
            loaded = pickle.load(f)
        ```
        
        ## References
        Schonlau M. The clustergram: a graph for visualizing hierarchical and non-hierarchical cluster analyses. The Stata Journal, 2002; 2 (4):391-402.
        
        Schonlau M. Visualizing Hierarchical and Non-Hierarchical Cluster Analyses with Clustergrams. Computational Statistics: 2004; 19(1):95-111.
        
        [https://www.r-statistics.com/2010/06/clustergram-visualization-and-diagnostics-for-cluster-analysis-r-code/](https://www.r-statistics.com/2010/06/clustergram-visualization-and-diagnostics-for-cluster-analysis-r-code/)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6
Description-Content-Type: text/markdown
