svdawg 0.5

SVD accessories, widgets, and graphics

svdawg

Created by Simone Longo at the University of Utah
March 2020

Accessories, widgets, and graphics for singular value decomposition (SVD) analysis.

NOTE: This is an initial release and may be unstable. Please contact with bug information or feature requests. More functionality will be added over time.

Installation

pip3 install svdawg

OR

Installation requires numpy, seaborn, matplotlib, pandas, and sklearn

In your terminal:

git clone https://github.com/SpacemanSpiff7/svdawg/
cd svdawg
pip3 install .

Available methods

fillnans(df, fill=0)
    Parameters:
            df:   Pandas DataFrame to clean
            fill: Value to use when replacing np.nan and np.inf (default=0)

    Returns:
            Returns a copy of the input Pandas DataFrame replacing all np.nan and np.inf with the specified value


generate_synthetic_data(m, n)
    Generate a toy dataset with dimension m x n

    Parameters:
            m: number of rows
            n: number of columns


lineplot_svs(svd, top=5)
    Create lineplots of top singular values in U and V^T sorted and unsorted

    Parameters:
            svd:  A 3-ple containing the result of a SVD
            top:  Integer indicating which top singular values to include


pd_scale(df)
    StandardScaler transform of Pandas DataFrame, maintaining row and column labels.

    Parameters:
            df: Pandas DataFrame

    Returns:
            A scaled and labelled Pandas DataFrame


pd_svd(df, labels=True)
    Compute SVD on a Pandas DataFrame, maintaining row and column labels.

    Parameters:
            df: Pandas DataFrame

    Returns:
            Returns decomposition of D = U.S.V^T as labelled
            Pandas DataFrames as a 3-ple, (U, S, V^T)


plot_mat(mat)
    Plot a scaled matrix using red for negative and green for positive values

    Parameter:
            mat: some 2D matrix of numerical values


plot_sv(svd, sv=0)
    Tool for plotting U and V^T sorted by a specified singular value

    Parameters:
            svd: A 3-ple containing the result of a SVD computed by 'pd_svd'
            sv:  Integer indicating which singular value to sort by


plot_svd(svd)
    Tool for plotting U and V^T

    Parameters:
            svd: A 3-ple containing the result of a SVD computed by 'pd_svd'
            sv: Integer indicating which singular value to sort by


plot_svs(svd, top=5)
    Tool for plotting U and V^T sorted by top singular values

    Parameters:
            svd: A 3-ple containing the result of a SVD computed by 'pd_svd'
            top: Integer indicating which top singular values to sort by


svd_fp(fp, header='infer', sep='\t', index_col=None)
    Compute SVD directly from filepath to a table of tab-separated numerical values

    Parameters:
                fp:         Path to file
                header:     Specify header, see Pandas.read_csv documentation for default option
                sep:        field separator (default is tab separated). NOTE: this is different than default Pandas behavior
                index_col:  specify if dataframe has an existing index (see default Pandas.read_csv documentation)

    Returns:
                A tuple containing the input dataframe and the result of a SVD on the data
                Tuple Contents: (Pandas.DataFrame, (U, S, V^T))


svd_overview(data, top=3, scale=True)
    Display original data with line plots of top singular values from V^T and U

    Parameters:
            data:   untransformed dataframe
            top:    top n singular values to plot
            scale:  Preprocess data before SVD (boolean)


svdfilter(svd, noise=[0])
    Tool for filtering a singular value and reconstructing a data set

    Parameters:
            svd:    A 3-ple containing the result of a SVD
            noise:  A list enumerating the singular values to set to 0

    Returns:
            Reconstruction of the filtered dataset as a NumPy array

Example:

import svdawg as sv

toydata = sv.generate_synthetic_data(100,10)
toydata.head()

Out[]:
          0         1         2         3         4         5         6         7         8         9
0  1.000000  0.809017  0.309017 -0.309017 -0.809017 -1.000000 -0.809017 -0.309017  0.309017  0.809017
1  0.998027  0.844328  0.368125 -0.248690 -0.770513 -0.998027 -0.844328 -0.368125  0.248690  0.770513
2  0.992115  0.876307  0.425779 -0.187381 -0.728969 -0.992115 -0.876307 -0.425779  0.187381  0.728969
3  0.982287  0.904827  0.481754 -0.125333 -0.684547 -0.982287 -0.904827 -0.481754  0.125333  0.684547
4  0.968583  0.929776  0.535827 -0.062791 -0.637424 -0.968583 -0.929776 -0.535827  0.062791  0.637424

# Visualize the data
sv.plot_mat(toydata)

Calculate SVD

# Generate SVD results to use with other methods
svd = sv.pd_svd(toydata)

# Visualize results
sv.plot_svd(svd)

# Examine top singular values
sv.lineplot_svs(svd, top=4)

# Filter out first singular value
sv.plot_mat(sv.svdfilter(svd))

# Plot SVD sorted by top singular values
sv.plot_svs(svd, top=4)

# Or just plot whichever one you choose
sv.plot_sv(svd, sv=0)

# Quickly visualize Singular values in the context of your original data
sv.svd_overview(toydata, top=3)