141 lines
4.3 KiB
Python
141 lines
4.3 KiB
Python
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import matplotlib.pyplot as plt
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import numpy as np
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def plot_scree(eigenvals, total_var, ncomp=None, x_label='factor'):
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"""
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Plot of the ordered eigenvalues and variance explained for the loadings
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Parameters
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----------
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eigenvals : array_like
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The eigenvalues
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total_var : float
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the total variance (for plotting percent variance explained)
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ncomp : int, optional
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Number of factors to include in the plot. If None, will
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included the same as the number of maximum possible loadings
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x_label : str
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label of x-axis
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Returns
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-------
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Figure
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Handle to the figure.
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"""
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fig = plt.figure()
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ncomp = len(eigenvals) if ncomp is None else ncomp
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vals = eigenvals
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vals = vals[:ncomp]
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# vals = np.cumsum(vals)
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ax = fig.add_subplot(121)
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ax.plot(np.arange(ncomp), vals[: ncomp], 'b-o')
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ax.autoscale(tight=True)
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xlim = np.array(ax.get_xlim())
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sp = xlim[1] - xlim[0]
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xlim += 0.02 * np.array([-sp, sp])
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ax.set_xticks(np.arange(ncomp))
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ax.set_xlim(xlim)
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ylim = np.array(ax.get_ylim())
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scale = 0.02
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sp = ylim[1] - ylim[0]
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ylim += scale * np.array([-sp, sp])
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ax.set_ylim(ylim)
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ax.set_title('Scree Plot')
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ax.set_ylabel('Eigenvalue')
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ax.set_xlabel(x_label)
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per_variance = vals / total_var
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cumper_variance = np.cumsum(per_variance)
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ax = fig.add_subplot(122)
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ax.plot(np.arange(ncomp), per_variance[: ncomp], 'b-o')
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ax.plot(np.arange(ncomp), cumper_variance[: ncomp], 'g--o')
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ax.autoscale(tight=True)
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xlim = np.array(ax.get_xlim())
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sp = xlim[1] - xlim[0]
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xlim += 0.02 * np.array([-sp, sp])
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ax.set_xticks(np.arange(ncomp))
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ax.set_xlim(xlim)
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ylim = np.array(ax.get_ylim())
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scale = 0.02
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sp = ylim[1] - ylim[0]
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ylim += scale * np.array([-sp, sp])
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ax.set_ylim(ylim)
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ax.set_title('Variance Explained')
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ax.set_ylabel('Proportion')
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ax.set_xlabel(x_label)
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ax.legend(['Proportion', 'Cumulative'], loc=5)
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fig.tight_layout()
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return fig
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def plot_loadings(loadings, col_names=None, row_names=None,
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loading_pairs=None, percent_variance=None,
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title='Factor patterns'):
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"""
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Plot factor loadings in 2-d plots
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Parameters
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----------
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loadings : array like
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Each column is a component (or factor)
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col_names : a list of strings
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column names of `loadings`
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row_names : a list of strings
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row names of `loadings`
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loading_pairs : None or a list of tuples
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Specify plots. Each tuple (i, j) represent one figure, i and j is
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the loading number for x-axis and y-axis, respectively. If `None`,
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all combinations of the loadings will be plotted.
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percent_variance : array_like
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The percent variance explained by each factor.
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Returns
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-------
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figs : a list of figure handles
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"""
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k_var, n_factor = loadings.shape
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if loading_pairs is None:
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loading_pairs = []
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for i in range(n_factor):
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for j in range(i + 1,n_factor):
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loading_pairs.append([i, j])
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if col_names is None:
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col_names = ["factor %d" % i for i in range(n_factor)]
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if row_names is None:
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row_names = ["var %d" % i for i in range(k_var)]
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figs = []
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for item in loading_pairs:
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i = item[0]
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j = item[1]
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fig = plt.figure(figsize=(7, 7))
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figs.append(fig)
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ax = fig.add_subplot(111)
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for k in range(loadings.shape[0]):
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plt.text(loadings[k, i], loadings[k, j],
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row_names[k], fontsize=12)
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ax.plot(loadings[:, i], loadings[:, j], 'bo')
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ax.set_title(title)
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if percent_variance is not None:
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x_str = f'{col_names[i]} ({percent_variance[i]:.1f}%)'
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y_str = f'{col_names[j]} ({percent_variance[j]:.1f}%)'
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ax.set_xlabel(x_str)
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ax.set_ylabel(y_str)
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else:
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ax.set_xlabel(col_names[i])
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ax.set_ylabel(col_names[j])
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v = 1.05
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xlim = np.array([-v, v])
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ylim = np.array([-v, v])
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ax.plot(xlim, [0, 0], 'k--')
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ax.plot([0, 0], ylim, 'k--')
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ax.set_aspect('equal', 'datalim')
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ax.set_xlim(xlim)
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ax.set_ylim(ylim)
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fig.tight_layout()
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return figs
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