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''' |
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Functions for descriptive analytics. |
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:author: Andreas Kanz |
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''' |
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# Imports |
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import matplotlib.pyplot as plt |
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import matplotlib.ticker as ticker |
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import numpy as np |
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import pandas as pd |
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import scipy |
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import seaborn as sns |
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from .utils import _corr_selector |
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from .utils import _missing_vals |
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from .utils import _validate_input_0_1 |
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from .utils import _validate_input_bool |
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# Functions |
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# Correlation Matrix |
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def corr_mat(data, split=None, threshold=0, method='pearson'): |
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''' |
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Parameters |
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---------- |
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data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \ |
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information is used to label the plots. |
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split: {None, 'pos', 'neg', 'high', 'low'}, default None |
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Type of split to be performed. |
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threshold: float, default 0 |
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Value between 0 <= threshold <= 1 |
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method: {'pearson', 'spearman', 'kendall'}, default 'pearson' |
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* pearson: measures linear relationships and requires normally distributed and homoscedastic data. |
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* spearman: ranked/ordinal correlation, measures monotonic relationships. |
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* kendall: ranked/ordinal correlation, measures monotonic relationships. Computationally more expensive but |
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more robus in smaller dataets than 'spearman'. |
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Returns |
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------- |
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returns a Pandas Styler object |
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''' |
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# Validate Inputs |
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_validate_input_0_1(threshold, 'threshold') |
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def color_negative_red(val): |
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color = '#FF3344' if val < 0 else None |
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return 'color: %s' % color |
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data = pd.DataFrame(data) |
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corr = data.corr(method=method) |
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corr = _corr_selector(corr, split=split, threshold=threshold) |
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return corr.style.applymap(color_negative_red).format("{:.2f}", na_rep='-') |
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# Correlation matrix / heatmap |
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def corr_plot(data, split=None, threshold=0, target=None, method='pearson', cmap='BrBG', figsize=(12, 10), annot=True, |
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dev=False, **kwargs): |
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''' |
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Two-dimensional visualization of the correlation between feature-columns, excluding NA values. |
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Parameters |
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---------- |
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data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \ |
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information is used to label the plots. |
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split: {None, 'pos', 'neg', 'high', 'low'}, default None |
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Type of split to be performed. |
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* None: visualize all correlations between the feature-columns. |
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* pos: visualize all positive correlations between the feature-columns above the threshold. |
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* neg: visualize all negative correlations between the feature-columns below the threshold. |
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* high: visualize all correlations between the feature-columns for which abs(corr) > threshold is True. |
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* low: visualize all correlations between the feature-columns for which abs(corr) < threshold is True. |
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threshold: float, default 0 |
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Value between 0 <= threshold <= 1 |
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target: string, list, np.array or pd.Series, default None |
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Specify target for correlation. E.g. label column to generate only the correlations between each feature\ |
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and the label. |
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method: {'pearson', 'spearman', 'kendall'}, default 'pearson' |
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* pearson: measures linear relationships and requires normally distributed and homoscedastic data. |
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* spearman: ranked/ordinal correlation, measures monotonic relationships. |
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* kendall: ranked/ordinal correlation, measures monotonic relationships. Computationally more expensive but |
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more robust in smaller dataets than 'spearman'. |
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cmap: matplotlib colormap name or object, or list of colors, default 'BrBG' |
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The mapping from data values to color space. |
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figsize: tuple, default (12, 10) |
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Use to control the figure size. |
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annot: bool, default True |
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Use to show or hide annotations. |
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dev: bool, default False |
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Display figure settings in the plot by setting dev = True. If False, the settings are not displayed.s |
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**kwargs: optional |
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Additional elements to control the visualization of the plot, e.g.: |
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* mask: bool, default True |
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If set to False the entire correlation matrix, including the upper triangle is shown. Set dev = False in this \ |
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case to avoid overlap. |
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* vmax: float, default is calculated from the given correlation coefficients. |
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Value between -1 or vmin <= vmax <= 1, limits the range of the colorbar. |
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* vmin: float, default is calculated from the given correlation coefficients. |
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Value between -1 <= vmin <= 1 or vmax, limits the range of the colorbar. |
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* linewidths: float, default 0.5 |
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Controls the line-width inbetween the squares. |
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* annot_kws: dict, default {'size' : 10} |
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Controls the font size of the annotations. Only available when annot = True. |
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* cbar_kws: dict, default {'shrink': .95, 'aspect': 30} |
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Controls the size of the colorbar. |
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* Many more kwargs are available, i.e. 'alpha' to control blending, or options to adjust labels, ticks ... |
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Kwargs can be supplied through a dictionary of key-value pairs (see above). |
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Returns |
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------- |
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ax: matplotlib Axes |
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Returns the Axes object with the plot for further tweaking. |
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''' |
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# Validate Inputs |
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_validate_input_0_1(threshold, 'threshold') |
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_validate_input_bool(annot, 'annot') |
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_validate_input_bool(dev, 'dev') |
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data = pd.DataFrame(data) |
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mask = False |
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square = False |
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# Obtain correlations |
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if isinstance(target, (str, list, pd.Series, np.ndarray)): |
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if isinstance(target, str): |
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target_data = data[target] |
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data = data.drop(target, axis=1) |
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elif isinstance(target, (list, pd.Series, np.ndarray)): |
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target_data = pd.Series(target) |
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corr = pd.DataFrame(data.corrwith(target_data)) |
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corr = _corr_selector(corr, split=split, threshold=threshold) |
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vmax = np.round(np.nanmax(corr)-0.05, 2) |
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vmin = np.round(np.nanmin(corr)+0.05, 2) |
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else: |
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corr = corr_mat(data, split=split, threshold=threshold, method=method).data |
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mask = np.triu(np.ones_like(corr, dtype=np.bool)) # Generate mask for the upper triangle |
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square = True |
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vmax = np.round(np.nanmax(corr.where(~mask))-0.05, 2) |
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vmin = np.round(np.nanmin(corr.where(~mask))+0.05, 2) |
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fig, ax = plt.subplots(figsize=figsize) |
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# Specify kwargs for the heatmap |
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kwargs = {'mask': mask, |
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'cmap': cmap, |
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'annot': annot, |
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'vmax': vmax, |
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'vmin': vmin, |
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'linewidths': .5, |
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'annot_kws': {'size': 10}, |
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'cbar_kws': {'shrink': .95, 'aspect': 30}, |
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**kwargs} |
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# Draw heatmap with mask and some default settings |
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sns.heatmap(corr, |
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center=0, |
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square=square, |
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fmt='.2f', |
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**kwargs |
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) |
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ax.set_title(f'Feature-correlation ({method})', fontdict={'fontsize': 18}) |
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# Display settings |
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if dev: |
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fig.suptitle(f"\ |
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Settings (dev-mode): \n\ |
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- split-mode: {split} \n\ |
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- threshold: {threshold} \n\ |
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- method: {method} \n\ |
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- annotations: {annot} \n\ |
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- cbar: \n\ |
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- vmax: {vmax} \n\ |
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- vmin: {vmin} \n\ |
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- linewidths: {kwargs['linewidths']} \n\ |
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- annot_kws: {kwargs['annot_kws']} \n\ |
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- cbar_kws: {kwargs['cbar_kws']}", |
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fontsize=12, |
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color='gray', |
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x=0.35, |
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y=0.85, |
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ha='left') |
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return ax |
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# Distribution plot |
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def dist_plot(data, mean_color='orange', figsize=(14, 2), fill_range=(0.025, 0.975), hist=False, showall=False, |
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kde_kws={}, rug_kws={}, fill_kws={}, font_kws={}): |
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''' |
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Two-dimensional visualization of the missing values in a dataset. |
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Parameters |
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---------- |
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data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \ |
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information is used to label the plots. |
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mean_color: any valid color, default 'orange' |
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Color of the vertical line indicating the mean of the data. |
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figsize: tuple, default (14, 2) |
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Use to control the figure size. |
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fill_range: tuple, default (0.025, 0.975) |
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Use to control set the quantiles for shading. Default spans 95% of the data, which is about two std. deviations\ |
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above and below the mean. |
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hist: bool, default False |
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Set to True to display histogram bars in the plot. |
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showall: bool, default False |
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Set to True to remove the output limit of 20 plots. |
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kdw_kws: dict, optional |
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Keyword arguments for kdeplot(). |
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rug_kws: dict, optional |
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Keyword arguments for rugplot(). |
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fill_kws: |
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Keyword arguments to control the fill. |
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font_kws: |
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Keyword arguments to control the font. |
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Returns |
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------- |
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ax: matplotlib Axes |
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Returns the Axes object with the plot for further tweaking. |
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''' |
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# Validate Inputs |
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_validate_input_bool(hist, 'hist') |
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_validate_input_bool(showall, 'showall') |
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data = pd.DataFrame(data).copy() |
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cols = list(data.select_dtypes(include=['number']).columns) # numeric cols |
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if len(cols) == 0: |
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print('No columns with numeric data were detected.') |
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elif len(cols) >= 20 and showall is False: |
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print( |
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f'Note: The number of features is very large ({len(cols)}), please consider splitting the data.\ |
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Showing plots for the first 20 numerical features. Override this by setting showall=True.') |
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cols = cols[:20] |
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# Default settings |
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kde_kws = {'color': 'k', 'alpha': 0.6, 'linewidth': 1, **kde_kws} |
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rug_kws = {'color': 'brown', 'alpha': 0.5, 'linewidth': 2, 'height': 0.04, **rug_kws} |
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fill_kws = {'color': 'brown', 'alpha': 0.1, **fill_kws} |
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font_kws = {'color': '#111111', 'weight': 'normal', 'size': 11, **font_kws} |
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ax = [] |
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for col in cols: |
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fig, ax = plt.subplots(figsize=figsize) |
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ax = sns.distplot(data[col], hist=hist, rug=True, kde_kws=kde_kws, rug_kws=rug_kws) |
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# Vertical lines and fill |
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line = ax.lines[0] |
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x = line.get_xydata()[:, 0] |
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y = line.get_xydata()[:, 1] |
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ax.fill_between(x, y, |
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where=( |
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(x >= np.quantile(data[col], fill_range[0])) & |
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(x <= np.quantile(data[col], fill_range[1]))), |
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label=f'{fill_range[0]*100:.0f}% - {fill_range[1]*100:.0f}%', |
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**fill_kws) |
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ax.vlines(x=np.mean(data[col]), |
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ymin=0, |
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ymax=np.interp(np.mean(data[col]), x, y), |
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ls='dotted', color=mean_color, lw=2, label='mean') |
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ax.vlines(x=np.median(data[col]), |
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ymin=0, |
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ymax=np.interp(np.median(data[col]), x, y), |
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ls=':', color='.4', label='median') |
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ax.vlines(x=np.quantile(data[col], 0.25), |
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305
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ymin=0, |
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ymax=np.interp(np.quantile(data[col], 0.25), x, y), ls=':', color='.6', label='25%') |
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ax.vlines(x=np.quantile(data[col], 0.75), |
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ymin=0, |
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ymax=np.interp(np.quantile(data[col], 0.75), x, y), ls=':', color='.6', label='75%') |
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310
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311
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ax.set_ylim(0,) |
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312
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313
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# Annotations and legend |
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314
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ax.text(0.01, 0.85, f'Mean: {np.round(np.mean(data[col]),2)}', |
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315
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fontdict=font_kws, transform=ax.transAxes) |
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316
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ax.text(0.01, 0.7, f'Std. dev: {np.round(scipy.stats.tstd(data[col]),2)}', |
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317
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fontdict=font_kws, transform=ax.transAxes) |
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318
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ax.text(0.01, 0.55, f'Skew: {np.round(scipy.stats.skew(data[col]),2)}', |
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319
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fontdict=font_kws, transform=ax.transAxes) |
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320
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ax.text(0.01, 0.4, f'Kurtosis: {np.round(scipy.stats.kurtosis(data[col]),2)}', # Excess Kurtosis |
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321
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fontdict=font_kws, transform=ax.transAxes) |
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322
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ax.text(0.01, 0.25, f'Count: {np.round(len(data[col]))}', |
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323
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fontdict=font_kws, transform=ax.transAxes) |
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324
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ax.legend(loc='upper right') |
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325
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326
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return ax |
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327
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328
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329
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# Missing value plot |
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330
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def missingval_plot(data, cmap='PuBuGn', figsize=(12, 12), sort=False, spine_color='#EEEEEE'): |
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331
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''' |
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332
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Two-dimensional visualization of the missing values in a dataset. |
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333
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334
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Parameters |
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335
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---------- |
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336
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data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \ |
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337
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information is used to label the plots. |
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338
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339
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cmap: colormap, default 'PuBuGn' |
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340
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Any valid colormap can be used. E.g. 'Greys', 'RdPu'. More information can be found in the matplotlib \ |
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341
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documentation. |
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342
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343
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figsize: tuple, default (20, 12) |
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344
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Use to control the figure size. |
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345
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|
|
346
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sort: bool, default False |
|
347
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Sort columns based on missing values in descending order and drop columns without any missing values |
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348
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|
|
349
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spine_color: color-code, default '#EEEEEE' |
|
350
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Set to 'None' to hide the spines on all plots or use any valid matplotlib color argument. |
|
351
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|
352
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Returns |
|
353
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|
------- |
|
354
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|
|
figure |
|
355
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''' |
|
356
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|
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|
357
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data = pd.DataFrame(data) |
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358
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|
359
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if sort: |
|
360
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mv_cols_sorted = data.isna().sum(axis=0).sort_values(ascending=False) |
|
361
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|
|
final_cols = mv_cols_sorted.drop(mv_cols_sorted[mv_cols_sorted.values == 0].keys().tolist()).keys().tolist() |
|
362
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|
|
data = data[final_cols] |
|
363
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|
|
print('Displaying only columns with missing values.') |
|
364
|
|
|
|
|
365
|
|
|
# Identify missing values |
|
366
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|
|
mv_cols = _missing_vals(data)['mv_cols'] # data.isna().sum(axis=0) |
|
367
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|
|
mv_rows = _missing_vals(data)['mv_rows'] # data.isna().sum(axis=1) |
|
368
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|
|
mv_total = _missing_vals(data)['mv_total'] |
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369
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|
mv_cols_ratio = _missing_vals(data)['mv_cols_ratio'] # mv_cols / data.shape[0] |
|
370
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|
|
total_datapoints = data.shape[0]*data.shape[1] |
|
371
|
|
|
|
|
372
|
|
|
if mv_total == 0: |
|
373
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|
|
print('No missing values found in the dataset.') |
|
374
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|
|
else: |
|
375
|
|
|
# Create figure and axes |
|
376
|
|
|
fig = plt.figure(figsize=figsize) |
|
377
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|
|
gs = fig.add_gridspec(nrows=6, ncols=6, left=0.05, wspace=0.05) |
|
378
|
|
|
ax1 = fig.add_subplot(gs[:1, :5]) |
|
379
|
|
|
ax2 = fig.add_subplot(gs[1:, :5]) |
|
380
|
|
|
ax3 = fig.add_subplot(gs[:1, 5:]) |
|
381
|
|
|
ax4 = fig.add_subplot(gs[1:, 5:]) |
|
382
|
|
|
|
|
383
|
|
|
# ax1 - Barplot |
|
384
|
|
|
colors = plt.get_cmap(cmap)(mv_cols / np.max(mv_cols)) # color bars by height |
|
385
|
|
|
ax1.bar(range(len(mv_cols)), np.round((mv_cols_ratio)*100, 2), color=colors) |
|
386
|
|
|
ax1.get_xaxis().set_visible(False) |
|
387
|
|
|
ax1.set(frame_on=False, xlim=(-.5, len(mv_cols)-0.5)) |
|
388
|
|
|
ax1.set_ylim(0, np.max(mv_cols_ratio)*100) |
|
389
|
|
|
ax1.grid(linestyle=':', linewidth=1) |
|
390
|
|
|
ax1.yaxis.set_major_formatter(ticker.PercentFormatter(decimals=0)) |
|
391
|
|
|
ax1.tick_params(axis='y', colors='#111111', length=1) |
|
392
|
|
|
|
|
393
|
|
|
# annotate values on top of the bars |
|
394
|
|
|
for rect, label in zip(ax1.patches, mv_cols): |
|
395
|
|
|
height = rect.get_height() |
|
396
|
|
|
ax1.text(.1 + rect.get_x() + rect.get_width() / 2, height+0.5, label, |
|
397
|
|
|
ha='center', |
|
398
|
|
|
va='bottom', |
|
399
|
|
|
rotation='90', |
|
400
|
|
|
alpha=0.5, |
|
401
|
|
|
fontsize='small') |
|
402
|
|
|
|
|
403
|
|
|
ax1.set_frame_on(True) |
|
404
|
|
|
for _, spine in ax1.spines.items(): |
|
405
|
|
|
spine.set_visible(True) |
|
406
|
|
|
spine.set_color(spine_color) |
|
407
|
|
|
ax1.spines['top'].set_color(None) |
|
408
|
|
|
|
|
409
|
|
|
# ax2 - Heatmap |
|
410
|
|
|
sns.heatmap(data.isna(), cbar=False, cmap='binary', ax=ax2) |
|
411
|
|
|
ax2.set_yticks(np.round(ax2.get_yticks()[0::5], -1)) |
|
412
|
|
|
ax2.set_yticklabels(ax2.get_yticks()) |
|
413
|
|
|
ax2.set_xticklabels( |
|
414
|
|
|
ax2.get_xticklabels(), |
|
415
|
|
|
horizontalalignment='center', |
|
416
|
|
|
fontweight='light', |
|
417
|
|
|
fontsize='medium') |
|
418
|
|
|
ax2.tick_params(length=1, colors='#111111') |
|
419
|
|
|
for _, spine in ax2.spines.items(): |
|
420
|
|
|
spine.set_visible(True) |
|
421
|
|
|
spine.set_color(spine_color) |
|
422
|
|
|
|
|
423
|
|
|
# ax3 - Summary |
|
424
|
|
|
fontax3 = {'color': '#111111', |
|
425
|
|
|
'weight': 'normal', |
|
426
|
|
|
'size': 12, |
|
427
|
|
|
} |
|
428
|
|
|
ax3.get_xaxis().set_visible(False) |
|
429
|
|
|
ax3.get_yaxis().set_visible(False) |
|
430
|
|
|
ax3.set(frame_on=False) |
|
431
|
|
|
|
|
432
|
|
|
ax3.text(0.1, 0.9, f"Total: {np.round(total_datapoints/1000,1)}K", |
|
433
|
|
|
transform=ax3.transAxes, |
|
434
|
|
|
fontdict=fontax3) |
|
435
|
|
|
ax3.text(0.1, 0.7, f"Missing: {np.round(mv_total/1000,1)}K", |
|
436
|
|
|
transform=ax3.transAxes, |
|
437
|
|
|
fontdict=fontax3) |
|
438
|
|
|
ax3.text(0.1, 0.5, f"Relative: {np.round(mv_total/total_datapoints*100,1)}%", |
|
439
|
|
|
transform=ax3.transAxes, |
|
440
|
|
|
fontdict=fontax3) |
|
441
|
|
|
ax3.text(0.1, 0.3, f"Max-col: {np.round(mv_cols.max()/data.shape[0]*100)}%", |
|
442
|
|
|
transform=ax3.transAxes, |
|
443
|
|
|
fontdict=fontax3) |
|
444
|
|
|
ax3.text(0.1, 0.1, f"Max-row: {np.round(mv_rows.max()/data.shape[1]*100)}%", |
|
445
|
|
|
transform=ax3.transAxes, |
|
446
|
|
|
fontdict=fontax3) |
|
447
|
|
|
|
|
448
|
|
|
# ax4 - Scatter plot |
|
449
|
|
|
ax4.get_yaxis().set_visible(False) |
|
450
|
|
|
for _, spine in ax4.spines.items(): |
|
451
|
|
|
spine.set_color(spine_color) |
|
452
|
|
|
ax4.tick_params(axis='x', colors='#111111', length=1) |
|
453
|
|
|
|
|
454
|
|
|
ax4.scatter(mv_rows, range(len(mv_rows)), s=mv_rows, c=mv_rows, cmap=cmap, marker=".", vmin=1) |
|
455
|
|
|
ax4.set_ylim((0, len(mv_rows))[::-1]) # limit and invert y-axis |
|
456
|
|
|
ax4.set_xlim(0, max(mv_rows)+0.5) |
|
457
|
|
|
ax4.grid(linestyle=':', linewidth=1) |
|
458
|
|
|
|
|
459
|
|
|
gs.figure.suptitle('Missing value plot', x=0.45, y=0.94, fontsize=18, color='#111111') |
|
460
|
|
|
|
|
461
|
|
|
return gs |
|
462
|
|
|
|
akanz1 /
klib
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