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''' |
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Utilities 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 seaborn as sns |
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# Missing value plot |
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def missingval_plot(data, cmap='PuBuGn', figsize=(20, 12), sort=False, spine_color='#EEEEEE'): |
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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 information is used to label the plots. |
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cmap: colormap, default 'PuBuGn' |
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Any valid colormap can be used. E.g. 'Greys', 'RdPu'. More information can be found in the matplotlib documentation. |
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figsize: tuple, default (20,12) |
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Use to control the figure size. |
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sort: bool, default False |
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Sort columns based on missing values in descending order and drop columns without any missing values |
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spine_color: color-code, default '#EEEEEE' |
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Set to 'None' to hide the spines on all plots or use any valid matplotlib color argument. |
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Returns |
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------- |
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ax: matplotlib Axes. Axes object with the heatmap. |
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''' |
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data = pd.DataFrame(data) |
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if sort: |
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mv_cols_sorted = data.isna().sum(axis=0).sort_values(ascending=False) |
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final_cols = mv_cols_sorted.drop(mv_cols_sorted[mv_cols_sorted.values == 0].keys().tolist()).keys().tolist() |
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data = data[final_cols] |
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print('Displaying only columns with missing values.') |
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# Identify missing values |
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mv_cols = data.isna().sum(axis=0) |
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mv_rows = data.isna().sum(axis=1) |
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mv_total = mv_cols.sum() |
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mv_cols_rel = mv_cols / data.shape[0] |
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total_datapoints = data.shape[0]*data.shape[1] |
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if mv_total == 0: |
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print('No missing values found in the dataset.') |
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else: |
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# Create figure and axes |
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fig = plt.figure(figsize=figsize) |
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grid = fig.add_gridspec(nrows=6, ncols=6, left=0.05, right=0.48, wspace=0.05) |
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ax1 = fig.add_subplot(grid[:1, :5]) |
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ax2 = fig.add_subplot(grid[1:, :5]) |
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ax3 = fig.add_subplot(grid[:1, 5:]) |
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ax4 = fig.add_subplot(grid[1:, 5:]) |
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# ax1 - Barplot |
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colors = plt.get_cmap(cmap)(mv_cols / np.max(mv_cols)) # color bars by height |
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ax1.bar(range(len(mv_cols)), np.round((mv_cols_rel)*100, 2), color=colors) |
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ax1.get_xaxis().set_visible(False) |
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ax1.set(frame_on=False, xlim=(-.5, len(mv_cols)-0.5)) |
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ax1.set_ylim(0, np.max(mv_cols_rel)*100) |
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ax1.grid(linestyle=':', linewidth=1) |
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ax1.yaxis.set_major_formatter(ticker.PercentFormatter(decimals=0)) |
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ax1.tick_params(axis='y', colors='#111111', length=1) |
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# annotate values on top of the bars |
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for rect, label in zip(ax1.patches, mv_cols): |
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height = rect.get_height() |
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ax1.text(.1 + rect.get_x() + rect.get_width() / 2, height+0.5, label, |
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ha='center', |
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va='bottom', |
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rotation='90', |
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alpha=0.5, |
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fontsize='small') |
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ax1.set_frame_on(True) |
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for _, spine in ax1.spines.items(): |
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spine.set_visible(True) |
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spine.set_color(spine_color) |
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ax1.spines['top'].set_color(None) |
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# ax2 - Heatmap |
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sns.heatmap(data.isna(), cbar=False, cmap='binary', ax=ax2) |
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ax2.set_yticks(np.round(ax2.get_yticks()[0::5], -1)) |
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ax2.set_yticklabels(ax2.get_yticks()) |
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ax2.set_xticklabels( |
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ax2.get_xticklabels(), |
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horizontalalignment='center', |
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fontweight='light', |
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fontsize='medium') |
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ax2.tick_params(length=1, colors='#111111') |
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for _, spine in ax2.spines.items(): |
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spine.set_visible(True) |
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spine.set_color(spine_color) |
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# ax3 - Summary |
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fontax3 = {'color': '#111111', |
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'weight': 'normal', |
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'size': 12, |
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} |
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ax3.get_xaxis().set_visible(False) |
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ax3.get_yaxis().set_visible(False) |
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ax3.set(frame_on=False) |
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ax3.text(0.1, 0.9, f"Total: {np.round(total_datapoints/1000,1)}K", |
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transform=ax3.transAxes, |
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fontdict=fontax3) |
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ax3.text(0.1, 0.7, f"Missing: {np.round(mv_total/1000,1)}K", |
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transform=ax3.transAxes, |
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fontdict=fontax3) |
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ax3.text(0.1, 0.5, f"Relative: {np.round(mv_total/total_datapoints*100,1)}%", |
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transform=ax3.transAxes, |
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fontdict=fontax3) |
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ax3.text(0.1, 0.3, f"Max-col: {np.round(mv_cols.max()/data.shape[0]*100)}%", |
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transform=ax3.transAxes, |
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fontdict=fontax3) |
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ax3.text(0.1, 0.1, f"Max-row: {np.round(mv_rows.max()/data.shape[1]*100)}%", |
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transform=ax3.transAxes, |
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fontdict=fontax3) |
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# ax4 - Scatter plot |
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ax4.get_yaxis().set_visible(False) |
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for _, spine in ax4.spines.items(): |
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spine.set_color(spine_color) |
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ax4.tick_params(axis='x', colors='#111111', length=1) |
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ax4.scatter(mv_rows, range(len(mv_rows)), s=mv_rows, c=mv_rows, cmap=cmap, marker=".") |
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ax4.set_ylim(0, len(mv_rows)) |
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ax4.set_ylim(ax4.get_ylim()[::-1]) # invert y-axis |
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ax4.grid(linestyle=':', linewidth=1) |
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# Correlation matrix / heatmap |
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def corr_plot(data, split=None, threshold=0, cmap='BrBG', figsize=(12, 10), annot=True, 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 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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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. Use for presentations. |
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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 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. Axes object with the heatmap. |
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''' |
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data = pd.DataFrame(data) |
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if split == 'pos': |
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corr = data.corr().where((data.corr() >= threshold) & (data.corr() > 0)) |
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print('Displaying positive correlations. Use "threshold" to further limit the results.') |
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elif split == 'neg': |
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corr = data.corr().where((data.corr() <= threshold) & (data.corr() < 0)) |
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print('Displaying negative correlations. Use "threshold" to further limit the results.') |
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elif split == 'high': |
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corr = data.corr().where(np.abs(data.corr()) >= threshold) |
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print('Displaying absolute correlations above a chosen threshold.') |
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elif split == 'low': |
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corr = data.corr().where(np.abs(data.corr()) <= threshold) |
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print('Displaying absolute correlations below a chosen threshold.') |
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else: |
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corr = data.corr() |
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split = 'None' |
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threshold = 'None' |
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# Generate mask for the upper triangle |
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mask = np.triu(np.ones_like(corr, dtype=np.bool)) |
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# Compute dimensions and correlation range to adjust settings |
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vmax = np.round(np.nanmax(corr.where(mask == False))-0.05, 2) |
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vmin = np.round(np.nanmin(corr.where(mask == False))+0.05, 2) |
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# Set up the matplotlib figure and generate colormap |
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fig, ax = plt.subplots(figsize=figsize) |
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# 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=True, |
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fmt='.2f', |
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**kwargs |
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) |
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ax.set_title('Feature-correlation Matrix', fontdict={'fontsize': 18}) |
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if dev: # show settings |
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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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- 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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# _functions |
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def _memory_usage(data): |
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''' |
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Gives the total memory usage in kilobytes. |
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277
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Parameters |
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278
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---------- |
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279
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data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column information is used to label the plots. |
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280
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281
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Returns |
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282
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------- |
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283
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memory_usage: float |
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284
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285
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''' |
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286
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287
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data = pd.DataFrame(data) |
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memory_usage = round(data.memory_usage(index=True, deep=True).sum()/1024, 2) |
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return memory_usage |
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293
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|
|
def _missing_vals(data): |
|
294
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|
|
''' |
|
295
|
|
|
Gives metrics of missing values in the dataset. |
|
296
|
|
|
|
|
297
|
|
|
Parameters |
|
298
|
|
|
---------- |
|
299
|
|
|
data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column information is used to label the plots. |
|
300
|
|
|
|
|
301
|
|
|
Returns |
|
302
|
|
|
------- |
|
303
|
|
|
total_mv: float, number of missing values in the entire dataset |
|
304
|
|
|
rows_mv: float, number of missing values in each row |
|
305
|
|
|
cols_mv: float, number of missing values in each column |
|
306
|
|
|
rows_mv_ratio: float, ratio of missing values for each row |
|
307
|
|
|
cols_mv_ratio: float, ratio of missing values for each column |
|
308
|
|
|
''' |
|
309
|
|
|
|
|
310
|
|
|
data = pd.DataFrame(data) |
|
311
|
|
|
rows_mv = data.isna().sum(axis=0) |
|
312
|
|
|
cols_mv = data.isna().sum(axis=1) |
|
313
|
|
|
total_mv = data.isna().sum().sum() |
|
314
|
|
|
rows_mv_ratio = rows_mv/data.shape[0] |
|
315
|
|
|
cols_mv_ratio = cols_mv/data.shape[1] |
|
316
|
|
|
|
|
317
|
|
|
return total_mv, rows_mv, cols_mv, rows_mv_ratio, cols_mv_ratio |
|
318
|
|
|
|
akanz1 /
klib
