hana_ml.visualizers package¶

The Visualizers Package consists of the following sections:

hana_ml.visualizers.eda
hana_ml.visualizers.m4_sampling
hana_ml.visualizers.metrics
hana_ml.visualizers.model_debriefing

hana_ml.visualizers.eda¶

This module represents an eda plotter. Matplotlib is used for all visualizations.

class hana_ml.visualizers.eda.EDAVisualizer(ax=None, size=None, cmap=None, title=None)¶

Bases: hana_ml.visualizers.visualizer_base.Visualizer

Class for all EDA visualizations, including:

Distribution plot
Pie plot
Correlation plot
Scatter plot
Bar plot
Box plot

Parameters

axmatplotlib.Axes, optional: The axes to use to plot the figure. Default value : Current axes
sizetuple of integers, optional: (width, height) of the plot in dpi Default value: Current size of the plot
titlestr, optional: This plot’s title. Default value : Empty str

Attributes

ax: Returns the matplotlib Axes where the Visualizer will draw.
cmap: Returns the color map being used for the plot.
size: Returns the size of the plot in pixels.
title: Returns the title of the plot.

Methods

`bar_plot`(self, data, column, aggregation[, …])	Returns a bar plot for the HANA DataFrame column specified.
`box_plot`(self, data, column[, outliers, …])	Returns a box plot for the HANA DataFrame column specified.
`correlation_plot`(self, data[, key, …])	Returns a correlation plot for the HANA DataFrame columns specified.
`distribution_plot`(self, data, column, bins)	Returns a distribution plot for the HANA DataFrame column specified.
`pie_plot`(self, data, column[, explode, …])	Returns a pie plot for the HANA DataFrame column specified.
`scatter_plot`(self, data, x, y, x_bins, y_bins)	Returns a scatter plot for the HANA DataFrame columns specified.
`set_ax`(self, ax)	Sets the Axes
`set_cmap`(self, cmap)	Sets the colormap
`set_size`(self, size)	Sets the size
`set_title`(self, title)	Sets the title of the plot

distribution_plot(self, data, column, bins, title=None, x_axis_fontsize=10, x_axis_rotation=90, debrief=True)¶

Returns a distribution plot for the HANA DataFrame column specified.

Parameters

dataDataFrame: DataFrame to use for the plot.
columnstr: Column in the DataFrame being plotted.
binsint: Number of bins to create based on the value of column.
titlestr, optional: Title for the plot.
x_axis_fontsizeint, optional: Size of x axis labels
x_axis_rotationint, optional: Rotation of x axis labels
debriefbool, optional: Include skewness debrief

Returns

axAxes: The axes for the plot.
bin_datapandas.DataFrame: The data used in the plot.

pie_plot(self, data, column, explode=0.03, title=None, legend=True)¶

Returns a pie plot for the HANA DataFrame column specified.

Parameters

dataDataFrame: DataFrame to use for the plot.
columnstr: Column in the DataFrame being plotted.
explodefloat, optional: Relative spacing between pie segments.
titlestr, optional: Title for the plot.
legendbool, optional: Legend for the plot.

Returns

axAxes: The axes for the plot. This can be used to set specific properties for the plot.
pie_datapandas.DataFrame: The data used in the plot.

correlation_plot(self, data, key=None, corr_cols=None, label=True, cmap='RdYlBu')¶

Returns a correlation plot for the HANA DataFrame columns specified.

Parameters

dataDataFrame: DataFrame to use for the plot.
keystr: Name of ID column.
corr_colslist of str, optional: Columns in the DataFrame being plotted. If None all numeric columns will be plotted.
labelbool, optional: Plot a colorbar.
cmapmatplotlib.pyplot.colormap, optional: Color map to use for the plot.

Returns

axAxes: The axes for the plot. This can be used to set specific properties for the plot.
corrpandas.DataFrame: The data used in the plot.

scatter_plot(self, data, x, y, x_bins, y_bins, title=None, label=True, cmap='Blues', debrief=True, rounding_precision=3)¶

Returns a scatter plot for the HANA DataFrame columns specified.

Parameters

dataDataFrame: DataFrame to use for the plot.
xstr: Column to be plotted on the x axis.
ystr: Column to be plotted on the y axis.
x_binsint: Number of x axis bins to create based on the value of column.
y_binsint: Number of y axis bins to create based on the value of column.
titlestr, optional: Title for the plot.
labelstr, optional: Label for the color bar.
cmapmatplotlib.pyplot.colormap, optional: Color map to use for the plot.
debriefbool, optional: Include correlation debrief.
rounding_precisionint, optional: The rounding precision for bin size.

Returns

axAxes: The axes for the plot.
bin_matrixpandas.DataFrame: The data used in the plot.

bar_plot(self, data, column, aggregation, title=None)¶

Returns a bar plot for the HANA DataFrame column specified.

Parameters

dataDataFrame: DataFrame to use for the plot.
columnstr: Column to be aggregated.
aggregationdict: Aggregation conditions (‘avg’, ‘count’, ‘max’, ‘min’).
titlestr, optional: Title for the plot.

Returns

axAxes: The axes for the plot.
bar_datapandas.DataFrame: The data used in the plot.

Examples

>>> ax1 = f.add_subplot(111)
>>> eda = EDAVisualizer(ax1)
>>> ax, bar_data = eda.bar_plot(data=data, column='COLUMN',
                                aggregation={'COLUMN':'count'})

Returns : bar plot (count) of ‘COLUMN’

>>> ax1 = f.add_subplot(111)
>>> eda = EDAVisualizer(ax1)
>>> ax, bar_data = eda.bar_plot(data=data, column='COLUMN',
                                aggregation={'OTHER_COLUMN':'avg'})

Returns : bar plot (avg) of ‘COLUMN’ against ‘OTHER_COLUMN’

box_plot(self, data, column, outliers=False, title=None, groupby=None)¶

Returns a box plot for the HANA DataFrame column specified.

Parameters

dataDataFrame: DataFrame to use for the plot.
columnstr: Column in the DataFrame being plotted.
outliersbool: Whether to plot suspected outliers and outliers.
titlestr, optional: Title for the plot.
groupbystr, optional: Column to group by and compare.

Returns

axAxes: The axes for the plot.
contpandas.DataFrame: The data used in the plot.

ax¶: Returns the matplotlib Axes where the Visualizer will draw.

cmap¶: Returns the color map being used for the plot.

set_ax(self, ax)¶: Sets the Axes

set_cmap(self, cmap)¶: Sets the colormap

set_size(self, size)¶: Sets the size

set_title(self, title)¶: Sets the title of the plot

size¶: Returns the size of the plot in pixels.

title¶: Returns the title of the plot.

class hana_ml.visualizers.eda.Profiler¶

Bases: object

Class to build a HANA Profiler, including: - Variable descriptions - Missing values % - High cardinality % - Skewness - Numeric distributions - Categorical distributions - Correlations - High correlaton warnings

Methods

`description`(self, data, key[, bins, …])	Returns a HANA profiler, including: - Variable descriptions - Missing values % - High cardinality % - Skewness - Numeric distributions - Categorical distributions - Correlations - High correlaton warnings
`set_size`(self, fig, figsize)	Set the size of the data description plot, in inches.

description(self, data, key, bins=20, missing_threshold=10, card_threshold=100, skew_threshold=0.5, figsize=None)¶

Returns a HANA profiler, including: - Variable descriptions - Missing values % - High cardinality % - Skewness - Numeric distributions - Categorical distributions - Correlations - High correlaton warnings

Parameters

dataDataFrame: DataFrame to use for the plot.
keystr, optional: Key in the DataFrame.
binsint, optional: Number of bins for numeric distributions. Default value = 20.
missing_thresholdfloat: Percentage threshold to display missing values.
card_thresholdint: Threshold for column to be considered with high cardinality.
skew_thresholdfloat: Absolute value threshold for column to be considered as highly skewed.
tight_layoutbool, optional: Use matplotlib tight layout or not.
figsizetuple, optional: Size of figure to be plotted. First element is width, second is height.
Note: categorical columns with cardinality warnings are not plotted.

Returns

figFigure: matplotlib axis of the profiler

set_size(self, fig, figsize)¶

Set the size of the data description plot, in inches.

Parameters

figax: The returned axes constructed by the description method.
figsizetuple: Tuple of width and height for the plot.

hana_ml.visualizers.metrics¶

This module represents a visualizer for metrics.

class hana_ml.visualizers.metrics.MetricsVisualizer(ax=None, size=None, cmap=None, title=None)¶

Bases: hana_ml.visualizers.visualizer_base.Visualizer, object

The MetricVisualizer is used to visualize metrics.

Parameters

axmatplotlib.Axes, optional: The axes to use to plot the figure. Default value : Current axes
sizetuple of integers, optional: (width, height) of the plot in dpi Default value: Current size of the plot
titlestr, optional: This plot’s title. Default value : Empty str

Attributes

ax: Returns the matplotlib Axes where the Visualizer will draw.
cmap: Returns the color map being used for the plot.
size: Returns the size of the plot in pixels.
title: Returns the title of the plot.

Methods

`plot_confusion_matrix`(self, df[, normalize])	This function plots the confusion matrix and returns the Axes where this is drawn.
`set_ax`(self, ax)	Sets the Axes
`set_cmap`(self, cmap)	Sets the colormap
`set_size`(self, size)	Sets the size
`set_title`(self, title)	Sets the title of the plot

plot_confusion_matrix(self, df, normalize=False)¶

This function plots the confusion matrix and returns the Axes where this is drawn.

Parameters

dfDataFrame: Data points to the resulting confusion matrix. This dataframe’s columns should match columns (‘CLASS’, ‘’)

ax¶: Returns the matplotlib Axes where the Visualizer will draw.

cmap¶: Returns the color map being used for the plot.

set_ax(self, ax)¶: Sets the Axes

set_cmap(self, cmap)¶: Sets the colormap

set_size(self, size)¶: Sets the size

set_title(self, title)¶: Sets the title of the plot

size¶: Returns the size of the plot in pixels.

title¶: Returns the title of the plot.

hana_ml.visualizers.m4_sampling¶

M4 algorithm for sampling query

hana_ml.visualizers.m4_sampling.get_min_index(data)¶

Get Minimum Timestamp of Time Series Data

Parameters

dataDataFrame: Time series data whose 1st column is index and 2nd one is value.

Returns

datetime: Return the minimum timestamp.

hana_ml.visualizers.m4_sampling.get_max_index(data)¶

Get Maximum Timestamp of Time Series Data

Parameters

dataDataFrame: Time series data whose 1st column is index and 2nd one is value.

Returns

datetime: Return the maximum timestamp.

hana_ml.visualizers.m4_sampling.m4_sampling(data, width)¶

M4 algorithm for big data visualization

Parameters

dataDataFrame: Data to be sampled. Time seires data whose 1st column is index and 2nd one is value.
widthint: Sampling Rate. It is an indicator of how many pixels being in the picture.

Returns

DataFrame: Return the sampled dataframe.

hana_ml.visualizers.model_debriefing¶

class hana_ml.visualizers.model_debriefing.TreeModelDebriefing¶

Bases: object

Visualize tree model.

Dependency packages

1.Graphviz
To render the generated DOT source code, you need to install Graphviz.

Download page: https://www.graphviz.org/download/.

Make sure that the directory containing the dot executable is on your system path.

2.graphviz

3.pydotplus

4.ipywidgets
To render the Jupyter widgets, you also need to install JupyterLab extension.

Install Page: https://ipywidgets.readthedocs.io/en/latest/user_install.

Methods

`tree_debrief`(self, model)	Visualize tree model by data in JSON or XML format.
`tree_debrief_from_file`(path)	Visualize tree model by a DOT, JSON or XML file.
`tree_debrief_with_dot`(self, model)	Visualize tree model by data in DOT format.
`tree_export`(self, model)	Export tree model as a JSON or XML file.
`tree_export_with_dot`(self, model)	Export tree model as a DOT file.
`tree_parse`(self, model)	Transform tree model content using DOT language.

tree_debrief(self, model)¶

Visualize tree model by data in JSON or XML format.

Parameters

modelDataFrame: Tree model.

Returns

JSON or XML Component: This object can be rendered by browser.

tree_debrief_with_dot(self, model)¶

Visualize tree model by data in DOT format.

Parameters

modelDataFrame: Tree model.

Returns

SVG Component: This object can be rendered by browser.

tree_parse(self, model)¶

Transform tree model content using DOT language.

Parameters

modelDataFrame: Tree model.

tree_export(self, model)¶

Export tree model as a JSON or XML file.

Parameters

modelDataFrame: Tree model.

Returns

Interactive Text and Button Widgets: Those widgets can be rendered by browser.

tree_export_with_dot(self, model)¶

Export tree model as a DOT file.

Parameters

modelDataFrame: Tree model.

Returns

Interactive Text and Button Widgets: Those widgets can be rendered by browser.

static tree_debrief_from_file(path)¶

Visualize tree model by a DOT, JSON or XML file.

Parameters

pathString: File path.

Returns

SVG, JSON or XML Component: This object can be rendered by browser.

hana_ml.visualizers package¶

hana_ml.visualizers.eda¶

hana_ml.visualizers.metrics¶

hana_ml.visualizers.m4_sampling¶

hana_ml.visualizers.model_debriefing¶

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