lorepy 0.1.1

Draw Logistic Regression Plots in Python

lorepy: Logistic Regression Plots for Python

Logistic Regression plots are used to plot the distribution of a categorical dependent variable in function of a continuous independent variable.

Installation

Lorepy can be installed using pip using the command below.

pip install lorepy

Usage

Data needs to be provided as a DataFrame and the columns for the x (independent continuous) and y (dependant categorical) variables need to be defined. Here the iris dataset is loaded and converted to an appropriate DataFrame. Once the data is in shape it can be plotted using a single line of code loreplot(data=iris_df, x="sepal width (cm)", y="species").

from lorepy import loreplot

from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import pandas as pd

iris_obj = load_iris()
iris_df = pd.DataFrame(iris_obj.data, columns=iris_obj.feature_names)

iris_df["species"] = [iris_obj.target_names[s] for s in iris_obj.target]

loreplot(data=iris_df, x="sepal width (cm)", y="species")

plt.show()

Options

While lorepy has very few customizations, it is possible to pass arguments through to Pandas' DataFrame.plot.area and Matplotlib's pyplot.scatter to change the aesthetics of the plots.

Disable sample dots

Dots indicating where samples are located can be en-/disabled using the add_dots argument.

loreplot(data=iris_df, x="sepal width (cm)", y="species", add_dots=False)
plt.show()

Custom styles

Additional keyword arguments are passed to Pandas' DataFrame.plot.area. This can be used, among other things, to define a custom colormap. For more options to customize these plots consult Pandas' documentation.

from matplotlib.colors import ListedColormap

colormap=ListedColormap(['red', 'green', 'blue'])

loreplot(data=iris_df, x="sepal width (cm)", y="species", colormap=colormap)
plt.show()

Using scatter_kws arguments for pyplot.scatter can be set to change the appearance of the sample markers.

scatter_options = {
    's': 20,                  # Marker size
    'alpha': 1,               # Fully opaque
    'color': 'black',         # Set color to black
    'marker': 'x'             # Set style to crosses
}

loreplot(data=iris_df, x="sepal width (cm)", y="species", scatter_kws=scatter_options)
plt.show()

You can use LoRePlots in subplots as you would expect.

fig, ax = plt.subplots(1,2, sharex=False, sharey=True)
loreplot(data=iris_df, x="sepal width (cm)", y="species", ax=ax[0])
loreplot(data=iris_df, x="petal width (cm)", y="species", ax=ax[1])

ax[0].get_legend().remove()
ax[0].set_title("Sepal Width")
ax[1].set_title("Petal Width")

plt.savefig('./docs/img/loreplot_subplot.png', dpi=150)
plt.show()