lorepy/example.py at main · raeslab/lorepy · GitHub

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import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from lorepy import loreplot
from matplotlib.colors import ListedColormap
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC

# Load iris dataset and convert to dataframe
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]

# Basic Lore Plot with default style
loreplot(data=iris_df, x="sepal width (cm)", y="species")
plt.savefig("./docs/img/loreplot.png", dpi=150)
plt.show()

# Key word arguments (like colormap) can be passed to the DataFrame.plot.area

colormap = ListedColormap(["red", "green", "blue"])
loreplot(data=iris_df, x="sepal width (cm)", y="species", colormap=colormap)
plt.savefig("./docs/img/loreplot_custom_color.png", dpi=150)
plt.show()

# En-/disable sample markers with add_dots
loreplot(data=iris_df, x="sepal width (cm)", y="species", add_dots=False)
plt.savefig("./docs/img/loreplot_no_dots.png", dpi=150)
plt.show()

# Pass custom styles for markers using scatter_kws
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.savefig("./docs/img/loreplot_custom_markers.png", dpi=150)
plt.show()

# Test in subplots

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()

# Basic Lore Plot with default style but different classifier
fig, ax = plt.subplots(1, 2, sharex=False, sharey=True)

svc = SVC(probability=True)
rf = RandomForestClassifier(n_estimators=10, max_depth=2)

loreplot(data=iris_df, x="sepal width (cm)", y="species", clf=svc, ax=ax[0])
loreplot(data=iris_df, x="sepal width (cm)", y="species", clf=rf, ax=ax[1])

ax[0].get_legend().remove()
ax[0].set_title("SVC")
ax[1].set_title("RF")

plt.savefig("./docs/img/loreplot_other_clf.png", dpi=150)
plt.show()

# Basic Lore Plot with default style with one confounder
loreplot(
    data=iris_df,
    x="sepal width (cm)",
    y="species",
    confounders=[("petal width (cm)", 1)],
)
plt.savefig("./docs/img/loreplot_confounder.png", dpi=150)
plt.show()

# Basic Lore Plot with some jitter
iris_df["sepal width (cm)"] = (
    np.round(iris_df["sepal width (cm)"] * 3) / 3
)  # Round values

loreplot(data=iris_df, x="sepal width (cm)", y="species", jitter=0.05)
plt.savefig("./docs/img/loreplot_jitter.png", dpi=150)
plt.show()

### Generate some plots that can be used for the documentation

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]

iris_df["sepal_group"] = iris_df["sepal width (cm)"].apply(
    lambda x: "small" if x < 3 else "large"
)
count_df = (
    iris_df.groupby(["species", "sepal_group"], as_index=False)
    .size()
    .pivot_table(index="sepal_group", columns="species", values="size")
)

totals = count_df.sum(axis=1)

count_df = count_df.div(totals, axis=0).sort_index(ascending=False)
count_df.plot.bar(stacked=True)

plt.tight_layout()
plt.savefig("./docs/img/threshold.png", dpi=150)
plt.show()

iris_df["sepal_bin"] = pd.cut(iris_df["sepal width (cm)"], 6)
count_df = (
    iris_df.groupby(["species", "sepal_bin"], as_index=False, observed=False)
    .size()
    .pivot_table(index="sepal_bin", columns="species", values="size", observed=False)
)

label_df = iris_df.groupby("sepal_bin", as_index=False, observed=False).size()
label_df["label"] = label_df.apply(
    lambda x: str(x["sepal_bin"]) + " (n=" + str(x["size"]) + ")", axis=1
)

totals = count_df.sum(axis=1)

count_df = count_df.div(totals, axis=0).sort_index(ascending=True)
count_df = (
    pd.merge(count_df, label_df, left_index=True, right_on="sepal_bin")
    .set_index("label")
    .drop(columns=["sepal_bin", "size"])
)
count_df.plot.bar(stacked=True)

plt.tight_layout()
plt.savefig("./docs/img/bins.png", dpi=150)
plt.show()


iris_df["sepal_cut"] = pd.qcut(iris_df["sepal width (cm)"], 6, duplicates="drop")
count_df = (
    iris_df.groupby(["species", "sepal_cut"], as_index=False, observed=False)
    .size()
    .pivot_table(index="sepal_cut", columns="species", values="size", observed=False)
)

label_df = iris_df.groupby("sepal_cut", as_index=False, observed=False).size()
label_df["label"] = label_df.apply(
    lambda x: str(x["sepal_cut"]) + " (n=" + str(x["size"]) + ")", axis=1
)

totals = count_df.sum(axis=1)

count_df = count_df.div(totals, axis=0).sort_index(ascending=True)
count_df = (
    pd.merge(count_df, label_df, left_index=True, right_on="sepal_cut")
    .set_index("label")
    .drop(columns=["sepal_cut", "size"])
)
count_df.plot.bar(stacked=True)

plt.tight_layout()
plt.savefig("./docs/img/percentiles.png", dpi=150)
plt.show()