"""
=============
Breast Cancer
=============

This example generates a Mapper built from the `Wisconsin Breast Cancer Dataset`_.

.. _Wisconsin Breast Cancer Dataset: https://www.kaggle.com/uciml/breast-cancer-wisconsin-data


The reasoning behind the choice of lenses in the demonstration below is:

- **For lens1:** Lenses that make biological sense; in other words, lenses that highlight special features in the data, that I know about.
- **For lens2:** Lenses that disperse the data, as opposed to clustering many points together.

In the case of this particular data, using an anomaly score (in this case calculated using the IsolationForest from sklearn) makes biological sense since cancer cells are anomalous. For the second lens, we use the :math:`l^2` norm.

For an interactive exploration of lens for the breast cancer, see the `Choosing a lens notebook <../../notebooks/Cancer-demo.html>`_.

KeplerMapper also permits setting multiple datapoint color functions and node color functions in its html visualizations.
The example code below demonstrates three ways this might be done. The rendered visualizations are also viewable:

- `Visualization of the breat cancer mapper using multiple datapoint color functions <../../_static/breast-cancer-multiple-color-functions.html>`_
- `Visualization of the breat cancer mapper using multiple node color functions <../../_static/breast-cancer-multiple-node-color-functions.html>`_
- `Visualization of the breat cancer mapper using multiple datapoint and node color functions <../../_static/breast-cancer-multiple-color-functions-and-multiple-node-color-functions.html>`_

.. image:: ../../../examples/breast-cancer/breast-cancer.png


"""
# sphinx_gallery_thumbnail_path = '../examples/breast-cancer/breast-cancer-d3.png'

import sys

try:
    import pandas as pd
except ImportError as e:
    print(
        "pandas is required for this example. Please install with `pip install pandas` and then try again."
    )
    sys.exit()

import numpy as np
import kmapper as km
import sklearn
from sklearn import ensemble

# For data we use the Wisconsin Breast Cancer Dataset
# Via:
df = pd.read_csv("data/breast-cancer.csv")
feature_names = [c for c in df.columns if c not in ["id", "diagnosis"]]
df["diagnosis"] = df["diagnosis"].apply(lambda x: 1 if x == "M" else 0)
X = np.array(df[feature_names].fillna(0))  # quick and dirty imputation
y = np.array(df["diagnosis"])

# We create a custom 1-D lens with Isolation Forest
model = ensemble.IsolationForest(random_state=1729)
model.fit(X)
lens1 = model.decision_function(X).reshape((X.shape[0], 1))

# We create another 1-D lens with L2-norm
mapper = km.KeplerMapper(verbose=3)
lens2 = mapper.fit_transform(X, projection="l2norm")

# Combine both lenses to create a 2-D [Isolation Forest, L^2-Norm] lens
lens = np.c_[lens1, lens2]

# Create the simplicial complex
graph = mapper.map(
    lens,
    X,
    cover=km.Cover(n_cubes=15, perc_overlap=0.4),
    clusterer=sklearn.cluster.KMeans(n_clusters=2, random_state=1618033),
)

# Visualization
mapper.visualize(
    graph,
    path_html="output/breast-cancer.html",
    title="Wisconsin Breast Cancer Dataset",
    custom_tooltips=y,
)


# Visualization with multiple color functions
mapper.visualize(
    graph,
    path_html="output/breast-cancer-multiple-color-functions.html",
    title="Wisconsin Breast Cancer Dataset",
    custom_tooltips=y,
    color_values=lens,
    color_function_name=["Isolation Forest", "L2-norm"],
)


# Visualization with multiple node color functions
mapper.visualize(
    graph,
    path_html="output/breast-cancer-multiple-node-color-functions.html",
    title="Wisconsin Breast Cancer Dataset",
    custom_tooltips=y,
    node_color_function=["mean", "std", "median", "max"],
)

# Visualization showing both multiple color functions, and also multiple node color functions
mapper.visualize(
    graph,
    path_html="output/breast-cancer-multiple-color-functions-and-multiple-node-color-functions.html",
    title="Wisconsin Breast Cancer Dataset",
    custom_tooltips=y,
    color_values=lens,
    color_function_name=["Isolation Forest", "L2-norm"],
    node_color_function=["mean", "std", "median", "max"],
)


import matplotlib.pyplot as plt

km.draw_matplotlib(graph)
plt.show()