"""
==========================================
Evaluation of outlier detection estimators
==========================================

This example benchmarks outlier detection algorithms, :ref:`local_outlier_factor`
(LOF) and :ref:`isolation_forest` (IForest), using ROC curves on
classical anomaly detection datasets. The algorithm performance
is assessed in an outlier detection context:

1. The algorithms are trained on the whole dataset which is assumed to
contain outliers.

2. The ROC curve from :class:`~sklearn.metrics.RocCurveDisplay` is computed
on the same dataset using the knowledge of the labels.

"""

# Author: Pharuj Rajborirug <pharuj.ra@kmitl.ac.th>
# License: BSD 3 clause

print(__doc__)

# %%
# Define a data preprocessing function
# ------------------------------------
#
# The example uses real-world datasets available in
# :class:`sklearn.datasets` and the sample size of some datasets is reduced
# to speed up computation. After the data preprocessing, the datasets' targets
# will have two classes, 0 representing inliers and 1 representing outliers.
# The `preprocess_dataset` function returns data and target.

import numpy as np
import pandas as pd

from sklearn.datasets import fetch_covtype, fetch_kddcup99, fetch_openml
from sklearn.preprocessing import LabelBinarizer

rng = np.random.RandomState(42)


def preprocess_dataset(dataset_name):
    # loading and vectorization
    print(f"Loading {dataset_name} data")
    if dataset_name in ["http", "smtp", "SA", "SF"]:
        dataset = fetch_kddcup99(subset=dataset_name, percent10=True, random_state=rng)
        X = dataset.data
        y = dataset.target
        lb = LabelBinarizer()

        if dataset_name == "SF":
            idx = rng.choice(X.shape[0], int(X.shape[0] * 0.1), replace=False)
            X = X[idx]  # reduce the sample size
            y = y[idx]
            x1 = lb.fit_transform(X[:, 1].astype(str))
            X = np.c_[X[:, :1], x1, X[:, 2:]]
        elif dataset_name == "SA":
            idx = rng.choice(X.shape[0], int(X.shape[0] * 0.1), replace=False)
            X = X[idx]  # reduce the sample size
            y = y[idx]
            x1 = lb.fit_transform(X[:, 1].astype(str))
            x2 = lb.fit_transform(X[:, 2].astype(str))
            x3 = lb.fit_transform(X[:, 3].astype(str))
            X = np.c_[X[:, :1], x1, x2, x3, X[:, 4:]]
        y = (y != b"normal.").astype(int)
    if dataset_name == "forestcover":
        dataset = fetch_covtype()
        X = dataset.data
        y = dataset.target
        idx = rng.choice(X.shape[0], int(X.shape[0] * 0.1), replace=False)
        X = X[idx]  # reduce the sample size
        y = y[idx]

        # inliers are those with attribute 2
        # outliers are those with attribute 4
        s = (y == 2) + (y == 4)
        X = X[s, :]
        y = y[s]
        y = (y != 2).astype(int)
    if dataset_name in ["glass", "wdbc", "cardiotocography"]:
        dataset = fetch_openml(
            name=dataset_name, version=1, as_frame=False, parser="pandas"
        )
        X = dataset.data
        y = dataset.target

        if dataset_name == "glass":
            s = y == "tableware"
            y = s.astype(int)
        if dataset_name == "wdbc":
            s = y == "2"
            y = s.astype(int)
            X_mal, y_mal = X[s], y[s]
            X_ben, y_ben = X[~s], y[~s]

            # downsampled to 39 points (9.8% outliers)
            idx = rng.choice(y_mal.shape[0], 39, replace=False)
            X_mal2 = X_mal[idx]
            y_mal2 = y_mal[idx]
            X = np.concatenate((X_ben, X_mal2), axis=0)
            y = np.concatenate((y_ben, y_mal2), axis=0)
        if dataset_name == "cardiotocography":
            s = y == "3"
            y = s.astype(int)
    # 0 represents inliers, and 1 represents outliers
    y = pd.Series(y, dtype="category")
    return (X, y)


# %%
# Define an outlier prediction function
# -------------------------------------
# There is no particular reason to choose algorithms
# :class:`~sklearn.neighbors.LocalOutlierFactor` and
# :class:`~sklearn.ensemble.IsolationForest`. The goal is to show that
# different algorithm performs well on different datasets. The following
# `compute_prediction` function returns average outlier score of X.


from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor


def compute_prediction(X, model_name):
    print(f"Computing {model_name} prediction...")
    if model_name == "LOF":
        clf = LocalOutlierFactor(n_neighbors=20, contamination="auto")
        clf.fit(X)
        y_pred = clf.negative_outlier_factor_
    if model_name == "IForest":
        clf = IsolationForest(random_state=rng, contamination="auto")
        y_pred = clf.fit(X).decision_function(X)
    return y_pred


# %%
# Plot and interpret results
# --------------------------
#
# The algorithm performance relates to how good the true positive rate (TPR)
# is at low value of the false positive rate (FPR). The best algorithms
# have the curve on the top-left of the plot and the area under curve (AUC)
# close to 1. The diagonal dashed line represents a random classification
# of outliers and inliers.


import math

import matplotlib.pyplot as plt

from sklearn.metrics import RocCurveDisplay

datasets_name = [
    "http",
    "smtp",
    "SA",
    "SF",
    "forestcover",
    "glass",
    "wdbc",
    "cardiotocography",
]

models_name = [
    "LOF",
    "IForest",
]

# plotting parameters
cols = 2
linewidth = 1
pos_label = 0  # mean 0 belongs to positive class
rows = math.ceil(len(datasets_name) / cols)

fig, axs = plt.subplots(rows, cols, figsize=(10, rows * 3), sharex=True, sharey=True)

for i, dataset_name in enumerate(datasets_name):
    (X, y) = preprocess_dataset(dataset_name=dataset_name)

    for model_idx, model_name in enumerate(models_name):
        y_pred = compute_prediction(X, model_name=model_name)
        display = RocCurveDisplay.from_predictions(
            y,
            y_pred,
            pos_label=pos_label,
            name=model_name,
            linewidth=linewidth,
            ax=axs[i // cols, i % cols],
            plot_chance_level=(model_idx == len(models_name) - 1),
            chance_level_kw={
                "linewidth": linewidth,
                "linestyle": ":",
            },
        )
    axs[i // cols, i % cols].set_title(dataset_name)
plt.tight_layout(pad=2.0)  # spacing between subplots
plt.show()
