149 lines
4.7 KiB
Python
149 lines
4.7 KiB
Python
"""
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==========================================
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IsolationForest benchmark
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==========================================
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A test of IsolationForest on classical anomaly detection datasets.
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"""
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from time import time
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.ensemble import IsolationForest
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from sklearn.metrics import roc_curve, auc
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from sklearn.datasets import fetch_kddcup99, fetch_covtype, fetch_mldata
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from sklearn.preprocessing import MultiLabelBinarizer
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from sklearn.utils import shuffle as sh
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print(__doc__)
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def print_outlier_ratio(y):
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"""
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Helper function to show the distinct value count of element in the target.
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Useful indicator for the datasets used in bench_isolation_forest.py.
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"""
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uniq, cnt = np.unique(y, return_counts=True)
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print("----- Target count values: ")
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for u, c in zip(uniq, cnt):
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print("------ %s -> %d occurrences" % (str(u), c))
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print("----- Outlier ratio: %.5f" % (np.min(cnt) / len(y)))
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np.random.seed(1)
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fig_roc, ax_roc = plt.subplots(1, 1, figsize=(8, 5))
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# Set this to true for plotting score histograms for each dataset:
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with_decision_function_histograms = False
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# Removed the shuttle dataset because as of 2017-03-23 mldata.org is down:
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# datasets = ['http', 'smtp', 'SA', 'SF', 'shuttle', 'forestcover']
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datasets = ['http', 'smtp', 'SA', 'SF', 'forestcover']
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# Loop over all datasets for fitting and scoring the estimator:
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for dat in datasets:
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# Loading and vectorizing the data:
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print('====== %s ======' % dat)
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print('--- Fetching data...')
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if dat in ['http', 'smtp', 'SF', 'SA']:
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dataset = fetch_kddcup99(subset=dat, shuffle=True, percent10=True)
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X = dataset.data
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y = dataset.target
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if dat == 'shuttle':
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dataset = fetch_mldata('shuttle')
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X = dataset.data
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y = dataset.target
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X, y = sh(X, y)
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# we remove data with label 4
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# normal data are then those of class 1
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s = (y != 4)
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X = X[s, :]
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y = y[s]
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y = (y != 1).astype(int)
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print('----- ')
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if dat == 'forestcover':
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dataset = fetch_covtype(shuffle=True)
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X = dataset.data
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y = dataset.target
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# normal data are those with attribute 2
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# abnormal those with attribute 4
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s = (y == 2) + (y == 4)
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X = X[s, :]
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y = y[s]
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y = (y != 2).astype(int)
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print_outlier_ratio(y)
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print('--- Vectorizing data...')
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if dat == 'SF':
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lb = MultiLabelBinarizer()
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x1 = lb.fit_transform(X[:, 1])
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X = np.c_[X[:, :1], x1, X[:, 2:]]
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y = (y != b'normal.').astype(int)
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print_outlier_ratio(y)
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if dat == 'SA':
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lb = MultiLabelBinarizer()
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x1 = lb.fit_transform(X[:, 1])
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x2 = lb.fit_transform(X[:, 2])
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x3 = lb.fit_transform(X[:, 3])
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X = np.c_[X[:, :1], x1, x2, x3, X[:, 4:]]
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y = (y != b'normal.').astype(int)
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print_outlier_ratio(y)
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if dat in ('http', 'smtp'):
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y = (y != b'normal.').astype(int)
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print_outlier_ratio(y)
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n_samples, n_features = X.shape
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n_samples_train = n_samples // 2
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X = X.astype(float)
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X_train = X[:n_samples_train, :]
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X_test = X[n_samples_train:, :]
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y_train = y[:n_samples_train]
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y_test = y[n_samples_train:]
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print('--- Fitting the IsolationForest estimator...')
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model = IsolationForest(n_jobs=-1)
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tstart = time()
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model.fit(X_train)
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fit_time = time() - tstart
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tstart = time()
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scoring = - model.decision_function(X_test) # the lower, the more abnormal
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print("--- Preparing the plot elements...")
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if with_decision_function_histograms:
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fig, ax = plt.subplots(3, sharex=True, sharey=True)
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bins = np.linspace(-0.5, 0.5, 200)
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ax[0].hist(scoring, bins, color='black')
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ax[0].set_title('Decision function for %s dataset' % dat)
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ax[1].hist(scoring[y_test == 0], bins, color='b', label='normal data')
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ax[1].legend(loc="lower right")
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ax[2].hist(scoring[y_test == 1], bins, color='r', label='outliers')
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ax[2].legend(loc="lower right")
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# Show ROC Curves
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predict_time = time() - tstart
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fpr, tpr, thresholds = roc_curve(y_test, scoring)
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auc_score = auc(fpr, tpr)
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label = ('%s (AUC: %0.3f, train_time= %0.2fs, '
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'test_time= %0.2fs)' % (dat, auc_score, fit_time, predict_time))
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# Print AUC score and train/test time:
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print(label)
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ax_roc.plot(fpr, tpr, lw=1, label=label)
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ax_roc.set_xlim([-0.05, 1.05])
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ax_roc.set_ylim([-0.05, 1.05])
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ax_roc.set_xlabel('False Positive Rate')
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ax_roc.set_ylabel('True Positive Rate')
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ax_roc.set_title('Receiver operating characteristic (ROC) curves')
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ax_roc.legend(loc="lower right")
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fig_roc.tight_layout()
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plt.show()
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