comparison tests/test_utils.py @ 30:e8084526f7e5 branch-tests

additional test functions

+# -*- coding: utf-8 -*-
+"""
+Created on Fri Sep  1 19:11:52 2017
+
+@author: mariapanteli
+"""
+
+import pytest
+
+import numpy as np
+import pandas as pd
+import pickle
+import os
+
+import scripts.utils as utils
+
+
+def test_get_outliers():
+    np.random.seed(1)
+    X = np.random.randn(100, 3)
+    # create outliers by shifting the entries of the last 5 samples
+    X[-5:, :] = X[-5:, :] + 10
+    Y = np.concatenate([np.repeat('a', 95), np.repeat('b', 5)])
+    threshold, y_pred, MD = utils.get_outliers(X)
+    # expect that items from country 'b' are detected as outliers
+    assert np.array_equal(y_pred[-5:], np.ones(5))
+
+
+def test_get_outliers():
+    np.random.seed(1)
+    X = np.random.randn(100, 3)
+    # create outliers by shifting the entries of the last 5 samples
+    X[-5:, :] = X[-5:, :] + 10
+    Y = np.concatenate([np.repeat('a', 95), np.repeat('b', 5)])
+    threshold, y_pred, MD = utils.get_outliers_Mahal(X)
+    # expect that items from country 'b' are detected as outliers
+    assert np.array_equal(y_pred[-5:], np.ones(5))
+
+
+def test_pca_data():
+    np.random.seed(1)
+    X = np.random.randn(100, 3)
+    X[-5:, :] = X[-5:, :] + 10
+    X_pca, n_pc = utils.pca_data(X, min_variance=0.8)
+    assert n_pc < X.shape[1]
+
+
+def test_get_local_outliers_from_neighbors_dict():
+    np.random.seed(1)
+    X = np.random.randn(100, 3)
+    n_outliers = 3
+    X[-n_outliers:, :] = X[-n_outliers:, :] + 10
+    Y = np.concatenate([np.repeat('a', 20), np.repeat('b', 20), np.repeat('c', 20), 
+                        np.repeat('k', 20), np.repeat('l', 20)])
+    w_dict = {'a': ['b', 'c'], 'b': ['a', 'c'], 'c': ['b', 'a'], 'k': ['l'], 'l':['k']}
+    spatial_outliers = utils.get_local_outliers_from_neighbors_dict(X, Y, w_dict)
+    # last n samples of 'l' country must be outliers
+    assert np.array_equal(spatial_outliers[-1][3][-n_outliers:], np.ones(n_outliers))
+
+
+def test_best_n_clusters_silhouette():
+    np.random.seed(1)
+    X = np.random.randn(100, 3)
+    X[:30, :] = X[:30, :] + 10
+    X[-30:, :] = X[-30:, :] + 20
+    bestncl, _ = utils.best_n_clusters_silhouette(X, max_ncl=10)
+    assert bestncl == 3
+