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/test/test_utils.py
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| a | b/test/test_utils.py | ||
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| 1 | import pytest |
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| 2 | import numpy as np |
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| 3 | import pandas as pd |
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| 4 | from scipy import spatial, cluster |
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| 5 | |||
| 6 | from maui import utils |
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| 7 | |||
| 8 | |||
| 9 | def test_merge_factors(): |
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| 10 | z = pd.DataFrame( |
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| 11 | [ |
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| 12 | [1, 1, 1, 0, 0, 0, 1, 0, 0], |
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| 13 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 14 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 15 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 16 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 17 | [1, 1, 1, 1, 1, 0, 0, 1, 0], |
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| 18 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 19 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 20 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 21 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 22 | [0, 0, 0, 1, 0, 1, 1, 1, 0], |
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| 23 | ], |
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| 24 | index=[f"sample {i}" for i in range(11)], |
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| 25 | columns=[f"LF{i}" for i in range(9)], |
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| 26 | dtype=float, |
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| 27 | ) # expect 0,1,2 to be merged, and 3,7 to be merged |
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| 28 | |||
| 29 | z_merged = utils.merge_factors(z, metric="euclidean", plot_dendro=False) |
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| 30 | |||
| 31 | assert z_merged.shape[1] == 6 |
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| 32 | assert "0_1_2" in z_merged.columns |
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| 33 | assert "3_7" in z_merged.columns |
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| 34 | |||
| 35 | |||
| 36 | def test_merge_factors_with_custom_linkage(): |
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| 37 | z = pd.DataFrame( |
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| 38 | [ |
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| 39 | [1, 1, 1, 0, 0, 0, 1, 0, 0], |
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| 40 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 41 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 42 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 43 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 44 | [1, 1, 1, 1, 1, 0, 0, 1, 0], |
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| 45 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 46 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 47 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 48 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 49 | [0, 0, 0, 1, 0, 1, 1, 1, 0], |
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| 50 | ], |
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| 51 | index=[f"sample {i}" for i in range(11)], |
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| 52 | columns=[f"LF{i}" for i in range(9)], |
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| 53 | dtype=float, |
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| 54 | ) # expect 0,1,2 to be merged, and 3,7 to be merged |
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| 55 | |||
| 56 | l = cluster.hierarchy.linkage(spatial.distance.pdist(z.T, "minkowski"), "average") |
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| 57 | |||
| 58 | z_merged = utils.merge_factors(z, l=l, plot_dendro=False) |
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| 59 | |||
| 60 | assert z_merged.shape[1] == 6 |
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| 61 | assert "0_1_2" in z_merged.columns |
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| 62 | assert "3_7" in z_merged.columns |
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| 63 | |||
| 64 | |||
| 65 | def test_filter_factors_by_r2(): |
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| 66 | dummy_z = pd.DataFrame( |
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| 67 | [[0, 1, 2], [1, 0, 2]], |
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| 68 | index=["sample 1", "sample 2"], |
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| 69 | columns=["LF1", "LF2", "LF3"], |
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| 70 | ) |
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| 71 | |||
| 72 | dummy_x = pd.DataFrame( |
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| 73 | [[1.0, 1.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 1.0, 1.0]], |
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| 74 | columns=[f"feature{i}" for i in range(6)], |
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| 75 | index=["sample 1", "sample 2"], |
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| 76 | ).T |
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| 77 | |||
| 78 | z_filt = utils.filter_factors_by_r2(dummy_z, dummy_x) |
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| 79 | assert z_filt.columns.tolist() == ["LF1", "LF2"] |
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| 80 | |||
| 81 | def test_map_factors_to_feaures_using_linear_models(): |
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| 82 | dummy_z = pd.DataFrame( |
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| 83 | [[0, 1], [1, 0]], index=["sample 1", "sample 2"], columns=["LF1", "LF2"] |
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| 84 | ) |
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| 85 | |||
| 86 | dummy_x = pd.DataFrame( |
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| 87 | [[1.0, 1.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 1.0, 1.0]], |
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| 88 | columns=[f"feature{i}" for i in range(6)], |
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| 89 | index=["sample 1", "sample 2"], |
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| 90 | ).T |
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| 91 | |||
| 92 | expected_w = np.array( |
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| 93 | [[-2.0, 2.0], [-2.0, 2.0], [-2.0, 2.0], [2.0, -2.0], [2.0, -2.0], [2.0, -2.0]] |
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| 94 | ) |
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| 95 | |||
| 96 | w = utils.map_factors_to_feaures_using_linear_models(dummy_z, dummy_x) |
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| 97 | |||
| 98 | assert np.allclose(w, expected_w) |
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| 99 | |||
| 100 | |||
| 101 | def test_correlate_factors_and_features(): |
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| 102 | dummy_z = pd.DataFrame( |
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| 103 | [[0, 1], [1, 0]], index=["sample 1", "sample 2"], columns=["LF1", "LF2"] |
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| 104 | ) |
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| 105 | |||
| 106 | dummy_x = pd.DataFrame( |
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| 107 | [[1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1]], |
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| 108 | columns=[f"feature{i}" for i in range(6)], |
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| 109 | index=["sample 1", "sample 2"], |
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| 110 | ) |
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| 111 | |||
| 112 | expected_corrs = np.array( |
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| 113 | [[-1.0, 1.0], [-1.0, 1.0], [-1.0, 1.0], [1.0, -1.0], [1.0, -1.0], [1.0, -1.0]] |
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| 114 | ) |
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| 115 | |||
| 116 | corrs = utils.correlate_factors_and_features(dummy_z, dummy_x) |
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| 117 | |||
| 118 | assert np.allclose(corrs, expected_corrs) |
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| 119 | |||
| 120 | |||
| 121 | def test_compute_roc(): |
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| 122 | np.random.seed(0) |
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| 123 | dummy_z = pd.DataFrame( |
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| 124 | [ |
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| 125 | [0, 1, 1, 1, 0, 1, 1, 0, 0], |
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| 126 | [1, 0, 0, 0, 0, 0, 1, 1, 0], |
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| 127 | [1, 0, 1, 0, 0, 0, 1, 1, 0], |
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| 128 | [1, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 129 | [1, 0, 0, 0, 1, 1, 1, 1, 0], |
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| 130 | [1, 1, 1, 0, 0, 0, 1, 1, 1], |
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| 131 | ], |
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| 132 | index=[f"sample {i}" for i in range(6)], |
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| 133 | columns=[f"LF{i}" for i in range(9)], |
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| 134 | ) |
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| 135 | dummy_y = pd.Series(["a", "b", "a", "c", "b", "c"], index=dummy_z.index) |
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| 136 | |||
| 137 | roc_curves = utils.compute_roc(dummy_z, dummy_y, cv_folds=2) |
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| 138 | assert np.allclose(roc_curves["a"].FPR, [0.0, 0.5, 0.5, 0.75, 1.0]) |
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| 139 | |||
| 140 | |||
| 141 | def test_compute_auc(): |
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| 142 | fpr = [0.0, 0.0, 0.5, 0.5, 1.0] |
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| 143 | tpr = [0.0, 0.5, 0.5, 1.0, 1.0] |
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| 144 | roc = utils.auc(fpr, tpr) |
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| 145 | assert roc - 0.75 < 1e-6 |
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| 146 | |||
| 147 | |||
| 148 | def test_estimate_km(): |
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| 149 | yhat = pd.Series( |
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| 150 | ["a", "a", "a", "b", "b", "b"], index=[f"Sample {i}" for i in range(6)] |
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| 151 | ) |
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| 152 | durations = np.random.poisson(6, 6) |
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| 153 | observed = np.random.randn(6) > 0.1 |
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| 154 | survival = pd.DataFrame( |
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| 155 | dict(duration=durations, observed=observed), |
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| 156 | index=[f"Sample {i}" for i in range(6)], |
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| 157 | ) |
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| 158 | km = utils.estimate_kaplan_meier(yhat, survival) |
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| 159 | |||
| 160 | assert "a" in km.columns |
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| 161 | assert "b" in km.columns |
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| 162 | |||
| 163 | |||
| 164 | def test_multivariate_logrank_test(): |
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| 165 | yhat = pd.Series( |
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| 166 | ["a", "a", "a", "b", "b", "b"], index=[f"Sample {i}" for i in range(6)] |
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| 167 | ) |
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| 168 | durations = np.random.poisson(6, 6) |
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| 169 | observed = np.random.randn(6) > 0.1 |
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| 170 | survival = pd.DataFrame( |
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| 171 | dict(duration=durations, observed=observed), |
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| 172 | index=[f"Sample {i}" for i in range(6)], |
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| 173 | ) |
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| 174 | test_stat, p_val = utils.multivariate_logrank_test(yhat, survival) |
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| 175 | assert p_val <= 1.0 |
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| 176 | |||
| 177 | |||
| 178 | def test_select_clinical_factors(): |
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| 179 | dummy_z = pd.DataFrame( |
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| 180 | [ |
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| 181 | [1, 1, 1, 0, 0, 0, 1, 0, 1], |
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| 182 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 183 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 184 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 185 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 186 | [1, 1, 1, 1, 1, 0, 0, 1, 0], |
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| 187 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 188 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 189 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 190 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 191 | [0, 0, 0, 1, 0, 1, 1, 1, 1], |
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| 192 | ], |
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| 193 | index=[f"sample {i}" for i in range(11)], |
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| 194 | columns=[f"LF{i}" for i in range(9)], |
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| 195 | ) # here the first 3 factors separate the groups and the last 6 do not |
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| 196 | |||
| 197 | durations = [ |
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| 198 | 1, |
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| 199 | 2, |
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| 200 | 3, |
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| 201 | 4, |
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| 202 | 5, |
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| 203 | 6, |
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| 204 | 1000, |
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| 205 | 2000, |
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| 206 | 3000, |
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| 207 | 4000, |
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| 208 | 5000, |
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| 209 | ] # here the first 3 have short durations, the last 3 longer ones |
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| 210 | observed = [True] * 11 # all events observed |
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| 211 | survival = pd.DataFrame( |
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| 212 | dict(duration=durations, observed=observed), |
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| 213 | index=[f"sample {i}" for i in range(11)], |
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| 214 | ) |
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| 215 | |||
| 216 | z_clinical = utils.select_clinical_factors(dummy_z, survival, cox_penalizer=1, alpha=.1) |
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| 217 | assert "LF0" in z_clinical.columns |
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| 218 | assert "LF1" in z_clinical.columns |
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| 219 | assert "LF2" in z_clinical.columns |
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| 220 | |||
| 221 | assert "LF3" not in z_clinical.columns |
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| 222 | assert "LF4" not in z_clinical.columns |
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| 223 | assert "LF5" not in z_clinical.columns |
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| 224 | |||
| 225 | |||
| 226 | def test_compute_harrells_c(): |
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| 227 | dummy_z = pd.DataFrame( |
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| 228 | [ |
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| 229 | [1, 1, 1, 0, 0, 0, 1, 0, 1], |
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| 230 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 231 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 232 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 233 | [1, 1, 1, 1, 0, 1, 1, 1, 0], |
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| 234 | [1, 1, 1, 1, 1, 0, 0, 1, 0], |
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| 235 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 236 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 237 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 238 | [0, 0, 0, 1, 0, 0, 1, 1, 0], |
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| 239 | [0, 0, 0, 1, 0, 1, 1, 1, 1], |
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| 240 | ], |
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| 241 | index=[f"sample {i}" for i in range(11)], |
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| 242 | columns=[f"LF{i}" for i in range(9)], |
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| 243 | ) # here the first 3 factors separate the groups and the last 6 do not |
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| 244 | |||
| 245 | durations = [ |
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| 246 | 1, |
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| 247 | 2, |
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| 248 | 3, |
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| 249 | 4, |
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| 250 | 5, |
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| 251 | 6, |
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| 252 | 1000, |
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| 253 | 2000, |
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| 254 | 3000, |
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| 255 | 4000, |
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| 256 | 5000, |
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| 257 | ] # here the first 3 have short durations, the last 3 longer ones |
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| 258 | observed = [True] * 11 # all events observed |
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| 259 | survival = pd.DataFrame( |
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| 260 | dict(duration=durations, observed=observed), |
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| 261 | index=[f"sample {i}" for i in range(11)], |
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| 262 | ) |
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| 263 | z_clinical = utils.select_clinical_factors(dummy_z, survival, cox_penalizer=1, alpha=.1) |
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| 264 | |||
| 265 | np.random.seed(0) |
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| 266 | c = utils.compute_harrells_c(z_clinical, survival, cv_folds=3) |
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| 267 | assert np.allclose(c, [0.5, 0.8, 0.5], atol=.05) |