Add Manova unit tests and fix other unit tests

CHANGELOG.md

@@ -5,6 +5,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ## [0.3.0] - 2019-02-07
 - Refactor the mgcpy package structure
+- Add Manova
 - Fix two sample tests
 - Add scripts for k sample test power curves
 - Update sphinx docs

mgcpy/benchmarks/unit_tests/power_test.py

@@ -1,16 +1,9 @@
-import pickle
-
 import h5py
 import numpy as np
 import pytest
 from mgcpy.benchmarks.power import power, power_given_data
-from mgcpy.benchmarks.simulations import (circle_sim, joint_sim,
-                                          multi_indep_sim, multi_noise_sim,
-                                          sin_sim, square_sim, ubern_sim,
-                                          w_sim)
+from mgcpy.benchmarks.simulations import joint_sim
 from mgcpy.independence_tests.dcorr import DCorr
-from mgcpy.independence_tests.rv_corr import RVCorr
-from scipy.spatial.distance import pdist, squareform
 
 
 def test_power():

mgcpy/hypothesis_tests/unit_test.py

@@ -23,7 +23,7 @@ def test_k_sample():
 
     # k sample case
     salaries = salary_data["Current Annual Salary"].values
-    department_labels = salary_data["Department"].values
+    department_labels = salary_data["Department"].values.reshape(-1, 1)
     u, v = k_sample_transform(salaries[:100], department_labels[:100], is_y_categorical=True)
     mgc = MGC()
     p_value, p_value_metadata = mgc.p_value(u, v)
@@ -43,7 +43,7 @@ def test_k_sample():
                   0.22417797,  0.45241074, -1.03024521,  0.6615743,  0.27216365, 2.4188678,  0.20561134,  0.71095061, -1.02478312,  0.54512964,
                   0.16582386, -0.39648338, -0.77905918, -0.33196771,  0.69407125, -0.81484451,  3.01568098, -0.49053868, -0.60987204,  1.72967348])
     # assign half of them as samples from 1 and the other half as samples from 2
-    y = np.concatenate([np.repeat(1, 50), np.repeat(2, 50)], axis=0)
+    y = np.concatenate([np.repeat(1, 50), np.repeat(2, 50)], axis=0).reshape(-1, 1)
 
     u, v = k_sample_transform(x, y, is_y_categorical=True)
     mgc = MGC()

mgcpy/independence_tests/unit_tests/manova_test.py

@@ -0,0 +1,22 @@
+import numpy as np
+import pytest
+from mgcpy.independence_tests.manova import Manova
+from mgcpy.benchmarks.hypothesis_tests.three_sample_test.power import generate_three_two_d_gaussians
+from mgcpy.hypothesis_tests.transforms import k_sample_transform
+
+
+def test_local_corr():
+    np.random.seed(0)
+    matrix_X, matrix_Y, matrix_Z = generate_three_two_d_gaussians(2, 100, 3)
+
+    data = np.concatenate([matrix_X, matrix_Y, matrix_Z], axis=0)
+    labels = np.concatenate([np.repeat(1, matrix_X.shape[0]), np.repeat(2, matrix_Y.shape[0]), np.repeat(3, matrix_Z.shape[0])], axis=0).reshape(-1, 1)
+
+    matrix_U, matrix_V = k_sample_transform(data, labels, is_y_categorical=True)
+
+    # Against linear simulations
+    manova = Manova()
+    test_stat = manova.test_statistic(matrix_U, matrix_V)[0]
+
+    assert manova.get_name() == 'manova'
+    assert np.allclose(test_stat, 0.06, atol=1.e-2)