test added

Author: LI-Yixuan

.gitignore

@@ -125,6 +125,4 @@ tex.cache/
 testtt.py
 real.py
 0to2_beforeduringafter.csv
-test.ipynb
-TrhCsCh.csv
-*.py
+

dabest/tests/test

@@ -0,0 +1,306 @@
+import sys
+import pytest
+import lqrt
+import numpy as np
+import scipy as sp
+import pandas as pd
+from .._stats_tools import effsize
+from .._classes import TwoGroupsEffectSize, PermutationTest, Dabest
+
+
+# Data for tests.
+# See: Asheber Abebe. Introduction to Design and Analysis of Experiments 
+# with the SAS, from Example: Two-way RM Design Pg 137.
+hr = [72, 78, 71, 72, 66, 74, 62, 69, 69, 66, 84, 80, 72, 65, 75, 71, 
+      86, 83, 82, 83, 79, 83, 73, 75, 73, 62, 90, 81, 72, 62, 69, 70]
+
+# Add experiment column
+e1 = np.repeat('Treatment1', 8).tolist()
+e2 = np.repeat('Control', 8).tolist()
+experiment = e1 + e2 + e1 + e2
+
+# Add a `Drug` column as the first variable
+d1 = np.repeat('AX23', 8).tolist()
+d2 = np.repeat('CONTROL', 8).tolist()
+drug = d1 + d2 + d1 + d2
+
+# Add a `Time` column as the second variable
+t1 = np.repeat('T1', 16).tolist()
+t2 = np.repeat('T2', 16).tolist()
+time = t1 + t2
+
+# Add an `id` column for paired data plotting.
+id_col = []
+for i in range(1, 9):
+    id_col.append(str(i)+"a")
+for i in range(1, 9):
+    id_col.append(str(i)+"c")
+id_col.extend(id_col)
+
+# Combine samples and gender into a DataFrame.
+df_test = pd.DataFrame({'ID'   : id_col,
+                   'Drug'      : drug,
+                   'Time'      : time, 
+                   'Experiment': experiment,
+                   'Heart Rate': hr
+                    })
+
+
+df_test_control = df_test[df_test["Experiment"]=="Control"]
+df_test_control = df_test_control.pivot(index="ID", columns="Time", values="Heart Rate")
+
+
+df_test_treatment1 = df_test[df_test["Experiment"]=="Treatment1"]
+df_test_treatment1 = df_test_treatment1.pivot(index="ID", columns="Time", values="Heart Rate")
+
+
+# kwargs for Dabest class init.
+dabest_default_kwargs = dict(ci=95, 
+                            resamples=5000, random_seed=12345,
+                            idx=None, proportional=False
+                            )
+
+
+# example of unpaired delta-delta calculation
+unpaired = Dabest(data = df_test, x = ["Time", "Drug"], y = "Heart Rate", 
+                  delta2 = True, experiment = "Experiment",
+                  experiment_label=None, x1_level=None, paired=None, id_col=None,
+                  **dabest_default_kwargs)
+
+
+# example of paired delta-delta calculation
+paired = Dabest(data = df_test, x = ["Time", "Drug"], y = "Heart Rate", 
+                  delta2 = True, experiment = "Experiment", paired="sequential", id_col="ID",
+                  experiment_label=None, x1_level=None,
+                  **dabest_default_kwargs)
+
+
+# example of paired data with specified experiment/x1 level
+paired_specified_level = Dabest(data = df_test, x = ["Time", "Drug"], y = "Heart Rate", 
+                  delta2 = True, experiment = "Experiment", paired="sequential", id_col="ID",
+                  experiment_label=["Control", "Treatment1"], x1_level=["T2", "T1"],
+                  **dabest_default_kwargs)
+
+
+def test_mean_diff_delta_unpaired():
+    mean_diff_results = unpaired.mean_diff.results
+    all_mean_diff = mean_diff_results['difference'].to_list()
+    diff1 = np.mean(df_test_treatment1["T2"])-np.mean(df_test_treatment1["T1"])
+    diff2 = np.mean(df_test_control["T2"])-np.mean(df_test_control["T1"])
+    diff3 = np.mean(mean_diff_results['bootstraps'][1]) - np.mean(mean_diff_results['bootstraps'][0])
+    np_result = [diff1, diff2, diff3]
+    assert all_mean_diff == pytest.approx(np_result)
+
+
+def test_mean_diff_delta_paired():
+    mean_diff_results = paired.mean_diff.results
+    all_mean_diff = mean_diff_results['difference'].to_list()
+    diff1 = np.mean(df_test_treatment1["T2"]-df_test_treatment1["T1"])
+    diff2 = np.mean(df_test_control["T2"]-df_test_control["T1"])
+    diff3 = np.mean(mean_diff_results['bootstraps'][1] - mean_diff_results['bootstraps'][0])
+    np_result = [diff1, diff2, diff3]
+    assert all_mean_diff == pytest.approx(np_result)
+
+
+def test_mean_diff_delta_paired_specified_level():
+    mean_diff_results = paired_specified_level.mean_diff.results
+    all_mean_diff = mean_diff_results['difference'].to_list()
+    diff1 = np.mean(df_test_control["T1"]-df_test_control["T2"])
+    diff2 = np.mean(df_test_treatment1["T1"]-df_test_treatment1["T2"])
+    diff3 = np.mean(mean_diff_results['bootstraps'][1] - mean_diff_results['bootstraps'][0])
+    np_result = [diff1, diff2, diff3]
+    assert all_mean_diff == pytest.approx(np_result)
+
+
+def test_median_diff_unpaired():
+    all_median_diff = unpaired.median_diff.results
+    median_diff = all_median_diff['difference'].to_list()
+    diff1 = np.median(df_test_treatment1["T2"])-np.median(df_test_treatment1["T1"])
+    diff2 = np.median(df_test_control["T2"])-np.median(df_test_control["T1"])
+    np_result = [diff1, diff2]
+    assert median_diff == pytest.approx(np_result)
+
+
+def test_median_diff_paired():
+    all_median_diff = paired.median_diff.results
+    median_diff = all_median_diff['difference'].to_list()
+    diff1 = np.median(df_test_treatment1["T2"]-df_test_treatment1["T1"])
+    diff2 = np.median(df_test_control["T2"]-df_test_control["T1"])
+    np_result = [diff1, diff2]
+    assert median_diff == pytest.approx(np_result)
+
+
+def test_median_diff_paired_specified_level():
+    all_median_diff = paired_specified_level.median_diff.results
+    median_diff = all_median_diff['difference'].to_list()
+    diff1 = np.median(df_test_control["T1"]-df_test_control["T2"])
+    diff2 = np.median(df_test_treatment1["T1"]-df_test_treatment1["T2"])
+    np_result = [diff1, diff2]
+    assert median_diff == pytest.approx(np_result)
+
+
+def test_cohens_d_unpaired():
+    all_cohens_d = unpaired.cohens_d.results
+    cohens_d = all_cohens_d['difference'].to_list()
+    diff1 = np.mean(df_test_treatment1["T2"])-np.mean(df_test_treatment1["T1"])
+    diff1 = diff1/np.sqrt((np.var(df_test_treatment1["T2"], ddof=1)+np.var(df_test_treatment1["T1"], ddof=1))/2) 
+    diff2 = np.mean(df_test_control["T2"])-np.mean(df_test_control["T1"])
+    diff2 = diff2/np.sqrt((np.var(df_test_control["T2"], ddof=1)+np.var(df_test_control["T1"], ddof=1))/2) 
+    np_result = [diff1, diff2]	        
+    assert cohens_d == pytest.approx(np_result)
+
+
+def test_cohens_d_paired():
+    all_cohens_d = paired.cohens_d.results
+    cohens_d = all_cohens_d['difference'].to_list()
+    diff1 = np.mean(df_test_treatment1["T2"]-df_test_treatment1["T1"])
+    diff1 = diff1/np.sqrt((np.var(df_test_treatment1["T2"], ddof=1)+np.var(df_test_treatment1["T1"], ddof=1))/2) 
+    diff2 = np.mean(df_test_control["T2"]-df_test_control["T1"])
+    diff2 = diff2/np.sqrt((np.var(df_test_control["T2"], ddof=1)+np.var(df_test_control["T1"], ddof=1))/2) 
+    np_result = [diff1, diff2]	        
+    assert cohens_d == pytest.approx(np_result)
+
+
+def test_cohens_d_paired_specified_level():
+    all_cohens_d = paired_specified_level.cohens_d.results
+    cohens_d = all_cohens_d['difference'].to_list()
+    diff1 = np.mean(df_test_control["T1"])-np.mean(df_test_control["T2"])
+    diff1 = diff1/np.sqrt((np.var(df_test_control["T2"], ddof=1)+np.var(df_test_control["T1"], ddof=1))/2)
+    diff2 = np.mean(df_test_treatment1["T1"])-np.mean(df_test_treatment1["T2"])
+    diff2 = diff2/np.sqrt((np.var(df_test_treatment1["T2"], ddof=1)+np.var(df_test_treatment1["T1"], ddof=1))/2)  
+    np_result = [diff1, diff2]	        
+    assert cohens_d == pytest.approx(np_result)
+
+
+def test_hedges_g_unpaired():
+    from math import gamma
+    hedges_g = unpaired.hedges_g.results['difference'].to_list()
+    a = 8*2-2
+    fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))
+    diff1 = (np.mean(df_test_treatment1["T2"])-np.mean(df_test_treatment1["T1"]))*fac
+    diff1 = diff1/np.sqrt((np.var(df_test_treatment1["T2"], ddof=1)+np.var(df_test_treatment1["T1"], ddof=1))/2) 
+    diff2 = (np.mean(df_test_control["T2"])-np.mean(df_test_control["T1"]))*fac
+    diff2 = diff2/np.sqrt((np.var(df_test_control["T2"], ddof=1)+np.var(df_test_control["T1"], ddof=1))/2) 
+    np_result=[diff1, diff2]
+    assert hedges_g == pytest.approx(np_result)
+
+
+def test_hedges_g_paired():
+    from math import gamma
+    hedges_g = paired.hedges_g.results['difference'].to_list()
+    a = 8*2-2
+    fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))
+    diff1 = (np.mean(df_test_treatment1["T2"]-df_test_treatment1["T1"]))*fac
+    diff1 = diff1/np.sqrt((np.var(df_test_treatment1["T2"], ddof=1)+np.var(df_test_treatment1["T1"], ddof=1))/2) 
+    diff2 = (np.mean(df_test_control["T2"]-df_test_control["T1"]))*fac
+    diff2 = diff2/np.sqrt((np.var(df_test_control["T2"], ddof=1)+np.var(df_test_control["T1"], ddof=1))/2) 
+    np_result=[diff1, diff2]
+    assert hedges_g == pytest.approx(np_result)
+
+
+def test_hedges_g_paired_specified_level():
+    from math import gamma
+    hedges_g = paired_specified_level.hedges_g.results['difference'].to_list()
+    a = 8*2-2
+    fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))
+    diff1 = (np.mean(df_test_control["T1"]-df_test_control["T2"]))*fac
+    diff1 = diff1/np.sqrt((np.var(df_test_control["T2"], ddof=1)+np.var(df_test_control["T1"], ddof=1))/2) 
+    diff2 = (np.mean(df_test_treatment1["T1"]-df_test_treatment1["T2"]))*fac
+    diff2 = diff2/np.sqrt((np.var(df_test_treatment1["T2"], ddof=1)+np.var(df_test_treatment1["T1"], ddof=1))/2) 
+    np_result=[diff1, diff2]
+    assert hedges_g == pytest.approx(np_result)
+
+
+def test_paired_stats_unpaired():
+    np_result = unpaired.mean_diff.results
+    
+    p1 = sp.stats.ttest_rel(np_result["bootstraps"][0], \
+                np_result["bootstraps"][1], nan_policy='omit').pvalue
+    assert np_result["pvalue_paired_students_t"].to_list()[2] == pytest.approx(p1)
+    
+    p2 = sp.stats.wilcoxon(np_result["bootstraps"][0], \
+                np_result["bootstraps"][1],).pvalue
+    assert np_result["pvalue_wilcoxon"].to_list()[2] == pytest.approx(p2)
+
+
+def test_paired_stats_paired():
+    np_result = paired.mean_diff.results
+    p1 = sp.stats.ttest_rel(df_test_treatment1["T1"], \
+                df_test_treatment1["T2"], nan_policy='omit').pvalue
+    p2 = sp.stats.ttest_rel(df_test_control["T1"], \
+                df_test_control["T2"], nan_policy='omit').pvalue
+    p3 = sp.stats.ttest_rel(np_result["bootstraps"][0], \
+                np_result["bootstraps"][1], nan_policy='omit').pvalue
+    assert np_result["pvalue_paired_students_t"].to_list() == pytest.approx([p1, p2, p3])
+    
+    p1 = sp.stats.wilcoxon(df_test_treatment1["T1"], \
+                df_test_treatment1["T2"]).pvalue
+    p2 = sp.stats.wilcoxon(df_test_control["T1"], \
+                df_test_control["T2"]).pvalue
+    p3 = sp.stats.wilcoxon(np_result["bootstraps"][0], \
+                np_result["bootstraps"][1]).pvalue
+    assert np_result["pvalue_wilcoxon"].to_list() == pytest.approx([p1, p2, p3])
+
+
+def test_paired_stats_paired_speficied_level():
+    np_result = paired_specified_level.mean_diff.results
+    p1 = sp.stats.ttest_rel(df_test_control["T2"], \
+                df_test_control["T1"], nan_policy='omit').pvalue
+    p2 = sp.stats.ttest_rel(df_test_treatment1["T2"], \
+                df_test_treatment1["T1"], nan_policy='omit').pvalue
+    p3 = sp.stats.ttest_rel(np_result["bootstraps"][0], \
+                np_result["bootstraps"][1], nan_policy='omit').pvalue
+    assert np_result["pvalue_paired_students_t"].to_list() == pytest.approx([p1, p2, p3])
+    
+    p1 = sp.stats.wilcoxon(df_test_control["T2"], \
+                df_test_control["T1"]).pvalue
+    p2 = sp.stats.wilcoxon(df_test_treatment1["T2"], \
+                df_test_treatment1["T1"]).pvalue
+    p3 = sp.stats.wilcoxon(np_result["bootstraps"][0], \
+                np_result["bootstraps"][1]).pvalue
+    assert np_result["pvalue_wilcoxon"].to_list() == pytest.approx([p1, p2, p3])
+
+
+def test_lqrt_delta_unpaired():
+    all_mean_diff = unpaired.mean_diff
+    lqrt_result = all_mean_diff.lqrt["pvalue_paired_lqrt"].to_list()[2]
+                             
+    p1 = lqrt.lqrtest_rel(all_mean_diff.results["bootstraps"][0], 
+          all_mean_diff.results["bootstraps"][1], 
+          random_state=12345).pvalue
+    
+    assert lqrt_result == pytest.approx(p1)
+
+
+def test_lqrt_delta_paired():
+    all_mean_diff = paired.mean_diff
+    lqrt_result = all_mean_diff.lqrt["pvalue_paired_lqrt"].to_list()
+
+    p1 = lqrt.lqrtest_rel(df_test_treatment1["T2"], 
+                df_test_treatment1["T1"], 
+          random_state=12345).pvalue
+    p2 = lqrt.lqrtest_rel(df_test_control["T2"], 
+                df_test_control["T1"], 
+          random_state=12345).pvalue
+    p3 = lqrt.lqrtest_rel(all_mean_diff.results["bootstraps"][0], 
+          all_mean_diff.results["bootstraps"][1], 
+          random_state=12345).pvalue
+    
+    assert lqrt_result == pytest.approx([p1, p2, p3])
+
+def test_lqrt_delta_paired_specified_level():
+    all_mean_diff = paired_specified_level.mean_diff
+    lqrt_result = all_mean_diff.lqrt["pvalue_paired_lqrt"].to_list()
+
+    p1 = lqrt.lqrtest_rel(df_test_control["T2"], 
+                df_test_control["T1"], 
+          random_state=12345).pvalue
+    p2 = lqrt.lqrtest_rel(df_test_treatment1["T2"], 
+                df_test_treatment1["T1"], 
+          random_state=12345).pvalue
+    p3 = lqrt.lqrtest_rel(all_mean_diff.results["bootstraps"][0], 
+          all_mean_diff.results["bootstraps"][1], 
+          random_state=12345).pvalue
+    
+    assert lqrt_result == pytest.approx([p1, p2, p3])
+