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b/tests/TestData/bnlearn_discrete_10000/data/barley.txt similarity index 100% rename from tests/TestData/bnlearn_discrete_10000/barley.txt rename to tests/TestData/bnlearn_discrete_10000/data/barley.txt diff --git a/tests/TestData/bnlearn_discrete_10000/cancer.txt b/tests/TestData/bnlearn_discrete_10000/data/cancer.txt similarity index 100% rename from tests/TestData/bnlearn_discrete_10000/cancer.txt rename to tests/TestData/bnlearn_discrete_10000/data/cancer.txt diff --git a/tests/TestData/bnlearn_discrete_10000/child.txt b/tests/TestData/bnlearn_discrete_10000/data/child.txt similarity index 100% rename from tests/TestData/bnlearn_discrete_10000/child.txt rename to tests/TestData/bnlearn_discrete_10000/data/child.txt diff --git a/tests/TestData/bnlearn_discrete_10000/earthquake.txt b/tests/TestData/bnlearn_discrete_10000/data/earthquake.txt similarity index 100% rename from tests/TestData/bnlearn_discrete_10000/earthquake.txt rename to tests/TestData/bnlearn_discrete_10000/data/earthquake.txt diff --git a/tests/TestData/bnlearn_discrete_10000/hailfinder.txt b/tests/TestData/bnlearn_discrete_10000/data/hailfinder.txt similarity index 100% rename from tests/TestData/bnlearn_discrete_10000/hailfinder.txt rename to tests/TestData/bnlearn_discrete_10000/data/hailfinder.txt diff --git a/tests/TestData/bnlearn_discrete_10000/hepar2.txt b/tests/TestData/bnlearn_discrete_10000/data/hepar2.txt similarity index 100% rename from tests/TestData/bnlearn_discrete_10000/hepar2.txt rename to tests/TestData/bnlearn_discrete_10000/data/hepar2.txt diff --git a/tests/TestData/bnlearn_discrete_10000/insurance.txt b/tests/TestData/bnlearn_discrete_10000/data/insurance.txt similarity index 100% rename from tests/TestData/bnlearn_discrete_10000/insurance.txt rename to tests/TestData/bnlearn_discrete_10000/data/insurance.txt diff --git a/tests/TestData/bnlearn_discrete_10000/sachs.txt b/tests/TestData/bnlearn_discrete_10000/data/sachs.txt similarity index 100% rename from tests/TestData/bnlearn_discrete_10000/sachs.txt rename to tests/TestData/bnlearn_discrete_10000/data/sachs.txt diff --git a/tests/TestData/bnlearn_discrete_10000/survey.txt b/tests/TestData/bnlearn_discrete_10000/data/survey.txt similarity index 100% rename from tests/TestData/bnlearn_discrete_10000/survey.txt rename to tests/TestData/bnlearn_discrete_10000/data/survey.txt diff --git a/tests/TestData/bnlearn_discrete_10000/water.txt b/tests/TestData/bnlearn_discrete_10000/data/water.txt similarity index 100% rename from tests/TestData/bnlearn_discrete_10000/water.txt rename to tests/TestData/bnlearn_discrete_10000/data/water.txt diff --git a/tests/TestData/bnlearn_discrete_10000/win95pts.txt b/tests/TestData/bnlearn_discrete_10000/data/win95pts.txt similarity index 100% rename from tests/TestData/bnlearn_discrete_10000/win95pts.txt rename to tests/TestData/bnlearn_discrete_10000/data/win95pts.txt diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/alarm.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/alarm.graph.txt new file mode 100644 index 00000000..98218d48 --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/alarm.graph.txt @@ -0,0 +1,50 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6;X7;X8;X9;X10;X11;X12;X13;X14;X15;X16;X17;X18;X19;X20;X21;X22;X23;X24;X25;X26;X27;X28;X29;X30;X31;X32;X33;X34;X35;X36;X37 + +Graph Edges: +1. X4 --> X5 +2. X4 --> X7 +3. X5 --> X2 +4. X5 --> X3 +5. X6 --> X1 +6. X6 --> X5 +7. X6 --> X7 +8. X7 --> X36 +9. X8 --> X9 +10. X11 --> X10 +11. X11 --> X12 +12. X13 --> X34 +13. X14 --> X15 +14. X15 --> X34 +15. X15 --> X37 +16. X17 --> X26 +17. X17 --> X31 +18. X19 --> X20 +19. X20 --> X21 +20. X21 --> X34 +21. X23 --> X22 +22. X23 --> X24 +23. X24 --> X21 +24. X25 --> X18 +25. X25 --> X24 +26. X25 --> X26 +27. X25 --> X31 +28. X25 --> X32 +29. X27 --> X30 +30. X28 --> X29 +31. X29 --> X30 +32. X30 --> X26 +33. X30 --> X31 +34. X31 --> X16 +35. X31 --> X18 +36. X31 --> X32 +37. X32 --> X20 +38. X32 --> X33 +39. X33 --> X16 +40. X33 --> X34 +41. X34 --> X35 +42. X35 --> X9 +43. X35 --> X10 +44. X35 --> X12 +45. X35 --> X36 +46. X36 --> X37 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/andes.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/andes.graph.txt new file mode 100644 index 00000000..b3e4237c --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/andes.graph.txt @@ -0,0 +1,342 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6;X7;X8;X9;X10;X11;X12;X13;X14;X15;X16;X17;X18;X19;X20;X21;X22;X23;X24;X25;X26;X27;X28;X29;X30;X31;X32;X33;X34;X35;X36;X37;X38;X39;X40;X41;X42;X43;X44;X45;X46;X47;X48;X49;X50;X51;X52;X53;X54;X55;X56;X57;X58;X59;X60;X61;X62;X63;X64;X65;X66;X67;X68;X69;X70;X71;X72;X73;X74;X75;X76;X77;X78;X79;X80;X81;X82;X83;X84;X85;X86;X87;X88;X89;X90;X91;X92;X93;X94;X95;X96;X97;X98;X99;X100;X101;X102;X103;X104;X105;X106;X107;X108;X109;X110;X111;X112;X113;X114;X115;X116;X117;X118;X119;X120;X121;X122;X123;X124;X125;X126;X127;X128;X129;X130;X131;X132;X133;X134;X135;X136;X137;X138;X139;X140;X141;X142;X143;X144;X145;X146;X147;X148;X149;X150;X151;X152;X153;X154;X155;X156;X157;X158;X159;X160;X161;X162;X163;X164;X165;X166;X167;X168;X169;X170;X171;X172;X173;X174;X175;X176;X177;X178;X179;X180;X181;X182;X183;X184;X185;X186;X187;X188;X189;X190;X191;X192;X193;X194;X195;X196;X197;X198;X199;X200;X201;X202;X203;X204;X205;X206;X207;X208;X209;X210;X211;X212;X213;X214;X215;X216;X217;X218;X219;X220;X221;X222;X223 + +Graph Edges: +1. 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X172 --> X191 +270. X172 --> X193 +271. X173 --> X174 +272. X173 --> X175 +273. X173 --> X199 +274. X173 --> X200 +275. X174 --> X179 +276. X174 --> X181 +277. X174 --> X184 +278. X175 --> X176 +279. X175 --> X178 +280. X176 --> X178 +281. X177 --> X178 +282. X177 --> X184 +283. X177 --> X202 +284. X177 --> X205 +285. X178 --> X209 +286. X178 --> X210 +287. X179 --> X183 +288. X179 --> X184 +289. X180 --> X181 +290. X181 --> X183 +291. X182 --> X183 +292. X185 --> X186 +293. X185 --> X222 +294. X186 --> X187 +295. X186 --> X189 +296. X186 --> X206 +297. X187 --> X189 +298. X187 --> X191 +299. X188 --> X189 +300. X188 --> X204 +301. X190 --> X191 +302. X190 --> X207 +303. X192 --> X193 +304. X192 --> X208 +305. X193 --> X209 +306. X194 --> X199 +307. X194 --> X200 +308. X195 --> X197 +309. X196 --> X198 +310. X197 --> X198 +311. X198 --> X207 +312. X198 --> X208 +313. X199 --> X203 +314. X199 --> X204 +315. X199 --> X205 +316. X200 --> X201 +317. X200 --> X202 +318. X201 --> X202 +319. X202 --> X210 +320. X202 --> X211 +321. X203 --> X204 +322. X203 --> X205 +323. X206 --> X207 +324. X206 --> X223 +325. X208 --> X211 +326. X209 --> X212 +327. X210 --> X213 +328. X211 --> X214 +329. X212 --> X215 +330. X213 --> X215 +331. X214 --> X215 +332. X215 --> X217 +333. X216 --> X217 +334. X217 --> X219 +335. X218 --> X219 +336. X219 --> X221 +337. X220 --> X221 +338. X222 --> X223 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/asia.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/asia.graph.txt new file mode 100644 index 00000000..5c014216 --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/asia.graph.txt @@ -0,0 +1,12 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6;X7;X8 + +Graph Edges: +1. X1 --> X2 +2. X2 --> X6 +3. X3 --> X4 +4. X3 --> X5 +5. X4 --> X6 +6. X5 --> X8 +7. X6 --> X7 +8. X6 --> X8 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/barley.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/barley.graph.txt new file mode 100644 index 00000000..77a7b6f5 --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/barley.graph.txt @@ -0,0 +1,88 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6;X7;X8;X9;X10;X11;X12;X13;X14;X15;X16;X17;X18;X19;X20;X21;X22;X23;X24;X25;X26;X27;X28;X29;X30;X31;X32;X33;X34;X35;X36;X37;X38;X39;X40;X41;X42;X43;X44;X45;X46;X47;X48 + +Graph Edges: +1. X1 --> X4 +2. X1 --> X5 +3. X1 --> X7 +4. X1 --> X10 +5. X1 --> X18 +6. X2 --> X3 +7. X2 --> X5 +8. X3 --> X4 +9. X4 --> X8 +10. X4 --> X11 +11. X5 --> X8 +12. X5 --> X11 +13. X6 --> X7 +14. X6 --> X10 +15. X6 --> X12 +16. X7 --> X8 +17. X8 --> X14 +18. X8 --> X16 +19. X8 --> X25 +20. X9 --> X10 +21. X9 --> X13 +22. X10 --> X12 +23. X10 --> X13 +24. X11 --> X12 +25. X12 --> X15 +26. X13 --> X14 +27. X14 --> X15 +28. X15 --> X16 +29. X15 --> X24 +30. X16 --> X41 +31. X17 --> X19 +32. X17 --> X23 +33. X17 --> X30 +34. X18 --> X19 +35. X18 --> X37 +36. X19 --> X35 +37. X19 --> X36 +38. X19 --> X41 +39. X19 --> X48 +40. X20 --> X21 +41. X20 --> X34 +42. X20 --> X39 +43. X21 --> X41 +44. X22 --> X24 +45. X23 --> X24 +46. X24 --> X25 +47. X25 --> X35 +48. X25 --> X36 +49. X25 --> X38 +50. X25 --> X43 +51. X25 --> X44 +52. X26 --> X28 +53. X27 --> X28 +54. X28 --> X33 +55. X29 --> X32 +56. X30 --> X31 +57. X31 --> X32 +58. X32 --> X33 +59. X33 --> X35 +60. X34 --> X35 +61. X34 --> X44 +62. X35 --> X36 +63. X35 --> X38 +64. X35 --> X42 +65. X35 --> X44 +66. X36 --> X40 +67. X36 --> X44 +68. X36 --> X45 +69. X36 --> X46 +70. X36 --> X47 +71. X37 --> X38 +72. X37 --> X43 +73. X37 --> X48 +74. X38 --> X40 +75. X38 --> X42 +76. X38 --> X45 +77. X38 --> X46 +78. X38 --> X47 +79. X39 --> X40 +80. X39 --> X45 +81. X39 --> X46 +82. X39 --> X47 +83. X40 --> X41 +84. X40 --> X43 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/cancer.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/cancer.graph.txt new file mode 100644 index 00000000..41231a7d --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/cancer.graph.txt @@ -0,0 +1,8 @@ +Graph Nodes: +X1;X2;X3;X4;X5 + +Graph Edges: +1. X1 --> X3 +2. X2 --> X3 +3. X3 --> X4 +4. X3 --> X5 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/child.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/child.graph.txt new file mode 100644 index 00000000..13e7f1c4 --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/child.graph.txt @@ -0,0 +1,29 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6;X7;X8;X9;X10;X11;X12;X13;X14;X15;X16;X17;X18;X19;X20 + +Graph Edges: +1. X1 --> X12 +2. X2 --> X8 +3. X3 --> X8 +4. X3 --> X9 +5. X4 --> X10 +6. X5 --> X11 +7. X6 --> X13 +8. X12 --> X14 +9. X12 --> X15 +10. X12 --> X16 +11. X12 --> X17 +12. X12 --> X18 +13. X12 --> X19 +14. X12 --> X20 +15. X15 --> X7 +16. X16 --> X2 +17. X17 --> X2 +18. X17 --> X3 +19. X18 --> X3 +20. X18 --> X4 +21. X18 --> X5 +22. X18 --> X6 +23. X19 --> X5 +24. X20 --> X6 +25. X20 --> X14 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/earthquake.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/earthquake.graph.txt new file mode 100644 index 00000000..41231a7d --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/earthquake.graph.txt @@ -0,0 +1,8 @@ +Graph Nodes: +X1;X2;X3;X4;X5 + +Graph Edges: +1. X1 --> X3 +2. X2 --> X3 +3. X3 --> X4 +4. X3 --> X5 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/hailfinder.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/hailfinder.graph.txt new file mode 100644 index 00000000..bb54899c --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/hailfinder.graph.txt @@ -0,0 +1,70 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6;X7;X8;X9;X10;X11;X12;X13;X14;X15;X16;X17;X18;X19;X20;X21;X22;X23;X24;X25;X26;X27;X28;X29;X30;X31;X32;X33;X34;X35;X36;X37;X38;X39;X40;X41;X42;X43;X44;X45;X46;X47;X48;X49;X50;X51;X52;X53;X54;X55;X56 + +Graph Edges: +1. X1 --> X15 +2. X2 --> X3 +3. X3 --> X4 +4. X3 --> X8 +5. X3 --> X35 +6. X3 --> X40 +7. X3 --> X43 +8. X3 --> X44 +9. X3 --> X45 +10. X3 --> X46 +11. X3 --> X48 +12. X3 --> X50 +13. X3 --> X51 +14. X3 --> X52 +15. X3 --> X53 +16. X3 --> X54 +17. X3 --> X55 +18. X3 --> X56 +19. X4 --> X42 +20. X5 --> X7 +21. X6 --> X7 +22. X7 --> X41 +23. X8 --> X11 +24. X9 --> X11 +25. X10 --> X11 +26. X11 --> X34 +27. X12 --> X33 +28. X13 --> X15 +29. X14 --> X15 +30. X15 --> X16 +31. X16 --> X20 +32. X16 --> X24 +33. X16 --> X32 +34. X17 --> X19 +35. X18 --> X19 +36. X19 --> X20 +37. X20 --> X24 +38. X21 --> X23 +39. X22 --> X23 +40. X23 --> X24 +41. X24 --> X26 +42. X24 --> X32 +43. X25 --> X26 +44. X26 --> X28 +45. X26 --> X31 +46. X26 --> X47 +47. X27 --> X28 +48. X27 --> X30 +49. X27 --> X31 +50. X28 --> X30 +51. X29 --> X30 +52. X30 --> X32 +53. X31 --> X32 +54. X32 --> X33 +55. X32 --> X38 +56. X33 --> X34 +57. X34 --> X41 +58. X35 --> X37 +59. X36 --> X37 +60. X37 --> X39 +61. X38 --> X39 +62. X39 --> X41 +63. X40 --> X41 +64. X41 --> X42 +65. X42 --> X49 +66. X47 --> X49 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/hepar2.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/hepar2.graph.txt new file mode 100644 index 00000000..1ab6b7f5 --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/hepar2.graph.txt @@ -0,0 +1,127 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6;X7;X8;X9;X10;X11;X12;X13;X14;X15;X16;X17;X18;X19;X20;X21;X22;X23;X24;X25;X26;X27;X28;X29;X30;X31;X32;X33;X34;X35;X36;X37;X38;X39;X40;X41;X42;X43;X44;X45;X46;X47;X48;X49;X50;X51;X52;X53;X54;X55;X56;X57;X58;X59;X60;X61;X62;X63;X64;X65;X66;X67;X68;X69;X70 + +Graph Edges: +1. X1 --> X4 +2. X1 --> X18 +3. X2 --> X11 +4. X2 --> X55 +5. X2 --> X56 +6. X2 --> X66 +7. X2 --> X67 +8. X2 --> X69 +9. X3 --> X4 +10. X3 --> X22 +11. X4 --> X23 +12. X4 --> X30 +13. X4 --> X39 +14. X4 --> X46 +15. X4 --> X50 +16. X4 --> X51 +17. X4 --> X53 +18. X4 --> X57 +19. X4 --> X58 +20. X4 --> X59 +21. X5 --> X9 +22. X5 --> X10 +23. X6 --> X9 +24. X6 --> X10 +25. X7 --> X8 +26. X7 --> X24 +27. X7 --> X26 +28. X7 --> X27 +29. X7 --> X29 +30. X7 --> X41 +31. X7 --> X52 +32. X8 --> X9 +33. X8 --> X10 +34. X9 --> X11 +35. X10 --> X11 +36. X11 --> X15 +37. X11 --> X23 +38. X11 --> X24 +39. X11 --> X29 +40. X11 --> X30 +41. X11 --> X39 +42. X11 --> X49 +43. X11 --> X50 +44. X11 --> X51 +45. X11 --> X53 +46. X11 --> X54 +47. X11 --> X55 +48. X11 --> X56 +49. X11 --> X66 +50. X11 --> X67 +51. X11 --> X69 +52. X12 --> X14 +53. X12 --> X20 +54. X13 --> X14 +55. X13 --> X20 +56. X14 --> X24 +57. X14 --> X29 +58. X14 --> X32 +59. X14 --> X33 +60. X14 --> X34 +61. X14 --> X35 +62. X14 --> X38 +63. X14 --> X43 +64. X14 --> X49 +65. X14 --> X53 +66. X14 --> X54 +67. X14 --> X70 +68. X15 --> X19 +69. X16 --> X17 +70. X17 --> X18 +71. X18 --> X19 +72. X18 --> X21 +73. X18 --> X28 +74. X18 --> X46 +75. X18 --> X49 +76. X18 --> X50 +77. X18 --> X51 +78. X18 --> X53 +79. X18 --> X54 +80. X19 --> X24 +81. X19 --> X30 +82. X19 --> X36 +83. X19 --> X37 +84. X19 --> X38 +85. X19 --> X39 +86. X19 --> X42 +87. X19 --> X43 +88. X19 --> X50 +89. X19 --> X51 +90. X19 --> X59 +91. X19 --> X61 +92. X19 --> X63 +93. X19 --> X64 +94. X19 --> X65 +95. X19 --> X68 +96. X19 --> X70 +97. X20 --> X24 +98. X20 --> X28 +99. X20 --> X39 +100. X20 --> X46 +101. X20 --> X49 +102. X20 --> X53 +103. X22 --> X23 +104. X22 --> X30 +105. X22 --> X46 +106. X22 --> X50 +107. X22 --> X51 +108. X22 --> X53 +109. X22 --> X57 +110. X22 --> X58 +111. X22 --> X59 +112. X24 --> X25 +113. X24 --> X31 +114. X24 --> X62 +115. X34 --> X35 +116. X36 --> X45 +117. X38 --> X40 +118. X39 --> X40 +119. X43 --> X44 +120. X43 --> X48 +121. X43 --> X60 +122. X46 --> X47 +123. X55 --> X56 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/insurance.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/insurance.graph.txt new file mode 100644 index 00000000..9079d8c7 --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/insurance.graph.txt @@ -0,0 +1,56 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6;X7;X8;X9;X10;X11;X12;X13;X14;X15;X16;X17;X18;X19;X20;X21;X22;X23;X24;X25;X26;X27 + +Graph Edges: +1. X2 --> X1 +2. X2 --> X3 +3. X2 --> X4 +4. X2 --> X13 +5. X2 --> X14 +6. X2 --> X23 +7. X3 --> X1 +8. X3 --> X4 +9. X3 --> X5 +10. X3 --> X9 +11. X3 --> X18 +12. X3 --> X19 +13. X3 --> X22 +14. X4 --> X5 +15. X4 --> X9 +16. X4 --> X10 +17. X4 --> X14 +18. X4 --> X18 +19. X4 --> X19 +20. X4 --> X27 +21. X5 --> X7 +22. X5 --> X12 +23. X5 --> X17 +24. X5 --> X25 +25. X6 --> X15 +26. X7 --> X6 +27. X7 --> X21 +28. X7 --> X24 +29. X8 --> X6 +30. X8 --> X21 +31. X8 --> X23 +32. X8 --> X26 +33. X9 --> X7 +34. X9 --> X12 +35. X9 --> X17 +36. X9 --> X25 +37. X10 --> X8 +38. X11 --> X8 +39. X11 --> X17 +40. X12 --> X8 +41. X13 --> X10 +42. X13 --> X27 +43. X14 --> X13 +44. X15 --> X20 +45. X16 --> X15 +46. X17 --> X15 +47. X17 --> X16 +48. X18 --> X16 +49. X19 --> X16 +50. X21 --> X20 +51. X24 --> X23 +52. X25 --> X24 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/sachs.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/sachs.graph.txt new file mode 100644 index 00000000..5157f60d --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/sachs.graph.txt @@ -0,0 +1,21 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6;X7;X8;X9;X10;X11 + +Graph Edges: +1. X2 --> X1 +2. X4 --> X2 +3. X7 --> X6 +4. X8 --> X1 +5. X8 --> X2 +6. X8 --> X3 +7. X8 --> X4 +8. X8 --> X5 +9. X8 --> X11 +10. X9 --> X3 +11. X9 --> X4 +12. X9 --> X5 +13. X9 --> X8 +14. X9 --> X11 +15. X10 --> X6 +16. X10 --> X7 +17. X11 --> X4 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/survey.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/survey.graph.txt new file mode 100644 index 00000000..92bdbaa9 --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/survey.graph.txt @@ -0,0 +1,10 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6 + +Graph Edges: +1. X1 --> X3 +2. X2 --> X3 +3. X3 --> X4 +4. X3 --> X5 +5. X4 --> X6 +6. X5 --> X6 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/water.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/water.graph.txt new file mode 100644 index 00000000..45e797f8 --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/water.graph.txt @@ -0,0 +1,70 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6;X7;X8;X9;X10;X11;X12;X13;X14;X15;X16;X17;X18;X19;X20;X21;X22;X23;X24;X25;X26;X27;X28;X29;X30;X31;X32 + +Graph Edges: +1. X1 --> X9 +2. X1 --> X11 +3. X2 --> X10 +4. X2 --> X11 +5. X2 --> X12 +6. X3 --> X11 +7. X3 --> X13 +8. X3 --> X14 +9. X4 --> X12 +10. X4 --> X15 +11. X5 --> X11 +12. X5 --> X13 +13. X5 --> X16 +14. X6 --> X11 +15. X6 --> X14 +16. X6 --> X16 +17. X7 --> X12 +18. X7 --> X15 +19. X7 --> X16 +20. X8 --> X13 +21. X8 --> X14 +22. X8 --> X16 +23. X9 --> X17 +24. X9 --> X19 +25. X10 --> X18 +26. X10 --> X19 +27. X10 --> X20 +28. X11 --> X19 +29. X11 --> X21 +30. X11 --> X22 +31. X12 --> X20 +32. X12 --> X23 +33. X13 --> X19 +34. X13 --> X21 +35. X13 --> X24 +36. X14 --> X19 +37. X14 --> X22 +38. X14 --> X24 +39. X15 --> X20 +40. X15 --> X23 +41. X15 --> X24 +42. X16 --> X21 +43. X16 --> X22 +44. X16 --> X24 +45. X17 --> X25 +46. X17 --> X27 +47. X18 --> X26 +48. X18 --> X27 +49. X18 --> X28 +50. X19 --> X27 +51. X19 --> X29 +52. X19 --> X30 +53. X20 --> X28 +54. X20 --> X31 +55. X21 --> X27 +56. X21 --> X29 +57. X21 --> X32 +58. X22 --> X27 +59. X22 --> X30 +60. X22 --> X32 +61. X23 --> X28 +62. X23 --> X31 +63. X23 --> X32 +64. X24 --> X29 +65. X24 --> X30 +66. X24 --> X32 diff --git a/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/win95pts.graph.txt b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/win95pts.graph.txt new file mode 100644 index 00000000..d3e8f74a --- /dev/null +++ b/tests/TestData/bnlearn_discrete_10000/truth_dag_graph/win95pts.graph.txt @@ -0,0 +1,116 @@ +Graph Nodes: +X1;X2;X3;X4;X5;X6;X7;X8;X9;X10;X11;X12;X13;X14;X15;X16;X17;X18;X19;X20;X21;X22;X23;X24;X25;X26;X27;X28;X29;X30;X31;X32;X33;X34;X35;X36;X37;X38;X39;X40;X41;X42;X43;X44;X45;X46;X47;X48;X49;X50;X51;X52;X53;X54;X55;X56;X57;X58;X59;X60;X61;X62;X63;X64;X65;X66;X67;X68;X69;X70;X71;X72;X73;X74;X75;X76 + +Graph Edges: +1. X1 --> X3 +2. X2 --> X3 +3. X3 --> X11 +4. X3 --> X12 +5. X3 --> X28 +6. X3 --> X29 +7. X3 --> X56 +8. X3 --> X57 +9. X4 --> X11 +10. X5 --> X12 +11. X5 --> X37 +12. X5 --> X38 +13. X6 --> X35 +14. X6 --> X76 +15. X7 --> X35 +16. X7 --> X73 +17. X8 --> X30 +18. X8 --> X58 +19. X8 --> X72 +20. X9 --> X15 +21. X9 --> X56 +22. X9 --> X57 +23. X10 --> X11 +24. X11 --> X12 +25. X12 --> X15 +26. X13 --> X15 +27. X14 --> X15 +28. X15 --> X17 +29. X16 --> X17 +30. X17 --> X30 +31. X17 --> X66 +32. X18 --> X21 +33. X18 --> X28 +34. X19 --> X21 +35. X19 --> X28 +36. X19 --> X57 +37. X19 --> X60 +38. X19 --> X67 +39. X19 --> X71 +40. X20 --> X21 +41. X20 --> X28 +42. X20 --> X67 +43. X21 --> X30 +44. X22 --> X25 +45. X22 --> X29 +46. X23 --> X25 +47. X23 --> X29 +48. X24 --> X25 +49. X24 --> X29 +50. X24 --> X56 +51. X24 --> X60 +52. X25 --> X30 +53. X26 --> X30 +54. X27 --> X28 +55. X28 --> X30 +56. X29 --> X30 +57. X30 --> X35 +58. X31 --> X35 +59. X31 --> X39 +60. X31 --> X43 +61. X31 --> X46 +62. X31 --> X48 +63. X31 --> X53 +64. X31 --> X54 +65. X31 --> X56 +66. X31 --> X57 +67. X31 --> X75 +68. X32 --> X35 +69. X33 --> X35 +70. X34 --> X35 +71. X34 --> X74 +72. X35 --> X36 +73. X37 --> X39 +74. X37 --> X59 +75. X38 --> X39 +76. X39 --> X71 +77. X39 --> X72 +78. X40 --> X43 +79. X41 --> X43 +80. X42 --> X54 +81. X43 --> X65 +82. X43 --> X69 +83. X44 --> X46 +84. X44 --> X48 +85. X45 --> X46 +86. X45 --> X48 +87. X46 --> X49 +88. X47 --> X49 +89. X47 --> X61 +90. X47 --> X62 +91. X47 --> X63 +92. X47 --> X68 +93. X47 --> X69 +94. X48 --> X49 +95. X50 --> X55 +96. X51 --> X53 +97. X51 --> X54 +98. X52 --> X53 +99. X53 --> X55 +100. X54 --> X55 +101. X56 --> X58 +102. X57 --> X58 +103. X58 --> X64 +104. X58 --> X68 +105. X61 --> X62 +106. X61 --> X63 +107. X61 --> X64 +108. X61 --> X65 +109. X64 --> X68 +110. X65 --> X69 +111. X70 --> X71 +112. X71 --> X72 diff --git a/tests/TestData/data_linear_missing_10.txt b/tests/TestData/data_linear_missing_10.txt new file mode 100644 index 00000000..bda73c41 --- /dev/null +++ b/tests/TestData/data_linear_missing_10.txt @@ -0,0 +1,10001 @@ +X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 +4.267000000000000237e-01 -3.834700000000000220e+00 8.205999999999999961e-01 2.210500000000000131e+00 -8.013000000000000123e-01 1.445999999999999952e+00 8.458999999999999853e-01 1.430800000000000072e+00 3.060400000000000009e+00 8.405000000000000249e-01 7.019999999999999851e-02 nan -3.002200000000000202e+00 2.868199999999999861e+00 1.054100000000000037e+00 2.733000000000000096e+00 3.022899999999999920e+00 9.423000000000000265e-01 nan 4.993900000000000006e+00 +-2.493100000000000094e+00 -2.550999999999999934e-01 -2.670399999999999885e+00 2.017799999999999816e+00 -7.048999999999999710e-01 7.437999999999999723e+00 3.972199999999999953e+00 -2.832199999999999829e+00 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-1.876905889634290281e+00 +8.234231845623113388e-01 -8.833304690299352835e-01 -8.101541118525346086e-01 2.031733147759218205e-01 -7.064267931892864727e-02 +9.456334275676296919e-01 6.540860884671306774e-01 1.294625069125997952e+00 -8.619570178499540436e-01 -1.456023329927864429e+00 diff --git a/tests/TestPC.py b/tests/TestPC.py index 5b363a7d..207e0fdd 100644 --- a/tests/TestPC.py +++ b/tests/TestPC.py @@ -1,228 +1,275 @@ +import os import sys -import time - sys.path.append("") import unittest - +import hashlib import numpy as np - from causallearn.search.ConstraintBased.PC import pc from causallearn.utils.cit import chisq, fisherz, gsq, kci, mv_fisherz +from causallearn.graph.SHD import SHD +from causallearn.utils.DAG2CPDAG import dag2cpdag +from causallearn.utils.TXT2GeneralGraph import txt2generalgraph + +######################################### Test Notes ########################################### +# All the benchmark results of loaded files (e.g. "./TestData/benchmark_returned_results/") # +# are obtained from the code of causal-learn as of commit # +# https://github.com/cmu-phil/causal-learn/commit/94d1536 (06-29-2022). # +# # +# We are not sure if the results are completely "correct" (reflect ground truth graph) or not. # +# So if you find your tests failed, it means that your modified code is logically inconsistent # +# with the code as of 94d1536, but not necessarily means that your code is "wrong". # +# If you are sure that your modification is "correct" (e.g. fixed some bugs in 94d1536), # +# please report it to us. We will then modify these benchmark results accordingly. Thanks :) # +######################################### Test Notes ########################################### + + +BENCHMARK_TXTFILE_TO_MD5 = { + "./TestData/data_linear_10.txt": "95a17e15038d4cade0845140b67c05a6", + "./TestData/data_discrete_10.txt": "ccb51c6c1946d8524a8b29a49aef2cc4", + "./TestData/data_linear_missing_10.txt": "4e3ee59becd0fbe5fdb818154457a558", + "./TestData/test_pc_simulated_linear_gaussian_data.txt": "ac1f99453f7e038857b692b1b3c42f3c", + "./TestData/graph.10.txt": "4970d4ecb8be999a82a665e5f5e0825b", + "./TestData/benchmark_returned_results/discrete_10_pc_chisq_0.05_stable_0_-1.txt": "87ebf9d830d75a5161b3a3a34ad6921f", + "./TestData/benchmark_returned_results/discrete_10_pc_gsq_0.05_stable_0_-1.txt": "87ebf9d830d75a5161b3a3a34ad6921f", + "./TestData/benchmark_returned_results/linear_10_pc_fisherz_0.05_stable_0_0.txt": "bfd3827bfa2a61bd201807925ad67d2b", + "./TestData/benchmark_returned_results/linear_10_pc_fisherz_0.05_stable_0_1.txt": 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Please check your code with the commit 94d1536." + +# verify files integrity first +for file_path, expected_MD5 in BENCHMARK_TXTFILE_TO_MD5.items(): + with open(file_path, 'rb') as fin: + assert hashlib.md5(fin.read()).hexdigest() == expected_MD5,\ + f'{file_path} is corrupted. Please download it again from https://github.com/cmu-phil/causal-learn/blob/94d1536/tests/TestData' class TestPC(unittest.TestCase): - - # example1 - def test_pc_with_fisher_z(self): - data_path = "data_linear_10.txt" - data = np.loadtxt(data_path, skiprows=1) # Import the file at data_path as data - cg = pc(data, 0.05, fisherz, False, 0, - -1) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example2 - def test_pc_with_g_sq(self): - data_path = "data_discrete_10.txt" - data = np.loadtxt(data_path, skiprows=1) # Import the file at data_path as data - cg = pc(data, 0.05, gsq, True, 0, - -1) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example3 - def test_pc_with_chi_sq(self): - data_path = "data_discrete_10.txt" - data = np.loadtxt(data_path, skiprows=1) # Import the file at data_path as data - cg = pc(data, 0.05, chisq, True, 0, - -1) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example4 - def test_pc_with_fisher_z_maxp(self): - data_path = "data_linear_10.txt" - data = np.loadtxt(data_path, skiprows=1) # Import the file at data_path as data - cg = pc(data, 0.05, fisherz, True, 1, - -1) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example5 - def test_pc_with_fisher_z_definite_maxp(self): - data_path = "data_linear_10.txt" - data = np.loadtxt(data_path, skiprows=1) # Import the file at data_path as data - cg = pc(data, 0.05, fisherz, True, 2, - -1) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example6 - def test_pc_with_fisher_z_with_uc_priority0(self): - data_path = "data_linear_10.txt" - data = np.loadtxt(data_path, skiprows=1) # Import the file at data_path as data - cg = pc(data, 0.05, fisherz, True, 0, - 0) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example7 - def test_pc_with_fisher_z_with_uc_priority1(self): - data_path = "data_linear_10.txt" - data = np.loadtxt(data_path, skiprows=1) # Import the file at data_path as data - cg = pc(data, 0.05, fisherz, True, 0, - 1) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example8 - def test_pc_with_fisher_z_with_uc_priority2(self): - data_path = "data_linear_10.txt" - data = np.loadtxt(data_path, skiprows=1) # Import the file at data_path as data - cg = pc(data, 0.05, fisherz, True, 0, - 2) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example9 - def test_pc_with_fisher_z_with_uc_priority3(self): - data_path = "data_linear_10.txt" - data = np.loadtxt(data_path, skiprows=1) # Import the file at data_path as data - cg = pc(data, 0.05, fisherz, True, 0, - 3) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example10 - def test_pc_with_fisher_z_with_uc_priority4(self): - data_path = "data_linear_10.txt" - data = np.loadtxt(data_path, skiprows=1) # Import the file at data_path as data - cg = pc(data, 0.05, fisherz, True, 0, - 4) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example11 - def test_pc_with_mv_fisher_z_with_uc_priority4(self): - data_path = "data_linear_missing_10.txt" - data = np.loadtxt(data_path, skiprows=1) # Import the file at data_path as data - - cg = pc(data, 0.05, mv_fisherz, True, 0, - 4, mvpc=True) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example12 - def test_pc_with_kci(self): - data_path = "data_linear_10.txt" - data = np.loadtxt(data_path, skiprows=1)[:50, :] # Import the file at data_path as data - cg = pc(data, 0.05, kci, True, 0, - -1) # Run PC and obtain the estimated graph (CausalGraph object) - - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) - - print('finish') - - # example13 - def test_bnlearn_discrete_datasets(self): - import os + # Load data from file "data_linear_10.txt". Run PC with fisherz test, and different uc_rule and uc_priority. + def test_pc_load_linear_10_with_fisher_z(self): + print('Now start test_pc_load_linear_10_with_fisher_z ...') + data_path = "./TestData/data_linear_10.txt" + truth_graph_path = "./TestData/graph.10.txt" + data = np.loadtxt(data_path, skiprows=1) + truth_dag = txt2generalgraph(truth_graph_path) # truth_dag is a GeneralGraph instance + truth_cpdag = dag2cpdag(truth_dag) + num_edges_in_truth = truth_dag.get_num_edges() + + # Run PC with deafult parameters: stable=True, uc_rule=0 (uc_sepset), uc_priority=2 (prioritize existing colliders) + cg = pc(data, 0.05, fisherz) # Run PC and obtain the estimated graph (cg is CausalGraph object) + benchmark_returned_graph = np.loadtxt("./TestData/benchmark_returned_results/linear_10_pc_fisherz_0.05_stable_0_2.txt") + assert np.all(cg.G.graph == benchmark_returned_graph), INCONSISTENT_RESULT_GRAPH_ERRMSG + shd = SHD(truth_cpdag, cg.G) + print(f" pc(data, 0.05, fisherz)\tSHD: {shd.get_shd()} of {num_edges_in_truth}") + + # Run PC with: stable=True, uc_rule=0 (uc_sepset), uc_priority=0 (overwrite) + cg = pc(data, 0.05, fisherz, True, 0, 0) + benchmark_returned_graph = np.loadtxt("./TestData/benchmark_returned_results/linear_10_pc_fisherz_0.05_stable_0_0.txt") + assert np.all(cg.G.graph == benchmark_returned_graph), INCONSISTENT_RESULT_GRAPH_ERRMSG + shd = SHD(truth_cpdag, cg.G) + print(f" pc(data, 0.05, fisherz, True, 0, 0)\tSHD: {shd.get_shd()} of {num_edges_in_truth}") + + # Run PC with: stable=True, uc_rule=0 (uc_sepset), uc_priority=1 (orient bi-directed) + cg = pc(data, 0.05, fisherz, True, 0, 1) + benchmark_returned_graph = np.loadtxt("./TestData/benchmark_returned_results/linear_10_pc_fisherz_0.05_stable_0_1.txt") + assert np.all(cg.G.graph == benchmark_returned_graph), INCONSISTENT_RESULT_GRAPH_ERRMSG + shd = SHD(truth_cpdag, cg.G) + print(f" pc(data, 0.05, fisherz, True, 0, 1)\tSHD: {shd.get_shd()} of {num_edges_in_truth}") + + # Run PC with: stable=True, uc_rule=0 (uc_sepset), uc_priority=3 (prioritize stronger colliders) + cg = pc(data, 0.05, fisherz, True, 0, 3) + benchmark_returned_graph = np.loadtxt("./TestData/benchmark_returned_results/linear_10_pc_fisherz_0.05_stable_0_3.txt") + assert np.all(cg.G.graph == benchmark_returned_graph), INCONSISTENT_RESULT_GRAPH_ERRMSG + shd = SHD(truth_cpdag, cg.G) + print(f" pc(data, 0.05, fisherz, True, 0, 3)\tSHD: {shd.get_shd()} of {num_edges_in_truth}") + + # Run PC with: stable=True, uc_rule=0 (uc_sepset), uc_priority=4 (prioritize stronger* colliders) + cg = pc(data, 0.05, fisherz, True, 0, 4) + benchmark_returned_graph = np.loadtxt("./TestData/benchmark_returned_results/linear_10_pc_fisherz_0.05_stable_0_4.txt") + assert np.all(cg.G.graph == benchmark_returned_graph), INCONSISTENT_RESULT_GRAPH_ERRMSG + shd = SHD(truth_cpdag, cg.G) + print(f" pc(data, 0.05, fisherz, True, 0, 4)\tSHD: {shd.get_shd()} of {num_edges_in_truth}") + + # Run PC with: stable=True, uc_rule=1 (maxP), uc_priority=-1 (whatever is default in uc_rule + cg = pc(data, 0.05, fisherz, True, 1, -1) + benchmark_returned_graph = np.loadtxt("./TestData/benchmark_returned_results/linear_10_pc_fisherz_0.05_stable_1_-1.txt") + assert np.all(cg.G.graph == benchmark_returned_graph), INCONSISTENT_RESULT_GRAPH_ERRMSG + shd = SHD(truth_cpdag, cg.G) + print(f" pc(data, 0.05, fisherz, True, 1, -1)\tSHD: {shd.get_shd()} of {num_edges_in_truth}") + + # Run PC with: stable=True, uc_rule=2 (definiteMaxP), uc_priority=-1 (whatever is default in uc_rule + cg = pc(data, 0.05, fisherz, True, 2, -1) + benchmark_returned_graph = np.loadtxt("./TestData/benchmark_returned_results/linear_10_pc_fisherz_0.05_stable_2_-1.txt") + assert np.all(cg.G.graph == benchmark_returned_graph), INCONSISTENT_RESULT_GRAPH_ERRMSG + shd = SHD(truth_cpdag, cg.G) + print(f" pc(data, 0.05, fisherz, True, 2, -1)\tSHD: {shd.get_shd()} of {num_edges_in_truth}") + + print('test_pc_load_linear_10_with_fisher_z passed!\n') + + # Simulate linear Gaussian data. Run PC with fisherz test with default parameters. + def test_pc_simulate_linear_gaussian_with_fisher_z(self): + print('Now start test_pc_simulate_linear_gaussian_with_fisher_z ...') + # Graph specification. + num_of_nodes = 5 + truth_DAG_directed_edges = {(0, 1), (0, 3), (1, 2), (1, 3), (2, 3), (2, 4), (3, 4)} + truth_CPDAG_directed_edges = {(0, 3), (1, 3), (2, 3), (2, 4), (3, 4)} + truth_CPDAG_undirected_edges = {(0, 1), (1, 2), (2, 1), (1, 0)} + # After the skeleton is discovered, the edges are oriented in the following way: + # Unshilded triples: + # 2 -- 1 -- 0: not v-structure. + # 1 -- 2 -- 4: not v-structure. + # 0 -- 3 -- 2: v-structure, oritented as 0 -> 3 <- 2. + # 0 -- 3 -- 4: not v-structure. + # 1 -- 3 -- 4: not v-structure. + # Then by Meek rule 1: 3 -> 4. + # Then by Meek rule 2: 2 -> 4. + # Then by Meek rule 3: 1 -> 3. + + ###### Simulation configuration: code to generate "./TestData/test_pc_simulated_linear_gaussian_data.txt" ###### + # np.random.seed(42) + # linear_weight_minabs, linear_weight_maxabs, linear_weight_netative_prob = 0.5, 0.9, 0.5 + # sample_size = 10000 + # adjacency_matrix = np.zeros((num_of_nodes, num_of_nodes)) + # adjacency_matrix[tuple(zip(*truth_DAG_directed_edges))] = 1 + # adjacency_matrix = adjacency_matrix.T + # weight_mask = np.random.uniform(linear_weight_minabs, linear_weight_maxabs, (num_of_nodes, num_of_nodes)) + # weight_mask[np.unravel_index(np.random.choice(np.arange(weight_mask.size), replace=False, + # size=int(weight_mask.size * linear_weight_netative_prob)), weight_mask.shape)] *= -1. + # adjacency_matrix = adjacency_matrix * weight_mask + # mixing_matrix = np.linalg.inv(np.eye(num_of_nodes) - adjacency_matrix) + # exogenous_noise = np.random.normal(0, 1, (num_of_nodes, sample_size)) + # data = (mixing_matrix @ exogenous_noise).T + ###### Simulation configuration: code to generate "./TestData/test_pc_simulated_linear_gaussian_data.txt" ###### + + data = np.loadtxt("./TestData/test_pc_simulated_linear_gaussian_data.txt", skiprows=1) + + # Run PC with deafult parameters: stable=True, uc_rule=0 (uc_sepset), uc_priority=2 (prioritize existing colliders) + cg = pc(data, 0.05, fisherz) + returned_directed_edges = set(cg.find_fully_directed()) + returned_undirected_edges = set(cg.find_undirected()) + returned_bidirected_edges = set(cg.find_bi_directed()) + self.assertEqual(truth_CPDAG_directed_edges, returned_directed_edges, "Directed edges are not correct.") + self.assertEqual(truth_CPDAG_undirected_edges, returned_undirected_edges, "Undirected edges are not correct.") + self.assertEqual(0, len(returned_bidirected_edges), "There should be no bi-directed edges.") + print(f" pc(data, 0.05, fisherz)\treturns exactly the same CPDAG as the truth.") + # cg.draw_pydot_graph(labels=list(map(str, range(num_of_nodes)))) + print('test_pc_simulate_linear_gaussian_with_fisher_z passed!\n') + + # Load data from file "data_discrete_10.txt". Run PC with gsq or chisq test. + def test_pc_load_discrete_10_with_gsq_chisq(self): + print('Now start test_pc_load_discrete_10_with_gsq_chisq ...') + data_path = "./TestData/data_discrete_10.txt" + truth_graph_path = "./TestData/graph.10.txt" + data = np.loadtxt(data_path, skiprows=1) + truth_dag = txt2generalgraph(truth_graph_path) # truth_dag is a GeneralGraph instance + truth_cpdag = dag2cpdag(truth_dag) + num_edges_in_truth = truth_dag.get_num_edges() + + # Run PC with gsq test. + cg = pc(data, 0.05, gsq, True, 0, -1) + benchmark_returned_graph = np.loadtxt("./TestData/benchmark_returned_results/discrete_10_pc_gsq_0.05_stable_0_-1.txt") + assert np.all(cg.G.graph == benchmark_returned_graph), INCONSISTENT_RESULT_GRAPH_ERRMSG + shd = SHD(truth_cpdag, cg.G) + print(f" pc(data, 0.05, gsq, True, 0, -1)\tSHD: {shd.get_shd()} of {num_edges_in_truth}") + + # Run PC with chisq test. + cg = pc(data, 0.05, chisq, True, 0, -1) + benchmark_returned_graph = np.loadtxt("./TestData/benchmark_returned_results/discrete_10_pc_chisq_0.05_stable_0_-1.txt") + assert np.all(cg.G.graph == benchmark_returned_graph), INCONSISTENT_RESULT_GRAPH_ERRMSG + shd = SHD(truth_cpdag, cg.G) + print(f" pc(data, 0.05, chisq, True, 0, -1)\tSHD: {shd.get_shd()} of {num_edges_in_truth}") + + print('test_pc_load_discrete_10_with_gsq_chisq passed!\n') + + # Load data from file "data_linear_missing_10.txt". Run Missing-Value PC with mv_fisherz. + def test_pc_load_linear_missing_10_with_mv_fisher_z(self): + print('Now start test_pc_load_linear_10_with_fisher_z ...') + data_path = "./TestData/data_linear_missing_10.txt" + truth_graph_path = "./TestData/graph.10.txt" + data = np.loadtxt(data_path, skiprows=1) + truth_dag = txt2generalgraph(truth_graph_path) + truth_cpdag = dag2cpdag(truth_dag) + num_edges_in_truth = truth_dag.get_num_edges() + + cg = pc(data, 0.05, mv_fisherz, True, 0, 4, mvpc=True) + benchmark_returned_graph = np.loadtxt("./TestData/benchmark_returned_results/linear_missing_10_mvpc_fisherz_0.05_stable_0_4.txt") + assert np.all(cg.G.graph == benchmark_returned_graph), INCONSISTENT_RESULT_GRAPH_ERRMSG + shd = SHD(truth_cpdag, cg.G) + print(f" pc(data, 0.05, mv_fisherz, True, 0, 4, mvpc=True)\tSHD: {shd.get_shd()} of {num_edges_in_truth}") + + print('test_pc_load_linear_missing_10_with_mv_fisher_z passed!\n') + + + # Load data from data in bnlearn repository. Run PC with chisq. Test speed. + def test_pc_load_bnlearn_discrete_datasets(self): + print('Now start test_pc_load_bnlearn_discrete_datasets ...') + print('Please check SHD with truth graph and time cost with https://github.com/cmu-phil/causal-learn/pull/6.') benchmark_names = [ "asia", "cancer", "earthquake", "sachs", "survey", "alarm", "barley", "child", "insurance", "water", "hailfinder", "hepar2", "win95pts", - "andes", + # "andes", ] - bnlearn_path = './TestData/bnlearn_discrete_10000' + bnlearn_data_dir = './TestData/bnlearn_discrete_10000/data' + bnlearn_truth_dag_graph_dir = './TestData/bnlearn_discrete_10000/truth_dag_graph' + bnlearn_benchmark_returned_results_dir = './TestData/bnlearn_discrete_10000/benchmark_returned_results' for bname in benchmark_names: - data = np.loadtxt(os.path.join(bnlearn_path, f'{bname}.txt'), skiprows=1) - cg = pc(data, 0.05, chisq, True, 0, -1) # Run PC and obtain the estimated graph (CausalGraph object) - print(f'{bname}: used {cg.PC_elapsed:.5f}s') - # visualization using pydot - # cg.draw_pydot_graph() - - # visualization using networkx - # cg.to_nx_graph() - # cg.draw_nx_graph(skel=False) + data = np.loadtxt(os.path.join(bnlearn_data_dir, f'{bname}.txt'), skiprows=1) + truth_dag = txt2generalgraph(os.path.join(bnlearn_truth_dag_graph_dir, f'{bname}.graph.txt')) + truth_cpdag = dag2cpdag(truth_dag) + num_edges_in_truth = truth_dag.get_num_edges() + num_nodes_in_truth = truth_dag.get_num_nodes() + cg = pc(data, 0.05, chisq, True, 0, -1) + benchmark_returned_graph = np.loadtxt( + os.path.join(bnlearn_benchmark_returned_results_dir, f'{bname}_pc_chisq_0.05_stable_0_-1.txt')) + assert np.all(cg.G.graph == benchmark_returned_graph), INCONSISTENT_RESULT_GRAPH_ERRMSG + shd = SHD(truth_cpdag, cg.G) + print(f'{bname} ({num_nodes_in_truth} nodes/{num_edges_in_truth} edges): used {cg.PC_elapsed:.5f}s, SHD: {shd.get_shd()}') + + print('test_pc_load_bnlearn_discrete_datasets passed!\n')