| \n", + " | TemplateId | \n", + "DateTime | \n", + "43e6c5b5_7 | \n", + "f2e46af4_11 | \n", + "f98340b4_12 | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "43e6c5b5 | \n", + "1.678827e+09 | \n", + "100.002860 | \n", + "NaN | \n", + "NaN | \n", + "
| 1 | \n", + "f2e46af4 | \n", + "1.678827e+09 | \n", + "NaN | \n", + "200.189963 | \n", + "NaN | \n", + "
| 2 | \n", + "43e6c5b5 | \n", + "1.678827e+09 | \n", + "100.812299 | \n", + "NaN | \n", + "NaN | \n", + "
| 3 | \n", + "f2e46af4 | \n", + "1.678827e+09 | \n", + "NaN | \n", + "200.458986 | \n", + "NaN | \n", + "
| 4 | \n", + "43e6c5b5 | \n", + "1.678827e+09 | \n", + "100.260164 | \n", + "NaN | \n", + "NaN | \n", + "
| \n", + " | TemplateText | \n", + "TemplateId | \n", + "VariableIndices | \n", + "RegexIndices | \n", + "TemplateExample | \n", + "Occurrences | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "<*0> DATA Short message with x = <*> | \n", + "43e6c5b5 | \n", + "[7] | \n", + "[0] | \n", + "<*0> DATA Short message with x = 100.00285967800117 | \n", + "10000 | \n", + "
| 1 | \n", + "<*0> DATA This is a log message that reports y = <*> | \n", + "f2e46af4 | \n", + "[11] | \n", + "[0] | \n", + "<*0> DATA This is a log message that reports y = 200.1899632341074 | \n", + "5006 | \n", + "
| 2 | \n", + "<*0> DATA The current line will include the value of z = <*> | \n", + "f98340b4 | \n", + "[12] | \n", + "[0] | \n", + "<*0> DATA The current line will include the value of z = 200.0 | \n", + "115 | \n", + "
| \n", + " | Name | \n", + "Tag | \n", + "TagOrigin | \n", + "Type | \n", + "IsUninteresting | \n", + "Occurrences | \n", + "Preceding 3 tokens | \n", + "Examples | \n", + "From regex | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "DateTime | \n", + "DateTime | \n", + "4 | \n", + "num | \n", + "False | \n", + "15121 | \n", + "[] | \n", + "[2023-03-14T20:55:49.165591Z, 2023-03-14T20:55:49.166591Z, 2023-03-14T20:55:49.167591Z, 2023-03-14T20:55:49.168591Z, 2023-03-14T20:55:49.169591Z] | \n", + "True | \n", + "
| 1 | \n", + "43e6c5b5_7 | \n", + "x | \n", + "0 | \n", + "num | \n", + "False | \n", + "10000 | \n", + "[with, x, =] | \n", + "[100.00285967800117, 100.81229891323964, 100.26016363495995, 199.4482406365968, 200.98748260974] | \n", + "False | \n", + "
| 2 | \n", + "f2e46af4_11 | \n", + "y | \n", + "0 | \n", + "num | \n", + "False | \n", + "5006 | \n", + "[reports, y, =] | \n", + "[200.1899632341074, 200.45898567905772, 399.6905765982258, 400.22712382793407, 400.08343040509993] | \n", + "False | \n", + "
| 3 | \n", + "f98340b4_12 | \n", + "z | \n", + "0 | \n", + "num | \n", + "False | \n", + "115 | \n", + "[of, z, =] | \n", + "[200.0, 100.0] | \n", + "False | \n", + "
| \n", + " | Name | \n", + "Tag | \n", + "TagOrigin | \n", + "Type | \n", + "IsUninteresting | \n", + "Occurrences | \n", + "Preceding 3 tokens | \n", + "Examples | \n", + "From regex | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "DateTime | \n", + "DateTime | \n", + "4 | \n", + "num | \n", + "False | \n", + "15121 | \n", + "[] | \n", + "[2023-03-14T20:55:49.165591Z, 2023-03-14T20:55:49.166591Z, 2023-03-14T20:55:49.167591Z, 2023-03-14T20:55:49.168591Z, 2023-03-14T20:55:49.169591Z] | \n", + "True | \n", + "
| 1 | \n", + "43e6c5b5_7 | \n", + "X | \n", + "0 | \n", + "num | \n", + "False | \n", + "10000 | \n", + "[with, x, =] | \n", + "[100.00285967800117, 100.81229891323964, 100.26016363495995, 199.4482406365968, 200.98748260974] | \n", + "False | \n", + "
| 2 | \n", + "f2e46af4_11 | \n", + "y | \n", + "0 | \n", + "num | \n", + "False | \n", + "5006 | \n", + "[reports, y, =] | \n", + "[200.1899632341074, 200.45898567905772, 399.6905765982258, 400.22712382793407, 400.08343040509993] | \n", + "False | \n", + "
| 3 | \n", + "f98340b4_12 | \n", + "z | \n", + "0 | \n", + "num | \n", + "False | \n", + "115 | \n", + "[of, z, =] | \n", + "[200.0, 100.0] | \n", + "False | \n", + "
| \n", + " | Variable | \n", + "Type | \n", + "Num Units | \n", + "IUS | \n", + "
|---|---|---|---|---|
| 0 | \n", + "DateTime | \n", + "num | \n", + "4 | \n", + "1.000000 | \n", + "
| 1 | \n", + "DateTime | \n", + "num | \n", + "8 | \n", + "1.000000 | \n", + "
| 2 | \n", + "DateTime | \n", + "num | \n", + "40 | \n", + "0.993734 | \n", + "
| 3 | \n", + "43e6c5b5_7 | \n", + "num | \n", + "4 | \n", + "0.330666 | \n", + "
| 4 | \n", + "43e6c5b5_7 | \n", + "num | \n", + "8 | \n", + "0.330666 | \n", + "
| 5 | \n", + "43e6c5b5_7 | \n", + "num | \n", + "40 | \n", + "0.330666 | \n", + "
| 6 | \n", + "f2e46af4_11 | \n", + "num | \n", + "4 | \n", + "0.165531 | \n", + "
| 7 | \n", + "f2e46af4_11 | \n", + "num | \n", + "8 | \n", + "0.165531 | \n", + "
| 8 | \n", + "f2e46af4_11 | \n", + "num | \n", + "40 | \n", + "0.165531 | \n", + "
| \n", + " | 43e6c5b5_7+mean | \n", + "f2e46af4_11+mean | \n", + "f98340b4_12+mean | \n", + "
|---|---|---|---|
| DateTime+max | \n", + "\n", + " | \n", + " | \n", + " |
| 1678827349.170591 | \n", + "199.659262 | \n", + "399.690577 | \n", + "200.0 | \n", + "
| 1678827349.173591 | \n", + "199.755998 | \n", + "400.227124 | \n", + "200.0 | \n", + "
| 1678827349.175591 | \n", + "200.098576 | \n", + "400.083430 | \n", + "200.0 | \n", + "
| 1678827349.178591 | \n", + "200.158634 | \n", + "399.188180 | \n", + "200.0 | \n", + "
| 1678827349.179591 | \n", + "200.934658 | \n", + "400.514334 | \n", + "200.0 | \n", + "
| 1678827349.181591 | \n", + "199.626578 | \n", + "399.894171 | \n", + "200.0 | \n", + "
| 1678827349.182591 | \n", + "200.937334 | \n", + "400.577257 | \n", + "200.0 | \n", + "
| 1678827349.183591 | \n", + "199.831092 | \n", + "399.077464 | \n", + "200.0 | \n", + "
| 1678827349.188591 | \n", + "199.372097 | \n", + "399.650472 | \n", + "200.0 | \n", + "
| 1678827349.193591 | \n", + "200.476665 | \n", + "400.613001 | \n", + "200.0 | \n", + "
| \n", + " | Name | \n", + "Base | \n", + "Pre-agg Value | \n", + "Agg | \n", + "Post-agg Value | \n", + "Tag | \n", + "Base Variable Occurences | \n", + "Type | \n", + "Examples | \n", + "From regex | \n", + "TemplateText | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "43e6c5b5_7+mean | \n", + "43e6c5b5_7 | \n", + "\n", + " | mean | \n", + "\n", + " | X mean | \n", + "10000 | \n", + "num | \n", + "[100.00285967800117, 100.81229891323964, 100.26016363495995, 199.4482406365968, 200.98748260974] | \n", + "False | \n", + "<*0> DATA Short message with x = <*> | \n", + "
| 1 | \n", + "f2e46af4_11+mean | \n", + "f2e46af4_11 | \n", + "\n", + " | mean | \n", + "\n", + " | y mean | \n", + "5006 | \n", + "num | \n", + "[200.1899632341074, 200.45898567905772, 399.6905765982258, 400.22712382793407, 400.08343040509993] | \n", + "False | \n", + "<*0> DATA This is a log message that reports y = <*> | \n", + "
| 2 | \n", + "f98340b4_12+mean | \n", + "f98340b4_12 | \n", + "\n", + " | mean | \n", + "\n", + " | z mean | \n", + "115 | \n", + "num | \n", + "[200.0, 100.0] | \n", + "False | \n", + "<*0> DATA The current line will include the value of z = <*> | \n", + "
| \n", + " | Source | \n", + "Source Tag | \n", + "Target | \n", + "Target Tag | \n", + "Change | \n", + "ATE | \n", + "Baseline ATE | \n", + "ATE Ratio | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "f98340b4_12+mean | \n", + "z mean | \n", + "f2e46af4_11+mean | \n", + "y mean | \n", + "Remove | \n", + "1.999968 | \n", + "-0.000376 | \n", + "5321.786986 | \n", + "
| 2 | \n", + "f98340b4_12+mean | \n", + "z mean | \n", + "43e6c5b5_7+mean | \n", + "X mean | \n", + "Reverse | \n", + "1.999968 | \n", + "-0.000376 | \n", + "5321.786986 | \n", + "
| 1 | \n", + "f98340b4_12+mean | \n", + "z mean | \n", + "43e6c5b5_7+mean | \n", + "X mean | \n", + "Remove | \n", + "0.265125 | \n", + "-0.000376 | \n", + "705.479462 | \n", + "
| \n", + " | Edge | \n", + "% Expectancy | \n", + "Status | \n", + "
|---|---|---|---|
| 1 | \n", + "(z mean, y mean) | \n", + "-0.555556 | \n", + "DOES NOT EXIST | \n", + "
| 0 | \n", + "(z mean, X mean) | \n", + "-0.333333 | \n", + "DOES NOT EXIST | \n", + "
| \n", + " | Edge | \n", + "% Expectancy | \n", + "Status | \n", + "
|---|---|---|---|
| 1 | \n", + "(z mean, y mean) | \n", + "1.666667 | \n", + "EXISTS | \n", + "
| 0 | \n", + "(z mean, X mean) | \n", + "1.000000 | \n", + "EXISTS | \n", + "
| \n", + " | Name | \n", + "Tag | \n", + "TagOrigin | \n", + "Type | \n", + "IsUninteresting | \n", + "Occurrences | \n", + "Preceding 3 tokens | \n", + "Examples | \n", + "From regex | \n", + "Aggregates | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "DateTime | \n", + "DateTime | \n", + "4 | \n", + "num | \n", + "False | \n", + "4687 | \n", + "[] | \n", + "[2023-11-01 17:54:53.006, 2023-11-01 17:54:53.017, 2023-11-01 17:54:53.018, 2023-11-01 17:54:53.019, 2023-11-01 17:54:53.027] | \n", + "True | \n", + "[mean, max, min] | \n", + "
| 1 | \n", + "sessionID | \n", + "sessionID | \n", + "4 | \n", + "str | \n", + "False | \n", + "4687 | \n", + "[] | \n", + "[6542c92d.1f943, 6542d880.1fc1b, 6542e76c.1fe63, 6543018c.20227, 65431bc7.20615] | \n", + "True | \n", + "[first] | \n", + "
| 2 | \n", + "tID | \n", + "tID | \n", + "4 | \n", + "str | \n", + "True | \n", + "4687 | \n", + "[] | \n", + "[, 3/5183, 3/5184, 3/0, 3/5185] | \n", + "True | \n", + "[] | \n", + "
| 3 | \n", + "7f9dd6da_22 | \n", + "port | \n", + "0 | \n", + "num | \n", + "False | \n", + "8 | \n", + "[127.0.0.1, port, =] | \n", + "[42396, 51214, 54898, 47566, 60122] | \n", + "False | \n", + "[mean, max, min] | \n", + "
| 4 | \n", + "9213789e_11 | \n", + "statement | \n", + "0 | \n", + "str | \n", + "False | \n", + "1552 | \n", + "[:, statement, :] | \n", + "[BEGIN, COMMIT] | \n", + "False | \n", + "[last, mode, first] | \n", + "
| 5 | \n", + "2dfe3d16_11 | \n", + "duration | \n", + "0 | \n", + "num | \n", + "False | \n", + "2327 | \n", + "[:, duration, :] | \n", + "[0.076, 0.062, 0.030, 0.023, 0.044] | \n", + "False | \n", + "[mean, max, min] | \n", + "
| 6 | \n", + "d046ac78_14 | \n", + "max_parallel_workers | \n", + "0 | \n", + "num | \n", + "False | \n", + "8 | \n", + "[SET, max_parallel_workers, =] | \n", + "[1, 2] | \n", + "False | \n", + "[mean, max, min] | \n", + "
| 7 | \n", + "0f62d144_15 | \n", + "work_mem | \n", + "0 | \n", + "num | \n", + "False | \n", + "8 | \n", + "[work_mem, =, '] | \n", + "[128.0, 64.0, 256.0] | \n", + "False | \n", + "[mean, max, min] | \n", + "
| 8 | \n", + "7c2f7377_14 | \n", + "Filename | \n", + "0 | \n", + "str | \n", + "False | \n", + "16 | \n", + "[--, Filename, :] | \n", + "[query012.sql, query020.sql] | \n", + "False | \n", + "[last, mode, first] | \n", + "
| 9 | \n", + "7c2f7377_28 | \n", + "7c2f7377_28 | \n", + "3 | \n", + "str | \n", + "False | \n", + "16 | \n", + "[,, sum, (] | \n", + "[ws_ext_sales_price, cs_ext_sales_price] | \n", + "False | \n", + "[last, mode, first] | \n", + "
| 10 | \n", + "7c2f7377_35 | \n", + "7c2f7377_35 | \n", + "3 | \n", + "str | \n", + "False | \n", + "16 | \n", + "[,, sum, (] | \n", + "[ws_ext_sales_price, cs_ext_sales_price] | \n", + "False | \n", + "[last, mode, first] | \n", + "
| 11 | \n", + "7c2f7377_41 | \n", + "7c2f7377_41 | \n", + "3 | \n", + "str | \n", + "False | \n", + "16 | \n", + "[(, sum, (] | \n", + "[ws_ext_sales_price, cs_ext_sales_price] | \n", + "False | \n", + "[last, mode, first] | \n", + "
| 12 | \n", + "7c2f7377_53 | \n", + "7c2f7377_53 | \n", + "3 | \n", + "str | \n", + "False | \n", + "16 | \n", + "[as, revenueratio, from] | \n", + "[web_sales, catalog_sales] | \n", + "False | \n", + "[last, mode, first] | \n", + "
| 13 | \n", + "7c2f7377_59 | \n", + "7c2f7377_59 | \n", + "3 | \n", + "str | \n", + "False | \n", + "16 | \n", + "[,, date_dim, where] | \n", + "[ws_item_sk, cs_item_sk] | \n", + "False | \n", + "[last, mode, first] | \n", + "
| 14 | \n", + "7c2f7377_79 | \n", + "7c2f7377_79 | \n", + "3 | \n", + "str | \n", + "False | \n", + "16 | \n", + "[', ), and] | \n", + "[ws_sold_date_sk, cs_sold_date_sk] | \n", + "False | \n", + "[last, mode, first] | \n", + "
| \n", + " | DateTime+mean | \n", + "7f9dd6da_22+mean | \n", + "2dfe3d16_11+mean | \n", + "d046ac78_14+mean | \n", + "0f62d144_15+mean | \n", + "2c402162_13+mean | \n", + "2c402162_25+mean | \n", + "9213789e_11+last=COMMIT | \n", + "7c2f7377_14+last=query020.sql | \n", + "7c2f7377_28+last=cs_ext_sales_price | \n", + "7c2f7377_35+last=cs_ext_sales_price | \n", + "7c2f7377_41+last=cs_ext_sales_price | \n", + "7c2f7377_53+last=catalog_sales | \n", + "7c2f7377_59+last=cs_item_sk | \n", + "7c2f7377_79+last=cs_sold_date_sk | \n", + "3e3b083e_14+last=query053.sql | \n", + "3e3b083e_20+last=i_manufact_id | \n", + "3e3b083e_39+last=i_manufact_id | \n", + "3e3b083e_41+last=avg_quarterly_sales | \n", + "3e3b083e_219+last=i_manufact_id | \n", + "3e3b083e_221+last=d_qoy | \n", + "3e3b083e_227+last=avg_quarterly_sales | \n", + "3e3b083e_235+last=avg_quarterly_sales | \n", + "3e3b083e_238+last=avg_quarterly_sales | \n", + "3e3b083e_246+last=avg_quarterly_sales | \n", + "3e3b083e_248+last=sum_sales | \n", + "3e3b083e_250+last=i_manufact_id | \n", + "a092a92c_14+last=query026.sql | \n", + "a092a92c_20+last=cs_quantity | \n", + "a092a92c_26+last=cs_list_price | \n", + "a092a92c_32+last=cs_coupon_amt | \n", + "a092a92c_38+last=cs_sales_price | \n", + "a092a92c_42+last=catalog_sales | \n", + "a092a92c_52+last=cs_sold_date_sk | \n", + "a092a92c_56+last=cs_item_sk | \n", + "a092a92c_60+last=cs_bill_cdemo_sk | \n", + "a092a92c_64+last=cs_promo_sk | \n", + "c1dbe68b_14+last=query099.sql | \n", + "c1dbe68b_27+last=cc_name | \n", + "c1dbe68b_34+last=cs_ship_date_sk | \n", + "c1dbe68b_36+last=cs_sold_date_sk | \n", + "c1dbe68b_58+last=cs_ship_date_sk | \n", + "c1dbe68b_60+last=cs_sold_date_sk | \n", + "c1dbe68b_66+last=cs_ship_date_sk | \n", + "c1dbe68b_68+last=cs_sold_date_sk | \n", + "c1dbe68b_90+last=cs_ship_date_sk | \n", + "c1dbe68b_92+last=cs_sold_date_sk | \n", + "c1dbe68b_98+last=cs_ship_date_sk | \n", + "c1dbe68b_100+last=cs_sold_date_sk | \n", + "c1dbe68b_122+last=cs_ship_date_sk | \n", + "c1dbe68b_124+last=cs_sold_date_sk | \n", + "c1dbe68b_130+last=cs_ship_date_sk | \n", + "c1dbe68b_132+last=cs_sold_date_sk | \n", + "c1dbe68b_154+last=cs_ship_date_sk | \n", + "c1dbe68b_156+last=cs_sold_date_sk | \n", + "c1dbe68b_172+last=catalog_sales | \n", + "c1dbe68b_178+last=call_center | \n", + "c1dbe68b_190+last=cs_ship_date_sk | \n", + "c1dbe68b_194+last=cs_warehouse_sk | \n", + "c1dbe68b_198+last=cs_ship_mode_sk | \n", + "c1dbe68b_202+last=cs_call_center_sk | \n", + "c1dbe68b_204+last=cc_call_center_sk | \n", + "c1dbe68b_218+last=cc_name | \n", + "c1dbe68b_232+last=cc_name | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sessionID+first | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| 6542c92d.1f943 | \n", + "1.698863e+09 | \n", + "42396.0 | \n", + "13483.595990 | \n", + "1.0 | \n", + "128.0 | \n", + "3923.846 | \n", + "42396.0 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "
| 6542d880.1fc1b | \n", + "1.698867e+09 | \n", + "51214.0 | \n", + "13126.180309 | \n", + "1.0 | \n", + "128.0 | \n", + "3819.821 | \n", + "51214.0 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "
| 6542e76c.1fe63 | \n", + "1.698872e+09 | \n", + "54898.0 | \n", + "22983.408399 | \n", + "2.0 | \n", + "64.0 | \n", + "6688.275 | \n", + "54898.0 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "
| 6543018c.20227 | \n", + "1.698879e+09 | \n", + "47566.0 | \n", + "23075.070997 | \n", + "2.0 | \n", + "64.0 | \n", + "6714.950 | \n", + "47566.0 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "
| 65431bc7.20615 | \n", + "1.698883e+09 | \n", + "60122.0 | \n", + "2707.956093 | \n", + "1.0 | \n", + "256.0 | \n", + "808.713 | \n", + "60122.0 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "
| 65431ef0.2073a | \n", + "1.698884e+09 | \n", + "47102.0 | \n", + "2781.880526 | \n", + "1.0 | \n", + "256.0 | \n", + "809.632 | \n", + "47102.0 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "
| 6543221a.2085f | \n", + "1.698886e+09 | \n", + "43972.0 | \n", + "13000.099663 | \n", + "2.0 | \n", + "128.0 | \n", + "3783.136 | \n", + "43972.0 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "
| 654330e1.20b8f | \n", + "1.698890e+09 | \n", + "37380.0 | \n", + "13005.023144 | \n", + "2.0 | \n", + "128.0 | \n", + "3784.566 | \n", + "37380.0 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "
| \n", + " | Name | \n", + "Tag | \n", + "TagOrigin | \n", + "Type | \n", + "IsUninteresting | \n", + "Occurrences | \n", + "Preceding 3 tokens | \n", + "Examples | \n", + "From regex | \n", + "Aggregates | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|
| 7 | \n", + "0f62d144_15 | \n", + "work_mem | \n", + "0 | \n", + "num | \n", + "False | \n", + "8 | \n", + "[work_mem, =, '] | \n", + "[128.0, 64.0, 256.0] | \n", + "False | \n", + "[mean, max, min] | \n", + "
| \n", + " | TemplateText | \n", + "TemplateId | \n", + "VariableIndices | \n", + "RegexIndices | \n", + "TemplateExample | \n", + "Occurrences | \n", + "
|---|---|---|---|---|---|---|
| 6 | \n", + "<*0> EDT [ <*1> <*2> ] postgres@tpcds1 LOG : statement : SET work_mem = ' <*> ' ; | \n", + "0f62d144 | \n", + "[15] | \n", + "[0, 3, 4] | \n", + "<*0> EDT [ <*1> <*2> ] postgres@tpcds1 LOG : statement : SET work_mem = ' 128.0 ' ; | \n", + "8 | \n", + "
| \n", + " | 0f62d144_15+mean (candidate) | \n", + "
|---|---|
| sessionID+first | \n", + "\n", + " |
| 6542c92d.1f943 | \n", + "128.0 | \n", + "
| 6542d880.1fc1b | \n", + "128.0 | \n", + "
| 6542e76c.1fe63 | \n", + "64.0 | \n", + "
| 6543018c.20227 | \n", + "64.0 | \n", + "
| 65431bc7.20615 | \n", + "256.0 | \n", + "
| 65431ef0.2073a | \n", + "256.0 | \n", + "
| 6543221a.2085f | \n", + "128.0 | \n", + "
| 654330e1.20b8f | \n", + "128.0 | \n", + "
| \n", + " | Name | \n", + "Tag | \n", + "TagOrigin | \n", + "Type | \n", + "IsUninteresting | \n", + "Occurrences | \n", + "Preceding 3 tokens | \n", + "Examples | \n", + "From regex | \n", + "Aggregates | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|
| 6 | \n", + "d046ac78_14 | \n", + "max_parallel_workers | \n", + "0 | \n", + "num | \n", + "False | \n", + "8 | \n", + "[SET, max_parallel_workers, =] | \n", + "[1, 2] | \n", + "False | \n", + "[mean, max, min] | \n", + "
| \n", + " | TemplateText | \n", + "TemplateId | \n", + "VariableIndices | \n", + "RegexIndices | \n", + "TemplateExample | \n", + "Occurrences | \n", + "
|---|---|---|---|---|---|---|
| 5 | \n", + "<*0> EDT [ <*1> <*2> ] postgres@tpcds1 LOG : statement : SET max_parallel_workers = <*> ; | \n", + "d046ac78 | \n", + "[14] | \n", + "[0, 3, 4] | \n", + "<*0> EDT [ <*1> <*2> ] postgres@tpcds1 LOG : statement : SET max_parallel_workers = 1 ; | \n", + "8 | \n", + "
| \n", + " | d046ac78_14+mean (candidate) | \n", + "
|---|---|
| sessionID+first | \n", + "\n", + " |
| 6542c92d.1f943 | \n", + "1.0 | \n", + "
| 6542d880.1fc1b | \n", + "1.0 | \n", + "
| 6542e76c.1fe63 | \n", + "2.0 | \n", + "
| 6543018c.20227 | \n", + "2.0 | \n", + "
| 65431bc7.20615 | \n", + "1.0 | \n", + "
| 65431ef0.2073a | \n", + "1.0 | \n", + "
| 6543221a.2085f | \n", + "2.0 | \n", + "
| 654330e1.20b8f | \n", + "2.0 | \n", + "
| \n", + " | work_mem | \n", + "max_parallel_workers | \n", + "mean_latency | \n", + "
|---|---|---|---|
| sessionID+first | \n", + "\n", + " | \n", + " | \n", + " |
| 6542c92d.1f943 | \n", + "128.0 | \n", + "1.0 | \n", + "13483.595990 | \n", + "
| 6542d880.1fc1b | \n", + "128.0 | \n", + "1.0 | \n", + "13126.180309 | \n", + "
| 6542e76c.1fe63 | \n", + "64.0 | \n", + "2.0 | \n", + "22983.408399 | \n", + "
| 6543018c.20227 | \n", + "64.0 | \n", + "2.0 | \n", + "23075.070997 | \n", + "
| 65431bc7.20615 | \n", + "256.0 | \n", + "1.0 | \n", + "2707.956093 | \n", + "
| 65431ef0.2073a | \n", + "256.0 | \n", + "1.0 | \n", + "2781.880526 | \n", + "
| 6543221a.2085f | \n", + "128.0 | \n", + "2.0 | \n", + "13000.099663 | \n", + "
| 654330e1.20b8f | \n", + "128.0 | \n", + "2.0 | \n", + "13005.023144 | \n", + "
+
+
+
+
+
+Based on this trivial causal graph, we can calculate the ATE:
+
+
+```python title="Code"
+s.get_unadjusted_ate("X mean", "y mean")
+```
+
+
+
+```title="Output"
+2.00007436079689
+```
+
+
+
+
+However, let's now assume that we are (thankfully) not yet fully convinced, and would like to look for any possible confounding. Let's ask the system for candidates causes of `X`:
+
+
+```python title="Code"
+s.explore_candidate_causes("X mean")
+```
+
+
+| |Candidate| Tag |Slope| P-value| Candidate->Target Edge Status |Target->Candidate Edge Status|
+|-|-|-|-|-|-|-|
+|0 |62ff3fb7_11+mean |y mean |0.499895 |0.0 |Rejected |Accepted|
+|1| ff661264_12+mean |z mean |0.999940| 0.0| Undecided |Undecided|
+
+
+
+`z` is successfully detected once more, and we can use domain knowledge to judge that `z` influencing `X` is the correct direction. Indeed, let's see what happens if we add it to the graph:
+
+
+```python title="Code"
+s.accept('z mean', 'X mean')
+```
+
+
+
+
+
+
+
+
+```python title="Code"
+s.get_unadjusted_ate("X mean", "y mean")
+```
+
+
+
+``` title="Output"
+2.00007436079689
+```
+
+
+
+
+```python title="Code"
+s.get_adjusted_ate("X mean", "y mean")
+```
+
+
+
+``` title="Output"
+-0.0249128594251147
+```
+
+
+That is, after adjusting for `z`, the effect of `x` on `y` is negligible.
+
+## PostgreSQL/TPC-DS Example
+
+Now that we saw the basics of the framework, let's try to apply it to logs from a real system.
+
+We set up a PostgreSQL instance and load it with the data from TPC-DS with scale factor 1. We then run a workload in which we issue the queries in the TPC-DS workload once each (except for 4 particularly long-running queries, to expedite development 😅).
+
+We use PostgreSQL's logging to capture the execution of this workload:
+
+
+``` title="Log Excerpt"
+2023-11-01 17:54:53.027 EDT [ 6542c92d.1f943 3/5186 ] postgres@tpcds1 LOG: statement: BEGIN
+2023-11-01 17:54:53.027 EDT [ 6542c92d.1f943 3/5186 ] postgres@tpcds1 LOG: duration: 0.033 ms
+2023-11-01 17:54:53.028 EDT [ 6542c92d.1f943 3/5186 ] postgres@tpcds1 LOG: statement: -- Filename: query080.sql
+
+ with ssr as
+ (select s_store_id as store_id,
+ sum(ss_ext_sales_price) as sales,
+ sum(coalesce(sr_return_amt, 0)) as returns,
+ sum(ss_net_profit - coalesce(sr_net_loss, 0)) as profit
+ from store_sales left outer join store_returns on
+```
+
+
+
+We configure logging to print out the latency of every query, and the prefix of each line is set to `%m [ %c %v ] %q%u@%d`, where:
+
+- %m = timestamp with milliseconds
+- %c = session ID
+- %v = virtual transaction ID
+- %q = stop here in non-session processes
+- %u = user name
+- %d = database name
+
+We run this workload 8 times total, each using a new connection - 2 runs each for each of 4 different parameter configurations.
+
+For each parameter configuration, we decide on a total memory budget (128 kB or 256 kB). We then set the number of `max_parallel_workers` to 1 or 2, and the amount of `work_mem` to the budget divided by the number of max parallel workers. For example:
+
+
+```python title="Log Excerpt"
+
+2023-11-01 17:54:53.018 EDT [ 6542c92d.1f943 3/5184 ] postgres@tpcds1 LOG: statement: BEGIN
+2023-11-01 17:54:53.019 EDT [ 6542c92d.1f943 3/5184 ] postgres@tpcds1 LOG: duration: 0.076 ms
+2023-11-01 17:54:53.019 EDT [ 6542c92d.1f943 3/5184 ] postgres@tpcds1 LOG: statement: SET max_parallel_workers = 1;
+2023-11-01 17:54:53.019 EDT [ 6542c92d.1f943 3/5184 ] postgres@tpcds1 LOG: duration: 0.062 ms
+2023-11-01 17:54:53.019 EDT [ 6542c92d.1f943 3/5184 ] postgres@tpcds1 LOG: statement: COMMIT
+2023-11-01 17:54:53.019 EDT [ 6542c92d.1f943 3/0 ] postgres@tpcds1 LOG: duration: 0.030 ms
+2023-11-01 17:54:53.019 EDT [ 6542c92d.1f943 3/5185 ] postgres@tpcds1 LOG: statement: BEGIN
+2023-11-01 17:54:53.019 EDT [ 6542c92d.1f943 3/5185 ] postgres@tpcds1 LOG: duration: 0.023 ms
+2023-11-01 17:54:53.019 EDT [ 6542c92d.1f943 3/5185 ] postgres@tpcds1 LOG: statement: SET work_mem = '128.0';
+2023-11-01 17:54:53.019 EDT [ 6542c92d.1f943 3/5185 ] postgres@tpcds1 LOG: duration: 0.044 ms
+2023-11-01 17:54:53.019 EDT [ 6542c92d.1f943 3/5185 ] postgres@tpcds1 LOG: statement: COMMIT
+2023-11-01 17:54:53.019 EDT [ 6542c92d.1f943 3/0 ] postgres@tpcds1 LOG: duration: 0.026 ms
+
+```
+
+Can we use Sawmill to find the causal effect of these modifications on query runtime?
+
+### Parsing and preparation
+
+Since we run each iteration of the workload in a separate connection, we can extract the connection ID from each log line and use it to define our causal units. We also specify that each log message begins with a timestamp, so that Samwill can collapse multi-line log messages into a single message:
+
+
+```python title="Code"
+s2 = Sawmill("datasets_raw/tpc-ds/work_mem_2_256kB_2_128kB_parallel_1_2.log", workdir="datasets/tpc-ds")
+s2.parse(regex_dict={"Date": r'\d{4}-\d{2}-\d{2}',
+ "Time": r'\d{2}:\d{2}:\d{2}\.\d{3}(?= EDT \[ )',
+ "sessionID" : r'(?<=EDT \[ )\S+\.\S+',
+ "tID": r'3/\d+(?= ] )'
+ },message_prefix=r'\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}.\d{3}', force=True)
+s2.set_causal_unit("sessionID")
+s2.prepare(force = True)
+```
+``` title="Output"
+Initialized Sawmill with log file datasets_raw/tpc-ds/work_mem_2_256kB_2_128kB_parallel_1_2.log
+Work directory set to datasets/tpc-ds
+Parsing file: datasets_raw/tpc-ds/work_mem_2_256kB_2_128kB_parallel_1_2.log
+
+
+Reading and tokenizing log lines...: 100%|██████████| 42108/42108 [00:00<00:00, 85860.79it/s]
+Determining template for each line...: 100%|██████████| 4687/4687 [00:00<00:00, 65351.69it/s]
+Extracting variables from each line...: 100%|██████████| 100/100 [00:00<00:00, 349.71it/s]
+
+
+Variables generated from regexes: 4
+Variables generated by Drain: 63
+Templates with at least 1 non-regex variable: 100
+Templates with at least 2 occurrences: 99
+
+
+Determining variable types...: 100%|██████████| 67/67 [00:00<00:00, 49414.17it/s]
+Casting date variables...: 100%|██████████| 1/1 [00:00<00:00, 431.11it/s]
+Casting time variables...: 100%|██████████| 2/2 [00:00<00:00, 275.95it/s]
+Casting numerical variables...: 100%|██████████| 5/5 [00:00<00:00, 1644.05it/s]
+Tagging variables...: 100%|██████████| 67/67 [00:00<00:00, 83018.72it/s]
+Detecting identifiers...: 100%|██████████| 67/67 [00:00<00:00, 4675.30it/s]
+Dumping pkl file to datasets/tpc-ds/work_mem_2_256kB_2_128kB_parallel_1_2.log_parsed_log_None.pkl...: 100%|██████████| 1/1 [00:00<00:00, 95.75it/s]
+Dumping pkl file to datasets/tpc-ds/work_mem_2_256kB_2_128kB_parallel_1_2.log_parsed_templates_None.pkl...: 100%|██████████| 1/1 [00:00<00:00, 1182.83it/s]
+Dumping pkl file to datasets/tpc-ds/work_mem_2_256kB_2_128kB_parallel_1_2.log_parsed_variables_None.pkl...: 100%|██████████| 1/1 [00:00<00:00, 1450.81it/s]
+
+
+Parsing complete in 1.088751 seconds!
+Causal unit set to sessionID (tag: sessionID)
+Dropped 3 identifier columns.
+Calculating aggregates for each causal unit...
+
+
+Imputing missing values...: 100%|██████████| 73/73 [00:00<00:00, 16921.86it/s]
+One-hot encoding categorical variables...: 100%|██████████| 57/57 [00:00<00:00, 996.50it/s]
+Dumping pkl file to datasets/tpc-ds/work_mem_2_256kB_2_128kB_parallel_1_2.log_prepared_log_sessionID.pkl...: 100%|██████████| 1/1 [00:00<00:00, 2413.29it/s]
+Dumping pkl file to datasets/tpc-ds/work_mem_2_256kB_2_128kB_parallel_1_2.log_prepared_variables_sessionID.pkl...: 100%|██████████| 1/1 [00:00<00:00, 1171.59it/s]
+
+Successfully prepared the log with causal unit sessionID
+ (tag: sessionID)
+Preparation complete in 0.343447 seconds!
+
+```
+
+
+
+
+Now, the prepared log consists of one causal unit per run of the workload:
+
+
+```python title="Code"
+s2.prepared_log
+```
+
+
+| | Date+earliest | 8ea7bb0e_23+mean | 8ea7bb0e_23+min | 8ea7bb0e_23+max | 424c4f52_12+mean | 424c4f52_12+min | 424c4f52_12+max | f1c25a57_15+mean | f1c25a57_15+min | f1c25a57_15+max | 80cfbe3b_16+mean | 80cfbe3b_16+min | 80cfbe3b_16+max | 758612aa_26+mean | 758612aa_26+min | 758612aa_26+max | f20ea3d3_12+latest=COMMIT | 7fce1bc0_15+latest=query020.sql | 7fce1bc0_29+latest=cs_ext_sales_price | 7fce1bc0_36+latest=cs_ext_sales_price | 7fce1bc0_42+latest=cs_ext_sales_price | 7fce1bc0_54+latest=catalog_sales | 7fce1bc0_60+latest=cs_item_sk | 7fce1bc0_80+latest=cs_sold_date_sk | f8592a76_15+latest=query053.sql | f8592a76_21+latest=i_manufact_id | f8592a76_40+latest=i_manufact_id | f8592a76_42+latest=avg_quarterly_sales | f8592a76_220+latest=i_manufact_id | f8592a76_222+latest=d_qoy | f8592a76_228+latest=avg_quarterly_sales | f8592a76_236+latest=avg_quarterly_sales | f8592a76_239+latest=avg_quarterly_sales | f8592a76_247+latest=avg_quarterly_sales | f8592a76_249+latest=sum_sales | f8592a76_251+latest=i_manufact_id | 7e11a9a5_15+latest=query026.sql | 7e11a9a5_21+latest=cs_quantity | 7e11a9a5_27+latest=cs_list_price | 7e11a9a5_33+latest=cs_coupon_amt | 7e11a9a5_39+latest=cs_sales_price | 7e11a9a5_43+latest=catalog_sales | 7e11a9a5_53+latest=cs_sold_date_sk | 7e11a9a5_57+latest=cs_item_sk | 7e11a9a5_61+latest=cs_bill_cdemo_sk | 7e11a9a5_65+latest=cs_promo_sk | 252996ec_15+latest=query099.sql | 252996ec_28+latest=cc_name | 252996ec_35+latest=cs_ship_date_sk | 252996ec_37+latest=cs_sold_date_sk | 252996ec_59+latest=cs_ship_date_sk | 252996ec_61+latest=cs_sold_date_sk | 252996ec_67+latest=cs_ship_date_sk | 252996ec_69+latest=cs_sold_date_sk | 252996ec_91+latest=cs_ship_date_sk | 252996ec_93+latest=cs_sold_date_sk | 252996ec_99+latest=cs_ship_date_sk | 252996ec_101+latest=cs_sold_date_sk | 252996ec_123+latest=cs_ship_date_sk | 252996ec_125+latest=cs_sold_date_sk | 252996ec_131+latest=cs_ship_date_sk | 252996ec_133+latest=cs_sold_date_sk | 252996ec_155+latest=cs_ship_date_sk | 252996ec_157+latest=cs_sold_date_sk | 252996ec_173+latest=catalog_sales | 252996ec_179+latest=call_center | 252996ec_191+latest=cs_ship_date_sk | 252996ec_195+latest=cs_warehouse_sk | 252996ec_199+latest=cs_ship_mode_sk | 252996ec_203+latest=cs_call_center_sk | 252996ec_205+latest=cc_call_center_sk | 252996ec_219+latest=cc_name | 252996ec_233+latest=cc_name |
+|-----------------:|-----------------:|----------------:|----------------:|-----------------:|----------------:|----------------:|-----------------:|----------------:|----------------:|-----------------:|----------------:|----------------:|-----------------:|----------------:|----------------:|--------------------------:|--------------------------------:|--------------------------------------:|--------------------------------------:|--------------------------------------:|---------------------------------:|------------------------------:|-----------------------------------:|--------------------------------:|---------------------------------:|---------------------------------:|---------------------------------------:|----------------------------------:|--------------------------:|----------------------------------------:|----------------------------------------:|----------------------------------------:|----------------------------------------:|------------------------------:|----------------------------------:|--------------------------------:|-------------------------------:|---------------------------------:|---------------------------------:|----------------------------------:|---------------------------------:|-----------------------------------:|------------------------------:|------------------------------------:|-------------------------------:|--------------------------------:|---------------------------:|-----------------------------------:|-----------------------------------:|-----------------------------------:|-----------------------------------:|-----------------------------------:|-----------------------------------:|-----------------------------------:|-----------------------------------:|-----------------------------------:|------------------------------------:|------------------------------------:|------------------------------------:|------------------------------------:|------------------------------------:|------------------------------------:|------------------------------------:|----------------------------------:|--------------------------------:|------------------------------------:|------------------------------------:|------------------------------------:|--------------------------------------:|--------------------------------------:|----------------------------:|----------------------------:|------|
+| **sessionID+latest** | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
+| 6542c92d.1f943 | 1.698797e+12 | 42396.0 | 42396.0 | 42396.0 | 13483.595990 | 0.020 | 2935111.471 | 1.0 | 1.0 | 1.0 | 128.0 | 128.0 | 128.0 | 42396.0 | 42396.0 | 42396.0 | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True |
+| 6542d880.1fc1b | 1.698797e+12 | 51214.0 | 51214.0 | 51214.0 | 13126.180309 | 0.021 | 2876689.649 | 1.0 | 1.0 | 1.0 | 128.0 | 128.0 | 128.0 | 51214.0 | 51214.0 | 51214.0 | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True |
+| 6542e76c.1fe63 | 1.698797e+12 | 54898.0 | 54898.0 | 54898.0 | 22983.408399 | 0.023 | 4460683.070 | 2.0 | 2.0 | 2.0 | 64.0 | 64.0 | 64.0 | 54898.0 | 54898.0 | 54898.0 | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True |
+| 6543018c.20227 | 1.698797e+12 | 47566.0 | 47566.0 | 47566.0 | 23075.070997 | 0.023 | 4477061.260 | 2.0 | 2.0 | 2.0 | 64.0 | 64.0 | 64.0 | 47566.0 | 47566.0 | 47566.0 | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True |
+| 65431bc7.20615 | 1.698797e+12 | 60122.0 | 60122.0 | 60122.0 | 2707.956093 | 0.021 | 423565.150 | 1.0 | 1.0 | 1.0 | 256.0 | 256.0 | 256.0 | 60122.0 | 60122.0 | 60122.0 | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True |
+| 65431ef0.2073a | 1.698883e+12 | 47102.0 | 47102.0 | 47102.0 | 2781.880526 | 0.019 | 423792.790 | 1.0 | 1.0 | 1.0 | 256.0 | 256.0 | 256.0 | 47102.0 | 47102.0 | 47102.0 | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True |
+| 6543221a.2085f | 1.698883e+12 | 43972.0 | 43972.0 | 43972.0 | 13000.099663 | 0.024 | 2868369.399 | 2.0 | 2.0 | 2.0 | 128.0 | 128.0 | 128.0 | 43972.0 | 43972.0 | 43972.0 | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True |
+| 654330e1.20b8f | 1.698883e+12 | 37380.0 | 37380.0 | 37380.0 | 13005.023144 | 0.024 | 2871176.389 | 2.0 | 2.0 | 2.0 | 128.0 | 128.0 | 128.0 | 37380.0 | 37380.0 | 37380.0 | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True | True |
+
+
+### Graph exploration and ATE calculation
+
+We ask for candidate causes of query duration:
+
+
+```python title="Code"
+s2.explore_candidate_causes("duration mean")
+```
+
+
+| |Candidate| Tag| Slope| P-value| Candidate->Target Edge Status| Target->Candidate Edge Status|
+|-|-|-|-|-|-|-|
+|0| 80cfbe3b_16+mean| work_mem mean| -1.005257e+02| 0.000029| Undecided |Undecided|
+|1 |Date+earliest |Date earliest |7.664300e-09 |0.001963 |Undecided |Undecided|
+|2| f1c25a57_15+mean| max_parallel_workers mean |9.990997e+03| 0.055041| Undecided |Undecided|
+|3 |8ea7bb0e_23+mean |port mean |-1.459892e-01 |0.745097 |Undecided |Undecided|
+|4| 758612aa_26+mean| port mean| -1.459892e-01| 0.745097 |Undecided |Undecided|
+
+
+
+Let's learn more about some of the top candidate causes:
+
+
+```python title="Code"
+s2.inspect('work_mem mean')
+```
+``` title="Output"
+Information about prepared variable 80cfbe3b_16+mean:
+
+--> Variable Information about 80cfbe3b_16:
+```
+
+|Name| Tag| Type| IsUninteresting| Occurrences| Preceding 3 tokens| Examples| From regex| Aggregates|
+|-|-|-|-|-|-|-|-|-|
+| 80cfbe3b_16 |work_mem| num| False| 8| [work_mem, =, ']| [128.0, 64.0, 256.0]| False |[mean, min, max]|
+
+
+``` title="Output"
+--> Template Information about 80cfbe3b:
+```
+
+|TemplateText| TemplateId| Occurrences| VariableIndices| RegexIndices|
+|-|-|-|-|-|
+|<\*0> <\*1> EDT [ <\*2> <\*3> ] postgres@tpcds1 LOG : statement : SET work_mem = ' <\*> ' ;| 80cfbe3b| 8| [16]| [0, 1, 4, 5]|
+
+``` title="Output"
+--> Causal Unit Partial Information:
+```
+
+| | 80cfbe3b_16+mean (candidate) |
+|-|-|
+| **sessionID+latest** | |
+|6542c92d.1f943 |128.0|
+|6542d880.1fc1b| 128.0|
+|6542e76c.1fe63 |64.0|
+|6543018c.20227| 64.0|
+|65431bc7.20615 |256.0|
+|65431ef0.2073a |256.0|
+|6543221a.2085f |128.0|
+|654330e1.20b8f |128.0|
+
+
+
+```python title="Code"
+s2.inspect('max_parallel_workers mean')
+```
+``` title="Output"
+Information about prepared variable f1c25a57_15+mean:
+
+--> Variable Information about f1c25a57_15:
+```
+
+|Name| Tag |Type| IsUninteresting |Occurrences| Preceding 3 tokens| Examples |From regex| Aggregates|
+|-|-|-|-|-|-|-|-|-|
+|f1c25a57_15| max_parallel_workers| num| False| 8| [SET, max_parallel_workers, =]| [1, 2]| False| [mean, min, max]|
+
+
+
+``` title="Output"
+--> Template Information about f1c25a57:
+```
+
+|TemplateText| TemplateId| Occurrences| VariableIndices| RegexIndices|
+|-|-|-|-|-|
+|<\*0> <\*1> EDT [ <\*2> <\*3> ] postgres@tpcds1 LOG : statement : SET max_parallel_workers = <\*> ; |f1c25a57| 8| [15]| [0, 1, 4, 5]|
+
+``` title="Output"
+--> Causal Unit Partial Information:
+```
+
+| |f1c25a57_15+mean (candidate)|
+|-|-|
+|**sessionID+latest** | |
+|6542c92d.1f943| 1.0|
+|6542d880.1fc1b |1.0|
+|6542e76c.1fe63 |2.0|
+|6543018c.20227 |2.0|
+|65431bc7.20615 |1.0|
+|65431ef0.2073a |1.0|
+|6543221a.2085f |2.0|
+|654330e1.20b8f |2.0|
+
+
+
+Indeed, two of them correspond to the two parameters we are tweaking. Assuming we do not know the experiment set up, let's accept them both as causes.
+
+
+```python title="Code"
+s2.accept('work_mem mean', 'duration mean', interactive=False)
+s2.accept('max_parallel_workers mean', 'duration mean')
+```
+
+
+
+
+
+
+Let's now look at the corresponding ATE of the number of `max_parallel_workers` on the mean query latency. Intuitively, more parallelism should help so this should be negative:
+
+
+```python title="Code"
+s2.get_adjusted_ate("max_parallel_workers mean", "duration mean")
+```
+
+
+``` title="Output"
+-1588.99225407086
+```
+
+
+
+What if we hadn't considered the amount of working memory as a cause?
+
+
+```python title="Code"
+s2.get_unadjusted_ate("max_parallel_workers mean", "duration mean")
+```
+
+
+
+``` title="Output"
+9990.99732122586
+```
+
+
+Surprising discrepancy! Let's look at the underlying data though:
+
+
+```python title="Code"
+t = s2.prepared_log[["80cfbe3b_16+mean", "f1c25a57_15+mean", "424c4f52_12+mean"]]
+t.columns = ["work_mem", "max_parallel_workers", "mean_latency"]
+t
+```
+
+| |work_mem| max_parallel_workers |mean_latency|
+|-|-|-|-|
+|**sessionID+latest** | | |
+|6542c92d.1f943 |128.0| 1.0| 13483.595990|
+|6542d880.1fc1b |128.0 |1.0 |13126.180309|
+|6542e76c.1fe63 |64.0 |2.0| 22983.408399|
+|6543018c.20227 |64.0 |2.0 |23075.070997|
+|65431bc7.20615 |256.0| 1.0| 2707.956093|
+|65431ef0.2073a |256.0 |1.0 |2781.880526|
+|6543221a.2085f |128.0| 2.0| 13000.099663|
+|654330e1.20b8f |128.0 |2.0 |13005.023144|
+
+
+
+Indeed, for a fixed amount of working memory, more parallelism leads to lower latency. But because of how we set up the experiment, more parallelism comes at the expense of working memory, so naively looking at only the 2 right-hand columns above would indeed make us think that more parallelism means *higher* latency!
diff --git a/docs/reference/SUMMARY.md b/docs/reference/SUMMARY.md
new file mode 100644
index 0000000..9669f85
--- /dev/null
+++ b/docs/reference/SUMMARY.md
@@ -0,0 +1,15 @@
+* src
+ * [sawmill](src/sawmill/index.md)
+ * aggimp
+ * [agg_funcs](src/sawmill/aggimp/agg_funcs.md)
+ * [imp_funcs](src/sawmill/aggimp/imp_funcs.md)
+ * [drain](src/sawmill/drain.md)
+ * [graph_renderer](src/sawmill/graph_renderer.md)
+ * [pickler](src/sawmill/pickler.md)
+ * [printer](src/sawmill/printer.md)
+ * [regression](src/sawmill/regression.md)
+ * [sawmill](src/sawmill/sawmill.md)
+ * [tag_utils](src/sawmill/tag_utils.md)
+ * variable_name
+ * [parsed_variable_name](src/sawmill/variable_name/parsed_variable_name.md)
+ * [prepared_variable_name](src/sawmill/variable_name/prepared_variable_name.md)
diff --git a/docs/reference/src/sawmill/aggimp/agg_funcs.md b/docs/reference/src/sawmill/aggimp/agg_funcs.md
new file mode 100644
index 0000000..70384ac
--- /dev/null
+++ b/docs/reference/src/sawmill/aggimp/agg_funcs.md
@@ -0,0 +1 @@
+::: src.sawmill.aggimp.agg_funcs
\ No newline at end of file
diff --git a/docs/reference/src/sawmill/aggimp/imp_funcs.md b/docs/reference/src/sawmill/aggimp/imp_funcs.md
new file mode 100644
index 0000000..0627a76
--- /dev/null
+++ b/docs/reference/src/sawmill/aggimp/imp_funcs.md
@@ -0,0 +1 @@
+::: src.sawmill.aggimp.imp_funcs
\ No newline at end of file
diff --git a/docs/reference/src/sawmill/drain.md b/docs/reference/src/sawmill/drain.md
new file mode 100644
index 0000000..e8e37d7
--- /dev/null
+++ b/docs/reference/src/sawmill/drain.md
@@ -0,0 +1 @@
+::: src.sawmill.drain
\ No newline at end of file
diff --git a/docs/reference/src/sawmill/graph_renderer.md b/docs/reference/src/sawmill/graph_renderer.md
new file mode 100644
index 0000000..25f0d1b
--- /dev/null
+++ b/docs/reference/src/sawmill/graph_renderer.md
@@ -0,0 +1 @@
+::: src.sawmill.graph_renderer
\ No newline at end of file
diff --git a/docs/reference/src/sawmill/index.md b/docs/reference/src/sawmill/index.md
new file mode 100644
index 0000000..6154652
--- /dev/null
+++ b/docs/reference/src/sawmill/index.md
@@ -0,0 +1 @@
+::: src.sawmill
\ No newline at end of file
diff --git a/docs/reference/src/sawmill/pickler.md b/docs/reference/src/sawmill/pickler.md
new file mode 100644
index 0000000..eefee84
--- /dev/null
+++ b/docs/reference/src/sawmill/pickler.md
@@ -0,0 +1 @@
+::: src.sawmill.pickler
\ No newline at end of file
diff --git a/docs/reference/src/sawmill/printer.md b/docs/reference/src/sawmill/printer.md
new file mode 100644
index 0000000..f371f73
--- /dev/null
+++ b/docs/reference/src/sawmill/printer.md
@@ -0,0 +1 @@
+::: src.sawmill.printer
\ No newline at end of file
diff --git a/docs/reference/src/sawmill/regression.md b/docs/reference/src/sawmill/regression.md
new file mode 100644
index 0000000..03668b9
--- /dev/null
+++ b/docs/reference/src/sawmill/regression.md
@@ -0,0 +1 @@
+::: src.sawmill.regression
\ No newline at end of file
diff --git a/docs/reference/src/sawmill/sawmill.md b/docs/reference/src/sawmill/sawmill.md
new file mode 100644
index 0000000..c7e1215
--- /dev/null
+++ b/docs/reference/src/sawmill/sawmill.md
@@ -0,0 +1 @@
+::: src.sawmill.sawmill
\ No newline at end of file
diff --git a/docs/reference/src/sawmill/tag_utils.md b/docs/reference/src/sawmill/tag_utils.md
new file mode 100644
index 0000000..0626ac5
--- /dev/null
+++ b/docs/reference/src/sawmill/tag_utils.md
@@ -0,0 +1 @@
+::: src.sawmill.tag_utils
\ No newline at end of file
diff --git a/docs/reference/src/sawmill/variable_name/parsed_variable_name.md b/docs/reference/src/sawmill/variable_name/parsed_variable_name.md
new file mode 100644
index 0000000..f0aaccc
--- /dev/null
+++ b/docs/reference/src/sawmill/variable_name/parsed_variable_name.md
@@ -0,0 +1 @@
+::: src.sawmill.variable_name.parsed_variable_name
\ No newline at end of file
diff --git a/docs/reference/src/sawmill/variable_name/prepared_variable_name.md b/docs/reference/src/sawmill/variable_name/prepared_variable_name.md
new file mode 100644
index 0000000..9e16ecf
--- /dev/null
+++ b/docs/reference/src/sawmill/variable_name/prepared_variable_name.md
@@ -0,0 +1 @@
+::: src.sawmill.variable_name.prepared_variable_name
\ No newline at end of file
diff --git a/evaluation/2.2-existing-causal-discovery/discovery.py b/evaluation/2.2-existing-causal-discovery/discovery.py
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index 0000000..61205b3
--- /dev/null
+++ b/evaluation/2.2-existing-causal-discovery/discovery.py
@@ -0,0 +1,284 @@
+import pandas as pd
+import sys
+
+sys.path.append("../..")
+import threading
+from causallearn.search.ConstraintBased.PC import pc
+from causallearn.search.ConstraintBased.FCI import fci
+from causallearn.search.FCMBased import lingam
+from causallearn.search.FCMBased.lingam.utils import make_dot
+from causallearn.search.HiddenCausal.GIN import GIN
+from causallearn.search.PermutationBased.GRaSP import grasp
+from causallearn.search.ScoreBased.GES import ges
+from causallearn.search.ScoreBased.ExactSearch import bic_exact_search
+from causallearn.graph.Endpoint import Endpoint
+import pickle
+from datetime import datetime
+import multiprocessing as mp
+from src.logos.graph_renderer import GraphRenderer
+import networkx as nx
+from src.logos.causal_discoverer import CausalDiscoverer
+
+
+TIMEOUT_SECONDS = 30 * 60
+
+PC_OPTIONS = [
+ "fisherz",
+ "mv_fisherz",
+ "mc_fisherz",
+ "kci",
+ "chisq",
+ "gsq",
+ "d_separation",
+]
+
+FCI_OPTIONS = ["fisherz", "kci", "chisq", "gsq"]
+
+LINGAM_OPTIONS = ['placeholder']
+
+GIN_OPTIONS = ["kci", "hsic"]
+
+GRASP_OPTIONS = [
+ "local_score_CV_general",
+ "local_score_marginal_general",
+ "local_score_CV_multi",
+ "local_score_marginal_multi",
+ "local_score_BIC",
+ "local_score_BDeu",
+]
+
+GES_OPTIONS = [
+ "local_score_CV_general",
+ "local_score_marginal_general",
+ "local_score_CV_multi",
+ "local_score_marginal_multi",
+ "local_score_BIC",
+ "local_score_BIC_from_cov",
+ "local_score_BDeu",
+]
+
+EXACT_SEARCH_OPTIONS = ["dp", "astar"]
+
+GPT_OPTIONS = ["gpt-4"]
+
+
+datasets = {
+ "PostgreSQL_filtered": {
+ "prepared_log": "~/causal-log/datasets/tpc-ds/parameter_sweep_1_filtered.log_prepared_log_sessionID_None.pkl",
+ "prepared_variables": "~/causal-log/datasets/tpc-ds/parameter_sweep_1_filtered.log_prepared_variables_sessionID_None.pkl",
+ },
+ "XYZ_10": {
+ "prepared_log": "~/causal-log/datasets/xyz_extended/log_2023-12-22_13:13:01.log_prepared_log_machine_None.pkl",
+ "prepared_variables": "~/causal-log/datasets/xyz_extended/log_2023-12-22_13:13:01.log_prepared_variables_machine_None.pkl",
+ },
+ "XYZ_100": {
+ "prepared_log": "~/causal-log/datasets/xyz_extended/log_2023-12-22_13:17:29.log_prepared_log_machine_None.pkl",
+ "prepared_variables": "~/causal-log/datasets/xyz_extended/log_2023-12-22_13:17:29.log_prepared_variables_machine_None.pkl",
+ },
+ "XYZ_1000": {
+ "prepared_log": "~/causal-log/datasets/xyz_extended/log_2023-12-22_13:27:02.log_prepared_log_machine_None.pkl",
+ "prepared_variables": "~/causal-log/datasets/xyz_extended/log_2023-12-22_13:27:02.log_prepared_variables_machine_None.pkl",
+ },
+ "OpenStack_Cinder": {
+ "prepared_log": "~/causal-log/datasets/OpenStack/Cinder/Cinder_combined_all.log_prepared_log_ID_None.pkl",
+ "prepared_variables": "~/causal-log/datasets/OpenStack/Cinder/Cinder_combined_all.log_prepared_variables_ID_None.pkl",
+ },
+ "OpenStack_Neutron": {
+ "prepared_log": "~/causal-log/datasets/OpenStack/Neutron/Neutron_combined_all.log_prepared_log_ID_None.pkl",
+ "prepared_variables": "~/causal-log/datasets/OpenStack/Neutron/Neutron_combined_all.log_prepared_variables_ID_None.pkl",
+ },
+ "OpenStack_Nova": {
+ "prepared_log": "~/causal-log/datasets/OpenStack/Nova/Nova_combined_all.log_prepared_log_ID_None.pkl",
+ "prepared_variables": "~/causal-log/datasets/OpenStack/Nova/Nova_combined_all.log_prepared_variables_ID_None.pkl",
+ },
+ "Proprietary": {
+ "prepared_log": "~/causal-log/datasets/proprietary_logs/proprietary_eval/proprietary_1000users_10faulty_20pctfailfaulty_10pctfailnormal.log_prepared_log_User_None.pkl",
+ "prepared_variables": "~/causal-log/datasets/proprietary_logs/proprietary_eval/proprietary_1000users_10faulty_20pctfailfaulty_10pctfailnormal.log_prepared_variables_User_None.pkl",
+ },
+}
+
+
+def run_method_with_timer(dataset_name, method_name, options, fres):
+ # Load the data
+ data_df = pd.read_pickle(datasets[dataset_name]["prepared_log"])
+ data = data_df.to_numpy().astype(float)
+ vars_df = pd.read_pickle(datasets[dataset_name]["prepared_variables"])
+ vars_df["Name_old"] = vars_df["Name"]
+ vars_df["Name"] = vars_df.index.to_list()
+
+ ##############################
+
+ # Define causal discovery methods
+
+ def general_graph_to_nx_digraph(gg_cg):
+ # Convert the graph to a NetworkX DiGraph
+ nx_cg = nx.DiGraph()
+ for edge in gg_cg.get_graph_edges():
+ node1 = edge.get_node1().get_name()
+ node2 = edge.get_node2().get_name()
+ points_left = edge.get_endpoint1() == Endpoint.ARROW and (
+ edge.get_endpoint2() == Endpoint.TAIL
+ or edge.get_endpoint2() == Endpoint.CIRCLE
+ )
+ points_right = edge.get_endpoint2() == Endpoint.ARROW and (
+ edge.get_endpoint1() == Endpoint.TAIL
+ or edge.get_endpoint1() == Endpoint.CIRCLE
+ )
+ if not points_left: # points right or undirected
+ nx_cg.add_edge(node1, node2)
+ if not points_right: # points left or undirected
+ nx_cg.add_edge(node2, node1)
+
+ return nx_cg
+
+ def run_pc(option):
+ cg = pc(data, indep_test=option, show_progress=False)
+ cg.to_nx_graph()
+ return cg.nx_graph
+
+ def run_fci(option):
+ cg, _ = fci(data, independence_test_method=option, show_progress=False)
+ nx_cg = general_graph_to_nx_digraph(cg)
+ return nx_cg
+
+ def run_lingam(option):
+ model = lingam.ICALiNGAM()
+ model.fit(data)
+ gv_cg = make_dot(model.adjacency_matrix_, labels=list(data_df.columns))
+
+ # Convert the Graphviz Digraph to NetworkX DiGraph
+ nx_cg = nx.DiGraph()
+ for line in gv_cg.body:
+ tokens = line.strip(";").split("->")
+ if len(tokens) == 2:
+ nx_cg.add_edge(tokens[0].strip(), tokens[1].strip())
+ tokens = tokens[0].split("--")
+ if len(tokens) == 2:
+ nx_cg.add_edge(tokens[0].strip(), tokens[1].strip())
+ nx_cg.add_edge(tokens[1].strip(), tokens[0].strip())
+ return nx_cg
+
+ def run_gin(option):
+ cg, _ = GIN.GIN(data, indep_test_method=option)
+ nx_cg = general_graph_to_nx_digraph(cg)
+ return nx_cg
+
+ def run_grasp(option):
+ cg = grasp(data, score_func=option)
+ nx_cg = general_graph_to_nx_digraph(cg)
+ return nx_cg
+
+ def run_ges(option):
+ record = ges(data, score_func=option)
+ nx_cg = general_graph_to_nx_digraph(record["G"])
+ return nx_cg
+
+ def run_exact_search(option):
+ matrix = bic_exact_search(data, search_method=option)
+ # Convert the matrix to a NetworkX DiGraph
+ # If matrix[i,j] == 1, then there is an edge from i to j
+ nx_cg = nx.DiGraph()
+ for i in range(matrix.shape[0]):
+ for j in range(matrix.shape[1]):
+ if matrix[i, j] == 1:
+ nx_cg.add_edge(i, j)
+ return nx_cg
+
+ def run_gpt(option):
+ vars_df["Name"] = vars_df["Name_old"]
+ nx_cg = CausalDiscoverer.gpt(data_df, model=option, vars_df=vars_df)
+ vars_df["Name"] = vars_df.index.to_list()
+ return nx_cg
+
+ # Run a function within a try except block
+ def run_safe(function, option):
+ try:
+ starttime = datetime.now()
+ nx_cg = function(option=option)
+
+ # Check if the graph is empty
+ if nx_cg.number_of_edges() == 0:
+ fres.write(
+ f"{dataset_name},{method_name},{option},empty,{(datetime.now() - starttime).seconds}\n"
+ )
+ fres.flush()
+ return
+
+ # Remove isolated nodes
+ nx_cg.remove_nodes_from(list(nx.isolates(nx_cg)))
+
+ print(f"{datetime.now()} Writing out {method_name} with {option}")
+ sys.stdout.flush()
+ fres.write(
+ f"{dataset_name},{method_name},{option},success,{(datetime.now() - starttime).seconds}\n"
+ )
+ fres.flush()
+
+ # Save the graph as png
+ GraphRenderer.save_graph(
+ nx_cg, vars_df, f"{dataset_name}_{method_name}_{option}.png"
+ )
+
+ # Write out graph as pickle
+ with open(f"{dataset_name}_{method_name}_{option}.pkl", "wb") as f:
+ pickle.dump(nx_cg, f)
+ except Exception as e:
+ print(f"{datetime.now()} Error running {function}: {e}")
+ sys.stdout.flush()
+ fres.write(f"{dataset_name},{method_name},{option},exception: {e},\n")
+ fres.flush()
+
+ ##############################
+
+ # Run the discovery
+
+ for option in options:
+ process = mp.Process(
+ target=run_safe,
+ kwargs={"function": locals()[f"run_{method_name}"], "option": option},
+ )
+ print(f"{datetime.now()} Running {method_name} with {option}")
+ sys.stdout.flush()
+
+ process.start()
+ process.join(TIMEOUT_SECONDS)
+ if process.is_alive():
+ print(f"{datetime.now()} Function timed out")
+ fres.write(
+ f"{dataset_name},{method_name},{option},timeout,{TIMEOUT_SECONDS}\n"
+ )
+ fres.flush()
+ process.terminate() # Terminate the process
+ process.join() # Clean up the process
+
+ print(f"{datetime.now()} Done running {method_name} with {option}")
+ sys.stdout.flush()
+
+
+def main():
+ # Open logging files
+ fout = open(f"discovery.log", "a")
+ sys.stdout = fout
+ sys.stderr = fout
+ fres = open(f"discovery-results.csv", "a")
+ fres.write(f"dataset_name,method_name,option,result,time\n")
+ fres.flush()
+
+ for dataset_name in datasets.keys():
+ print("\n\n========================================\n\n")
+ print(f"{datetime.now()} Starting {dataset_name}\n")
+ #run_method_with_timer(dataset_name, "pc", PC_OPTIONS, fres)
+ #run_method_with_timer(dataset_name, "fci", FCI_OPTIONS, fres)
+ #run_method_with_timer(dataset_name, "lingam", LINGAM_OPTIONS, fres)
+ #run_method_with_timer(dataset_name, "gin", GIN_OPTIONS, fres)
+ #run_method_with_timer(dataset_name, "grasp", GRASP_OPTIONS, fres)
+ #run_method_with_timer(dataset_name, "ges", GES_OPTIONS, fres)
+ #run_method_with_timer(dataset_name, "exact_search", EXACT_SEARCH_OPTIONS, fres)
+ run_method_with_timer(dataset_name, "gpt", GPT_OPTIONS, fres)
+
+ fout.close()
+ fres.close()
+
+
+if __name__ == "__main__":
+ main()
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diff --git a/evaluation/2.2-existing-causal-discovery/results/discovery-results.csv b/evaluation/2.2-existing-causal-discovery/results/discovery-results.csv
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index 0000000..66cfd31
--- /dev/null
+++ b/evaluation/2.2-existing-causal-discovery/results/discovery-results.csv
@@ -0,0 +1,301 @@
+dataset_name,method_name,option,result,time
+PostgreSQL,pc,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+PostgreSQL,pc,mv_fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+PostgreSQL,pc,mc_fisherz,exception: 'm',
+PostgreSQL,pc,kci,timeout,1800
+PostgreSQL,pc,chisq,success,1
+PostgreSQL,pc,gsq,empty,1
+PostgreSQL,pc,d_separation,exception: 'NoneType' object has no attribute 'is_directed',
+PostgreSQL,fci,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+PostgreSQL,fci,kci,timeout,1800
+PostgreSQL,fci,chisq,success,1
+PostgreSQL,fci,gsq,empty,2
+PostgreSQL,gin,kci,timeout,1800
+PostgreSQL,gin,hsic,timeout,1800
+PostgreSQL,grasp,local_score_CV_general,success,1545
+PostgreSQL,grasp,local_score_marginal_general,exception: Array must not contain infs or NaNs,
+PostgreSQL,grasp,local_score_CV_multi,exception: 147,
+PostgreSQL,grasp,local_score_marginal_multi,exception: 145,
+PostgreSQL,grasp,local_score_BIC,exception: Singular matrix,
+PostgreSQL,grasp,local_score_BDeu,success,183
+PostgreSQL,ges,local_score_CV_general,timeout,1800
+PostgreSQL,ges,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+PostgreSQL,ges,local_score_CV_multi,timeout,1800
+PostgreSQL,ges,local_score_marginal_multi,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+PostgreSQL,ges,local_score_BIC,exception: Singular matrix,
+PostgreSQL,ges,local_score_BIC_from_cov,exception: Singular matrix,
+PostgreSQL,ges,local_score_BDeu,success,1352
+PostgreSQL,exact_search,dp,exception: can only convert an array of size 1 to a Python scalar,
+PostgreSQL,exact_search,astar,exception: can only convert an array of size 1 to a Python scalar,
+XYZ_10,pc,fisherz,success,1
+XYZ_10,pc,mv_fisherz,success,3
+XYZ_10,pc,mc_fisherz,exception: 'm',
+XYZ_10,pc,kci,success,341
+XYZ_10,pc,chisq,success,10
+XYZ_10,pc,gsq,empty,1
+XYZ_10,pc,d_separation,exception: 'NoneType' object has no attribute 'is_directed',
+XYZ_10,fci,fisherz,success,1
+XYZ_10,fci,kci,success,314
+XYZ_10,fci,chisq,success,10
+XYZ_10,fci,gsq,empty,1
+XYZ_10,gin,kci,success,156
+XYZ_10,gin,hsic,success,197
+XYZ_10,grasp,local_score_CV_general,success,1729
+XYZ_10,grasp,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+XYZ_10,grasp,local_score_CV_multi,exception: 0,
+XYZ_10,grasp,local_score_marginal_multi,exception: 10,
+XYZ_10,grasp,local_score_BIC,success,1
+XYZ_10,grasp,local_score_BDeu,success,243
+XYZ_10,ges,local_score_CV_general,timeout,1800
+XYZ_10,ges,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+XYZ_10,ges,local_score_CV_multi,timeout,1800
+XYZ_10,ges,local_score_marginal_multi,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+XYZ_10,ges,local_score_BIC,success,54
+XYZ_10,ges,local_score_BIC_from_cov,success,57
+XYZ_10,ges,local_score_BDeu,success,428
+dataset_name,method_name,option,result,time
+dataset_name,method_name,option,result,time
+XYZ_100,pc,fisherz,success,95
+XYZ_100,pc,mv_fisherz,success,183
+XYZ_100,pc,mc_fisherz,exception: 'm',
+XYZ_100,pc,kci,timeout,1800
+XYZ_100,pc,chisq,empty,93
+XYZ_100,pc,gsq,empty,121
+XYZ_100,pc,d_separation,exception: 'NoneType' object has no attribute 'is_directed',
+XYZ_100,fci,fisherz,success,31
+XYZ_100,fci,kci,timeout,1800
+XYZ_100,fci,chisq,empty,95
+XYZ_100,fci,gsq,empty,122
+XYZ_100,gin,kci,timeout,1800
+XYZ_100,gin,hsic,timeout,1800
+XYZ_100,grasp,local_score_CV_general,timeout,1800
+XYZ_100,grasp,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+XYZ_100,grasp,local_score_CV_multi,exception: 55,
+XYZ_100,grasp,local_score_marginal_multi,exception: 79,
+XYZ_100,grasp,local_score_BIC,success,29
+XYZ_100,grasp,local_score_BDeu,timeout,1800
+XYZ_100,ges,local_score_CV_general,timeout,1800
+XYZ_100,ges,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+XYZ_100,ges,local_score_CV_multi,timeout,1800
+XYZ_100,ges,local_score_marginal_multi,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+XYZ_100,ges,local_score_BIC,success,1464
+XYZ_100,ges,local_score_BIC_from_cov,success,1466
+XYZ_100,ges,local_score_BDeu,timeout,1800
+XYZ_100,exact_search,dp,timeout,1800
+XYZ_100,exact_search,astar,timeout,1800
+XYZ_1000,pc,fisherz,timeout,1800
+XYZ_1000,pc,mv_fisherz,timeout,1800
+XYZ_1000,pc,mc_fisherz,exception: 'm',
+XYZ_1000,pc,kci,timeout,1800
+XYZ_1000,pc,chisq,timeout,1800
+XYZ_1000,pc,gsq,timeout,1800
+XYZ_1000,pc,d_separation,exception: 'NoneType' object has no attribute 'is_directed',
+XYZ_1000,fci,fisherz,timeout,1800
+XYZ_1000,fci,kci,timeout,1800
+XYZ_1000,fci,chisq,timeout,1800
+XYZ_1000,fci,gsq,timeout,1800
+XYZ_1000,gin,kci,timeout,1800
+XYZ_1000,gin,hsic,timeout,1800
+XYZ_1000,grasp,local_score_CV_general,timeout,1800
+XYZ_1000,grasp,local_score_marginal_general,exception: Array must not contain infs or NaNs,
+XYZ_1000,grasp,local_score_CV_multi,exception: 191,
+XYZ_1000,grasp,local_score_marginal_multi,exception: 66,
+XYZ_1000,grasp,local_score_BIC,exception: Singular matrix,
+XYZ_1000,grasp,local_score_BDeu,timeout,1800
+XYZ_1000,ges,local_score_CV_general,timeout,1800
+XYZ_1000,ges,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+XYZ_1000,ges,local_score_CV_multi,timeout,1800
+XYZ_1000,ges,local_score_marginal_multi,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+XYZ_1000,ges,local_score_BIC,timeout,1800
+XYZ_1000,ges,local_score_BIC_from_cov,timeout,1800
+XYZ_1000,ges,local_score_BDeu,timeout,1800
+XYZ_1000,exact_search,dp,timeout,1800
+XYZ_1000,exact_search,astar,timeout,1800
+OpenStack_Cinder,pc,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+OpenStack_Cinder,pc,mv_fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+OpenStack_Cinder,pc,mc_fisherz,exception: 'm',
+OpenStack_Cinder,pc,kci,timeout,1800
+dataset_name,method_name,option,result,time
+OpenStack_Cinder,pc,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+OpenStack_Cinder,pc,mv_fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+OpenStack_Cinder,pc,mc_fisherz,exception: 'm',
+OpenStack_Cinder,pc,kci,timeout,1800
+OpenStack_Cinder,pc,chisq,timeout,1800
+OpenStack_Cinder,pc,gsq,timeout,1800
+OpenStack_Cinder,pc,d_separation,exception: 'NoneType' object has no attribute 'is_directed',
+OpenStack_Cinder,fci,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+OpenStack_Cinder,fci,kci,timeout,1800
+OpenStack_Cinder,fci,chisq,timeout,1800
+OpenStack_Cinder,fci,gsq,timeout,1800
+OpenStack_Cinder,gin,kci,timeout,1800
+OpenStack_Cinder,gin,hsic,timeout,1800
+OpenStack_Cinder,grasp,local_score_CV_general,timeout,1800
+OpenStack_Cinder,grasp,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+OpenStack_Cinder,grasp,local_score_CV_multi,exception: 576,
+OpenStack_Cinder,grasp,local_score_marginal_multi,exception: 390,
+OpenStack_Cinder,grasp,local_score_BIC,exception: Singular matrix,
+OpenStack_Cinder,grasp,local_score_BDeu,timeout,1800
+OpenStack_Cinder,ges,local_score_CV_general,timeout,1800
+OpenStack_Cinder,ges,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+OpenStack_Cinder,ges,local_score_CV_multi,timeout,1800
+OpenStack_Cinder,ges,local_score_marginal_multi,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+OpenStack_Cinder,ges,local_score_BIC,exception: Singular matrix,
+OpenStack_Cinder,ges,local_score_BIC_from_cov,exception: Singular matrix,
+OpenStack_Cinder,ges,local_score_BDeu,timeout,1800
+OpenStack_Cinder,exact_search,dp,exception: can only convert an array of size 1 to a Python scalar,
+OpenStack_Cinder,exact_search,astar,exception: can only convert an array of size 1 to a Python scalar,
+OpenStack_Neutron,pc,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+OpenStack_Neutron,pc,mv_fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+OpenStack_Neutron,pc,mc_fisherz,exception: 'm',
+OpenStack_Neutron,pc,kci,timeout,1800
+OpenStack_Neutron,pc,chisq,timeout,1800
+OpenStack_Neutron,pc,gsq,timeout,1800
+OpenStack_Neutron,pc,d_separation,exception: 'NoneType' object has no attribute 'is_directed',
+OpenStack_Neutron,fci,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+OpenStack_Neutron,fci,kci,timeout,1800
+OpenStack_Neutron,fci,chisq,timeout,1800
+OpenStack_Neutron,fci,gsq,timeout,1800
+OpenStack_Neutron,gin,kci,timeout,1800
+OpenStack_Neutron,gin,hsic,timeout,1800
+OpenStack_Neutron,grasp,local_score_CV_general,timeout,1800
+OpenStack_Neutron,grasp,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+OpenStack_Neutron,grasp,local_score_CV_multi,exception: 256,
+OpenStack_Neutron,grasp,local_score_marginal_multi,exception: 191,
+OpenStack_Neutron,grasp,local_score_BIC,exception: Singular matrix,
+OpenStack_Neutron,grasp,local_score_BDeu,timeout,1800
+OpenStack_Neutron,ges,local_score_CV_general,timeout,1800
+OpenStack_Neutron,ges,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+OpenStack_Neutron,ges,local_score_CV_multi,timeout,1800
+OpenStack_Neutron,ges,local_score_marginal_multi,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+OpenStack_Neutron,ges,local_score_BIC,timeout,1800
+OpenStack_Neutron,ges,local_score_BIC_from_cov,timeout,1800
+OpenStack_Neutron,ges,local_score_BDeu,timeout,1800
+OpenStack_Neutron,exact_search,dp,exception: can only convert an array of size 1 to a Python scalar,
+OpenStack_Neutron,exact_search,astar,exception: can only convert an array of size 1 to a Python scalar,
+OpenStack_Nova,pc,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+OpenStack_Nova,pc,mv_fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+OpenStack_Nova,pc,mc_fisherz,exception: 'm',
+OpenStack_Nova,pc,kci,timeout,1800
+OpenStack_Nova,pc,chisq,timeout,1800
+OpenStack_Nova,pc,gsq,timeout,1800
+OpenStack_Nova,pc,d_separation,exception: 'NoneType' object has no attribute 'is_directed',
+OpenStack_Nova,fci,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+OpenStack_Nova,fci,kci,timeout,1800
+OpenStack_Nova,fci,chisq,timeout,1800
+OpenStack_Nova,fci,gsq,timeout,1800
+OpenStack_Nova,gin,kci,timeout,1800
+OpenStack_Nova,gin,hsic,timeout,1800
+OpenStack_Nova,grasp,local_score_CV_general,timeout,1800
+OpenStack_Nova,grasp,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+OpenStack_Nova,grasp,local_score_CV_multi,exception: 611,
+OpenStack_Nova,grasp,local_score_marginal_multi,exception: 590,
+OpenStack_Nova,grasp,local_score_BIC,exception: Singular matrix,
+OpenStack_Nova,grasp,local_score_BDeu,timeout,1800
+OpenStack_Nova,ges,local_score_CV_general,timeout,1800
+OpenStack_Nova,ges,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+OpenStack_Nova,ges,local_score_CV_multi,timeout,1800
+OpenStack_Nova,ges,local_score_marginal_multi,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+OpenStack_Nova,ges,local_score_BIC,exception: Singular matrix,
+OpenStack_Nova,ges,local_score_BIC_from_cov,exception: Singular matrix,
+OpenStack_Nova,ges,local_score_BDeu,timeout,1800
+OpenStack_Nova,exact_search,dp,exception: can only convert an array of size 1 to a Python scalar,
+OpenStack_Nova,exact_search,astar,exception: can only convert an array of size 1 to a Python scalar,
+Proprietary,pc,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+Proprietary,pc,mv_fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+Proprietary,pc,mc_fisherz,exception: 'm',
+Proprietary,pc,kci,timeout,1800
+Proprietary,pc,chisq,success,0
+Proprietary,pc,gsq,success,0
+Proprietary,pc,d_separation,exception: 'NoneType' object has no attribute 'is_directed',
+Proprietary,fci,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+Proprietary,fci,kci,timeout,1800
+Proprietary,fci,chisq,success,0
+Proprietary,fci,gsq,success,0
+Proprietary,gin,kci,timeout,1800
+Proprietary,gin,hsic,timeout,1800
+Proprietary,grasp,local_score_CV_general,timeout,1800
+Proprietary,grasp,local_score_marginal_general,exception: Array must not contain infs or NaNs,
+Proprietary,grasp,local_score_CV_multi,exception: 32,
+Proprietary,grasp,local_score_marginal_multi,exception: 17,
+Proprietary,grasp,local_score_BIC,exception: Singular matrix,
+Proprietary,grasp,local_score_BDeu,empty,19
+Proprietary,ges,local_score_CV_general,timeout,1800
+Proprietary,ges,local_score_marginal_general,exception: Array must not contain infs or NaNs,
+Proprietary,ges,local_score_CV_multi,timeout,1800
+Proprietary,ges,local_score_marginal_multi,exception: Array must not contain infs or NaNs,
+Proprietary,ges,local_score_BIC,exception: Singular matrix,
+Proprietary,ges,local_score_BIC_from_cov,exception: Singular matrix,
+Proprietary,ges,local_score_BDeu,empty,73
+Proprietary,exact_search,dp,exception: can only convert an array of size 1 to a Python scalar,
+Proprietary,exact_search,astar,exception: can only convert an array of size 1 to a Python scalar,
+dataset_name,method_name,option,result,time
+PostgreSQL,lingam,placeholder,exception: cost matrix is infeasible,
+XYZ_10,lingam,placeholder,success,0
+XYZ_100,lingam,placeholder,success,59
+XYZ_1000,lingam,placeholder,exception: index 1000 is out of bounds for axis 0 with size 1000,
+OpenStack_Cinder,lingam,placeholder,exception: index 581 is out of bounds for axis 0 with size 538,
+OpenStack_Neutron,lingam,placeholder,exception: index 429 is out of bounds for axis 0 with size 406,
+OpenStack_Nova,lingam,placeholder,exception: index 879 is out of bounds for axis 0 with size 878,
+Proprietary,lingam,placeholder,exception: cost matrix is infeasible,
+dataset_name,method_name,option,result,time
+XYZ_10,exact_search,dp,timeout,1800
+XYZ_10,exact_search,astar,timeout,1800
+dataset_name,method_name,option,result,time
+PostgreSQL_filtered,pc,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+PostgreSQL_filtered,pc,mv_fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+PostgreSQL_filtered,pc,mc_fisherz,exception: 'm',
+PostgreSQL_filtered,pc,kci,timeout,1800
+PostgreSQL_filtered,pc,chisq,success,1
+PostgreSQL_filtered,pc,gsq,empty,1
+PostgreSQL_filtered,pc,d_separation,exception: 'NoneType' object has no attribute 'is_directed',
+PostgreSQL_filtered,fci,fisherz,exception: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.,
+PostgreSQL_filtered,fci,kci,timeout,1800
+PostgreSQL_filtered,fci,chisq,success,1
+PostgreSQL_filtered,fci,gsq,empty,1
+PostgreSQL_filtered,lingam,placeholder,exception: cost matrix is infeasible,
+PostgreSQL_filtered,gin,kci,timeout,1800
+PostgreSQL_filtered,gin,hsic,timeout,1800
+PostgreSQL_filtered,grasp,local_score_CV_general,success,919
+PostgreSQL_filtered,grasp,local_score_marginal_general,exception: Array must not contain infs or NaNs,
+PostgreSQL_filtered,grasp,local_score_CV_multi,exception: 147,
+PostgreSQL_filtered,grasp,local_score_marginal_multi,exception: 122,
+PostgreSQL_filtered,grasp,local_score_BIC,exception: Singular matrix,
+PostgreSQL_filtered,grasp,local_score_BDeu,success,173
+PostgreSQL_filtered,ges,local_score_CV_general,timeout,1800
+PostgreSQL_filtered,ges,local_score_marginal_general,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+PostgreSQL_filtered,ges,local_score_CV_multi,timeout,1800
+PostgreSQL_filtered,ges,local_score_marginal_multi,exception: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.,
+PostgreSQL_filtered,ges,local_score_BIC,exception: Singular matrix,
+PostgreSQL_filtered,ges,local_score_BIC_from_cov,exception: Singular matrix,
+PostgreSQL_filtered,ges,local_score_BDeu,success,1141
+PostgreSQL_filtered,exact_search,dp,exception: can only convert an array of size 1 to a Python scalar,
+PostgreSQL_filtered,exact_search,astar,exception: can only convert an array of size 1 to a Python scalar,
+dataset_name,method_name,option,result,time
+PostgreSQL_filtered,lingam,placeholder,exception: cost matrix is infeasible,
+XYZ_10,lingam,placeholder,success,0
+XYZ_100,lingam,placeholder,success,64
+XYZ_1000,lingam,placeholder,exception: index 1000 is out of bounds for axis 0 with size 1000,
+dataset_name,method_name,option,result,time
+PostgreSQL_filtered,lingam,placeholder,exception: cost matrix is infeasible,
+XYZ_10,lingam,placeholder,success,0
+XYZ_100,lingam,placeholder,success,62
+XYZ_1000,lingam,placeholder,exception: index 1000 is out of bounds for axis 0 with size 1000,
+dataset_name,method_name,option,result,time
+PostgreSQL_filtered,lingam,placeholder,exception: cost matrix is infeasible,
+XYZ_10,lingam,placeholder,success,0
+XYZ_100,lingam,placeholder,success,55
+XYZ_1000,lingam,placeholder,exception: index 1000 is out of bounds for axis 0 with size 1000,
+OpenStack_Cinder,lingam,placeholder,exception: index 602 is out of bounds for axis 0 with size 538,
+OpenStack_Neutron,lingam,placeholder,exception: index 490 is out of bounds for axis 0 with size 406,
+OpenStack_Nova,lingam,placeholder,exception: index 900 is out of bounds for axis 0 with size 878,
+Proprietary,lingam,placeholder,exception: cost matrix is infeasible,
+dataset_name,method_name,option,result,time
+PostgreSQL_filtered,gpt,gpt-4,timeout,1800
+XYZ_10,gpt,gpt-4,timeout,1800
+XYZ_100,gpt,gpt-4,timeout,1800
+XYZ_1000,gpt,gpt-4,timeout,1800
+OpenStack_Cinder,gpt,gpt-4,timeout,1800
+OpenStack_Neutron,gpt,gpt-4,timeout,1800
+OpenStack_Nova,gpt,gpt-4,timeout,1800
+Proprietary,gpt,gpt-4,timeout,1800
+
diff --git a/evaluation/2.2-existing-causal-discovery/results/discovery.log b/evaluation/2.2-existing-causal-discovery/results/discovery.log
new file mode 100644
index 0000000..6d9e9cf
--- /dev/null
+++ b/evaluation/2.2-existing-causal-discovery/results/discovery.log
@@ -0,0 +1,1820 @@
+
+
+========================================
+
+
+2024-01-03 06:05:50.942151 Starting PostgreSQL
+
+2024-01-03 06:05:50.946116 Running pc with fisherz
+~/logs-venv/lib/python3.11/site-packages/causallearn/search/ConstraintBased/PC.py:36: UserWarning: The number of features is much larger than the sample size!
+ warnings.warn("The number of features is much larger than the sample size!")
+~/logs-venv/lib/python3.11/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide
+ c /= stddev[:, None]
+~/logs-venv/lib/python3.11/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide
+ c /= stddev[None, :]
+2024-01-03 06:05:51.071805 Error running | \n", + " | Name | \n", + "Tag | \n", + "Type | \n", + "IsUninteresting | \n", + "Occurrences | \n", + "Preceding 3 tokens | \n", + "Examples | \n", + "From regex | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Date | \n", + "Date | \n", + "num | \n", + "False | \n", + "78080 | \n", + "[] | \n", + "[2023-11-06, 2023-11-07, 2023-11-08, 2023-11-0... | \n", + "True | \n", + "
| 1 | \n", + "Time | \n", + "Time | \n", + "num | \n", + "False | \n", + "78080 | \n", + "[] | \n", + "[16:34:40.799, 16:34:40.810, 16:34:40.811, 16:... | \n", + "True | \n", + "
| 2 | \n", + "sessionID | \n", + "sessionID | \n", + "str | \n", + "False | \n", + "78080 | \n", + "[] | \n", + "[65495bf0.179a, 65496dbc.231b, 65497f35.2c04, ... | \n", + "True | \n", + "
| 3 | \n", + "tID | \n", + "tID | \n", + "str | \n", + "True | \n", + "78080 | \n", + "[] | \n", + "[, 3/2461, 3/2462, 3/0, 3/2463] | \n", + "True | \n", + "
| 4 | \n", + "aea309d3_23 | \n", + "port | \n", + "num | \n", + "False | \n", + "128 | \n", + "[127.0.0.1, port, =] | \n", + "[52708, 39780, 50446, 45248, 56574] | \n", + "False | \n", + "