diff --git a/dev-notebooks/HipsterHailvsVS_covariates.ipynb b/dev-notebooks/HipsterHailvsVS_covariates.ipynb
index 746c43d6..0e6d008e 100644
--- a/dev-notebooks/HipsterHailvsVS_covariates.ipynb
+++ b/dev-notebooks/HipsterHailvsVS_covariates.ipynb
@@ -21,26 +21,21 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "using variant-spark jar at '/Users/reg032/workspace/VariantSpark/target/variant-spark_2.11-0.5.0-a0-dev0-all.jar'\n",
- "22/05/09 14:34:16 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
+ "using variant-spark jar at '/Users/reg032/workspace/VariantSpark/target/variant-spark_2.12-0.5.3-SNAPSHOT-all.jar'\n",
+ "23/01/24 10:36:42 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
"Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n",
"Setting default log level to \"WARN\".\n",
"To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
- "22/05/09 14:34:16 WARN Hail: This Hail JAR was compiled for Spark 3.1.1, running with Spark 3.1.2.\n",
+ "23/01/24 10:36:43 WARN Hail: This Hail JAR was compiled for Spark 3.1.1, running with Spark 3.1.2.\n",
" Compatibility is not guaranteed.\n",
- "22/05/09 14:34:17 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n",
- "22/05/09 14:34:17 WARN Utils: Service 'SparkUI' could not bind on port 4041. Attempting port 4042.\n",
- "22/05/09 14:34:17 WARN Utils: Service 'SparkUI' could not bind on port 4042. Attempting port 4043.\n",
- "22/05/09 14:34:17 WARN Utils: Service 'SparkUI' could not bind on port 4043. Attempting port 4044.\n",
- "22/05/09 14:34:17 WARN Utils: Service 'SparkUI' could not bind on port 4044. Attempting port 4045.\n",
"Running on Apache Spark version 3.1.2\n",
- "SparkUI available at http://192.168.86.23:4045\n",
+ "SparkUI available at http://192.168.86.30:4040\n",
"Welcome to\n",
" __ __ <>__\n",
" / /_/ /__ __/ /\n",
" / __ / _ `/ / /\n",
" /_/ /_/\\_,_/_/_/ version 0.2.74-0c3a74d12093\n",
- "LOGGING: writing to /Users/reg032/workspace/hail-20220509-1434-0.2.74-0c3a74d12093.log\n"
+ "LOGGING: writing to /Users/reg032/workspace/VariantSpark/dev-notebooks/hail-20230124-1036-0.2.74-0c3a74d12093.log\n"
]
}
],
@@ -402,7 +397,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-05-09 14:34:22 Hail: INFO: Reading table without type imputation\n",
+ "2023-01-24 10:36:49 Hail: INFO: Reading table without type imputation\n",
" Loading field 'samples' as type str (not specified)\n",
" Loading field 'score' as type float64 (user-supplied)\n",
" Loading field 'label' as type float64 (user-supplied)\n",
@@ -501,7 +496,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-05-09 14:34:25 Hail: INFO: Coerced almost-sorted dataset (0 + 1) / 1]\n",
+ "2023-01-24 10:36:52 Hail: INFO: Coerced almost-sorted dataset (0 + 1) / 1]\n",
"[Stage 1:> (0 + 1) / 1]\r"
]
},
@@ -536,8 +531,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-05-09 14:34:29 Hail: INFO: Coerced almost-sorted dataset\n",
- "2022-05-09 14:34:32 Hail: INFO: logistic_regression_rows: running score on 2504 samples for response variable y,\n",
+ "2023-01-24 10:36:56 Hail: INFO: Coerced almost-sorted dataset\n",
+ "2023-01-24 10:36:58 Hail: INFO: logistic_regression_rows: running score on 2504 samples for response variable y,\n",
" with input variable x, and 5 additional covariates...\n"
]
}
@@ -607,7 +602,7 @@
"\n",
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+ " <div class=\"bk-root\" id=\"bff7ed2c-a225-4e4d-83de-1b8dedb75e2c\" data-root-id=\"1003\"></div>\n"
]
},
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@@ -619,8 +614,8 @@
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- " var render_items = [{\"docid\":\"ac18815b-0eb9-4df0-894c-610631249b9f\",\"roots\":{\"1003\":\"0fd03b49-4886-4f4e-9d59-5213c8c2bbb3\"}}];\n",
+ " var docs_json = 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Application\",\"version\":\"1.4.0\"}};\n",
+ " var render_items = [{\"docid\":\"1e5fd8a0-a26b-471d-8741-0b96d9e3789d\",\"roots\":{\"1003\":\"bff7ed2c-a225-4e4d-83de-1b8dedb75e2c\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
@@ -681,8 +676,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-05-09 14:35:04 Hail: INFO: Coerced almost-sorted dataset\n",
- "[Stage 696:> (0 + 8) / 9]\r"
+ "2023-01-24 10:37:29 Hail: INFO: Coerced almost-sorted dataset\n",
+ "[Stage 634:==================================================> (8 + 1) / 9]\r"
]
}
],
@@ -709,23 +704,23 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "0.18130990415335463\n"
+ "0.1869009584664537\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-05-09 14:38:43 Hail: INFO: Coerced sorted dataset\n"
+ "2023-01-24 10:42:05 Hail: INFO: Coerced sorted dataset\n"
]
},
{
"data": {
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- "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2:109511463</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">["G","A"]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.13e-01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">24</td></tr>\n",
+ "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2:109511398</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">["G","A"]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.62e-03</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1</td></tr>\n",
+ "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2:109511454</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">["C","A"]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
+ "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2:109511463</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">["G","A"]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">6.66e-02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">18</td></tr>\n",
"</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 3 rows</p>\n"
],
"text/plain": [
@@ -734,9 +729,9 @@
"+---------------+------------+------------+------------+\n",
"| locus<GRCh37> | array<str> | float64 | int64 |\n",
"+---------------+------------+------------+------------+\n",
- "| 2:109511398 | [\"G\",\"A\"] | 0.00e+00 | 0 |\n",
- "| 2:109511454 | [\"C\",\"A\"] | 1.84e-02 | 4 |\n",
- "| 2:109511463 | [\"G\",\"A\"] | 1.13e-01 | 24 |\n",
+ "| 2:109511398 | [\"G\",\"A\"] | 5.62e-03 | 1 |\n",
+ "| 2:109511454 | [\"C\",\"A\"] | 0.00e+00 | 0 |\n",
+ "| 2:109511463 | [\"G\",\"A\"] | 6.66e-02 | 18 |\n",
"+---------------+------------+------------+------------+\n",
"showing top 3 rows"
]
@@ -767,18 +762,18 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-05-09 14:38:44 Hail: INFO: Coerced sorted dataset\n",
- "2022-05-09 14:38:44 Hail: INFO: Coerced dataset with out-of-order partitions.\n"
+ "2023-01-24 10:42:06 Hail: INFO: Coerced sorted dataset\n",
+ "2023-01-24 10:42:06 Hail: INFO: Coerced dataset with out-of-order partitions.\n"
]
},
{
"data": {
"text/html": [
"<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">covariate</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">importance</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">splitCount</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">str</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int64</td></tr>\n",
- "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">"PC0"</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.53e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">471</td></tr>\n",
- "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">"PC1"</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.52e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">479</td></tr>\n",
- "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">"PC2"</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.57e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">464</td></tr>\n",
- "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">"age"</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.55e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">473</td></tr>\n",
+ "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">"PC0"</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.53e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">486</td></tr>\n",
+ "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">"PC1"</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.11e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">433</td></tr>\n",
+ "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">"PC2"</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.09e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">548</td></tr>\n",
+ "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">"age"</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.48e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">473</td></tr>\n",
"</tbody></table>"
],
"text/plain": [
@@ -787,10 +782,10 @@
"+-----------+------------+------------+\n",
"| str | float64 | int64 |\n",
"+-----------+------------+------------+\n",
- "| \"PC0\" | 2.53e+00 | 471 |\n",
- "| \"PC1\" | 2.52e+00 | 479 |\n",
- "| \"PC2\" | 2.57e+00 | 464 |\n",
- "| \"age\" | 2.55e+00 | 473 |\n",
+ "| \"PC0\" | 2.53e+00 | 486 |\n",
+ "| \"PC1\" | 2.11e+00 | 433 |\n",
+ "| \"PC2\" | 3.09e+00 | 548 |\n",
+ "| \"age\" | 2.48e+00 | 473 |\n",
"+-----------+------------+------------+"
]
},
@@ -819,9 +814,9 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-05-09 14:38:44 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
- " 'alleles' -> 'alleles_1'\n",
- " 'locus' -> 'locus_1'\n"
+ "2023-01-24 10:42:06 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+ " 'locus' -> 'locus_1'\n",
+ " 'alleles' -> 'alleles_1'\n"
]
}
],
@@ -839,7 +834,7 @@
"output_type": "stream",
"text": [
"\r",
- "[Stage 746:> (0 + 1) / 1]\r"
+ "[Stage 760:> (0 + 1) / 1]\r"
]
},
{
@@ -851,7 +846,7 @@
"\n",
"\n",
"\n",
- " <div class=\"bk-root\" id=\"8b3cb01c-4499-4681-9d01-af20d9f8f6df\" data-root-id=\"1161\"></div>\n"
+ " <div class=\"bk-root\" id=\"23eab958-44f7-4c01-85e1-2e5542853a6a\" data-root-id=\"1161\"></div>\n"
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},
"metadata": {},
@@ -863,8 +858,8 @@
"(function(root) {\n",
" function embed_document(root) {\n",
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+ " var render_items = [{\"docid\":\"9c15035c-fd2c-440f-8940-2bd81f477efd\",\"roots\":{\"1161\":\"23eab958-44f7-4c01-85e1-2e5542853a6a\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
@@ -933,7 +928,7 @@
"\n",
"\n",
"\n",
- " <div class=\"bk-root\" id=\"28a45c93-2f53-4c0d-a6d2-4156b2d77c12\" data-root-id=\"1326\"></div>\n"
+ " <div class=\"bk-root\" id=\"9e4e5efe-13de-405c-9257-5fac9b77d3d0\" data-root-id=\"1326\"></div>\n"
]
},
"metadata": {},
@@ -945,8 +940,8 @@
"(function(root) {\n",
" function embed_document(root) {\n",
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\",\"dtype\":\"float64\",\"shape\":[561]}},\"selected\":{\"id\":\"1391\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1390\",\"type\":\"UnionRenderers\"}},\"id\":\"1361\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1334\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null},\"id\":\"1328\",\"type\":\"DataRange1d\"},{\"attributes\":{\"axis_label\":\"gini 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- " var render_items = [{\"docid\":\"a7e48548-75db-40d9-b01b-b5a7df3b244e\",\"roots\":{\"1326\":\"28a45c93-2f53-4c0d-a6d2-4156b2d77c12\"}}];\n",
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\",\"dtype\":\"float64\",\"shape\":[526]}},\"selected\":{\"id\":\"1391\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1392\",\"type\":\"UnionRenderers\"}},\"id\":\"1361\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1346\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1351\",\"type\":\"HelpTool\"}],\"root_ids\":[\"1326\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
+ " var render_items = [{\"docid\":\"1b079820-2e46-4bb5-b7f1-bb93e264331d\",\"roots\":{\"1326\":\"9e4e5efe-13de-405c-9257-5fac9b77d3d0\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
@@ -994,6 +989,15 @@
"source": [
"_Fig 3: Compare gini importance vs logistic regresion p-values._"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hl.stop()"
+ ]
}
],
"metadata": {
diff --git a/examples/compute_local_fdr.ipynb b/examples/compute_local_fdr.ipynb
index d46a97ab..54ad504c 100644
--- a/examples/compute_local_fdr.ipynb
+++ b/examples/compute_local_fdr.ipynb
@@ -40,21 +40,21 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "using variant-spark jar at '/Users/szu004/dev/VariantSpark/target/variant-spark_2.11-0.5.0-a0-dev2-SNAPSHOT-all.jar'\n",
- "22/07/06 12:34:55 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
+ "using variant-spark jar at '/Users/reg032/workspace/VariantSpark/target/variant-spark_2.12-0.5.3-SNAPSHOT-all.jar'\n",
+ "23/08/01 15:18:29 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
"Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n",
"Setting default log level to \"WARN\".\n",
"To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
- "22/07/06 12:34:55 WARN Hail: This Hail JAR was compiled for Spark 3.1.1, running with Spark 3.1.2.\n",
+ "23/08/01 15:18:29 WARN Hail: This Hail JAR was compiled for Spark 3.1.1, running with Spark 3.1.2.\n",
" Compatibility is not guaranteed.\n",
"Running on Apache Spark version 3.1.2\n",
- "SparkUI available at http://192.168.0.195:4040\n",
+ "SparkUI available at http://192.168.86.121:4040\n",
"Welcome to\n",
" __ __ <>__\n",
" / /_/ /__ __/ /\n",
" / __ / _ `/ / /\n",
" /_/ /_/\\_,_/_/_/ version 0.2.74-0c3a74d12093\n",
- "LOGGING: writing to /Users/szu004/dev/VariantSpark/examples/hail-20220706-1234-0.2.74-0c3a74d12093.log\n"
+ "LOGGING: writing to /Users/reg032/workspace/VariantSpark/examples/hail-20230801-1518-0.2.74-0c3a74d12093.log\n"
]
}
],
@@ -101,10 +101,10 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-07-06 12:34:58 Hail: WARN: Name collision: field 'sample' already in object dict. \n",
+ "2023-08-01 15:18:34 Hail: WARN: Name collision: field 'sample' already in object dict. \n",
" This field must be referenced with __getitem__ syntax: obj['sample']\n",
- "2022-07-06 12:34:58 Hail: INFO: Reading table to impute column types\n",
- "2022-07-06 12:35:00 Hail: INFO: Finished type imputation\n",
+ "2023-08-01 15:18:34 Hail: INFO: Reading table to impute column types\n",
+ "2023-08-01 15:18:39 Hail: INFO: Finished type imputation (0 + 1) / 1]\n",
" Loading field 'sample' as type str (imputed)\n",
" Loading field 'x22_16050408' as type int32 (imputed)\n",
" Loading field 'x22_16050612' as type str (imputed)\n",
@@ -141,10 +141,10 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-07-06 12:35:00 Hail: WARN: cols(): Resulting column table is sorted by 'col_key'.\n",
+ "2023-08-01 15:18:39 Hail: WARN: cols(): Resulting column table is sorted by 'col_key'.\n",
" To preserve matrix table column order, first unkey columns with 'key_cols_by()'\n",
- "2022-07-06 12:35:00 Hail: INFO: Coerced almost-sorted dataset\n",
- "2022-07-06 12:35:01 Hail: INFO: Coerced sorted dataset\n"
+ "2023-08-01 15:18:40 Hail: INFO: Coerced almost-sorted dataset\n",
+ "2023-08-01 15:18:41 Hail: INFO: Coerced sorted dataset\n"
]
},
{
@@ -226,8 +226,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-07-06 12:35:01 Hail: INFO: Coerced almost-sorted dataset\n",
- "[Stage 9:=====================================================> (16 + 1) / 17]\r"
+ "2023-08-01 15:18:43 Hail: INFO: Coerced almost-sorted dataset\n",
+ "[Stage 9:====================================================> (8 + 1) / 9]\r"
]
}
],
@@ -264,7 +264,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-07-06 12:35:11 Hail: INFO: Coerced sorted dataset\n"
+ "2023-08-01 15:19:00 Hail: INFO: Coerced sorted dataset\n"
]
},
{
@@ -331,7 +331,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-07-06 12:35:12 Hail: INFO: Coerced sorted dataset\n"
+ "2023-08-01 15:19:01 Hail: INFO: Coerced sorted dataset\n",
+ "[Stage 209:> (0 + 1) / 1]\r"
]
}
],
@@ -354,15 +355,19 @@
"metadata": {},
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+ "ename": "TypeError",
+ "evalue": "plot_log_densities() got an unexpected keyword argument 'find_automatic_best'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/var/folders/c5/g6ky3r7d2qjc07pmv0my66k80000gp/T/ipykernel_12045/3753571859.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'constrained'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfdrCalc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_log_densities\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_split_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_split_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfind_automatic_best\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mTypeError\u001b[0m: plot_log_densities() got an unexpected keyword argument 'find_automatic_best'"
]
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
@@ -389,23 +394,10 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"id": "c95c8a44",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x360 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"fig, ax2 = plt.subplots(figsize=(10, 5), layout='constrained')\n",
"fdrCalc.plot_log_hist(ax2, split_count=2)\n",
@@ -422,35 +414,10 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"id": "771a23e0",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "( variant_id logImportance pvalue is_significant\n",
- " 0 22_17705196_C_G -9.685114 1.000000e+00 False\n",
- " 1 22_17685358_C_T -9.134216 1.000000e+00 False\n",
- " 2 22_17774329_TC_T -7.530889 9.999952e-01 False\n",
- " 3 22_17763349_T_A -7.214255 9.999780e-01 False\n",
- " 4 22_17691887_C_T -6.603883 9.996942e-01 False\n",
- " .. ... ... ... ...\n",
- " 682 22_16052838_T_A 2.698982 0.000000e+00 True\n",
- " 683 22_16053509_A_G 2.703196 2.220446e-16 True\n",
- " 684 22_16051480_T_C 2.732347 2.220446e-16 True\n",
- " 685 22_16050678_C_T 3.224218 1.110223e-16 True\n",
- " 686 22_16050408_T_C 3.387460 1.110223e-16 True\n",
- " \n",
- " [687 rows x 4 columns],\n",
- " 0.000997620934933333)"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"pvalsDF, fdr = fdrCalc.compute_fdr(countThreshold = 2, local_fdr_cutoff = 0.05)\n",
"pvalsDF, fdr"
@@ -466,23 +433,10 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"id": "c854b79d",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x360 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"fig, ax3 = plt.subplots(figsize=(10, 5), layout='constrained')\n",
"fdrCalc.plot(ax3)\n",
@@ -491,7 +445,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 10,
"id": "fa2e6e96",
"metadata": {},
"outputs": [],
diff --git a/python/varspark/hail/lfdrvs.py b/python/varspark/hail/lfdrvs.py
index ccfdc685..8b8d5c69 100644
--- a/python/varspark/hail/lfdrvs.py
+++ b/python/varspark/hail/lfdrvs.py
@@ -40,13 +40,12 @@ def from_imp_table(cls, impTable):
return LocalFdrVs.from_imp_df(impTable.to_spark(flatten=False).toPandas())
def plot_log_densities(self, ax, min_split_count=1, max_split_count=6, palette='Set1',
- find_automatic_best=False, xLabel='log(importance)', yLabel='density'):
+ xLabel='log(importance)', yLabel='density'):
"""
Plotting the log densities to visually identify the unimodal distributions.
:param ax: Matplotlib axis as a canvas for this plot.
:param min_split_count: n>=1, from which the split count plotting starts.
:param max_split_count: when to stop the split count filtering.
- :param find_automatic_best: The user may let the computer highlight the potential best option.
:param palette: Matplotlib color palette used for the plotting.
:param xLabel: Label on the x-axis of the plot.
:param yLabel: Label on the y-axis of the plot.
@@ -65,14 +64,7 @@ def plot_log_densities(self, ax, min_split_count=1, max_split_count=6, palette='
sns.kdeplot(df.logImportance[df.splitCount >= i],
ax=ax, c=c, bw_adjust=0.5) #bw low show sharper distributions
- if find_automatic_best:
- potential_best = self.find_split_count_th( min_split_count, max_split_count)
- sns.kdeplot(df.logImportance[df.splitCount >= potential_best],
- ax = ax, c=colors[potential_best-1], bw_adjust=0.5, lw=8, linestyle=':')
- best_split = [str(x) if x != potential_best else str(x)+'*' for x in range(
- min_split_count, max_split_count+1)]
- else:
- best_split = list(range(min_split_count, max_split_count+1))
+ best_split = list(range(min_split_count, max_split_count+1))
ax.legend(title='Minimum split counts in distribution')
ax.legend(labels=best_split, bbox_to_anchor=(1,1))
diff --git a/src/main/scala/au/csiro/variantspark/algo/DecisionTree.scala b/src/main/scala/au/csiro/variantspark/algo/DecisionTree.scala
index 573655c2..8a5907a3 100644
--- a/src/main/scala/au/csiro/variantspark/algo/DecisionTree.scala
+++ b/src/main/scala/au/csiro/variantspark/algo/DecisionTree.scala
@@ -275,7 +275,7 @@ case class StdVariableSplitter(labels: Array[Int], mTryFraction: Double = 1.0,
// for impurity)
// This should obtain a statefule and somehow compartmenalised impurity calculator
// (Try trhead local perhaps here, but creating a new one (per partition) also fits the bill)
- new DefStatefullIndexedSpliterFactory(GiniImpurity, labels, nCategories)
+ new DefStatefulIndexedSplitterFactory(GiniImpurity, labels, nCategories)
}
/** Returns the result of a split based on a variable
@@ -384,7 +384,7 @@ case class AirVariableSplitter(labels: Array[Int], permutationOrder: Array[Int],
// used for impurity)
// This should obtain a statefule and somehow compartmenalised impurity calculator
// (Try trhead local perhaps here, but creating a new one (per partition) also fits the bill)
- new DefStatefullIndexedSpliterFactory(GiniImpurity, labels, nCategories)
+ new DefStatefulIndexedSplitterFactory(GiniImpurity, labels, nCategories)
}
/** Returns the result of a split based on a variable
@@ -469,7 +469,7 @@ object DecisionTree extends Logging with Prof {
// format: off
indexedData
.mapPartitions(it => br_splitter.value.splitSubsets(it, br_subsets.value,
- br_bestSplits.value))
+ br_bestSplits.value))
.collectAsMap()
// format: on
}
diff --git a/src/main/scala/au/csiro/variantspark/algo/GiniImpurity.scala b/src/main/scala/au/csiro/variantspark/algo/GiniImpurity.scala
index b32e8303..9b091c29 100644
--- a/src/main/scala/au/csiro/variantspark/algo/GiniImpurity.scala
+++ b/src/main/scala/au/csiro/variantspark/algo/GiniImpurity.scala
@@ -5,7 +5,7 @@ import au.csiro.variantspark.algo.impurity.GiniImpurityAggregator
/**
* Gini impurity measure
*/
-case object GiniImpurity extends ClassficationImpurity {
+case object GiniImpurity extends ClassificationImpurity {
def createAggregator(nCategories: Int): ClassificationImpurityAggregator =
new GiniImpurityAggregator(nCategories)
}
diff --git a/src/main/scala/au/csiro/variantspark/algo/Impurity.scala b/src/main/scala/au/csiro/variantspark/algo/Impurity.scala
index f8ac3fdf..a2e370b6 100644
--- a/src/main/scala/au/csiro/variantspark/algo/Impurity.scala
+++ b/src/main/scala/au/csiro/variantspark/algo/Impurity.scala
@@ -28,8 +28,8 @@ trait ImpurityAggregator {
}
/**
- * Mutable class that encapsulates classification impirity calculation.
- * The state is modified by adding or removing speficic labels.
+ * Mutable class that encapsulates classification impurity calculation.
+ * The state is modified by adding or removing specific labels.
*/
trait ClassificationImpurityAggregator extends ImpurityAggregator {
def addLabel(label: Int)
@@ -37,28 +37,43 @@ trait ClassificationImpurityAggregator extends ImpurityAggregator {
}
/**
- * Mutatabe class that encapsulates regression impority calculation.
- * The stat is modified by adding or removing continous values.
+ * Mutable class that encapsulates regression impurity calculation.
+ * The stat is modified by adding or removing continuous values.
*/
trait RegressionImpurityAggregator extends ImpurityAggregator {
+
def addValue(value: Double)
def subValue(value: Double)
}
/**
- * Base trait for representing impurituy measure
+ * Base trait for representing impurity measure
*/
trait Impurity
/**
* Base trait for representing classification impurity measures.
*/
-trait ClassficationImpurity extends Impurity {
+trait ClassificationImpurity extends Impurity {
/**
* Creates an aggregator for this impurity.
*
- * @param nCategories the number of categories (lables) in the response variable.
+ * @param nCategories the number of categories (labels) in the response variable.
*/
def createAggregator(nCategories: Int): ClassificationImpurityAggregator
}
+
+/**
+ * Base trait for representing regression impurity measures.
+ */
+trait RegressionImpurity extends Impurity {
+
+ /**
+ * Creates an aggregator for this impurity.
+ *
+ */
+ def createAggregator(): RegressionImpurityAggregator
+}
+
+
diff --git a/src/main/scala/au/csiro/variantspark/algo/PredictiveModel.scala b/src/main/scala/au/csiro/variantspark/algo/PredictiveModel.scala
index 7bf472c7..337d1ce1 100644
--- a/src/main/scala/au/csiro/variantspark/algo/PredictiveModel.scala
+++ b/src/main/scala/au/csiro/variantspark/algo/PredictiveModel.scala
@@ -16,3 +16,17 @@ trait PredictiveModelWithImportance extends PredictiveModel {
def variableImportance(): Map[Long, Double] = variableImportanceAsFastMap.asScala
def variableSplitCount(): Map[Long, Long] = variableSplitCountAsFastMap.asScala
}
+
+
+/** REGRESSION */
+trait RegressionPredictiveModel {
+ def predict(data: RDD[(Feature, Long)]): Array[Double]
+ def printout()
+}
+
+trait RegressionPredictiveModelWithImportance extends RegressionPredictiveModel {
+ def variableImportanceAsFastMap: Long2DoubleOpenHashMap
+ def variableSplitCountAsFastMap: Long2LongOpenHashMap
+ def variableImportance(): Map[Long, Double] = variableImportanceAsFastMap.asScala
+ def variableSplitCount(): Map[Long, Long] = variableSplitCountAsFastMap.asScala
+}
diff --git a/src/main/scala/au/csiro/variantspark/algo/RegressionDecisionTree.scala b/src/main/scala/au/csiro/variantspark/algo/RegressionDecisionTree.scala
new file mode 100644
index 00000000..cb4c3db4
--- /dev/null
+++ b/src/main/scala/au/csiro/variantspark/algo/RegressionDecisionTree.scala
@@ -0,0 +1,941 @@
+package au.csiro.variantspark.algo
+
+import au.csiro.pbdava.ssparkle.common.utils.FastUtilConversions._
+import au.csiro.pbdava.ssparkle.common.utils.{Logging, Prof}
+import au.csiro.pbdava.ssparkle.spark.SparkUtils._
+import au.csiro.variantspark.data.{DataBuilder, DataLike, Feature, StdFeature, VariableType}
+import au.csiro.variantspark.metrics.Gini
+import au.csiro.variantspark.utils.IndexedRDDFunction._
+import au.csiro.variantspark.utils._
+import it.unimi.dsi.fastutil.longs.{Long2DoubleOpenHashMap, Long2LongOpenHashMap}
+import it.unimi.dsi.util.XorShift1024StarRandomGenerator
+import org.apache.commons.lang3.builder.ToStringBuilder
+import org.apache.commons.math3.random.RandomGenerator
+import org.apache.commons.math3.util.MathArrays
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.RDD.rddToPairRDDFunctions
+
+/** Allows for a general description of the construct
+ *
+ * Specify the 'indices', 'impurtity', and 'meanLabel' these values will not be visible
+ * outside the class
+ *
+ * {{{
+ * val subInfo = RegressionSubsetInfo(indices, impurtity, meanLabel)
+ * val subInfoAlt = RegressionSubsetInfo(indices, impurity, labels, nLabels)
+ * }}}
+ *
+ * @constructor creates value based on the indices, impurity, and meanLabel
+ * @param indices: input an array of integers representing the indices of the values
+ * @param impurity: input the value of impurity of the data construct
+ * @param meanLabel: input mean value of all labels in the class
+ */
+case class RegressionSubsetInfo(indices: Array[Int], impurity: Double, meanLabel: Double) {
+
+ // val meanLabel: Double = meanLabel
+
+ /** An alternative constructor for the RegressionSubsetInfo class, use this if the meanLabel
+ * has not already been defined
+ *
+ * Specify the 'indices', 'impurity', 'labels', and 'nLabels'
+ *
+ * {{{
+ * val subInfo = RegressionSubsetInfo(indices, impurity, labels, nLables)
+ * }}}
+ *
+ * @param indices: input an array of integers that contains the indices required
+ * @param impurity: a value based on the gini impurity of the dataset sent in
+ * @param labels: in put an array of integers that contains the labels of the values for each row
+ *
+ */
+ def this(indices: Array[Int], impurity: Double, labels: Array[Double]) {
+ this(indices, impurity, labels.sum / labels.size)
+ }
+
+ def length: Int = indices.length
+ override def toString: String = s"RegressionSubsetInfo(${indices.toList},${impurity})"
+}
+
+/** Class utilized to give an insight into the split data
+ *
+ * Specify the 'variableIndex', 'splitPoint', 'gini', 'leftGini', and 'rightGini'
+ *
+ * @constructor creates information about the split that occured on a specifc variable
+ * @param variableIndex: specifies the index of the variable that the dataset will
+ * split on
+ * @param splitPoint: specifies the point in the index of the exact split
+ * @param gini: general gini value of the dataset
+ * @param leftGini: the gini impurity of the left split of the dataset
+ * @param rightGini: the gini impurity of the right split of the dataset
+ */
+case class RegressionVarSplitInfo(variableIndex: Long, splitPoint: Double, impurity: Double,
+ leftImpurity: Double, rightImpurity: Double, isPermuted: Boolean) {
+
+ /** Creates a list of the RegressionSubsetInfos for the dataset split
+ *
+ * @param v: input the specific data construct
+ * @param labels: input an array of integer labels
+ * @param subset: specify the RegressionSubsetInfo class touched on previously at
+ * [[au.csiro.variantspark.algo.RegressionSubsetInfo]]
+ * @return returns a tupple of the subset information
+ */
+ def split(v: TreeFeature, labels: Array[Double])(
+ subset: RegressionSubsetInfo): (RegressionSubsetInfo, RegressionSubsetInfo) = {
+ val leftIndexes: Array[Int] = subset.indices.filter(v.at(_) <= splitPoint)
+ val rightIndexes: Array[Int] = subset.indices.filter(v.at(_) > splitPoint)
+ (new RegressionSubsetInfo(leftIndexes, leftImpurity, leftIndexes.map(labels)),
+ new RegressionSubsetInfo(rightIndexes, rightImpurity, rightIndexes.map(labels)))
+ }
+ def splitpermuted(v: TreeFeature, labels: Array[Double], permutationOder: Array[Int])(
+ subset: RegressionSubsetInfo): (RegressionSubsetInfo, RegressionSubsetInfo) = {
+ val leftIndexes: Array[Int] =
+ subset.indices.filter(i => v.at(permutationOder(i)) <= splitPoint)
+ val rightIndexes: Array[Int] =
+ subset.indices.filter(i => v.at(permutationOder(i)) > splitPoint)
+ (new RegressionSubsetInfo(leftIndexes, leftImpurity, leftIndexes.map(labels)),
+ new RegressionSubsetInfo(rightIndexes, rightImpurity, rightIndexes.map(labels)))
+ }
+}
+
+/** Utilized to return a RegressionVarSplitInfo object
+ */
+object RegressionVarSplitInfo {
+
+ /** Applies the values obtained from the [[au.csiro.variantspark.algo.SplitInfo]] class to
+ * create an [[au.csiro.variantspark.algo.RegressionVarSplitInfo]] object
+ *
+ * @param variableIndex: input an index of where the variable split from
+ * @param split: input a [[au.csiro.variantspark.algo.SplitInfo]] object
+ * @return returns a [[au.csiro.variantspark.algo.RegressionVarSplitInfo]] object
+ */
+ def apply(variableIndex: Long, split: SplitInfo, isPermuted: Boolean): RegressionVarSplitInfo =
+ apply(variableIndex, split.splitPoint, split.gini, split.leftGini, split.rightGini,
+ isPermuted)
+}
+
+/** Defines the trait for the case class
+ * [[au.csiro.variantspark.algo.DeterministicRegressionMerger]]
+ */
+trait RegressionMerger {
+
+ /** Operates a merging function utilizing two arrays of the
+ * class [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ *
+ * @param a1: input an array of [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @param a2: input an array of [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @return Returns an array of [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ */
+ def merge(a1: Array[RegressionVarSplitInfo],
+ a2: Array[RegressionVarSplitInfo]): Array[RegressionVarSplitInfo]
+}
+
+/** Utilizes the Deterministic Decision Tree model found here:
+ * [[https://en.wikipedia.org/wiki/Decision_tree_model#Randomized_decision_tree]]
+ * Extends the [[au.csiro.variantspark.algo.RegressionMerger]] class
+ */
+case class DeterministicRegressionMerger() extends RegressionMerger {
+
+ /** Operates a merging function utilizing two arrays of the
+ * class [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ *
+ * @param a1: input an array of [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @param a2: input an array of [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @return Returns the merged array a1
+ */
+ def merge(a1: Array[RegressionVarSplitInfo],
+ a2: Array[RegressionVarSplitInfo]): Array[RegressionVarSplitInfo] = {
+
+ /** Takes the [[au.csiro.variantspark.algo.RegressionVarSplitInfo]] from two seperate splits
+ * and returns the value from either s1 or s2 based on the gini impurity
+ *
+ * @param s1: input an [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @param s2: input an [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @return Returns either s1 or s2 based on the gini impurity calculation
+ */
+ def mergeSplitInfo(s1: RegressionVarSplitInfo, s2: RegressionVarSplitInfo) = {
+ if (s1 == null) s2
+ else if (s2 == null) s1
+ else if (s1.impurity < s2.impurity) s1
+ else if (s2.impurity < s1.impurity) s2
+ else if (s1.variableIndex < s2.variableIndex) s1
+ else s2
+ }
+ a1.indices.foreach(i => a1(i) = mergeSplitInfo(a1(i), a2(i)))
+ a1
+ }
+}
+
+/**
+ * UsedsMurmur3 hashing to create random ordering of variables
+ * dependent on the initial seed and split number.
+ *
+ * The assumption is that comparing the hashes of variable indexes will produce
+ * sufficently randomzized orderings given different seeds and split ids.
+ *
+ * @param seed: input a seed value to initialize the random number generator for rnd
+ */
+case class RandomizingRegressionMergerMurmur3(seed: Long) extends RegressionMerger {
+
+ def hashOrder(varIndex: Long, splitId: Int): Int = {
+ MurMur3Hash.hashLong(varIndex, MurMur3Hash.hashLong(seed, splitId))
+ }
+
+ def chooseEqual(s1: RegressionVarSplitInfo, s2: RegressionVarSplitInfo,
+ id: Int): RegressionVarSplitInfo = {
+ if (hashOrder(s1.variableIndex, id) < hashOrder(s2.variableIndex, id)) s1 else s2
+ }
+
+ /** Operates a merging function utilizing two arrays of the
+ * class [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ *
+ * @param a1: input an array of [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @param a2: input an array of [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @return Returns the merged array a1
+ */
+ def merge(a1: Array[RegressionVarSplitInfo],
+ a2: Array[RegressionVarSplitInfo]): Array[RegressionVarSplitInfo] = {
+
+ /** Takes the [[au.csiro.variantspark.algo.RegressionVarSplitInfo]] from two seperate splits
+ * and returns the value from either s1 or s2 based on the gini impurity
+ *
+ * @note if the gini values of each split are equal then the value returns one at random
+ *
+ * @param s1: input an [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @param s2: input an [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @return Returns either s1 or s2 based on the gini impurity calculation
+ */
+ def mergeSplitInfo(s1: RegressionVarSplitInfo, s2: RegressionVarSplitInfo, id: Int) = {
+ if (s1 == null) s2
+ else if (s2 == null) s1
+ else if (s1.impurity < s2.impurity) s1
+ else if (s2.impurity < s1.impurity) s2
+ else chooseEqual(s1, s2, id)
+ }
+ a1.indices.foreach(i => a1(i) = mergeSplitInfo(a1(i), a2(i), i))
+ a1
+ }
+}
+
+trait RegressionVariableSplitter {
+
+ def initialSubset(sample: Sample): RegressionSubsetInfo
+
+ /** Splits the subsets of the RDD and returns a split based on the variable of split index
+ *
+ * @param varData: input an interator containing the dataset and an index
+ * @param subsets: input an array of [[au.csiro.variantspark.algo.RegressionSubsetInfo]]
+ * @param bestSplits: input an array of the [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @return returns a flattened iterator
+ */
+ def splitSubsets(varData: Iterator[TreeFeature], subsets: Array[RegressionSubsetInfo],
+ bestSplits: Array[RegressionVarSplitInfo])
+ : Iterator[(Int, (RegressionSubsetInfo, RegressionSubsetInfo))]
+ def findSplitsForVars(varData: Iterator[TreeFeature], splits: Array[RegressionSubsetInfo])(
+ implicit rng: RandomGenerator): Iterator[Array[RegressionVarSplitInfo]]
+ def createRegressionMerger(seed: Long): RegressionMerger
+}
+
+/** This is the main split function
+ *
+ * 1. specifies the number of categories based on the label input
+ * 2. Finds the splits in the data based on the gini value
+ *
+ * @param labels: input an array of labels used by the dataset
+ * @param mTryFraction: the fraction of variable to try at each split (default to 1.0)
+ * @param randomizeEquality: default to false
+ */
+case class StdRegressionVariableSplitter(labels: Array[Double], mTryFraction: Double = 1.0,
+ randomizeEquality: Boolean = false)
+ extends RegressionVariableSplitter with Logging with Prof {
+
+ def initialSubset(sample: Sample): RegressionSubsetInfo = {
+ val currentSet = sample.indexes
+ // val (totalGini, classCounts) = Gini.giniImpurity(currentSet, labels, nCategories) // TODO
+ val totalImpurity = VarianceImpurity.createAggregator().getValue // TODO: Will it work?
+ RegressionSubsetInfo(currentSet, totalImpurity, labels.sum / labels.size)
+ }
+
+ /** Find the splits in the data based on the gini value
+ *
+ * Specify the 'data' and 'splits' inputs
+ *
+ * @param typedData: input the data from the dataset of generic type V
+ * @param splits: input an array of the [[au.csiro.variantspark.algo.RegressionSubsetInfo]] class
+ * @return returns an array [[au.csiro.variantspark.algo.SplitInfo]]
+ */
+ def findSplits(typedData: TreeFeature, splits: Array[RegressionSubsetInfo],
+ sbf: IndexedSplitterFactory)(implicit rng: RandomGenerator): Array[SplitInfo] = {
+
+ val splitter = sbf.create(typedData)
+ splits.map { RegressionSubsetInfo =>
+ if (rng.nextDouble() <= mTryFraction) {
+ val splitInfo = splitter.findSplit(RegressionSubsetInfo.indices)
+ if (splitInfo != null && splitInfo.gini < RegressionSubsetInfo.impurity) splitInfo
+ else null
+ } else null
+ }
+ }
+
+ def threadSafeSplitterBuilderFactory(labels: Array[Double]): RegressionSplitterFactory = {
+ // TODO: this should be actually passed externally (or at least part of it
+ // as it essentially determines what kind of tree are we building (e.g what is the metric
+ // used for impurity)
+ // This should obtain a stateful and somehow compartmentalised impurity calculator
+ // (Try thread local perhaps here, but creating a new one (per partition) also fits the bill)
+ new RegressionSplitterFactory(VarianceImpurity, labels)
+ }
+
+ /** Returns the result of a split based on a variable
+ *
+ * @param varData: input an Iterator of a tuple containing the dataset and indices
+ * @param splits: input an Array of the [[au.csiro.variantspark.algo.RegressionSubsetInfo]] class
+ * @return takes the varData and maps the value of the dataset
+ */
+ def findSplitsForVars(varData: Iterator[TreeFeature], splits: Array[RegressionSubsetInfo])(
+ implicit rng: RandomGenerator): Iterator[Array[RegressionVarSplitInfo]] = {
+ profIt("Local: splitting") {
+ val sbf = threadSafeSplitterBuilderFactory(labels)
+ val result = varData
+ .map { vi =>
+ val thisVarSplits = findSplits(vi, splits, sbf)
+ thisVarSplits
+ .map(si => if (si != null) RegressionVarSplitInfo(vi.index, si, false) else null)
+ }
+ result
+ }
+ }
+
+ /** Splits the subsets of the RDD and returns a split based on the variable of split index
+ *
+ * @param varData: input an interator containing the dataset and an index
+ * @param subsets: input an array of [[au.csiro.variantspark.algo.RegressionSubsetInfo]]
+ * @param bestSplits: input an array of the [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @return returns a flattened iterator
+ */
+ def splitSubsets(varData: Iterator[TreeFeature], subsets: Array[RegressionSubsetInfo],
+ bestSplits: Array[RegressionVarSplitInfo])
+ : Iterator[(Int, (RegressionSubsetInfo, RegressionSubsetInfo))] = {
+
+ val usefulSubsetSplitAndIndex =
+ subsets.zip(bestSplits).filter(_._2 != null).zipWithIndex.toList
+ val splitByVarIndex = usefulSubsetSplitAndIndex.groupBy(_._1._2.variableIndex)
+ varData.flatMap { vi =>
+ splitByVarIndex.getOrElse(vi.index, Nil).map {
+ case ((subsetInfo, splitInfo), si) =>
+ (si, splitInfo.split(vi, labels)(subsetInfo))
+ }
+ }
+ }
+
+ def createRegressionMerger(seed: Long): RegressionMerger =
+ if (randomizeEquality) RandomizingRegressionMergerMurmur3(seed)
+ else DeterministicRegressionMerger()
+
+}
+
+/** This is the main split function
+ *
+ * 1. specifies the number of categories based on the label input
+ * 2. Finds the splits in the data based on the gini value
+ *
+ * @param labels: input an array of labels used by the dataset
+ * @param mTryFraction: the fraction of variable to try at each split (default to 1.0)
+ * @param randomizeEquality: default to false
+ */
+case class RegressionAirVariableSplitter(labels: Array[Double], permutationOrder: Array[Int],
+ mTryFraction: Double, randomizeEquality: Boolean)
+ extends RegressionVariableSplitter with Logging with Prof {
+
+ lazy val permutedLabels: Array[Double] = permutationOrder.map(labels(_))
+
+ def initialSubset(sample: Sample): RegressionSubsetInfo = {
+ val currentSet = sample.indexes
+ // val (totalGini, classCounts) = Gini.giniImpurity(currentSet, labels, nCategories) // TODO
+ val totalImpurity = VarianceImpurity.createAggregator().getValue // TODO: Will it work?
+ RegressionSubsetInfo(currentSet, totalImpurity, labels.sum / labels.size)
+ }
+
+ /** Find the splits in the data based on the gini value
+ *
+ * Specify the 'data' and 'splits' inputs
+ *
+ * @param typedData: input the data from the dataset of generic type V
+ * @param splits: input an array of the [[au.csiro.variantspark.algo.RegressionSubsetInfo]] class
+ * @return returns an array [[au.csiro.variantspark.algo.SplitInfo]]
+ */
+ def findSplits(typedData: TreeFeature, splits: Array[RegressionSubsetInfo],
+ sbf: IndexedSplitterFactory, permutedSbf: IndexedSplitterFactory,
+ permSubsets: Array[Array[Int]])(
+ implicit rng: RandomGenerator): Array[RegressionVarSplitInfo] = {
+
+ val splitter = sbf.create(typedData)
+ val permutedSplitter = permutedSbf.create(typedData)
+
+ splits.zip(permSubsets).map {
+ case (subsetInfo, permIndexes) =>
+ val rnd = rng.nextDouble()
+ if (rnd <= mTryFraction) {
+ // check wheter to use informative or permuted labels
+ val permuted = rnd > mTryFraction / 2
+ val selectedSplitter = if (!permuted) splitter else permutedSplitter
+ val indices = if (!permuted) subsetInfo.indices else permIndexes
+ val splitInfo = selectedSplitter.findSplit(indices)
+ if (splitInfo != null && splitInfo.gini < subsetInfo.impurity) {
+ RegressionVarSplitInfo(typedData.index, splitInfo, permuted)
+ } else { null }
+ } else null
+ }
+ }
+
+ def threadSafeSplitterBuilderFactory(labels: Array[Double]): IndexedSplitterFactory = {
+ // TODO: this should be actually passed externally (or at least part of it
+ // as it essentially determines what kind of tree are we bulding (e.g what is the metric
+ // used for impurity)
+ // This should obtain a statefule and somehow compartmenalised impurity calculator
+ // (Try trhead local perhaps here, but creating a new one (per partition) also fits the bill)
+ new RegressionSplitterFactory(VarianceImpurity, labels)
+ }
+
+ /** Returns the result of a split based on a variable
+ *
+ * @param varData: input an Iterator of a tuple containing the dataset and indices
+ * @param splits: input an Array of the [[au.csiro.variantspark.algo.RegressionSubsetInfo]] class
+ * @return takes the varData and maps the value of the dataset
+ */
+ def findSplitsForVars(varData: Iterator[TreeFeature], splits: Array[RegressionSubsetInfo])(
+ implicit rng: RandomGenerator): Iterator[Array[RegressionVarSplitInfo]] = {
+ profIt("Local: splitting") {
+ val sbf = threadSafeSplitterBuilderFactory(labels)
+ val permutedSbf = threadSafeSplitterBuilderFactory(permutedLabels)
+
+ // TODO: [Performance] maybe there is not need to permutata all the splits up front
+
+ val permSubsets = splits.map(s => ArraysUtils.permutate(s.indices, permutationOrder))
+ varData.map(vi => findSplits(vi, splits, sbf, permutedSbf, permSubsets))
+ }
+ }
+
+ /** Splits the subsets of the RDD and returns a split based on the variable of split index
+ *
+ * @param varData: input an interator containing the dataset and an index
+ * @param subsets: input an array of [[au.csiro.variantspark.algo.RegressionSubsetInfo]]
+ * @param bestSplits: input an array of the [[au.csiro.variantspark.algo.RegressionVarSplitInfo]]
+ * @return returns a flattened iterator
+ */
+ def splitSubsets(varData: Iterator[TreeFeature], subsets: Array[RegressionSubsetInfo],
+ bestSplits: Array[RegressionVarSplitInfo])
+ : Iterator[(Int, (RegressionSubsetInfo, RegressionSubsetInfo))] = {
+
+ val usefulSubsetSplitAndIndex =
+ subsets.zip(bestSplits).filter(_._2 != null).zipWithIndex.toList
+ val splitByVarIndex = usefulSubsetSplitAndIndex.groupBy(_._1._2.variableIndex)
+ varData.flatMap { vi =>
+ splitByVarIndex.getOrElse(vi.index, Nil).map {
+ case ((subsetInfo, splitInfo), si) =>
+ if (!splitInfo.isPermuted) {
+ (si, splitInfo.split(vi, labels)(subsetInfo))
+ } else {
+ (si, splitInfo.splitpermuted(vi, labels, permutationOrder)(subsetInfo))
+ }
+ }
+ }
+ }
+
+ def createRegressionMerger(seed: Long): RegressionMerger =
+ if (randomizeEquality) RandomizingRegressionMergerMurmur3(seed)
+ else DeterministicRegressionMerger()
+
+}
+
+object RegressionAirVariableSplitter {
+ def apply(labels: Array[Double], seed: Long, mTryFraction: Double = 1.0,
+ randomizeEquality: Boolean = false): RegressionAirVariableSplitter = {
+ val rng = new XorShift1024StarRandomGenerator(seed)
+ val permutationOrder = labels.indices.toArray
+ MathArrays.shuffle(permutationOrder, rng)
+ RegressionAirVariableSplitter(labels, permutationOrder, mTryFraction, randomizeEquality)
+ }
+}
+
+/** Object utilized with the RegressionDecisionTreeModel class
+ */
+object RegressionDecisionTree extends Logging with Prof {
+
+ /** Returns the splitted subsets input through the indexedData param and outputs a list of
+ * the Splitted Subsets
+ *
+ * @param indexedData: input an RDD of the dataset plus indexes of type long
+ * @param bestSplits: input an Array containing
+ * the [[au.csiro.variantspark.algo.RegressionVarSplitInfo]] class
+ * @param br_subsets: input a Broadcast of Arrays containing
+ * the [[au.csiro.variantspark.algo.RegressionSubsetInfo]] class
+ * @param br_splitter: Broadcast of the [[au.csiro.variantspark.algo.RegressionVariableSplitter]]
+ * @return Returns an indexed list of splited subsets
+ */
+ def splitSubsets(indexedData: RDD[TreeFeature], bestSplits: Array[RegressionVarSplitInfo],
+ br_subsets: Broadcast[Array[RegressionSubsetInfo]],
+ br_splitter: Broadcast[RegressionAirVariableSplitter]): List[RegressionSubsetInfo] = {
+ profIt("REM: splitSubsets") {
+ val indexedSplittedSubsets = withBroadcast(indexedData)(bestSplits) { br_bestSplits =>
+ // format: off
+ indexedData
+ .mapPartitions(it => br_splitter.value.splitSubsets(it, br_subsets.value,
+ br_bestSplits.value))
+ .collectAsMap()
+ // format: on
+ }
+ indexedSplittedSubsets
+ .foldLeft(Array.fill[RegressionSubsetInfo](indexedSplittedSubsets.size * 2)(null)) {
+ case (a, (i, st)) =>
+ a(2 * i) = st._1
+ a(2 * i + 1) = st._2
+ a
+ }
+ .toList
+ }
+ }
+
+ /** Returns an indexed
+ *
+ * @param treeFeatures: input an RDD of tree features
+ * @param br_splits : input a broadcast containing an array of
+ * the [[au.csiro.variantspark.algo.RegressionSubsetInfo]] class
+ * @param br_splitter : input a broadcast containing
+ * the [[au.csiro.variantspark.algo.RegressionVariableSplitter]] class of
+ * the dataset
+ * @return Returns the indexedData variable that contains the indexed best splits
+ */
+ def findBestSplits(treeFeatures: RDD[TreeFeature],
+ br_splits: Broadcast[Array[RegressionSubsetInfo]],
+ br_splitter: Broadcast[RegressionAirVariableSplitter])(
+ implicit rng: RandomGenerator): Array[RegressionVarSplitInfo] = {
+ val seed = rng.nextLong()
+ val RegressionMerger = br_splitter.value.createRegressionMerger(seed)
+ profIt("REM: findBestSplits") {
+ treeFeatures
+ .mapPartitionsWithIndex {
+ case (pi, it) =>
+ br_splitter.value.findSplitsForVars(it,
+ br_splits.value)(new XorShift1024StarRandomGenerator(seed ^ pi))
+ }
+ .fold(Array.fill(br_splits.value.length)(null))(RegressionMerger.merge)
+ }
+ }
+}
+
+@SerialVersionUID(1L)
+abstract class RegressionDecisionTreeNode(val meanLabel: Double, val size: Int,
+ val nodeImpurity: Double)
+ extends Serializable {
+ def isLeaf: Boolean
+
+ def printout(level: Int)
+ def impurityContribution: Double = nodeImpurity * size
+ def traverse(f: RegressionSplitNode => Boolean): RegressionLeafNode = this match {
+ case leaf: RegressionLeafNode => leaf
+ case split: RegressionSplitNode => (if (f(split)) split.left else split.right).traverse(f)
+ }
+ def toStream: Stream[RegressionDecisionTreeNode]
+ def splitsToStream: Stream[RegressionSplitNode] =
+ toStream.filter(!_.isLeaf).asInstanceOf[Stream[RegressionSplitNode]]
+ def leafsToStream: Stream[RegressionLeafNode] =
+ toStream.filter(_.isLeaf).asInstanceOf[Stream[RegressionLeafNode]]
+}
+
+@SerialVersionUID(1L)
+case class RegressionLeafNode(override val meanLabel: Double, override val size: Int,
+ override val nodeImpurity: Double)
+ extends RegressionDecisionTreeNode(meanLabel, size, nodeImpurity) {
+ val isLeaf: Boolean = true
+
+ def printout(level: Int) {
+ print(new String(Array.fill(level)(' ')))
+ val nodeType = "leaf"
+ println(s"${nodeType}[${meanLabel}, ${size}, ${nodeImpurity}]")
+ }
+
+ override def toString: String = s"leaf[${meanLabel}, ${size}, ${nodeImpurity}]"
+
+ def toStream: Stream[RegressionDecisionTreeNode] = this #:: Stream.empty
+}
+
+object RegressionLeafNode {
+ def apply(subset: RegressionSubsetInfo): RegressionLeafNode =
+ apply(subset.meanLabel, subset.length, subset.impurity)
+
+ /**
+ * Create a tree leaf for a standard classifier tree with voting information onlu
+ * @param meanLabel
+ * @param size
+ * @param nodeImpurity
+ * @return
+ */
+ def voting(meanLabel: Double, size: Int, nodeImpurity: Double): RegressionLeafNode = {
+ RegressionLeafNode(meanLabel, size, nodeImpurity)
+ }
+
+}
+
+@SerialVersionUID(1L)
+case class RegressionSplitNode(override val meanLabel: Double, override val size: Int,
+ override val nodeImpurity: Double, splitVariableIndex: Long, splitPoint: Double,
+ impurityReduction: Double, left: RegressionDecisionTreeNode,
+ right: RegressionDecisionTreeNode, isPermuted: Boolean = false)
+ extends RegressionDecisionTreeNode(meanLabel, size, nodeImpurity) {
+
+ val isLeaf: Boolean = false
+
+ def printout(level: Int) {
+ print(new String(Array.fill(level)(' ')))
+ val nodeType = "split"
+ println(
+ s"${nodeType}[${splitVariableIndex}, ${splitPoint}, ${meanLabel},"
+ + s" ${size}, ${impurityReduction}, ${nodeImpurity}]")
+ left.printout(level + 1)
+ right.printout(level + 1)
+ }
+ override def toString: String =
+ (s"split[${splitVariableIndex}, ${splitPoint}, ${meanLabel}, ${size},"
+ + s" ${impurityReduction}, ${nodeImpurity}]")
+
+ def childFor(value: Double): RegressionDecisionTreeNode =
+ if (value <= splitPoint) left else right
+
+ def impurityDelta: Double = {
+ val deltaAbs = impurityContribution - (left.impurityContribution + right.impurityContribution)
+ if (isPermuted) -deltaAbs else deltaAbs
+ }
+ def toStream: Stream[RegressionDecisionTreeNode] = this #:: left.toStream #::: right.toStream
+}
+
+object RegressionSplitNode {
+ def apply(subset: RegressionSubsetInfo, split: RegressionVarSplitInfo,
+ left: RegressionDecisionTreeNode, right: RegressionDecisionTreeNode): RegressionSplitNode =
+ apply(subset.meanLabel, subset.length, subset.impurity, split.variableIndex, split.splitPoint,
+ subset.impurity - split.impurity, left, right, split.isPermuted)
+
+ def voting(meanLabel: Int, size: Int, nodeImpurity: Double, splitVariableIndex: Long,
+ splitPoint: Double, impurityReduction: Double, left: RegressionDecisionTreeNode,
+ right: RegressionDecisionTreeNode, isPermuted: Boolean = false): RegressionSplitNode = {
+ RegressionSplitNode(meanLabel, size, nodeImpurity, splitVariableIndex, splitPoint,
+ impurityReduction, left, right, isPermuted)
+ }
+
+}
+
+@SerialVersionUID(1L)
+case class RegressionDecisionTreeModel(rootNode: RegressionDecisionTreeNode)
+ extends RegressionPredictiveModelWithImportance with Logging with Serializable {
+
+ def splitVariableIndexes: Set[Long] = rootNode.splitsToStream.map(_.splitVariableIndex).toSet
+
+ def predict[T](indexedData: RDD[(T, Long)], variableType: VariableType)(
+ implicit db: DataBuilder[T]): Array[Double] = {
+ predict(indexedData.map({ case (v, i) => (StdFeature.from(null, variableType, v), i) }))
+ }
+
+ def predict(indexedData: RDD[(Feature, Long)]): Array[Double] = {
+ val treeVariableData = indexedData.collectAtIndexes(splitVariableIndexes)
+ Range(0, indexedData.size)
+ .map(i =>
+ rootNode
+ .traverse(s => treeVariableData(s.splitVariableIndex).at(i) <= s.splitPoint)
+ .meanLabel)
+ .toArray
+ }
+
+ def printout() {
+ rootNode.printout(0)
+ }
+
+ def printoutByLevel() {
+ @scala.annotation.tailrec
+ def printLevel(levelNodes: Seq[RegressionDecisionTreeNode]) {
+ if (levelNodes.nonEmpty) {
+ println(levelNodes.mkString(" "))
+ printLevel(levelNodes.flatMap(_ match {
+ case t: RegressionSplitNode => List(t.left, t.right)
+ case _ => Nil
+ }))
+ }
+ }
+ printLevel(Seq(rootNode))
+ }
+
+ override def variableImportanceAsFastMap: Long2DoubleOpenHashMap = {
+ rootNode.splitsToStream.foldLeft(new Long2DoubleOpenHashMap()) {
+ case (m, splitNode) =>
+ m.increment(splitNode.splitVariableIndex, splitNode.impurityDelta)
+ }
+ }
+
+ override def variableSplitCountAsFastMap: Long2LongOpenHashMap = {
+ rootNode.splitsToStream.foldLeft(new Long2LongOpenHashMap()) {
+ case (m, splitNode) =>
+ m.increment(splitNode.splitVariableIndex, 1L)
+ }
+ }
+
+ def impurity: List[Double] = rootNode.toStream.map(_.nodeImpurity).toList
+ def variables: List[Long] = rootNode.splitsToStream.map(_.splitVariableIndex).toList
+ def thresholds: List[Double] = rootNode.splitsToStream.map(_.splitPoint).toList
+
+}
+
+/** Contains the object for the [[au.csiro.variantspark.algo.RegressionDecisionTreeModel]] class
+ */
+object RegressionDecisionTreeModel {
+
+ /** Returns the resolved list of the split nodes and indices
+ *
+ * @param indexedData: input an RDD of tuples with the valeus in the dataset and the index
+ * @param RegressionSplitNodes: input a list of tuples with the
+ * [[au.csiro.variantspark.algo.RegressionSplitNode]] class and an index
+ * @return returns a List of the resolved [[au.csiro.variantspark.algo.RegressionSplitNode]]
+ * class and it's index
+ */
+ def resolveRegressionSplitNodes(indexedData: RDD[(DataLike, Long)],
+ RegressionSplitNodes: List[(RegressionSplitNode, Int)])
+ : List[(RegressionDecisionTreeNode, Int)] = {
+ val varsAndIndexesToCollect = RegressionSplitNodes
+ .asInstanceOf[List[(RegressionSplitNode, Int)]]
+ .map { case (n, i) => (n.splitVariableIndex, i) }
+ .zipWithIndex
+ .toArray
+ val varValuesForSplits = withBroadcast(indexedData)(varsAndIndexesToCollect) {
+ br_varsAndIndexesToCollect =>
+ indexedData.mapPartitions { it =>
+ val varsAndIndexesToCollectMap =
+ br_varsAndIndexesToCollect.value.toList.groupBy(_._1._1)
+ it.flatMap {
+ case (v, vi) =>
+ varsAndIndexesToCollectMap.getOrElse(vi, Nil).map {
+ case (n, si) => (si, v.at(n._2))
+ }
+ }
+ }.collectAsMap
+ }
+ RegressionSplitNodes.asInstanceOf[List[(RegressionSplitNode, Int)]].zipWithIndex.map {
+ case ((n, i), v) => (n.childFor(varValuesForSplits(v)), i)
+ }
+ }
+
+ def batchPredict(indexedData: RDD[(DataLike, Long)], trees: Seq[RegressionDecisionTreeModel],
+ indexes: Seq[Array[Int]]): Seq[Array[Double]] = {
+
+ /** Takes the decision tree nodes and outputs the leaf nodes
+ * Partitions the nodesAndIndexes variable and recursively iterates through each
+ * model until a leaf node is reached
+ *
+ * @param nodesAndIndexes: input a list of tuples of tuple
+ * @return a list of tuples of tuple
+ */
+ def predict(nodesAndIndexes: List[((RegressionDecisionTreeNode, Int), Int)])
+ : List[((RegressionLeafNode, Int), Int)] = {
+ val (leaves, splits) = nodesAndIndexes.partition(_._1._1.isLeaf)
+ if (splits.isEmpty) {
+ leaves.asInstanceOf[List[((RegressionLeafNode, Int), Int)]]
+ } else {
+ val (bareSplits, splitIndexes) = splits.unzip
+ val transformedSplits =
+ resolveRegressionSplitNodes(indexedData,
+ bareSplits.asInstanceOf[List[(RegressionSplitNode, Int)]])
+ .zip(splitIndexes)
+ leaves.asInstanceOf[List[((RegressionLeafNode, Int), Int)]] ::: predict(transformedSplits)
+ }
+ }
+
+ val rootNodesAndIndexes = trees
+ .map(_.rootNode)
+ .zip(indexes)
+ .flatMap { case (n, idx) => idx.map(i => (n, i)) }
+ .zipWithIndex
+ .toList
+ val leaveNodesAndIndexes = predict(rootNodesAndIndexes)
+
+ val orderedPredictions = leaveNodesAndIndexes.sortBy(_._2).map(_._1).map(_._1.meanLabel)
+ val orderedPredictionsIter = orderedPredictions.toIterator
+
+ indexes.map(a => Array.fill(a.length)(orderedPredictionsIter.next()))
+ }
+}
+
+/** Class for the Decision tree model
+ *
+ * Specify the 'params' using the [[au.csiro.variantspark.algo.DecisionTreeParams]]
+ *
+ * {{{
+ *
+ * val maxDepth = 5
+ * val minNodeSize = 10
+ * val seed = 1
+ * val randomizeEquality = false
+ *
+ * val params = DecisionTreeParams(maxDepth, minNodeSize, seed, randomizeEquality)
+ * val model = RegressionDecisionTree(params)
+ *
+ * }}}
+ *
+ * @param params: input the [[au.csiro.variantspark.algo.DecisionTreeParams]] class
+ * containing the main aspects of the model
+ */
+class RegressionDecisionTree(val params: DecisionTreeParams = DecisionTreeParams(),
+ val trf: TreeRepresentationFactory = DefTreeRepresentationFactory)
+ extends Logging with Prof {
+
+ implicit lazy val rnd: XorShift1024StarRandomGenerator =
+ new XorShift1024StarRandomGenerator(params.seed)
+
+ implicit def toRepresenation(indexedFeatures: RDD[(Feature, Long)]): RDD[TreeFeature] =
+ trf.createRepresentation(indexedFeatures)
+
+ /** Basic training operation taking the in the data, the type, and the labels
+ *
+ * @param indexedData: input an RDD of the dataset
+ * @param labels: input an array of integers changed to an integer representation
+ */
+ def train(indexedData: RDD[(Feature, Long)],
+ labels: Array[Double]): RegressionDecisionTreeModel =
+ train(indexedData, labels, 1.0, Sample.all(indexedData.first._1.size))
+
+ /** Alternative train function
+ *
+ * @param indexedData: input an RDD of the values of the dataset with the indices
+ * @param labels: input an array of integers changed to an integer representation
+ * @param nvarFraction: fraction of variable to test at each split
+ * @param sample: input the [[au.csiro.variantspark.utils.Sample]] class that
+ * contains the size and the indices
+ */
+ def train(indexedData: RDD[(Feature, Long)], labels: Array[Double], nvarFraction: Double,
+ sample: Sample): RegressionDecisionTreeModel =
+ batchTrain(indexedData, labels, nvarFraction, List(sample)).head
+
+ /** Trains all the trees for specified samples at the same time
+ *
+ * @param indexedFeatures: input an RDD of the values of the dataset with the indices
+ * @param labels: input an array of integers changed to an integer representation
+ * @param nvarFraction: fraction of variable to test for each split
+ * @param sample: input the [[au.csiro.variantspark.utils.Sample]] class that
+ * contains the size and the indices
+ * @return Returns a Sequence of [[au.csiro.variantspark.algo.RegressionDecisionTreeModel]]
+ * classes containing the dataset
+ */
+ def batchTrain(indexedFeatures: RDD[(Feature, Long)], labels: Array[Double],
+ nvarFraction: Double, sample: Seq[Sample]): Seq[RegressionDecisionTreeModel] = {
+ batchTrainInt(trf.createRepresentation(indexedFeatures), labels, nvarFraction, sample)
+ }
+
+ /** Trains all the trees for specified samples at the same time
+ *
+ * @param features: input an RDD of the internal tree feature representation
+ * @param labels: input an array of integers changed to an integer representation
+ * @param nvarFraction: fraction of variable to test for each split
+ * @param sample: input the [[au.csiro.variantspark.utils.Sample]] class that
+ * contains the size and the indices
+ * @return Returns a Sequence of [[au.csiro.variantspark.algo.RegressionDecisionTreeModel]]
+ * classes containing the dataset
+ */
+ def batchTrainInt(features: RDD[TreeFeature], labels: Array[Double], nvarFraction: Double,
+ sample: Seq[Sample]): Seq[RegressionDecisionTreeModel] = {
+
+ // manage persistence here - cache the features if not already cached
+ withCached(features) { cachedFeatures =>
+ val splitter: RegressionAirVariableSplitter =
+ RegressionAirVariableSplitter(labels,
+ if (params.airRandomSeed != 0L) params.airRandomSeed else params.seed, nvarFraction,
+ randomizeEquality = params.randomizeEquality)
+ val subsets = sample.map(splitter.initialSubset).toList
+ val rootNodes = withBroadcast(cachedFeatures)(splitter) { br_splitter =>
+ buildSplit(cachedFeatures, subsets, br_splitter, 0)
+ }
+ rootNodes.map(new RegressionDecisionTreeModel(_))
+ }
+ }
+
+ private def summarize(subsets: List[RegressionSubsetInfo]): String = {
+ s"#${subsets.size} => ${subsets.map(_.length)}"
+ }
+
+ /** Builds (recursively) the decision tree level by level
+ *
+ * @param indexedTypedData: input an RDD of tree features
+ * @param subsets: input an Array containing the
+ * [[au.csiro.variantspark.algo.RegressionVarSplitInfo]] class
+ * @param br_splitter: input a Broadcast of Arrays containing
+ * the [[au.csiro.variantspark.algo.RegressionSubsetInfo]] class
+ * @param treeLevel: specify the current level of the tree being built
+ * @return Returns a subset of the splits
+ */
+ private def buildSplit(indexedTypedData: RDD[TreeFeature], subsets: List[RegressionSubsetInfo],
+ br_splitter: Broadcast[RegressionAirVariableSplitter],
+ treeLevel: Int): List[RegressionDecisionTreeNode] = {
+
+ logDebug(s"Building level ${treeLevel}")
+ logDebug(s"Initial subsets: ${summarize(subsets)}")
+ logTrace(s"Initial subsets (details): ${subsets}")
+
+ profReset()
+
+ val subsetsToSplit = subsets.zipWithIndex.filter {
+ case (si, _) =>
+ si.length >= params.minNodeSize && treeLevel < params.maxDepth
+ }
+ logDebug(s"Splittable subsets: ${summarize(subsetsToSplit.map(_._1))}")
+ logTrace(s"Splittable subsets (details): ${subsetsToSplit}")
+
+ val (bestSplits, nextLevelSubsets) =
+ findBestSplitsAndSubsets(indexedTypedData, subsetsToSplit.map(_._1), br_splitter)
+ logDebug(s"Best splits: ${bestSplits.toList}")
+ logDebug(s"Next level subsets ${summarize(nextLevelSubsets)}")
+ logTrace(s"Next level subsets (details): ${nextLevelSubsets}")
+
+ profPoint("Best splits and splitting done")
+
+ val nextLevelNodes =
+ if (nextLevelSubsets.nonEmpty) {
+ buildSplit(indexedTypedData, nextLevelSubsets, br_splitter, treeLevel + 1)
+ } else { List() }
+
+ profPoint("Sublevels done")
+
+ val (usefulSplits, usefulSplitsIndices) =
+ bestSplits.zip(subsetsToSplit.map(_._2)).filter(_._1 != null).unzip
+ val subsetIndexToSplitIndexMap = usefulSplitsIndices.zipWithIndex.toMap
+ val result = subsets.zipWithIndex.map {
+ case (subset, i) =>
+ // format: off
+ subsetIndexToSplitIndexMap
+ .get(i)
+ .map(splitIndex => RegressionSplitNode(subset, usefulSplits(splitIndex),
+ nextLevelNodes(2 * splitIndex), nextLevelNodes(2 * splitIndex + 1)))
+ .getOrElse(RegressionLeafNode(subset))
+ // format: on
+ }
+ profPoint("building done")
+
+ result
+ }
+
+ /** Finds the best split using the [[au.csiro.variantspark.algo.RegressionDecisionTree]]
+ * class's findBestSplits function then broadcast to the bestSplits variable
+ *
+ * @param treeFeatures: input an RDD of tree freatures
+ * @param subsetsToSplit: input a list of [[au.csiro.variantspark.algo.RegressionSubsetInfo]]
+ * @param br_splitter: input a Broadcast of Arrays containing
+ * the [[au.csiro.variantspark.algo.RegressionSubsetInfo]] class
+ */
+ private def findBestSplitsAndSubsets(treeFeatures: RDD[TreeFeature],
+ subsetsToSplit: List[RegressionSubsetInfo],
+ br_splitter: Broadcast[RegressionAirVariableSplitter]) = {
+ profIt("findBestSplitsAndSubsets") {
+ val subsetsToSplitAsIndices = subsetsToSplit.toArray
+ withBroadcast(treeFeatures)(subsetsToSplitAsIndices) { br_splits =>
+ val bestSplits =
+ RegressionDecisionTree.findBestSplits(treeFeatures, br_splits, br_splitter)
+ (bestSplits,
+ RegressionDecisionTree.splitSubsets(treeFeatures, bestSplits, br_splits, br_splitter))
+ }
+ }
+ }
+}
diff --git a/src/main/scala/au/csiro/variantspark/algo/RegressionRandomForest.scala b/src/main/scala/au/csiro/variantspark/algo/RegressionRandomForest.scala
new file mode 100644
index 00000000..c879bedb
--- /dev/null
+++ b/src/main/scala/au/csiro/variantspark/algo/RegressionRandomForest.scala
@@ -0,0 +1,321 @@
+package au.csiro.variantspark.algo
+
+import au.csiro.pbdava.ssparkle.common.utils.FastUtilConversions._
+import au.csiro.pbdava.ssparkle.common.utils.Logging
+import au.csiro.pbdava.ssparkle.common.utils.Timed._
+import au.csiro.variantspark.data.Feature
+import au.csiro.variantspark.metrics.Metrics
+import au.csiro.variantspark.utils.IndexedRDDFunction._
+import au.csiro.variantspark.utils.{Sample, defRng}
+import it.unimi.dsi.fastutil.longs.{Long2DoubleOpenHashMap, Long2LongOpenHashMap}
+import it.unimi.dsi.util.XorShift1024StarRandomGenerator
+import org.apache.commons.lang3.builder.ToStringBuilder
+import org.apache.spark.rdd.RDD
+
+/** Allows for normalization(scaling)of the input map values
+ */
+trait VarImportanceNormalizer {
+ def normalize(varImportance: Map[Long, Double]): Map[Long, Double]
+}
+
+/** Defines normalization variable conditionally
+ */
+case object RawVarImportanceNormalizer extends VarImportanceNormalizer {
+ override def normalize(varImportance: Map[Long, Double]): Map[Long, Double] = varImportance
+}
+
+/** Implements normalization variable scaling
+ */
+class StandardImportanceNormalizer(val scale: Double) extends VarImportanceNormalizer {
+ override def normalize(varImportance: Map[Long, Double]): Map[Long, Double] = {
+ val total = varImportance.values.sum * scale
+ varImportance.mapValues(_ / total)
+ }
+}
+
+/** Defines two different scaling values conditionally - 100% and 1%
+ */
+case object To100ImportanceNormalizer extends StandardImportanceNormalizer(100.0)
+case object ToOneImportanceNormalizer extends StandardImportanceNormalizer(1.0)
+
+/*
+TODO: IMPLEMENT THE RIGHT Voting aggregator
+*/
+
+
+/** Implements voting aggregator conditionally
+ *
+ * @param nLabels the number of labels
+ * @param nSamples the number of samples
+ */
+
+/*
+case class VotingAggregator(nSamples: Int) {
+ lazy val votes: Array[Array[Int]] = Array.fill(nSamples)(Array.fill(nLabels)(0))
+
+ /** Adds a vote with predictions and indexes
+ * @param predictions the number of predictions
+ * @param indexes the number of indexes
+ */
+ def addVote(predictions: Array[Int], indexes: Iterable[Int]) {
+ require(predictions.length <= nSamples, "Valid number of samples")
+ predictions.zip(indexes).foreach { case (v, i) => votes(i)(v) += 1 }
+ }
+
+ /** Adds a vote with predictions
+ * @param predictions the number of predictions
+ */
+ def addVote(predictions: Array[Int]): VotingAggregator = {
+ require(predictions.length == nSamples, "Full prediction range")
+ predictions.zipWithIndex.foreach { case (v, i) => votes(i)(v) += 1 }
+ this
+ }
+
+ /** Maps votes to predictions
+ *
+ */
+ def predictions: Array[Int] = votes.map(v => v.indices.maxBy(v))
+
+ /**
+ * Computes class probabilities.
+ * The result is an array with one item per sample, where
+ * each item is a vector with class probabilities for this sample.
+ * @return predicted class probabilities for each sample.
+ */
+ def classProbabilities: Array[Array[Double]] = {
+ votes.map { row =>
+ val sampleTotal = row.sum.toDouble
+ row.map(classCount => classCount / sampleTotal)
+ }
+ }
+}
+*/
+
+/** Implements random forest members conditionally
+ * @param predictor the predictor model
+ * @param oobIndexes an array of out-of-bag index values
+ */
+@SerialVersionUID(2L)
+case class RandomForestMember(predictor: PredictiveModelWithImportance,
+ oobIndexes: Array[Int] = null, oobPred: Array[Int] = null) {}
+
+/** Implements random forest models conditionally
+ * @param members the RF members
+ * @param labelCount the label count
+ * @param oobErrors the out-of-bag errors
+ */
+@SerialVersionUID(2L)
+case class RegressionRandomForestModel(members: List[RandomForestMember],
+ oobErrors: List[Double] = List.empty, params: RandomForestParams = null) {
+
+ def oobError: Double = oobErrors.last
+
+ def printout() {
+ trees.zipWithIndex.foreach {
+ case (tree, index) =>
+ println(s"Tree: ${index}")
+ tree.printout()
+ }
+ }
+
+ def trees: List[PredictiveModelWithImportance] = members.map(_.predictor)
+
+ def normalizedVariableImportance(
+ norm: VarImportanceNormalizer = To100ImportanceNormalizer): Map[Long, Double] =
+ norm.normalize(variableImportance)
+
+ /** Sets the variable importance by averaging the importance of each variable over all trees
+ * if a variable is not used in a tree it's importance for this tree is assumed to be 0
+ */
+ def variableImportance: Map[Long, Double] = {
+
+ trees
+ .map(_.variableImportanceAsFastMap)
+ .foldLeft(new Long2DoubleOpenHashMap())(_.addAll(_))
+ .asScala
+ .mapValues(_ / size)
+ }
+
+ /**
+ * Computes the number of time each of the variables appears as the splitting variable
+ * in the forest.
+ * @return map variableIndex -> variableSplitCount
+ */
+ def variableSplitCount: Map[Long, Long] = {
+ trees
+ .map(_.variableSplitCountAsFastMap)
+ .foldLeft(new Long2LongOpenHashMap())(_.addAll(_))
+ .asScala
+ }
+
+ def size: Int = members.size
+
+ def predict(indexedData: RDD[(Feature, Long)]): Array[Int] =
+ predict(indexedData, indexedData.size)
+
+ def predict(indexedData: RDD[(Feature, Long)], nSamples: Int): Array[Int] = {
+ trees
+ .map(_.predict(indexedData))
+ .foldLeft(VotingAggregator(nSamples))(_.addVote(_))
+ .predictions
+ }
+
+ def predictProb(indexedData: RDD[(Feature, Long)]): Array[Array[Double]] =
+ predictProb(indexedData, indexedData.size)
+
+ def predictProb(indexedData: RDD[(Feature, Long)], nSamples: Int): Array[Array[Double]] = {
+ val treeVotes = trees
+ .map(_.predict(indexedData))
+ .foldLeft(VotingAggregator(nSamples))(_.addVote(_))
+ treeVotes.classProbabilities
+ }
+}
+
+/** Implements random forest params conditionally
+ * @param oob the out-of-bag value
+ * @param nTryFraction the n-try fraction value
+ * @param bootstrap the bootstrap value
+ * @param subsample the subsample value
+ * @param seed the seed value
+ * @param maxDepth the maxDepth value
+ * @param minNodeSize the minNodeSize value
+ */
+case class RandomForestParams(oob: Boolean = true, nTryFraction: Double = Double.NaN,
+ bootstrap: Boolean = true, subsample: Double = Double.NaN, randomizeEquality: Boolean = true,
+ seed: Long = defRng.nextLong, maxDepth: Int = Int.MaxValue, minNodeSize: Int = 1,
+ correctImpurity: Boolean = false, airRandomSeed: Long = 0L) {
+ def resolveDefaults(nSamples: Int, nVariables: Int): RandomForestParams = {
+ RandomForestParams(oob = oob,
+ nTryFraction =
+ if (!nTryFraction.isNaN) nTryFraction else Math.sqrt(nVariables.toDouble) / nVariables,
+ bootstrap = bootstrap,
+ subsample = if (!subsample.isNaN) subsample else if (bootstrap) 1.0 else 0.666,
+ randomizeEquality = randomizeEquality, seed = seed, maxDepth = maxDepth,
+ minNodeSize = minNodeSize, correctImpurity = correctImpurity, airRandomSeed = airRandomSeed)
+ }
+ def toDecisionTreeParams(seed: Long): DecisionTreeParams = {
+ DecisionTreeParams(seed = seed, randomizeEquality = randomizeEquality, maxDepth = maxDepth,
+ minNodeSize = minNodeSize, correctImpurity = correctImpurity, airRandomSeed = airRandomSeed)
+ }
+ override def toString: String = ToStringBuilder.reflectionToString(this)
+}
+
+object RandomForestParams {
+ def fromOptions(oob: Option[Boolean] = None, mTryFraction: Option[Double] = None,
+ bootstrap: Option[Boolean] = None, subsample: Option[Double] = None,
+ seed: Option[Long] = None, maxDepth: Option[Int] = None, minNodeSize: Option[Int] = None,
+ correctImpurity: Option[Boolean] = None,
+ airRandomSeed: Option[Long] = None): RandomForestParams =
+ RandomForestParams(oob.getOrElse(true), mTryFraction.getOrElse(Double.NaN),
+ bootstrap.getOrElse(true), subsample.getOrElse(Double.NaN), true,
+ seed.getOrElse(defRng.nextLong), maxDepth.getOrElse(Int.MaxValue), minNodeSize.getOrElse(1),
+ correctImpurity.getOrElse(false), airRandomSeed.getOrElse(0L))
+}
+
+trait RandomForestCallback {
+ def onParamsResolved(actualParams: RandomForestParams) {}
+ def onTreeComplete(nTrees: Int, oobError: Double, elapsedTimeMs: Long) {}
+}
+
+// REGRESSION
+
+// TODO (Design): Avoid using type cast change design
+trait BatchRegressionTreeModel {
+ def batchTrain(indexedData: RDD[TreeFeature], labels: Array[Double], nTryFraction: Double,
+ samples: Seq[Sample]): Seq[RegressionPredictiveModelWithImportance]
+
+ def batchPredict(indexedData: RDD[TreeFeature], models: Seq[PredictiveModelWithImportance],
+ indexes: Seq[Array[Int]]): Seq[Array[Int]]
+}
+
+object RegressionRandomForest {
+ type ModelBuilderFactory = DecisionTreeParams => BatchRegressionTreeModel
+ val defaultBatchSize: Int = 10
+
+ def wideDecisionTreeBuilder(params: DecisionTreeParams): BatchRegressionTreeModel = {
+ val decisionTree = new RegressionDecisionTree(params)
+ new BatchRegressionTreeModel() {
+ override def batchTrain(indexedData: RDD[TreeFeature], labels: Array[Double],
+ nTryFraction: Double,
+ samples: Seq[Sample]): Seq[RegressionPredictiveModelWithImportance] =
+ decisionTree.batchTrainInt(indexedData, labels, nTryFraction, samples)
+
+ override def batchPredict(indexedData: RDD[TreeFeature],
+ models: Seq[PredictiveModelWithImportance], indexes: Seq[Array[Int]]): Seq[Array[Int]] =
+ DecisionTreeModel.batchPredict(indexedData.map(tf => (tf, tf.index)),
+ models.asInstanceOf[Seq[DecisionTreeModel]], indexes)
+ }
+ }
+}
+
+/** Implements random forest
+ * @param params the RF params
+ * @param modelBuilderFactory the type of model, i.e. 'wide decision tree builder'
+ */
+class RegressionRandomForest(params: RandomForestParams = RandomForestParams(),
+ modelBuilderFactory: RegressionRandomForest.ModelBuilderFactory =
+ RegressionRandomForest.wideDecisionTreeBuilder,
+ trf: TreeRepresentationFactory = DefTreeRepresentationFactory)
+ extends Logging {
+
+ // TODO (Design):make this class keep random state (could be externalised to implicit random)
+ implicit lazy val rng: XorShift1024StarRandomGenerator =
+ new XorShift1024StarRandomGenerator(params.seed)
+ def batchTrain(indexedData: RDD[(Feature, Long)], labels: Array[Double], nTrees: Int,
+ nBatchSize: Int = RandomForest.defaultBatchSize): RegressionRandomForestModel = {
+ val treeFeatures: RDD[TreeFeature] = trf.createRepresentation(indexedData)
+ batchTrainTyped(treeFeatures, labels, nTrees, nBatchSize)
+ }
+
+ // TODO (Design): Make a param rather then an extra method
+ // TODO (Func): Add OOB Calculation
+ def batchTrainTyped(treeFeatures: RDD[TreeFeature], labels: Array[Double], nTrees: Int,
+ nBatchSize: Int)(
+ implicit callback: RandomForestCallback = null): RegressionRandomForestModel = {
+
+ require(nBatchSize > 0)
+ require(nTrees > 0)
+ val nSamples = labels.length
+ val nVariables = treeFeatures.count().toInt
+ val nLabels = labels.max + 1
+
+ logDebug(s"Data: nSamples:${nSamples}, nVariables: ${nVariables}, nLabels:${nLabels}")
+
+ val actualParams = params.resolveDefaults(nSamples, nVariables)
+
+ Option(callback).foreach(_.onParamsResolved(actualParams))
+ logDebug(s"Parameters: ${actualParams}")
+ logDebug(s"Batch Training: ${nTrees} with batch size: ${nBatchSize}")
+
+ // TODO: Implement oob for regression
+ val oobAggregator = None
+
+ val builder = modelBuilderFactory(actualParams.toDecisionTreeParams(rng.nextLong))
+ val allSamples = Stream
+ .fill(nTrees)(Sample.fraction(nSamples, actualParams.subsample, actualParams.bootstrap))
+
+ val (allTrees, errors) = allSamples
+ .sliding(nBatchSize, nBatchSize)
+ .flatMap { samplesStream =>
+ time {
+
+ val samples = samplesStream.toList
+ val predictors = List.empty
+ val members = predictors.map(RandomForestMember(_))
+ val oobError = List.empty
+ members.zip(oobError)
+
+ }.withResultAndTime {
+ case (treesAndErrors, elapsedTime) =>
+ logDebug(s"Trees: ${treesAndErrors.size} >> oobError: ${treesAndErrors.last._2}, "
+ + s"time: ${elapsedTime}")
+ Option(callback).foreach(_.onTreeComplete(treesAndErrors.size, 0.0, elapsedTime))
+ }.result
+ }
+ .toList
+ .unzip
+
+ RegressionRandomForestModel(allTrees, errors, actualParams)
+ }
+
+}
diff --git a/src/main/scala/au/csiro/variantspark/algo/Split.scala b/src/main/scala/au/csiro/variantspark/algo/Split.scala
index 30810e0a..8062e106 100644
--- a/src/main/scala/au/csiro/variantspark/algo/Split.scala
+++ b/src/main/scala/au/csiro/variantspark/algo/Split.scala
@@ -14,7 +14,7 @@ case class SplitInfo(splitPoint: Double, gini: Double, leftGini: Double, rightGi
/**
* An aggregator for calculating split impurity for two sets of labels or values
- * indireclty referenced by theid indexes.
+ * indirectly referenced by their indexes.
*/
trait IndexedSplitAggregator {
def left: ImpurityAggregator
@@ -70,7 +70,7 @@ class ClassificationSplitAggregator private (val labels: Array[Int],
}
object ClassificationSplitAggregator {
- def apply(impurity: ClassficationImpurity, labels: Array[Int],
+ def apply(impurity: ClassificationImpurity, labels: Array[Int],
nCategories: Int): ClassificationSplitAggregator =
new ClassificationSplitAggregator(labels, impurity.createAggregator(nCategories),
impurity.createAggregator(nCategories))
@@ -83,7 +83,7 @@ object ClassificationSplitAggregator {
class ConfusionAggregator private (val matrix: Array[ClassificationImpurityAggregator],
val labels: Array[Int]) {
- def this(impurity: ClassficationImpurity, size: Int, nCategories: Int, labels: Array[Int]) {
+ def this(impurity: ClassificationImpurity, size: Int, nCategories: Int, labels: Array[Int]) {
this(Array.fill(size)(impurity.createAggregator(nCategories)), labels)
}
@@ -111,7 +111,7 @@ trait IndexedSplitter {
}
/**
- * A helper trait for IndexedSplitter that select the actual implementaiton
+ * A helper trait for IndexedSplitter that select the actual implementation
* base on the set of indexes themselves.
*/
trait SwitchingIndexedSplitter extends IndexedSplitter {
@@ -133,7 +133,7 @@ trait SplitterProvider {
trait FastSplitterProvider extends SplitterProvider {
/**
- * The size of the required confusino aggregator
+ * The size of the required confusion aggregator
*/
def confusionSize: Int
def createSplitter(impCalc: IndexedSplitAggregator,
@@ -150,18 +150,18 @@ trait IndexedSplitterFactory {
/**
* Depending on weather the fast memory consuming splitter can be created
* and the size of the current subset select either the fast memory consuming option
- * slower but memory efficien one
+ * slower but memory efficient one
* The way ranger does it is
* if (sampleSize/numOfUniqueValues < Q_THRESHOLD {
- * useSlowAlgorirm()
+ * useSlowAlgorithm()
* else {
- * useFastAltorithm() if (available I assume)
+ * useFastAlgorithm() if (available I assume)
*
* The value of Q_THRESHOLD is 0.02
*/
case class ThresholdIndexedSplitter(fastSplitter: IndexedSplitter, confusionSize: Int,
defaultSplitter: IndexedSplitter,
- qThreshold: Double = ThresholdIndexesSplitter.DefaultQThredhold)
+ qThreshold: Double = ThresholdIndexesSplitter.DefaultQThreshold)
extends SwitchingIndexedSplitter {
override def select(splitIndices: Array[Int]): IndexedSplitter = {
@@ -171,16 +171,16 @@ case class ThresholdIndexedSplitter(fastSplitter: IndexedSplitter, confusionSize
}
object ThresholdIndexesSplitter {
- val DefaultQThredhold: Double = 0.02
+ val DefaultQThreshold: Double = 0.02
}
/**
* The default implementation of the {{IndexedSplitterFactory}} for classification
*
*/
-class DefStatefullIndexedSpliterFactory(val impurity: ClassficationImpurity,
+class DefStatefulIndexedSplitterFactory(val impurity: ClassificationImpurity,
val labels: Array[Int], val nCategories: Int, val maxConfusionSize: Int = 10,
- val qThreshold: Double = ThresholdIndexesSplitter.DefaultQThredhold)
+ val qThreshold: Double = ThresholdIndexesSplitter.DefaultQThreshold)
extends IndexedSplitterFactory {
lazy val splitAggregator: ClassificationSplitAggregator =
@@ -197,3 +197,45 @@ class DefStatefullIndexedSpliterFactory(val impurity: ClassficationImpurity,
}
}
}
+
+/** REGRESSION SECTION */
+
+
+/**
+ * Split aggregator for classification. The indexes refer to nominal labels.
+ */
+class RegressionSplitterFactory(
+ val impurity: RegressionImpurity, val labels: Array[Double])
+ extends IndexedSplitterFactory {
+
+ lazy val splitAggregator: RegressionSplitAggregator =
+ RegressionSplitAggregator(impurity, labels)
+
+ def create(sf: SplitterProvider): IndexedSplitter = {
+ sf.createSplitter(splitAggregator)
+ }
+}
+
+class RegressionSplitAggregator private (val labels: Array[Double],
+ val left: RegressionImpurityAggregator, val right: RegressionImpurityAggregator)
+ extends IndexedSplitAggregator {
+
+ def initLabel(value: Double) {
+ right.addValue(value)
+ }
+
+ def updateLabel(value: Double) {
+ left.addValue(value)
+ right.subValue(value)
+ }
+
+ override def init(index: Int): Unit = initLabel(labels(index))
+
+ override def update(index: Int): Unit = updateLabel(labels(index))
+}
+
+object RegressionSplitAggregator {
+ def apply(impurity: RegressionImpurity, labels: Array[Double]): RegressionSplitAggregator =
+ new RegressionSplitAggregator(labels, impurity.createAggregator(),
+ impurity.createAggregator())
+}
diff --git a/src/main/scala/au/csiro/variantspark/algo/VarianceImpurity.scala b/src/main/scala/au/csiro/variantspark/algo/VarianceImpurity.scala
new file mode 100644
index 00000000..2b886928
--- /dev/null
+++ b/src/main/scala/au/csiro/variantspark/algo/VarianceImpurity.scala
@@ -0,0 +1,11 @@
+package au.csiro.variantspark.algo
+
+import au.csiro.variantspark.algo.impurity.VarianceImpurityAggregator
+
+/**
+ * Variance impurity measure
+ */
+case object VarianceImpurity extends RegressionImpurity {
+ def createAggregator(): RegressionImpurityAggregator =
+ new VarianceImpurityAggregator()
+}
\ No newline at end of file