diff --git a/.gitignore b/.gitignore index 5f15c28..8836146 100644 --- a/.gitignore +++ b/.gitignore @@ -271,4 +271,4 @@ scripts/zippedLambdaFunction/* *reddit*.cfg !example_reddit.cfg -model/pickledModels/latestModel.sav +model/pickledModels/sklearn-1.0.2/latestModel.sav diff --git a/README.md b/README.md index 50d3f09..9fa8a74 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,7 @@ The purpose of this repo is to: 1. Install Terraform CLI 2. Install AWS CLI and run `aws configure` and enter in your aws credentials. 3. JDK 17 installed (8, 11 or 17 are compatible with spark 3.4.0) + 1. You will need to add this to you're `.zshrc`: `export JAVA_HOME=\$(/usr/libexec/java_home)` 3. Clone this repository 4. You can run the tests locally yourself by doing the following (it is recommended that you manage your python environments with something like [asdf](https://asdf-vm.com/) and use python==3.12.3 as your local runtime): @@ -25,6 +26,16 @@ The purpose of this repo is to: pip install -e ."[dev]" # installs this packages in local env with dependencies pytest . -r f -s # -r f shows extra info for failures, -s disables capturing ``` + 1. If everything installed without issue then test that pyspark works, open a fresh terminal and type `pyspark` and hit enter. This is dependent upon setting JAVA_HOME in the earlier step. `exit()` out of this if it worked. + 2. You need to follow the steps in the [Getting Started](https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html#Getting_Started) section for connecting to S3, see also StackOverflow posts [like this one](https://stackoverflow.com/questions/44411493/java-lang-noclassdeffounderror-org-apache-hadoop-fs-storagestatistics/44500698#44500698) for clarifications. The important thing is that you install these 2 JARs in the pyspark classpath and that their versions match each other: + 1. **hadoop-aws** JAR must match the version of hadoop required by this version of spark. Spark 3.4.0 requires hadoop 3.3.4. + 2. the **AWS SDK For Java Bundle** JAR - this one you need to find the version that hadoop-aws was created with by looking at its [dependencies](https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-aws/3.3.4). For hadoop-aws 3.3.4 this is 1.12.262. + 3. The installed by navigating to something like the following: + ```shell + cd venv/lib/python3.12/site-packages/pyspark/jars/ + curl -O https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.3.4/hadoop-aws-3.3.4.jar + curl -O https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.12.262/aws-java-sdk-bundle-1.12.262.jar + ``` 5. From within this repository run the following: diff --git a/model/model-GBM.ipynb b/model/model-GBM.ipynb index 7f449d7..08a97d3 100644 --- a/model/model-GBM.ipynb +++ b/model/model-GBM.ipynb @@ -49,11 +49,12 @@ "import seaborn as sns\n", "import numpy as np\n", "from pyspark.sql import SparkSession\n", + "import pyspark.sql.functions as F\n", "import os\n", "import pandas as pd\n", "import sys\n", "sys.path.append('..')\n", - "import viral_reddit_posts_utils.configUtils as cu\n", + "import viral_reddit_posts_utils.config_utils as cu\n", "import pickle\n", "\n", "\n", @@ -92,22 +93,39 @@ "start_time": "2023-05-10T18:31:59.943209Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: Ignoring non-Spark config property: fs.s3a.access.key\n", + "Warning: Ignoring non-Spark config property: fs.s3a.secret.key\n", + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", + "24/04/26 07:49:05 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n", + "24/04/26 07:49:06 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties\n", + " \r" + ] + } + ], "source": [ - "cfg_file = cu.findConfig()\n", - "cfg = cu.parseConfig(cfg_file)\n", + "cfg_file = cu.find_config()\n", + "cfg = cu.parse_config(cfg_file)\n", "spark = (\n", - " SparkSession\n", - " .builder\n", - " .appName('redditData')\n", - " .config('spark.driver.extraJavaOptions', '-Duser.timezone=GMT') \n", - " .config('spark.executor.extraJavaOptions', '-Duser.timezone=GMT')\n", - " .config('spark.sql.session.timeZone', 'UTC')\n", - " .config(\"fs.s3a.access.key\", cfg['S3_access']['ACCESSKEY'])\n", - " .config(\"fs.s3a.secret.key\", cfg['S3_access']['SECRETKEY'])\n", - " .getOrCreate()\n", + " SparkSession\n", + " .builder\n", + " .appName('redditData')\n", + " .config('spark.driver.extraJavaOptions', '-Duser.timezone=GMT') \n", + " .config('spark.executor.extraJavaOptions', '-Duser.timezone=GMT')\n", + " .config('spark.sql.session.timeZone', 'UTC')\n", + " .config(\"fs.s3a.access.key\", cfg['S3_access']['ACCESSKEY'])\n", + " .config(\"fs.s3a.secret.key\", cfg['S3_access']['SECRETKEY'])\n", + " .getOrCreate()\n", ")\n", - "df = spark.read.parquet(filename).toPandas()\n", + "df = spark.read.parquet(filename)\n", + "# type issue https://stackoverflow.com/questions/76072664/convert-pyspark-dataframe-to-pandas-dataframe-fails-on-timestamp-column\n", + "df = df.withColumn(\"createdTSUTC\", F.date_format(\"createdTSUTC\", \"yyyy-MM-dd HH:mm:ss\"))\n", + "df = df.toPandas()\n", "spark.stop() # we don't need spark now" ] }, @@ -152,6 +170,15 @@ { "cell_type": "code", "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df['createdTSUTC'] = df['createdTSUTC'].apply(pd.to_datetime)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2023-05-10T18:38:35.393700Z", @@ -183,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2023-05-10T18:38:35.800002Z", @@ -197,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2023-05-10T18:38:37.334015Z", @@ -212,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2023-05-10T18:38:42.413707Z", @@ -283,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T23:55:10.778614Z", @@ -296,28 +323,26 @@ "output_type": "stream", "text": [ "total posts: 5627, viral posts: 147\n", - "AUCPR: 0.6330\n", + "AUCPR: 0.6414\n", " featureName featureImportance\n", - "0 maxScore41_60m 0.681268\n", - "4 maxScoreGrowth21_40m41_60m 0.246214\n", - "2 maxNumComments41_60m 0.032091\n", - "1 maxNumComments21_40m 0.016060\n", - "3 maxUpvoteRatio41_60m 0.012905\n", - "6 randomVar 0.009379\n", - "5 maxNumCommentsGrowth21_40m41_60m 0.002084\n", + "0 maxScore41_60m 0.683986\n", + "4 maxScoreGrowth21_40m41_60m 0.247258\n", + "2 maxNumComments41_60m 0.036959\n", + "1 maxNumComments21_40m 0.016926\n", + "3 maxUpvoteRatio41_60m 0.012954\n", + "5 maxNumCommentsGrowth21_40m41_60m 0.001917\n", + "6 randomVar 0.000000\n", "Plots for top 5% of scores\n" ] }, { "data": { - "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -369,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T23:55:33.721981Z", @@ -391,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T23:55:34.797598Z", @@ -402,19 +427,19 @@ { "data": { "text/plain": [ - "[(0.6639895052948158, 1),\n", - " (0.6639895052948158, 1),\n", - " (0.6639895052948158, 1),\n", - " (0.6423623326790783, 1),\n", - " (0.6019444311920222, 0),\n", - " (0.6019444311920222, 1),\n", - " (0.6003847625592882, 1),\n", - " (0.49642274710031775, 1),\n", - " (0.49642274710031775, 1),\n", - " (0.47257728279381206, 0)]" + "[(0.6646363726374206, 1),\n", + " (0.6646363726374206, 1),\n", + " (0.6646363726374206, 1),\n", + " (0.6429612871102689, 1),\n", + " (0.6024965993790518, 1),\n", + " (0.6024965993790518, 0),\n", + " (0.6024965993790518, 1),\n", + " (0.4956585679697745, 1),\n", + " (0.4956585679697745, 1),\n", + " (0.47174250475989055, 1)]" ] }, - "execution_count": 26, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -433,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2023-05-10T19:07:41.021891Z", @@ -465,13 +490,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T23:56:15.004497Z", "start_time": "2023-05-03T23:56:14.879378Z" - }, - "scrolled": false + } }, "outputs": [ { @@ -523,7 +547,7 @@ " 1\n", " 12z2v8n\n", " https://reddit.com/12z2v8n\n", - " 0.663990\n", + " 0.664636\n", " 2023-04-26 01:48:36+00:00\n", " 180.0\n", " 29.0\n", @@ -538,7 +562,7 @@ " 1\n", " 135tlqe\n", " https://reddit.com/135tlqe\n", - " 0.663990\n", + " 0.664636\n", " 2023-05-02 16:56:49+00:00\n", " 301.0\n", " 13.0\n", @@ -553,7 +577,7 @@ " 1\n", " 134omh1\n", " https://reddit.com/134omh1\n", - " 0.663990\n", + " 0.664636\n", " 2023-05-01 14:51:49+00:00\n", " 108.0\n", " 21.0\n", @@ -568,7 +592,7 @@ " 1\n", " 12s2767\n", " https://reddit.com/12s2767\n", - " 0.642362\n", + " 0.642961\n", " 2023-04-19 16:40:28+00:00\n", " 167.0\n", " 17.0\n", @@ -583,7 +607,7 @@ " 1\n", " 132gkp5\n", " https://reddit.com/132gkp5\n", - " 0.601944\n", + " 0.602497\n", " 2023-04-29 02:11:53+00:00\n", " 114.0\n", " 12.0\n", @@ -598,7 +622,7 @@ " 1\n", " 13637rz\n", " https://reddit.com/13637rz\n", - " 0.600385\n", + " 0.602497\n", " 2023-05-02 22:59:56+00:00\n", " 89.0\n", " 12.0\n", @@ -613,7 +637,7 @@ " 1\n", " 12lxody\n", " https://reddit.com/12lxody\n", - " 0.496423\n", + " 0.495659\n", " 2023-04-14 13:41:09+00:00\n", " 57.0\n", " 16.0\n", @@ -628,7 +652,7 @@ " 1\n", " 12jijwn\n", " https://reddit.com/12jijwn\n", - " 0.496423\n", + " 0.495659\n", " 2023-04-12 11:07:02+00:00\n", " 52.0\n", " 6.0\n", @@ -643,7 +667,7 @@ " 1\n", " 131ch4k\n", " https://reddit.com/131ch4k\n", - " 0.470956\n", + " 0.471743\n", " 2023-04-28 00:29:45+00:00\n", " 56.0\n", " 12.0\n", @@ -658,7 +682,7 @@ " 1\n", " 12iolqv\n", " https://reddit.com/12iolqv\n", - " 0.467449\n", + " 0.467947\n", " 2023-04-11 16:07:21+00:00\n", " 67.0\n", " 6.0\n", @@ -673,7 +697,7 @@ " 1\n", " 12mx6hj\n", " https://reddit.com/12mx6hj\n", - " 0.311706\n", + " 0.311868\n", " 2023-04-15 10:00:12+00:00\n", " 54.0\n", " 5.0\n", @@ -688,7 +712,7 @@ " 1\n", " 12km1m9\n", " https://reddit.com/12km1m9\n", - " 0.305131\n", + " 0.306546\n", " 2023-04-13 11:39:59+00:00\n", " 87.0\n", " 6.0\n", @@ -703,7 +727,7 @@ " 1\n", " 12qynw7\n", " https://reddit.com/12qynw7\n", - " 0.281166\n", + " 0.294694\n", " 2023-04-18 18:08:05+00:00\n", " 70.0\n", " 11.0\n", @@ -718,7 +742,7 @@ " 1\n", " 132e6vu\n", " https://reddit.com/132e6vu\n", - " 0.281166\n", + " 0.294694\n", " 2023-04-29 00:15:57+00:00\n", " 72.0\n", " 5.0\n", @@ -733,7 +757,7 @@ " 1\n", " 12kn4oy\n", " https://reddit.com/12kn4oy\n", - " 0.281166\n", + " 0.294694\n", " 2023-04-13 12:18:16+00:00\n", " 52.0\n", " 2.0\n", @@ -748,7 +772,7 @@ " 1\n", " 135gdnn\n", " https://reddit.com/135gdnn\n", - " 0.281166\n", + " 0.294694\n", " 2023-05-02 09:46:58+00:00\n", " 47.0\n", " 5.0\n", @@ -763,7 +787,7 @@ " 1\n", " 134e9tp\n", " https://reddit.com/134e9tp\n", - " 0.264704\n", + " 0.277746\n", " 2023-05-01 06:07:32+00:00\n", " 54.0\n", " 12.0\n", @@ -778,7 +802,7 @@ " 1\n", " 131q4lm\n", " https://reddit.com/131q4lm\n", - " 0.168092\n", + " 0.166035\n", " 2023-04-28 12:21:03+00:00\n", " 45.0\n", " 27.0\n", @@ -793,7 +817,7 @@ " 1\n", " 12hhgkx\n", " https://reddit.com/12hhgkx\n", - " 0.148474\n", + " 0.148463\n", " 2023-04-10 12:33:35+00:00\n", " 42.0\n", " 13.0\n", @@ -808,7 +832,7 @@ " 1\n", " 12pdqrz\n", " https://reddit.com/12pdqrz\n", - " 0.148474\n", + " 0.148463\n", " 2023-04-17 12:13:07+00:00\n", " 31.0\n", " 13.0\n", @@ -823,7 +847,7 @@ " 1\n", " 12p49vs\n", " https://reddit.com/12p49vs\n", - " 0.143906\n", + " 0.143905\n", " 2023-04-17 05:56:23+00:00\n", " 40.0\n", " 11.0\n", @@ -838,7 +862,7 @@ " 1\n", " 132mjp2\n", " https://reddit.com/132mjp2\n", - " 0.123780\n", + " 0.123769\n", " 2023-04-29 07:42:14+00:00\n", " 38.0\n", " 6.0\n", @@ -849,26 +873,11 @@ " 0\n", " \n", " \n", - " 1167\n", - " 1\n", - " 130dea2\n", - " https://reddit.com/130dea2\n", - " 0.111868\n", - " 2023-04-27 07:40:40+00:00\n", - " 22.0\n", - " 14.0\n", - " 16.0\n", - " 0.95\n", - " 0.157895\n", - " 0.142857\n", - " 0\n", - " \n", - " \n", " 104\n", " 1\n", " 12o4j95\n", " https://reddit.com/12o4j95\n", - " 0.109598\n", + " 0.116201\n", " 2023-04-16 10:49:20+00:00\n", " 53.0\n", " 7.0\n", @@ -876,6 +885,21 @@ " 0.98\n", " 0.325000\n", " 0.285714\n", + " 1\n", + " \n", + " \n", + " 1167\n", + " 1\n", + " 130dea2\n", + " https://reddit.com/130dea2\n", + " 0.111867\n", + " 2023-04-27 07:40:40+00:00\n", + " 22.0\n", + " 14.0\n", + " 16.0\n", + " 0.95\n", + " 0.157895\n", + " 0.142857\n", " 0\n", " \n", " \n", @@ -883,7 +907,7 @@ " 1\n", " 12n6ja4\n", " https://reddit.com/12n6ja4\n", - " 0.074778\n", + " 0.074776\n", " 2023-04-15 15:06:30+00:00\n", " 33.0\n", " 4.0\n", @@ -898,7 +922,7 @@ " 1\n", " 12hdvy0\n", " https://reddit.com/12hdvy0\n", - " 0.064132\n", + " 0.064151\n", " 2023-04-10 09:59:05+00:00\n", " 15.0\n", " 4.0\n", @@ -913,7 +937,7 @@ " 1\n", " 12qkrun\n", " https://reddit.com/12qkrun\n", - " 0.031148\n", + " 0.031147\n", " 2023-04-18 12:00:57+00:00\n", " 34.0\n", " 2.0\n", @@ -928,7 +952,7 @@ " 1\n", " 135dndb\n", " https://reddit.com/135dndb\n", - " 0.030027\n", + " 0.030025\n", " 2023-05-02 06:53:13+00:00\n", " 39.0\n", " 4.0\n", @@ -936,7 +960,7 @@ " 0.94\n", " 0.695652\n", " 0.000000\n", - " 1\n", + " 0\n", " \n", " \n", " 3697\n", @@ -958,7 +982,7 @@ " 0\n", " 12z0e15\n", " https://reddit.com/12z0e15\n", - " 0.601944\n", + " 0.602497\n", " 2023-04-25 23:57:03+00:00\n", " 111.0\n", " 3.0\n", @@ -973,7 +997,7 @@ " 0\n", " 12pg4tm\n", " https://reddit.com/12pg4tm\n", - " 0.472577\n", + " 0.469706\n", " 2023-04-17 13:33:03+00:00\n", " 65.0\n", " 7.0\n", @@ -984,11 +1008,26 @@ " 0\n", " \n", " \n", + " 1187\n", + " 0\n", + " 133ryam\n", + " https://reddit.com/133ryam\n", + " 0.385497\n", + " 2023-04-30 13:49:37+00:00\n", + " 46.0\n", + " 4.0\n", + " 5.0\n", + " 0.93\n", + " 0.916667\n", + " 0.250000\n", + " 0\n", + " \n", + " \n", " 2877\n", " 0\n", " 12jj0ew\n", " https://reddit.com/12jj0ew\n", - " 0.385997\n", + " 0.385497\n", " 2023-04-12 11:25:56+00:00\n", " 50.0\n", " 4.0\n", @@ -999,26 +1038,11 @@ " 0\n", " \n", " \n", - " 1187\n", - " 0\n", - " 133ryam\n", - " https://reddit.com/133ryam\n", - " 0.384457\n", - " 2023-04-30 13:49:37+00:00\n", - " 46.0\n", - " 4.0\n", - " 5.0\n", - " 0.93\n", - " 0.916667\n", - " 0.250000\n", - " 1\n", - " \n", - " \n", " 1157\n", " 0\n", " 12noe45\n", " https://reddit.com/12noe45\n", - " 0.306512\n", + " 0.306546\n", " 2023-04-16 00:23:13+00:00\n", " 79.0\n", " 8.0\n", @@ -1033,7 +1057,7 @@ " 0\n", " 12v5wac\n", " https://reddit.com/12v5wac\n", - " 0.306512\n", + " 0.306546\n", " 2023-04-22 13:26:42+00:00\n", " 104.0\n", " 9.0\n", @@ -1044,26 +1068,11 @@ " 0\n", " \n", " \n", - " 987\n", - " 0\n", - " 133fwem\n", - " https://reddit.com/133fwem\n", - " 0.281166\n", - " 2023-04-30 04:38:50+00:00\n", - " 56.0\n", - " 4.0\n", - " 4.0\n", - " 0.97\n", - " 0.555556\n", - " 0.000000\n", - " 0\n", - " \n", - " \n", " 3398\n", " 0\n", " 12zk2q2\n", " https://reddit.com/12zk2q2\n", - " 0.281166\n", + " 0.294694\n", " 2023-04-26 14:59:30+00:00\n", " 73.0\n", " 4.0\n", @@ -1074,11 +1083,26 @@ " 0\n", " \n", " \n", + " 987\n", + " 0\n", + " 133fwem\n", + " https://reddit.com/133fwem\n", + " 0.294694\n", + " 2023-04-30 04:38:50+00:00\n", + " 56.0\n", + " 4.0\n", + " 4.0\n", + " 0.97\n", + " 0.555556\n", + " 0.000000\n", + " 0\n", + " \n", + " \n", " 4584\n", " 0\n", " 12o6cbq\n", " https://reddit.com/12o6cbq\n", - " 0.281166\n", + " 0.294694\n", " 2023-04-16 12:07:08+00:00\n", " 55.0\n", " 6.0\n", @@ -1093,7 +1117,7 @@ " 0\n", " 12q0mbr\n", " https://reddit.com/12q0mbr\n", - " 0.281166\n", + " 0.294694\n", " 2023-04-17 23:00:46+00:00\n", " 65.0\n", " 9.0\n", @@ -1104,26 +1128,11 @@ " 0\n", " \n", " \n", - " 301\n", - " 0\n", - " 12z0h89\n", - " https://reddit.com/12z0h89\n", - " 0.264704\n", - " 2023-04-26 00:00:50+00:00\n", - " 46.0\n", - " 5.0\n", - " 5.0\n", - " 0.87\n", - " 0.533333\n", - " 0.000000\n", - " 0\n", - " \n", - " \n", " 576\n", " 0\n", " 12h08ll\n", " https://reddit.com/12h08ll\n", - " 0.264704\n", + " 0.277746\n", " 2023-04-09 23:49:41+00:00\n", " 44.0\n", " 4.0\n", @@ -1134,11 +1143,26 @@ " 0\n", " \n", " \n", + " 301\n", + " 0\n", + " 12z0h89\n", + " https://reddit.com/12z0h89\n", + " 0.277746\n", + " 2023-04-26 00:00:50+00:00\n", + " 46.0\n", + " 5.0\n", + " 5.0\n", + " 0.87\n", + " 0.533333\n", + " 0.000000\n", + " 0\n", + " \n", + " \n", " 1821\n", " 0\n", " 1357pwr\n", " https://reddit.com/1357pwr\n", - " 0.217024\n", + " 0.217010\n", " 2023-05-02 01:39:08+00:00\n", " 24.0\n", " 13.0\n", @@ -1169,48 +1193,48 @@ ], "text/plain": [ " target postId link prediction \\\n", - "3479 1 12z2v8n https://reddit.com/12z2v8n 0.663990 \n", - "3651 1 135tlqe https://reddit.com/135tlqe 0.663990 \n", - "1786 1 134omh1 https://reddit.com/134omh1 0.663990 \n", - "3659 1 12s2767 https://reddit.com/12s2767 0.642362 \n", - "5233 1 132gkp5 https://reddit.com/132gkp5 0.601944 \n", - "3096 1 13637rz https://reddit.com/13637rz 0.600385 \n", - "3529 1 12lxody https://reddit.com/12lxody 0.496423 \n", - "2247 1 12jijwn https://reddit.com/12jijwn 0.496423 \n", - "263 1 131ch4k https://reddit.com/131ch4k 0.470956 \n", - "1973 1 12iolqv https://reddit.com/12iolqv 0.467449 \n", - "2406 1 12mx6hj https://reddit.com/12mx6hj 0.311706 \n", - "1935 1 12km1m9 https://reddit.com/12km1m9 0.305131 \n", - "3465 1 12qynw7 https://reddit.com/12qynw7 0.281166 \n", - "4067 1 132e6vu https://reddit.com/132e6vu 0.281166 \n", - "3758 1 12kn4oy https://reddit.com/12kn4oy 0.281166 \n", - "2413 1 135gdnn https://reddit.com/135gdnn 0.281166 \n", - "985 1 134e9tp https://reddit.com/134e9tp 0.264704 \n", - "1933 1 131q4lm https://reddit.com/131q4lm 0.168092 \n", - "4046 1 12hhgkx https://reddit.com/12hhgkx 0.148474 \n", - "2908 1 12pdqrz https://reddit.com/12pdqrz 0.148474 \n", - "1040 1 12p49vs https://reddit.com/12p49vs 0.143906 \n", - "2454 1 132mjp2 https://reddit.com/132mjp2 0.123780 \n", - "1167 1 130dea2 https://reddit.com/130dea2 0.111868 \n", - "104 1 12o4j95 https://reddit.com/12o4j95 0.109598 \n", - "3824 1 12n6ja4 https://reddit.com/12n6ja4 0.074778 \n", - "86 1 12hdvy0 https://reddit.com/12hdvy0 0.064132 \n", - "3957 1 12qkrun https://reddit.com/12qkrun 0.031148 \n", - "292 1 135dndb https://reddit.com/135dndb 0.030027 \n", + "3479 1 12z2v8n https://reddit.com/12z2v8n 0.664636 \n", + "3651 1 135tlqe https://reddit.com/135tlqe 0.664636 \n", + "1786 1 134omh1 https://reddit.com/134omh1 0.664636 \n", + "3659 1 12s2767 https://reddit.com/12s2767 0.642961 \n", + "5233 1 132gkp5 https://reddit.com/132gkp5 0.602497 \n", + "3096 1 13637rz https://reddit.com/13637rz 0.602497 \n", + "3529 1 12lxody https://reddit.com/12lxody 0.495659 \n", + "2247 1 12jijwn https://reddit.com/12jijwn 0.495659 \n", + "263 1 131ch4k https://reddit.com/131ch4k 0.471743 \n", + "1973 1 12iolqv https://reddit.com/12iolqv 0.467947 \n", + "2406 1 12mx6hj https://reddit.com/12mx6hj 0.311868 \n", + "1935 1 12km1m9 https://reddit.com/12km1m9 0.306546 \n", + "3465 1 12qynw7 https://reddit.com/12qynw7 0.294694 \n", + "4067 1 132e6vu https://reddit.com/132e6vu 0.294694 \n", + "3758 1 12kn4oy https://reddit.com/12kn4oy 0.294694 \n", + "2413 1 135gdnn https://reddit.com/135gdnn 0.294694 \n", + "985 1 134e9tp https://reddit.com/134e9tp 0.277746 \n", + "1933 1 131q4lm https://reddit.com/131q4lm 0.166035 \n", + "4046 1 12hhgkx https://reddit.com/12hhgkx 0.148463 \n", + "2908 1 12pdqrz https://reddit.com/12pdqrz 0.148463 \n", + "1040 1 12p49vs https://reddit.com/12p49vs 0.143905 \n", + "2454 1 132mjp2 https://reddit.com/132mjp2 0.123769 \n", + "104 1 12o4j95 https://reddit.com/12o4j95 0.116201 \n", + "1167 1 130dea2 https://reddit.com/130dea2 0.111867 \n", + "3824 1 12n6ja4 https://reddit.com/12n6ja4 0.074776 \n", + "86 1 12hdvy0 https://reddit.com/12hdvy0 0.064151 \n", + "3957 1 12qkrun https://reddit.com/12qkrun 0.031147 \n", + "292 1 135dndb https://reddit.com/135dndb 0.030025 \n", "3697 1 133maqp https://reddit.com/133maqp 0.011083 \n", - "5105 0 12z0e15 https://reddit.com/12z0e15 0.601944 \n", - "131 0 12pg4tm https://reddit.com/12pg4tm 0.472577 \n", - "2877 0 12jj0ew https://reddit.com/12jj0ew 0.385997 \n", - "1187 0 133ryam https://reddit.com/133ryam 0.384457 \n", - "1157 0 12noe45 https://reddit.com/12noe45 0.306512 \n", - "5259 0 12v5wac https://reddit.com/12v5wac 0.306512 \n", - "987 0 133fwem https://reddit.com/133fwem 0.281166 \n", - "3398 0 12zk2q2 https://reddit.com/12zk2q2 0.281166 \n", - "4584 0 12o6cbq https://reddit.com/12o6cbq 0.281166 \n", - "5325 0 12q0mbr https://reddit.com/12q0mbr 0.281166 \n", - "301 0 12z0h89 https://reddit.com/12z0h89 0.264704 \n", - "576 0 12h08ll https://reddit.com/12h08ll 0.264704 \n", - "1821 0 1357pwr https://reddit.com/1357pwr 0.217024 \n", + "5105 0 12z0e15 https://reddit.com/12z0e15 0.602497 \n", + "131 0 12pg4tm https://reddit.com/12pg4tm 0.469706 \n", + "1187 0 133ryam https://reddit.com/133ryam 0.385497 \n", + "2877 0 12jj0ew https://reddit.com/12jj0ew 0.385497 \n", + "1157 0 12noe45 https://reddit.com/12noe45 0.306546 \n", + "5259 0 12v5wac https://reddit.com/12v5wac 0.306546 \n", + "3398 0 12zk2q2 https://reddit.com/12zk2q2 0.294694 \n", + "987 0 133fwem https://reddit.com/133fwem 0.294694 \n", + "4584 0 12o6cbq https://reddit.com/12o6cbq 0.294694 \n", + "5325 0 12q0mbr https://reddit.com/12q0mbr 0.294694 \n", + "576 0 12h08ll https://reddit.com/12h08ll 0.277746 \n", + "301 0 12z0h89 https://reddit.com/12z0h89 0.277746 \n", + "1821 0 1357pwr https://reddit.com/1357pwr 0.217010 \n", "4656 0 130j6wn https://reddit.com/130j6wn 0.203376 \n", "\n", " createdTSUTC maxScore41_60m maxNumComments21_40m \\\n", @@ -1236,8 +1260,8 @@ "2908 2023-04-17 12:13:07+00:00 31.0 13.0 \n", "1040 2023-04-17 05:56:23+00:00 40.0 11.0 \n", "2454 2023-04-29 07:42:14+00:00 38.0 6.0 \n", - "1167 2023-04-27 07:40:40+00:00 22.0 14.0 \n", "104 2023-04-16 10:49:20+00:00 53.0 7.0 \n", + "1167 2023-04-27 07:40:40+00:00 22.0 14.0 \n", "3824 2023-04-15 15:06:30+00:00 33.0 4.0 \n", "86 2023-04-10 09:59:05+00:00 15.0 4.0 \n", "3957 2023-04-18 12:00:57+00:00 34.0 2.0 \n", @@ -1245,16 +1269,16 @@ "3697 2023-04-30 11:04:20+00:00 12.0 2.0 \n", "5105 2023-04-25 23:57:03+00:00 111.0 3.0 \n", "131 2023-04-17 13:33:03+00:00 65.0 7.0 \n", - "2877 2023-04-12 11:25:56+00:00 50.0 4.0 \n", "1187 2023-04-30 13:49:37+00:00 46.0 4.0 \n", + "2877 2023-04-12 11:25:56+00:00 50.0 4.0 \n", "1157 2023-04-16 00:23:13+00:00 79.0 8.0 \n", "5259 2023-04-22 13:26:42+00:00 104.0 9.0 \n", - "987 2023-04-30 04:38:50+00:00 56.0 4.0 \n", "3398 2023-04-26 14:59:30+00:00 73.0 4.0 \n", + "987 2023-04-30 04:38:50+00:00 56.0 4.0 \n", "4584 2023-04-16 12:07:08+00:00 55.0 6.0 \n", "5325 2023-04-17 23:00:46+00:00 65.0 9.0 \n", - "301 2023-04-26 00:00:50+00:00 46.0 5.0 \n", "576 2023-04-09 23:49:41+00:00 44.0 4.0 \n", + "301 2023-04-26 00:00:50+00:00 46.0 5.0 \n", "1821 2023-05-02 01:39:08+00:00 24.0 13.0 \n", "4656 2023-04-27 11:37:41+00:00 29.0 9.0 \n", "\n", @@ -1281,8 +1305,8 @@ "2908 17.0 0.88 0.722222 \n", "1040 12.0 0.98 0.428571 \n", "2454 8.0 0.98 0.900000 \n", - "1167 16.0 0.95 0.157895 \n", "104 9.0 0.98 0.325000 \n", + "1167 16.0 0.95 0.157895 \n", "3824 8.0 1.00 0.375000 \n", "86 12.0 1.00 0.250000 \n", "3957 5.0 0.90 0.416667 \n", @@ -1290,16 +1314,16 @@ "3697 3.0 0.81 0.200000 \n", "5105 7.0 0.94 0.947368 \n", "131 12.0 0.97 1.096774 \n", - "2877 9.0 0.90 0.923077 \n", "1187 5.0 0.93 0.916667 \n", + "2877 9.0 0.90 0.923077 \n", "1157 13.0 0.98 0.837209 \n", "5259 15.0 0.99 0.824561 \n", - "987 4.0 0.97 0.555556 \n", "3398 9.0 0.96 0.697674 \n", + "987 4.0 0.97 0.555556 \n", "4584 11.0 1.00 0.666667 \n", "5325 10.0 0.89 0.756757 \n", - "301 5.0 0.87 0.533333 \n", "576 4.0 0.91 0.517241 \n", + "301 5.0 0.87 0.533333 \n", "1821 20.0 1.00 0.714286 \n", "4656 13.0 0.97 0.526316 \n", "\n", @@ -1326,30 +1350,30 @@ "2908 0.307692 0 \n", "1040 0.090909 0 \n", "2454 0.333333 0 \n", + "104 0.285714 1 \n", "1167 0.142857 0 \n", - "104 0.285714 0 \n", "3824 1.000000 0 \n", "86 2.000000 0 \n", "3957 1.500000 0 \n", - "292 0.000000 1 \n", + "292 0.000000 0 \n", "3697 0.500000 0 \n", "5105 1.333333 0 \n", "131 0.714286 0 \n", + "1187 0.250000 0 \n", "2877 1.250000 0 \n", - "1187 0.250000 1 \n", "1157 0.625000 0 \n", "5259 0.666667 0 \n", - "987 0.000000 0 \n", "3398 1.250000 0 \n", + "987 0.000000 0 \n", "4584 0.833333 0 \n", "5325 0.111111 0 \n", - "301 0.000000 0 \n", "576 0.000000 0 \n", + "301 0.000000 0 \n", "1821 0.538462 0 \n", "4656 0.444444 0 " ] }, - "execution_count": 31, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1380,21 +1404,30 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T17:02:51.219438Z", "start_time": "2023-05-03T17:02:51.145599Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ken/Documents/side_projects/RedditWork/Model/venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "import shap" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T17:03:05.548725Z", @@ -1404,14 +1437,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1433,7 +1464,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T17:03:06.944003Z", @@ -1443,14 +1474,12 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1464,7 +1493,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T17:03:08.091590Z", @@ -1474,9 +1503,19 @@ "outputs": [ { "data": { - "image/png": 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", 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" + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -1489,7 +1528,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T17:03:09.101703Z", @@ -1499,9 +1538,19 @@ "outputs": [ { "data": { - "image/png": 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", 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" + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -1525,7 +1574,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T23:53:09.046979Z", @@ -1560,7 +1609,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T20:04:47.320460Z", @@ -1576,8 +1625,447 @@ "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ken/Documents/side_projects/RedditWork/Model/venv/lib/python3.12/site-packages/sklearn/metrics/_scorer.py:548: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.\n", + " warnings.warn(\n" + ] + }, { "data": { + "text/html": [ + "
GridSearchCV(cv=StratifiedShuffleSplit(n_splits=10, random_state=0, test_size=0.2,\n",
+       "            train_size=0.8),\n",
+       "             estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
+       "             param_grid={'learning_rate': [0.1, 0.01, 0.001],\n",
+       "                         'max_depth': [2, 3, 4],\n",
+       "                         'min_samples_leaf': [1, 3, 5, 7],\n",
+       "                         'n_estimators': [50, 100, 150]},\n",
+       "             refit='top5Pctaucpr',\n",
+       "             scoring={'aucpr': make_scorer(aucpr, response_method='predict_proba'),\n",
+       "                      'top2PctPrecision': make_scorer(top2PctPrecision, response_method='predict_proba'),\n",
+       "                      'top2PctRecall': make_scorer(top2PctRecall, response_method='predict_proba'),\n",
+       "                      'top5Pctaucpr': make_scorer(top5Pctaucpr, response_method='predict_proba')},\n",
+       "             verbose=3)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ "GridSearchCV(cv=StratifiedShuffleSplit(n_splits=10, random_state=0, test_size=0.2,\n", " train_size=0.8),\n", @@ -1587,14 +2075,14 @@ " 'min_samples_leaf': [1, 3, 5, 7],\n", " 'n_estimators': [50, 100, 150]},\n", " refit='top5Pctaucpr',\n", - " scoring={'aucpr': make_scorer(aucpr, needs_proba=True),\n", - " 'top2PctPrecision': make_scorer(top2PctPrecision, needs_proba=True),\n", - " 'top2PctRecall': make_scorer(top2PctRecall, needs_proba=True),\n", - " 'top5Pctaucpr': make_scorer(top5Pctaucpr, needs_proba=True)},\n", + " scoring={'aucpr': make_scorer(aucpr, response_method='predict_proba'),\n", + " 'top2PctPrecision': make_scorer(top2PctPrecision, response_method='predict_proba'),\n", + " 'top2PctRecall': make_scorer(top2PctRecall, response_method='predict_proba'),\n", + " 'top5Pctaucpr': make_scorer(top5Pctaucpr, response_method='predict_proba')},\n", " verbose=3)" ] }, - "execution_count": 216, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1678,7 +2166,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T20:24:23.333240Z", @@ -1694,7 +2182,7 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T20:13:40.128256Z", @@ -1704,9 +2192,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -1784,7 +2272,7 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T20:18:13.695193Z", @@ -1815,7 +2303,7 @@ }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T20:18:30.390730Z", @@ -1853,31 +2341,31 @@ " \n", " 48\n", " 0.656392\n", - " 0.0669879\n", + " 0.066988\n", " {'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 1, 'n_estimators': 50}\n", " \n", " \n", " 54\n", " 0.656361\n", - " 0.0666646\n", + " 0.066665\n", " {'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 5, 'n_estimators': 50}\n", " \n", " \n", " 51\n", " 0.656361\n", - " 0.0666646\n", + " 0.066665\n", " {'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 3, 'n_estimators': 50}\n", " \n", " \n", " 57\n", " 0.656159\n", - " 0.0664449\n", + " 0.066445\n", " {'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 7, 'n_estimators': 50}\n", " \n", " \n", " 95\n", " 0.652558\n", - " 0.0692917\n", + " 0.069292\n", " {'learning_rate': 0.001, 'max_depth': 3, 'min_samples_leaf': 7, 'n_estimators': 150}\n", " \n", " \n", @@ -1885,12 +2373,12 @@ "" ], "text/plain": [ - " mean aucpr std aucpr \\\n", - "48 0.656392 0.0669879 \n", - "54 0.656361 0.0666646 \n", - "51 0.656361 0.0666646 \n", - "57 0.656159 0.0664449 \n", - "95 0.652558 0.0692917 \n", + " mean aucpr std aucpr \\\n", + "48 0.656392 0.066988 \n", + "54 0.656361 0.066665 \n", + "51 0.656361 0.066665 \n", + "57 0.656159 0.066445 \n", + "95 0.652558 0.069292 \n", "\n", " params \n", "48 {'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 1, 'n_estimators': 50} \n", @@ -1900,7 +2388,7 @@ "95 {'learning_rate': 0.001, 'max_depth': 3, 'min_samples_leaf': 7, 'n_estimators': 150} " ] }, - "execution_count": 226, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1944,7 +2432,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T23:53:15.735776Z", @@ -1961,11 +2449,418 @@ }, { "data": { + "text/html": [ + "
GradientBoostingClassifier(learning_rate=0.01, n_estimators=50)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ "GradientBoostingClassifier(learning_rate=0.01, n_estimators=50)" ] }, - "execution_count": 13, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1983,7 +2878,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T23:53:29.300451Z", @@ -1992,26 +2887,40 @@ }, "outputs": [], "source": [ - "from datetime import datetime\n", + "import datetime\n", "\n", - "now = datetime.utcnow().strftime('%Y%m%d-%H%M%S')\n", + "now = datetime.datetime.now(datetime.UTC).strftime('%Y%m%d-%H%M%S')\n", "filename = f'./pickledModels/Reddit_model_{now}_GBM.sav'\n", "pickle.dump(model, open(filename, 'wb'))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2023-05-10T18:23:43.068876Z", "start_time": "2023-05-10T18:23:42.289402Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.98341046 0.01658954]\n", + " [0.98352997 0.01647003]\n", + " [0.98352997 0.01647003]\n", + " ...\n", + " [0.98352997 0.01647003]\n", + " [0.98352997 0.01647003]\n", + " [0.98352997 0.01647003]]\n" + ] + } + ], "source": [ "# testing the model\n", - "filename = './pickledModels/Reddit_model_20230503-235329_GBM.sav'\n", + "filename = './pickledModels/Reddit_model_20240426-075204_GBM.sav'\n", "loaded_model = pickle.load(open(filename, 'rb'))\n", "result = loaded_model.predict_proba(X)\n", "print(result)" @@ -2026,7 +2935,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2023-05-03T23:54:04.652248Z", @@ -2053,7 +2962,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2023-05-10T18:39:39.824970Z", @@ -2070,7 +2979,7 @@ } ], "source": [ - "filename = './pickledModels/Reddit_model_20230503-235329_GBM.sav'\n", + "filename = './pickledModels/Reddit_model_20240426-075204_GBM.sav'\n", "model = pickle.load(open(filename, 'rb'))\n", "\n", "features = model.feature_names_in_\n", @@ -2082,7 +2991,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2023-05-10T18:39:42.361717Z", @@ -2099,14 +3008,12 @@ }, { "data": { - "image/png": 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", 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"text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2124,7 +3031,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2023-05-10T19:00:27.580940Z", @@ -2138,7 +3045,7 @@ "0.2941209400738473" ] }, - "execution_count": 19, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -2150,13 +3057,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2023-05-10T19:08:19.134536Z", "start_time": "2023-05-10T19:08:19.003644Z" - }, - "scrolled": false + } }, "outputs": [ { @@ -2417,9 +3323,1095 @@ "[154 rows x 11 columns]" ] }, - "execution_count": 30, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.588) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.729) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.540) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.765) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.588) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.713) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.549) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.782) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.586) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.667) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.587) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.669) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.546) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.778) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.642) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.830) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.593) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.774) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.544) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.773) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.587) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.738) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.527) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.718) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.630) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.840) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.601) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.793) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.584) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.766) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.617) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.821) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.541) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.769) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.514) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.786) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.547) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.781) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.544) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.745) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.531) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.777) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.533) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.757) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.588) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.730) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.579) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.727) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.562) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.700) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.639) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.726) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.672) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.759) total time= 0.5s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.691) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.690) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.795) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.722) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.770) total time= 0.5s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.683) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.780) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.533) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.688) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.501) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.666) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.591) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.654) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.561) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.670) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.452) top2PctPrecision: (test=0.435) top2PctRecall: (test=0.345) top5Pctaucpr: (test=0.635) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.670) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.826) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.587) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.737) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.554) top2PctPrecision: (test=0.481) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.732) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.670) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.826) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.566) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.751) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.611) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.689) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.571) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.717) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.696) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.829) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.603) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.671) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.542) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.650) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.663) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.757) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.655) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.744) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.686) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.813) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.708) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.794) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.761) top2PctPrecision: (test=0.692) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.827) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.760) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.844) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.555) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.673) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.766) top2PctPrecision: (test=0.720) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.833) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.719) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.827) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.681) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.795) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.675) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.784) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.567) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.690) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.693) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.793) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.774) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.841) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.733) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.808) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.671) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.796) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.751) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.813) total time= 0.5s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.542) top2PctPrecision: (test=0.520) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.617) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.565) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.642) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.637) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.729) total time= 0.5s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.550) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.628) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.668) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.782) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.655) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.780) total time= 0.6s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.617) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.705) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.608) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.686) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.574) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.664) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.646) top2PctPrecision: (test=0.472) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.701) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.646) top2PctPrecision: (test=0.472) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.701) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.657) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.825) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.647) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.771) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.646) top2PctPrecision: (test=0.472) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.701) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.670) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.723) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.630) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.712) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.475) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.566) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.657) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.755) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.523) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.681) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.628) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.721) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.630) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.720) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.744) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.812) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.747) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.815) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.707) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.804) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.696) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.816) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.714) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.828) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.716) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.831) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.480) top2PctPrecision: (test=0.543) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.573) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.587) top2PctPrecision: (test=0.571) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.773) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.485) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.734) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.502) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.566) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.500) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.577) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.501) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.578) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.498) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.571) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.525) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.617) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.529) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.608) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.534) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.616) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.537) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.629) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.537) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.619) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.754) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.816) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.591) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.733) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.692) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.773) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.760) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.822) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.759) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.819) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.674) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.833) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.619) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.674) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.666) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.726) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.613) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.786) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.615) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.670) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.763) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.823) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.617) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.692) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.676) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.752) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.666) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.743) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.638) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.723) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.681) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.760) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.681) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.763) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.710) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.855) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.684) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.764) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.728) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.837) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.713) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.818) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.693) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.795) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.606) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.803) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.712) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.890) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.623) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.698) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.580) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.770) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.491) top2PctPrecision: (test=0.435) top2PctRecall: (test=0.345) top5Pctaucpr: (test=0.752) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.615) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.786) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.668) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.765) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.597) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.796) total time= 0.5s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.554) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.664) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.722) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.771) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.566) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.714) total time= 0.5s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.658) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.754) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.644) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.744) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.615) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.715) total time= 0.5s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.682) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.818) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.696) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.829) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.650) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.823) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.682) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.818) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.696) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.829) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.584) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.734) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.566) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.750) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.566) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.751) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.554) top2PctPrecision: (test=0.481) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.732) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.571) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.717) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.566) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.751) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.554) top2PctPrecision: (test=0.481) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.732) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.572) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.782) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.551) top2PctPrecision: (test=0.542) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.686) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.586) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.708) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.572) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.782) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.576) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.847) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.584) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.705) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.572) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.782) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.547) top2PctPrecision: (test=0.542) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.678) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.625) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.716) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.639) top2PctPrecision: (test=0.615) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.738) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.637) top2PctPrecision: (test=0.615) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.735) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.628) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.722) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.526) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.743) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.528) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.787) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.531) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.830) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.547) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.600) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.495) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.598) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.721) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.804) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.597) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.672) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.592) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.765) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.590) top2PctPrecision: (test=0.542) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.763) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.662) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.764) total time= 0.5s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.659) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.782) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.648) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.758) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.649) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.733) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.505) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.711) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.589) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.693) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.574) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.664) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.586) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.674) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.505) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.711) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.665) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.825) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.669) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.808) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.647) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.771) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.639) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.771) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.657) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.825) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.539) top2PctPrecision: (test=0.560) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.659) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.550) top2PctPrecision: (test=0.560) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.732) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.552) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.731) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.539) top2PctPrecision: (test=0.560) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.659) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.550) top2PctPrecision: (test=0.560) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.732) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.552) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.731) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.550) top2PctPrecision: (test=0.560) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.732) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.496) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.753) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.496) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.754) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.511) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.612) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.531) top2PctPrecision: (test=0.483) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.640) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.518) top2PctPrecision: (test=0.444) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.623) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.478) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.704) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.518) top2PctPrecision: (test=0.444) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.623) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.461) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.676) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.458) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.673) total time= 0.5s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.672) top2PctPrecision: (test=0.679) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.814) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.496) top2PctPrecision: (test=0.400) top2PctRecall: (test=0.345) top5Pctaucpr: (test=0.594) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.458) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.673) total time= 0.5s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.719) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.824) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.718) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.804) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.701) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.796) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.588) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.729) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.601) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.746) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.540) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.763) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.589) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.723) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.599) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.737) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.750) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.809) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.718) top2PctPrecision: (test=0.680) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.821) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.550) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.670) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.535) top2PctPrecision: (test=0.435) top2PctRecall: (test=0.345) top5Pctaucpr: (test=0.652) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.589) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.720) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.580) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.710) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.748) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.801) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.741) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.799) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.546) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.669) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.535) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.673) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.572) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.702) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.549) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.677) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.538) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.664) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.560) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.681) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.532) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.650) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.518) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.674) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.583) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.705) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.536) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.664) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.534) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.664) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.723) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.771) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.553) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.683) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.677) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.804) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.523) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.652) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.688) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.785) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.646) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.766) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.746) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.800) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.737) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.789) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.726) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.775) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.764) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.835) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.611) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.679) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.747) top2PctPrecision: (test=0.704) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.813) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.764) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.835) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.561) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.685) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.568) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.690) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.542) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.650) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.561) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.685) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.697) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.873) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.683) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.869) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.687) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.865) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.696) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.874) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.696) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.866) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.691) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.843) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.697) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.825) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.637) top2PctPrecision: (test=0.615) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.730) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.627) top2PctPrecision: (test=0.583) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.720) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.641) top2PctPrecision: (test=0.615) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.738) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.696) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.866) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.688) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.841) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.581) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.702) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.715) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.804) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.710) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.796) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.687) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.784) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.581) top2PctPrecision: (test=0.542) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.667) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.605) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.675) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.587) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.784) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.591) top2PctPrecision: (test=0.542) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.763) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.597) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.775) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.575) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.680) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.534) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.630) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.513) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.608) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.561) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.660) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.626) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.814) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.497) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.590) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.588) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.760) total time= 0.5s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.647) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.771) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.639) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.771) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.670) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.723) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.646) top2PctPrecision: (test=0.472) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.701) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.639) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.771) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.669) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.808) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.646) top2PctPrecision: (test=0.472) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.701) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.646) top2PctPrecision: (test=0.472) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.701) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.537) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.659) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.505) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.711) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.669) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.808) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.714) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.833) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.714) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.828) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.539) top2PctPrecision: (test=0.560) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.659) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.628) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.721) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.628) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.721) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.583) top2PctPrecision: (test=0.577) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.675) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.598) top2PctPrecision: (test=0.577) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.690) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.597) top2PctPrecision: (test=0.577) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.704) total time= 0.6s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.480) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.707) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.480) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.707) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.590) top2PctPrecision: (test=0.571) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.755) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.596) top2PctPrecision: (test=0.607) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.797) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.501) top2PctPrecision: (test=0.400) top2PctRecall: (test=0.345) top5Pctaucpr: (test=0.601) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.521) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.621) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.584) top2PctPrecision: (test=0.607) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.779) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.608) top2PctPrecision: (test=0.586) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.782) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.511) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.620) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.538) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.766) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.700) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.813) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.745) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.801) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.668) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.783) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.642) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.827) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.693) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.773) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.563) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.685) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.763) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.823) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.645) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.729) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.679) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.798) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.636) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.702) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.676) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.816) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.687) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.781) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.686) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.784) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.665) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.795) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.695) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.793) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.715) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.818) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.730) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.782) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.702) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.806) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.654) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.745) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.634) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.720) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.686) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.761) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.654) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.727) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.613) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.679) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.754) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.810) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.691) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.777) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.736) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.784) total time= 0.6s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.595) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.717) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.722) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.770) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.542) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.692) total time= 0.5s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.685) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.769) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.602) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.792) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.704) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.752) total time= 0.5s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.658) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.717) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.690) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.811) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.688) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.814) total time= 0.5s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.685) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.769) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.658) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.833) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.687) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.816) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.571) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.717) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.658) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.833) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.687) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.816) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.682) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.818) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.608) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.674) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.579) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.727) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.566) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.750) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.585) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.734) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.610) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.688) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.641) top2PctPrecision: (test=0.615) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.738) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.636) top2PctPrecision: (test=0.615) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.729) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.624) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.715) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.641) top2PctPrecision: (test=0.615) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.738) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.696) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.866) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.689) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.843) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.695) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.825) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.641) top2PctPrecision: (test=0.615) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.737) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.581) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.701) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.566) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.709) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.547) top2PctPrecision: (test=0.542) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.678) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.577) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.836) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.615) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.726) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.595) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.685) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.596) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.684) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.516) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.737) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.520) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.814) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.534) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.838) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.520) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.743) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.516) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.815) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.620) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.684) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.578) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.669) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.672) top2PctPrecision: (test=0.680) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.790) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.612) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.699) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.633) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.724) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.523) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.681) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.672) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.834) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.665) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.825) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.657) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.825) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.672) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.834) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.710) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.778) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.701) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.874) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.697) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.874) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.628) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.721) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.710) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.778) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.701) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.874) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.697) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.874) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.562) top2PctPrecision: (test=0.441) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.774) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.552) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.731) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.515) top2PctPrecision: (test=0.483) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.624) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.496) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.637) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.531) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.638) total time= 0.5s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.504) top2PctPrecision: (test=0.576) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.603) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.718) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.792) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.717) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.789) total time= 0.5s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.672) top2PctPrecision: (test=0.679) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.814) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.677) top2PctPrecision: (test=0.679) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.820) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.679) top2PctPrecision: (test=0.679) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.816) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.518) top2PctPrecision: (test=0.444) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.623) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.677) top2PctPrecision: (test=0.679) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.820) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.609) top2PctPrecision: (test=0.586) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.782) total time= 0.5s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.572) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.700) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.669) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.805) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.615) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.690) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.724) top2PctPrecision: (test=0.680) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.829) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.544) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.679) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.751) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.809) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.648) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.796) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.535) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.754) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.529) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.742) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.655) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.823) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.578) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.659) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.574) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.654) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.579) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.650) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.589) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.662) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.537) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.759) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.608) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.755) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.595) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.784) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.593) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.748) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.606) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.749) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.624) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.781) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.586) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.798) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.617) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.824) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.699) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.845) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.594) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.819) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.610) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.808) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.538) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.733) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.572) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.760) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.531) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.749) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.551) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.808) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.547) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.765) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.564) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.646) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.537) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.628) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.516) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.606) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.510) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.726) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.495) top2PctPrecision: (test=0.435) top2PctRecall: (test=0.345) top5Pctaucpr: (test=0.730) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.530) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.638) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.620) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.718) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.683) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.869) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.687) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.865) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.696) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.871) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.587) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.737) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.603) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.671) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.620) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.718) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.614) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.709) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.611) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.689) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.620) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.718) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.677) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.776) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.561) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.685) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.565) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.687) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.719) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.827) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.686) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.783) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.672) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.782) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.719) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.827) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.686) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.783) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.677) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.787) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.760) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.844) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.710) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.796) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.769) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.835) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.760) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.844) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.556) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.671) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.581) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.701) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.485) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.581) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.545) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.657) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.674) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.800) total time= 0.5s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.666) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.781) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.564) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.685) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.755) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.818) total time= 0.5s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.504) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.610) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.562) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.681) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.552) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.666) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.727) top2PctPrecision: (test=0.680) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.787) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.761) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.825) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.761) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.823) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.729) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.881) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.729) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.881) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.740) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.885) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.444) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.534) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.630) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.712) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.647) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.771) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.639) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.771) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.657) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.825) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.672) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.834) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.665) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.825) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.537) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.659) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.716) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.831) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.696) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.816) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.714) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.828) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.707) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.804) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.552) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.731) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.539) top2PctPrecision: (test=0.560) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.659) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.550) top2PctPrecision: (test=0.560) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.732) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.662) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.852) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.603) top2PctPrecision: (test=0.607) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.774) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.425) top2PctPrecision: (test=0.543) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.503) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.587) top2PctPrecision: (test=0.571) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.774) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.596) top2PctPrecision: (test=0.607) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.797) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.590) top2PctPrecision: (test=0.571) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.756) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.534) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.639) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.460) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.676) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.590) top2PctPrecision: (test=0.571) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.756) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.588) top2PctPrecision: (test=0.571) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.753) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.460) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.676) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.461) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.676) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.679) top2PctPrecision: (test=0.679) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.837) total time= 0.5s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.629) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.686) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.597) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.669) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.576) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.656) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.595) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.667) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.537) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.759) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.588) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.711) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.687) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.807) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.686) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.807) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.681) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.804) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.585) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.715) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.538) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.731) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.747) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.805) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.694) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.746) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.724) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.775) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.560) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.686) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.562) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.686) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.736) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.790) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.659) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.738) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.769) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.830) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.775) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.832) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.732) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.784) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.778) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.840) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.775) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.833) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.766) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.822) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.688) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.758) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.741) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.797) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.617) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.718) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.587) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.668) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.655) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.743) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.637) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.732) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.649) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.760) total time= 0.5s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.658) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.759) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.637) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.735) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.628) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.743) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.678) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.806) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.664) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.791) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.640) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.764) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.566) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.751) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.584) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.734) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.566) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.750) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.614) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.710) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.554) top2PctPrecision: (test=0.481) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.732) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.764) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.835) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.658) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.833) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.688) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.817) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.685) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.769) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.699) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.832) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.747) top2PctPrecision: (test=0.720) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.813) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.566) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.693) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.772) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.840) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.682) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.761) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.686) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.813) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.693) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.793) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.697) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.795) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.566) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.693) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.555) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.673) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.700) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.829) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.696) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.866) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.681) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.777) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.703) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.832) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.663) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.793) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.750) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.829) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.718) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.805) total time= 0.5s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.719) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.786) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.667) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.798) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.661) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.792) total time= 0.5s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.674) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.793) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.565) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.642) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.630) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.719) total time= 0.6s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.514) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.778) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.534) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.844) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.444) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.534) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.475) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.566) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.474) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.564) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.729) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.881) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.729) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.881) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.740) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.885) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.444) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.534) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.729) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.881) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.740) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.885) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.630) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.712) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.630) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.712) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.657) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.755) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.689) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.794) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.686) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.790) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.690) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.793) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.689) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.794) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.686) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.790) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.697) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.874) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.628) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.721) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.628) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.721) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.630) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.720) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.710) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.778) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.686) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.790) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.697) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.803) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.646) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.711) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.561) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.694) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.733) top2PctPrecision: (test=0.760) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.795) total time= 0.6s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.564) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.698) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.562) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.694) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.570) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.699) total time= 0.5s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.659) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.758) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.642) top2PctPrecision: (test=0.760) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.720) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.582) top2PctPrecision: (test=0.519) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.718) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.651) top2PctPrecision: (test=0.704) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.733) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.635) top2PctPrecision: (test=0.704) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.710) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.695) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.763) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.655) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.850) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.637) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.691) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.674) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.814) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.615) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.669) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.675) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.749) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.639) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.707) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.716) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.836) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.717) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.816) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.711) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.826) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.720) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.840) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.730) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.831) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.682) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.778) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.698) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.787) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.687) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.792) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.668) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.767) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.728) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.890) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.696) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.804) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.673) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.794) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.691) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.781) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.683) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.788) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.637) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.732) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.711) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.848) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.687) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.813) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.643) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.756) total time= 0.5s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.667) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.772) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.639) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.740) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.639) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.746) total time= 0.6s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.677) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.790) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.641) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.748) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.614) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.741) total time= 0.5s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.662) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.755) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.634) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.731) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.610) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.713) total time= 0.5s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.672) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.750) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.699) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.807) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.683) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.799) total time= 0.5s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.561) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.685) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.568) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.690) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.685) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.769) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.611) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.679) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.747) top2PctPrecision: (test=0.704) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.813) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.685) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.769) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.748) top2PctPrecision: (test=0.704) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.861) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.747) top2PctPrecision: (test=0.720) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.813) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.682) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.818) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.748) top2PctPrecision: (test=0.704) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.861) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.684) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.812) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.715) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.804) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.555) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.674) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.574) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.693) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.715) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.804) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.710) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.796) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.581) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.702) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.715) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.804) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.765) top2PctPrecision: (test=0.606) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.833) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.689) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.770) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.717) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.824) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.688) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.841) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.684) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.764) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.714) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.789) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.601) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.809) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.631) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.720) total time= 0.5s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.546) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.639) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.571) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.661) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.591) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.762) total time= 0.5s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.676) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.807) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.657) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.758) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.585) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.756) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.502) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.591) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.609) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.790) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.544) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.653) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.672) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.834) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.665) top2PctPrecision: (test=0.528) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.825) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.537) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.659) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.537) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.659) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.574) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.664) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.523) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.681) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.589) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.693) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.574) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.664) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.586) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.674) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.589) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.693) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.630) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.720) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.628) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.721) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.628) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.721) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.630) top2PctPrecision: (test=0.581) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.720) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.565) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.698) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.557) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.688) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.747) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.815) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.696) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.816) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.699) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.816) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.747) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.815) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.744) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.812) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.747) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.815) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.747) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.815) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.645) top2PctPrecision: (test=0.760) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.737) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.634) top2PctPrecision: (test=0.760) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.686) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.705) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.800) total time= 0.5s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.713) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.812) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.550) top2PctPrecision: (test=0.576) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.660) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.555) top2PctPrecision: (test=0.576) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.660) total time= 0.5s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.713) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.812) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.718) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.792) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.718) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.790) total time= 0.5s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.577) top2PctPrecision: (test=0.519) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.717) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.572) top2PctPrecision: (test=0.519) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.710) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.675) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.775) total time= 0.5s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.712) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.831) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.759) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.818) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.638) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.847) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.656) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.851) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.669) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.824) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.616) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.814) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.585) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.643) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.564) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.627) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.547) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.607) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.568) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.634) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.679) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.819) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.618) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.797) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.598) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.740) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.602) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.773) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.572) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.742) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.565) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.809) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.602) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.777) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.579) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.781) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.619) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.823) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.593) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.782) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.597) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.749) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.603) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.666) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.499) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.701) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.574) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.679) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.531) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.836) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.531) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.821) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.504) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.734) total time= 0.6s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.635) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.736) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.538) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.743) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.497) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.674) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.615) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.812) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.664) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.780) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.680) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.798) total time= 0.5s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.596) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.787) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.602) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.780) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.575) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.749) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.571) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.717) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.614) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.710) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.611) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.689) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.620) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.718) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.677) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.776) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.663) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.757) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.671) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.764) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.677) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.776) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.663) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.757) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.558) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.678) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.764) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.835) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.608) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.674) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.603) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.671) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.761) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.845) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.693) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.793) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.623) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.808) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.566) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.693) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.762) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.829) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.694) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.776) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.686) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.813) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.693) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.793) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.694) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.792) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.686) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.813) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.765) top2PctPrecision: (test=0.606) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.833) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.683) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.794) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.568) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.649) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.569) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.647) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.566) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.641) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.620) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.705) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.625) top2PctPrecision: (test=0.542) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.714) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.617) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.698) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.618) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.678) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.524) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.824) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.520) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.743) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.621) top2PctPrecision: (test=0.593) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.717) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.668) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.780) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.669) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.778) total time= 0.5s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.657) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.755) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.657) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.755) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.729) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.881) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.630) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.712) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.670) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.723) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.646) top2PctPrecision: (test=0.472) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.701) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.729) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.881) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.474) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.564) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.565) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.698) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.747) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.815) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.707) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.804) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.714) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.833) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.699) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.816) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.716) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.831) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.714) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.833) total time= 0.1s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.714) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.828) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.716) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.831) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.714) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.833) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.699) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.816) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.707) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.804) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.711) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.810) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.708) top2PctPrecision: (test=0.690) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.802) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.568) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.698) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.657) top2PctPrecision: (test=0.760) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.740) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.640) top2PctPrecision: (test=0.760) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.719) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.737) top2PctPrecision: (test=0.760) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.834) total time= 0.5s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.577) top2PctPrecision: (test=0.519) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.717) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.572) top2PctPrecision: (test=0.519) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.710) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.667) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.766) total time= 0.5s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.659) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.758) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.653) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.751) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.582) top2PctPrecision: (test=0.519) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.717) total time= 0.5s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.668) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.787) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.538) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.654) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.522) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.637) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.714) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.816) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.718) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.835) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.709) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.804) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.768) top2PctPrecision: (test=0.826) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.833) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.541) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.660) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.693) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.777) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.573) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.720) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.710) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.792) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.725) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.823) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.722) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.858) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.682) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.803) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.694) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.799) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.679) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.779) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.685) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.787) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.649) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.744) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.577) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.661) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.558) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.689) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.589) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.731) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.593) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.678) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.488) top2PctPrecision: (test=0.435) top2PctRecall: (test=0.345) top5Pctaucpr: (test=0.710) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.555) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.634) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.533) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.610) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.516) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.618) total time= 0.6s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.554) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.669) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.560) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.631) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.508) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.610) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.535) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.795) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.519) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.806) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.494) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.731) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.554) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.681) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.547) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.723) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.535) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.718) total time= 0.5s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.566) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.750) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.677) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.776) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.663) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.757) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.671) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.764) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.683) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.869) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.687) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.865) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.696) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.871) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.683) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.869) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.687) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.865) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.655) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.744) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.614) top2PctPrecision: (test=0.630) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.709) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.579) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.727) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.642) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.855) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.641) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.836) total time= 0.3s\n", + "[CV 5/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.705) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.805) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.566) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.709) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.547) top2PctPrecision: (test=0.542) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.678) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.571) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.836) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.642) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.855) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.576) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.847) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.572) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.836) total time= 0.4s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.572) top2PctPrecision: (test=0.600) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.782) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.578) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.816) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.578) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.717) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.538) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.625) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.554) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.667) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.573) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.675) total time= 0.5s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.553) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.653) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.551) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.660) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.709) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.788) total time= 0.5s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.720) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.787) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.761) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.824) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.760) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.822) total time= 0.5s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.522) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.633) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.569) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.689) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.583) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.704) total time= 0.5s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.630) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.712) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.630) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.712) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.630) top2PctPrecision: (test=0.450) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.712) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.475) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.566) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.474) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.564) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.474) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.564) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.729) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.881) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.646) top2PctPrecision: (test=0.472) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.701) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.670) top2PctPrecision: (test=0.640) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.723) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.696) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.816) total time= 0.1s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.699) top2PctPrecision: (test=0.654) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.816) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.747) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.815) total time= 0.4s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.744) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.812) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.747) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.815) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.557) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.702) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.565) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.698) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.686) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.790) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.690) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.793) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.689) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.794) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.701) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.874) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.697) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.874) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.635) top2PctPrecision: (test=0.533) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.692) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.633) top2PctPrecision: (test=0.533) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.687) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.636) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.782) total time= 0.5s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.662) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.753) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.628) top2PctPrecision: (test=0.533) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.761) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.622) top2PctPrecision: (test=0.533) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.761) total time= 0.5s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.639) top2PctPrecision: (test=0.533) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.758) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.640) top2PctPrecision: (test=0.533) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.753) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.638) top2PctPrecision: (test=0.533) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.738) total time= 0.5s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.648) top2PctPrecision: (test=0.533) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.734) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.645) top2PctPrecision: (test=0.533) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.744) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.644) top2PctPrecision: (test=0.533) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.744) total time= 0.5s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.597) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.667) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.641) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.854) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.530) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.749) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.565) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.691) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.706) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.773) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.534) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.653) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.706) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.806) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.700) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.796) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.543) top2PctPrecision: (test=0.435) top2PctRecall: (test=0.345) top5Pctaucpr: (test=0.664) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.773) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.838) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.635) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.798) total time= 0.2s\n", + "[CV 3/10] END learning_rate=0.1, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.584) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.718) total time= 0.2s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.550) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.788) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.540) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.767) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.570) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.643) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.579) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.638) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.581) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.647) total time= 0.3s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.557) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.634) total time= 0.4s\n", + "[CV 1/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.591) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.653) total time= 0.1s\n", + "[CV 9/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.727) top2PctPrecision: (test=0.783) top2PctRecall: (test=0.621) top5Pctaucpr: (test=0.865) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.682) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.785) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.666) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.790) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.711) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.804) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.687) top2PctPrecision: (test=0.739) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.793) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.686) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.792) total time= 0.5s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.674) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.751) total time= 0.3s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.673) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.772) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.662) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.768) total time= 0.6s\n", + "[CV 4/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.587) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.775) total time= 0.4s\n", + "[CV 5/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.641) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.748) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.723) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.773) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.535) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.656) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.510) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.640) total time= 0.5s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.537) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.644) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.527) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.684) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.1, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.525) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.689) total time= 0.5s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.542) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.650) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.748) top2PctPrecision: (test=0.704) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.861) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.603) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.671) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.542) top2PctPrecision: (test=0.478) top2PctRecall: (test=0.379) top5Pctaucpr: (test=0.650) total time= 0.1s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.748) top2PctPrecision: (test=0.704) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.861) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.650) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.823) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.670) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.826) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.585) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.734) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.650) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.823) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.670) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.826) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.658) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.833) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.651) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.824) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.566) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.708) total time= 0.1s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.576) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.847) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.571) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.836) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.642) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.855) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.641) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.836) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.621) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.832) total time= 0.4s\n", + "[CV 3/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.566) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.709) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.639) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.834) total time= 0.3s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.620) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.834) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.642) top2PctPrecision: (test=0.708) top2PctRecall: (test=0.586) top5Pctaucpr: (test=0.855) total time= 0.1s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.638) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.833) total time= 0.2s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.626) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.843) total time= 0.4s\n", + "[CV 4/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.592) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.768) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.721) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.811) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.564) top2PctPrecision: (test=0.522) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.677) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.744) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.836) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.757) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.821) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.550) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.660) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.744) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.836) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.711) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.792) total time= 0.3s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.706) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.786) total time= 0.5s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.717) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.796) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.698) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.774) total time= 0.4s\n", + "[CV 6/10] END learning_rate=0.01, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.684) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.746) total time= 0.6s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.586) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.674) total time= 0.2s\n", + "[CV 5/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.669) top2PctPrecision: (test=0.488) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.808) total time= 0.3s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.505) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.711) total time= 0.1s\n", + "[CV 3/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.523) top2PctPrecision: (test=0.565) top2PctRecall: (test=0.448) top5Pctaucpr: (test=0.681) total time= 0.2s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.589) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.693) total time= 0.3s\n", + "[CV 2/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.546) top2PctPrecision: (test=0.417) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.730) total time= 0.1s\n", + "[CV 1/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.586) top2PctPrecision: (test=0.432) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.674) total time= 0.2s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.662) top2PctPrecision: (test=0.467) top2PctRecall: (test=0.724) top5Pctaucpr: (test=0.865) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.444) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.534) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.475) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.566) total time= 0.2s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=2, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.740) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.885) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.744) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.812) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.557) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.688) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.557) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.702) total time= 0.4s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.710) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.778) total time= 0.1s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.701) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.690) top5Pctaucpr: (test=0.874) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.690) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.793) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.689) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.794) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.557) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.688) total time= 0.2s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.557) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.702) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.565) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.698) total time= 0.1s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.557) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.688) total time= 0.3s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=3, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.557) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.414) top5Pctaucpr: (test=0.702) total time= 0.4s\n", + "[CV 8/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=50; aucpr: (test=0.564) top2PctPrecision: (test=0.500) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.698) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=100; aucpr: (test=0.664) top2PctPrecision: (test=0.625) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.731) total time= 0.4s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=1, n_estimators=150; aucpr: (test=0.669) top2PctPrecision: (test=0.609) top2PctRecall: (test=0.483) top5Pctaucpr: (test=0.752) total time= 0.5s\n", + "[CV 10/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=50; aucpr: (test=0.630) top2PctPrecision: (test=0.533) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.766) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=100; aucpr: (test=0.678) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.771) total time= 0.3s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=3, n_estimators=150; aucpr: (test=0.674) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.766) total time= 0.5s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=50; aucpr: (test=0.657) top2PctPrecision: (test=0.760) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.740) total time= 0.2s\n", + "[CV 9/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=100; aucpr: (test=0.653) top2PctPrecision: (test=0.652) top2PctRecall: (test=0.517) top5Pctaucpr: (test=0.751) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=5, n_estimators=150; aucpr: (test=0.740) top2PctPrecision: (test=0.760) top2PctRecall: (test=0.655) top5Pctaucpr: (test=0.836) total time= 0.5s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=50; aucpr: (test=0.681) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.773) total time= 0.2s\n", + "[CV 6/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=100; aucpr: (test=0.692) top2PctPrecision: (test=0.696) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.762) total time= 0.3s\n", + "[CV 7/10] END learning_rate=0.001, max_depth=4, min_samples_leaf=7, n_estimators=150; aucpr: (test=0.628) top2PctPrecision: (test=0.667) top2PctRecall: (test=0.552) top5Pctaucpr: (test=0.700) total time= 0.5s\n" + ] } ], "source": [ @@ -2449,9 +4441,9 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "py37", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "py37" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2463,7 +4455,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.12.3" }, "toc": { "base_numbering": 1, @@ -2485,5 +4477,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/model/notebookArchive/model-GBM-20240426.ipynb b/model/notebookArchive/model-GBM-20240426.ipynb new file mode 100644 index 0000000..bef317c --- /dev/null +++ b/model/notebookArchive/model-GBM-20240426.ipynb @@ -0,0 +1,3395 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "20230510: Rerunning last section to increase step up threshold to top 1%. When I added more subreddits it started sending too many posts throughout the day and the model is not currently trained for the activity levels in the various subreddits. Increasing the threshold will reduce the amount it steps up." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:31:57.198159Z", + "start_time": "2023-05-10T18:31:57.142023Z" + } + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:31:59.840540Z", + "start_time": "2023-05-10T18:31:57.328401Z" + } + }, + "outputs": [], + "source": [ + "import modelUtils as mu\n", + "import pyarrow.parquet as pq\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "from pyspark.sql import SparkSession\n", + "import pyspark.sql.functions as F\n", + "import os\n", + "import pandas as pd\n", + "import sys\n", + "sys.path.append('..')\n", + "import viral_reddit_posts_utils.config_utils as cu\n", + "import pickle\n", + "\n", + "\n", + "os.environ['TZ'] = 'UTC'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read from S3\n", + "\n", + "After the data has been collected, the dataset size is relatively small with significant reduction over scraped dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:31:59.927659Z", + "start_time": "2023-05-10T18:31:59.847319Z" + } + }, + "outputs": [], + "source": [ + "filename = 's3a://data-kennethmyers/model_data/redditAggregatedData-20230502.parquet'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:32:31.503506Z", + "start_time": "2023-05-10T18:31:59.943209Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: Ignoring non-Spark config property: fs.s3a.access.key\n", + "Warning: Ignoring non-Spark config property: fs.s3a.secret.key\n", + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", + "24/04/26 07:49:05 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n", + "24/04/26 07:49:06 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties\n", + " \r" + ] + } + ], + "source": [ + "cfg_file = cu.find_config()\n", + "cfg = cu.parse_config(cfg_file)\n", + "spark = (\n", + " SparkSession\n", + " .builder\n", + " .appName('redditData')\n", + " .config('spark.driver.extraJavaOptions', '-Duser.timezone=GMT') \n", + " .config('spark.executor.extraJavaOptions', '-Duser.timezone=GMT')\n", + " .config('spark.sql.session.timeZone', 'UTC')\n", + " .config(\"fs.s3a.access.key\", cfg['S3_access']['ACCESSKEY'])\n", + " .config(\"fs.s3a.secret.key\", cfg['S3_access']['SECRETKEY'])\n", + " .getOrCreate()\n", + ")\n", + "df = spark.read.parquet(filename)\n", + "# type issue https://stackoverflow.com/questions/76072664/convert-pyspark-dataframe-to-pandas-dataframe-fails-on-timestamp-column\n", + "df = df.withColumn(\"createdTSUTC\", F.date_format(\"createdTSUTC\", \"yyyy-MM-dd HH:mm:ss\"))\n", + "df = df.toPandas()\n", + "spark.stop() # we don't need spark now" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:38:30.825618Z", + "start_time": "2023-05-10T18:38:30.325073Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.model_selection import StratifiedShuffleSplit\n", + "from sklearn.metrics import accuracy_score, recall_score, precision_score, precision_recall_curve, auc\n", + "from sklearn.preprocessing import StandardScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:38:30.919898Z", + "start_time": "2023-05-10T18:38:30.832950Z" + } + }, + "outputs": [], + "source": [ + "df = df.fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df['createdTSUTC'] = df['createdTSUTC'].apply(pd.to_datetime)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:38:35.393700Z", + "start_time": "2023-05-10T18:38:35.310757Z" + } + }, + "outputs": [], + "source": [ + "# testing some new features, \n", + "# did not want to add these to the ETL work until understanding them better\n", + "def timeFeatures(df):\n", + " df = df.copy()\n", + " # make it timezone aware\n", + " df['createdTSUTC'] = df['createdTSUTC'].dt.tz_localize('UTC')\n", + " # convert to EST\n", + " df['createdTSEST'] = df['createdTSUTC'].dt.tz_convert('US/Eastern')\n", + " \n", + " df['hour'] = df['createdTSEST'].apply(lambda x: x.hour)\n", + " df['dayofweek'] = df['createdTSEST'].apply(lambda x: x.dayofweek)\n", + " \n", + " df['time0006'] = df['hour'].apply(lambda x: 1 if 0 <= x < 6 else 0)\n", + " df['time0612'] = df['hour'].apply(lambda x: 1 if 6 <= x < 12 else 0)\n", + " df['time1218'] = df['hour'].apply(lambda x: 1 if 12 <= x < 18 else 0)\n", + " df['time1800'] = df['hour'].apply(lambda x: 1 if 18 <= x else 0)\n", + " df['sunday'] = df['dayofweek'].apply(lambda x: 1 if x == 6 else 0)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:38:35.800002Z", + "start_time": "2023-05-10T18:38:35.531456Z" + } + }, + "outputs": [], + "source": [ + "df = timeFeatures(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:38:37.334015Z", + "start_time": "2023-05-10T18:38:37.257380Z" + } + }, + "outputs": [], + "source": [ + "# adding a random variable, I like to see how it performs against other variables\n", + "df['randomVar'] = np.random.binomial(1, df['target'].mean(), len(df))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:38:42.413707Z", + "start_time": "2023-05-10T18:38:42.300471Z" + } + }, + "outputs": [], + "source": [ + "def plotPrecRecAUCPR(y, y_pred):\n", + " \"\"\"\n", + " To do: let the user pass in q (lower bound threshold)\n", + " \"\"\"\n", + " topQuantiles = np.arange(.95,1,0.005)\n", + " threshold = np.quantile(y_pred, 0.95)\n", + " topQuantilesThresholds = np.quantile(y_pred, topQuantiles)\n", + " totalTargets = y.sum()\n", + " topQPrecisions = [y[y_pred>=t].mean() for t in topQuantilesThresholds]\n", + " topQRecalls = [y[y_pred>=t].sum()/totalTargets for t in topQuantilesThresholds]\n", + " \n", + " y2 = y[y>=threshold]\n", + " y_pred2 = y_pred[y>=threshold]\n", + " precisions, recalls, thresholds = precision_recall_curve(y, y_pred)\n", + " aucpr = auc(recalls, precisions)\n", + "\n", + " print(\"Plots for top 5% of scores\")\n", + " \n", + " fig, axes = plt.subplots(1, 3, figsize=(15, 5), sharey=False, squeeze=False)\n", + "\n", + " axes[0,0].plot(topQuantiles, topQPrecisions)\n", + " axes[0,0].set_title(f'Precisions against quantile')\n", + " axes[0,0].set_ylim([0,1.05])\n", + " axes[0,0].set_xlabel('quantile')\n", + " axes[0,0].set_ylabel('precisions')\n", + "\n", + " axes[0,1].plot(topQuantiles, topQRecalls)\n", + " axes[0,1].set_title(f'Recalls against quantile')\n", + " axes[0,1].set_ylim([0,1.05])\n", + " axes[0,1].set_xlabel('quantile')\n", + " axes[0,1].set_ylabel('recalls')\n", + "\n", + " axes[0,2].plot(recalls, precisions)\n", + " axes[0,2].set_title(f'AUCPR={aucpr:.04f}')\n", + " axes[0,2].set_xlabel('recalls')\n", + " axes[0,2].set_ylabel('precisions')\n", + "\n", + " plt.show()\n", + " \n", + "\n", + "def modelMetrics(X_train, y_train, X_test, y_test):\n", + " model = GradientBoostingClassifier(n_estimators=100, learning_rate=.01, min_samples_leaf=5, max_depth=3)\n", + " model.fit(X_train, y_train)\n", + " y_pred = model.predict(X_test)\n", + " y_pred_proba = model.predict_proba(X_test)[:, 1]\n", + " # print(f\"accuracy: {accuracy_score(y_test,y_pred)}\") # doesn't mean much with imbalanced data\n", + " # print(f\"precision: {precision_score(y_test,y_pred)}\")\n", + " # print(f\"recall: {recall_score(y_test,y_pred)}\")\n", + " precisions, recalls, thresholds = precision_recall_curve(y_test, y_pred_proba)\n", + " aucpr = auc(recalls, precisions)\n", + " print(f\"AUCPR: {aucpr:.04f}\")\n", + "\n", + " fi = pd.DataFrame({'featureName':model.feature_names_in_, 'featureImportance':model.feature_importances_})\n", + " print(fi.sort_values('featureImportance', ascending=False).to_string())\n", + "\n", + " plotPrecRecAUCPR(y=y_test, y_pred=y_pred_proba)\n", + " \n", + " return y_pred_proba" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T23:55:10.778614Z", + "start_time": "2023-05-03T23:55:09.721120Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total posts: 5627, viral posts: 147\n", + "AUCPR: 0.6414\n", + " featureName featureImportance\n", + "0 maxScore41_60m 0.683986\n", + "4 maxScoreGrowth21_40m41_60m 0.247258\n", + "2 maxNumComments41_60m 0.036959\n", + "1 maxNumComments21_40m 0.016926\n", + "3 maxUpvoteRatio41_60m 0.012954\n", + "5 maxNumCommentsGrowth21_40m41_60m 0.001917\n", + "6 randomVar 0.000000\n", + "Plots for top 5% of scores\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# scaler = StandardScaler()\n", + "# scaler.fit(X_train)\n", + "# X_train_std, X_test_std = pd.DataFrame(scaler.transform(X_train), columns=X_train.columns), scaler.transform(X_test)\n", + " \n", + "features = [\n", + " #'maxScore20m',\n", + "# 'maxScore21_40m',\n", + " 'maxScore41_60m',\n", + " #'maxNumComments20m',\n", + " 'maxNumComments21_40m',\n", + " 'maxNumComments41_60m',\n", + "# 'maxUpvoteRatio20m', \n", + "# 'maxUpvoteRatio21_40m',\n", + " 'maxUpvoteRatio41_60m',\n", + "# 'maxNumGildings20m', # we know these are bad features from prior analysis\n", + "# 'maxNumGildings21_40m',\n", + "# 'maxNumGildings41_60m',\n", + " 'maxScoreGrowth21_40m41_60m',\n", + " 'maxNumCommentsGrowth21_40m41_60m',\n", + " 'randomVar',\n", + "# 'time0006', \n", + "# 'time0612',\n", + "# 'time1218',\n", + "# 'time1800',\n", + "# 'sunday'\n", + "]\n", + "\n", + "X = df[features]\n", + "y = df['target']\n", + "print(f\"total posts: {len(y)}, viral posts: {y.sum()}\") # how many targets are there, This is a highly imbalanced problem, only ~2.5% of posts in rising go viral\n", + "\n", + "sss = StratifiedShuffleSplit(n_splits=1, train_size=0.8, test_size=0.2, random_state=0)\n", + "train_index, test_index = next(sss.split(X,y))\n", + "X_train, y_train, X_test, y_test = X.iloc[train_index], y.iloc[train_index], X.iloc[test_index], y.iloc[test_index]\n", + "\n", + "y_pred_proba = modelMetrics(X_train, y_train, X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a pretty good model so far. There seems to be evidence for removing the upvote ratio features. I ran an experiment without them (not shown here) and it seemed to perform well without them but I want to experiment more before making a decision." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T23:55:33.721981Z", + "start_time": "2023-05-03T23:55:33.662204Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], + "source": [ + "print(sum(y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T23:55:34.797598Z", + "start_time": "2023-05-03T23:55:34.734148Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0.6646363726374206, 1),\n", + " (0.6646363726374206, 1),\n", + " (0.6646363726374206, 1),\n", + " (0.6429612871102689, 1),\n", + " (0.6024965993790518, 1),\n", + " (0.6024965993790518, 0),\n", + " (0.6024965993790518, 1),\n", + " (0.4956585679697745, 1),\n", + " (0.4956585679697745, 1),\n", + " (0.47174250475989055, 1)]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sorted top 10 predictions\n", + "sorted(list(zip(y_pred_proba, y_test)), key=lambda x:x[0])[::-1][:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is cool to see many of the posts being scored at the top." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T19:07:41.021891Z", + "start_time": "2023-05-10T19:07:40.935663Z" + } + }, + "outputs": [], + "source": [ + "# I wanted to see some data on the viral posts (why were some accurately predicted and some not)\n", + "# and the posts that had high viral probability but were considered non-viral\n", + "def getOriginalPostId(df, test_index, y_test, y_pred_proba, threshold, features):\n", + " fullTestData = df.iloc[test_index].copy(deep=True)\n", + " fullTestData['prediction'] = y_pred_proba\n", + " fullTestData['link'] = fullTestData['postId'].apply(lambda x: \"https://reddit.com/\"+x)\n", + " target1Indexes = np.where(y_test==1)[0]\n", + " viralPosts = fullTestData[fullTestData['target']==1].sort_values('prediction', ascending=False)\n", + " nonViralPosts = fullTestData[fullTestData['target']==0].sort_values('prediction', ascending=False)\n", + " nonViralPosts = nonViralPosts[nonViralPosts['prediction']>=threshold]\n", + " \n", + " p = len(viralPosts)\n", + " tp = len(viralPosts[viralPosts['prediction']>=threshold])\n", + " fp = len(nonViralPosts)\n", + " \n", + " print(f\"recall = {tp/p}\")\n", + " print(f\"precision = {tp/(tp+fp)}\")\n", + "\n", + " return pd.concat([viralPosts, nonViralPosts], axis=0)[['target', 'postId', 'link', 'prediction', 'createdTSUTC']+features]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T23:56:15.004497Z", + "start_time": "2023-05-03T23:56:14.879378Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "recall = 0.5862068965517241\n", + "precision = 0.5483870967741935\n" + ] + }, + { + "data": { + "text/html": [ + "
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targetpostIdlinkpredictioncreatedTSUTCmaxScore41_60mmaxNumComments21_40mmaxNumComments41_60mmaxUpvoteRatio41_60mmaxScoreGrowth21_40m41_60mmaxNumCommentsGrowth21_40m41_60mrandomVar
3479112z2v8nhttps://reddit.com/12z2v8n0.6646362023-04-26 01:48:36+00:00180.029.051.00.902.1034480.7586210
36511135tlqehttps://reddit.com/135tlqe0.6646362023-05-02 16:56:49+00:00301.013.041.00.902.7625002.1538460
17861134omh1https://reddit.com/134omh10.6646362023-05-01 14:51:49+00:00108.021.053.00.881.5116281.5238100
3659112s2767https://reddit.com/12s27670.6429612023-04-19 16:40:28+00:00167.017.039.00.951.8793101.2941180
52331132gkp5https://reddit.com/132gkp50.6024972023-04-29 02:11:53+00:00114.012.025.00.961.0727271.0833330
3096113637rzhttps://reddit.com/13637rz0.6024972023-05-02 22:59:56+00:0089.012.016.00.961.3421050.3333330
3529112lxodyhttps://reddit.com/12lxody0.4956592023-04-14 13:41:09+00:0057.016.041.00.841.1111111.5625000
2247112jijwnhttps://reddit.com/12jijwn0.4956592023-04-12 11:07:02+00:0052.06.016.00.871.0800001.6666670
2631131ch4khttps://reddit.com/131ch4k0.4717432023-04-28 00:29:45+00:0056.012.016.00.950.9310340.3333330
1973112iolqvhttps://reddit.com/12iolqv0.4679472023-04-11 16:07:21+00:0067.06.09.00.941.3103450.5000000
2406112mx6hjhttps://reddit.com/12mx6hj0.3118682023-04-15 10:00:12+00:0054.05.08.00.970.8620690.6000000
1935112km1m9https://reddit.com/12km1m90.3065462023-04-13 11:39:59+00:0087.06.06.00.960.8125000.0000000
3465112qynw7https://reddit.com/12qynw70.2946942023-04-18 18:08:05+00:0070.011.023.00.960.7073171.0909090
40671132e6vuhttps://reddit.com/132e6vu0.2946942023-04-29 00:15:57+00:0072.05.05.00.970.7142860.0000000
3758112kn4oyhttps://reddit.com/12kn4oy0.2946942023-04-13 12:18:16+00:0052.02.04.00.880.7333331.0000000
24131135gdnnhttps://reddit.com/135gdnn0.2946942023-05-02 09:46:58+00:0047.05.08.01.000.5666670.6000000
9851134e9tphttps://reddit.com/134e9tp0.2777462023-05-01 06:07:32+00:0054.012.014.00.920.5428570.1666670
19331131q4lmhttps://reddit.com/131q4lm0.1660352023-04-28 12:21:03+00:0045.027.046.00.920.6071430.7037040
4046112hhgkxhttps://reddit.com/12hhgkx0.1484632023-04-10 12:33:35+00:0042.013.020.00.880.7500000.5384620
2908112pdqrzhttps://reddit.com/12pdqrz0.1484632023-04-17 12:13:07+00:0031.013.017.00.880.7222220.3076920
1040112p49vshttps://reddit.com/12p49vs0.1439052023-04-17 05:56:23+00:0040.011.012.00.980.4285710.0909090
24541132mjp2https://reddit.com/132mjp20.1237692023-04-29 07:42:14+00:0038.06.08.00.980.9000000.3333330
104112o4j95https://reddit.com/12o4j950.1162012023-04-16 10:49:20+00:0053.07.09.00.980.3250000.2857141
11671130dea2https://reddit.com/130dea20.1118672023-04-27 07:40:40+00:0022.014.016.00.950.1578950.1428570
3824112n6ja4https://reddit.com/12n6ja40.0747762023-04-15 15:06:30+00:0033.04.08.01.000.3750001.0000000
86112hdvy0https://reddit.com/12hdvy00.0641512023-04-10 09:59:05+00:0015.04.012.01.000.2500002.0000000
3957112qkrunhttps://reddit.com/12qkrun0.0311472023-04-18 12:00:57+00:0034.02.05.00.900.4166671.5000000
2921135dndbhttps://reddit.com/135dndb0.0300252023-05-02 06:53:13+00:0039.04.04.00.940.6956520.0000000
36971133maqphttps://reddit.com/133maqp0.0110832023-04-30 11:04:20+00:0012.02.03.00.810.2000000.5000000
5105012z0e15https://reddit.com/12z0e150.6024972023-04-25 23:57:03+00:00111.03.07.00.940.9473681.3333330
131012pg4tmhttps://reddit.com/12pg4tm0.4697062023-04-17 13:33:03+00:0065.07.012.00.971.0967740.7142860
11870133ryamhttps://reddit.com/133ryam0.3854972023-04-30 13:49:37+00:0046.04.05.00.930.9166670.2500000
2877012jj0ewhttps://reddit.com/12jj0ew0.3854972023-04-12 11:25:56+00:0050.04.09.00.900.9230771.2500000
1157012noe45https://reddit.com/12noe450.3065462023-04-16 00:23:13+00:0079.08.013.00.980.8372090.6250000
5259012v5wachttps://reddit.com/12v5wac0.3065462023-04-22 13:26:42+00:00104.09.015.00.990.8245610.6666670
3398012zk2q2https://reddit.com/12zk2q20.2946942023-04-26 14:59:30+00:0073.04.09.00.960.6976741.2500000
9870133fwemhttps://reddit.com/133fwem0.2946942023-04-30 04:38:50+00:0056.04.04.00.970.5555560.0000000
4584012o6cbqhttps://reddit.com/12o6cbq0.2946942023-04-16 12:07:08+00:0055.06.011.01.000.6666670.8333330
5325012q0mbrhttps://reddit.com/12q0mbr0.2946942023-04-17 23:00:46+00:0065.09.010.00.890.7567570.1111110
576012h08llhttps://reddit.com/12h08ll0.2777462023-04-09 23:49:41+00:0044.04.04.00.910.5172410.0000000
301012z0h89https://reddit.com/12z0h890.2777462023-04-26 00:00:50+00:0046.05.05.00.870.5333330.0000000
182101357pwrhttps://reddit.com/1357pwr0.2170102023-05-02 01:39:08+00:0024.013.020.01.000.7142860.5384620
46560130j6wnhttps://reddit.com/130j6wn0.2033762023-04-27 11:37:41+00:0029.09.013.00.970.5263160.4444440
\n", + "
" + ], + "text/plain": [ + " target postId link prediction \\\n", + "3479 1 12z2v8n https://reddit.com/12z2v8n 0.664636 \n", + "3651 1 135tlqe https://reddit.com/135tlqe 0.664636 \n", + "1786 1 134omh1 https://reddit.com/134omh1 0.664636 \n", + "3659 1 12s2767 https://reddit.com/12s2767 0.642961 \n", + "5233 1 132gkp5 https://reddit.com/132gkp5 0.602497 \n", + "3096 1 13637rz https://reddit.com/13637rz 0.602497 \n", + "3529 1 12lxody https://reddit.com/12lxody 0.495659 \n", + "2247 1 12jijwn https://reddit.com/12jijwn 0.495659 \n", + "263 1 131ch4k https://reddit.com/131ch4k 0.471743 \n", + "1973 1 12iolqv https://reddit.com/12iolqv 0.467947 \n", + "2406 1 12mx6hj https://reddit.com/12mx6hj 0.311868 \n", + "1935 1 12km1m9 https://reddit.com/12km1m9 0.306546 \n", + "3465 1 12qynw7 https://reddit.com/12qynw7 0.294694 \n", + "4067 1 132e6vu https://reddit.com/132e6vu 0.294694 \n", + "3758 1 12kn4oy https://reddit.com/12kn4oy 0.294694 \n", + "2413 1 135gdnn https://reddit.com/135gdnn 0.294694 \n", + "985 1 134e9tp https://reddit.com/134e9tp 0.277746 \n", + "1933 1 131q4lm https://reddit.com/131q4lm 0.166035 \n", + "4046 1 12hhgkx https://reddit.com/12hhgkx 0.148463 \n", + "2908 1 12pdqrz https://reddit.com/12pdqrz 0.148463 \n", + "1040 1 12p49vs https://reddit.com/12p49vs 0.143905 \n", + "2454 1 132mjp2 https://reddit.com/132mjp2 0.123769 \n", + "104 1 12o4j95 https://reddit.com/12o4j95 0.116201 \n", + "1167 1 130dea2 https://reddit.com/130dea2 0.111867 \n", + "3824 1 12n6ja4 https://reddit.com/12n6ja4 0.074776 \n", + "86 1 12hdvy0 https://reddit.com/12hdvy0 0.064151 \n", + "3957 1 12qkrun https://reddit.com/12qkrun 0.031147 \n", + "292 1 135dndb https://reddit.com/135dndb 0.030025 \n", + "3697 1 133maqp https://reddit.com/133maqp 0.011083 \n", + "5105 0 12z0e15 https://reddit.com/12z0e15 0.602497 \n", + "131 0 12pg4tm https://reddit.com/12pg4tm 0.469706 \n", + "1187 0 133ryam https://reddit.com/133ryam 0.385497 \n", + "2877 0 12jj0ew https://reddit.com/12jj0ew 0.385497 \n", + "1157 0 12noe45 https://reddit.com/12noe45 0.306546 \n", + "5259 0 12v5wac https://reddit.com/12v5wac 0.306546 \n", + "3398 0 12zk2q2 https://reddit.com/12zk2q2 0.294694 \n", + "987 0 133fwem https://reddit.com/133fwem 0.294694 \n", + "4584 0 12o6cbq https://reddit.com/12o6cbq 0.294694 \n", + "5325 0 12q0mbr https://reddit.com/12q0mbr 0.294694 \n", + "576 0 12h08ll https://reddit.com/12h08ll 0.277746 \n", + "301 0 12z0h89 https://reddit.com/12z0h89 0.277746 \n", + "1821 0 1357pwr https://reddit.com/1357pwr 0.217010 \n", + "4656 0 130j6wn https://reddit.com/130j6wn 0.203376 \n", + "\n", + " createdTSUTC maxScore41_60m maxNumComments21_40m \\\n", + "3479 2023-04-26 01:48:36+00:00 180.0 29.0 \n", + "3651 2023-05-02 16:56:49+00:00 301.0 13.0 \n", + "1786 2023-05-01 14:51:49+00:00 108.0 21.0 \n", + "3659 2023-04-19 16:40:28+00:00 167.0 17.0 \n", + "5233 2023-04-29 02:11:53+00:00 114.0 12.0 \n", + "3096 2023-05-02 22:59:56+00:00 89.0 12.0 \n", + "3529 2023-04-14 13:41:09+00:00 57.0 16.0 \n", + "2247 2023-04-12 11:07:02+00:00 52.0 6.0 \n", + "263 2023-04-28 00:29:45+00:00 56.0 12.0 \n", + "1973 2023-04-11 16:07:21+00:00 67.0 6.0 \n", + "2406 2023-04-15 10:00:12+00:00 54.0 5.0 \n", + "1935 2023-04-13 11:39:59+00:00 87.0 6.0 \n", + "3465 2023-04-18 18:08:05+00:00 70.0 11.0 \n", + "4067 2023-04-29 00:15:57+00:00 72.0 5.0 \n", + "3758 2023-04-13 12:18:16+00:00 52.0 2.0 \n", + "2413 2023-05-02 09:46:58+00:00 47.0 5.0 \n", + "985 2023-05-01 06:07:32+00:00 54.0 12.0 \n", + "1933 2023-04-28 12:21:03+00:00 45.0 27.0 \n", + "4046 2023-04-10 12:33:35+00:00 42.0 13.0 \n", + "2908 2023-04-17 12:13:07+00:00 31.0 13.0 \n", + "1040 2023-04-17 05:56:23+00:00 40.0 11.0 \n", + "2454 2023-04-29 07:42:14+00:00 38.0 6.0 \n", + "104 2023-04-16 10:49:20+00:00 53.0 7.0 \n", + "1167 2023-04-27 07:40:40+00:00 22.0 14.0 \n", + "3824 2023-04-15 15:06:30+00:00 33.0 4.0 \n", + "86 2023-04-10 09:59:05+00:00 15.0 4.0 \n", + "3957 2023-04-18 12:00:57+00:00 34.0 2.0 \n", + "292 2023-05-02 06:53:13+00:00 39.0 4.0 \n", + "3697 2023-04-30 11:04:20+00:00 12.0 2.0 \n", + "5105 2023-04-25 23:57:03+00:00 111.0 3.0 \n", + "131 2023-04-17 13:33:03+00:00 65.0 7.0 \n", + "1187 2023-04-30 13:49:37+00:00 46.0 4.0 \n", + "2877 2023-04-12 11:25:56+00:00 50.0 4.0 \n", + "1157 2023-04-16 00:23:13+00:00 79.0 8.0 \n", + "5259 2023-04-22 13:26:42+00:00 104.0 9.0 \n", + "3398 2023-04-26 14:59:30+00:00 73.0 4.0 \n", + "987 2023-04-30 04:38:50+00:00 56.0 4.0 \n", + "4584 2023-04-16 12:07:08+00:00 55.0 6.0 \n", + "5325 2023-04-17 23:00:46+00:00 65.0 9.0 \n", + "576 2023-04-09 23:49:41+00:00 44.0 4.0 \n", + "301 2023-04-26 00:00:50+00:00 46.0 5.0 \n", + "1821 2023-05-02 01:39:08+00:00 24.0 13.0 \n", + "4656 2023-04-27 11:37:41+00:00 29.0 9.0 \n", + "\n", + " maxNumComments41_60m maxUpvoteRatio41_60m maxScoreGrowth21_40m41_60m \\\n", + "3479 51.0 0.90 2.103448 \n", + "3651 41.0 0.90 2.762500 \n", + "1786 53.0 0.88 1.511628 \n", + "3659 39.0 0.95 1.879310 \n", + "5233 25.0 0.96 1.072727 \n", + "3096 16.0 0.96 1.342105 \n", + "3529 41.0 0.84 1.111111 \n", + "2247 16.0 0.87 1.080000 \n", + "263 16.0 0.95 0.931034 \n", + "1973 9.0 0.94 1.310345 \n", + "2406 8.0 0.97 0.862069 \n", + "1935 6.0 0.96 0.812500 \n", + "3465 23.0 0.96 0.707317 \n", + "4067 5.0 0.97 0.714286 \n", + "3758 4.0 0.88 0.733333 \n", + "2413 8.0 1.00 0.566667 \n", + "985 14.0 0.92 0.542857 \n", + "1933 46.0 0.92 0.607143 \n", + "4046 20.0 0.88 0.750000 \n", + "2908 17.0 0.88 0.722222 \n", + "1040 12.0 0.98 0.428571 \n", + "2454 8.0 0.98 0.900000 \n", + "104 9.0 0.98 0.325000 \n", + "1167 16.0 0.95 0.157895 \n", + "3824 8.0 1.00 0.375000 \n", + "86 12.0 1.00 0.250000 \n", + "3957 5.0 0.90 0.416667 \n", + "292 4.0 0.94 0.695652 \n", + "3697 3.0 0.81 0.200000 \n", + "5105 7.0 0.94 0.947368 \n", + "131 12.0 0.97 1.096774 \n", + "1187 5.0 0.93 0.916667 \n", + "2877 9.0 0.90 0.923077 \n", + "1157 13.0 0.98 0.837209 \n", + "5259 15.0 0.99 0.824561 \n", + "3398 9.0 0.96 0.697674 \n", + "987 4.0 0.97 0.555556 \n", + "4584 11.0 1.00 0.666667 \n", + "5325 10.0 0.89 0.756757 \n", + "576 4.0 0.91 0.517241 \n", + "301 5.0 0.87 0.533333 \n", + "1821 20.0 1.00 0.714286 \n", + "4656 13.0 0.97 0.526316 \n", + "\n", + " maxNumCommentsGrowth21_40m41_60m randomVar \n", + "3479 0.758621 0 \n", + "3651 2.153846 0 \n", + "1786 1.523810 0 \n", + "3659 1.294118 0 \n", + "5233 1.083333 0 \n", + "3096 0.333333 0 \n", + "3529 1.562500 0 \n", + "2247 1.666667 0 \n", + "263 0.333333 0 \n", + "1973 0.500000 0 \n", + "2406 0.600000 0 \n", + "1935 0.000000 0 \n", + "3465 1.090909 0 \n", + "4067 0.000000 0 \n", + "3758 1.000000 0 \n", + "2413 0.600000 0 \n", + "985 0.166667 0 \n", + "1933 0.703704 0 \n", + "4046 0.538462 0 \n", + "2908 0.307692 0 \n", + "1040 0.090909 0 \n", + "2454 0.333333 0 \n", + "104 0.285714 1 \n", + "1167 0.142857 0 \n", + "3824 1.000000 0 \n", + "86 2.000000 0 \n", + "3957 1.500000 0 \n", + "292 0.000000 0 \n", + "3697 0.500000 0 \n", + "5105 1.333333 0 \n", + "131 0.714286 0 \n", + "1187 0.250000 0 \n", + "2877 1.250000 0 \n", + "1157 0.625000 0 \n", + "5259 0.666667 0 \n", + "3398 1.250000 0 \n", + "987 0.000000 0 \n", + "4584 0.833333 0 \n", + "5325 0.111111 0 \n", + "576 0.000000 0 \n", + "301 0.000000 0 \n", + "1821 0.538462 0 \n", + "4656 0.444444 0 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "getOriginalPostId(df, test_index, y_test, y_pred_proba, threshold=0.2, features=features)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Below notes are a little outdated and were from the Logistic Regression model, but some of it is still relevant.**\n", + "\n", + "I created the above to spotcheck a few examples. The 2 that I failed to predict were both pretty viral (exceeding two I succeeded in predicting) despite having relatively weak metrics (3 comments after an hour, <25 upvotes after an hour). This shows that there is pretty good lag in posts going viral and that perhaps more than an hour is needed to accurately predict virality. Another thing I noticed was that those two posts were both about tragedies, so perhaps there there is something about tragedies going extremely viral but are slower to gain traction.\n", + "\n", + "The three posts that were predicted to go viral but ultimately were not all had pretty high upvote counts after the first hour but must have lost traction afterwards, this is further evidence that maybe it would be better to extend the data collection another 30-60 minutes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SHAP analysis\n", + "\n", + "For imbalanced datasets, I like to look at the SHAP values for the target of interest (viral posts)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T17:02:51.219438Z", + "start_time": "2023-05-03T17:02:51.145599Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ken/Documents/side_projects/RedditWork/Model/venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import shap" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T17:03:05.548725Z", + "start_time": "2023-05-03T17:02:51.778819Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# https://shap.readthedocs.io/en/latest/example_notebooks/api_examples/plots/beeswarm.html\n", + "model = GradientBoostingClassifier(n_estimators=100, learning_rate=.01, min_samples_leaf=5, max_depth=3)\n", + "model.fit(X, y)\n", + "explainer = shap.Explainer(model, X, feature_names=X.columns)\n", + "shap_values = explainer(X)\n", + "shap_values_0 = explainer(X[y==0])\n", + "shap_values_1 = explainer(X[y==1])\n", + "\n", + "shap.plots.beeswarm(shap_values_1, show=False)\n", + "ax = plt.gca()\n", + "# You can change the min and max value of xaxis by changing the arguments of:\n", + "#ax.set_xlim(-3, 3) \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T17:03:06.944003Z", + "start_time": "2023-05-03T17:03:05.567827Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "shap.plots.beeswarm(shap_values_0, show=False)\n", + "ax = plt.gca()\n", + "# You can change the min and max value of xaxis by changing the arguments of:\n", + "#ax.set_xlim(-3, 3) \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T17:03:08.091590Z", + "start_time": "2023-05-03T17:03:06.960223Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "shap.plots.bar(shap_values.cohorts([i for i in map(str, y.values)]).abs.mean(0), show=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T17:03:09.101703Z", + "start_time": "2023-05-03T17:03:08.101429Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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fbHUB/Pz8nNrx8fEBsI3OsCpatKgtkQD2PyO//fYb/fr1c6hbvXp1Zs+ezZ9//pnuZIKVpefXcCTB/P36Y6nV9/GEAS0gIgcMmYNlb+wNz0n1eaEwGNgSjpzCMjQKLie53kazitCqKny3GkvMby6drzgUh+JQHIpDcSiO2x/H9eNfr3/ueV0cntfF4X5dfTeuWwOgQE58Jzl+eXT9DXjFihUdnpcoUcLheZ48eRyeBwQEOH3RdH2bd+MaGaVkgsh9rGjRog7Pw8LCAAgPD3eqGxgYyPnz523P//jjD3r16sWGDRu4ePGiQ91rM8dWuXPnZsyYMbRu3RqAH374wTYa4Fp58+blm2++AeDYsWMsXryYWbNmsWzZMjp06ECJEiUoV66cLbFRrly5G8a4b98+AB555BGnY9ayPXv2OJTnypXLIasM5loJAF999VWaiyiePn3a9vsrr7yCYRiprstwJ1h3jnjllVccyps3b0727NnZsmWLrcyaMLh48aLTyARrouH68uv/4wHzzwjg9B+QNYkTFxfneiAHT8FeF897aTSM6Qh9G0PXCbDziGvn7z4GB07Cl53g9frQfaL5zYgrhsdA7BnoUsf8OXm5a+eD4rBSHHaKw05xmBSHneKwUxymeyUOSZMmnYjcx66dNpCecqv4+Hhq1qzJ+vXradOmDWPHjmX69OnMnDmTMmXKkNbsp2t3KNiwYcNN+xceHk6HDh1YsmQJ7dq148qVK0ycOPGm590q6811alq1asXMmTNTfVjXRFi+fDk///wzrVu35sSJE2zZsoUtW7bwzz//AGaSZMuWLU5JmMxknYZhHbJ2/bELFy7YnluTOqktkhgbG4vFYiEyMtKh/EZ/Rq5PxFhduwBnujV61PVzLl0xPzDsizU/QJTIwJoLO4+YH1wiw8wPMr4ZWBxz8nIYt9j8ANLhadfPVxx2isNOcZgUh53isFMcJsVhdy/EIWlSMkHkIRQVFUV8fDz9+/dnwoQJdO3alTZt2tCqVSvbN9nXmzhxItHR0dStW5cSJUowcuRIVq9ene5rVq1aFYDjx48D5sKAwE0X9StcuHCa9bZt2+ZQ50ZKliwJmDfRrVq1SvVhXfBx3759GIbB+PHjKV++vO0xevRowFyzonz58rZdLG4H6+iA1LbTjIuLIyAgwPbcOvVg6dKlTnX//PNPh3l1d1zzSg/3BxDFYac47BSHSXHYKQ47xWFSHHb3QhySKiUTRB5CHh7mDKfrRyDMmDGDv//+26n+nj176N27N+Hh4cyePZuYmBi8vLxo1aqVw1z8BQsW2L69v551VIN1m8annnqKiIgIfvrpJ9auXetU3/oteIsWLfD29mbq1KkO0xBOnz7N1KlT8fb2pmXLljeNuUaNGkRERBATE8PWrVudjl+5csWW6KhZsyZjxoxxejRt2hQwRzeMGTPGlhC5Hdq3bw/AuHHjHMonTZpEfHy8bQcNMHec8PLy4uuvvyYpKcmh7smTJ2nUqNFt6+dNRa/TBxDFYac47BSHSXHYKQ47xWFSHHb3QhyZ5dcd0HA4RHQEn+chrAPUHQJr/krf+d//Bs+PgoJdwO8FKNod3pwCZy441529Gl78DAp3A0tTqD7Auc4tUDJB5CFUv359AgMDGT58OK+++iqDBw+mSZMmvPLKK07D6pOTk2natCmJiYnMmjWLgIAAihUrxqhRozh69KhtQUWAyZMnkzdvXp566il69+7N8OHD6d27N2XLlmXhwoXkyZOHt99+GwA3NzcmTpyIh4cHNWrUoEWLFgwePJg33niDxx9/nPfeew8wh/D37duX2NhYypYtyxtvvMEbb7xB2bJliY2N5d133yUkJOSmMbu5uTFt2jTc3Nx44oknaNGiBe+//z59+/aladOm5MyZk2nTpgFQqFAhunXr5vSw7oZQpUoVunXrZlujAmDu3Ll07dqVrl272taDGD58uK3MVa1ataJGjRqsW7eOxx57jAEDBvDSSy/RtWtXgoKC+PTTT2118+bNS/fu3dmzZw8VKlRg8ODBdOzYkW7dupE7d24++OADl6+faX74XR9AQHFcS3HYKQ6T4rBTHHaKw6Q47O6FODLD38fBzQ0614GxHaFPQ3M9h2oDYNHmm5/faRz8dQxerAafvwJ1y8GYn6HSO3Ap0bHuuMXwwwaIyA7BWTI9FCUTRB5CoaGhzJ8/n2LFivHdd98xYsQI9u7dy6xZs2wjB6y6devG9u3beeedd2xTFQA6d+5MkyZN+PHHH/nyyy8B6N27N23atOHkyZNMmTKFgQMH8uWXX5KQkEDHjh3ZsmWLwy4SderU4ddff+XJJ59k6dKlDB06lGnTpuHh4WGbcgAwdOhQxo0bR5YsWRg3bpzt96+++opBgwalO+4aNWqwfv166tSpwy+//MKwYcMYN24cW7dupVGjRjRp0iSDryj8/PPPtr5ZR3d89913trKMWLRoEa+//jrHjh3jww8/5IcffuCpp55i3bp1FCpUyKHuJ598wvDhw0lISGDYsGHMnj2bGjVqsHbt2rs3xcFKH0BMisNOcdgpDpPisFMcdorDpDjs7oU4bqb6AGj/RdrHX30G5r0D7zWHV2pBn8awdgSEBMBnP928/ei+sO1TGNLKbGv0KzCxC+w6BjN+daz7bU84Ox1WDIHcmf950GKktdKaiIhIBiUkJBAYGMjZQp0IsO7m0OFp8z/ucYsztpqyr5f5wSEyLGOrQoP5weXLTuYHmYysCg2Kw0px2CkOO8VhUhx2isNOcZjulziKhsOKwZA7m2vtVh8A+XPC1B6unVf6DfD3ht8+cu08gHOXIKAN9G4An7ycep1SPSFHAPwy1PX206CRCSIicmfoGw2T4rBTHHaKw6Q47BSHneIwKQ67eyGOW5VwEU4lwK6j0G86bD8MT5fJWFux8ebPHAE3rpfJlEwQEblD4uPj2b9//w0fhw8fvtvdvL30AcSkOOwUh53iMCkOO8VhpzhMisPuXojjVrQcBSHtofjr8Ml8eK02DGiRsbY+mgvubuYuWneQkgkiInfI22+/TWRk5A0fFSpUuNvdvP30AcSkOOwUh53iMCkOO8VhpzhMisPubseRdNUcXXDtI+kqJCY5l/9/pzKbD1+CJQNhUjeoWASuXIWrya73f+avMGk5vNkQCud2/fxboDUTRETukK1bt7Jz584b1vH396dhw4Z3qEe3T6prJlzvYZlzeTOKw05x2CkOk+KwUxx2isOkOOxuRxzpWTPhl+1QY2D62j8w3lxLITVXkqB8HygWDtFvpb/P/90JtYfAUyXgp/fAwz3turdhzQQlE0REJNOlK5kAD+4HEFcpDjvFYac4TIrDTnHYKQ6T4rDL7DjW7Lp5MiH+PGza51j25lQIC4a+jRzLqxYHnxuMnOg3HT6cCxdmgq/3zfu69QBUHwiFwmDlEMjie+P6WoBRREQeKBoiaVIcdorDTnGYFIed4rBTHCbFYZfZcTSrePP6wVmgVlnHR3AWyBXsXH6jRAKYr4FhmDsz3My+WKg7FHIGwsL+N08k3CZKJoiIyN31IH4AURyKAxTHtRSHSXHYKQ47xWG61+JoVRU8bzBtIKNOnnEuO3MBYn6DiByQM8hefjjO3O3hWrHxUHswuLnB4oEQEpj5fUwnTXMQEZFMl+5pDtd6EIdIKg7FAYrjWorDpDjsFIed4jDdK3H0awZ9G0OQv2vnVR9gro0wtUfqxyv0gTzZ4YnC5siCw6dgygo4Hg+ze0Pzyo5trdoBxvf2skd6w9aD8FZjKJ3Pse3QQHjmEfvzX3fAr/9fr+uLheDnDa/8P9FTrQRUK+labNdRMkFERDJdhpIJ8OB8AFEcJsVhpzjsFIdJcdgpDjvFYboX4igaDv8d5vo3/zdLJoz9GWathl3HzBEJwf7mbg59G8OTJZzbuj6ZYGma9rWfKum4JsL7s2DwnNTrDmoJ77+QrpDSomSCiIhkugwnE+DB+AACisNKcdgpDjvFYVIcdorDTnGY7nYc6dnN4SGnNRNEROT28fFw/ZwHbc6l4lAcVorDTnGYFIed4rBTHKZ7JQ5Jk5IJIiJy+7zV5OH+AKI4TIrDTnHYKQ6T4rBTHHaKw3SvxCGpUjJBRERun/Ds+gCiOEyKw05x2CkOk+KwUxx2isN0r8QhTpRMEBGR2+ej7/UBBBSHleKwUxx2isOkOOwUh53iMN0rcYgDLcAoIiKZzrYAY61+BHj7wcCWcOQUDI2Cy0muN9isornf83erzX2YXeXjCQNamPs3D5kDe2Ndb6NQmOKwUhwmxWGnOOwUh0lx2CkOu/spjgI5YVI3LcB4A0omiIhIprMlE3YdICBrALhZwNsTUgxIzMCHDwBPd/D0gKSrkJScsTa8Pc2+JCaZfXGV4rBTHHaKw6Q47BSHneIwKQ67+ykOf28I9M9YHx8CGVhmW0REJJ1yZYOAgLvdCxERERHJZFozQURERERERERcomSCiIiIiIiIiLhEyQQRERERERERcYmSCSIiIiIiIiLiEiUTRERERERERMQlSiaIiIiIiIiIiEuUTBARERERERERlyiZICIiIiIiIiIuUTJBRERERERERFyiZIKIiIiIiIiIuETJBBERERERERFxiZIJIiIiIiIiIuISJRNERERERERExCVKJoiIiIiIiIiIS5RMEBERERERERGXKJkgIiIiIiIiIi5RMkFEREREREREXKJkgoiIiIiIiIi4xONud0BERB5gJ/6F81fvdi9ERO4d/t4Q6H+3eyEicsuUTBARkdun+0Q4knC3eyEicm8okBMmdVMyQUQeCEomiIjI7XPwFOyNu9u9EBEREZFMpjUTRERERERERMQlSiaIiIiIiIiIiEs0zUFERERE5H534l8YvQDW74Hf98L5y7ByCFQvlf42lm2FD2Lgz0NwNRmK5IYez8JL1e11pq6Al8ek3cb0ntDmqQyHISL3DyUTRERERETud7uPw0dzoXAuKJ0P1u127fz5G6DxR1CpCLz/PFgsMGcNtP0cTp2DXg3MetVKwLc9nc//9EfYehCeLnPLoYjI/UHJBBERERGRe131AZA/J0ztkfrxCpFwehpkywrRa6GFi8mEMT9DrmBYMQS8Pc2y12pDsR7maARrMqFgmPm41qVE6DoBapaGsGDXrisi9y0lE0RERERE7ndZfW/t/ISLEOxvTyQAeLhDjqw3P/fH3+HcJWhT7db6ICL3FS3AKCIiIiLysKteCnYcgQEzYe8J2BcLQ+fA7/vgrSY3PnfGr+DrBU0r3pm+isg9QSMTREREREQedgNawIF/zAUYh0WbZX7eEPMWNHo87fP+PQeLtkDjx299dISI3FeUTBARERERuZckXYWzF53LEpPgVIJjebYs4JYJg429Pc3dG5pXMkcYJCfDhKXw4mewdBBULJr6edHr4MpVTXEQeQgpmSAiIiIici9ZswtqDHQuX7sbZq12LDsw3lyY8VZ1nwi//Q2bR9mTEy2rQMme0HMyrP8o9fNm/GomNOqVv/U+iMh9RckEEREREZF7Sdn85miAa7051dwpoW8jx/KwoFu/3pUkmLQc3mrsOMrB08NMEoz52azj5el43uE4+O9f0OkZs66IPFT0t15ERERE5F4SnAVqlXUuyxXsXJ4ZTp+Dq8mQnOJ8LOkqpKSkfuy7/4JhaIqDyENKuzmIiIiIiDxMDsfBrqP25zkDIcgf5q43RyBYnb9kbvtYLBx8vZ3bmflfyJsDqha//X0WkXuORiaIiIiIiDwIhkWZP3ccMX9+uwpW/2X+3r+FvV7bz2HVDjC+N5+7u0OfRtB/JlR8B9pWN0ciTFoOR0/D9J7O19p+CLYdgneagsVy20ISkXuXkgkiIiIiIg+CAd85Pp+83P77tcmE1LzXHArkhNELYPAcc+eIMvkgui80q+Rcf8Z/zZ+tn7y1PovIfctiGIZxtzshIiIPloSEBAIDAzlbqBMBe+PudndERO4NRcNhxWDIne1u90RE5JZpzQR5qFgsFiwWC7Vq1Ur1eOnSpfHx8bnDvUrb77//TpMmTQgPD8fb2xsvLy9y5cpFgwYNWLx48d3u3j1txIgRvPXWW5nWXkJCAjlz5sRisdCsWTOn44sWLaJFixYUL14cHx8fLBYLI0aMuOXrHj9+nNatWxMWFoanpycBAQGUKVOGefPmOdX9+eefeeSRR/Dx8cHX15cKFSqwcuXKW+6DiIiIiMj1NM1BHkorVqxg5cqV1KhR4253JU3Dhg3j/fffx9PTk2eeeYZHHnkEDw8Pdu/ezdKlS/npp59Yu3YtlSqlMvRQmDlzJrGxsYwcOTJT2uvcuTMJCQlpHv/++++JiYkhPDycAgUKsGvXrlu+5o4dO6hRowaXL1+mcePGFC1alDNnzrB9+3YOHjzoUHfhwoU0atSIbNmy0aNHDwC++eYb6tWrx7Jly6hateot90dERERExErJBHno5M2bl+PHj9OnTx82bdp0t7uTqu+++46BAweSJ08eVqxYQaFChRyOX7lyhffee+8u9e7hs3z5cmbPnk2vXr345JNPUq3z7rvvMnLkSIKCghg7dizdu3e/5eu2bNmS5ORktmzZQmRk5A3r9uzZEw8PD1avXk3hwoUB6NSpE2XKlOH1119n8+bNt9wfERERERErTXOQTDdixAgsFguzZ8+mc+fO5MiRAy8vL4oUKcLChQsBiImJoUSJEnh7exMUFESXLl0c2pgxYwbVq1cnZ86ceHl54efnR/ny5YmJiXGot2LFCjw9PSlXrhwpKfb9j5OSkihZsiTe3t6sWbPG4ZywsDCaNWvG5s2bmT179k3jKV26NCEhIU7l27dvx2Kx0KlTJ1tZdHS0bXj7gAEDyJ07N56enuTNm5fJkycDsHr1aipUqICvry9ZsmShRYsWJCYmOrTdv39/AGbPnu2USADw8vLi448/dhiVcObMGdq1a0doaCienp4EBQVRu3ZtduzY4XBuZvXR+rps376dKlWq4Ofnh7+/P3Xr1iU+Pp7k5GS6d+9Ozpw58fT0pGDBgsyfP98plpSUFAYOHEhkZCTe3t74+PhQpkwZoqKi0ny9J0+eTKFChWxxtmnThitXrtjqWvt16tQp29QWi8VCdHQ0AGvXrqVatWpky5bN1kaZMmWYMmWKU/+SkpLo1KkTjz76KO3bt3c6blWgQAGCgoLSPO6quXPnsnPnTjp16kRkZCSJiYlpjozYsmULe/fupWbNmrZEAkDhwoWpWbMmf/zxB/v377eVZ9Z7JyIiIiIPL41MkNvmvffeIyUlhfbt23PlyhWmTp1Ks2bNGD16NL1796ZZs2Y0b96cefPmMX78eCIjI+nTpw8AkyZN4uzZszRu3JiIiAiOHj1KdHQ0LVu2JCYmhsaNGwNQs2ZN+vTpw4cffkjPnj354osvAOjSpQs7d+5k0KBBVKlSxalvn3zyCfPnz+e9996jRYsWuLllbl5t0qRJnD9/npYtW+Lj48PUqVPp2LEjV69epU+fPtSpU4fnnnuO5cuXEx0dTUhICF9++SUA27ZtY//+/ZQoUSLdUxgSExOpXLkyf/31F08++SRPPfUUf//9N99//z1VqlRh/fr1FC1aNNP6eO11a9asSYUKFejbty+///47CxcupGnTpgQHB7Nt2zbatm1LUlISU6ZMoXXr1hw6dIjs2bPb2qhduzYrVqygWrVqPP/88yQmJjJ37lxeeOEFzpw5Q8eOHR2uuXLlSmbNmkXLli3JnTs3CxYsYObMmQQHBzNmzBgAPvjgAwYPHsy5c+cYOHCg7dwKFSpw7Ngx6tWrB5jf/OfPn5+4uDi2bNnCmjVrePnllx2u9/bbb3P06FF++OGHdL0XmWXu3LmAmaR44okn+P3330lJSSEsLIw333zT9ncF4JdffgGgYsWKTu088cQTLFy4kFWrVlGwYEFbeWa8d+lSuQgUinD9BRARuV+duwinzqV+rEDOO9sXEZHbyRDJZMOHDzcAo0CBAsbFixdt5RMmTDAAw83NzViyZImt/OLFi0ZgYKBRpEgRW1l8fLxTu/v37zeyZMliPProo07HKlasaLi5uRkLFiwwoqKiDIvFYlStWtWpHmA8/vjjhmEYRrdu3QzA+PTTT23HS5UqZXh7ezucU6pUKSNHjhxObf35558GYHTs2NFWFhUVZQBGcHCwcfLkSVv5qlWrDMCwWCzGl19+6dBOwYIFjcDAQNvzr7/+2gCM5s2bO10zLYMHDzYAo02bNg7lkydPNgCjVq1amdpHwzBfF8B44403HMqrVKliWCwWIzIy0rh8+bKt3Pr+Dxo0yFY2btw4AzDef/99hzYSExONyMhIIyQkxEhOTjYMw/56e3l5GX/++aetbnJyshEREZFq/1J736z9GD16tNOx623dutXw8vIyXn/9dYc+NG3a9IbnjRkzxgCM4cOH3/QaaalcubIBGFmzZjWKFStmjBo1yvjggw+MiIgIAzA++OADW93evXsbgDF16lSndqZMmeL0GmfGe3czZ8+eNQDjzI79hnE4zjAOxxkp/384PD900vbcsD4/EW8Yp88Zxon4VI+n3Oi59RpxZw3jdIKRcvRU6te4yfOUo6cM43SCYcSdTb3fikNxKA7FkVYccWcN48S/RuKBE+Y1jp02jGOnjSsHY43kI3GGcea8YRiGceTIEePQoUMO/25e+/+bYRjGmjVrbvh83bp1xtWrV23Pd+zYYfz777+257qGrqFr6BrpuUZGaWtIyXQjRoygX79+DB482OFb4djYWHLlykXx4sXZuXOnwzmVKlVi+/btnDvnnMmPj4/n4sWLGIZB06ZN2blzJ+fPn3eoc/z4cUqXLo27uztXr17F3d2d7du3Exoa6lDPYrHw+OOPs379ehISEsifPz9eXl4cOnQIb29vSpcuzZ49e7h8+bLtnNKlSxMbG0tcnOP2dtu3b6d06dJ07NiRCRMmAOYUghYtWtC+fXunIfN+fn74+Pjw77//OpS3bNmSqKgo/v33X4KDg/nkk0/o06cPL7/8sm3awc089thjbNq0iePHjxMWFuZwrECBAsTGxnL+/Hnc3d0zpY/W12Xnzp2cPXuWLFmy2Or27duXUaNGMWTIEAYMGGArt77/rVq1YubMmQBUrVqVTZs2sWvXLtzd3R2uOXToUCZMmMDGjRt59NFHba/3008/zbJlyxzqNm/enJiYGKf+pfa+xcTE0Lx5c+rWrcu3335Ljhw50nxdK1SoQFxcHHv27MHb29vWh6ZNmzpNubmWdc2E4cOH8+6776ZZ70YeeeQRtm7dSs6cOTl48CC+vr6A+TpGRkbi6enJ6dOncXd3p0uXLowfP57Zs2fTsmVLh3bmzJnD888/T9++fW2LUWbGe3cztq0hnx1EwL7Trr8AzSpCq6rw3WqI+c318308YUALiMgBQ+bA3ljX2ygUBgNbwpFTMDQKLie53obiMCkOO8Vh9yDGsXk/TOqmrR9F5KGgaQ5y21w/rN56kxseHu5UNzAw0CFB8Mcff9CrVy82bNjAxYsXHepaLBan83Pnzs2YMWNo3bo1AD/88INTIuF6AQEBvPHGGwwaNIj3338/U7bxs0ptsTx/f39y5nQe3midZ3/8+HGCg4Ntz69PmNyI9dzrEwnWvhw8eJCjR4+SL1++TOnjteXX3owCZMtmfoAqUqSIQ7m1b/Hx8baygwcPcvnyZfLnz59mbEePHuXRRx+1Pb82huuveX3/UtOsWTNq167NokWLCAsLo3DhwlStWpUOHTo4TCsZNWoUW7ZsYe7cuXh7e9+wzdvBes369evbEglgvo5Vq1ZlyZIlbNy4kYoVK+Ln5wfApUuXnNqx/v3x9/d3KL/V9y7dXqgKr30Nl67cvO61hsdA7BnoUsf8OXm569d+aTSM6Qh9G0PXCbDziGvn7z4GB07Cl53g9frQfaLiUByKw0px2F0bh4jIQ0QLMMptc/03zTcrt4qPj6dmzZqsX7+eNm3aMHbsWKZPn87MmTMpU6YMaQ2m+f77722/b9iwIV19fPfdd8mdOzdffvklp0+n/u1paskLMBfmS0taMd5obQZrXI899hiA08KJme1W+pieumm1f20bhmGQNWtWZs6cmebj+nUAbvTnJ70DrRYvXszq1avp0aMHgYGBfPvtt1StWpV+/foB5g34kCFDeOyxx8ibNy9btmxhy5Yt7N69GzC/dd+yZQv//PNPuq6XEbly5QJINUFkTZRZR13kyZMHgMOHDzvVtZblzZvXofxW37t0C89uftD39XL93MnLYdxi8wN6h6ddP//SFfPGYF+seaNQIgNrN+w8Yt6gRIYpDsVhUhx2isPOGkerquB54885IiIPCiUT5J4TFRVFfHw8/fv3Z8KECXTt2pU2bdrQqlUrh+kH15o4cSLR0dHUrVuXEiVKMHLkSFavXn3Ta3l6ejJo0CASEhJ45513Uq0TEBCQ6iiB66dqZJYyZcpQoEAB/vrrr3QnRcLDw4mPj0/15nb//v34+vrabjjvJREREZw/f566devSqlWrVB+p3UynR1pJIKsqVarw6aef8ttvv3Ho0CFCQ0P5z3/+Q0pKCufOnePcuXNs2LCB8uXL2x7NmzcHYNmyZZQvX56PPvooQ31Lj8cffxwwR2Zc7/jx44A9iVC9enUAfvvNeZjw+vXrsVgsPPXUU7eppzfx0fe60QDFYaU47BSH3YMUx3erwfM2Dfw98S+88y3UGAhZW4OlKfyy3bU2lm01z8/RDoJehMffgm9/caxz5BQMnm0eC37JrFt9gHmuiMg1lEyQe46Hh/mf8PXfgs6YMYO///7bqf6ePXvo3bs34eHhzJ49m5iYGLy8vGjVqlWqazBcr1OnThQrVoxvv/021WHckZGRXL58mUWLFtnKkpOT+fTTT10NLd2GDRsGwPPPP++wpZ9VUlISb731FuvWrQPgueeewzAM+vbt61Bv2rRpHDhwgCpVqtx0RMjd8NJLL2EYhtOODVYHDhzIcNu+vr5cuHDBYctQMG/Ek5OTHcpCQ0PJlSsXV65c4cKFCwQFBTFmzBinh3UdgQoVKjhMq7kdOnTogI+PDz/99JPDn8t9+/axevVqcuXKRbly5QAoV64ckZGRrFixgr1799rq7t27lxUrVlCmTBmHnRzuqP0ndaNhpThMisNOcdg9KHHE/AZJV10/Lz12H4eP5sKx01DaecrfTc3fALWHwJUkeP95+KCNGWPbz+HTH+31fthgXqdQLhjW2lyX4twleGYwTMnAVBQReWBpzQS559SvX5/AwECGDx/OgQMHiIiI4I8//uDnn38mb968DkO5k5OTadq0KYmJicyaNYuAgAACAgIYNWoUXbp0oU2bNsyfP/+m1/zwww9p3Lgxx44dc5of/+abbzJjxgxat25N27Zt8fLy4qeffnK6Ic1MrVu3Zv/+/bz//vuUKlWKZ555hkceeQQPDw/27NnD0qVL+eeff2jSpAlgbl84a9Ysvv32W44cOULVqlXZs2cPMTExBAYG2rbMvNd069aNn376iZiYGIoXL06tWrUICQnhyJEjbNq0iaNHj3Ly5MkMtf3oo4+yYcMGmjdvbkumNGnShLFjxzJ58mRq1qxJoUKF8PLy4tdff2Xz5s089dRTZM2a1da3623fvp2hQ4eSL18+p+M7duxg7NixAOzatQuABQsWcOTIEVt7JUuWTHf/w8LCeOedd3j//fcpW7YsLVu25MqVK8ycOZOrV6/y8ccfO9T/9NNPadq0KVWqVKFdu3aAmUwyDIPRo0en+7q3hfUD+pedzA/oGZmTbJ1LbZ2T7OrcauuNxpiOZj8yMrdacdgpDpPisFMcdkkZ/HxQfQDkzwlTe6R+vEIknJ4G2bJC9Fposdu19sf8DLmCYcUQ8PY0y16rDcV6wNQV0KuBWVajFByeADkC7Od2rgOP9IaBs+DlDCR7ROSBpJEJcs8JDQ1l/vz5FCtWjO+++44RI0awd+9eZs2aRfHixR3qduvWje3bt/POO+9QtWpVW3nnzp1p0qQJP/74I19++eVNr9moUSOnuflWZcqU4euvvyYwMJCxY8cyceJEKlSowPTp028t0Jvo378/69ato3bt2mzYsIEPP/yQIUOGsGzZMh577DGWLVtmWzDQ29ubtWvX0rZtW3bs2MGIESNYuHAh1atXZ82aNRQrVuy29vVW/Pzzz3z00Ue4ubnx9ddfM3ToUObNm4efnx/9+/fPcLsjRozg2WefZfny5fTt25devXqxceNG6tWrR4UKFfj111/55JNP+Oijj9i/fz+9e/dm4cKFGb7eX3/9xbhx4xg3bhwrV64EYM2aNbayv/76y+U2Bw0axJdffomvry9ffPEFEyZMIG/evHz//fe0adPGoW6DBg2YN28eYWFhfP7553z++efkyZOHBQsW3L0pDtfSN5d2isOkOOwUh92DEsftkNXXTCRkVMJFCPa3JxIAPNwhR1bHGEvmdUwkgHnOs+Xh6GlzlIKICKCtIUVEJNPZtoYs1ImAvddsz1kiwvyAvy82Y9/4gXmD0aWOecORkdXffb3MG4TIsIx9cwmK41qKw6Q47B7mOIqGw4rBGdsa8mYjE64VvRZajIKVQ6B6qfS1/8635vSF/s2hXQ2wWGDmrzB4DszpA01T/1LFps2nMG8DJEyHe3DqpIjceRqZICIid46+ubRTHCbFYac47B6UOO4lA1pAy8rwQQwU7gaFusKHcyHmrZsnEvaegO/XQ7OKSiSIiI2SCSIid8ilS5fYv3//TR832nb0gaAbDTvFYVIcdorD7kGJIzVJV+FUguMj6SokJjmXX7eQcIZ5e0KR3NC8EnzXG6b3hEcj4cXP4LcbrL9wMdEcBeHrBR++lDl9EZEHgqY5iIjcIdHR0bRo0eKm9f78809KlUrnsNV7VJrTHK71MA+Fvp7iMCkOO8Vhdz/Fkd5pDr9sN7doTI8D483pD9fKyDSHzuPht79h8yhw+//3iUlXoWRPCM4C61PZ7jg5GZp8BIv/gJ8HQM3S6buWiDwUlEwQEblDYmNjbYsz3kiDBg3IkiXLHejR7ZOuZAI8fDcaN6I4TIrDTnHY3S9xpDeZEH8eNu1zLHtzKoQFQ99GjuVVi4PPdSMiXE0mXEkC/9bwVmNzS8hr9Zxk7vRw6Tvw8nQ81mEMTF0JM96AVk/e/Doi8lBRMkFERDJdupMJ8HDdaNyM4jApDjvFYXc/xHGvLsB44l/I/Sq83cR5qkLXr8zX9OJ34HvN9th9p8GoH+CzDtDzOddiEZGHgtZMEBGRu0tzq+0Uh0lx2CkOuwcljjvhcBzsOmp/njMQgvxh7npzlILV+Uvw4+9QLNwxkfDxPDOR0K+ZEgkikiYlE0RE5O7TjYad4jApDjvFYfegxJFRw6LMR8xv5vNvV9nLrtX2cyj+uv25uzv0aQR/H4eK78BnP8InP8Djb8PR0+Z2kVZzf4O3voHCuaB4Hpi+yvHxz5nbHqaI3B80zUFERDKdS9McrvWwDIVOD8VhUhx2isPuXo3jdk9zsDRN+5jxvWNbq3Y4lgHM/BVGLzCTColJUCYf9G0MzSrZ67w/CwbPSfs6riz6KCIPNCUTREQk02U4mQAP9o2GqxSHSXHYKQ67ezGOvCEZTyaIiNxnNM1BRERun4I5b17nehoKbac4TIrDTnHY3Ytx+Hje/BwRkQeEkgkiInL7vN1UNxqKw6Q4TIrD7kGMY0AL188XEblPKZkgIiK3z7HTutFQHHaKw6Q47B60OCJygLdGJ4jIw0HJBBERuX1GztWNBiiOaykOk+Kwe5DiGDIH3Cyunysich/SAowiIpLpbAsw1upHQNwlc+hvRA7zg/beWNcbLBQGA1vCkVMwNAouJ938nOs1qwitqsJ3q+3bqrnCx1NxWCkOO8VhUhymAjlhSncIC3b92iIi9xklE0REJNPZkgm7DhCQNcAs9PY0v7FLTIKUDPzX42Yx20gxzDYywtMdPD0g6SokJWesDcVhUhx2isNOcYC/NwT6Z+y6IiL3EY+73QEREXmA5coGAQF3uxciIiIiksm0ZoKIiIiIiIiIuETJBBERERERERFxiZIJIiIiIiIiIuISJRNERERERERExCVKJoiIiIiIiIiIS5RMEBERERERERGXKJkgIiIiIiIiIi5RMkFEREREREREXKJkgoiIiIiIiIi4RMkEEREREREREXGJkgkiIiIiIiIi4hIlE0RERERERETEJUomiIiIiIiIiIhLlEwQEREREREREZcomSAiIiIiIiIiLlEyQURERERERERcomSCiIiIiIiIiLhEyQQRERERERERcYnH3e6AiIg8wE78C+ev3u1eiNxd/t4Q6H+3eyEiIpKplEwQEZHbp/tEOJJwt3shcvcUyAmTuimZICIiDxwlE0RE5PY5eAr2xt3tXoiIiIhIJtOaCSIiIiIiIiLiEiUTRERERERERMQlmuYgIiIicj868S+MXgDr98Dve+H8ZVg5BKqXSt/5u4/B+MXm+Zv3Q2ISHBgP+XM61529Gn783ay79wQ8VRJ+GZq58YiIyH1FIxNERERE7ke7j8NHc+HYaSidz/Xz1+2GzxfCuUtQPM+N645bDD9sgIjsEJwlY/0VEZEHikYmiIiIiNyLqg8wRwlM7ZH68QqRcHoaZMsK0WuhxW7X2m/4GJz5FrL6wqh58MeBtOt+2xPCs4GbG5Tq6dp1RETkgaRkgoiIiMj9KKvvrZ2fLWv660bkuLVriYjIA0fTHERERERERETEJUomiIiIiIiIiIhLNM1BRERE5G5LugpnLzqXJSbBqQTH8mxZzLULRERE7iIlE0RERETutjW7oMZA5/K1u2HWaseytLZvFBERuYOUTBARERG528rmh6WDHMvenAphwdC3kWN5WNAd6pSIiEjalEwQERERuduCs0Ctss5luYKdy0VERO4BmnAnIiIi8qA7HAe7jt7tXoiIyANEIxNERERE7lfDosyfO46YP79dBav/Mn/v38Jer+3nsGoHGN/by85egC8Wmr+v2WX+HLMQgvzNR/dn7XV/3QG/7jR/j0uAC4n2a1crAdVKZm5cIiJyz1MyQUREROR+NeA7x+eTl9t/vzaZkJr4C87nfzLf/JkvxDGZsOJPGDwn9WsPaqlkgojIQ8hiGIZxtzshIpkjJCSEsLAw/vzzz7vdFXnIJSQkEBgYyNlCnQjYG3e3uyNy9xQNhxWDIXe2u90TERGRTKU1E8QlcXFxdO3alQIFCuDn54ePjw85c+akcuXKDB069G53z2X79+/n5ZdfJjIyEj8/P9zd3QkICKBs2bL07t2bY8eO3e0uOnnrrbcYMWLELbezfPlyWrduTcGCBfH398ff359ChQoxYMAAEhMTnerPnDmTRo0aUahQITw9PbFYLERHR99yP6z27duHv78/FouF3r17p1rn559/5pFHHsHHxwdfX18qVKjAypUrM60PN5KcnEzRokWxWCw88cQTTsc3btxI69atKVu2LH5+flgsFjp16nTL101ISKBz585ERETg5eVFlixZKFq0KOPHj0+1D5UrV8bf3x9vb29KlizJnDlzUmlVREREROTWaJqDpNupU6coU6YMJ0+epFq1arzwwgt4eXmxb98+fv/9dyZOnMiAAQPudjfT7dtvv+W1117jypUrPPnkkzRr1oygoCDi4uLYuHEjX3zxBbNmzeL48eN3u6sOpkyZQlhYGO++++4ttTNs2DB+//13qlWrxosvvsjVq1dZvHgxw4YNY+HChWzcuBE3N3u+8ZtvvmHZsmXkzZuXPHnycPDgwVuMxFH79u1JTk5O8/jChQtp1KgR2bJlo0ePHrY+1atXj2XLllG1atVM7c/1BgwYwKFDh9I8vmzZMmbNmkVoaCiFCxdm27Ztt3zN2NhYKleuzPHjx2nQoAFly5blwoUL/PXXX+zfv9+h7h9//EGNGjVwd3enQ4cOBAUFMX36dFq1akVycjKtWrW65f6IiIiIiFgpmSDpNmLECGJjY3nrrbf46KOPnI5ff3Nzp5w+fZrs2bO7dM7atWt59dVXyZo1KytWrKBixYpOdQ4cOMCQIUNuy/XvBb169aJmzZpkyZLFVjZ8+HBq1arF8uXLmTp1Kh06dLAd++KLLwgPD8fPz4/evXvz6aefZlpfJk6cyJo1a+jZsyefffZZqnV69uyJh4cHq1evpnDhwgB06tSJMmXK8Prrr7N58+ZM68/1du/ezaeffkr37t355JNPUq3Trl07OnToQGhoKEuXLqV27dq3fN2XXnqJEydOsHLlSipVqnTDuj179uTixYssX76cGjVqANC7d28KFy7Mm2++yfPPP++QHBIRERERuRX6ZHkPGjFiBBaLhdmzZ9O5c2dy5MiBl5cXRYoUYeFCc9XlmJgYSpQogbe3N0FBQXTp0sWhjRkzZlC9enVy5syJl5cXfn5+lC9fnpiYGId6K1aswNPTk3LlypGSkmIrT0pKomTJknh7e7NmzRoA9u7dC0DDhg1T7XfBggWdyrZs2UK9evXIli0bHh4eBAcHU6lSJZYtW+ZQb8KECRQvXhxvb298fHwoXrw4EyZMcGovJCSE0qVLs2LFCipUqICfnx/Fixe3Hd+8eTPPPPMMQUFBeHh4EBISQps2bThz5oxDO2+99RZXrlzh888/TzWRAFCgQAGmTJniUFa6dGlCQkLYunUrTz75JFmyZCFHjhy247/++itVqlQhS5YseHp6kidPHt544w2SkpJsdT766CMsFgtRUVG2ssTERHx8fLBYLKxYscJWfvr0adzd3WncuDEAFouFU6dOsX37diwWi+2xfft2h35u2LCBxx9/HB8fH/z8/HjyySedkj0NGzZ0SCRYWb/B/uOPPxzKCxcujJ+fX6qv1a04ffo077zzDk2aNKFKlSqp1tmyZQt79+6lZs2atkSCtU81a9bkjz/+cIjP+j5t376dKlWq4Ofnh7+/P3Xr1iU+Pp7k5GS6d+9Ozpw58fT0pGDBgsyfPz/NPrZv356wsLAbTi/JnTs3oaGhGXgFUrd9+3aWL19O48aNqVSpEklJScTHx6da98yZM6xdu5bSpUvbEgkAwcHBtGjRghMnTrBkyRJbed26dbFYLBw7doy6deuSNWtWfHx8qFy5su11HDx4MOHh4Xh6ehIeHp7q30cREREReXhpZMI97L333iMlJYX27dtz5coVpk6dSrNmzRg9ejS9e/emWbNmNG/enHnz5jF+/HgiIyPp06cPAJMmTeLs2bM0btyYiIgIjh49SnR0NC1btiQmJsZ2c1qzZk369OnDhx9+SM+ePfniiy8A6NKlCzt37mTQoEG2GzxrsmDMmDE89thjeHl53bD/S5cupVGjRiQnJ9OgQQNKlSrF6dOnWbt2LcuXL6dWrVqAOXx82LBh5M6dm86dOwMQHR3Na6+9xokTJxg0aJBDu//88w8NGjSgZs2aNGzYkHPnzgHmMPOGDRvi5+dHy5YtyZMnD1u3bmXOnDls2rSJrVu34u3tzfnz51m/fj05cuSgdevWLr8vly9fpmbNmpQpU4bevXvzzz//2OJ97rnncHd35/nnnycsLIzFixczevRotm/fbkugNGrUiHfeeYdFixbRooW50vaiRYtITEzEYrGwYMECatasCcD8+fNJSUmxvVYff/wxQ4YMIWvWrPTq1cvWpzx58th+P3XqFLVr16Z69erUq1ePrVu3Mn/+fJo3b56ub++tQ/nDwsJcfm0yolOnTqSkpDB+/HhWrVqVap1ffvkFINXEzxNPPMHChQtZtWqVQ0IrMTGRmjVrUqFCBfr27cvvv//OwoULadq0KcHBwWzbto22bduSlJTElClTaN26NYcOHXIaZTJ27FjWr1/PTz/9hKenZ+YFfhNRUVEYhkHJkiV55pln+OWXX7h69SrBwcG88sorfPzxx7a6a9as4erVq1SoUMGpnSeffJLx48ezevVq6tat63DsqaeeIjQ0lDfeeIM9e/YQFRVFvXr1ePbZZ5k1axYtWrTAx8eHqVOn0qVLFypWrEiZMmVcC6RyESgUkaHXQOSBkCcbeOi7GxEReQAZcs8ZPny4ARgFChQwLl68aCufMGGCARhubm7GkiVLbOUXL140AgMDjSJFitjK4uPjndrdv3+/kSVLFuPRRx91OlaxYkXDzc3NWLBggREVFWVYLBajatWqDnVOnDhhZM+e3QCMgIAAo2rVqsbrr79uzJs3z7h69apD3eTkZCMiIsLw8PAwVq1a5XQ9a/0TJ04Y3t7eRmhoqBEXF2c7HhcXZ+TMmdPw9vY2YmNjbeU5cuQwAGPIkCFObebLl8/InTu3cerUKYfycePGGYAxfPhwwzAM47///a8BGBUrVnRq49y5c8aRI0ccHomJibbjpUqVMgDj5Zdfdjq3WLFihpubm0O8ycnJRrVq1QzAmDVrlq08LCzMKFasmO35a6+9ZmTNmtWoUKGCw/vzwgsvGBaLxThy5IjDa1CqVCmn61/7+owePdqhvEmTJgZgbNiwIdXzrP79918jJCTE8PX1NY4dO5ZmvV69ehmAERUVdcP2bmbBggWGxWKx9TcqKsoAjF69ejnU6927twEYU6dOdWpjypQpBmC8//77tjLr+/TGG2841K1SpYphsViMyMhI4/Lly7Zy69+tQYMGOdQ/efKkERQUZDRq1MhWBhiPP/74DeNasmSJARgdO3a8Yb0badmypQEYWbNmNcLDw40PPvjAGDVqlFGiRAkDMF599VVb3bFjxxqAMWDAAKd21q5dawBGu3btbGV16tQxAKNJkyYOdZ9//nkDMLJnz+7w93HVqlUGYLRt2zbd/T979qwBGGd27DeMw3GGcTjOSPn/w+H5oZO254b1+dFThnE6wTDizhoph52Pp9zoufUaJ+IN4/Q5I+VEfOrXuMnzlMNxhhF31jBOJxgpR0+lfg3FoTjSG8eRU0bigVgj5dhpwziVYBj/njOuHDlpJB+JM4xjpw3j2Gnj6uGTRtLhk7bnyUfijCsHY23PjWOnjcQDJwzjxL+G8e85wziVYD6/7njK0VO251cOxqZ+jZNnDCP+vJEcG5/6NW7y3BZHwkVj165dxr///mv7u3/kyBHj0KFDDv8W/Pnnnw7/PqxZs+aGz9etW+fwuWLHjh26hq6ha+gausZtvEZGaWvIe9CIESPo168fgwcPZuDAgbby2NhYcuXKRfHixdm5c6fDOZUqVWL79u22b+mvFR8fz8WLFzEMg6ZNm7Jz507Onz/vUOf48eOULl0ad3d3rl69iru7O9u3b3catn348GH69+/P0qVLiY2NtZWHhITw6aef0qZNG8CcPvH000/TqFEj5s2bl2asn3/+OT179uSdd95xGkL+9ttvM3LkSMaMGUO3bt1s17l06RLx8fEO3xKvXr2aJ598ktdee43+/fs7tJOSkkKRIkWoXLkyK1asYP78+TRq1IhatWqxdOlSh7r9+vVz6seSJUt45plnAHP4/Pbt24mNjXV4bQ4dOkT+/PmpUqUKq1evdjh/1apVVK9enaZNm9qmmTRs2JCFCxcSFxdHcHAwpUuXJlu2bDz66KOMHTuWs2fP4u3tTcGCBUlJSXFY7PBG2z+GhISQnJzMv//+61D+5Zdf0q1bNyZNmuSwDsK1kpKSeOqpp1i3bh3/+c9/HEY+XM+6ZkJUVBTNmzdPs96NJCYmUrhwYUJCQti0aRNgjkhp0aIFvXr14j//+Y+tbpcuXRg/fjyzZ8+mZcuWDu3MmTOH559/nr59+zJy5EjAfJ927tzJ2bNnHaZy9O3bl1GjRjFkyBCHxUKtf7datWrFzJkzbeUNGzbk119/Zffu3bb322Kx8Pjjj7N+/fo0Y7OumdCxY8cMTw+oX78+CxcuxNfXlz179hAeHm573fLnz8/p06c5fPgwYWFhjBw5krfffpthw4bx3nvvObSzdetWHnnkEVq0aGHb2aFu3bosXryYTZs2Ub58eVvd8ePH06VLF15++WUmT57s0I6fnx8VKlTgv//9b7r6b9sasv1/CFiXgfVUCoXBwJZw5BQMjYLLSTc/53rNKkKrqvDdaoj5zfXzfTxhQAuIyAFD5sDe2Jufcz3FYac4TA9KHMXCYVI3yJ7V9XNFROSBoGkO97CiRYs6PLcOO7feVFwrMDDQIUHwxx9/0KtXLzZs2MDFixcd6losFqfzc+fOzZgxY2zD/n/44YdU53/nzZuXb775BoBjx46xePFiZs2axbJly+jQoQMlSpSgXLlytjn85cqVu2GM+/btA+CRRx5xOmYt27Nnj0N5rly5nIabW4fvf/XVV3z11VepXuv06dMAZMtm7vV9fUIFzEX0SpcuDZi7JlyfbADImjWr02tjTe5c/54BPP7441gsFg4fPmwrq1WrFj/++CPz58+nadOm7Nq1i379+vHUU0/xn//8h8WLF/Poo49y8OBB21SI9MqVK5dTWc6cOQFza8/UJCcn89xzz7Fu3Tq6det2w0RCZunVqxexsbE3XKvAyrpWw6VLl5yOWf98+/v7O5QHBQU5rQlhfe+LFCniUG79u3XtmgTz5s3jp59+4uOPP87UtRDSy9fXF4DKlSs7/J339vbmueee4+uvv2bx4sW0a9fOFmdqW3paX5/U1ruw/lm3sq7/kdr6J/7+/pw9e9b1QF6qDhsOws4jrp23+xgcOAlfdoLX60P3iXDpimttDI+B2DPQpY75c/Jy184HeGk0jOkIfRtD1wmKQ3EojmslZiCJISIiDwxN4ruHubu7u1RuFR8fT82aNVm/fj1t2rRh7NixTJ8+nZkzZ1KmTBnSGozy/fff237fsGHDTfsXHh5Ohw4dWLJkCe3atePKlStMnDjxpufdKh8fnzSPWb9ZTu1hXYX/kUcewcPDw7ag5LWKFi1Kq1ataNWqFfnz50/1Gt7e3rccQ6NGjbBYLCxZsoSffvqJq1ev0qBBA6pXr46/vz8LFy7khx9+wDAMl3cFuNGK/dcusmllTSQsWbKEjh07MmbMGJfjcdW+ffv4+uuvqVevHoZhsGXLFrZs2WJbr+H06dNs2bLFtnCmdU2IaxMyVtayvHnzOpTf6HVI6+/QtX833njjDfLly0fNmjVt/duyZQtgrpuxZcsWjh49ms6IXWdNIFgTQdfKnTs3YE+Q5cuXDyDV/hw4cABwXFfDKq01INLz+qTbsdPmDU+JDKybsPOIeaMVGWbeePneeJ2WVE1eDuMWmzdMHZ52/fxLV8wbtX2xikNx2CkOERERJRMeRFFRUcTHx9O/f38mTJhA165dadOmDa1ateLy5cupnjNx4kSio6OpW7cuJUqUYOTIkU7D9W+katWqgDldAuzfeF6/I8D1rCvzp1Zv27ZtDnVupGTJkoB5E2RNBlz/sC5imCVLFp544glOnTrlMKT9VpQoUQIwtxC83saNGzEMw+FmN1++fOTJk4d169axaNEi2xQHNzc3ypUrx+rVq1m+fDlubm40atTIob3URpZklDWRsGjRIjp06HDHVuw/fPgwSUlJzJ8/n/Lly9se1gVEv/nmG8qXL8+MGTMAqF69OgC//eY8FHf9+vVYLBaeeuqpTO3jyZMnOXjwoEP/rFMCtm3bRvny5enZs2emXvNa1r9TJ06ccDp25Ij5LaZ1FErlypXx8PCwTRe5lnVaQlo7Zdx2I+fe/zdMD8qNn+KwUxx2SiiIiEgGKZnwAPLwMGevXP8t4owZM/j777+d6u/Zs4fevXsTHh7O7NmziYmJwcvLi1atWjmswbBgwQLbzgXXs45qsG7T+NRTTxEREcFPP/3E2rVrnepbvyFv0aIF3t7eTJ061fYtK5jfuE6dOhVvb2+nOfKpqVGjBhEREcTExLB161an41euXLElOgBGjhyJl5cXr7/+eqo3qODat7D58uWjWLFirFu3zraVpjXO999/H8ApjooVK3Lw4EGWLVvGo48+aiuvXr06u3fvZvXq1URGRjpsPQnmyIyEhIR09y0tKSkpNGzYkEWLFtG+fXsmTZp0y22mV6lSpRgzZozTo1OnTgDUrl2bMWPG2EZllCtXjsjISFasWOEwomTv3r2sWLGCMmXKpDo0/1aMHj061T4CREZGMmbMGLp3756p17xW48aNyZEjB2vXrnWY6nPmzBnmz5+Pn58f9erVA7Btufrnn3867IgRHx9PVFQUYWFh1KlT57b19YYuX30wbpgelBs/xWGnOOzupYTCiX/hnW+hxkDI2hosTeGX7Tc/Ly3PvG+20f26kZNHTsHg2fD4WxD8EuRoB9UHwDLnzxAiIpI6rZnwAKpfvz6BgYEMHz6cAwcOEBERwR9//MHPP/9M3rx5HYaKJycn07RpUxITE5k1axYBAQEEBAQwatQounTpQps2bWxz2idPnkzTpk2pWLEiFSpUIEeOHJw6dYrly5ezbds28uTJw9tvvw2YQ8wnTpxI48aNqVGjBg0bNqRUqVLEx8ezdu1ann76aUaMGEFoaCh9+/Zl2LBhlC1b1raYX3R0NLGxsbz//vuEhITcNGY3NzemTZtGgwYNeOKJJ2jQoAElS5bkwoUL7Nu3jxUrVvD222/z7rvvAuY3uV9//TWvvfYaVatW5cknn+Txxx8nMDCQkydPsnnzZtauXYuvr2+6rg/mYpLPPfcctWvXdtgacvPmzTz99NM8//zzDvVr1apFVFQUx48f5/XXX7eVN2jQgGHDhvHPP/+kegNYpkwZFi5cyCuvvELJkiVxc3Ojffv2BAUFpaufVi+++CILFy4kf/78lCxZklGjRjkcL168OPXr17c9//XXX5k1axZgnwYzZcoUVqxYAcDgwYPT/VqFhITYFtW8VnR0NBMmTKBkyZJOxz/99FOaNm1KlSpVaNeuHQDTpk3DMAxGjx6dzqjTr2PHjqmWd+/enezZszv1Ly4uzraNqXU0wfr16+natSsAL7zwAtWqVUv39T09Pfn444955ZVXqFSpEq1bt8bLy4s5c+Zw+vRphg0b5vCejx49mieffJKGDRvSrl07AgMDmT59OvHx8UybNu2G0z5uO+sN05iO5g1TRuaIW2+YvuxktpOROeLWOeFd6jg+Ty/FYac47BRH5tp9HD6aC4VzQel8sM55xF+6ff8brHP+EgWAHzaY12n8BLSrAVeT4Ztf4JnBMLkbvKxRGiIiN6NkwgMoNDSU+fPn06tXL7777juSk5MpXLgws2bNYvz48Q7JhG7durF9+3YGDBhgG1YN0LlzZ5YsWcLcuXP58ssv6dq1K7179yYwMJB169YxZcoUzp07h4eHB7ly5aJjx44MHz7c4eamTp06/Prrr7z77rssXbqUuXPnkjVrVooXL26bcgAwdOhQwsPD+eyzzxg3bhwABQoU4KuvvrJ9U50eNWrUYP369fTr149ffvmFuXPn4uPjQ2hoKI0aNaJJkyYO9V966SUqV67MsGHDWLVqFevXrycxMRF/f38KFCjA66+/zptvvpnqgpepeeaZZ1iyZAnvvvsuc+bMITExkZw5c9KzZ08+/vhjp/qNGjWiS5cupKSkOExlePzxxwkODiY+Pj7VZMLYsWNp06YNs2fPtu3SUatWLZeTCdbdIA4ePEjfvn2djtepU8chmbBmzRrb+2O1cOFC2+9du3ZNdzIhIxo0aMC8efPo168fn3/+OWBOb5k5c2amT3HIiH/++cfp9dm2bZttuk5ERIRLyQSA9u3bkzVrVoYMGcKECRMwDIOCBQvy9ddf88orrzjULVeuHMuWLaNXr158/fXXJCcnExkZyfTp02nVqtWtBZcZHpQbJsVhpzjsFEf6VR8A+XPC1B6pH68QCaenQbasEL0WWmQwmXD5Crw5Fd5uDANnOR+vUQoOT4AcAfayznXgkd5mfSUTRERuSltDiohIprNtDVmoEwF7r9lFxNfLvNGJDMvYDROYQ8G/7GQODc/IDROYQ7m71DGHdmfkhklx2CkOu4cpjqLhsGIw5M7mWts3SyZcK3ottBgFK4dA9VKuXWfIHJi0DHZ9AX6toFs987W9mTenwH9+hIQZkNXXtWuKiDxktGaCiIjcOQ/KHHHFYac47BTHveFwHHz4PXzUFnxd3IEp9gz4eYNfBl43EZGHjJIJIpJpzp07x/79+2/6eFglJSWl6/W5dOnS3e7q7fWg3DApDjvFYac47r43p0K5AvBC1ZtWdbD3BHy/HppVhJtswy0iIkomiEgmGjNmDJGRkTd9PKx2796drtdnwYIFd7urt9+DcsOkOOwUh53iMCVdhVMJjo+kq5CY5Fz+/12ebtnKPyHmN/isg2vnXUw0p1T4esGHL2VOX0REHnBaM0FEMs2+fftsOz3cyD2xIOBdcP78eX788ceb1qtRowZhYWF3oEe3T5prJlzvfpojfiOKw05x2D3IcaRnzYRftptbPKbHgfHmWgrXcnXNhKvJUO5NKF8Qptl3ScLS9MZrJiQnQ5OPYPEf8PMAqFk6fX0WEXnIKZkgIiKZLt3JBLi3b5hcoTjsFIfdgxpHepIJ8edh0z7HsjenQlgw9G3kWF61OPhcN3rC1WTC5OXQ+Sv4ZYhjvwp0hrbVYfALkDPQXBPBIbYxMHUlzHgDWj158+uIiAigaQ4iInK3PShDuhWHneKwe5jjCM4Ctco6PoKzQK5g5/LrEwkZcTjOnEZRpZ+ZQLA+AL75xfx9yR+O5/SdBlNWwKcvK5EgIuIiJRNEROTuu59vmK6lOOwUh92DGEeziq6fn9kOx8Guo/bnL1SFuW87PwCeLW/+/kRhe/2P58GoH6BfM+j53B3tuojIg0DTHEREJNO5NM3hWvfqkG5XKQ47xWH3IMXR6zkIDYKQQNfOrT7AXBthao+06wyLMn/uOAKzVpvXK/D/9RT6t3Bsa9UOML6/8TVTWzNh7m/QdCQUzgUDWzqf80xZMz4REUmTRiaIiMi94178BvZB+SZZcSgOyLw4vlsNnh6un5seA74zH7NW269nLcssWw+aP/ecgJdGOz/+OnrD00VERCMTRETkNrCNTHhzAgGf/Ox6A/fSN7APyjfJikNxXOtW4ygaDv8d5vrIBBEReWBoZIKIiNw+zSvd/9/APijfJCsOxXGtzIgjKdn1c0RE5IGhZIKIiNw+0evu/xumB+XGT3GYFIddZsQhIiIPLSUTRETk9vnh9wfjhulBufFTHCbFYaeEgoiIZJCSCSIicns9KDdMisOkOOwUh4iIPMS0AKOIiGQ62wKMtfoRcCTBLGxWEVpVNVeBj/nN9UZ9PGFAC4jIAUPmwN5Y19soFGZuA3fkFAyNgstJrrehOEyKw+5hjKNATpjUDXJnc/06IiLyQFAyQUREMp0tmbDrAAFZA+wHPN3N7eSSrmZ88TZvT3CzQGISpGTgvzA3i9lGimG2kRGKw05xmB7GOPy9IdA/Y/0UEZH73m3aIFhERATIlQ0CAm5eT0RERETuK1ozQURERERERERcomSCiIiIiIiIiLhEyQQRERERERERcYmSCSIiIiIiIiLiEiUTRERERERERMQlSiaIiIiIiIiIiEuUTBARERERERERlyiZICIiIiIiIiIuUTJBRERERERERFyiZIKIiIiIiIiIuETJBBERERERERFxiZIJIiIiIiIiIuISJRNERERERERExCVKJoiIiIiIiIiIS5RMEBERERERERGXKJkgIiIiIiIiIi5RMkFEREREREREXKJkgoiIiIiIiIi4xONud0BERB5gJ/6F81fvdi/s/L0h0P9u90JERETkvqdkgoiI3D7dJ8KRhLvdC1OBnDCpm5IJIiIiIplAyQQREbl9Dp6CvXF3uxciIiIiksm0ZoKIiIiIiIiIuETJBBERERERERFxiaY5iIiIZIYzF+Ctb2DueriYCI8Xhk/aQfnIG5+XkgLf/ALf/wZbDsC/5831HV6oCn0agY+XY/1xi2DFn7B+Dxw5Be1qwNQety0sERERkdRoZIKIiMitSkmB+sNg5n+hez0Y2RZOnoXqA2HP8RufezERXh4DcQnQuQ581sFMRAyaDfWGgWE41v9orplMKBkBHu63LyYRERGRG9DIBBERkZupPgDy50x7BED0Oli7G6L6QPPKZlnLylCku5kUmNkr7ba9PGDNcKhczF7W8RnzeoNmwfJtUKus/diqoZA3BCwWyNL61mMTERERyQCNTBAREblV0esgNAiaVrSXhQSaCYUfNkBiUtrnenk6JhKsmjxh/vzrqGN5vpxmIkFERETkLlIyQURE5FZtOQDlC4Lbdf+tPl7YnMbw902mOqQmNt78mSPg1vsnIiIiksmUTBAREblVJ+IhV7BzubXs+L+utzlyHgT4Qb3yt9Q1ERERkdtBayaIiIhcK+kqnL3oXJaYBKcSHMuzZTFHI1y6At6p/Jdq3Ynh0hXX+jA8GpZtgy87QZC/a+eKiIiI3AFKJoiIiFxrzS6oMdC5fO1umLXasezAeHOhRF8vSLzqfM7l/ycRfL2cj6Vl9mro/x288jR0qZv+80RERETuICUTRERErlU2Pywd5Fj25lQIC4a+jRzLw4LMn7mCzakO17OW5c6Wvmsv/QPafg71K8D4zunvs4iIiMgdpmSCiIjItYKzOG7FaC3LFexcbvVIfvjvX5CS4rgI4/o94OcNRXLf/Lrr/4YmI+HRSJjzJni4ZzgEERERkdtNCzCKiIjcquaV4J8z8P1v9rJTCRC1Fho8Ct6e9vJ9sebjWn8dhfofQP4Q+Ok98PW+I90WERERySiNTBAREblVzStBxSLw8hjYeRRyZIUvF0FyCgx+wbHu0/+fQnHwK/PnuUtQZwjEXzCnUSzY5Fg/MgwqFbU//3EjbD1o/p50FbYdhGFR5vOGj0GZ/JkcnIiIiIgzJRNERERulbs7LOwPfafB5wvM3RseKwRTe0DR8Bufe/ocHDll/v7OdOfj7Wo4JhNifoNpK+3PtxwwHwB5siuZICIiIneExTAM4253QkQks0VHR9OiRQuGDx/Ou+++e7e789BJSEggMDCQs4U6EbA37m53x1Q0HFYMTv9iiCIiIiKSJq2ZIHIbbd++HYvFQt26aW/vFhISQkhIyB3sVfqMGDGCt95665bb6dSpExaLxfZwc3MjS5YslC1blokTJ95S2ytXrqRTp05s3779lvt5M8nJyRQtWhSLxcITTzzhdHzjxo20bt2asmXL4ufnh8VioVOnTrd83YSEBDp37kxERAReXl5kyZKFokWLMn78+FT7ULlyZfz9/fH29qZkyZLMmTPnlvsgIiIiInI9TXMQkVTNnDmT2NhYRo4cmSntde7cmcjISJKSkti7dy9RUVF06tSJc+fO0bt37wy1+dtvvzFx4kRq165NqVKlHI41adKEc+fO4e2dOQvZDRgwgEOHDqV5fNmyZcyaNYvQ0FAKFy7Mtm3bbvmasbGxVK5cmePHj9OgQQPKli3LhQsX+Ouvv9i/f79D3T/++IMaNWrg7u5Ohw4dCAoKYvr06bRq1Yrk5GRatWp1y/0REREREbFSMkFE7oimTZvyzDPP2J63bduW6tWr89lnn2U4mXAj7u7uZMmSJVPa2r17N59++indu3fnk08+SbVOu3bt6NChA6GhoSxdupTatWvf8nVfeuklTpw4wcqVK6lUqdIN6/bs2ZOLFy+yfPlyatSoAUDv3r0pXLgwb775Js8//zxubhqMJiIiIiKZQ58s5YEwYsQILBYLs2fPpnPnzuTIkQMvLy+KFCnCwoULAYiJiaFEiRJ4e3sTFBREly5dHNqYMWMG1atXJ2fOnHh5eeHn50f58uWJiYlxqLdixQo8PT0pV64cKSkptvKkpCRKliyJt7c3a9asuaV4QkJCKF26NMuXL6ds2bL4+PiQJUsW6tSp4/Dt+LRp07BYLGmuCVC0aFECAgJITEy0lc2dO5dy5crh5+eHl5cXBQsWZNiwYU7X3759O6dOnXKYohAdHW2rs3nzZp555hmCgoLw8PAgJCSENm3acObMmXTF+NRTT5E1a1ZOnDjhUL53715atWpFgQIF8Pf3x9PTkzx58tCzZ0+SkpJs9Tp16kS/fv0AaNGiha2P1ikl0dHRWCwWRowY4dD+mTNnaNeuHaGhoXh6ehIUFETt2rXZsWNHmn1t3749YWFhTm1dK3fu3ISGhqYr9vTYvn07y5cvp3HjxlSqVImkpCTi4+NTrXvmzBnWrl1L6dKlbYkEgODgYFq0aMGJEydYsmSJrbxu3bpYLBaOHTtG3bp1yZo1Kz4+PlSuXNk24mHw4MGEh4fj6elJeHg4EyZMyFgglYtA3fL3xuPJ4uBmyVgcIiIiIuLIEHkADB8+3ACMyMhIo0CBAsabb75p9OjRw8iaNavh4+NjfPXVV4a/v7/Rtm1bY8CAAUbp0qUNwPj4449tbdSoUcN45JFHjI4dOxpDhgwxOnXqZGTLls1wc3Mz5s6d63C9d955xwCM7t2728peeeUVAzAGDRpkK/vzzz8NwKhTp06afc+RI4eRI0cOp7LQ0FDDz8/PePbZZ43+/fsbzz77rGGxWIw8efIYZ8+eNQzDMK5cuWIEBgYakZGRTu1u2rTJAIwWLVrYyr7++mvDzc3NCA4ONl577TWjT58+RuHChQ3AaN++va3eV199ZeTOndvImjWr8fHHH9se+/fvNwzDMJYuXWr4+voa2bNnNzp27GgMHjzYaNq0qeHh4WEULVrUuHz5sq2tjh07GoCxZMkSh/6dOHHCsFgsRlBQkEP5N998Y4SEhBjPP/+88d577xl9+/Y1ypcvbwBGgwYNbPVWrVplNGjQwNZ3ax9/+uknwzAMIyoqygCM4cOH2865fPmyUbx4cQMwnnzySaN///5Gy5YtDQ8PDyMwMNDYtWuX0+s4ZswYw2KxGAsWLDAMwzAA4/HHH0/trbRZsmSJARgdO3a8Yb0bGThwoAEYQ4cONWrVqmV4eHgYgBEcHGz06dPHoe5PP/1kAMbLL7/s1M6MGTMMwHjvvfdsZXXq1LH9falcubLRv39/4/nnnzfc3NyMIkWKGG+88YYRFhZm9OjRw+jbt68REhJiuLm5GVu3bk13/8+ePWsAxpkd+w3jcJxhHI4zUv7/cHh+6KTtuXHt87izhnE6wUg5esrpeMqNnluvcfSUef7Jsw7HjdMJxubNmx36umbNGofn69atM65evWp7vmPHDuPff/+1PT9y5Ihx6NAhh1j//PPPG7apa+gauoauoWvoGrqGrnGvXiOjlEyQB4I1mVCgQAHj4sWLtvIJEyYYgOHm5uZwM3vx4kUjMDDQKFKkiK0sPj7eqd39+/cbWbJkMR599FGnYxUrVjTc3NyMBQsWGFFRUYbFYjGqVq3qUOdWkgmA0bdvX4fyPn36GIDRo0cPW1nr1q0NwFi7dq1D3Q4dOhiAsWzZMsMwzMRD9uzZDV9fX2PPnj0Or0WxYsUMi8VibNq0yVZeqlQpp35Z5cuXz8idO7dx6tQph/Jx48Y53cBbkwmzZs0yjhw5Yuzbt8/44YcfbAmdNm3aOLSRkJBgJCcnO12zVq1ahsViMfbu3Wsrs77vUVFRTvVTSyYMHjw41WtOnjzZAIxatWo5lJ88edIICgoyGjVqZCu7U8mEli1bGoCRNWtWIzw83Pjggw+MUaNGGSVKlDAA49VXX7XVHTt2rAEYAwYMcGpn7dq1BmC0a9fOVmZNJjRp0sSh7vPPP28ARvbs2Y24uDhb+apVqwzAaNu2bbr7b00mnB0+yzCKdnf9UbaXYUSvNYz1fxtG/WEZa6P+MPP86LVme3WHGMax0y68CyIiIiKSFk1zkAdK+/bt8fX1tT1v0KABYA73v3a+vq+vL8WLF+f48eO2sqCgINvv8fHxHDt2DE9PT4oXL85ff/3ldK2YmBiCgoJo3749nTp1Inv27A7TAG6Vr68vgwcPdigbPHgwvr6+LFq0yFbWtWtXAMaNG2crS0lJ4ccffyQiIoKnn34agOXLl3P69GkaNmxIoUKFHK7Tq1cvDMNg+vRU9ri/zurVqzl06BANGjTg0qVLHD161PZ49tln8fb2ZunSpU7nvfDCC0RERBAZGUmjRo3466+/eOmll5g8ebJDvaxZs9rm9l+6dInjx49z9OhR6tSpg2EY/PLLLzftY1p+/PFHLBYLo0aNcih/+eWXyZ8/P6tXryY5OdlW/sorr2AYBl999VWGr5lR58+fB+Dq1ausX7+efv368eabb7J582bCwsKYNm0asbGxDnVTW2zSz88PgIsXLzod69+/v8Pz6tWrA9CwYUNy5MhhK69WrRq+vr5Oiz6mS42SUKUY7D7m2mPrQXhpNGw/DH0bg7ub620s2AQvj4EcAfB6fTiR+jQREREREXGdkgnyQClatKjD87CwMADCw8Od6gYGBtpuwsC+Gr6/vz/ZsmUjT548REREsHHjxlRvxHLnzs2YMWOIi4sjPj6eSZMmZXjOvMXiPI87NDTUITEC5o1haGio7SYSoEqVKhQsWJAFCxbYboTnzp1LXFwczZo1s9X7+++/AZx2PQB47LHHADhw4MBN+7p582YAvvrqKyIiIhwe+fLlIzExkdOnTzud179/f2bOnMmECRNo2bIlycnJnDlzBi8vL4d6V65coWvXruTKlQt/f3/Cw8OJiIigb9++AKm2nV7Hjx8nODjY9ufiWpGRkVy+fJmjR48CMG/ePH766ScGDBiQqWshpJf1va9cubLDn19vb2+ee+45kpKSWLx4MYBtoclr18awsv7ZtSYVrlW6dGmH59YEQsGCBZ3q+vv7c/bsWdcDiV4HXepAh6ddP/fSFeg+EfbFwpedoESE623sPAJdJ0BkGAxo4fr5IiIiIpIq7eYgDxR3d3eXyq3i4+OpWbMmly9f5sUXX+SRRx4hMDAQNzc3PvzwwzS3+fv+++9tv2/YsIGGDRs6HPf39wfMb9jTkpiYSEBAwA37dzPNmzdn5MiRzJkzh1atWjF58mTc3Nzo3r37LbV7I61atbKN/LheSEiIU1m1atVso0M6duxIaGgoX3zxBYMGDXIYgdGmTRuio6OpXr06b7zxBmFhYXh5ebF+/XpGjx7tMHLgdnrjjTfIly8fNWvWZMuWLQ7HLl++zJYtWwgJCSFPnjy35frWBELOnDmdjuXOnRuwJ1by5csHYEuEXMuaIEqtn56enqleO62/L4Zh3Kzbzn74HeKvmAkFgMnLXTvfmlAY09FMKHSdYCYIXGFNKEzpDt6pxywiIiIirlEyQQSIiooiPj6eDz74wLZDgNX777+f6jkTJ04kOjqaunXrcvjwYUaOHEndunWpWrWqrU6ePHnw9vZO8xv/w4cPc+7cOYoVK+Z07J9//uHSpUsOoxMuXrzIP//843Rj2K1bNz755BOmTJnCc889x8qVKylXrhyRkZG2OtZRG9u3b3e61u+//w5AgQIFbGWpjZYAKFmyJGDecLZq1SrVOunx8ccfExUVxX/+8x969Ohh+1Z84cKFlCxZkpUrVzrU37Vrl1Mbrm51GB4ezu+//84///zjNNpg//79+Pr62l7bkydPcunSJcqXL+/UzrZt2yhfvjxNmzZ12u0js1StWpXPP//cabcLgCNHzJvpXLlyAeboBQ8PDzZt2uRU97///S9gjmC5a6wJhLudUBgyB77t6dp5rjhzAd76Buauh4uJ8Hhh+KQdlI+88XkpKfDNL/D9b7DlAPx7HgrkhBeqQp9G4OM4eodxi2DFn7B+Dxw5Be1qwNQety0sERERkdRomoMI4OFh5tWu/+Z1xowZtukB19qzZw+9e/cmPDyc2bNnExMTg5eXF61ateLcuXO2ep6enlSuXJkjR44wY8YMp3YGDBgAwHPPPed07NKlSwwaNMihbNCgQVy6dIk6deo4lOfNm5fHHnuMX3/9lc8++4xLly7x4osvOtSpWbMm2bNn58cff3SY+56YmMhnn32GxWJxOMfX15cLFy44bH8JUKNGDSIiIoiJiWHr1q1O/b5y5YrDWhRp8fb2pkePHpw/f56BAwfayt3c3JzehzNnzjBx4kSnNrJmzQpAXFzcTa8H5utsGIZtyoTVtGnTOHDgAFWqVLF9Kz969GjGjBnj9ABzSsSYMWNu68iPxo0bkyNHDtauXcuePXts5WfOnGH+/Pn4+flRr149wNwCslKlSvz555+sWrXKVjc+Pp6oqCjCwsKc/szccZOXw7jFd3fKw95YSEy6eb2MSEmB+sNg5n+hez0Y2RZOnoXqA2HPTf4+XEw013aIS4DOdeCzDmYiYtBsqDcMrh8R8tFcM5lQMgI8bjzqSkREROR20cgEEaB+/foEBgYyfPhwDhw4QEREBH/88Qc///wzefPm5fDhw7a6ycnJNG3alMTERGbNmkVAQAABAQGMGjWKLl260KZNG+bPn2+rP3bsWCpVqkTbtm2ZOXMm5cqV4+LFi/z6669s2rSJUqVK8fbbbzv1KTQ0lLFjx7Jjxw4qVKjApk2b+Pnnn8mdOzdDhw51qt+2bVt+++03RowYga+vL6+++qrDcU9PTz766CM6derEY489RsuWLcmaNSs//PADf//9N+3bt3f4Fv7RRx9lw4YNNG/e3HaT3aRJE/Lly8e0adNo0KABTzzxBA0aNKBkyZJcuHCBffv2sWLFCt5++23efffdm77uffr04bPPPuObb75hyJAh5MiRg5o1azJ//nyqV69OzZo1iY2NJTo6OtWpIDVq1LAtqHj69GmyZMlC0aJFbTfZ13v77beZNWsW3377LUeOHKFq1ars2bOHmJgYAgMD+eKLL2x1O3bsmGob3bt3J3v27HTr1s2hPC4uzpb8sY4mWL9+vW2BzBdeeIFq1ard9DWx8vT05OOPP+aVV16hUqVKtG7dGi8vL+bMmcPp06cZNmyYw6Kho0eP5sknn6Rhw4a0a9eOwMBApk+fTnx8PNOmTXN5FMdtcS+MUEjJwFQNgOoDIH/OtEcARK+Dtbshqg80r2yWtawMRbqbSYGZvdJu28sD1gyHyteMUOr4jHm9QbNg+TaoVdZ+bNVQyBsCFgtkaZ2xeERERERu1V3cSUIk09xoi0DS2JrRuj2e1apVq4zy5csbfn5+hre3t1GqVClj7ty5TvVee+21NLfha9KkiQEYY8eOdSjfs2eP0bRpUyNnzpyGh4eH4eXlZeTPn9/o0aOHceHCBad2cuTIYZQqVcpYtmyZUaZMGcPLy8vw8/MzatWqZezfvz/V1+DixYuGv7+/ARj169dP87WKjo42HnnkEcPHx8fw8PAw8ufPbwwdOtSp3tmzZ41nn33WCAgIMCwWi9Pru337dqNhw4ZGjhw5DHd3d8Pf398oWLCg0bZtW+Ovv/6y1bNuDXnt1pzXGjRokAEYnTp1sl23devWRvbs2Q0PDw8jLCzM6NGjhzFr1iynrR4NwzA+/PBDIzw83HB3d3d4r1PbGtIwzC1A27Zta4SEhBju7u5G1qxZjVq1ahnbt29P8zW7FmlsDWndBjStx/X9SK/o6GijTJkyhre3t+Hl5WUUK1bM+Prrr1Otu27dOqNixYqGr6+v4eXlZRQvXtyYOXOmU73r/0xbpfWaGYb9z2R62baGLNTJMGji+OgwxjA27jF/Xn8sPQ/f5w1j0jLD+GW7YZR4Pf3nFe2e8a0hn+pvGO0+T/t4i48NI/Rlw7h+W9NOXxqG3wuGcfmK69fcdtDs9+c/pV3Hv9WN+yUiIiJym1gMIyMraonI7RQSEkJYWBh//vnn3e6KSIYkJCQQGBjI2UKdCNibyjSUDk+bIxTGLXZ9hAKAr5c5QiEyLP0jFIqGw4rBkDub69e72ciEwt2gcC5Y6LjdJpOWwatfwrZPoXQ+16659A+oPcQc1dDqydTrZGkNzStpzQQRERG54+6Bca8iIvLQuRfWUMhMJ+IhV7BzubXs+L+utzlyHgT4QT3nRUBFRERE7jatmSAicockJSXZdmK4kVy5cjns4vHAuhfWUEhN0lU4e9G5LDEJTiU4lmfLAm5uZl+8U/kv1boTw6UrrvVheDQs22bGFeTv2rkiIiIid4CSCSIid8ju3bspXbr0TetFRUXRvHnzO9Cje8C9mFBYswtqDHQuX7sbZq12LDsw3pz+4OsFiVedz7n8/ySCr5fzsbTMXg39v4NXnoYuddN/noiIiMgdpGSCyD0ovVsdyv0lf/78zJw586b1qlategd6cw+51xIKZfPDUsdtWXlzKoQFQ99GjuVhQebPXMHmVIfrWcvSu07D0j+g7edQvwKM75z+PouIiIjcYUomiIjcIVmyZKFVq1Z3uxv3pnspoRCcxXErRmtZrmDncqtH8sN//4KUFHPag9X6PeDnDUVy3/y66/+GJiPh0UiY8yZ4uGes/yIiIiJ3gBZgFBGRe8P9vChj80rwzxn4/jd72akEiFoLDR4Fb097+b5Y83Gtv45C/Q8gfwj89B74et+RbouIiIhklEYmiIjIveNeGqHgiuaVoGIReHkM7DwKObLCl4sgOQUGv+BY9+n/T6E4+JX589wlqDME4i+Y0ygWbHKsHxkGlYran/+4EbYeNH9PugrbDsKwKPN5w8egTP5MDk5ERETEmZIJIiJy+/hk4L+Z+zGh4O4OC/tD32nw+QKzD48Vgqk9oGj4jc89fQ6OnDJ/f2e68/F2NRyTCTG/wbSV9udbDpgPgDzZlUwQERGRO8JiGIZxtzshIiIPloSEBAIDAzn7zVICXvva9a0RwZzq0KWOOfXB1YQCmDsojOlofrPfdYI5SmDF4PQvhigiIiIiadKaCSIicvuEZzdv6F3ZGtEqs9dQKBTmehsiIiIikiolE0RE5Pb56HtzZMC9kFAY2BLcLK63ISIiIiJOlEwQEZHbZ/9Jc4rBvZBQOHLKcVcFEREREckwJRNEROT22nnk3kgoDI2CFC0TJCIiIpIZtJuDiIjcPvlzgLuXufjhx/PMqQbf9jRv7C8nudbWml0QFgS9njN/xvzm2vm5giHRxWuKiIiISKq0m4OIiGQ6224Ouw4QkDXAfsDNYk41SDEyfmPv6Q6eHpB0FZKSXTvX3xsC/TN2XRERERGx0cgEERG5fXJlg4CAm9cTERERkfuK1kwQEREREREREZcomSAiIiIiIiIiLlEyQURERERERERcomSCiIiIiIiIiLhEyQQRERERERERcYmSCSIiIiIiIiLiEiUTRERERERERMQlSiaIiIiIiIiIiEuUTBARERERERERlyiZICIiIiIiIiIuUTJBRERERERERFyiZIKIiIiIiIiIuETJBBERERERERFxiZIJIiIiIiIiIuISJRNERERERERExCVKJoiIiIiIiIiIS5RMEBERERERERGXKJkgIiIiIiIiIi7xuNsdEBGRB9iJf+H81dt/HX9vCPS//dcREREREUDJBBERuZ26T4QjCbf3GgVywqRuSiaIiIiI3EFKJoiIyO1z8BTsjbvbvRARERGRTKY1E0RERERERETEJUomiIiIiIiIiIhLNM1BRETkRs5cgLe+gbnr4WIiPF4YPmkH5SNvfu6GPTB1BazfA9sOwdVkML5Pu/4/Z2Dgd/DTJjh9DsKC4Oky5poQIiIiIvcQJRNERETSkpIC9YfB1kPQtxHkCIAvF0H1gbDpYyic+8bnL9wEXy+HMvmgYCj8fTztukdOQZV+5u+da0N4djj+r5mQEBEREbnHKJkgIiIPr+oDIH9OmNoj9ePR62DtbojqA80rm2UtK0OR7jBoNszsdeP2u9SFt5uAr7e5s8WNkgmvjQcPd9g4ErJnzVg8IiIiIneI1kwQERFJS/Q6CA2CphXtZSGBZkLhhw2QmHTj80ODzETCzew6Cj9vNkc/ZM8Kl69A0tVb6bmIiIjIbaVkgoiISFq2HIDyBcHtuv8uHy9srp9wo5EGrli2zfwZGgRPDwLfF8xHvaFw8GTmXENEREQkEymZICIikpYT8ZAr2LncWnb838y5zp4T5s9O48DLA2a/CR++CKv/glrvm4kLERERkXuI1kwQEZGHQ9JVOHvRuSwxCU4lOJZny2KORrh0BbxT+a/Sx8v8eelK5vTt/GXzZ1gQLHjPPhIiTw5o9R+Y+Su8+kzmXEtEREQkEyiZICIiD4c1u6DGQOfytbth1mrHsgPjzYUZfb0gMZW1Cy7/P4ng65U5fbO207KK45SKFpXgJXezj0omiIiIyD1EyQQREXk4lM0PSwc5lr05FcKCzYUPrxUWZP7MFWxOdbietSx3tszpW+7/T5sIDXQsd3c3F2SMP5851xERERHJJEomiIjIwyE4C9Qq61yWK9i53OqR/PDfvyAlxXHEwPo94OcNRXJnTt8qRJo/j123BsOV/0/BCAl0PkdERETkLtICjCIiImlpXgn+OQPf/2YvO5UAUWuhwaPg7Wkv3xdrPjKieinIGQgzfrVPoQCYuhKSU+CZNJIdIiIiIneJRiaIiIikpXklqFgEXh4DO49Cjqzw5SLzBn/wC451n/7/FIqDX9nLDp2Eb1eZv/++1/w5LMr8mS8EXqpu/u7tCR+3hXZfQLX+8NJTcPgUjF4AT5aApk/cthBFREREMkLJBBERkbS4u8PC/tB3Gny+wNy94bFCMLUHFA2/+fkHTsKA7xzLrM+fKmlPJgC0rQFenvDh99D3Gwjyh9eegeEvmv0QERERuYdYDMMw7nYnRCRz1K1bl8WLF6O/1nK3JSQkEBgYyNlCnQjYG3d7L1Y0HFYMzrzFEEVERETkph6oNRMsFgsWi4VatWqlerx06dL4+Pjc4V6l7ffff6dJkyaEh4fj7e2Nl5cXuXLlokGDBixevPhud++eNmLECN56661bbuf8+fMMGDCAsmXLEhAQgLu7O35+fhQqVIiXXnqJDRs2ZEJvM9eECRPo1KnTLbezZ88eevToQalSpQgKCsLb25uIiAjatGnDsWPHnOpv3LiR1q1bU7ZsWfz8/LBYLJnSD6vk5GSKFi2KxWLhiSdSH9K9d+9eateuTUBAAF5eXhQqVIgxY8ZkWh9u5qWXXsJisaT670h8fDyvvfYaFStWJFu2bFgsFkqXLp0p1x05ciTFixfH19cXHx8f8ubNS5cuXZzqxcXF0bx5c7Jly4anpycREREMHDiQlJSUTOmHiIiIiIjVAznNYcWKFaxcuZIaNWrc7a6kadiwYbz//vt4enryzDPP8Mgjj+Dh4cHu3btZunQpP/30E2vXrqVSpUp3u6v3pJkzZxIbG8vIkSMz3MbWrVupX78+x44do1SpUrRt25bcuXNz7tw5tm3bxg8//MCMGTPYvXs3hQsXzsTe35rvv/+exYsXM2HChFtqZ/r06YwfP54nnniCunXrEhAQwIYNG5g1axaLFi1i48aNFCxY0FZ/2bJlzJo1i9DQUAoXLsy2bdtuNRQHAwYM4NChQ2keP378OJUrVyYhIYEXX3yRiIgIoqOj6dGjBwkJCfTr1y9T+3O9lStXMnPmTLy9vVM9fuzYMSZMmEBgYCBFihRh06ZNmXLdevXqsXjxYp588klatGiBm5sb+/fv5/Dhww71Ll26ROXKldm/fz8tWrSgRIkSLFmyhKFDhxIbG3vLf15ERERERK71wCUT8ubNy/Hjx+nTp0+mfZjPbN999x0DBw4kT548rFixgkKFCjkcv3LlCu+9995d6t3D4dy5czz77LP8888/jBs3js6dOzvVOX/+PO+++y4Wi+WGbSUmJpKUlESWLFluV3dvizp16tCuXTuHhAHA0KFDGThwIP3792fmzJm28nbt2tGhQwdCQ0NZunQptWvXzrS+7N69m08//ZTu3bvzySefpFrnrbfeIi4ujq+//ppXXnkFgH79+lGiRAmGDx9Oly5dCA4OzrQ+XSspKYmOHTvy2GOPceHCBfbs2eNUp2DBguzatYuiRYsCZMooqGHDhrFo0SI++uijm47EGT58OHv37uWdd95hxIgRAAwcOJCqVasyefJkevbsScmSJW+5TyIiIiIikIFpDiNGjMBisTB79mw6d+5Mjhw58PLyokiRIixcuBCAmJgYSpQogbe3N0FBQU7DcWfMmEH16tXJmTMnXl5e+Pn5Ub58eWJiYhzqrVixAk9PT8qVK+cwTDcpKYmSJUvi7e3NmjVrHM4JCwujWbNmbN68mdmzZ980ntKlSxMSEuJUvn37dqdh3NHR0VgsFkaMGMGAAQPInTs3np6e5M2bl8mTJwOwevVqKlSogK+vL1myZKFFixYkJiY6tN2/f38AZs+e7ZRIAPDy8uLjjz92GJVw5swZ2rVrR2hoKJ6engQFBVG7dm127NjhcG5m9dH6umzfvp0qVarg5+eHv78/devWJT4+nuTkZLp3707OnDnx9PSkYMGCzJ8/3ymWlJQUBg4cSGRkJN7e3vj4+FCmTBmioqLSfL0nT55MoUKFbHG2adOGK1fsW6VZ+3Xq1Cnb1BaLxUJ0dDQAa9eupVq1arah3kFBQZQpU4YpU6bY2hg+fDjHjx/npZdeSjWRAJAlSxa++OILh/eoU6dOWCwW1q5dywsvvEC2bNnw9fVl0aJFABw9epSmTZuSPXt2PDw8yJ49O02bNnWYNrBjxw4sFgsdO3Z0uF6FChWwWCz07dvXobxIkSKEh4fb3hfrFJhrY7fePFrFxcXRuHFjAgIC8PT0pHjx4vz8888OdSpXruyUSADo2rUrYN7gXyt37tyEhoam+lrdqvbt2xMWFuYUx7UWLlxIaGioLZEA4OnpSceOHblw4QLffvutrdz6Pq1bt46WLVvapnGULVuWjRs3AjBu3DgKFiyIl5cXISEhDB48OM1r9+vXjyNHjjBp0qQ06/j5+dkSCZkhJSWFMWPGULBgQVsi4fTp02lOWYiJicHLy8spEdmnTx+Sk5OZOHGirSwz/h0XERERkYdbhkcmvPfee6SkpNC+fXuuXLnC1KlTadasGaNHj6Z37940a9aM5s2bM2/ePMaPH09kZCR9+vQBYNKkSZw9e5bGjRsTERHB0aNHiY6OpmXLlsTExNC4cWMAatasSZ8+ffjwww/p2bMnX3zxBQBdunRh586dDBo0iCpVqjj17ZNPPmH+/Pm89957tmHBmWnSpEmcP3+eli1b4uPjw9SpU+nYsSNXr16lT58+1KlTh+eee47ly5cTHR1NSEgIX375JQDbtm1j//79lChRIt1TGBITE6lcuTJ//fUXTz75JE899RR///0333//PVWqVGH9+vVONzG30sdrr1uzZk0qVKhA3759+f3331m4cCFNmzYlODiYbdu20bZtW5KSkpgyZQqtW7fm0KFDZM+e3dZG7dq1WbFiBdWqVeP5558nMTGRuXPn8sILL3DmzBmnG+qVK1cya9YsWrZsSe7cuVmwYAEzZ84kODjYNjf+gw8+YPDgwZw7d46BAwfazq1QoQLHjh2jXr16ALRs2ZL8+fP/r737jq/x/P84/jqJLJGFSIzUiFoNtUepPVutrVWqpV9ao0arm6Jm0aE1qkW1RhFU1apRtUurtFq0tthBJIhEJNfvj/PLqeMkkhOJoO/n43EeyX3d133dn/s+d8T55BpERkayc+dONm/eTJcuXQD4/vvvAejfv3+63oObdezYEXd3d1544QUsFguFCxcmMjKSKlWqcObMGZo1a0bFihXZuXMnixcvZtu2bfzxxx/kyZOHhx56iHz58rFlyxZbe1evXrUlVDZs2GArP3fuHAcPHqRFixYAvPXWW4wYMYI9e/YwduxYW71GjRrZxVerVi38/f3p3bs358+fZ+bMmbRv354jR47YvT8pSf6re968eTN0b5w1ceJEtm3bxtKlS3Fzc0uxzsGDB4mKiqJBgwYO+5LnSNm2bZvDvmeffZacOXPSo0cPIiMjmTlzJo899hivvfYaI0eOtCWEvvnmG4YMGUL58uVt9zrZX3/9xaeffkqPHj3u6F/2f/31V86cOUObNm3o0aMHM2fO5MqVK3h5edGoUSNmzJhh64mRmJjIwYMHCQ0Ndegh07hxYywWC7/99pvDOW7n3/F0e6QEFA/J8H1Il0K5weXWPYhEREREJJMZJ40cOdIApmjRoiY2NtZW/vnnnxvAuLi4mFWrVtnKY2NjjZ+fnylRooStLCoqyqHdQ4cOmVy5cpnKlSs77KtevbpxcXExy5YtM+Hh4cZisZhatWo51ANM1apVjTHG9OrVywDmo48+su0PCwszHh4edseEhYWZvHnzOrS1e/duA5hu3brZysLDww1gAgICzNmzZ23l69evN4CxWCxm0qRJdu0UK1bM+Pn52banTp1qANO2bVuHc6Zm6NChBjAdO3a0K58+fboBTMOGDTM1RmOs9wUw/fr1syuvWbOmsVgsJjQ01MTFxdnKk9//wYMH28omT55sADNkyBC7NuLj401oaKgJDAw0iYmJxph/77e7u7vZvXu3rW5iYqIJCQlJMb6U3rfkOMaPH++w70be3t7Gy8vLofzatWsmIiLC7hUTE2Pb361bNwOYsLAwEx8fb3fs888/bwAzcOBAu/J33nnHAKZr1662sscff9y4urqa6OhoY4wxixYtsr2Xnp6etraTn5eJEyfajm3SpIlJ7Uc3eV/Lli3tyj/99FOH9yc1tWvXNoCZN29eqnVWrVrl8POREWfPnjX+/v6mRYsWtrIbf46TrV692gCmU6dODm1ER0cbwNSrV89Wlvw+Va1a1faMGWPMm2++aQDj6elp9u7days/evSoyZEjh10bySpXrmweeOAB23uS0r8jKfHw8DBhYWFp1kvNlClTDGB8fHyMl5eX6d+/v5k4caJp2rSpAUzZsmVt13b8+HEDmDp16qTYlo+PjylZsqRtOzP+HU9L8vty8WCEMccijTkWaZL+/2W3ffSsbdvcvH0qyiSdjzHmVJTd/qSb6icdizTmvPXnNCIiwhw9etQujhv/TTHGmM2bN99ye+vWreb69eu27b/++stcuHDBtq1z6Bw6h86hc+gcOofOcT+dI6MynEwYOnSoXfmpU6cMYEqXLu1wTPXq1U2uXLlSbO/ChQvm+PHjJiIiwlSpUsV4e3s71Dlx4oTJnTu3CQwMNAEBASZv3rzm9OnTjhdzw4eQ6OhoExAQYIKCgmwfejMrmfD888871Pfy8jIBAQEO5e3atTOA7Q0fN26cAUyXLl1SvB8pqVy5srFYLObUqVMO+4oUKWI8PT1tD1hmxGiM9b64uLiYS5cu2dUdMGCAAcx7771nV578/nfo0MFWVrNmTePp6WmOHDni8AG9e/fuBjC//PKLMebf+92gQQOH+Nq0aZNifCm9bwsWLDCAadq0qYmMjHTYn8zFxcXkzp3boXzTpk0GsHv179/ftj/5Q+qUKVMcji1YsKDx8fFxSDLEx8cbHx8fExISYiv78MMPDWBmzpxpa9fX19f2IX3p0qXGGGOeeuopY7FYzIkTJ2zHpieZsGPHDrvy5A+bN74/KXnllVcMYJ544olb1susZMITTzxh/Pz87H6eU0omfPvttwYwL7zwgkMb169fN4CpUaOGrSz5fZo9e7Zd3bVr16b6nBUuXNiEhobalX344YfGYrGYJUuW2MruVDJh7Nixtmdwzpw5dvuS3+cZM2YYY4zZu3evAUyjRo1SbCtPnjymSJEitu3M/nc8JcnJhOgtu41ZsMWYh/sbU7K386+3Zxmz+4j1a2p1mr5nzInz6Y5NRERERG5fhvv/39ytPjg4GMA2tvtGfn5+XL582ba9a9cu6tWrh7e3N7lz56ZQoUKEhITwyy+/EBsb63B8gQIFmDBhApGRkURFRTFt2rQ0x277+vrSr18/zpw5w5AhQzJwhakLDQ11KPP29iZ//vwO5f7+/oB1Jvobt2+8H2k5efIkAQEBtnt8cyxxcXEcP34802K8sfzmLtO5c1vXcS9RooRdeXJsUVFRtrIjR44QFxdHkSJFCAkJsXslzyx/c9yFCxd2iC/5nDfHl5I2bdrQuHFjVq5cSXBwMKVLl6Zbt25s3brVrp6XlxdXr151OP6hhx5izpw5zJkzh1deeSXV86S05N/Zs2cJCQnB3d3drtzd3Z1ChQpx5swZW1nyUJ7k+Q82b95MxYoVqV+/Pt7e3rZx69u2baNw4cIUKFAgzWu/VXzJP5c3vj83GzZsGB999BFVq1Z1mNMiKyxevJilS5cyaNCgNH+ek5/Dm+f2ALhy5QqQ8oSHZcqUsdvOly8fkPJz5uPjQ0xMjG375MmTDBkyhMcee4wnnngijavJfDlz5gQgICCADh062O1LnjdizZo1gDV2wG5ukRtdu3YtxVUobuff8XQbMg/y+kKfx+FYJPx9wrnXyIXw0VJoVQ1qlkq5zuGzzsclIiIiIrclw3MmuLq6OlWeLCoqivr16xMXF0enTp0oX748fn5+uLi4MHr06FSXm1u0aJHt++3bt/Pkk0+mGeNbb73FlClTmDRpUqrjfFObqT8hISHVdlO7xlvNzWCMAaBKlSoADhMnZrbbiTE9dVNr/8Y2jDH4+PgwZcqUVNupXr16utpNKb7U/PDDD2zevJkFCxawdetWZs6cyfTp03njjTcYOXIkAEWKFOGvv/7ijz/+oFy5crZj/f39bR/cUhu/D/9+eMuookWLUqhQIbZu3UpMTAz//PMP7du3x9XVlYcffphNmzZx4sQJjh49ylNPPeV0+6nFnto9HDFiBIMHD6ZChQqsW7cu1eUPM1O/fv0oXLgw9evXZ+fOnXb74uLi2LlzJ4GBgRQqVIiiRYsCcOrUKYd2Dhw4AJBioixHjpT/iUvP89uvXz/i4+Pp3bu3XXzx8fEYY9i5cydeXl6UKlUqjSvNmORrTk6m3Sg5GXLx4kXAmgRwd3fn7FnHD9WxsbFcvnzZlki5UUb/HXfKobPQ83OY1B0mdIPeX8DVlJMeqZq+1vq1RxP7bRERERHJNnd8acjw8HCioqIYMWKEw7rwqfUg+OKLL1iwYAFNmzbl2LFjjBkzhqZNm1KrVq1bnsvNzY3Bgwfz4osv8uabb6ZYx9fXN8Vl3vbs2ZO+C3JSuXLlKFq0KHv37mX79u1UrVo1zWMKFixom4zt5r/gHjp0CC8vLwoVKpQl8d6OkJAQtm/fTtOmTTN9yb60lmusWbOmbXLOM2fOUKFCBT788EOGDx+Oi4sLTzzxBH/99RcfffSR3SoPtyMoKIiIiAiuXbtm1zvh2rVrHD9+3OG9q1atGosWLWLGjBlcv37dNvFfnTp1GDt2LN988w3GGIclGNO6dmeNGDGCQYMGUb58edavX2/7i3hWO3v2LFevXqVixYoO+/744w8qVqxI69atWbhwIaGhoQQEBPDnn3861E3+63y1atUyNb7jx48THx9vm9DzZhUrViQkJIRjx45l6nmT1axZEzc3N86dO+ew7+DBgwC2lWhcXV0JDQ3l4MGDxMbG2r2Hq1atwhhDhQoVsiTOdNkTcW8nFC5egde/hm+3QWw8VH0QPngOKjr2AHOwfT/M+BG27Yc/jsL1RDCLUq9/5iK8+w0s3QHnL0GwPzQoB9N6ZdbViIiIiGSKzF3mIB2S/1J4819IZ8+ezT///ONQf//+/bzyyisULFiQefPm2ZY/69ChA5cuXUrzfN27d6dUqVLMnDkzxS7eycMEkpf2A+vM6B999JGzl5Zuw4cPB+Cpp57i0KFDDvsTEhJ4/fXXbV3zmzdvjjHGYcnAr776isOHD1OzZs3M/UtiJnn22Wcxxjis2JDs8OHDGW7by8uLK1euOCyTd/LkSRITE+3KgoKCyJ8/P9euXbN1iX/77bcpUKAAM2fO5LPPPkvxHOntCZGsYcOGXLp0iREjRtiVDx8+nEuXLjmsuNCwYUOMMYwZM4Y8efJQvnx5AJ544gmuX7/O+PHjcXFxceiF4+3tbbvW25W8hGi5cuVYv369w7CWrDR+/HgmTJjg8ALrz+WECRPo3bu3rX6zZs04c+aM3fKMCQkJfPHFF+TMmZOOHTtmanzvvPNOivEVKlQINzc3JkyYwOjRozP1nDfy9fWldu3aREdHOzyjEydOBKBVq1a2statW3Pt2jWGDRtmV3fcuHG4uLik+nN4xyQnFEKDrQkFL/e0j7nZ9LUw+QdrQqGr48oeWSIpCR4fDnM2Qu9mMKYznI2Guu/C/nT8DC7fAVPXgsUCxdJYWjXiHFR5HVbshJcaW5Mv/2sIkdGZcy0iIiIimeiO90x4/PHH8fPzY+TIkRw+fJiQkBB27drFihUreOCBB+z+ypeYmEjr1q2Jj49n7ty5+Pr64uvry7hx4+jRowcdO3ZkyZIlaZ5z9OjRtGzZkhMnTjh033711VeZPXs2zzzzDJ07d8bd3Z2lS5c6fCDNTM888wyHDh1iyJAhhIWF0ahRI8qXL0+OHDnYv38/q1ev5syZM7YPCm+88QZz585l5syZREREUKtWLfbv38/ChQvx8/OzLZl5t+nVqxdLly5l4cKFlC5dmoYNGxIYGEhERAQ7duzg+PHjKXbLTo/KlSuzfft22rZta0umtGrViokTJzJ9+nTq169P8eLFcXd3Z8OGDfz222/UqVPHNjzBx8eH5cuX8/jjj9OjRw8mTZpEnTp1yJ8/P9HR0fz999+sWbMGFxcXihQpkq6YxowZw8qVKxk2bBi//fYbFSpUYOfOnSxbtowCBQowZswYu/otW7akZ8+enDhxgqZNm9rKa9SogZ+fH8ePH6dEiRIOSznWqFGDhQsX0rFjR5o1a4a7uzv169e3G66RHl988QXvvPMOXl5etG7d2mE4iq+vL927d7dtR0ZGMnjwYODf4Qbbtm2jZ8+eADz99NPUrl073edP7cNt7969yZMnD7162f8ldsyYMaxatYpevXrx888/U6hQIRYuXMiBAwcYNmxYmkteOuvxxx9Psfyzzz4jMjLSIT6wJqmShx4kJiZy5swZ2/2pVKmSba6D9Pr000+pUaMGffr0YdOmTRQtWpRVq1axfft2GjdubDeXwzvvvMPcuXMZM2YMR44coUyZMrYhP127diUsLMypc2eJu7GHQt1BUCQfzHg55f0LtsKWvyF8ALR9xFrW/hEo0RsGz4M5aSwv26MpvNEKvDys1/vPLRIQL34GOVzhlzGQ5/aGUomIiIhktTueTAgKCmLJkiX079+fb775hsTERB588EHmzp3LZ599ZpdM6NWrF3/++SeDBg2yG9Lw0ksvsWrVKr799lsmTZpk+896alq0aEH16tX5+eefHfaVK1eOqVOnMnToUCZOnEiuXLlo3rw5ffr0SdcQhIwaOHAgTZo0YcSIEWzbto2VK1eSlJREYGAgVapUoV+/ftSoUQMADw8PtmzZQt++fVmxYgUbN24kZ86c1K1bl48//jjLxmxnhhUrVjBmzBi++uorpk6dyvXr1/H396dkyZIMHDgww+2OGjWKI0eOsHbtWhYvXowxhkKFCtGsWTN+//13NmzYwHfffYeLiwtBQUG88sorDn+xffjhh9m7dy/vv/8+S5YsYcaMGcTGxuLh4UGBAgVo2bIlffv2tc1zkZbAwEC2bdtG3759Wb9+PcuXL8fPz4+WLVvyySefOHzYDQ4OpmjRohw6dIh69erZ7atcuTJr167lkUcecThPv3792LFjBytXrmT9+vUYYxg5cqTTyYRffvkFYwyxsbG2JMGN8ubNa5dMOHPmDJMnT7ar88cff9jmOQkJCXEqmeCsggULsmXLFnr27MncuXOJi4sjJCSE8ePH06dPnyw7rzO++OILu2EJkZGRtnvWpEkTp5MJpUuXZtOmTfTr148lS5YQGxtre55vTk55eXmxefNmevbsycqVK1mwYAFBQUG89dZbtt5Qd4WsSChs3pe5Md5owVYI8ofWN8zvEuhnTSjM2gDxCeCR+vwqBPmn7zz7jsOK36z3JY8PxF0DVxdwu+O/pkVERETSxWKc7cstIiKShpiYGPz8/Igu3h3fA5GOFcqEWD84HzydsYQCWIc69GhincugVzMo4DhZZZrS6pnwYC94MD8svyn5OW0N/G8S/PERlHVcHSRFvb+AiStSnjNhwnJ4eSosfN1a58fd1mRCo4dh8ovWGEVERETuInd8zgQREZFMnUOhQy1wy6J5Y05FQf4UJpBNLjt5IXPOs///VyrpPhncc8C8V2F0J9i0FxoOsU78KCIiInIXUf9JEck0CQkJREREpFkvf/78eHl53YGI7j7Hjh3j+vXrt6wTEBCQ6Sug3JUya8hDsD+81jLtugnXITrWsSw+Ac7F2JfnzgUuLtZ4PFL4Ven5/8mPjPSoSMnlOOvXYH9Y9o713ACF8kKHD2HOBvhfo1QPFxEREbnTlEwQkUzz999/U7Zs2TTrhYeH07Zt2zsQ0d2nUqVKKS73eKNu3brx+eef36GIsllmJBQW/gz9mqddb/M+qPeuY/mWv2HuJvuyw59ZhxZ4uUN8CsmfuP+PMSM9KlKS3E77mv8mEgDa1YBnXa0xKpkgIiIidxElE0Qk0xQpUoQ5c+akWe/GCVX/a6ZNm2ZbojQ1ZcqUuUPR3CUyI6GQkI4VeB4uAqtvmmz01RkQHACvtbAvD/a3fs0fYB3qcLPksozM05CSAv/fEyXIz77c1dU6IWPU5cw5j4iIiEgmUTJBRDJNrly56NChQ3aHcVd78sknszuEu1NmJBTSEpALGj7sWJY/wLE8WfkisHEvJCXZ9xjYth9yekCJApkTW6VQ69cTN83BcO3/h2AE+jkeIyIiIpKNNAGjiIjcHTJjUsbM1rYGnLkIi25YWvhcDIRvgScq2y8LefC09ZURdcMgnx/M3vDvEAqAGesgMcm6qoOIiIjIXUQ9E0RE5O5xJ3ooOKNtDaheArpMgD3HIa8PTFpp/YA/9Gn7ug3+fwjFkSn/lh09CzPXW7//9YD16/Bw69fCgfBsXev3Hm4wtjM89ynUHgjP1oFj52D8Mni0DLSulmWXKCIiIpIRSiaIiEjWKZYPDkQ6d8zdlFBwdYXlA+G1r+CTZdY4qhSHGS9DyYJpH3/4LAz6xr4sebvOQ/8mEwA61wN3Nxi9CF77Gvy94cVGMLKTNQ4RERGRu4jFGGOyOwgREbm/xMTE4OfnR/Ta3/B9eYY1QeCsMiHWhMLB07dOKJQsCD8OzbzJEEVEREQkTZozQUREss6J89aEQJkQ54+9G+dQEBERERFAyQQREclKY7619ixQQkFERETkvqJkgoiIZJ2469YhCkooiIiIiNxXlEwQEZGsdfWaEgoiIiIi9xlNwCgiIpnONgFjw7fxjYixFnq6waB2EJIX3psPB04733DxYHi3PUScg2HhEJcARfPBtF6agFFERETkDlIyQUREMp0tmbDvML4+vvY7PdzAxQLxCZCUgV9BLhZrG0nG2gaAtwf4ed9+4CIiIiKSLjmyOwAREbmP5c8Nvr5p1xMRERGRe4rmTBARERERERERpyiZICIiIiIiIiJOUTJBRERERERERJyiZIKIiIiIiIiIOEXJBBERERERERFxipIJIiIiIiIiIuIUJRNERERERERExClKJoiIiIiIiIiIU5RMEBERERERERGnKJkgIiIiIiIiIk5RMkFEREREREREnKJkgoiIiIiIiIg4RckEEREREREREXGKkgkiIiIiIiIi4hQlE0RERERERETEKUomiIiIiIiIiIhTlEwQEREREREREacomSAiIiIiIiIiTlEyQURERERERESckiO7AxARkfvYqQtw+XrmtuntAX7emdumiIiIiDhFyQQREck6vb+AiJjMa69oPpjWS8kEERERkWymZIKIiGSdI+fgQGR2RyEiIiIimUxzJoiIiIiIiIiIU9QzQUREBODiFXj9a/h2G8TGQ9UH4YPnoGJo2sdu3w8zfoRt++GPo3A9Ecwix3oR52D6Wli2A/afAlcXCHsABraFhg9n/jWJiIiIZBH1TBAREUlKgseHw5yN0LsZjOkMZ6Oh7ruw/2Taxy/fAVPXgsUCxYJSr/fddnj/WyieH4Y/A4PawaWr0GgofLk2865HREREJItZjDEmu4MQEZH7S0xMDH5+fkQX745vZs6ZULIg/DgUCuR27ri6g6BIPpjxcsr752+Gpz6A8AHQ9hFrWWQ0lOgNzSrCnP63bv/MRfD1Ai8P66STE1ek3DPhr2MQ5A95ff8ti0+A8q/A5TiI+MK56xIRERHJJuqZICIismCr9UN+6+r/lgX6QftHrL0J4hNufXyQvzWRkJaHHrBPJAB4uMFjFeH4eWsvBREREZF7gJIJIiIiOw9DxWLgctOvxaoPWudP+CcdQx1ux+mLkNMDcrpn7XlEREREMomSCSIiIqeiIH+AY3ly2ckLWXfuA6dg0TZoUx1cXbPuPCIiIiKZSKs5iIjI/SXhOkTHOpbFJ8C5GPvy3LmsvRGuXgOPFH4lev5/T4Gr17Im1th4aDcOvNxh9LNZcw4RERGRLKBkgoiI3F8274N67zqWb/kb5m6yLzv8mXViRi93iL/ueEzc/ycRvLJg+EFiIjz9AeyJgBWDnJ9UUkRERCQbKZkgIiL3l4eLwOrB9mWvzoDgAHithX15sL/1a/4A61CHmyWXZcUH/W6TYekOmN0P6pfN/PZFREREspCSCSIicn8JyAUNH3Ysyx/gWJ6sfBHYuBeSkuwnYdy23zoxYokCmRvja1/Blz/Cx12hw6OZ27aIiIjIHaAJGEVERNrWgDMXYdHP/5adi4HwLfBEZevyjckOnra+MmrsYhj3HbzdBvo2z3g7IiIiItlIPRNERETa1oDqJaDLBNhzHPL6wKSVkJgEQ5+2r9vg/4dQHJnyb9nRszBzvfX7Xw9Yvw4Pt34tHAjP1rV+/+3P8PrX8GB+KF0IZq23b7vRwxDkn5lXJiIiIpIllEwQERFxdYXlA63DDz5ZZl29oUpxmPEylCyY9vGHz8Kgb+zLkrfrPPRvMuH3I9av+0/Bs+Md21n3npIJIiIick+wGGNMdgchIiL3l5iYGPz8/Igu3h3fA5GZ13DJgvDjUK18ICIiIpLNNGeC/KdYLBYsFgsNGzZMcX/ZsmXx9PS8w1Gl7tdff6VVq1YULFgQDw8P3N3dyZ8/P0888QQ//PBDdod3Vxs1ahSvv/56ho//9ddf+d///kfJkiXx9fXF09OTokWL0qtXLy5evOhQf+XKlbRr147SpUvj6emJxWJh1KhRt3EF9mJiYsiXLx8Wi4U2bdqkWOeXX37hkUcewdvbGw8PDx566CHmz5+faTGIiIiIiCTTMAf5T/rxxx9Zt24d9erVy+5QUjV8+HCGDBmCm5sbjRo1onz58uTIkYO///6b1atXs3TpUrZs2UKNGjWyO9S70pw5czh9+jRjxozJ0PGffPIJ8+fPp1atWrRu3Rp3d3fWr1/PpEmTWLZsGbt378bHx8dWf9GiRSxcuJCCBQtStGhR9u3bl1mXAsBLL71ETExMqvt37dpFvXr1cHV1pWvXrvj7+zNr1iw6dOhAYmIiHTp0yNR4REREROS/TckE+c954IEHOHnyJAMGDGDHjh3ZHU6KvvnmG959910KFSrEjz/+SPHixe32X7t2jXfeeSebovtvePbZZ/nggw8IDAy0K+/atStffvklo0aNYuTIkbbyt956izFjxuDv78/EiRPp3bt3psWydu1a5s2bR//+/fnggw9SrNO3b19iY2NZu3atLUn2yiuv8OCDD/Lqq6/y1FNP4eKizmgiIiIikjn0P0vJdKNGjcJisTBv3jxeeukl8ubNi7u7OyVKlGD58uUALFy4kDJlyuDh4YG/vz89evSwa2P27NnUrVuXfPny4e7uTs6cOalYsSILFy60q/fjjz/i5uZGhQoVSEpKspUnJCTw0EMP4eHhwebNm+2OCQ4Opk2bNvz222/MmzcvzespW7aswwdKgD///BOLxUL37t1tZQsWLLB1bx80aBAFChTAzc2NBx54gOnTpwOwadMmKlWqhJeXF7ly5aJdu3bEx8fbtT1w4EAA5s2b55BIAHB3d2fs2LF2vRIuXrzIc889R1BQEG5ubvj7+9O4cWP++usvu2MzK8bk+/Lnn39Ss2ZNcubMibe3N02bNiUqKorExER69+5Nvnz5cHNzo1ixYixZssThWpKSknj33XcJDQ3Fw8MDT09PypUrR3h4eKr3e/r06RQvXtx2nR07duTatWu2uslxnTt3zja0xWKxsGDBAgC2bNlC7dq1yZ07t62NcuXK8eWXX9raaNSoUYrve9euXQEc7mvRokXx9/d3qH+7EhIS6N69O5UrV+b5559Psc7FixfZsmULZcuWtettExAQQLt27Th16hSrVq2ylTdt2hSLxcKJEydo2rQpPj4+eHp68sgjj3Do0CEAhg4dSsGCBXFzc6NgwYJ8/vnnmX5tIiIiInLvUs8EyTLvvPMOSUlJPP/881y7do0ZM2bQpk0bxo8fzyuvvEKbNm1o27Ytixcv5rPPPiM0NJQBAwYAMG3aNKKjo2nZsiUhISEcP36cBQsW0L59exYuXEjLli0BqF+/PgMGDGD06NH07duXTz/9FIAePXqwZ88eBg8eTM2aNR1i++CDD1iyZAnvvPMO7dq1y/S/2E6bNo3Lly/Tvn17PD09mTFjBt26deP69esMGDCAJk2a0Lx5c9auXcuCBQsIDAxk0qRJAPzxxx8cOnSIMmXKpHsIQ3x8PI888gh79+7l0UcfpU6dOvzzzz8sWrSImjVrsm3bNkqWLJlpMd543vr161OpUiVee+01fv31V5YvX07r1q0JCAjgjz/+oHPnziQkJPDll1/yzDPPcPToUfLkyWNro3Hjxvz444/Url2bp556ivj4eL799luefvppLl68SLdu3ezOuW7dOubOnUv79u0pUKAAy5YtY86cOQQEBDBhwgQARowYwdChQ7l06RLvvvuu7dhKlSpx4sQJmjVrBkD79u0pUqQIkZGR7Ny5k82bN9OlS5db3uuDBw8CkC9fvnS9N7frjTfe4Pjx43z33Xep1tm8eTPXr1+nUqVKDvseffRRPvvsMzZt2kTTpk3t9tWpU4egoCD69evH/v37CQ8Pp1mzZjz22GPMnTuXdu3a2Z6NHj16UL16dcqVK+fcBTxSAoqHOHfMrRTKDS6WzGtPRERERDLGiGSykSNHGsAULVrUxMbG2so///xzAxgXFxezatUqW3lsbKzx8/MzJUqUsJVFRUU5tHvo0CGTK1cuU7lyZYd91atXNy4uLmbZsmUmPDzcWCwWU6tWLYd6gKlataoxxphevXoZwHz00Ue2/WFhYcbDw8PumLCwMJM3b16Htnbv3m0A061bN1tZeHi4AUxAQIA5e/asrXz9+vUGMBaLxUyaNMmunWLFihk/Pz/b9tSpUw1g2rZt63DO1AwdOtQApmPHjnbl06dPN4Bp2LBhpsZojPW+AKZfv3525TVr1jQWi8WEhoaauLg4W3ny+z948GBb2eTJkw1ghgwZYtdGfHy8CQ0NNYGBgSYxMdEY8+/9dnd3N7t377bVTUxMNCEhISnGl9L7lhzH+PHjHfal5dq1a6ZkyZLGxcXFbN++PdV6EyZMMIAZOXKk0+e40e+//27c3d1Nnz59jDH/3oPWrVvb1Zs4caIBzKBBgxza2LJliwHMc889Zytr0qSJAUyrVq3s6j711FMGMHny5DGRkZG28uRno3PnzumOPTo62gDm4uHjxhw/Z8yxSJP0/y/z/6+kY5Em6ehZ27a5efv4OZN0LsaYyGi7/UnnY2zn+euvv8yFCxds2xEREebo0aN2cdz4vBhjzObNm2+5vXXrVnP9+nWdQ+fQOXQOnUPn0Dl0jv/EOTJKyQTJdMnJhKFDh9qVnzp1ygCmdOnSDsdUr17d5MqVK8X2Lly4YI4fP24iIiJMlSpVjLe3t0OdEydOmNy5c5vAwEATEBBg8ubNa06fPu1Q78ZkQnR0tAkICDBBQUG2D72ZlUx4/vnnHep7eXmZgIAAh/J27doZwPaPwrhx4wxgunTpkuL9SEnlypWNxWIxp06dcthXpEgR4+npaftHKDNiNMZ6X1xcXMylS5fs6g4YMMAA5r333rMrT37/O3ToYCurWbOm8fT0NEeOHDERERF2r+7duxvA/PLLL8aYf+93gwYNHOJr06ZNivGl9L4tWLDAAKZp06Z2H5jTI/k8vXv3vmW9zEomVKxY0YSEhNiez9SSCe+//74BzPDhwx3a2LVrlwFMu3btbGXJyYQdO3bY1U1O7qT07Hl5eaWYoEtNcjIhetFGY7b9Y8zjw40p2dv51+PDrccv2GLMw/2NafqeMSfOpzsOEREREckamjNBsszN3eqDg4MBKFiwoENdPz8/Ll++bNtOnpne29ub3LlzU6hQIUJCQvjll1+IjY11OL5AgQJMmDCByMhIoqKimDZtGkFBQbeMz9fXl379+nHmzBmGDBmSgStMXWhoqEOZt7c3+fPndyhPHmd/8uRJu+0b70daTp48SUBAgO0e3xxLXFwcx48fz7QYbyzPlSuXXVnu3LkBKFGihF15cmxRUVG2siNHjhAXF0eRIkUICQmxeyWP0b857sKFCzvEl3zOm+NLSZs2bWjcuDErV64kODiY0qVL061bN7Zu3XrL41544QUWLlzIE088YRtOk5XGjRvHzp07+fTTT/Hw8Lhl3eT34OZ5LQDbz0vOnDkd9pUtW9ZuO2/evAAUK1bMoa63tzfR0dHpC/5G734Dfx6D11qCqwv8fcK517Id0GUC5PWFPo/Dqag0TykiIiIiWU9zJkiWcXV1dao8WVRUFPXr1ycuLo5OnTpRvnx5/Pz8cHFxYfTo0fzxxx8pHrdo0SLb99u3b+fJJ59MM8a33nqLKVOmMGnSJNt8DTezWFIen52QkJBqu6ld463mZjDGAFClShXAcYK/zHY7Maanbmrt39iGMQYfHx+mTJmSajvVq1dPV7spxZeaH374gc2bN7NgwQK2bt3KzJkzmT59Om+88YbdCg3Jkid9bNq0KYsXL07XOW5HbGws7733HlWqVOGBBx5g586dALbJEWNiYti5cycFChQgKCjIlmC5OfECcPjwYQAKFSrksM/NzS3F86fnvUu3uOvQ+wuY0A0mdYeen8OeCOfa2BNhPW5SdxjUzvkYRERERCTTKZkgd53w8HCioqIYMWIEb7/9tt2+1HoQfPHFFyxYsICmTZty7NgxxowZQ9OmTalVq9Ytz+Xm5sbgwYN58cUXefPNN1Os4+vry/79+x3K9+zZk74LclK5cuUoWrQoe/fuZfv27VStWjXNYwoWLMivv/7KmTNnHHpkHDp0CC8vrxQ/TGa3kJAQtm/fTtOmTQkICMjUtlNLAiWrWbOmbXLOM2fOUKFCBT788EOGDx9ulyTp3r07X3zxBU2aNGHp0qV3ZHnFS5cucenSJbZv307FihUd9q9Zs4aKFSvSv39/PvzwQx555BFy5MiR4lKnGzduBEhxItI75uq1zEsofNkbPFJOgoiIiIjInaNhDnLXyZHDmuO6+a+gs2fP5p9//nGov3//fl555RUKFizIvHnzWLhwIe7u7nTo0IFLly6leb7u3btTqlQpZs6cadcFP1nyMIGVK1fayhITE/noo4+cvbR0Gz58OABPPfWU7a/RN0pISOD111+3dc1v3rw5xhhee+01u3pfffUVhw8fpmbNmmn2CMkOzz77LMYYhxUbkiX/VT0jvLy8uHLlit2SoWAdCpGYmGhXFhQURP78+bl27RpXrlyxlb/00kt88cUXNGrUiGXLlt2xe+jv78+ECRMcXoMGDQKsq1JMmDCBZ555BrAuAVmjRg12797N+vXrbe1ERUURHh5OcHAwTZo0uSOxpyo5oXDwtDWhUCYDKzzsiYD35mfdag4Xr0D3yRD4PHh3gHrvwm8H03fs9v3QcwpUGgBu7cDSOuV6Eedg6Dyo+joEPAt5n4O6g2DN75l2GSIiIiJ3gnomyF3n8ccfx8/Pj5EjR3L48GFCQkLYtWsXK1as4IEHHuDYsWO2uomJibRu3Zr4+Hjmzp2Lr68vvr6+jBs3jh49etCxY0eWLFmS5jlHjx5Ny5YtOXHihMP49FdffZXZs2fzzDPP0LlzZ9zd3Vm6dKnDB9LM9Mwzz3Do0CGGDBlCWFgYjRo1onz58uTIkYP9+/ezevVqzpw5Q6tWrQDr8oFz585l5syZREREUKtWLfbv38/ChQvx8/O7I2P8M6JXr14sXbqUhQsXUrp0aRo2bEhgYCARERHs2LGD48ePc/bs2Qy1XblyZbZv307btm1tyZRWrVoxceJEpk+fTv369SlevDju7u5s2LCB3377jTp16uDj4wPAwIEDmTJlCnny5KFhw4YOyaMCBQrYPsyDdVjKxIkTAdi3bx8Ay5YtIyIiwnatDz30ULpi9/DwoFevXg7lf/75J8OGDaNw4cIO+8ePH8+jjz7Kk08+yXPPPYefnx+zZs0iKiqKr7766o70qEhTZvRQOHAa4lMfYpRhSUnw+HD4/Si81sI6R8OklVD3XdgxFh4scOvjl++AqWuhXGEoFgT/pDJ/x3fb4f1voWU1eK4eXE+Er3+CRkNhei/o0iDTL01EREQkKyiZIHedoKAglixZQv/+/fnmm29ITEzkwQcfZO7cuXz22Wd2yYRevXrx559/MmjQILshDS+99BKrVq3i22+/ZdKkSfTs2fOW52zRogXVq1fn559/dthXrlw5pk6dytChQ5k4cSK5cuWiefPm9OnTJ11DEDJq4MCBNGnShBEjRrBt2zZWrlxJUlISgYGBVKlShX79+lGjRg3A+uFzy5Yt9O3blxUrVrBx40Zy5sxJ3bp1+fjjjylVqlSWxXm7VqxYwZgxY/jqq6+YOnUq169fx9/fn5IlSzJw4MAMtztq1CiOHDnC2rVrWbx4McYYChUqRLNmzfj999/ZsGED3333HS4uLgQFBfHKK68wbNgw2/G//vorAOfPn+eNN95waD8sLMwumbB3714mT55sV2fz5s1s3rwZgPr166c7mZARFSpUYM2aNfTv35+pU6eSmJhIaGgos2bNokOHDll2XqdlRkIhKQNzN9QdBEXywYyXU96/YCts+RvCB0DbR6xl7R+BEr1h8DyY0//W7fdoCm+0Ai8P6/WllkyoFwbHPrcmK5K91ATKvwLvzlUyQURERO4ZFpOhGbVERERSFxMTg5+fH9HFu+N7INKxgpe7NaEQGuxcQqFkQfhxKBTI7VxAaSUT2o+DDXvg5FS4sRfHi5Nh1ga48HX652ro/QVMXAFmUdp1k736JXz4PcTMBh+v9B8nIiIikk3ugn6vIiLyn5MZcyhkpp2HoWIx+0QCQNUHITY+9Z4GmeX0RcjpATnds/Y8IiIiIplEwxxERO6Qq1evcurUqTTrhYSEpLps430lM4Y8ZJZTUVC7jGN5/v9fZeTkBShbOGvOfeAULNoG7WrAXThRqoiIiEhKlEwQEblDli1bRrt27dKst3v3bsLCwu5ARHeBrEgoJFyH6FjHsvgEOBdjX547l7U3wtVr4JHCr0RP93/jzAqx8dBunHXYx+hns+YcIiIiIllAyQQRkTukVq1azJkzJ816RYoUyfpg7iaZnVDYvM+6rOPNtvwNczfZlx3+zDqXgpc7xF93PCbu/5MIXlkw/CAxEZ7+wHqtKwY5Pw+EiIiISDZSMkFE5A4JDg6+u1ZWuJtkZkLh4SKwerB92aszIDjAuuzjjYL9rV/zB1iHOtwsuSwrPuh3mwxLd8DsflC/bOa3LyIiIpKFlEwQEZG7Q2YlFAJyQcOHHcvyBziWJytfBDbuhaQk+0kYt+23ToxYooDzcdzKa1/Blz/Cx12hw6OZ27aIiIjIHaDVHERE5O6RXas8tK0BZy7Cop//LTsXA+Fb4InK9stCHjxtfWXU2MUw7jt4uw30bZ7xdkRERESykXomiIhI1mlRGT5Y4dwx2bHKQ9saUL0EdJkAe45DXh+YtBISk2Do0/Z1G/z/EIojU/4tO3oWZq63fv/rAevX4eHWr4UD4dm61u+//Rle/xoezA+lC8Gs9fZtN3oYgvwz88pEREREsoSSCSIiknXa1oCoazB9rXPH3emEgqsrLB9oHX7wyTLr+asUhxkvQ8mCaR9/+CwM+sa+LHm7zkP/JhN+P2L9uv8UPDvesZ117ymZICIiIvcEizHGZHcQIiJyf4mJicHPz4/oVz/H9+l6MPkH5xMKYF1FYUI3CA22JhQSk+DHoVr5QERERCSbac4EERHJOt/9ak0k9GgCXRs4f/zNcygUD878GEVERETEaRrmICIiWSu5R0KPJvbb6XXjkId324OLJXPjExERERGnqWeCiIhkvelrM6eHQsQ5+5UVRERERCRbqGeCiIjcGZnRQ2FYONQNy9y4RERERMRpSiaIiEjWKZIXXN3/3d68D4L9oX9z69eFPzvXXv4AiE/IzAhFREREJAO0moOIiGQ622oO+w7j6+PrWMHNFdxyQMJ1SEh0rnFvD/DzzpxARURERCRD1DNBRESyTv7c4JtCMkFERERE7mmagFFEREREREREnKJkgoiIiIiIiIg4RckEEREREREREXGKkgkiIiIiIiIi4hQlE0RERERERETEKUomiIiIiIiIiIhTlEwQEREREREREacomSAiIiIiIiIiTlEyQUREREREREScomSCiIiIiIiIiDhFyQQRERERERERcYqSCSIiIiIiIiLiFCUTRERERERERMQpSiaIiIiIiIiIiFOUTBARERERERERpyiZICIiIiIiIiJOUTJBRERERERERJyiZIKIiIiIiIiIOEXJBBERERERERFxipIJIiIiIiIiIuIUJRNERERERERExCk5sjsAERGRxMREEhISsjsMucPc3NxwdXXN7jBEREQkA5RMEBGRbGOM4fTp01y8eDG7Q5Fs4u/vT3BwMBaLJbtDEREREScomSAiItkmOZGQL18+cubMmf4PlNcT4cxFiLkKSQZyukOQP+T0yMpwJRMZY4iNjeXs2bMA5M+fP5sjEhEREWcomSAiItkiMTHRlkjIkydP+g80Bo6cgNhrEJwbcrjC2Wg4ch7KFAJP96wLWjKVl5cXAGfPniVfvnwa8iAiInIP0QSMIiKSLZLnSMiZM6f9jr9PwOEzqR8YdQUux0GRfFAgN+Tzg5IFwAKcjMq6gCVLJL//mjNDRETk3qJkgoiIZCunx8pHXQa3HBDg/W+ZWw7InQsuXoGkpMwNULKU5koQERG5NymZICIi95bYeOscCTd/CM3paU0kxOkv3CIiIiJZTckEERG5tyQkWnsi3Mzd9d/9IiIiIpKlNAGjiIhkH2OsH/5dE+3LkstvlMPF2hshyTj2SoB/yzTMQURERCTLqWeCiIhkn/gE+Ps4/H7439flOOuSj38ft38lXLce4+VuTSzczAJ4ullXd7jHFSlSBIvFgsVioXfv3tkdjtN27dpli99isbBgwYLsDklEREQymZIJIiKSfdxzWFdlKFHg35ePl3WVBv9c9q/IGDh5AXJ5QmKS9fsbXxdjIZcXxMQ67svoK/pKhi8tPj6eN954gwIFCuDl5UW1atVYvXp1uo9/9NFHmTlzJs8995xd+Y0f0m98jR49+pbtNWrUKNXkRHR0NK+//joPPvggXl5eFC5cmBdeeIFjx4451D1x4gTt27fH398fX19fWrRowaFDh+zqFC5cmJkzZ/L222+n+3pFRETk3qJhDiIikn1cXKwJAE/Pf8vOX4K4a/DiZ3D4bPbFVjQfTOsFft5p103B888/z4IFC+jXrx8PPvggM2bM4LHHHmPdunXUqlUrzeOLFStGp06dUtzXqFEjOnfubFdWoUKFVNtatGgRW7duTXFfUlISjRo1Ys+ePfTs2ZMSJUpw4MABJk2axA8//MDevXvx8fEB4PLly9SrV4/o6Gjefvtt3Nzc+Oijj6hTpw67du0iT548AAQEBNCpUyd++uknRo4cmea1ioiIyL1HyQQREbk7HT4Lf5/I7igyZPv27cydO5exY8cyYMAAADp37kxYWBivv/46W7Zsua32S5QokWqi4WZxcXG8+uqrvPHGG7z77rsO+3/++Wd++eUXJkyYQK9evWzlJUuWpGvXrqxZs4ZWrVoBMGnSJPbv38/27dupUqUKAM2aNSMsLIwPPvhAiQMREZH/EA1zEBERyWQLFizA1dWV7t2728o8PT154YUX2Lp1KxEREbd9jqtXrxIXF5dmvTFjxpCUlGRLatwsJiYGgKCgILvy/PnzA+Dl5WUrW7BgAVWqVLElEgBKlSpFgwYNmD9/vtPXICIiIvcuJRNERG5DYGAgZcuWze4w5C6zc+dOSpQoga+vr1151apVAesEhbdjxowZeHt74+XlRZkyZZgzZ06K9Y4dO8bo0aN5//337ZICN6pcuTLe3t4MGjSIH3/8kRMnTrB+/Xpef/11qlSpQsOGDQHrcIg//viDypUrO7RRtWpVDh48yKVLl27rukREROTeoWSCiMg9rHjx4uTIkSPFifKSRUVF4enpSYECBe5gZLehaNA9vyLDqVOnbH/Zv1Fy2cmTJzPc9iOPPMKIESNYvHgxkydPxtXVlY4dOzJ58mSHuq+++ioVKlTg6aefTrW9vHnzMm/ePKKjo2nQoAGFChWibt26FChQgB9//JEcOawjIi9cuEB8fHyWXZeIiIjcW5RMEBG5h3Xs2JHExEQ+/fTTVOtMmjSJ+Ph42rZtewcj+2+7evUqHh4eDuWe/z/R5NWrVzPc9ubNm+nbty9PPvkkL730Ejt27CAsLIy3337brt1169axcOFCPv744zTbDAwMpEKFCrYkxZAhQ9i4cSNdunSxuyYgy65LRERE7i1KJojIPS8hIcE27vu/pnfv3ri7u99yvPqcOXNwcXGhb9++t32+//K9doaXlxfx8fEO5clzHKQ25CAj3N3d6d27NxcvXmTHjh0AXL9+nT59+vDss8/azW+QkkOHDlGvXj26du3K22+/TYsWLRg8eDCTJk1iwYIFrFixwi7mO3VdIiIicndTMkFE7imjRo3CYrHwzTff0KNHD4KCgvD09GTixInMnj2bunXrki9fPtzd3cmZMycVK1Zk4cKFDu2ULVuWwMBADhw4QP369fH29sbDw4NKlSrx66+/OtTft28fderUIWfOnHh5eVGtWrVbjnsfNmwYxYoVs4vju+++c6hnsVho2rQp8+fPp2TJknh4eJA7d27bB//Tp0/z+OOP4+vri4eHB9WqVePAgQO24wMDA6lTpw7Hjh1j9erVDu3/9ttv7Nmzh8qVKxMaGsqBAwfo0KEDRYsWxdvbGzc3NwoVKkTfvn1JSEhI972WW8ufPz+nTp1yKE8uy+whJyEhIYB1KALA119/zd9//82LL77IkSNHbC+AS5cuceTIEWJjYwHr/AtxcXE0b97crs0nn3wSsPaEAMidOzceHh539LpERETk7qWlIUXknvT2229z/fp1nnrqKfz8/ChXrhwffPAB0dHRtGzZkpCQEI4fP86CBQto3749CxcupGXLlnZtxMfHU6tWLcLCwnj11Vc5dOgQ8+bNo0WLFhw5cgQ3NzcAzpw5Q+3atTl//jwtW7akTJkybNy4kYYNG3Lt2jWH2J599llmzZrFgw8+SN++fbl06RLz58+ndevWTJs2jeeff96u/r59++jatStt2rThqaeeYvHixXzyySd4enoSHh5OgQIF6Nu3L/v37yc8PJy2bdvaJTK6d+/O6tWrmTx5Mo0aNbJr+5NPPgHghRdeAGDr1q2sXbuW+vXrU7x4ca5du8batWv55JNPOHz4MEuWLEnXvZZbK1++POvWrSMmJsZuEsZt27bZ9memQ4cOAdbkElgnXkxISKBmzZoOdb/++mu+/vprvv32W1q2bMmZM2cwxpCYmGhXLzm5dP36dQBcXFwoW7Zsism2bdu2UaxYMXx8fDL1ukREROTupWSCiNyT4uPj2bdvn90HtZo1a+Lv729X780336RcuXKMGDHCIZlw6dIlunbtajemPDAwkI8//ph58+bRqVMnAN544w0iIyMZMWIEb7/9tq1u+/btCQ8Pt2vzl19+Yfbs2ZQuXZodO3bYun0PGDCAcuXK8dprr9GxY0dbogKsH/yWLVtGs2bNAHjrrbfInz8/Y8eOpU2bNg7nmDdvHr/88out+3rr1q0JCgpi1apVxMbGkjNnTgASExNZsmQJvr6+tgRGy5Yt6dixIy4u9h3TGjVqxNKlSzl48CChoaFp3us7omi+O3u+TDx/27ZtGTduHJ9//rltScb4+Hi+/PJLqlWrZutJ4KzIyEhbwiDZpUuX+Pjjj8mbNy+VKlUC4Omnn04xYdGqVSsee+wxunXrRrVq1QAoUaIExhjmz59vl+j65ptvAKhQoYLddb355pv8+uuvtlUd/v77b3788cdUl54UERGR+5QREbmHjBw50gDmtddeu2W9CxcumOPHj5uIiAhTpUoV4+3tbbc/LCzMWCwWc+nSJbvy1atXG8C89dZbtrKCBQsaPz8/c+3aNbu6Bw4cMIAJCwuzlfXt29cA5vPPP3eIqX379gYwq1atspUBplSpUg51q1evbgDz22+/2ZVPnjzZAGbatGl25b169TKAGT9+vK3s66+/NoB55plnHNo3xpjY2Fhz4sQJExERYcaOHWsAM3XqVNv+9N7rlERHRxvAHDt2zFYWERFhjh49atuOjIw0u3btMlevXrWV2d6Pi5eNOXHeJEZEGnPivO2VGBFpko7fsH38nEk6fs62nZTCduIN26m1meo5Ll42sbGxJiEhwRZjfHy8iYuLs21fv37dxMbG2l3/pUuXTLt27UyOHDnMa6+9ZsaPH28eeeQRkyNHDrN+/Xpz6dIlk5SUZPde3HiOwoULm06dOjmcY/Dgwebhhx82AwcONJ988okZOnSoKVy4sLFYLHbvXXIMN58DML169bK7jnPnzpng4GDj7u5uevbsaaZMmWJefPFF4+rqakqXLm3i4+NtbZw8edKEhoaafPnymTFjxpjRo0ebkJAQU6BAAXP27FmH61i1apUBTHh4eKr36ty5c2bPnj2252Dz5s12+7du3WquX79u2/7rr7/MhQsXbNs3P1fR0dFm9+7ddm3c3KbOoXPoHDqHzqFz6By3T8kEEbmnJH/A/fLLLx327dy509StW9fkzJnTAHYvi8ViVzcsLMzkzp3boY3du3cbwHTv3t1WliNHDlOmTJkU48mZM6ddMqFFixYGMDt37nSo+9577zl84AdMw4YNHeo2adLEAA4JjPDwcAOY0aNH25Xv37/fuLi4mIoVK9rKateubQCzdetWW1l8fLzp0aOHCQ4ONhaLxeE+vf/++7a6t7rXaUlOJkRHR6da5+rVq3YfIu83V69eNQMGDDDBwcHGw8PDVKlSxaxcuTJdxxYuXNg899xzDuWrVq0yjRo1MsHBwcbNzc34+/ubxo0bm7Vr16ar3RuTCTc6fvy46dq1qylatKhxd3c3+fPnN926dTORkZEOdSMiIkzbtm2Nr6+vyZUrl2nevLnZv39/iudbt26dXTIhJff7cyAiInK/0jAHEbkn5cqVy247KiqK+vXrExcXR6dOnShfvjx+fn64uLgwevRo/vjjD4c2bu7qfyNjTKbHnBpXV9dU9904HOJGSUlJdtvFixe3TR75999/4+Pjw5YtWyhZsiTVq1e31evYsSMLFiygbt269OvXj+DgYNzd3dm2bRvjx493GDcPjvda0sfT05OxY8cyduzYDB0fHx/PuXPn8PLywtvbG7AOR7l5XgxnpPZcFyxYkGnTpqWrjUKFCjkMvblZYmIiUVFRREdHOx2jiIiI3BuUTBCR+0J4eDhRUVEO8xoADBky5LbaDgoK4sSJEyQkJNh9uD948KBtRvxkRYsWBaxzJ9w8Zv2vv/4CoHTp0rcVT2q6dOnCL7/8wieffIKfnx/Xr1+nY8eOdnWWL1/OQw89xLp16+zK9+3blyUxScbNnTuXuXPn0qtXLyZMmJDd4Thl9+7ddnMtiIiIyP1HS0OKyH0hRw5rbvTmv7zOnj2bf/7557babtCgAdHR0Q5/YX7rrbcc6j7zzDNYLBY+/vhj4uPjbeUHDx7k+++/J2/evNStW/e24klN165d8fHxYdGiRcydOxcPDw969uxpV8fFxcXhHl28eJEvvvgiS2LKMtcT4chZ2HUYfjsEf5+AK3HZHVWmmT17NqtXr2b16tW8/PLL2R2O04oXL26Lf/Xq1dSuXTu7QxIREZFMpp4JInJfePzxx/Hz82PkyJEcPnyYkJAQdu3axYoVK3jggQc4duxYhtt+//33WbZsGYMGDWLHjh089NBDbNiwgT///NNhKbwqVarQsWNHZs2aRbly5WjRooVtaci4uDgmTpyY6tCF2+Xh4UHz5s1ts/A3bdqUPHny2NWpX78+S5YsoW7dutSvX5/Tp0+zYMGCO79Sw+0wBg6cgthrEOwPOVzhbDT8fRLKFAJP9+yO8LaltKTjvSRXrlw0bNgwu8MQERGRLKSeCSJyXwgKCmLJkiWUKlWKb775hlGjRnHgwAHmzp1728MKgoOD2bhxI4888gjLly9n7NixXL16lTVr1uDh4eFQf+bMmQwdOpRr167x0UcfMWPGDB544AEWLlxot/ReVujTp4/t+xdffDHF2J555hn+/PNPhg0bxrfffsvTTz/NiBEjsjQup/x9Ag6fSX1/1BW4HAdF8kGB3JDPD0oWAAtwMuqOhSkiIiLyX2Yxd3KWMRER+U+IiYnBz8+P6OjoVHs9xMXFcfjwYYoWLYqnp+e/O/4+Ae45oGhQyo0fPG1NJpQrDBbLv+VHz8L5y1C+CNxick25u6T6HIiIiMhdTf/bEhGRe0tsPOR0t08kAOT0hKQkiEvInrhERERE/kOUTBARkXtLQiK4pTDlj7vrv/tFREREJEtpAkYREck+xlg//Lsm2pcll98oh4u1N0KSceyVAP+WJSVlXbwiIiIiAiiZICIi2Sk+Af4+Drg67rtw2X67bGHwcAMXizXZcLPkMs2XICIiIpLllEwQEZHs454DgvKB+w2rYkScBzdX67KPN3Jz/fdrwnXHtq4l2tcTERERkSyjZIKIiGQfFxfI5QU3zuKfI8qaEPDNmfIxOT2sqzmYm4Y7XImztufplrUxi4iIiIgmYBQRkbuQ6y1+PeXxAVcLRMf+W5aQaF3lIa/PfTHMoW7dulgsFiwWC82bN8/ucGw+/vhjW1wWi4Vz585ld0giIiKSTe79/3GJiMj9xdUFvNwhMhouXrF+PXnh39eVePDJCdFXIOKc9XXyAnh7WI+9se75SxB1GU5H2Zen9xV9JUOXcPnyZQYPHkzTpk3JnTs3FouFGTNmONVGqVKlmDlzJgMGDLArnzdvHp06deLBBx/EYrFQt27ddLU3YsQILBYLYWFhduVHjhyxSxDc/OrWrZutbtOmTZk5cyatWrVy6lpERETk/qNhDiIicndxdYGr1+DFz6BiMehQC77ZBAt/dr4tTzcY1A5C8sJ78+HA6fQfWzQfTOsFft5On/bcuXO89957PPDAAzz88MP89NNPTrcRFBREp06dHMonT57Mjh07qFKlCufPn09XW8ePH2fkyJF4ezteS2BgIDNnznQoX7lyJbNnz6Zx48a2slKlSlGqVCkOHDjAt99+68TViIiIyP1GyQQREbm7FA2y9go4fBZW7oTTF6FHE+vX6Wudb+/Z8TChG7zWEnp+DnsiMjlgR/nz5+fUqVMEBwfz66+/UqVKlUxre+bMmRQsWBAXFxeHXgapGTBgANWrVycxMdFhaIK3t3eKSYsZM2bg6+vLE088kSlxi4iIyP1FwxxEROTuNn0tTP7BmlDo2sD5469eg95fwMHTMKk7lAnJ/Bhv4uHhQXBwcJa0HRISgosT80Js2LCBBQsW8PHHH6f7mFOnTrFu3Tpat26N542TY4qIiIj8PyUTRETk7ncPJhTuBomJibz88sv873//o2zZsuk+bu7cuSQlJdGxY8csjE5ERETuZUomiIjIvUEJBad99tlnHD16lGHDhjl13OzZs8mfPz/169fPoshERETkXqdkgoiI3DuUUEi38+fP8+677zJo0CACAwPTfdw///zDjh07ePrpp50aTiEiIiL/LfpfgoiI3FuUUEiXgQMHkjt3bl5++WWnjps9ezaAhjiIiIjILSmZICIi9x4lFG5p//79fP755/Tp04eTJ09y5MgRjhw5QlxcHAkJCRw5coQLFy6keOycOXMoWbIklSpVusNRi4iIyL1EyQQREbk3KaGQqhMnTpCUlESfPn0oWrSo7bVt2zb++ecfihYtynvvvedw3LZt2zhw4IB6JYiIiEiacmR3ACIiIhk2fa31a48m9tvplZxQmNDNmlDo+TnsicjcGLNBWFgY3377rUP5wIEDuXTpEuPHjyc0NNRh/5w5cwB45plnsjxGERERubcpmSAiInenovnSV2/zPgj2h/7NrV8X/uz8uT5ZBoPawZe94b35cOB0+s+figkTJnDx4kVOnjwJwPfff8/x48cBePnll/Hz88tQuxs2bGDDhg0AREZGcuXKFYYPHw5A7dq1qV27Nnnz5qVly5YOx3788ccAKe5LTExk3rx5VK9ePcVEg4iIiMiNlEwQEZG7j7cHTOvl3DFurvBaS+jXHBISM3ZeDzeY2RfiEyDJWOPIoHHjxnH06FHb9qJFi1i0aBEAnTp1ynAy4ccff2To0KF2ZYMGDQJg8ODB1K5dO0PtrlmzhjNnzvDOO+9k6HgRERH5b1EyQURE7j5+3tbXPezIkSO3dXxCQgLnzp3D3d0dX19fW/mQIUMYMmRIhtr86aefUt3XpEkTjDG3PD4uLo7Lly8TGxubofOLiIjI/UMTMIqIiNyFtmzZQmBg4F01f8Fnn31GYGAgY8eOze5QREREJJupZ4KIiMhd5oMPPiAqKgqAwMDAbI7mX23atCEsLMy2ndGhGiIiInLvUzJBRETkLlOpUqXsDiFFISEhhITcP0toioiISMZpmIOIiIiIiIiIOEXJBBERERERERFxipIJIiKSrdJaQUDub3r/RURE7k1KJoiISLZwc3MD0DKD/3HJ73/y8yAiIiL3Bk3AKCIi2cLV1RV/f3/Onj0LQM6cObFYLNkcldwpxhhiY2M5e/Ys/v7+uLq6ZndIIiIi4gQlE0REJNsEBwcD2BIK8t/j7+9vew5ERETk3qFkgoiIZBuLxUL+/PnJly8fCQkJ2R2O3GFubm7qkSAiInKPUjJBRESynaurqz5UioiIiNxDNAGjiIiIiIiIiDhFyQQRERERERERcYqSCSIiIiIiIiLiFCUTRERERERERMQpSiaIiIiIiIiIiFO0moOIiGQ6YwwAMTEx2RyJiIiIiNyKj48PFovF6eOUTBARkUx3/vx5AEJCQrI5EhERERG5lejoaHx9fZ0+TskEERHJdLlz5wbg2LFj+Pn5ZXM095+YmBhCQkKIiIjI0C9/SZ3ubdbS/c1aur9ZR/c2a+n+Zp303FsfH58Mta1kgoiIZDoXF+uUPH5+fvpPQRby9fXV/c0iurdZS/c3a+n+Zh3d26yl+5t1suLeagJGEREREREREXGKkgkiIiIiIiIi4hQlE0REJNN5eHgwePBgPDw8sjuU+5Lub9bRvc1aur9ZS/c36+jeZi3d36yTlffWYpLX7xIRERERERERSQf1TBARERERERERpyiZICIiIiIiIiJOUTJBRERERERERJyiZIKIiDhl3759NGrUCG9vb4KDg3n99de5du1amscZYxg9ejQPPPAAXl5e1KhRg59//vkORHxvyej9LVKkCBaLxeEVFxd3B6K+Nxw4cICXXnqJ8uXLkyNHDsLCwtJ1nJ7d9Mno/dWzm7bw8HBatGhBoUKF8Pb2pnz58kyfPp20pj7Ts5u2jN5bPbfps3z5curUqUNgYCAeHh4UK1aMV155hejo6DSPnTZtGiVKlMDT05OHH36YpUuX3oGI7y0Zvb9169ZN8fndt2+fU+fPcTvBi4jIf0tUVBT169fnwQcfZNGiRZw4cYJXXnmF2NhYJkyYcMtj33//fQYPHszo0aMpV64cEydOpHHjxuzatYtixYrdoSu4u93O/QVo27Ytr776ql2ZZsb+119//cWyZcuoVq0aSUlJJCUlpes4Pbvpk9H7C3p20/Lhhx9SpEgRPvjgAwIDA1m9ejXdunUjIiKCwYMHp3qcnt20ZfTegp7b9Lhw4QLVqlWjT58+5MmThz///JMhQ4bw559/smrVqlSPmzt3Lt26deOdd96hfv36zJs3j1atWrFx40aqV69+B6/g7pbR+wtQs2ZNxo0bZ1dWpEgR5wIwIiIi6TRy5Ejj7e1tzp8/byubMmWKcXV1NSdOnEj1uKtXrxpfX1/z1ltv2cri4+NN4cKFTY8ePbI05ntJRu+vMcYULlzY9OrVK6tDvKclJibavn/uuefMQw89lOYxenbTLyP31xg9u+kRGRnpUNatWzfj6+trd99vpGc3fTJyb43Rc3s7Pv/8cwPc8vdaiRIlTIcOHezKatSoYZo1a5bV4d3z0nN/69SpYx5//PHbPpeGOYiISLqtWLGChg0bkjt3bltZ+/btSUpKumUGfMuWLcTExNC+fXtbmbu7O61bt2b58uVZGvO9JKP3V9LHxcX5//bo2U2/jNxfSZ+8efM6lFWoUIGYmBiuXLmS4jF6dtMnI/dWbk+ePHkAUh3Cd+jQIf755x+7Zxfg6aefZu3atcTHx2d5jPeytO5vZtK/+iIikm779u2jVKlSdmX+/v7kz5//luPskvfdfGzp0qU5duwYV69ezfxg70EZvb/JZs+ejYeHB7ly5eKxxx5j9+7dWRXqf4ae3TtDz67zNm3aRMGCBfHx8Ulxv57djEvr3ibTc5t+iYmJxMXF8dtvv/Hee+/x5JNPptql/lbP7rVr1zh8+HBWh3vPceb+Jlu/fj3e3t54enpSp04dNmzY4PR5lUwQEZF0i4qKwt/f36E8ICCACxcu3PI4Dw8PPD09HY4zxhAVFZXZod6TMnp/AZ588kkmTJjAmjVrmDhxIgcOHKBWrVocOnQoi6L9b9Czm/X07Dpv06ZNzJ07lwEDBqRaR89uxqTn3oKeW2cVLlwYLy8vKlWqRP78+ZkzZ06qdZOfzZt/HwYEBACk+fvwv8iZ+wtQp04dxo8fz8qVK/nqq6+IjY2lYcOGbN261anzagJGERGR+8Ann3xi+/7RRx+lcePGlCpVinHjxjFp0qRsjEzk1vTsOuf48eM89dRT1KtXjz59+mR3OPcVZ+6tnlvnLF++nCtXrvDXX38xfPhwnnjiCVavXo2rq2t2h3ZfcPb+Dh061G67efPmPPTQQwwbNsypYVBKJoiISLoFBASkuNxQVFSU3Tj/lI6Lj48nLi7O7q9kUVFRWCwW218b/usyen9Tkj9/fmrVqsWOHTsyK7z/JD27d56e3dRdvHiRZs2akSdPHhYuXHjLeSr07DrHmXubEj23t1auXDkAatSoQZUqVShfvjzffvstbdu2daib/GxGR0cTHBxsK0/useDs78P/Amfub0q8vb15/PHHWbBggVPn1TAHERFJt1KlSjmM3Y+OjubUqVMOYxtvPg7g77//tivft2+fbf1zyfj9layjZ1fuFlevXqV58+ZER0ezYsUK/Pz8bllfz276OXtv5faUK1cONzc3Dhw4kOL+5Gf35t+H+/btw93dXcuapiGt+5uZlEwQEZF0a9asGWvWrOHixYu2svDwcFxcXGjcuHGqxz3yyCP4+voSHh5uK0tISGDRokU89thjWRnyPSWj9zclJ0+eZNOmTVSpUiWTo/xv0bN75+nZdXT9+nXat2/P3r17WblyJQULFkzzGD276ZORe5sSPbfpt23bNhISElJNChQrVowSJUrYPbsA8+bNo0GDBri7u9+JMO9Zad3flFy5coWlS5c6/fxqmIOIiKTbSy+9xKeffkrLli15++23OXHiBK+99hovvfQSBQoUsNVr0KABR48etWXFPT09eeuttxgyZAiBgYGULVuWSZMmcf78+TQnufovyej9/eabb1i6dCmPPfYYBQoU4NChQ4waNQpXV1deffXV7Lqcu05sbKxtLOjRo0eJiYmxdemsU6cOgYGBenZvQ0bur57d9OnZsydLly7lgw8+ICYmhp9//tm2r0KFCnh4eOjZzaCM3Fs9t+nXunVrKleuTLly5fDy8uL3339n7NixlCtXjpYtWwLwwgsv8NVXX3H9+nXbcUOGDKFjx46EhoZSr1495s2bx7Zt2zK04sD9LCP3d+PGjYwdO5ZWrVpRpEgRTp48yQcffMDp06cdEjhpMiIiIk7Ys2ePadCggfHy8jL58uUzAwYMMPHx8XZ16tSpYwoXLmxXlpSUZEaOHGkKFSpkPDw8TLVq1cyWLVvuYOT3hozc361bt5q6deuavHnzmhw5cpi8efOa9u3bm3379t3h6O9uhw8fNkCKr3Xr1hlj9OzejozcXz276VO4cOFU7+3hw4eNMXp2Myoj91bPbfqNGjXKlC9f3vj4+Bhvb2/z0EMPmUGDBpno6Ghbneeee86k9LF06tSppnjx4sbd3d2ULVvWfP/993cy9HtCRu7v/v37TZMmTUxwcLBxc3Mz/v7+5rHHHjPbtm1z+vwWY4zJUBpERERERERERP6TNGeCiIiIiIiIiDhFyQQRERERERERcYqSCSIiIiIiIiLiFCUTRERERERERMQpSiaIiIiIiIiIiFOUTBARERERERERpyiZICIiIiIiIiJOUTJBRERERERERJyiZIKIiIiIZKu4uDiKFCnCO++8Y1c+ZMgQLBZLNkV1f5gxYwYWi4Wffvrpjpzvp59+cjifMYaKFSvSpUuXOxKDiNwZSiaIiIiISLb68MMPuXjxIgMGDEhX/ejoaIYPH0758uXx9/cnV65cFC1alJYtWzJ16lS7us8//zwWi4Vz586l2Fbyh+0FCxakuD8xMZGCBQtisVgYNmxYqjHVrVsXi8Vie7m5uVGwYEE6dOjAX3/9la7rul9ZLBaGDBnC119/za5du7I7HBHJJDmyOwARERER+e+6evUqY8eOpUuXLgQEBKRZPyYmhipVqnDo0CHatm1L165dcXd359ChQ2zatInx48fzv//9L9PiW7FiBSdPniQ0NJQZM2YwcODAVHtLeHh42JIZV69eZdu2bXz11VcsW7aMX375hZIlS2ZaXPeaJ598kiJFijBixAjCw8OzOxwRyQRKJoiIiIhItpkzZw4XL16kc+fO6ar/xRdfsH//fj7++GP69u3rsP/06dOZGt+0adMIDQ3lww8/pEWLFvz000/Uq1cvxbo5cuSgU6dOtu1u3bpRunRpBgwYwCeffMLEiRMzNbZ7TadOnRg1ahSnT58mODg4u8MRkdukYQ4iIiIi97Hkbvxr167lvffeo3Dhwnh5eVGtWjV+/vlnANavX0+tWrXw9vYmf/78KXbnX7VqFU899RTFihXDy8sLf39/GjduzPr16+3q7dy5E09PTxo1aoQxxlaemJhI7dq1yZkzJ3/++aetPDw8nODgYCpUqJCu69m/fz8ADRo0SHF/Zn5IPXPmDEuXLqVz58489thj5MuXj2nTpjnVRpMmTQA4cOBAqnX27t2LxWLhlVdeSXF/hw4dcHd3JzIyEoB9+/bRs2dPHnroIXx8fMiZMyeVKlVyGOKRmuS5KI4cOeKwr0iRItStW9ehfM2aNTRu3Bh/f388PT0pV64cn332WbrOl6xZs2YkJCSwePFip44TkbuTkgkiIiIi/wFvvvkmixcvpm/fvgwePJhDhw7RuHFjFi9eTOvWrXn00UcZN24cpUqV4t1332XWrFl2x8+YMYMLFy7QuXNnPv30U/r378/evXtp0KABGzdutNWrUKECY8aMYc2aNYwePdpWPnToUDZu3MhHH31EWFgYYE0wbN68mapVq6b7OkJDQwH48ssvuX79erqPu3DhAufOnXN4Xb58OdVjvv76axITE+ncuTM5cuSgY8eOLFq0iOjo6HSfNzn5kTdv3lTrlC5dmipVqjBnzhwSExPt9sXExPDdd9/RrFkzAgMDAeskhxs2bKB58+aMHTuWYcOG4ebmRrdu3Rg1alS6Y0uvzz//nMaNG3P58mXeeecdPvzwQ0JDQ+nRowevvfZautupWLEiHh4ed2wySBHJYkZERERE7ltffvmlAUyFChVMfHy8rfy7774zgMmRI4f55ZdfbOXx8fEmODjYVK9e3a6dy5cvO7R9+vRpkydPHtOsWTOHfU8++aTJkSOH2bp1q1m3bp1xcXExbdq0satz6NAhA5j+/funGPvgwYPNzf9dvXDhggkJCTGAyZcvn2nTpo0ZPXq02bhxo0lMTHRo47nnnjNAmq/w8HCHY0uVKmXq1Klj2961a5cBzKRJkxzq1qlTx3h7e5vIyEgTGRlpjh07ZsLDw02hQoUMYFauXJniNSabMGGCAcyyZcvsyqdOnWoAs3DhQltZSu9FYmKiqVOnjvH19TXXrl2zlSe//+vWrbOVJd/Xw4cPO7RTuHBhu2s+efKk8fDwMB06dHCo26dPH+Pi4mIOHjxoK1u3bp3D+W4UGhpqwsLCUtwnIvcW9UwQERER+Q/o0aMH7u7utu1HH30UgGrVqlG5cmVbubu7O1WrVrX9RT2Zt7e37fvLly9z/vx5XF1dqVatGtu2bXM435dffklwcDAdOnSgU6dOhISEOHTDT+62nzt37nRfR0BAADt27OCNN97Az8+PhQsX8uabb/Loo48SGhrKqlWrUjxu4cKFrF692uGV2l/Wt2zZwr59+3juuedsZQ8//DDly5dn+vTpKR5z5coVAgMDCQwM5IEHHqBdu3Zcv36dGTNm2IY7pCZ5KMPXX39tV/7111+TO3dumjdvbiu78b2Ii4vj/PnzXLhwgcaNGxMTE8O+fftueS5nLFiwgPj4eF544QWHXh1PPPEESUlJrFmzJt3t5cmTh7Nnz2ZafCKSfTQBo4iIiMh/QLFixey2k1dOKFq0qEPdgIAAzp8/b1d28OBB3nnnHX744QcuXrxoty+l1Q1y587Nl19+SaNGjQDYtGkT/v7+KR5nbphbIT0CAwMZPXo0o0eP5vz582zdupX58+cza9YsWrVqxe+//07x4sXtjqldu3aKQw2OHz+e4jmmTZuGm5sbFSpUsJvvoEmTJrz//vv88ccflCtXzu4YT09Pvv/+e8A6GWNQUBAlS5bExSXtv98lJwy+++47YmJi8PX15ciRI2zcuNEhEXT58mWGDBnC/PnziYiIcGgrKioqzfOl1969ewFo2LBhqnXOnDmT7vaMMamuhiEi9xYlE0RERET+A1xdXZ0qv9Hly5epXbs2V65coV+/fpQtWxYfHx9cXFwYNWoUP/74Y4rHLVmyxPb9rl27qFmzpt3+5DkALly4kN7LcJAnTx6aN29O8+bNCQkJYeTIkcydO5eBAwdmuM3Lly8zf/58EhISUp0Ycvr06Xz88cd2Za6urrf80J2Wzp07s2jRIubPn8///vc/Zs6ciTHGrncEwDPPPMPSpUvp3r07tWvXJk+ePLi6urJ8+XI++ugjkpKSbnmeW32Yv3keiuREz9dff03+/PlTPObmRNWtXLhwwfa+i8i9TckEEREREbmltWvXcvLkSaZPn06XLl3s9qX2oX3JkiV8+umndOnShQMHDjBgwABq165N2bJlbXVCQkLw9fV1GFKRUdWrVwfgxIkTt9XO/PnzuXz5MiNHjuTBBx902P/JJ58wa9YsxowZY9dj4HY99thj5M2bl6+//tqWTChVqpTdBJUXL15k6dKlPPvssw6rKaR3uEHysJILFy5QpEgRW3lcXBynTp2y69WRfP158+a9rUQJQHx8PBEREbRu3fq22hGRu4PmTBARERGRW0ruvXDzcIRVq1alOF/CiRMn6Nq1K6VLl2bChAnMnj0bLy8vnn76aa5evWrX7qOPPppiG6nZunWrwzCLZMlLDpYpUybd7aVk2rRp5M6dm9dee422bds6vF544QXOnz/Pd999d1vnuZmbmxvPPPMMmzZtYs6cOezfv9+hV0Jq78WpU6fSvTRkiRIlAMfkQ0q9Gtq3b4+HhweDBw+2e++SRUdHEx8fn67z7ty5k2vXrlGnTp101ReRu5t6JoiIiIjILdWqVYvg4GBeffVVjhw5QqFChdi1axczZ86kbNmy7N6921Y3KSmJjh07cuXKFebNm0fOnDnJmTMn06ZNo3Xr1vTr148pU6bY6rdr145ly5axffv2dC0ROXv2bL788ksef/xxqlatSp48eTh//jzLly9n3bp1lClThq5du2b4Wvft28eWLVt4/vnnyZEj5f8qP/nkk7i5uTFt2jTatWuX4XOl5LnnnuOTTz6hR48euLi40KlTJ7v9Pj4+NG7cmFmzZuHl5UWVKlU4evQoU6ZMoWjRog5zXaSkYcOGlCxZknfffZfz589TtGhRNm3axM8//+wwr0ShQoWYPHky//vf/yhdujTPPvsshQsXJjIykt27d7N48WL27Nlj18MhNcuXL8fNzY2WLVs6c0tE5C6lngkiIiIickv+/v788MMPVKtWjU8//ZRXX32VPXv2sHz5cipWrGhXd/jw4axfv54PP/zQbkhDq1at6NmzJ59//jkLFy60lT/11FPkzp2bmTNnpiuWl156if79+3PixAk+/PBDXnzxRYYOHUpUVBSDBw9m69atdqsdOGvatGkAt+yKHxAQQL169Vi9enWKEyDejooVKxIWFkZMTAz169enUKFCDnVmzZpF165d+f777+nduzeLFy9mxIgR9OrVK13ncHV1ZcmSJdStW5dPP/2UN998k2vXrrF+/foU712XLl3YsGEDFSpUYMqUKfTs2ZNPP/2UU6dOMWzYMIKDg9N13lmzZtGiRYt01xeRu5vFODt9roiIiIhIJho9ejSjRo3i8OHDdstEDhkyhKFDhzq92oNkn59++ol69eqxbt066tatayv/7rvvaN26NTt27KB8+fLZFp+IZB71TBARERGRbNWvXz8CAgIYN25cdociWcAYw5AhQ+jcubMSCSL3Ec2ZICIiIiLZytPTkyNHjmR3GJJFLBYLO3fuzO4wRCSTqWeCiIiIiIiIiDhFcyaIiIiIiIiIiFPUM0FEREREREREnKJkgoiIiIiIiIg4RckEEREREREREXGKkgkiIiIiIiIi4hQlE0RERERERETEKUomiIiIiIiIiIhTlEwQEREREREREacomSAiIiIiIiIiTvk/gYSYhbW03p8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "shap.plots.bar(shap_values.cohorts([i for i in map(str, y.values)]).abs.max(0), show=False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grid search regularization parameter\n", + "\n", + "Here I'm removing features based on the above findings, then I want to check how the model performs on different splits.\n", + "\n", + "I'm removing the randomVar and the upvoteRatio features since they seem to be the least useful." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T23:53:09.046979Z", + "start_time": "2023-05-03T23:53:08.987712Z" + } + }, + "outputs": [], + "source": [ + "features = [\n", + " #'maxScore20m',\n", + "# 'maxScore21_40m',\n", + " 'maxScore41_60m',\n", + " #'maxNumComments20m',\n", + " 'maxNumComments21_40m',\n", + " 'maxNumComments41_60m',\n", + "# 'maxUpvoteRatio20m', \n", + "# 'maxUpvoteRatio21_40m',\n", + " 'maxUpvoteRatio41_60m',\n", + "# 'maxNumGildings20m', # we know these are bad features from prior analysis\n", + "# 'maxNumGildings21_40m',\n", + "# 'maxNumGildings41_60m',\n", + " 'maxScoreGrowth21_40m41_60m',\n", + " 'maxNumCommentsGrowth21_40m41_60m',\n", + "# 'randomVar',\n", + "# 'time0006', \n", + "# 'time0612',\n", + "# 'time1218',\n", + "# 'time1800',\n", + "# 'sunday'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T20:04:47.320460Z", + "start_time": "2023-05-03T20:01:27.709073Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total posts: 5627, viral posts: 147\n", + "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ken/Documents/side_projects/RedditWork/Model/venv/lib/python3.12/site-packages/sklearn/metrics/_scorer.py:548: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
GridSearchCV(cv=StratifiedShuffleSplit(n_splits=10, random_state=0, test_size=0.2,\n",
+       "            train_size=0.8),\n",
+       "             estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
+       "             param_grid={'learning_rate': [0.1, 0.01, 0.001],\n",
+       "                         'max_depth': [2, 3, 4],\n",
+       "                         'min_samples_leaf': [1, 3, 5, 7],\n",
+       "                         'n_estimators': [50, 100, 150]},\n",
+       "             refit='top5Pctaucpr',\n",
+       "             scoring={'aucpr': make_scorer(aucpr, response_method='predict_proba'),\n",
+       "                      'top2PctPrecision': make_scorer(top2PctPrecision, response_method='predict_proba'),\n",
+       "                      'top2PctRecall': make_scorer(top2PctRecall, response_method='predict_proba'),\n",
+       "                      'top5Pctaucpr': make_scorer(top5Pctaucpr, response_method='predict_proba')},\n",
+       "             verbose=3)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GridSearchCV(cv=StratifiedShuffleSplit(n_splits=10, random_state=0, test_size=0.2,\n", + " train_size=0.8),\n", + " estimator=GradientBoostingClassifier(), n_jobs=-1,\n", + " param_grid={'learning_rate': [0.1, 0.01, 0.001],\n", + " 'max_depth': [2, 3, 4],\n", + " 'min_samples_leaf': [1, 3, 5, 7],\n", + " 'n_estimators': [50, 100, 150]},\n", + " refit='top5Pctaucpr',\n", + " scoring={'aucpr': make_scorer(aucpr, response_method='predict_proba'),\n", + " 'top2PctPrecision': make_scorer(top2PctPrecision, response_method='predict_proba'),\n", + " 'top2PctRecall': make_scorer(top2PctRecall, response_method='predict_proba'),\n", + " 'top5Pctaucpr': make_scorer(top5Pctaucpr, response_method='predict_proba')},\n", + " verbose=3)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import defaultdict\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.metrics import make_scorer\n", + "\n", + "X = df[features]\n", + "y = df['target']\n", + "print(f\"total posts: {len(y)}, viral posts: {y.sum()}\") # how many targets are there, This is a highly imbalanced problem, only ~2.5% of posts in rising go viral\n", + "\n", + "sss = StratifiedShuffleSplit(n_splits=10, train_size=0.8, test_size=0.2, random_state=0)\n", + "model = GradientBoostingClassifier()#(n_estimators=100, learning_rate=.01, min_samples_leaf=5, max_depth=3)\n", + "parameters = {\n", + " 'n_estimators':[50, 100, 150], \n", + " 'learning_rate':[.1, .01, .001],\n", + " 'min_samples_leaf':[1,3,5,7],\n", + " 'max_depth':[2,3,4]\n", + "}\n", + "\n", + "# parameters = { # for debugging, comment out when not needed\n", + "# 'n_estimators':[50],\n", + "# 'learning_rate':[.1],\n", + "# 'min_samples_leaf':[1],\n", + "# 'max_depth':[2]\n", + "# }\n", + "\n", + "# I couldn't figure out a way to make multiple scorers that don't repeat work, each scorer has to return a scaler\n", + "def getThreshold(y, y_pred, q=0.98):\n", + " return np.quantile(y_pred, q)\n", + "\n", + "def top2PctPrecision(y, y_pred):\n", + " top2PctThreshold = getThreshold(y, y_pred)\n", + " return y[y_pred>=top2PctThreshold].mean()\n", + " \n", + "def top2PctRecall(y, y_pred):\n", + " top2PctThreshold = getThreshold(y, y_pred)\n", + " totalTargets = y.sum()\n", + " return y[y_pred>=top2PctThreshold].sum()/totalTargets\n", + "\n", + "def aucpr(y, y_pred):\n", + " precisions, recalls, thresholds = precision_recall_curve(y, y_pred)\n", + " return auc(recalls, precisions)\n", + "\n", + "def top5Pctaucpr(y, y_pred):\n", + " \"\"\"\n", + " This is basically a partial aucpr for the top 5% where we are most likely to decision\n", + " \"\"\"\n", + " top5PctThreshold = getThreshold(y, y_pred, q=0.95)\n", + " y_pred2 = y_pred[y_pred >= top5PctThreshold]\n", + " y2 = y[y_pred >= top5PctThreshold]\n", + " precisions, recalls, thresholds = precision_recall_curve(y2, y_pred2)\n", + " return auc(recalls, precisions)\n", + "\n", + "top2PctPrecision_scorer = make_scorer(top2PctPrecision, needs_proba=True)\n", + "top2PctRecall_scorer = make_scorer(top2PctRecall, needs_proba=True)\n", + "aucpr_scorer = make_scorer(aucpr, needs_proba=True)\n", + "top5Pctaucpr_scorer = make_scorer(top5Pctaucpr, needs_proba=True)\n", + "\n", + "scoring = {\n", + " 'top2PctPrecision':top2PctPrecision_scorer,\n", + " 'top2PctRecall':top2PctRecall_scorer,\n", + " 'aucpr':aucpr_scorer,\n", + " 'top5Pctaucpr': top5Pctaucpr_scorer,\n", + "}\n", + "\n", + "gs = GridSearchCV(\n", + " estimator=model,\n", + " param_grid=parameters,\n", + " scoring=scoring,\n", + " cv=sss,\n", + " n_jobs=-1,\n", + " refit='top5Pctaucpr',\n", + " verbose=3\n", + " #, error_score=\"raise\" # uncomment this to test for scorer errors, changing n_jobs=1 can help\n", + ")\n", + "gs.fit(X,y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T20:24:23.333240Z", + "start_time": "2023-05-03T20:24:23.218101Z" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "# this outputs a lot but it's used in the next steps\n", + "# gs.cv_results_" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T20:13:40.128256Z", + "start_time": "2023-05-03T20:13:26.015875Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "sns.set_theme(style='white')\n", + "\n", + "def swarmPlotForMetric(gs, metric=aucpr):\n", + " metric_col = f'mean_test_{metric}'\n", + " dfs = []\n", + " for param in parameters.keys():\n", + " sub_df = pd.DataFrame({\n", + " 'param': [param for _ in range(len(gs.cv_results_['param_min_samples_leaf'].data))], \n", + " 'param_value':gs.cv_results_[f'param_{param}'].data,\n", + " metric_col:gs.cv_results_[metric_col]\n", + " })\n", + " dfs.append(sub_df)\n", + "\n", + " data = pd.concat(dfs)\n", + "\n", + " sns.catplot(\n", + " data=data, \n", + " kind=\"swarm\",\n", + " x=\"param_value\", y=metric_col, col=\"param\",\n", + " aspect=.5,\n", + " sharex=False,\n", + " color='blue',\n", + " s=3.5,\n", + " facecolor='white'\n", + " )\n", + " plt.show()\n", + "\n", + "swarmPlotForMetric(gs, 'top2PctPrecision')\n", + "swarmPlotForMetric(gs, 'top2PctRecall')\n", + "swarmPlotForMetric(gs, 'aucpr')\n", + "swarmPlotForMetric(gs, 'top5Pctaucpr')\n", + "\n", + "\n", + "\n", + "# sns.swarmplot(data=data, x=\"min_samples_leaf\", y=\"mean_test_aucpr\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T20:18:13.695193Z", + "start_time": "2023-05-03T20:18:13.596908Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "best model params: {'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 1, 'n_estimators': 50}\n", + "best model aucpr: 0.7929563746137056\n" + ] + } + ], + "source": [ + "print(\"best model params:\", gs.best_params_)\n", + "print(\"best model aucpr:\", gs.best_score_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What I don't like about this model is I think having only 1 sample in the leaf risks overfitting. Let's look at the top 5 models:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T20:18:30.390730Z", + "start_time": "2023-05-03T20:18:30.293614Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mean aucprstd aucprparams
480.6563920.066988{'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 1, 'n_estimators': 50}
540.6563610.066665{'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 5, 'n_estimators': 50}
510.6563610.066665{'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 3, 'n_estimators': 50}
570.6561590.066445{'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 7, 'n_estimators': 50}
950.6525580.069292{'learning_rate': 0.001, 'max_depth': 3, 'min_samples_leaf': 7, 'n_estimators': 150}
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" + ], + "text/plain": [ + " mean aucpr std aucpr \\\n", + "48 0.656392 0.066988 \n", + "54 0.656361 0.066665 \n", + "51 0.656361 0.066665 \n", + "57 0.656159 0.066445 \n", + "95 0.652558 0.069292 \n", + "\n", + " params \n", + "48 {'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 1, 'n_estimators': 50} \n", + "54 {'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 5, 'n_estimators': 50} \n", + "51 {'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 3, 'n_estimators': 50} \n", + "57 {'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 7, 'n_estimators': 50} \n", + "95 {'learning_rate': 0.001, 'max_depth': 3, 'min_samples_leaf': 7, 'n_estimators': 150} " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "mean_aucpr = np.array(gs.cv_results_['mean_test_aucpr'])\n", + "std_aucpr = np.array(gs.cv_results_['std_test_aucpr'])\n", + "params = np.array(gs.cv_results_['params'])\n", + "\n", + "pd.set_option('display.max_colwidth', None)\n", + "d = pd.DataFrame(\n", + " np.concatenate([mean_aucpr.reshape(-1,1), std_aucpr.reshape(-1,1), params.reshape(-1,1)], axis=1)\n", + " , columns = ['mean aucpr', 'std aucpr', 'params']\n", + " #, index=best5idx\n", + ")\n", + "\n", + "d.sort_values('mean aucpr', ascending=False).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Couple of things that stand out here:\n", + "\n", + "1. The only difference in the first 4 models is the min_samples_leaf\n", + "2. Furthermore in the swarm plots above we could see for the top5Pctaucpr metric, the min_samples_leaf has similar spreads for all values. However max_depth shows clear and distinctive distributions and likely has a stronger effect on the model.\n", + "\n", + "Thus, although it risks overfitting, I'll keep it with min_samples_leaf = 1 since it seems to not have much difference among the best models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create Final Model and Save Model\n", + "\n", + "Final model is just going to use all of the data. This isn't typical practice but we don't have a lot of data so I want to use all of the data available. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T23:53:15.735776Z", + "start_time": "2023-05-03T23:53:15.376048Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total posts: 5627, viral posts: 147\n" + ] + }, + { + "data": { + "text/html": [ + "
GradientBoostingClassifier(learning_rate=0.01, n_estimators=50)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GradientBoostingClassifier(learning_rate=0.01, n_estimators=50)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "X = df[features]\n", + "y = df['target']\n", + "print(f\"total posts: {len(y)}, viral posts: {y.sum()}\") # how many targets are there, This is a highly imbalanced problem, only ~2.5% of posts in rising go viral\n", + "\n", + "params = {'learning_rate': 0.01, 'max_depth': 3, 'min_samples_leaf': 1, 'n_estimators': 50}\n", + "model = GradientBoostingClassifier(**params)\n", + "model.fit(X, y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T23:53:29.300451Z", + "start_time": "2023-05-03T23:53:29.132048Z" + } + }, + "outputs": [], + "source": [ + "import datetime\n", + "\n", + "now = datetime.datetime.now(datetime.UTC).strftime('%Y%m%d-%H%M%S')\n", + "filename = f'./pickledModels/Reddit_model_{now}_GBM.sav'\n", + "pickle.dump(model, open(filename, 'wb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:23:43.068876Z", + "start_time": "2023-05-10T18:23:42.289402Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.98341046 0.01658954]\n", + " [0.98352997 0.01647003]\n", + " [0.98352997 0.01647003]\n", + " ...\n", + " [0.98352997 0.01647003]\n", + " [0.98352997 0.01647003]\n", + " [0.98352997 0.01647003]]\n" + ] + } + ], + "source": [ + "# testing the model\n", + "filename = './pickledModels/Reddit_model_20240426-075204_GBM.sav'\n", + "loaded_model = pickle.load(open(filename, 'rb'))\n", + "result = loaded_model.predict_proba(X)\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Upload Model to S3" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-03T23:54:04.652248Z", + "start_time": "2023-05-03T23:54:03.620849Z" + } + }, + "outputs": [], + "source": [ + "import boto3\n", + "\n", + "s3 = boto3.client('s3', region_name='us-east-2')\n", + "\n", + "response = s3.upload_file(filename, \"data-kennethmyers\", f\"models/{filename.split('/')[-1]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get Threshold of Top 1% of data\n", + "\n", + "Really this should be done with a holdout set, but I don't have enough data right now so I'm just going to generate it from the in-sample data. We can always adjust the threshold later." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:39:39.824970Z", + "start_time": "2023-05-10T18:39:39.749010Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total posts: 5627, viral posts: 147\n" + ] + } + ], + "source": [ + "filename = './pickledModels/Reddit_model_20240426-075204_GBM.sav'\n", + "model = pickle.load(open(filename, 'rb'))\n", + "\n", + "features = model.feature_names_in_\n", + "X = df[features]\n", + "y = df['target']\n", + "print(f\"total posts: {len(y)}, viral posts: {y.sum()}\") # how many targets are there, This is a highly imbalanced problem, only ~2.5% of posts in rising go viral\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T18:39:42.361717Z", + "start_time": "2023-05-10T18:39:41.636684Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plots for top 5% of scores\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_pred_proba = model.predict_proba(X[model.feature_names_in_])[:, 1]\n", + "plotPrecRecAUCPR(y=y, y_pred=y_pred_proba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking the top ~1% to get higher precision and reduce spame from the subreddits this wasn't trained on." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T19:00:27.580940Z", + "start_time": "2023-05-10T19:00:27.482122Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.2941209400738473" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "threshold = np.quantile(y_pred_proba, 0.99)\n", + "threshold" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-10T19:08:19.134536Z", + "start_time": "2023-05-10T19:08:19.003644Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "recall = 0.3469387755102041\n", + "precision = 0.8793103448275862\n" + ] + }, + { + "data": { + "text/html": [ + "
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targetpostIdlinkpredictioncreatedTSUTCmaxScore41_60mmaxNumComments21_40mmaxNumComments41_60mmaxUpvoteRatio41_60mmaxScoreGrowth21_40m41_60mmaxNumCommentsGrowth21_40m41_60m
36511135tlqehttps://reddit.com/135tlqe0.4249452023-05-02 16:56:49+00:00301.013.041.00.902.7625002.153846
5069112v4y7khttps://reddit.com/12v4y7k0.4249452023-04-22 12:47:21+00:00417.016.030.00.961.4244190.875000
2106112q3ti7https://reddit.com/12q3ti70.4249452023-04-18 00:44:08+00:003070.047.0191.00.935.2781193.063830
1919112mh34chttps://reddit.com/12mh34c0.4249452023-04-14 23:04:35+00:00661.087.0231.00.923.7553961.655172
3479112z2v8nhttps://reddit.com/12z2v8n0.4249452023-04-26 01:48:36+00:00180.029.051.00.902.1034480.758621
....................................
52500132itl0https://reddit.com/132itl00.3209192023-04-29 04:09:53+00:0075.09.016.01.001.2727270.777778
5105012z0e15https://reddit.com/12z0e150.3106572023-04-25 23:57:03+00:00111.03.07.00.940.9473681.333333
803012vs177https://reddit.com/12vs1770.3064092023-04-23 02:15:49+00:0081.07.014.00.931.0769231.000000
131012pg4tmhttps://reddit.com/12pg4tm0.2941212023-04-17 13:33:03+00:0065.07.012.00.971.0967740.714286
2401012q1sh3https://reddit.com/12q1sh30.2941212023-04-17 23:38:19+00:0079.011.015.00.981.1944440.363636
\n", + "

154 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " target postId link prediction \\\n", + "3651 1 135tlqe https://reddit.com/135tlqe 0.424945 \n", + "5069 1 12v4y7k https://reddit.com/12v4y7k 0.424945 \n", + "2106 1 12q3ti7 https://reddit.com/12q3ti7 0.424945 \n", + "1919 1 12mh34c https://reddit.com/12mh34c 0.424945 \n", + "3479 1 12z2v8n https://reddit.com/12z2v8n 0.424945 \n", + "... ... ... ... ... \n", + "5250 0 132itl0 https://reddit.com/132itl0 0.320919 \n", + "5105 0 12z0e15 https://reddit.com/12z0e15 0.310657 \n", + "803 0 12vs177 https://reddit.com/12vs177 0.306409 \n", + "131 0 12pg4tm https://reddit.com/12pg4tm 0.294121 \n", + "2401 0 12q1sh3 https://reddit.com/12q1sh3 0.294121 \n", + "\n", + " createdTSUTC maxScore41_60m maxNumComments21_40m \\\n", + "3651 2023-05-02 16:56:49+00:00 301.0 13.0 \n", + "5069 2023-04-22 12:47:21+00:00 417.0 16.0 \n", + "2106 2023-04-18 00:44:08+00:00 3070.0 47.0 \n", + "1919 2023-04-14 23:04:35+00:00 661.0 87.0 \n", + "3479 2023-04-26 01:48:36+00:00 180.0 29.0 \n", + "... ... ... ... \n", + "5250 2023-04-29 04:09:53+00:00 75.0 9.0 \n", + "5105 2023-04-25 23:57:03+00:00 111.0 3.0 \n", + "803 2023-04-23 02:15:49+00:00 81.0 7.0 \n", + "131 2023-04-17 13:33:03+00:00 65.0 7.0 \n", + "2401 2023-04-17 23:38:19+00:00 79.0 11.0 \n", + "\n", + " maxNumComments41_60m maxUpvoteRatio41_60m maxScoreGrowth21_40m41_60m \\\n", + "3651 41.0 0.90 2.762500 \n", + "5069 30.0 0.96 1.424419 \n", + "2106 191.0 0.93 5.278119 \n", + "1919 231.0 0.92 3.755396 \n", + "3479 51.0 0.90 2.103448 \n", + "... ... ... ... \n", + "5250 16.0 1.00 1.272727 \n", + "5105 7.0 0.94 0.947368 \n", + "803 14.0 0.93 1.076923 \n", + "131 12.0 0.97 1.096774 \n", + "2401 15.0 0.98 1.194444 \n", + "\n", + " maxNumCommentsGrowth21_40m41_60m \n", + "3651 2.153846 \n", + "5069 0.875000 \n", + "2106 3.063830 \n", + "1919 1.655172 \n", + "3479 0.758621 \n", + "... ... \n", + "5250 0.777778 \n", + "5105 1.333333 \n", + "803 1.000000 \n", + "131 0.714286 \n", + "2401 0.363636 \n", + "\n", + "[154 rows x 11 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this was defined earlier, not that it's overfit, because it's evaluating on trained data\n", + "getOriginalPostId(df, list(range(len(df))), y, y_pred_proba, threshold, list(features))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If there are 250 posts per day reaching r/pics/rising, then this means we will find about 2.5 posts on average.\n", + "\n", + "Of those 2.5 posts, 2 of them will be viral (0.8 precision) and this will be out of 7.5 posts that go viral (0.33 recall, those that reach top 3 of Hot). \n", + "\n", + "Once more data is collected this should be rerun to determine the proper out of sample scoring threshold." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "234.667px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/model/pickledModels/README.md b/model/pickledModels/README.md new file mode 100644 index 0000000..6a62e7f --- /dev/null +++ b/model/pickledModels/README.md @@ -0,0 +1,3 @@ +When I first started this project in April 2023, I used sklearn 1.0.2 which I think was because I was on python 3.7 (already a bit old) and locked into some older packages (older versions of pyspark, numpy, pandas, etc). + +Upon returning to this project in April 2024 I had a newer Mac and upgraded to python 3.12.3 and I was able to upgrade all of the above packages. However, because of this the pickled models could no longer be loaded. Therefore, the sklearn 1.0.2 have been moved to a separate folder for archival purposes. And newly trained models using sklearn 1.4.2 will be stored in the current directory. \ No newline at end of file diff --git a/model/pickledModels/Reddit_model_20240426-075204_GBM.sav b/model/pickledModels/Reddit_model_20240426-075204_GBM.sav new file mode 100644 index 0000000..b3d8e34 Binary files /dev/null and b/model/pickledModels/Reddit_model_20240426-075204_GBM.sav differ diff --git a/model/pickledModels/Reddit_model_20230414-061009_LR.sav b/model/pickledModels/sklearn-1.0.2/Reddit_model_20230414-061009_LR.sav similarity index 100% rename from model/pickledModels/Reddit_model_20230414-061009_LR.sav rename to model/pickledModels/sklearn-1.0.2/Reddit_model_20230414-061009_LR.sav diff --git a/model/pickledModels/Reddit_model_20230503-235329_GBM.sav b/model/pickledModels/sklearn-1.0.2/Reddit_model_20230503-235329_GBM.sav similarity index 100% rename from model/pickledModels/Reddit_model_20230503-235329_GBM.sav rename to model/pickledModels/sklearn-1.0.2/Reddit_model_20230503-235329_GBM.sav diff --git a/model/pickledModels/test_latestModel.sav b/model/pickledModels/test_latestModel.sav index 61e0f3b..b3d8e34 100644 Binary files a/model/pickledModels/test_latestModel.sav and b/model/pickledModels/test_latestModel.sav differ diff --git a/pyproject.toml b/pyproject.toml index a6b71d0..d95011f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,6 @@ dynamic = ["version"] dependencies = [ "boto3==1.26.117", - "matplotlib==3.8", "numpy==1.26", "pandas==2.2.2", # 1.3 at least needed for M1 Mac "pg8000==1.29.4", # this was easier to pip install than psycopg2 @@ -18,8 +17,6 @@ dependencies = [ "pyspark==3.4.0", "requests==2.31.0", "scikit-learn==1.4.2", - "seaborn==0.11.2", - "shap==0.41.0", "sqlalchemy==1.4.46", # originally tried 2.0.10, but this was incompatible with old versions of pandas https://stackoverflow.com/a/75282604/5034651, "viral_reddit_posts_utils @ git+https://github.com/ViralRedditPosts/Utils.git@main", "Reddit-Scraping @ git+https://github.com/ViralRedditPosts/Reddit-Scraping.git@main", @@ -48,8 +45,12 @@ build = [ "Reddit-Model[test]" ] dev = [ + "matplotlib==3.8", # packages for plotting and notebook work are only needed in dev + "notebook==7.1.3", "pre-commit==2.21.0", "Reddit-Model[build]" + "seaborn==0.11.2", + "shap==0.45.0", ] [tool.setuptools.packages.find]