From 9b406cb9f34bed62415ce1536f9383a8c8f0bd8b Mon Sep 17 00:00:00 2001 From: Sarah Wooders Date: Tue, 5 Apr 2022 18:59:03 -0700 Subject: [PATCH 1/4] add als plot nb --- nb/03-17 als_simulation.ipynb | 227 +- nb/03-26-22_als_simulations_joey.ipynb | 127578 +++++++++++++++++++++- nb/03-29-22-als_plots.ipynb | 456 + workloads/recsys/stream_als.py | 54 +- workloads/recsys/train_als.py | 4 +- 5 files changed, 127078 insertions(+), 1241 deletions(-) create mode 100644 nb/03-29-22-als_plots.ipynb diff --git a/nb/03-17 als_simulation.ipynb b/nb/03-17 als_simulation.ipynb index 236c240..fa54590 100644 --- a/nb/03-17 als_simulation.ipynb +++ b/nb/03-17 als_simulation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 44, + "execution_count": 94, "id": "8b25a596", "metadata": {}, "outputs": [ @@ -20,6 +20,7 @@ "from pyspark.sql import SparkSession\n", "from pyspark.ml.evaluation import RegressionEvaluator\n", "from pyspark.mllib.recommendation import ALS as newALS\n", + "from pyspark.ml.recommendation import ALS\n", "from sklearn.metrics import mean_squared_error\n", "from pyspark.sql.functions import *\n", "from pyspark.sql.types import *\n", @@ -50,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 103, "id": "b7688ace", "metadata": {}, "outputs": [ @@ -58,21 +59,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "/data/wooders/ralf-vldb//datasets/ml-latest-small\n", - "Downloading from aws: vldb\n" + "/data/wooders/ralf-vldb//datasets/ml-1m\n" ] } ], "source": [ - "dataset_dir = use_dataset(\"ml-latest-small\", download=True)\n", + "dataset_dir = use_dataset(\"ml-1m\")\n", "ratings_path = f\"{dataset_dir}/ratings.csv\"\n", "tags_path = f\"{dataset_dir}/tags.csv\" \n", - "movies_path = f\"{dataset_dir}/movies.csv\"" + "movies_path = f\"{dataset_dir}/movies.csv\"\n", + "path = dataset_dir" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 72, "id": "0ab59e64", "metadata": {}, "outputs": [], @@ -83,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 73, "id": "ccdd8301", "metadata": {}, "outputs": [], @@ -92,7 +93,7 @@ "\n", "def split_data(df): \n", " start_ts = df['timestamp'].min()\n", - " med_ts = df['timestamp'].quantile(.5)\n", + " med_ts = df['timestamp'].quantile(.25)\n", " end_ts = df['timestamp'].max()\n", " train_df = df[df['timestamp'] <= med_ts]\n", " stream_df = df[df['timestamp'] > med_ts]\n", @@ -113,22 +114,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 75, "id": "2bf51807", "metadata": {}, "outputs": [], "source": [ - "tags = pd.read_csv(tags_path)\n", - "tags.columns = ['user_id', 'movie_id', 'tag', 'timestamp']\n", + "#tags = pd.read_csv(tags_path)\n", + "#tags.columns = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "ratings = pd.read_csv(ratings_path)\n", "ratings.columns = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "movies = pd.read_csv(movies_path)\n", - "movies.columns = ['movie_id', 'title', 'genres']" + "#movies = pd.read_csv(movies_path)\n", + "#movies.columns = ['movie_id', 'title', 'genres']" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 76, "id": "80579d50", "metadata": {}, "outputs": [ @@ -136,7 +137,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "5567 7720 50418\n" + "3346 3676 750156\n" ] } ], @@ -147,73 +148,19 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "6ac525ca", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n", - "UserWarning: sort_values defaulting to pandas implementation.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3551 3643 500104\n" - ] - }, - { - "data": { - "text/plain": [ - "(956703932, 973018006.0, 1046454590)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "split_data(path)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, + "execution_count": 80, "id": "4a38666a", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "22/03/25 16:38:46 WARN Utils: Your hostname, flaminio resolves to a loopback address: 127.0.1.1; using 169.229.48.114 instead (on interface enp1s0f0)\n", - "22/03/25 16:38:46 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address\n", - "WARNING: An illegal reflective access operation has occurred\n", - "WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/pyspark/jars/spark-unsafe_2.12-3.2.1.jar) to constructor java.nio.DirectByteBuffer(long,int)\n", - "WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform\n", - "WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations\n", - "WARNING: All illegal access operations will be denied in a future release\n", - "Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n", - "Setting default log level to \"WARN\".\n", - "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", - "22/03/25 16:38:47 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n" - ] - } - ], + "outputs": [], "source": [ "spark = SparkSession.builder.master('local').appName('als').getOrCreate()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 100, "id": "a3fbc1e7", "metadata": {}, "outputs": [], @@ -223,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 101, "id": "4f286b1b", "metadata": {}, "outputs": [ @@ -234,26 +181,26 @@ "+-------+--------+------+\n", "|user_id|movie_id|rating|\n", "+-------+--------+------+\n", - "| 1| 1| 4.0|\n", - "| 1| 3| 4.0|\n", - "| 1| 6| 4.0|\n", - "| 1| 47| 5.0|\n", - "| 1| 50| 5.0|\n", - "| 1| 70| 3.0|\n", - "| 1| 101| 5.0|\n", - "| 1| 110| 4.0|\n", - "| 1| 151| 5.0|\n", - "| 1| 157| 5.0|\n", - "| 1| 163| 5.0|\n", - "| 1| 216| 5.0|\n", - "| 1| 223| 3.0|\n", - "| 1| 231| 5.0|\n", - "| 1| 235| 4.0|\n", - "| 1| 260| 5.0|\n", - "| 1| 296| 3.0|\n", - "| 1| 316| 3.0|\n", - "| 1| 333| 5.0|\n", - "| 1| 349| 4.0|\n", + "| 4268| 3064| 1|\n", + "| 4268| 955| 3|\n", + "| 4268| 260| 4|\n", + "| 4268| 356| 5|\n", + "| 4268| 1270| 4|\n", + "| 4268| 1625| 3|\n", + "| 4269| 3791| 1|\n", + "| 4269| 3793| 4|\n", + "| 4269| 1| 5|\n", + "| 4269| 2064| 4|\n", + "| 4269| 2065| 5|\n", + "| 4269| 3022| 5|\n", + "| 4269| 2080| 4|\n", + "| 4269| 2081| 3|\n", + "| 4269| 908| 4|\n", + "| 4269| 910| 4|\n", + "| 4269| 921| 4|\n", + "| 4269| 3061| 5|\n", + "| 4269| 3070| 5|\n", + "| 4269| 3072| 4|\n", "+-------+--------+------+\n", "only showing top 20 rows\n", "\n" @@ -275,7 +222,94 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 104, + "id": "d5a101ba", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "als = ALS(\n", + " userCol=\"user_id\", \n", + " itemCol=\"movie_id\",\n", + " ratingCol=\"rating\", \n", + " nonnegative = True, \n", + " implicitPrefs = False,\n", + " coldStartStrategy=\"drop\",\n", + " rank=50,\n", + " maxIter=10,\n", + " regParam=.1\n", + ")\n", + "model=als.fit(spark_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "d47294c0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "0.7407670697276212" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions = model.transform(spark_df)\n", + "evaluator = RegressionEvaluator().setMetricName(\"rmse\").setLabelCol(\"rating\").setPredictionCol(\"prediction\")\n", + "rmse = evaluator.evaluate(predictions)\n", + "rmse" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "7aa65951", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'MatrixFactorizationModel' object has no attribute 'transform'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [88]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Evaluate the model by computing the RMSE on the test data\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mnew_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m(spark_df\u001b[38;5;241m.\u001b[39mrdd)\n\u001b[1;32m 3\u001b[0m evaluator \u001b[38;5;241m=\u001b[39m RegressionEvaluator(metricName\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrmse\u001b[39m\u001b[38;5;124m\"\u001b[39m, labelCol\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrating\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4\u001b[0m predictionCol\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m rmse \u001b[38;5;241m=\u001b[39m evaluator\u001b[38;5;241m.\u001b[39mevaluate(predictions)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'MatrixFactorizationModel' object has no attribute 'transform'" + ] + } + ], + "source": [ + "# Evaluate the model by computing the RMSE on the test data\n", + "predictions = new_model.transform(spark_df.rdd)\n", + "evaluator = RegressionEvaluator(metricName=\"rmse\", labelCol=\"rating\",\n", + " predictionCol=\"prediction\")\n", + "\n", + "rmse = evaluator.evaluate(predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, "id": "4b6b9644", "metadata": {}, "outputs": [ @@ -283,7 +317,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "22/03/25 16:39:18 WARN BlockManager: Task 49 already completed, not releasing lock for rdd_318_0\n", + "22/03/26 18:49:39 WARN BlockManager: Task 99 already completed, not releasing lock for rdd_658_0\n", + "22/03/26 18:49:40 WARN BlockManager: Task 101 already completed, not releasing lock for rdd_659_0\n", " \r" ] } diff --git a/nb/03-26-22_als_simulations_joey.ipynb b/nb/03-26-22_als_simulations_joey.ipynb index 1914618..7546800 100644 --- a/nb/03-26-22_als_simulations_joey.ipynb +++ b/nb/03-26-22_als_simulations_joey.ipynb @@ -2,10 +2,19 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "22a80d9f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "import pandas as pd\n", "from pyspark.sql import SparkSession\n", @@ -21,6 +30,7 @@ "from tqdm import tqdm \n", "import concurrent.futures\n", "\n", + "from sklearn.linear_model import Ridge, RidgeCV\n", "\n", "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n", "\n", @@ -32,6 +42,27 @@ "%autoreload 2" ] }, + { + "cell_type": "code", + "execution_count": 46, + "id": "63b52ee8", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "sns.set_theme(style=\"whitegrid\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6fdca5cd", + "metadata": {}, + "outputs": [], + "source": [ + "d=50" + ] + }, { "cell_type": "code", "execution_count": 302, @@ -61,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "3d95242c", "metadata": {}, "outputs": [], @@ -78,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "e439fb99", "metadata": {}, "outputs": [], @@ -87,7 +118,7 @@ "\n", "\n", "# ratings is an n by m sparse matrix\n", - "def update_user(user_id, user_data, d=50):\n", + "def update_user(user_id, user_data, movie_features, d=50):\n", " user_data = user_data.tocoo() # lookup all rating data for this user\n", " movie_ids = user_data.col\n", " values = user_data.data\n", @@ -163,10 +194,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "id": "a55d394b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/data/wooders/ralf-vldb//datasets/ml-1m\n" + ] + } + ], "source": [ "experiment = \"ml-1m\"\n", "dataset_dir = use_dataset(experiment)\n", @@ -175,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "318ffb25", "metadata": {}, "outputs": [], @@ -188,50 +227,149 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "b02ee031", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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7DIC77rqr5u2tlkoM+EngUiGEXwghgCuB54CHgDdb21wPVKapqNFoqsIuXeZxTnahnLNBCdT1xbvmtE0AGSRBqfpwY5mFacDPP/98LrjgArZu3crb3vY2XvKSl+TX3XjjjbzmNa/JT2JWwm233cb73/9+du7cSTwep7GxsR7NrpiyLhQp5WNCiB8CTwJZ4CmUS+SnwF1CiP/PWnZbPRuq0SxXUhk1fPe4/JPWtYQ7acyZDGZ757pZpIUkYHqABMkFVBdz/fr17Nu3L//+W9/6VsntbrrpJm666ab8++PHjwPQ2tpatP9HPvKR/Ott27bxzDNKuuBzn/scu3ZNUnidUyqKQpFSfgr41ITFR4FLat4ijUZTRCqj4rx97kDJ9Rszfg46BjFzOQyHo+Q29SAjwG94ETJOylyYPuJa89Of/pR/+qd/IpvNsm7duikfDnOFViPUaBY4dg/c6wmWXL+j8SKeSv2WXz95L1dc/Cdz1q60ABcufFKSXCYG/LrrrpuyUs98oFPpNZoFTsoqXeadogf+xss+AMCD+++cszaBpc9iuPCZkDYLdTHnssrXUqPa304bcI1mgZPKqt6t31u6B37Wup1sSQmeSx+aszaZuRxpQ+AyPHilICWVMqHX62VwcFAb8RkgpWRwcBCvd/Jk9VRoF4pGs8DJ5FTv1u+dOuLhXPcWfiwO8sKxpzh7wwV1b9NoQiXuuAw33oxBytJFWb16NV1dXeikvZnh9XpZvXp1xdtrA67RLHDSlgEP+hqm3ObKbW/jx899mv9+7GucveGrdW9TNG7rs3hxY5ASyoC7XC42bNhQ9+NrFNqFotEscLKmyhz0+0q7UABefuG1dGRMnhndM+U2tSRm9cDdDg8e6SQtdEGH+UAbcI1mgZPOKf9yyB+echvD4WCbXMFzrjGGR+rvvoiP2en9XjzCRVJon/d8oA24RrPAycoMDinxeiYn8oznRWtfTcoQ/Og3/7+6t2kspVwoHqcfN25twOcJbcA1mgVO1kzjriCq45rLP4DfNNnT86u6t8kuoeZx+fEYHpLakswL+mfXaBY4GZnBVUEHN+APsS7jol+O1L1NyZQS2PK6/XgNLwnDIJudn8ISyxltwDWaBU5WZisy4ABBPESNbPkNZ0kirQy4xxXA61SunYVaVm0pow24RrPAyZLDLUVF2wZFgFGj/v5oOzvU5w7icaoM0aHI3AtqLXe0AddoFjhZsjgr7YE7Gxl1GCSS9S10nLYMuN8bwu8KATAc0z3wuUYbcI1mgZOROVxU1gNv9LQAcKr7YD2blJeP9XtC+D0qwWg0Nrkupqa+aAOu0SxwssLEKSu7VZsCHQCc7K2vAU9nVXJRwB/G71Ep/qOJoboeUzMZbcA1mgVOFhNXhbdqW8MaAPoiJ+rZJNI5ZcCDvgZCviYA4iXqYmrqS9mrQghxthBi77i/USHEXwkhmoUQDwghDln/m+aiwRrNciNL5T3wzpb1AAxGz9SxRQWBraC/gYaActvEU/UPX9QUU/aqkFK+IKXcKaXcCVwEJIAfAR8DHpRSbgEetN5rNJoakxESJ5VV2lnTuRWASLK+E4oZS/875G+iKdQGwFh6YdbFXMpU60K5EjgipTwBXAPcbi2/Hbi2hu3SLECOntrP//76q+oe4aApJiMkTlGZAV/ZsgaXlETTw3VtU9pM45QSt9tTMOCZWF2PqZlMtQb8rcB3rdcdUspu63UP0FFqByHEjUKI3UKI3VojeHFz3++/xi9d3Tz61H3z3ZRlheqBV6b8bDgchHOSaK6+veGszOCx0vubGzsBFlRh4+VCxQZcCOEG3gj8YOI6qcpvlIxUlVLeIqXcJaXc1dbWNuOGauaf0eQAAPGk9nVWSjabmbU6YFaAU1Qu3R8yDWKyvjUqMzKD27rjA/4QLimXTWHjhUQ1PfDXAk9KKe10q14hxAoA639frRunWVhEMxFAG/Bq+Pe7/4o/uvsVs3I7pQW4hKvi7YOmm5iRnvHxKiE7QZ/FZ0pSlm65Zu6oxoD/GQX3CcB9wPXW6+uBH9eqUZqFSTynfJyJ9Og8t2TxcCZ2lBGHQe/gqRl/RlqIqnrgQeEjatS3wEJmQnq/zyRfF1Mzd1RkwIUQAeBq4J5xiz8HXC2EOARcZb3XLGHsYXkipaMNKiWRU37hwUhP0fKegVN8/Jt/TDqdKrVbnmw2Q0YInIa74mMGjSAjhsDMzdyIHzv9PP/wzT8hmSrt187ILM5xBtwjDW3A54GKDLiUMi6lbJGyoFMppRyUUl4ppdwipbxKSqnTsJY4caFu0GRWR6FUSsJ66EUm6ITc85svc59xiIf23D3t/rbutqsKA97gbiJlCPonPDSq4UeP/js/Ng5y76+/VnJ9VuRwjTPgXmmQpv4qiJpidCampmJi1rA8paMNKiZhPfRG4sUG3E56SSSnd0dFE3bxYE/Fx2z0qmCBkz3PV7zPREaSStfk6dO/LrleZYcWQhvdOEjpuphzjjbgy4ybbr2Cd3/tkhntG7VkSlM5PVlVKQnLqMXGiuOyExllmBOp6Q14YiwCgNNReQ+8JbgSgDP9RyreZyIxa8L6WKZ0Sn5mQnaoRzpJCXPGx9PMDG3AlxldcpAzjup70IlknKhDXS5pbcArJm4ooxZLRYqW226oeJn5hJhdPNjhrfiY7U1rAegf7ap4n0nHNVX7DrsyROORSeszQhb3wIWLpDbgc4424MuMiCNHfAaC/129h/Kv03L6iTdNgaih/MQTe9pjVsx0Kj199qJduszt9FV8zFVtmwAYis/cBx6X6iGdMgQPPv69Sesnpvd7hFvXxZwH9E++jEinUww7BFFDVF2/8HRfYTie1tEGFRGJDpCyDPhYprinnbRipsfKTAgnktUb8A0rzwVgNDlzfe64SLMppdr+5MlfTlqv0vsLoY0ew8uYqEyzXFM7tAFfRhzvPkBOCKQQ9Aydrmrf3uGT+ddpdPHaSjjddzz/eixXbKjtkLt0dvrsxTHLxeJxVu5CCfhDhHIm0ezME66iRo52QqxJS44kj05anxbKbWLjMXykDaF1cuYYbcCXEcfOPJd/3TdQnV70UEzJ3nhNSVrqcLFK6B08nn+dzBUb6pT1ECw3IZxMq/kKj8tf1bEbTEHMnLm41KgDAkaAjbRx2J2cFA+eFuAcZ8DtwsaDui7mnKIN+DKie6jgBukfqa4HPjKmwuDaspDW4WIVMTBS0OSemGaeFFlr+fQ98FRG9Wh97kBVxw6ZTmJiZnMVkegAY4ZByNXI1uaLSBgGD+/5UdE2aSGKYtN9riAAw6Mz97trqkcb8GXE+KiEwdHuabaczKglT9pkenS8b4VEEoXe6MQsRTtiI2NOP59gly7zeoJVHTsoPUTFzFxdJ3sOAxByN3PFzrcC8MSRn+XXJ5JxckLgEuMMuFsVNh6JKcGzr9/3KfYdfmxGx9dUjjbgy4jhZEFvbCQxUNW+8ewoDTkTDy7SovooluXI6JiaRGzOmiQnzBuMWZFA6TIGPGVVf/dW2QMPGH5GHTM7T90DyufdFOhg26ZdrMhIjiQKNTbtsELXuNj0gF3YODHE9x74V/5t+B5ue/gTMzq+pnK0AV9GjGSGMSwN52iVEQpxM0GDKXALFyltwCsimlKjlpacg5QonjdIWHdemjIG3Jrk9Hur64GHnI2MGmJKLZPpGBhRI7XW0CoANppNHHbF85FL8UQEKI5ND3hURcWh0R5uP/51AAbN+haV0GgDvqwYlTFWWHYknq4uQiEuUgRMh5WwUYfGLUHi2VFcUhKSnqIkl3Q6RcJQt16mzISwnTTl9zZWdexGdzNSiLw7pBqGosq91tGyDoCzGs9nxGHw+2eUGyU2pq6d8Qa8IdAMwH1dd3HKLWjJmgwKnfBVb7QBX0aMGilacx5cUhLPVicJGxNZgtKD2/CQNLQFr4RELkFDTqokl3EGfGCkMNGXYfr5hExO9dCDvoaqjt3kVwWyunoPltlyMiNW4Y7VVkLQ5hUXAHC0+1kA4pZ+y/jYdNuAP+/JcUHSy4VyNb1OOStFRE15tAFfRkQMkwYRIJiTjJnVDa2jhknA8OExvGTEzIbmy40xmSRgGniFh7Fxd9pApDCBXC6iJ2tFr/h91blQWhpWA9AzXF24KMBoZhiHlKxoWw/A6vazABiMqciluJXe73EVDHg4qAS0PKbkr17+Jdp8K0kZgkMnn6n6+JrK0QZ8mZBMJYg4BI3OMAEpGJOVD2/NXI5RhyBgBPFY8b5Do7q+aTniIoVfOvAYPhLj9LmHRwvRKeUMeNrqgYf84aqO3dmyAYDBaHXhogDxbIyGnMTpVHHeG1edA8CwFUpaSC4qxKav7thEY87kjc7zuXDr5axq2gLAgeOPV318TeVoA75MOHb6AKYQhL3tBEwHY2Umz8Zzuv8EWSEIuZvwOtRNOxKtLoplOZIQOfzShc/pJysEUct3bGuDB3Nm2YiejEzjkBKvp7pEnnWdW4FC/H41xGWCkFkwDeFQK8GcyWhGTUrayUXjI2NCgTD3v+1xPvnOOwDYsvpCAI7376/6+JrKqbQiT1gI8UMhxPNCiANCiMuEEM1CiAeEEIes/031bqxm5hzvVlmYbaE1+KSLhKg8m7KrT/lRG72teF3qph2Jz1xnY7kQN0z8wovPqX6zwWHlOokm1G8XNkXZiJ6cmcEtq4/6WdW2DqeUjKarr7MSF2mCsriEW3hcZmfSEuDyuovdOn5vwaBv33QpQkp64yephH/8zvX87W2vr7qty51Ke+D/BvxcSrkVOB84AHwMeFBKuQV40HqvWaB0D6nY3hXNG/EJD/EqpD/7htRN2BToxGslbETjOkSsHFFD4DcC+FxqAnLQylKMJiMANJgu0mXmgzMTigdXiuFw0JiTxHLVl7+LGVkCsriARMh0ErWiSuyCHtNlh4YCYdpyksFMZSOAx8ee5GlZvb9+uVPWgAshGoGXAbcBSCnTUsoIcA1wu7XZ7cC19Wmiphb0R1VR3fUrzsUnfMSrcJ71jyo/alt4DQGPMuATCxRoirGVCAOuEH5PY34ZQMLSBg/hJVlGwS8rszMy4AANpkFUVj/ZHDUkQaPYZROSXkYNK/3fjk33TR/a2JpzM0R5PZZ0OkWXUxKbgczxcqeS23gD0A98UwjxlBDi61aR4w4ppT2d3gN0lNpZCHGjEGK3EGJ3f7+e+JovIsk+DCnZsOoc/M4gMYdRtqBufl8rJXxl6wYCljGKJ2eudLccsJUIg64wDT7lXRyJKwMet6rxhIwQqTLSvhmyRdXfqyFouvJ1TCslnU4xaggCzlDR8pARJGJldtrZoX5vaNL+42khRJ+jvKtu94FfkTYEUYeho5uqpBID7gQuBL4ipbwAiDPBXSKllEDJx6eU8hYp5S4p5a62trbZtlczQ0ayEZpyajIs4FRD+t7ByvyToynlR13TsYWALwxAIl1dHPlyo29IuQMafa2E/CpGOpZUv6OtAR5yhQEYiU09n5CVOZwz7Jh6qL5KzsmeQ0ghaHAXT2k1uJuIGwbDI/355KJgmR54i7uDYafBYJniys8eeyT/unugsmtSo6jEgHcBXVJKW5nmhyiD3iuEWAFg/e+bYn/NAmBUxgnnVAWVoCcMQHeFkrKxzAheU9LU2EYooG7ssTKVZJY7/RHldmoMtNMYbAcgZo1akrkEftPMT24Oj04d0ZMlh4uZ9cA9wkWySreErYPS6GsvWh72qvfHTj9XMOD+6Q34igYVyvjMod9Ou92J4QP51z3jJHg15SlrwKWUPcApIcTZ1qIrgeeA+4DrrWXXAz+uSws1NWHUSBOSSnyowdcKFMudTkfMjNGYU4agMdACQDKjhfunw3Y7tTSspCmkjJ89akmZSXwmeCwDPpqYej4hK4qLB1eDR3iqlj3osSasm4OdRcttXZRT/Yfy2aGhMgZ8fft2AI50T5/M050uXIf2g09TGc7ymwBwE3CHEMINHAVuQBn/7wsh3gucAP60Pk3U1IKIw2SNVGFfzcEOGILhWGXazQmZJChV772pQRkjOxJBUxpbibCjZR2t4RVAoaxaUqbxmwKfKwBJiE9nwDFxzTBdw2N48wlEhsNRfgdgyEr8aQ+vLVre2bQB+qE3coKsmUIYkkAZH/h5my6DI3BmZHo9lh5HnJasZNBpMBStrFOhUVRkwKWUe4FdJVZdWdPWaOpCIhln2GHQaIXqtzQqg1KppGzMyBAwVVZes23Ac9MXIlju2FXoV7WtpzFojVpy6qGXIoNXGvk46qil7leKjDDxmK4p10+H1+knh2A0MUw41FrRPhEr8ccujGyzrnMrHISh+BnSZhq3oOxDYVX7RoI5k4H01NrzI7Ehzjjh4lQDg85Y1TLHyx2dibkMOHxKiRA1WX7M9qY1QOVFb2MiR0Ao5Tm324PblJMqzGiKiWVHcElJU6gNp9OF3zTzZdWSIotXOvFbIZm2OFQpshRXf68G28c+MFx5lZz8hPWKs4qWr1t5NkJKRlKDZGUGTwXJRYbDQXvOYNCcOmLpsWd/gSkEZ4WUuyWa1OGp1aAN+DLghJ2FaQkcdbYpmdB4hZEkIw5B0Chk3XmlJG3OrFzXciGRSxDKyXwv1WcWyqolhSqM4beKIIxNcx5U9fcZGnCXekBEqpA9iGVHCebMoqxKAK/HT9iURLORqpKLWqSfQcfU18qBrj8AcMlZr0NISXwWhZiXI9qALwO6h48BsLJ5MwCNwWY8piSRLZ+lNzzST9IQBF0FOVOPhLTUlemnY0wmCY7TE/FJQdIqqzZmmHiEOx+GZ4tDlSIjJM6Kp6qKsR8QtvZKJcTNOA1m6ZnPxpxB1IxXFZve4mih18mUOQenRg/ikpJLtr+KoCmJV3BNagpoA74MGIypCivrV27LLwualUnKnuo9BECDpyW/zCNF2UoyNodP7luWyRm2EqGN1zTylegTBngNLyG/+k3HponoyQhwipkZ8KA3DBS0VyohLlIEzdI9/pDpIipSZGUOV4UGvM2/mqwQPH98T8n1Pdk+VmUEfm+AkClIlCnyrClGG/BlwHCqH4eUeVlQgIBpkKhAUvbMgOq9h/2FRFuPaZAuU0kG4OCJvfzZg2/lX+++aQatXtzYSoQ2XlRZtWw2Q0IIfIaPhmAYmD6iJyPAJWY2iRn0qQSi6FjlglYxkSGAu+S6oPAxauTIULkBX9uiVBEPnCgtK3vGmaRTKldPwDSqUsnUaAO+LBjNjtA8Tt8ZwC8djFVQtbxQH3Flfpkbo6yONcB/PfSPJA1Bf2L5xfbGDROfKJQc80iVFTk40ocUAq8zQIMVUz+dAU8LMeMeeGNARZ5UI3sQNUwCeEuuazAaGHYIsiJXcWjjWasvAqBr8IVJ6870n6DfabDCo+ZmfNJF3NCuuWrQBnwZEJUJGnPFw2IflUnKDsWK6yMCuKWDNNOnaKfTKX6fVZOncbPypJ9v/Pdn+NqP/r7i7RcqUUMQMAoTgXZZNbsaj9/VQDhkGfBc6ZFQNpshIwROo3SPuBxNISVdUansgV24I+goHd/d4GkmbQhGRQZnhabj3E0X45xCVvbxfT8HYGPreQD4hYdElan/yx1twJc4yVSC464k7RTXVPTjIWGUv1km1kcEcAsnqTL73vXLf6bXZSCkJEHlIYc/6/4RPxn4ScXbL0TGKxHaeISbMQOGLUnZgKcRr8ePa5qIHrt4sGumBrxBZVOOZSqTPegbPkNGiLxGy6TP8yk3Wp+TirNDvR4/zTnJSG7yKOBg924Adm6+AgC/8GtFwirRBnyJ89PffotRh8GFbZcXLfcZfmIVnP1oJlJUHxHAjbtsIYIHT/2IppzJ2Wkn8Sr8mimRY7SCB8tCZrwSoY3XKqs2FFUp9vYEo9eczoArw+s2PCXXl6OtyTLg2cpGQKesAsgN3paS69saVP5AyhC4qoiMCeUcxOXkycnT8aP4TZMdmy8DwO8MEq1CJVOjDfiS53dH78MpJddc/sGi5X5HgIRhkEhOf3PHc9Gi+ogAbuEiNc0c1r7Dj7HXM8alcj0N0kvcqLwyeVJIIg6xqG9iW4kw5CtkP3qtsmoDI2o+oMGnjKRHqrJppbDL1rkdpX3S5fB6/HhMWXHWbM+gandToLPk+lWthVFYNbHpAekiVsK33SuHWJVx5GPlgy4VVnm6/1jFn73c0QZ8ibOfLs5Ju+lsXVO03G/dLD39x6fdP26O0WAWXyZuwzNtIYI7H/kcEnjrZX9HwPATrWJYPGZITCE43j150muxMDCi/NxNgYKin8+pEqH6opbMbFAZd7cUU8bUHziuBEBXNm2ecVt8UpKssID1gF24o3FNyfXrVxSimFxVTKwGhJdYiUnvUZEhTKGyfYNHRc1oRcLK0QZ8CfO7p3/GaZfgvMCOSesaPEoXpXf41LSfERMpAhPqI3oML2mjdC85nU7xe/Mg56U8XHjuywk4gkSn2LYUCevBcLJn8Rrw4bgy4C0NhcgdOytyKKlcKE0Nyp+sYupLTyYf61MqfuduePGM2+IzBSlZ2W8/HFf++c7mdSXXr2rfiNNKoXdReWhjwAgwWqLDPuKQBEVhorcxoCZdtSJh5WgDvoR54OlvA/DaXe+btK7Br3qA5W6WuJGbVB/R41C9pqHRyRLwjz/3IANOg4vCyq8ZdDchhahIezyeiJI2lAHvsWp4LkbySoTNhZ6s7fMeyUYAaGlUxt0tDTJThGT2xE/gMSU7Nl8647Z4pCBVYdasPWG9buXZJdcbDgdNlqyw06jcgIdcjYwZRlFKfyIZZ9RhEHQWJGmbLAnb4Wjl2i3LHW3AlzD7kgdYn4adZ7900rpwUPUAI7HeaT9jtER9RI9TvR8uYcCPdyvhrHVt5wKFYfHp/iNl22uH2AEMRLvKbr9QySsRtm/ML7MN+ChqzsGeYHThIENpA95vDtKZFUXzD9XikQbpCsJFAUYzw/hNk5ZwaR84QIOVpemqYmI15FbXwMnug/llp6zX9vUB0Nak4sEjCV0bplK0AV+inDhzkIPuLNscG0qut3uA08l3JlMJRh1GvgSbjdelhr0j8ckp2mciylBvWb0TgHBAPSj6y7hqAAbHGXBb1nQxMl6J0MYe8USMDD7TxOtRD0G3dJCaIva5zxijzfSXXFcpHukkWUHSFUA0FyWcmz7DMmSqkMZqQhubrGuge5xv+1Sf0ghvHjdhuqpVXatakbBytAFfotz72//EFIKXnfXmkus7m5Vgfyw19c3S1avcGA3ucNFyn61jHZ+878DYGRxScta6nQC0NqpKLoPRqTWhbcaLLo1kKk//XmiM5EZozsoivewGa9JyyCHxj7PXLpykS4RkJpJxep3Q5ppdHVkPrikfEBOJiSQN5vSTkyHLZ11NaKNdzac/UniI90WOA4XQRICOlrVakbBKtAFfojw9/AdasiavuvTPSq5f2a56O7H01DeL7faYWB/R71Y98vhYZNI+w9khWrMy38PsaFIPikqGxXbVdlC9wcXKCAmaJhRhaLEmLROGgW+cjogKyZxswPe+8BuyQrAyuHHSumrwCBdjZWL2bUZFhhDTG+YGy2ddTWijncVrZ/UCDI6qyjudLevzy9xuj1YkrJKKDLgQ4rgQ4lkhxF4hxG5rWbMQ4gEhxCHrf1O5z9HMHV1GnE25hin9p35vAJ9pkshNnaXXa8UzNwdXFC23/bnx1GTjHyFB8zjjtapDhcBFU+V71LbokktKYmLxFoyIGFkax4XHATQ3FlwF3nFhmW7hJl3Ca/H8SSX+tKlz56za4jG8jFXYTYs4JCERnHabRkuV0u2s3IDbWbzjC4gMJ9UDfU3n1qJtg1qRsCqq6YFfIaXcKaW0S6t9DHhQSrkFeNB6r1kgJAxZJKZUiqAJY9PcLMNWj6k9XBwXHPCFAYiX0LEedGQIUwgNawt34pSSWKb8sDiWjADQkVW9wcWImcsx6IBGo7jgb0tjO8IKwfOOq7DjMjwkjckW/NTw8wDsPOsVs2qP1/AxJlRdzOmIxiNEHcaUafQ2zQH1MLcnsithRdt6HFIymim43KLpYZxSsrKl+NoKakXCqpiNC+Ua4Hbr9e3AtbNujaZmjAmBt4wBD5gGY9MkeQyPqV7SxPqIoYAabCXTxQY8kYwz6BC0OAup2IbDQWNOEs+VT+e2RZfacj4ijsWpiXH8zPOkDUGzt6NoudPpwmcZcM84mVm34SEjxCTN9N7kGcI5kzWds3OheB1+pBAMjk4fbXTstBIeC3un97l3Nqn2+DzT99TH43S6aMhJ4uPcYmrCVE6qq+mTLhKL9OE9H1RqwCVwvxBijxDiRmtZh5TSdmr1AB2ldhRC3CiE2C2E2N3fv3gjCxYTiWSctCHy8dpT4ZOOaW+W6BT1ERstGdTkhEIEzx9/ElMIWv2ripYHTYNECS2MSe22qra3OZqJOgyi8UjZfRYah7tU8k1baPWkdfbkpVcUIjg8TnWOItHiiJ5+RujMzjx80MaOGBqKTG/AT/WpsL6W4Kppt3v9S67nBu/LecsVf1VVO0KmQXzcaC8uJ2f4glIkjC9yLZy5pFID/lIp5YXAa4EPCSFeNn6llFKijPwkpJS3SCl3SSl3tbXNbkZdUxmDw2qCyC5qOxV+6WZsmhCzWDZasj5io1Xh3K6ybnOk62kAVkxI/fZLB3FRflictHSxO/xq4tPuFU7k4Iln+K//+dy0n5VIxvnPe/62rOug1nQNqApGq1q2TFrntSYvPeN1wq2H7OiEkMxeZ4a2CQqSM8FvZYAOlymr1jus5F47m9ZPu53T6eKvr/sPAv7SkrNTEZTOomsgJtIEzcmhiFqRsDoqMuBSytPW/z7gR8AlQK8QYgWA9V9H3y8Q7OGyzzX9MDcg/EQcU/d24rJ0fcTmBvUgTmWLe9Wnh1QvbuPK84qPIz3EK0gmSeYSeE1JW4My4HavcCL/cf9f8aW+70ybnv+dX3yWr0Z/zo9/c2vZ49YSW+tk4m8A4LN6nD5HwX+cj6mPFQx4V99xhh0GHd6VzBa/p9H6/OkLGw/GVEbumo6zpt1upgSkh5hRuAZGHTmCYvIIUSsSVkdZAy6ECAghQvZr4FXAPuA+4Hprs+uBH9erkZrqiERVb8sO95uKdu8KRhwGp3pKp63HSZasj2jrWKcmTID2x1X25DkbdhUt9wsfsQp82imZxG/KfC/Q7hVO5Aj9ZIVgeJpq6yetScCuwdIPgXoxNNaDU0o2rd4+aZ3Hmrz0OScb8Fgikl/29MGHAVjTVBCPmin5DND49FFAkaS6ZjasPHfWxyzZDsPPqNWzNnM5RgxB0JjcwbAVCSuRXtBU1gPvAB4VQjwNPA78VEr5c+BzwNVCiEPAVdZ7zQLAzpAMeBun3c42EE8dfKjk+pjIEpClM+5K6VgPZwZozpo0BpuLlgecQUaN8pEQSZnGK0W+F2j3Csdz7PTznLSaNDQytWZGX1qtG4yfmfaYtSaSi9Cck7jdk+Op7clLn7PgfrCTouziDQCHu58CYOvaS2bdngarcHKsTHZjNBuhMWdW7RqplIAzRNRQk7X9kR5ShpiUIAYQskTWzgwsXi2cuaSsJqSU8ihwfonlg8CV9WiUZnbErHjqkK952u3OWXcJDP2Aoz17S66PGiarZelIFq9kkgzqMFFacpN77EFXIzlTcGbwFKvb10/ZnpTM4DONfC/Q7hWO58E9d+ZfR6bx6w6gJkSH03M7cT5KgqZc6dvKI9zAWN6tAQUXx/i6lWeiR3EYkvO3TNawqRY7A7RUzP54omacxjrm9TW4m5BZQVfvUYatohaNJSJewoF2SGhFwkrRmZhLEPtmDfqmz606/6zLcUrJmejk3o6ZyzEyTX1ETwkd6yEjQ5OcHB/cYCV/dJcRtEqJLF4cBPwhGnImUUu5bzz7+/6Qfz2VW8DM5eh2qt7+iDm3WX3DRoZGWTr6xytUrzw4bmQU8Co311i6kFDVn+2jI0tNesNNIZVFW64uZlQkaZCVa3xXi53Ne7r/CGcGVMGGUhEvWpGwOrQBX4IkUupmDYdap93O7w3QkYX+7GRfcs9QF9lp6iN6TKNIxzqbzdDvhCbH5IdG2NJ5tiu+TEVS5HBbRqQxJ4iWKIZ8zOzBbSpf6lQG/HDXfmIOdWlHjLnL6LSTeMKO0q4rj6FGMyFfIU4+6FPbJscZ8D6RoD03syo8E2lpVNG9yTJ1MUeNHEGmDzudDc2WYe4dOkH/iJrbaLdkFsZjKxKOJHTIcSVoA74EGbPiqZtCJUPzi2g3ffQbiUnLT1tqcVPVR3RTLFN65NQ+MkLQ4pssRdocUtEUQ2UErZJC5mOkG6SL6IR0+p6BUxxzm5yTVj3Z+BRCXM8eeRSAtqzJcBXl3GbLqd4jpAxBk6e95HqvFdZpFy4ACAWUm8suPJzNZuhxStodpX/3amm1pGHHcpPPsY2ZyzHsgJBRH/83QHtYGevhWDdDVuGIiQliACssbZTR5OIVM5tLtAFfgthFbFubptZ1tmkzWuh2yklhW7b051T1Ed3SQXqcyt0Lp54EoLNxcuZgZ5MSMyonaDVmSMtPDCF8jE4wvvc//l+YQrAtqEL04qnSbgG7ks1ZuSaGHYJ4Ym7cKIdOqTj4tuDkJB5QIXIAzaGCgW/wqxFLyoqB33fkMVKGoDOwviZtcjpd+Exz2rqY/ZEekoag0V0/OSPbWA+P9eU1UdatmFw4YkXrOq1IWAXagC9BktkEDinzxmE6VgTXkzYE+w7/oWi5XXx3qvqIbuEkTcGAdw2oEmjrOyaHoa1oU0Z9vJhRKRKGwGuoYXzICDHsoChy5dnu3+KUkpdvvw6AsXRpw9wdO47blGwMnYsUghdO7p32uLWia0CFLq5oKV3D8k0vvolrzLOKCmw0hlRPO20Z2P3HfgfA+rbJceQzxWuqEM2pOHHmAABhX/kR20yxs3mjqSGi2RGCOZNQIDxpO1uRMJGd3uWjUWgDvgRJmSqeeqLORCk2tKt6mfuP/75oeSShIgWmqo/oxkVqXMZcX1T5Nc/ZMDn0bUXrGhxlBK2i8QgZUUj/b3Q3kTQE/ZHCZNaxXBcb0w7WWsV1J6by2/Sbg6zIClaGVa/vRPf+KY9bS/os3+6mVZOCtgA4e8MF/H833F10XhqDlixBThnY4wMq+7QWESg2PilIyUIW5Kmeo/zu6Z/l358eUO6ytobSI4da4PcGCOZMYtkoMRmnsUSCmE3QFMTNqV0+mgLagC9BUjKFt8Js5PM2KUNxYrA4bd3uLa9ZMTklHJQMamrcPTiU7iOUM+lsndxjdzpdhMxiMaOJDFiG2o6RtnuDdu9weKSfI+4cGx2raG1ULgjbVTQRu5LNug5l6M8Mly/nVgsGkz04pGTLmsp7z06nC48pyeSUC6t37CQB02Tz6hr2wCfUxfzn/76Rj+z5SF5Aqy+iJpdXNM9OOKscDaYgLuPESBKapnBEQCsSVow24EuQlMzgnaaHM57Nq7cRzJn0JotLnkUzETymzBcimIhbeBgThWMMm6O05Ka+nIKmmFbQanikOP3f7g3avcP7H7+DrBBs73wJXo8fjzk5ExTGVbJxtrJl7QUADMTmpr7mSHZ4yiSe6fBKSdqqHN8vI6zIOCoaPVWKRzpIjZtw7jOHiDoMfvXEDwEYSqjJ5XUTtLlrTdB0ECdJVGQIyql/I79WJKwYbcCXICkyeGVlp9ZwOFiRdTAgI0XL42aMhmncMG6Hl7Qh8pOfw0aKJnPq0Leg6SQ+Ta9qKKomOO0Yabs3aPcOnzz1IEJKrr7kHYAyeilzsl/36YOPqEo2oY10tq7Bb5oMp6fXAakVI8SnTOKZDs+4pKheR4o2ahsN4pFOUuNEywYc6jzsPvpzACKpQRxSsnZFfXRQbAK4iYksow5JwJhaT3ypKRLe+Ysvcu2tO3hi/4M1/2xtwJcgKZHDUz7JNk8rIXodxcY1LpOESsh92nisklpDo32YuRz9TpMmY+rUfb90TStoFU0ol42t3WH3BocS3Rw4uoeHxVHOT3lZ2aZ88mpibvID4cAJVclmo1XJpiUrGMlNn8RSKyLTJPFMh1sKMjLD8Eg//U6Ddlf56KFq8AgXSausWjKVoM+6NA6PKeXEaHaEcE5OWb2pVgTwMuIwGTUEIefUOj1+4Se6hBQJu4ePcsQtcTmrG5lVwpI34Ol0ikef+knZ7Z7Y/+CSUUBLChNPFVl17e5O+p0Gg+MmDOMiTUBOfUPbMc0j0QFO958gYRi0TBH/DBAQPmLT9KpGLQNuJ7msXXEWDimJpAb55wf+F1kEN724ILfjlcakTFCAU8PKZ37BFqV4HDbdjIj6l+gyczkGHNDoqF4C1k6K2vP8wwCsaiwdxTJTPMKdr4u57/Dj5ITAb5ocdiXIZjNEZYLGaR7WtSLoCDHsMJBC0OCeWuYh6Gwg5jDmLPyz3sTSEQA6W0oHBMyGJW/Av/OLz/MXz/w9N916BYlk6UmvJ59/hPc/8Zd87cdLoypc0jDz8dSVsKpRDZ33HCiIWkUNkwBTu0RsFb3+yGn+6b4bAFjXsm3K7f2OAKOOqQWt7PT/sKXd4XS6COckz+aO8Zg3xqvYzCXnXZXf3iONIr+uTW/yDI05Mz/52igCDDnqn8xzuv8ESUPQ5Kle896FQUbkOHR6NwBbVu0qs0d1eISXMats28GuPQBcmFFFM37z5I+JijShEtrctWZ8Vm+zf+pRRoNHGfeTPXOrJFkv4plRHFLS3jR7eeCJLHkDPhBVk3MPuwd4z7dfyokzky+Kh5++i5wQnBx9Ya6bVxfGBFUZ8LNXXQTAoe4n88uUn3LqghC2it7/2/MxHvEM8drsWt7+6r+bcvuQq5HMNKW9bAPeNG7StNE0OOUWrMhIPvrmrxdt78FBmsmGWVWyKYw+wq5mhhxiyod3rTh8SikItgUnp4eXwy3Vd+kaUS6Ni7a+opZNw+PwkbTmK7oGVaz6VZv/DIDHDv3E0uauvMblTBmf1dsyTchi2Kcegt2WZspiJ2EmCFYY1lstS96AxzPK//lO92UccmV4///8McdOP1+0zcERlbk3mJs+0WQxYOZyjBkCj1G5L/bCc16BkJIzIyriIxqPkDAMgtP5KS2t8UEH3Bi4mi+896fT+lBDVq/qVG/pkL4xK3FjfPV2u1f49hV/NknXxSNdJMVkl0yvo7iSTbO3EykEh08+PWXbasGpfvXwX9k8OT28HG6cpIWkL9NDe9akqbG2lavsAhIDIz30jXXhkpJrXvZ+2rImB2MHGHYIGmbg+qmW8Vm9nS0bptyuOaRErvoipfXgZ4uZy/E3X38NP//dHTX93F/8/k7+7rbXTxpljskkgQqjwqplyRvwRDaKS0o+8qdf4aOrb6TbJfjew18o2uaooTQ1BsXiTx6IJ6NkhcDrqLxHFQ610p6V9GVU7/hEtzJGtopgKS7d9nouSvr4zJaPcNObv1T2GA1Wr6pvqPRNmU//H2fAL229gtfm1nH96z8+aXu3cJGaYMDP9J9g2GnQ4Smo3HU0Kr/jsTP7yrZxNvSOqGiZDSsnF3Ioh0soA94vYnRkaz/R5bVCM4dGehjMDtGRVS6qTblGnnPHyApBo2d64bNaMD6rd11H6fwCgM5mNYoZjE2vnTNTfvn497nfdZpfHvhObT/3uTv4mfPkpFHmGCkCdZpjWPIGfGzc8OXNr/wQKzOSZ2OF3ti+w4/R7RJ4TEmfw5zzGoq1xk6I8ZaphzmRdtNDP6oX3D1wHICwf+qe4Oa12/nWnz/O61/67oo+v9UStBoYKR2TncqN4TPNohjq//UnN/OF95SegPYIN2MTrt6nXlA+/NVNBY2Nte1WMs9Qoed/9NT+mp/nobFuDCk5a+2Oqvd1oaJEup052oza65HYo6VIbJBBI0GLpXR4Vug8Eob6EafzSdcKO6vXKSUr2tZPud2KVkt6Yaw+ioQPP/99AGLTJJbNBDsvYeIoc4wsvmkCAmZDxQZcCOEQQjwlhPiJ9X6DEOIxIcRhIcT3hKjC6TqHjMkUfmv4YjgcbGcVBzwpegeV1sdDe78HwAUZNfN9um9xVwKZmBBTKZ1GGyfcJv95z9/m5T5bQtNXKK+G1ibV+xqOl/aBJ81kvmp7JXiMwsSczdEe5Qo7e/VF+WWbrWQeu9zbw7t/xJ88eB23/+yzlR+sAiLZYZpyEq+nel+y23ATcQgShkG7r7T2zGwIWEUjRmMD9DlNWqxwz5ece21+m7bG6n331ZKfWC4Tsri2cwtCSkbT01cRmin7s5arcJrEspkwZunNTBxlxg0TX53MYzU98L8EDox7/3ngX6SUm4Fh4L21bFitGCOFf9zw5bINbyQjBPc+8p8AvDD8JAHTZFf7KwB49sjvS33MosGuUhPwVOfTvOm1/8bmtJOvRn/OPSfU0LKjeX3N2rUqL2hVWiY0RTpftb0SPA4fGVGsNGin/68dl1G4smUNPtNkOKV+l3ue+jJZIegZqW16fUyO0TDDYbLb8CCtrNb1rbWvSRn0ql79if4DxA2DNqtY8qXbX0VTTj01S0m71pqWhg48piz7O7ndHhpMSSxb+/j9fYcf46hlS0eN2qbrJ628hMHR4mpCcUPiL1HAuRZUdMUJIVYDrwe+br0XwCuBH1qb3A5cW4f2zZrkhOHLG156A6GcyZN9vwHgKANsTvvYsvJCAI731c5Xeu9DX+Pzd76vZp9XCaN2PUzP1Ek1pdiwaivfescjXJ5q5gWPuqnXtNcuHnlV2waElMQypXtVKZmuOHsUwOtQLqLxdTFjmQhCSla0FuJtDYeDlpxgxBxhMNLD4041AohPoWQ4UxIiQ9Cc2TDZbRTCNbdteHGtmpSnwdIcPzb8LAArw+q8Gg4Hm7NqpLZh5dQhoLXCcDhoMCXBCtwJIVMQLyGVMFt++vitAGxNORgpkZfwz3f9BXfdX35OpxRJK6x1KFa4JrPZDDFD4HNU59KslErvmH8F/g7y+qEtQERKaQfidgElx9tCiBuFELuFELv7++e+ykbCMPNFAkBVVN+eDbPfOcwLx57ilFuwybuJ87aoG6d7tHahSz84eAv3pOa2Rx8dUwYyVKacWikC/hD/8d5f8TbnLi5IelnVPnWkQLW43R7COcnIVAZc5PDKysOs/FYY43C0cE0lsjGC5mQtkrDpYoQk3/r5Z4hbPt+xXG3DCqNGDj8zm4C0FRg9pmT7phfVslkANFqx9d0ZZVg2rdyZX3fl2j/hRclgXWKUS3GJ2MSFDeWLNQdNJ3FR+8S6Z6NPsSIj2excy4hDTEreuzfxG+468a0ZfXbSkisYP8rsGz6DKQQBZ23lEWzKGnAhxBuAPinlnpkcQEp5i5Ryl5RyV1tbbcOjKiFuSHyiOCHlgraXMeIw+OqDHwXgwvVX09a0kqacyWC6tI+2WpKpBIfdSRKGQSQ6N1ocADErnrohMLOKLobDwd+//Zt8+8+fqHlqdVvOyRCle75Jkasqe9TnUjdEkQGXYwRLhGs1EmTYkeWR6G9Zm4bmrMlYjXt3ow5JcJq4+enwOJUBX5EVdUlnb25QGbJdTvWdd2wu9PLf/pq/4+t//vu6xCiX4nPvuY+PvPWrZbcLSKWbUkv6h89wwJ1iGytp9LRiCsHx7oJXeCQ2RMRhcMQtOXC0enM3ZkVFxTKR/LKe/uMABD3h2TR9Sirpgb8EeKMQ4jhwF8p18m9AWAhh33GrgQVXRtrM5dTwZYJwzpsu/yBOKXnYeQaPKbnqYlUgoC3rZHAKA1MtD+/5UX6G/2T33GWUjaWVAW8MTZ3WPl+0EGRgCr9jUsiqko/8lo/fdhkBJGTpcK2ws4l+p7oxX+y7CL8UJGXtenfReIS4YRB0Vue2svE61fXZVqIgdC1osgz4gNOgpQ5x5vUgIPxEHbUVtPrRb75C2hBcuu51NAdXAIWQWYCDJ57Kv/7vx75W9ecnrEsvPs53328VRmnw1SdMs6wBl1L+vZRytZRyPfBW4FdSyrcDDwFvtja7HvhxXVo4C4aj/WRLDF86W9dwTspNVgg2Z1z56t/NBPJKbbPliSMFwfwuq77kXJCwiuNOJQM7n7S4Wul3li5xNmbIfNX2SghaLqJ4MpJflhAZ/CX8q81e9Vt4TMn1r/o0PtMgWUO96S4rbMxOAa8Wn0ddf23O+jx0Wxo6EFJpobTOQC1xPgg6Q4wagmy2drKyT/Y+TChn8keXvz+fH9AzVHCZju+NPzv65KT9p8PM5UhYUVHjZZOHo8pt1eivz7mdTRz4R4G/FkIcRvnEb6tNk2pHz6AKHbPjYMdzXlDF6250rc8va3W10e8QROORWR/7SOIghnXT9I1MX429liTthJjwijk7ZqV0BNZiClEy0idhiHzV9kqwffyxcQY8bpj4SzwE2q0QuV2ZJla3r8cnXfkJp1pwul+FnjZOEzc/HbYswcpQfQoqGA4HPutabKmxVG29CLnC5ISg23JBzIRkKsFHv/FH3HTrFdx06xU84xxiW7YRvzfAqjal/9MfLeQl9FiFP3Yk3TznThaJu5VjcLSXnBVJFKcwuhuJKxdfS0N94uyrMuBSyoellG+wXh+VUl4ipdwspXyLlDUck9aIgYgavoQ8kyf0/uSlf8XGNLx6x7vzyzoCa5FCsO/IHyZtXw3ZbIbDrjjnpFVvcPysdL1JmglcUuZHFQuJdW0q0uHQqeLejV1OzVvFTH04qIxlYlxh49gU4Vov3n4NG9Pw1gv/GgAvLhI11Ju2U75nGje/6+yr2ZQWXHH+dTVr00R81tdtcS189wlAo1e5HE7NYvT66yfv5X8cx3nC2cdeRx9uCVeuV06DDatUqGkkWSi0PZA4A8CVK99I2hD86DdfqfhYA8OFrNHxxShGrNDW1qb6lKtbHOOpGTI0qn7UxsBk/9NZ63by4/c/W7RsXds2OPkwh7qe5LIdr5nxcX/79P8w4jB4vWcn+7O7GUnO3SRmykziFwtTS3nruovhJJweLp4TmEn2aLhBndNERrlj0ukUMUPgF5MTmM5at6PoXHuFl4SoXRjhcFy1f6r6oeU4e8MF3Pv+Z2rWnlL4rBj7zmDtJU3rQXNoBSSmll6ohN7h4wD8/Ya/5JpX3Fi0riXcid80ieYi+WXD6X5CDpO3Xv0Rbr3z++zp+RXv4zMVHWtwRNkatymJjdMyj1tzUqumyTydDUs6lT5iDV/Cgcr8wVvXXQzA6eFDszru71+4D4Crd16PzzSJjpuVrjcpmca7QIuZnL1up5IsGCue77aHqgF35aMGe2IuZbmMugdOIIUgOE6ydCp8ho+4UTtxoUhCXWcrW2sXdllrPFaM/bq22icK1YPWRtVjHYjOPDZiyNJSWTlFklI4J4iahQf5iBmlOWfg9wbYlm1gv3OoYh/8UFRFr7XnYHTctZXIRnFKSVOoPiOfJW3Ao2Nq+DJe4W46pjIw1XIo9hytWZMLt15OY04QM2Oz+rxqSMvKy6nNNYbDQXsWhnLFseB29qi/iuzRplAbQkqSOTVh1D14HCjtLpuIzxkkbQhGYqWzQqsllh5CSMnqjtoWYqgldoz9ORsuneeWVIathzKV9EIljCTVdbV2ilqfDaaTKIWyfCMiRdhSwDy/+TKGHQb/87tvV3Ss0bgaZbfmvCQNkQ8dTuQShOokJQtL3YCnIgB0NFem8zCVgakGM5fjiGOUTdkGDIeDkGkQq7HmwnSkxMI14ACt0seAUfx72BM9QW/lURxOpwvfOAPeP6wmoyqZSPRbOjG9g7UpdhzLRWkokUC0kHBLJz7TZPPq+mdc1oJ1Vn3OaHrmD9nRtCrM3RYu3YELSS+jRqGHPeTI0Wioa+Pal/4vHFLy6KF7KjpW1JpMbzbCQCEyqZ5SsrDEDXjC0gLvaKlcIKhV+hg0JhfLrZTdzz3EoNNgS0jdKEHpIWrULhSqHCmqS4iZa5qNMH3OYtXH2AyzR30m+WruQ1E1XG4KlR9tBd1hAAZHzlR1vKmIywShOt6ktWCjdxM7Mg1zlrAzWxqDzcpHnRmZ8WfEzBiN0/R+Q0aAEctfHY1HGHYahJ2qE7GmcyMb0wYnc5VdI/GUuobbvCr6q2dQ+e7HRAq/Wb/ffEkb8LFcHJ9p4vdWPjnWbITpdcoZy40++px6Yr946xsBCAo/0TmssJ0UJh6xcA14m3cVY4bB4a79+WUxa6TUVGXykUeKfGHjEcsP3d5U/mEd9KmbdGi0NtFBMVIEzYX7mwN8+vq7+PqNi0uoLZSDuDlzyYOYHCM0jfEMucJEHQYjsSEOnVAS0y3+QvhtGB8jFXa+EmnVWbR1ZmzZ5ISon5QsLHID/oXv3sif3HL+lMWIVSmj6j6zzbuKpCE43PVs+Y1LcHDkWRpzJi85//WASkgYcdQ2IWE6kobEzYJU9gVgVZOaUDpw/PH8MjsUMFzlRI9XClJWYeOo1QNa0bK+7H7hgDqO7bqZLXGRIygXrvtksRIyHcTlzEfDMWP6Wp9NXtVhOHb6OY71qA5FZ7gwEd0gQgw7Jkd0fer263jvLZcVLRvLxhBSsr5DjbyHrdDhhJB1k5KFRW7AD44+y0GPyR33f6Hk+qQslpKthNVNSrP4wLHHy2xZmn5GWZ1154dtDa5msrNMSKiGMUFVGY1zzWar/uaJcaqPY1YoYGu4OkElj3SQtgSEbP2J8UqEU9EUUlFJI4naTGJGHWbd5EKXMwHpJi5m3vEZNUwC05yXFqvIyKneg3QPq2QsuwAIQKO7hbhh0D9c7EY5mDrEC85iqdsxcwy/lHlZXjt0uJ5SsrDIDfiIUBNYvzpdOot/TGTwVaFwB7BplSoAcLx/f5ktS9PvzNI8LtstbKXQnuidXWhiJdjpvLa63ULkvM2XIqSkN1bITh3LqVJ2rVNMNk2FWzpJWYWNE9kYoZxZ0URic6MaJtt+y9mQzWYYNQTBOqnNLWcCwkvUMTNXppnLEXEIQo6pz0tnk4p06Y2cYNAq+LHFKgAChSpFR7qKJaYjRpoRh1FUKNsuSGJHIkVTw6TTKaIOA7+zuuIq1bCoDXjEyCKk5BlPkmcO/m7S+jGRxUd1/qftm5TUpV3BpRp6B08z7DBo9RTizu1SYt0D9a/0MxIbwqyyHuZc0xhspjUnGcgU3BfJXAK/aVatxOcRLlLW/MJUSoSl6GhWfnI7yWI2dPcfJycEIVftS6Etd4JGgJEZxuufGTxFVghC7qnPy5oOVXpvKN7NUGqAgGnS0TK5nuqp3kLimZnLMWi5VU6NE6lLyhQ+0yDgD+E3TeK5KL3WRGZgmuLgs2XRGvBsNsOgEy5Oh5DAdx/93KRtEsLEW6U/OBxqxWtKEtnqY7f3WxofnaGCH62zWb2eTUJCpfRHVCSGrW63UGnNuRii8PumzFQ+1bsaPLgYs7JOp1IiLEVbuBNDynwh5dlgp3o3emcm36uZmqArTMoQDI9UP1dxqud5AMLeqedVNqxS7pKRZD8j5ijN2eKHxWpLL6V35Hh+Wc9QF2OWyuj4NP+kyOCzwncbcoKEmaDHyiKtl5QsLGIDfvT0ATJCsNF/NuenvPxeHika0gDEDfDNwP8UMiUJs/oK9Ye71Uz2ho7z8stWtyufeiRRm4iHkdgQb7llJ99/4N8nrYuMKl0HWyt7odJMiH5HwbeZkql8qnc1eAwvSesKnkqJsBSGw0HAlDUp6tA7pFxBzaGFJx622LEfiid6Xiiz5WTOWCNe289dCr83QGPOZDQbYUQkaTKL3W8bV6v7eChe8IEfPFHQ8emLHM+/TpLDa4XvBqRBXCYZiKj96iUlC4vagKsokY6GtVyx8o0MOg2+84tCodpsNkPcEPirrM4OEDANEjOY/bbrLG7fWMh2KxRorc2E2cN77uF5T47fn5hcrb2Q0TgzXeq5otXVzqDTyPesUmTyqd7V4DG8jAmBmctNqUQ4FX6zUIR2NvRb9Q/bGmtfjHi5E/YrV2T3QPVVsgZGlQu0o2l6eYNwziBqxhl25GgUxbais3k1XlMyki5ozp/qKzxMBkcLhn1sXOWvgOkiITKF3IRg/aSdF4UBv+v+L/H1+z5ZtKxrQPmfVrVs4R2v/ihtWZOHewsa3D1Dp5FC4J/B5FJAOkmI6vWi+1PdhHJmvvo2qFJijaYkWqMCrftOPQpAb26yQNaoFVUR9IZrcqx60RFSvsVnDqt5i6TIVlVOzcbj8GEKlRI/lRLhVPhlbTTBRxJq1NO5gHVQFistDcofPTBSvftxOKFS8MvVdQ1JJxGRZMghaHQVZwIbDgdNORjJFe7d3tHjhWOMUzIcr2fvFx5ihslIQt2jzQ31G50tCgP+02N3cm/vj4qW9UeVf2nT6vNwuz1cLDbyrCeTD/npG1BD24C7+t6oT7pJiOpnv4fMUdpyk3/ShpxBfAYumVKcSii/22nn5Nj3mFWLbyb1MOeS9e3bAThyZi//dMcNHHbniiJ3KsVnja76hrqUEmEVs/1e6SDJ7GPzR1PqN7dTvzW1Y0XzekBNMlaLfV7WlDkvQXyccplIIWjxTY6CajSdjIqC9MPgWA9CSpxSEk0XopjiQuC1OhCqmpAkat2P7XWSkoVFYsDbHC30OGVRMsxQsg+nlGxcpQLnNzYrf9WTzz8MwMCI7X+qfnLJb3iLJCErZcCRosWc7LIJSSdRMfvhOsApoSInhpwGx04/X7QubifEBOvnc6sF2zaowr3f6/0Bd2Z3syPt45N/fEfVn+O1NE0On34aKQSBKkqa+aSLsRk8pCcSy47gN00agzOrxqOZmpVWSN5M5Jhj2REacuWzsBuMEGkr0qWjYf3k9fgYMQrFPyKZQZpzknBOEs2p/IVoPELaEPnggYCzgbhhMJpSrpcVbfWT8K2kqLFXCPG4EOJpIcR+IcRnrOUbhBCPCSEOCyG+J0T90o06A+tIGYJnDhcKLURyw7RkZT707OzVSgr20Bk1yTBklTKayQSC3wgSdYiq0unT6RR9TmhxTr6Rg3iJGrM3Fv3DZ+hySc5KqdO2+8Avi9aPpdUFtRDrYY5n/cqt+E2TMy7Ba7Jr+fq7H6WztXofsu3r7+pXfsmGKsSwvMKTL0I7G+JmnIbcwtZBWaysbFmDU0pi6UjV+8ZknIYKwkob3IVrZm3HOZPXGyob07YFI8RpyjloMA3ilkhd/5By8QSs4IGQpbXTn+nDY0rCofmdxEwBr5RSng/sBF4jhLgU+DzwL1LKzcAw8N56NXJDmyp/9tyxQqz3CAmazELUwQVbX46QktOjysUwOqae2nbWXTUEXSGyQtBfRUmlfUcfJysE7f7JhigoAozOoEc/kUf33ocpBC8Kqlj1w727i9aPZVRoXqXyufOF4XBwnf8Kbgpfyxff+9MZq/gFvSq+tjeq3GXVlDTzCS/xGow/4zJJsMpsX01lGA4HjTlJLFd9SG+UFKEK9GmafAX7sGXtzknrw+4WxgyDPss1GxFpGqWXoOkmZs2TDVjhu37LXdvoU9fhIDGCZn2Lq1RS1FhKKe1f0GX9SVR1+h9ay28Hrq1HAwHO3/JSAE4MFoqORowsjRQmrRqDzXRkoT+jJi9iSUsdrKn6MldBK/i/mtnvgyeVMV3TMll7OOhqJOYwZl1rc3+XeoBdc9kHCeVMTieOF63P18NsXNg9cIC/vu4/uPGa/zerzwh41XkaTKnJpEqUCG18Dj/xGhTNjRlpAnLhas8sdkKmQVxWP38UM7IEZfkaq60NqsPlN006myf7qpstcStbG2nIKQkbDQSFj1ErS9Qu5hCw4r3tqJNeZxZ/nVUqK+o6CCEcQoi9QB/wAHAEiEgpbedQF1DSUgohbhRC7BZC7O7vn5l40ObV5xEwTXrH1MSlyoaCRqPY59me89Av1LPGVrhrb65+aB6y3C59Q6cq3ufUoPJHn7V216R1jR71eSe6q49nHc/JsSO0Zk3O3nABq7IuemRxKngyN4bHlHg9CzuRp1Y0BtT8RsRU8wKVKBHa+FwhpBD5ntVMiRkmQVF5MWZNdQSkk/gMIsJGDEnQKH8frGxR6fTNOVFSdrajcT0Ap/sP0jNwirhh0ORpJWgEGTGUmzUSUx0Ie77Njp4ZcRj4ZxBdVQ0VGXApZU5KuRNYDVwClC5xUXrfW6SUu6SUu9raZlZWyHA4WJFx0C8jAJzoOUTaEDR7i90jbUYTPZbWdMJSB2tvqk4gCaA5qHpydhxnJfQlunBKyfaNl0xa1xRQn3e6/0jVbRnPKWOENVl1Ua4QzXS5skV++pRM4pcLsx5mPWiwJmuHLf32SpQIbQIu5X4ZGJ5dhuyIIQgY1ecaaCojgJdYlfNH8USUqMMgVMGk9roVyu/dlCs9ilrToaJY+iInOHTyKQBaAqtpcKss0f5IT360bxfaXtG6Pr+/v87KoNVWpY8ADwGXAWEh8sLTq4G65oq3EqTXoULn7B+yLVQ85On0ryVhGBw4/hRjuThBU1atrwHQ2qiM/nCs8nJOg9lB2rOU9OfaSR52kdWZ0Dt4mtNOWONWn7UysIG4YbDv6BP5bVQ9zOUzodbSoB7gAw41GVmJEqFN0HK/2NlyE/nrr7+ar9zz0Wk/YzDSQ8qorA6nZmYEhb/q+aOTPSpHpMFTPgJtTccmnFLSIEo/hDeusrIxx7o52adG2SuaNtJoSdGe7Hk+b8DteO/VHYUanD7qqwxaSRRKmxAibL32AVcDB1CG/M3WZtcDpSUBa0S7q5N+p8FgpIeuAaXst6plS9E269tUfPGzRx4hIcdmXMrILsE2mhwss2WBIZGgNVf6ZK1qVeFQdpHVmfDI3nuRQnB2h3LRbOlUsqxPHXwwv02KDN4ZpKQvVpotX3/UYVSsRGgTsoa7w7G+SetOnDnIA64z3D7yEw6e2DvlZ5zq1Too9abR3ULUMVnSdTq6LI2SpkD5BBqn08UbOIerNr215PqOllX4TZNIeiivibKhcxstQdXJOzNwjLhVzKHNGu3bekqgCmjXk0p64CuAh4QQzwBPAA9IKX8CfBT4ayHEYaAFuK1+zYTVjcpYP/n8r+mzog42rjyvaJvtG18CwPGBfSRlGv8MjdnKdpVVF6tCra7PmaPZKK06tm6lGobZRVZnwnNn1ATmi7erSj+XnPsaAI4NFApPpMjiqbPPbSERCoRxWi6jSpUIbZqCyviPxCfHGD+wW8Wkxw2DL/7ig1N+hj3Jbad8a2rPCksYbt/hyqsJ2SPd9obKEmj+37t/wJuv/NCU65tygtHcKIMJ1QE7e/2FtDepTl7/yEkSlp59W3NhGjBkGXC/o35SslBZFMozUsoLpJQ7pJTbpZT/11p+VEp5iZRys5TyLVLK0mVxasSWVarnefjMkwwle3FKyabV24u22br+QrympDdxytICn1mZq8Zgs6VIWFn6+6meo0QdBm2e0k/8loYOPKZkdAbxrDYnE0dpy5psXqu+85rOjbRmTc6kChOtC70eZj3wWTdKpUqENk2W+8XOXh3Pc31/wG1KXp1ZzR880ZLCYQD9I+q3tyetNLVnQ6cKIT5iCcVVwmBM9dZrJW/QaLoYFUmGM0M0ZU0C/lC+cMNQvIdkLo7HlEVJQyHregy46qtLtGgCWC/a+goAukYOEckO05ybXAXc6XSxIivozw2SEDl8swjvCpmy4vT3fUd+C0Bn46aS6w2Hg3BOEjOrj2e1OeWIsjZb7KdbmfXSIwoPmaRh4hH1q7+3EPFa7tFKlQht2sPK6MZLPFSPym42ZZx8/C3fpiNj8q3jtxBPRCdtNxxXcyR2yrem9mzbqEqXnRmpPABgZEyNdNd2nl2TNtjZmCMyRnNOjXDXrVCfPZocJCmTBCbEe9vXY6iOUrIAi6a71tTYRnvWpC/XQ5QxmnKlm94mg5xyREkLiXcWBrwaRcLjvapix+YV50+5Tch0EJNjU66fjjP9JzjjElziLJ6k63S08byji2QqwVgyTsyQeKapAbgUsSdtq1EiBOhoUUPgeLrYMPcOnuaYy+R15nqaGtt4e+db+dLg93nnd16Svyk3e7fwD2//r3yKt53yrak9azo3EsqZDKQrnz8azQzjcsiScd0zocFoYMgxikumaTVVyGgoECaUM4nmRkiaKfyi2IXnxwNkaPDXd35k0fTAoRDnPWxkaJSlJwfaXe30OmHUIfBXEAc6FQHpZKzCenzdUaU9vG3TZVNuE5RuYsbM1O+e2P8LADa27ihavqZhC2lDcM/DX+Hd338lo4bg/PaXzOgYixWvJUNbbd3BgD+Ep4Qm+AOPfwdTCM5b9XIAbnjDJ3htbh0SiIsMESPN3eznfbe9hEHLlbeyRUvJ1pP2rINBs/L5qFguRjgnS8Z1z4Swp5WUIeh2qSQemwZTEJNxkmTwTnDh2bU4mwL1nR9ZND1wgDYR5jFnLzkE2yntW1oZ2oQZP4oJ+GYxgeCTbnqNygT/B1J9NDmLyzFNJCh8dBkz64Ef6VX+v23rih8Q56y5DA4+zBe7v4nHKfmbtrdx/es/PqNjLFY8OIDsjOoOBkzJmFl8Tp7pfgSnIXn1i96eX/aF9xS0181cjs/e+W5+4HkKU4zRkq2dodCUpkX66XJU7n6MyTEacrU7Jy2BlRB9mowQhN0FXZOQ6SRGkqww8U4wpQFnCOinJVw/JUJYZD3wDp+K804ZgiZP6XTxzSsKRUkD7plXpqlGkbCPCG3Z6Z+FIUcDEYcgna5+rrc7dgynlOw468VFy1+0/VV4TElbFr54/j8tO+MNqrAxUJUSoY1fCpIT5t6PZbvYkDZomaLAsuFw8A/v/C/+tuOdBHMmYVMb73rT7Gyhz0nF905MpAlWOScyHe2NBddlq7+QGBiUHqIiw5jI4Z1wvFbfSpxSsq6jONS51iwqA76+9dz867Zg6SfbBWe/Iv86OE1B03L4jUBFioQnzhzkkDvHFuf0SSQdgbVkhWDfOEXFSunPDtCZZVKKfDjUyr9f+EW+fe3PuPzCN1b9uUsBtzVpW40SoY3PLC7qEIkOcMSdZaNRPqrkHa/9KLe+7Bv8n0u/VPVxNdXREVhDVgieO/ZE+Y2BUUeO4AxKKU7F2vbCZGhnUyFQIWD4GXVIq5hD8dzTB974Bf753E8XFXepB4vKgG/bUOiBrmgpPXHU2bqGlqzKzAv5Z67RHHQ1kBWCwdHpszHv/e1/YgrBy85687TbrW9XMev7j1cez2rTZyRoz5W+IF98/mtnJMW6VPBYk5fVKBHa+KSLpChoPd//hzvJCMG2zhdPs1eB7ZtfxCXnXVX1cTXVsbpZGdDnTxQM+L7Dj/Gxb7yRZKo4UszM5RgxBEGjdnVhN60phCuvX1GQnA05Gxk1BFFD4J2QsBPwh3jlJdPbhFqwqAz49k0vwmOF62xaNXXER4elazCbCYS8ImH/8Wm3e3roMZqzJq+69M+m3e78zS8D4MTgc1W1I51O0eOCNufCLtIwX9gGvBolQhsvLhJGQRP86a6HEFJy9SXvrFn7NLPHFojrGiqIwd3xyGf5qeMY9zz8laJt+4bPkDZEXpO7FrSEOwnm1HVy1rqCi7bR3YwUgpQh8DrmR0BuURlwp9NFZ1bgkJIta86bcrtWa4KzuWHmuti2ImHP4Ikpt4nGI+x3R9lutpbVXNm46hxCOZPeZOUKhwBPH3qUjBCsDOqai6XwOlTPpxolwvy+wktCFOY5jmaOsz4jWN2+vlbN09SA7RsvwSklvfHCvXM0o5RJHzv5s6JtT1qKn2FfbSWVm3KCcK648lLTuAzcmUyi14JFZcABOs0gbVOIRtms9K9HSMmK1o0zPk4lioT3PXILCcNg14ryw2jD4aAzW1BUrJTnjiuf+Yb2HWW2XJ4E3WEMKVnVVn0sts/wEbfKafUPn+GQK8NGdFr8QsPt9tCehaGs0iaKxiMccakQ3/2iu2ie6tBpVZGrNVTb7Ngm00NbtnjCumVcqn7AXVpGo94sqjBCgP991ZfpG5q6VwzwoWtu5qzH7sqnnc+E1saV0AXD8an1Sx4/9Qs8TsmbXv4XFX1mGw0ccgyX33AcJ4dUEYvzN19e1X7Lhfe97h/Zuvc+1nRW/7D2OYOkrar23/z5p0kZgivOKi1qpJlfWnMeBoXydz/4+PdIGYKdSQ97vSl+/eS9XHHxnwDw8Ml7CTpNXnfZDTU9/t9c/iVS2eKQ086WDaoSAoViDnPNouuBb9u0K3+ypiIcauUtV/2vWR3HViSMjpUuqGrmcuwzejg37au45l2buyOvqFgpvckuQjmT9SsrlmBfVrQ1reRNV3xgRvv6raLIvYNd/Db2e9an4Y9eWtsbX1Mbmo0G+pyqp/3kSVUL9r0XqbDZX+2/C4Bjp59nj3uUS3IraGqcWe2Bqbjw3Jdz2Y7XFC0bHyIYmkHx9Fqw6Az4XGErEkanEKB6eM+P6HManN84uQLPVKxpVKqEew48VPE+A3KEFVmnThapA0FrousXj3+To254if9i/TsvUNo8K4g6DE71HOVI8ihr0/CKXW9iS0qwP6383t9+8DNkhOCPL7xpTtq0qn1jXg3Trg4112gDPgXlFAl/tf+7APzRZX9e8WeetVoZ+0PdT1a8T48jTRu1C4nSFAj61ITU/ZFf4jUl73rVp+a5RZqpsIXi9h58mMPuJBtRo95z3Vs45JG8cOwpfpd+lrNTBi+/6Jo5aZNddBlmVjy9Jm2Yl6MuEqZTJHwuc4gtKcFZ63ZW/HkXbH05QkpOjxzOL7vj51/g8m9s4+ip/ZO27x08zaDToH0KmVrN7AgH1DD7uBsuzrSwsq3yij6aucUWivv1oR+QMAzOabkYgFeeq8J3v3j/BznjErys6Yo5bVeDpYHSGp6fe1Qb8GkImAZjJRQJewdPc9Rtls2+nEg41Ep7VtKXLvjAHz55LxGHwcN7fzhp+6deeBiAVY31zeZarozvNf3RtvfNY0s05bCF4p5AhQ9esfM6AF5x0ZvoyJg85o3RkDO5/rWfnNN22Sn77TVSPqwWbcCnISCdJEooEv7yiTvICcGOldVHhnTkvAwIJZKVSMbZ71Qqa4f690za9tAZtezs1RdXfRxNeZobVa9pU0rw2pfo5J2FTEfLKppyJkNOg5UZyTkbVUlBw+Fgm1Tn8UXm6qI47bkgiE/V1PTPXLZjNlRSE3ONEOIhIcRzQoj9Qoi/tJY3CyEeEEIcsv7PzzeoIz7pJj4u1drmmTOP4JCSV73oHVV/ZqvRRLcrh5nL8ZNHbiPqMDCk5FRmck3oM9EjCCm5YOvLZ9R+zfRsWLmV1RnJa9vfMN9N0VSALRi30Sw2Na8+5120ZU3e+qK/nfM2bQ5t45yUe94mvyuJA88CfyOlfFIIEQL2CCEeAN4NPCil/JwQ4mPAx1B1MpcMfsNL3JgsY3k0e4qNGPkiptXQ6V9DItvLCyf28vsTP8HllOxMBTnsimLmckUXQl+mjw6DOe9VLBdCgTA/e9+++W6GpkKaCQBRzg7vLFr+upe+i9e99F3z0qa/+7Nb5uW4NpXUxOyWUj5pvY6iKtKvAq4Bbrc2ux24tk5tnDdKKRKOxIY44s6ywajeeAOsa1XJRc8ceYT9nOHclIezgucy7DDYd7RYbW1AxGifotK9RrPcaHWpSeeXbps+D2Q5UZUPXAixHrgAeAzokFLaeeY9UDoHWQhxoxBitxBid3//zKuyzwdBVwOZCYqEDzz2XaVY11GZYt1Etm9UFXN+f/wndLsE20M72bbmpQD84bmf5rczczm6nSZtxpLzTGk0M+JdL/8k7/dfya5tr5jvpiwYKjbgQoggcDfwV1LKouBoKaUESlY/kFLeIqXcJaXc1dZW2+yoelNKkXDvyV8pxbqLq/d/A5y74SK8puR3TvXse8MlN3L5BW/EKSWHB5/Kb/fcsd2MGQad/rUz/wIazRLinI0X8eG3/Ot8N2NBUZEBF0K4UMb7DinlPdbiXiHECmv9CqCvPk2cP2xFwt6hcSpo6WOsy4gZaW+AUlRckRWMGQYb00pTOhxqZU1GcDpTEM761VMqUWjryhfN4htoNJqlTCVRKAK4DTggpRxffuQ+4Hrr9fXAj2vfvPnF1hMfHD0DqLC/w+4UG5mdVGWbGQBgm7OgoLdaNnPClcz72x8ZfpiVGckbLn/PrI6l0WiWLpVEobwEeCfwrBBir7Xs/wCfA74vhHgvcAL407q0cB5pC6+C0zAcV4OLh3b/gDHDYGvrJbP63HZXO3CMl299S37ZuuAWHskMsffQ74jGh3jek+PNYntZnXGNRrN8KWvApZSPAmKK1VfWtjkLi4mKhLuP/gKAKy+cvvpOOa57yd/hf/yrXH3JdfllO9a9HA4/xhPP/4zn+h/H5ZK885WfmNVxNBrN0mbR6YHPJbYi4QvRZ/nX73+Yp5P7WW1Izlo3u+IKO89+KTvPfmnRspfu/COchz7Pgf7HedzRzYWpBjau2Tar42g0mqWNNuDT0Bhspi1r8rg3zuNjD4EHrkzPvEzbdIQCYdalDR5x9ZA2DF6zbmZRLhqNZvmgDXgZvnvt/ZzqOZR/v2PLZXU71iqaOWIMsiYt+eNXzKxIgUajWT5oA16GjpZVdLTUtr7eVKwPnc1v0r/jxd4LdWEBjUZTFq1GuID405f/DVdlVvD+1/3TfDdFo9EsAnQPfAGxbuVZ/Mv77p/vZmg0mkWC7oFrNBrNIkUbcI1Go1mkaAOu0Wg0ixRtwDUajWaRog24RqPRLFK0AddoNJpFijbgGo1Gs0jRBlyj0WgWKUJVQ5ujgwnRj9IOnwmtwEANm7NYWI7fezl+Z1ie31t/58pYJ6WcVJNyTg34bBBC7JZS7prvdsw1y/F7L8fvDMvze+vvPDu0C0Wj0WgWKdqAazQazSJlMRnwW+a7AfPEcvzey/E7w/L83vo7z4JF4wPXaDQaTTGLqQeu0Wg0mnFoA67RaDSLlEVhwIUQrxFCvCCEOCyE+Nh8t6ceCCHWCCEeEkI8J4TYL4T4S2t5sxDiASHEIet/03y3tdYIIRxCiKeEED+x3m8QQjxmne/vCSHc893GWiOECAshfiiEeF4IcUAIcdlSP9dCiP9tXdv7hBDfFUJ4l+K5FkJ8QwjRJ4TYN25ZyXMrFP9uff9nhBAXVnOsBW/AhRAO4D+B1wLnAn8mhDh3fltVF7LA30gpzwUuBT5kfc+PAQ9KKbcAD1rvlxp/CRwY9/7zwL9IKTcDw8B756VV9eXfgJ9LKbcC56O+/5I910KIVcCHgV1Syu2AA3grS/Ncfwt4zYRlU53b1wJbrL8bga9Uc6AFb8CBS4DDUsqjUso0cBdwzTy3qeZIKbullE9ar6OoG3oV6rvebm12O3DtvDSwTgghVgOvB75uvRfAK4EfWpssxe/cCLwMuA1ASpmWUkZY4ucaVcLRJ4RwAn6gmyV4rqWUvwGGJiye6txeA3xbKv4AhIUQKyo91mIw4KuAU+Ped1nLlixCiPXABcBjQIeUstta1QN0zFe76sS/An8HmNb7FiAipcxa75fi+d4A9APftFxHXxdCBFjC51pKeRq4GTiJMtwjwB6W/rm2merczsq+LQYDvqwQQgSBu4G/klKOjl8nVcznkon7FEK8AeiTUu6Z77bMMU7gQuArUsoLgDgT3CVL8Fw3oXqbG4CVQIDJboZlQS3P7WIw4KeBNePer7aWLTmEEC6U8b5DSnmPtbjXHlJZ//vmq3114CXAG4UQx1GusVeifMNha5gNS/N8dwFdUsrHrPc/RBn0pXyurwKOSSn7pZQZ4B7U+V/q59pmqnM7K/u2GAz4E8AWa7bajZr4uG+e21RzLN/vbcABKeWXxq26D7jeen098OO5blu9kFL+vZRytZRyPeq8/kpK+XbgIeDN1mZL6jsDSCl7gFNCiLOtRVcCz7GEzzXKdXKpEMJvXev2d17S53ocU53b+4B3WdEolwIj41wt5ZFSLvg/4HXAQeAI8PH5bk+dvuNLUcOqZ4C91t/rUD7hB4FDwC+B5vlua52+/yuAn1ivNwKPA4eBHwCe+W5fHb7vTmC3db7vBZqW+rkGPgM8D+wD/gvwLMVzDXwX5efPoEZb753q3AICFWV3BHgWFaVT8bF0Kr1Go9EsUhaDC0Wj0Wg0JdAGXKPRaBYp2oBrNBrNIkUbcI1Go1mkaAOu0Wg0ixRtwDULGku174PW65VCiB+W22cWx9ophHhdvT5fo6k12oBrFjph4IMAUsozUso3T7/5rNiJir3XaBYFOg5cs6ARQtjqky+gkiDOkVJuF0K8G6XoFkBJcd4MuIF3AingdVLKISHEJlSiRBuQAN4vpXxeCPEW4FNADiWsdBUqmcSHSmX+J+AYKrXfC4wBN0gpX6ji2A8DTwMvR+mfvEdK+Xg9fifNMmW+s5b0n/6b7g9YD+wr8frdKIMbQhnnEeAD1rp/QYmBgcp+22K9fhEqXR9U1tsq63V43Gf+x7hjNwBO6/VVwN1VHvth4Fbr9cvstus//VerP1tERqNZjDwklXZ6VAgxAvy3tfxZYIel7Phi4AdKfgNQ6dsAvwW+JYT4PkpYqRSNwO1CiC0omQNXpccet913QWlECyEahBBhqbS/NZpZow24ZjGTGvfaHPfeRF3bBkpveufEHaWUHxBCvAhVTGKPEOKiEp///1CG+k2WRvvDVRw7f6iJh57m+2g0VaEnMTULnSjKVVE1UumpH7P83Xb9wfOt15uklI9JKT+JKq6wpsSxGilIe757Zs3nOut4L0UpzY3M8HM0mkloA65Z0EgpB4HfWgVivziDj3g78F4hxNPAfgrl+L4ohHjW+tzfoSYbHwLOFULsFUJcB3wB+CchxFPMfLSatPb/Kkuj3qNmAaGjUDSaOmFFoXxESrl7vtuiWZroHrhGo9EsUnQPXKPRaBYpugeu0Wg0ixRtwDUajWaRog24RqPRLFK0AddoNJpFijbgGo1Gs0j5/wMpTI/9Jw7ZNwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "test_df.groupby(by=\"timestamp\").count().iloc[:100].plot()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "78537d48", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "95508 23\n", + "95484 21\n", + "95497 21\n", + "96458 20\n", + "95522 20\n", + " ..\n", + "303695 1\n", + "656192 1\n", + "656189 1\n", + "656188 1\n", + "656142 1\n", + "Name: timestamp, Length: 312, dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "test_df[test_df[\"user_id\"] == 1941].timestamp.value_counts()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "68c393ef", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████| 89396/89396 [00:21<00:00, 4154.59it/s]\n" + ] + } + ], "source": [ "data = {}\n", "\n", @@ -241,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "a5f916bf", "metadata": {}, "outputs": [], @@ -269,7 +407,7 @@ " pred = predict_user_movie_rating(user_features[uid], movie_features[mid])\n", " error = abs(pred - rating) #error*error # square \n", " \n", - " if uid not in past_updates and policy == \"total_error_cold\": # unseen keys start with high total error\n", + " if uid not in self.past_updates and self.policy == \"total_error_cold\": # unseen keys start with high total error\n", " self.total_error[uid] = 1000000\n", " else:\n", " # update per key \n", @@ -333,8 +471,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "a90806f5", + "execution_count": 22, + "id": "22bb6bee", "metadata": {}, "outputs": [], "source": [ @@ -346,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "7990d3ae", "metadata": {}, "outputs": [], @@ -403,7 +541,7 @@ " if uid is None: \n", " print(f\"{ts}: No updates in queue\")\n", " break \n", - " user_features[uid] = update_user(uid, ratings, d)\n", + " user_features[uid] = update_user(uid, ratings, movie_features, d)\n", " update_times[uid].append(ts)\n", " #print(\"update\", uid)\n", " \n", @@ -426,37 +564,10 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "2a969e53", - "metadata": {}, - "outputs": [], - "source": [ - "experiment(\"total_error\", 1, result_dir, 100)" - ] - }, - { - "cell_type": "code", - "execution_count": 337, - "id": "35429601", + "execution_count": 33, + "id": "6e8b51d1", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6041 1772\n", - "6041 1772\n", - "waiting\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 0%| | 0/10000 [00:00, {4268: [], 4269: [], 4271: 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[], 4174: [], 4173: [], 4172: [], 5689: [], 4171: [], 4170: [], 4167: [], 4169: [], 4165: [], 4166: [], 4168: [], 4164: [], 4163: [], 4162: [], 4161: [], 4387: [], 4160: [], 4185: [], 4159: [], 4158: [], 4157: [], 4155: [], 4156: [], 4318: [], 4154: [], 4335: [], 4153: [], 4751: [], 4152: [], 4151: [], 4150: [], 4386: [], 5433: [], 5233: [], 4149: [], 5175: [], 4148: [], 4146: [], 4147: [], 4145: [], 4144: [], 4143: [], 4337: [], 4142: [], 4141: [], 4140: [], 4138: [], 4139: [], 4137: [], 4136: [], 4135: [], 4134: [], 4133: [], 4132: [], 4131: [], 4310: [], 4130: [], 4128: [], 4129: [], 4458: [], 4647: [], 4689: [], 4127: [], 4126: [], 4447: [], 4125: [], 4124: [], 4122: [], 4123: [], 4121: [], 5034: [], 4120: [], 4119: [], 4118: [], 4117: [], 4116: [], 4115: [], 4384: [], 5312: [], 4114: [], 4113: [], 4112: [], 4111: [], 4531: [], 4110: [], 4109: [], 4351: [], 4108: []})\n", + "729: No updates in queue\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 2/10000 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"756: No updates in queue\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\r", - " 0%| | 3/10000 [00:01<1:08:47, 2.42it/s]" + " 15%|███████▎ | 766/5000 [09:10<49:04, 1.44it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0.]\n", - "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0.]\n" + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 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queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 21%|█████████▋ | 1037/5000 [09:38<47:56, 1.38it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 21%|█████████▊ | 1042/5000 [09:42<46:04, 1.43it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 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16%|███████▊ | 808/5000 [09:44<54:03, 1.29it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 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queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 11%|█████▏ | 563/5000 [11:48<1:47:56, 1.46s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: 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33%|███████████████▋ | 1674/5000 [11:49<32:17, 1.72it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1255: No updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 24%|███████████▍ | 1213/5000 [11:49<48:00, 1.31it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: 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"1263: No updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 24%|███████████▍ | 1217/5000 [11:53<48:01, 1.31it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 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"stream", + "text": [ + " 24%|███████████▎ | 1205/5000 [13:24<25:19, 2.50it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 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44%|████████████████████▌ | 2187/5000 [14:47<20:43, 2.26it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 44%|████████████████████▋ | 2197/5000 [14:52<18:45, 2.49it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 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"text": [ + " 44%|████████████████████▋ | 2198/5000 [14:52<19:57, 2.34it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 25%|███████████▋ | 1239/5000 [14:53<50:39, 1.24it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 29%|█████████████▌ | 1440/5000 [15:00<25:04, 2.37it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: 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44%|████████████████████▋ | 2199/5000 [15:38<18:57, 2.46it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: 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31%|██████████████▍ | 1533/5000 [15:38<21:20, 2.71it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 26%|████████████▍ | 1324/5000 [15:53<45:51, 1.34it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: 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47%|█████████████████████▉ | 2338/5000 [15:54<20:07, 2.20it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: 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2339/5000 [15:54<20:18, 2.18it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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1332/5000 [15:59<48:49, 1.25it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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27%|████████████▌ | 1340/5000 [16:05<47:37, 1.28it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 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"output_type": "stream", + "text": [ + "\r", + " 14%|██████▌ | 690/5000 [16:09<57:05, 1.26it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 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" 31%|██████████████▌ | 1555/5000 [16:10<43:59, 1.31it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 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queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 45%|█████████████████████▍ | 2274/5000 [16:11<19:28, 2.33it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 46%|█████████████████████▍ | 2280/5000 [16:14<18:36, 2.44it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 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48%|██████████████████████▌ | 2395/5000 [16:19<18:51, 2.30it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 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27%|████████████▊ | 1367/5000 [16:25<46:34, 1.30it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 33%|███████████████▍ | 1646/5000 [16:26<23:25, 2.39it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: 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2421/5000 [16:30<18:45, 2.29it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 48%|██████████████████████▊ | 2425/5000 [16:32<19:19, 2.22it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: 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28%|█████████████ | 1384/5000 [16:34<54:15, 1.11it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 34%|███████████████▉ | 1696/5000 [16:46<21:39, 2.54it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 34%|███████████████▉ | 1697/5000 [16:47<22:13, 2.48it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: 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47%|██████████████████████▏ | 2366/5000 [16:51<19:51, 2.21it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 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" 33%|███████████████▎ | 1634/5000 [17:09<42:53, 1.31it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 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37%|█████████████████▌ | 1866/5000 [18:01<21:56, 2.38it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 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30%|██████████████▎ | 1524/5000 [18:17<47:41, 1.21it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 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2675/5000 [18:19<16:30, 2.35it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 39%|██████████████████▏ | 1931/5000 [18:29<22:04, 2.32it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 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[18:30<18:03, 2.23it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 35%|████████████████▌ | 1761/5000 [18:30<43:45, 1.23it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 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55%|█████████████████████████▉ | 2754/5000 [18:53<16:34, 2.26it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 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2.35it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 4191: [], 4190: [], 4189: 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[]})\n", + "1242: No updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 55%|█████████████████████████▋ | 2732/5000 [19:31<16:46, 2.25it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 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queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 57%|██████████████████████████▋ | 2840/5000 [19:32<15:45, 2.28it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: 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[19:36<22:06, 2.19it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1255: No updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 55%|█████████████████████████▊ | 2745/5000 [19:37<16:38, 2.26it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: 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[]})\n", + "1263: No updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 32%|███████████████▏ | 1616/5000 [19:43<46:14, 1.22it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 59%|███████████████████████████▌ | 2928/5000 [20:12<14:48, 2.33it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 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"stream", + "text": [ + " 33%|███████████████▌ | 1657/5000 [20:16<44:36, 1.25it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: 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[20:25<42:56, 1.29it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 4191: [], 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" 34%|████████████████▏ | 1718/5000 [20:41<40:06, 1.36it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: 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45%|█████████████████████▎ | 2262/5000 [20:51<17:38, 2.59it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: 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[20:52<46:22, 1.19it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 4191: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 61%|████████████████████████████▌ | 3043/5000 [21:04<16:06, 2.02it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 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+ { + "name": "stderr", + "output_type": "stream", + "text": [ + " 46%|█████████████████████▌ | 2294/5000 [21:05<20:39, 2.18it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: 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"output_type": "stream", + "text": [ + "\r", + " 34%|████████████████▏ | 1717/5000 [21:06<47:50, 1.14it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 59%|███████████████████████████▋ | 2948/5000 [21:06<14:31, 2.36it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 46%|█████████████████████▋ | 2302/5000 [21:09<19:12, 2.34it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 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"name": "stderr", + "output_type": "stream", + "text": [ + " 35%|████████████████▍ | 1754/5000 [21:09<43:25, 1.25it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 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59%|███████████████████████████▊ | 2960/5000 [21:11<14:01, 2.42it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 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59%|███████████████████████████▊ | 2962/5000 [21:12<14:16, 2.38it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 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2311/5000 [21:12<18:35, 2.41it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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[], 4089: []})\n", + "1255: No updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 35%|████████████████▎ | 1730/5000 [21:16<42:55, 1.27it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 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"1256: No updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 46%|█████████████████████▊ | 2322/5000 [21:17<18:03, 2.47it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 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in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 19%|█████████▏ | 955/5000 [21:18<46:54, 1.44it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 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1998/5000 [21:25<39:05, 1.28it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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"output_type": "stream", + "text": [ + " 63%|█████████████████████████████▍ | 3130/5000 [21:49<13:38, 2.28it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 20%|█████████ | 982/5000 [22:02<1:43:42, 1.55s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 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4119: [], 4118: [], 4117: [], 4116: [], 4115: [], 4384: [], 5312: [], 4114: [], 4113: [], 4112: [], 4111: [], 4531: [], 4110: [], 4109: [], 4351: [], 4108: [], 4322: [], 4277: [], 4107: [], 5359: [], 4106: [], 4105: [], 4104: [], 5267: [], 5096: [], 4334: [], 4103: [], 4102: [], 4101: [], 4100: [], 4099: [], 4098: [], 4097: [], 4096: [], 5629: [], 5654: [], 4523: [], 5782: [], 4095: [], 4094: [], 4093: [], 4092: [], 4784: [], 4091: [], 4090: [], 4089: [], 4088: [], 4087: [], 4509: []})\n", + "1295: No updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 20%|█████████ | 990/5000 [22:07<1:33:42, 1.40s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 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22%|█████████▋ | 1079/5000 [22:44<1:42:38, 1.57s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: 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"stream", + "text": [ + " 51%|████████████████████████ | 2558/5000 [22:59<17:40, 2.30it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: 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4079: [], 4077: [], 4078: [], 4076: [], 4075: [], 4298: [], 4074: [], 4592: []})" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 40%|██████████████████▋ | 1993/5000 [24:17<39:46, 1.26it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "1466: No updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 22%|█████████▋ | 1081/5000 [24:17<1:25:10, 1.30s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 23%|██████████▊ | 1149/5000 [24:17<51:16, 1.25it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 68%|███████████████████████████████▉ | 3396/5000 [24:20<12:02, 2.22it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 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3400/5000 [24:22<12:10, 2.19it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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"name": "stderr", + "output_type": "stream", + "text": [ + " 39%|██████████████████▍ | 1967/5000 [24:31<45:20, 1.11it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: 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55%|██████████████████████████ | 2772/5000 [24:32<16:40, 2.23it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 56%|██████████████████████████▏ | 2781/5000 [24:36<17:37, 2.10it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 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3045/5000 [26:32<14:40, 2.22it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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"2734: No updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 50%|███████████████████████▋ | 2514/5000 [26:37<16:05, 2.57it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: 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74%|██████████████████████████████████▋ | 3691/5000 [26:46<20:00, 1.09it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 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51%|████████████████████████ | 2560/5000 [26:56<18:04, 2.25it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 26%|████████████▎ | 1314/5000 [26:56<51:05, 1.20it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: 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3728/5000 [26:57<09:41, 2.19it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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63%|█████████████████████████████▍ | 3130/5000 [27:09<13:50, 2.25it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: 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75%|███████████████████████████████████▎ | 3760/5000 [27:43<17:41, 1.17it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 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[28:09<37:16, 1.20it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 4191: [], 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3912/5000 [28:19<07:51, 2.31it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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1306/5000 [28:24<47:01, 1.31it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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2796/5000 [28:37<15:58, 2.30it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 4193: [], 4350: [], 4192: [], 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79%|█████████████████████████████████████▎ | 3963/5000 [28:41<07:31, 2.30it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 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56%|██████████████████████████▍ | 2817/5000 [28:47<16:35, 2.19it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 4195: [], 4194: [], 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"text": [ + "\r", + " 80%|█████████████████████████████████████▌ | 3998/5000 [28:57<07:11, 2.32it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1754: No updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 77%|████████████████████████████████████▏ | 3847/5000 [28:57<18:06, 1.06it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 68%|███████████████████████████████▋ | 3376/5000 [28:58<12:21, 2.19it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 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updates in queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 78%|████████████████████████████████████▋ | 3907/5000 [29:48<15:29, 1.18it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 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"stream", + "text": [ + " 30%|█████████████▎ | 1479/5000 [30:01<1:23:58, 1.43s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates no updatesdefaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: 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93%|███████████████████████████████████████████▋ | 4651/5000 [38:37<02:40, 2.17it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: [], 4203: [], 4201: [], 4200: [], 4199: [], 4198: [], 4416: [], 4197: [], 4196: [], 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queue\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 94%|███████████████████████████████████████████▉ | 4680/5000 [42:05<02:12, 2.42it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no updates defaultdict(, {4268: [], 4269: [], 4271: [], 4274: [], 4267: [], 4266: [], 4265: [], 4264: [], 4263: [], 4262: [], 4259: [], 4260: [], 4261: [], 4258: [], 4256: [], 4257: [], 4255: [], 4254: [], 4315: [], 4344: [], 4252: [], 4253: [], 4251: [], 4250: [], 4249: [], 4248: [], 4247: [], 4245: [], 4246: [], 4244: [], 4242: [], 4241: [], 4240: [], 4239: [], 4238: [], 4243: [], 4237: [], 4236: [], 4235: [], 4233: [], 4234: [], 4232: [], 4231: [], 4230: [], 5653: [], 4229: [], 4225: [], 4226: [], 4227: [], 4228: [], 4224: [], 4222: [], 4223: [], 5289: [], 4221: [], 4220: [], 4219: [], 4218: [], 4217: [], 4216: [], 4215: [], 4214: [], 4213: [], 4212: [], 4211: [], 4210: [], 4208: [], 4209: [], 4205: [], 4206: [], 4207: [], 4204: [], 4202: 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"stderr", "output_type": "stream", "text": [ - " 0%| | 13/10000 [00:05<1:15:22, 2.21it/s]\n", - " 0%| | 11/10000 [00:05<1:26:06, 1.93it/s]\n", - " 0%| | 6/10000 [00:05<2:41:05, 1.03it/s]\n", - " 0%| | 13/10000 [00:05<1:13:04, 2.28it/s]\n", - " 0%| | 6/10000 [00:06<2:49:11, 1.02s/it]\n", - " 0%| | 13/10000 [00:06<1:17:29, 2.15it/s]\n", - "Process ForkProcess-89:\n", - "Exception ignored in: Exception ignored in: Process ForkProcess-91:\n" + "100%|█████████████████████████████████████████████| 5000/5000 [1:32:23<00:00, 1.11s/it]\n" ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [337]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m: \n\u001b[0;32m---> 18\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "File \u001b[0;32m~/miniconda3/lib/python3.9/concurrent/futures/_base.py:440\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__get_result()\n\u001b[0;32m--> 440\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_condition\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", - "File \u001b[0;32m~/miniconda3/lib/python3.9/threading.py:312\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 313\u001b[0m gotit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: ", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [337]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 21\u001b[0m res \u001b[38;5;241m=\u001b[39m concurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mwait(futures)\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdone\u001b[39m\u001b[38;5;124m\"\u001b[39m, res)\n\u001b[0;32m---> 23\u001b[0m executor\u001b[38;5;241m.\u001b[39mshutdown()\n", - "File \u001b[0;32m~/miniconda3/lib/python3.9/concurrent/futures/_base.py:636\u001b[0m, in \u001b[0;36mExecutor.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, exc_type, exc_val, exc_tb):\n\u001b[0;32m--> 636\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshutdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwait\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 637\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/lib/python3.9/concurrent/futures/process.py:740\u001b[0m, in \u001b[0;36mProcessPoolExecutor.shutdown\u001b[0;34m(self, wait, cancel_futures)\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_executor_manager_thread_wakeup\u001b[38;5;241m.\u001b[39mwakeup()\n\u001b[1;32m 739\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_executor_manager_thread \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m wait:\n\u001b[0;32m--> 740\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_executor_manager_thread\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 741\u001b[0m \u001b[38;5;66;03m# To reduce the risk of opening too many files, remove references to\u001b[39;00m\n\u001b[1;32m 742\u001b[0m \u001b[38;5;66;03m# objects that use file descriptors.\u001b[39;00m\n\u001b[1;32m 743\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_executor_manager_thread \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/lib/python3.9/threading.py:1053\u001b[0m, in \u001b[0;36mThread.join\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1050\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot join current thread\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1052\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1053\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wait_for_tstate_lock\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1054\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1055\u001b[0m \u001b[38;5;66;03m# the behavior of a negative timeout isn't documented, but\u001b[39;00m\n\u001b[1;32m 1056\u001b[0m \u001b[38;5;66;03m# historically .join(timeout=x) for x<0 has acted as if timeout=0\u001b[39;00m\n\u001b[1;32m 1057\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wait_for_tstate_lock(timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mmax\u001b[39m(timeout, \u001b[38;5;241m0\u001b[39m))\n", - "File \u001b[0;32m~/miniconda3/lib/python3.9/threading.py:1069\u001b[0m, in \u001b[0;36mThread._wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m 1067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lock \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \u001b[38;5;66;03m# already determined that the C code is done\u001b[39;00m\n\u001b[1;32m 1068\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_stopped\n\u001b[0;32m-> 1069\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[43mlock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 1070\u001b[0m lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m 1071\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stop()\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Process ForkProcess-90:\n", - "Process ForkProcess-87:\n", - "Process ForkProcess-84:\n", - "Process ForkProcess-85:\n", - "Exception ignored in: Exception ignored in: \n", - "\n", - "Traceback (most recent call last):\n", - "\n", - "\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 315, in _bootstrap\n", - " self.run()\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - "Traceback (most recent call last):\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 315, in _bootstrap\n", - " self.run()\n", - "Traceback (most recent call last):\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 1195, in __iter__\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 315, in _bootstrap\n", - " self.run()\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 1195, in __iter__\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 1195, in __iter__\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 315, in _bootstrap\n", - " self.run()\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 315, in _bootstrap\n", - " self.run()\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 1195, in __iter__\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 315, in _bootstrap\n", - " self.run()\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/concurrent/futures/process.py\", line 237, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/process.py\", line 108, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/concurrent/futures/process.py\", line 237, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/concurrent/futures/process.py\", line 237, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/concurrent/futures/process.py\", line 237, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - "self.close()self.close() File \"/home/eecs/wooders/miniconda3/lib/python3.9/concurrent/futures/process.py\", line 237, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/concurrent/futures/process.py\", line 237, in _process_worker\n", - " call_item = call_queue.get(block=True)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - "self.close() File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - "\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/queues.py\", line 103, in get\n", - " res = self._recv_bytes()\n", - "\n", - "self.close() File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/queues.py\", line 102, in get\n", - " with self._rlock:\n", - "KeyboardInterrupt\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - "\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 1274, in close\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/connection.py\", line 221, in recv_bytes\n", - " buf = self._recv_bytes(maxlength)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 1274, in close\n", - "\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/synchronize.py\", line 95, in __enter__\n", - " return self._semlock.__enter__()\n", - "KeyboardInterrupt\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 1274, in close\n", - "KeyboardInterrupt\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/connection.py\", line 419, in _recv_bytes\n", - " buf = self._recv(4)\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 1274, in close\n", - "KeyboardInterrupt\n", - " KeyboardInterrupt\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/multiprocessing/connection.py\", line 384, in _recv\n", - " chunk = read(handle, remaining)\n", - " self._decr_instances(self)self._decr_instances(self) self._decr_instances(self)KeyboardInterrupt\n", - "\n", - "self._decr_instances(self)\n", - "\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 593, in _decr_instances\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 593, in _decr_instances\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 593, in _decr_instances\n", - "\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 593, in _decr_instances\n", - " with cls._lock:with cls._lock:with cls._lock: \n", - "\n", - "\n", - "with cls._lock: File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 113, in __enter__\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 113, in __enter__\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 113, in __enter__\n", - "\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 113, in __enter__\n", - " self.acquire()self.acquire()self.acquire()\n", - "\n", - " \n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 106, in acquire\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 106, in acquire\n", - "self.acquire() File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 106, in acquire\n", - "\n", - " File \"/home/eecs/wooders/miniconda3/lib/python3.9/site-packages/tqdm/std.py\", line 106, in acquire\n", - " lock.acquire(*a, **k)lock.acquire(*a, **k)lock.acquire(*a, **k)\n", - "\n", - " \n", - "KeyboardInterruptKeyboardInterruptlock.acquire(*a, **k)KeyboardInterrupt: : \n", - ": \n", - "\n", - "KeyboardInterrupt\n", - ": \n" + "name": "stdout", + "output_type": "stream", + "text": [ + "done DoneAndNotDoneFutures(done={, , , , , , , , , }, not_done=set())\n" ] } ], @@ -1860,7 +127146,7 @@ }, { "cell_type": "code", - "execution_count": 317, + "execution_count": 78, "id": "68d54144", "metadata": {}, "outputs": [ @@ -1896,51 +127182,251 @@ " 0\n", " 1\n", " total_error\n", - " 4.735482\n", + " 3.511024\n", " 100\n", " \n", " \n", " 1\n", + " 2\n", + " total_error\n", + " 2.283483\n", + " 200\n", + " \n", + " \n", + " 2\n", + " 4\n", + " total_error\n", + " 1.767486\n", + " 395\n", + " \n", + " \n", + " 3\n", + " 8\n", + " total_error\n", + " 1.695238\n", + " 615\n", + " \n", + " \n", + " 4\n", + " 10\n", + " total_error\n", + " 1.695236\n", + " 631\n", + " \n", + " \n", + " 5\n", + " 100\n", + " total_error\n", + " 1.695235\n", + " 636\n", + " \n", + " \n", + " 6\n", " 1\n", " total_error_cold\n", - " 4.735482\n", + " 1.948788\n", " 100\n", " \n", " \n", - " 2\n", + " 7\n", + " 2\n", + " total_error_cold\n", + " 1.748832\n", + " 200\n", + " \n", + " \n", + " 8\n", + " 4\n", + " total_error_cold\n", + " 1.712741\n", + " 395\n", + " \n", + " \n", + " 9\n", + " 8\n", + " total_error_cold\n", + " 1.695238\n", + " 615\n", + " \n", + " \n", + " 10\n", + " 10\n", + " total_error_cold\n", + " 1.695236\n", + " 631\n", + " \n", + " \n", + " 11\n", + " 100\n", + " total_error_cold\n", + " 1.695235\n", + " 636\n", + " \n", + " \n", + " 12\n", " 1\n", " max_pending\n", - " 6.706238\n", + " 6.070381\n", " 100\n", " \n", " \n", - " 3\n", + " 13\n", + " 2\n", + " max_pending\n", + " 4.204328\n", + " 200\n", + " \n", + " \n", + " 14\n", + " 4\n", + " max_pending\n", + " 2.480546\n", + " 395\n", + " \n", + " \n", + " 15\n", + " 8\n", + " max_pending\n", + " 1.716084\n", + " 614\n", + " \n", + " \n", + " 16\n", + " 10\n", + " max_pending\n", + " 1.695236\n", + " 632\n", + " \n", + " \n", + " 17\n", + " 100\n", + " max_pending\n", + " 1.695235\n", + " 636\n", + " \n", + " \n", + " 18\n", " 1\n", " min_past\n", - " 4.984848\n", + " 4.288212\n", " 100\n", " \n", " \n", - " 4\n", + " 19\n", + " 2\n", + " min_past\n", + " 2.682922\n", + " 200\n", + " \n", + " \n", + " 20\n", + " 4\n", + " min_past\n", + " 1.716769\n", + " 394\n", + " \n", + " \n", + " 21\n", + " 8\n", + " min_past\n", + " 1.695228\n", + " 612\n", + " \n", + " \n", + " 22\n", + " 10\n", + " min_past\n", + " 1.695236\n", + " 632\n", + " \n", + " \n", + " 23\n", + " 100\n", + " min_past\n", + " 1.695235\n", + " 636\n", + " \n", + " \n", + " 24\n", " 1\n", " round_robin\n", - " 7.127969\n", + " 6.563035\n", + " 100\n", + " \n", + " \n", + " 25\n", + " 2\n", + " round_robin\n", + " 4.006099\n", + " 200\n", + " \n", + " \n", + " 26\n", + " 4\n", + " round_robin\n", + " 2.294047\n", + " 395\n", + " \n", + " \n", + " 27\n", + " 8\n", + " round_robin\n", + " 1.751973\n", + " 612\n", + " \n", + " \n", + " 28\n", + " 10\n", + " round_robin\n", + " 1.716081\n", + " 631\n", + " \n", + " \n", + " 29\n", " 100\n", + " round_robin\n", + " 1.695235\n", + " 636\n", " \n", " \n", "\n", "" ], "text/plain": [ - " updates policy error total_updates\n", - "0 1 total_error 4.735482 100\n", - "1 1 total_error_cold 4.735482 100\n", - "2 1 max_pending 6.706238 100\n", - "3 1 min_past 4.984848 100\n", - "4 1 round_robin 7.127969 100" + " updates policy error total_updates\n", + "0 1 total_error 3.511024 100\n", + "1 2 total_error 2.283483 200\n", + "2 4 total_error 1.767486 395\n", + "3 8 total_error 1.695238 615\n", + "4 10 total_error 1.695236 631\n", + "5 100 total_error 1.695235 636\n", + "6 1 total_error_cold 1.948788 100\n", + "7 2 total_error_cold 1.748832 200\n", + "8 4 total_error_cold 1.712741 395\n", + "9 8 total_error_cold 1.695238 615\n", + "10 10 total_error_cold 1.695236 631\n", + "11 100 total_error_cold 1.695235 636\n", + "12 1 max_pending 6.070381 100\n", + "13 2 max_pending 4.204328 200\n", + "14 4 max_pending 2.480546 395\n", + "15 8 max_pending 1.716084 614\n", + "16 10 max_pending 1.695236 632\n", + "17 100 max_pending 1.695235 636\n", + "18 1 min_past 4.288212 100\n", + "19 2 min_past 2.682922 200\n", + "20 4 min_past 1.716769 394\n", + "21 8 min_past 1.695228 612\n", + "22 10 min_past 1.695236 632\n", + "23 100 min_past 1.695235 636\n", + "24 1 round_robin 6.563035 100\n", + "25 2 round_robin 4.006099 200\n", + "26 4 round_robin 2.294047 395\n", + "27 8 round_robin 1.751973 612\n", + "28 10 round_robin 1.716081 631\n", + "29 100 round_robin 1.695235 636" ] }, - "execution_count": 317, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -1969,88 +127455,83 @@ }, { "cell_type": "code", - "execution_count": 318, - "id": "656312b3", - "metadata": {}, - "outputs": [], - "source": [ - "window_error = pd.DataFrame()" - ] - }, - { - "cell_type": "code", - "execution_count": 319, - "id": "9a262740", + "execution_count": 71, + "id": "72f28503", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1 max_pending\n" + "1 max_pending\n", + "1 min_past\n", + "1 round_robin\n", + "1 total_error\n", + "1 total_error_cold\n", + "2 max_pending\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "4144it [00:00, 5438.91it/s]\n" + "10000it [00:00, 16832.76it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "1 min_past\n" + "2 min_past\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "4144it [00:00, 5443.14it/s]\n" + "10000it [00:00, 17093.20it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "1 round_robin\n" + "2 round_robin\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "4144it [00:00, 5428.82it/s]\n" + "10000it [00:00, 16884.50it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "1 total_error\n" + "2 total_error\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "4144it [00:00, 5687.86it/s]\n" + "10000it [00:00, 17054.32it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "1 total_error_cold\n" + "2 total_error_cold\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "4144it [00:00, 5496.07it/s]\n" + "10000it [00:00, 16875.01it/s]\n" ] }, { @@ -2084,43 +127565,43 @@ " \n", " \n", " 0\n", - " 2.752320\n", - " 1\n", + " 2.209699\n", + " 2\n", " max_pending\n", " 0\n", " 3\n", " \n", " \n", " 0\n", - " 2.999977\n", - " 1\n", + " 3.415470\n", + " 2\n", " max_pending\n", - " 0\n", " 3\n", + " 6\n", " \n", " \n", " 0\n", - " 2.213762\n", - " 1\n", + " 1.939810\n", + " 2\n", " max_pending\n", - " 0\n", - " 3\n", + " 6\n", + " 8\n", " \n", " \n", " 0\n", - " 2.562452\n", - " 1\n", + " 3.062870\n", + " 2\n", " max_pending\n", - " 0\n", - " 4\n", + " 8\n", + " 12\n", " \n", " \n", " 0\n", - " 2.735813\n", - " 1\n", + " 3.306425\n", + " 2\n", " max_pending\n", - " 1\n", - " 4\n", + " 12\n", + " 15\n", " \n", " \n", " ...\n", @@ -2132,67 +127613,67 @@ " \n", " \n", " 0\n", - " 4.562074\n", - " 1\n", + " 2.973199\n", + " 2\n", " total_error_cold\n", - " 95\n", - " 98\n", + " 245\n", + " 247\n", " \n", " \n", " 0\n", - " 5.691296\n", - " 1\n", + " 1.127414\n", + " 2\n", " total_error_cold\n", - " 95\n", - " 99\n", + " 247\n", + " 249\n", " \n", " \n", " 0\n", - " 5.245053\n", - " 1\n", + " 1.078621\n", + " 2\n", " total_error_cold\n", - " 96\n", - " 99\n", + " 249\n", + " 251\n", " \n", " \n", " 0\n", - " 4.987265\n", - " 1\n", + " 2.504983\n", + " 2\n", " total_error_cold\n", - " 96\n", - " 99\n", + " 251\n", + " 254\n", " \n", " \n", " 0\n", - " 3.526618\n", - " 1\n", + " 1.290743\n", + " 2\n", " total_error_cold\n", - " 97\n", - " 99\n", + " 254\n", + " 257\n", " \n", " \n", "\n", - "

2025 rows × 5 columns

\n", + "

500 rows × 5 columns

\n", "" ], "text/plain": [ " error updates policy start_timestamp end_timestamp\n", - "0 2.752320 1 max_pending 0 3\n", - "0 2.999977 1 max_pending 0 3\n", - "0 2.213762 1 max_pending 0 3\n", - "0 2.562452 1 max_pending 0 4\n", - "0 2.735813 1 max_pending 1 4\n", + "0 2.209699 2 max_pending 0 3\n", + "0 3.415470 2 max_pending 3 6\n", + "0 1.939810 2 max_pending 6 8\n", + "0 3.062870 2 max_pending 8 12\n", + "0 3.306425 2 max_pending 12 15\n", ".. ... ... ... ... ...\n", - "0 4.562074 1 total_error_cold 95 98\n", - "0 5.691296 1 total_error_cold 95 99\n", - "0 5.245053 1 total_error_cold 96 99\n", - "0 4.987265 1 total_error_cold 96 99\n", - "0 3.526618 1 total_error_cold 97 99\n", + "0 2.973199 2 total_error_cold 245 247\n", + "0 1.127414 2 total_error_cold 247 249\n", + "0 1.078621 2 total_error_cold 249 251\n", + "0 2.504983 2 total_error_cold 251 254\n", + "0 1.290743 2 total_error_cold 254 257\n", "\n", - "[2025 rows x 5 columns]" + "[500 rows x 5 columns]" ] }, - "execution_count": 319, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -2200,16 +127681,17 @@ "source": [ "#df_all.groupby(by=[\"updates\", \"policy\"]).rolling(window=50).apply(lambda w: print(w))\n", "\n", - "#window_error = pd.DataFrame()\n", + "window_error = pd.DataFrame()\n", + "wsize = 100\n", "for (updates, policy), group in df_all.groupby(by=[\"updates\", \"policy\"]):\n", " print(updates, policy)\n", - " if updates != 1: continue\n", - " for w in tqdm(group.iloc[:5000].rolling(window=100)):\n", + " if updates != 2: continue\n", + " for w in tqdm(group.iloc[:10000].rolling(window=wsize)):\n", " \n", " start_ts = w.index.min()\n", " end_ts = w.index.max()\n", " #print(start_ts, end_ts)\n", - " if start_ts % 10 != 0 or end_ts - start_ts + 1 < 100: continue \n", + " if start_ts % wsize != 0 or end_ts - start_ts + 1 < wsize: continue \n", " \n", " e = mean_squared_error(w.y_pred, w.y_true)\n", " wdf = pd.DataFrame({\"error\": [e], \"updates\": [updates], \"policy\": [policy], \"start_timestamp\": [w.timestamp.min()], \"end_timestamp\": [w.timestamp.max()]})\n", @@ -2223,22 +127705,22 @@ }, { "cell_type": "code", - "execution_count": 320, - "id": "c8b89d94", + "execution_count": 72, + "id": "7cc1c9fa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "max_pending 405\n", - "min_past 405\n", - "round_robin 405\n", - "total_error 405\n", - "total_error_cold 405\n", + "max_pending 100\n", + "min_past 100\n", + "round_robin 100\n", + "total_error 100\n", + "total_error_cold 100\n", "Name: policy, dtype: int64" ] }, - "execution_count": 320, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -2249,19 +127731,20 @@ }, { "cell_type": "code", - "execution_count": 321, - "id": "ccdf9aec", + "execution_count": 1, + "id": "44bb7080", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "NameError", + "evalue": "name 'window_error' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# plot lines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpolicy\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"max_pending\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"min_past\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"round_robin\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"total_error\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"total_error_cold\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwindow_error\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwindow_error\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"policy\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mpolicy\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_timestamp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwindow_error\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwindow_error\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"policy\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mpolicy\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpolicy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'window_error' is not defined" + ] } ], "source": [ @@ -2274,13 +127757,13 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", " \n", - "# create data\n", "\n", " \n", "# plot lines\n", - "for policy in [\"max_pending\", \"min_past\", \"round_robin\", \"total_error\"]:\n", - " x = window_error[window_error[\"policy\"] == policy].start_timestamp\n", - " plt.plot(x, window_error[window_error[\"policy\"] == policy].error.tolist(), label = policy)\n", + "for policy in [\"max_pending\", \"min_past\", \"round_robin\", \"total_error\", \"total_error_cold\"]:\n", + " x = window_error[window_error[\"policy\"] == policy].start_timestamp.tolist()\n", + " y = window_error[window_error[\"policy\"] == policy].error.tolist()\n", + " plt.plot(x[:10], y[:10], label = policy)\n", "\n", "plt.legend()\n", "plt.show()" @@ -2288,33 +127771,15 @@ }, { "cell_type": "code", - "execution_count": 222, - "id": "093e2807", + "execution_count": 74, + "id": "ba538609", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 5000\n", - "0 5010\n", - "0 5020\n", - "0 5030\n", - "0 5040\n", - " ... \n", - "0 9860\n", - "0 9870\n", - "0 9880\n", - "0 9890\n", - "0 9900\n", - "Name: start_timestamp, Length: 491, dtype: int64\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -2326,30 +127791,21 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", " \n", - "# create data\n", - "x = window_error[window_error[\"policy\"] == \"max_pending\"].iloc[500:].start_timestamp\n", - "print(x)\n", "\n", " \n", "# plot lines\n", - "for policy in [\"max_pending\", \"min_past\", \"round_robin\", \"total_error\"]:\n", - " plt.plot(x, window_error[window_error[\"policy\"] == policy].iloc[500:].error.tolist(), label = policy)\n", + "for policy in [\"max_pending\", \"min_past\", \"round_robin\", \"total_error\", \"total_error_cold\"]:\n", + " x = window_error[window_error[\"policy\"] == policy].start_timestamp.tolist()\n", + " y = window_error[window_error[\"policy\"] == policy].error.tolist()\n", + " plt.plot(x[10:50], y[10:50], label = policy)\n", "\n", "plt.legend()\n", "plt.show()" ] }, - { - "cell_type": "raw", - "id": "d09b75f1", - "metadata": {}, - "source": [ - "sns.lineplot(data=window_error, x=\"start_timestamp\", y=\"error\", hue=\"policy\", ci=None)" - ] - }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 75, "id": "657a46e1", "metadata": { "scrolled": true @@ -2357,175 +127813,43 @@ "outputs": [ { "data": { - "text/html": [ - "
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.....................
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\n", "text/plain": [ - " Unnamed: 0 policy runtime key i time\n", - "0 0 total_error 1.000 4274 0 0\n", - "1 1 total_error 1.000 4271 0 1\n", - "2 2 total_error 1.000 4271 1 2\n", - "3 3 total_error 1.000 4267 0 3\n", - "4 4 total_error 1.000 4267 1 4\n", - "... ... ... ... ... .. ...\n", - "4022 4022 round_robin 0.125 4085 1 1303\n", - "4023 4023 round_robin 0.125 4085 2 1304\n", - "4024 4024 round_robin 0.125 4085 3 1305\n", - "4025 4025 round_robin 0.125 4085 4 1306\n", - "4026 4026 round_robin 0.125 4085 5 1308\n", - "\n", - "[40144 rows x 6 columns]" + "
" ] }, - "execution_count": 109, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "updates_df" + "# importing package\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + " \n", + "\n", + " \n", + "# plot lines\n", + "for policy in [\"max_pending\", \"min_past\", \"round_robin\", \"total_error\", \"total_error_cold\"]:\n", + " x = window_error[window_error[\"policy\"] == policy].start_timestamp.tolist()\n", + " y = window_error[window_error[\"policy\"] == policy].error.tolist()\n", + " plt.plot(x[50:], y[50:], label = policy)\n", + "\n", + "plt.legend()\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 323, + "execution_count": 79, "id": "d6350762", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, "metadata": {}, diff --git a/nb/03-29-22-als_plots.ipynb b/nb/03-29-22-als_plots.ipynb new file mode 100644 index 0000000..5176211 --- /dev/null +++ b/nb/03-29-22-als_plots.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "bedcb268", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.metrics import mean_squared_error\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import sys \n", + "sys.path.insert(1, \"../\")\n", + "from workloads.util import use_results, use_dataset, read_config, log_dataset\n", + "\n", + "import seaborn as sns\n", + "sns.set_theme(style=\"whitegrid\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb5d482f", + "metadata": {}, + "outputs": [], + "source": [ + "experiment = \"ml-1m\"\n", + "dataset_dir = use_dataset(experiment)\n", + "result_dir = use_results(experiment, download=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2913b4b", + "metadata": {}, + "outputs": [], + "source": [ + "updates_per_ts = [1, 2, 4]\n", + "ts_factor = [10, 100] #, 1000]\n", + "policies = [\"total_error\", \"total_error_cold\", \"max_pending\", \"min_past\", \"round_robin\", \"last_query\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "753cb3b1", + "metadata": {}, + "outputs": [], + "source": [ + "updates_df = pd.DataFrame()\n", + "df_all = pd.DataFrame()\n", + "results = [] \n", + "\n", + "for p in policies: \n", + " for u in updates_per_ts: \n", + " for t in ts_factor: \n", + " update_df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_updates.csv\")\n", + " df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_results.csv\")\n", + " df[\"policy\"] = p\n", + " df[\"updates\"] = u \n", + " df[\"ts_factor\"] = t\n", + "\n", + " error = mean_squared_error(df.y_pred, df.y_true)\n", + "\n", + " total_updates = len(update_df.index)\n", + " results.append([u , p, error, total_updates, t])\n", + " updates_df = pd.concat([updates_df, update_df])\n", + " df_all = pd.concat([df_all, df])\n", + " \n", + "results_df = pd.DataFrame(results, columns=[\"updates\", \"policy\", \"error\", \"total_updates\", \"ts_factor\"])\n", + "results_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3020d09d", + "metadata": {}, + "outputs": [], + "source": [ + "plot_df = results_df[(results_df[\"ts_factor\"] == 100) & (results_df.policy.isin([\"total_error_cold\", \"min_past\", \"round_robin\", \"max_pending\", \"last_query\"]))]\n", + "plot_df.policy = plot_df.policy.map({\n", + " 'total_error_cold': 'Accuracy Optimized', \n", + " 'min_past': 'Minimum Past Updates', \n", + " 'round_robin': \"Round-Robin\", \n", + " 'max_pending': \"Maximum Pending Updates\", \n", + " 'last_query': \"Read-Optimized\"\n", + "})\n", + "plot_df = plot_df[plot_df[\"updates\"] <=4]\n", + "print(plot_df)\n", + "#sns.set(rc={'figure.figsize':(10.7,5.27)})\n", + "sns.set_theme(style=\"whitegrid\", font_scale=1.2)\n", + "\n", + "g = sns.barplot(x=\"updates\", y=\"error\", hue=\"policy\", data=plot_df)\n", + "g.set(title=\"Prediction Accuracy per Policy (Recommendation)\", xlabel=\"Updates per Timestep\", ylabel=\"MSE\")\n", + "#plt.legend(np.flip(plot_df['policy'].unique()), loc='upper right', title=\"Event Type\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f93439a7", + "metadata": {}, + "outputs": [], + "source": [ + "ax = sns.barplot(x=\"updates\", y=\"error\", hue=\"policy\", data=results_df[results_df[\"ts_factor\"] == 10])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b559447b", + "metadata": {}, + "outputs": [], + "source": [ + "runtime = 1.0\n", + "sns.set(rc={'figure.figsize':(10.7,5.27)})\n", + "hist_df = updates_df[updates_df[\"runtime\"] == runtime].groupby(by=[\"key\", \"policy\"]).size()\n", + "hist_df = hist_df.to_frame(name=\"updates\").reset_index()\n", + "hist_df[\"policy\"] = hist_df.policy.map({'total_error': 'Accuracy Optimized', 'min_past': 'Minimum Past Updates'})\n", + "\n", + "sns.set_theme(style=\"whitegrid\", font_scale=1.5)\n", + "g = sns.histplot(data=hist_df, x=\"updates\", hue=\"policy\", log_scale=(False, True), discrete=True)\n", + "g.set(title=\"Distribution of Updates Across Keys\", xlabel=\"Scheduled Updates per Key\", ylabel=\"Number Keys\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2135c0fd", + "metadata": {}, + "outputs": [], + "source": [ + "updates_df[(updates_df[\"policy\"] == \"total_error\") & (updates_df[\"runtime\"] == 1.00)]" + ] + }, + { + "cell_type": "markdown", + "id": "31c30365", + "metadata": {}, + "source": [ + "# Plot Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "432cb608", + "metadata": {}, + "outputs": [], + "source": [ + "factor = 100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86ad8ec1", + "metadata": {}, + "outputs": [], + "source": [ + "test_df = pd.read_csv(f'{dataset_dir}/stream.csv')\n", + "train_df = pd.read_csv(f'{dataset_dir}/train.csv')\n", + "start_ts = test_df.timestamp.min()\n", + "test_df.timestamp = test_df.timestamp.apply(lambda ts: int((ts - start_ts)/factor))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc5c7b6b", + "metadata": {}, + "outputs": [], + "source": [ + "test_df.groupby(by=\"timestamp\").user_id.nunique().plot() #.rolling(window=100).mean().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b3aa039", + "metadata": {}, + "outputs": [], + "source": [ + "test_df.groupby(by=\"timestamp\").count().plot()#.rolling(window=100).mean().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db992d7f", + "metadata": {}, + "outputs": [], + "source": [ + "train_df.movie_id.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44870c12", + "metadata": {}, + "outputs": [], + "source": [ + "test_df.movie_id.value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "99777003", + "metadata": {}, + "source": [ + "# Results Over Time " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0c7f9e6", + "metadata": {}, + "outputs": [], + "source": [ + "results[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c03eaa5e", + "metadata": {}, + "outputs": [], + "source": [ + "x0 = test_df.timestamp.min()\n", + "x1 = test_df.timestamp.max()\n", + "\n", + "p = \"max_pending\"\n", + "u = 1\n", + "t = 100\n", + "df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_results.csv\")\n", + "df[\"square_error\"] = df.y_pred - df.y_true\n", + "df[\"square_error\"] = df[\"square_error\"].apply(lambda x: x*x)\n", + "#df.groupby(by=\"timestamp\").square_error.mean().rolling(1000).mean().plot(xlim=(x0, x1))\n", + "\n", + "p = \"total_error_cold\"\n", + "df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_results.csv\")\n", + "df[\"square_error\"] = df.y_pred - df.y_true\n", + "df[\"square_error\"] = df[\"square_error\"].apply(lambda x: x*x)\n", + "df.groupby(by=\"timestamp\").square_error.mean().expanding().sum().plot(xlim=(x0, x1), label=p)\n", + "\n", + "\n", + "p = \"min_past\"\n", + "df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_results.csv\")\n", + "df[\"square_error\"] = df.y_pred - df.y_true\n", + "df[\"square_error\"] = df[\"square_error\"].apply(lambda x: x*x)\n", + "df.groupby(by=\"timestamp\").square_error.mean().expanding().sum().plot(xlim=(x0, x1), label=p).legend()\n", + "\n", + "p = \"max_pending\"\n", + "df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_results.csv\")\n", + "df[\"square_error\"] = df.y_pred - df.y_true\n", + "df[\"square_error\"] = df[\"square_error\"].apply(lambda x: x*x)\n", + "df.groupby(by=\"timestamp\").square_error.mean().expanding().sum().plot(xlim=(x0, x1), label=p).legend()\n", + "\n", + "p = \"round_robin\"\n", + "df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_results.csv\")\n", + "df[\"square_error\"] = df.y_pred - df.y_true\n", + "df[\"square_error\"] = df[\"square_error\"].apply(lambda x: x*x)\n", + "df.groupby(by=\"timestamp\").square_error.mean().expanding().sum().plot(xlim=(x0, x1), label=p).legend()\n", + "\n", + "\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e59201ab", + "metadata": {}, + "outputs": [], + "source": [ + "p = \"min_past\"\n", + "df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_results.csv\")\n", + "df[df[\"y_pred\"] > 10]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39fc347d", + "metadata": {}, + "outputs": [], + "source": [ + "x0 = test_df.timestamp.min()\n", + "x1 = 1000 #100000\n", + "\n", + "\n", + "p = \"min_past\"\n", + "df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_results.csv\")\n", + "df[\"square_error\"] = df.y_pred - df.y_true\n", + "df[\"square_error\"] = df[\"square_error\"].apply(lambda x: x*x)\n", + "df.groupby(by=\"timestamp\").square_error.mean().rolling(1).mean().plot(xlim=(x0, x1), label=p).legend()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1284900b", + "metadata": {}, + "outputs": [], + "source": [ + "p = \"total_error_cold\"\n", + "df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_results.csv\")\n", + "df[\"square_error\"] = df.y_pred - df.y_true\n", + "df[\"square_error\"] = df[\"square_error\"].apply(lambda x: x*x)\n", + "df.groupby(by=\"timestamp\").square_error.mean().rolling(1).mean().plot(xlim=(x0, x1), label=p).legend()\n", + "#df[df[\"square_error\"] > 100]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ef30734", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "movie_features = pickle.load(open(f\"{result_dir}/train_movie_features.pkl\", \"rb\"))\n", + "user_features = pickle.load(open(f\"{result_dir}/train_user_features.pkl\", \"rb\"))\n", + "past_updates = pickle.load(open(f\"{result_dir}/past_updates.pkl\", \"rb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ddba3c3", + "metadata": {}, + "outputs": [], + "source": [ + "from workloads.recsys.stream_als import predict_user_movie_rating" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bbc366fc", + "metadata": {}, + "outputs": [], + "source": [ + "movie_features[481][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e39a0143", + "metadata": {}, + "outputs": [], + "source": [ + "predict_user_movie_rating(user_features[4312], movie_features[481])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2d08f01", + "metadata": {}, + "outputs": [], + "source": [ + "train_df[train_df[\"movie_id\"] == 481]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "071d519f", + "metadata": {}, + "outputs": [], + "source": [ + "test_df.groupby(by=\"timestamp\").user_id.nunique().rolling(window=1000).mean().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f259176", + "metadata": {}, + "outputs": [], + "source": [ + "test_df.groupby(by=\"timestamp\").user_id.nunique().plot(xlim=(x0, x1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec1925ea", + "metadata": {}, + "outputs": [], + "source": [ + "test_df.groupby(by=\"timestamp\").user_id.nunique().rolling(window=1000).mean().plot(xlim=(x0, x1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "edf89d30", + "metadata": {}, + "outputs": [], + "source": [ + "test_df.groupby(by=\"timestamp\").count().plot(xlim=(x0, x1))#.rolling(window=100).mean().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "442d7d84", + "metadata": {}, + "outputs": [], + "source": [ + "test_df.groupby(by=\"timestamp\").count().rolling(window=1).mean().plot(xlim=(x0, x1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2ddefe5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workloads/recsys/stream_als.py b/workloads/recsys/stream_als.py index 802722b..0782db4 100644 --- a/workloads/recsys/stream_als.py +++ b/workloads/recsys/stream_als.py @@ -4,9 +4,8 @@ from scipy.sparse import dok_matrix from sklearn.linear_model import Ridge, RidgeCV from sklearn.metrics import mean_squared_error -from pyspark.sql import SparkSession -from pyspark.ml.evaluation import RegressionEvaluator -from pyspark.ml.recommendation import ALS + +import time from sklearn.metrics import mean_squared_error @@ -75,6 +74,7 @@ def __init__(self, num_keys, policy, past_updates): self.past_updates = past_updates self.queue = defaultdict(list) self.staleness = defaultdict(lambda: 0) + self.last_key = defaultdict(lambda: 0) def push(self, uid, mid, rating, user_features, movie_features): @@ -88,6 +88,9 @@ def push(self, uid, mid, rating, user_features, movie_features): # update per key self.total_error[uid] += error self.queue[uid].append((uid, mid, rating)) + + # TODO: try moving existing keys to front? + self.last_key[uid] = time.time() def arg_max(self, data_dict): max_key = None @@ -111,6 +114,8 @@ def arg_max(self, data_dict): def choose_key(self): if self.policy == "total_error_cold" or self.policy == "total_error": key = self.arg_max(self.total_error) + elif self.policy == "last_query": + key = self.arg_max(self.last_key) elif self.policy == "max_pending": key = self.arg_max({key: len(self.queue[key]) for key in self.queue.keys()}) elif self.policy == "min_past": @@ -143,13 +148,13 @@ def pop(self): return key -def experiment(policy, updates_per_ts, dataset_dir=".", result_dir=".", limit=None, d=50): +def experiment(policy, updates_per_ts, ts_factor, dataset_dir=".", result_dir=".", limit=None, d=50): # read data test_df = pd.read_csv(f'{dataset_dir}/stream.csv') train_df = pd.read_csv(f'{dataset_dir}/train.csv') start_ts = test_df.timestamp.min() - test_df.timestamp = test_df.timestamp.apply(lambda ts: int((ts - start_ts)/100)) + test_df.timestamp = test_df.timestamp.apply(lambda ts: int((ts - start_ts)/ts_factor)) data = {} for ts, group in tqdm(test_df.groupby("timestamp")): data[ts] = group.to_dict("records") @@ -177,7 +182,8 @@ def experiment(policy, updates_per_ts, dataset_dir=".", result_dir=".", limit=No limit = len(list(data.keys())) queue = UserEventQueue(user_features.shape[0], policy, past_updates) - for ts in tqdm(list(data.keys())[:limit]): + #for ts in tqdm(list(data.keys())[:limit]): + for ts in list(data.keys())[:limit]: # process events updated_users = set([]) @@ -206,7 +212,21 @@ def experiment(policy, updates_per_ts, dataset_dir=".", result_dir=".", limit=No #print("update", uid) # TODO: make sure overall loss is decreasing (training error) - + + if ts % 1000 == 0: + runtime = 1/updates_per_ts + update_df = pd.DataFrame([ + [policy, runtime, k, i, update_times[k][i]] + for k, v in update_times.items() for i in range(len(v)) + ], columns = ["policy", "runtime", "key", "i", "time"]) + results_df = pd.DataFrame({"y_true": y_true, "y_pred": y_pred, "user_id": users, "movie_id": movies, "timestamp": timestamps}) + + + print("saving", {ts}, f"{result_dir}/{policy}_{updates_per_ts}_results.csv") + update_df.to_csv(f"{result_dir}/{policy}_{updates_per_ts}_{ts_factor}_updates.csv") + results_df.to_csv(f"{result_dir}/{policy}_{updates_per_ts}_{ts_factor}_results.csv") + + runtime = 1/updates_per_ts @@ -218,22 +238,23 @@ def experiment(policy, updates_per_ts, dataset_dir=".", result_dir=".", limit=No print("saving", f"{result_dir}/{policy}_{updates_per_ts}_results.csv") - update_df.to_csv(f"{result_dir}/{policy}_{updates_per_ts}_updates.csv") - results_df.to_csv(f"{result_dir}/{policy}_{updates_per_ts}_results.csv") + update_df.to_csv(f"{result_dir}/{policy}_{updates_per_ts}_{ts_factor}_updates.csv") + results_df.to_csv(f"{result_dir}/{policy}_{updates_per_ts}_{ts_factor}_results.csv") if __name__ == "__main__": name = "ml-1m" - dataset_dir = use_dataset(name) - result_dir = use_results(name) + dataset_dir = use_dataset(name, download=False) + result_dir = use_results(name, download=False) #os.makedirs(result_dir, exist_ok=True) - workers = 40 + workers = 144 limit = None - policies = ["total_error", "total_error_cold", "max_pending", "min_past", "round_robin"] - #policies = ["min_past"] - updates_per_ts = [1, 2, 4, 8, 10, 100] + #policies = ["total_error", "total_error_cold", "max_pending", "min_past", "round_robin"] + policies = ["last_query"] + updates_per_ts = [1, 2, 4] #, 8, 10] + ts_factors = [10, 100] #, 1000] #experiments = [(p, u, ".") for p in policies for u in updates_per_ts] @@ -241,7 +262,8 @@ def experiment(policy, updates_per_ts, dataset_dir=".", result_dir=".", limit=No with concurrent.futures.ProcessPoolExecutor(workers) as executor: for p in policies: for u in updates_per_ts: - futures.append(executor.submit(experiment, p, u, dataset_dir, result_dir, limit)) + for ts_factor in ts_factors: + futures.append(executor.submit(experiment, p, u, ts_factor, dataset_dir, result_dir, limit)) for f in futures: try: diff --git a/workloads/recsys/train_als.py b/workloads/recsys/train_als.py index 9ce8938..d4fc4cb 100644 --- a/workloads/recsys/train_als.py +++ b/workloads/recsys/train_als.py @@ -142,8 +142,8 @@ def predict_user_movie_rating(user_feature, movie_feature): pickle.dump(ratings, open(f"{result_dir}/ratings.pkl", "wb")) # store past updates - past_updates = {uid: ratings.getrow(uid).size for uid in uids} - pickle.dump(past_updates, open(f"{result_dir}/past_updates.pkl", "wb")) + #past_updates = {uid: ratings.getrow(uid).size for uid in uids} + #pickle.dump(past_updates, open(f"{result_dir}/past_updates.pkl", "wb")) max_iter = 50 From a694d492e192bbd5605e86ecfcd8105595fd0857 Mon Sep 17 00:00:00 2001 From: Amit Narang Date: Thu, 7 Apr 2022 14:47:36 -0700 Subject: [PATCH 2/4] cold policy analysis --- nb/03-29-22-als_plots.ipynb | 1324 ++++++++++++++++++++++++++++++++++- 1 file changed, 1302 insertions(+), 22 deletions(-) diff --git a/nb/03-29-22-als_plots.ipynb b/nb/03-29-22-als_plots.ipynb index 5176211..a957d0d 100644 --- a/nb/03-29-22-als_plots.ipynb +++ b/nb/03-29-22-als_plots.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "bedcb268", "metadata": {}, "outputs": [], @@ -13,7 +13,7 @@ "import numpy as np\n", "import sys \n", "sys.path.insert(1, \"../\")\n", - "from workloads.util import use_results, use_dataset, read_config, log_dataset\n", + "from util import use_results, use_dataset, read_config, log_dataset\n", "\n", "import seaborn as sns\n", "sns.set_theme(style=\"whitegrid\")" @@ -21,38 +21,1072 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "bb5d482f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/amitnarang/results_experiments/datasets/ml-1m\n", + "Downloading from aws: vldb\n" + ] + } + ], "source": [ "experiment = \"ml-1m\"\n", - "dataset_dir = use_dataset(experiment)\n", - "result_dir = use_results(experiment, download=False)" + "dataset_dir = use_dataset(experiment, download=False)\n", + "result_dir = use_results(experiment, download=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, + "id": "40b6b9a8", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = f\"{dataset_dir}/train.csv\"\n", + "train_df = pd.read_csv(train_data).sort_values('timestamp')\n", + "train_users = set(train_df[\"user_id\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "5a8854d8", + "metadata": {}, + "outputs": [], + "source": [ + "data = f\"{dataset_dir}/stream.csv\"\n", + "temp_df = pd.read_csv(data).sort_values('timestamp')" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "e6c4f0dc", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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te_uneq[\"te\"]\n", + "/var/folders/p5/zwk89rgd0cdf07_7k1wdqjgw0000gn/T/ipykernel_19865/4108400031.py:47: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " te_uneq[\"round_diff\"] = te_uneq[\"cold_round\"] - te_uneq[\"te_round\"]\n", + "/var/folders/p5/zwk89rgd0cdf07_7k1wdqjgw0000gn/T/ipykernel_19865/4108400031.py:51: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " mp_uneq[\"diff\"] = mp_uneq[\"cold\"] - mp_uneq[\"mp\"]\n", + "/var/folders/p5/zwk89rgd0cdf07_7k1wdqjgw0000gn/T/ipykernel_19865/4108400031.py:52: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " mp_uneq[\"round_diff\"] = te_uneq[\"cold_round\"] - te_uneq[\"mp_round\"]\n" + ] + }, + { + "data": { + "text/plain": [ + "user_id 4318.000000\n", + "movie_id 3849.000000\n", + "y_true 5.000000\n", + "cold 6.864934\n", + "te 9.830508\n", + "mp 6.864814\n", + "cold_round 5.000000\n", + "te_round 5.000000\n", + "mp_round 5.000000\n", + "diff 0.390332\n", + "round_diff 0.088759\n", + "dtype: float64" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "policies_analyze = [\"total_error_cold\", \"total_error\", \"min_past\"]\n", + "ts_analyze = [100]\n", + "u_analyze = []\n", + "\n", + "cold_df = pd.read_csv(f\"{result_dir}/total_error_cold_1_100_results.csv\").sort_values(\"timestamp\")\n", + "cold_df = cold_df[~cold_df['user_id'].isin(train_users)]\n", + "te_df = pd.read_csv(f\"{result_dir}/total_error_1_100_results.csv\").sort_values(\"timestamp\")\n", + "te_df = te_df[~te_df['user_id'].isin(train_users)]\n", + "mp_df = pd.read_csv(f\"{result_dir}/min_past_1_100_results.csv\").sort_values(\"timestamp\")\n", + "mp_df = mp_df[~mp_df['user_id'].isin(train_users)]\n", + "\n", + "'''\n", + "for t in ts_factor:\n", + " for u in updates_per_ts:\n", + " for p in policies_analyze:\n", + " update_df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_updates.csv\")\n", + " df = pd.read_csv(f\"{result_dir}/{p}_{u}_{t}_results.csv\")\n", + " df[\"policy\"] = p\n", + " df[\"updates\"] = u \n", + " df[\"ts_factor\"] = t\n", + " \n", + " strip_old_users = df[~df['user_id'].isin(train_users)]\n", + " #rounded_pred = df.y_pred.apply(round_pred)\n", + " #df[\"round\"] = rounded_pred\n", + " \n", + " print(p)\n", + " narrow_df = strip_old_users[['user_id', 'y_true', 'y_pred']]\n", + " print(narrow_df)'''\n", + "overall_df = cold_df[[\"user_id\", \"movie_id\", \"y_true\"]]\n", + "overall_df[\"cold\"] = cold_df[\"y_pred\"]\n", + "overall_df[\"te\"] = te_df[\"y_pred\"]\n", + "overall_df[\"mp\"] = mp_df[\"y_pred\"]\n", + "\n", + "overall_df[\"cold_round\"] = overall_df[\"cold\"].apply(round_pred)\n", + "overall_df[\"te_round\"] = overall_df[\"te\"].apply(round_pred)\n", + "overall_df[\"mp_round\"] = overall_df[\"mp\"].apply(round_pred)\n", + "\n", + "mp_diff = mean_squared_error(overall_df[\"cold\"], overall_df[\"mp\"])\n", + "te_diff = mean_squared_error(overall_df[\"cold\"], overall_df[\"te\"])\n", + "\n", + "mp_round_diff = mean_squared_error(overall_df[\"cold_round\"], overall_df[\"mp_round\"])\n", + "te_round_diff = mean_squared_error(overall_df[\"cold_round\"], overall_df[\"te_round\"])\n", + "\n", + "print(mp_diff, te_diff, mp_round_diff, te_round_diff)\n", + "te_uneq = overall_df[overall_df[\"cold\"] != overall_df['te']]\n", + "te_uneq[\"diff\"] = te_uneq[\"cold\"] - te_uneq[\"te\"]\n", + "te_uneq[\"round_diff\"] = te_uneq[\"cold_round\"] - te_uneq[\"te_round\"]\n", + "te_uneq.mean()\n", + "\n", + "mp_uneq = overall_df[overall_df[\"cold\"] != overall_df['mp']]\n", + "mp_uneq[\"diff\"] = mp_uneq[\"cold\"] - mp_uneq[\"mp\"]\n", + "mp_uneq[\"round_diff\"] = te_uneq[\"cold_round\"] - te_uneq[\"mp_round\"]\n", + "mp_uneq.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, "id": "d2913b4b", "metadata": {}, "outputs": [], "source": [ "updates_per_ts = [1, 2, 4]\n", "ts_factor = [10, 100] #, 1000]\n", - "policies = [\"total_error\", \"total_error_cold\", \"max_pending\", \"min_past\", \"round_robin\", \"last_query\"]" + "policies = [\"total_error\", \"total_error_cold\", \"max_pending\", \"min_past\", \"round_robin\"]#, \"last_query\"]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "753cb3b1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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updatespolicyerrorround_errorscaled_errortotal_updatests_factor
01total_error1.2422711.20157010.01011527002010
11total_error1.9230511.79848010.67326989396100
22total_error1.2296041.19099510.00635332267310
32total_error1.5439041.46484510.102091117557100
44total_error1.2279961.18962210.00586133925010
54total_error1.4002111.33672910.058174135767100
61total_error_cold1.2287741.18974710.00583727002010
71total_error_cold1.5257861.44218110.56495789396100
82total_error_cold1.2279651.18959010.00584932266810
92total_error_cold1.3586171.29903310.044536117540100
104total_error_cold1.2279611.18958910.00585233925010
114total_error_cold1.3469211.28964210.041421135762100
121max_pending1.3349251.28396610.03890427002010
131max_pending2.7858912.55764810.47447189396100
142max_pending1.2490411.20782110.01234532286410
152max_pending2.0428491.90890410.253157117769100
164max_pending1.2288081.19032610.00612233923510
174max_pending1.6309121.54416910.128805135832100
181min_past1.2303591.19052010.00588027002010
191min_past1.5573521.44735612.04117589396100
202min_past1.2279691.18959710.00584932253210
212min_past1.3679321.30123310.521990117322100
224min_past1.2279611.18958910.00585233922310
234min_past1.3476141.28992110.041411135451100
241round_robin1.3466681.29329510.04254727002010
251round_robin3.4213933.07560010.66232789396100
262round_robin1.2607721.21810410.01615932235010
272round_robin2.3283472.14080810.335691116419100
284round_robin1.2347591.19543610.00795533921810
294round_robin1.6902041.59068910.145827135211100
\n", + "
" + ], + "text/plain": [ + " updates policy error round_error scaled_error \\\n", + "0 1 total_error 1.242271 1.201570 10.010115 \n", + "1 1 total_error 1.923051 1.798480 10.673269 \n", + "2 2 total_error 1.229604 1.190995 10.006353 \n", + "3 2 total_error 1.543904 1.464845 10.102091 \n", + "4 4 total_error 1.227996 1.189622 10.005861 \n", + "5 4 total_error 1.400211 1.336729 10.058174 \n", + "6 1 total_error_cold 1.228774 1.189747 10.005837 \n", + "7 1 total_error_cold 1.525786 1.442181 10.564957 \n", + "8 2 total_error_cold 1.227965 1.189590 10.005849 \n", + "9 2 total_error_cold 1.358617 1.299033 10.044536 \n", + "10 4 total_error_cold 1.227961 1.189589 10.005852 \n", + "11 4 total_error_cold 1.346921 1.289642 10.041421 \n", + "12 1 max_pending 1.334925 1.283966 10.038904 \n", + "13 1 max_pending 2.785891 2.557648 10.474471 \n", + "14 2 max_pending 1.249041 1.207821 10.012345 \n", + "15 2 max_pending 2.042849 1.908904 10.253157 \n", + "16 4 max_pending 1.228808 1.190326 10.006122 \n", + "17 4 max_pending 1.630912 1.544169 10.128805 \n", + "18 1 min_past 1.230359 1.190520 10.005880 \n", + "19 1 min_past 1.557352 1.447356 12.041175 \n", + "20 2 min_past 1.227969 1.189597 10.005849 \n", + "21 2 min_past 1.367932 1.301233 10.521990 \n", + "22 4 min_past 1.227961 1.189589 10.005852 \n", + "23 4 min_past 1.347614 1.289921 10.041411 \n", + "24 1 round_robin 1.346668 1.293295 10.042547 \n", + "25 1 round_robin 3.421393 3.075600 10.662327 \n", + "26 2 round_robin 1.260772 1.218104 10.016159 \n", + "27 2 round_robin 2.328347 2.140808 10.335691 \n", + "28 4 round_robin 1.234759 1.195436 10.007955 \n", + "29 4 round_robin 1.690204 1.590689 10.145827 \n", + "\n", + " total_updates ts_factor \n", + "0 270020 10 \n", + "1 89396 100 \n", + "2 322673 10 \n", + "3 117557 100 \n", + "4 339250 10 \n", + "5 135767 100 \n", + "6 270020 10 \n", + "7 89396 100 \n", + "8 322668 10 \n", + "9 117540 100 \n", + "10 339250 10 \n", + "11 135762 100 \n", + "12 270020 10 \n", + "13 89396 100 \n", + "14 322864 10 \n", + "15 117769 100 \n", + "16 339235 10 \n", + "17 135832 100 \n", + "18 270020 10 \n", + "19 89396 100 \n", + "20 322532 10 \n", + "21 117322 100 \n", + "22 339223 10 \n", + "23 135451 100 \n", + "24 270020 10 \n", + "25 89396 100 \n", + "26 322350 10 \n", + "27 116419 100 \n", + "28 339218 10 \n", + "29 135211 100 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "updates_df = pd.DataFrame()\n", "df_all = pd.DataFrame()\n", - "results = [] \n", + "results = []\n", + "\n", + "def round_pred(x):\n", + " if x > 5:\n", + " return 5\n", + " if x < 0:\n", + " return 0\n", + " return x\n", "\n", "for p in policies: \n", " for u in updates_per_ts: \n", @@ -62,26 +1096,32 @@ " df[\"policy\"] = p\n", " df[\"updates\"] = u \n", " df[\"ts_factor\"] = t\n", - "\n", + " \n", + " rounded_pred = df.y_pred.apply(round_pred)\n", + " \n", + " scaled_amt = 5 / df.y_pred.max()\n", + " scaled_pred = df.y_pred * scaled_amt\n", + " \n", " error = mean_squared_error(df.y_pred, df.y_true)\n", + " rounded_error = mean_squared_error(rounded_pred, df.y_true)\n", + " scaled_error = mean_squared_error(scaled_pred, df.y_true)\n", "\n", " total_updates = len(update_df.index)\n", - " results.append([u , p, error, total_updates, t])\n", + " results.append([u , p, error, rounded_error, scaled_error, total_updates, t])\n", + " \n", " updates_df = pd.concat([updates_df, update_df])\n", " df_all = pd.concat([df_all, df])\n", " \n", - "results_df = pd.DataFrame(results, columns=[\"updates\", \"policy\", \"error\", \"total_updates\", \"ts_factor\"])\n", + "results_df = pd.DataFrame(results, columns=[\"updates\", \"policy\", \"error\", \"round_error\", \"scaled_error\", \"total_updates\", \"ts_factor\"])\n", "results_df" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "3020d09d", + "cell_type": "markdown", + "id": "76443ba2", "metadata": {}, - "outputs": [], "source": [ - "plot_df = results_df[(results_df[\"ts_factor\"] == 100) & (results_df.policy.isin([\"total_error_cold\", \"min_past\", \"round_robin\", \"max_pending\", \"last_query\"]))]\n", + "# plot_df = results_df[(results_df[\"ts_factor\"] == 100) & (results_df.policy.isin([\"total_error_cold\", \"min_past\", \"round_robin\", \"max_pending\", \"last_query\"]))]\n", "plot_df.policy = plot_df.policy.map({\n", " 'total_error_cold': 'Accuracy Optimized', \n", " 'min_past': 'Minimum Past Updates', \n", @@ -96,17 +1136,257 @@ "\n", "g = sns.barplot(x=\"updates\", y=\"error\", hue=\"policy\", data=plot_df)\n", "g.set(title=\"Prediction Accuracy per Policy (Recommendation)\", xlabel=\"Updates per Timestep\", ylabel=\"MSE\")\n", + "\n", "#plt.legend(np.flip(plot_df['policy'].unique()), loc='upper right', title=\"Event Type\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, + "id": "0277bf81", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.5, 1.0, 'Prediction Accuracy per Policy (Recommendation)'),\n", + " Text(0.5, 0, 'Updates per Timestep'),\n", + " Text(0, 0.5, 'Rounded MSE')]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "g1 = sns.barplot(x=\"updates\", y=\"round_error\", hue=\"policy\", data=plot_df)\n", + "g1.set(title=\"Prediction Accuracy per Policy (Recommendation)\", xlabel=\"Updates per Timestep\", ylabel=\"Rounded MSE\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, "id": "f93439a7", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.barplot(x=\"updates\", y=\"error\", hue=\"policy\", data=results_df[results_df[\"ts_factor\"] == 10])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "be5ec187", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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updatespolicyerrorround_errorscaled_errortotal_updatests_factor
191Minimum Past Updates1.5573521.44735612.04117589396100
212Minimum Past Updates1.3679321.30123310.521990117322100
234Minimum Past Updates1.3476141.28992110.041411135451100
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" + ], + "text/plain": [ + " updates policy error round_error scaled_error \\\n", + "19 1 Minimum Past Updates 1.557352 1.447356 12.041175 \n", + "21 2 Minimum Past Updates 1.367932 1.301233 10.521990 \n", + "23 4 Minimum Past Updates 1.347614 1.289921 10.041411 \n", + "\n", + " total_updates ts_factor \n", + "19 89396 100 \n", + "21 117322 100 \n", + "23 135451 100 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_df[plot_df['policy'] == \"Minimum Past Updates\"]" ] }, { @@ -448,7 +1728,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.8.13" } }, "nbformat": 4, From ef4b8d5075045e9758838fcec47e82dd066cd08a Mon Sep 17 00:00:00 2001 From: Sarah Wooders Date: Wed, 13 Apr 2022 11:27:14 -0700 Subject: [PATCH 3/4] Error scheduler (#19) * some modifications to the scheduler * make error cumulative --- run_stl.sh | 34 +++++++++++++--- workloads/stl/stl_server.py | 77 +++++++++++++++++++++++++++++++++---- 2 files changed, 99 insertions(+), 12 deletions(-) diff --git a/run_stl.sh b/run_stl.sh index 8337f8c..ceef934 100644 --- a/run_stl.sh +++ b/run_stl.sh @@ -1,10 +1,34 @@ set -ex +num_keys=20 +workers=1 +slide_size=288 +window_size=864 +source_sleep_per_batch=0.01 + +# make sure update_throughput < event_throughput +let update_throughput="$workers*2" +echo $update_throughput +echo "scale=2 ; $num_keys/ ($source_sleep_per_batch*$slide_size)" | bc + python workloads/stl/stl_server.py \ --scheduler=ce \ - --window_size=864 \ - --slide_size=288 \ - --workers=16 \ + --window_size=${window_size}\ + --slide_size=${slide_size}\ + --workers=${workers}\ + --azure_database /home/ubuntu/cleaned_sqlite_3_days_min_ts.db \ + --num_keys=${num_keys}\ + --source_sleep_per_batch ${source_sleep_per_batch} + +python workloads/stl/stl_server.py \ + --scheduler=rr \ + --window_size=${window_size}\ + --slide_size=${slide_size}\ + --workers=${workers}\ --azure_database /home/ubuntu/cleaned_sqlite_3_days_min_ts.db \ - --num_keys=5000 \ - --source_sleep_per_batch 0.001 + --num_keys=${num_keys}\ + --source_sleep_per_batch ${source_sleep_per_batch} + + + + diff --git a/workloads/stl/stl_server.py b/workloads/stl/stl_server.py index 1e3ad75..f7b9f07 100644 --- a/workloads/stl/stl_server.py +++ b/workloads/stl/stl_server.py @@ -73,12 +73,20 @@ required=False, ) +flags.DEFINE_float( + "epsilon", + default=None, + help="Default error to add to each time a query is made", + required=False, +) + KeyType = int class BasePriorityScheduler(BaseScheduler): def __init__(self): self.key_to_event: Dict[KeyType, Record] = dict() + # set initial priority scores to infinity self.key_to_priority: Dict[KeyType, float] = dict() self.sorted_keys_by_timestamp = SortedSet( key=lambda key: self.key_to_priority[key] @@ -87,6 +95,8 @@ def __init__(self): self.stop_iteration = None self._writer_lock = None + self.max_prio = 100000000000 + @property def writer_lock(self): if self._writer_lock is None: @@ -109,8 +119,12 @@ def push_event(self, record: Record): # Remove the key so we can recompute sort key if record_key in self.sorted_keys_by_timestamp: self.sorted_keys_by_timestamp.remove(record_key) + + # Update priority self.key_to_priority[record_key] = self.compute_priority(record) + self.sorted_keys_by_timestamp.add(record_key) + print("add", record_key) self.wake_waiter_if_needed() def pop_event(self) -> Record: @@ -120,9 +134,14 @@ def pop_event(self) -> Record: with self.writer_lock: if len(self.key_to_event) == 0: return Record.make_wait_event(self.new_waker()) + + print(len(self.key_to_event), len(self.sorted_keys_by_timestamp)) latest_key = self.sorted_keys_by_timestamp.pop() record = self.key_to_event.pop(latest_key) - self.key_to_priority.pop(latest_key) + prio = self.key_to_priority.pop(latest_key) + #self.key_to_priority[latest_key] = 0 + if self.qsize() == 0: + logger.msg(f"Queue size is zero - system not fully utilized") return record def qsize(self) -> int: @@ -133,6 +152,13 @@ class KeyAwareLifo(BasePriorityScheduler): """Always prioritize the latest record by arrival time.""" def compute_priority(self, _: Record) -> float: + feature: Record[TimeSeriesValue] = self._operator.get(record.shard_key) + if feature is None: + logger.msg( + f"Missing feature for key {record.shard_key}, returning max_prio" + ) + return self.max_prio + return time.time() @@ -140,24 +166,40 @@ class RoundRobinScheduler(BasePriorityScheduler): """Prioritize the key that hasn't been updated for longest.""" def compute_priority(self, record: Record) -> float: + feature: Record[TimeSeriesValue] = self._operator.get(record.shard_key) + if feature is None: + logger.msg( + f"Missing feature for key {record.shard_key}, returning max_prio" + ) + return self.max_prio + + return self.key_to_priority.get(record.shard_key, 0) + 1 class CumulativeErrorScheduler(BasePriorityScheduler): """Prioritize the key that has highest prediction error so far""" - max_prio = 10000000 - def __init__(self): + def __init__(self, epsilon = None): # TODO: bring back the logic that temporarily disable a key if it is pending update # If that ever becomes an issue. # self.pending_updates: Dict[KeyType, float] = [] super().__init__() + self.epsilon = epsilon + self.last_seqno = {} def compute_priority(self, record: Record["WindowValue"]) -> float: assert isinstance(record.entry, WindowValue) + # start from last seen seqno + start = self.last_seqno.get(record.shard_key, -1) + incoming_seqnos = np.array([n for n in record.entry.seq_nos if n > start]) + self.last_seqno[record.shard_key] = incoming_seqnos.max() + #print("length", incoming_seqnos.shape, start) + + # lookup current feature feature: Record[TimeSeriesValue] = self._operator.get(record.shard_key) if feature is None: logger.msg( @@ -165,7 +207,7 @@ def compute_priority(self, record: Record["WindowValue"]) -> float: ) return self.max_prio - incoming_seqnos = np.array(record.entry.seq_nos) + forecast = np.array(feature.forecast) window_last_seqno = feature.last_seqno forecast_indicies = incoming_seqnos - window_last_seqno - 1 @@ -177,12 +219,22 @@ def compute_priority(self, record: Record["WindowValue"]) -> float: y_pred = np.take(forecast, forecast_indicies) y_train = np.array(feature.y_train) + assert len(record.entry.seq_nos) == 864, f"Unexpected length {len(record.entry.seq_nos)}" + assert len(y_true) % 288 == 0, f"Unexpected length {len(y_true)}" + # TODO: sample if too heavy weight # TODO: maybe scale this by staleness error = mean_absolute_scaled_error( y_true=y_true, y_pred=y_pred, y_train=y_train - ) - return error + ) * len(y_true) + + print(record.shard_key, "Marginal error", error, forecast_indicies.max() - start, len(y_true), self.key_to_priority.get(record.shard_key, 0)) + + if self.epsilon is not None: + error = max(error, self.epsilon) # minimum error + + # add to current error + return self.key_to_priority.get(record.shard_key, 0) + error @dataclass @@ -268,6 +320,8 @@ def on_event(self, _: Record) -> List[Record[SourceValue]]: if len(batch) == 0: return + #print(f"Sending {len(batch)} rows at {self.ts} at {ingest_time}") + self.result_file.write(json.dumps([i.entry.__dict__ for i in batch])) self.result_file.write("\n") self.result_file.flush() @@ -334,6 +388,8 @@ class STLFitForecast(BaseTransform): def __init__(self, results_dir): self.results_dir = results_dir + self.start_time = None + self.num_updates = 0 def prepare(self): self.data = defaultdict(lambda: None) @@ -347,6 +403,8 @@ def get(self, key): def on_event(self, record: Record[WindowValue]): key_id = record.shard_key + if self.start_time is None: self.start_time = time.time() + with warnings.catch_warnings(): # catch warning for ML fit warnings.filterwarnings("ignore") @@ -358,6 +416,9 @@ def on_event(self, record: Record[WindowValue]): ).fit() forecast = model.forecast(9000) + self.num_updates += 1 + print("avg throughput", self.num_updates, self.num_updates / (time.time() - self.start_time)) + forecast_record = TimeSeriesValue( key_id=key_id, forecast=forecast.tolist(), @@ -407,6 +468,7 @@ def _get_config() -> Dict: def main(argv): logger.msg("Running STL pipeline on ralf...") + print("Results", f"{FLAGS.results_dir}/metrics") # Setup dataset directory conn = sqlite3.connect(FLAGS.azure_database) @@ -453,7 +515,7 @@ def main(argv): schedulers = { "lifo": KeyAwareLifo(), "rr": RoundRobinScheduler(), - "ce": CumulativeErrorScheduler(), + "ce": CumulativeErrorScheduler(FLAGS.epsilon), } app.source( @@ -483,6 +545,7 @@ def main(argv): app.deploy() app.wait() + print("Finished", FLAGS.results_dir) if __name__ == "__main__": From dae13b2a739d8cf777828b79692152dd03a8f94d Mon Sep 17 00:00:00 2001 From: Sarah Wooders Date: Wed, 13 Apr 2022 11:27:19 -0700 Subject: [PATCH 4/4] Revert "Error scheduler (#19)" This reverts commit ef4b8d5075045e9758838fcec47e82dd066cd08a. --- run_stl.sh | 34 +++------------- workloads/stl/stl_server.py | 77 ++++--------------------------------- 2 files changed, 12 insertions(+), 99 deletions(-) diff --git a/run_stl.sh b/run_stl.sh index ceef934..8337f8c 100644 --- a/run_stl.sh +++ b/run_stl.sh @@ -1,34 +1,10 @@ set -ex -num_keys=20 -workers=1 -slide_size=288 -window_size=864 -source_sleep_per_batch=0.01 - -# make sure update_throughput < event_throughput -let update_throughput="$workers*2" -echo $update_throughput -echo "scale=2 ; $num_keys/ ($source_sleep_per_batch*$slide_size)" | bc - python workloads/stl/stl_server.py \ --scheduler=ce \ - --window_size=${window_size}\ - --slide_size=${slide_size}\ - --workers=${workers}\ - --azure_database /home/ubuntu/cleaned_sqlite_3_days_min_ts.db \ - --num_keys=${num_keys}\ - --source_sleep_per_batch ${source_sleep_per_batch} - -python workloads/stl/stl_server.py \ - --scheduler=rr \ - --window_size=${window_size}\ - --slide_size=${slide_size}\ - --workers=${workers}\ + --window_size=864 \ + --slide_size=288 \ + --workers=16 \ --azure_database /home/ubuntu/cleaned_sqlite_3_days_min_ts.db \ - --num_keys=${num_keys}\ - --source_sleep_per_batch ${source_sleep_per_batch} - - - - + --num_keys=5000 \ + --source_sleep_per_batch 0.001 diff --git a/workloads/stl/stl_server.py b/workloads/stl/stl_server.py index f7b9f07..1e3ad75 100644 --- a/workloads/stl/stl_server.py +++ b/workloads/stl/stl_server.py @@ -73,20 +73,12 @@ required=False, ) -flags.DEFINE_float( - "epsilon", - default=None, - help="Default error to add to each time a query is made", - required=False, -) - KeyType = int class BasePriorityScheduler(BaseScheduler): def __init__(self): self.key_to_event: Dict[KeyType, Record] = dict() - # set initial priority scores to infinity self.key_to_priority: Dict[KeyType, float] = dict() self.sorted_keys_by_timestamp = SortedSet( key=lambda key: self.key_to_priority[key] @@ -95,8 +87,6 @@ def __init__(self): self.stop_iteration = None self._writer_lock = None - self.max_prio = 100000000000 - @property def writer_lock(self): if self._writer_lock is None: @@ -119,12 +109,8 @@ def push_event(self, record: Record): # Remove the key so we can recompute sort key if record_key in self.sorted_keys_by_timestamp: self.sorted_keys_by_timestamp.remove(record_key) - - # Update priority self.key_to_priority[record_key] = self.compute_priority(record) - self.sorted_keys_by_timestamp.add(record_key) - print("add", record_key) self.wake_waiter_if_needed() def pop_event(self) -> Record: @@ -134,14 +120,9 @@ def pop_event(self) -> Record: with self.writer_lock: if len(self.key_to_event) == 0: return Record.make_wait_event(self.new_waker()) - - print(len(self.key_to_event), len(self.sorted_keys_by_timestamp)) latest_key = self.sorted_keys_by_timestamp.pop() record = self.key_to_event.pop(latest_key) - prio = self.key_to_priority.pop(latest_key) - #self.key_to_priority[latest_key] = 0 - if self.qsize() == 0: - logger.msg(f"Queue size is zero - system not fully utilized") + self.key_to_priority.pop(latest_key) return record def qsize(self) -> int: @@ -152,13 +133,6 @@ class KeyAwareLifo(BasePriorityScheduler): """Always prioritize the latest record by arrival time.""" def compute_priority(self, _: Record) -> float: - feature: Record[TimeSeriesValue] = self._operator.get(record.shard_key) - if feature is None: - logger.msg( - f"Missing feature for key {record.shard_key}, returning max_prio" - ) - return self.max_prio - return time.time() @@ -166,40 +140,24 @@ class RoundRobinScheduler(BasePriorityScheduler): """Prioritize the key that hasn't been updated for longest.""" def compute_priority(self, record: Record) -> float: - feature: Record[TimeSeriesValue] = self._operator.get(record.shard_key) - if feature is None: - logger.msg( - f"Missing feature for key {record.shard_key}, returning max_prio" - ) - return self.max_prio - - return self.key_to_priority.get(record.shard_key, 0) + 1 class CumulativeErrorScheduler(BasePriorityScheduler): """Prioritize the key that has highest prediction error so far""" + max_prio = 10000000 - def __init__(self, epsilon = None): + def __init__(self): # TODO: bring back the logic that temporarily disable a key if it is pending update # If that ever becomes an issue. # self.pending_updates: Dict[KeyType, float] = [] super().__init__() - self.epsilon = epsilon - self.last_seqno = {} def compute_priority(self, record: Record["WindowValue"]) -> float: assert isinstance(record.entry, WindowValue) - # start from last seen seqno - start = self.last_seqno.get(record.shard_key, -1) - incoming_seqnos = np.array([n for n in record.entry.seq_nos if n > start]) - self.last_seqno[record.shard_key] = incoming_seqnos.max() - #print("length", incoming_seqnos.shape, start) - - # lookup current feature feature: Record[TimeSeriesValue] = self._operator.get(record.shard_key) if feature is None: logger.msg( @@ -207,7 +165,7 @@ def compute_priority(self, record: Record["WindowValue"]) -> float: ) return self.max_prio - + incoming_seqnos = np.array(record.entry.seq_nos) forecast = np.array(feature.forecast) window_last_seqno = feature.last_seqno forecast_indicies = incoming_seqnos - window_last_seqno - 1 @@ -219,22 +177,12 @@ def compute_priority(self, record: Record["WindowValue"]) -> float: y_pred = np.take(forecast, forecast_indicies) y_train = np.array(feature.y_train) - assert len(record.entry.seq_nos) == 864, f"Unexpected length {len(record.entry.seq_nos)}" - assert len(y_true) % 288 == 0, f"Unexpected length {len(y_true)}" - # TODO: sample if too heavy weight # TODO: maybe scale this by staleness error = mean_absolute_scaled_error( y_true=y_true, y_pred=y_pred, y_train=y_train - ) * len(y_true) - - print(record.shard_key, "Marginal error", error, forecast_indicies.max() - start, len(y_true), self.key_to_priority.get(record.shard_key, 0)) - - if self.epsilon is not None: - error = max(error, self.epsilon) # minimum error - - # add to current error - return self.key_to_priority.get(record.shard_key, 0) + error + ) + return error @dataclass @@ -320,8 +268,6 @@ def on_event(self, _: Record) -> List[Record[SourceValue]]: if len(batch) == 0: return - #print(f"Sending {len(batch)} rows at {self.ts} at {ingest_time}") - self.result_file.write(json.dumps([i.entry.__dict__ for i in batch])) self.result_file.write("\n") self.result_file.flush() @@ -388,8 +334,6 @@ class STLFitForecast(BaseTransform): def __init__(self, results_dir): self.results_dir = results_dir - self.start_time = None - self.num_updates = 0 def prepare(self): self.data = defaultdict(lambda: None) @@ -403,8 +347,6 @@ def get(self, key): def on_event(self, record: Record[WindowValue]): key_id = record.shard_key - if self.start_time is None: self.start_time = time.time() - with warnings.catch_warnings(): # catch warning for ML fit warnings.filterwarnings("ignore") @@ -416,9 +358,6 @@ def on_event(self, record: Record[WindowValue]): ).fit() forecast = model.forecast(9000) - self.num_updates += 1 - print("avg throughput", self.num_updates, self.num_updates / (time.time() - self.start_time)) - forecast_record = TimeSeriesValue( key_id=key_id, forecast=forecast.tolist(), @@ -468,7 +407,6 @@ def _get_config() -> Dict: def main(argv): logger.msg("Running STL pipeline on ralf...") - print("Results", f"{FLAGS.results_dir}/metrics") # Setup dataset directory conn = sqlite3.connect(FLAGS.azure_database) @@ -515,7 +453,7 @@ def main(argv): schedulers = { "lifo": KeyAwareLifo(), "rr": RoundRobinScheduler(), - "ce": CumulativeErrorScheduler(FLAGS.epsilon), + "ce": CumulativeErrorScheduler(), } app.source( @@ -545,7 +483,6 @@ def main(argv): app.deploy() app.wait() - print("Finished", FLAGS.results_dir) if __name__ == "__main__":