{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time series forecasting with DeepAR - Telecom data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time series forecasasting with DeepAR is a supervised learning algorithm for forecasting scalar time series with Telecom data. This notebook demonstrates how to prepare a dataset of time series for training DeepAR with telecom Call Detail Record(CDR) data, classify Call Disconnect Reason and how to use the trained model for inference. The notebook uses a hybrid approach of Spark ML Random Forest Classifier and DeepAR.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config IPCompleter.greedy=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This demonstrates the use of sparkml RandomForestClassifier for classification and feeds as input to DeepAR for Time series Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Start_Time_HH_MM_SS_s_index', 'Called_Number_index', 'Call_Service_Duration_index', 'Accounting_ID_index', 'Calling_Number_index', 'Start_Time_MM_DD_YYYY_index']\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.types import *\n",
    "from pyspark.sql import SparkSession\n",
    "from sagemaker import get_execution_role\n",
    "import sagemaker_pyspark\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "role = get_execution_role()\n",
    "\n",
    "# Configure Spark to use the SageMaker Spark dependency jars\n",
    "jars = sagemaker_pyspark.classpath_jars()\n",
    "\n",
    "classpath = \":\".join(sagemaker_pyspark.classpath_jars())\n",
    "\n",
    "spark = SparkSession.builder.config(\"spark.driver.extraClassPath\", classpath)\\\n",
    "    .master(\"local[*]\").getOrCreate()\n",
    "\n",
    "def getCdrDataframe():\n",
    "    cdr_start_loc = \"<%CDRStartFile%>\"\n",
    "    cdr_stop_loc = \"<%CDRStopFile%>\"\n",
    "    cdr_start_sample_loc = \"<%CDRStartSampleFile%>\"\n",
    "    cdr_stop_sample_loc = \"<%CDRStopSampleFile%>\"\n",
    "    \n",
    "    df = spark.read.format(\"s3select\").parquet(cdr_stop_sample_loc)\n",
    "    df.createOrReplaceTempView(\"cdr\")\n",
    "    return df\n",
    "\n",
    "getCdrDataframe()\n",
    "\n",
    "def build_schema():\n",
    "    \"\"\"Build and return a schema to use for the sample data.\"\"\"\n",
    "    schema = StructType(\n",
    "            [\n",
    "                StructField(\"Accounting_ID\", StringType(), True),\n",
    "                StructField(\"Start_Time_MM_DD_YYYY\", StringType(), True),\n",
    "                StructField(\"Start_Time_HH_MM_SS_s\", StringType(), True),\n",
    "                StructField(\"Call_Service_Duration\", StringType(), True),\n",
    "                StructField(\"Call_Disconnect_Reason\", StringType(), True),\n",
    "                StructField(\"Calling_Number\", StringType(), True),\n",
    "                StructField(\"Called_Number\", StringType(), True)\n",
    "            ]\n",
    "        )\n",
    "    return schema\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "dataDF = spark.sql(\"SELECT _c2,_c5,_c6,_c13,_c14,_c19,_c20 from cdr where _c0 = 'STOP'\")\n",
    "dataPanda = dataDF.toPandas()\n",
    "newDataDF = spark.createDataFrame(dataPanda.dropna(),build_schema())\n",
    "dataPd = newDataDF.toPandas()\n",
    "\n",
    "integerColumns = [\"Call_Service_Duration\" , \"Call_Disconnect_Reason\", \"Calling_Number\", \"Called_Number\"]\n",
    "for col in integerColumns:\n",
    "    dataPd[col] = dataPd[col].astype(int)\n",
    "    \n",
    "#Mock Data\n",
    "def mock_data():\n",
    "    from pyspark.sql.functions import rand,when\n",
    "    addDF = newDataDF\n",
    "    unionDF = addDF.union(newDataDF)\n",
    "    df = unionDF.drop('Call_Disconnect_Reason')  \n",
    "    df1 = df.withColumn('Call_Disconnect_Reason', when(rand(seed=1234) > 0.5, 16).otherwise(17))         \n",
    "    return df1\n",
    "\n",
    "df1 = mock_data()    \n",
    "    \n",
    "    \n",
    "from pyspark.sql.functions import rand\n",
    "\n",
    "trainingFraction = 0.75; testingFraction = (1-trainingFraction);\n",
    "seed = 1234;\n",
    "trainData, testData = df1.randomSplit([trainingFraction, testingFraction], seed=seed);\n",
    "\n",
    "# # CACHE TRAIN AND TEST DATA\n",
    "trainData.cache()\n",
    "testData.cache()\n",
    "trainData.count(),testData.count()\n",
    "\n",
    "from pyspark.ml.feature import StringIndexer\n",
    "columns_list = list(set(newDataDF.columns)-set(['Call_Disconnect_Reason']) ) \n",
    "indexers = []\n",
    "for column in columns_list:\n",
    "    indexer = StringIndexer(inputCol=column, outputCol=column+\"_index\")\n",
    "    indexer.setHandleInvalid(\"skip\")\n",
    "    indexers.append(indexer)\n",
    "\n",
    "from pyspark.ml.feature import StringIndexer\n",
    "# Convert target into numerical categories\n",
    "labelIndexer = StringIndexer(inputCol=\"Call_Disconnect_Reason\", outputCol=\"label\")\n",
    "labelIndexer.setHandleInvalid(\"skip\")\n",
    "   \n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "from array import array\n",
    "\n",
    "inputcolsIndexer = []\n",
    "for col in columns_list:\n",
    "    inputcolsIndexer.append(col+\"_index\")\n",
    "print(inputcolsIndexer)\n",
    "\n",
    "vecAssembler = VectorAssembler(inputCols=inputcolsIndexer, outputCol=\"features\")\n",
    "\n",
    "from pyspark.ml.classification import RandomForestClassifier\n",
    "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
    "\n",
    "# Train a RandomForest model.\n",
    "rf = RandomForestClassifier(labelCol=\"label\", featuresCol=\"features\", maxDepth=8, maxBins=2400000, numTrees=128,impurity=\"gini\")\n",
    "\n",
    "from pyspark.ml.feature import ChiSqSelector\n",
    "chisqSelector = ChiSqSelector(numTopFeatures=3, featuresCol=\"features\",\n",
    "                         outputCol=\"selectedFeatures\", labelCol=\"label\")\n",
    "\n",
    "from pyspark.ml import Pipeline\n",
    "stages = []\n",
    "stages += indexers \n",
    "stages += [labelIndexer]\n",
    "stages += [vecAssembler]\n",
    "stages += [rf]\n",
    "stages += [chisqSelector]\n",
    "\n",
    "pipeline = Pipeline(stages=stages)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33607, 11219)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainData.count(),testData.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3403, 4487)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sdata = trainData.sample(False,0.1)\n",
    "test_sdata = testData.sample(False,0.4)\n",
    "train_sdata.count(),test_sdata.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 8.34 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "356"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "model = pipeline.fit(train_sdata)\n",
    "predictions = model.transform(test_sdata)\n",
    "predictions.createOrReplaceTempView(\"predicted_table\")\n",
    "predictions.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+---------------------+---------------------+---------------------+--------------+-------------+----------------------+---------------------------+-------------------+---------------------------+-------------------+--------------------+---------------------------+-----+--------------------+--------------------+--------------------+----------+----------------+\n",
      "|     Accounting_ID|Start_Time_MM_DD_YYYY|Start_Time_HH_MM_SS_s|Call_Service_Duration|Calling_Number|Called_Number|Call_Disconnect_Reason|Start_Time_HH_MM_SS_s_index|Called_Number_index|Call_Service_Duration_index|Accounting_ID_index|Calling_Number_index|Start_Time_MM_DD_YYYY_index|label|            features|       rawPrediction|         probability|prediction|selectedFeatures|\n",
      "+------------------+---------------------+---------------------+---------------------+--------------+-------------+----------------------+---------------------------+-------------------+---------------------------+-------------------+--------------------+---------------------------+-----+--------------------+--------------------+--------------------+----------+----------------+\n",
      "|0x00016E0F11780902|           08/10/2018|           12:57:43.1|                    5|    9645000099|   3512000099|                    16|                     1339.0|                8.0|                      277.0|             2840.0|                 8.0|                        0.0|  1.0|[1339.0,8.0,277.0...|[0.47079490632979...|[0.00367808520570...|       1.0| [8.0,277.0,8.0]|\n",
      "|0x00016E0F1240F35C|           08/10/2018|           12:49:03.1|                  135|    9645000072|   3512000072|                    16|                       38.0|               95.0|                        9.0|             2958.0|                93.0|                        0.0|  1.0|[38.0,95.0,9.0,29...|[81.7280438800052...|[0.63850034281254...|       0.0| [95.0,9.0,93.0]|\n",
      "+------------------+---------------------+---------------------+---------------------+--------------+-------------+----------------------+---------------------------+-------------------+---------------------------+-------------------+--------------------+---------------------------+-----+--------------------+--------------------+--------------------+----------+----------------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions.show(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- _Call Disconnect Reason prediction count is computed to classify Normal Call Clearing(16) records and non Normal Call Clearing records as anomalous._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_sql = spark.sql(\"Select Start_Time_MM_DD_YYYY,Start_Time_HH_MM_SS_s,Call_Disconnect_Reason,prediction, CASE WHEN Call_Disconnect_Reason = 16 AND prediction = 0.0 THEN 0 ELSE 1 END AS anomaly from predicted_table\")\n",
    "dft = pred_sql.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start_Time_MM_DD_YYYY</th>\n",
       "      <th>Start_Time_HH_MM_SS_s</th>\n",
       "      <th>Call_Disconnect_Reason</th>\n",
       "      <th>prediction</th>\n",
       "      <th>anomaly</th>\n",
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       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>08/10/2018 11:37:28.1</th>\n",
       "      <td>08/10/2018</td>\n",
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       "      <td>08/10/2018</td>\n",
       "      <td>11:37:37.1</td>\n",
       "      <td>17</td>\n",
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       "      <th>08/10/2018 11:38:00.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:38:00.1</td>\n",
       "      <td>17</td>\n",
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       "      <th>08/10/2018 11:38:33.1</th>\n",
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       "    <tr>\n",
       "      <th>08/10/2018 11:39:33.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:39:33.1</td>\n",
       "      <td>16</td>\n",
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       "      <td>11:39:43.1</td>\n",
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       "      <th>08/10/2018 11:39:48.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:39:48.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>08/10/2018 11:41:13.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:41:13.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>08/10/2018 11:42:17.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:42:17.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>08/10/2018 11:42:21.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:42:21.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>08/10/2018 11:42:32.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:42:32.1</td>\n",
       "      <td>16</td>\n",
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       "      <th>08/10/2018 11:42:38.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:42:38.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>08/10/2018 11:43:10.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:43:10.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>08/10/2018 11:43:20.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:43:20.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>08/10/2018 11:43:38.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:43:38.1</td>\n",
       "      <td>16</td>\n",
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       "      <th>08/10/2018 11:44:35.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:44:35.1</td>\n",
       "      <td>16</td>\n",
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       "      <th>08/10/2018 11:45:05.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:45:05.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>08/10/2018 11:45:13.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:45:13.1</td>\n",
       "      <td>16</td>\n",
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       "      <th>08/10/2018 11:45:33.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:45:33.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>08/10/2018 11:46:59.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:46:59.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 11:48:55.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:48:55.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 11:48:56.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:48:56.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 11:49:00.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:49:00.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 11:49:07.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:49:07.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 11:49:08.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:49:08.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 11:49:16.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:49:16.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 11:50:13.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:50:13.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 11:50:22.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:50:22.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 11:50:35.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:50:35.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:54:28.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:54:28.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:54:33.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:54:33.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:55:19.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:55:19.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:56:27.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:56:27.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:56:36.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:56:36.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:58:12.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:58:12.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:58:43.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:58:43.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:58:54.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:58:54.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:59:02.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:59:02.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:59:40.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:59:40.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:59:46.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:59:46.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 13:59:47.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:59:47.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:00:12.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:00:12.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:00:32.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:00:32.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:00:38.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:00:38.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:01:12.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:01:12.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:01:40.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:01:40.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:02:04.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:02:04.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:02:07.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:02:07.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:02:19.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:02:19.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:03:02.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:03:02.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:03:38.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:03:38.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:04:18.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:04:18.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:04:36.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:04:36.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:04:46.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:04:46.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:04:56.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:04:56.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:05:34.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:05:34.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:06:03.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:06:03.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:06:43.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:06:43.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/10/2018 14:06:53.1</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>14:06:53.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>356 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Start_Time_MM_DD_YYYY Start_Time_HH_MM_SS_s  \\\n",
       "Date                                                                \n",
       "08/10/2018 11:37:28.1            08/10/2018            11:37:28.1   \n",
       "08/10/2018 11:37:30.1            08/10/2018            11:37:30.1   \n",
       "08/10/2018 11:37:37.1            08/10/2018            11:37:37.1   \n",
       "08/10/2018 11:38:00.1            08/10/2018            11:38:00.1   \n",
       "08/10/2018 11:38:33.1            08/10/2018            11:38:33.1   \n",
       "08/10/2018 11:39:33.1            08/10/2018            11:39:33.1   \n",
       "08/10/2018 11:39:43.1            08/10/2018            11:39:43.1   \n",
       "08/10/2018 11:39:48.1            08/10/2018            11:39:48.1   \n",
       "08/10/2018 11:41:13.1            08/10/2018            11:41:13.1   \n",
       "08/10/2018 11:42:17.1            08/10/2018            11:42:17.1   \n",
       "08/10/2018 11:42:21.1            08/10/2018            11:42:21.1   \n",
       "08/10/2018 11:42:32.1            08/10/2018            11:42:32.1   \n",
       "08/10/2018 11:42:38.1            08/10/2018            11:42:38.1   \n",
       "08/10/2018 11:43:10.1            08/10/2018            11:43:10.1   \n",
       "08/10/2018 11:43:20.1            08/10/2018            11:43:20.1   \n",
       "08/10/2018 11:43:38.1            08/10/2018            11:43:38.1   \n",
       "08/10/2018 11:44:35.1            08/10/2018            11:44:35.1   \n",
       "08/10/2018 11:45:05.1            08/10/2018            11:45:05.1   \n",
       "08/10/2018 11:45:13.1            08/10/2018            11:45:13.1   \n",
       "08/10/2018 11:45:33.1            08/10/2018            11:45:33.1   \n",
       "08/10/2018 11:46:59.1            08/10/2018            11:46:59.1   \n",
       "08/10/2018 11:48:55.1            08/10/2018            11:48:55.1   \n",
       "08/10/2018 11:48:56.1            08/10/2018            11:48:56.1   \n",
       "08/10/2018 11:49:00.1            08/10/2018            11:49:00.1   \n",
       "08/10/2018 11:49:07.1            08/10/2018            11:49:07.1   \n",
       "08/10/2018 11:49:08.1            08/10/2018            11:49:08.1   \n",
       "08/10/2018 11:49:16.1            08/10/2018            11:49:16.1   \n",
       "08/10/2018 11:50:13.1            08/10/2018            11:50:13.1   \n",
       "08/10/2018 11:50:22.1            08/10/2018            11:50:22.1   \n",
       "08/10/2018 11:50:35.1            08/10/2018            11:50:35.1   \n",
       "...                                     ...                   ...   \n",
       "08/10/2018 13:54:28.1            08/10/2018            13:54:28.1   \n",
       "08/10/2018 13:54:33.1            08/10/2018            13:54:33.1   \n",
       "08/10/2018 13:55:19.1            08/10/2018            13:55:19.1   \n",
       "08/10/2018 13:56:27.1            08/10/2018            13:56:27.1   \n",
       "08/10/2018 13:56:36.1            08/10/2018            13:56:36.1   \n",
       "08/10/2018 13:58:12.1            08/10/2018            13:58:12.1   \n",
       "08/10/2018 13:58:43.1            08/10/2018            13:58:43.1   \n",
       "08/10/2018 13:58:54.1            08/10/2018            13:58:54.1   \n",
       "08/10/2018 13:59:02.1            08/10/2018            13:59:02.1   \n",
       "08/10/2018 13:59:40.1            08/10/2018            13:59:40.1   \n",
       "08/10/2018 13:59:46.1            08/10/2018            13:59:46.1   \n",
       "08/10/2018 13:59:47.1            08/10/2018            13:59:47.1   \n",
       "08/10/2018 14:00:12.1            08/10/2018            14:00:12.1   \n",
       "08/10/2018 14:00:32.1            08/10/2018            14:00:32.1   \n",
       "08/10/2018 14:00:38.1            08/10/2018            14:00:38.1   \n",
       "08/10/2018 14:01:12.1            08/10/2018            14:01:12.1   \n",
       "08/10/2018 14:01:40.1            08/10/2018            14:01:40.1   \n",
       "08/10/2018 14:02:04.1            08/10/2018            14:02:04.1   \n",
       "08/10/2018 14:02:07.1            08/10/2018            14:02:07.1   \n",
       "08/10/2018 14:02:19.1            08/10/2018            14:02:19.1   \n",
       "08/10/2018 14:03:02.1            08/10/2018            14:03:02.1   \n",
       "08/10/2018 14:03:38.1            08/10/2018            14:03:38.1   \n",
       "08/10/2018 14:04:18.1            08/10/2018            14:04:18.1   \n",
       "08/10/2018 14:04:36.1            08/10/2018            14:04:36.1   \n",
       "08/10/2018 14:04:46.1            08/10/2018            14:04:46.1   \n",
       "08/10/2018 14:04:56.1            08/10/2018            14:04:56.1   \n",
       "08/10/2018 14:05:34.1            08/10/2018            14:05:34.1   \n",
       "08/10/2018 14:06:03.1            08/10/2018            14:06:03.1   \n",
       "08/10/2018 14:06:43.1            08/10/2018            14:06:43.1   \n",
       "08/10/2018 14:06:53.1            08/10/2018            14:06:53.1   \n",
       "\n",
       "                       Call_Disconnect_Reason  prediction  anomaly  \n",
       "Date                                                                \n",
       "08/10/2018 11:37:28.1                      17         0.0        1  \n",
       "08/10/2018 11:37:30.1                      16         1.0        1  \n",
       "08/10/2018 11:37:37.1                      17         0.0        1  \n",
       "08/10/2018 11:38:00.1                      17         0.0        1  \n",
       "08/10/2018 11:38:33.1                      16         1.0        1  \n",
       "08/10/2018 11:39:33.1                      16         1.0        1  \n",
       "08/10/2018 11:39:43.1                      17         0.0        1  \n",
       "08/10/2018 11:39:48.1                      16         0.0        0  \n",
       "08/10/2018 11:41:13.1                      17         1.0        1  \n",
       "08/10/2018 11:42:17.1                      16         1.0        1  \n",
       "08/10/2018 11:42:21.1                      17         1.0        1  \n",
       "08/10/2018 11:42:32.1                      16         0.0        0  \n",
       "08/10/2018 11:42:38.1                      16         0.0        0  \n",
       "08/10/2018 11:43:10.1                      16         0.0        0  \n",
       "08/10/2018 11:43:20.1                      17         0.0        1  \n",
       "08/10/2018 11:43:38.1                      16         0.0        0  \n",
       "08/10/2018 11:44:35.1                      16         0.0        0  \n",
       "08/10/2018 11:45:05.1                      17         1.0        1  \n",
       "08/10/2018 11:45:13.1                      16         0.0        0  \n",
       "08/10/2018 11:45:33.1                      16         0.0        0  \n",
       "08/10/2018 11:46:59.1                      17         1.0        1  \n",
       "08/10/2018 11:48:55.1                      16         1.0        1  \n",
       "08/10/2018 11:48:56.1                      17         0.0        1  \n",
       "08/10/2018 11:49:00.1                      17         0.0        1  \n",
       "08/10/2018 11:49:07.1                      17         0.0        1  \n",
       "08/10/2018 11:49:08.1                      16         0.0        0  \n",
       "08/10/2018 11:49:16.1                      16         0.0        0  \n",
       "08/10/2018 11:50:13.1                      17         0.0        1  \n",
       "08/10/2018 11:50:22.1                      16         1.0        1  \n",
       "08/10/2018 11:50:35.1                      17         0.0        1  \n",
       "...                                       ...         ...      ...  \n",
       "08/10/2018 13:54:28.1                      16         0.0        0  \n",
       "08/10/2018 13:54:33.1                      16         0.0        0  \n",
       "08/10/2018 13:55:19.1                      16         1.0        1  \n",
       "08/10/2018 13:56:27.1                      17         0.0        1  \n",
       "08/10/2018 13:56:36.1                      17         0.0        1  \n",
       "08/10/2018 13:58:12.1                      16         1.0        1  \n",
       "08/10/2018 13:58:43.1                      17         1.0        1  \n",
       "08/10/2018 13:58:54.1                      17         1.0        1  \n",
       "08/10/2018 13:59:02.1                      16         1.0        1  \n",
       "08/10/2018 13:59:40.1                      16         0.0        0  \n",
       "08/10/2018 13:59:46.1                      16         0.0        0  \n",
       "08/10/2018 13:59:47.1                      16         1.0        1  \n",
       "08/10/2018 14:00:12.1                      16         1.0        1  \n",
       "08/10/2018 14:00:32.1                      16         1.0        1  \n",
       "08/10/2018 14:00:38.1                      17         1.0        1  \n",
       "08/10/2018 14:01:12.1                      16         0.0        0  \n",
       "08/10/2018 14:01:40.1                      16         0.0        0  \n",
       "08/10/2018 14:02:04.1                      17         1.0        1  \n",
       "08/10/2018 14:02:07.1                      17         1.0        1  \n",
       "08/10/2018 14:02:19.1                      17         1.0        1  \n",
       "08/10/2018 14:03:02.1                      16         1.0        1  \n",
       "08/10/2018 14:03:38.1                      17         1.0        1  \n",
       "08/10/2018 14:04:18.1                      17         1.0        1  \n",
       "08/10/2018 14:04:36.1                      17         1.0        1  \n",
       "08/10/2018 14:04:46.1                      16         1.0        1  \n",
       "08/10/2018 14:04:56.1                      17         1.0        1  \n",
       "08/10/2018 14:05:34.1                      17         1.0        1  \n",
       "08/10/2018 14:06:03.1                      16         1.0        1  \n",
       "08/10/2018 14:06:43.1                      16         1.0        1  \n",
       "08/10/2018 14:06:53.1                      16         0.0        0  \n",
       "\n",
       "[356 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#dataframetime\n",
    "dft['Date']=dft['Start_Time_MM_DD_YYYY'].astype(str)+' '+dft['Start_Time_HH_MM_SS_s']\n",
    "dft['Date'] = pd.to_datetime(dft['Date'], format='%m/%d/%Y %H:%M:%S.%f', errors = 'coerce')\n",
    "dft = dft.sort_values(by=['Date'])\n",
    "dft.set_index('Date', inplace=True)\n",
    "dft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start_Time_MM_DD_YYYY</th>\n",
       "      <th>Start_Time_HH_MM_SS_s</th>\n",
       "      <th>Call_Disconnect_Reason</th>\n",
       "      <th>prediction</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:37:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:37:28.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:38:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:37:30.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:38:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:37:37.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:38:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:38:00.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:39:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:38:33.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:40:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:39:33.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:40:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:39:43.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:40:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:39:48.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:41:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:41:13.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:42:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:42:17.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Start_Time_MM_DD_YYYY Start_Time_HH_MM_SS_s  \\\n",
       "Date                                                              \n",
       "2018-08-10 11:37:00            08/10/2018            11:37:28.1   \n",
       "2018-08-10 11:38:00            08/10/2018            11:37:30.1   \n",
       "2018-08-10 11:38:00            08/10/2018            11:37:37.1   \n",
       "2018-08-10 11:38:00            08/10/2018            11:38:00.1   \n",
       "2018-08-10 11:39:00            08/10/2018            11:38:33.1   \n",
       "2018-08-10 11:40:00            08/10/2018            11:39:33.1   \n",
       "2018-08-10 11:40:00            08/10/2018            11:39:43.1   \n",
       "2018-08-10 11:40:00            08/10/2018            11:39:48.1   \n",
       "2018-08-10 11:41:00            08/10/2018            11:41:13.1   \n",
       "2018-08-10 11:42:00            08/10/2018            11:42:17.1   \n",
       "\n",
       "                     Call_Disconnect_Reason  prediction  anomaly  \n",
       "Date                                                              \n",
       "2018-08-10 11:37:00                      17         0.0        1  \n",
       "2018-08-10 11:38:00                      16         1.0        1  \n",
       "2018-08-10 11:38:00                      17         0.0        1  \n",
       "2018-08-10 11:38:00                      17         0.0        1  \n",
       "2018-08-10 11:39:00                      16         1.0        1  \n",
       "2018-08-10 11:40:00                      16         1.0        1  \n",
       "2018-08-10 11:40:00                      17         0.0        1  \n",
       "2018-08-10 11:40:00                      16         0.0        0  \n",
       "2018-08-10 11:41:00                      17         1.0        1  \n",
       "2018-08-10 11:42:00                      16         1.0        1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft.index = pd.to_datetime(dft.index)\n",
    "dfn = dft\n",
    "#dataframeindex\n",
    "dfn.index = dfn.index.round('min')\n",
    "dfn.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = dfn\n",
    "ts.plot()\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "time_series = []\n",
    "data = np.array(dfn['anomaly'])\n",
    "freq = '1min'\n",
    "idx = dfn.index\n",
    "# Note: Setting dataframe index frequency to 1 minute requires passed values to conform to one minute\n",
    "# frequency. For reference see also DataFrame.asfreq() and DataFrame.drop_duplicates()\n",
    "idx.freq = pd.tseries.frequencies.to_offset(freq)\n",
    "time_series.append(pd.Series(data=data, index=idx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Times Series Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_series[0].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start_Time_MM_DD_YYYY</th>\n",
       "      <th>Start_Time_HH_MM_SS_s</th>\n",
       "      <th>Call_Disconnect_Reason</th>\n",
       "      <th>prediction</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:37:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:37:28.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:38:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:37:30.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:38:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:37:37.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:38:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:38:00.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:39:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:38:33.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:40:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:39:33.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:40:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:39:43.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:40:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:39:48.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:41:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:41:13.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:42:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:42:17.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:42:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:42:21.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:43:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:42:32.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:43:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:42:38.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:43:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:43:10.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:43:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:43:20.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:44:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:43:38.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:45:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:44:35.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:45:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:45:05.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:45:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:45:13.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:46:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:45:33.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:47:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:46:59.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:49:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:48:55.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:49:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:48:56.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:49:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:49:00.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:49:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:49:07.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:49:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:49:08.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:49:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:49:16.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:50:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:50:13.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:50:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:50:22.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 11:51:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>11:50:35.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:44:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:44:08.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:45:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:44:41.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:45:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:44:59.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:45:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:45:16.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:45:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:45:25.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:46:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:45:31.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:46:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:46:09.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:46:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:46:13.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:48:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:47:59.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:49:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:48:34.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:50:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:49:32.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:50:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:49:40.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:50:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:50:07.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:50:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:50:17.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:51:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:50:30.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:51:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:50:49.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:52:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:51:44.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:52:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:51:50.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:53:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:52:54.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:54:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:53:52.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:54:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:54:28.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:55:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:54:33.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:55:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:55:19.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:56:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:56:27.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:57:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:56:36.1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:58:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:58:12.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:59:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:58:43.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:59:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:58:54.1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 13:59:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:59:02.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 14:00:00</th>\n",
       "      <td>08/10/2018</td>\n",
       "      <td>13:59:40.1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>336 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Start_Time_MM_DD_YYYY Start_Time_HH_MM_SS_s  \\\n",
       "Date                                                              \n",
       "2018-08-10 11:37:00            08/10/2018            11:37:28.1   \n",
       "2018-08-10 11:38:00            08/10/2018            11:37:30.1   \n",
       "2018-08-10 11:38:00            08/10/2018            11:37:37.1   \n",
       "2018-08-10 11:38:00            08/10/2018            11:38:00.1   \n",
       "2018-08-10 11:39:00            08/10/2018            11:38:33.1   \n",
       "2018-08-10 11:40:00            08/10/2018            11:39:33.1   \n",
       "2018-08-10 11:40:00            08/10/2018            11:39:43.1   \n",
       "2018-08-10 11:40:00            08/10/2018            11:39:48.1   \n",
       "2018-08-10 11:41:00            08/10/2018            11:41:13.1   \n",
       "2018-08-10 11:42:00            08/10/2018            11:42:17.1   \n",
       "2018-08-10 11:42:00            08/10/2018            11:42:21.1   \n",
       "2018-08-10 11:43:00            08/10/2018            11:42:32.1   \n",
       "2018-08-10 11:43:00            08/10/2018            11:42:38.1   \n",
       "2018-08-10 11:43:00            08/10/2018            11:43:10.1   \n",
       "2018-08-10 11:43:00            08/10/2018            11:43:20.1   \n",
       "2018-08-10 11:44:00            08/10/2018            11:43:38.1   \n",
       "2018-08-10 11:45:00            08/10/2018            11:44:35.1   \n",
       "2018-08-10 11:45:00            08/10/2018            11:45:05.1   \n",
       "2018-08-10 11:45:00            08/10/2018            11:45:13.1   \n",
       "2018-08-10 11:46:00            08/10/2018            11:45:33.1   \n",
       "2018-08-10 11:47:00            08/10/2018            11:46:59.1   \n",
       "2018-08-10 11:49:00            08/10/2018            11:48:55.1   \n",
       "2018-08-10 11:49:00            08/10/2018            11:48:56.1   \n",
       "2018-08-10 11:49:00            08/10/2018            11:49:00.1   \n",
       "2018-08-10 11:49:00            08/10/2018            11:49:07.1   \n",
       "2018-08-10 11:49:00            08/10/2018            11:49:08.1   \n",
       "2018-08-10 11:49:00            08/10/2018            11:49:16.1   \n",
       "2018-08-10 11:50:00            08/10/2018            11:50:13.1   \n",
       "2018-08-10 11:50:00            08/10/2018            11:50:22.1   \n",
       "2018-08-10 11:51:00            08/10/2018            11:50:35.1   \n",
       "...                                   ...                   ...   \n",
       "2018-08-10 13:44:00            08/10/2018            13:44:08.1   \n",
       "2018-08-10 13:45:00            08/10/2018            13:44:41.1   \n",
       "2018-08-10 13:45:00            08/10/2018            13:44:59.1   \n",
       "2018-08-10 13:45:00            08/10/2018            13:45:16.1   \n",
       "2018-08-10 13:45:00            08/10/2018            13:45:25.1   \n",
       "2018-08-10 13:46:00            08/10/2018            13:45:31.1   \n",
       "2018-08-10 13:46:00            08/10/2018            13:46:09.1   \n",
       "2018-08-10 13:46:00            08/10/2018            13:46:13.1   \n",
       "2018-08-10 13:48:00            08/10/2018            13:47:59.1   \n",
       "2018-08-10 13:49:00            08/10/2018            13:48:34.1   \n",
       "2018-08-10 13:50:00            08/10/2018            13:49:32.1   \n",
       "2018-08-10 13:50:00            08/10/2018            13:49:40.1   \n",
       "2018-08-10 13:50:00            08/10/2018            13:50:07.1   \n",
       "2018-08-10 13:50:00            08/10/2018            13:50:17.1   \n",
       "2018-08-10 13:51:00            08/10/2018            13:50:30.1   \n",
       "2018-08-10 13:51:00            08/10/2018            13:50:49.1   \n",
       "2018-08-10 13:52:00            08/10/2018            13:51:44.1   \n",
       "2018-08-10 13:52:00            08/10/2018            13:51:50.1   \n",
       "2018-08-10 13:53:00            08/10/2018            13:52:54.1   \n",
       "2018-08-10 13:54:00            08/10/2018            13:53:52.1   \n",
       "2018-08-10 13:54:00            08/10/2018            13:54:28.1   \n",
       "2018-08-10 13:55:00            08/10/2018            13:54:33.1   \n",
       "2018-08-10 13:55:00            08/10/2018            13:55:19.1   \n",
       "2018-08-10 13:56:00            08/10/2018            13:56:27.1   \n",
       "2018-08-10 13:57:00            08/10/2018            13:56:36.1   \n",
       "2018-08-10 13:58:00            08/10/2018            13:58:12.1   \n",
       "2018-08-10 13:59:00            08/10/2018            13:58:43.1   \n",
       "2018-08-10 13:59:00            08/10/2018            13:58:54.1   \n",
       "2018-08-10 13:59:00            08/10/2018            13:59:02.1   \n",
       "2018-08-10 14:00:00            08/10/2018            13:59:40.1   \n",
       "\n",
       "                     Call_Disconnect_Reason  prediction  anomaly  \n",
       "Date                                                              \n",
       "2018-08-10 11:37:00                      17         0.0        1  \n",
       "2018-08-10 11:38:00                      16         1.0        1  \n",
       "2018-08-10 11:38:00                      17         0.0        1  \n",
       "2018-08-10 11:38:00                      17         0.0        1  \n",
       "2018-08-10 11:39:00                      16         1.0        1  \n",
       "2018-08-10 11:40:00                      16         1.0        1  \n",
       "2018-08-10 11:40:00                      17         0.0        1  \n",
       "2018-08-10 11:40:00                      16         0.0        0  \n",
       "2018-08-10 11:41:00                      17         1.0        1  \n",
       "2018-08-10 11:42:00                      16         1.0        1  \n",
       "2018-08-10 11:42:00                      17         1.0        1  \n",
       "2018-08-10 11:43:00                      16         0.0        0  \n",
       "2018-08-10 11:43:00                      16         0.0        0  \n",
       "2018-08-10 11:43:00                      16         0.0        0  \n",
       "2018-08-10 11:43:00                      17         0.0        1  \n",
       "2018-08-10 11:44:00                      16         0.0        0  \n",
       "2018-08-10 11:45:00                      16         0.0        0  \n",
       "2018-08-10 11:45:00                      17         1.0        1  \n",
       "2018-08-10 11:45:00                      16         0.0        0  \n",
       "2018-08-10 11:46:00                      16         0.0        0  \n",
       "2018-08-10 11:47:00                      17         1.0        1  \n",
       "2018-08-10 11:49:00                      16         1.0        1  \n",
       "2018-08-10 11:49:00                      17         0.0        1  \n",
       "2018-08-10 11:49:00                      17         0.0        1  \n",
       "2018-08-10 11:49:00                      17         0.0        1  \n",
       "2018-08-10 11:49:00                      16         0.0        0  \n",
       "2018-08-10 11:49:00                      16         0.0        0  \n",
       "2018-08-10 11:50:00                      17         0.0        1  \n",
       "2018-08-10 11:50:00                      16         1.0        1  \n",
       "2018-08-10 11:51:00                      17         0.0        1  \n",
       "...                                     ...         ...      ...  \n",
       "2018-08-10 13:44:00                      17         0.0        1  \n",
       "2018-08-10 13:45:00                      16         0.0        0  \n",
       "2018-08-10 13:45:00                      16         0.0        0  \n",
       "2018-08-10 13:45:00                      16         1.0        1  \n",
       "2018-08-10 13:45:00                      16         0.0        0  \n",
       "2018-08-10 13:46:00                      17         0.0        1  \n",
       "2018-08-10 13:46:00                      17         0.0        1  \n",
       "2018-08-10 13:46:00                      17         0.0        1  \n",
       "2018-08-10 13:48:00                      17         0.0        1  \n",
       "2018-08-10 13:49:00                      16         0.0        0  \n",
       "2018-08-10 13:50:00                      17         0.0        1  \n",
       "2018-08-10 13:50:00                      17         0.0        1  \n",
       "2018-08-10 13:50:00                      17         1.0        1  \n",
       "2018-08-10 13:50:00                      16         1.0        1  \n",
       "2018-08-10 13:51:00                      17         0.0        1  \n",
       "2018-08-10 13:51:00                      17         1.0        1  \n",
       "2018-08-10 13:52:00                      17         0.0        1  \n",
       "2018-08-10 13:52:00                      17         0.0        1  \n",
       "2018-08-10 13:53:00                      16         0.0        0  \n",
       "2018-08-10 13:54:00                      17         0.0        1  \n",
       "2018-08-10 13:54:00                      16         0.0        0  \n",
       "2018-08-10 13:55:00                      16         0.0        0  \n",
       "2018-08-10 13:55:00                      16         1.0        1  \n",
       "2018-08-10 13:56:00                      17         0.0        1  \n",
       "2018-08-10 13:57:00                      17         0.0        1  \n",
       "2018-08-10 13:58:00                      16         1.0        1  \n",
       "2018-08-10 13:59:00                      17         1.0        1  \n",
       "2018-08-10 13:59:00                      17         1.0        1  \n",
       "2018-08-10 13:59:00                      16         1.0        1  \n",
       "2018-08-10 14:00:00                      16         0.0        0  \n",
       "\n",
       "[336 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_length = 20\n",
    "context_length = 20\n",
    "ts[:-prediction_length]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_series_training = []\n",
    "for ts in time_series:\n",
    "    time_series_training.append(ts[:-prediction_length])\n",
    "time_series[0].plot(label='test')\n",
    "time_series_training[0].plot(label='train', ls=':')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DeepAR supervised learning algorithm for forecasting scalar time series of Telecom Call Details Record."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "np.random.seed(1)\n",
    "import pandas as pd\n",
    "import json\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the sagemaker client library for easy interface with sagemaker and s3fs for uploading the training data to S3. (Use `pip` to install missing libraries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.4.10\n",
      "  latest version: 4.5.11\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base conda\n",
      "\n",
      "\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda install -y s3fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import s3fs\n",
    "import sagemaker\n",
    "from sagemaker import get_execution_role"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The S3 bucket and prefix that you want to use for training and model data. This should be within the same region as the Notebook Instance, training, and hosting.\n",
    "- The IAM role arn used to give training and hosting access to your data. We use the `get_execution_role` function to obtain the role arn which was specified when creating the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "isConfigCell": true
   },
   "outputs": [],
   "source": [
    "bucket = '<%bucket_name%>' #<-- your bucket_name\n",
    "version = '%%VERSION%%'\n",
    "prefix = 'sagemaker/Telecom-RandomForest/DeepAR'\n",
    "prefix = 'machine-learning-for-all/{}/data/cdr-stop'.format(version)  \n",
    "\n",
    "sagemaker_session = sagemaker.Session()\n",
    "role = get_execution_role()\n",
    "\n",
    "s3_data_path = \"{}/{}\".format(bucket, prefix)\n",
    "s3_output_path = \"{}/{}/output\".format(bucket, prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we configure the container image to be used for the region that we are running in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
    "image_name = get_image_uri(boto3.Session().region_name, 'forecasting-deepar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating and uploading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to train a model that can predict the next 20 points of syntheticly generated time series.\n",
    "The time series that we use have minutes granularity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also Configure the `context_length`, which determines how much context of the time series the model should take into account when making the prediction, i.e. how many previous points to look at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = 'min'\n",
    "# we predict for 20 Minutes\n",
    "prediction_length = 20\n",
    "\n",
    "# we also use 20 Minutes as context length, this is the number of state updates accomplished before making predictions\n",
    "context_length = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_dataset = pd.Timestamp(\"2018-07-15 00:00:00\", freq=freq)\n",
    "end_training = pd.Timestamp(\"2018-08-09 00:00:00\", freq=freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following utility functions convert pandas.Series objects into the appropriate JSON strings that DeepAR can consume. We will use these to write the data to S3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def series_to_obj(ts, cat=None):\n",
    "    obj = {\"start\": str(ts.index[0]), \"target\": list(ts)}\n",
    "    if cat is not None:\n",
    "        obj[\"cat\"] = cat\n",
    "    return obj\n",
    "\n",
    "def series_to_jsonline(ts, cat=None):\n",
    "    return json.dumps(series_to_obj(ts, cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding = \"utf-8\"\n",
    "s3filesystem = s3fs.S3FileSystem()\n",
    "\n",
    "with s3filesystem.open(s3_data_path + \"/train/train.json\", 'wb') as fp:\n",
    "    for ts in time_series_training:\n",
    "        fp.write(series_to_jsonline(ts).encode(encoding))\n",
    "        fp.write('\\n'.encode(encoding))\n",
    "\n",
    "with s3filesystem.open(s3_data_path + \"/test/test.json\", 'wb') as fp:\n",
    "    for ts in time_series:\n",
    "        fp.write(series_to_jsonline(ts).encode(encoding))\n",
    "        fp.write('\\n'.encode(encoding))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now define the estimator that will launch the training job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = sagemaker.estimator.Estimator(\n",
    "    sagemaker_session=sagemaker_session,\n",
    "    image_name=image_name,\n",
    "    role=role,\n",
    "    train_instance_count=1,\n",
    "    train_instance_type='ml.c4.xlarge',\n",
    "    base_job_name='Ml-Telcom-DemoForecast-deepar',\n",
    "    output_path=\"s3://\" + s3_output_path\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparameters = {\n",
    "    \"time_freq\": freq,\n",
    "    \"context_length\": str(context_length),\n",
    "    \"prediction_length\": str(prediction_length),\n",
    "    \"num_cells\": \"40\",\n",
    "    \"num_layers\": \"3\",\n",
    "    \"likelihood\": \"gaussian\",\n",
    "    \"epochs\": \"20\",\n",
    "    \"mini_batch_size\": \"32\",\n",
    "    \"learning_rate\": \"0.001\",\n",
    "    \"dropout_rate\": \"0.05\",\n",
    "    \"early_stopping_patience\": \"10\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator.set_hyperparameters(**hyperparameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SageMaker will start an EC2 instance, download the data from S3, start training the model and save the trained model.\n",
    "\n",
    "DeepAR will also calculate accuracy metrics for the trained model on the test data set. This is done by predicting the last `perdiction_length` points of each time series in the test set and comparing this to the actual value of the time series.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: Ml-Telcom-DemoForecast-deepar-2018-09-27-19-04-18-999\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".....................\n",
      "\u001b[31mArguments: train\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/default-input.json: {u'num_dynamic_feat': u'auto', u'dropout_rate': u'0.10', u'mini_batch_size': u'128', u'test_quantiles': u'[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]', u'_tuning_objective_metric': u'', u'_num_gpus': u'auto', u'num_eval_samples': u'100', u'learning_rate': u'0.001', u'num_cells': u'40', u'num_layers': u'2', u'embedding_dimension': u'10', u'_kvstore': u'auto', u'_num_kv_servers': u'auto', u'cardinality': u'auto', u'likelihood': u'student-t', u'early_stopping_patience': u''}\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'dropout_rate': u'0.05', u'learning_rate': u'0.001', u'num_cells': u'40', u'prediction_length': u'20', u'epochs': u'20', u'time_freq': u'min', u'context_length': u'20', u'num_layers': u'3', u'mini_batch_size': u'32', u'likelihood': u'gaussian', u'early_stopping_patience': u'10'}\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Final configuration: {u'dropout_rate': u'0.05', u'test_quantiles': u'[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]', u'_tuning_objective_metric': u'', u'num_eval_samples': u'100', u'learning_rate': u'0.001', u'num_layers': u'3', u'epochs': u'20', u'embedding_dimension': u'10', u'num_cells': u'40', u'_num_kv_servers': u'auto', u'mini_batch_size': u'32', u'likelihood': u'gaussian', u'num_dynamic_feat': u'auto', u'cardinality': u'auto', u'_num_gpus': u'auto', u'prediction_length': u'20', u'time_freq': u'min', u'context_length': u'20', u'_kvstore': u'auto', u'early_stopping_patience': u'10'}\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Detected entry point for worker worker\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Using early stopping with patience 10\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] [cardinality=auto] `cat` field was NOT found in the file `/opt/ml/input/data/train/train.json` and will NOT be used for training.\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] [num_dynamic_feat=auto] `dynamic_feat` field was NOT found in the file `/opt/ml/input/data/train/train.json` and will NOT be used for training.\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Training set statistics:\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Integer time series\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] number of time series: 1\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] number of observations: 336\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] mean target length: 336\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] min/mean/max target: 0.0/0.752976190476/1.0\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] mean abs(target): 0.752976190476\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] contains missing values: no\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Small number of time series. Doing 10 number of passes over dataset per epoch.\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Test set statistics:\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Integer time series\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] number of time series: 1\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] number of observations: 356\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] mean target length: 356\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] min/mean/max target: 0.0/0.755617977528/1.0\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] mean abs(target): 0.755617977528\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] contains missing values: no\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] nvidia-smi took: 0.0252199172974 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Create Store: local\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"get_graph.time\": {\"count\": 1, \"max\": 99.35808181762695, \"sum\": 99.35808181762695, \"min\": 99.35808181762695}}, \"EndTime\": 1538075259.603323, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075259.501878}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"initialize.time\": {\"count\": 1, \"max\": 275.1431465148926, \"sum\": 275.1431465148926, \"min\": 275.1431465148926}}, \"EndTime\": 1538075259.777122, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075259.60341}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:39 INFO 139992056387392] Epoch[0] Batch[0] avg_epoch_loss=1.668614\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:40 INFO 139992056387392] Epoch[0] Batch[5] avg_epoch_loss=1.000207\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:40 INFO 139992056387392] Epoch[0] Batch [5]#011Speed: 598.22 samples/sec#011loss=1.000207\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:40 INFO 139992056387392] processed a total of 308 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"epochs\": {\"count\": 1, \"max\": 20, \"sum\": 20.0, \"min\": 20}, \"update.time\": {\"count\": 1, \"max\": 685.8439445495605, \"sum\": 685.8439445495605, \"min\": 685.8439445495605}}, \"EndTime\": 1538075260.463144, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075259.777206}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:40 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=449.001835844 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:40 INFO 139992056387392] #progress_metric: host=algo-1, completed 5 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:40 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:40 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_fc9c47d7-a4b2-440c-9cc0-f21127499520-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 18.35489273071289, \"sum\": 18.35489273071289, \"min\": 18.35489273071289}}, \"EndTime\": 1538075260.482053, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075260.46323}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:40 INFO 139992056387392] Epoch[1] Batch[0] avg_epoch_loss=0.718298\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:40 INFO 139992056387392] Epoch[1] Batch[5] avg_epoch_loss=0.681346\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:40 INFO 139992056387392] Epoch[1] Batch [5]#011Speed: 685.95 samples/sec#011loss=0.681346\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:40 INFO 139992056387392] processed a total of 315 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 517.9588794708252, \"sum\": 517.9588794708252, \"min\": 517.9588794708252}}, \"EndTime\": 1538075261.000167, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075260.482125}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=607.989588937 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_caa09677-5bcb-445c-9314-d27563bb62c3-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 17.38905906677246, \"sum\": 17.38905906677246, \"min\": 17.38905906677246}}, \"EndTime\": 1538075261.018225, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075261.000244}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] Epoch[2] Batch[0] avg_epoch_loss=0.632169\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] Epoch[2] Batch[5] avg_epoch_loss=0.638416\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] Epoch[2] Batch [5]#011Speed: 706.46 samples/sec#011loss=0.638416\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] Epoch[2] Batch[10] avg_epoch_loss=0.642646\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] Epoch[2] Batch [10]#011Speed: 698.65 samples/sec#011loss=0.647721\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] processed a total of 331 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 540.679931640625, \"sum\": 540.679931640625, \"min\": 540.679931640625}}, \"EndTime\": 1538075261.559035, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075261.018301}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=612.065280397 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] #progress_metric: host=algo-1, completed 15 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_850c6060-f374-4591-8f03-0d8fd132f66c-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 19.886016845703125, \"sum\": 19.886016845703125, \"min\": 19.886016845703125}}, \"EndTime\": 1538075261.579381, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075261.559114}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] Epoch[3] Batch[0] avg_epoch_loss=0.634267\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] Epoch[3] Batch[5] avg_epoch_loss=0.613932\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:41 INFO 139992056387392] Epoch[3] Batch [5]#011Speed: 675.70 samples/sec#011loss=0.613932\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] processed a total of 315 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 517.29416847229, \"sum\": 517.29416847229, \"min\": 517.29416847229}}, \"EndTime\": 1538075262.096798, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075261.579449}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=608.806252817 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_dcc74a08-84f0-4e77-a55a-c332767fb320-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 19.624948501586914, \"sum\": 19.624948501586914, \"min\": 19.624948501586914}}, \"EndTime\": 1538075262.11683, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075262.096875}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] Epoch[4] Batch[0] avg_epoch_loss=0.613078\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] Epoch[4] Batch[5] avg_epoch_loss=0.596061\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] Epoch[4] Batch [5]#011Speed: 657.28 samples/sec#011loss=0.596061\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] processed a total of 304 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 522.3469734191895, \"sum\": 522.3469734191895, \"min\": 522.3469734191895}}, \"EndTime\": 1538075262.639297, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075262.116898}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=581.868320453 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] #progress_metric: host=algo-1, completed 25 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_f2bfa6cf-96da-490f-adb7-436f73fb51b3-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 25.178194046020508, \"sum\": 25.178194046020508, \"min\": 25.178194046020508}}, \"EndTime\": 1538075262.664913, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075262.63937}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:42 INFO 139992056387392] Epoch[5] Batch[0] avg_epoch_loss=0.582463\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] Epoch[5] Batch[5] avg_epoch_loss=0.577364\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] Epoch[5] Batch [5]#011Speed: 646.17 samples/sec#011loss=0.577364\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] Epoch[5] Batch[10] avg_epoch_loss=0.576717\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] Epoch[5] Batch [10]#011Speed: 674.95 samples/sec#011loss=0.575940\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] processed a total of 343 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 589.5130634307861, \"sum\": 589.5130634307861, \"min\": 589.5130634307861}}, \"EndTime\": 1538075263.254557, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075262.664987}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=581.729553385 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_6febd383-9f70-4d45-b70b-b5ac826b0804-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 16.642093658447266, \"sum\": 16.642093658447266, \"min\": 16.642093658447266}}, \"EndTime\": 1538075263.271619, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075263.254627}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] Epoch[6] Batch[0] avg_epoch_loss=0.543216\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] Epoch[6] Batch[5] avg_epoch_loss=0.558766\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] Epoch[6] Batch [5]#011Speed: 686.83 samples/sec#011loss=0.558766\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] processed a total of 299 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 514.3611431121826, \"sum\": 514.3611431121826, \"min\": 514.3611431121826}}, \"EndTime\": 1538075263.786106, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075263.271693}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=581.184020435 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] #progress_metric: host=algo-1, completed 35 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_42559fb0-06ed-4370-916e-f6ee8f8bd256-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 22.788047790527344, \"sum\": 22.788047790527344, \"min\": 22.788047790527344}}, \"EndTime\": 1538075263.809307, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075263.786179}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:43 INFO 139992056387392] Epoch[7] Batch[0] avg_epoch_loss=0.560542\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] Epoch[7] Batch[5] avg_epoch_loss=0.568886\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] Epoch[7] Batch [5]#011Speed: 695.29 samples/sec#011loss=0.568886\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] Epoch[7] Batch[10] avg_epoch_loss=0.564763\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] Epoch[7] Batch [10]#011Speed: 706.21 samples/sec#011loss=0.559817\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] processed a total of 331 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 553.7979602813721, \"sum\": 553.7979602813721, \"min\": 553.7979602813721}}, \"EndTime\": 1538075264.363235, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075263.809382}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=597.574099398 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] loss did not improve for 1 epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] Epoch[8] Batch[0] avg_epoch_loss=0.553834\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] Epoch[8] Batch[5] avg_epoch_loss=0.548489\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] Epoch[8] Batch [5]#011Speed: 650.23 samples/sec#011loss=0.548489\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] processed a total of 306 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 549.6490001678467, \"sum\": 549.6490001678467, \"min\": 549.6490001678467}}, \"EndTime\": 1538075264.913256, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075264.363309}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=556.60351806 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] #progress_metric: host=algo-1, completed 45 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:44 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_6c5ba5e5-4907-4f06-9714-9789161d36e6-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 16.722917556762695, \"sum\": 16.722917556762695, \"min\": 16.722917556762695}}, \"EndTime\": 1538075264.930667, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075264.91333}\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] Epoch[9] Batch[0] avg_epoch_loss=0.570835\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] Epoch[9] Batch[5] avg_epoch_loss=0.539807\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] Epoch[9] Batch [5]#011Speed: 691.67 samples/sec#011loss=0.539807\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] Epoch[9] Batch[10] avg_epoch_loss=0.531483\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] Epoch[9] Batch [10]#011Speed: 699.48 samples/sec#011loss=0.521493\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] processed a total of 343 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 557.8279495239258, \"sum\": 557.8279495239258, \"min\": 557.8279495239258}}, \"EndTime\": 1538075265.488624, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075264.930735}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=614.754606879 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] #progress_metric: host=algo-1, completed 50 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_32da4cfd-4674-4f8c-b33f-a4d3c962f225-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 17.2271728515625, \"sum\": 17.2271728515625, \"min\": 17.2271728515625}}, \"EndTime\": 1538075265.506317, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075265.4887}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] Epoch[10] Batch[0] avg_epoch_loss=0.535287\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] Epoch[10] Batch[5] avg_epoch_loss=0.526376\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:45 INFO 139992056387392] Epoch[10] Batch [5]#011Speed: 689.97 samples/sec#011loss=0.526376\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] processed a total of 299 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 496.1738586425781, \"sum\": 496.1738586425781, \"min\": 496.1738586425781}}, \"EndTime\": 1538075266.002614, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075265.506386}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=602.477603881 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] #progress_metric: host=algo-1, completed 55 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_28758133-0f00-4f01-862a-62dcebbfb8d7-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 23.020029067993164, \"sum\": 23.020029067993164, \"min\": 23.020029067993164}}, \"EndTime\": 1538075266.026067, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075266.00269}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] Epoch[11] Batch[0] avg_epoch_loss=0.513396\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] Epoch[11] Batch[5] avg_epoch_loss=0.521285\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] Epoch[11] Batch [5]#011Speed: 682.70 samples/sec#011loss=0.521285\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] Epoch[11] Batch[10] avg_epoch_loss=0.509232\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] Epoch[11] Batch [10]#011Speed: 630.55 samples/sec#011loss=0.494767\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] processed a total of 336 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 586.3878726959229, \"sum\": 586.3878726959229, \"min\": 586.3878726959229}}, \"EndTime\": 1538075266.61258, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075266.026138}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=572.849808059 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_3533afb6-9c9f-4d46-832c-66ca8fffa49f-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 24.536848068237305, \"sum\": 24.536848068237305, \"min\": 24.536848068237305}}, \"EndTime\": 1538075266.637568, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075266.612697}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] Epoch[12] Batch[0] avg_epoch_loss=0.516536\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] Epoch[12] Batch[5] avg_epoch_loss=0.506413\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:46 INFO 139992056387392] Epoch[12] Batch [5]#011Speed: 714.65 samples/sec#011loss=0.506413\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] Epoch[12] Batch[10] avg_epoch_loss=0.490687\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] Epoch[12] Batch [10]#011Speed: 622.12 samples/sec#011loss=0.471815\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] processed a total of 325 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 580.3749561309814, \"sum\": 580.3749561309814, \"min\": 580.3749561309814}}, \"EndTime\": 1538075267.21806, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075266.637633}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=559.880462346 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] #progress_metric: host=algo-1, completed 65 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_1239933f-c4ba-44e1-87c2-6ad2a865a72c-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 17.4560546875, \"sum\": 17.4560546875, \"min\": 17.4560546875}}, \"EndTime\": 1538075267.235959, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075267.218132}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] Epoch[13] Batch[0] avg_epoch_loss=0.501668\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] Epoch[13] Batch[5] avg_epoch_loss=0.488324\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] Epoch[13] Batch [5]#011Speed: 718.44 samples/sec#011loss=0.488324\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] processed a total of 318 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 498.7211227416992, \"sum\": 498.7211227416992, \"min\": 498.7211227416992}}, \"EndTime\": 1538075267.7348, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075267.236025}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=637.496808187 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] #progress_metric: host=algo-1, completed 70 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_c4cd172a-8f3f-4e95-a0d8-b0c2c32675ca-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 25.576114654541016, \"sum\": 25.576114654541016, \"min\": 25.576114654541016}}, \"EndTime\": 1538075267.760798, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075267.734871}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:47 INFO 139992056387392] Epoch[14] Batch[0] avg_epoch_loss=0.490021\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] Epoch[14] Batch[5] avg_epoch_loss=0.470555\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] Epoch[14] Batch [5]#011Speed: 715.76 samples/sec#011loss=0.470555\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] Epoch[14] Batch[10] avg_epoch_loss=0.470830\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] Epoch[14] Batch [10]#011Speed: 707.86 samples/sec#011loss=0.471160\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] processed a total of 331 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 553.5869598388672, \"sum\": 553.5869598388672, \"min\": 553.5869598388672}}, \"EndTime\": 1538075268.31451, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075267.760867}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=597.733872377 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] #progress_metric: host=algo-1, completed 75 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_8bd0b083-dd55-49cc-b79e-7cbb099a688e-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 17.199039459228516, \"sum\": 17.199039459228516, \"min\": 17.199039459228516}}, \"EndTime\": 1538075268.332312, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075268.314643}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] Epoch[15] Batch[0] avg_epoch_loss=0.423810\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] Epoch[15] Batch[5] avg_epoch_loss=0.460837\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] Epoch[15] Batch [5]#011Speed: 682.42 samples/sec#011loss=0.460837\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] Epoch[15] Batch[10] avg_epoch_loss=0.456859\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] Epoch[15] Batch [10]#011Speed: 616.69 samples/sec#011loss=0.452087\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] processed a total of 345 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 578.6659717559814, \"sum\": 578.6659717559814, \"min\": 578.6659717559814}}, \"EndTime\": 1538075268.911105, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075268.332382}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=596.074357969 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] #progress_metric: host=algo-1, completed 80 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:48 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_0e946b2d-7fc5-4f85-82d2-8de273be0043-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 17.600059509277344, \"sum\": 17.600059509277344, \"min\": 17.600059509277344}}, \"EndTime\": 1538075268.929193, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075268.911187}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] Epoch[16] Batch[0] avg_epoch_loss=0.452340\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] Epoch[16] Batch[5] avg_epoch_loss=0.428428\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] Epoch[16] Batch [5]#011Speed: 709.95 samples/sec#011loss=0.428428\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] processed a total of 318 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 507.0209503173828, \"sum\": 507.0209503173828, \"min\": 507.0209503173828}}, \"EndTime\": 1538075269.436315, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075268.92925}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=627.075363928 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] #progress_metric: host=algo-1, completed 85 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_504570da-358b-4cff-930b-6dc2b9891b20-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 16.973018646240234, \"sum\": 16.973018646240234, \"min\": 16.973018646240234}}, \"EndTime\": 1538075269.453712, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075269.436378}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] Epoch[17] Batch[0] avg_epoch_loss=0.390457\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] Epoch[17] Batch[5] avg_epoch_loss=0.417007\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] Epoch[17] Batch [5]#011Speed: 695.72 samples/sec#011loss=0.417007\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] processed a total of 304 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 512.854814529419, \"sum\": 512.854814529419, \"min\": 512.854814529419}}, \"EndTime\": 1538075269.966671, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075269.453771}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=592.638851783 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] #progress_metric: host=algo-1, completed 90 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:49 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_33f07b23-7078-4d1d-8c49-6702c376fb90-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 16.416072845458984, \"sum\": 16.416072845458984, \"min\": 16.416072845458984}}, \"EndTime\": 1538075269.983498, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075269.966743}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:50 INFO 139992056387392] Epoch[18] Batch[0] avg_epoch_loss=0.421876\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:50 INFO 139992056387392] Epoch[18] Batch[5] avg_epoch_loss=0.417273\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:50 INFO 139992056387392] Epoch[18] Batch [5]#011Speed: 717.33 samples/sec#011loss=0.417273\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:50 INFO 139992056387392] processed a total of 315 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 510.85495948791504, \"sum\": 510.85495948791504, \"min\": 510.85495948791504}}, \"EndTime\": 1538075270.494473, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075269.983565}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:50 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=616.470957122 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:50 INFO 139992056387392] #progress_metric: host=algo-1, completed 95 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:50 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:50 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_7d565c4c-05a4-4086-8285-9c74be72a0ac-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 17.915010452270508, \"sum\": 17.915010452270508, \"min\": 17.915010452270508}}, \"EndTime\": 1538075270.51285, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075270.494554}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:50 INFO 139992056387392] Epoch[19] Batch[0] avg_epoch_loss=0.332859\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:50 INFO 139992056387392] Epoch[19] Batch[5] avg_epoch_loss=0.344839\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:50 INFO 139992056387392] Epoch[19] Batch [5]#011Speed: 603.75 samples/sec#011loss=0.344839\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] processed a total of 310 examples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 550.1449108123779, \"sum\": 550.1449108123779, \"min\": 550.1449108123779}}, \"EndTime\": 1538075271.063111, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075270.512915}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] #throughput_metric: host=algo-1, train throughput=563.36997467 records/second\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] #progress_metric: host=algo-1, completed 100 % of epochs\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] best epoch loss so far\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/state_34c78c01-d822-458c-a25b-243b88513dcf-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 24.517059326171875, \"sum\": 24.517059326171875, \"min\": 24.517059326171875}}, \"EndTime\": 1538075271.088053, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075271.063192}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] Loading parameters from best epoch (19)\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"state.deserialize.time\": {\"count\": 1, \"max\": 6.698131561279297, \"sum\": 6.698131561279297, \"min\": 6.698131561279297}}, \"EndTime\": 1538075271.094933, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075271.088121}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] Final loss: 0.356049019098 (occurred at epoch 19)\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] #quality_metric: host=algo-1, train final_loss <loss>=0.356049019098\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] Worker algo-1 finished training.\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 WARNING 139992056387392] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] All workers finished. Serializing model for prediction.\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"get_graph.time\": {\"count\": 1, \"max\": 127.70795822143555, \"sum\": 127.70795822143555, \"min\": 127.70795822143555}}, \"EndTime\": 1538075271.223266, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075271.094984}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"finalize.time\": {\"count\": 1, \"max\": 186.57302856445312, \"sum\": 186.57302856445312, \"min\": 186.57302856445312}}, \"EndTime\": 1538075271.28209, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075271.223339}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] Serializing to /opt/ml/model/model_algo-1\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] Saved checkpoint to \"/opt/ml/model/model_algo-1-0000.params\"\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"model.serialize.time\": {\"count\": 1, \"max\": 10.080099105834961, \"sum\": 10.080099105834961, \"min\": 10.080099105834961}}, \"EndTime\": 1538075271.29228, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075271.282159}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] Successfully serialized the model for prediction.\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:51 INFO 139992056387392] Evaluating model accuracy on testset using 100 samples\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"model.bind.time\": {\"count\": 1, \"max\": 0.04410743713378906, \"sum\": 0.04410743713378906, \"min\": 0.04410743713378906}}, \"EndTime\": 1538075271.293088, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075271.292329}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"model.score.time\": {\"count\": 1, \"max\": 836.759090423584, \"sum\": 836.759090423584, \"min\": 836.759090423584}}, \"EndTime\": 1538075272.129804, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075271.293147}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #test_score (algo-1, RMSE): 0.404396450293\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #test_score (algo-1, mean_wQuantileLoss): 0.287107\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #test_score (algo-1, wQuantileLoss[0.1]): 0.299846\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #test_score (algo-1, wQuantileLoss[0.2]): 0.35851\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #test_score (algo-1, wQuantileLoss[0.3]): 0.381453\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #test_score (algo-1, wQuantileLoss[0.4]): 0.367567\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #test_score (algo-1, wQuantileLoss[0.5]): 0.334978\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #test_score (algo-1, wQuantileLoss[0.6]): 0.302277\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #test_score (algo-1, wQuantileLoss[0.7]): 0.243726\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #test_score (algo-1, wQuantileLoss[0.8]): 0.183429\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #test_score (algo-1, wQuantileLoss[0.9]): 0.112176\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #quality_metric: host=algo-1, test RMSE <loss>=0.404396450293\u001b[0m\n",
      "\u001b[31m[09/27/2018 19:07:52 INFO 139992056387392] #quality_metric: host=algo-1, test mean_wQuantileLoss <loss>=0.287106812\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 12833.847999572754, \"sum\": 12833.847999572754, \"min\": 12833.847999572754}, \"setuptime\": {\"count\": 1, \"max\": 10.254859924316406, \"sum\": 10.254859924316406, \"min\": 10.254859924316406}}, \"EndTime\": 1538075272.145939, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1538075272.129872}\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Billable seconds: 97\n"
     ]
    }
   ],
   "source": [
    "data_channels = {\n",
    "    \"train\": \"s3://{}/train/\".format(s3_data_path),\n",
    "    \"test\": \"s3://{}/test/\".format(s3_data_path)\n",
    "}\n",
    "\n",
    "estimator.fit(inputs=data_channels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create endpoint and predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is trained , we can use it to perform predictions by deploying it to an endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: Ml-Telcom-DemoForecast-deepar-2018-09-27-19-04-18-999\n",
      "INFO:sagemaker:Creating endpoint-config with name Ml-Telcom-DemoForecast-deepar-2018-09-27-19-04-18-999\n",
      "INFO:sagemaker:Creating endpoint with name Ml-Telcom-DemoForecast-deepar-2018-09-27-19-04-18-999\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------!"
     ]
    }
   ],
   "source": [
    "job_name = estimator.latest_training_job.name\n",
    "\n",
    "endpoint_name = sagemaker_session.endpoint_from_job(\n",
    "    job_name=job_name,\n",
    "    initial_instance_count=1,\n",
    "    instance_type='ml.m4.xlarge',\n",
    "    deployment_image=image_name,\n",
    "    role=role\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To query the endpoint and perform predictions, we can define the following utility class: this allows making requests using `pandas.Series` objects rather than raw JSON strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepARPredictor(sagemaker.predictor.RealTimePredictor):\n",
    "\n",
    "    def set_prediction_parameters(self, freq, prediction_length):\n",
    "        \"\"\"Set the time frequency and prediction length parameters. This method **must** be called\n",
    "        before being able to use `predict`.\n",
    "        \n",
    "        Parameters:\n",
    "        freq -- string indicating the time frequency\n",
    "        prediction_length -- integer, number of predicted time points\n",
    "        \n",
    "        Return value: none.\n",
    "        \"\"\"\n",
    "        self.freq = freq\n",
    "        self.prediction_length = prediction_length\n",
    "        \n",
    "    def predict(self, ts, cat=None, encoding=\"utf-8\", num_samples=100, quantiles=[\"0.1\", \"0.5\", \"0.9\"]):\n",
    "        \"\"\"Requests the prediction of for the time series listed in `ts`, each with the (optional)\n",
    "        corresponding category listed in `cat`.\n",
    "        \n",
    "        Parameters:\n",
    "        ts -- list of `pandas.Series` objects, the time series to predict\n",
    "        cat -- list of integers (default: None)\n",
    "        encoding -- string, encoding to use for the request (default: \"utf-8\")\n",
    "        num_samples -- integer, number of samples to compute at prediction time (default: 100)\n",
    "        quantiles -- list of strings specifying the quantiles to compute (default: [\"0.1\", \"0.5\", \"0.9\"])\n",
    "        \n",
    "        Return value: list of `pandas.DataFrame` objects, each containing the predictions\n",
    "        \"\"\"\n",
    "        prediction_times = [x.index[-1]+1 for x in ts]\n",
    "        req = self.__encode_request(ts, cat, encoding, num_samples, quantiles)\n",
    "        res = super(DeepARPredictor, self).predict(req)\n",
    "        return self.__decode_response(res, prediction_times, encoding)\n",
    "    \n",
    "    def __encode_request(self, ts, cat, encoding, num_samples, quantiles):\n",
    "        instances = [series_to_obj(ts[k], cat[k] if cat else None) for k in range(len(ts))]\n",
    "        configuration = {\"num_samples\": num_samples, \"output_types\": [\"quantiles\"], \"quantiles\": quantiles}\n",
    "        http_request_data = {\"instances\": instances, \"configuration\": configuration}\n",
    "        return json.dumps(http_request_data).encode(encoding)\n",
    "    \n",
    "    def __decode_response(self, response, prediction_times, encoding):\n",
    "        response_data = json.loads(response.decode(encoding))\n",
    "        list_of_df = []\n",
    "        for k in range(len(prediction_times)):\n",
    "            prediction_index = pd.DatetimeIndex(start=prediction_times[k], freq=self.freq, periods=self.prediction_length)\n",
    "            list_of_df.append(pd.DataFrame(data=response_data['predictions'][k]['quantiles'], index=prediction_index))\n",
    "        return list_of_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = DeepARPredictor(\n",
    "    endpoint=endpoint_name,\n",
    "    sagemaker_session=sagemaker_session,\n",
    "    content_type=\"application/json\"\n",
    ")\n",
    "predictor.set_prediction_parameters(freq, prediction_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make predictions and plot results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the previously created `predictor` object. For simplicity, we will predict only the first few time series used for training, and compare the results with the actual data we kept in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_df = predictor.predict(time_series_training[:10])\n",
    "actual_data = time_series[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_length = 10\n",
    "context_length = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in range(len(list_of_df)):\n",
    "    plt.figure(figsize=(12,6))\n",
    "    actual_data[k][-prediction_length-context_length:].plot(label='target')\n",
    "    p10 = list_of_df[k]['0.1']\n",
    "    p90 = list_of_df[k]['0.9']\n",
    "    plt.fill_between(p10.index, p10, p90, color='y', alpha=0.5, label='80% confidence interval')\n",
    "    list_of_df[k]['0.5'].plot(label='prediction median')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Deleting endpoint with name: Ml-Telcom-DemoForecast-deepar-2018-09-27-19-04-18-999\n"
     ]
    }
   ],
   "source": [
    "sagemaker_session.delete_endpoint(endpoint_name)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_python3",
   "language": "python",
   "name": "conda_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": "3.6.5"
  },
  "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
 },
 "nbformat": 4,
 "nbformat_minor": 2
}