{ "cells": [ { "cell_type": "markdown", "id": "3ac6400c", "metadata": {}, "source": [ "# Modeling (US MODEL)\n", "## Track Pre-release Forecasting Using CM Data Alone\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "6c0f2110", "metadata": {}, "outputs": [], "source": [ "!pip -q install snowflake-connector-python pytest pytest-sugar xgboost langdetect holidays" ] }, { "cell_type": "code", "execution_count": 4, "id": "28f18073", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Imports completed :)\n" ] } ], "source": [ "# main imports\n", "import snowflake.connector\n", "import pandas as pd\n", "import numpy as np\n", "import pprint\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "\n", "# utils etc\n", "from getpass import getpass\n", "from absl import logging\n", "import re\n", "import random\n", "import json\n", "\n", "# tensorflow imports\n", "import tensorflow as tf\n", "\n", "# scikitlearn \n", "from sklearn.dummy import DummyRegressor\n", "from sklearn.model_selection import KFold\n", "# model evaluation metrics\n", "from sklearn.metrics import (\n", " r2_score,\n", " mean_absolute_error,\n", " mean_absolute_percentage_error,\n", " mean_squared_error\n", ")\n", "\n", "from langdetect import detect\n", "import holidays\n", "from sklearn.compose import ColumnTransformer\n", "from collections import Counter\n", "from sklearn.preprocessing import (\n", " StandardScaler, \n", " OrdinalEncoder,\n", " OneHotEncoder,\n", " FunctionTransformer,\n", " MinMaxScaler\n", ")\n", "from sklearn import set_config\n", "from sklearn.pipeline import (\n", " Pipeline,\n", " FeatureUnion\n", ")\n", "# covariance and correlation and other stats functions \n", "from numpy import cov\n", "from scipy.stats import pearsonr\n", "from scipy.stats import ttest_ind\n", "import dask.dataframe as dd\n", "import shelve\n", "\n", "from sklearn.ensemble import GradientBoostingRegressor\n", "from sklearn.model_selection import TimeSeriesSplit\n", "from xgboost.sklearn import XGBModel\n", "from tqdm import tqdm\n", "\n", "log_level = 'DEBUG'\n", "logging.set_verbosity(log_level)\n", "print('Imports completed :)')" ] }, { "cell_type": "code", "execution_count": 8, "id": "8f59832b", "metadata": {}, "outputs": [], "source": [ "def get_snowflake_creds(username=\"aadamu\", account=\"orchard\",\n", " warehouse=\"DEV_PERFORMANCE_WAREHOUSE\"):\n", " \"\"\"\n", " Fetches and returns snowflake creds for connecting to snowflake\n", "\n", " Please use this within the scope of a function to ensure its memory safe \n", "\n", " returns:\n", " - creds (dict) - a dictionary containing user creds\n", "\n", " \"\"\"\n", " creds = {\n", " \"user\": getpass('Enter snowflake username : ') or username,\n", " \"password\": getpass('Enter snowflake password : '),\n", " \"account\": \"orchard\",\n", " \"warehouse\": warehouse,\n", " \"protocol\":'https',\n", " \"passcode\": getpass('Enter passcode: ') or None\n", " }\n", " return creds\n", "\n", "def snowflake_connector_factory(creds=None):\n", " \"\"\"\n", " A Factory for creating snowflake connectors.\n", "\n", " This returns the cursor after opening a session with snowflake.\n", "\n", " params:\n", " - creds - snowflake credentials \n", "\n", " returns:\n", " - cursor - snowflake session cursor\n", " \"\"\"\n", " try:\n", " if creds is None:\n", " _creds = get_snowflake_creds()\n", " else:\n", " _creds = creds\n", " return snowflake.connector.connect(**_creds).cursor()\n", " except Exception as e:\n", " logging.error(f\"Something went wrong - {str(e)}\")\n", "\n", "\n", "def _is_version_number(s):\n", " \"Check and returns true if its a version number\"\n", " return re.search(\"^[0-9][.0-9]*[0-9]$\", s) is not None\n", "\n", "\n", "def test_connection():\n", " \"\"\" tests connection to snowflake \"\"\"\n", " _creds = get_snowflake_creds()\n", " with snowflake_connector_factory(_creds) as cs:\n", " try:\n", " cs.execute(\"SELECT current_version()\")\n", " one_row = cs.fetchone()\n", " # make sure its just one row\n", " assert len(one_row) == 1\n", " # make sure it is a version number\n", " assert _is_version_number(one_row[0])\n", " logging.info(f\"Your snowflake version - {one_row[0]} PASSED!\")\n", " except Exception as e:\n", " logging.error(f\"Something went wrong - {str(e)}\")\n", " \n", "\n", "def set_env(conn_cursor, warehouse=\"DEV_OWS_WAREHOUSE\", \n", " database=None, \n", " schema=None):\n", " \"\"\" Setups Environment\"\"\"\n", " conn_cursor.execute(f\"USE WAREHOUSE {warehouse};\")\n", " if database:\n", " conn_cursor.execute(f\"USE DATABASE {database};\")\n", " if schema:\n", " conn_cursor.execute(f\"USE SCHEMA {database}.{schema};\")" ] }, { "cell_type": "markdown", "id": "7f9bbea8", "metadata": {}, "source": [ "## Test Snowflake connection" ] }, { "cell_type": "code", "execution_count": null, "id": "9652b9e4", "metadata": {}, "outputs": [], "source": [ "# quick test of connection\n", "test_connection()" ] }, { "cell_type": "markdown", "id": "514bfc67", "metadata": {}, "source": [ "## Snowflake SQL Executor" ] }, { "cell_type": "code", "execution_count": 9, "id": "f70dc314", "metadata": {}, "outputs": [], "source": [ "def _unpack_cols(description):\n", " try:\n", " cols = list(map(lambda meta: meta[0], description))\n", " return cols\n", " \n", " except Exception as e:\n", " print(f\"Error occured while unpacking cols: {e}\")\n", " return None\n", "\n", "def execute_sql(conn, sql, limit=None):\n", " \"\"\" Executes SQL and returns data as pandas dataframe \"\"\"\n", " try:\n", " res = conn.execute(sql)\n", " if limit is None:\n", " rows = res.fetchall()\n", " else:\n", " rows = res.fetchmany(limit)\n", " return pd.DataFrame(rows, \n", " columns=_unpack_cols(res.description))\n", " except Exception as e:\n", " print(f\"Opps...something went wrong. You might need to set snowflake env. {e}\")\n", " \n", "def execute_sql_dask(conn, sql, limit=None):\n", " \"\"\" Executes SQL and returns data as pandas dataframe \"\"\"\n", " try:\n", " res = conn.execute(sql)\n", " if limit is None:\n", " rows = res.fetchall()\n", " else:\n", " rows = res.fetchmany(limit)\n", " return dd.from_pandas(pd.DataFrame(rows, \n", " columns=_unpack_cols(res.description)))\n", " except Exception as e:\n", " print(f\"Opps...something went wrong. You might need to set snowflake env. {e}\")\n", " " ] }, { "cell_type": "markdown", "id": "71c2fb83", "metadata": {}, "source": [ "## Connect to Snowflake" ] }, { "cell_type": "code", "execution_count": 10, "id": "c187ecf8", "metadata": {}, "outputs": [ { "name": "stdin", "output_type": "stream", "text": [ "Enter snowflake username : \n", "Enter snowflake password : ························································\n", "Enter passcode: \n" ] } ], "source": [ " # connect to snowflake\n", "conn = snowflake_connector_factory()" ] }, { "cell_type": "code", "execution_count": null, "id": "8489cc7b", "metadata": {}, "outputs": [], "source": [ "# set snowflake environment\n", "set_env(conn_cursor=conn, \n", " warehouse=\"DEV_PERFORMANCE_WAREHOUSE\", \n", " database=\"DEV_ENGINEERING\",\n", " schema = \"AADAMU\")" ] }, { "cell_type": "markdown", "id": "14a9bf82", "metadata": {}, "source": [ "## (1) Pull TRACK_FORECASTING_DATASET_LATEST from Snowflake\n" ] }, { "cell_type": "markdown", "id": "06b3f8b8", "metadata": {}, "source": [ "#### Only Sample" ] }, { "cell_type": "code", "execution_count": 11, "id": "340455c3", "metadata": {}, "outputs": [], "source": [ "sample_size = 50e6\n", "dataset_latest_sample_sql = f\"\"\"SELECT * FROM DEV_ENGINEERING.AADAMU.TRACK_FORECASTING_DATASET_LATEST SAMPLE ({sample_size} ROWS)\n", " ORDER BY ARTIST_ID, RELEASE_ID, RELEASE_DATE ASC, SNAPSHOT_YEAR ASC, SNAPSHOT_ISO_WEEK ASC\"\"\"" ] }, { "cell_type": "markdown", "id": "d0b4a125", "metadata": {}, "source": [ "#### All Data" ] }, { "cell_type": "code", "execution_count": 12, "id": "d01f40b8", "metadata": {}, "outputs": [], "source": [ "dataset_latest_all_sql = \"\"\"SELECT * FROM DEV_ENGINEERING.AADAMU.TRACK_FORECASTING_DATASET_LATEST \n", " ORDER BY ARTIST_ID, RELEASE_ID, RELEASE_DATE ASC, SNAPSHOT_YEAR ASC, SNAPSHOT_ISO_WEEK ASC\"\"\"" ] }, { "cell_type": "markdown", "id": "1118de6c", "metadata": {}, "source": [ "#### Only Data for the US" ] }, { "cell_type": "code", "execution_count": 13, "id": "0a6b3e36", "metadata": {}, "outputs": [], "source": [ "dataset_latest_us_only_sql = \"\"\"SELECT * FROM DEV_ENGINEERING.AADAMU.TRACK_FORECASTING_DATASET_LATEST \n", " WHERE COUNTRY_CODE = 'US'\n", " ORDER BY ARTIST_ID, RELEASE_ID, RELEASE_DATE ASC, SNAPSHOT_YEAR ASC, SNAPSHOT_ISO_WEEK ASC\"\"\"" ] }, { "cell_type": "markdown", "id": "fe1c8b04", "metadata": {}, "source": [ "#### Run Query" ] }, { "cell_type": "code", "execution_count": 14, "id": "a8859f35", "metadata": {}, "outputs": [], "source": [ "dataset_df = execute_sql(conn=conn, sql=dataset_latest_us_only_sql)" ] }, { "cell_type": "code", "execution_count": 15, "id": "f6c4b740", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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5 rows × 33 columns

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" ], "text/plain": [ " ARTIST_ID ARTIST_NAME RELEASE_ID RELEASE_NAME RELEASE_FORMAT \\\n", "0 539614 Whiskey Myers 193483876009 Whiskey Myers Full Length \n", "1 539614 Whiskey Myers 193483876009 Whiskey Myers Full Length \n", "2 539614 Whiskey Myers 193483876009 Whiskey Myers Full Length \n", "3 539614 Whiskey Myers 193483876009 Whiskey Myers Full Length \n", "4 539614 Whiskey Myers 193483876009 Whiskey Myers Full Length \n", "\n", " THIRD_PARTY_PUBLISHER UPC ISRC RELEASE_TRACK_NAME \\\n", "0 N 193483876009 QMYLU1900001 Die Rockin \n", "1 N 193483876009 QMYLU1900001 Die Rockin \n", "2 N 193483876009 QMYLU1900001 Die Rockin \n", "3 N 193483876009 QMYLU1900001 Die Rockin \n", "4 N 193483876009 QMYLU1900001 Die Rockin \n", "\n", " RELEASE_DATE ... RELEASE_DAY_OF_WEEK_ISO RELEASE_YEAR SALES_START_YEAR \\\n", "0 2019-06-14 ... 5 2019 2019 \n", "1 2019-06-14 ... 5 2019 2019 \n", "2 2019-06-14 ... 5 2019 2019 \n", "3 2019-06-14 ... 5 2019 2019 \n", "4 2019-06-14 ... 5 2019 2019 \n", "\n", " AVG_DIFF_RELEASE_SALES_DAYS AVG_DIFF_RELEASE_SALES_WEEKS \\\n", "0 105.000000 15.000000 \n", "1 105.000000 15.000000 \n", "2 105.000000 15.000000 \n", "3 105.000000 15.000000 \n", "4 105.000000 15.000000 \n", "\n", " MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS \\\n", "0 199.500 \n", "1 199.500 \n", "2 199.500 \n", "3 199.500 \n", "4 199.500 \n", "\n", " MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS TOTAL_STREAMS TOTAL_SKIPS \\\n", "0 29.000 3700 922 \n", "1 29.000 6182 2682 \n", "2 29.000 1166 178 \n", "3 29.000 215 34 \n", "4 29.000 10447 4759 \n", "\n", " TOTAL_SAVES \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 268 \n", "\n", "[5 rows x 33 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.head()" ] }, { "cell_type": "code", "execution_count": 21, "id": "837ac845", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['ARTIST_ID', 'ARTIST_NAME', 'RELEASE_ID', 'RELEASE_NAME',\n", " 'RELEASE_FORMAT', 'THIRD_PARTY_PUBLISHER', 'UPC', 'ISRC',\n", " 'RELEASE_TRACK_NAME', 'RELEASE_DATE', 'SALES_START_DATE', 'TRACKNAME',\n", " 'RELEASE_GENREID', 'GENRENAME', 'FEED_ID', 'FEEDNAME', 'STORE_ID',\n", " 'STORENAME', 'COUNTRY_CODE', 'SNAPSHOT_ISO_WEEK', 'SNAPSHOT_YEAR',\n", " 'RELEASE_DATE_ISO_WEEK', 'SALES_START_ISO_WEEK',\n", " 'RELEASE_DAY_OF_WEEK_ISO', 'RELEASE_YEAR', 'SALES_START_YEAR',\n", " 'AVG_DIFF_RELEASE_SALES_DAYS', 'AVG_DIFF_RELEASE_SALES_WEEKS',\n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS',\n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS', 'TOTAL_STREAMS',\n", " 'TOTAL_SKIPS', 'TOTAL_SAVES'],\n", " dtype='object')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.columns" ] }, { "cell_type": "markdown", "id": "bda4f581", "metadata": {}, "source": [ "### Add Some additioal columns \n", "- SNAPSHOT_YEAR_ISOWEEK \n", "- Holidays\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "e3523ecf", "metadata": {}, "outputs": [], "source": [ "US_COVID_LOCKDOWN_1 = {\n", " \n", " 'start': {'iso_week': 10, 'year': 2020 }, \n", " 'end': {'iso_week':25, 'year': 2020 }\n", "}\n", "\n", "\n", "events = [\n", " US_COVID_LOCKDOWN_1,\n", "]\n", "\n", "def is_within_covid_lockdowns(iso_week, year, lockdown=US_COVID_LOCKDOWN_1):\n", " \"\"\"checks if event within covid\"\"\"\n", " bool_is_within_iso_week = (int(iso_week) >= lockdown['start']['iso_week'] and int(iso_week) <= lockdown['end']['iso_week'])\n", " bool_is_within_year = (lockdown['start']['year'] >= year and year <= lockdown['end']['year'])\n", " return bool_is_within_iso_week and bool_is_within_year\n", " \n", " \n", "\n", "def add_additional_features_dask(df):\n", " df['SNAPSHOT_YEAR_ISOWEEK'] = df.apply(lambda row: '{}-{}'.format(row['SNAPSHOT_YEAR'], row['SNAPSHOT_ISO_WEEK']), \n", " axis=1\n", " )\n", " df['HOLIDAY_CHRISTMAS'] = df.apply(lambda row: row['SNAPSHOT_ISO_WEEK'] == 52,\n", " axis=1\n", " )\n", " #extra ordinary events\n", " df['SNAPSHOT_WITHIN_COVID_LOCKDOWN'] = df.apply(lambda row: is_within_covid_lockdowns(\n", " iso_week=row['SNAPSHOT_ISO_WEEK'], \n", " year=row['SNAPSHOT_YEAR']\n", " ),\n", " axis=1\n", " )\n", " df['RELEASE_WITHIN_COVID_LOCKDOWN'] = df.apply(lambda row: is_within_covid_lockdowns(\n", " iso_week=row['RELEASE_DATE_ISO_WEEK'], \n", " year=row['RELEASE_YEAR']\n", " ),\n", " axis=1\n", " )\n", " return df" ] }, { "cell_type": "code", "execution_count": 23, "id": "33ec3ef7", "metadata": {}, "outputs": [], "source": [ "dataset_df = add_additional_features_dask(df=dataset_df)" ] }, { "cell_type": "code", "execution_count": 25, "id": "43c832fc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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170873 rows × 4 columns

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" ], "text/plain": [ " RELEASE_DATE SNAPSHOT_YEAR_ISOWEEK RELEASE_WITHIN_COVID_LOCKDOWN \\\n", "0 2019-06-14 2019-1 True \n", "1 2019-06-14 2019-1 True \n", "2 2019-06-14 2019-1 True \n", "3 2019-06-14 2019-1 True \n", "4 2019-06-14 2019-1 True \n", "... ... ... ... \n", "375961 2020-10-23 2020-25 False \n", "375962 2020-10-23 2020-25 False \n", "375963 2020-10-23 2020-25 False \n", "375964 2020-10-23 2020-25 False \n", "375965 2020-10-23 2020-25 False \n", "\n", " SNAPSHOT_WITHIN_COVID_LOCKDOWN \n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", "... ... \n", "375961 True \n", "375962 True \n", "375963 True \n", "375964 True \n", "375965 True \n", "\n", "[170873 rows x 4 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df[(dataset_df['SNAPSHOT_WITHIN_COVID_LOCKDOWN'] == True) | (dataset_df['RELEASE_WITHIN_COVID_LOCKDOWN'] == True) ][['RELEASE_DATE','SNAPSHOT_YEAR_ISOWEEK','RELEASE_WITHIN_COVID_LOCKDOWN', 'SNAPSHOT_WITHIN_COVID_LOCKDOWN']]" ] }, { "cell_type": "markdown", "id": "fbf06662", "metadata": {}, "source": [ "## (2) Feature Processing Pipeline \n", "\n", "Here we define the feature processing pipeline \n", " " ] }, { "cell_type": "code", "execution_count": 26, "id": "37c24d54", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['ARTIST_ID', 'ARTIST_NAME', 'RELEASE_ID', 'RELEASE_NAME',\n", " 'RELEASE_FORMAT', 'THIRD_PARTY_PUBLISHER', 'UPC', 'ISRC',\n", " 'RELEASE_TRACK_NAME', 'RELEASE_DATE', 'SALES_START_DATE', 'TRACKNAME',\n", " 'RELEASE_GENREID', 'GENRENAME', 'FEED_ID', 'FEEDNAME', 'STORE_ID',\n", " 'STORENAME', 'COUNTRY_CODE', 'SNAPSHOT_ISO_WEEK', 'SNAPSHOT_YEAR',\n", " 'RELEASE_DATE_ISO_WEEK', 'SALES_START_ISO_WEEK',\n", " 'RELEASE_DAY_OF_WEEK_ISO', 'RELEASE_YEAR', 'SALES_START_YEAR',\n", " 'AVG_DIFF_RELEASE_SALES_DAYS', 'AVG_DIFF_RELEASE_SALES_WEEKS',\n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS',\n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS', 'TOTAL_STREAMS',\n", " 'TOTAL_SKIPS', 'TOTAL_SAVES', 'SNAPSHOT_YEAR_ISOWEEK',\n", " 'HOLIDAY_CHRISTMAS', 'SNAPSHOT_WITHIN_COVID_LOCKDOWN',\n", " 'RELEASE_WITHIN_COVID_LOCKDOWN'],\n", " dtype='object')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.columns" ] }, { "cell_type": "code", "execution_count": 27, "id": "65d6fde2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ARTIST_ID 0\n", "ARTIST_NAME 0\n", "RELEASE_ID 0\n", "RELEASE_NAME 0\n", "RELEASE_FORMAT 0\n", "THIRD_PARTY_PUBLISHER 0\n", "UPC 0\n", "ISRC 0\n", "RELEASE_TRACK_NAME 0\n", "RELEASE_DATE 0\n", "SALES_START_DATE 0\n", "TRACKNAME 0\n", "RELEASE_GENREID 0\n", "GENRENAME 0\n", "FEED_ID 0\n", "FEEDNAME 0\n", "STORE_ID 0\n", "STORENAME 0\n", "COUNTRY_CODE 0\n", "SNAPSHOT_ISO_WEEK 0\n", "SNAPSHOT_YEAR 0\n", "RELEASE_DATE_ISO_WEEK 0\n", "SALES_START_ISO_WEEK 0\n", "RELEASE_DAY_OF_WEEK_ISO 0\n", "RELEASE_YEAR 0\n", "SALES_START_YEAR 0\n", "AVG_DIFF_RELEASE_SALES_DAYS 0\n", "AVG_DIFF_RELEASE_SALES_WEEKS 0\n", "MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS 0\n", "MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS 0\n", "TOTAL_STREAMS 0\n", "TOTAL_SKIPS 0\n", "TOTAL_SAVES 0\n", "SNAPSHOT_YEAR_ISOWEEK 0\n", "HOLIDAY_CHRISTMAS 0\n", "SNAPSHOT_WITHIN_COVID_LOCKDOWN 0\n", "RELEASE_WITHIN_COVID_LOCKDOWN 0\n", "dtype: int64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.isna().sum()" ] }, { "cell_type": "markdown", "id": "2da89b94", "metadata": {}, "source": [ "### Features (X - inputs)" ] }, { "cell_type": "code", "execution_count": 28, "id": "3404f92b", "metadata": {}, "outputs": [], "source": [ "def add_text_length_feature(df, text_column='TRACKNAME'):\n", " \"\"\"returns length of the string\"\"\"\n", " df['TRACKNAME_LENGTH'] = df.apply(lambda row: len(row[text_column]),\n", " axis=1\n", " )\n", " return df\n", "\n", "\n", "def add_text_language_feature(df, lang_detect_fn=detect, text_column ='TRACKNAME'):\n", " \"\"\"returns language of the text\"\"\"\n", " lst_distinct_text = Counter(df[text_column]).keys()\n", " distinct_text_lang_dict = {k: lang_detect_fn(k) for k in lst_distinct_text}\n", " \n", " df['TRACKNAME_LANG_CODE'] = df.apply(lambda row: distinct_text_lang_dict[row[text_column]],\n", " axis=1\n", " )\n", "\n", " return df\n", "\n", "\n", "# categorical\n", "CATEGORICAL_COLS = ['ARTIST_ID', \n", " 'FEED_ID',\n", " 'RELEASE_GENREID',\n", " 'STORE_ID',\n", " 'RELEASE_FORMAT'\n", "# 'COUNTRY_CODE'\n", " ]\n", "# numerical cols (ints) - counting\n", "TIME_COLS = [\n", " 'RELEASE_YEAR',\n", " 'RELEASE_DATE_ISO_WEEK',\n", " 'RELEASE_DAY_OF_WEEK_ISO',\n", " \n", " 'SNAPSHOT_YEAR', \n", " 'SNAPSHOT_ISO_WEEK', \n", " \n", " 'SALES_START_YEAR',\n", " 'SALES_START_ISO_WEEK',\n", " ]\n", "# numerical cols (float) - distance measure\n", "NUMERICAL_FLOAT_COLS = ['AVG_DIFF_RELEASE_SALES_DAYS', \n", " 'AVG_DIFF_RELEASE_SALES_WEEKS', \n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS', \n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS']\n", "# boolean cols\n", "BOOL_COLS = ['THIRD_PARTY_PUBLISHER', \n", " 'HOLIDAY_CHRISTMAS', \n", " 'SNAPSHOT_WITHIN_COVID_LOCKDOWN',\n", " 'RELEASE_WITHIN_COVID_LOCKDOWN'\n", " ]\n", "# textual-based features\n", "TEXT_FEATURE_COLS = ['TRACKNAME_LENGTH']\n" ] }, { "cell_type": "code", "execution_count": 29, "id": "8f232bca", "metadata": {}, "outputs": [], "source": [ "dataset_df = add_text_length_feature(dataset_df)" ] }, { "cell_type": "code", "execution_count": 30, "id": "d2284bf0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ARTIST_IDARTIST_NAMERELEASE_IDRELEASE_NAMERELEASE_FORMATTHIRD_PARTY_PUBLISHERUPCISRCRELEASE_TRACK_NAMERELEASE_DATE...MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYSMEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKSTOTAL_STREAMSTOTAL_SKIPSTOTAL_SAVESSNAPSHOT_YEAR_ISOWEEKHOLIDAY_CHRISTMASSNAPSHOT_WITHIN_COVID_LOCKDOWNRELEASE_WITHIN_COVID_LOCKDOWNTRACKNAME_LENGTH
0539614Whiskey Myers193483876009Whiskey MyersFull LengthN193483876009QMYLU1900001Die Rockin2019-06-14...199.50029.000370092202019-1FalseFalseTrue10
1539614Whiskey Myers193483876009Whiskey MyersFull LengthN193483876009QMYLU1900001Die Rockin2019-06-14...199.50029.0006182268202019-1FalseFalseTrue10
2539614Whiskey Myers193483876009Whiskey MyersFull LengthN193483876009QMYLU1900001Die Rockin2019-06-14...199.50029.000116617802019-1FalseFalseTrue10
3539614Whiskey Myers193483876009Whiskey MyersFull LengthN193483876009QMYLU1900001Die Rockin2019-06-14...199.50029.0002153402019-1FalseFalseTrue10
4539614Whiskey Myers193483876009Whiskey MyersFull LengthN193483876009QMYLU1900001Die Rockin2019-06-14...199.50029.0001044747592682019-1FalseFalseTrue10
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5 rows × 38 columns

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" ], "text/plain": [ " ARTIST_ID ARTIST_NAME RELEASE_ID RELEASE_NAME RELEASE_FORMAT \\\n", "0 539614 Whiskey Myers 193483876009 Whiskey Myers Full Length \n", "1 539614 Whiskey Myers 193483876009 Whiskey Myers Full Length \n", "2 539614 Whiskey Myers 193483876009 Whiskey Myers Full Length \n", "3 539614 Whiskey Myers 193483876009 Whiskey Myers Full Length \n", "4 539614 Whiskey Myers 193483876009 Whiskey Myers Full Length \n", "\n", " THIRD_PARTY_PUBLISHER UPC ISRC RELEASE_TRACK_NAME \\\n", "0 N 193483876009 QMYLU1900001 Die Rockin \n", "1 N 193483876009 QMYLU1900001 Die Rockin \n", "2 N 193483876009 QMYLU1900001 Die Rockin \n", "3 N 193483876009 QMYLU1900001 Die Rockin \n", "4 N 193483876009 QMYLU1900001 Die Rockin \n", "\n", " RELEASE_DATE ... MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS \\\n", "0 2019-06-14 ... 199.500 \n", "1 2019-06-14 ... 199.500 \n", "2 2019-06-14 ... 199.500 \n", "3 2019-06-14 ... 199.500 \n", "4 2019-06-14 ... 199.500 \n", "\n", " MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS TOTAL_STREAMS TOTAL_SKIPS \\\n", "0 29.000 3700 922 \n", "1 29.000 6182 2682 \n", "2 29.000 1166 178 \n", "3 29.000 215 34 \n", "4 29.000 10447 4759 \n", "\n", " TOTAL_SAVES SNAPSHOT_YEAR_ISOWEEK HOLIDAY_CHRISTMAS \\\n", "0 0 2019-1 False \n", "1 0 2019-1 False \n", "2 0 2019-1 False \n", "3 0 2019-1 False \n", "4 268 2019-1 False \n", "\n", " SNAPSHOT_WITHIN_COVID_LOCKDOWN RELEASE_WITHIN_COVID_LOCKDOWN \\\n", "0 False True \n", "1 False True \n", "2 False True \n", "3 False True \n", "4 False True \n", "\n", " TRACKNAME_LENGTH \n", "0 10 \n", "1 10 \n", "2 10 \n", "3 10 \n", "4 10 \n", "\n", "[5 rows x 38 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.head()" ] }, { "cell_type": "code", "execution_count": 31, "id": "88dce46e", "metadata": {}, "outputs": [], "source": [ "def feature_preprocessing_pipeline_factory_v1(categorical_cols=CATEGORICAL_COLS, \n", " numerical_float_cols=NUMERICAL_FLOAT_COLS, \n", " time_cols=TIME_COLS, \n", " bool_cols=BOOL_COLS,\n", " text_cols=TEXT_FEATURE_COLS):\n", " \"\"\"\n", " Feature processing pipeline (v1)\n", " \n", " params:\n", " - categorical_cols (list[str]) - list of categorical features\n", " - numerical_float_cols (list[str]) - list of float cols,\n", " - time_cols (list[str]) - list of time features, such as ISOWEEK, YEAR.\n", " - bool_cols (list[str]) - list of boolean feature\n", " - has_trackname_feature (bool) - adds trackname features\n", " \"\"\"\n", " # categorical feature\n", " categorical_features_pipeline = [\n", " (\"categorical_features\", OneHotEncoder(), categorical_cols)\n", " ]\n", " \n", " # count cols \n", " time_cols = [\n", " (\"time_features\", OneHotEncoder(), time_cols)\n", " ]\n", " \n", " # \n", " numerical_floats_features_pipeline = [\n", " (\"float_features\", MinMaxScaler(), numerical_float_cols)\n", " ]\n", " \n", " # bool features\n", " bool_features_pipeline = [\n", " (\"boolean_features\", OneHotEncoder(), bool_cols),\n", " ] \n", " \n", " text_features_pipeline = []\n", " \n", " if 'TRACKNAME_LENGTH' in text_cols:\n", " text_features_pipeline.append((\"trackname_length_feature\", MinMaxScaler(), ['TRACKNAME_LENGTH']))\n", " if 'TRACKNAME_LANG_CODE' in text_cols:\n", " text_features_pipeline.append((\"trackname_lang_feature\", OneHotEncoder(), ['TRACKNAME_LANG_CODE']))\n", " \n", " # column transformers\n", " column_transform_fns = ColumnTransformer(transformers=categorical_features_pipeline + \\\n", " time_cols + \\\n", " numerical_floats_features_pipeline +\\\n", " bool_features_pipeline + \\\n", " text_features_pipeline,\n", " remainder = 'drop'\n", " )\n", " return Pipeline(steps=[(\"feature_processor\", column_transform_fns)]) \n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 32, "id": "7ac65a84", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('feature_processor',\n",
       "                 ColumnTransformer(transformers=[('categorical_features',\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  ['ARTIST_ID', 'FEED_ID',\n",
       "                                                   'RELEASE_GENREID',\n",
       "                                                   'STORE_ID',\n",
       "                                                   'RELEASE_FORMAT']),\n",
       "                                                 ('time_features',\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  ['RELEASE_YEAR',\n",
       "                                                   'RELEASE_DATE_ISO_WEEK',\n",
       "                                                   'RELEASE_DAY_OF_WEEK_ISO',\n",
       "                                                   'SNAPSHOT_YEAR',\n",
       "                                                   'SNAPSHOT_ISO_WEEK',\n",
       "                                                   'SALES_START_YEAR',\n",
       "                                                   'SALES_START_ISO...\n",
       "                                                  ['AVG_DIFF_RELEASE_SALES_DAYS',\n",
       "                                                   'AVG_DIFF_RELEASE_SALES_WEEKS',\n",
       "                                                   'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS',\n",
       "                                                   'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS']),\n",
       "                                                 ('boolean_features',\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  ['THIRD_PARTY_PUBLISHER',\n",
       "                                                   'HOLIDAY_CHRISTMAS',\n",
       "                                                   'SNAPSHOT_WITHIN_COVID_LOCKDOWN',\n",
       "                                                   'RELEASE_WITHIN_COVID_LOCKDOWN']),\n",
       "                                                 ('trackname_length_feature',\n",
       "                                                  MinMaxScaler(),\n",
       "                                                  ['TRACKNAME_LENGTH'])]))])
ColumnTransformer(transformers=[('categorical_features', OneHotEncoder(),\n",
       "                                 ['ARTIST_ID', 'FEED_ID', 'RELEASE_GENREID',\n",
       "                                  'STORE_ID', 'RELEASE_FORMAT']),\n",
       "                                ('time_features', OneHotEncoder(),\n",
       "                                 ['RELEASE_YEAR', 'RELEASE_DATE_ISO_WEEK',\n",
       "                                  'RELEASE_DAY_OF_WEEK_ISO', 'SNAPSHOT_YEAR',\n",
       "                                  'SNAPSHOT_ISO_WEEK', 'SALES_START_YEAR',\n",
       "                                  'SALES_START_ISO_WEEK']),\n",
       "                                ('float_features', MinMaxScal...\n",
       "                                 ['AVG_DIFF_RELEASE_SALES_DAYS',\n",
       "                                  'AVG_DIFF_RELEASE_SALES_WEEKS',\n",
       "                                  'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS',\n",
       "                                  'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS']),\n",
       "                                ('boolean_features', OneHotEncoder(),\n",
       "                                 ['THIRD_PARTY_PUBLISHER', 'HOLIDAY_CHRISTMAS',\n",
       "                                  'SNAPSHOT_WITHIN_COVID_LOCKDOWN',\n",
       "                                  'RELEASE_WITHIN_COVID_LOCKDOWN']),\n",
       "                                ('trackname_length_feature', MinMaxScaler(),\n",
       "                                 ['TRACKNAME_LENGTH'])])
['ARTIST_ID', 'FEED_ID', 'RELEASE_GENREID', 'STORE_ID', 'RELEASE_FORMAT']
OneHotEncoder()
['RELEASE_YEAR', 'RELEASE_DATE_ISO_WEEK', 'RELEASE_DAY_OF_WEEK_ISO', 'SNAPSHOT_YEAR', 'SNAPSHOT_ISO_WEEK', 'SALES_START_YEAR', 'SALES_START_ISO_WEEK']
OneHotEncoder()
['AVG_DIFF_RELEASE_SALES_DAYS', 'AVG_DIFF_RELEASE_SALES_WEEKS', 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS', 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS']
MinMaxScaler()
['THIRD_PARTY_PUBLISHER', 'HOLIDAY_CHRISTMAS', 'SNAPSHOT_WITHIN_COVID_LOCKDOWN', 'RELEASE_WITHIN_COVID_LOCKDOWN']
OneHotEncoder()
['TRACKNAME_LENGTH']
MinMaxScaler()
" ], "text/plain": [ "Pipeline(steps=[('feature_processor',\n", " ColumnTransformer(transformers=[('categorical_features',\n", " OneHotEncoder(),\n", " ['ARTIST_ID', 'FEED_ID',\n", " 'RELEASE_GENREID',\n", " 'STORE_ID',\n", " 'RELEASE_FORMAT']),\n", " ('time_features',\n", " OneHotEncoder(),\n", " ['RELEASE_YEAR',\n", " 'RELEASE_DATE_ISO_WEEK',\n", " 'RELEASE_DAY_OF_WEEK_ISO',\n", " 'SNAPSHOT_YEAR',\n", " 'SNAPSHOT_ISO_WEEK',\n", " 'SALES_START_YEAR',\n", " 'SALES_START_ISO...\n", " ['AVG_DIFF_RELEASE_SALES_DAYS',\n", " 'AVG_DIFF_RELEASE_SALES_WEEKS',\n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS',\n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS']),\n", " ('boolean_features',\n", " OneHotEncoder(),\n", " ['THIRD_PARTY_PUBLISHER',\n", " 'HOLIDAY_CHRISTMAS',\n", " 'SNAPSHOT_WITHIN_COVID_LOCKDOWN',\n", " 'RELEASE_WITHIN_COVID_LOCKDOWN']),\n", " ('trackname_length_feature',\n", " MinMaxScaler(),\n", " ['TRACKNAME_LENGTH'])]))])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_config(display=\"diagram\")\n", "feature_preprocessing_pipeline_factory_v1()" ] }, { "cell_type": "markdown", "id": "919ebdfc", "metadata": {}, "source": [ "### Sanity Checks" ] }, { "cell_type": "code", "execution_count": 33, "id": "2ccffa5e", "metadata": {}, "outputs": [], "source": [ "def check_dimensions(df, expected=3912858):\n", " # check to make sure we\n", " try:\n", " assert int(df.shape[0]) == int(expected)\n", " except Exception as e:\n", " logging.warning(f\"Dataset has more rows than expected! Expected {expected} but got {df.shape[0]}\")\n", " \n", " return None\n", " \n", " logging.debug(\"TEST PASSED!!!\")" ] }, { "cell_type": "code", "execution_count": 34, "id": "14b451b8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:absl:TEST PASSED!!!\n" ] } ], "source": [ "check_dimensions(dataset_df, expected=399895)" ] }, { "cell_type": "markdown", "id": "dd286ef3", "metadata": {}, "source": [ "# (2) Modeling \n", "\n", "- Create Feature preprocessing pipeline (done)\n", "-- Track name length (done)\n", "-- release format (done)\n", "- Model using Light ML models: DCT, SVM\n", "- Model using Ensemble ML models: XGBoost." ] }, { "cell_type": "markdown", "id": "67ad6309", "metadata": {}, "source": [ "### Create feature processing pipleine" ] }, { "cell_type": "code", "execution_count": 35, "id": "a210b47b", "metadata": {}, "outputs": [], "source": [ "feat_pipeline_v1 = feature_preprocessing_pipeline_factory_v1()" ] }, { "cell_type": "code", "execution_count": 36, "id": "45e21294", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ARTIST_IDARTIST_NAMERELEASE_IDRELEASE_NAMERELEASE_FORMATTHIRD_PARTY_PUBLISHERUPCISRCRELEASE_TRACK_NAMERELEASE_DATE...MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYSMEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKSTOTAL_STREAMSTOTAL_SKIPSTOTAL_SAVESSNAPSHOT_YEAR_ISOWEEKHOLIDAY_CHRISTMASSNAPSHOT_WITHIN_COVID_LOCKDOWNRELEASE_WITHIN_COVID_LOCKDOWNTRACKNAME_LENGTH
36949539614Whiskey Myers884977958669FirewaterFull LengthN884977958669US2761001051Ballad Of A Southern Man2011-04-26...-3665.000-524.0002002001-15FalseTrueTrue24
36947539614Whiskey Myers884977958669FirewaterFull LengthN884977958669US2761001055Turn It Up2011-04-26...-3664.000-524.0001002001-15FalseTrueTrue10
36948539614Whiskey Myers884977958669FirewaterFull LengthN884977958669US2761001049Bar, Guitar and a Honky Tonk Crowd2011-04-26...-3664.000-524.0001002001-15FalseTrueTrue34
71203539614Whiskey Myers886444396097Early Morning ShakesFull LengthN886444396097QMYLU1300005Home2014-02-04...-36.000-5.0005002013-1FalseFalseFalse4
36966539614Whiskey Myers884977958669FirewaterFull LengthN884977958669US2761001060Song For You2011-04-26...979.500140.0006002013-1FalseFalseTrue12
\n", "

5 rows × 38 columns

\n", "
" ], "text/plain": [ " ARTIST_ID ARTIST_NAME RELEASE_ID RELEASE_NAME \\\n", "36949 539614 Whiskey Myers 884977958669 Firewater \n", "36947 539614 Whiskey Myers 884977958669 Firewater \n", "36948 539614 Whiskey Myers 884977958669 Firewater \n", "71203 539614 Whiskey Myers 886444396097 Early Morning Shakes \n", "36966 539614 Whiskey Myers 884977958669 Firewater \n", "\n", " RELEASE_FORMAT THIRD_PARTY_PUBLISHER UPC ISRC \\\n", "36949 Full Length N 884977958669 US2761001051 \n", "36947 Full Length N 884977958669 US2761001055 \n", "36948 Full Length N 884977958669 US2761001049 \n", "71203 Full Length N 886444396097 QMYLU1300005 \n", "36966 Full Length N 884977958669 US2761001060 \n", "\n", " RELEASE_TRACK_NAME RELEASE_DATE ... \\\n", "36949 Ballad Of A Southern Man 2011-04-26 ... \n", "36947 Turn It Up 2011-04-26 ... \n", "36948 Bar, Guitar and a Honky Tonk Crowd 2011-04-26 ... \n", "71203 Home 2014-02-04 ... \n", "36966 Song For You 2011-04-26 ... \n", "\n", " MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS \\\n", "36949 -3665.000 \n", "36947 -3664.000 \n", "36948 -3664.000 \n", "71203 -36.000 \n", "36966 979.500 \n", "\n", " MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS TOTAL_STREAMS TOTAL_SKIPS \\\n", "36949 -524.000 2 0 \n", "36947 -524.000 1 0 \n", "36948 -524.000 1 0 \n", "71203 -5.000 5 0 \n", "36966 140.000 6 0 \n", "\n", " TOTAL_SAVES SNAPSHOT_YEAR_ISOWEEK HOLIDAY_CHRISTMAS \\\n", "36949 0 2001-15 False \n", "36947 0 2001-15 False \n", "36948 0 2001-15 False \n", "71203 0 2013-1 False \n", "36966 0 2013-1 False \n", "\n", " SNAPSHOT_WITHIN_COVID_LOCKDOWN RELEASE_WITHIN_COVID_LOCKDOWN \\\n", "36949 True True \n", "36947 True True \n", "36948 True True \n", "71203 False False \n", "36966 False True \n", "\n", " TRACKNAME_LENGTH \n", "36949 24 \n", "36947 10 \n", "36948 34 \n", "71203 4 \n", "36966 12 \n", "\n", "[5 rows x 38 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.sort_values( by=['SNAPSHOT_YEAR_ISOWEEK'] ).head()" ] }, { "cell_type": "markdown", "id": "0cd08385", "metadata": {}, "source": [ "### Prep dataset\n", "\n", "- Filtering out Tracks with descrepancies (noise) between release and ingestion (out of sync)\n", "- Filtering for only the US " ] }, { "cell_type": "code", "execution_count": 37, "id": "07272b3f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['2019-1', '2019-1', '2019-1', ..., '2022-17', '2022-17', '2022-17'],\n", " dtype=object)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DATES = np.array(dataset_df['SNAPSHOT_YEAR_ISOWEEK'].values)\n", "DATES" ] }, { "cell_type": "code", "execution_count": 38, "id": "ceeaf94e", "metadata": {}, "outputs": [], "source": [ "# vectorise features\n", "X_feat_v1 = feat_pipeline_v1.fit_transform(dataset_df.sort_values( by=['SNAPSHOT_YEAR_ISOWEEK'] ))\n", "\n", "# prepare target (i.e. streams)\n", "y_raw = np.array( dataset_df['TOTAL_STREAMS'].values, dtype = np.float32)" ] }, { "cell_type": "code", "execution_count": 39, "id": "f752f262", "metadata": {}, "outputs": [], "source": [ "# for models which will need more rescaled \n", "y_scaled = np.array(dataset_df['TOTAL_STREAMS'].values/1e3, dtype = np.float32)" ] }, { "cell_type": "code", "execution_count": 40, "id": "ae0244e4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(399895, 206)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_feat_v1.shape" ] }, { "cell_type": "code", "execution_count": 41, "id": "135c007e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<399895x206 sparse matrix of type ''\n", "\twith 7707683 stored elements in Compressed Sparse Row format>" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_feat_v1" ] }, { "cell_type": "code", "execution_count": 47, "id": "7ccd29fd", "metadata": {}, "outputs": [], "source": [ "dataset_df['ROW_COUNT'] = 1\n", "sample_per_track_df = dataset_df[['ARTIST_ID', 'RELEASE_ID', 'ARTIST_NAME', 'ISRC', 'ROW_COUNT']].groupby(['ARTIST_ID', 'RELEASE_ID', 'ARTIST_NAME', 'ISRC']).sum().reset_index()" ] }, { "cell_type": "code", "execution_count": 48, "id": "08db2a50", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ARTIST_IDRELEASE_IDARTIST_NAMEISRCROW_COUNT
0539614193483876009Whiskey MyersQMYLU19000012684
1539614193483876009Whiskey MyersQMYLU19000021178
2539614193483876009Whiskey MyersQMYLU19000032668
3539614193483876009Whiskey MyersQMYLU19000042436
4539614193483876009Whiskey MyersQMYLU19000053873
..................
3172549794196292668254Ted NugentUSME32101242201
3182549794196292681284Ted NugentUSME32101242201
3192549794196292681284Ted NugentUSME3210124490
3202549794196626104830Ted NugentUSME3210124490
3212592973196626363589Easton CorbinQM4TW225825571
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322 rows × 5 columns

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" ], "text/plain": [ " ARTIST_ID RELEASE_ID ARTIST_NAME ISRC ROW_COUNT\n", "0 539614 193483876009 Whiskey Myers QMYLU1900001 2684\n", "1 539614 193483876009 Whiskey Myers QMYLU1900002 1178\n", "2 539614 193483876009 Whiskey Myers QMYLU1900003 2668\n", "3 539614 193483876009 Whiskey Myers QMYLU1900004 2436\n", "4 539614 193483876009 Whiskey Myers QMYLU1900005 3873\n", ".. ... ... ... ... ...\n", "317 2549794 196292668254 Ted Nugent USME32101242 201\n", "318 2549794 196292681284 Ted Nugent USME32101242 201\n", "319 2549794 196292681284 Ted Nugent USME32101244 90\n", "320 2549794 196626104830 Ted Nugent USME32101244 90\n", "321 2592973 196626363589 Easton Corbin QM4TW2258255 71\n", "\n", "[322 rows x 5 columns]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_per_track_df" ] }, { "cell_type": "code", "execution_count": 49, "id": "c650500f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 322.000000\n", "mean 1241.909938\n", "std 882.166419\n", "min 4.000000\n", "25% 752.250000\n", "50% 971.000000\n", "75% 1701.000000\n", "max 4556.000000\n", "Name: ROW_COUNT, dtype: float64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_per_track_df['ROW_COUNT'].describe()" ] }, { "cell_type": "code", "execution_count": 50, "id": "d744ee5b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sample_per_track_df[['ROW_COUNT']].boxplot()" ] }, { "cell_type": "code", "execution_count": 51, "id": "1b2d5ed2", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:absl:mean_track_row_count: 1241.91\n" ] } ], "source": [ "mean_track_row_count = sample_per_track_df['ROW_COUNT'].mean()\n", "logging.debug(f\"mean_track_row_count: {mean_track_row_count :.2f}\")" ] }, { "cell_type": "markdown", "id": "5b1c93e7", "metadata": {}, "source": [ "### CV Fold Size Selection" ] }, { "cell_type": "code", "execution_count": 52, "id": "5b07b8a1", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:absl:CV fold size: 10\n", "DEBUG:absl:dataset size: 399895.000 \n" ] } ], "source": [ "cv_folds = 10\n", "N = X_feat_v1.shape[0]\n", "\n", "if X_feat_v1.shape[0] > 10e5:\n", " cv_folds = 10\n", "logging.debug(f\"CV fold size: {cv_folds}\")\n", "logging.debug(f\"dataset size: {N :.3f} \") \n", "\n" ] }, { "cell_type": "code", "execution_count": 53, "id": "d5653164", "metadata": {}, "outputs": [], "source": [ "# max train size\n", "max_train_size = int(N/7)\n", "# test size\n", "test_size = int(mean_track_row_count)" ] }, { "cell_type": "code", "execution_count": 54, "id": "d8c9c4e0", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:absl:max_train_size: 57127\n", "DEBUG:absl:test size: 1241\n" ] } ], "source": [ "logging.debug(f\"max_train_size: {max_train_size}\") \n", "logging.debug(f\"test size: {test_size}\") " ] }, { "cell_type": "markdown", "id": "c84b7706", "metadata": {}, "source": [ "### Define TimeSeries CV Split" ] }, { "cell_type": "code", "execution_count": 55, "id": "755aee6c", "metadata": {}, "outputs": [], "source": [ "## we pick max_train size as 1/3 of the average row count (rough estimate)\n", "timeseries_cv = TimeSeriesSplit(\n", " n_splits=cv_folds, # since we\n", " max_train_size= max_train_size,\n", " gap=4,\n", " test_size=test_size,\n", ")" ] }, { "cell_type": "markdown", "id": "4bc009dd", "metadata": {}, "source": [ "## Evaluation Pipeline\n", "\n", "Evaluation Pipeline for Forecasting Models" ] }, { "cell_type": "code", "execution_count": 56, "id": "508727fe", "metadata": {}, "outputs": [], "source": [ "def evaluate_generalization_performance_sklearn(model_factory, X, y, cv=timeseries_cv, model_params={}, \n", " results_store={}, plot_residuals=True, dates=DATES):\n", " \"\"\"\n", " Evaluates the MAE on unseen data using Time series Cross Validation\n", " \n", " params:\n", " model_factory - model factory to evaluate the mode\n", " X (DataFrame/numpy-array) Features with shape (n_samples, n_features)\n", " y (Series) - Target variables with shape (n_samples)\n", " \n", " returns:\n", " evaluation results (dictionary) - dictionary of evaluation results for the \n", " \"\"\"\n", " pp = pprint.PrettyPrinter(indent=4)\n", " \n", " logging.debug(\"Fitting Params\")\n", " logging.debug(pp.pprint(model_params))\n", "\n", " for cv_fold, (train_indicies, val_indicies) in enumerate(cv.split(X,y)):\n", " logging.info(f'**Running CV Fold : {cv_fold + 1}**')\n", " cv_fold_str = f'fold_{cv_fold}'\n", " \n", " results_store[cv_fold_str] = {}\n", " logging.debug(results_store.keys())\n", " #split train validation split\n", " X_train, X_val = X[train_indicies], X[val_indicies]\n", " y_train, y_val = y[train_indicies], y[val_indicies]\n", " \n", " # create model\n", " logging.info('Creating model with model factory using defined parameters')\n", " model = model_factory(model_params=model_params)\n", " \n", " # fit model\n", " logging.info('Fitting Model')\n", " fitted_model = model.fit(X_train, y_train)\n", "\n", " logging.info('Evaluating Model')\n", " logging.debug('-running inference')\n", " # predict using dataset\n", " y_hat_train = model.predict(X_train)\n", " y_hat_val = model.predict(X_val)\n", " \n", " \n", " # evaluate metrics\n", " logging.debug('-evaluating forecast predictions')\n", " \n", " # MAE\n", " mae_train = mean_absolute_error(y_pred=y_hat_train, y_true=y_train)\n", " mae_val = mean_absolute_error(y_pred=y_hat_val, y_true=y_val)\n", " # save results\n", " results_store[cv_fold_str][\"mae_val\"] = mae_val\n", " results_store[cv_fold_str][\"mae_train\"] = mae_train\n", " print(f'MAE (train) : {results_store[cv_fold_str][\"mae_train\"]: .2f}')\n", " print(f'MAE (val) : {results_store[cv_fold_str][\"mae_val\"]: .2f}')\n", " \n", " # RMSE\n", " rmse_train = mean_squared_error(y_pred=y_hat_train, y_true=y_train, squared=False)\n", " rmse_val = mean_squared_error(y_pred=y_hat_val, y_true=y_val, squared=False)\n", " # save results\n", " results_store[cv_fold_str][\"rmse_val\"] = rmse_val\n", " results_store[cv_fold_str][\"rmse_train\"] = rmse_train\n", " print(f'RMSE (train) : {results_store[cv_fold_str][\"rmse_train\"]: .2f}')\n", " print(f'RMSE (val) : {results_store[cv_fold_str][\"rmse_val\"]: .2f}\\n')\n", " \n", " # plot residuals\n", " if plot_residuals:\n", " _plot_data = pd.DataFrame({'residuals(val)': np.abs(y_hat_val - y_val),\n", " 'time_index': val_indicies})\n", " sns.lineplot(data=_plot_data, x='time_index', y='residuals(val)')\n", " plt.title(f'Residual(val) - MAE({mae_val :.3f})')\n", " plt.show()\n", " " ] }, { "cell_type": "markdown", "id": "9df37064", "metadata": {}, "source": [ "## SVM Regression Model\n", " - Predict First seven days only.\n", " - Drop Noisy samples with negative dist.\n", " - Do for the US only.\n", " " ] }, { "cell_type": "code", "execution_count": 977, "id": "68211e28", "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import LinearSVR\n", "\n", "svm_model_params = {\n", " 'loss': 'squared_epsilon_insensitive', # keeps features as much as possible\n", " 'max_iter': 3000,\n", " 'fit_intercept': True,\n", " 'C': 1,\n", " 'random_state': 99, # seed value\n", "}\n", "\n", "def svm_regressor_pipeline_factory(model_params={}):\n", " \"\"\"SVM Regressor Pipeline\"\"\"\n", " return Pipeline(steps=[\n", " (\"sklearn_svm_regressor\", LinearSVR(**model_params))\n", " ])\n" ] }, { "cell_type": "code", "execution_count": 978, "id": "6bb5bc71", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('sklearn_svm_regressor', LinearSVR())])
LinearSVR()
" ], "text/plain": [ "Pipeline(steps=[('sklearn_svm_regressor', LinearSVR())])" ] }, "execution_count": 978, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_config(display=\"diagram\")\n", "svm_regressor_pipeline_factor()" ] }, { "cell_type": "code", "execution_count": 979, "id": "bdbe8ab2", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:absl:Fitting Params\n", "DEBUG:absl:None\n", "INFO:absl:**Running CV Fold : 1**\n", "DEBUG:absl:dict_keys(['fold_0'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{ 'C': 1,\n", " 'fit_intercept': True,\n", " 'loss': 'squared_epsilon_insensitive',\n", " 'max_iter': 3000,\n", " 'random_state': 99}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.77\n", "MAE (val) : 0.85\n", "RMSE (train) : 1.83\n", "RMSE (val) : 1.29\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 2**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.79\n", "MAE (val) : 1.35\n", "RMSE (train) : 1.84\n", "RMSE (val) : 2.57\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 3**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.82\n", "MAE (val) : 1.87\n", "RMSE (train) : 1.88\n", "RMSE (val) : 7.38\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 4**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.87\n", "MAE (val) : 4.18\n", "RMSE (train) : 2.17\n", "RMSE (val) : 9.85\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 5**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.00\n", "MAE (val) : 3.03\n", "RMSE (train) : 2.59\n", "RMSE (val) : 6.36\n", "\n" ] }, { "data": { "image/png": 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VMca0cvn8svV0An+dOmt6PvC4qj4O2GsZjTEmz6TTjr9CRO4Afgp8T0TaAx3CyZYxxrQeeVy495VOif/HQDVwjduKZ3fgoVByZYwxrVw+nwtSLvG7wf5Rz/ByrI7fGGPyTipP7lbgf3ITQFV1u6znyhhjWpF8vpHrJ5UHuOwGrjHGxMnnc0E6N3cBEJGdgc6RYbfKxxhjTJ5I5wGu80RkAbAE+AxYCowKKV/GGNNq5HHh3lc6rXruA44B5qvqXsCpwIRQcmWMMa1e/p4O0gn8taq6HmgnIu1UdSzQL5xsGWOMCUs6gb9MRLoB44BXRORxnF46jTGmTcvFjdxVZZUMGTWPRaXZf9VJOoH/fKASuA0YDSwCzs16jowxJo+EdU5Ys6mKpz9bxIoNW7OedjoPcG3xDA7Nek6MMSaPtNRVgIhkPc2UA3/cg1wdcfrp2WIPcBlj2jpNUK7PflhuXGpY0inxxzzIJSIXAEdlO0PGGJNPwgrPkSuKME4s6dTxx1DVd4FTkk0nIu1FZLqIDHeHu4vIhyKywP2/Y6Z5MMaYFpHDVy+GUNOTVlXPhZ7BdsAAUlv3XwFzgUiV0GDgY1UdIiKD3eFBqebDGGNaSipBN6yqnjBPLOmU+M/1/J0BVOC09AkkInsA5wDPeUafT+PN4aHABWnkwRhjWkwqN3DDr+rJ4c1dVb0qg/Qfw3lNo/f+wC6qWuymWez2/dOEiFwHXAfQu3fvDBZtjDHZ4RfcW65VT/bTTKVb5idIcFJT1VsC5vsBUKKqU0XkpHQzpqrPAs8CDBgwIH+fjTbG5K2cVvWEeGZJpapnCjAVp0fO/sAC968fUJ9gvoHAeSKyFHgdOEVE/gOsFZFeAO7/kkwzb4wxYcpl18uRReekVY+qDlXVocB+wMmq+oSqPoHTSVu/BPPdoap7qGpf4FLgE1X9KfAecIU72RXAsOatgjHGhMvvBJCobX9WhRD507m5uxuxdfXd3HHpGgKc7nbxfLo7bIwxrU4Y9eupCvNqI50XsQwBpovIWHf4RODuVGZU1U+BT93P63GuFowxplXLbVWPs/Bct+p5QURGAUe7owa7L2A3xpg2za9aJ/STQqQ5Zy6qekTkAPd/f5yqnRXu327uOGOMaZNSCboV1XXc/d6c8PIQQpqplPh/jdOe/hGf75QUum0wxph8lGqp/sWJS7n7vIOavbw1m6rYXF3LvjtvG+qt46SBX1Wvc/+fHGI+jDGm1fJt1RNCZD7mLx8DsHTIOY1P7oZQ15POy9YvFpFt3c9/FJG3ReTwrOfIGGNaiVy26gkzD+k057xTVStE5HicvnqGAk9nP0vGGNM6tIZWPWFIJ/BHntI9B/inqg7DeSGLMca0ab599YT8AFdr6Y9/lYg8A1wCjBSRTmnOb4wxecWqepyAPwY4U1XLgO7A77KfJWOMaV3C7DAtcJkhpp1y4FfVrTgdqh3vjqrD6azNGGMKTtjngsaTTW5b9dyF86asO9xRHYD/ZD1HxhjTBtz2xgxOf/SzZqeT01cvAj8EDgemAajq6kjzTmOMacsyKdy/M31Viy8zVenU8deoc+2hACLSNZwsGWOMIdetesR5dGy426pnBxH5BfAR8K8Q8mSMMQUv2jtnCHU9KVX1qKqKyAU4dfzlwHeAP6nqh1nPkTHGtDY5fJArV520RUwCylTVmnAaYwpe+K16wks7ncB/MnC9iCwDtkRGquqhWc+VMcYUOM1lf/weZwH74HTDfK7nr1XbtLWWvoNHMOrr4lxnxRiTp8LsnmHsvBL6Dh5BSUVVdFxFVS3XvjQFyP0buJZlfektYGHpZgCeHb+Ysw7plePcGGPaimydDF6cuBSAOavLo+Pem7k6K2kHsb52jDEmRI9/tKBZXT7kuqrHGGMKUnNutP7to/msKqvMXmaywAK/McakYN3malZ7Ang6J4Nc9uvvJ51WPcYYU5BUYcD9HwHOaxHT1ZzqGqvqMcaYAhNGq56CCfzTl5fx0Jh5uc6GMaaNaGW1N2kpmMAP8OTYRbnOgjEmD+UyyFtVjzHGFBgL/BnJ5wsyY0wh+nzBuuhnq+M3xpgc8HsAK8z38I6avSa0tMECvzHGhC6lc0TANFbVE7Kpyzbyz0/tBrAxpvXIdX/8bd6P/jkRgBtP2ifHOTHGtCZ+hfGsV/SEEeEDWInfGGNaMavqyUgLnkaNMQWtqraesq01WU41j1r1iMieIjJWROaKyBwR+ZU7vruIfCgiC9z/O4aVh2yrqq1na00dACs2bOXd6atynCNjClPZ1hremb6yxZbnd3PWb9wFT06g372t/1XkYZb464DfqOp3gWOAm0XkQGAw8LGq7gd87A7nheOGfMKBfxoDwPlPTuDWN2bkNkPGFKhbXp/BbW/MZLH7oqXWYt6aisDvVDWjJqB5VdWjqsWqOs39XAHMBXYHzgeGupMNBS4IKw/ZtmFLje/nTZW1PPLBt9TVN+QiW8YUnLWbnNcU1uTJMacKe90xkrvem5P2vGFUVrdIHb+I9AUOByYDu6hqMTgnB2DnlshDWFSVIaPm8cQnCxkZ8kMXxphcaV67nshrGl+a1DreYBt64BeRbsD/gFtVtTzZ9J75rhORKSIypbS0NLwMNlODOnX/gJX4TVJPfbqQG16emutsmBbWnId8JYS6nlADv4h0wAn6r6jq2+7otSLSy/2+F1DiN6+qPquqA1R1QM+ePcPMZrOE+di2yQ/FmyrpO3gEs1aWJZ32wdHfMnqOXRnmi/LKWqD5b9Cqb847d5u3aF9htuoR4N/AXFV91PPVe8AV7ucrgGFh5cERbmC2sJ+fHv9oAac9+lmT8es2V1O8qen7UeetKeeI+z6ktKK6yXfj5jtXpC+3kst4kz2LSrcEfpdOLF9bXpVxHvLq5i4wEPgZcIqIzHD/zgaGAKeLyALgdHe4VUmnFN+QZNrZqzaxfnPTYJGuLxavp+/gEcwtTrm2zCTwt4/ms7CkaYuQAfd/xLF/+aTJ+H+NW8L6LTWMned7gZqR6rr6rKVlmufEh8bygyfGh5b+5c9NDi3tTITWZYOqfk7wVcqpYS03G1RTP8smO0f84InP6bV9Zybd0bxVHu3eOJ60aD3f7bVds9IyuTdvTTlnPjaepy7vz9mH9Mp1dgresvVbE37f3Cv7ZtXx59MDXPksnd/I+4MG/bjFmzK/zDOtQ7Qg4HMMZnJgzlq5CYBPsngFUYha6hbbiFnFTZfdMovOu6qevOVXfTNt+UbfaTXEn794UyUrNyYuibQmQycuZcWG/MlvtmSyD0SqE9tZjyJ5YcTXTQN/PmvzgT9RiWDNpipOemhsk2AVP89XSzdw4VMTfdNo8Eybzpl5a00dz45bRH1DcAaP/csnHP/XsaknmkObq+u46705XPrsF7nOSl6ItPxtl8Xi3NhvS9jktkIxJpG2H/gTfPffqStYun4rr3+1PGZ8fIl/dVnTVh7R9D3TpnPZ+fCY+Twwcl6zShKqyquTl1Ne1XIH+/UvT2HA/U37Iolss/ICDDyZVPVEmve1y1KRf/3maq564StueqUwnhEIo/ojXS1VzWRVPRkI+8fJNPkKN1hXup2+ZWLa8jJ+/87X/P7trzNOI11j5qxl3ebMeh+cv7aCu4bNpqFB+de4xQXdQilSYGifpaO6us65hFhUEtz80CRX0oxml5ksa+oy/yrksBVA4E8eml//cgXLPXf1kzXRjEk/w4d1s3G8R54Y9vYblIlhM1b5Nm0MWl6mrnrhK4ZOWsaqskr+PHIuZz0eXvO5REa3gq41GhqyW8ffGkrAbcFRD3zcYsH4jMfGRV/+lEjePbnbGqQSwtdvqeGipxt/gGRxf/is1Z70m3dJ0Roe/P3V6zN8H2byWlVWyQF3jg78Pp31yHWQuuE/4VSHpPNTRm7tZPugDrOxQaGYvza4h02v+ELll0s2pLWcjVtTqxbNqyd3W4tUA1KZ50dINssvX50e/Zzo5m6iq41IvXC+HKbJur9N56G3RJOu21zNg6PnJbzp3RY0RFv1WFG9OUoqqpvVkmxVWSUDhzR9YC8Tt+VRN+1tP/CnGlo9x19aVT0Jbu6mkkw2SvwtcdWQLA6nE6cT5fcP73zNU58uYsLCdTHjq2rrufu9OS16Iztd6YTwhiw354wWJNr2+bKJK57/khMezLzl21tTVrAqQeONdIRVWLGbu5lIP+6ndfAk+q3TOYFkItX9YXVZJRPjAmm6kpXoo+uaQqYSbZeqWuemSXynVq9/uZwXJy7liY8XBM5717DZvPnViuQZCEk6v3akOWf7LDfkL7C4nzNXvfAlH81dGzOuOR2xAYE/XhhP7obWZUNrkein8Naves+qaVVbJFhCopNCS17hn/HYOCqq6lg65JyM00i2SVI5yUW2a6Jpg76pd7+orQ+ed6jbSdolR+6ZNC/ePIVx8yyZaIk/S4E/sh+WVlSzsKSCfXfeNivpFqJkv4iqMvbbUsZ+G9tdfEMzS/xBscRK/BlItdTtrWtN68SdYYk/sriWuBlXUZV5k9GIZNsxW1dJQcIKzbmqGsn2k7vebXrao+Oyk2gb969xi3nso+AryCBBVTp1nvHpFB4/c3t3bcl9sc0H/qb17gFnVc/ndKpoEgWxxMm0jpt6qe6gidZzcelmjn7gYzfBVNJKdNO7ZWWlOi6DTEe6d87Wzd3mljYL0WMfzc9ovqAqHe++lMnPETSPterJQPy2jPw2lTX1fOt5MbL3cj+9An+iqp5Uqj/SWBhQVVfPfn8Y6dtpVCZSXX6idXln+qq0llmXoLom6JvoFVKWi0VBqa3cuDXm2Y6U0koja5FqqWxVMxXaTd1syHSTBZX4GzIs8SdLN4zI3/br+ON+gMjQLa9P58NvGm/OZPvmblVtPTNXlAXO11jVk541m6qorVceHDOPB354SJpzN5VqiTfRZOnu46m0fojf18O6Egha/0gfScnui/QdPKJZy+9UlJ2yV9gNCVqbeWtSa2sfhlSqejIp8bfk2/zafuCPH1YFhC8Wr48Zn/HN3YBp73x3Nm9NXRk4X3Rxaf7YkaqBTJuOVdXW06moXbSkmWoy2dwpaxsyfzdxUC5mr9qUWXo5ipeH7bE9M1duomvH9s1KZ8LCdQiw6/ads5MxExW0zwftvjFdtGdwPRFUhWT98WcibltGn5iMm8zbuiKtqh71L41G+lsPkukVfqT5n/eyMtW0SsqrOODO0Tw3fkl0XKolxZTPMwnyEknC76R117DZCUvPkRNVUHaHjJqXYgZj5aqkHNnfmls1f/lzk7nsucnNTsc0FbRN6wIivzdwZ7JbBRXmrFVPBuLPvJHh+E3s3ba3vDY99fS1eW2n/eZduXEr//x0UcL5MmkzvGKj86DK+94uJ7JQx58uvzr+SJ13UCkrUm0WVJJKtYT1wMi5sfOlfMWT2nSpiqSXre3qt92qauu58T9TWbbeOm7LRNC+GHTs1Tc0L/Bbq54sSvVpWu9Ntslp9LmRaXPMyOXb2vIq/vDO19TWN5YirnlxCn8d7V+Cjexc9RnUlvjtyKmX+JtON3vVpqRVQF8t3cDSdbGBJ91qqg+/Wcvbad5ADvLsuMUxw7kq8UeWm63F+23SCQvXMWr2Gu56b052FpIn/vHJAl6YsITN1c47L9Jt8RQJBUGzpVJTmSgu3Pv+N75Xt4El/uSLS1ubruMfM2cN17+cWodcmW7cTC+xIzvXk2Odkv1J39mZ0w/cBXBeahK8PI35DzBx0XoWlmxm3527JVym347lLb1E3gM78pYTOHC32Pf6xgfICQvXcflzk7nv/IMS7uQXPz0JiL1JmqiO36+Vyy9emhL9HBQoMw2gqf5+2b7cjvwW2XqOo9Bu7iby8AdOM80FJZt5dfJy+u7UlZ2368xOXTuyZ/cuKacTtE2Dqnpi5w3+7vkJS3zHB9bxW++c6Xl7WtObqwfcOZqvljYt0cdv25LyKvoOHsHrXy5vMq1XfIn3f1NXptRLX6Y/ZaTlQPxO+ecR3ySd12/H8nYrHemuePTspk1F45+YXepWH3xTnH7rioTNOZM9KJb20pLIUbysj/6O2UnPL0hF9unW8cRIy9vkdrxYXdfABU9OaFafPl4plfgzOBG35LMYbTrwB3nB54wbf1Zd5vb4998ELXPAOXC9c/7mrZlc8sykZucxcHnRqh5NqZT3zvTG/PtN7k0jUeuB+GCdSQEzknp9c1r1BFbVZZZeNpqzZiL7dfxZSabN+/Ezk3hu/OLkExL826Ryfy2TGB7cqif72nTgT6daIKjdePIDM7OL9Uwv36IlxQaN2bmC8jBtWVmTeb381s8vraBgnc5qRNKtS3BUJK//D7i5m2Hgq8nkZokrvoSWzp4QOcizV8dvkT+Idx+dvGQD949wbvAn73/Kf3xKBZcMfo6gEr+16smmuG0cv3FTfcAqjKuzRP36RwJGXYolfi+/6VN9WXyiztH8slFVW0/ZVv83gyUK7vEnhSbBNct1/HcNS+3Gp9+2aU5vjA2eE3g2JMrKsvVbW/ThoDA0NCh/GTU3sO/9RPfFMl5mYOudzOdNPE/as2SsIAO/30Fc1K5d3DSN7ayTPbWaytXBxrjXI6ZzFvfuEA2eOv50D+ZIwI19StmnxO+TbHywDlpyJO1Ln/2Cfvc2fSk74HuPJX45DZ4TXCoyDcLjFpQmnyhomc04UqMl/oxTiJVo31u8bgvPT1iapSXlxrdrK3jms8Wc8ODYJscSwOX/+iLtNJMdg0GbNJWbu5n8rsGteuzmbloSbvy4bVnUXvy/1sQla+/lfaLY8/AH3ybKTUx24nfIhpgSv/u/QWNuMqUS97xNRmvrG/jTsNl8PK+kyXTedaqrb+DLJRuatsRxF/jq5OU85fPMwYwE3VX854vgG+aNVVmxw3GLbZrngC9ufnUaP/v35MDlba1J/B7hWSvLKK+qbVyu9yTcpK0wTFy4zjcvG7fUxDThiz/BNVeyc9DUZem9FrC18W4nv4LDTJ8HJpNVvQW9Q7rxpTb+8ydqnBAR9LsetVf3tOcJo5K/bQf+RL9P3HdFAf3jKkletuINvgl2tPidJb5XxsTVLI0LaSzxx+4oa8urkl4B1HjyMH5BKS9NWsYdb3/dmAefef7+yUIueWZSk5ZKYV2WRgLir16fDjQtXQVt46DS0ohZxYxfkPlLaM77xwSufbGxOemz4xdzidtENX6RI2cXc9lzk3nVpyXY4rhnGby/I8Bz4xfHdBqYrnyvyknGu3rpnix/79nHvTJ9q1wqV6FBWSwprwqcx57czYH4qh5ve/lkJf7It4nq/qJPDLtVNPFXGODsFFuq65pc2lXXNibs3Tm8+Zq3poJXJjcNON5AWVvX4I7zv7nc2ANm47h5xeUAFJfF7rBBO2lFdR1/+9C/u9tUDtjIQbXFLYmnWuJPoRCWsanLN0a3zcKSzXzpljjj8xZ5e9iyFHr1rG1o3B/qG5T7R8zlgicnZJxHv5/Du60Wlmym7+ARzX4TW2uQ7v348gzfRxH45G5Kgd9/mqUJ9o2WrONv0w9wpVPTFv8KvEgJXTXxDTjv71tT13jpWF0XexkZSeK4IZ/QuUN7zj5k1ybpHPXAx+zds2uTM3x1nSfwexb4TNxTqBMXreOnx/QBoKKqlhUbYt8l6r1y8LvCiTz44ie+5H3vcP/nBlThcZ/XI5ZWVCe8QRwR32IivnSVSpe4YYhv1jtt+UZmBVRnRbasqrKqrJI9duxC/L4Y6Y+/QTVa5VBZW8/7M1dz9F7d2Xm79DpdS3bjfv7azQC8Mnk5HYracWTf4CqH1si7eonq2BOVqL3qEpw9Ggt96c8bkcneGFQwsuacaUrnirBDXAk8+mSlJn/ZSmROb9NAb7CGxh+1eFMVS9ZtiT6xG12e+/3i0qb9qnhPIt4AN315WZO8RFz30lTO/vv4mNJRpJQpJL5hpD6fmxtXT33kU2rqmh4wfxo2m601jSWy+MB+R9xlevx2jUh2NdHQoPzgifGpZjepC5+ayN3vBzw0527a/01bxfF/HcvUZRsStkby1jX/32vT+fnzX6adH7/192t2OOLrYi5+epLvb9GaeQs8iX7ri55O7RmaRIWQyC4YVGpPdl/ISSP9Aya4qsfeuRuaJiV+z823RD+i9zvvwRQfoJLtB4kuH71p1SaJwLe+Pp11m2uY4t7M2+Jp5ubNX0198M7rzWvkc20z2ruDc7nd0afv+ZcmLaPX9ttEh+O3g/edCdD0Sgqck8Oc1eUJl7+1tp7ZqxJPk21fLnG6/p6/Nrg7jQaFqrh9ZdXGSt9pE/HbvxLVRd/25gy236YDt562Hx3atWPHrh2j39XUNfBNcTn99twh7XyExbv/Jbq5utzT3DPRMZfo+Y1oP0oB32+pSV51lMktl5Z8FqMgA//Ir9c0GTctrvT80qSlgFN3vqmyNjCtYTNWRzsQK3Ev3wE2xDU5W1y6mXHzg5sOJgqssXX8ifq5gXdnOD1vdu3Yntr6et6b2dgTp3cZ3jRTkUpLhmSC1tFb4k124+yjuSU0NGhMN9qvxd1Mra1voEP72JNMUAuOMDS2CmkcF7T9VJXK+BJkggLepspavlqygdPcfp0i/Ev8wdsy8ga3Vycvp3OHdsy776zodr3rvdm89uUKPh90MuWVdfTavnPMiSEXahNcTTc3vXh/fHc2dfUNnHHwrr7fb6kOp8Qf+ABX2ikl17arepox70dzG5s5PjQmuCmmt8Oll9yuhf3MXLkp4SW8d2eK/6G9pdxU6skBOviUrmsTXJF4qU9lT2UWAmfQsVAVUJUVFLjW+7TjjknPJ69NgmtMvhJv00xbzETmEoIDjbeOH8/0QW55bTrXvjSFkorYuuz4TfXq5OUpP2dQVdvA+zNXs/fvR7KqrDLaO+1n80s5++/jOfw+/+cxwjS3uDymldPSdY0l+YmLUrtBnSj4JruCffqzxYGv3twaUok/uJO29NNKRvKhGdiAAQN0ypQpySeM8/IXy7jz3dkh5Cg1vbt3ibn0zCc7de3Iwbtvz2cJrlJy5S8XHkKfnbrQqagdtfXKpc/GPrxzwn496NqxiOtP3JsfPjURgF+csBf/8ryAxuvE/XtSVVtP6eZq33ssmdht+86s3pTajUY/D110KFOXbeSDb9Zy1XF9OXafnRg6aRnvu1dwT17Wn6L2Eu19tn/vHWKuWvfYcRsO2HXbmAJMNnXv2jF6VXvTSfvQqch5k9hPj+nNncNms3zDVnptvw219Q0c2bc74xeU8sXiDTx+aT9+9fqMaDp79+jK81ceyd8+mk/v7l2oqKrj61WbmLpsIwDH7N2dF686igPuHJ00T+/ePDDlllGXHd2bV31awaXittP2529JXtTesahd1u6jfHPvGXTpmFnljIhMVdUBTca35cC/taaOA/80Jmbc3358GDOWl0Vf/BGxT8+uLPIc9EFBu1unopjHwy88fPfAvuJf/cXRrNxYye3/nRUd9+b1x4baiZsxpm2Ze++ZbJPhKzqDAn9OqnpE5EwR+VZEForI4LCW06VjEVcP3IsL++/OtcfvBcDAfXpwz/kH8+vT9+fBiw6NTvvxb05i6ZBzmHPPGUwcfAo9t+0EwPNXDuDb+8/k1WuP5snL+jP7njOYd9+ZXHrkngD03LYTg886gAd/dCjf3HsGH9z2vWianYrac8mAPRl81gHRcfvu3I1Zd3+fJy/rz7F775R0Ha48rm/087adi/hR/z14+6bj0toO23aOLS1MGHxKWvMD/PGc76b9fthbTt0vZvjGk/YJnPYPZ383pTR3C3i37MMXH5Z6xlw9uoVTb/2dXbZNabqRt5yQ0nS777BN8okCvPfLgRnPmw+K2gm3n/mdmHEXHbFH2unclGDf/PS3JzUZ16moHbPvOYNv7z+THx6+e8x3v/3+/k2mP3qv7jx6if8+emHc/C2hxQO/iLQHngTOAg4EfiIiB4a1vD+deyCPXtKPP/7gQBY9cHa0ffQtp+7HJQP25Nrj94oJrl07FbHbDtvwKzdo9e+9I52K2nPcvj0459BeAHTu0J7rvrc3Re2ES47ckxtO3IdLjtyTLh2L2H+XbTlsj+3ddXXSvOHExp1q285FbNe5A+cc2ouXrzmKkbecwKIHzo5+P/n3p9K5g/OzDL36KO4+7yCm33k60+88na/vPoNHLjmM/r13jE7/2e9O4qqBjfn38/Nj+8QMewPJhMGn0KVje0bccnx03Ihbjo/ZmR+95DCuPWFv9nFbpgzcdyeGXn0Uv/3+/jx00aHMuecMXr32aF6+5ij+cdnhAOzQpUOTlixXDezLRPekc5DnRS9PXtafa0/YKzoc2V5+Qf7A3baPfj6/3268ef2xfPa7k/jh4btz9cC9mkzv58WrjmTRA2fz+aBTnCu2m45jz+5Ng2uXju05os+O/PGc77Lrdp2ZMPgURv3KCdbbb9OhyfS9tu/MwH13YvStJ/C/G4+N+W5An8bf7PNBJ/Phbd+LednNlcf15bTv7sxjP+4XHTfs5oEMvfooxt9+Mof5tLDp7b5UZLvORbxz03GcfciuPPOzIxh280AO7LUdb15/LIfusQPT7jydg3ffjnvOOyil7ePn80EnA061WPw6zb33zCaFCyCmEJSqyPMllx65JxcdsQc7uTeVIwU3r+u+tzcLHzibm07al/n3n8VPjuoNwJ3nHMjL1xzFK9cezYMXHcpp33VuhN97/kHs07MrAI9f2g+Aqwfuxbz7zuT2Mw+I7psAZx+yKz89pjcL/3wWfXt0jY7fz92nf3pMH7p1KqJTUXv+9uN+HLO381zEC1cdyS9P2Y/xt59MpP3Bb07fnzeuP5YL++/BtDtPZ/ztJ0fTW/Dns3jkksPo0a1TdNyXfziVA3bdlid+cjg/O6ZPNB5kVeRJ0pb6A44FxniG7wDuSDTPEUccofnkltemaZ9Bw/XbNeXRcX0GDdc+g4YHzlNSXqXz3em3VNdqeWVNwmXU1NVrXX1DdPi2N6br4fd+oPPXlOvkxeujy+szaLgOn7la3/hqufYZNFzvfPdrVVV9edJSvWvY7Jg035m2Us98bJw2NDjpPjJmnvYZNFzHzlurqqq/fmOG9hk0PJpPP5U1ddpn0HB948vlurh0s/YZNFx/MfQrra6tj05TtqVGa+rq9bEP5+vsVWUx2+j4v36s4+aXaJ9Bw3XK0g3aZ9BwveqFL6PrMmVp47pF8un17GeLdNz8Ej3l4bHR6X7/9ixtaGjQhoYGXbC2wjff89eUa59Bw/WtKSv04THz9A/vzNKS8irdWl2nqqr1nm09fOZqXVhSoX0GDdf3Z67Sd6at1D6DhmtlTV1Mnj5fUKpTlm7Qb9eUa3Vtva7fXK1bqmtjljtk1FztM2i4jvp6dXRcv3vGNFm/TZU1+va0Fbq4dLNe+swk7TNouJZWVGmfQcP1zMfGBf4eXmVba7TPoOF6/UtTtM+g4dr/3g/0as+2fWDkN1q2pSZm37nt9en6xlfLVVX12zXlurmq1jftmrr6aH43bK7W0bOLY+aZumyDlpRX6To3z+/PXKXH//Vj/fEzE3XG8o26sKRCx8wu1s1VtfroB99Gt9Omyhp9aPQ8raqt0wVry7W4rDKat/htGWRTZY2+NHFJNH+R33L2qrIm+9APn/zcd9+68KkJetSfP9SGhgbdUl3b5PvNVbW6auPWmHG1dfVNxkVMXLhON8Ud4w+M/EaHz1ztO32mgCnqF4f9Rob5B1wEPOcZ/hnwD5/prgOmAFN69+6d1Y0Rts1VtTrq6+KYcRMXrtPPvi1psTy8M22lvj1thX61ZH3MAZmOmrp6HfV1cXT+LdW1+tE3a9JKY215ZUzQTKRsa030YK6pc04UZVtqtKq2Tt+dvlJfnrQ0Oq1f0Peqq2/QdRVOoElV2ZbEJ9uwlJTH5nH5+i36iXuy9VO2tUbHzXf2pY++WaNrN1WmvKzqWidA/3v8Yl1YUqH19Q1aXVuvc1Zt0qpa5yRXW1evfxk5N61t15I+mbdWX/h8cShpl1fW6JLSzU3GJ9vfWqugwN/iN3dF5GLgDFW91h3+GXCUqv5f0DyZ3tw1xphC1ppu7q4E9vQM7wGsDpjWGGNMluUi8H8F7Ccie4lIR+BS4L0c5MMYYwpSi3fZoKp1IvJLYAzQHnheVVN7/50xxphmy0lfPao6EhiZi2UbY0yha9N99RhjjGnKAr8xxhQYC/zGGFNgLPAbY0yByYveOUWkFIh0p9kDyPc3Rts6tA5tYR2gbayHrUM4+qhqz/iReRH4vURkit+TaPnE1qF1aAvrAG1jPWwdWpZV9RhjTIGxwG+MMQUmHwP/s7nOQBbYOrQObWEdoG2sh61DC8q7On5jjDHNk48lfmOMMc1ggd8YYwqN39tZwvgDOgNfAjOBOcA97vjDgEnA18D7wHbu+NOBqe74qcApPmm+B8z2DHcC3gAWApOBvp7vrgAWuH9X5HodgI44dYLzgXnAj/JwHX7ijp8FjAZ6tNJ1OAqY4f7NBH7oSesId/qFwN9prP7Mi3UAugAj3H1oDjCkFR8Pgb9DHh3TifalnBzTGa13iy0IBOjmfu7gboBjcPrnP9EdfzVwn/v5cGA39/PBwKq49C4EXo3bSW4CnnY/Xwq84X7uDix2/+/oft4xl+sA3APc735uR2PQzIt1wOnZtcST7weBu1vpOnQBitzPvdx8R4a/xHkPtACjgLPyaR3c8Se74zsC4/NtHfLsmE60L+XkmM7kr8UCf9zG7gJMA44GymksZe0JfBPw46wHOrnD3YDPgQPjdpIxwLHu5yKcp+gEp2T6jGe6Z4Cf5HgdVgBdfabLi3VwD5JSoI87/mngujxYh72AtW6+egHzPN9F85cv6+Dz3ePAL/JtHcjPYzp+HXJ+TKf616J1/CLSXkRm4JwlP1TVycBs4Dx3kouJfS1jxI+A6apa7Q7fBzwCbI2bbnecjY+q1gGbgJ28410r3XE5WQcR2SGyHiIyTUTeEpFd8mkdVLUWuBHnUng1zgH779a6DiJytIjMcfN7g5uv3d08+OUnX9bBm94OwLnAx3m4DnlzTPutQ66P6XS1aOBX1XpV7Yfznt2jRORgnMuom0VkKrAtUOOdR0QOAv4KXO8O9wP2VdV3fBYhfotNMD4n64Bz1t8DmKCq/XHqEh/Op3UQkQ44gf9wYDecev47Wus6qOpkVT0IOBK4Q0Q6J8lPvqyDk1mRIuA14O+qujif1iHfjumA3yGnx3S6ctKqR1XLgE+BM1V1nqp+X1WPwNlxF0WmE5E9gHeAn6tqZPyxwBEishTn0nB/EfnU/S76Inf3QNge2EAIL3hv5jqsxynZRHb0t4D+ebYO/dw0FqlznfomcFxrXQfP9HOBLTj3K1a6efDLT76sQ8SzwAJVfcwzLl/WIa+O6YB1aBXHdMpaoj7JiQv0BHZwP2+DcxPqB8DO7rh2wEvA1e7wDjh3zX+UIM2+xNYH3kzsTZQ33c/dgSU4N1B2dD93z+U6AK/jtpABrgTeyqd1wCnlFwM93eH7gEda6TrsRWM9bB+cgyty4+0rnJt5kZu7Z+fhOtwP/A9oF7eMvFmHPDqmE/0OOTmmM/kLfQGeDXwoMB2nSmA28Cd3/K9wmj/NB4bQeEPljzhn0xmev52T7CSdcc60C3Faa+zt+e5qd/xC4Kpcr4O704xz0/oY6J2H63ADMNdN631gp1a6Dj/Daao3A+fm3QWetAa4aSwC/uGZJy/WAaeUqO7vEPl9rs2ndcizYzrRvpSTYzqTP+uywRhjCow9uWuMMQXGAr8xxhQYC/zGGFNgLPAbY0yBscBvjDEFxgK/McYUGAv8ps0RkR1E5Cb3824i8t8Ql3WDiPw8zXk+FZEBYeXJmGSsHb9pc0SkLzBcVQ9ONm0uuN0R/FZVp+Q6L6YwWYnftEVDgH1EZIbbS+JsABG5UkTeFZH3RWSJiPxSRH4tItNF5AsR6e5Ot4+IjBaRqSIyXkQOCFqQiNwtIr91P38qIn8VkS9FZL6InOCO30ZEXheRWSLyBk7XAJH5vy8ikzw9OnYTkT4iskBEeohIOzcP3w9zg5nCYoHftEWDgUXq9Lj4u7jvDgYuw3mT0p+Brap6OE5vipEqm2eB/1Ong67fAk+lsewiVT0KuBW4yx13o7ucQ91lHgEgIj1wusQ4TZ0eHacAv1bVZTg9oT4N/AanL/gP0siDMQkV5ToDxrSwsapaAVSIyCacPobA6Vv9UBHphtPL6Fsi0V5zO6WR/tvu/6k4/c4AfA/ntY6o6iwRmeWOPwbnPQYT3GV1xDkBoarPicjFOP0h9Utj+cYkZYHfFJpqz+cGz3ADzvHQDihzrxaak349sceX3800wXnxx0+afCHShcYuo7sBFRnmx5gmrKrHtEUVOC/PSJuqlgNL3NI24jismfkZB1zupncwTo+QAF8AA0VkX/e7LiKyv/vdX4FXgD8B/2rm8o2JYYHftDmquh6n+mQ28FAGSVwOXCMiM3G64D2/mVn6J9DNreK5HadrXlS1FKff9tfc774ADhCRE3He7vRXVX0FqBGRq5qZB2OirDmnMcYUGCvxG2NMgbGbu8akQET+AFwcN/otVf1zLvJjTHNYVY8xxhQYq+oxxpgCY4HfGGMKjAV+Y4wpMBb4jTGmwPw/NhG9/41oU6sAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 6**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.05\n", "MAE (val) : 5.16\n", "RMSE (train) : 2.73\n", "RMSE (val) : 10.92\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 7**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.20\n", "MAE (val) : 2.74\n", "RMSE (train) : 3.16\n", "RMSE (val) : 3.87\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 8**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6', 'fold_7'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.24\n", "MAE (val) : 2.56\n", "RMSE (train) : 3.21\n", "RMSE (val) : 3.63\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 9**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6', 'fold_7', 'fold_8'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.27\n", "MAE (val) : 2.93\n", "RMSE (train) : 3.24\n", "RMSE (val) : 3.69\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 10**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6', 'fold_7', 'fold_8', 'fold_9'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.31\n", "MAE (val) : 5.10\n", "RMSE (train) : 3.28\n", "RMSE (val) : 13.50\n", "\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# evaluate model\n", "# -- define results store\n", "svm_results_store_dict = {}\n", "# -- evaluate models performance\n", "evaluate_generalization_performance_sklearn(model_factory = svm_regressor_pipeline_factory,\n", " X = X_feat_v1,\n", " y = y_scaled,\n", " model_params = svm_model_params,\n", " results_store=svm_results_store_dict\n", " )" ] }, { "cell_type": "code", "execution_count": 980, "id": "27ad1080", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mae_valmae_trainrmse_valrmse_train
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fold_62.7381051.2034083.8668833.162487
fold_72.5587211.2431793.6268613.209677
fold_82.9288261.2685003.6933623.241158
fold_95.0979971.31138513.4978503.282567
\n", "
" ], "text/plain": [ " mae_val mae_train rmse_val rmse_train\n", "fold_0 0.854719 0.769444 1.291414 1.833210\n", "fold_1 1.346156 0.786942 2.573196 1.842901\n", "fold_2 1.869571 0.817476 7.376577 1.880973\n", "fold_3 4.175871 0.869008 9.847075 2.166194\n", "fold_4 3.032980 0.997383 6.361962 2.585072\n", "fold_5 5.157938 1.051296 10.924838 2.731734\n", "fold_6 2.738105 1.203408 3.866883 3.162487\n", "fold_7 2.558721 1.243179 3.626861 3.209677\n", "fold_8 2.928826 1.268500 3.693362 3.241158\n", "fold_9 5.097997 1.311385 13.497850 3.282567" ] }, "execution_count": 980, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svm_results_df = pd.DataFrame(svm_results_store_dict).T\n", "svm_results_df" ] }, { "cell_type": "code", "execution_count": 981, "id": "e8521417", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 981, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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mae_valmae_trainrmse_valrmse_train
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" ], "text/plain": [ " mae_val mae_train rmse_val rmse_train\n", "count 10.000000 10.000000 10.000000 10.000000\n", "mean 2.976089 1.031802 6.306002 2.593597\n", "std 1.465151 0.213727 4.024614 0.619013\n", "min 0.854719 0.769444 1.291414 1.833210\n", "25% 2.041859 0.830359 3.643487 1.952278\n", "50% 2.833465 1.024340 5.114422 2.658403\n", "75% 3.890148 1.233236 9.229450 3.197879\n", "max 5.157938 1.311385 13.497850 3.282567" ] }, "execution_count": 621, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svm_results_df.describe()" ] }, { "cell_type": "code", "execution_count": 622, "id": "9af4a8d6", "metadata": {}, "outputs": [], "source": [ "# export results\n", "svm_results_df.to_csv('svm_results_best_case.csv')" ] }, { "cell_type": "markdown", "id": "dfd6924c", "metadata": {}, "source": [ "#### Investigating Spikes" ] }, { "cell_type": "code", "execution_count": 623, "id": "f0f70a60", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64Index([390000, 390001, 390002, 390003, 390004, 390005, 390006, 390007,\n", " 390008, 390009,\n", " ...\n", " 393790, 393791, 393792, 393793, 393794, 393795, 393796, 393797,\n", " 393798, 393799],\n", " dtype='int64', length=3800)" ] }, "execution_count": 623, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df[['ISRC']].index[list(range(390000,393800))]" ] }, { "cell_type": "code", "execution_count": 624, "id": "13fb27a3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ARTIST_IDARTIST_NAMERELEASE_IDRELEASE_NAMERELEASE_FORMATTHIRD_PARTY_PUBLISHERUPCISRCRELEASE_TRACK_NAMERELEASE_DATE...MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKSTOTAL_STREAMSTOTAL_SKIPSTOTAL_SAVESSNAPSHOT_YEAR_ISOWEEKHOLIDAY_CHRISTMASSNAPSHOT_WITHIN_COVID_LOCKDOWNRELEASE_WITHIN_COVID_LOCKDOWNTRACKNAME_LENGTHROW_COUNT
3920001539265Millyz196006967727Blanco 4Full LengthN196006967727QMBZ92187648Cantaloupe2021-07-20...31.0001002022-8FalseFalseFalse101
3920011539265Millyz196006967727Blanco 4Full LengthN196006967727QMBZ92187648Cantaloupe2021-07-20...31.00027002022-8FalseFalseFalse101
3920021539265Millyz196006967727Blanco 4Full LengthN196006967727QM6P42127445Fish Tank2021-07-20...31.0001115602022-8FalseFalseFalse91
3920031539265Millyz196006967727Blanco 4Full LengthN196006967727QM6P42127445Fish Tank2021-07-20...31.00010002022-8FalseFalseFalse91
3920041539265Millyz196006967727Blanco 4Full LengthN196006967727QM6P42127445Fish Tank2021-07-20...31.00067730102022-8FalseFalseFalse91
..................................................................
3937951539265Millyz196006967727Blanco 4Full LengthN196006967727QMBZ92187741Highbeams2021-08-20...1.000125425402021-34FalseFalseFalse91
3937961539265Millyz196006967727Blanco 4Full LengthN196006967727QM6P42127445Fish Tank2021-08-20...1.00046717105665722021-34FalseFalseFalse91
3937971539265Millyz196006967727Blanco 4Full LengthN196006967727QMBZ92187753Trust Me2021-08-20...1.0003742162763992021-34FalseFalseFalse81
3937981539265Millyz196006967727Blanco 4Full LengthN196006967727QMBZ92187753Trust Me2021-08-20...1.0001203939285362021-34FalseFalseFalse81
3937991539265Millyz196006967727Blanco 4Full LengthN196006967727QM6P42127445Fish Tank2021-08-20...1.000156002021-34FalseFalseFalse91
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1800 rows × 39 columns

\n", "
" ], "text/plain": [ " ARTIST_ID ARTIST_NAME RELEASE_ID RELEASE_NAME RELEASE_FORMAT \\\n", "392000 1539265 Millyz 196006967727 Blanco 4 Full Length \n", "392001 1539265 Millyz 196006967727 Blanco 4 Full Length \n", "392002 1539265 Millyz 196006967727 Blanco 4 Full Length \n", "392003 1539265 Millyz 196006967727 Blanco 4 Full Length \n", "392004 1539265 Millyz 196006967727 Blanco 4 Full Length \n", "... ... ... ... ... ... \n", "393795 1539265 Millyz 196006967727 Blanco 4 Full Length \n", "393796 1539265 Millyz 196006967727 Blanco 4 Full Length \n", "393797 1539265 Millyz 196006967727 Blanco 4 Full Length \n", "393798 1539265 Millyz 196006967727 Blanco 4 Full Length \n", "393799 1539265 Millyz 196006967727 Blanco 4 Full Length \n", "\n", " THIRD_PARTY_PUBLISHER UPC ISRC RELEASE_TRACK_NAME \\\n", "392000 N 196006967727 QMBZ92187648 Cantaloupe \n", "392001 N 196006967727 QMBZ92187648 Cantaloupe \n", "392002 N 196006967727 QM6P42127445 Fish Tank \n", "392003 N 196006967727 QM6P42127445 Fish Tank \n", "392004 N 196006967727 QM6P42127445 Fish Tank \n", "... ... ... ... ... \n", "393795 N 196006967727 QMBZ92187741 Highbeams \n", "393796 N 196006967727 QM6P42127445 Fish Tank \n", "393797 N 196006967727 QMBZ92187753 Trust Me \n", "393798 N 196006967727 QMBZ92187753 Trust Me \n", "393799 N 196006967727 QM6P42127445 Fish Tank \n", "\n", " RELEASE_DATE ... MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS \\\n", "392000 2021-07-20 ... 31.000 \n", "392001 2021-07-20 ... 31.000 \n", "392002 2021-07-20 ... 31.000 \n", "392003 2021-07-20 ... 31.000 \n", "392004 2021-07-20 ... 31.000 \n", "... ... ... ... \n", "393795 2021-08-20 ... 1.000 \n", "393796 2021-08-20 ... 1.000 \n", "393797 2021-08-20 ... 1.000 \n", "393798 2021-08-20 ... 1.000 \n", "393799 2021-08-20 ... 1.000 \n", "\n", " TOTAL_STREAMS TOTAL_SKIPS TOTAL_SAVES SNAPSHOT_YEAR_ISOWEEK \\\n", "392000 1 0 0 2022-8 \n", "392001 27 0 0 2022-8 \n", "392002 111 56 0 2022-8 \n", "392003 10 0 0 2022-8 \n", "392004 677 301 0 2022-8 \n", "... ... ... ... ... \n", "393795 1254 254 0 2021-34 \n", "393796 46717 10566 572 2021-34 \n", "393797 37421 6276 399 2021-34 \n", "393798 12039 3928 536 2021-34 \n", "393799 156 0 0 2021-34 \n", "\n", " HOLIDAY_CHRISTMAS SNAPSHOT_WITHIN_COVID_LOCKDOWN \\\n", "392000 False False \n", "392001 False False \n", "392002 False False \n", "392003 False False \n", "392004 False False \n", "... ... ... \n", "393795 False False \n", "393796 False False \n", "393797 False False \n", "393798 False False \n", "393799 False False \n", "\n", " RELEASE_WITHIN_COVID_LOCKDOWN TRACKNAME_LENGTH ROW_COUNT \n", "392000 False 10 1 \n", "392001 False 10 1 \n", "392002 False 9 1 \n", "392003 False 9 1 \n", "392004 False 9 1 \n", "... ... ... ... \n", "393795 False 9 1 \n", "393796 False 9 1 \n", "393797 False 8 1 \n", "393798 False 8 1 \n", "393799 False 9 1 \n", "\n", "[1800 rows x 39 columns]" ] }, "execution_count": 624, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.iloc[list(range(392000 , 393800))]" ] }, { "cell_type": "markdown", "id": "1739e7ba", "metadata": {}, "source": [ "## SVM (v2)\n", "- (version 2 with Polynomial Features - For feature interactions)" ] }, { "cell_type": "code", "execution_count": 812, "id": "405c2acc", "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import (\n", " PolynomialFeatures,\n", " Normalizer\n", ")\n", "svm_model_params_v2 = {\n", " 'loss': 'squared_epsilon_insensitive', # keeps features as much as possible\n", " 'max_iter': 3000,\n", " 'fit_intercept': True,\n", " 'C': 1,\n", " 'random_state': 99, # seed value\n", "}\n", "\n", "def svm_regressor_pipeline_factory_v2(model_params={}):\n", " \"\"\"SVM Regressor Pipeline\"\"\"\n", " return Pipeline(steps=[\n", " (\"poly_feature\", PolynomialFeatures(order=2)),\n", " (\"normalisation\", Normalizer()),\n", " (\"sklearn_svm_regressor\", LinearSVR(**model_params))\n", " ])" ] }, { "cell_type": "code", "execution_count": 813, "id": "33272e41", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('poly_feature', PolynomialFeatures(order=2)),\n",
       "                ('normalisation', Normalizer()),\n",
       "                ('sklearn_svm_regressor', LinearSVR())])
PolynomialFeatures(order=2)
Normalizer()
LinearSVR()
" ], "text/plain": [ "Pipeline(steps=[('poly_feature', PolynomialFeatures(order=2)),\n", " ('normalisation', Normalizer()),\n", " ('sklearn_svm_regressor', LinearSVR())])" ] }, "execution_count": 813, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_config(display=\"diagram\")\n", "svm_regressor_pipeline_factory_v2()" ] }, { "cell_type": "code", "execution_count": 814, "id": "2f6ad7cd", "metadata": {}, "outputs": [], "source": [ "svm_regressor_v2 = svm_regressor_pipeline_factory_v2()" ] }, { "cell_type": "code", "execution_count": 815, "id": "d354a370", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:absl:Fitting Params\n", "DEBUG:absl:None\n", "INFO:absl:**Running CV Fold : 1**\n", "DEBUG:absl:dict_keys(['fold_0'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{ 'C': 1,\n", " 'fit_intercept': True,\n", " 'loss': 'squared_epsilon_insensitive',\n", " 'max_iter': 3000,\n", " 'random_state': 99}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.76\n", "MAE (val) : 0.91\n", "RMSE (train) : 1.79\n", "RMSE (val) : 1.34\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 2**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.78\n", "MAE (val) : 1.31\n", "RMSE (train) : 1.80\n", "RMSE (val) : 2.61\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 3**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.81\n", "MAE (val) : 1.91\n", "RMSE (train) : 1.84\n", "RMSE (val) : 7.36\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 4**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.87\n", "MAE (val) : 4.62\n", "RMSE (train) : 2.11\n", "RMSE (val) : 9.70\n", "\n" ] }, { "data": { "image/png": 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azBBEKVwCbAV+C4wCFgAXpXjfR4BRqtobOAqYBQwExqjqgcAY99wwDCPrBN2OM7x4rcTHFK4Heqhqg6oOVdVHXXNSIERkZ+B04DkAVa1T1SocpTPUjTYUuDRo3oZhGEZ6BFEKOwMfiMhnInKziHRP8Z77AZXA8yLytYg8KyI7At1VdSWA+7+bX2IRuV5EykWkvLKyMkURDMMw0qeVjyn7krRSUNW/qOphwM1AD+ATEfkohXu2BfoAT6jqMUA1AUxFqvq0qvZV1b5du3ZN4faGYRiRBK3cJai9qRWRyn4Ka4BVwDpitOYTsAxYpqoT3fM3cZTEahHZA8D9vyaFvA3DMIw0CLJ47SYRGYczCNwF+IWqHhn0hqq6ClgqIge7Qf2Ab4F3gQFu2ABgeNC8DcMwUiFiP4UA3YZiNB8FcXOxD3CLqk7NwH1/hTOtdTtgIXAtjoIaJiLXAUuAyzJwH8MwjIQENh+F0hXh7KNkvKR2VNUtqhrT7h+Kk+xNXcXS1+dSv2TzMAzDyAbFPF6QDMmYj4aLyIMicro7SwgAEdlPRK4TkdCObIZhGK0SMx81k4zr7H4icgFwA3CKu9K4AZiDs0fzAHecwDAMoyQI+z7KrxhZIakxBVUdCYzMsiyGYRgFQzH2ApIhyOyjU0LmIxH5iYg8JCL7ZE80wzCM3JCqAkjHI+q3Kzbx5uRlKafPFkHWKTwBbBWRo4A/AouBF7MilWEYRkHj7ryWRg4XPPoZt77xTWbEySBBlEKDOmrxEuARVX0EsK06DcNo9ZT4hKMIgqxT2CwitwE/AU4XkTZAu+yIZRiGkTv8rEDJ9AKKcdwhSE/hx0AtcJ0722hP4B9ZkcowDKOAae5ZFJ9WSLqn4CqChzznS7AxBcMwigAzHzWTzIrmzfirQwFUVXfOuFSGYRg5JPXZR5mVoxBIZvGaDSYbhlFyxJtuWrm51omTK2FySGDX2SLSTUT2Dv1lQ6hss3LjNu4fNZsFlUm7azIMwygJgixeu1hE5gGLgE+ACuD9LMmVVVZvqmXIuAUsXledb1EMw2hleHsQxWg+CtJTuAc4EZirqvvieDQdnxWpskyZO6jU1JRfOQzDKAxSHWhOZ0VzoRJEKdSr6jqgTETKVHUscHR2xMoukoHViIZhFA9B1ikUoR6IIMjitSoR6Qh8irNBzhocb6mtjrCHw2J/uoZhZByNcVwsBOkpXAJsA34LjAIWABdlQ6hsU8xubw3DCE7EfgoB0iXTrvxy4TrGzm49W84HWbzmHZUdmgVZckbYfGQ9BcMwCGYSClpvXP70lwBUDL4wULp8kbRSiFrEth2O36Pq1rh4rdl8lF85DMMoXGLVD5Hmo+KrRIL0FCIWsYnIpcDxmRYoF5SJDTQbhpEBirASCbx4LYSq/gc4O3Oi5I5QT6HJugqGUdL4zURtDvOvH4q92ghiPvq+57QM6Esr1ZOhh17sD9cwjPj4VQGJqgWvyagYq5AgU1K9M40acFY0X5JRaXKEmPko78xcsZEPZ67mt+celG9RDCNlirFhGWRM4dpsCpJLbJ1C/rn0n+Opb1R+0+9AysrMb7HReij2aiMZ19mPEadRraq/zqhEOSBUBY2cvhIR4eKjeuRVnlKkvrHIvyyjVZGym4sitDckM9BcDkwGOgB9gHnu39FAY9YkyyKh2UcfzFzNr1/9Os/SlDbF90kZrRFfNxdJvJzF2GtIZj+FoQAi8lPgLFWtd8+fBD7MqnRZwnZZMgwjVYpREXgJMiW1B+Bdq9DRDWt1iO9ENCMf2LiOUQj4NRSj38zmjXXizz5avmFbynIcN+gjBo34NuX0mSCIUhgMfC0iL4jIC8AU4L6sSJVlrKdQOJhKMAqBRG2Tb5ZWcdygj3hz8rKodC0Trt1Sm7IclZtreeazRSmnzwRJKwVVfR44AXjH/TspZFpqbZhSKByso2AUKt53c87qzYDj3K7Y39mESkFEerv/++CYi5a6fz3csJQQkTYi8rWIvOeedxaR0SIyz/2/a6p5J3HvbGVtGEaR4DezSLX4XWcns07hd8D1wIM+15TUXV38BpgFhBzqDQTGqOpgERnonv8pxbzjYiqhcCjGKX1GCVGEr28ys4+ud/+flambikhP4EJgEI7SAWd19Jnu8VBgHFlSCmXWUygYir0rbrQOfAeafd5NkeKfHJH0mIKIXCYiO7nHd4jI2yJyTIr3fRj4I+DdJbm7qq4EcP93iyHH9SJSLiLllZWVKd3cdIJhGF4CbccZcVx8CiLI7KM/q+pmETkVOA+nNf9k0BuKyHeBNao6OWhaAFV9WlX7qmrfrl27ppKFmY8Mw8gImeg0FFrPI4hSCK1evhB4QlWH42y2E5RTgItFpAJ4DThbRF4GVovIHgDu/6ztX2cDzYVDgX0PRonibz6KMdBc5O9sEKWwXESeAn4EjBSR9gHTA6Cqt6lqT1XtBVwOfKyqPwHeBQa40QYAw4PmnSymEwqHYux+G62PQBW9J25megrp55FJglTqPwI+AM5X1SqgM/CHDMoyGDhXROYB57rnWcF0gmEYsYhXSUc3KAusPs8IQVxnbxWRNcCpOA7xGtz/KaOq43BmGaGq64B+6eSXLDb7qHAotFaSUZo0qVJT30iHdm3CYbH3aM7sS1ton0CQ2Ud34UwRvc0Nage8nA2hso3phMKh0D4Io7QIVQWPjJlH7z+PcsJC+60ksR1nJgaJW/NA8/eAi4FqAFVdQaSDvFaDOcQzDANibMcZp46OvlZY1XlmCKIU6tRRaQogIjtmR6TsI4GHx41sUWitJMMI4X01vc3ITL+xsfL7cOYqhk9dnuG7JSap6lGcOZzvubOPOonIL4CPgGeyKVy2iO4n1DY00mvgCB75KK0hEiMFTCUYhUqEmSgiXBPGSfU+Xq5/aTK/eW1qirmmTlJKwe0hXAq8CbwFHAzcqaqPZU+07BG9TmFrrbME4/kJ+XVZW4pYR8EoVPxezXjjkcXyLic9+wj4AqhS1UxOQ80Ltk+8YRiJiGXa1DhnXsbOXkOvLomt7IW2VieIUjgLuEFEFuMONgOo6pEZlyrL2EBzAVFY34NhxCV6RXOk3oh8ma994aucyJRpgiiF/lmTIsfYlNTCodBaSYYRwvtmZrPKKDSzU5DFa4uzKYhRmhTaB2GUFnEr+1gDzTH2aA7yLm+ra0wcKU+U5ORMW9GcP7bUNtDUZJrAKAySfROXrd8KuFaGDPg++scHc1JLmANKUimUgv+SQmTasioOv+sD3pzSvPm5/fZGoeLtETz68fzw8Z+Hz4gRP3kqt9Q2pyuwj6A0lULAcCMzTFu2EXAW5YSwxWtGIZHofVSFD2aubj5PsVlT39C8v1ihjauVpFKIZT7asLWeSYvW51ia0mOrx5762Mfzee5zWx9iFAaxZxZlJn6I+samxJHyREkqhRbmI8/T/NFTX+RYmtIh9CtXe5TCCxMquOe9b/MjkGFEoTGO49HUpIEr+TpP/ELrLJeoUojUCjbumVtqCnjmhVHaBDVnjpm1mv95dQoH3v5+oLReJaJA1da6QPfNJiWpFCCyt2B27dzSZL+3UaAEnWL6n6krGDl9VYu0iajzjCm8MH4RR/91NIvXVcdJkTtKVyl4jq2nkFtMKRiFQvToYqSDO589mjM0KFzf2JzP6FnOdvRL3Gmv+aZ0lYKnq2CVVI5wf+dkfu0FlVv4qsIG/Y3s4rufgvv/9ndaTj0NstdCPBKNQayvzp85qWSVgtcpnimF7FJT30h1bUNzQBI/d78HP+GyJ23Q38gsc1Zt5k9vTou5gDJRTyBTpuYmn2lL3qA+94zOyH1SoWSVgtcpXlOU0u41cASvf7UkxxIVL+c//CmH3fVB+HMzFWzkixteKuf18qUsDq1Qjrqumvp6pXRNS4XyXZSsUiBBT+H58RW5k6XIqVgXaSu1gX2jUAj6JmbqzfXbnKdQvouSVQpmPso9oZ/Zfm2jUElUFcS9nuKLXWjVT8kqhQjzUYE9lGKn0D4CwwihCYxA2WxAFspnUbpKwdYp5I1C8/ViGCES9hRSvJZU3AL5LEpWKZRJ4p7C6k01PDR6rimNDGM/p1EotBhoThA/G3VBU3iqdmF8GCWrFCIXr/k/jN8Nm8qjY+YxdWlVTmQqdtRn6p1h5JPoVzEZL6mpXEtKlgL5LkpWKSSafQRQW+/MVW2wQYeMYL+iUegk7inES+vZkS1ADR+egFEgH0jJKgVvTyHWwyhzpyjZTmGZJdYHM2LaSu6MsYGJYWSDeG4u/EjWxOOXT8Xaas91PxcahUHJKoWyssRuLkJRGt3rdQ1N/H3U7MjVuUbSJJqSevO/p/DiF7YVuJE7WryLaUxJ9V7zq1POfGBchCO8QiXnSkFE9hKRsSIyS0Rmishv3PDOIjJaROa5/3fNqhye48YYPYHQYPSHM1dT19DE6+VLeWLcAh4dMy+bohU9hdJNNlo3Exeui/ntpkpCNxdJXosllp+yCA80F8iHkY+eQgPwe1U9BDgRuFlEDgUGAmNU9UBgjHueNSSJ2UchpfDChAruHzU7vIXeCxMqOPqvHxbMQ8wkjU3K8KnLs2Iym7NqM1A4syyM1suXC9fx46e/5LGP02ugBXVpEe+T91b4idYzxHPEl29yrhRUdaWqTnGPNwOzgD2BS4ChbrShwKXZlKMsiXUKXhPTsg3bwse1DU1Uba0vyhbv8+MX8ZvXpvLWlGUZz/v18qVAaj2FKUs28Ic3vilKRWwEZ/WmGgAWVKa3B0HL2UdBU3jTJn43szl7KVPkdUxBRHoBxwATge6quhIcxQF0i5HmehEpF5HyysrKdO4ePordU4ifQzG6x1izuRaAdVl03ev3eyfaYGTAc5N4Y/IyNtt4jpFF0pp9lGBMwck/C34yMkzelIKIdATeAm5R1U3JplPVp1W1r6r27dq1axr3bz6OPdAcXysUxiNMjgkL1tJr4IhwCyu/tPzlzvjHuNyLYZQssVr1CdcpxLnWpP7HfnFS9cSaC/KiFESkHY5CeEVV33aDV4vIHu71PYA12ZQhwiFezIHm+Hm0pp7C1c9NAmDy4g15liRYN9mmAxvxSNWcGCtVOiuaNYkxhfACTk9Y80BzgpvniHzMPhLgOWCWqj7kufQuMMA9HgAMz6ocSZmP4muFhkbl+0PG88nclmasr5dsYPS3q9OSMSjTl21k3ZZa32uhWRpNqlRuro05rTbdFszGrfVsSWDiCfLu/+LF8sBpDMOPsXPWhN24a4wWezq+j65/aXJzvBgzT2vjTEktlHc8Hz2FU4CrgbNFZKr7dwEwGDhXROYB57rnWSOR+UhEWiiFaB2xvrqOKUuq+P2wqS3Sf2/IhHCFlisuevxzLnrscwAufPQzfvf6VNZGKYkmheMGfUT/Rz7LigxH/fVDThj0Udw4QVp3Y2ZntcNotHIkQcPNy7XPfxU+VlUu/ed4Hv14fkQcReO+n8l2XGP1FPreG/vbKJSeQttc31BVPyd2g7RfruQoS7BHs6rSJoH9qNC6fQArNtbQ1KTMXLGJmSs28e3KTYy65fTw9dALn8lNwm98aTJlZTDkqmMBqK5rjBs/nZ8rVgvMKE3SMR/5+jRLuHgtyRXNASI0L+osjIok50qhEPF7zvPWbImYhvrFwnWs3xo5I8cv3cwVG+m843aZFjEQXl9Ni9ZGzurJxjjIqJmrWoRt3FrPLju0y9g9Quq5NY3jGNkjSA/Bj1ivUaberiDvaaG90SWrFLzvlN+qyMYmjbCNb9xWz6RF6yPihB68d/rmhY9+nmFJg+MtT3TJovejjkW6de9x933E3Hv7+15LZfA4lMKUggHpr/6N1SpPa+c1D4GUQoFZHErX91EC81EyeFONm5Oe7Xvasip+8MQEaurjm16SoT5OzZ+wpBmaKxfPx0ta5qM00hpGiFifiKJxeyHpOMSLzCe5sHxgPQVS347T2+INLfpKhV4DR4SPZ63cxDF7p+f2qbEx3kBZGtMrMkWCe3xVsT7mtZD8TU3KVxXrOWG/3TIpmdFKSNt8FKenEHegOcmedioNzV+/+nXgNNmgZHsKka6zU6sJG7PQ30s0uJ0M8fZ/KAQ3EYkkuG/krBZh0YP6/xq/iB8//SVjbXaSkQKpfgb1jclphSANzfx/kZGUrFJIZjvORHht97Gq8tmrYi/Wrq5taGFfT7Q2Ihbeyj6e58iEZQ14+3grpBdWbmHCgrUtwhMppgafns5Wd0ZTKOmCyi0ArNi4rUVcw0hEqgPN5Uku/gw0blZgWqFklYK38ku1xe/tSsbK4fyHP/NdUPbAB3M47K4PeDjKDbefUli1sYbvDxkfc2EaRFb2DXEEC9KtXVC5hRVVkZXu+uo6lnvCTrhvTMz0Zz/4CVc+M7FFeCIJ4rXGbKDZiGbEtJW+vrMmzF/LjOUbfdPENh9pRgZ8Ew9YF+57XLJKIRPmo4YkDYxb6xr533emc+sb34TDHh/rLJp5d+ryiLhl7hMZP38tH7pTPZ/7fCFTllTx5+Ezwu6nowdymzLUU/Cu9O734CecPPjjiOvHD/qIU6LCgpLo544uWyL3AZWba9lUU5+WTEbr5eZ/T+G8hz9tEX7lsxP57mP+swFj9hQ0Mw2P1jwltXSVQgZmHyW7wUdZmfDviUt4c3Jid9ShnsJVz07k+pcmR6yKHjl9Fec9/Cn/+Xo5B93xPovWVtPYpKyo2hZRhrh7Sie9+MY/Xib2q040gyPaFUDEFFufpMcN+ojT7x+btlxG6yP0OtTUB1vVGO8NzLVSCELF2uqsu88pWaUQ6RAvtTySGVNIdC2aNZtqWbKuebWx3wsQCpuxfCMPfjiHkwd/zIqqZtt+WmMKOSBhTyHKfNQQRync/o6zp3PVVuspGA6qyt3vzowbJ7bDusx8I4ny8F4OYqk468FxWXefU7pTUsldTyFI7j95rqUNPpr2bR1dXtvQFHbGt8Yz4Os3UBsinRbMygwN6iYSIXpMwfs7B5X/d8OmctGRPTirt+/2HEYRsnpTLS9MqIgbJ/ZAs2bIM29mzUe/GzaV7du1yckCt9JVChE7r6WWx9AvKpKKl+5LFi1f+3ZtAKipb/SVfZi7wxk4re5ZK5tnQHlFCeqKYsbypLe9iEsi81F9lPnIq+SCKoW3pyzn7SnLqRh8YaB0Rusg9QHbeD2FTJiP0s4igrenLE8cKUOUrPnIO6aQ6uyjD2YmZ9uL16NI5c6hnkJNfWPz/H3P9fLFkYu/vB5RvR/RHcNnBLpvmwy9LYl7CpERvAP6BWD9MgqA0Nfr9U8Wj2jlEeuT1DjXghDMzUX698skpasUPMfZnuYYT+kkY4KKjtHB7Sl4B2S9t0jWfLQlzoyd+0fNaRGW7irSEIlKHP17RY4pJP+s/HpoY2evodfAEcxfsznpfIzCI/Rkpy3zn3IaTfSrEHv2kWamp5BgnLLQFIGX0lUKGXBzkSzxzEfJvBzPfb4o4rxdG0f4hsbmOdVek0xaU1Lj1PttMqQUEuUTLX9DxJhC7HTRHmEvHTK+RZz3pq0EYMqSqgRSGq2N0HfmV6lHh8XcjjNG+sCyBJqSGvx+2VznULJKwbtILFM/cKx8vC3fR6MWqyU7WO1l5PSVzfckrBWSyjOV+4V8M6W62jqaoNk0eAae431si9ZuiTj3a0WGfq8yEV78oqKFm4xXJi6m18AR1Dak75jQyB5+r9CWugben77Sd8Ze9HsT03ykqc9GTJZJi9YH/gaiSeU7ThYbaCYz+wCLSMxWv/cBPjR6Lr/ud2DztRQU0oJKp0Xs7BLVMp90zVWxKMtQEyKwUkiwTiGcbzKTfzUUF+4c7kxb9A5CP/ThXAA2bWug605tgglq5BVVuOmVKTGvRZzHaJ2f89AndOnYPm1Z4jVePpm7JkKeVNqkjapZq7xLVyl4juOY4JMmni3y5S8XR5y/6Jm1tC3BLmXxaFJY5U5F9Y4jxBtTSHXxWXnFepZmaLe2oCKkM/soxPw1W5i4aF24KoilmKJzDzUYyjLgqNDILvG30WyhFWISvYVtKsR7x1uu2E8h/yz2ZkrWfCRZMB/FaqG/OmlpxHmohQok3OQ+LqpsrnHSe+f2x+sNNCTw8hjr+g+f/II/vTU9blqvC/B4BO2ZRcw+ipc0Tr393cc+4/Z3ZiS/naIb79h7R3PGA2OTStNambBgLY9/PC9xRA/1jU1c+/wk/y0tc4DfU4zX4Gkx0Ay0zaKij9d4iV6xnwrZ8NAcooSVQvNxpmYf5XpGgfd23g8i3scR98NpUp75bFHM65kiaG/F21MID6z7ZBHvEw+5QQjdO5EJq96Nt2FrPUvXtx5PrOPnr2XIuPmJI3q48pmJPPDhXIaMm8/GJFeGV6ytZuycygh/XrnET7nHagwNn7rcZ0xBs7ptbrzGR11DU9r7McfbMyVdSlcpeI4zMWYjIjn34BkxDdVTiMY4fct4Hkij3UsUCsOnrggfx/uNk5kyGyr/b1/3r8xCH3NDYxPlns1+JifpMjkb1NQ3sjlJh39XPTuR+0fN4eslG3j3mxWJE3i4f9QcfjdsalJxQ69bvoxqtT6+jmI1Np4Yt6DlmIJmd9ZhvLyjewqpWCqsp5AFMrEdZzS59isU4QTPU6HHa4nHUwrJbiCSa/41vrn3ku6zirdNKDg9A3BWkP7wyS/C4T94YkJa902H7w2ZwBF3fxg4TbI7ee3Zafvw8ao4+2N4CbV04+nhz+ZVMjbNbWpj8ce3prUIi9V6VoULPAs4wellZ3NaZ/xp6JHXVmxM7jf3ks3ZRyWrFDI9+wiy+6D88HqG9JpY4o8pxFMYAeZW52n1zbNpmreSLePc1S0Xt8Uq86aa+rAPqhCTFq3n76NmBxfQB6+bkmywXdvmaiBpf15utLmrt/D3UbNZUbWNLbUNEY2Tq5+bxLXPfxWRbvWmGm5/Zzobt9Vn3N35X/7r7wRP0Yg9QAD++80K1lXXZfT+XrZlYK/1eGTTKlHCs4/S33nNi7M5R24ryq11zYPU9U3J9hTi2zqTpbFJadsm98aDEdNX8lCMNQTJ/P7xTGTe9H69pm9XbuKwHrvwq1e/5qieu/Dz0/YD4KaXJzN+/jqm3nkunXZw7NQ/esrpZfzp/N4JZYrmuc8X0XnHdnzvmJ4R4UvXb6XnrtuHzWSPfzyPBz6cy6K/XZDQdFbf2ESTKu3btpxm632PYimFx8bM46ze3Th8z12AyErpiXELeGLcgvD5jL+cF7HpTa+BI+i7z64c1mNnNtU08M7Xy3ll4hIAnvxJH4aVL2NddR1rN9cy9GfHcUC3nWKWY2tdAxc/3nJRIsCYGFuz+r0WybixT4e74nhpdXopyecVZPwkE5RsT4EMDzQ/NHpuzs1Ha7c0t3SS7Slkynx07v+13NQkV9TUNfH+jFUtwr9eUsWE+WupWNtyF64Q8RSfV2HW+SjP0C5y//1mBfeOaN5HetrSjS3SN+fZxIQFa+k1cESEJ9u6hqaYPdR73vuW377+DS+MXxQR57T7x0ZMb37AXVOxta6Rr5ds4JlPF8Ys23kPf8rBd4zyvbbVMy3a793ZWtfAg6PncpnHnBavxzli2go+nxe5DWv54g0M/WJxi/xvfHkKH89ewzdLq1hetS1hT/Cixz5n/potceNEk48+7eJ1sadvB61u/OJ7FXmmKVml4J2Nlgmtu3JjTc4Hmj+a1bxyM3KgOTWlEGSgOdqlRC556tMFbNzW0vTwyJh5XPnsRM58YFzMgWG/8t/2tjPVtsbTA6nz6Y1E37O+sYn5a7aw1TUV1DU2sXFrfcS9axuaeGF8BRA5WH3QHe/zqwQ2/7v/+y3/nRY5WPzmlOVU1zbwkkc5lC/ewPeGTGDQyFnRWYSVykJ3weP66joqN9e6Hnada6FpzeA/gLl6kzNvf1u9o3xmr9oUt2JuaNKYDaRE00Dnrt4ct8cXWrgZhEIbKws6AcDv18hmo8zMR8BjHwebwheLTI1NpEKi9Qch4pmPCu3jicXSJDxjvjJxsW+4nx371UlL+Nv3j4iY0fLlwvUt4kFkV/5Pb02LcGn82qQlfLlwHV9VNFf+NfWNtHErwsottRHpR0xfyT8TlOM3r02NOP9maRXXPv8Vkzwzowb8a1LM9HWNTVRvba70+9wzOny8XdsytotyfVvX0MSG6joeGTOPj2ev4fYLD+Fxz/fxvSGJB9ybmjTmlEs/Ze5lypIqXvtqKVccv3fC+yRLOgtEs0WQtQp+SjKb7c/SVQpZMIfPXR2sW5uIG87Yj6c+iW0S8JJshR4v3tcBncQd66lgcsl/k2lpxfho4q05SKb3s+9tI8PH0T7u/RoXNfWNYVPXncNn8o9RcyLevc019bQtK2Ph2i0MGjGLf1x2VEIZvAohEbUNTZzxj3G+1+oamlqY01ZurOEYz3O94aXJSd8rRE19U8xKa8PWloO77duWRVSSw8qXMmbWGk4/qAtdO7bn7EO60b5tm5QbXdke9E2FZKcYA/xnarCeRbqUrFLIxkbvyeyaFoQgDuhC9uVEeLv9Y+dUcvVzE/ksyv6bLNmcvZEuiwO65LjhpfKk98cIwql/j1wNvTlqBXv0VNNTBn8cM68/nn+wr0vzeNQ2NCZcNb9Th7bs22XHpNxQ99x1e179xYmcFmdP7Jr6xphuQap8ego3nL4fj3oUaqhx4jWPevnukXswY/lGKuLY7b14zWOFQpA9pXO9QLBkxxQytYtYNsmUq2ov0VPzUlUIXg7q3jHi/MT9OoeP/3rJYRHXLj6qR9r3S4agi81SUQiPXXFMi7C/XHyYT8zM8MszD+DYfXYNn/+ob884sR3OefCThHEeveKY8KwpgD9/91Ae9SkbwLhbz2Svzjvw/T57xszv/z6ay7Of+fdwF/qMCSwMOD61ZnMtA07uFShNMXJnwE2ykqXglIKInC8ic0RkvogMzLc86dAuzpTNndon7qTNczeCGdg/9rRG78KjbPOL0/Zl0u39uOfSw3nvV6eGp1ueekBXxt16Zjjea9efxML7LqBi8IWcvH+XiDz267pjSvc+sucuKcudLDeftT+Tbu9HxeALqRh8IaNuOY0hV/Vh1C2n+cbvf/ju9NilQ/i8z96d6H/E7mnLce6h3fnT+b0j1g+EGHJVn/DxPrsl/i03xWglf+fQ7uHjPTttH7Ey+fQDu7Qw1dx4xv6MH3g2bd0xiLsuiq38mrR5EWAy/KBPYuXmff4d2rXh2lP2jVCQ0ZxzSPeY17x8eVs/vrjtbE7ab7eYccbeeiaH9dg5IizWVGPv97hzh7bMvud8wBm/eW5AX980D//46IjzOfeezys/P4FzDmneV3yH7VpOJW7v835kgoJSCiLSBvgn0B84FLhCRA7Nxr1+eeb+Ma/dcMZ+dNupPbP+ej73fe8Irjt1Xyb9bz8Azjy4K/MH9Y+Z9orj9wKgX+9uzBt0QcRHPPzmU8ItMO8c//t/eKRvXvNcU88J+zot7122b8ewG04C4IBuHakYfCH/iEob6+W+9TsHMf3u74TPH/7x0fzw2MiPceSvmyu/qXeey6/OPoDnrz2O8QPPZmD/3tzW/xC67dSBq0/ch8P33CX8UZ68/2706rIjD152FBPd3ylkPjigW0eG3XASI399Gm/eeBIHd3fmoB+zdydfOQG6dNyORX+7gI9+d0Y47M0bT2b+oP4RLfGKwRcy7e7vcFv/3lx7Si+e8PzW4JhFvAOWs+85n4rBF4Y/tvI7zuGtm05izr1O+B/O6023nZor+d6778wFR+xB7913DlcyZx3cNXy9bZsy2nie4/5dO7Jzh5Z7Xu++c4cWYSGiZQY4dI+duenM/cPP3Uv3nTuE37EuHSN992zfrg2nHdilRRqA168/MeL8V2c3u2/fs9P27NvFUTDPXNOXA7vv1GLv7lvOOTCiwttl+5bl/PC3p9OhXcsq5ayDuzLgpH1ahJ9xUFfeuulkzurdjUV/uyDi2rxB/fn8T2eFG08vXXdC+NqgSw8HIgeQT94/8r0fclUfHrn8aL7963m8fN0J3HWRU41cc9I+PH31sQD88Nie7L5LB/bYZXv+9v0j2LPT9rz3q1OZdvd3GHXLafTr3Y3xA89m3y478uLPjufBy44K1xuh3wscpbHobxfwh/MO5s2bTuJeV77rTt2PDu3aMH9Qf+bccz79ohTVdm3KeOaavlx6THOv69pTetG+bRtOOaAL933/CMBpQD70o6M5qHvHsBL7cd+9wutkMo3ka2WqHyJyEnC3qp7nnt8GoKp/84vft29fLS8vT/l+9773Lecfvju9uuzIO1OWs2hdNUfv1Ykf9d0rYdpvllbxp7emMXvVZt666SR+8IQzh3vIVX2YvHgDvzhtP3Z3W5E/H1rOR7NWM+Mv57Hjdm24+92ZXNZ3L4aMm8/I6at4+boTWoxHdN+5PWcc1JVh5cuY+ZfzGPpFBecd5rRC+z34Ccf36sywG09iU009/R/+LGwWWnDfBez/vyPpvnN7/nLx4dz48mT+/YsTwi32nw/9isYm5V8/PQ4RCXs2PfWALgz92fHs/7/OQGqyG91vqK5j1wCOxVSVCQvWcfy+nXnpi8U0NDWxZP1WdtuxPY1NynH7duaMg5or3ilLNrBxWz1nHdzcatq4rZ5lG7ZyWI+WvYfB78/myU8WsF/XHXnmmr7s37UjDY1NVNc2hiu6xialoanJdyFXLOoamtha18BrXy1l8PvOSuWKwRfy2qQlDHx7Okf13IXHr+zDXp13YMi4+dw/ag7H9dqVryo2cMgeO3PjGfvRo9P24bn+V5+4DzNWbOSdX57CDS+Vs62+iQcuO5IvF66n/+G7065NGbe9PZ1XJy3hjIO6cvWJ+3CO27rfVtfIKxMXc/nxe3P4XR+EZdxntx345A9nMXvVJpZv2MZ1Q51vY/zAs9mz0/bcOXwGG7fV88jlTsPkpS8X8/z4RXz8+zOpbWjks7lrw/dQVUZMX8lRPTsBsFfnHVr8JtFecUPvzLzVmzmgW0du/vcUftCnZ7gyrK5t4LC7PuCWcw7kkqP3jKhYAZ79bCH3jpjF3Hv7+/aSXpu0hBkrNnLvpU5lOXb2Gm57ezrj/nAmHdq1YVNNPX98YxoXHrkHF0WZKVWVYeVLufDIHnRMopcej8Xrqtlntx1ZXrWNNiLh79zLwsot7NtlxxaLChetrWZY+VL26bwDlycxw+r+UbPpd0j3uL2iVBCRyarq33UJrcQthD/gh8CznvOrgcej4lwPlAPle++9txYKK6q26h/f+EZr6htaXNtSU6/Tl1W1CG9sbNK6hkatrW/UP7wxVb9dsVHXb6nV6cuqdO3mGq2pb9Cl66sj0jQ1Nekzny7QVRu3RYSv3VyjyzdsVVXVj75dpSurnOtV1XVx5f541mpdvLb5HlMWr9f7R81KrtAFSENjky5ZV504Yoo0Njbpy19W6IsTFsWNt3httdbUN+iQsfMj5Hn6kwX6yZw1Sd2rurZe3yxfqk1NTTHjrN60Tf/7zXJ96pP5Ec9RVfWrRev03anLk7pXKvzr84U6ctoKXb1xm26ra/ne+xGvLEbuAMo1Rj1caD2Fy4DzVPXn7vnVwPGq+iu/+On2FAzDMEqReD2FghpTAJYBXttNTyC3k3QNwzBKmEJTCl8BB4rIviKyHXA58G6eZTIMwygZCmrxmqo2iMj/AB8AbYB/qWpsd4OGYRhGRikopQCgqiOBkQkjGoZhGBmn0MxHhmEYRh4xpWAYhmGEMaVgGIZhhDGlYBiGYYQpqMVrQRGRSsB/N5X4dAHSdw+aX6wMhYGVoTCwMgRjH1Xt6nehVSuFVBGR8lir+VoLVobCwMpQGFgZMoeZjwzDMIwwphQMwzCMMKWqFJ7OtwAZwMpQGFgZCgMrQ4YoyTEFwzAMw59S7SkYhmEYPphSMAzDMMK0GqUgIh1EZJKIfCMiM0XkL274USLyhYhMF5H/isjObvhuIjJWRLaIyOOefHYQkREiMtvNZ7DnWnsReV1E5ovIRBHp5bk2QETmuX8D8lmGqDzfFZEZrbEMIrKdiDwtInPd5/GDVliGK9z400RklIh0yUUZUizHuSIy2Q2fLCJne/I61g2fLyKPijj7SBbgs/Atg7Su7zrmc/DkmdPvOoJYW7IV2h8gQEf3uB0wETgRZw+GM9zwnwH3uMc7AqcCN+LZ0hPYATjLPd4O+Azo757/EnjSPb4ceN097gwsdP/v6h7vmq8yePL7PvBvYIYnrNWUAfgLcK97XAZ0aU1lwPEyvMYj9/04e4xnvQwpluMYoId7fDiw3JPXJOAkN8/3KdxvwrcMtK7vOuZzyNd3HXH/dBLn6899AaYAJwCbaB4w3wv4NiruT/GpUD3XHwF+4R5/AJzkHrfFWV0owBXAU540TwFX5LMMQEfgc+DQqJenNZVhKbCjT76togw4FUAlsI8r35PA9bkuQ9ByuOECrAPaA3sAsz3XwjIW6rOILoPPtYL/rv3KQAF8163GfAQgIm1EZCpO62y0qk4EZgAXu1EuI3I7z0T5dQIuAsa4QXviVFSoagOwEdjNG+6yzA3LZxnuAR4EtkaFt4oyuL89wD0iMkVE3hCR7q2pDKpaD9wETMfZNvZQ4LlclSHNcvwA+FpVa937L4shUyE/C28ZvPl1ovV819FlyNt3HaJVKQVVbVTVo3H2bj5eRA7H6ZbdLCKTgZ2AumTyEpG2wKvAo6q6MBTsd9s44YHJRBlE5GjgAFV9x+9yDFkLqgw4rZ2ewHhV7QN8ATzgXmsVZRCRdjhK4RigBzANuC1XZYDUyiEihwF/B25IIGu8a3l9Fj5lCIW3mu86ugz5/q5DtCqlEEJVq4BxwPmqOltVv6Oqx+K8DAuSzOZpYJ6qPuwJW4arzd2XaxdgvTfcpSdOyzBl0izDScCxIlKB09U8SETGtbIyrMNpDYU+gDeAPq2sDEe7eSxQp+8+DDg512UIUg4R6Ynzm1+jqqHwZa4cfjIV3LOIUYYQreK7jlGGgviu07Jj5vIP6Ap0co+3xxlI+i7QzQ0rA14EfhaV7qe0tGXfC7wFlEWF30zkYM4w97gzsAhnIGdX97hzPsvgudaLSNtjqykD8Bpwtuf6G62pDDi9g5VAV/f8HuDBXJQhlXIAnYBvgB/45PUVzuBoaKD5gkJ8FgnK0Cq+63hlyNd3HXHvdBLn8g84Evgap4s+A7jTDf8NMNf9G4w7sONeq8DRpltwNOqhOJpUgVnAVPfv5278Djgt1vk4szH28+T1Mzd8PnBtPsuQ4OVpNWXAGaD91M1rDLB3KyzDje67NA34L7BbLsqQSjmAO4Bqmt/7qTRXXH3dPBYAj3vSFNSziFUGWtF3He855Ou79v6ZmwvDMAwjTKscUzAMwzCygykFwzAMI4wpBcMwDCOMKQXDMAwjjCkFwzAMI4wpBcMwDCOMKQWjZBCRTiLyS/e4h4i8mcV73Sgi1wRMM05E+mZLJsNIBlunYJQMrg/691T18HzL4ofr0uBWVS3PtyxG6WI9BaOUGAzsLyJTXa+sMwBE5Kci8h93I5RFIvI/IvI7EflaRL4Ukc5uvP3F2Uhnsoh8JiK9Y91IRO4WkVvd43Ei8nd3I5a5InKaG769iLwmzgY9r+O4SAil/467QUvIg2xHEdnH3Uili4iUuTJ8J5s/mFF6mFIwSomBwAJ1vFn+Iera4cCVwPHAIGCrqh6D4701ZAZ6GviVOg7ObgWGBLh3W1U9HrgFuMsNu8m9z5HuPY8FEGf3tjuAc9TxIFsO/E5VF+N41XwS+D2Of/4PA8hgGAlpm28BDKNAGKuqm4HNIrIRx48ROHslHCkiHXE8oL4hEvZW3D5A/m+7/yfj+LUBOB14FEBVp4nINDf8RBw/XePde22Ho5xQ1WdF5DIcn0tHB7i/YSSFKQXDcPBu1NLkOW/C+U7KgCq3l5FO/o1Efnd+g3qCs1HLFS0uiOxAs5vrjsDmFOUxDF/MfGSUEptxNjsJjKpuAha5rXTE4ag05fkUuMrN73Acb5sAXwKniMgB7rUdROQg99rfgVeAO4Fn0ry/YbTAlIJRMqjqOhyTzAzgHylkcRVwnYh8A8wELklTpCeAjq7Z6I84LpFR1UqcvRteda99CfQWkTOA44C/q+orQJ2IXJumDIYRgU1JNQzDMMJYT8EwDMMIYwPNhpEGInI7cFlU8BuqOigf8hhGupj5yDAMwwhj5iPDMAwjjCkFwzAMI4wpBcMwDCOMKQXDMAwjzP8DibnxHrJ+YYkAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 5**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.98\n", "MAE (val) : 2.96\n", "RMSE (train) : 2.51\n", "RMSE (val) : 6.41\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 6**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.03\n", "MAE (val) : 5.27\n", "RMSE (train) : 2.66\n", "RMSE (val) : 10.87\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 7**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.18\n", "MAE (val) : 2.48\n", "RMSE (train) : 3.07\n", "RMSE (val) : 3.87\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 8**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6', 'fold_7'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.22\n", "MAE (val) : 2.34\n", "RMSE (train) : 3.12\n", "RMSE (val) : 3.62\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 9**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6', 'fold_7', 'fold_8'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.26\n", "MAE (val) : 2.51\n", "RMSE (train) : 3.16\n", "RMSE (val) : 3.56\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 10**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6', 'fold_7', 'fold_8', 'fold_9'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.30\n", "MAE (val) : 4.73\n", "RMSE (train) : 3.20\n", "RMSE (val) : 13.60\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# evaluate model\n", "# -- define results store\n", "svm_results_store_dict_v2 = {}\n", "# -- evaluate models performance\n", "evaluate_generalization_performance_sklearn(model_factory = svm_regressor_pipeline_factory_v2,\n", " X = X_feat_v1,\n", " y = y_scaled,\n", " model_params = svm_model_params,\n", " results_store=svm_results_store_dict_v2\n", " )" ] }, { "cell_type": "markdown", "id": "872c995a", "metadata": {}, "source": [ "## Gradient Boosting Trees Regression Model" ] }, { "cell_type": "code", "execution_count": 57, "id": "6458efa2", "metadata": {}, "outputs": [], "source": [ "gbt_regressor_model_params = {\n", " 'n_estimators': 200, \n", " 'learning_rate': 0.1,\n", " 'subsample': 1.0,\n", " 'min_samples_split':3,\n", " 'min_samples_leaf': 10,\n", " 'max_depth': 3,\n", " 'validation_fraction': 0.3,\n", " 'n_iter_no_change': 5\n", "}\n", "\n", "def gbt_regressor_pipeline_factory(model_params = {}):\n", " \"\"\"GBT Regressor Pipeline\"\"\"\n", " return Pipeline(steps= [\n", " (\"sklearn_gbt_regressor\", GradientBoostingRegressor(**model_params) )\n", " ])" ] }, { "cell_type": "code", "execution_count": 58, "id": "15d3b4ce", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('sklearn_gbt_regressor', GradientBoostingRegressor())])
GradientBoostingRegressor()
" ], "text/plain": [ "Pipeline(steps=[('sklearn_gbt_regressor', GradientBoostingRegressor())])" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_config(display=\"diagram\")\n", "gbt_regressor_pipeline_factory()" ] }, { "cell_type": "code", "execution_count": 59, "id": "7cd146ab", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:absl:Fitting Params\n", "DEBUG:absl:None\n", "INFO:absl:**Running CV Fold : 1**\n", "DEBUG:absl:dict_keys(['fold_0'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{ 'learning_rate': 0.1,\n", " 'max_depth': 3,\n", " 'min_samples_leaf': 10,\n", " 'min_samples_split': 3,\n", " 'n_estimators': 200,\n", " 'n_iter_no_change': 5,\n", " 'subsample': 1.0,\n", " 'validation_fraction': 0.3}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.76\n", "MAE (val) : 0.88\n", "RMSE (train) : 1.83\n", "RMSE (val) : 1.28\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 2**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.79\n", "MAE (val) : 1.31\n", "RMSE (train) : 1.84\n", "RMSE (val) : 2.58\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 3**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.81\n", "MAE (val) : 1.92\n", "RMSE (train) : 1.87\n", "RMSE (val) : 7.34\n", "\n" ] }, { "data": { "image/png": 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eyA7dfbOZrTezI9z9U4Kb6RaHr4nApPDvCweyHxGRlqQmJmRMcDCzXHcvcfd6q35q1klgv98huFeiHbAK+CpBKeZpM7sGWAdcmkB6IiKtQorao+MqObxgZnMJruRnuXspgJkdApwOfBn4G0H1UFzC0sfYGIvOiDcNEZHWpCYmpOomuHiG7D7DzM4DvgGcFN6cVgl8SvAM6Ynuvjm52RQREUhdb6W42hzc/RXglSTnRURE6lFTYsi4m+DM7KTwfgTM7Eozu8fMhiQvayIiUleq2hwSuc/hL8AuMxsF3AisBR5NSq5ERKSWVN3fUCOR4FDpQbnmIuAP7v4HQI8QFRFpgRK5z6HYzG4GrgRONbM2QNvkZEtERCKlqiG6RiIlh/8ByoBrwt5JA4DfJiVXIiKSVnGXHMKAcE/E+3WozUFEJCU8xa0O8dwhXUzsthAD3N27NHmuREQkreK5CU6NziIi6ZbiNodEGqQBMLPeQPua92H1koiItCCJ3AQ3wcyWA6uBd4A1wJQk5UtERCJk8n0OtwPjgGXufjDBIHnvJyVXIiKSVokEhwp33w5kmVmWu78FjE5OtkREJFKq73NIpM2h0MxygekEz2LYSjA6q4iItDCJlBwuAnYD3wdeBVYCFyYjUyIiUlvG3edQo+YhP6HJSciLiIhkiLiDQ52b4doRjKtUqpvgRESSL2PbHOreDGdmFwPHN3WGREQkWiZ3Za3F3f8DfL7psiIiIpkikWqlL0a8zQLGkvpgJiLNTHW1U+1OdptGX4sK+x4TmiqJdGWN7JlUSXCH9EVNmhsRaXFufn4BT81cz5pJ56c7K5KARNocvprMjIhIy/TUzPXpzkKLkHEN0mb2RxqoPnL37zZpjkREJO3iqQScCcwiGIl1DLA8fI0GqpKWMxERSZt4nucwGcDMrgZOd/eK8P1fgdeTmjsREQHglLveSun+Euk+0B+IvNchN5wnIiItTCK9lSYBc8ysJnydBtzW5DkSEZG0S6S30t/NbArw2XDWTe6+OTnZEhGRdNpvtZKZDQ//jiGoRlofvvqH80REpIWJp+TwA+Ba4HcxljmNHELDzNoQ9ITa6O4XmFkP4ClgKMENdl9294LGpC0iIgcmnt5K14Z/T2/ifd8ALAFqRnW9CZjm7pPM7Kbw/U+aeJ8ikibujpmlOxsSp7h7K5nZpWbWOZy+xcyeN7NjG7NTMxsInA88FDH7IvY9J2IycHFj0haRzJTqO3zlwCTSlfXn7l5sZicD5xCcwP/ayP3eC9wIVEfM6+PueQDh396xNjSza81sppnNzM/Pb+TuRUSkIYkEh5q7oc8H/uLuLxA89CchZnYBsNXdZyW6LYC7P+juY919bK9evRqThIikgQoOzUsi9zlsNLMHgDOBO80sh8Y9D+IkYIKZnUcwJEcXM/snsMXM+rl7npn1A7Y2Im0RyVDBkNNqc2guEjm5fxl4DRjv7oVAD+DHie7Q3W9294HuPhS4DHjT3a8EXgQmhqtNBF5ING0RkZagtCz9w9bFHRzcfRfB1fzJ4axKggH4msok4CwzWw6cFb4XkRZC1Urxu/PVpenOQkJPgruV4OlvRwB/B9oC/ySoJmoUd38beDuc3g6c0di0RCSzqbdS85JItdIXgAlAKYC7b6L2QHwiIvVylR2alUSCQ7kHLUoOYGadkpMlERFpyOJNO5O+j7iCgwW3Nb4U9lbqZmb/B7wB/C2ZmRORlkPVSk3nvPveTfo+4mpzcHc3s4sJhrPYSdDu8At3n5rEvImISJokcp/DDKDQ3RPuvioiIs1LIsHhdOAbZraWsFEawN2PafJciUiLo2ql5iWR4HBu0nIhIi2eeis1L4k8CW5tMjMiIi2bSg7NS2PGRhIRkRZOwUFEUkIFh+ZFwUFEUmLxpp08/cn6dGdD4pRIg7SISKN9+YEZwd/PDEpzTiQeKjmIiEgUBQcREYmi4CAiKbervJLx905n3vrCdGdF6qHgICIpN3d9IUs3F/ObKUvSnRWph4KDiKSNbozLXAoOIpJyhqU7C7IfCg4ikjYqOGQuBQcRSTmrKTgoOmQsBQcREYmi4CAiIlEUHEQk5dQcnfkUHEQkpaqrnarqoLFBDwDKXBp4T0RS6muTP+HtT/PTnQ3ZD5UcRCSlIgODboLLXAoOIpI2ig2ZS8FBROKyeltpurMgKaTgICL79erCPE6/+21eX7S5ydPeXlLGc7M2NHm6cmBSHhzMbJCZvWVmS8xskZndEM7vYWZTzWx5+Ld7qvMmIrEt2rQTgKWbi5s87W8/PpsfPjOPDQW7mjxtabx0lBwqgR+6+5HAOOA6MxsB3ARMc/dhwLTwvYi0YO7Olp17AKioUgtEJkl5cHD3PHefHU4XA0uAAcBFwORwtcnAxanOm4iIBNLa5mBmQ4FjgY+APu6eB0EAAXrXs821ZjbTzGbm56uvtEgqNXXXU5UVMlfagoOZ5QLPAd9z953xbufuD7r7WHcf26tXr+RlUESkFUtLcDCztgSB4XF3fz6cvcXM+oXL+wFb05E3EamfaVCkViMdvZUMeBhY4u73RCx6EZgYTk8EXkh13kQktXSHdOZKx9hKJwH/Cywws7nhvJ8Ck4CnzewaYB1waRryJiIipCE4uPt71D9i7xmpzIuIJCaZDdJV1c7kD9Zw2fGDyMlu07Q7agY8w4pRukNaRDLC87M3cOuLi/jzWyvTnZW0qM6s2KDgICKZoaSsEoCi3RVpzkl6qOQgIiJRMis0KDiISALWF+xiyoK8pksw4mo5wy6cUy7Tjl/BoZXZUVrO/W+tyLgirDQPz87awLcen91k6UV+C6tb+Xcy0x6ZquDQytz47Hx++9qnzFxbkO6siNRSc2rcULCbF+ZuTGte0iHTYqOCQytTUhY09lVUVac5J9KcVTdR15rIE2LN9BtLtnDDk3ObbB/NhYKDiDR7FdVNf3FRt6rzkzU7uGfqsibfT6ZStZKINHuVSXj2Qt0r5/958EPum7a8yfeTqVRyEJFmrzLOKp9xv54Wd5pqkM4sCg4ikrDKONusNodPeatPZFVKffFm9roCht70MlvDtJZu3snW4obTbY4yrQehgkMr4e7kFe1OdzakhYi35LA/tRqk67l2fuS91QDMWLUdgPH3vsupd73VJPvPJJnW/q7g0Eo8OmMtJ/zmTZbkhQ+Id3jgnZX87N8L0psxaZYe/3Btk6dZ34VzzfzV20r3zttT0QJ72yk4SDrMWBlcdUWOW/ObKUt5/KN16cqSNCN1h1G+780VvLZoM795ZUnUug+8s5IfPD2XoTe9vN90IwNCfW0OVeEl9b1v1G6cfnTGGuasazn36xTsKk93FmpJx/McJBPoiV6SgFin7W88NguAkw7ryTWTP2HmLWfRtUNbfjNlaUJpW/h4ufpKDvVVYf3ihUUArJl0fkL7y1Tfe2puurNQi0oOrVWGFWGl+brqkY+pqHLmrS9s1PY1DbH1lRxaSy+m/OKydGehFgUHEdmvuXGc+K965OMDOsHVFwKq9tNSW7Srgg9Wbmv0fptSVbVTXtm49pBMC4IKDsLiTTuZ3YLqbqVpFe2u4N3l8Z18py7eklDa1bVGZW1cyeHrj37CFX/7iF3llQntO5b3V2yjeE/jnydx5UMfcfgtUxq1bYbFBrU5CJx337tAy6m7lab1zwR6Jv00wd5vGwt2Uxw+5Gd/vZXqU9MDb1txOe3bVtK2TRbdO7Vja/EethSVcfTArvvNx4erttMzN4evPPQRxw3pzqy1Bdz5paO5cFR/srOyaJcd33V0TXfbxsi04TMUHFqpzPoaSmtVExhg/72V9ufU3+6792HNpPM55/fTKdhVEfOiZ3PRHs6/712e+sYJPPHxOh5+bzV9uuQAMCscsfifH67jJ88t4Lgh3XnuWyeycGMR/bq256DcnKj0xtw+lRH9uux9vzK/hIfeXc2Xxw5kYPeOfLR6O+eO7Mf5971LTnYWL1x/MivzS+jQtg39u3UAVHKQDBHrB/fBym0c3qczPWN8+UWSbeHGnTHnV0WcNeMNFAAFu4LqoRufncddl4yqteyVBXlsLy3n4fdW88THQXfuLTtrt5eEnaj2BosL/vgeA7t34L2ffJ6FG4vo2K4Nh/TKBYLnpLy3Yl/V23WPz2bp5uK9aQPcdO5ulm4u3vv+jN+9A+wrsesmOMkIsa7SrvjbR1z24IdpyI0IbCyMfQd/5NDdjWnsfXrmBgDmbyiMCi5llVX1bhert/eGgt24Oxf88T2+/ujMereNVQrYXLS/IT8yKzooOLQSdesz6xtVc8XWEvZU1P+DEUm1yJJDeSOfQ7JwYxET/vQ+f3ij9hDgZQ0FG9sXHjxGHlbll0Ztsnf9RpzoE61WSvZwOAoOrVRDVz3Df/4qAMu3FLN+x65UZUkkpv2VHOIZsC4vvGpftCmouqo575c1cCEUWXKoTLD0UhHj4mt/+Uw0nMxeW5jgFolRcGglLMFboiurqjnr99M5pQUOcCbNy+K8fW0R763Ib1QaNSfmaUu38pNn5++d32DJIULkkxPjCQ6Nqf5KdFTWZPduUnCQmPZEfLlfmr+Jcb+eVutHVdfP/r2AX720mKpqV7WUNKnIq/DvPzUvanlp+f6/b5FX/k/NXL93uqHvamT7REXlvul4Akpjqr8yrUFavZUkpt0RP7jr/zUHCH5UR/brzMQTh1JWWc26Hbs4vE9npizI2zuAX97OPbw8P0/3TEha7SitPYhdfc9Mb+hEX6u0kIklhyQHE5UcWqBHZ6xh2Zbi/a/YgPquqG7772KW5BVz8/MLOPv30ynaXcG3Hp+9d/nL8/MA+MMby9lUuJuyyqq4HwyTCTYV7uaxGWtiLnN31myrvxFSMseY26fWev/+ith3eM/fUFRvGrVKDvUECqjdJrJ3nVhtIxHTsUYkSPRcn+yCRqsODq8syGP9jl2szC9h7fbgR19ZVR31Ya/fsYs3l26htKwSd0/rE5tq9r29pIySskrKKqv4+/urufj+99leUsb9b63gFy8s4vzwrufGaqiLX3lV9d7hFOpeodX4/RvL+P5Tcznillc57GdTeGn+Jr752CzOvOcdVm8rZdqSLWwvKWNPRRVllVXMXV/IzgMYtiARVdXOR+GdrOWV1dz6wkI2Fu7mZ/9ewImT3uTnLywiv7iMi+9/n6E3vcyO0nJ2lVfy2Idr+dzdb3P/Wyv4NOyvvnBj0d7vS3W1Uxre1LWhYNfeE8rWnXtYEJ6E0v39AdhWsq8/v7vv7UK6YEMRGwp2UVJWyfaSzBoE7kDVdGetEU8LXGR377c/3dfW8fHqHXunX5q/iUN++krUtrtjXFw9OmPfneZf/PMH0TtM8GuR7O+RpfuLWpeZjQf+ALQBHnL3SfWtO3bsWJ85s/5eN/WZt76Qi+5/P651TxnWs8FxZT4/vDdvLt3KmMHdmL2ukP5d23PiYT054ZCD2FS4myWbd2IYFVXVdGjXBgNG9O/CxoLdlFc5M1Zuo6LKOW5Idw7u2YkV+SUc0aczCzYWUbS7gjnrChg/sh8VldX0yG3HvxJ4/sKAbh04rHcunXLa8MqCzXFvl4irTxzKPz5Y06htc3OyKSmrPR7OcUO6c9Ho/hwzsBsLNxbRLjuL7CxjR2k5c9cX4sDpR/Tml/9dRPGeSg7u2YnvnTmMDm3bsGjTTt5dns/1nz+Mn/9nEV07tCW3fTYXHNOP6cu2sXNPBVt37qFddhbLtpTU2m+W1a7zveuSY7ixgTYWgG+ceggPTF8FwLkj+/LWp1trPYTm2MHd+M7nD+Nr/wi+o7ecfyS/enkJA7p14LQjejGsdy7Pzd7A6EHdeH/Fdi45biAAvTrn0KdLe9qYsWXnHjrlZFNZXU2nnGw27NjFzj2VbC7aQ8ecNhzUqR2lZVUs2lTEsYO7c+aRfViwsYjZ6woY2b8rndtn06VDW6rdeWV+HvM2FO499l9/4ei9w13cOP4I7nr101rH97tLR5HTNot7pi5rsNtmc3T2iD68nuA4UMnyywlHUbyngrtfX7b/lSPcfO5wvnHaoY3ap5nNcvexDa6TScHBzNoAy4CzgA3AJ8Dl7r441vqNDQ55Rbs54TdvHkhWRUTS6ugBXfnvd05u1LbxBIdMq1Y6Hljh7qvcvRx4ErioqXfSt0v7Jk3vwlH9G71tTsSAXh3btaFTuzZ73x/aq1OtdU8Z1rPedJ66dhwA3Tq2Zd6tZ9dadv4x/bjm5INp3zb6487JzuKQnp244wsjOWN4b64cN5irThjCZ4Z256snDeXuS0fx43OO4JRhPbnhjGF7t+vaoS1nHtmHkw47iBH9unDj+CM46bCDOGVYTy4c1Z9rTz2E44Z0B+AzQ7vH/f/48TlHxL0uwBePHRDXegPC8WtiOe3wXnunP3twj4T2D9GfUyyd2ye/78fgHh33Ttf87/fn8D65Mef36xr8RiKHUrnu9H1XqZcfP6gxWcwINccWadIXj671fljv2P+Xhlw5bvDe6Vi/tSs+Ozhq3k3nDm8wzT5dcur9LH9x4YgEc5iYTCs5XAKMd/evh+//F/isu18fsc61wLUAgwcPPm7t2sY/y3bOugJGD+rG2u27GHJQR15duJnh/brw3optdM7J5vA+nendJYfSskoqq52c7Cx6d26/ty7y33M2siq/hJ+edySfbimmZE8lc9YVcsKhB7FgYxF/eXslV44bTElZFRce04+euTnkFe3B8TAoGIf26sSu8iq2FpcxpEdHissq+fv7q9leUs6Pxx/Buu3BTWhZZhzSqxP/nbeJMUO60zknmxfmbiKvaA9XjhvMIb1y2bmngtx22WRlGXPWFXBIr1y6dmi793jdnRfnbWL0oG57TyRmid3/UFZZxaQpS/n25w6jV+fExmDaVR6MmFlaVkm1B3XfB3VqR3ZWFllZwdDQA7t35J6py3hsxhqe+eYJPDpjLRcfO4A56wrJK9zN2Uf1pUendjwzaz0j+nXhotEDWLG1JGjTOO1Q7nx1KVedMJT731pB5/bZjB7UjZKySi45biD/+mgdVdXOOUf1ZVtJGdOWbOX04b05bkh3PlixjfdXbuOHZx3B+oJdfLhqO2eN6Mt7K7aRZbB8SwmH9+lMeVUVw/t2YfGmnXTr2Ja8oj184dgB/PTfCyjeU8npw3vTvWNbRvbvyrwNhXRp35aP1+zghjOGccOTc+jesR13fOFosgymLNxMv67tuePlJfz4nCOYungLE0b3p2RPJR+u3sE5R/VheN8u7KmoYlV+KVOXbGF43858urmYCaP7M6h7R574eB3rduziC8cOYFCPjlz3+GxuOnc4Iwd0ZdmWYnp0asfyLSUsydvJFZ8dzMw1BZRXVZGT3Ybpy/P5yTnDqaiu5s0lWxk/si8PTF/F6EHdGHfIQXs/t2dmrmfMkO4c2iuXh95dxRlH9mFQ9w58vGYHg7p3pE2W0bdLex5+bzUnD+tJu+ws9lRU0TmnLY6T3SaLuesKOe/ovhSXVbJ4005mrNzOtaceQk52Fu8u30bR7gpGDepGv67tmb2ugILSCnrmtqNf1w7c9dpSRg3sxuF9O7NmWyl9urRnwcZCxgzuzsbC3Vxy3ECmL8vnrBF9KdpdweptJbjD2KE9KK+sZs66Avp368D20nJGD+oGQEFpOR+v2cHoQd2Ys66Q8SP7snjTTvKKdjOifxf6de3A1p17WLalhOI9FZx7dD/cnQ9WbmdY71zmbSjimIFdWbChiNLySiaM6o+ZsX7HLjq3z6Zrh7b8d34eZx7Zm1++uJgJo/tz0mE9qa52pi/PZ+hBnchpm0W/rh1YsbWYzu2D3+n9b63gxvHD+WDFNgp3V/ClMQPJMpi+fBsVldXMWV/A9acPo8qd3JzGX3A0x2qlS4Fz6gSH4939O7HWb2y1kohIa9Ycq5U2AJHl1YHApjTlRUSk1cq04PAJMMzMDjazdsBlwItpzpOISKuTUXdIu3ulmV0PvEbQlfURd1+U5myJiLQ6GRUcANz9FSD6rhIREUmZTKtWEhGRDKDgICIiURQcREQkioKDiIhEyaib4BJlZvlA42+R3r+eQP2j7jUPOobMoGPIDDqGwBB379XQCs06OCSbmc3c312EmU7HkBl0DJlBxxA/VSuJiEgUBQcREYmi4NCwB9OdgSagY8gMOobMoGOIk9ocREQkikoOIiISRcFBRESitLjgYGbtzexjM5tnZovM7Jfh/FFmNsPMFpjZf82sS8Q2N5vZCjP71MzOiZh/XLj+CjO7z8LHpplZjpk9Fc7/yMyGRmwz0cyWh6+J6TwGM+toZi+b2dIwnUkR6zeLY6iT5otmtjBVx9DUx2Fm7czsQTNbFn4mX0rFcTTxMVwerj/fzF41s56ZeAxmdpCZvWVmJWb2pzppNYvfdX3HYKn6Xbt7i3oBBuSG022Bj4BxBM+KOC2c/zXg9nB6BDAPyAEOBlYCbcJlHwMnhGlOAc4N538b+Gs4fRnwVDjdA1gV/u0eTndP1zEAHYHTw3XaAe82t2OISO+LwL+AhRHzknoMSfg+/RL4VTidBfRsTp8FwSjOWyPyfRdwW4YeQyfgZOCbwJ/qpNVcftcxj4EU/a7TfjJP5iv8J84GPgvsZF8D/CBgcTh9M3BzxDavhV+cfsDSiPmXAw9ErhNOZxPcrWiR64TLHgAuT9cxxEjrD8D/NbdjAHKB9whOWgvrWSepx9BEx7Ee6BQj3WbxWRCc0PKBIWH+/gpcm4nHELHu1dQ+sTab33V9xxAjraT8rltctRKAmbUxs7kEVzlT3f0jYCEwIVzlUvY9jnQAwY+2xoZw3oBwuu78Wtu4eyVQBBzUQFrpOobI9LoBFwLTmuEx3A78DthVZxdJP4amOo7w/w9wu5nNNrNnzKxPqo6jKY7B3SuAbwELCB7fOwJ4OEOPoT7N6XcdT3rdSNLvukUGB3evcvfRBM+gPt7MRhIU164zs1lAZ6A8XN1iJdHA/MZuk5AmOoZgoVk28ARwn7uvak7HYGajgcPc/d8xlif9GKDJPovscPv33X0MMAO4O1XH0USfRVuC4HAs0B+YT1DKyMRjqE9z+l03KNm/6xYZHGq4eyHwNjDe3Ze6+9nufhzBP3RluNoGakfqgQRXRRvC6brza20TfkBdgR0NpJWuY6jxILDc3e+NmNdcjuEE4DgzW0NQtXS4mb2d6mNoguPYTlDyqQlyzwBjUn0cB3gMo8M0VnpQN/E0cGKGHkN9mtPven+S+7s+kHqzTHwBvYBu4XQHgsaaC4De4bws4FHga+H7o6jd+LaKfQ2InxA0GNU0XJ0Xzr+O2o0+T4fTPYDVBA0+3cPpHmk+hl8BzwFZdfbRbI4hIs2h1G5zSOoxJOGzeBL4fDh9NfBMc/osCEoLeUCvcL3bgd9l4jFEbHc10Q3SzeJ3vZ9jSPrvOu0n86Z+AccAcwiKvAuBX4TzbwCWha9JhA1A4bKfEUTrTwlb/cP5Y8M0VgJ/qtkGaE9w5beCoOfDIRHbfC2cvwL4ajqPgeDqwIElwNzw9fXmdAx10hxK7eCQ1GNIwvdpCDA9TGsaMLi5fRYEPWeWhGn9Fzgog49hDcFVcwnBlfOIZvi7jjoGUvS71vAZIiISpUW3OYiISOMoOIiISBQFBxERiaLgICIiURQcREQkioKDiIhEUXCQVsPMupnZt8Pp/mb2bBL39U0zuyrBbd42s7HJypNIInSfg7Qa4dj2L7n7yHTnJZZwWJAfufvMdOdFRCUHaU0mAYea2dxwVNSFAGZ2tZn9J3zQymozu97MfmBmc8zsQzPrEa53qAUPuJllZu+a2fD6dmRmt5nZj8Lpt83szvBBL8vM7JRwfgcze9KCB+c8RTCkQs32Z4cPgKkZwTXXzIaED2rpaWZZYR7OTuY/TFovBQdpTW4CVnowKuaP6ywbCVwBHA/cAexy92MJRk+tqR56EPiOBwOk/Qj4cwL7znb344HvAbeG874V7ueYcJ/HAVjwdLVbgDM9GMF1JvADd18L3EnwHIUfEoz7/3oCeRCJW3a6MyCSId5y92Kg2MyKCMYNguDZBceYWS7BCKTPmO0d/TgngfSfD//OIhgjCuBU4D4Ad59vZvPD+eMIxtB5P9xXO4Ighbs/ZGaXEoxxNDqB/YskRMFBJFAWMV0d8b6a4HeSBRSGpY4DSb+K2r+7WI1+RvAgmMujFph1ZN+Q07lAcSPzI9IgVStJa1JM8DCVhLn7TmB1eNWOBUYdYH6mA18J0xtJMGonwIfASWZ2WLiso5kdHi67E3gc+AXwtwPcv0i9FByk1XD37QRVNQuB3zYiia8A15jZPGARcNEBZukvQG5YnXQjwRDLuHs+wRj+T4TLPgSGm9lpwGeAO939caDczL56gHkQiUldWUVEJIpKDiIiEkUN0iIHwMx+BlxaZ/Yz7n5HOvIj0lRUrSQiIlFUrSQiIlEUHEREJIqCg4iIRFFwEBGRKP8PIsdoR2NRaToAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 4**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.86\n", "MAE (val) : 4.86\n", "RMSE (train) : 2.13\n", "RMSE (val) : 10.03\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 5**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 0.97\n", "MAE (val) : 2.69\n", "RMSE (train) : 2.55\n", "RMSE (val) : 6.49\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 6**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.01\n", "MAE (val) : 5.08\n", "RMSE (train) : 2.70\n", "RMSE (val) : 10.96\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 7**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.15\n", "MAE (val) : 2.17\n", "RMSE (train) : 3.12\n", "RMSE (val) : 3.93\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 8**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6', 'fold_7'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.20\n", "MAE (val) : 2.13\n", "RMSE (train) : 3.17\n", "RMSE (val) : 3.61\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 9**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6', 'fold_7', 'fold_8'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.24\n", "MAE (val) : 1.83\n", "RMSE (train) : 3.20\n", "RMSE (val) : 3.49\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:**Running CV Fold : 10**\n", "DEBUG:absl:dict_keys(['fold_0', 'fold_1', 'fold_2', 'fold_3', 'fold_4', 'fold_5', 'fold_6', 'fold_7', 'fold_8', 'fold_9'])\n", "INFO:absl:Creating model with model factory using defined parameters\n", "INFO:absl:Fitting Model\n", "INFO:absl:Evaluating Model\n", "DEBUG:absl:-running inference\n", "DEBUG:absl:-evaluating forecast predictions\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MAE (train) : 1.27\n", "MAE (val) : 4.70\n", "RMSE (train) : 3.25\n", "RMSE (val) : 13.63\n", "\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# evaluate model\n", "# -- define results store\n", "gbt_results_store_dict = {}\n", "# -- evaluate models performance\n", "evaluate_generalization_performance_sklearn(model_factory = gbt_regressor_pipeline_factory,\n", " X = X_feat_v1,\n", " y = y_scaled,\n", " model_params = gbt_regressor_model_params,\n", " results_store=gbt_results_store_dict\n", " )" ] }, { "cell_type": "code", "execution_count": 60, "id": "5bb50c3a", "metadata": {}, "outputs": [], "source": [ "gbt_results_df = pd.DataFrame(gbt_results_store_dict).T" ] }, { "cell_type": "code", "execution_count": 61, "id": "2b674c5b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fold_94.6976701.27110913.6289303.248829
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" ], "text/plain": [ " mae_val mae_train rmse_val rmse_train\n", "fold_0 0.881432 0.761911 1.283864 1.827933\n", "fold_1 1.310641 0.787814 2.576291 1.836118\n", "fold_2 1.922542 0.812456 7.343700 1.867888\n", "fold_3 4.857800 0.859360 10.025838 2.127623\n", "fold_4 2.686871 0.972966 6.492536 2.549014\n", "fold_5 5.079630 1.012135 10.956213 2.695101\n", "fold_6 2.165469 1.149712 3.934823 3.116924\n", "fold_7 2.128778 1.200971 3.614876 3.172217\n", "fold_8 1.832298 1.236253 3.488560 3.197051\n", "fold_9 4.697670 1.271109 13.628930 3.248829" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gbt_results_df" ] }, { "cell_type": "code", "execution_count": 62, "id": "0e7c28d4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gbt_results_df.boxplot()" ] }, { "cell_type": "code", "execution_count": 63, "id": "c4dea543", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mae_valmae_trainrmse_valrmse_train
count10.00000010.00000010.00000010.000000
mean2.7563131.0064696.3345632.563870
std1.5450550.1969884.0841920.606393
min0.8814320.7619111.2838641.827933
25%1.8548590.8241823.5201391.932822
50%2.1471240.9925505.2136792.622057
75%4.1949711.1881579.3553043.158394
max5.0796301.27110913.6289303.248829
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" ], "text/plain": [ " mae_val mae_train rmse_val rmse_train\n", "count 10.000000 10.000000 10.000000 10.000000\n", "mean 2.756313 1.006469 6.334563 2.563870\n", "std 1.545055 0.196988 4.084192 0.606393\n", "min 0.881432 0.761911 1.283864 1.827933\n", "25% 1.854859 0.824182 3.520139 1.932822\n", "50% 2.147124 0.992550 5.213679 2.622057\n", "75% 4.194971 1.188157 9.355304 3.158394\n", "max 5.079630 1.271109 13.628930 3.248829" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gbt_results_df.describe()" ] }, { "cell_type": "code", "execution_count": 64, "id": "e8df1d04", "metadata": {}, "outputs": [], "source": [ "gbt_results_df.to_csv('gbt_results_best_case.csv')" ] }, { "cell_type": "markdown", "id": "8e29f8a4", "metadata": {}, "source": [ "## Quick Visual Check" ] }, { "cell_type": "code", "execution_count": 65, "id": "172d0393", "metadata": {}, "outputs": [], "source": [ "gbt_model = gbt_regressor_pipeline_factory(gbt_regressor_model_params)" ] }, { "cell_type": "code", "execution_count": 67, "id": "8e793a65", "metadata": {}, "outputs": [], "source": [ "# svm_model = svm_regressor_pipeline_factory(svm_model_params)" ] }, { "cell_type": "code", "execution_count": 68, "id": "726c1ab9", "metadata": {}, "outputs": [], "source": [ "fitted_gbt_model = gbt_model.fit(X_feat_v1,y_scaled)" ] }, { "cell_type": "code", "execution_count": 70, "id": "830cc4f8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "done\n" ] } ], "source": [ "import pickle\n", "\n", "with open('fitted_gbt_model_latest.pkl', 'wb') as pkl_file:\n", " pickle.dump(fitted_gbt_model, pkl_file)\n", " print('done')" ] }, { "cell_type": "code", "execution_count": 994, "id": "ac1da9ab", "metadata": {}, "outputs": [], "source": [ "# fitted_svm_model = svm_model.fit(X_feat_v1, y_scaled)" ] }, { "cell_type": "code", "execution_count": 71, "id": "371579a4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1539265MillyzUSCGJ208956048201930375183248201930375183214784414561032352653604980020985609979211088497789649778963873264746352513369200147842464
USDY42018517340165303160000340165303160000104402730675564946431351665773660748207540351480035148001181535292488130416000174001740
2549794Ted NugentUSME32101242789096552571387890965525713840271841612109198812720180901246220108124428126433509221614921538630006432402
USME321012443536269075026035362690750260180286673090193836396090019809003639603639601347347271263040003060180
2592973Easton CorbinQM4TW22582551396047181481913960471814819639128628686912143562639639284143562143562134410820504749100000106571
\n", "

257 rows × 20 columns

\n", "
" ], "text/plain": [ " RELEASE_ID UPC \\\n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 4857715329286660 4857715329286660 \n", " QMYLU1300002 2408469424195549 2408469424195549 \n", " QMYLU1300003 2376557425936057 2376557425936057 \n", " QMYLU1300004 2402264313422870 2402264313422870 \n", " QMYLU1300005 4873670858626632 4873670858626632 \n", "... ... ... \n", "1539265 Millyz USCGJ2089560 482019303751832 482019303751832 \n", " USDY42018517 340165303160000 340165303160000 \n", "2549794 Ted Nugent USME32101242 78909655257138 78909655257138 \n", " USME32101244 35362690750260 35362690750260 \n", "2592973 Easton Corbin QM4TW2258255 13960471814819 13960471814819 \n", "\n", " RELEASE_GENREID FEED_ID STORE_ID \\\n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 49320 77104 1752138 \n", " QMYLU1300002 24453 38128 869041 \n", " QMYLU1300003 24129 38474 859963 \n", " QMYLU1300004 24390 37729 869257 \n", " QMYLU1300005 49482 77072 1762440 \n", "... ... ... ... \n", "1539265 Millyz USCGJ2089560 14784 41456 1032352 \n", " USDY42018517 10440 27306 755649 \n", "2549794 Ted Nugent USME32101242 402 7184 161210 \n", " USME32101244 180 2866 73090 \n", "2592973 Easton Corbin QM4TW2258255 639 1286 28686 \n", "\n", " SNAPSHOT_ISO_WEEK SNAPSHOT_YEAR \\\n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 145374 11061860 \n", " QMYLU1300002 72158 5484516 \n", " QMYLU1300003 71307 5411845 \n", " QMYLU1300004 71963 5470331 \n", " QMYLU1300005 146444 11097810 \n", "... ... ... \n", "1539265 Millyz USCGJ2089560 65360 4980020 \n", " USDY42018517 46431 3516657 \n", "2549794 Ted Nugent USME32101242 9198 812720 \n", " USME32101244 1938 363960 \n", "2592973 Easton Corbin QM4TW2258255 912 143562 \n", "\n", " RELEASE_DATE_ISO_WEEK \\\n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 27400 \n", " QMYLU1300002 16302 \n", " QMYLU1300003 16086 \n", " QMYLU1300004 16260 \n", " QMYLU1300005 129203 \n", "... ... \n", "1539265 Millyz USCGJ2089560 98560 \n", " USDY42018517 73660 \n", "2549794 Ted Nugent USME32101242 18090 \n", " USME32101244 900 \n", "2592973 Easton Corbin QM4TW2258255 639 \n", "\n", " SALES_START_ISO_WEEK \\\n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 27400 \n", " QMYLU1300002 16302 \n", " QMYLU1300003 16086 \n", " QMYLU1300004 16260 \n", " QMYLU1300005 129203 \n", "... ... \n", "1539265 Millyz USCGJ2089560 99792 \n", " USDY42018517 74820 \n", "2549794 Ted Nugent USME32101242 12462 \n", " USME32101244 1980 \n", "2592973 Easton Corbin QM4TW2258255 639 \n", "\n", " RELEASE_DAY_OF_WEEK_ISO RELEASE_YEAR \\\n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 10960 11036720 \n", " QMYLU1300002 5434 5472038 \n", " QMYLU1300003 5362 5399534 \n", " QMYLU1300004 5420 5457940 \n", " QMYLU1300005 10996 11070223 \n", "... ... ... \n", "1539265 Millyz USCGJ2089560 11088 4977896 \n", " USDY42018517 7540 3514800 \n", "2549794 Ted Nugent USME32101242 2010 812442 \n", " USME32101244 900 363960 \n", "2592973 Easton Corbin QM4TW2258255 284 143562 \n", "\n", " SALES_START_YEAR TOTAL_STREAMS \\\n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 11036720 48412702 \n", " QMYLU1300002 5472038 5992522 \n", " QMYLU1300003 5399534 33387926 \n", " QMYLU1300004 5457940 6847527 \n", " QMYLU1300005 11070223 10146960 \n", "... ... ... \n", "1539265 Millyz USCGJ2089560 4977896 3873264 \n", " USDY42018517 3514800 1181535 \n", "2549794 Ted Nugent USME32101242 812643 350922 \n", " USME32101244 363960 134734 \n", "2592973 Easton Corbin QM4TW2258255 143562 1344108 \n", "\n", " TOTAL_SKIPS TOTAL_SAVES \\\n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 9466288 394066 \n", " QMYLU1300002 1225534 48341 \n", " QMYLU1300003 3196727 130088 \n", " QMYLU1300004 1707837 63888 \n", " QMYLU1300005 3172460 129460 \n", "... ... ... \n", "1539265 Millyz USCGJ2089560 746352 51336 \n", " USDY42018517 292488 13041 \n", "2549794 Ted Nugent USME32101242 161492 15386 \n", " USME32101244 72712 6304 \n", "2592973 Easton Corbin QM4TW2258255 205047 49100 \n", "\n", " HOLIDAY_CHRISTMAS \\\n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 122 \n", " QMYLU1300002 59 \n", " QMYLU1300003 60 \n", " QMYLU1300004 58 \n", " QMYLU1300005 122 \n", "... ... \n", "1539265 Millyz USCGJ2089560 92 \n", " USDY42018517 60 \n", "2549794 Ted Nugent USME32101242 30 \n", " USME32101244 0 \n", "2592973 Easton Corbin QM4TW2258255 0 \n", "\n", " SNAPSHOT_WITHIN_COVID_LOCKDOWN \\\n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 1250 \n", " QMYLU1300002 621 \n", " QMYLU1300003 603 \n", " QMYLU1300004 624 \n", " QMYLU1300005 1246 \n", "... ... \n", "1539265 Millyz USCGJ2089560 0 \n", " USDY42018517 0 \n", "2549794 Ted Nugent USME32101242 0 \n", " USME32101244 0 \n", "2592973 Easton Corbin QM4TW2258255 0 \n", "\n", " RELEASE_WITHIN_COVID_LOCKDOWN \\\n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 0 \n", " QMYLU1300002 0 \n", " QMYLU1300003 0 \n", " QMYLU1300004 0 \n", " QMYLU1300005 0 \n", "... ... \n", "1539265 Millyz USCGJ2089560 0 \n", " USDY42018517 0 \n", "2549794 Ted Nugent USME32101242 0 \n", " USME32101244 0 \n", "2592973 Easton Corbin QM4TW2258255 0 \n", "\n", " TRACKNAME_LENGTH ROW_COUNT \n", "ARTIST_ID ARTIST_NAME ISRC \n", "539614 Whiskey Myers QMYLU1300001 109600 5480 \n", " QMYLU1300002 40755 2717 \n", " QMYLU1300003 18767 2681 \n", " QMYLU1300004 56910 2710 \n", " QMYLU1300005 21992 5498 \n", "... ... ... \n", "1539265 Millyz USCGJ2089560 14784 2464 \n", " USDY42018517 17400 1740 \n", "2549794 Ted Nugent USME32101242 6432 402 \n", " USME32101244 3060 180 \n", "2592973 Easton Corbin QM4TW2258255 1065 71 \n", "\n", "[257 rows x 20 columns]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.groupby(by=['ARTIST_ID', 'ARTIST_NAME', 'ISRC']).sum()" ] }, { "cell_type": "code", "execution_count": 1035, "id": "9f2fc03c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['ARTIST_ID', 'ARTIST_NAME', 'RELEASE_ID', 'RELEASE_NAME',\n", " 'RELEASE_FORMAT', 'THIRD_PARTY_PUBLISHER', 'UPC', 'ISRC',\n", " 'RELEASE_TRACK_NAME', 'RELEASE_DATE', 'SALES_START_DATE', 'TRACKNAME',\n", " 'RELEASE_GENREID', 'GENRENAME', 'FEED_ID', 'FEEDNAME', 'STORE_ID',\n", " 'STORENAME', 'COUNTRY_CODE', 'SNAPSHOT_ISO_WEEK', 'SNAPSHOT_YEAR',\n", " 'RELEASE_DATE_ISO_WEEK', 'SALES_START_ISO_WEEK',\n", " 'RELEASE_DAY_OF_WEEK_ISO', 'RELEASE_YEAR', 'SALES_START_YEAR',\n", " 'AVG_DIFF_RELEASE_SALES_DAYS', 'AVG_DIFF_RELEASE_SALES_WEEKS',\n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS',\n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS', 'TOTAL_STREAMS',\n", " 'TOTAL_SKIPS', 'TOTAL_SAVES', 'SNAPSHOT_YEAR_ISOWEEK',\n", " 'HOLIDAY_CHRISTMAS', 'SNAPSHOT_WITHIN_COVID_LOCKDOWN',\n", " 'RELEASE_WITHIN_COVID_LOCKDOWN', 'TRACKNAME_LENGTH', 'ROW_COUNT'],\n", " dtype='object')" ] }, "execution_count": 1035, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df = dataset_df[(dataset_df['ARTIST_ID'] == 2549794) & (dataset_df['ISRC'] == 'USME32101244')].sort_values(by=['SNAPSHOT_YEAR_ISOWEEK'])\n", "test_df.columns" ] }, { "cell_type": "code", "execution_count": 1036, "id": "bb313069", "metadata": {}, "outputs": [], "source": [ "X_feat_test = feat_pipeline_v1.transform(test_df[['ARTIST_ID', 'ARTIST_NAME', 'RELEASE_ID', 'RELEASE_NAME',\n", " 'RELEASE_FORMAT', 'THIRD_PARTY_PUBLISHER', 'UPC', 'ISRC',\n", " 'RELEASE_TRACK_NAME', 'RELEASE_DATE', 'SALES_START_DATE', 'TRACKNAME',\n", " 'RELEASE_GENREID', 'GENRENAME', 'FEED_ID', 'FEEDNAME', 'STORE_ID',\n", " 'STORENAME', 'COUNTRY_CODE', 'SNAPSHOT_ISO_WEEK', 'SNAPSHOT_YEAR',\n", " 'RELEASE_DATE_ISO_WEEK', 'SALES_START_ISO_WEEK',\n", " 'RELEASE_DAY_OF_WEEK_ISO', 'RELEASE_YEAR', 'SALES_START_YEAR',\n", " 'AVG_DIFF_RELEASE_SALES_DAYS', 'AVG_DIFF_RELEASE_SALES_WEEKS',\n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_DAYS',\n", " 'MEDIAN_SNAPSHOT_DIST_FROM_RELEASE_WEEKS', 'TOTAL_STREAMS',\n", " 'TOTAL_SKIPS', 'TOTAL_SAVES', 'SNAPSHOT_YEAR_ISOWEEK',\n", " 'HOLIDAY_CHRISTMAS', 'SNAPSHOT_WITHIN_COVID_LOCKDOWN',\n", " 'RELEASE_WITHIN_COVID_LOCKDOWN', 'TRACKNAME_LENGTH']])" ] }, { "cell_type": "code", "execution_count": 1037, "id": "a059c72a", "metadata": {}, "outputs": [], "source": [ "# y_pred_svm = fitted_svm_model.predict(X_feat_test)\n", "y_pred_gbt = fitted_gbt_model.predict(X_feat_test)" ] }, { "cell_type": "code", "execution_count": 1038, "id": "f1b93652", "metadata": {}, "outputs": [], "source": [ "model_err = 4.240894\n", "# test_df['PREDICTIONS_SCALED_SVM'] = y_pred_svm\n", "test_df['PREDICTIONS_SCALED_GBT'] = y_pred_gbt\n", "test_df['PREDICTIONS_SCALED_GBT_LOWER'] = y_pred_gbt - model_err\n", "test_df['PREDICTIONS_SCALED_GBT_UPPER'] = y_pred_gbt + model_err" ] }, { "cell_type": "code", "execution_count": 1039, "id": "f2e07354", "metadata": {}, "outputs": [], "source": [ "test_df['TOTAL_STREAMS_SCALED'] = test_df['TOTAL_STREAMS']/1e3" ] }, { "cell_type": "code", "execution_count": 1040, "id": "4d98d851", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'STREAMS * 1e3')" ] }, "execution_count": 1040, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "forecast_size = 500\n", "sns.lineplot(data=test_df.iloc[:forecast_size], x='SNAPSHOT_YEAR_ISOWEEK', y='TOTAL_STREAMS_SCALED', ci=None, label='actual')\n", "sns.lineplot(data=test_df.iloc[:forecast_size], x='SNAPSHOT_YEAR_ISOWEEK', y='PREDICTIONS_SCALED_GBT', \n", " color='purple', label='model(gbt)', err_style='band', ci=None)\n", "ax.fill_between(x=test_df['SNAPSHOT_YEAR_ISOWEEK'].iloc[:forecast_size], \n", " y1=test_df['PREDICTIONS_SCALED_GBT_LOWER'].iloc[:forecast_size],\n", " y2=test_df['PREDICTIONS_SCALED_GBT_UPPER'].iloc[:forecast_size], alpha=0.2)\n", "\n", "plt.title('STREAMS * 1e3')" ] }, { "cell_type": "markdown", "id": "e581ec6b", "metadata": {}, "source": [ "## TODO\n", "- Noise\n", "- Social Data\n", "- Binning Streams (e.g. [0 - 10k, 10k - 50k, 50k - 100k, 100k - 500k, 500k +])\n", "- Last week data (as test dataset)\n", "- Negative Predictions\n", "- Prior Singles releases - good indicator for next track.\n" ] }, { "cell_type": "code", "execution_count": 857, "id": "dedbe6f5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ARTIST_IDARTIST_NAMERELEASE_IDRELEASE_NAMERELEASE_FORMATTHIRD_PARTY_PUBLISHERUPCISRCRELEASE_TRACK_NAMERELEASE_DATE...HOLIDAY_CHRISTMASSNAPSHOT_WITHIN_COVID_LOCKDOWNRELEASE_WITHIN_COVID_LOCKDOWNTRACKNAME_LENGTHROW_COUNTPREDICTIONS_SCALED_SVMPREDICTIONS_SCALED_GBTPREDICTIONS_SCALED_GBT_LOWERPREDICTIONS_SCALED_GBT_UPPERTOTAL_STREAMS_SCALED
71247539614Whiskey Myers886444396097Early Morning ShakesFull LengthN886444396097QMYLU1300001Early Morning Shakes2014-02-04...FalseFalseFalse20118.66276419.75468615.51379223.9955800.018
71250539614Whiskey Myers886444396097Early Morning ShakesFull LengthN886444396097QMYLU1300001Early Morning Shakes2014-02-04...FalseFalseFalse20115.67150318.59573714.35484322.8366313.005
71261539614Whiskey Myers886444396097Early Morning ShakesFull LengthN886444396097QMYLU1300001Early Morning Shakes2014-02-04...FalseFalseFalse20116.08465418.59573714.35484322.8366310.697
103714539614Whiskey Myers886444410212Early Morning ShakesSingleN886444410212QMYLU1300001Early Morning Shakes2014-01-21...FalseFalseFalse20116.33005018.59573714.35484322.8366310.697
103712539614Whiskey Myers886444410212Early Morning ShakesSingleN886444410212QMYLU1300001Early Morning Shakes2014-01-21...FalseFalseFalse20118.90816019.75468615.51379223.9955800.018
..................................................................
102744539614Whiskey Myers886444396097Early Morning ShakesFull LengthN886444396097QMYLU1300001Early Morning Shakes2014-02-04...FalseFalseFalse201-1.1120481.736407-2.5044875.9773010.015
102729539614Whiskey Myers886444396097Early Morning ShakesFull LengthN886444396097QMYLU1300001Early Morning Shakes2014-02-04...FalseFalseFalse201-1.2567651.736407-2.5044875.97730137.746
102722539614Whiskey Myers886444396097Early Morning ShakesFull LengthN886444396097QMYLU1300001Early Morning Shakes2014-02-04...FalseFalseFalse201-0.8822701.736407-2.5044875.9773018.067
106368539614Whiskey Myers886444410212Early Morning ShakesSingleN886444410212QMYLU1300001Early Morning Shakes2014-01-21...FalseFalseFalse201-0.9471731.736407-2.5044875.97730141.761
106370539614Whiskey Myers886444410212Early Morning ShakesSingleN886444410212QMYLU1300001Early Morning Shakes2014-01-21...FalseFalseFalse201-0.8666531.736407-2.5044875.9773010.015
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5480 rows × 44 columns

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" ], "text/plain": [ " ARTIST_ID ARTIST_NAME RELEASE_ID RELEASE_NAME \\\n", "71247 539614 Whiskey Myers 886444396097 Early Morning Shakes \n", "71250 539614 Whiskey Myers 886444396097 Early Morning Shakes \n", "71261 539614 Whiskey Myers 886444396097 Early Morning Shakes \n", "103714 539614 Whiskey Myers 886444410212 Early Morning Shakes \n", "103712 539614 Whiskey Myers 886444410212 Early Morning Shakes \n", "... ... ... ... ... \n", "102744 539614 Whiskey Myers 886444396097 Early Morning Shakes \n", "102729 539614 Whiskey Myers 886444396097 Early Morning Shakes \n", "102722 539614 Whiskey Myers 886444396097 Early Morning Shakes \n", "106368 539614 Whiskey Myers 886444410212 Early Morning Shakes \n", "106370 539614 Whiskey Myers 886444410212 Early Morning Shakes \n", "\n", " RELEASE_FORMAT THIRD_PARTY_PUBLISHER UPC ISRC \\\n", "71247 Full Length N 886444396097 QMYLU1300001 \n", "71250 Full Length N 886444396097 QMYLU1300001 \n", "71261 Full Length N 886444396097 QMYLU1300001 \n", "103714 Single N 886444410212 QMYLU1300001 \n", "103712 Single N 886444410212 QMYLU1300001 \n", "... ... ... ... ... \n", "102744 Full Length N 886444396097 QMYLU1300001 \n", "102729 Full Length N 886444396097 QMYLU1300001 \n", "102722 Full Length N 886444396097 QMYLU1300001 \n", "106368 Single N 886444410212 QMYLU1300001 \n", "106370 Single N 886444410212 QMYLU1300001 \n", "\n", " RELEASE_TRACK_NAME RELEASE_DATE ... HOLIDAY_CHRISTMAS \\\n", "71247 Early Morning Shakes 2014-02-04 ... False \n", "71250 Early Morning Shakes 2014-02-04 ... False \n", "71261 Early Morning Shakes 2014-02-04 ... False \n", "103714 Early Morning Shakes 2014-01-21 ... False \n", "103712 Early Morning Shakes 2014-01-21 ... False \n", "... ... ... ... ... \n", "102744 Early Morning Shakes 2014-02-04 ... False \n", "102729 Early Morning Shakes 2014-02-04 ... False \n", "102722 Early Morning Shakes 2014-02-04 ... False \n", "106368 Early Morning Shakes 2014-01-21 ... False \n", "106370 Early Morning Shakes 2014-01-21 ... False \n", "\n", " SNAPSHOT_WITHIN_COVID_LOCKDOWN RELEASE_WITHIN_COVID_LOCKDOWN \\\n", "71247 False False \n", "71250 False False \n", "71261 False False \n", "103714 False False \n", "103712 False False \n", "... ... ... \n", "102744 False False \n", "102729 False False \n", "102722 False False \n", "106368 False False \n", "106370 False False \n", "\n", " TRACKNAME_LENGTH ROW_COUNT PREDICTIONS_SCALED_SVM \\\n", "71247 20 1 18.662764 \n", "71250 20 1 15.671503 \n", "71261 20 1 16.084654 \n", "103714 20 1 16.330050 \n", "103712 20 1 18.908160 \n", "... ... ... ... \n", "102744 20 1 -1.112048 \n", "102729 20 1 -1.256765 \n", "102722 20 1 -0.882270 \n", "106368 20 1 -0.947173 \n", "106370 20 1 -0.866653 \n", "\n", " PREDICTIONS_SCALED_GBT PREDICTIONS_SCALED_GBT_LOWER \\\n", "71247 19.754686 15.513792 \n", "71250 18.595737 14.354843 \n", "71261 18.595737 14.354843 \n", "103714 18.595737 14.354843 \n", "103712 19.754686 15.513792 \n", "... ... ... \n", "102744 1.736407 -2.504487 \n", "102729 1.736407 -2.504487 \n", "102722 1.736407 -2.504487 \n", "106368 1.736407 -2.504487 \n", "106370 1.736407 -2.504487 \n", "\n", " PREDICTIONS_SCALED_GBT_UPPER TOTAL_STREAMS_SCALED \n", "71247 23.995580 0.018 \n", "71250 22.836631 3.005 \n", "71261 22.836631 0.697 \n", "103714 22.836631 0.697 \n", "103712 23.995580 0.018 \n", "... ... ... \n", "102744 5.977301 0.015 \n", "102729 5.977301 37.746 \n", "102722 5.977301 8.067 \n", "106368 5.977301 41.761 \n", "106370 5.977301 0.015 \n", "\n", "[5480 rows x 44 columns]" ] }, "execution_count": 857, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# test_df" ] }, { "cell_type": "markdown", "id": "b483792d", "metadata": {}, "source": [ "## Forecast Data\n", "PREP query to Pull Data from Snowflake" ] }, { "cell_type": "code", "execution_count": 72, "id": "10152302", "metadata": {}, "outputs": [], "source": [ "inference_data_sql = \"SELECT * FROM DEV_ENGINEERING.AADAMU.MODEL_INFERENCE_DATA\"" ] }, { "cell_type": "code", "execution_count": 73, "id": "fee91108", "metadata": {}, "outputs": [], "source": [ "inference_data_df = execute_sql(conn=conn, sql=inference_data_sql)" ] }, { "cell_type": "code", "execution_count": 74, "id": "d2687081", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ARTIST_IDARTIST_NAMERELEASE_NAMETRACK_UPCUPCISRCTRACK_ISRCLABEL_IDLABEL_NAMERELEASE_FORMAT...RELEASE_YEARSALES_START_YEARRELEASE_DATE_ISO_WEEKSALES_START_ISO_WEEKRELEASE_DAY_OF_WEEK_ISOSALES_START_WEEK_ISODIFF_RELEASE_SALES_DAYSDIFF_RELEASE_SALES_WEEKSAVG_DIFF_RELEASE_SALES_DAYSAVG_DIFF_RELEASE_SALES_WEEKS
01007533Devil MasterEcstasies Of Never Ending Night781676425112781676425112US2642245705US264224570510440Relapse RecordsFull Length...202220221717550000
11007533Devil MasterEcstasies Of Never Ending Night781676425112781676425112US2642245703US264224570310440Relapse RecordsFull Length...202220221717550000
22549794Ted NugentDetroit Muscle196292681284196292681284USME32101251USME3210125121899MRI EntertainmentFull Length...202220221717550000
32549794Ted NugentDetroit Muscle196292681284196292681284USME32101242USME3210124221899MRI EntertainmentFull Length...202220221717550000
41007533Devil MasterEcstasies Of Never Ending Night781676425112781676425112US2642245702US264224570210440Relapse RecordsFull Length...202220221717550000
52549794Ted NugentDetroit Muscle196292681284196292681284USME32101248USME3210124821899MRI EntertainmentFull Length...202220221717550000
6846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200004USFZH220000415115Traffic Entertainment Group, Inc.Full Length...202220221717550000
72549794Ted NugentDetroit Muscle196292681284196292681284USME32101241USME3210124121899MRI EntertainmentFull Length...202220221717550000
82549794Ted NugentDetroit Muscle196292681284196292681284USME32101249USME3210124921899MRI EntertainmentFull Length...202220221717550000
91007533Devil MasterEcstasies Of Never Ending Night781676425112781676425112US2642245706US264224570610440Relapse RecordsFull Length...202220221717550000
101007533Devil MasterEcstasies Of Never Ending Night781676425112781676425112US2642245708US264224570810440Relapse RecordsFull Length...202220221717550000
11539614Whiskey MyersWhole World Gone Crazy196626311153196626311153QMYLU2100003QMYLU210000321989Thirty TigersSingle...202220221717550000
12846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200011USFZH220001115115Traffic Entertainment Group, Inc.Full Length...202220221717550000
13846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200012USFZH220001215115Traffic Entertainment Group, Inc.Full Length...202220221717550000
142549794Ted NugentDetroit Muscle196292681284196292681284USME32101250USME3210125021899MRI EntertainmentFull Length...202220221717550000
15846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200008USFZH220000815115Traffic Entertainment Group, Inc.Full Length...202220221717550000
16846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200005USFZH220000515115Traffic Entertainment Group, Inc.Full Length...202220221717550000
171539265MillyzRearview Mirror196626673855196626673855QM4TX2219925QM4TX221992525046Navy Blue Music, LLCSingle...202220221919550000
18846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200001USFZH220000115115Traffic Entertainment Group, Inc.Full Length...202220221717550000
191007533Devil MasterEcstasies Of Never Ending Night781676425112781676425112US2642245701US264224570110440Relapse RecordsFull Length...202220221717550000
201007533Devil MasterEcstasies Of Never Ending Night781676425112781676425112US2642245709US264224570910440Relapse RecordsFull Length...202220221717550000
212549794Ted NugentDetroit Muscle196292681284196292681284USME32101247USME3210124721899MRI EntertainmentFull Length...202220221717550000
22846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200007USFZH220000715115Traffic Entertainment Group, Inc.Full Length...202220221717550000
23539614Whiskey MyersWhole World Gone Crazy196626316271196626316271QMYLU2100005QMYLU210000521989Thirty TigersSingle...202220221717550000
242592973Easton CorbinI Can't Decide196626401717196626401717QM4TW2279566QM4TW227956633927Brown Sellers Brown LLCSingle...202220221717550000
25539614Whiskey MyersWhole World Gone Crazy196626311153196626311153QMYLU2100002QMYLU210000221989Thirty TigersSingle...202220221717550000
262549794Ted NugentDetroit Muscle196292681284196292681284USME32101243USME3210124321899MRI EntertainmentFull Length...202220221717550000
27846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200003USFZH220000315115Traffic Entertainment Group, Inc.Full Length...202220221717550000
28539614Whiskey MyersWhole World Gone Crazy196626311153196626311153QMYLU2100005QMYLU210000521989Thirty TigersSingle...202220221717550000
292549794Ted NugentDetroit Muscle196292681284196292681284USME32101244USME3210124421899MRI EntertainmentFull Length...202220221717550000
30846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200009USFZH220000915115Traffic Entertainment Group, Inc.Full Length...202220221717550000
311007533Devil MasterEcstasies Of Never Ending Night781676425112781676425112US2642245707US264224570710440Relapse RecordsFull Length...202220221717550000
322549794Ted NugentDetroit Muscle196292681284196292681284USME32101245USME3210124521899MRI EntertainmentFull Length...202220221717550000
33846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200006USFZH220000615115Traffic Entertainment Group, Inc.Full Length...202220221717550000
342549794Ted NugentDetroit Muscle196292681284196292681284USME32101246USME3210124621899MRI EntertainmentFull Length...202220221717550000
35846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200002USFZH220000215115Traffic Entertainment Group, Inc.Full Length...202220221717550000
362592973Easton CorbinI Can't Decide196626401717196626401717QM4TW2283646QM4TW228364633927Brown Sellers Brown LLCSingle...202220221717550000
371007533Devil MasterEcstasies Of Never Ending Night781676425112781676425112US2642245704US264224570410440Relapse RecordsFull Length...202220221717550000
381007533Devil MasterEcstasies Of Never Ending Night781676425112781676425112US2642245710US264224571010440Relapse RecordsFull Length...202220221717550000
39846820CzarfaceCzarmageddon!706091202728706091202728USFZH2200010USFZH220001015115Traffic Entertainment Group, Inc.Full Length...202220221717550000
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" ], "text/plain": [ " ARTIST_ID ARTIST_NAME RELEASE_NAME TRACK_UPC \\\n", "0 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "1 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "2 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "3 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "4 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "5 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "6 846820 Czarface Czarmageddon! 706091202728 \n", "7 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "8 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "9 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "10 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "11 539614 Whiskey Myers Whole World Gone Crazy 196626311153 \n", "12 846820 Czarface Czarmageddon! 706091202728 \n", "13 846820 Czarface Czarmageddon! 706091202728 \n", "14 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "15 846820 Czarface Czarmageddon! 706091202728 \n", "16 846820 Czarface Czarmageddon! 706091202728 \n", "17 1539265 Millyz Rearview Mirror 196626673855 \n", "18 846820 Czarface Czarmageddon! 706091202728 \n", "19 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "20 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "21 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "22 846820 Czarface Czarmageddon! 706091202728 \n", "23 539614 Whiskey Myers Whole World Gone Crazy 196626316271 \n", "24 2592973 Easton Corbin I Can't Decide 196626401717 \n", "25 539614 Whiskey Myers Whole World Gone Crazy 196626311153 \n", "26 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "27 846820 Czarface Czarmageddon! 706091202728 \n", "28 539614 Whiskey Myers Whole World Gone Crazy 196626311153 \n", "29 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "30 846820 Czarface Czarmageddon! 706091202728 \n", "31 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "32 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "33 846820 Czarface Czarmageddon! 706091202728 \n", "34 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "35 846820 Czarface Czarmageddon! 706091202728 \n", "36 2592973 Easton Corbin I Can't Decide 196626401717 \n", "37 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "38 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "39 846820 Czarface Czarmageddon! 706091202728 \n", "\n", " UPC ISRC TRACK_ISRC LABEL_ID \\\n", "0 781676425112 US2642245705 US2642245705 10440 \n", "1 781676425112 US2642245703 US2642245703 10440 \n", "2 196292681284 USME32101251 USME32101251 21899 \n", "3 196292681284 USME32101242 USME32101242 21899 \n", "4 781676425112 US2642245702 US2642245702 10440 \n", "5 196292681284 USME32101248 USME32101248 21899 \n", "6 706091202728 USFZH2200004 USFZH2200004 15115 \n", "7 196292681284 USME32101241 USME32101241 21899 \n", "8 196292681284 USME32101249 USME32101249 21899 \n", "9 781676425112 US2642245706 US2642245706 10440 \n", "10 781676425112 US2642245708 US2642245708 10440 \n", "11 196626311153 QMYLU2100003 QMYLU2100003 21989 \n", "12 706091202728 USFZH2200011 USFZH2200011 15115 \n", "13 706091202728 USFZH2200012 USFZH2200012 15115 \n", "14 196292681284 USME32101250 USME32101250 21899 \n", "15 706091202728 USFZH2200008 USFZH2200008 15115 \n", "16 706091202728 USFZH2200005 USFZH2200005 15115 \n", "17 196626673855 QM4TX2219925 QM4TX2219925 25046 \n", "18 706091202728 USFZH2200001 USFZH2200001 15115 \n", "19 781676425112 US2642245701 US2642245701 10440 \n", "20 781676425112 US2642245709 US2642245709 10440 \n", "21 196292681284 USME32101247 USME32101247 21899 \n", "22 706091202728 USFZH2200007 USFZH2200007 15115 \n", "23 196626316271 QMYLU2100005 QMYLU2100005 21989 \n", "24 196626401717 QM4TW2279566 QM4TW2279566 33927 \n", "25 196626311153 QMYLU2100002 QMYLU2100002 21989 \n", "26 196292681284 USME32101243 USME32101243 21899 \n", "27 706091202728 USFZH2200003 USFZH2200003 15115 \n", "28 196626311153 QMYLU2100005 QMYLU2100005 21989 \n", "29 196292681284 USME32101244 USME32101244 21899 \n", "30 706091202728 USFZH2200009 USFZH2200009 15115 \n", "31 781676425112 US2642245707 US2642245707 10440 \n", "32 196292681284 USME32101245 USME32101245 21899 \n", "33 706091202728 USFZH2200006 USFZH2200006 15115 \n", "34 196292681284 USME32101246 USME32101246 21899 \n", "35 706091202728 USFZH2200002 USFZH2200002 15115 \n", "36 196626401717 QM4TW2283646 QM4TW2283646 33927 \n", "37 781676425112 US2642245704 US2642245704 10440 \n", "38 781676425112 US2642245710 US2642245710 10440 \n", "39 706091202728 USFZH2200010 USFZH2200010 15115 \n", "\n", " LABEL_NAME RELEASE_FORMAT ... RELEASE_YEAR \\\n", "0 Relapse Records Full Length ... 2022 \n", "1 Relapse Records Full Length ... 2022 \n", "2 MRI Entertainment Full Length ... 2022 \n", "3 MRI Entertainment Full Length ... 2022 \n", "4 Relapse Records Full Length ... 2022 \n", "5 MRI Entertainment Full Length ... 2022 \n", "6 Traffic Entertainment Group, Inc. Full Length ... 2022 \n", "7 MRI Entertainment Full Length ... 2022 \n", "8 MRI Entertainment Full Length ... 2022 \n", "9 Relapse Records Full Length ... 2022 \n", "10 Relapse Records Full Length ... 2022 \n", "11 Thirty Tigers Single ... 2022 \n", "12 Traffic Entertainment Group, Inc. Full Length ... 2022 \n", "13 Traffic Entertainment Group, Inc. Full Length ... 2022 \n", "14 MRI Entertainment Full Length ... 2022 \n", "15 Traffic Entertainment Group, Inc. Full Length ... 2022 \n", "16 Traffic Entertainment Group, Inc. Full Length ... 2022 \n", "17 Navy Blue Music, LLC Single ... 2022 \n", "18 Traffic Entertainment Group, Inc. 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72549794Ted NugentDetroit Muscle196292681284196292681284USME32101241USME3210124121899MRI EntertainmentFull Length...0002022182022-18FalseFalseFalse14
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201007533Devil MasterEcstasies Of Never Ending Night781676425112781676425112US2642245709US264224570910440Relapse RecordsFull Length...0002022182022-18FalseFalseFalse32
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322549794Ted NugentDetroit Muscle196292681284196292681284USME32101245USME3210124521899MRI EntertainmentFull Length...0002022182022-18FalseFalseFalse13
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" ], "text/plain": [ " ARTIST_ID ARTIST_NAME RELEASE_NAME TRACK_UPC \\\n", "0 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "1 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "2 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "3 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "4 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "5 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "6 846820 Czarface Czarmageddon! 706091202728 \n", "7 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "8 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "9 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "10 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "11 539614 Whiskey Myers Whole World Gone Crazy 196626311153 \n", "12 846820 Czarface Czarmageddon! 706091202728 \n", "13 846820 Czarface Czarmageddon! 706091202728 \n", "14 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "15 846820 Czarface Czarmageddon! 706091202728 \n", "16 846820 Czarface Czarmageddon! 706091202728 \n", "17 1539265 Millyz Rearview Mirror 196626673855 \n", "18 846820 Czarface Czarmageddon! 706091202728 \n", "19 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "20 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "21 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "22 846820 Czarface Czarmageddon! 706091202728 \n", "23 539614 Whiskey Myers Whole World Gone Crazy 196626316271 \n", "24 2592973 Easton Corbin I Can't Decide 196626401717 \n", "25 539614 Whiskey Myers Whole World Gone Crazy 196626311153 \n", "26 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "27 846820 Czarface Czarmageddon! 706091202728 \n", "28 539614 Whiskey Myers Whole World Gone Crazy 196626311153 \n", "29 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "30 846820 Czarface Czarmageddon! 706091202728 \n", "31 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "32 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "33 846820 Czarface Czarmageddon! 706091202728 \n", "34 2549794 Ted Nugent Detroit Muscle 196292681284 \n", "35 846820 Czarface Czarmageddon! 706091202728 \n", "36 2592973 Easton Corbin I Can't Decide 196626401717 \n", "37 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "38 1007533 Devil Master Ecstasies Of Never Ending Night 781676425112 \n", "39 846820 Czarface Czarmageddon! 706091202728 \n", "\n", " UPC ISRC TRACK_ISRC LABEL_ID \\\n", "0 781676425112 US2642245705 US2642245705 10440 \n", "1 781676425112 US2642245703 US2642245703 10440 \n", "2 196292681284 USME32101251 USME32101251 21899 \n", "3 196292681284 USME32101242 USME32101242 21899 \n", "4 781676425112 US2642245702 US2642245702 10440 \n", "5 196292681284 USME32101248 USME32101248 21899 \n", "6 706091202728 USFZH2200004 USFZH2200004 15115 \n", "7 196292681284 USME32101241 USME32101241 21899 \n", "8 196292681284 USME32101249 USME32101249 21899 \n", "9 781676425112 US2642245706 US2642245706 10440 \n", "10 781676425112 US2642245708 US2642245708 10440 \n", "11 196626311153 QMYLU2100003 QMYLU2100003 21989 \n", "12 706091202728 USFZH2200011 USFZH2200011 15115 \n", "13 706091202728 USFZH2200012 USFZH2200012 15115 \n", "14 196292681284 USME32101250 USME32101250 21899 \n", "15 706091202728 USFZH2200008 USFZH2200008 15115 \n", "16 706091202728 USFZH2200005 USFZH2200005 15115 \n", "17 196626673855 QM4TX2219925 QM4TX2219925 25046 \n", "18 706091202728 USFZH2200001 USFZH2200001 15115 \n", "19 781676425112 US2642245701 US2642245701 10440 \n", "20 781676425112 US2642245709 US2642245709 10440 \n", "21 196292681284 USME32101247 USME32101247 21899 \n", "22 706091202728 USFZH2200007 USFZH2200007 15115 \n", "23 196626316271 QMYLU2100005 QMYLU2100005 21989 \n", "24 196626401717 QM4TW2279566 QM4TW2279566 33927 \n", "25 196626311153 QMYLU2100002 QMYLU2100002 21989 \n", "26 196292681284 USME32101243 USME32101243 21899 \n", "27 706091202728 USFZH2200003 USFZH2200003 15115 \n", "28 196626311153 QMYLU2100005 QMYLU2100005 21989 \n", "29 196292681284 USME32101244 USME32101244 21899 \n", "30 706091202728 USFZH2200009 USFZH2200009 15115 \n", "31 781676425112 US2642245707 US2642245707 10440 \n", "32 196292681284 USME32101245 USME32101245 21899 \n", "33 706091202728 USFZH2200006 USFZH2200006 15115 \n", "34 196292681284 USME32101246 USME32101246 21899 \n", "35 706091202728 USFZH2200002 USFZH2200002 15115 \n", "36 196626401717 QM4TW2283646 QM4TW2283646 33927 \n", "37 781676425112 US2642245704 US2642245704 10440 \n", "38 781676425112 US2642245710 US2642245710 10440 \n", "39 706091202728 USFZH2200010 USFZH2200010 15115 \n", "\n", " LABEL_NAME RELEASE_FORMAT ... \\\n", "0 Relapse Records Full Length ... \n", "1 Relapse Records Full Length ... \n", "2 MRI Entertainment Full Length ... \n", "3 MRI Entertainment Full Length ... \n", "4 Relapse Records Full Length ... \n", "5 MRI Entertainment Full Length ... \n", "6 Traffic Entertainment Group, Inc. 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