{ "cells": [ { "cell_type": "markdown", "id": "7a2d1980", "metadata": {}, "source": [ "# Modeling (US MODEL V2)\n", "## Track Pre-release Forecasting Using CM Data Alone\n", "\n" ] }, { "cell_type": "code", "execution_count": 215, "id": "2159e043", "metadata": {}, "outputs": [], "source": [ "!pip -q install snowflake-connector-python pytest pytest-sugar xgboost langdetect holidays" ] }, { "cell_type": "code", "execution_count": 1, "id": "14f915a7", "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": 2, "id": "a08657cb", "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": "f3d07d42", "metadata": {}, "source": [ "## Test Snowflake connection" ] }, { "cell_type": "code", "execution_count": 4, "id": "c654028e", "metadata": {}, "outputs": [ { "name": "stdin", "output_type": "stream", "text": [ "Enter snowflake username : ······\n", "Enter snowflake password : ························································\n", "Enter passcode: \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Your snowflake version - 6.13.1 PASSED!\n" ] } ], "source": [ "# quick test of connection\n", "test_connection()" ] }, { "cell_type": "markdown", "id": "7548c758", "metadata": {}, "source": [ "## Snowflake SQL Executor" ] }, { "cell_type": "code", "execution_count": 3, "id": "2203de67", "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": "751dbedd", "metadata": {}, "source": [ "## Connect to Snowflake" ] }, { "cell_type": "code", "execution_count": 4, "id": "ccf9b2f5", "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": 220, "id": "d0948a93", "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": "c82bcdef", "metadata": {}, "source": [ "## (1) Pull TRACK_FORECASTING_DATASET_LATEST from Snowflake\n" ] }, { "cell_type": "markdown", "id": "1299d933", "metadata": {}, "source": [ "#### Only Sample" ] }, { "cell_type": "code", "execution_count": 221, "id": "b2507de7", "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": "5dd958d7", "metadata": {}, "source": [ "#### All Data" ] }, { "cell_type": "code", "execution_count": 222, "id": "5b684992", "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": "86e2f8d7", "metadata": {}, "source": [ "#### Only Data for the US" ] }, { "cell_type": "code", "execution_count": 223, "id": "34b0be62", "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": "29b6b3f1", "metadata": {}, "source": [ "#### Run Query" ] }, { "cell_type": "code", "execution_count": 366, "id": "f2a55d76", "metadata": {}, "outputs": [], "source": [ "dataset_df = execute_sql(conn=conn, sql=dataset_latest_us_only_sql)" ] }, { "cell_type": "code", "execution_count": 367, "id": "62ff80ef", "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": 372, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.head()" ] }, { "cell_type": "code", "execution_count": 373, "id": "872237a4", "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": 373, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.columns" ] }, { "cell_type": "markdown", "id": "4ee5b416", "metadata": {}, "source": [ "### Add Some additioal columns \n", "- SNAPSHOT_YEAR_ISOWEEK \n", "- Holidays\n" ] }, { "cell_type": "code", "execution_count": 412, "id": "f0c3291c", "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": 413, "id": "f8f376a6", "metadata": {}, "outputs": [], "source": [ "dataset_df = add_additional_features_dask(df=dataset_df)" ] }, { "cell_type": "code", "execution_count": 418, "id": "13c3cf69", "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": 418, "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": "a9888ca4", "metadata": {}, "source": [ "## (2) Feature Processing Pipeline \n", "\n", "Here we define the feature processing pipeline \n", " " ] }, { "cell_type": "code", "execution_count": 420, "id": "0660d025", "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": 420, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.columns" ] }, { "cell_type": "code", "execution_count": 421, "id": "7b5be738", "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": 421, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.isna().sum()" ] }, { "cell_type": "markdown", "id": "8f2e7bdf", "metadata": {}, "source": [ "### Features (X - inputs)" ] }, { "cell_type": "code", "execution_count": 429, "id": "6e19f036", "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": 430, "id": "45340a0a", "metadata": {}, "outputs": [], "source": [ "dataset_df = add_text_length_feature(dataset_df)" ] }, { "cell_type": "code", "execution_count": 431, "id": "885a4b70", "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": 431, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.head()" ] }, { "cell_type": "code", "execution_count": 432, "id": "06a760b3", "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": 436, "id": "938f4052", "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": 436, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_config(display=\"diagram\")\n", "feature_preprocessing_pipeline_factory_v1()" ] }, { "cell_type": "markdown", "id": "4f2515e7", "metadata": {}, "source": [ "### Sanity Checks" ] }, { "cell_type": "code", "execution_count": 437, "id": "ca24c66f", "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": 438, "id": "228eccc1", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:absl:TEST PASSED!!!\n" ] } ], "source": [ "check_dimensions(dataset_df, expected=399895)" ] }, { "cell_type": "markdown", "id": "148be4af", "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": "50830802", "metadata": {}, "source": [ "### Create feature processing pipleine" ] }, { "cell_type": "code", "execution_count": 439, "id": "5256633c", "metadata": {}, "outputs": [], "source": [ "feat_pipeline_v1 = feature_preprocessing_pipeline_factory_v1()" ] }, { "cell_type": "code", "execution_count": 440, "id": "a51f313c", "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": 440, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.sort_values( by=['SNAPSHOT_YEAR_ISOWEEK'] ).head()" ] }, { "cell_type": "markdown", "id": "39667e68", "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": 468, "id": "0345863f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['2019-1', '2019-1', '2019-1', ..., '2022-17', '2022-17', '2022-17'],\n", " dtype=object)" ] }, "execution_count": 468, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DATES = np.array(dataset_df['SNAPSHOT_YEAR_ISOWEEK'].values)\n", "DATES" ] }, { "cell_type": "code", "execution_count": 443, "id": "8c133668", "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": 604, "id": "70dc755d", "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": 605, "id": "9d21a6bf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(399895, 206)" ] }, "execution_count": 605, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_feat_v1.shape" ] }, { "cell_type": "code", "execution_count": 606, "id": "9bd79482", "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": 607, "id": "4e49f17c", "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": 607, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_per_track_df" ] }, { "cell_type": "code", "execution_count": 608, "id": "57ff1ddf", "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": 608, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_per_track_df['ROW_COUNT'].describe()" ] }, { "cell_type": "code", "execution_count": 609, "id": "573c58f2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 609, "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": 610, "id": "5fc8cfca", "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": "3320645f", "metadata": {}, "source": [ "### CV Fold Size Selection" ] }, { "cell_type": "code", "execution_count": 611, "id": "178d9e7d", "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": 612, "id": "d289cc69", "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": 613, "id": "57133c6f", "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": "64e556fd", "metadata": {}, "source": [ "### Define TimeSeries CV Split" ] }, { "cell_type": "code", "execution_count": 614, "id": "62b3afec", "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": "32de9a1e", "metadata": {}, "source": [ "## Evaluation Pipeline\n", "\n", "Evaluation Pipeline for Forecasting Models" ] }, { "cell_type": "code", "execution_count": 615, "id": "dc94fdee", "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": "7466b05b", "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": 740, "id": "df62c955", "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": 741, "id": "2379746d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('sklearn_svm_regressor', LinearSVR())])
LinearSVR()
" ], "text/plain": [ "Pipeline(steps=[('sklearn_svm_regressor', LinearSVR())])" ] }, "execution_count": 741, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_config(display=\"diagram\")\n", "svm_regressor_pipeline_factor()" ] }, { "cell_type": "code", "execution_count": 742, "id": "7243ac51", "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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\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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MhkIxCqIMpNlcUQ0ccuNYTrr99UqL0erYsLmJV2aYqeOtGaMgDAaDL1c9MY1z73ubjz5bXWlRDBXCKAhDyShmpJSWUVZmS5OUyJMk+fwts79YC8DqDZuTF6ZIWpPvqJwYBWEwuGhNK6nToANL9bbTkJdaxCgIQyowFdxgSB9GQRhKhmnkq4vWM1YyFIpREIZUkLbzF9IlTTKkIY9pkMEQjFEQBoOLajh3wxCPsdMX886cQk4eMCS2ktrQ+kjbKKAYomRFVRGjUSpKlLd//v3vADDnhuOTFaYGMSOIhPhizUaaU95gpqlBT48khrQyb9m6nGum3CSLURAxGXDF0/zg0cmhYRav2sCw617gjy98XB6hUoKprNVFGsY+UWV4/N35HHLjWN6Y+UWi8hiyMQqiAB5/d0Ho/cWrNwIwce7ycohTE6RlMONYjKLsxZQWmQulmsSf/OkKAGZ8blZ1lxOjIFox1d7AJUFrWihnMOTDKAhDyWhtCqeVZTcRzDtMN0ZBGLL4/ZjKbE1uttcuH1HHSGmaxGCoDEZBVAlJ1FW/KO8YO6v0CVUhtdw2pilrkZVVkc8bCsMoCEPpqIEjR+Msa4jbw35v/kpmLVkTUyJDGCkpNjVLkmdS3ycii0Vkmuva70VkuohMFZHHRaRbwLNz7LOrJ4vIhKRkNBiCSKLhOfH2cRx58ysJxGwwJEOSI4j7geM8154H9lTVvYEZwJUhz49Q1SGqOiwh+aqKJGz0abIxp0eS6FSjzG6MecaQj8QUhKq+CizzXHtOVbfYX8cD/ZNK31B+SqHEzM4V5SMJBZdUp8MUi8pQSR/EN4FnAu4p8JyITBSRC8MiEZELRWSCiExYsqR2z88tl5O60qRlUJOm0ZUhGKM4kqUiCkJEfg5sAR4KCHKQqg4FRgKXiMihQXGp6j2qOkxVh/Xo0SMBaQ1RqY0jR6M3OWmROU2Yd1JblF1BiMi5wAnA2RrQTVPVhfb/xcDjwPDySWgwpHN0VSpMr9sQlbIqCBE5DrgcOElVc7dmtMJ0FJHOzmfgGGCaX9hqoRS2+WTsxfGfmfTpcv75zrzSy1KFTXI1ygy1pfxqKS9pJNJ5ECJyAPB14BCgD7Aeq9F+GnhQVVf6PPMwcDjQXUTmA7/EmrXUFnjeHsqPV9WLRKQv8FdVHQX0Ah637zcA/1DVZ4vJpKF0fPnONwA440vb5twrRWU1TurqJqkG2yiCypBXQYjIM8BC4EngemAx0A4YCIwAnhSRW1T1KfdzqnqWT3T3+qVhm5RG2Z9nA4Nj5KFVUOtOUyd7lc6m0U/Vhfm9kiXKCOIbqurdhH0NMMn+u1lEupdcMkPiVKuJpCz4vJqJc5fzwoeftwQxr89Q4+RVED7KoaAwhtqnFkY5YSaur9z1RvkESZAke91WGcifQvWXlNZBFBPTavx/TwFUVbuUXCpDDmlxUidFikQBant0Vbs5M5SaKCOIzuUQxGCA9Dip06Q8qwnz2mqLSLOY3IhITywnNQCq+mlJJTJULUVt5pqSFtnRT1HESYnIsUmJDjZUAZHXQYjISSLyMfAJ8Aowh+CtMgwlplobo2qlll93GvJmlFR1EGeh3LXA/sAMVd0BOBJ4PRGpDFVJKZSYUYTVjfn9aos4CmKzqi4F6kSkTlXHAkOSEcuQS23XvLQ1LFFMXrXsyE4rcfbKMhRPHB/EChHpBLwKPCQii7E23DNUKWlrlKHyTmqnAUrhq6kpzPutDuKMIE4G1gE/AJ4FZgEnJiGUoTqphR61cVIXRy2UAUMLcUYQFwL/UtX5wN8TkscQQLU2Rob0Uc1FKS2z3VoLcUYQXYAxIvKaiFwiIr2SEqrWSGuZTlNvL33vKHUCVYw4Zr/0/Y6GYoisIFT1V6q6B3AJ0Bd4RUReSEwyQxZVUe+qQshoRDIxJS9GIqTBzZsGGQz5KeQ8iMXAZ8BSoGdpxTGUkzT29iotk9NbTuGrqSnM+60O4iyU+46IvAy8CHQHvq2qeyclmKH6KGoldcqajGhO6nTJbDCUmjhO6u2By1R1ckKyGEKo9rZIVSPNYa/0NNfWQJUXJUMZibKbaydVXaOqV+QLU1rRDEmTpoYibQowbSOaaiHp3zGok2FGc8kQxcT0pIjcLCKH2mdEAyAiO4rIBSIyBjjO+5CI3Ccii0Vkmuva1iLyvIh8bP/fyi9BETlORD4SkZkiEqiYaon3F65k+PUvsGztpkqLUjBhdbR66q+9UM44qQ2G/ApCVY/E8jv8H/C+iKwUkaXAg0Bv4FxVfczn0fvJVRxXAC+q6i52nDmNv4jUA3cAI4HdgbNEZPfIOapS7nx5FotXb+T1mf5nL7WWHm2lFUlrcFInnbc3Zy3lky/WJpyKoRxE8kGo6mhgdJyIVfVVERnguXwycLj9+e/Ay8DlnjDDgZn22dSIyCP2cx/ESd+Qn3IOy/OlVI0NcqWVWSn562uzuXfcJ7x55ZFFxaMoZ/1lPABzbji+FKJl8dHnqwPSNSRBnFlMBzkmJhH5uojcIiLbx0yvl6ouArD/+02T7QfMc32fb18rO6rKHWNnsmjl+kok75Gl0hLkpxSjnHI7qVWVuUtze7u1bNP2e8XXPf0hi1ZuKL8sMX/vf7xljp8pJ3HWQdwFrBORwcBPgbnAAwnI5FdkAmuriFwoIhNEZMKSJUtKKsjMxWv4/ZiP+M6Dk0oabxxqZVZPvga3Ug3yg+PnctjvX+bdT5dXJP3WTql+9hrW5xUljoLYolYtPhm4VVVvBeIeR/q5iPQBsP8v9gkzH9jW9b0/sDAoQlW9R1WHqeqwHj16xBQnnCa71K3f1FTSeOOQZME3dQre/XQFALOWWKOIOJv1mReYi2moa4s4CmK1iFwJfB142nYmN8ZM7yngXPvzucCTPmHeAXYRkR1EpA1wpv1cq6YaKl4xMlYqe/V1lkpobs6WoNKTAp54d0FicVdBUSqA2sxVpYmjIL4KbAQuUNXPsPwCvw8KLCIPA28Cg0RkvohcANwAHG0fXXq0/R0R6SsiowFUdQtwKTAG+BD4p6q+HztnJaCcjfLo9xaVLzGbcubPL6k/vfgxE+YsK58QPjgKYktz/JeRpBK57NHJicWdJKaZri0ir6S2lcItru+fEuKDUNWzAm7lTJNQ1YXAKNf32LOmkiQz9THBFjVf1JXu0UYhroQ3Pz8Dns+e7bJuUxP7/eYF3vrZUaUVLoA6W0E0eX6AahixFUqNuLUMZSDKSurV+Nd9AVRVu5RcqhqjmLamdpzU0e9/vmpjssK4qJdsE1NLZyD/s7WsRKoN81skQ14FoapxHdE1g7fQVaIQJpqmqVQZE1OToyCcldQVk6i6iTvKrpUOUK0Se7tvEekpIts5f0kIlVYq2WhUQw8prHHIbyKrTAYzTupqeMElolI5VVVemv55Iqba1vPrlZc4C+VOsp3LnwCvAHOAZxKSKxWkoXeTBhlqmSAndZRGzDRKuYS9k4ffnsc375/AvyclN0PLUFrijCCuBfYHZqjqDljO5tcTkSqlRO35FNNB8iqEZNdBpKeJq1QHvk6yTUwO5RSnuVl54M05bNhcnvU2lepzLFixDoDPXDsTmIVy6SaOgtisqkuBOhGpU9WxwJBkxEoHhRa6Yhreai7o1biba4PXB5FZKZf/2VKZSp774DOufvJ9bnz2o5LEl4+oUqf0JzOUkTgHBq0QkU7Aq8BDIrIY2JKMWOmkEhUmSRNTWhvtclJXFzSCKN/LWb3BqkYr1ld+q/eoBzsFPx8vvDGhpps4I4iTgfXAD4BngVnAiUkIVe0k0fDWemNeqew1eJzUlWywpEzGn1psk2t5c8VKEmehnHvLy78nIEvqKUcZND2q8hLspM7/bC02Sarl9YMZ0k1kBeFZMNcGax+mtbW8UM5rZohqdiimPpVz7UU5631YPtZt2sKWpsq0QnWehXIOrbVNLDrfFXpxrfX3Spo4I4isBXMicgrW4T4GYMPmJpat3UTfbu0TiT9NM47A31ZdqDLb/eox7NmvMv2MetvImrNQroZXUoeJbZlqPL9rqdMvIMJqfdfVTuyFcg6q+gRwROlESR9em3BYIT3/b+9w4A0v2eGyA27c0sz3H3m3oIOH0mpyilth8ym4aQtWFSFN4TgjiIougqxg2l6KlSXsd07Sx2IUSDLEMTF92fW1DhhGusp2yYnTa39z9lLXc9lMmbeCKfNWsGlLM3d9fd/QeIIUQjKO75r++WKRY9qrQNEuV2cgLBm/IpHqYpLWHlSNEGeaq3vG0hasldQnl1SalFLMtL+4pLoyuvATM06jmnblFM1Jne48tApSXo6qnTg+iPOTFKSWaA1l1s9WHR4+/HtqcHZzLWeaBdnki1uvECxKrjBxlHmlflejrJMhynbffyKkCKvq90oqUYqp5DTXJJI2dvcQKjDPNQ3GktQqbkNFiOKkngBMBNoBQ4GP7b8hQOUOay4Dhdul/cPVUuXzNTGFbbWREzadL6MSjXQhvd9KnwZYbvIOltIgZA0S5TyIvwOIyHnACFXdbH+/G3guUelaIWWt+EWklbScSZlQAtPLWfNSftLgby32dy37e0vDS6th4kxz7Qu410J0sq/FQkQGichk198qEbnME+ZwEVnpCnN13HSSIGrlMTOOcvHKny835cpukBJK+0rqohZjht7z80EUkVhQOiWOtLprR3qJM4vpBuBdERlrfz8MuCZugqr6EfYusCJSDywAHvcJ+pqqnhA3/lJSaOekmMJaLR0i34YkzvMpcsy6ceKvdoVcKEWPIBJ6b63056g4cWYx/U1EngH2sy9doaqfFZn+kcAsVZ1bZDyJkOuDiPJMcKiitgEv+MmwOIuQp2hTRHgElW4Pov3WiYsRkna8WWRu4j5V6hlCSgmnjhvNkSh5TUwisqv9fyiWSWme/dfXvlYMZwIPB9w7QESmiMgzIrJHiHwXisgEEZmwZMmSIsUJSCNGWNXWW2bDlaM3bPy4avW9pilfSYpitq6vPqKMIH4IXAjc7HNPKXC7DRFpA5wEXOlzexKwvaquEZFRwBPALn7xqOo9wD0Aw4YNS7SYmGMoy0e532Ma1mnE2YoiKfF8y3iMxEz5ry2izGK60P4/osRpjwQmqernPmmucn0eLSJ3ikh3Vf2ixDKUHFVN5lD2lC2EiL0XUwka4CRegbdJjnGgXE0uzqrWHNXib5EGIs9iEpHTRaSz/fkXIvIfEdmniLTPIsC8JCK9xTZSishwW86lfmHLgcRYXRslzEvTPy9o4740UbSTOp8PokL1PfNbp9xmkZR4RQ4gyv+7VcusjiolzjTXq1R1tYgcDByLdWjQ3YUkKiIdgKOB/7iuXSQiF9lfTwOmicgU4DbgTK1gjXVSjroFdFAw5/lv3j+Bk25/Pa4UMcOnjJgjiGK3fCgHpRInVbkqkzAl+y1TViZqjTjTXJ1V08cDd6nqkyJyTSGJquo6YBvPtbtdn28Hbi8k7kqjaKQyu2T1xuSFyUNRc+lNvSwZzruM0xkup0mlGn7rapCxGokzglggIn8GzgBGi0jbmM9XHTmFLuIIojUQd6uNKM/niysRH0TI9urX/u8DLn5oYuCzpZYnDdaSqIrns5Ub+HzVhtDn5y1bx60vfJy6kZ8hOnFGEGcAxwE3qeoKEekD/CQZsdKBU9jjVtwkenfJrGYtfZyBaXm3skhJo5FxStvytDiplXvHfVIRmaJQXh9E7sX9f/siAHNuOD4wrgv+/g4zPl/Dqfv0Y7ttOpRMRj+irlEq5/YttUDkEYBtFloMHGxf2oK1aV/NUshmffk2rEtLw1gsxeajoBFEEkoyRvqtgVJme/3mJjvO3FhL1lDHiKe1/qbFEGcW0y+By2lZt9AIPJiEUGmhkPKkhHipSbaQNjUrN435iKVrkvdv+GcjZKFcCtYZ+FHchoWlyYS3AV2/qXKbJBe9QNEVNuNb8Tm6t+R7MZk1SokQx4dwKtbCtrUAqrqQ7M37ao6cDeYilLDmPGGaC6wYUZ569eMl3D52Jlc9OS1inIVXmaLrdyGzmJIw3cW8nixWQ3rPq7MrkjoUn++w8l9p406tjN7LSRwFscmeaqoAItIxGZHSg1OcYq2D0PBmLMki2tRkxb5hc3OCqQQT5zyIwb8O3ym+3HW55bd2NuuL8ExCMm5qyj+CiCZfqUY4ccJG6cmX8MXFOe2udKm2GiIpCHvR2v/sWUzdROTbwAvAX5IUrtLEWf+QeSYkvGoRI4i0le6E5Sl2llTkdDyRup3U1U5hO+aWLs0o03fLuZYkdXWoCog0i0lVVUROwfJBrAIGAVer6vMJypY6Itk585lOUlRIgxVZ/tkecRtQM7wvLdF66qWJN9aZ1AWkWRRxnNQ1oPTLTZxprm8CK1S1pqe2ZlPANFetXEEs1cSQBSvW03+r+NMSS5nrcimUsNFeuUhuyqoS2/Jf9AgiZsehuORiRWT6KPGJoyBGAP8nInOxHdUAqrp3yaVKCQWdB5EnVOEmpvI5aCM9WwETUzLppKfViLWSOkqDWIAM+Z7ZuKUp3NeUnteZQ5plSytxFMTIxKRIKQVVMM3jrC1DIS1H7ztuCrHD+zwQJVtn/3U8u/Xuwi9O2L2w9GJs1leLezHlW8G+/29eZPm6zZGezyw+LMP0pbI7x1sJcRbKzfX7S1K4SlPI3P3wIJp3BBF0JkCUol3ORaJpPdDn9ZlL+WuMFdBpMDE5xPn5Xv4o/+FYSeQhTDlAnkbYp4CW0zeVhvJZbdT0XkrF0rL9gj31MdJK6uKmuXqfTvYULiutDxauyhOyFGnFfcDvUvJmtjiH9lSSS/4xKW+YQrZU930mziy+Akd+5SAlYlQVRkGEUKgNN/TozZhLFMpRuUbd9lrW9yjbIPhOQy1hFSyXOSBwBBHl2VLJmNCPXIlprpuaWgq4E1XYSLxUOS/nupDWhFEQIWTWQZD5EPmZwPsFVolS7pRaSJzFPluKxrRU9Xvdpi0tcQZtGlIJE5Otl8s1ivHrBxSr+H8z+sPA5+Pm6pbnZzBx7rKYTwVj1EN8jIIoMRpSnfI5sKG8Jo67XplVtrTiklQD/casL9j96jG8PjP79Frvzr3FbsyYBgobQRSXqWkLWsyVTlT5tp8J4rYXP+Yrd70ZKWwlV77XMkZBhNDS83F8EJEeCiWfk7qcMy2efHdBwc/6L6gqRhpv/NGuxeWt2VaP9K1PrP+13GgUUpaS8CGEKp0Svf85S9cybcHKPIKUJq3WhFEQYRTSA8vzXKFltLwniCUwyd4O//rML9i4JcpeQ5WtzXGSX7RyPas2hM/uaT34dBzKkOp1T3/ICX8aFxrGTHONT0UUhIjMEZH3RGSyiEzwuS8icpuIzBSRqSIyNGmZLnvkXcZ+tDjrmrc4lWIYW+g013KSVNs8df4Kzv7rW9zwzPSCni+F0vAsdwiMM5qT2uKA377EUTe/UrRMpaZk6zQKnMXkNMihkzbK2vEpW1I1QyVHECNUdYiqDvO5NxLYxf67ELgraWGemLyQ8//2DvOWrWP+8nVAS4GKZZcO8ULkG13kiTgvGVNY0SaBwsTJZ0n4wj6nYtaStcEBQ+IvJZnfVP3/x32Ji0twxnipOwcFzcJT/8+FpFnIZpctz2Y/VJJJDkXH0PpIq4npZOABtRiPtYNsn3IkfMiNYzn4d2OBwm24YRUin8OurOsgIsrgG6aA2ubMgKyLkKd8K3pLTc5oMcozKe+S5pMv6jqIYvs0Yc8HiVioYzuMtP9eaaRSCkKB50Rkoohc6HO/HzDP9X2+fS0HEblQRCaIyIQlS/KvLvWjOaA0ln4ldXylU4kyHaVyFpKPpubo0x0TMz3k9Exrl+AOQMgziTTM8Z/xmmILHV25lUIt/9ZJUSkFcZCqDsUyJV0iIod67vuVBt/fV1XvUdVhqjqsR48eBQnTVIQd2kee2Hsxley4xJh1KCh4oT2tqBsV1hU4LCpl4xVkjmuxm0eQp0SyJLeba/j9yOsgYgjoF7aQDSoL3dSy3HHWOhVREPZxpajqYuBxYLgnyHxgW9f3/sDCpOSJWnC8oaZ/toqV67NnrxTipC7W7lsoQUlFGkHElFNRtjgjiGhDiLIQ7C8qp/M0xnuJFXFggnllKUnyEXwQBYhYkAyhiRkCKbuCEJGOItLZ+QwcA3gPUX4KOMeezbQ/sFJVFyUlU3PA9hf5Kstxf3yNM+8Zn/tcQPiXpi9m9YYtOdejlOFyDv0LHdHkXUWeaQgL28qjFBXcicIrQotz2vO9jFR+/po/cV6Fb7nwXI0SXxK9faMf4hNnu+9S0Qt43G4kGoB/qOqzInIRgKreDYwGRgEzgXXA+UkKFNXE5NdwfrholSdMeFp+s13CGuRCepXFVoSkKlIsH0TCZhfxfC8mrrRS2GZ9pZXAnY6vSSthJ3VW5yvlv1caKbuCUNXZwGCf63e7PitwSblkagoqjaG+hGDTRFiD7+cQzy7Ent5WRZzUEUYQMQVzO6kL9kEkoLq8LkzNuV4+Ply0mhXrNpUsPvdP9L+pCzli1550aNMQ20ld6DqIL9ZsylxrblaemmxZiZuam1mzMXyxZKlGENlOaqMh4pLWaa5lJXAWE9k9XneZDdQpGt64bPFTEJEcouW0iZcmjBen0kfRD0nlNyde9XdKR8tfaWV8e84yTrs72t5DUXCkmzJvBZf+412ufvL9rPv+v0MSSlh5dMI8Zn9hrX+5Y+wsXvjw8/BnYu56HEmOKtAPl/xjEgOueLrSYmQwCoLg3kpYgdoS4LjIVwb9RivuRsubZtzzsK04iqsJSdl/W9ZBRPBBFNmTzUfL4sfsuJ13V1aF7Po8c/Gakjmrnbw4fq+FK9Z77vs9k0/C+DQrLAlYSBj0nr1lsOBdkAM+p5Wnpybmai0IoyAI8UGElKggs1S+aa75ZjHFuZcTtkRVIKmeVpOjVCONIIpnw+YmtjRF64o67y7zs1ZDa5KHoCwUMqsoKt5ZfVZ64XXCj6A6CfDup8vjipWRwxAPoyAImcXk/e664Gcq8nvGS9BzLc+33J+9ZA3zl6/PSTuIoHzEpVATU7ii01hOat84Yobf9apn+fq9b3nk8MqV/T/QH+UnT6kcqQk75IOIOlIpVj4luPMS7KQOTvTUO9+Inrb6fzZEwygIwnsrgc80hRX44PiafFrxoEL87Qdy9jEMJSwXt7/0MX95dXYkp10kJ3UBfU3nlUUzMZWmNo+f7X/gjDPVNrMwzr7umA6jpl4KOcu1gKtl/6ng9JKaTh0n3vWbmvj+w5NLL4ghNpWY5po6grfaCLaDBo8EwmtCRItHThpR6ldYQ3PTczMAuODgHfLGk1Rz1RxjoZz/CKV4yYJGhd4RRNS1IKWYjpnYCCKj/GKMinzCFite2Dntftf/O2Uhb85eWmSqTvzB/j1DfswIghAndYRn/BZchfsufEYQAYXYz9yxcUsTd78yi80+mibabKgWgva3iTbN1S/u8J6pM1IrdJprKcgxMWX+W5+2ZBREtPjimKSCSGwEkSfapCcCuMWIo9yTKh5mmmt8jIIgzOFsf3DMET4+CG9jp4TXy3zTXDXgusM9r8zmhmem8/Dbn/rEE68CFHMecyFVLWkfRFGNUAE+CIixTUvY2pgy64ew5JJQGnFNTPURtvsNGvX7pe332RANoyAIq+ReE1MLjg/CW5bzFcIoC+XeX7iSRSvXZ8mVmbK40ZqyuG5T7kKjKOU/ywcR8ECUhjbubCxwKYgCp7km+UzGB9GU7ZMI45WPlvhunRKXQkcQhWzn7cZ/s77St6LNGnJSu4+QUUaY+SZ7BMlhiIfxQeDfg1u4Yj2bXI7otRu3sGlLi1nHcWZ6GztrJXVwWpt9nNvein78beNo31hPl/a5P09mPyOfuKM5l1sIqodRqlEhphVnymnUg5dyroU8tn5TE7td/WzseL2nnjXFMDFdP/pDXpv5Rf6AeeIr1LfSrFAf0pYWstVGIfHkfT7mCKIuwggibJp5UAekmtRDc7NGeg9JYxQE/oXtwBteomfntpnve/xyDJ3bNuQ8E3cE4es7cH12Gvn1m5vo1K4hJ4wTf9i+NpErfkC40+9+kxd+eCg79+wMWOsJdr3qWX5/2t6ZMHEVhCpstp+Jah7IiSOkii9Ysc6TXngaGf+LdwRhK/4NEc7NBvjos1X5A+UhzsQFN1YegxsRb3lwOidx1900NSufr9pAry7tCpMz5Jfzu17vU7i9cm1ubqY99TnhvEqzWk1MTarUpWD7RmNiwm36yL7u3VjPMe9AsMNVNbwhy+eDWLp2k+t6bljncb9heDQTU4RAwP97c26OTH94fkbmmv+K8Ba8C9Tenbecu16eZT0bxccRozKvXL+ZZWuzF2hFVWBexes894+3Wnw8YcpmS5TMEP7bFGr6iKpnnbL69if+U37zceOzH7Hfb17MHBkbl9gjiAjtYtA08/DfvXo0RCkmP5QCoyBwzUgKuN+9Y5uca07D4O3t5BuOu81UrocyrHNtYuZXRsJnChVmuvHDPUz3izdfAT7whpeyvt/03EeuZ/N3mX1jD0hy8K+e44w/Z+9hFGijDrjslIG4e2X5jQjjUriJKY8Jyf7vbUxDm1CfmwvsLTqWrS1sI8GfP/5erAkRfqYVb7DNAWUobIuOtI0gnE7UveM+YcAVTzP2o8WZe1+s2chrHy/huD++yobN2aPZaQtW8vPH3yv494iDMTHR0hCLiG8p2spHQdw77hPAGlU8NaXlLKNT7njd18/g4PSiAcZOX8xBO3fnOw9NzFxzL9rLKuwec4GfnbWUFUDEMgWddvcbjNor9zjwfIsLvaMv95TaKL2jYtc8BNqoHXk8i8YyjWmekZGXqM7SJGYx5XtFqsriVRuyfqvVGza3+LECnNR/eH4Gz077jPZtsk04UUdLXuYsXZcl69n7bUffbu35/ZiPfMP7jo49SeedeejDhs0J7ABYII7Z1s35f3sn8/ng343NfN71qme57pQ92a1PF56cvIAH7NH9Q299yleG9ufXJ+9Bkypd2jWWXE6jIMjfo/148Zqca4+/uyDz+XsPv5v5HKYcADa5epzn3/9Ozn23fd5vXxuHa//3Ae0a6zh7v+0BeH3mF5meXhgT5yzPGwZg+dpNzF22jkmfrmDSpyty7vs2pCG1090pzPeOACbOXc6OPTplx5/3qRbyNdw5O/Rq8HMDf/EMT3/vYN94vLPJVq7fTPvGeto0RB+cF2piuv+NOXzn8J0C7z/y9jxuHzuTi11h9rrmucznT5eu48Hxczlo5+6Za78f8xGvfWw53ods2y0rPr/R0uamZsa8/1leWd0j51+fvCcr128OURB5owtUVt6Oi/vrT/89lWe+f0j+yMvAuz51KoxfPOE9U83i35Pm06ZBeP6DxUz4xVElkCwboyBoabDq68S34Zsyb0XZZMlaPe0SZcz7n9F/q/bc/8aczLXrn/6Qs/fbnumfreLsv7bsObR+c7CD9Wt/fSvwnpsnJi/kicnZp7wuXLkh8znKKMCtsOYsbXEivzR9sV/wLH7y2FQOG9SDK//9XubaCX8ax+uXHxGp8Q2b5ZL1PfNfA59ratYsn0QQi1dvYPj1L2a+D+zViYsO24kf/nMKl47YOXP94bc/Zer8lYwY1IMx73/Of6dkv+eorsnfPTudCw7egSNufpkLD92Rcw4YkHX/9rEzARgfsCp53MwvGDfzC748tF/mmqMcIFchL/c5q+LecZ9wwzPT88q60eX0d+fPr1PhP801O9zStZvo07VdzvPNqsxbto5xM7/g8EE9mLespQx+uGgV85ev45n3PmNzczOd2zbQrHDQzt15+5NlnDi4D+0a61m+dhMd2zbQsW0DS9dspG1jPe8vWEm/rdrTu0s7Gupbyt+z0xaxZ7+u9N+qQ+bavGXraFblhQ8Xc9jA7uzQvRNNzcrmpmba2mXXu7NuMSxbu4mGhGY8GQVBsD+hUI7arRefLlvLjM9zRx5+uC1bbjukm0femccj78zLurZuUxNH3/IKPxu1W9b1iXOXM+CKp/nxMQPZo2/XwAZ545bmgveejzITadma4myk7sYWrC2jF6xYzw7dO+Z9Nmg7docWE5P1//kPPufJyQsCt6XeGME84ZV3xudr+OE/pwAtjTXAlf+xlJ7fYkeIN1IacdPLLFixnquffJ89+nZl3+23ygmzJI9zeeJc/1HlIk8jdt7fcke8UZQDZL8/kXiLJRev3sCrM7KnE59yx+u+YTdvaeaQG8f63oNs042Xnz3+XuA9h05tGzhln748OP5THvjmcC56cBLdOjQy+epjAGvk7U7/2rwxFs+Y9z+nX7f2icRddgUhItsCDwC9gWbgHlW91RPmcOBJ4BP70n9U9ddJyeQ4vEqlhX9y7CC+/8i7+QPauDtBs5esjZXWx4vXBDYAzv5LSeCMRNo21PHRdSOB3IZtzcbiF5F5WbxqA398YQYDe3Vm9pK1XHPS7r7hgkYQzuV7Xv2Ebx28I/e9bhWxzU3K9x+ZHJjuoxPmBd6rJO5R2r3jZrNLr71zwrh70b5xLPe/73c8bqG4pw27/WfX/PcDzjvI2h+suVlZt7kpx5TlVbxh7HvdC0VKGs6ajVt4cLyl2M+5720AVqxrMQUvXVu6dxaH1RuCzdHFUIkRxBbgR6o6SUQ6AxNF5HlV/cAT7jVVPaEcAm227aNRFqbcdfZQvvPQpNAw9XUBs5US4qePTS1bWl42bmlm4txl7NanS869u16Z5fNEfNwjrK/eMz7r3r8nzfd9Zq1rNthtL35Mz85t2We7rZi2YCVgzRKZszSeMi4XT0xekPX9w0WruO5pb/XIZfR7nzH6vfz+AC+FrEqOi9dX4x6sj7r1NX775b249n8fMCFgNFMMhw/qwcsfLcm6dsgu3bPMadtt3YFPl63zPhqZLU3NjJ72WdZaqXKyqgQr+v0o+zRXVV2kqpPsz6uBD4F+4U8li1NBouwBs++ArbjqBP9eq4OIJPaDpZGv3PUmI299LafX/uqMJQFPtPDLE8PfZbcOjXz46+Niy3TULa9kPt/y/Ayu+M97HPvHV3nLtRbgiJtf8Xu04nh7/CNvfY3XZ5Zmd9NK8bq94nyf7boB2Urpg0WruGPszKKUQ1g52n/HbXKuXX/KXlnfe3VpmxMmDjv//Bm+9/C7vhNP/Ni1d2f+79Ad84Zz+4f8aFOfbBNe0XUQIjIA2Afw85weICJTROQZEdkjJI4LRWSCiExYsiR/g+SHM6TNpyDuO28YPTu3y+skrROJPdTcb4etY4XPxw+PHshRu/UsaZxhzF26jvGz4jdiX96nf+j9288amnHsGQrn/V8dW9H0NzcpjfXC4xcfBJAzJfO5D8LPqM5Hz87Bq7y91bpr+8acKb7tGnNXZXuZ9qtjue6UPQsRL4d9tsv1FQF8ZWhLfTh5SF+uO2VPrg7pkMaZLVcIFat5ItIJ+Ddwmap69yuYBGyvqoOBPwFPBMWjqveo6jBVHdajR4+CZHFmMQU5KF0yA9AmbAMcLGd33JmLN56Wazv28sA3h0eOr01DHX8990s516/x6WmN2qs3R+xavDK52bXSeqsOuXOy7/76vpnPfzvvSzz9vYPp6hPOzW59Okfa3K81Eae3e96BA/juETvTMUHTx38vPZifeyZK+OGe3tymoY57vrFvSOh4eBvKs4Zvl/nsnRXVtX1jjjm5fQQF0altAx1ca0O+e8TOIaHDCSrSv3T51L46bFs6tGkI7bg25GmLiqUiCkJEGrGUw0Oq+h/vfVVdpapr7M+jgUYR6e4NVyqinlvsFLTGPMO6Qtqzzj6LXLzlok1DHVN+eUzmu5/d3yFIxrY+FeE3p+7lO/slKsft0Tvnmlc/Dt2uG8ft2RJuxK492aNv17xxN9QFv2s/ZVcKjtujN988yP9gJXdDtHPPTr5hkiaOMr/0iJ350TGDAOjRuTgzShB79e/Kzr3iv4tSNm7etSTujS69CqJNQ11O3fKOINo1+pc7d2PtfSaKiTofXdo1sv+OtjXBji4s2rD6UQrKriDE6g7eC3yoqrcEhOlth0NEhmPJmZgRNup2Cc4PlU9BFLIL49Yd2zD2x4eHhmmsF7q2b1EkHdsE93qCRjl+Bb+uTvIqtbOGbxt47yKfxVqqcMmIlutOJb333GGxGvZ6Ox9f3qdf5v0ftVtPvn3IDvRNYGrfoQN7cPc39uWCQ7IVhGPmauv67Us1LToulx6xS+Sw3Tu1KIWxPz6cX52Ua6390dED+cXx+UcAYQS9i7d+diS3nDHY/5kSNW7fPGiHHP+XWyl4q6OQqzSCFIIX93RSb5Y7hNRHL2Elx2t9CGtPki6ClRhBHAR8AzhCRCbbf6NE5CIRucgOcxowTUSmALcBZ2qpDin24aon388bpkfntuy3g+XsyqcgCm04enp6eN4MeyvUlaN25ftH7sI7P89dQRkko99Qul4k7x78QT2Vrwztz3Zbd8i5rqr85NhdM9+d+I/crVdmWmMUnKnHt3x1SMZssHvfrvz8+N3plIDZxClm3inPGQXhakii9hgfu+gAHvrWfpFl+NHRAzl6916B9xsL7Kl2attAN49Jb/ttOvDdI3fhW4fkd5iG4X0XToPbq0s7hgf41wrNh5f9d9w6p5PnjtqvgfUW96jKatiAlrx460wUM1VQ+m4y28HYaiSsPUl6f6myz8lS1XHkWSejqrcDt5dHomjcfPrgjHmhTUN4wQ4r953bNQQeMuO3M6wbb6PVtX0bfnD0wKxVqg5Bzis/E1N9neTd3iBo4dmNp+3tm76XQns67obHeT9OhekQQ0F4pzUG4bxzb4PXtrEeNmyhbUPL+2uMaCKJM4Vyzg3HAy2L6fwolU/m798czm69O0cKe/HhO3Hny8HTlr1l980rjsysfQiaaVMKkwxYnaG1m7LrlHvvL+/7EsltdAuZCOEV37t3lcPfzvsS59//DgfutA1v2BM5BIls8qvkEb1mekhE3IU5vw+isB80XyfGa7N1RPIrQEEytrMbOPdopS7CCCJo75v6OvFtAHJHP4W9k3ofU4GTXJiJzYtbxms9M1Hc78LZcqPR82M4z7sbkqh5attQX1JHezHtqrvTcdjAHvSMeMbD/x0avOcT5L6LrTq2oU9XyxwT1FlpKGCK5qMX7u+b9sg9+3DGsJYZQO5dXP164O7yfufZQws66+LQgdmTYs47cEDm89PfOzgzmWDErj358NfH8XfXJJMObes578ABfO/IXHOhM/uw/1a2OauCczSMggjgoJ23ydrYy12g8pqYItTgG0/bO2c2Slwzj5NOmILwOpCdob/bPFMn4Upt1F69QxdT+VZ0rx21YKWZ2xN0rsUZQTjv41sH78A39t8+c33sjw9nF5eDNTOC8ChjZ3+rbTq17Owb1UHYtrGuZL1lqEyP0vs+cu6H5C+ovhSyc4FfWasToV1jPTee5u/r8EvG/Q79diuOwq69u/DcDw7NfHeb0vbo25UXfngY4688ErBGF+738P0jd6Ghvo7T982d5v3tQ3bk3auOZlvHdFvBbcqNgvDhiUsO4u6v75s1S8jde8/rpA4p986to3frleuMylPxvSYNJ7xfek5Pd/e+2TOd+tm9kvMPGpC5FmRiGtirE389Zxh3nr0vlxYxpQ9K60xzeoRxRhCN9vvIGdl4piQ3B/ggDtzJ8j9tv421D9ShA3vkbfRPHNzXSrs+d9aMH+6pomHvq5ijKAs9PjSfXy3sXQSNIApRmn6P+Il2qmt9jd99idDy9d+qfc4oIVeelsi9PojO7Rrp3dV/ZNKhjdW58XsHIpJ1xEAS54RHxSgIH4Zs2y1n2qm7IORbvRilAteJ5EzNy/eYtzA56fj1/h2F5r3TpV0jc244nm+4dv6UABNT767tOcp2lu7UI3wa429OzV6Z6i3Spej1ZnwQzgjCrmRh030dHIeoVymLZE+RDPJB3HT6YF776YhMY3f07r3yTtO8+fTBvP2zI20FnD//33atrA0LXYmjivM15mEKJGikkK+jFVUOv9h37tnJt3duhY/2e4y7/AjftUd9XI2+O29BPgg3Fx++E//4dsuEBXd++gQokyhEnYUVF7Oba0TqYxQEd+G7ZMROPDZxPp+vshbhde/UllUbtiB1uRvK5bNTeytUWKUMqtBBldV3pkeoNNl4ndjeSWelMLF4d2Bt01DHgxfsxx59u7BgxXpO+NO4wGedd+ftjdXXSdaBPc5H73tq11jfMuR3PRtGm4a6jI2/WP1YJ+HHzUal0Fkv+cxBYdY2EeHkIX05ZUj2thGFjSCiP+MtL9nx5Hk2pPSP/fHhvtvzRJnF9NPjds367s5P0DR3P/m7d2qbOQL2zSuPyPgWS40ZQbi499xh/PWcYb733I2xd77z1/ffjptOb7F/ugvfT47dNctW/f++tR+//fJedGnXGPsksdwRRHDYIPt4UKWMOnR3455Tn88eH2p2s++dPKRvaBxOFO5G/uBdurNVxzbs2c9/0d3221iNemOD/wiiTiRruNNy2pq/wO6r15+6F6fu04/B/fMv+IvbGObOsQ9e+BWHQvblu+6UPfOOirNnm+Xev/XMfRjhWeBXSDZ88x4Qj9PIe7Mskv8dhpl12jXWZ3x47vcSZbuOHBldYsR5/qTBLXWlT9f2vqdelgKjIFwcuVuvjEnFoWXmTMsv2bFN9sDrulP24jTXcNZb+Nxf+3Vrn5nPH+VMBTfemTVhhTxo9B7U8PnFFRT7iEE9+MmxgzjXNWvjNM9wPrdSBsv6s5GW7d3p5Y/cM3dlNrRUxji9YCfVoHdVJ9mNQZyfpF+39vzhq0NyRnaPX3wg7151tCed4Pz77eXv7cG6R63l9FHvu/1WfN3l1A/C6UDt2KMjs397fKS4CxnNxFlbFzaC8L7DQjfrc3ccy7ln2Nf22y5/oBJgTEzA8Xv3YWnAmQqN9XVs3NIc28Q07vIRfL7KOoEtqELnO9fZi3cmiZ+JadfenZn+2eqcBun0fftnnKb+MudeC5qGeN95X8pp8L2OyDhZ+/ahO/LtQ3fkp49NAazpl9eesmegoo373rLl8vh96rKd1F9yLYSKy179uvLegpVsu3WHnB5dkIJorBde+vFhBJ1vtFWHRpav28wFB+/AoF6d6dG5bVHmuhGD4u1XFpTSj44eyJT5K3L2eIozusm39nXUXr1zti93yvwO3TvSo3Nb3v5kWaA5KKMgUH509MCsvcK8cp66Tz/uHfcJ7y/0bgsXjlthFTKV2XkiTLn4vSVnwkrSnQWjIIA7vjY08F7ndg1sXLMpy5Hpbgx7+8yfrhPov1WHzDGEQZUm37GdXx22bdZBNe0a8o8gHMep916vLu1CZ2T4Fe4gZ3whFSHKQnhHZiV7iwgHZwQVtCbDD0fWfbffigfenMvhg7LNHO7JApeO2JnLjoq+jYWDI83Pj9+N/lu195U9qE3/+PpRAXJb/y87aiDnHLC9fc266C03Jw3uy1P2saW3nDE4c4qdH9v4yJa517ENS9fmPwXQWcznxhEpTsno1C68+bnulL0YsE3HzAK9W88cklX2utjPBy9YbBlxfvfIXThsUA9Ouv1130kZIsLRu/eKrSC8ptVde3duWb8QAUfBfjtkJbu/D6U8w0hjYsrDHV8byu59uuQ4KB3G/+zInGs5vgL7xxxz2aFZ18MOq3/sogNyzF3eHr3fcNvZccArQ76pcu6e2c9GWY60qCuF/XDSO8X2K0Q5lMYp80HvxTHlRN1cEeCm0/fmgB23YdRefZhx3cgcO3i9tDipd+nVKesd+zWEfjg+qTYNdVlnE2elU2CvX1URkayG0RuVO27vtOY4TPSYxeLg/N5x2q0+Xdvz5CUHBd7v0KY+o9DOGr4tJw/plxX/jacN5qfHDQrcaLJlBGERtj9ToXhH8c9edqjvLspBtGus5+PrR/KjYwZGfqZz24aymRnNCCIP++24DaNdC+aikLO03/7vbSTCRhBOof/1yXtwdcBeUX6NTp+u7fhw0apYG4dBi7IZ3L9rZq/+Yvaad9r4Lw/tzxOTF+YdLUHLewsK6oyONsUYQey7/dY87LP6NpNmnXvmUmH5vfmMwTzy9jz22bZbYJi4Pb4Wh7zPvRAfV5R07j//S5EPtHJH17ltA6sDjpF1TGRx8zk45J21de266jea3bpjGy4+PHh9TkYST4dDiDcK3qtfVwZv6z8RoRT7Deab7uvu3PXu0o7/XHxg8YlGxCiIMtBSFrMLalib6RTgcw4YEKgg/CrjH84YwsszFrNjnnULXo7ZvTdH7LqIHx87iLHTFwOFzVP34kyPjGIWchqDIHOU00hE3X03CtYspugnCn59/+3518T5HOkaifTs3M53ywQ3SW21DS0O7ZMG943UM/aa2aLy1s+PDCyzzv5kfua1QhHXIsZCFgd6RxCxnnUZy/773YMDw5VyhXwURu3Vh77d2vPZyg1lSc8oiDKQr2ecj1vOGMyq9bmHkvspiK4dGjl5SPwTXDu2beC+86yhsdNDL0ZBOFl1KlCUEYSTXlCVc0xecUxM+XCbmKJs/bBbny7MuG5k7HTaNdZbCxTvfSvSpoFOmYnijxeBqdccQ4fGeuYWca5yPjq0CW4udu7Zmd+culfWmR9ROXu/7RjUu7NvR8gxNxZicxeiv0PIvxjUj3Jve1Iu57SDURBlwHFEFfqbfnmo/4rQKL2XsII05rJDmefToDib1+3Yo2M0AX3Yw7aFOzb9oN1g3Vx21EA2NzVz+jD/syecuKKamG49c0jeMO73k/TpXFZ6yaThmAUrufNnoVMvr7dX4fspCKdxLyRXkmdE6uWEvfuwTac2fO0vficg+1PIflJxcYvvbHlTrl/ZKIgycNfZQ3ls4vySn0BWbNkc1Lszg3y2ez5h7z50bd/IIbsUfojf/edZWxTEGUF0bd/IdZ7D5N04fpUoA5u9+nWNNJKqE8nI6OeDaFNfx6YSjljcP1kUP1HcQWelDjFKiswIwlPYo+QyyI8T9IpEhMH9u8URr6wmpq/tt13OFkA1dx5ELfPDowfyl9dm51zv2629r426fWN9ZpfQQkiqNyoieTcpy4dz1nTGB1Gofc3FiYP78vHiNVx0WPjW03Gor5OMjH6VfdLVR0dSblFx/2TjLj8iUrhC4y+G/ztsR/78Sm5ZLgc/Onog85evBwqbPuvgNdNFNdfFTWPrjm34QQHTo6NSwc1cjYIoJd87cpe8zko3//vewRx58ysJSlR5nF5yKZyXjfV1XO7Zy6ZY6iTcAVrqU+vcJqCtI2yPEMU84pa+mJ1e3RwxqCd/fmV26J5Epab/Vu0ZuWdvvuuqQy3TZy05trHf2fF7F7ZFtxVX8D2nk7BXhO1THCYVMT04CgfsaO0kfOLe4VvRJIFREBWkEKdYJRm+w9a8/cmyWM/s2KMTN58+mCN2LWzmTNKItIwgwtallCy9yOGiN8zuBq9YE9ND39qPiXOXFxVHofiNqDI+CDtb3Tq0Yeo1x9ApxGGeE0fmf/7ft21DPf+5+MCSm4OLYeeenQLX5NTkSmoROQ64FagH/qqqN3jui31/FLAOOE9VJ5Vd0DJw8eE75V1R6mXMZYfy9pzshvrBC/bz3U/mkF16cNNzMxhR4NRGbxqF2OO/ErDtcpKcFLKtiJf6EprB8hG1Qsep+B09hz8Vw0E7d+egnbszfvbS4iIqEc5+Ze58dfHY4d385+ID2bi52X7GMTF510GEv6Sh2/kvvEsTji/izC/5T+goFWVXECJSD9wBHA3MB94RkadU9QNXsJHALvbffsBd9v+aw7v9bxT8nMsHBziUB2/bLfKK4Hy0aagravFcuZh+7XGxNk5ryDjSS+eMDsIxlVx8eGn8KD85dlDWUZcl90tV2Ocdd4tzd+Pu3ayvDAPEstG+TT0zrhtZ1G4HUahEbR8OzFTV2aq6CXgEONkT5mTgAbUYD3QTkcKNjlWG3+6e1cCfztqn0iLQuW0D7RrznwHtnBAHUF/AHk+F4hx3eo7rwCY/BvWyOgDOCXZebj1zCKO/dwiXjNg5awTRtb3VsxwxqAf/+FbhfSqnDFbaNOhMlw7azj2MIfYq7V37WO+ymz1xwu1fiHLYVFpp01CX2EQVB4k6R7hkCYqcBhynqt+yv38D2E9VL3WF+R9wg6qOs7+/CFyuqhN84rsQuBBgu+2223fu3LllyAV8unQdG7c0sUuv3GmixbB87SbaNtaFLkpKM3O+WAvAgO6Fr6EolOVrN9FQLzlTAf3YsLmJ5es20adrexatXM/Nz83g+lP3pG1CB6/ERVWZtmBVLGdpqVm6ZiNbd2yTeCOUj3nL1gXuhRb32SnzVrBrn860bahnyeqNdGrbEOkkuFpGRCaqqu9BOJVohfxKW85U5QhhrIuq9wD3AAwbNqxs2m67bQorsPlI6uCPclEJxeAQ5921a6ynT1erl9yna/usA5/SgIhUVDlA+M6v5aRQ5eD3rHvvpyS3P6kVKmFimg+4PSv9gYUFhDEYDAZDglRCQbwD7CIiO4hIG+BM4ClPmKeAc8Rif2Clqi4qt6AGg8HQmim7iUlVt4jIpcAYrGmu96nq+yJykX3/bmA01hTXmVjTXM8vt5wGg8HQ2qmIJ1RVR2MpAfe1u12fFbik3HIZDAaDoYX0T2o3GAwGQ0UwCsJgMBgMvhgFYTAYDAZfjIIwGAwGgy9lX0mdJCKyBIizlLo7kP/8x3Rj8pAOTB7SgclDfLZXVd8DYGpKQcRFRCYELTGvFkwe0oHJQzoweSgtxsRkMBgMBl+MgjAYDAaDL61dQdxTaQFKgMlDOjB5SAcmDyWkVfsgDAaDwRBMax9BGAwGgyEAoyAMBoPB4I+qVtUf0A54G5gCvA/8yr4+GHgTeA/4L9DFvn42MNn11wwM8cT5FDDN9b0t8CjWbrJvAQNc984FPrb/zq10HoA2WDbLGcB04CtVmIez7PBTgWeB7inNQyPwd/v6h8CVrrj2ta/PBG6jxXybaB5KmQ+gA/C0XY7exzrVMa11IvC3qKJ6HVaeKlKvc/JUikjK+Yd12lwn1wt+C9gf65yJw+zr3wSu9Xl2L2C259qXgX94CtLFwN325zOBR+3PWwOz7f9b2Z+3qmQegF8B19mf62hpXKsiD1g7Ci92yX0jcE0a8wB8DXjE/twBmONUUKyG4QA7zmeAkeXIQynzYX8eYV9vA7xWrnyU8reolnqdpzxVpF7n5KnYCCr5Z7/UScB+wCpaem3bAh/4hP8NcL3reydgHLC7pyCNAQ6wPzdgrWoUrJ7un13h/gycVeE8zAM6+oSrijzYFWkJsL0t393AhWnMg53uf21ZtsHq3W0N9AGmu+LKyFfOPBSbD5+4bgW+XU2/RTXV6zx5qHi9VtXq9EGISL2ITMbqeT6vqm8B04CT7CCnk31kqcNXgYdd368FbsY6lMhNP6wfCFXdAqzE+gEz123m29cqkgcR6ebkQ0Qmici/RKRXNeVBVTcD38EaZi/EqtT3pjQPjwFrgUXAp8BNqrrMTnt+gDyJ56GE+XDH1w04EXixXPkoYR6qpV775qHS9dpNVSoIVW1S1SFYZ1UPF5E9sYZul4jIRKAzsMn9jIjsB6xT1Wn29yHAzqr6uE8S4pdsyPWK5AGrB9EfeF1Vh2LZOW+qpjyISCOWgtgH6Ivlh7gypXkYDjTZcu4A/EhEdswjT+J5KGE+LIFFGrAU+G2qOrtc+ShFHqqsXgf9DhWt126qUkE4qOoK4GXgOFWdrqrHqOq+WIV7lif4mWSPHg4A9hWROVjD0YEi8rJ9bz62lrcrS1dgmfu6TX+sXm+l8rAUq5fkVIZ/AUOrLA9D7DhmqTU2/idwYErz8DXgWVXdrKqLgdeBYbY8/QPkKVseSpAPh3uAj1X1j65r1fJbVFO9DspDKuq1k5Gq+gN6AN3sz+2xHGknAD3ta3XAA8A3Xc/U2S9wx4A4B5Btq7yEbEfQP+3PWwOfYDmBtrI/59huy5kH4BHgCPvzecC/qikPWL2nRUAP+/u1wM1pzANwOfA3rN5aR+ADYG/73jtYDknHST2qHHlIIB/XAf8G6jxpVM1vUS31Os/vUJF6nZOnYiMo9x+wN/AuliliGnC1ff37WE6eGcAN2E4h+97hwPiQOL0FqR2W1p6JNTvF3aB9074+Ezi/0nnAcu6+asf1IrBdFebhIqxpflOxnHbbpDEPWM7Pf2FNYfwA+IkrrmF2HLOA213PJJqHUuYDq9ep9m8x2f77VrX9FtVSr/OUp4rUa++f2WrDYDAYDL5UtQ/CYDAYDMlhFITBYDAYfDEKwmAwGAy+GAVhMBgMBl+MgjAYDAaDL0ZBGAwGg8EXoyAMrRYR6SYiF9uf+4rIYwmmdZGInBPzmZdFZFj+kAZDMph1EIZWi4gMAP6nqntWWhY/7C0ifqyqEyoti6F1YkYQhtbMDcBOIjLZ3jHT2UDwPBF5QkT+KyKfiMilIvJDEXlXRMaLyNZ2uJ1E5FkRmSgir4nIrkEJicg1IvJj+/PLIvI7EXlbRGaIyCH29fYi8oiITBWRR7G2a3CeP0ZE3nTt7tlJRLYXkY9FpLuI1NkyHJPkCzO0LoyCMLRmrgBmqbX75k889/bE2kxtOHA91g60+2DtrOmYiu4BvqvWJmw/Bu6MkXaDqg4HLgN+aV/7jp3O3naa+wKISHfgF8BRau3uOQH4oarOBX6HdYbGj7DOGXguhgwGQygNlRbAYEgpY1V1NbBaRFZi7REF1rkVe4tIJ6xdZ/8lktlpuW2M+P9j/5+ItWcQwKFYx5WiqlNFZKp9fX+sczJet9Nqg6WoUNW/isjpWPtZDYmRvsGQF6MgDAZ/Nro+N7u+N2PVmzpghT36KCb+JrLroZ9TULAOnzkr54ZIB1q2Gu8ErC5QHoMhB2NiMrRmVmMd4BIbVV0FfGL33hGLwUXK8ypwth3fnli7gwKMBw4SkZ3tex1EZKB973fAQ8DVwF+KTN9gyMIoCEOrRVWXYpltpgG/LyCKs4ELRGQK1pbNJxcp0l1AJ9u09FOs7ZxR1SVYZwI8bN8bD+wqIocBXwJ+p6oPAZtE5PwiZTAYMphprgaDwWDwxYwgDAaDweCLcVIbDCVERH4OnO65/C9Vvb4S8hgMxWBMTAaDwWDwxZiYDAaDweCLURAGg8Fg8MUoCIPBYDD4YhSEwWAwGHz5/5DnBko8c1iWAAAAAElFTkSuQmCC\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": 619, "id": "1f279980", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mae_valmae_trainrmse_valrmse_train
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fold_43.0329800.9973836.3619622.585072
fold_55.1579381.05129610.9248382.731734
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": 619, "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": 620, "id": "73d66eec", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 620, "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": "53876601", "metadata": {}, "outputs": [], "source": [ "# export results\n", "svm_results_df.to_csv('svm_results_best_case.csv')" ] }, { "cell_type": "markdown", "id": "de653243", "metadata": {}, "source": [ "#### Investigating Spikes" ] }, { "cell_type": "code", "execution_count": 623, "id": "50b89bd6", "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": "4108f684", "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
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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

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" ], "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": "9ecd4109", "metadata": {}, "source": [ "## SVM (v2)\n", "- (version 2 with Polynomial Features - For feature interactions)" ] }, { "cell_type": "code", "execution_count": 812, "id": "5dfcef40", "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": "01a3bf19", "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": "048b0988", "metadata": {}, "outputs": [], "source": [ "svm_regressor_v2 = svm_regressor_pipeline_factory_v2()" ] }, { "cell_type": "code", "execution_count": 815, "id": "66abe7ef", "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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\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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WPaeGUj0ico2IPCQiAzJXNGOMaR1yuXbvJ5F2/E8Bu0XkWOBPwFrg5YyUyhhjTMYkEvjr1Hms/GXAo6r6KGCPZTTG5CXN4Su+ibTjLxOR24BrgNNFpB3QITPFMsaY1iOHY7yvRGr83weqgZ+4rXj2A/6VkVIZY4zJmLhr/G6wf8jzfh2W4zfG5KlcPgmI587dMvzXUQBV1T3SXipjjGlFcjmf7yeeG7jsAq4xxrQhiVzcBUBE+gGdQ+/dlI8xxrRZfvX9XD4JSOQGrktFpBBYDUwC1gBjMlQuY4zJOpFslyAzEmnVcw9wMrBcVQ8CzgY+y0ipjDGmFcjlWn00iQT+WlXdDhSISIGqTgAGZaZYxhjTevgfAHL3qJBIjr9ERLoDk4FXRKQIp5dOY4wxabatvJpxi7dyxlf7sm/PLmmddyI1/suASuC3wEfASuCStJbGGGMMAOt27Oa2t79k+daytM87kRu4KjxvR6S9JMYY00qpT1on0/n/TM4/7sAfcSNXR5x+eirsBi5jjMkEJ9xKBpoWJVLjD7uRS0QuB05Md4GMMcY01fgz0aI0kRx/GFV9F/hmrPFEpJ2IzBWRD933vUVknIgUuv97JVsGY4xpET5pl5Zq05OJewkSSfV8x/O2ABhMfOv+G2AJEEoJDQU+VdVhIjLUff/neMthjDH5IJMHlkRq/Jd4/s4HynBa+gQSkf2Bi4HnPIMvo+ni8Ajg8gTKYIwxeaEp1ZPdHP8NScz/EZzHNHqvD/RX1c3uPDe7ff80IyI3AjcCHHjggUks2hhj0iObffVkJdUjIv8mylmHqv46YLpvAUWqOltEzky0YKr6DPAMwODBg3P3FjljjElCJruCjifVMwuYjdMj5/FAofs3CKiPMt2pwKUisgZ4DfimiPwX2Coi+wC4/4uSLbwxxrSEbPTZE1pkVlr1qOoIVR0BHAqcpar/VtV/43TSNijKdLep6v6qOhD4ATBeVa8B3geud0e7HngvtVUwxpjMiJZm8bupKzOFSP8sE7m4uy/hufru7rBEDQPOdbt4Ptd9b4wxrU42e+dsFXfu4gTouSIywX1/BnBXPBOq6kRgovt6O87ZgjHG5IQWq937LDMTrXrirvGr6ovAScA77t8QNwVkjDFtUtRUj3ssKKuu4/Z3vsxKGZIVM/CLyOHu/+NxUjvr3b993WHGGNMmxZtuefXzDDyBNsupnt/htKd/0OczJY5uG4wxJpe1tVY9MQO/qt7o/j8rA8s3xphWK55UTzot31rGzooaTjp4r6Y7dzOQ60nkYetXikgP9/UdIvK2iByX9hIZY0wr0dI1/fMensz3n5kRNiwrOX6Pv6pqmYh8A6evnhHA8PQXyRhjWpdstOrMZEuiRAJ/6C7di4GnVPU9nAeyGGNMm5TNG7haS3/8G0XkaeB7wGgR6ZTg9MYYk1OyeQNXSLZTPd8DxgIXqGoJ0Bv4Y/qLZIwxrUsmO0wLXGYG553IDVy7cTpU+4Y7qA6nszZjjGmTUqlt//vTQm59bW7S0zcdbLLbqudOnCdl3eYO6gD8N+0lMsaYViaZ/vgfHLecd+dtSnnZ2U71fBu4FKgAUNVNhHfaZowxJk1aRaoHqFHn3EMBRKRbZopkjDGGbLfqEefWsQ/dVj09ReRnwCfAsxkokzHGtC5Z6bLB7Z0zA7meuLplVlUVkctxcvylwGHA31R1XNpLZIwxplFW+urxmA6UqKo14TTGmAzLZAvSRHL8ZwHTRWSliCwI/WWqYMYY01r43aWbrsCsqmwsqQycfyZa9SRS478w/Ys3xpj89szkVfxzzFI++d3pYcO3lFYBmXkCV9yBX1XXpn3pxhiT56at3A7A+p1Ntf7Za3dwx7sLM7ZM62vHGGNi8EvrxNtJ24hpaxJe3tItZY2vs30DlzHG5K0VReUs3Lgr4enufH8Rm3c1z+FnU5sP/Dsqahg4dBRvz9mQ7aIYY3KMt6Z/zkOT+Na/pyY1n7r6VtDNp0ebD/yrt1UA8J8ZdonCGJMc31RPC8VyS/UYY0wLSlfQTWU+mWjVY4HfGGMCtIYHsWSCBX5jjInBt1vmTC7PM3NL9RhjTAvKRNCNx6w1OzJaBgv8xhgTQ0s/etH7ABfL8adg/Y5K5q8vyXYxjDFtRDaew5sueRP4t5VXc9kTn2W7GMYYkxBL9RhjTBZks26ftSdwGWOMCZe7iR4L/MYY06pZqscYY7Ig3uu41XX1lFfXJT29vxxq1SMiB4jIBBFZIiKLROQ37vDeIjJORArd/70yVYZEfbxoC396c362i2GMiWFKYTGn/PNTqmrrs1YGv2D+veHTOfrOsS1fmARlssZfB/xeVY8ATgZuFpEjgaHAp6p6KPCp+75VuPE/sxk5y3rxNKa1+8eHS9i0q4o12yuyXZQw8zcEd9v80LjlSTUpz6lUj6puVtU57usyYAmwH3AZMMIdbQRweabKkEk7K2qYvda5u664rJo/vTk/q7UPY0wmpXYpVxUe+7QwqSblOduqR0QGAscBnwP9VXUzOAcHoF9LlCHdrnp2Blc8NR2Af45ewshZGxi1YHOWS2Vau8WbSpmwtCjbxTBpEf/BIN6ndfmRDFT5Mx74RaQ78BZwq6qWJjDdjSIyS0RmFRcXZ66ACXh8fCF3vb8ICH80WugnzVa/Hia7VJUJy4qoq2+IOe5Fj03hhpe+aIFSmXSYu64kLfNpaGVtPzMa+EWkA07Qf0VV33YHbxWRfdzP9wF8qz+q+oyqDlbVwX379k2hFOn7xh/4eDkvRTw/U1Vz+tbtfPXs5FV858n4T7vLq+t4fHwh9T578IRlRdzw4hc8NXFlOotoWlBdfUPUA3equ/iKovKkp82pVI845yfPA0tU9SHPR+8D17uvrwfey1QZWkJLHclr6hr438x1NLS2qkOOunf0Eub41OamrdjGRwubp+weGLuMBz5ezocLNjX7rLisGoD1O3envZymZRx911hOvX98QtMkcjD42cuzEixRk0xkEtqnf5aNTgWuBb4UkXnusNuBYcBIEfkJsA64MoNlIDPHyyaqmpEcXKSnJ63kwXHL6dCugO+esH/Gl5evrn7ucwDWDLs4bHiobXZ1Xex0Trx2VNTQu1vHtM3PJK+qtoGq2urAz9tadStjgV9VpxIcdc/O1HJbWoO2TC992ytqACitrM34skxyEtkMFm7cxbf+PZUHrzyWK+xA3uptKw8+KGSadcvcQhIJ5LGu1l/06BSen7o61SKZNmbxJqedw/RV27NcktzWUpfX/vbeopZZkI+casffeiS+ZSSyMalGb261eHMp93y4OOEyADwzeSUPjVue1LSZsqKoPOzpQF4vfbaaddstzx2PBncjK0jjTl1b32ANDVpQLn/TeRD4E9eQSI0/yVTP0i2lnHDPuMYLg37uG72Uxz4tDPy8tr6BoW8tYP2Olgu25zw0ie8On95seHl1HXd9sJirnp3RYmXJZaFr9O3SFPkrqus49C9jeOST4O3FmJA2H/iTqQAlMkkiBwmv56asZntFTUo388xcvYPXvljPn95ckPQ80iX0PcR7DWLm6h1sz2LeNBMSOSWvd7+vdDUM2OV+769/sT4t88tXoxZsbrGzpqra+qgVvxBL9SQhmZ8woRp/EvOH9LY1SnXD+Mb94xk+KXYb9KVb4r7/zteWXVWMcO+D+N7T07ny6eZnDi3h8fGZqRUnliJ0Rm6Xpr3abh5Mj5tfncPYRVvjGjfV48PVz87g6/d+ktpMktT2A38yNf4Y08zzdLSUbI2/cVmtIFO4YWclw8YsjTpO4dYyLnhkSkrL+cmIL7jz/UVs3lUJwKri7HSw9cDH2b9uErofI505fmgd21Ou27m7Jqnp4qm9e/ndR+InJ7tsyLZop22qyrjFW5vdFBU5SUOD8tyUVY3vL/d0tJRs3A/9lrlyLW5LaVXUz+NZj51uk9S6+uCR6+obmL12Z0Jly0WhryBdO3Ummvzlg/nrSxg4dFRa5pXKTVrR5NSdu61FtBtd35qzkZ+9PItXZq6LmCZ8ok+WbOUfo5b4ziPWgSVIru2osQJ7uvKiD3y8nCuemsbCjeHd2y7atIuBQ0cxbeW2tCwn27SxVU96Uz25UpFIl7nrSpi8PPm+vCYsS/4aW+Q2v2VX9MpRsizHn4Rop75b3Vrs5pLKsOGRgb8ySnfL3lEjf6B4eldIdD/N1n4dK6XVuK5xbKTRZrVks3MdoTjiwu+MVU4T0nGLg/Ov63fsbjyraO1Cff60S/MemGdxn9vf+ZLrXpiZ1nnGE2evemYGHy3aEjYs1bRvS2rzgT+ePSFylES6w4n2Y/t16BXSkjW0+etLeCHFm8hilTORjb4+hZWONulp/zeB0/81Iel5t6TQplGQpiR/6HspLqtmzbbW9XCS1mp3TR3Lt5Y1Gx5r61RVpq/azoufrQkbnqnAb3fuJiHaTxEKvk9NXMmCDSXxTZTA/KNtCI2BP8E6WjKbwGVPfMbfk7yJLCRWORvXNY7ViXZADPok3vUuq2r+vNNosnXDU0OaUz3ebe3MByamZZ5t3S2vzmX0l1tijxghaPuNtl1HMymUqgqY3FI9SYi2X3uPpN9+clrj60SO3NHGzVRM2VVZm7ZeOuMNfLFz/LHnEbqQGc8yI7f1pjOk9H6p2To7T3ernlxKM7QWM5LsLqMujsCfzHbaki2y2nzgj6fWDeGBJnKaqL9hlM+ipzRCQTDKKD5Kdtdw7N0fp60rh3iXH228qtp6Hhi7LO5lxpPqiRxDAoanKh0BM5lT8Qfd3y9dNX6L+y0naJvxBv5k6mVBv6G16klC7NDrvva8SezO3WifxZPqSczO3c4dmn79wicj3sAXbbznpqzijdmxH1IfqgUlc0rcdLaQ8KRRBc3ugkcmc+qw2P2zTyksZu765Jufpqs5Z77V+Jf55ObTJdYvEpjq8fwGydT4A/eLHOuPv1WI9wdwam3OuInduRs8brR0TLK/Zahvl1QukHrFG4OjjZdoH/XxBP7AVE/A910bx2MP/QT91t5Ha0Zz7fOptSjp1D61utc7czdQIMKx+/dMaT6muaDYEbT9egcns3e25ME772r83h8zqLKVyPffoME/clxBNcEfO5QaaEjyeSAfLdzS+FARSGRji3O8OI5ofjnSNdsq+HhR8IW2WLNNJNXkla2K8rEH9ASgW8d2Kc3nt6/P5zevzcu7Gn9LCPpKAwN/WKon8d8juMJvrXoSF/FlxvN7JNPnip8NUR7FFy3VM2LamsC7CTWJs5KQwq1l/Py/s/mzp1O3dOT4Ey2K345z5gMTufE/s4O/zxipnoWbdvl/EEO242W6nqRpT+RMv6CvNOhsOzzVk/jygg4o1qonCc0u1Lr/i8uqw5tySfA00agG10YvfTz4Yd6ho/jqbRVc/8JMKmuabhJ7OkqHaaFaRTKBv9Rt6uh9Nmy8KaNkU0t3vb+IN2aF9xiZTFpmitvkrTVe3E2KJv87+s+u+Xyqauu58eVZedeuf9GmXRRuLUNVKUzhWkA8F3G9vKNH+1k376r07ZXXUj1p1KzfHXfA9S/MDOtsLWqrnijhRqOkeuLx4mdrmLS8mCmFTbedR7vgF9rmvLGzsrY+rmsZofUKWtcVRWUMHDrKd2eJ3NhVlYnLimLm61+atoY/RnQbHVeO3/MdfLZiGx+7d+ymum98EnHnbzp3tWQaBaRrX/f7SqetdL63uz7I3tOjsuHix6Zy7sOTeX7qas59eDLz1pdwz4eLeSuOBgheiaZ6wqaNsjVc+vhn3PDSF82GBwV+a9WThOY5fuf/+og0TFirnoRy/MntucmevtX71BTnrith5KzY/bD7dY6mngPIhws2A/CB+z9suREb+6dLivjRi1/wzORVzcYNEppDUDvosHE96/dD9wHo4XNJzk8jOtLKVo0/tNx0td32W49c6w8q3ea6vV+u37Gb56eu5vdvzE9o+qBtI57ra9E28aBePINOhK13ziRURfSzE8+Odv9H0bsoDp9fckfkZH/KUKqnvkHDDlAfLfS/MDrKE8QbN2TPhhRv4Is8aIR661yXxNO/6qP0zhlL2ptzJnmRPFX1jSm79MzP73uxLpqbGzh0FLe8OieledTFEfmTac4Z1ArQavwJemfuBn71v7lhw+L5PT70qfEGadDw3Wtq4Tbf/j8iJXoUD41e78nxe4N20GpNX9XUm6XfKapv4PcZFllLD71LpjISrcbfdGzyn3HQ75fsASHVh8skK1TedJ1xWKueYJGbUmj/Trb/qXi+62QO6Olqoh2PNh34g2rBQLNIGe20ON4WLapwzfOfc97Dk5td0Iwl1oGgKVA0vU90Z/fbsOLdQOsDajnO3Q/NZ7J8a1ng9x8tRxorf5ruWuzNKdT+Imt1iRwDQ79FuvZ1i/vNpbqtBG2KcbVNaOWtetr0DVx+O8OIaWu45uQBzbsEiPLlRotF3p3fO97Qt7+Mr5BxCF+Gf6on0flEG+anWY0/xnTnPTw58LM124NbmTQFRPfMJsZDcmINL9ldQ32Dslf3Tr6f764J7nI7lmYXvGOMP+ZLT9qtIXw9UxWtErBxZyWqmpFccTa0VMd6QcuJJ9WTXDv+oFSP5fhT9s8xS/nnGP+HqnjtqKhh4NBRvDdvY2BtF8J3du8PF6vmmsg+6J1VUKonntmFairhrXo8ZQr1H+T5fO12p7lpaWV4r5ehciiJn9b+K8rNVt75Qvynv0E7zaC/j+OEfwQ/1zSlwO+zzCmFxb652qVbSvnFK01nF6Hypq8df/DF3cKickZMW8OXG3ZlrTfSVKkqT0xYwWsz1/l+Z4uSvI/DT2jfDPpt4rm4G/Qtn3hQ78Bpku3dMxltOvAHfY074nhYx1q3VvrC1NVRT+3CdrgUfjfBeeygX9BoUA3I8TeNM2FZMauKy6Muo85nReZ7u6P2cd/oJUxaXsz4peHNIEPLfvXzdTw1MfaD2uMVOrMI3cUbb606U/Fs4NBRPPhx04Gquq6BEveZrJEB4MMFm7j2+ZnNnugGUB7RXXRk66zHxxemFLxirf6D45ZzyeNTeXn62qSXkU2FReX8a+wyhr79JbPW7Gj2+cWPTQ2cdmqh/1Pboj1gCQj8UlOp8c9c3bzsTdMEfGA3cCUm8CZQpNmPF/nddu7g3EpfXl0XtdZ576glvDfP6TAt2niRrYv8emX8yl/G8COf9r219Q3NLgbW1muzQPHAx9G7LahxA78I1NQ1MPStBdz0n9lRpwmJXLeiMv/HzJVV1QXfdRxHcA7VSP8307lGEpliCmr5kMkLY94D21/fXcigv4/zXWZVrfP9rvdp6RRZutDm16DOwe2Bj5dzWZQb/mKJVZMPPacg3j6IWpsaT39Q8VTcoGl7e+2LxK63hQQF7/iaIyexPLtzN138v8hRX25u3ElDKmr8A/PK4opmj2b0muKpTUS7I/WRTwrD3ncIeOae3/NDvRu9twYcOc9YucBaTzPKKYXFzXYIv6aZoQ04sjnn05Pib78fEs8OE7mcyKafgbfLZ/g0efrK8L7bd1XWsilgu/D7FSKLvdGdVlUbf9+6BuWFqasbzzYTEe/ql1XV8oVPjbm1835/6fipo3WgGKvFVTx3nifVO6e16kmPVL5Hb7fHT8aZyois1XsVlVXR0NC0kw+P0i1DJG/vl1EvGnkiTlFZVbPbwkMbrOD/yL+35gTf2ZhqYJ24rCiuU+TI5UROE3TwiLWjqSp/e29hzOUH2RgR5M97eFLwBWz3q91UUsl1L8yktKo28HdTheq6pu3m7x8u5upnP/cdNxq/QOb3m324YDNXDp8etsxc4P3+om1H8Qbc2ijzaGw5F/B5dW0cgT+uUoQLbNWTxLxiadOtelLx7/ErEp6msiZ4g9hUUsnBt48G4Irj92/2ebQNpbq2ofF0L1ateeyiLeyuqePJCSspLCrnqhMPaPzMW1NpH+ejnxovsqYY+H/04hdx9UQZWeuJXN+g7iRifS+7a+rTmt/eWup/96XXo58UMnl5MR/O38xBfbr5jtOg2qxb612VtQmXx2/1o30n949Zxp5dOnDVSQfQoaCAXt06Nn6mquyuqadbp9YTHuJtOBHvI0b97mKPXFbQwTpaBS8kmUpn0EHL7txNUCZOnO6/4pjAz96YHZxLnLGq6fTar2YdreVQdV1944YUbaMvEOGm/8zmt6/Pb2wyOXl5UyqqJuzMIXA2vhttOk5Da30W+r3h08NuYY98fN23nwjPey/fWh62HgCH3D6aRZui34gVz86abqF25CLRbgai2fok9dg+n2mibSsvfLaahz9Zzon3fsrx/xgX9tkzk1dx1J1jKS6r5v6PlvLZCv+Loy3JexCLtl7eB6BH+xqjBf473l3I7LU7wzpO9Ip5UZjkmnMGdtmQ8Jxia9uB3+fLv37IAOb89VxW3neR7zTPXTc46jwP6N018LMNO5vSAScd1JsTBwY33YoUrXbmrRFG22C9G0joGoI3ReHN8VfHGQiDcvzJ8MuNzlyzg2cmN6W9vKfx9Q3Kpl3NLyIH7ZAhfmmPeHbWdGlsFuspRlBeWFWbpV2ifdO7a+p8c/SR0yzeVBpXai1UztXbKvjZy7OorKnnnbkbAfjvjLU8NXFlRF9JLaO+QcMC/Dy33x1oaqSQimipHoArnprGGf+a6PtZ5PVBP0mleizHnx6/OvvQxte//uZXAOjcsR29u3VsfJKVV8d2BZxzZH8uPmafxmHXDRkQNk7k9nLRMXv7Lvv1m4Yw8udD+Nu3jgwb/tqNJ/uOH21j8gaG6VEeEO09I/S7eByaj4hQleBTsxJ9ypafoO362SmrG197v9+gHbyips53eLTpYh0sMkkIP+h6NajGFUhCfj9yPlcOn8628vBUU2QN83cj5yV0Af5v7y1k3OKtzFi1vfF3KoroTOy5KasYPmklZVW1MZsOp+q0+8dzyrBPqaypZ3dNHfeObrr35i/v+F+rad70NziQJvvENojv7DHaxeMgwamehGcVU5sO/If26974OpQn69Q+Sp45oq08QPuC8K8osiVHz64difTnCw5vfP3jbxzEHp2bcqV9e3TitEP7NJvGWyONvJB43+ilvDRtTXC5w4sP4HtgC7UCmr12J7+O6MPIS3Gaii7dUtqYgmqpVIk3gNXW+e8IFdXRA395dR1lVeF58mg1/s27glttJePduRupqWtg9rqdgHM94J25/hfOC4vKech98HqkOet2ctN/ZrGtvJpd7rOWQz1ORn4H/50Rfv1i6ZYyFm+Ovx+i0AXLDu0KGmuekcv4x6glDBuzlGPu+phvPjgJaGqV9HmUCknYcurqG++DACcA19Y3MGfdTm55dY57R7pzpre1tJoj/vYRR/5tbFzzfuzT8FZuCzcGr/8nS5r3hx+veM4ek2k2G3RtJxN37raeqzcZ0K1j0+qFaqydOwQf60Jfb9iTdCJqDWce3pcDe3dlyMF78fqs9b4XLL+2/56By+jUvsA3R/nXd4NbnMxeuzPwM6935zW1RIq3rbOfJyas5IkJ4a2OymME23TZ7EntHPv3j33Hmb9hFwP26sbW0irGLGzeod5g907dV396UuOwyKavXkP+6TxU/ZC+/hdg42mG6rWltIqv3jGm8f3Dn/gHdoCJy5o3391dU88Rf/2oMcBMXFZMdV0DN591SGOvqJMLt/HeyKZuhscu2tpsPomY6aaPrn3h88Ya//vzm7anY+5sHnyPuWssZVV1tCsQ6huUPt078sj3j2P80iI2luymR+cOrNu+mysH78+iTaWMWbiZkt21VNc1cFj/HlTW1jdrQlxUVu17g1Y8Ho0I/JEVKK9o+1ss8aRJfxbR/Xc8WvLmuqwEfhG5AHgUaAc8p6rDMrEcb5PF8mrnaOo9GDQb3z0ruPDovRkX8OCPfj06M/lPZzXW0rr6zC9aqq5j+4LGbgKGHLxX1NQNOKmksCeFtVH3X3EMf34rvv6N/vDGfP4QR9/qV3ty0+N9nngUaWVxyz6p6qh99wi8KO2tVYYqLd6DcbyB6y8XHRGWJoklaNst8znwh24KC1VktpXXcM3zza8HzPQJ5MsCerCNdmdrvDq2K0jLdYAgj09IvMVfKtpEqkdE2gFPABcCRwJXiciR0adK3ZZdTr6y/x5NnXWFmjQ++cPj3bI5w79z/P4cd2BPwP8OW4BrTjqQwQN6cc3JA5p9NuSQvQLL0al9O/bs0gGAW8851Hec3p6mdY/+4DgAenRuz20XHs5LN3yd8b8/o/HzsbeeHnjdIOSWs74Sdi3C27TwxIG96dO9ebrKzwG9u9CtY7vG78brj+cf1mx9Hvn+oMbXV590IFP/fBa3XXg4kdYMu5jLj9svrjIE+c9PTmx8ffNZhyQ07e0XNS8TOGeH1w0Z0Jiqu+Wsr/DOL0/h8kH78pS7zfiJ3GT69ejE8GvCxx9+zQmM+vVpje+/e0LzJr7XD2m+bSXqe18/gLd+MSTl+URzzhH9Mjr/aA7fuwdrhl3Mh7/6RuOwC47emw9u+QZP/vD4uIPmYf17BH72zi9PCXvfoM5yl/z9Amb+5eywz+64+IjG/durQzthqM+2D/iOD/CTbxzE09ee0NiLQDplo8Z/IrBCVVcBiMhrwGVAfA1wk9C+QDjxoF58smQrR+3blIa569KjuPuDRZx5WF/AqYGH7LtnF+ZSwjlH9KNBlVMO2YsO7ZuOk/326Mybv3A2iF+ceQh9unfi+18/gPYF0iy/3qd7p8bn3XZqX8Bdlx7FB/M3cfyAXiz++/ms31HJ+Y84NwM9e91gzjmiHwfdNppeXTvQoV0BU/50Fv336ExHz/IH7tWVfj06c9jezgb7+NXHUVRazZBD9mJ3TR3ff3oGdQ1K5w4FfPeE/fls5bbGM4cxvzmNw//6EQAjf+4Ehcqaek667xNKq+pYes8FzF1Xws2vzuH1G09mzy4d6NujEyJCQ4M2nkm9PWcD45cW8dD3BjWW7ZRD+vDZim387PSD6d6pPTPX7GDRxl3c922nGexNZxxCp/YFfO2AnnznyWncePrB7vfSjsJ7L2T6yu1s2VXFaV/tw+y1O3ns00K+N/gAHhq3nN019cy47WxenbmOTSWVvDl7A98ffAAXHL03px3al5E3DeHYA/akU/t2nH5oX3p160htfQO3vjaP8uo67r70KF75fB2T3LujJ/zhTNZsq+Csw/txxlf7MX5pEQf27srXD+pFvx6dG7/rMw/ryxuzNnDzWV+hS8d2HHdgLwBm3HY2BQWwbvtujjuwl9MKqaSSvffsTKf2BZTsrqVLx3bNdtz5d57XuLP/8sxDeHLiSs4/am/269mFbxzah+raBqau2MbQCw/n7suOBpxHRv7hzflM+dNZPPJJIeOXFtGtUzsWbizlqH334OzD+/HY+BV8tX93br/oCH70otP1x55dOnDCgN688tOTYrbO6b9Hp8b7Ewbs1ZW125vSMKHpv3P8frw9x2n107F9Ad88rB/Drz2BJyasaOx87/Pbz2b1tgpOHNibl6at4e8fLmb4NcfTp3snvjt8OhcdszfnHtmfju3acfcHi8IuIofOgq4bMoCO7Qp4bqpz4f/gPt1Yta2Cnl07cNqhfflg/ibu+/YxXH3SgQAcvV/Tfv2Xi4+g/x6dOWb/PZl9x7mUVdUyfNJK+vXo3JgO6tGpPWXVdQw5eC8uOmZvrh0ykCWbS5m9did3eM6mQr/VqV/Zi89WNJ2dH7Z3D7p0bEeXju34/Paz2VZezcriCi49dl9+fOpBLNlSysadlVTW1nPZoKZKzcC9urK7pp7fjZzPL888hB+ePIAuHdpx/D1Ok9q9unXkX1d+jR+/NItD+3Xn/KP8G4+kTFVb9A/4Lk56J/T+WuBxn/FuBGYBsw488EBN1ppt5VpUWqX19Q26rawqcLylm0u1orq28X1JRY0+O3mlNjQ0JL3skPU7KvTO9xbq4+MLA8cZu3Czzlm7o/H9pGVFumHn7qSXWVtXr3X1TWVvaGjQZVtKG+c5fslWHTFtddg0pZU1On/9zqSXmUwZ4/1+SytrtKSiJmxYTQLTexWVVjWbVzbtrKhOetqPF23RLbsqta6+QUsrm9ZpR3m1ri4uDxt30rIiXbxpl9762lzdWVGtX24o0WcmrdTPCot16FsLtKyqVrfuqtSNO3drfX2D1tU36NbSSn1v3kZVVS3ZXaO1dfX64fxNWlQavC8lorSypnFe5VW1WltXr69/sU5r6upV1fmtfj9yXuN4u9x1LC6ravbbb9i5O+Y+U15Vq1W1daqqumTzLt/tp6q2TneUV2t5VVM8aGho0IaGBt1Uslt/NuIL3VxSmfxK+/hyQ4kOn7hCv9xQoqqqizbu0vr61GMPMEt94rBoC3fTKiJXAuer6k/d99cCJ6rqr4KmGTx4sM6alfjFEmOMyWciMltVm92clI3mnBuAAzzv9wc2BYxrjDEmzbIR+L8ADhWRg0SkI/AD4P0slMMYY/JSi1/cVdU6EbkFGIvTnPMFVV3U0uUwxph8lZV2/Ko6GhidjWUbY0y+a9NdNhhjjGnOAr8xxuQZC/zGGJNnLPAbY0yeafEbuJIhIsVAqOu6PkD2HwmUGluH1qEtrAO0jfWwdciMAaraN3JgTgR+LxGZ5XcnWi6xdWgd2sI6QNtYD1uHlmWpHmOMyTMW+I0xJs/kYuB/JtsFSANbh9ahLawDtI31sHVoQTmX4zfGGJOaXKzxG2OMSYEFfmOMyTd+T2fJxB/QGZgJzAcWAXe7w48FpgNfAh8Ae7jDzwVmu8NnA9/0mef7wELP+07A68AK4HNgoOez64FC9+/6bK8D0BEnJ7gcWApckYPrcJU7fAHwEdCnla7DicA8928+8G3PvE5wx18BPEZT+jMn1gHoCoxyt6FFwLBWvD8E/g45tE9H25aysk8ntd4ttiAQoLv7uoP7BZyM0z//Ge7wHwP3uK+PA/Z1Xx8NbIyY33eAVyM2kl8Cw93XPwBed1/3Bla5/3u5r3tlcx2Au4F/uK8LaAqaObEOOD27FnnK/X/AXa10HboC7d3X+7jlDr2fCQxx5zkGuDCX1sEdfpY7vCMwJdfWIcf26WjbUlb26WT+WizwR3zZXYE5wElAKU21rAOAxQE/znagk/u+OzAVODJiIxkLDHFft8e5i05waqZPe8Z7Grgqy+uwHujmM15OrIO7kxQDA9zhw4Ebc2AdDgK2uuXaB1jq+ayxfLmyDj6fPQr8LNfWgdzcpyPXIev7dLx/LZrjF5F2IjIP5yg5TlU/BxYCl7qjXEn4YxlDrgDmqmq1+/4e4EFgd8R4++F8+ahqHbAL2Ms73LXBHZaVdRCRnqH1EJE5IvKGiPTPpXVQ1VrgFzinwptwdtjnW+s6iMhJIrLILe/P3XLt55bBrzy5sg7e+fUELgE+zcF1yJl92m8dsr1PJ6pFA7+q1qvqIJzn7J4oIkfjnEbdLCKzgR5AjXcaETkKuB+4yX0/CPiKqr7jswjxW2yU4VlZB5yj/v7AZ6p6PE4u8YFcWgcR6YAT+I8D9sXJ89/WWtdBVT9X1aOArwO3iUjnGOXJlXVwCivSHvgf8Jiqrsqldci1fTrgd8jqPp2orLTqUdUSYCJwgaouVdXzVPUEnA13ZWg8EdkfeAe4TlVDw4cAJ4jIGpxTw6+KyET3s8YHubs7wp7ADjLwgPcU12E7Ts0mtKG/ARyfY+swyJ3HSnXOU0cCp7TWdfCMvwSowLlescEtg195cmUdQp4BClX1Ec+wXFmHnNqnA9ahVezTcWuJfJITF+gL9HRfd8G5CPUtoJ87rAB4Gfix+74nzlXzK6LMcyDh+cCbCb+IMtJ93RtYjXMBpZf7unc21wF4DbeFDPAj4I1cWgecWv5moK/7/h7gwVa6DgfRlIcdgLNzhS68fYFzMS90cfeiHFyHfwBvAQURy8iZdcihfTra75CVfTqZv4wvwPMFfw2Yi5MSWAj8zR3+G5zmT8uBYTRdULkD52g6z/PXL8ZG0hnnSLsCp7XGwZ7PfuwOXwHckO11cDeaye68PgUOzMF1+DmwxJ3XB8BerXQdrsVpqjcP5+Ld5Z55DXbnsRJ43DNNTqwDTi1R3d8h9Pv8NJfWIcf26WjbUlb26WT+rMsGY4zJM3bnrjHG5BkL/MYYk2cs8BtjTJ6xwG+MMXnGAr8xxuQZC/zGGJNnLPCbNkdEeorIL93X+4rImxlc1s9F5LoEp5koIoMzVSZjYrF2/KbNEZGBwIeqenSscbPB7Y7gD6o6K9tlMfnJavymLRoGHCIi89xeEhcCiMiPRORdEflARFaLyC0i8jsRmSsiM0SktzveISLykYjMFpEpInJ40IJE5C4R+YP7eqKI3C8iM0VkuYic5g7vIiKvicgCEXkdp2uA0PTnich0T4+O3UVkgIgUikgfESlwy3BeJr8wk18s8Ju2aCiwUp0eF/8Y8dnRwNU4T1K6F9itqsfh9KYYStk8A/xKnQ66/gA8mcCy26vqicCtwJ3usF+4y/mau8wTAESkD06XGOeo06PjLOB3qroWpyfU4cDvcfqC/ziBMhgTVftsF8CYFjZBVcuAMhHZhdPHEDh9q39NRLrj9DL6hkhjr7mdEpj/2+7/2Tj9zgCcjvNYR1R1gYgscIefjPMcg8/cZXXEOQChqs+JyJU4/SENSmD5xsRkgd/km2rP6wbP+wac/aEAKHHPFlKZfz3h+5ffxTTBefDHVc0+EOlKU5fR3YGyJMtjTDOW6jFtURnOwzMSpqqlwGq3to04jk2xPJOBH7rzOxqnR0iAGcCpIvIV97OuIvJV97P7gVeAvwHPprh8Y8JY4Ddtjqpux0mfLAT+lcQsfgj8RETm43TBe1mKRXoK6O6meP6E0zUvqlqM02/7/9zPZgCHi8gZOE93ul9VXwFqROSGFMtgTCNrzmmMMXnGavzGGJNn7OKuMXEQkb8AV0YMfkNV781GeYxJhaV6jDEmz1iqxxhj8owFfmOMyTMW+I0xJs9Y4DfGmDzz/xb5SBslk7NZAAAAAElFTkSuQmCC\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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eahEMbYRSzEF8X0Rm2iaonj7ndwGWuL4vtY9VHNM//ow3Fq7lqidnl1qUsqecG+Ry4+J73ym1CIY2QrEVxD+BPYARwHLgOp80foPPwPZDRC4UkWkiMm316vJcsdtWhsFt5T4NhrZCURWEqq5U1UZVbQJuwzIneVkKDHR9HwAsC8nzVlUdpaqj+vTpk6zAhqJRNsrF7p6UjTwp0hpusRK8+yqZoioIEenn+volYJZPsreBISKym4i0B84EniiGfGlhXmJDJTJvZS3fuXsadQ1NpRbFUCLSdHO9H3gDGCoiS0XkAuBPIvK+iMwEjgZ+ZKftLyITAVS1Afg+8CzwIfCQqn6QlpyVQiUomUJCbRiKT65X6vJH32fS7JXMXLq+GOIYypDUYjGp6lk+h/8dkHYZMM71fSKQ5QLblmn1Jo8yub8K0MNFo1xcj8OoABErGrOS2mDwwYyGDAajIAwJUkhvrhIb5EroYReCVIJd05AqRkHEZMw1z3Pt0x+WWgyDobJp3bq11WAURExWbtzOLS8vLLUYrY5y6YzH6TSXi8xtmSi/1/xVtSxZtyV9YVohZsMgQ2Kk1V5urWtke0MjPTq3T6mEtklJ9VsRYzEdd/0UoG1soJQ0ZgSREs/MWk7ttoZSi9EqOOGGlxlx1aRSi2EoIRu21vPrx99nW31jqUVpU5gRRAosXL2Ji+99h64dyvvxqmrZTESGdQSXrNtaNDmaidAzrXQLU3n88tG44fm53Dv1E4bs1I1zDx1canHaDGYEkQJb6qxezqbtbWsE0Rq8epxw35V/J2VOzAfc2GRd0BresUrCKIgiUEnv9Geb60pSbiVW/EqU2WCIg1EQbRi/9u2U/321+IIY2h4x7VuVZA5rTRgFYcjg0/X52/sL6U+XS1+8TKZkDBExv1e6GAVhKCsqqcLHVWpPzFjGK/PKc8+SSsVY+dKlvN1sDM2kURHyybKpSWlUpV11On2L1lzhL73/XaB8/PFb8aM2JIQZQRhice4dbzHkV0/7nisoFlOZtVZR5Ck3mQ2GpDEKwhCLV+atKbUIhoQoqTXPKNeKwJiYKoQ0op1abpoJNhOtPJrrqtptzF2xqdRiGAxFwygIg8GFM0nup7C+9PfXM7y8KkGplS0JD18qybmhkkhzy9HbRWSViMxyHfuziMwRkZki8piI9Ai4drG9Nel7IjItLRkNZUSZtbV+8wuFuAAb0sXMB6VDJAUhIoeIyN/thn21iHwiIhNF5BIR2SHgsjuBsZ5jk4B9VXV/YC7wi5Bij1bVEao6KoqMrZ1y8WIKz6/wHEvdE5QYXVvTKBlaOzkVhIg8DXwbeBarwe8HDAN+DXQE/isiX/Bep6pTgHWeY8+pqhOgaCowoCDpDa2Ocml0y0SMiiNy+BHzgCuCKHMQ31BVr+vKJuAd++86EemdR9nfAh4MOKfAcyKiwC2qemtQJiJyIXAhwKBBg/IQozJo7fWp3O6vLcRZav13aCiUnCMIH+WQVxo3IvIroAG4LyDJYao6EjgJuEREjggp+1ZVHaWqo/r06RNHjDZP0m1gG2hTDTmI/A6YSeWKIOcIQkRq8e9sCKCq2j1OgSJyLnAKcKwGdNNUdZn9/yoReQwYDUyJU46hsig35VJm4qSCaaMNucipIFS1W1KFichY4OfAkarqu0msiHQBqlS11v58AnBVUjJUKm3B5AFlMEntlN8GVlJXuPgZlPq9aa3EdnMVkZ1EZJDzF5LufuANYKiILBWRC4CbgG7AJNuF9WY7bX8RmWhf2hd4VURmAG8BE1T1mbhyGnKTtB9/YdFcy6u5Kjd5SkE+nZLIVxjzZkUQeaGc7al0HdAfWAXsCnwI7OOXXlXP8jn874C0y4Bx9ueFwPCochkMhvwwnW5DLuKMIK4GDgbmqupuwLHAa6lIZcgijQ5SOfa6ykWmSMH6zCjD0MqJoyDqVXUtUCUiVar6IjAiHbEMlUgh8yTlohgcyk2eSqGtzJW1FeLEYlovIl2xvInuE5FVWK6qhiJg6l35YX6TAohp38r1qM0kdTrEGUGcBmwBfgQ8AywATk1DKEPbo9za2nKTp1Iwz611EWcEcSHwsKouBe5KSR5DBZNE41DqnqDYAhhTicEQbwTRHXhWRF6xg/T1TUsogx+tu8EqtwY5ijTlJXF8Siq/cXOtCCIrCFX9naruA1yC5er6sog8n5pkbYwrn/iAb97+VlHLNJXKkDTmnWpd5LMfxCpgBbAW2ClZcdoud76+mClzV/P6/Mrd0jOJxqFcGphoe1KXibB54rbmrd9SxwfLNpSmcEPZEllBiMh3ReQlYDLQG/iOva9Dq2ZLXUNRG4K1m+t8j1d4W5TzGZbL/bW0W2UiUJE4/Z+vc/KNr5ZaDEOZEWcEsSvwQ1XdR1V/q6qz0xKqXFi3uY5hVzzL31+cX2pRUiH5UBvx8ht8+QR+8/isjGPFnqRev6WOayd+SH1jU3ELLjMWrt6cSD6lWjxYaueG1kqUDYO6Aqjq5ar6Xlia1sbKjdsAeHLG8hJLUvkEjRDumfpxcQXxcO3EOdwyZSET38/8jaOtpDbkTZ4Pb9N2/6VX5TICbW1EGUH8V0SuE5Ej7OiqAIjI7iJygYg4O80ZUsS8/+nQ0GQ92bqGzBGEed75oQovfbSKeStrU8n/L8/NTSVfgz9Rwn0fKyLjgIuAw0SkJ9YK6o+ACcC5qroiXTENaZB4r6uA/ErVA6ypsmwTjbaiKBdTxbL1W0stQt6cd8fbACwef3LRyjQKPR0iLZRT1YnAxJwJWynl0GhU+hC6XMWvshWEM5JwiObFlIZEFoeOfyG9zG3K9TcxlA9xvJgOc0xMIvJ1EbleRHZNTzRDW6TYirDargFNnoJNpNaUKYNOlyE3cbyY/glsEZHhwM+Aj4G7U5HKUBSKaWHK6eZaoga5psqqAg2NtonJkacNLKUuaRud+ErqCv8xypQ4CqLB3kP6NOBvqvo3rN3hfBGR20VklYjMch3rJSKTRGSe/X/PgGvHishHIjJfRC6PIWOrpa30aIttzqv2zEEYDIYW4iiIWhH5BfB1YIKIVAPtQtLfSbZ30+XAZFUdgrXgLqvxt/P9O3ASMAw4S0SGxZAzMUynJDlyPcpSPetmBZFlYjLkQ6l+R/N7pUMcBfE1YDtwge21tAvw56DEqjoFWOc5fBotkWDvAr7oc+loYL6qLlTVOuAB+zpDwiQ9LK9EhRo0gojybJIa1a3auI0hv5rIzKXrE8nPYEiKOMH6Vqjq9ar6iv39E1WNOwfRV1WX29cvxz+W0y7AEtf3pfYxX0TkQhGZJiLTVq9eHVOc4tCkyj9fWkDttvq886jExjcOpbq9aim9ienluaupb1TufH1xyWRIishKM2FTYmuvH6UiykrqWhHZ6PNXKyIbU5DJ79UJ/PlV9VZVHaWqo/r06ZOCOBll5XXdm4vW8cdn5nDVk60+OkkgUR/dlrpGXvpoVbrCuChkDiLpRkmMa4+hzMipIFS1m6p29/nrpqrdY5a3UkT6Adj/+7UES4GBru8DgGUxyylLNtflv0NrGj2k5L2YksnRWWhVDLIXyjkbBhVNhJKNnkyn25CL2OG+RWQnERnk/MW8/AngXPvzucB/fdK8DQwRkd1EpD1wpn1d0fE2eGYYmz+5lEep3BQDF8qVoPkshwWZhRL5Z0z88ZrKmQZxFsp9QUTmAYuAl4HFwNMh6e8H3gCGishSEbkAGA8cb+dzvP0dEekvIhMBVLUB+D7wLPAh8JCqfpDHvRlyYBRei4nJWSgXp41O7PGV6HfwteX6vBStQXEZ8iPOntRXAwcDz6vqASJyNHBWUGJVDTp3rE/aZcA41/eyDO1Ryva0EtZBFKJwSnV3XhOTQ3FNTPGVUzLlth5MZycd4piY6lV1LVAlIlWq+iIwIh2xygtJqAvVll/icr13CfBiKsWOcuXQUy/0lsr0ZzbkSRwFsd7e92EKcJ+I/A0rqmurxVtZwhqEuStrue/N0u5tEJuCoq8W1hR4ry9XBVIMSnXvsdwFE2BbfSM3Tp5nhVZPSBk6o682/PqkShwT02nANuBHwDnADsBVaQhVroS9hCf8dQoA54wJjl9YSA+x3BpQ1ez7KTMRIxH0k5TiXsrBzdVS3PnLEdZx+Peri7h+0lw6t6+uzJelDRJZQaiqe0/CuwITGgIb83Jr5EtJuT4LR+lFW0mdDGX6KBJni+3mva2+MbE8y0GptmYiKwgRqaXlXW6PFYdpcx5rISqWcm3U8qWQie+4V2aZ6wrOMV1KMoIoclvnd4/l9StEp7XVzXIhzggiI3KriHwRK25SqyduvQ1qeMthEjIp/EwRceYlWkN45qRuoZwehZmkNriJvVDOQVUfB45JTpTyp1BX03JqCAol8VXY5fZsSuHmWuQOREn7K4nHYiq3F6h1EMfEdLrraxUwCtNh8CWVsBgVkGdYflmr0pMtOkHsUBslmqYuJv4mJh/jXxqPonxfAIOLOF5Mp7o+N2CtpG5TYbijVpS28O4XbIpwXV+7rZ76xtI8tYI8yxL6pcup85vk71oMKmEBaSUTZw7i/DQFMVhUyjxF3IqZPUndcmC/K59jr50DNycsCaVotMvVxFQJTXAlyFiJ5FQQIvK/hIfbvjRRicqQuBW3EHtooItsClWgnCrVnBW1JS3f+c2a3VwjXZRQ2clkUx5EWoGeXHHGzTVdokxSTwOmAx2BkcA8+28EkJxDcyuhrUyWxb1Nb/Jyf0wlGUEUv8gs/O47qWeRZmNe7u9TpZJzBKGqdwGIyHnA0apab3+/GXguVenKjGLMQVSKiam1kVS8rbwoUesWdZLa0HaJ4+baH3Abirvax1ot+dTbtCb50vFiSjbTONnlSlvskVhWbCiUdZvrWLVxW/A1SZVt/x9HR6X1fPxHEDHWt5Rqy1Gj2FIhjhfTeOBdEXnR/n4kcGXiEpUhLXbpaOEX2sJwN7aJqUwfiredcr6rwsirJwGwePzJRZIl2VZTVUNHRiUdrJbn62DwEMeL6Q4ReRoYYx+6XFVXpCNWeRGvZ1zYmx9Un8utPhW8aDDnDnPlb24rpc5L4vlEDbVRbu+eGzNySJecJiYR2cv+fySWSWmJ/dffPtZmiLRHgOvfJMpIs5EspGr5miJCcow7SV3qal/M8p1n4fzWm7YnE0U/PxNphUYLKPUL00qJMoL4MXAhcJ3POSVmuA0RGQo86Dq0O3CFqt7gSnMU1n7Vi+xDj6pqyUKLx7MN506zbnMdXTvU0L4m9xRQmVpmCibXbRUadrpgivjgm11s7e+3TlmY+5oo+eY4H3kdRBm/g8bNNV2ieDFdaP9/dBIFqupH2DvRiUg18CnwmE/SV1T1lCTKzJd8wkMomrNCjbx6EscP68tt3xyVdS7QxFRmtbRQaVrD/aRl3mhsakol3ygU/Lu6Pt86ZQHXTJzD+1eeQLeO7TITJj5JbUiDyF5MInKGiHSzP/9aRB4VkQMKLP9YYIGqlvVWbE4vJdIeARrtZZ00e2W0stM0MRVQq/yeRWgspgLXTaRF0CRuUU1M+VwT6V2Mn3Nc02EY90y1qvW6zXV+mRoqgDhurr9R1VoR+TxwItamQTcXWP6ZwP0B5w4RkRki8rSI7BOUgYhcKCLTRGTa6tWrCxSn/CizjnZi5DYxFUWMLFo2DMqdNulw30mvxchLvBSeu9cMlGQRZpI6XeIoCGfV9MnAP1X1v1gbB+WFiLQHvgA87HP6HWBXVR0O/C/weFA+qnqrqo5S1VF9+vTJV5xIRDIxaUprFiKkqWto4uf/mcnyDVsj5lnAZHqBF+SepC72Ogjr/zijxVJSVOl8CpuzYiNzV2aHR3E/tyQdLnKvm8k/b0MwcRTEpyJyC/BVYKKIdIh5vZeTgHdUNcvWoqobVXWT/Xki0E5EehdQVtFIumGLU6mmzF3Ng9OW8KvHZhVUZlNTNFNa1rE4hZgKnTp5eTFF/GHG3vBK8z7suSh3d2VDMHEa+K8CzwJjVXU90Av4aQFln0WAeUlEdhZ7vC0io2051xZQVl5kRSCNWOGCep/W6CJerc2nkhdaH5dFHIHEodDor9ax9LSKtxFLwksoKvmspE6y3IxjhSr+kLzS7OUbU1M6RFYQqroFWAV83j7UgBW0LzYi0hk4HnjUdexiEbnY/voVYJaIzABuBM7Uch/z26Q1FC67TVsKlCetCv2HCbO57834Pg/FbMyyy3bcXKNriGhrcopfZaKUKAGf88G4uaZLnB3lfou1i9xQ4A6gHXAvcFjcQm1ls6Pn2M2uzzcBN8XNN2myXvaIC+WCkokUx7JSnDL8vJiil5yPIo2yevi2V6ylM+eM2TWiHAXMw5Swz3LwtZN55zfHh6bJJZ7fo9SAz4UQ9pslNgqriO5j5RHHxPQlrEnlzQCquozM4H2tDu8eAXGu8T9XSKOS+7pytvVmbxhUHgTJUS7yBeHrOhoTfxNTPPflsLQVMug3hBAnWF+dqqqIKICIdElJprLB+3onEawvwvxvBkmv4s5IHy95zrLSdl8sRnPT4uZavMbNG2qjlOS66/+dPI9NdcGhQPx+tzRDqZu5h3SJpCDsCeOnbC+mHiLyHeBbwG1pCleJJO2+WYpJ6igVOq5YWco2x40FT1InHfG0NNf6UWz9EH3L0ZYbvW7S3Mj5N0++Rxcpb4yaSIdICsIeOXwR+DmwEWse4gpVnZSibCUnr4lLDVcCxZikjlvEtvr4GwMWHNStwPNJ4S2nHHrxSU28lmI/E/cDLaeREZAz/LkhmzgmpjeA9apaiGtrhRG/tpRyHUT8vbOt/6P6s0fNL+m0zdfEvyRG3pm5F9WLifhzXXHyLfSaOM/CbUJ18vKdf0r6XiOGwTH6IR5xFMTRwEUi8jH2RDWAqu6fuFRlQsvqWvt71GtCEsbfaCde+nz4ZN2W2NfENjF5biRXLKpizQEErlkpotEirVAbOcvNcT4fadzPzbmvZlOTZCRMhFiuwckU2aaIoyBOSk2KVkQO/UBTng1fGi/32s3b2XmHjnldW2j7/cvH3g/PP4Uy2xp5Pa8CnQ/qGwpzf3Yz/ePPGLxjZ3bs2iEwTRxFXvIQ8hVInIVyH/v9pSlcqYk7sRolTdyqkman8n/ufzfhHEPmXuLmVGJlUNRgfclkEzvfXOsg8uGaiR9m5ZXvc/ryP1/nS/94PVLaiNODhpgUEkupVbHPFc9w65QFGcfSmFDOtzeVRoO5pnZ73teWZpVu8mVmBZRzgvUlXlJuGYrdt406Sovzzs5atiF/gXzIx/wZRKk7HZWIURA2m+sauWbinIxj2mIctr5HyKdcvHNSJ+25lCI/qFKG2mimBBO3SZO5UC77WHoFR0nSampf0TAKIgoxQ0ik4eYah8L3FS79KKdQb5pCyinGteWAv4nJ57nnXYIG5pk0F90znSP//GK4NJX9c5UEoyAI82TxpouQV67KkLfZqvheNaFpykCGNMspqheT4+aa9CLAfK4p8Lbj7zSYzHOua2zi47XJmaMMFkZBEBz+ovndbTYxRWs5w0NtpN/wRHWXLESS+GE9yqf7tmLDtuCTJd1RLpn8vPkWnlFh5eenqKJdlXQEXEMmRkGQ2WjXNzbR0GhtGp/fQqNglq7fUhRvnsJNTBHSpBwrKa26/OKcVRx87WSet9dhtOU2w3eSOo08wwJYBh2PKEgsN9c2/Wvnh1EQZCqI4b97joOvnWx98XqXROxVBiWb9elG1m3Oz3MoyqudVA80lYoUe1I7nTmImUs32P+vTyzP+as2sWZT/h5hDq/OWxN5u9hI2PdWu62eQ66dzPSP1+W+xO+5x1prkFV8nqba5DEjiPgYBUHmi7OlrpE1m/IPpZyrMm3YWh8rv1KEBiimeSUw/3SzDyyvedV8lDUv9v/HXf8yh41/If+y7bLe/3QD4/72St75ZOVrSzhz6QaWb9jGdc9lBtrznaQudA4ixLnAXd6j734amk8aplijH+JTEgUhIotF5H0ReU9EpvmcFxG5UUTmi8hMERmZpjxBL2NWz8fzffDlE7jh+cxKl2vPh7jhvlONbxSQPkrl9DclhF/T1KRcP2kua/PsbScxsmkO550jz7jPcntDU/5CufhsS7wORBSCJ+ILuz73ddaFi9ZsDknkfzgVBWGGELEp5QjiaFUdoaqjfM6dBAyx/y4E/pmmII0BrXaU9+mG5zN3XVXCK16TT1lRA40Vi1SG98DrC9Zy4+R5/PrxWbnTp3S/LSME/3LiFJtUg5PWvWYtAizCaNTPxPSdu6exvaGRG1+Yn1c+SWHUQ3zK1cR0GnC3WkzF2oOiX1qFBXoxeV6paKaX8ERxe96pRnMNOp7AffqxvaHR/j93bzutdRAtI4jwbnUxG5NSNVxRTUyxRrGuz+tdo6G7Xl8ckD6ZEVwUzAAiPqVSEAo8JyLTReRCn/O7AEtc35fax7IQkQtFZJqITFu9enV+wuTx5oReE3IqztB5W30jSz/bamdZXk2Wb0OSY4Fgg62Jq6siaLK0RhCOy3KO/FtDY5JrcriYE8Fb6uLtOVIOjhKG0imIw1R1JJYp6RIROcJzPnIcMVW9VVVHqeqoPn365CVMznUQzQK0HIh6TZTzQZf84tHwiKeBZeR1lev6lCqSY16rztPWkU6D5hklxnKbLG/y6vik5L7sZ1oNTZ+Kfij3X6z8KImCUNVl9v+rgMeA0Z4kS4GBru8DgGVpyZNrktqvPQuat7CuC5ukzj0HsXLjNjZuq+e9JesD80mCqCvIo+cXcg6lUaOPIJKoyre/uojXF6yJlL8je1OTc754jUmxRitR9HJqnYOYHap0JqkTz7LVE2c/iEQQkS5AlarW2p9PAK7yJHsC+L6IPACMATao6vK0ZApUEN7epetr8DW5yvK5xvN9zDWT2bl7Rzq2qwpOFEKhc5Fpubk6SjXfhipuj/iqp2YDsHj8yc3HxLNSOmiUWNSV1Ckpo3xy9Z8ji57TlroG3+NxG3wzSV0elGIE0Rd4VURmAG8BE1T1GRG5WEQuttNMBBYC84HbgO+lKVBTHh6KgZ5PaCKxZ1Zs3Jb6Cx0UkiNKg5VPo+Y8s6oIGiL9ldrqm2dr6mXmupc0nJqC5hoaXcKsdS0W9ROxrqGJ3z35QWAZ17r2nMiFu74ZN9f4FF1BqOpCVR1u/+2jqn+wj9+sqjfbn1VVL1HVPVR1P1XNWiuRJLlMTO9+sp4fPfhexsuc7wgil5dI0Oc4RL0s0MSUb7mhitGtIPLLPwmaY/cEmjZCT6dCqdqtXKOFJMVyz0Hc/Ub4PmNPzFjGo+8EL6S7ZcrCvGSoBPXw2vw1/OuV/O4vDcrVzbWoBA5/XYcf86z8DBp1KLlt8WHHgi6N8nLHjQYalGekhXI5krzzyWdZx2KNIHIo0nzxFp1lYrIP3P3G4gi5lXeT0zJKyrrJkGuiHYtLnDmIuBPaYSTR4Som5/zrTX4/IfoIKW2MgiDY3j1vVW3WMYfGwN53+FvYGMOcFdeMEzd9UDP97KwVWSa026YsZOlnLeGUc5V0umerSKXlmUWJNhvnTuobm9hS18BROfYDCM8/0+S0PoUVzdFlSYc3F2bGYkoj1EYQYU4daaIZnytAQ5QZRZ+kLkf8eswPvL0ka4c5N8FzEOEV3u+6zF6OBhwPydQm7lxKUJY3vjCfzh1quPjIPQBYvmErf5j4If+ZvtRXzpb8cinH6CYmX0UbkP3ev3mmeY1FLryxlrwyx5lMLfseqUc+7zOKboosXJRYk9RpmSDL/fcqQ8wIAv/h79uLsyNfJuPFlF4DFJZ83eY6PtscPQjh8vUtUUUbGq2cN21v8VDJxw0xjhdTHKIqB3fZQV5McTq6X73lDaZ/nG1Ki41HiLjPZ8OWes6/462siLL5mSvTaUWD5/myjyf5eqQ1p9JWMAqC/Ia/wddoqJkp1wiiEMLKHXn1JA64elIyBRF/IVNTkzavCs97DiKBKu7M0wT+ej4F//6p2RnK0eGzLfX84tGZBcuU712t32Ip/Pve+pgXP1rNbQlMbvq/QoU/9zjvS9QNr+JS9iO+MsQoCFoahTjvZVhvOOw9zNXzDp4vjzBxnDNFtLKi4L/gLzj9LVMW8O9XFwFQVaAb0+n/eI0H3/6koDwcHJGbF8r53MS/Xl3E/06el3XcfV0pGHFVpsL3OikErvXIIfP4p+fw+T+2hC/fvL2R1+f7LziMStDEc/4ec9EuNHMQhWEUBC29m1zNlp+JydsbzvXeOuaajGsyvJjccxD+mQVtmZlGY/Wf6UtZtj57E5u4o67XF6xt/hxtDiL42DufrOfnj+QXhsRrYnI+eFdSe6kL8C5IYsVvoVnk26kIimdz88sLmkd7AJc9PIOz//VmQZsZxXlfonQf8pnzLtE8eUVjJqkJqOQ5XqbmEYTPZWEV3s/7KYKXLX959iM6tqtm7aY6Lr53OvdcMJrDh2TGnkpyIZBiBQv8ycMzmo+5dWHcompcWiGKiSltsiL1BrmE5sqnDBqdPz/7EZBMNN+w+9m8PXsR3CvzVvONf7+Vs6wgrz8/otxHY5NGC9kS4ABiiIYZQdCiIHLZPjOD9eU3gtjos6NcxjBY/T/PWLqBM25+g3eXWJOiztaZQflEoZAhd9yec3VVy6uW5ErqbfXxooR6o7lmm5gC5Ak4HqfhCyIp00dWZ8Vzjy3Hg8uLK8vE96NFwInSCYpD1Pdv6sKWkavXzdeQG6MgCHh5c7RhzQ2Jd+FVDkfXXItgcg2DwxrXIJtzZproVTLMLBDXxNSuOuYivsCGOvPEP19aECvfXI1o3F5mOZiYHPxejTWbtnPu7f49/PjjuDwqik0sE1PEEUQUHny7ZdeAy1yj4dbEjCXree6DFankbRQEweYiL+6KHGhiyqOyuxsl92RemAtgvpvLuy/LtfLa2/i5K25ce67bxJT/lqaaVe5mH++iQojvnRUtXZLb0EZFgY/XBm/3GdfE5EdUs1ZwtAK/dzx3plEV88ZtxVvwmCRxOioPvL2EX0XYpTEfjILAbWKKfk1Q2AjVwtYvuH36/fLJmmT1lB2v3PALwhq/uF5MNS4TU65YPKEyRfVeCUjX8vwy5xzCvJiSkCeMQkxMj73bsngx24tJ+fZd2WHMwkoLH33GlS6/a6PUw6iKeeO2ZDsQSbJ+Sx0NAc4PcUZc9Y1NtEspwJmZpCZGo+NzTVZsnzwqe+bIpOWF8R/QB/vxR1F0UaVTDbev+7ktvjI/eEe/SLvIZZSvvDhnFb27dmg5RvSKEyR6sIkpU1FEJbrCCjsZr0w3v3tydvNnv9/9s5CQIXFGzOA/0on6syYdaiMs1I0zz1TX0MSMlPdUyZfGJm12U77j/IPYb5cd+Oukuc3n4zyuhsYmaqrT6esbBUFLfCRrNBAyged6KVfXWqtWtzc0ZaxQvnfqxwzZqVus8t3ug6s3ha92DhtBtMgZdi6qMtTQxs/vBf7TMx8Fpq+JOwcBnH/n2z7lRpM/KF3zJLVfgcSfg3B3AAdfPoH3rjieHp3bZ6SZv2oTu/To5Hv9e0vW8/yHK2OVmVG+y206qrkz9P3wccRwaPDptkcNEBnYoEe6OrvjFaRwmhScVy3INbkc2OQa2Vxw59sc+bk+vPhRSwfL/ezXbNpOt441dKipBqx7r5KWd7m+SWPP8UXFmJhoefFzede4f7R5qzYB1o/lXqF8/1tLmjeqiUJTkzL2hleav9/oWpDl11g5roaOLC/PXc2m7Q2s2bSdm16YH7ncqLK5cTcGcU0rNbFHENnHXl+wNnJPNCiZ8xM78jdPTue4Lgjvb7R8wzZuemFe89xIXUMTx13/MhfdOz3jmpUbrbUsX/z7ayxYHTxPkIuMhlekYFdO9+XeNTt+Zp2oZtkNPt57QUQK5hhwn2nsRJcG7rmRJiVDOQB8uHwjt7y8gCXrtjDq98/z3XvfsdI2KXv8ciK7/WJi8ztU39BEOzOCSI+m5hFEeLq7Xm+xnScxZD7z1jeYGuJ651fG7a8tav68fMNWzr39LY7buy9Nqs1Ky0tQPJpt9eE9LG/xmZPUmSdzNUxuN9doZOd36f3vMuO3J0S6OlesrOZ9H7LMKDFHEJ70N70wnwnvL+cvz81l+MAe3H2+tZvulLktDcDtry3m6qdmc/J+/WKV5UecOFRecl3pff/8RhBReWuR/3se9XF70wWNSNy/X6kiyEYhl8L8kh0N+dqnrYChL8xZxdgbprDWZa045NrJPPU/h9PQpLFH6FEp+ghCRAaKyIsi8qGIfCAiP/BJc5SIbBCR9+y/K9KUKeoI4p6pLQoiiUU3YcrBkivE3EVLAz9vVW3G+oqX565uNoEBPOyKwupuqHIROgfhrbA5KuOiNf7KK4igncnc6x5UlX+9usg3XZDoTgPS2Oiv4PKJMeVmgmtdwIwl63nho2zz0Rv2PtkTIq4hiFp+ZBNTxLy9CsHvN06jWfLmuWFrfZbMjixeK1IxF8ZNfH85gy+fwPXPBZtWg6iNOXnerlqYs6I2o143KYy78RVrkjqlEUQpTEwNwGWqujdwMHCJiAzzSfeKqo6w/7x7VieKX1ylD5fXBqS2+Mtzc0PPJ0FYo6uqXPFfy7Xt47VbeNczGefesnHRmhYTRthOXV68jd/Ha7cEnsvVsMY1o2zc6l+B1rrmaMJc+3KPIBwTk8fUFDL5GZZfED96MNv3PsnK7FbiIrnlcd9Hrv0gvL+pX4el0MB6fs/Vm+V1Pg2wo7u2N2R2JPxc0dPgDxNm8737LLPPjXmYdr1y52LnHToGnmtoVNrFHqFHoxRbji5X1Xfsz7XAh8AuxZbDTbPLqsvG9OHyjaUSpxm/uE0OqvDKvJYAat7KsNXVA3/ctRtefYyJu/BJaq+CSLYyTp7jP3G7zVWx/u/N4IB9QfI0jyA8K+SiLDL0I5+V1O44R4USdcLZL73fpe5rIo0gChxCTPKZoPdOfC/9bGugKXC7x0zq/t3T0g8PTVvCba/4j1yjsK2+kSv+G7zntpcvHbBL1n262VLX0HpMTG5EZDBwAPCmz+lDRGSGiDwtIvukKUecrTCLSZjNN5c7rduDY7kruN9zszMr5JVP+L+oquE9sCgmJvdwOC53vLbY93hYRXGTq3FobFKO+NOLzfsbt0xS525V3C67QaawMN7/NDtMShIIkvFW/OPF7FXmTRo9HL3XDOc7gijQyPTa/LVZx7zvtrXPReaxFz9aBWR7KjWpsmbTdr5z97RI79/MpetZuXEb/3plYeSwLTe/HG/1vpd7p37MJ+u25E4IHD20D9071rC9Ifi9n7F0Q+ubpBaRrsAjwA9V1dtdfwfYVVU3icg44HFgSEA+FwIXAgwaNCgvWZwXf12MDXWKQVgjl6sdqwt5odzc+fpi3+OPvfspXTsGvx5eheDXk05jFWvkoXmOOYgmJaOStsxB5FYQ5Rr0zdu/eXDakqw0jTlMTF9ybRXr/U39Qna4nSbyZcm6LQzs1bn5u/fx+k3o/u7J2fTv0Yntnka9SWHU758HYNLsYPfhJeu2cPifXsw4VrutgR8d/7mMY28sWMsOndoxrH/35mMLfcylqsobC9dyyO47hprdnn5/eew9pzu0q2ZLXficRatycxWRdljK4T5VfdR7XlU3quom+/NEoJ2I9PbLS1VvVdVRqjqqT58+fklyUimucW5y9ZAdU9IvH8svLPaWukZueTl4AxpvI3m7z2RxkpvPO+TyvGouO9DEZP8fc38CDfgcheEDdoh5hafsiO+nREh75ROzeWGO1fv+1BXGvZtPZyAtG377mioe/d6hzd8P/9OLvL90A/WNTXy0ojbr+W7YWu/7u1x0z/SsldLPzooWk2j8M9nbCfuFbTnrtqmMu7HFDT1olHH/W0s4+7Y3mfi+Vf5r89cw+PIJrKrNDM3/s//E22DqJycOpX11FfUh5mbIjFSQJKXwYhLg38CHqnp9QJqd7XSIyGgsObPHogkRZutPg28esmvBefhtiermnU/WM3/VplA7fSF4244bns/eUKcQF8wgoo4gck1Se813cUxMcfsTzgKnfHEah47twqvrhys2MmdFuHPF/W990rw3x0su3/udunXISpurUYrLMXvtxKzfnci7vzmeQa4RA8A7n3zG9ZPmcuINU5jlMcFt2d4Y+Zn/7JHsBtjbu35jwVomzMz2IIuyGvl01wjLzZLPrNGo463nbI713ifrM9JtizE5fft5o9in/w50qMktV9hovxBKMYI4DPgGcIzLjXWciFwsIhfbab4CzBKRGcCNwJma4rg+ak/phGF9ue6M4TnT/eOckfTtnl3hHLyVIx+i7IV83PUvF1xOEIvWbOKO1xZZAfQCnp/Xbhr2TIL46YlDefjiQ1ryLHAOwm1iyqDZu8n/urkrwxveMAoNCe4os3ZVVXzpgF0Y2Mt/VfbE91dwyv++GivvlRu3sXLjNl8vsziL26IgQNcONXTpUEPvrh1o72r4fvvEB82ReW+dkjlyrWts8jWXRcX9m/5n+lLOum2qb7oqsVzEX5u/hmOve4nBl09oPveNf7/JmwvXMjvAecVpxOsammiwR0IAT8xYxqbtDTQ1Ka/NXxNL6Tpzou0jKIjBOxbepvhR9DkIVX2VHO7TqnoTcFNxJIpegXfo1I4OOXpxAAfu2jPUFFKJJi0vjpvvqF17cd+b/sH3vvj315o/99+hI8/86Aj2v/K5WOV8ddRA+rh6t1FHEO9/ut73ePNcg0cT5NowKNealTA6tYs3gvjve8syvtc3WhPLm+oaGNCzE/vtslus1fphjLlmMl8c0T+RvHJR6zHhnDa8f8YanXw58nN9eGPB2sDQGkP7dmtu2H8SEvL7Hy8t4B8B4eNfmbcmw2vQixNu58YX5vPou582m++emrmc5z5YSX1TU+yRp6MgosQxO3n/dH5DE2qD6KaQDu2qOGBQz5zpaqoktPeVhumlVDz67lIeeDt3727nHTrSvWO72Pm39wz7o85BfOvOzCimjgNCi4nJoyCa5yZii5iTa760X6z0Xg+X7Q2NfP//3kXVsjUn7Wy3Jkf8L4A9d+pacDlex4mkasHIQT25/mvBI/vzDh2cUEnB3OWKUOx1Y65rjKccHFNiFAXRp1sHFo8/md16d4khbXSMgiD6ZGrHmmp26dGJX5+8d2i6XLbMbh2iDdziRkCNwj/PGZlofkHuqF6cl71z+3i96XY11nVPfv/zQPwd5ACenLGMkVdP4qmZy5rNDS97VpQ/PWsFgy+fkDFxWyj3XDCaGVecwKAdO/P3s/N/7i/NWd286npLfUNiGww5WG6k4cSNpeWH97fboVP8DoMfvbq0y+pIfG3UwObPiuasc7nmd4rFz8fuxQEDrU6o88jTaAeiUh5PpcTEGUFAbptgrsp01uhB/PbUzMXj54zJdtH1ZtO1Qw1Tf3FsBEmD6exTUa48dRiHD/F1EksM5yV/74posZQcnIrvPHPH/z0O/3P/u4ClKD7bUjxX5sOH9GGHzlYj2LNz/o3hrGUtk7aXHjMk1jP405f3z5nGmdgePrBHYJqwd75LRKXvXTPyY49Lab5079SOdh753A2+avbk8KzfnZjx/aDBvRKRJSpnjR7Edw7fLev4d4/ao8XMaRREedDosSsEDUnH7LYjkDtUgntV48hBPTLOTbj089RUV3H+YZkvx8VH7pGVj3cR0h3nH5S15P6YvXbKuu7bn9+NWb87kZd/elTWOb8e/BmjBvrmkyTOS96+piqjdwfhJgDnuj36WEPodzxeIXF49oOVPDFjWe6EKVBISAr3BktdOtTEmjwOa/Td9OzcjkdczgBewuT/4Kqx3PWt0RnH7vrWaA7ePbPR3eoZQXSJOJLORfeO7bI8ETu45n2aNNsjy9uJi+Jpdsf5BzWPZAtFJPiZ/vkrwzlr9EBG20qruoQLeI2CAK6ZmOkT7e3dOxzxOWudRS6F7o6L4rXd7tPf3yd+QM9szxTvhKl3GA1w2oj+HOBRQr8+ZRhdO9Sw647ZdsmOPhWhuqrQ9bC5cfeCOnmU1JVf2IdfjtuL3l3bey9rrkQ11VXsX+B6ArDmIvqHxLVJiyQ7gXHMbEEeT14+21If2lP90XG+61Sb8TZiR36uD1/1dAS25rHqPBejB/dizO69spwX3ArArx557zWK88nRQ3diP9c76H1fo7ijOgjBnjoDe3Xm2tP3bzZVV5kRRHlRaAAy9w964RG7Ry5zwTXjMo553W+9w/z9dtmBcfv1454LxjQfy7Wi0i9mS5VIwfecC3cYE79RzIVH7MHn+oZvtNSrS7YCcbj569Ft/O5VscUiyUp+2J7RzYGd29dEbrjC3oH9B/QIvTaKGcQ7gsiHhz2jnAcvOpjO7WuywuSE7Z/erlqyFFqcxt3h1Z8fw7B+Le9S3DwO3mPHjO9BE81mBFGGeM0g+bLnTt3YuXu0Hqu3knlfbK+C+OW4vWlXXZUxsvCuqPzpiUMB+N5Re7B4/Mm+FdnanSpYritPHZZREbyce8iufOPg8MV/7mLjTlQHXXfq8BbXvrH7Rt9bYaeIv4cbp/KGPYcwkojzdaU9sv3ZiXvFuu7Vnx/D8z8+gmtP3y+jEdrbdS+5JmlzBYPzO5+1h4PPXN/ufeJ532QrAuv7CcP68oNjW0Y5Rw11mUw9grSrqcpS2N7GvWuI+cvphHVsV81Vp7WEiesQw51ZxBqRvPSTo5qPPfejI3zTphWILwpGQQTwx6/szweuiaxBIbFicpFU59FrYnIUhrvRz/Uy+clSXRU+gvjiAbuEujmedsAuGXtH+5FpYmqpfM/88PDmz7naUG/jsHuern1RvcjcOGsxLj6qZa7IMTlGIYl34Dx73iqux02fbh3Yc6dunDV6ED8f26Jcnv7B4Tx00SHc+o0DefOXx4XmkSucdNgIonvHGgb16swj3z0069wTMW36QeXUVFdlxFE6aHCv5k5L9ggi+17ijKDf/tVxvPXLY7PkuSiitQBa5hfdIU6C5jZLGUTUKAgX+/TvzlCXmcM9iXb9V3OvoB4+YAdeuOxIAB757qH88cuW/7vz8sVdMOXF28txFIa7zniViHcBut/LJhI8B3Hz1w+kR+f29HPZ7b11tH11VZZi8o6a/ExMXx01gL12zu6RH7d3XwB2zbE69LtHZU/sB/G5vi0KLszc071jDX/+yv7cdPYBGced2FZuj50xu/XKOhZEvp4oV5+WHci4EHOgd5Hm6N16ccI+O+d0Oc0lv58Z5JTh/Thr9CBe/MlRTPnZ0Ry4a/YaorCeetRygnBE9tYBv7m8zp66GRa4oUfn9s2jUHej7p5f3GvnlnbkxZ8cxU1nH5AxWnBwnmvYvJjfLQftcZ40RkG4mHDp4TwbMMxzvy5+P9jwATtw9wVj2L2P1RAduGtPvnbQoIz0T3z/sILk85qYnO/uBsPbUO+5k/WiDrVfWOfF/tW4zLUcQfXuuL2tofqPT2jpnc2+amxGmgE9O2UpnkZVfnRcyzXuBma03bCeMGznTBlsNbXvLt3t75k4v8Evx+3F4vEn09FTqef+/iT/myBTOYeNAA8Y1JMzRg3MasycEB+dXaOf7x65B8//+Ej2imB2itILfOS7mfb1X5+8N2eNzi9CcRD5ruLP5brtp0A61FRz7en7sWOO0WUc4uhGp17kMtX+95LD6OXjIBEFd31z5/vMD4/gh8cNYeSgHuzWuwun7N+fwa4RrzPi7tG5Pb//4r487DO6cvDzwn/wooPzkjcuZk/qiLjtp3v0aemN/mzsUP70zEccvPuOgb0wp3EoNGZ7kIJw4y1j7L478/QPDm/u0XTtUMPi8ScD8IeJLWGHg8YQjieF2w3Q3Rg4eXllUVV+cNwQ/vq8FZLDXbH36NO1+To3ThqnlxjUUw6KXNm+poprT9+PXzyaHcHWLX/YXhpOA+otY729fqJPt5aGpKpKIq8wjtKwHbhrLxZdO45t9U2sqt3m64XmUF0ldG5XnRW+ws2lx2Z7HsWN0Dpkp67ccf5BOSfZnYZyQM9OTPrRkbHKiIP73bvk6PARpPPMvXfsdeQYPrAHry0IDqMRhvs98Y7wf3jc5/jhcf5rPdyj36/nmL/zG80Ua22EGUFExN3zGuVaVOMcDrP9B72ocfEOjf08lvyGz3v3657TLOF3OsjE4zfMP9vT0/W2Q3UN0e/eiUwZNMfgvu/eXTtkuHIGTSJ3dJmBurSvybDFu2n+PT0V0GmI+0aY4D55/358/eDM5xE0gjj9gMzNFEWETu2rQ5UDwIJrxnHlF8L30fL7neKOIHp2ac+AntnvwU9PHMqlx+zJz8YOzSirfU1VlhtzvgTNl4HlNPDTHJP1TqdHVTPemR6dLSXft3uHwHmDqE/J/Z5EnRs6ef9+kQLwNcviI0z6jukWZgQRkaAYPc4k045dgofRVc1D3cJUhDeEh99Llq/Hg/eqO84/KLCx9etNdmpfzZkHDWyOy+S91yhB9hwlNqBnZ+447yBGDc62WUPmc3j7V9FWlndyVd4Lj9idju2queiI3Zm6aC1n39ayoWHzCMLzHO+5YAxPv7+cbiHxpAb07MTSz7ZyxSnDshRJUI/v+q+N4NF3o+8T7iZXkEm/AWvYexqFL47ozw1nHpB13JEkyQnVYf27M+vTzOipcfLvbY/2dujUjhcuO4rFazczc+kGzjhwAEDGxLxfg/vIdw+hS4caxt7wStY5B/d7EmWx3Zyrx8a2JKSx1WtUjIKISND2n2ePHkRNVRVfHTUg8NrmEUQEBfH5PXvz6nxruNu5fXXolpYdqrNfyHw3DvG+cEcPjb+y2q2wvC91lB3u2le39PiO3qtvYDp3Y+sdGfltfAM0z1ecd+jg5s9VVcKhe2SuKQgyMY0Y2IMRrlXJfpOK40/fn24da3xHGYU0nDt2aZ/hkuqQK0Bbtc+7EMfzCmBv12TrvD+cFDhJ7LzaSVo+7v7WGP46aS73TP0YgMOH9G5+T6MUc+Hhu9Onawe+PHIAVVXCwF6dOXyI//17zY6qlskvF+7G3jExhTlXeOfNouDtbCURFysqxsQUkaDef011FWePGRQaoM/5OYP0wyjXhKj7RR2VIz6M/xxEniMIcQ/Bc8cN8ntH3Y2qc68n7WtNRAeFYnbjVLbAmPnqpAu+x937dOWeC1rCPjjuqSfv14+aKuFrB4Wvb3HkDrPxzrl6LC/99Ojm787CtV137BwY2qKQOj39N8dz77fHZB0/aHAvJlwa7CYa9Ere/PWRXHt6tAizv3QFpmxXnb1+wMGpH0mOIHp1ad/8/gzr1z1jQWgUaqqrOGPUwEiLFP1Ms1FwvycdaqqZ94eTmPzjZOdgvO3GxB8c7p8wBYyCwLJ153K367dD/m5lVQHeFA7/950WjwTvyzD5suCXza+hzHci3Mmpb/cOTLw09ws49ZfHZsnmRF6FlgbjkqP3BKKNIBwlGzRac/DrGbs5fEif5uB4jl14t95dmH/NON+e+C3fODBL7rBeWsd21RnK+QfHDuG1y4/J2Fc5W+bg/K46bZ9YLrtu/EK3OCvOgxrrsfv2i+wdFXU3POe55eOCW1MlHD3Uv2fveN2t9wRZTDpgfq6J4iDc60M6tKuiXXVVpJ3p4uDunJ536GA+17dbkWYgjIkJgOcjaHxvw/LU/3w+cuiAcw8dzK8fnxW4ojqo8Thlv34ZHlNe/F5EvwYwCk7FPmzP3vSP4GO9U7eO7OSJjNHBJY/zUjvD7e8cnnsRkaPwgpRJcyOUMyfL93xzXSNn3WrtHhZWad1rPBwFGyc0RnWV5PRL92usHSX0zUMGRy7Lj95dO7Bm03YOGtyT7x29Jy/OWcXdb3xcVFPE0L7d+PLIAVx8ZPTFYg7z7RAz7h3cHJznuotPrDI/rj5tn6x9qqPQsV01Pzh2CH+bbG2du2NEt9cMN9eEFYODu2PpPA+nzci1QLVQSqIgRGQs8DegGviXqo73nBf7/DhgC3Ceqr6Tljz5xMnZd5fogeO+fvCuoT0Ud/HOD37/dw7mEE+sFodLj9mTG1+Y73vuN6f4BxrMRbMIBXTN+rkaSafT061jO1+XVj/a5zAxOVFMo5jAenRuT4/O0Uw77gnK6yIsiMwH53c9Z8wg7nvzE0YO6sGt3xyVSN6vX34MC9dsYmDPznTpUMPzs1cCxQ0TXVNdVfCze+iiQ+i3Q0cO/9OLzcc6ta/mvm+PyVh4BsGdhG8UoGwdHb5z9448cGG0dQZuBZF2UL0+3Trwrc/vBsCOXTvwpy/vz5EBI6+kKLqCEJFq4O/A8cBS4G0ReUJV3XsongQMsf/GAP+0/y86+/Tvzj4pB3dzD8v/8KV9GbN7r6xQyW5+fMJQfnzCUN9zcdznMmWw/i9k6P61UQPp2qGGWZ9uyIiTFJWetmkkaI5hs+1qGscTRyJ4kDn3Pqxf94JMiWF0al/dsv4k5g5zuWhfU5WxIr15PqCEUUDzwVlAeef5B7F+S0tI8zjBCZPgqwcN9HXt9SNfp5A4nD5yF2YsWc/lJ+2VofS/mmM+LQlKMYIYDcxX1YUAIvIAcBrgVhCnAXer5fYzVUR6iEg/VV1ebGEnRLDH58vhQ3pn7XPbrWM7zhmTOdro3bV9pG0h47YH//rmqOa9eh0TTFiv84XLjmTx2uzN7ZvLrxJOHd4/L+UAli1/h07t+JJnbYDDdV8dwX1vfpzVmwzj1OH9uXHyPHp1DjYZOPMUO3XPVDy79+nCwtWbOXGfYI+qcuQnJwxle0MTpx8Q7FmXi9vPG8VO3YofFh08gfaKyEh7O2HvHi5hOPUlqc2P/OjYrpo/fiX3xk9pIFFcLxMtUOQrwFhV/bb9/RvAGFX9vivNU8B4VX3V/j4Z+LmqTvPJ70LgQoBBgwYd+PHHHxfhLpJhW30jn22py9lr3VbfSJNqRpgHL/NX1dK9Y7u8IpWCFWvoj0/P4ZKj92zuybcGmpqUzXUNoesXAB6atoQThvVtXkTl8NnmOrp0qMl7ZFZpvDZ/DRu21jNuv+jRcYtJU5Pym//O4rxDBzMkR3j4fFi7aXtWaJCXPlpF764dYpmVKwkRma6qvvbOUiiIM4ATPQpitKr+jyvNBOBaj4L4mapOD8t71KhROm1alg4xGAwGQwBhCqIU3aKlgNt4NgDw7gMZJY3BYDAYUqQUCuJtYIiI7CYi7YEzgSc8aZ4AvikWBwMbSjH/YDAYDG2Zok9Sq2qDiHwfeBbLzfV2Vf1ARC62z98MTMRycZ2P5eZ6frHlNBgMhrZOSdZBqOpELCXgPnaz67MClxRbLoPBYDC00DZcMwwGg8EQG6MgDAaDweCLURAGg8Fg8MUoCIPBYDD4UvSFcmkiIquBOEupewP5bUZbPph7KA/MPZQH5h7is6uq+kb9a1UKIi4iMi1oBWGlYO6hPDD3UB6Ye0gWY2IyGAwGgy9GQRgMBoPBl7auIG4ttQAJYO6hPDD3UB6Ye0iQNj0HYTAYDIZg2voIwmAwGAwBGAVhMBgMBn9UtaL+gI7AW8AM4APgd/bx4cAbwPvAk0B3+/g5wHuuvyZghCfPJ4BZru8dgAexosm+CQx2nTsXmGf/nVvqewDaY9ks5wJzgC9X4D2cZaefCTwD9C7Te2gH3GUf/xD4hSuvA+3j84EbaTHfpnoPSd4H0BmYYL9HH2Dt6liudSLwt6igeh32PpWkXmfdUxKZFPMPEKCr6wG/CRyMtc/EkfbxbwFX+1y7H7DQc+x04P88L9L3gJvtz2cCD9qfewEL7f972p97lvIegN8Bv7c/V9HSuFbEPWBFFF7lkvtPwJXleA/A2cAD9ufOwGKngmI1DIfYeT4NnFSMe0jyPuzPR9vH2wOvFOs+kvwtKqVe53ifSlKvs+6p0AxK+Wc/1HeAMcBGWnptA4HZPumvAf7g+t4VeBUY5nmRngUOsT/XYK1qFKye7i2udLcAZ5X4HpYAXXzSVcQ92BVpNbCrLd/NwIXleA92uU/asuyI1bvrBfQD5rjyapavmPdQ6H345PU34DuV9FtUUr3OcQ8lr9eqWplzECJSLSLvYfU8J6nqm8As4At2kjPI3LLU4WvA/a7vVwPXYW1K5GYXrB8IVW0ANmD9gM3HbZbax0pyDyLSw7kPEXlHRB4Wkb6VdA+qWg98F2uYvQyrUv+7TO/hP8BmYDnwCfAXVV1nl700QJ7U7yHB+3Dn1wM4FZhcrPtI8B4qpV773kOp67WbilQQqtqoqiOw9qoeLSL7Yg3dLhGR6UA3oM59jYiMAbao6iz7+whgT1V9zKcI8Ss25HhJ7gGrBzEAeE1VR2LZOf9SSfcgIu2wFMQBQH+seYhflOk9jAYabTl3Ay4Tkd1zyJP6PSR4H5bAIjVYCvxGVV1YrPtI4h4qrF4H/Q4lrdduKlJBOKjqeuAlYKyqzlHVE1T1QKyXe4En+Zlkjh4OAQ4UkcVYw9HPichL9rml2Freriw7AOvcx20GYPV6S3UPa7F6SU5leBgYWWH3MMLOY4FaY+OHgEPL9B7OBp5R1XpVXQW8Boyy5RkQIE/R7iGB+3C4FZinqje4jlXKb1FJ9TroHsqiXjs3UlF/QB+gh/25E9ZE2inATvaxKuBu4Fuua6rsB7h7QJ6DybRVXkLmRNBD9udewCKsSaCe9ucs220x7wF4ADjG/nwe8HAl3QNW72k50Mf+fjVwXTneA/Bz4A6s3loXYDawv33ubawJSWeSelwx7iGF+/g98AhQ5SmjYn6LSqnXOX6HktTrrHsqNINi/wH7A+9imSJmAVfYx3+ANckzFxiPPSlknzsKmBqSp/dF6oiltedjeae4G7Rv2cfnA+eX+h6wJnen2HlNBgZV4D1cjOXmNxNr0m7HcrwHrMnPh7FcGGcDP3XlNcrOYwFwk+uaVO8hyfvA6nWq/Vu8Z/99u9J+i0qp1znep5LUa++fCbVhMBgMBl8qeg7CYDAYDOlhFITBYDAYfDEKwmAwGAy+GAVhMBgMBl+MgjAYDAaDL0ZBGAwGg8EXoyAMbRYR6SEi37M/9xeR/6RY1sUi8s2Y17wkIqNypzQY0sGsgzC0WURkMPCUqu5baln8sENE/ERVp5VaFkPbxIwgDG2Z8cAeIvKeHTHTCSB4nog8LiJPisgiEfm+iPxYRN4Vkaki0stOt4eIPCMi00XkFRHZK6ggEblSRH5if35JRP4oIm+JyFwROdw+3klEHhCRmSLyIFa4Buf6E0TkDVd0z64isquIzBOR3iJSZctwQpoPzNC2MArC0Ja5HFigVvTNn3rO7YsVTG008AesCLQHYEXWdExFtwL/o1YQtp8A/4hRdo2qjgZ+CPzWPvZdu5z97TIPBBCR3sCvgePUiu45Dfixqn4M/BFrD43LsPYZeC6GDAZDKDWlFsBgKFNeVNVaoFZENmDFiAJr34r9RaQrVtTZh0WaIy13iJH/o/b/07FiBgEcgbVdKao6U0Rm2scPxton4zW7rPZYigpV/ZeInIEVz2pEjPINhpwYBWEw+LPd9bnJ9b0Jq95UAevt0Uch+TeSWQ/9JgUFa/OZs7JOiHSmJdR4V6A2T3kMhiyMicnQlqnF2sAlNqq6EVhk994Ri+EFyjMFOMfOb1+s6KAAU4HDRGRP+1xnEfmcfe6PwH3AFcBtBZZvMGRgFIShzaKqa7HMNrOAP+eRxTnABSIyAytk82kFivRPoKttWvoZVjhnVHU11p4A99vnpgJ7iciRwEHAH1X1PqBORM4vUAaDoRnj5mowGAwGX8wIwmAwGAy+mElqgyFBRORXwBmeww+r6h9KIY/BUAjGxGQwGAwGX4yJyWAwGAy+GAVhMBgMBl+MgjAYDAaDL0ZBGAwGg8GX/wd7TYQSgGzuPAAAAABJRU5ErkJggg==\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": "c2880a28", "metadata": {}, "source": [ "## Gradient Boosting Trees Regression Model" ] }, { "cell_type": "code", "execution_count": 635, "id": "b412a998", "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": 636, "id": "ff75a359", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('sklearn_gbt_regressor', GradientBoostingRegressor())])
GradientBoostingRegressor()
" ], "text/plain": [ "Pipeline(steps=[('sklearn_gbt_regressor', GradientBoostingRegressor())])" ] }, "execution_count": 636, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set_config(display=\"diagram\")\n", "gbt_regressor_pipeline_factory()" ] }, { "cell_type": "code", "execution_count": 637, "id": "554ebcaf", "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) : 0.87\n", "RMSE (train) : 1.82\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) : 0.78\n", "MAE (val) : 1.30\n", "RMSE (train) : 1.83\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) : 1.93\n", "RMSE (train) : 1.87\n", "RMSE (val) : 7.34\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 : 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) : 4.85\n", "RMSE (train) : 2.13\n", "RMSE (val) : 10.01\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) : 2.75\n", "RMSE (train) : 2.53\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) : 5.00\n", "RMSE (train) : 2.69\n", "RMSE (val) : 11.00\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) : 2.14\n", "RMSE (train) : 3.10\n", "RMSE (val) : 3.97\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) : 1.86\n", "RMSE (train) : 3.21\n", "RMSE (val) : 3.51\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) : 4.58\n", "RMSE (train) : 3.25\n", "RMSE (val) : 13.70\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": 638, "id": "259fd0f6", "metadata": {}, "outputs": [], "source": [ "gbt_results_df = pd.DataFrame(gbt_results_store_dict).T" ] }, { "cell_type": "code", "execution_count": 639, "id": "60bbf97e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fold_94.5845541.27002213.6986343.247480
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" ], "text/plain": [ " mae_val mae_train rmse_val rmse_train\n", "fold_0 0.867173 0.764050 1.284980 1.823519\n", "fold_1 1.296184 0.783453 2.580786 1.832836\n", "fold_2 1.929096 0.813528 7.341684 1.873969\n", "fold_3 4.847899 0.865321 10.009413 2.127670\n", "fold_4 2.747319 0.973266 6.486264 2.532766\n", "fold_5 5.000961 1.011644 11.003844 2.687914\n", "fold_6 2.144759 1.148629 3.967820 3.104175\n", "fold_7 2.131957 1.202066 3.610092 3.172086\n", "fold_8 1.859036 1.243883 3.505996 3.211885\n", "fold_9 4.584554 1.270022 13.698634 3.247480" ] }, "execution_count": 639, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gbt_results_df" ] }, { "cell_type": "code", "execution_count": 640, "id": "d84183c3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 640, "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": 641, "id": "89c2a8e5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mae_valmae_trainrmse_valrmse_train
count10.00000010.00000010.00000010.000000
mean2.7408941.0075866.3489512.561430
std1.5168460.1975024.0985850.606828
min0.8671730.7640501.2849801.823519
25%1.8765510.8264763.5320201.937394
50%2.1383580.9924555.2270422.610340
75%4.1252451.1887079.3424813.155108
max5.0009611.27002213.6986343.247480
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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 2.740894 1.007586 6.348951 2.561430\n", "std 1.516846 0.197502 4.098585 0.606828\n", "min 0.867173 0.764050 1.284980 1.823519\n", "25% 1.876551 0.826476 3.532020 1.937394\n", "50% 2.138358 0.992455 5.227042 2.610340\n", "75% 4.125245 1.188707 9.342481 3.155108\n", "max 5.000961 1.270022 13.698634 3.247480" ] }, "execution_count": 641, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gbt_results_df.describe()" ] }, { "cell_type": "code", "execution_count": 642, "id": "c2a65b43", "metadata": {}, "outputs": [], "source": [ "gbt_results_df.to_csv('gbt_results_best_case.csv')" ] }, { "cell_type": "code", "execution_count": 820, "id": "6d7e1256", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.240894" ] }, "execution_count": 820, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2.740894 + 1.5" ] }, { "cell_type": "markdown", "id": "fe15d89b", "metadata": {}, "source": [ "## Quick Visual Check" ] }, { "cell_type": "code", "execution_count": 643, "id": "888f8668", "metadata": {}, "outputs": [], "source": [ "gbt_model = gbt_regressor_pipeline_factory(gbt_regressor_model_params)" ] }, { "cell_type": "code", "execution_count": 745, "id": "a0bf84f8", "metadata": {}, "outputs": [], "source": [ "svm_model = svm_regressor_pipeline_factory(svm_model_params)" ] }, { "cell_type": "code", "execution_count": 644, "id": "ece8be5d", "metadata": {}, "outputs": [], "source": [ "fitted_gbt_model = gbt_model.fit(X_feat_v1,y_scaled)" ] }, { "cell_type": "code", "execution_count": 746, "id": "43badc7e", "metadata": {}, "outputs": [], "source": [ "fitted_svm_model = svm_model.fit(X_feat_v1, y_scaled)" ] }, { "cell_type": "code", "execution_count": 747, "id": "0d93769f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 747, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_df.groupby(by=['ARTIST_ID', 'ARTIST_NAME', 'ISRC']).sum().to_csv('isrc')" ] }, { "cell_type": "code", "execution_count": 842, "id": "af019ab2", "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": 842, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df = dataset_df[(dataset_df['ARTIST_ID'] == 539614) & (dataset_df['ISRC'] == 'QMYLU1900001')].sort_values(by=['SNAPSHOT_YEAR_ISOWEEK'])\n", "test_df.columns" ] }, { "cell_type": "code", "execution_count": 843, "id": "e82dfe97", "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": 844, "id": "c320c6a5", "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": 845, "id": "11bd6a5f", "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": 846, "id": "b019c40c", "metadata": {}, "outputs": [], "source": [ "test_df['TOTAL_STREAMS_SCALED'] = test_df['TOTAL_STREAMS']/1e3" ] }, { "cell_type": "code", "execution_count": 847, "id": "112dfa51", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'STREAMS * 1e3')" ] }, "execution_count": 847, "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", "sns.lineplot(data=test_df.iloc[:50], x='SNAPSHOT_YEAR_ISOWEEK', y='TOTAL_STREAMS_SCALED', ci=None, label='actual')\n", "sns.lineplot(data=test_df.iloc[:50], x='SNAPSHOT_YEAR_ISOWEEK', y='PREDICTIONS_SCALED_GBT', color='purple', label='model(gbt)', err_style='band')\n", "ax.fill_between(x=test_df['SNAPSHOT_YEAR_ISOWEEK'].iloc[:50], \n", " y1=test_df['PREDICTIONS_SCALED_GBT_LOWER'].iloc[:50],\n", " y2=test_df['PREDICTIONS_SCALED_GBT_UPPER'].iloc[:50], alpha=0.2)\n", "\n", "plt.title('STREAMS * 1e3')" ] }, { "cell_type": "code", "execution_count": null, "id": "30e2345e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "27222f1d", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "conda_tensorflow2_p36", "language": "python", "name": "conda_tensorflow2_p36" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.13" } }, "nbformat": 4, "nbformat_minor": 5 }