1_Creating Sample_Traffic_Data.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["# Mock Data\n","import warnings\n","warnings.filterwarnings('ignore')\n","from datetime import timedelta, datetime, date, timedelta\n","from mockseries.trend import LinearTrend\n","from mockseries.seasonality import SinusoidalSeasonality\n","from mockseries.noise import RedNoise\n","from datetime import datetime\n","from mockseries.utils import datetime_range\n","from pandas.tseries.holiday import USFederalHolidayCalendar\n","import numpy as np\n","import random\n","\n","\n","## Snowpark \n","import snowflake.snowpark\n","from snowflake.snowpark.session import Session\n","from snowflake.snowpark import functions as F\n","from snowflake.snowpark.functions import col\n","from snowflake.snowpark.types import *\n","from snowflake.ml.utils.connection_params import SnowflakeLoginOptions\n","\n","# Model Deployment\n","from time import time\n","import io\n","import pickle\n","import pandas as pd\n","import xgboost as xgb\n","from sklearn.metrics import mean_squared_error, mean_absolute_error"]},{"cell_type":"markdown","metadata":{},"source":["## Creates the trend and seasonality for mock data"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["trend = LinearTrend(coefficient= .1, time_unit=timedelta(days=4), flat_base=100)\n","seasonality = \\\n","SinusoidalSeasonality(amplitude=20, period=timedelta(days=7)) \\\n","+ SinusoidalSeasonality(amplitude=4, period=timedelta(days=1))\n","noise = RedNoise(mean=0, std=3, correlation=0.5)\n","timeseries = trend + seasonality + noise"]},{"cell_type":"markdown","metadata":{},"source":["## Create daily mock time series data from Janurary 2020 - Current Day"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["time_points = datetime_range(\n","    granularity=timedelta(days=1),\n","    start_time=datetime.datetime(2020,1,1),\n","    end_time= datetime.datetime.today(),\n",")\n","ts_values = timeseries.generate(time_points=time_points)"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["from mockseries.utils import plot_timeseries, write_csv\n","plot_timeseries(time_points, ts_values)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>DATE</th>\n","      <th>TRAFFIC</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2020-01-01</td>\n","      <td>96.942483</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2020-01-02</td>\n","      <td>112.012114</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2020-01-03</td>\n","      <td>114.728719</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2020-01-04</td>\n","      <td>107.652657</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2020-01-05</td>\n","      <td>87.51364</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        DATE     TRAFFIC\n","0 2020-01-01   96.942483\n","1 2020-01-02  112.012114\n","2 2020-01-03  114.728719\n","3 2020-01-04  107.652657\n","4 2020-01-05    87.51364"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","df = pd.DataFrame({\"DATE\":time_points,\"TRAFFIC\":ts_values})\n","df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Create a unique time series for 1000 different stores"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["dfs = []\n","\n","for i in range (1,1001):\n","    _ = df.copy()\n","    _['STORE_ID'] = i\n","    _['TRAFFIC'] = _['TRAFFIC']*random.uniform(.60, 1.40)\n","    dfs.append(_)\n","\n","df = pd.concat(dfs)"]},{"cell_type":"markdown","metadata":{},"source":["## Create the final dataframe by rounding the traffic value to mimic realistic foot traffic"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["df['TRAFFIC'] = df['TRAFFIC'].astype(int)"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>DATE</th>\n","      <th>TRAFFIC</th>\n","      <th>STORE_ID</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1513</th>\n","      <td>2024-02-22</td>\n","      <td>141</td>\n","      <td>1000</td>\n","    </tr>\n","    <tr>\n","      <th>1514</th>\n","      <td>2024-02-23</td>\n","      <td>141</td>\n","      <td>1000</td>\n","    </tr>\n","    <tr>\n","      <th>1515</th>\n","      <td>2024-02-24</td>\n","      <td>130</td>\n","      <td>1000</td>\n","    </tr>\n","    <tr>\n","      <th>1516</th>\n","      <td>2024-02-25</td>\n","      <td>115</td>\n","      <td>1000</td>\n","    </tr>\n","    <tr>\n","      <th>1517</th>\n","      <td>2024-02-26</td>\n","      <td>110</td>\n","      <td>1000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           DATE  TRAFFIC  STORE_ID\n","1513 2024-02-22      141      1000\n","1514 2024-02-23      141      1000\n","1515 2024-02-24      130      1000\n","1516 2024-02-25      115      1000\n","1517 2024-02-26      110      1000"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df.tail()"]},{"cell_type":"markdown","metadata":{},"source":["## Connect to snowflake to load our mock data"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["SnowflakeLoginOptions() is in private preview since 0.2.0. Do not use it in production. \n"]}],"source":["session = Session.builder.configs(SnowflakeLoginOptions()).getOrCreate()"]},{"cell_type":"markdown","metadata":{},"source":["## Turn Pandas Dataframe into a Snowpark Dataframe so we can upload it to snowflake"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/plain":["[Row(status='TIME_SERIES already exists, statement succeeded.')]"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["session.sql('create database if not exists store_traffic').collect()\n","session.sql('create schema if not exists time_series').collect()"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[],"source":["session.use_database('store_traffic')\n","session.use_schema('time_series')"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["final_snow_df = session.create_dataframe(df).select('DATE','TRAFFIC','STORE_ID')"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["------------------------------------------------\n","|\"DATE\"               |\"TRAFFIC\"  |\"STORE_ID\"  |\n","------------------------------------------------\n","|2020-01-01 00:00:00  |125        |1           |\n","|2020-01-02 00:00:00  |145        |1           |\n","|2020-01-03 00:00:00  |148        |1           |\n","|2020-01-04 00:00:00  |139        |1           |\n","|2020-01-05 00:00:00  |113        |1           |\n","|2020-01-06 00:00:00  |96         |1           |\n","|2020-01-07 00:00:00  |107        |1           |\n","|2020-01-08 00:00:00  |130        |1           |\n","|2020-01-09 00:00:00  |146        |1           |\n","|2020-01-10 00:00:00  |152        |1           |\n","------------------------------------------------\n","\n"]}],"source":["final_snow_df.show()"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["---------------------------------------\n","|\"DATE\"      |\"STORE_ID\"  |\"TRAFFIC\"  |\n","---------------------------------------\n","|2020-01-01  |1           |125        |\n","|2020-01-02  |1           |145        |\n","|2020-01-03  |1           |148        |\n","|2020-01-04  |1           |139        |\n","|2020-01-05  |1           |113        |\n","|2020-01-06  |1           |96         |\n","|2020-01-07  |1           |107        |\n","|2020-01-08  |1           |130        |\n","|2020-01-09  |1           |146        |\n","|2020-01-10  |1           |152        |\n","---------------------------------------\n","\n"]}],"source":["final_snow_df = final_snow_df.select(\n","    F.cast(F.to_varchar(\"DATE\"), DateType()).alias(\"DATE\"), \"STORE_ID\", \"TRAFFIC\"\n",")\n","final_snow_df.show()"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["final_snow_df.write.saveAsTable('store_traffic.time_series.traffic', mode='overwrite', create_temp_table=False)"]},{"cell_type":"markdown","metadata":{},"source":["## Create calendar table with holidays"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[],"source":["def create_date_table(start='2018-01-01', end='2025-12-31'):\n","    df = pd.DataFrame({\"CALENDAR_DATE\": pd.date_range(start, end)})\n","    df[\"CALENDAR_WEEK_DAY_NBR\"] = df.CALENDAR_DATE.dt.dayofweek\n","    df[\"CALENDAR_MTH_DAY_NBR\"] = df.CALENDAR_DATE.dt.day\n","    df[\"CALENDAR_MTH\"] = df.CALENDAR_DATE.dt.month\n","    df[\"CALENDAR_YEAR\"] = df.CALENDAR_DATE.dt.year\n","    return df"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["calendar_df = create_date_table()"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[],"source":["calendar = USFederalHolidayCalendar()\n","holiday_df = (\n","    pd.DataFrame(\n","        calendar.holidays(start='2018-01-01', end='2025-12-31', return_name=True)\n","    )\n","    .reset_index()\n","    .rename(columns={\"index\": \"date\", 0: \"holiday_name\"})\n",")"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[],"source":["holiday_df['date'] = holiday_df['date'].dt.date"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[],"source":["calendar_df['CALENDAR_DATE'] = calendar_df['CALENDAR_DATE'].dt.date"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[],"source":["calendar_final = calendar_df.merge(holiday_df, left_on='CALENDAR_DATE', right_on='date', how = 'left')"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[],"source":["calendar_final = calendar_final.rename(columns={\"holiday_name\":\"HOLIDAY_NAME\"})\n","calendar_final_snow_df = session.create_dataframe(calendar_final).select('CALENDAR_DATE','CALENDAR_WEEK_DAY_NBR','CALENDAR_MTH_DAY_NBR','CALENDAR_MTH','CALENDAR_YEAR','HOLIDAY_NAME')"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[],"source":["calendar_final_snow_df = calendar_final_snow_df.\\\n","select(col('CALENDAR_DATE'),\\\n","       col('CALENDAR_WEEK_DAY_NBR').cast(StringType()).alias('WEEK_DAY_NBR'),\\\n","       col('CALENDAR_MTH_DAY_NBR').cast(StringType()).alias('MTH_DAY_NBR'),\\\n","       col('CALENDAR_MTH').cast(StringType()).alias('CALENDAR_MTH'),\\\n","       col('CALENDAR_YEAR').cast(StringType()).alias('CALENDAR_YEAR'),\\\n","       col('HOLIDAY_NAME'))"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--------------------------------------------------------------------------------------------------------\n","|\"CALENDAR_DATE\"  |\"WEEK_DAY_NBR\"  |\"MTH_DAY_NBR\"  |\"CALENDAR_MTH\"  |\"CALENDAR_YEAR\"  |\"HOLIDAY_NAME\"  |\n","--------------------------------------------------------------------------------------------------------\n","|2018-01-01       |0               |1              |1               |2018             |New Year's Day  |\n","|2018-01-02       |1               |2              |1               |2018             |NULL            |\n","|2018-01-03       |2               |3              |1               |2018             |NULL            |\n","|2018-01-04       |3               |4              |1               |2018             |NULL            |\n","|2018-01-05       |4               |5              |1               |2018             |NULL            |\n","|2018-01-06       |5               |6              |1               |2018             |NULL            |\n","|2018-01-07       |6               |7              |1               |2018             |NULL            |\n","|2018-01-08       |0               |8              |1               |2018             |NULL            |\n","|2018-01-09       |1               |9              |1               |2018             |NULL            |\n","|2018-01-10       |2               |10             |1               |2018             |NULL            |\n","--------------------------------------------------------------------------------------------------------\n","\n"]}],"source":["calendar_final_snow_df.show()"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[],"source":["calendar_final_snow_df.write.saveAsTable('store_traffic.time_series.CALENDAR_INFO_2018', mode='overwrite', create_temp_table=False)"]}],"metadata":{"hex_info":{"author":"Chase Romano","exported_date":"Tue Feb 27 2024 22:19:04 GMT+0000 (Coordinated Universal Time)","project_id":"c67c10e1-31d4-47b7-a0eb-fad5290eae67","version":"draft"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.1.0"},"orig_nbformat":4},"nbformat":4,"nbformat_minor":4}