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gheesung/ntu-project-testing-of-prediction-models-v2.ipynb

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NTU Project - Testing of Prediction Models-v2
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ntu-project-testing-of-prediction-models-v2.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "NTU Project - Testing of Prediction Models-v2",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"machine_shape": "hm",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/gheesung/ed6ca83ec12c7fd63c1feae8030fc678/ntu-project-testing-of-prediction-models-v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_PxaJdiHBtF2",
"colab_type": "text"
},
"source": [
"# Ingredient Usage Forecasting\n",
"\n",
"This notebook forecasts the usage of the ingredients using 5 models.\n",
"\n",
"1. Linear Regression\n",
"2. Polynomial Regression\n",
"3. Random Forest\n",
"4. XGBoost\n",
"5. Neural Network\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bXz2bbhYL3Vz",
"colab_type": "code",
"outputId": "de6ba46d-e9ea-4f60-988c-09acb19315a1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# get the authentication token to GCP\n",
"from google.colab import auth\n",
"auth.authenticate_user()\n",
"print('Authenticated')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Authenticated\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hKxcVqbEcOcL",
"colab_type": "code",
"outputId": "cdca19e5-c811-4256-c946-5b775db88304",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"!gcloud config set project formosa-delights-186103"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Updated property [core/project].\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "P9IzS8eOheTd",
"colab_type": "code",
"outputId": "1e57a668-0047-4446-f14f-9ef5095f5ef1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# get the authentication token to Google drive\n",
"from google.colab import drive\n",
"drive.mount('/gdrive')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /gdrive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G_I18VOhaaj7",
"colab_type": "text"
},
"source": [
"## Setup and Preprocessing"
]
},
{
"cell_type": "code",
"metadata": {
"id": "66-FyC0hPwjS",
"colab_type": "code",
"colab": {}
},
"source": [
"project_id = \"formosa-delights-186103\"\n",
"\n",
"from google.cloud import bigquery\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "SkwOY-6DSbWd",
"colab_type": "text"
},
"source": [
"### Create the Holiday dataset\n",
"* Create a holiday table\n",
"* Create a Singapore Event table\n",
"* Create a school holiday table\n",
"\n",
"Day of the week\n",
"1 -Sun, 2 - Mon, 7 - Sat"
]
},
{
"cell_type": "code",
"metadata": {
"id": "geCLHmX1kzMz",
"colab_type": "code",
"outputId": "1b7662b6-9008-415c-b737-d618730b7b0c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 492
}
},
"source": [
"from datetime import datetime, timedelta\n",
"!cd /content\n",
"!git clone https://github.com/gheesung/ntu-data-analytics.git\n",
"holiday_df = pd.read_csv(\"/content/ntu-data-analytics/data/specialdays.csv\")\n",
"holiday_df['holiday_dt'] = pd.to_datetime(holiday_df['holiday_dt'],infer_datetime_format=True)\n",
"holiday_df.set_index('holiday_dt', inplace=True, drop=False)\n",
"holiday_df.head(10)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Cloning into 'ntu-data-analytics'...\n",
"remote: Enumerating objects: 17, done.\u001b[K\n",
"remote: Counting objects: 5% (1/17)\u001b[K\rremote: Counting objects: 11% (2/17)\u001b[K\rremote: Counting objects: 17% (3/17)\u001b[K\rremote: Counting objects: 23% (4/17)\u001b[K\rremote: Counting objects: 29% (5/17)\u001b[K\rremote: Counting objects: 35% (6/17)\u001b[K\rremote: Counting objects: 41% (7/17)\u001b[K\rremote: Counting objects: 47% (8/17)\u001b[K\rremote: Counting objects: 52% (9/17)\u001b[K\rremote: Counting objects: 58% (10/17)\u001b[K\rremote: Counting objects: 64% (11/17)\u001b[K\rremote: Counting objects: 70% (12/17)\u001b[K\rremote: Counting objects: 76% (13/17)\u001b[K\rremote: Counting objects: 82% (14/17)\u001b[K\rremote: Counting objects: 88% (15/17)\u001b[K\rremote: Counting objects: 94% (16/17)\u001b[K\rremote: Counting objects: 100% (17/17)\u001b[K\rremote: Counting objects: 100% (17/17), done.\u001b[K\n",
"remote: Compressing objects: 9% (1/11)\u001b[K\rremote: Compressing objects: 18% (2/11)\u001b[K\rremote: Compressing objects: 27% (3/11)\u001b[K\rremote: Compressing objects: 36% (4/11)\u001b[K\rremote: Compressing objects: 45% (5/11)\u001b[K\rremote: Compressing objects: 54% (6/11)\u001b[K\rremote: Compressing objects: 63% (7/11)\u001b[K\rremote: Compressing objects: 72% (8/11)\u001b[K\rremote: Compressing objects: 81% (9/11)\u001b[K\rremote: Compressing objects: 90% (10/11)\u001b[K\rremote: Compressing objects: 100% (11/11)\u001b[K\rremote: Compressing objects: 100% (11/11), done.\u001b[K\n",
"remote: Total 17 (delta 2), reused 16 (delta 1), pack-reused 0\u001b[K\n",
"Unpacking objects: 5% (1/17) \rUnpacking objects: 11% (2/17) \rUnpacking objects: 17% (3/17) \rUnpacking objects: 23% (4/17) \rUnpacking objects: 29% (5/17) \rUnpacking objects: 35% (6/17) \rUnpacking objects: 41% (7/17) \rUnpacking objects: 47% (8/17) \rUnpacking objects: 52% (9/17) \rUnpacking objects: 58% (10/17) \rUnpacking objects: 64% (11/17) \rUnpacking objects: 70% (12/17) \rUnpacking objects: 76% (13/17) \rUnpacking objects: 82% (14/17) \rUnpacking objects: 88% (15/17) \rUnpacking objects: 94% (16/17) \rUnpacking objects: 100% (17/17) \rUnpacking objects: 100% (17/17), done.\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"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>holiday_dt</th>\n",
" <th>holiday_type</th>\n",
" <th>holiday_desc</th>\n",
" </tr>\n",
" <tr>\n",
" <th>holiday_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-01-01</th>\n",
" <td>2017-01-01</td>\n",
" <td>8</td>\n",
" <td>New Year</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-02</th>\n",
" <td>2017-01-02</td>\n",
" <td>8</td>\n",
" <td>New Year</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-28</th>\n",
" <td>2017-01-28</td>\n",
" <td>8</td>\n",
" <td>CNY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-29</th>\n",
" <td>2017-01-29</td>\n",
" <td>8</td>\n",
" <td>CNY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-30</th>\n",
" <td>2017-01-30</td>\n",
" <td>8</td>\n",
" <td>CNY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-11</th>\n",
" <td>2017-03-11</td>\n",
" <td>9</td>\n",
" <td>School holiday</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-12</th>\n",
" <td>2017-03-12</td>\n",
" <td>9</td>\n",
" <td>School holiday</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-13</th>\n",
" <td>2017-03-13</td>\n",
" <td>9</td>\n",
" <td>School holiday</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-14</th>\n",
" <td>2017-03-14</td>\n",
" <td>9</td>\n",
" <td>School holiday</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-15</th>\n",
" <td>2017-03-15</td>\n",
" <td>9</td>\n",
" <td>School holiday</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" holiday_dt holiday_type holiday_desc\n",
"holiday_dt \n",
"2017-01-01 2017-01-01 8 New Year\n",
"2017-01-02 2017-01-02 8 New Year\n",
"2017-01-28 2017-01-28 8 CNY\n",
"2017-01-29 2017-01-29 8 CNY\n",
"2017-01-30 2017-01-30 8 CNY\n",
"2017-03-11 2017-03-11 9 School holiday\n",
"2017-03-12 2017-03-12 9 School holiday\n",
"2017-03-13 2017-03-13 9 School holiday\n",
"2017-03-14 2017-03-14 9 School holiday\n",
"2017-03-15 2017-03-15 9 School holiday"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Lv-APw5UukNA",
"colab_type": "text"
},
"source": [
"### Fetch from big query all the data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PRLcbtY4YDP1",
"colab_type": "code",
"outputId": "1e01946e-b364-4174-f693-2a6aa0997162",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 580
}
},
"source": [
"# fetch 3 year data\n",
"# The day,month and day_of_week is CAST to string so that it is treated as nominal data.\n",
"client = bigquery.Client(project=project_id)\n",
"\n",
"\n",
"df = client.query('''\n",
"WITH ITEM_USAGE_CLEAN_RESULT AS (\n",
"(SELECT outlet_code, outlet_name,\n",
" item_id, SAFE_CAST(item_qty as INT64) as item_qty, \n",
" item_desc, item_type, month, year, order_dt,\n",
" CAST(Extract(DAY from CAST(order_dt as date)) as INT64) as day,\n",
" CAST(Extract(DAYOFWEEK from CAST(order_dt as date)) as INT64) as day_of_week\n",
" FROM `item_usage.item_usage_norm_*` \n",
" WHERE\n",
" _TABLE_SUFFIX BETWEEN '20170701' AND '20190831'\n",
" and item_id in ('C038','C039','C040','C041')\n",
" and outlet_code in ('OUTLET01', 'OUTLET02','OUTLET03',\n",
" 'OUTLET05','OUTLET06','OUTLET07','OUTLET08','OUTLET09',\n",
" 'OUTLET10','OUTLET11', 'OUTLET13','OUTLET14','OUTLET17',\n",
" 'OUTLET18','OUTLET19') \n",
") \n",
"\n",
"UNION ALL\n",
"\n",
"(SELECT outlet_code, outlet_name,\n",
" item_id, SAFE_CAST(item_qty as INT64) as item_qty, \n",
" item_desc, item_type, month, year, order_dt,\n",
" CAST(Extract(DAY from CAST(order_dt as date)) as INT64) as day,\n",
" CAST(Extract(DAYOFWEEK from CAST(order_dt as date)) as INT64) as day_of_week\n",
" FROM `item_usage.item_usage_*` \n",
" WHERE\n",
" _TABLE_SUFFIX BETWEEN '20190901' AND '20191231'\n",
" and item_id in ('C038','C039','C040','C041')\n",
" and outlet_code in ('OUTLET01', 'OUTLET02','OUTLET03',\n",
" 'OUTLET05','OUTLET06','OUTLET07','OUTLET08','OUTLET09',\n",
" 'OUTLET10','OUTLET11', 'OUTLET13','OUTLET14','OUTLET17',\n",
" 'OUTLET18','OUTLET19') \n",
")\n",
")\n",
"\n",
"SELECT outlet_code, SUBSTR(outlet_code,7,7) as outlet_code_num, outlet_name, item_id, item_qty, item_desc, item_type, month, year, order_dt, day, day_of_week \n",
"from ITEM_USAGE_CLEAN_RESULT ORDER BY order_dt\n",
"#where item_id = 'C038'\n",
"\n",
"''').to_dataframe()\n",
"df.set_index('order_dt', inplace=True, drop=False)\n",
"df_traintestset = df[:\"2019-08-31\"]\n",
"df_traintestset.head(5)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"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>outlet_code</th>\n",
" <th>outlet_code_num</th>\n",
" <th>outlet_name</th>\n",
" <th>item_id</th>\n",
" <th>item_qty</th>\n",
" <th>item_desc</th>\n",
" <th>item_type</th>\n",
" <th>month</th>\n",
" <th>year</th>\n",
" <th>order_dt</th>\n",
" <th>day</th>\n",
" <th>day_of_week</th>\n",
" </tr>\n",
" <tr>\n",
" <th>order_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET01</td>\n",
" <td>01</td>\n",
" <td>新达成</td>\n",
" <td>C041</td>\n",
" <td>0</td>\n",
" <td>卤肉</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET01</td>\n",
" <td>01</td>\n",
" <td>新达成</td>\n",
" <td>C039</td>\n",
" <td>30</td>\n",
" <td>排刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET01</td>\n",
" <td>01</td>\n",
" <td>新达成</td>\n",
" <td>C040</td>\n",
" <td>30</td>\n",
" <td>鸡刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET01</td>\n",
" <td>01</td>\n",
" <td>新达成</td>\n",
" <td>C038</td>\n",
" <td>50</td>\n",
" <td>牛刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET02</td>\n",
" <td>02</td>\n",
" <td>莱佛士</td>\n",
" <td>C039</td>\n",
" <td>10</td>\n",
" <td>排刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET02</td>\n",
" <td>02</td>\n",
" <td>莱佛士</td>\n",
" <td>C040</td>\n",
" <td>10</td>\n",
" <td>鸡刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET02</td>\n",
" <td>02</td>\n",
" <td>莱佛士</td>\n",
" <td>C041</td>\n",
" <td>10</td>\n",
" <td>卤肉</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET02</td>\n",
" <td>02</td>\n",
" <td>莱佛士</td>\n",
" <td>C038</td>\n",
" <td>30</td>\n",
" <td>牛刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET03</td>\n",
" <td>03</td>\n",
" <td>武吉士</td>\n",
" <td>C040</td>\n",
" <td>10</td>\n",
" <td>鸡刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET03</td>\n",
" <td>03</td>\n",
" <td>武吉士</td>\n",
" <td>C041</td>\n",
" <td>10</td>\n",
" <td>卤肉</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" outlet_code outlet_code_num ... day day_of_week\n",
"order_dt ... \n",
"2017-07-01 OUTLET01 01 ... 1 7\n",
"2017-07-01 OUTLET01 01 ... 1 7\n",
"2017-07-01 OUTLET01 01 ... 1 7\n",
"2017-07-01 OUTLET01 01 ... 1 7\n",
"2017-07-01 OUTLET02 02 ... 1 7\n",
"2017-07-01 OUTLET02 02 ... 1 7\n",
"2017-07-01 OUTLET02 02 ... 1 7\n",
"2017-07-01 OUTLET02 02 ... 1 7\n",
"2017-07-01 OUTLET03 03 ... 1 7\n",
"2017-07-01 OUTLET03 03 ... 1 7\n",
"\n",
"[10 rows x 12 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gLkw5s_AaKQ0",
"colab_type": "text"
},
"source": [
"### Setup the Bigquery parameters for insertion into the table"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ukF6hnYiINKb",
"colab_type": "code",
"colab": {}
},
"source": [
"from google.cloud import bigquery\n",
"client = bigquery.Client(project=project_id)\n",
"dataset_id = \"item_usage\"\n",
"table_id = project_id+'.'+dataset_id+\".forecasted_item_usage\"\n",
"dataset = bigquery.Dataset(client.dataset(dataset_id))\n",
"dataset.location = \"US\"\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "XCTWWCU9Mz8W",
"colab_type": "code",
"colab": {}
},
"source": [
"#outlet_code,outlet_name,item_id,item_qty,item_desc,item_type,month,year,order_dt,day,day_of_week,holiday_type,holiday_desc,predicted\n",
"schema = [\n",
" bigquery.SchemaField(\"outlet_code\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"outlet_name\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"item_id\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"item_qty\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"item_desc\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"item_type\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"month\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"year\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"order_dt\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"day\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"day_of_week\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"holiday_type\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"holiday_desc\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"predicted\", \"STRING\", mode=\"NULLABLE\")\n",
" ]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "oTvCSAoaOiph",
"colab_type": "code",
"colab": {}
},
"source": [
"# function to insert the data to bigquery\n",
"def insert_into_bigquery(clientobj, source_file, table_id, schema_fld) :\n",
" print(source_file, table_id, schema_fld)\n",
" job_config = bigquery.LoadJobConfig()\n",
" job_config.write_disposition = bigquery.job.WriteDisposition.WRITE_TRUNCATE \n",
" job_config.create_disposition = bigquery.job.CreateDisposition.CREATE_IF_NEEDED \n",
" # In this example, the existing table contains only the 'full_name' column.\n",
" # 'REQUIRED' fields cannot be added to an existing schema, so the\n",
" # additional column must be 'NULLABLE'.\n",
" job_config.schema = schema_fld\n",
" job_config.source_format = bigquery.SourceFormat.CSV\n",
" job_config.skip_leading_rows = 1\n",
"\n",
" with open(source_file, \"rb\") as source_file1:\n",
" job = clientobj.load_table_from_file(\n",
" source_file1,\n",
" table_id,\n",
" location=\"US\", # Must match the destination dataset location.\n",
" job_config=job_config,\n",
" ) # API request\n",
" job.result() "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "KHhe5WzaaqNZ",
"colab_type": "text"
},
"source": [
"## Merge the holiday data and the Bigquery data\n",
"Panda dataframe merging\n",
"(https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vLusjnxGYGE8",
"colab_type": "code",
"outputId": "b9fb231f-2713-44bd-8e2b-5df3d0897c4b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 309
}
},
"source": [
"df_cd = pd.merge(df_traintestset, holiday_df, how='left', left_index=True, right_index=True) \n",
"df_cd.holiday_type.fillna('0', inplace=True)\n",
"del df_cd['holiday_dt']\n",
"df_cd = df_cd.astype(str)\n",
"df_cd.head(5)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"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>outlet_code</th>\n",
" <th>outlet_code_num</th>\n",
" <th>outlet_name</th>\n",
" <th>item_id</th>\n",
" <th>item_qty</th>\n",
" <th>item_desc</th>\n",
" <th>item_type</th>\n",
" <th>month</th>\n",
" <th>year</th>\n",
" <th>order_dt</th>\n",
" <th>day</th>\n",
" <th>day_of_week</th>\n",
" <th>holiday_type</th>\n",
" <th>holiday_desc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET01</td>\n",
" <td>01</td>\n",
" <td>新达成</td>\n",
" <td>C041</td>\n",
" <td>0</td>\n",
" <td>卤肉</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>nan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET01</td>\n",
" <td>01</td>\n",
" <td>新达成</td>\n",
" <td>C039</td>\n",
" <td>30</td>\n",
" <td>排刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>nan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET01</td>\n",
" <td>01</td>\n",
" <td>新达成</td>\n",
" <td>C040</td>\n",
" <td>30</td>\n",
" <td>鸡刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>nan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET01</td>\n",
" <td>01</td>\n",
" <td>新达成</td>\n",
" <td>C038</td>\n",
" <td>50</td>\n",
" <td>牛刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>nan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>OUTLET02</td>\n",
" <td>02</td>\n",
" <td>莱佛士</td>\n",
" <td>C039</td>\n",
" <td>10</td>\n",
" <td>排刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>nan</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" outlet_code outlet_code_num ... holiday_type holiday_desc\n",
"2017-07-01 OUTLET01 01 ... 0 nan\n",
"2017-07-01 OUTLET01 01 ... 0 nan\n",
"2017-07-01 OUTLET01 01 ... 0 nan\n",
"2017-07-01 OUTLET01 01 ... 0 nan\n",
"2017-07-01 OUTLET02 02 ... 0 nan\n",
"\n",
"[5 rows x 14 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Pf7g4WSTX_QT",
"colab_type": "code",
"outputId": "3331eff8-901c-453e-917b-9bc8cb666f54",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"import pandas as pd \n",
"import numpy as np \n",
"import matplotlib.pyplot as plt \n",
"import seaborn as seabornInstance \n",
"from sklearn.model_selection import train_test_split \n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn import metrics\n",
"from sklearn import preprocessing\n",
"from sklearn.metrics import mean_absolute_error \n",
"%matplotlib inline\n",
"\n",
"y= df_cd['item_qty'].astype(float)\n",
"\n",
"print(y.shape)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"(47396,)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rmlygS7vvIlM",
"colab_type": "text"
},
"source": [
"## 1-hot encode the features\n",
"* day_of_week\n",
"* item_id\n",
"* outlet_code\n",
"* holiday_type"
]
},
{
"cell_type": "code",
"metadata": {
"id": "issZtUgjUmKI",
"colab_type": "code",
"outputId": "b6d31a4a-245b-4fcd-cbfb-b20867a1d908",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"# Encode day of week\n",
"dfDummies = pd.get_dummies(df_cd['day_of_week'], prefix = 'day_of_week')\n",
"df_analyse = pd.concat([df_cd, dfDummies], axis=1) \n",
"dfDummies.head(5)\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"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>day_of_week_1</th>\n",
" <th>day_of_week_2</th>\n",
" <th>day_of_week_3</th>\n",
" <th>day_of_week_4</th>\n",
" <th>day_of_week_5</th>\n",
" <th>day_of_week_6</th>\n",
" <th>day_of_week_7</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" day_of_week_1 day_of_week_2 ... day_of_week_6 day_of_week_7\n",
"2017-07-01 0 0 ... 0 1\n",
"2017-07-01 0 0 ... 0 1\n",
"2017-07-01 0 0 ... 0 1\n",
"2017-07-01 0 0 ... 0 1\n",
"2017-07-01 0 0 ... 0 1\n",
"\n",
"[5 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5_6j6ANJZu_w",
"colab_type": "code",
"outputId": "4498a3fc-f3d2-41eb-9643-285b798da82e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"# Encode item id\n",
"dfDummies = pd.get_dummies(df_cd['item_id'], prefix = 'item_id') \n",
"df_analyse = pd.concat([df_analyse, dfDummies], axis=1) \n",
"dfDummies.head(5)\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"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>item_id_C038</th>\n",
" <th>item_id_C039</th>\n",
" <th>item_id_C040</th>\n",
" <th>item_id_C041</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" item_id_C038 item_id_C039 item_id_C040 item_id_C041\n",
"2017-07-01 0 0 0 1\n",
"2017-07-01 0 1 0 0\n",
"2017-07-01 0 0 1 0\n",
"2017-07-01 1 0 0 0\n",
"2017-07-01 0 1 0 0"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "u-HMpiEqjiGi",
"colab_type": "code",
"outputId": "75cee3fe-20f1-4019-e548-1ba7154ed236",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 309
}
},
"source": [
"# Encode outlet code\n",
"\n",
"dfDummies = pd.get_dummies(df_cd['outlet_code'], prefix = 'outlet_code')\n",
"df_analyse = pd.concat([df_analyse, dfDummies], axis=1)\n",
"dfDummies.head(5)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"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>outlet_code_OUTLET01</th>\n",
" <th>outlet_code_OUTLET02</th>\n",
" <th>outlet_code_OUTLET03</th>\n",
" <th>outlet_code_OUTLET05</th>\n",
" <th>outlet_code_OUTLET06</th>\n",
" <th>outlet_code_OUTLET07</th>\n",
" <th>outlet_code_OUTLET08</th>\n",
" <th>outlet_code_OUTLET09</th>\n",
" <th>outlet_code_OUTLET10</th>\n",
" <th>outlet_code_OUTLET11</th>\n",
" <th>outlet_code_OUTLET13</th>\n",
" <th>outlet_code_OUTLET14</th>\n",
" <th>outlet_code_OUTLET17</th>\n",
" <th>outlet_code_OUTLET18</th>\n",
" <th>outlet_code_OUTLET19</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" outlet_code_OUTLET01 ... outlet_code_OUTLET19\n",
"2017-07-01 1 ... 0\n",
"2017-07-01 1 ... 0\n",
"2017-07-01 1 ... 0\n",
"2017-07-01 1 ... 0\n",
"2017-07-01 0 ... 0\n",
"\n",
"[5 rows x 15 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IDPrZjK6j2y1",
"colab_type": "code",
"outputId": "e7fcd3e2-97ed-4674-80d7-45cad84f2f98",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"# Encode holiday type\n",
"dfDummies = pd.get_dummies(df_cd['holiday_type'], prefix = 'holiday_type')\n",
"df_analyse = pd.concat([df_analyse, dfDummies], axis=1) \n",
"dfDummies.head(5)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"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>holiday_type_0</th>\n",
" <th>holiday_type_10.0</th>\n",
" <th>holiday_type_11.0</th>\n",
" <th>holiday_type_8.0</th>\n",
" <th>holiday_type_9.0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-07-01</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" holiday_type_0 ... holiday_type_9.0\n",
"2017-07-01 1 ... 0\n",
"2017-07-01 1 ... 0\n",
"2017-07-01 1 ... 0\n",
"2017-07-01 1 ... 0\n",
"2017-07-01 1 ... 0\n",
"\n",
"[5 rows x 5 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "bm0zRiicsOGQ",
"colab_type": "code",
"colab": {}
},
"source": [
"#dfDummies = pd.get_dummies(df_cd['month'], prefix = 'month')\n",
"#df_analyse = pd.concat([df_analyse, dfDummies], axis=1) "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8s7zLIE60k6X",
"colab_type": "code",
"colab": {}
},
"source": [
"#dfDummies = pd.get_dummies(df_cd['day'], prefix = 'day')\n",
"#df_analyse = pd.concat([df_analyse, dfDummies], axis=1) "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "P8V9tenNZ3LK",
"colab_type": "code",
"outputId": "d82168f7-c0b4-4f2a-d42c-36c24eba085d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"#Pick only those features we are using in the predictions.\n",
"\n",
"df_b = df_analyse[[\n",
" 'outlet_code_OUTLET01','outlet_code_OUTLET02','outlet_code_OUTLET03','outlet_code_OUTLET05','outlet_code_OUTLET06',\n",
" 'outlet_code_OUTLET07','outlet_code_OUTLET08','outlet_code_OUTLET09','outlet_code_OUTLET10','outlet_code_OUTLET11',\n",
" 'outlet_code_OUTLET13','outlet_code_OUTLET14','outlet_code_OUTLET17','outlet_code_OUTLET18','outlet_code_OUTLET19',\n",
" 'day_of_week_1','day_of_week_2','day_of_week_3','day_of_week_4','day_of_week_5','day_of_week_6','day_of_week_7',\n",
" 'item_id_C038','item_id_C039','item_id_C040','item_id_C041',\n",
" 'holiday_type_0','holiday_type_8.0','holiday_type_9.0','holiday_type_10.0','holiday_type_11.0',\n",
"]].astype(float)\n",
"\n",
"X=df_b.values\n",
"print(X.shape, y.shape)\n",
"\n",
"# Split the training and test set to 80/20\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"(47396, 31) (47396,)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n8V08DTKxYCy",
"colab_type": "text"
},
"source": [
"## The prediction Models\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZN7u6IaSavKp",
"colab_type": "text"
},
"source": [
"### Setup the parameters\n",
"\n",
"The purpose is to 1-hot encode data for processing by the machine learning algorithms."
]
},
{
"cell_type": "code",
"metadata": {
"id": "nmXRGr5Iti7z",
"colab_type": "code",
"colab": {}
},
"source": [
"#Pass in the outlet code, order date and item id, the function will return array of encoded value\n",
"outlet_dict={\n",
" 'OUTLET01':[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\n",
" 'OUTLET02':[0,1,0,0,0,0,0,0,0,0,0,0,0,0,0],\n",
" 'OUTLET03':[0,0,1,0,0,0,0,0,0,0,0,0,0,0,0],\n",
" 'OUTLET05':[0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],\n",
" 'OUTLET06':[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0],\n",
" 'OUTLET07':[0,0,0,0,0,1,0,0,0,0,0,0,0,0,0],\n",
" 'OUTLET08':[0,0,0,0,0,0,1,0,0,0,0,0,0,0,0],\n",
" 'OUTLET09':[0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],\n",
" 'OUTLET10':[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0],\n",
" 'OUTLET11':[0,0,0,0,0,0,0,0,0,1,0,0,0,0,0],\n",
" 'OUTLET13':[0,0,0,0,0,0,0,0,0,0,1,0,0,0,0],\n",
" 'OUTLET14':[0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],\n",
" 'OUTLET17':[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0],\n",
" 'OUTLET18':[0,0,0,0,0,0,0,0,0,0,0,0,0,1,0],\n",
" 'OUTLET19':[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]\n",
"}\n",
"\n",
"#python used 0 as monday wherelse bigquery used 0 as Sunday\n",
"day_of_week = {\n",
" 'Monday' : [0,1,0,0,0,0,0],\n",
" 'Tuesday' : [0,0,1,0,0,0,0],\n",
" 'Wednesday': [0,0,0,1,0,0,0],\n",
" 'Thursday' : [0,0,0,0,1,0,0],\n",
" 'Friday' : [0,0,0,0,0,1,0],\n",
" 'Saturday' : [0,0,0,0,0,0,1],\n",
" 'Sunday' : [1,0,0,0,0,0,0]\n",
" \n",
"}\n",
"\n",
"ingredient={\n",
" 'C038' :[1,0,0,0],\n",
" 'C039' :[0,1,0,0],\n",
" 'C040' :[0,0,1,0],\n",
" 'C041' :[0,0,0,1]\n",
"}\n",
"\n",
"holiday_type ={\n",
" '0': [1,0,0,0,0],\n",
" '8': [0,1,0,0,0],\n",
" '9': [0,0,1,0,0],\n",
" '10': [0,0,0,1,0],\n",
" '11': [0,0,0,0,1]\n",
"}\n",
"import datetime\n",
"from datetime import datetime, date\n",
"def formula_1hot(outlet,order_dt,ingredientidx):\n",
" #print(type(order_dt))\n",
" outletid=outlet_dict[outlet]\n",
" ingredientid=ingredient[ingredientidx]\n",
" \n",
" \n",
" year, month, day = (int(x) for x in order_dt.split('-')) \n",
" dayofweek = date(year, month, day)\n",
" dayofweek= dayofweek.strftime(\"%A\")\n",
" \n",
" datedict=day_of_week[dayofweek]\n",
" \n",
" \n",
" a=holiday_df.loc[order_dt]\n",
" a=a[\"holiday_type\"]\n",
" if a.size > 0 :\n",
" a=str(a[0])\n",
" else:\n",
" a=str(0)\n",
" holidayidx=holiday_type[a]\n",
" #print(outletid,ingredientid,datedict,holidayidx)\n",
" \n",
" mylist = outletid + datedict +ingredientid +holidayidx\n",
" \n",
" \n",
" return mylist\n",
" \n",
"\n",
"\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "DUEy3nz58xVU",
"colab_type": "code",
"colab": {}
},
"source": [
"# Prediction Schema that stores the forcasted results.\n",
"\n",
"pred_schema = [\n",
" bigquery.SchemaField(\"order_dt\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"outlet_code\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"item_id\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"predicted\", \"STRING\", mode=\"NULLABLE\"),\n",
" bigquery.SchemaField(\"holiday_type\", \"STRING\", mode=\"NULLABLE\")\n",
"] "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "s7punnTdIaR2",
"colab_type": "code",
"colab": {}
},
"source": [
"# This function predicts the next X days starting from 1 Sep 2019\n",
"\n",
"from datetime import timedelta\n",
"from datetime import datetime, date\n",
"import csv\n",
"from google.cloud import storage\n",
"\n",
"def pred_next_xdays(model,filename,days, poly=False) :\n",
" today = date.today()\n",
"\n",
" #fromdt = today.strftime(\"%Y-%m-%d\")\n",
" fromdt='2019-09-01'\n",
" \n",
" todaydt = today.strftime(\"%Y-%m-%d\")\n",
" #todt= (datetime.strptime(fromdt,'%Y-%m-%d') + timedelta(days=days)).strftime(\"%Y-%m-%d\")\n",
" todt= (datetime.strptime(todaydt,'%Y-%m-%d') + timedelta(days=days)).strftime(\"%Y-%m-%d\")\n",
"\n",
" dateval = fromdt\n",
" row=[]\n",
" while dateval <= todt:\n",
"\n",
" for outlet_id in outlet_dict:\n",
" for ingredient_id in ingredient:\n",
" coded_val=formula_1hot(outlet_id,dateval,ingredient_id)\n",
" if poly == True:\n",
" np_val = np.array(coded_val)\n",
" np_val = np_val.reshape(1,31)\n",
" np_val=polynomial_features.fit_transform(np_val)\n",
"\n",
" else:\n",
" np_val = np.array(coded_val)\n",
" np_val = np_val.reshape(1,31)\n",
"\n",
" predicted_val = model.predict(np_val)\n",
" \n",
" if(dateval in holiday_df.index):\n",
" holiday_ty = holiday_df[dateval].holiday_type.values[0]\n",
" else:\n",
" holiday_ty=0\n",
" \n",
" newrow=[dateval,outlet_id,ingredient_id,int(predicted_val[0]),holiday_ty]\n",
" \n",
" #print(newrow)\n",
"\n",
" row.append(newrow)\n",
" dateval= (datetime.strptime(dateval, '%Y-%m-%d') + timedelta(days=1)).strftime('%Y-%m-%d')\n",
"\n",
" pred_df = pd.DataFrame(row)\n",
" \n",
" pred_df.columns = ['order_dt', 'outlet_code', 'item_id','predicted','holiday_type']\n",
" \n",
" pred_df.to_csv('/gdrive/My Drive/Colab Notebooks/' + filename, sep=',', encoding='utf-8', index=False)\n",
" \n",
" #upload to cloud storage\n",
" client_gcs = storage.Client(project=project_id)\n",
"\n",
" bucket = client_gcs.get_bucket('formosa-delights')\n",
" blob = bucket.blob(filename)\n",
" fullpath = '/gdrive/My Drive/Colab Notebooks/'+filename\n",
" blob.upload_from_filename(fullpath)\n",
" \n",
"\n",
" # insert to bigquery\n",
" table_id=project_id+'.'+dataset_id+'.' + filename.rstrip('.csv')\n",
" #print(fullpath, table_id)\n",
" insert_into_bigquery(client,fullpath, table_id, pred_schema)\n",
" \n",
" return pred_df\n",
" "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "FO493Qp5adUe",
"colab_type": "text"
},
"source": [
"### Linear Regression\n",
"This is using Linear Regression. The code is taken from this link\n",
"\n",
"(https://towardsdatascience.com/a-beginners-guide-to-linear-regression-in-python-with-scikit-learn-83a8f7ae2b4f)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZGNhDgUeZppr",
"colab_type": "code",
"outputId": "61cc032b-f58c-4443-8d6f-653ac28eea4c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"source": [
"regressor = LinearRegression() \n",
"regressor.fit(X_train, y_train)\n",
"y_pred = regressor.predict(X_test)\n",
"df_pred = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
"print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) \n",
"print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) \n",
"print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
"print('R2 Score:', metrics.r2_score(y_test, y_pred))\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Mean Absolute Error: 8.875862115143724\n",
"Mean Squared Error: 145.83791847571038\n",
"Root Mean Squared Error: 12.076337129929355\n",
"R2 Score: 0.6136373702544475\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "A-c6tsszpbcf",
"colab_type": "code",
"colab": {}
},
"source": [
"# add a new column \"predicted\"\n",
"df_cd['predicted'] = 0\n",
"df_cd=df_cd.reset_index(drop=True)\n",
"df_cd=df_cd.drop(columns=['outlet_code_num'])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "TN5zFZoNmnLI",
"colab_type": "text"
},
"source": [
"#### Predict the result"
]
},
{
"cell_type": "code",
"metadata": {
"id": "f1vqGrKFKMLY",
"colab_type": "code",
"outputId": "5dab76e5-84f5-4f28-8917-a125414722b9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
}
},
"source": [
"preddf=pred_next_xdays(regressor, 'regression_pred.csv',30)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/gdrive/My Drive/Colab Notebooks/regression_pred.csv formosa-delights-186103.item_usage.regression_pred [SchemaField('order_dt', 'STRING', 'NULLABLE', None, ()), SchemaField('outlet_code', 'STRING', 'NULLABLE', None, ()), SchemaField('item_id', 'STRING', 'NULLABLE', None, ()), SchemaField('predicted', 'STRING', 'NULLABLE', None, ()), SchemaField('holiday_type', 'STRING', 'NULLABLE', None, ())]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "enJHhBoR3um4",
"colab_type": "code",
"outputId": "efe7de09-0003-4f8d-adbe-39aa5b700301",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
}
},
"source": [
"fig, ax = plt.subplots()\n",
"ax.scatter(y_test, y_pred)\n",
"ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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Do4AGQDNgHYHT3Fg7yznXArgI6GFm5+SvdIHzWl+GwJlZOeBS4I1g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\njgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H0xv1n9d0_Cm",
"colab_type": "text"
},
"source": [
"### Polynomial Regression\n",
"\n",
"The code is adapted from [Towards Data Science - Polynomial Regression](https://towardsdatascience.com/polynomial-regression-bbe8b9d97491)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "M5smcwmU_Kzl",
"colab_type": "code",
"outputId": "f09bcec8-cfaa-4c02-fbe1-2f14d2ccae89",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 892
}
},
"source": [
"# polynomial regression\n",
"polynomial_features= PolynomialFeatures(degree=2)\n",
"x_poly = polynomial_features.fit_transform(X_train)\n",
"\n",
"polynomial_regressor = LinearRegression()\n",
"polynomial_regressor.fit(x_poly,y_train)\n",
"y_poly_pred = polynomial_regressor.predict(polynomial_features.fit_transform(X_test))\n",
"\n",
"\n",
"print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_poly_pred)) \n",
"print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_poly_pred)) \n",
"print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_poly_pred)))\n",
"print('R2 Score:', metrics.r2_score(y_test, y_poly_pred))\n",
"df_pred = pd.DataFrame({'Actual': y_test, 'Predicted': y_poly_pred})\n",
"df_pred.head(25)\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Mean Absolute Error: 6.704469936708861\n",
"Mean Squared Error: 93.03201214110298\n",
"Root Mean Squared Error: 9.645310370387413\n",
"R2 Score: 0.7535339695118916\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"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>Actual</th>\n",
" <th>Predicted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-04-07</th>\n",
" <td>50.0</td>\n",
" <td>23.796875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-03</th>\n",
" <td>40.0</td>\n",
" <td>47.187500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-28</th>\n",
" <td>30.0</td>\n",
" <td>29.734375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-24</th>\n",
" <td>30.0</td>\n",
" <td>30.125000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-12</th>\n",
" <td>0.0</td>\n",
" <td>14.015625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-19</th>\n",
" <td>0.0</td>\n",
" <td>5.609375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-17</th>\n",
" <td>10.0</td>\n",
" <td>12.109375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-13</th>\n",
" <td>30.0</td>\n",
" <td>25.859375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-15</th>\n",
" <td>10.0</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-24</th>\n",
" <td>10.0</td>\n",
" <td>19.437500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-25</th>\n",
" <td>10.0</td>\n",
" <td>7.906250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-17</th>\n",
" <td>30.0</td>\n",
" <td>25.296875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-30</th>\n",
" <td>10.0</td>\n",
" <td>4.765625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-05</th>\n",
" <td>20.0</td>\n",
" <td>22.640625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-28</th>\n",
" <td>0.0</td>\n",
" <td>8.218750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-04-01</th>\n",
" <td>0.0</td>\n",
" <td>3.828125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-20</th>\n",
" <td>50.0</td>\n",
" <td>35.359375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-29</th>\n",
" <td>0.0</td>\n",
" <td>3.859375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-30</th>\n",
" <td>20.0</td>\n",
" <td>26.687500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-26</th>\n",
" <td>40.0</td>\n",
" <td>22.140625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-28</th>\n",
" <td>20.0</td>\n",
" <td>23.859375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-30</th>\n",
" <td>20.0</td>\n",
" <td>16.640625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-09</th>\n",
" <td>0.0</td>\n",
" <td>0.437500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-09-15</th>\n",
" <td>10.0</td>\n",
" <td>7.484375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-26</th>\n",
" <td>0.0</td>\n",
" <td>4.093750</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Actual Predicted\n",
"2019-04-07 50.0 23.796875\n",
"2017-07-03 40.0 47.187500\n",
"2017-08-28 30.0 29.734375\n",
"2018-10-24 30.0 30.125000\n",
"2019-05-12 0.0 14.015625\n",
"2018-12-19 0.0 5.609375\n",
"2017-11-17 10.0 12.109375\n",
"2017-08-13 30.0 25.859375\n",
"2017-08-15 10.0 3.000000\n",
"2018-06-24 10.0 19.437500\n",
"2017-08-25 10.0 7.906250\n",
"2019-06-17 30.0 25.296875\n",
"2019-04-30 10.0 4.765625\n",
"2018-10-05 20.0 22.640625\n",
"2019-08-28 0.0 8.218750\n",
"2018-04-01 0.0 3.828125\n",
"2018-12-20 50.0 35.359375\n",
"2019-06-29 0.0 3.859375\n",
"2019-05-30 20.0 26.687500\n",
"2018-06-26 40.0 22.140625\n",
"2017-08-28 20.0 23.859375\n",
"2018-01-30 20.0 16.640625\n",
"2018-10-09 0.0 0.437500\n",
"2018-09-15 10.0 7.484375\n",
"2019-03-26 0.0 4.093750"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "CUuw_NYB_pHA",
"colab_type": "code",
"outputId": "f6a5012b-ee89-4442-cf66-40c5f5a5cf03",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
}
},
"source": [
"fig, ax = plt.subplots()\n",
"ax.scatter(y_test, y_poly_pred)\n",
"ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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UxNixY+nVqxd5eXl8/fXXZGVl2R2WiiHiqXqUxweKdAd+Zt390BizLWhR+SgtLc3ocgEq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\nKeWRJgillFIeaYJQSinlkSYIpZRSHv0/oCoZQEDZUxYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-zwkMaIdmYl7",
"colab_type": "code",
"outputId": "3ed063e6-260b-4cc9-bf3d-c79e61c72772",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"pred_next_xdays(polynomial_regressor, 'polyregressor_pred.csv',30, True)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/gdrive/My Drive/Colab Notebooks/polyregressor_pred.csv formosa-delights-186103.item_usage.polyregressor_pred [SchemaField('order_dt', 'STRING', 'NULLABLE', None, ()), SchemaField('outlet_code', 'STRING', 'NULLABLE', None, ()), SchemaField('item_id', 'STRING', 'NULLABLE', None, ()), SchemaField('predicted', 'STRING', 'NULLABLE', None, ()), SchemaField('holiday_type', 'STRING', 'NULLABLE', None, ())]\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"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>order_dt</th>\n",
" <th>outlet_code</th>\n",
" <th>item_id</th>\n",
" <th>predicted</th>\n",
" <th>holiday_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET01</td>\n",
" <td>C038</td>\n",
" <td>36</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET01</td>\n",
" <td>C039</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET01</td>\n",
" <td>C040</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET01</td>\n",
" <td>C041</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET02</td>\n",
" <td>C038</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET02</td>\n",
" <td>C039</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET02</td>\n",
" <td>C040</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET02</td>\n",
" <td>C041</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET03</td>\n",
" <td>C038</td>\n",
" <td>32</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET03</td>\n",
" <td>C039</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET03</td>\n",
" <td>C040</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET03</td>\n",
" <td>C041</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
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" <td>C038</td>\n",
" <td>72</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET05</td>\n",
" <td>C039</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET05</td>\n",
" <td>C040</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET05</td>\n",
" <td>C041</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET06</td>\n",
" <td>C038</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
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" <td>C039</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET06</td>\n",
" <td>C040</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET06</td>\n",
" <td>C041</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>20</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C038</td>\n",
" <td>71</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C039</td>\n",
" <td>49</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C040</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C041</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C038</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C039</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C040</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C041</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET09</td>\n",
" <td>C038</td>\n",
" <td>46</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET09</td>\n",
" <td>C039</td>\n",
" <td>28</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5010</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET09</td>\n",
" <td>C040</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5011</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET09</td>\n",
" <td>C041</td>\n",
" <td>15</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5012</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C038</td>\n",
" <td>27</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5013</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C039</td>\n",
" <td>15</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5014</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C040</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5015</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C041</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5016</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C038</td>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5017</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C039</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5018</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C040</td>\n",
" <td>-1</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5019</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C041</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5020</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C038</td>\n",
" <td>31</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5021</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C039</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5022</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5023</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C041</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5024</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C038</td>\n",
" <td>30</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5025</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C039</td>\n",
" <td>18</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5026</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C040</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5027</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C041</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5028</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C038</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5029</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C039</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5030</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C040</td>\n",
" <td>-1</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5031</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C041</td>\n",
" <td>-2</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5032</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C038</td>\n",
" <td>62</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5033</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C039</td>\n",
" <td>23</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5034</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C040</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5035</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C041</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5036</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C038</td>\n",
" <td>41</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5037</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C039</td>\n",
" <td>18</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5038</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C040</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5039</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C041</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5040 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" order_dt outlet_code item_id predicted holiday_type\n",
"0 2019-09-01 OUTLET01 C038 36 0\n",
"1 2019-09-01 OUTLET01 C039 16 0\n",
"2 2019-09-01 OUTLET01 C040 6 0\n",
"3 2019-09-01 OUTLET01 C041 6 0\n",
"4 2019-09-01 OUTLET02 C038 25 0\n",
"5 2019-09-01 OUTLET02 C039 12 0\n",
"6 2019-09-01 OUTLET02 C040 5 0\n",
"7 2019-09-01 OUTLET02 C041 5 0\n",
"8 2019-09-01 OUTLET03 C038 32 0\n",
"9 2019-09-01 OUTLET03 C039 26 0\n",
"10 2019-09-01 OUTLET03 C040 9 0\n",
"11 2019-09-01 OUTLET03 C041 8 0\n",
"12 2019-09-01 OUTLET05 C038 72 0\n",
"13 2019-09-01 OUTLET05 C039 45 0\n",
"14 2019-09-01 OUTLET05 C040 14 0\n",
"15 2019-09-01 OUTLET05 C041 9 0\n",
"16 2019-09-01 OUTLET06 C038 40 0\n",
"17 2019-09-01 OUTLET06 C039 22 0\n",
"18 2019-09-01 OUTLET06 C040 4 0\n",
"19 2019-09-01 OUTLET06 C041 0 0\n",
"20 2019-09-01 OUTLET07 C038 71 0\n",
"21 2019-09-01 OUTLET07 C039 49 0\n",
"22 2019-09-01 OUTLET07 C040 10 0\n",
"23 2019-09-01 OUTLET07 C041 20 0\n",
"24 2019-09-01 OUTLET08 C038 19 0\n",
"25 2019-09-01 OUTLET08 C039 6 0\n",
"26 2019-09-01 OUTLET08 C040 2 0\n",
"27 2019-09-01 OUTLET08 C041 5 0\n",
"28 2019-09-01 OUTLET09 C038 46 0\n",
"29 2019-09-01 OUTLET09 C039 28 0\n",
"... ... ... ... ... ...\n",
"5010 2019-11-23 OUTLET09 C040 16 9\n",
"5011 2019-11-23 OUTLET09 C041 15 9\n",
"5012 2019-11-23 OUTLET10 C038 27 9\n",
"5013 2019-11-23 OUTLET10 C039 15 9\n",
"5014 2019-11-23 OUTLET10 C040 3 9\n",
"5015 2019-11-23 OUTLET10 C041 3 9\n",
"5016 2019-11-23 OUTLET11 C038 20 9\n",
"5017 2019-11-23 OUTLET11 C039 4 9\n",
"5018 2019-11-23 OUTLET11 C040 -1 9\n",
"5019 2019-11-23 OUTLET11 C041 4 9\n",
"5020 2019-11-23 OUTLET13 C038 31 9\n",
"5021 2019-11-23 OUTLET13 C039 8 9\n",
"5022 2019-11-23 OUTLET13 C040 0 9\n",
"5023 2019-11-23 OUTLET13 C041 5 9\n",
"5024 2019-11-23 OUTLET14 C038 30 9\n",
"5025 2019-11-23 OUTLET14 C039 18 9\n",
"5026 2019-11-23 OUTLET14 C040 4 9\n",
"5027 2019-11-23 OUTLET14 C041 6 9\n",
"5028 2019-11-23 OUTLET17 C038 6 9\n",
"5029 2019-11-23 OUTLET17 C039 3 9\n",
"5030 2019-11-23 OUTLET17 C040 -1 9\n",
"5031 2019-11-23 OUTLET17 C041 -2 9\n",
"5032 2019-11-23 OUTLET18 C038 62 9\n",
"5033 2019-11-23 OUTLET18 C039 23 9\n",
"5034 2019-11-23 OUTLET18 C040 9 9\n",
"5035 2019-11-23 OUTLET18 C041 8 9\n",
"5036 2019-11-23 OUTLET19 C038 41 9\n",
"5037 2019-11-23 OUTLET19 C039 18 9\n",
"5038 2019-11-23 OUTLET19 C040 7 9\n",
"5039 2019-11-23 OUTLET19 C041 9 9\n",
"\n",
"[5040 rows x 5 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VzVbx0yI1etS",
"colab_type": "text"
},
"source": [
"### Random Forest\n",
"\n",
"The code is adapted from [Towards Data Science - Random Forest Regression](https://towardsdatascience.com/random-forest-and-its-implementation-71824ced454f)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "WpnYakQTQRDI",
"colab_type": "code",
"outputId": "774e0cde-f73d-4f9d-cd70-e659d4c47f3b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 139
}
},
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.metrics import mean_absolute_error \n",
"\n",
"randomforestregressor= RandomForestRegressor()\n",
"randomforestregressor.fit(X_train,y_train)\n",
"\n",
"# Get the mean absolute error on the validation data\n",
"y_pred = randomforestregressor.predict(X_test)\n",
"MAE = mean_absolute_error(y_test , y_pred)\n",
"\n",
"print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) \n",
"print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) \n",
"print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
"print('R2 Score:', metrics.r2_score(y_test, y_pred))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
" \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Mean Absolute Error: 6.730560513175636\n",
"Mean Squared Error: 96.45255319323923\n",
"Root Mean Squared Error: 9.821026076395441\n",
"R2 Score: 0.7444720653797636\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ajWhUDGAH_jP",
"colab_type": "text"
},
"source": [
"#### Predict the result"
]
},
{
"cell_type": "code",
"metadata": {
"id": "myBlmDhIDjJj",
"colab_type": "code",
"outputId": "82d83d11-5bfd-4580-d236-f6e8733e2f4c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"pred_next_xdays(randomforestregressor, 'randomforest_pred.csv',30)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/gdrive/My Drive/Colab Notebooks/randomforest_pred.csv formosa-delights-186103.item_usage.randomforest_pred [SchemaField('order_dt', 'STRING', 'NULLABLE', None, ()), SchemaField('outlet_code', 'STRING', 'NULLABLE', None, ()), SchemaField('item_id', 'STRING', 'NULLABLE', None, ()), SchemaField('predicted', 'STRING', 'NULLABLE', None, ()), SchemaField('holiday_type', 'STRING', 'NULLABLE', None, ())]\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>order_dt</th>\n",
" <th>outlet_code</th>\n",
" <th>item_id</th>\n",
" <th>predicted</th>\n",
" <th>holiday_type</th>\n",
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" <td>32</td>\n",
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" <th>12</th>\n",
" <td>2019-09-01</td>\n",
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" <td>78</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET05</td>\n",
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" <td>45</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
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" <th>15</th>\n",
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" <td>OUTLET06</td>\n",
" <td>C040</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET06</td>\n",
" <td>C041</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C038</td>\n",
" <td>68</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C039</td>\n",
" <td>51</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C040</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C041</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C038</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C039</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C040</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C041</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET09</td>\n",
" <td>C038</td>\n",
" <td>46</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET09</td>\n",
" <td>C039</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5010</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET09</td>\n",
" <td>C040</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5011</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET09</td>\n",
" <td>C041</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5012</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C038</td>\n",
" <td>26</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5013</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C039</td>\n",
" <td>19</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5014</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C040</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5015</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C041</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5016</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C038</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5017</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C039</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5018</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5019</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C041</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5020</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C038</td>\n",
" <td>25</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5021</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C039</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5022</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5023</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C041</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5024</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C038</td>\n",
" <td>31</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5025</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C039</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5026</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C040</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5027</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C041</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5028</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C038</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5029</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C039</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5030</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5031</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C041</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5032</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C038</td>\n",
" <td>64</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5033</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C039</td>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5034</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C040</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5035</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C041</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5036</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C038</td>\n",
" <td>43</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5037</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C039</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5038</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C040</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5039</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C041</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5040 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" order_dt outlet_code item_id predicted holiday_type\n",
"0 2019-09-01 OUTLET01 C038 35 0\n",
"1 2019-09-01 OUTLET01 C039 16 0\n",
"2 2019-09-01 OUTLET01 C040 5 0\n",
"3 2019-09-01 OUTLET01 C041 9 0\n",
"4 2019-09-01 OUTLET02 C038 25 0\n",
"5 2019-09-01 OUTLET02 C039 13 0\n",
"6 2019-09-01 OUTLET02 C040 5 0\n",
"7 2019-09-01 OUTLET02 C041 5 0\n",
"8 2019-09-01 OUTLET03 C038 32 0\n",
"9 2019-09-01 OUTLET03 C039 26 0\n",
"10 2019-09-01 OUTLET03 C040 9 0\n",
"11 2019-09-01 OUTLET03 C041 7 0\n",
"12 2019-09-01 OUTLET05 C038 78 0\n",
"13 2019-09-01 OUTLET05 C039 45 0\n",
"14 2019-09-01 OUTLET05 C040 10 0\n",
"15 2019-09-01 OUTLET05 C041 5 0\n",
"16 2019-09-01 OUTLET06 C038 38 0\n",
"17 2019-09-01 OUTLET06 C039 20 0\n",
"18 2019-09-01 OUTLET06 C040 5 0\n",
"19 2019-09-01 OUTLET06 C041 3 0\n",
"20 2019-09-01 OUTLET07 C038 68 0\n",
"21 2019-09-01 OUTLET07 C039 51 0\n",
"22 2019-09-01 OUTLET07 C040 11 0\n",
"23 2019-09-01 OUTLET07 C041 21 0\n",
"24 2019-09-01 OUTLET08 C038 21 0\n",
"25 2019-09-01 OUTLET08 C039 8 0\n",
"26 2019-09-01 OUTLET08 C040 2 0\n",
"27 2019-09-01 OUTLET08 C041 5 0\n",
"28 2019-09-01 OUTLET09 C038 46 0\n",
"29 2019-09-01 OUTLET09 C039 25 0\n",
"... ... ... ... ... ...\n",
"5010 2019-11-23 OUTLET09 C040 10 9\n",
"5011 2019-11-23 OUTLET09 C041 8 9\n",
"5012 2019-11-23 OUTLET10 C038 26 9\n",
"5013 2019-11-23 OUTLET10 C039 19 9\n",
"5014 2019-11-23 OUTLET10 C040 4 9\n",
"5015 2019-11-23 OUTLET10 C041 5 9\n",
"5016 2019-11-23 OUTLET11 C038 16 9\n",
"5017 2019-11-23 OUTLET11 C039 3 9\n",
"5018 2019-11-23 OUTLET11 C040 0 9\n",
"5019 2019-11-23 OUTLET11 C041 7 9\n",
"5020 2019-11-23 OUTLET13 C038 25 9\n",
"5021 2019-11-23 OUTLET13 C039 6 9\n",
"5022 2019-11-23 OUTLET13 C040 0 9\n",
"5023 2019-11-23 OUTLET13 C041 6 9\n",
"5024 2019-11-23 OUTLET14 C038 31 9\n",
"5025 2019-11-23 OUTLET14 C039 16 9\n",
"5026 2019-11-23 OUTLET14 C040 6 9\n",
"5027 2019-11-23 OUTLET14 C041 3 9\n",
"5028 2019-11-23 OUTLET17 C038 4 9\n",
"5029 2019-11-23 OUTLET17 C039 1 9\n",
"5030 2019-11-23 OUTLET17 C040 0 9\n",
"5031 2019-11-23 OUTLET17 C041 0 9\n",
"5032 2019-11-23 OUTLET18 C038 64 9\n",
"5033 2019-11-23 OUTLET18 C039 20 9\n",
"5034 2019-11-23 OUTLET18 C040 8 9\n",
"5035 2019-11-23 OUTLET18 C041 7 9\n",
"5036 2019-11-23 OUTLET19 C038 43 9\n",
"5037 2019-11-23 OUTLET19 C039 16 9\n",
"5038 2019-11-23 OUTLET19 C040 4 9\n",
"5039 2019-11-23 OUTLET19 C041 8 9\n",
"\n",
"[5040 rows x 5 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Sw8k8GP5W160",
"colab_type": "code",
"outputId": "a3cacb1b-dbd8-4647-c354-20d582c590cd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
}
},
"source": [
"fig, ax = plt.subplots()\n",
"ax.scatter(y_test, y_pred)\n",
"ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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VvB7XCmXHIlIZ6APMdYr+AZyBp/lpKwHud4jIEBHJEpGsHTt2hBJCuYXyDxyK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\ngg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "d3WuRg_6RjcF",
"colab_type": "code",
"outputId": "7fdb25c3-4fae-4265-e181-c36a4a5eb56e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 824
}
},
"source": [
"df_pred = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
"df1 = df_pred.head(25)\n",
"df1"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"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>Actual</th>\n",
" <th>Predicted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-04-07</th>\n",
" <td>50.0</td>\n",
" <td>23.912955</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-03</th>\n",
" <td>40.0</td>\n",
" <td>49.167422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-28</th>\n",
" <td>30.0</td>\n",
" <td>32.585095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-24</th>\n",
" <td>30.0</td>\n",
" <td>30.365002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-12</th>\n",
" <td>0.0</td>\n",
" <td>14.565611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-19</th>\n",
" <td>0.0</td>\n",
" <td>4.852514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-17</th>\n",
" <td>10.0</td>\n",
" <td>14.366667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-13</th>\n",
" <td>30.0</td>\n",
" <td>25.204376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-15</th>\n",
" <td>10.0</td>\n",
" <td>4.308965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-24</th>\n",
" <td>10.0</td>\n",
" <td>20.580106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-25</th>\n",
" <td>10.0</td>\n",
" <td>6.802192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-17</th>\n",
" <td>30.0</td>\n",
" <td>27.176356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-30</th>\n",
" <td>10.0</td>\n",
" <td>3.367833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-05</th>\n",
" <td>20.0</td>\n",
" <td>25.264191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-28</th>\n",
" <td>0.0</td>\n",
" <td>6.918949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-04-01</th>\n",
" <td>0.0</td>\n",
" <td>6.517201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-20</th>\n",
" <td>50.0</td>\n",
" <td>37.227433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-29</th>\n",
" <td>0.0</td>\n",
" <td>3.451715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-30</th>\n",
" <td>20.0</td>\n",
" <td>27.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-26</th>\n",
" <td>40.0</td>\n",
" <td>20.859067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-28</th>\n",
" <td>20.0</td>\n",
" <td>24.341439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-30</th>\n",
" <td>20.0</td>\n",
" <td>17.007555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-09</th>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-09-15</th>\n",
" <td>10.0</td>\n",
" <td>15.257937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-26</th>\n",
" <td>0.0</td>\n",
" <td>3.616105</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Actual Predicted\n",
"2019-04-07 50.0 23.912955\n",
"2017-07-03 40.0 49.167422\n",
"2017-08-28 30.0 32.585095\n",
"2018-10-24 30.0 30.365002\n",
"2019-05-12 0.0 14.565611\n",
"2018-12-19 0.0 4.852514\n",
"2017-11-17 10.0 14.366667\n",
"2017-08-13 30.0 25.204376\n",
"2017-08-15 10.0 4.308965\n",
"2018-06-24 10.0 20.580106\n",
"2017-08-25 10.0 6.802192\n",
"2019-06-17 30.0 27.176356\n",
"2019-04-30 10.0 3.367833\n",
"2018-10-05 20.0 25.264191\n",
"2019-08-28 0.0 6.918949\n",
"2018-04-01 0.0 6.517201\n",
"2018-12-20 50.0 37.227433\n",
"2019-06-29 0.0 3.451715\n",
"2019-05-30 20.0 27.500000\n",
"2018-06-26 40.0 20.859067\n",
"2017-08-28 20.0 24.341439\n",
"2018-01-30 20.0 17.007555\n",
"2018-10-09 0.0 0.000000\n",
"2018-09-15 10.0 15.257937\n",
"2019-03-26 0.0 3.616105"
]
},
"metadata": {
"tags": []
},
"execution_count": 31
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LlkuroXfS156",
"colab_type": "text"
},
"source": [
"### XGBoost"
]
},
{
"cell_type": "code",
"metadata": {
"id": "TYj5DmpaSKGS",
"colab_type": "code",
"outputId": "784c8f88-c40f-4abd-e818-b687b64e421e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 173
}
},
"source": [
"from xgboost import XGBRegressor\n",
"\n",
"XGBModel = XGBRegressor(n_estimators=500, learning_rate=0.05)\n",
"XGBModel.fit(X_train,y_train , verbose=False)\n",
"\n",
"# Get the mean absolute error on the validation data :\n",
"y_pred = XGBModel.predict(X_test)\n",
"\n",
"y_train_pred5 = XGBModel.predict(X_train)\n",
"MAE = mean_absolute_error(y_train , y_train_pred5)\n",
"print('XGBoost training MAE = ',MAE)\n",
"\n",
"print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) \n",
"print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) \n",
"print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
"print('R2 Score:', metrics.r2_score(y_test, y_pred))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
" if getattr(data, 'base', None) is not None and \\\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"[08:48:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
"XGBoost training MAE = 6.7955854462420575\n",
"Mean Absolute Error: 6.735954869434804\n",
"Mean Squared Error: 94.02776011967829\n",
"Root Mean Squared Error: 9.696791228013435\n",
"R2 Score: 0.7508959737725993\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tqb7W8qlTOzE",
"colab_type": "text"
},
"source": [
"#### Predict the result"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xSWsnVwKTRml",
"colab_type": "code",
"outputId": "ed0c3158-8752-4b00-e2f7-e55aaea2a272",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"# Predict the result\n",
"pred_next_xdays(XGBModel, 'xgboost_pred.csv',30)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/gdrive/My Drive/Colab Notebooks/xgboost_pred.csv formosa-delights-186103.item_usage.xgboost_pred [SchemaField('order_dt', 'STRING', 'NULLABLE', None, ()), SchemaField('outlet_code', 'STRING', 'NULLABLE', None, ()), SchemaField('item_id', 'STRING', 'NULLABLE', None, ()), SchemaField('predicted', 'STRING', 'NULLABLE', None, ()), SchemaField('holiday_type', 'STRING', 'NULLABLE', None, ())]\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>order_dt</th>\n",
" <th>outlet_code</th>\n",
" <th>item_id</th>\n",
" <th>predicted</th>\n",
" <th>holiday_type</th>\n",
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" <th>8</th>\n",
" <td>2019-09-01</td>\n",
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" <td>33</td>\n",
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" <th>12</th>\n",
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" <td>C038</td>\n",
" <td>69</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET05</td>\n",
" <td>C039</td>\n",
" <td>41</td>\n",
" <td>0</td>\n",
" </tr>\n",
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" <td>2019-09-01</td>\n",
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" <td>C040</td>\n",
" <td>12</td>\n",
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" <th>16</th>\n",
" <td>2019-09-01</td>\n",
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" <td>C038</td>\n",
" <td>42</td>\n",
" <td>0</td>\n",
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" <th>18</th>\n",
" <td>2019-09-01</td>\n",
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" <td>C040</td>\n",
" <td>7</td>\n",
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" <td>2019-09-01</td>\n",
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" <td>C041</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>20</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C038</td>\n",
" <td>70</td>\n",
" <td>0</td>\n",
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" <th>21</th>\n",
" <td>2019-09-01</td>\n",
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" <td>C039</td>\n",
" <td>48</td>\n",
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" <th>22</th>\n",
" <td>2019-09-01</td>\n",
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" <td>C040</td>\n",
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" <th>24</th>\n",
" <td>2019-09-01</td>\n",
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" <td>C038</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
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" <th>25</th>\n",
" <td>2019-09-01</td>\n",
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" <td>8</td>\n",
" <td>0</td>\n",
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" <th>26</th>\n",
" <td>2019-09-01</td>\n",
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" <td>1</td>\n",
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" <td>3</td>\n",
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" <th>28</th>\n",
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" <td>C038</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>29</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET09</td>\n",
" <td>C039</td>\n",
" <td>28</td>\n",
" <td>0</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <tr>\n",
" <th>5010</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET09</td>\n",
" <td>C040</td>\n",
" <td>15</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5011</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET09</td>\n",
" <td>C041</td>\n",
" <td>14</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5012</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C038</td>\n",
" <td>27</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5013</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C039</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5014</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C040</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5015</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C041</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5016</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C038</td>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5017</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C039</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5018</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5019</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C041</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5020</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C038</td>\n",
" <td>32</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5021</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C039</td>\n",
" <td>11</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5022</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C040</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5023</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C041</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5024</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C038</td>\n",
" <td>30</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5025</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C039</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5026</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C040</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5027</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C041</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5028</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C038</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5029</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C039</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5030</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C040</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5031</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C041</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5032</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C038</td>\n",
" <td>60</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5033</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C039</td>\n",
" <td>21</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5034</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C040</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5035</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C041</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5036</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C038</td>\n",
" <td>37</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5037</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C039</td>\n",
" <td>17</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5038</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C040</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5039</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C041</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5040 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" order_dt outlet_code item_id predicted holiday_type\n",
"0 2019-09-01 OUTLET01 C038 37 0\n",
"1 2019-09-01 OUTLET01 C039 17 0\n",
"2 2019-09-01 OUTLET01 C040 7 0\n",
"3 2019-09-01 OUTLET01 C041 6 0\n",
"4 2019-09-01 OUTLET02 C038 26 0\n",
"5 2019-09-01 OUTLET02 C039 12 0\n",
"6 2019-09-01 OUTLET02 C040 3 0\n",
"7 2019-09-01 OUTLET02 C041 3 0\n",
"8 2019-09-01 OUTLET03 C038 33 0\n",
"9 2019-09-01 OUTLET03 C039 22 0\n",
"10 2019-09-01 OUTLET03 C040 6 0\n",
"11 2019-09-01 OUTLET03 C041 6 0\n",
"12 2019-09-01 OUTLET05 C038 69 0\n",
"13 2019-09-01 OUTLET05 C039 41 0\n",
"14 2019-09-01 OUTLET05 C040 12 0\n",
"15 2019-09-01 OUTLET05 C041 7 0\n",
"16 2019-09-01 OUTLET06 C038 42 0\n",
"17 2019-09-01 OUTLET06 C039 23 0\n",
"18 2019-09-01 OUTLET06 C040 7 0\n",
"19 2019-09-01 OUTLET06 C041 4 0\n",
"20 2019-09-01 OUTLET07 C038 70 0\n",
"21 2019-09-01 OUTLET07 C039 48 0\n",
"22 2019-09-01 OUTLET07 C040 11 0\n",
"23 2019-09-01 OUTLET07 C041 20 0\n",
"24 2019-09-01 OUTLET08 C038 22 0\n",
"25 2019-09-01 OUTLET08 C039 8 0\n",
"26 2019-09-01 OUTLET08 C040 1 0\n",
"27 2019-09-01 OUTLET08 C041 3 0\n",
"28 2019-09-01 OUTLET09 C038 47 0\n",
"29 2019-09-01 OUTLET09 C039 28 0\n",
"... ... ... ... ... ...\n",
"5010 2019-11-23 OUTLET09 C040 15 9\n",
"5011 2019-11-23 OUTLET09 C041 14 9\n",
"5012 2019-11-23 OUTLET10 C038 27 9\n",
"5013 2019-11-23 OUTLET10 C039 16 9\n",
"5014 2019-11-23 OUTLET10 C040 4 9\n",
"5015 2019-11-23 OUTLET10 C041 5 9\n",
"5016 2019-11-23 OUTLET11 C038 20 9\n",
"5017 2019-11-23 OUTLET11 C039 5 9\n",
"5018 2019-11-23 OUTLET11 C040 0 9\n",
"5019 2019-11-23 OUTLET11 C041 6 9\n",
"5020 2019-11-23 OUTLET13 C038 32 9\n",
"5021 2019-11-23 OUTLET13 C039 11 9\n",
"5022 2019-11-23 OUTLET13 C040 2 9\n",
"5023 2019-11-23 OUTLET13 C041 7 9\n",
"5024 2019-11-23 OUTLET14 C038 30 9\n",
"5025 2019-11-23 OUTLET14 C039 16 9\n",
"5026 2019-11-23 OUTLET14 C040 4 9\n",
"5027 2019-11-23 OUTLET14 C041 6 9\n",
"5028 2019-11-23 OUTLET17 C038 7 9\n",
"5029 2019-11-23 OUTLET17 C039 5 9\n",
"5030 2019-11-23 OUTLET17 C040 1 9\n",
"5031 2019-11-23 OUTLET17 C041 0 9\n",
"5032 2019-11-23 OUTLET18 C038 60 9\n",
"5033 2019-11-23 OUTLET18 C039 21 9\n",
"5034 2019-11-23 OUTLET18 C040 8 9\n",
"5035 2019-11-23 OUTLET18 C041 8 9\n",
"5036 2019-11-23 OUTLET19 C038 37 9\n",
"5037 2019-11-23 OUTLET19 C039 17 9\n",
"5038 2019-11-23 OUTLET19 C040 7 9\n",
"5039 2019-11-23 OUTLET19 C041 8 9\n",
"\n",
"[5040 rows x 5 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "l98fjvJoZeo0",
"colab_type": "code",
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 824
}
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"source": [
"df_pred = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
"df1 = df_pred.head(25)\n",
"df1"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Actual</th>\n",
" <th>Predicted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-04-07</th>\n",
" <td>50.0</td>\n",
" <td>23.987719</td>\n",
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" <tr>\n",
" <th>2017-07-03</th>\n",
" <td>40.0</td>\n",
" <td>48.325134</td>\n",
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" <tr>\n",
" <th>2017-08-28</th>\n",
" <td>30.0</td>\n",
" <td>29.984762</td>\n",
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" <tr>\n",
" <th>2018-10-24</th>\n",
" <td>30.0</td>\n",
" <td>32.509281</td>\n",
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" <th>2019-05-12</th>\n",
" <td>0.0</td>\n",
" <td>13.614963</td>\n",
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" <th>2018-12-19</th>\n",
" <td>0.0</td>\n",
" <td>3.995248</td>\n",
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" <tr>\n",
" <th>2017-11-17</th>\n",
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" <tr>\n",
" <th>2017-08-13</th>\n",
" <td>30.0</td>\n",
" <td>26.974371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-15</th>\n",
" <td>10.0</td>\n",
" <td>4.465562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-24</th>\n",
" <td>10.0</td>\n",
" <td>20.653194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-25</th>\n",
" <td>10.0</td>\n",
" <td>9.477861</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-17</th>\n",
" <td>30.0</td>\n",
" <td>26.973803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-30</th>\n",
" <td>10.0</td>\n",
" <td>3.478553</td>\n",
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" <tr>\n",
" <th>2018-10-05</th>\n",
" <td>20.0</td>\n",
" <td>21.433132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-28</th>\n",
" <td>0.0</td>\n",
" <td>5.968414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-04-01</th>\n",
" <td>0.0</td>\n",
" <td>7.927432</td>\n",
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" <tr>\n",
" <th>2018-12-20</th>\n",
" <td>50.0</td>\n",
" <td>31.096466</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-29</th>\n",
" <td>0.0</td>\n",
" <td>3.553644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-30</th>\n",
" <td>20.0</td>\n",
" <td>26.400726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-26</th>\n",
" <td>40.0</td>\n",
" <td>22.819540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-28</th>\n",
" <td>20.0</td>\n",
" <td>21.982077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-30</th>\n",
" <td>20.0</td>\n",
" <td>16.630430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-09</th>\n",
" <td>0.0</td>\n",
" <td>-1.548566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-09-15</th>\n",
" <td>10.0</td>\n",
" <td>9.001460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-26</th>\n",
" <td>0.0</td>\n",
" <td>1.221424</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Actual Predicted\n",
"2019-04-07 50.0 23.987719\n",
"2017-07-03 40.0 48.325134\n",
"2017-08-28 30.0 29.984762\n",
"2018-10-24 30.0 32.509281\n",
"2019-05-12 0.0 13.614963\n",
"2018-12-19 0.0 3.995248\n",
"2017-11-17 10.0 10.459047\n",
"2017-08-13 30.0 26.974371\n",
"2017-08-15 10.0 4.465562\n",
"2018-06-24 10.0 20.653194\n",
"2017-08-25 10.0 9.477861\n",
"2019-06-17 30.0 26.973803\n",
"2019-04-30 10.0 3.478553\n",
"2018-10-05 20.0 21.433132\n",
"2019-08-28 0.0 5.968414\n",
"2018-04-01 0.0 7.927432\n",
"2018-12-20 50.0 31.096466\n",
"2019-06-29 0.0 3.553644\n",
"2019-05-30 20.0 26.400726\n",
"2018-06-26 40.0 22.819540\n",
"2017-08-28 20.0 21.982077\n",
"2018-01-30 20.0 16.630430\n",
"2018-10-09 0.0 -1.548566\n",
"2018-09-15 10.0 9.001460\n",
"2019-03-26 0.0 1.221424"
]
},
"metadata": {
"tags": []
},
"execution_count": 34
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Rw1k1pYTWc_a",
"colab_type": "code",
"outputId": "18faf8f0-3602-49d3-f7b4-ca4cb1ffa409",
"colab": {
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"height": 283
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},
"source": [
"fig, ax = plt.subplots()\n",
"ax.scatter(y_test, y_pred)\n",
"ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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4eGRnZ/OXv/yF2bNnBz2m5uUDqHz2RRGMKvKqBxlTClbuL1grNtytWy9Fursl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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0-BAPDuKs0s-",
"colab_type": "text"
},
"source": [
"### Neural Net"
]
},
{
"cell_type": "code",
"metadata": {
"id": "JkTDDjYcThgU",
"colab_type": "code",
"outputId": "8f49588f-6d2e-4459-ae1a-8b7389ff267d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"from keras.callbacks import ModelCheckpoint\n",
"#from keras.models import Sequential\n",
"from keras.optimizers import Adam,RMSprop\n",
"from sklearn.metrics import accuracy_score\n",
"from keras import Sequential\n",
"from keras.layers import Dense,Activation\n",
"from keras.layers import Flatten, Embedding, Conv1D, MaxPooling1D, Dropout, BatchNormalization\n",
"from keras.callbacks import EarlyStopping\n",
"from sklearn.preprocessing import StandardScaler"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Pg1QrXbOXEiG",
"colab_type": "code",
"colab": {}
},
"source": [
"def build_regressor():\n",
" regressor = Sequential()\n",
" regressor.add(Dense(units=500, activation='relu', input_dim=31))\n",
" regressor.add(Dropout(0.2))\n",
" regressor.add(Dense(units=500, activation='relu'))\n",
" regressor.add(Dropout(0.2))\n",
" regressor.add(Dense(units=500, activation='relu'))\n",
" regressor.add(Dropout(0.2))\n",
" regressor.add(Dense(units=1, activation='linear'))\n",
" \n",
" opt = Adam(lr=1e-3, decay=1e-3 / 200)\n",
" \n",
" regressor.compile(optimizer=opt, loss='mean_absolute_error', metrics=['mean_absolute_error'])\n",
" #regressor.compile(optimizer=opt, loss='mean_squared_error', metrics=['mean_absolute_error', 'mean_squared_error'])\n",
" \n",
" return regressor"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mOuPT43zXMIz",
"colab_type": "code",
"outputId": "7475f8fe-5bf7-4bb1-b5cb-8a1d7aae6293",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 391
}
},
"source": [
"from keras.wrappers.scikit_learn import KerasRegressor\n",
"regressor = KerasRegressor(build_fn=build_regressor, batch_size=32,epochs=50)\n",
"model = build_regressor()\n",
"model.summary()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"sequential_4\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_13 (Dense) (None, 500) 16000 \n",
"_________________________________________________________________\n",
"dropout_10 (Dropout) (None, 500) 0 \n",
"_________________________________________________________________\n",
"dense_14 (Dense) (None, 500) 250500 \n",
"_________________________________________________________________\n",
"dropout_11 (Dropout) (None, 500) 0 \n",
"_________________________________________________________________\n",
"dense_15 (Dense) (None, 500) 250500 \n",
"_________________________________________________________________\n",
"dropout_12 (Dropout) (None, 500) 0 \n",
"_________________________________________________________________\n",
"dense_16 (Dense) (None, 1) 501 \n",
"=================================================================\n",
"Total params: 517,501\n",
"Trainable params: 517,501\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8f8WohoBXPvb",
"colab_type": "code",
"outputId": "397dfafa-dd2b-48f5-8521-5c10148c77c7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 700
}
},
"source": [
"#X_train, X_test, y_train, y_test\n",
"\n",
"early_stop =EarlyStopping(monitor='val_loss', patience=10)\n",
"results=regressor.fit(X_train,y_train,validation_data=(X_test, y_test), callbacks=[early_stop])\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 37916 samples, validate on 9480 samples\n",
"Epoch 1/50\n",
"37916/37916 [==============================] - 8s 199us/step - loss: 7.2461 - mean_absolute_error: 7.2461 - val_loss: 6.4793 - val_mean_absolute_error: 6.4793\n",
"Epoch 2/50\n",
"37916/37916 [==============================] - 7s 186us/step - loss: 6.6954 - mean_absolute_error: 6.6954 - val_loss: 6.4697 - val_mean_absolute_error: 6.4697\n",
"Epoch 3/50\n",
"37916/37916 [==============================] - 7s 184us/step - loss: 6.6533 - mean_absolute_error: 6.6533 - val_loss: 6.3430 - val_mean_absolute_error: 6.3430\n",
"Epoch 4/50\n",
"37916/37916 [==============================] - 7s 180us/step - loss: 6.6018 - mean_absolute_error: 6.6018 - val_loss: 6.3351 - val_mean_absolute_error: 6.3351\n",
"Epoch 5/50\n",
"37916/37916 [==============================] - 7s 184us/step - loss: 6.5463 - mean_absolute_error: 6.5463 - val_loss: 6.3211 - val_mean_absolute_error: 6.3211\n",
"Epoch 6/50\n",
"37916/37916 [==============================] - 7s 186us/step - loss: 6.5528 - mean_absolute_error: 6.5528 - val_loss: 6.3845 - val_mean_absolute_error: 6.3845\n",
"Epoch 7/50\n",
"37916/37916 [==============================] - 7s 183us/step - loss: 6.5186 - mean_absolute_error: 6.5186 - val_loss: 6.4302 - val_mean_absolute_error: 6.4302\n",
"Epoch 8/50\n",
"37916/37916 [==============================] - 7s 186us/step - loss: 6.4986 - mean_absolute_error: 6.4986 - val_loss: 6.5371 - val_mean_absolute_error: 6.5371\n",
"Epoch 9/50\n",
"37916/37916 [==============================] - 7s 187us/step - loss: 6.4654 - mean_absolute_error: 6.4654 - val_loss: 6.2738 - val_mean_absolute_error: 6.2738\n",
"Epoch 10/50\n",
"37916/37916 [==============================] - 7s 185us/step - loss: 6.4603 - mean_absolute_error: 6.4603 - val_loss: 6.2882 - val_mean_absolute_error: 6.2882\n",
"Epoch 11/50\n",
"37916/37916 [==============================] - 7s 185us/step - loss: 6.4223 - mean_absolute_error: 6.4223 - val_loss: 6.2746 - val_mean_absolute_error: 6.2746\n",
"Epoch 12/50\n",
"37916/37916 [==============================] - 7s 193us/step - loss: 6.4177 - mean_absolute_error: 6.4177 - val_loss: 6.3103 - val_mean_absolute_error: 6.3103\n",
"Epoch 13/50\n",
"37916/37916 [==============================] - 7s 185us/step - loss: 6.4202 - mean_absolute_error: 6.4202 - val_loss: 6.3688 - val_mean_absolute_error: 6.3688\n",
"Epoch 14/50\n",
"37916/37916 [==============================] - 7s 185us/step - loss: 6.3868 - mean_absolute_error: 6.3868 - val_loss: 6.2872 - val_mean_absolute_error: 6.2872\n",
"Epoch 15/50\n",
"37916/37916 [==============================] - 7s 191us/step - loss: 6.3676 - mean_absolute_error: 6.3676 - val_loss: 6.3051 - val_mean_absolute_error: 6.3051\n",
"Epoch 16/50\n",
"37916/37916 [==============================] - 7s 182us/step - loss: 6.3807 - mean_absolute_error: 6.3807 - val_loss: 6.2806 - val_mean_absolute_error: 6.2806\n",
"Epoch 17/50\n",
"37916/37916 [==============================] - 7s 182us/step - loss: 6.3875 - mean_absolute_error: 6.3875 - val_loss: 6.2797 - val_mean_absolute_error: 6.2797\n",
"Epoch 18/50\n",
"37916/37916 [==============================] - 7s 184us/step - loss: 6.3561 - mean_absolute_error: 6.3561 - val_loss: 6.4150 - val_mean_absolute_error: 6.4150\n",
"Epoch 19/50\n",
"37916/37916 [==============================] - 7s 190us/step - loss: 6.3477 - mean_absolute_error: 6.3477 - val_loss: 6.2976 - val_mean_absolute_error: 6.2976\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KvndyJNZBE19",
"colab_type": "code",
"outputId": "fc35b159-3065-4859-c972-11c0c2397681",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 72
}
},
"source": [
"model.model.save('saved_model.h5')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.\n",
" warnings.warn('`Sequential.model` is deprecated. '\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Zv0EOsEKh7D3",
"colab_type": "code",
"outputId": "ac807871-758b-441d-a414-cb18f530423e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
}
},
"source": [
"def plot_history(history):\n",
" hist = pd.DataFrame(history.history)\n",
" hist['epoch'] = history.epoch\n",
" \n",
" plt.figure()\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Mean Abs Error [MPG]')\n",
" plt.plot(hist['epoch'], hist['mean_absolute_error'],\n",
" label='Train Error')\n",
" plt.plot(hist['epoch'], hist['val_mean_absolute_error'],\n",
" label = 'Val Error')\n",
" #plt.ylim([0,5])\n",
" plt.legend()\n",
"\n",
" #plt.figure()\n",
" #plt.xlabel('Epoch')\n",
" #plt.ylabel('Mean Square Error [$MPG^2$]')\n",
" #plt.plot(hist['epoch'], hist['mean_squared_error'],\n",
" # label='Train Error')\n",
" #plt.plot(hist['epoch'], hist['val_mean_squared_error'],\n",
" # label = 'Val Error')\n",
" \n",
" #plt.legend()\n",
" plt.show()\n",
"\n",
"\n",
"plot_history(results)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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UlFJByFVH8xnY5qOhwEJgpjEmyxeB+Utqkr0+YUueDoynlAo+rvoUFgKrgK+ASOzcB9fV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\nnby3aje9O8RxwcA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BW2HwO8eyB7FfaIAobNPCJmAZkO6HGE/HNhWsAlY6bucB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\nVUuTglJKqVqaFJRSStX6f5TDYnd4DK60AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "OmI79S_VJfee",
"colab_type": "code",
"outputId": "8ee394bf-5854-4966-eca9-4a6544f0b417",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 824
}
},
"source": [
"y_pred = regressor.predict(X_test)\n",
"df_pred = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
"df1 = df_pred.head(25)\n",
"df1"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\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>Actual</th>\n",
" <th>Predicted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-04-07</th>\n",
" <td>50.0</td>\n",
" <td>21.185818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-03</th>\n",
" <td>40.0</td>\n",
" <td>47.219410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-28</th>\n",
" <td>30.0</td>\n",
" <td>30.309542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-24</th>\n",
" <td>30.0</td>\n",
" <td>30.318447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-05-12</th>\n",
" <td>0.0</td>\n",
" <td>10.561611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-19</th>\n",
" <td>0.0</td>\n",
" <td>0.006997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-17</th>\n",
" <td>10.0</td>\n",
" <td>10.138051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-13</th>\n",
" <td>30.0</td>\n",
" <td>27.689070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-15</th>\n",
" <td>10.0</td>\n",
" <td>0.006997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-24</th>\n",
" <td>10.0</td>\n",
" <td>19.620787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-25</th>\n",
" <td>10.0</td>\n",
" <td>9.969027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-17</th>\n",
" <td>30.0</td>\n",
" <td>26.449509</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-04-30</th>\n",
" <td>10.0</td>\n",
" <td>0.006997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-05</th>\n",
" <td>20.0</td>\n",
" <td>22.565140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-08-28</th>\n",
" <td>0.0</td>\n",
" <td>10.020723</td>\n",
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" <tr>\n",
" <th>2018-04-01</th>\n",
" <td>0.0</td>\n",
" <td>9.946705</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-12-20</th>\n",
" <td>50.0</td>\n",
" <td>39.810631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-29</th>\n",
" <td>0.0</td>\n",
" <td>0.006997</td>\n",
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" <tr>\n",
" <th>2019-05-30</th>\n",
" <td>20.0</td>\n",
" <td>30.811691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-26</th>\n",
" <td>40.0</td>\n",
" <td>19.961226</td>\n",
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" <tr>\n",
" <th>2017-08-28</th>\n",
" <td>20.0</td>\n",
" <td>21.939608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-30</th>\n",
" <td>20.0</td>\n",
" <td>18.356581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-10-09</th>\n",
" <td>0.0</td>\n",
" <td>0.006997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-09-15</th>\n",
" <td>10.0</td>\n",
" <td>14.508426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-26</th>\n",
" <td>0.0</td>\n",
" <td>0.006997</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Actual Predicted\n",
"2019-04-07 50.0 21.185818\n",
"2017-07-03 40.0 47.219410\n",
"2017-08-28 30.0 30.309542\n",
"2018-10-24 30.0 30.318447\n",
"2019-05-12 0.0 10.561611\n",
"2018-12-19 0.0 0.006997\n",
"2017-11-17 10.0 10.138051\n",
"2017-08-13 30.0 27.689070\n",
"2017-08-15 10.0 0.006997\n",
"2018-06-24 10.0 19.620787\n",
"2017-08-25 10.0 9.969027\n",
"2019-06-17 30.0 26.449509\n",
"2019-04-30 10.0 0.006997\n",
"2018-10-05 20.0 22.565140\n",
"2019-08-28 0.0 10.020723\n",
"2018-04-01 0.0 9.946705\n",
"2018-12-20 50.0 39.810631\n",
"2019-06-29 0.0 0.006997\n",
"2019-05-30 20.0 30.811691\n",
"2018-06-26 40.0 19.961226\n",
"2017-08-28 20.0 21.939608\n",
"2018-01-30 20.0 18.356581\n",
"2018-10-09 0.0 0.006997\n",
"2018-09-15 10.0 14.508426\n",
"2019-03-26 0.0 0.006997"
]
},
"metadata": {
"tags": []
},
"execution_count": 46
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UwT0Vrgb0yae",
"colab_type": "code",
"outputId": "6a04141d-ff60-411f-fa6d-5415ed4e7369",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
}
},
"source": [
"df_cd.head(5)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>outlet_code</th>\n",
" <th>outlet_name</th>\n",
" <th>item_id</th>\n",
" <th>item_qty</th>\n",
" <th>item_desc</th>\n",
" <th>item_type</th>\n",
" <th>month</th>\n",
" <th>year</th>\n",
" <th>order_dt</th>\n",
" <th>day</th>\n",
" <th>day_of_week</th>\n",
" <th>holiday_type</th>\n",
" <th>holiday_desc</th>\n",
" <th>predicted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>OUTLET01</td>\n",
" <td>新达成</td>\n",
" <td>C041</td>\n",
" <td>0</td>\n",
" <td>卤肉</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>nan</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>OUTLET01</td>\n",
" <td>新达成</td>\n",
" <td>C039</td>\n",
" <td>30</td>\n",
" <td>排刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>nan</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>OUTLET01</td>\n",
" <td>新达成</td>\n",
" <td>C040</td>\n",
" <td>30</td>\n",
" <td>鸡刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>nan</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>OUTLET01</td>\n",
" <td>新达成</td>\n",
" <td>C038</td>\n",
" <td>50</td>\n",
" <td>牛刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>nan</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>OUTLET02</td>\n",
" <td>莱佛士</td>\n",
" <td>C039</td>\n",
" <td>10</td>\n",
" <td>排刀</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2017</td>\n",
" <td>2017-07-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>nan</td>\n",
" <td>12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" outlet_code outlet_name item_id ... holiday_type holiday_desc predicted\n",
"0 OUTLET01 新达成 C041 ... 0 nan 6\n",
"1 OUTLET01 新达成 C039 ... 0 nan 16\n",
"2 OUTLET01 新达成 C040 ... 0 nan 6\n",
"3 OUTLET01 新达成 C038 ... 0 nan 36\n",
"4 OUTLET02 莱佛士 C039 ... 0 nan 12\n",
"\n",
"[5 rows x 14 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 73
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PWodbRb4_knj",
"colab_type": "code",
"outputId": "8ea926ff-28ea-4ad1-c585-973d6900f7b8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"source": [
"y_pred = regressor.predict(X_test)\n",
"print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) \n",
"print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) \n",
"print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
"print('R2 Score:', metrics.r2_score(y_test, y_pred))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Mean Absolute Error: 6.297633879050565\n",
"Mean Squared Error: 107.49801179927341\n",
"Root Mean Squared Error: 10.368124796667592\n",
"R2 Score: 0.71520976872621\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "j19VERFQxYXK",
"colab_type": "code",
"outputId": "79645bfd-a387-4d42-815a-430b7dccc553",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"# Predict the result\n",
"pred_next_xdays(regressor, 'nn_pred.csv',30)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/gdrive/My Drive/Colab Notebooks/nn_pred.csv formosa-delights-186103.item_usage.nn_pred [SchemaField('order_dt', 'STRING', 'NULLABLE', None, ()), SchemaField('outlet_code', 'STRING', 'NULLABLE', None, ()), SchemaField('item_id', 'STRING', 'NULLABLE', None, ()), SchemaField('predicted', 'STRING', 'NULLABLE', None, ()), SchemaField('holiday_type', 'STRING', 'NULLABLE', None, ())]\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
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" <th></th>\n",
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" <th>predicted</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET01</td>\n",
" <td>C038</td>\n",
" <td>36</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET01</td>\n",
" <td>C039</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>2019-09-01</td>\n",
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" <td>C040</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET02</td>\n",
" <td>C038</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET02</td>\n",
" <td>C039</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET02</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET02</td>\n",
" <td>C041</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET03</td>\n",
" <td>C038</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET03</td>\n",
" <td>C039</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2019-09-01</td>\n",
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" <td>C040</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2019-09-01</td>\n",
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" <td>C041</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET05</td>\n",
" <td>C038</td>\n",
" <td>70</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET05</td>\n",
" <td>C039</td>\n",
" <td>46</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET05</td>\n",
" <td>C040</td>\n",
" <td>10</td>\n",
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" <tr>\n",
" <th>15</th>\n",
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" <td>9</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET06</td>\n",
" <td>C038</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET06</td>\n",
" <td>C039</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET06</td>\n",
" <td>C040</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET06</td>\n",
" <td>C041</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C038</td>\n",
" <td>67</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C039</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C040</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET07</td>\n",
" <td>C041</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C038</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C039</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET08</td>\n",
" <td>C041</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET09</td>\n",
" <td>C038</td>\n",
" <td>44</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2019-09-01</td>\n",
" <td>OUTLET09</td>\n",
" <td>C039</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5010</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET09</td>\n",
" <td>C040</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5011</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET09</td>\n",
" <td>C041</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5012</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C038</td>\n",
" <td>27</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5013</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C039</td>\n",
" <td>19</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5014</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5015</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET10</td>\n",
" <td>C041</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5016</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C038</td>\n",
" <td>18</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5017</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C039</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5018</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5019</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET11</td>\n",
" <td>C041</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5020</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C038</td>\n",
" <td>26</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5021</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C039</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5022</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5023</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET13</td>\n",
" <td>C041</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5024</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C038</td>\n",
" <td>31</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5025</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C039</td>\n",
" <td>21</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5026</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5027</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET14</td>\n",
" <td>C041</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5028</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C038</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5029</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C039</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5030</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5031</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET17</td>\n",
" <td>C041</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5032</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C038</td>\n",
" <td>64</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5033</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C039</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5034</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C040</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5035</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET18</td>\n",
" <td>C041</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5036</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C038</td>\n",
" <td>44</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5037</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C039</td>\n",
" <td>19</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5038</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C040</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5039</th>\n",
" <td>2019-11-23</td>\n",
" <td>OUTLET19</td>\n",
" <td>C041</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5040 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" order_dt outlet_code item_id predicted holiday_type\n",
"0 2019-09-01 OUTLET01 C038 36 0\n",
"1 2019-09-01 OUTLET01 C039 17 0\n",
"2 2019-09-01 OUTLET01 C040 9 0\n",
"3 2019-09-01 OUTLET01 C041 10 0\n",
"4 2019-09-01 OUTLET02 C038 27 0\n",
"5 2019-09-01 OUTLET02 C039 10 0\n",
"6 2019-09-01 OUTLET02 C040 0 0\n",
"7 2019-09-01 OUTLET02 C041 9 0\n",
"8 2019-09-01 OUTLET03 C038 31 0\n",
"9 2019-09-01 OUTLET03 C039 25 0\n",
"10 2019-09-01 OUTLET03 C040 9 0\n",
"11 2019-09-01 OUTLET03 C041 9 0\n",
"12 2019-09-01 OUTLET05 C038 70 0\n",
"13 2019-09-01 OUTLET05 C039 46 0\n",
"14 2019-09-01 OUTLET05 C040 10 0\n",
"15 2019-09-01 OUTLET05 C041 9 0\n",
"16 2019-09-01 OUTLET06 C038 40 0\n",
"17 2019-09-01 OUTLET06 C039 21 0\n",
"18 2019-09-01 OUTLET06 C040 9 0\n",
"19 2019-09-01 OUTLET06 C041 0 0\n",
"20 2019-09-01 OUTLET07 C038 67 0\n",
"21 2019-09-01 OUTLET07 C039 50 0\n",
"22 2019-09-01 OUTLET07 C040 10 0\n",
"23 2019-09-01 OUTLET07 C041 18 0\n",
"24 2019-09-01 OUTLET08 C038 0 0\n",
"25 2019-09-01 OUTLET08 C039 0 0\n",
"26 2019-09-01 OUTLET08 C040 0 0\n",
"27 2019-09-01 OUTLET08 C041 0 0\n",
"28 2019-09-01 OUTLET09 C038 44 0\n",
"29 2019-09-01 OUTLET09 C039 26 0\n",
"... ... ... ... ... ...\n",
"5010 2019-11-23 OUTLET09 C040 10 9\n",
"5011 2019-11-23 OUTLET09 C041 9 9\n",
"5012 2019-11-23 OUTLET10 C038 27 9\n",
"5013 2019-11-23 OUTLET10 C039 19 9\n",
"5014 2019-11-23 OUTLET10 C040 0 9\n",
"5015 2019-11-23 OUTLET10 C041 0 9\n",
"5016 2019-11-23 OUTLET11 C038 18 9\n",
"5017 2019-11-23 OUTLET11 C039 0 9\n",
"5018 2019-11-23 OUTLET11 C040 0 9\n",
"5019 2019-11-23 OUTLET11 C041 9 9\n",
"5020 2019-11-23 OUTLET13 C038 26 9\n",
"5021 2019-11-23 OUTLET13 C039 10 9\n",
"5022 2019-11-23 OUTLET13 C040 0 9\n",
"5023 2019-11-23 OUTLET13 C041 10 9\n",
"5024 2019-11-23 OUTLET14 C038 31 9\n",
"5025 2019-11-23 OUTLET14 C039 21 9\n",
"5026 2019-11-23 OUTLET14 C040 0 9\n",
"5027 2019-11-23 OUTLET14 C041 0 9\n",
"5028 2019-11-23 OUTLET17 C038 0 9\n",
"5029 2019-11-23 OUTLET17 C039 0 9\n",
"5030 2019-11-23 OUTLET17 C040 0 9\n",
"5031 2019-11-23 OUTLET17 C041 0 9\n",
"5032 2019-11-23 OUTLET18 C038 64 9\n",
"5033 2019-11-23 OUTLET18 C039 22 9\n",
"5034 2019-11-23 OUTLET18 C040 10 9\n",
"5035 2019-11-23 OUTLET18 C041 9 9\n",
"5036 2019-11-23 OUTLET19 C038 44 9\n",
"5037 2019-11-23 OUTLET19 C039 19 9\n",
"5038 2019-11-23 OUTLET19 C040 0 9\n",
"5039 2019-11-23 OUTLET19 C041 10 9\n",
"\n",
"[5040 rows x 5 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FwgAYi2aAj7q",
"colab_type": "code",
"outputId": "5ca0426c-2b5b-4d64-fff1-f417c2f6d19b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
}
},
"source": [
"fig, ax = plt.subplots()\n",
"ax.scatter(y_test, y_pred)\n",
"ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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BgwwYMIDGjRvz/fff+31MUlISt9xyiy2VYQpx8xpET+BTr/uNRWSJiHwlIpcFOkhEeotI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\nrkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "U0dd9pkHdSq2",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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