Retail_TimeSeries_Forecast_Modelling_V1.00.ipynb

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    "# TABLE OF CONTENTS: \n",
    "\n",
    "\n",
    "* [Data Import & Cleaning](#data_load_and_clean)\n",
    "* [Data Preparation & Dummy Variable Creation](#exog_var)\n",
    "* [Breakpoint Subsampling Split for Training/Test Sets](#sub_sampling)\n",
    "* [Initial Data Exploration: ACF / PACF](#initial_data_exploration)\n",
    "* [Forecasting Overview](#forecasting_intro)\n",
    "    * [Naive Mean (Baseline)](#naive_forecast)\n",
    "    * [Walk-Forward SARIMAX](#sarimax)\n",
    "    * [Static SARIMAX Forecast](#static_sarimax)\n",
    "    * [TBATS](#tbats)\n",
    "    * [XGBoost](#xgboost)\n",
    "        * [Feature Engineering](#feature_engineering)\n",
    "        * [XGBoost Parameter Tuning Guide & Process](#xgboost_param_tuning)\n",
    "    * [Final Results: XGBoost Features Breakdown & Final Models Comparisons and Conclusion](#xgboost_shap)"
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    "### BUSINESS PROBLEM / PURPOSE OF THIS PROJECT \n",
    "\n",
    "* Because purchase budgets are contingent on the sales forecast (calculated as COGS benchmarks) at the end of each month for the proceding month, the sales forecasts are running 4w static forecasts. This means that the next month's sales (forecasted by week) must be forecasted in advance and cannot be iteratively updated (walk-forward predictions). This problem has been further exacerbated by COVID creating a sort of paradigm shift in supermarket trends\n",
    "    * Thus, sales (and hence, expense budgets) must be confirmed on a rolling 1M (~4W) basis\n",
    "    * The overarching challenge is to create a robust 1M static forecast method, from which the relative department COGS benchmarks can be applied and purchasing budgets can be distributed in advance to stores on a rolling 1 month basis\n",
    "        * overly optimistic sales forecasts result in high budgets, inventory ballooning and potentially lower bottom-line GP/NP through higher shrinkage\n",
    "        * overly pessimistic forecasts could result in inventory gaps which result in less sales, and less GP/NP as a result\n",
    "  \n",
    "\n",
    "#### THE DATA ... \n",
    "* Roughly 3Y of daily sales data\n",
    "    * includes items by day, baskets (# transactions), average spend per customers, marked-down sales, promotional sales\n",
    "    \n",
    "### PROJECT FLOW \n",
    "* Data cleaning (for privacy concerns, the name of the store and other sensitive information has been censored) \n",
    "* Create Data Features: \n",
    "    * Create dummy variables to mark public holidays (automatically achieved by use of a package called \"holidays\" and specialised to the QLD region\n",
    "    * Acknowledge the impact of COVID occuring half-way through the sample. Statistically determine the data's breakpoint (noted by a drastic change in trend to reflect customer's new shopping habits)- use this knowledge to further restrict the time horizon of the sample to maintain representativeness\n",
    "    * Add additional time series features in the XGboost section, including: day of the week, week of the year\n",
    "        * Also experiment with a \"days since last public holiday\" and \"days until\" time series feature, but it was ultimately found to be insignificant. \n",
    "* Split the now restricted post structural break data into test-train split to match the business problem (4W static forecast)\n",
    "* Initial Data Exploration:\n",
    "    * View some time series characteristics of the data which can help inform the SARIMAX / TBATS models\n",
    "    * ACF / PACF etc.\n",
    "\n",
    "##### MODELS\n",
    "* Baseline (Naive Forecast) (~ 10% MAPE)\n",
    "* Walk-forward SARIMAX Vs. Static SARIMAX (~ 3.56% MAPE)\n",
    "    * Automate the procedure of optimisting the BIC for selecting the length of the (AR, MA, SAR, SMA) terms in the SARIMAX model; whereby, X = holiday_dummy variable\n",
    "    * Note: the walk-forward procedure only beat the static SARIMAX procedure MAPE by (0.08%)\n",
    "* TBATS forecast (~ 7.56% MAPE) with seasonality informed from the ACF/PACF plots\n",
    "* XGBoost - lengthy parameter tuning and refinement process to settle on the final model iteration which achieved (~2.99% MAPE)\n",
    "    * Present an analysis on the significance of each feature in the XGboost model \n",
    "\n",
    "##### PRESENT A FINAL MODEL COMPARISON AND GRAPHICAL BREAKDOWN \n",
    "* Generally the static sarimax is presented as performing surprisingly well given its ease of implementation and relative simplicity to the XGBoost\n",
    "* XGboost performd the best, but the trade-off is computational time and complexity\n",
    "\n",
    "### FUTURE PROJECT DEVELOPMENT:\n",
    "* Increase the forecast horizon from 1M to 3M and recalibrate the models to determine if the SARIMAX still performs reasonably well Vs. the more complicated XGBoost model"
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    "## FINAL MODEL RESULTS PREVIEW:"
   ]
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+9uwZSpcujV69eqF169Y4ffo0AgMDsWzZMhw6dCjP2vKiUqlyvT8ACCGQmZkJfX39XPXkV4eenl6u+URGRqJEiRKYNm0aVCoVOnbsiFatWiEyMhJCCOjo6GDz5s24du0azp49i1mzZqF58+YYO3ZsvvVmL1f2ewkhXtuoZG8I38TAwADt27fHwYMHce/ePbi6uuZ6Xq1W51oWtVqda0Micpzckv3eKpUK1atXx44dO6TnoqOjYWpqKg3LaFrPkydPcPLkSYSFheHIkSMAsjZsGzZskMLpXcPnbRQKBYQQUKvVr7WjSqXC77//jkWLFmHfvn2oXLkyfHx84OLigvbt26NEiRIAsr5Durq6uaYtXbo0vvzyS1y5cgVNmjTJ9dzIkSMxePDgt7b7q8OEeX1XAeRaH7LXtbCwMAwZMgSenp5o2rQpGjVqhGnTpkmvy+t7BwDz58/HnTt3cObMGaxZswZ79+7FkCFD3vidASC1A/B/7fkqW1tbPHr0CDExMTAzM8v1XEhIiLTx7t69O1xdXeHm5oakpCQ0btwYL1++fOP2KD4+HgYGBq99JwAgMDAQiYmJ0kkOkZGROH36NF6+fImRI0eiRYsWGDt2LD777DNp+M7a2hrp6enw9/dH/fr189yu+Pv748WLF+jWrRscHBzg4OAALy8vtGzZEjdu3MC1a9fw999/w93dHU5OTjAxMUFERIQ0/Yd8rjmp1WppqB/ICvmEhIQ85/WqQnsqubGxMWxtbfHnn38CyAqU7777Dv7+/q+9Ni4uDosXL5YSWwiBu3fvonbt2gCAZs2aYcOGDRBCQKlUYvDgwXmO3efUrFkzHDx4EM+ePQOQtRf9ww8/AAB69eqFmzdvomvXrpgxYwYSExMRExPzzstma2uLBw8e4OrVqwCAu3fv4vz582jcuPF71WFvbw8/Pz+o1WoolUqMGDEC58+fR1BQEIYOHSr1MK9cuQKVSoVbt26hc+fOqF69OgYNGgRPT09cu3YNQNYe65v2KvPSsmVL7Nu3D0qlEpmZmW/cU83JxcUFfn5+OH/+vLSnlq158+bYtWuXNIa+adMmNGrUCF988QXq1KkjBVBYWBju3LkjteOjR49w/vx5AMDNmzfh4OCA6Ojod1qG/OrZvn07GjZsiMDAQAQEBCAgIAC7d+/GjRs3cOnSpXea/7sqU6YMPv/88zyPVW3duhVt2rSBubm5dExVR0cHmZmZuT6viIgIWFpavjb9sGHDMHPmTDx69AhAVtitWLECt27dgqWl5Rvb/dWN19vs27cParUaCQkJ+Pfff9GmTRucP38eNjY2+PHHH9G4cWP4+/tDpVLlO5+4uDi0bNkSJiYm8PT0xKhRo3Dt2jVYWlpCT09P2lGIjo7G4cOHXwvd/Jibm8PDwwOjR4/OtY7s2rULR44cwYABA6TX1atXD97e3ujevTuA99se5dSpUycEBARg79692Lt3L9q0aQNPT0+MHDkSQNaoSVJSEvz9/XMdK23ZsiVWr16d5/EmAChZsiQWLFiAe/fuSY+Fh4dDV1cXFhYWCAoKkgK2WrVqCAgIeGvb5yWvzzWnZs2aYefOnXj58iWArDN937azm022Pad3MX/+fMyYMQNOTk5QKpXo3LlzrqGMbFOnTsXChQvRpUsXGBgYIDMzE9988w28vb0BAJMmTcLMmTPh5OSEjIwMNGnSBP3798/3vZs1a4YBAwagb9++UCgUMDY2xrJly6BQKPDzzz9j1qxZWLRoERQKBYYNG5ZrSOZtTE1NsXjxYsyYMQNpaWlQKBT47bffUK1aNVy+fPmd68je8Dg7O0OlUqFTp05o3769NNRlZGQEY2NjNGrUCI8fP4abmxs6duyIbt26wcjICCVKlMDkyZMBAG3atMGCBQuQkZHxTsvQtWtXPHz4EC4uLjAyMkLlypWlPco3qV+/PlJTU9GmTZvX9jK7d++OyMhIuLm5Qa1Wo2rVqpg/fz4AYMGCBZgwYQJ8fX1hYWEhbYRNTU2xZMkSzJ07F+np6RBCYO7cuahcuXKuoYn3rUepVGLnzp25TvMFgC+//BKOjo7YsGGDNOSWn5zDfjmtX78eZcuWzfVY9hBV9h4okLWh3r9/v7QjlX0AfufOnXBxcZGGfp8/f47Y2Fg0aNDgtfdycnKCEAKjR49GZmYm0tPTUadOHfz1118wMDDIt93fR1paGrp3747k5GT07t0b9vb2sLKywpEjR9CxY0eo1Wq0bt0aCQkJ0oYsL6amphg8eDA8PT1RokQJ6OrqwsfHB/r6+lixYgV8fHywdOlSqFQqDB06FN98881bLyvIacyYMdixYwcGDx4MpVIJpVKJunXrwtfXF5UqVZJe5+bmhpEjR2LlypXSY2/aHuXskWiiSZMmOHPmDGrUqCE91qpVK2zduhUtW7bMc5pvvvkGU6ZMwbhx45CUlARdXV2YmZlh7dq1KF26NPr27Qtvb2/pUIatra20U/c+8vpccy6vm5sboqOj0aNHDygUClSoUAGzZ89+p3krRF79W6IPFBQUhNjYWGnj6+PjA0NDQ2n4kd5PeHg4Ro4ciV27dr02fPU2S5cuhamp6TsF5sfg4eEBd3d3dOjQQSvvTx/Hx/5cC+2wHsmblZWVdPq1o6Mj4uPj8zzNn95NlSpV4OLikutU9ncRGRmJsLCwXCfMEBUG7DkREZHssOdERESyw3AiIiLZ+SRn68XEJH2Kt9GaMmWMEB+f8vYXUoFhm396bHPtKMrtbmZW6o3PvVPP6cqVK/Dw8AAAPHr0CN999x169+6NqVOn5nt1eXGhp6f79hdRgWKbf3psc+0oru3+1nBau3YtJk+ejPT0dADAb7/9hlGjRmHr1q0QQrz1IjMiIqL39dZwsrCwwNKlS6W/w8LCpDsVtGjRAmfOnPl41RERUbH01mNODg4Oua74FUJIFwGWLFkSSUlvP55UpoxRke+a5jd2Sh8H2/zTY5trR3Fs9/c+ISLnjf2Sk5Px+eefv3WaonowL5uZWakif9KH3LDNPz22uXYU5Xb/4BMicqpdu7Z0r6pTp07l+XMSREREH+K9w2ncuHFYunQpevbsiYyMjPf+hVciIqK3+SS3LyqqXdJsRbnbLVds80+Pba4dRbndC3RYj4iI6GNjOBERkewUqh8b7Ds7QNslFBrrx7d5+4uIiGSKPSciIpIdhhMREckOw4mIiGSH4URERLLDcCIiItlhOBERkewwnIiISHYYTkREJDsMJyIikh2GExERyQ7DiYiIZIfhREREssNwIiIi2WE4ERGR7DCciIhIdhhOREQkOwwnIiKSHYYTERHJDsOJiIhkh+FERESyw3AiIiLZYTgREZHsMJyIiEh2GE5ERCQ7DCciIpIdhhMREckOw4mIiGSH4URERLLDcCIiItlhOBERkewwnIiISHYYTkREJDt6mkyUkZGB8ePH48mTJ9DR0cGMGTNQvXr1gq6NiIiKKY16TidPnkRmZiZ8fX0xdOhQLFq0qIDLIiKi4kyjcKpWrRpUKhXUajVevnwJPT2NOmBERER50ihVjIyM8OTJE3Ts2BHx8fFYtWpVvq8vU8YIenq6GhVImjEzK6XtEj664rCMcsM2147i2O4ahdOGDRvQrFkzjBkzBpGRkfjhhx+wf/9+GBoa5vn6+PiUDyqS3l9MTJK2S/iozMxKFflllBu2uXYU5XbPL3Q1CqfPP/8c+vr6AIDSpUsjMzMTKpVKs+qIiIheoVE4eXp6YuLEiejduzcyMjLg5eUFIyOjgq6NiIiKKY3CqWTJkli8eHFB10JERASAF+ESEZEMMZyIiEh2GE5ERCQ7DCciIpIdhhMREckOw4mIiGSH4URERLLDcCIiItlhOBERkewwnIiISHYYTkREJDsMJyIikh2GExERyQ5/X51IhvrODtB2CYXG+vFttF0CfQTsORERkewwnIiISHYYTkREJDsMJyIikh2GExERyQ7DiYiIZIfhREREssNwIiIi2WE4ERGR7DCciIhIdhhOREQkOwwnIiKSHYYTERHJDsOJiIhkh+FERESyw3AiIiLZYTgREZHsMJyIiEh2GE5ERCQ7DCciIpIdhhMREcmOnqYTrl69GgEBAcjIyMB3330HNze3gqyLiIiKMY3CKSQkBJcvX8a2bduQmpqK9evXF3RdRERUjGkUTkFBQbC2tsbQoUPx8uVLjB07Nt/XlyljBD09XY0KJM2YmZXSdgkfXXFYRnq74rAeFIdlfJVG4RQfH4+nT59i1apViIiIwODBg3Ho0CEoFIo3vD7lg4qk9xcTk6TtEj4qM7NSRX4Z6d0U9fWgKK/r+YWuRuFkYmICS0tLGBgYwNLSEoaGhoiLi0PZsmU1LpKIiCibRmfrNWzYEIGBgRBCIDo6GqmpqTAxMSng0oiIqLjSqOfUunVrnD9/Ht27d4cQAt7e3tDV5TElIiIqGBqfSv62kyCIiIg0xYtwiYhIdhhOREQkOwwnIiKSHYYTERHJDsOJiIhkh+FERESyw3AiIiLZYTgREZHsMJyIiEh2GE5ERCQ7DCciIpIdhhMREckOw4mIiGSH4URERLLDcCIiItlhOBERkewwnIiISHYYTkREJDsMJyIikh2GExERyQ7DiYiIZIfhREREssNwIiIi2WE4ERGR7DCciIhIdhhOREQkOwwnIiKSHYYTERHJDsOJiIhkh+FERESyw3AiIiLZYTgREZHsMJyIiEh2GE5ERCQ7DCciIpKdDwqn2NhYtGzZEvfv3y+oeoiIiDQPp4yMDHh7e6NEiRIFWQ8RERH0NJ1wzpw56NWrF9asWfPW15YpYwQ9PV1N34o0YGZWStslfHTFYRnp7YrDelAclvFVGoXT7t27YWpqiubNm79TOMXHp2jyNvQBYmKStF3CR2VmVqrILyO9m6K+HhTldT2/0NVoWG/Xrl04c+YMPDw8cPPmTYwbNw4xMTEaF0hERJSTRj2nLVu2SP/38PDAr7/+CjMzswIrioiIijeeSk5ERLKj8QkR2TZt2lQQdRAREUnYcyIiItlhOBERkewwnIiISHYYTkREJDsffEIEFX19Zwdou4RCYf34NtougajIYM+JiIhkh+FERESyw3AiIiLZYTgREZHsMJyIiEh2GE5ERCQ7DCciIpIdhhMREckOw4mIiGSH4URERLLDcCIiItnhvfWIiP4/3kfy3XyK+0iy50RERLLDcCIiItlhOBERkewwnIiISHYYTkREJDsMJyIikh2GExERyQ7DiYiIZIfhREREssNwIiIi2WE4ERGR7DCciIhIdhhOREQkOwwnIiKSHYYTERHJDsOJiIhkh+FERESyo9Ev4WZkZGDixIl48uQJlEolBg8ejLZt2xZ0bUREVExpFE779u2DiYkJ5s2bh/j4eLi6ujKciIiowGgUTh06dICDg4P0t66uboEVREREpFE4lSxZEgDw8uVLjBgxAqNGjcr39WXKGEFPjwH2KZmZldJ2CcUO21w72O6f3qdoc43CCQAiIyMxdOhQ9O7dG05OTvm+Nj4+RdO3IQ3FxCRpu4Rih22uHWz3T6+g2jy/kNMonJ4/f46+ffvC29sb9vb2GhdGRESUF41OJV+1ahUSExOxYsUKeHh4wMPDA2lpaQVdGxERFVMa9ZwmT56MyZMnF3QtREREAHgRLhERyRDDiYiIZIfhREREssNwIiIi2WE4ERGR7DCciIhIdhhOREQkOwwnIiKSHYYTERHJDsOJiIhkh+FERESyw3AiIiLZYTgREZHsMJyIiEh2GE5ERCQ7DCciIpIdhhMREckOw4mIiGSH4URERLLDcCIiItlhOBERkewwnIiISHYYTkREJDsMJyIikh2GExERyQ7DiYiIZIfhREREssNwIiIi2WE4ERGR7DCciIhIdhhOREQkOwwnIiKSHYYTERHJDsOJiIhkh+FERESyo6fJRGq1Gr/++itu374NAwMD+Pj4oGrVqgVdGxERFVMa9ZyOHTsGpVKJ7du3Y8yYMZg9e3ZB10VERMWYRuF08eJFNG/eHABga2uL69evF2hRRERUvCmEEOJ9J5o0aRLat2+Pli1bAgBatWqFY8eOQU9Po1FCIiKiXDTqORkbGyM5OVn6W61WM5iIiKjAaBRODRo0wKlTpwAAoaGhsLa2LtCiiIioeNNoWC/7bL07d+5ACIFZs2ahevXqH6M+IiIqhjQKJyIioo+JF+ESEZHsMJyIiEh2GE5ERCQ7DCciIpIdhhNRDjw/iAqrW7du5fpbrVZrqZKCwXCSsadPn+Ly5cvaLqNYUSgUABhSBS3nhjIzM1OLlRRd586dw7hx43D48GEAgI5O4d68F+7qi7jU1FQcO3YM27ZtQ1RUlLbLKbKyN5xCCKSlpWHMmDGIiIjQclVFS/aGcuvWrVi/fj3u37+v5YqKBpVKJf2/ffv2OHnyJLZs2QKg8O8EMJxkJucepo6ODvbs2YNdu3ZBX18fAPfoP4bsDWdUVBRKlCiBEiVKwMDAQMtVFX5xcXGIjo4GkLUR9fHxQWhoKOzs7PDs2TPpFmiFffhJW9RqNXR1dQEA169fR2pqKiZOnIhnz54hLi4Oenp6UKvVhXabwXCSEbVaLW0oL168iKpVq2LixIlo1aoVjh8/DuD/hp3ow7y6Qfznn38wbdo0uLq64urVq3m+ht7PvXv3sHnzZgQGBkKpVOL69eto27YtDh8+jH/++Qf9+vVDampqoR9+0hYdHR1ERUVh2LBh2LhxI6KiotClSxc4ODhg4sSJiIuLgxCi0G4zuFbIiI6ODpKSkjB8+HBs2bIF+/btg6OjI2rVqoWHDx/i2LFjiIyM1HaZhV7OnYCzZ8/i2LFj6NSpE1atWoUBAwZAV1cX69evx4ABA3Do0KFCPzzyKQkhpD31xo0b4+TJkxg5ciTi4uIwdOhQREVFoUaNGpgxYwYqVKiAuLg4LVdcuGQP4wkhoFarMW/ePNSqVQuTJ0/GpUuXcOLECXh5ecHY2BgeHh64dOmSlivWHMNJy3KOGavVamzduhWWlpZYsGABHj58iMDAQFSqVAnVqlXDvHnzcO/ePS1WWzTo6OhACIEFCxbgjz/+wObNmzFx4kQAQOvWraGnpwd3d3d07twZ1tbWvOP+O1Kr1VAoFFAoFIiOjkZsbCyGDBmC2rVrIzExEc2bN4eVlRXi4uLQr18/VKhQAZUqVdJ22YWKrq4uUlNT8eDBA6hUKjRo0ACxsbEYO3YsypUrh4MHD+LChQuYNm0aNm3ahEaNGmm7ZI3xW6dFQgjo6upCCIGbN2/C2toaaWlpSE5OxrBhw1C/fn1s3rwZ7u7u6N69O1q0aIFy5cppu+xCSaVSSePzAHDo0CGkpKRg3bp12Lt3L6ZMmYL27dujUaNGMDY2hoWFBSwsLLRYceGT3Rtdu3YtDh8+jEaNGuHHH3+Ejo4Oli9fjmnTpsHc3BxhYWH4/vvvpd+Do3d3/PhxbNy4EaampihdujQ6deqEb7/9Fo8fP4axsTH27NmDMmXKoGTJkihZsqS2y/0gvPGrll27dg0zZ85EuXLloFarsWzZMkRFRSEiIgJ16tTB8OHD0adPH7Rq1QoACvUYshycOHEC33zzDV68eIHTp0/jwoULaN68OR4/fowNGzbAz88PT548wddff63tUguFnKGvUqkwb948AIC7uzv++usvKBQKTJo0CXPmzEFQUBB69eoFd3d3aXquz2/2atvExMRg3LhxGDt2LMzNzXHkyBGcPXsWPXv2hL+/P27evAkvL68is+6y5/SJ5Tze8ejRIyxatAje3t6wsLCAi4sL/vrrL3Tu3Bl79+7FrFmzMGzYMCmYAJ4Q8T5ytvXLly/h4+OD69evo1WrVmjVqhXq16+P8PBw2NraIiQkBG3atEHZsmVRoUIFLVcuf0lJSShVqpQUTGlpaShRogRq1aqFWrVqwc/PDxkZGXj+/DnWrFmDcePGoWvXrrCysso1H67PecsZ+ikpKfjss8+QkJCAhIQE1KpVCwBQv359PHz4EI0aNYKVlRVMTU2L1Mkl7Dl9Ijn3giIjI5GRkQEjIyNcvXoVkZGRuH//PqpVq4bly5dj3bp1sLS0hK6uLgwNDbVceeGUmZkpHSs6cOAAHj58iPLly8PNzQ1//PEH0tPTYWZmhrCwMOmgvYuLi3aLLiSUSiW8vLzQtm1bdO3aFVOnTgUAuLq6QkdHB1evXoWFhQVsbW0xffp0GBgYYOrUqdK6zN7Su1uzZg1CQ0NhaGiIadOmYdq0aahWrRqGDRuG5cuX4+bNm/j9999hYGBQ5Nq06MSszGWvOH5+fujbty927tyJc+fOoU6dOoiIiMBPP/0ECwsLVKxYEbGxsTAyMoKhoWGhvUZBW06dOoXk5GTo6ekhJSUFq1atwqFDh3D//n08ePAAaWlpaNq0KZRKJeLj4zF8+HDs2LFDCia2d/7Cw8NhYGCAPn36YPv27ejXrx9sbGxgY2OD7du3Iy4uDmfOnMGZM2fQp08fNGrUCLNmzcq1k1XUNqIFJfvkKJVKBSEEfHx8cPfuXcyfPx8WFhaYMWMGJk6ciKCgIAwfPhz37t3DzJkzYWhoWCTblMN6H1HOYSUg67oPPz8/rF27FpUrVwYA7NmzBzo6OhgyZAjKly+PlStXwtzcXJqmKK50H1NsbCxUKhUyMzMxZ84cxMfHY/78+Xj8+DF27tyJM2fOoE2bNtIB5LJlywL4v8+K7f1m6enp8PPzQ82aNVGpUiV069YNq1evhpubG4Csi5gfPHgALy8vhISEYPr06bC1tQXA3lJ+sodEs4fxss94NDY2Rrt27WBkZAQvLy+4uLjg4cOH2LhxI+Lj43NtJ4oi9pw+kpzBlH2rFoVCga+++kp6TWxsLK5cuQInJycMHjwYy5Ytg7m5OS/+1ED2tUiurq7YsWMHDh8+jF69ekGhUODq1auwtraGlZUVAgICcP/+fbRv3x5NmjSRpi9KY/UfgxAChoaGKF++PLy8vLBjxw44OzujTp06+PPPPwEAX331FaKiomBlZYU+ffrA1tZW6okymF6nVquRmpoKZ2dnaRvh4+OD33//HZcvX0ZERASioqKkO2lYWFjg888/h4GBQZEPJoA9p48m+1oaHx8fPHnyBA0aNACQNV5/5swZ2NnZ4a+//oKJiYl0EBl4vbdF70ZPTw9XrlzB8ePHUbZsWSxcuBC+vr6ws7PDiRMnUKVKFTg6OqJs2bKoXr26tsstdLLDpV69enBxcUGpUqWgp6eH4cOHY/Dgwbh58yaioqIwfPjwPKej3IQQUCqV+Oyzz+Dp6YkhQ4agQYMGqFmzJlJSUhAcHAxzc3MEBwfj8uXLuH//Pho2bAhra2ttl/7J8ISIApTzDJuMjAwcPHgQT58+xcCBAzFy5EhYWFigcuXKUCqVCAoKgo2NDby8vLRcdeGVM8hv376Nn3/+WTpVedmyZbh58yYWLlyICRMmoHHjxujZs6eWKy46BgwYgG7duqFJkybw9/fH9evXMXr06EJ/bc2ncPnyZfz5558wNDRE48aN4ebmhjFjxuDhw4fYvXs3MjIysG7dOpQrVw5t2rTB/fv3YWRkhNq1a2u79E+K4fQRbNq0CWXKlMGZM2dQsWJFREVFoVSpUmjYsCGqVq0KKysrPHv2TLqglr2l9/PqmY9A1h76unXroFQqMX36dABA9+7d4eDgIO3plyhRQms1Fyb5rY/ZO2AXLlzAypUrERsbi2XLlknHUHlsKX8rVqxAcHAwhgwZgtKlS6Nq1apIS0uDsbExunTpggkTJqBly5bYunUrnj59ip9//lnbJWsNw+kD5fwiK5VKLFq0COfOncPOnTsRFBSEwYMHY/bs2ejYsSN++ukndOjQAV27dpWm55dZc9u3b8f27dvx5Zdfok2bNrCwsICfnx/q1asHV1dXhIeH4+rVq3B0dATAtn6bnOtySkoK9PT08rw7e3Y73r9/H6VKlZJ2sti+b5aZmYnY2Fj8/vvv8PHxgYGBAVJTU7FkyRKcPn0aQ4cORaVKlfDjjz/C09MT58+fx+DBg2FnZ6ft0rWGu+sfIDMzU/oyP3/+HAqFApUrV4aenh7S0tLQrFkz9OnTByEhIejRowfat2+fK5gAjsm/q1dPEjl58iQuX76Mv//+GxYWFjh9+jSUSiVatGiBAwcOIDw8XDrOlI1tnbfsU5iz1+WAgAB4enpi+fLluHHjRq7X5ryxq4WFRa7babF9XyeEwIULFzBhwgQ8ffoUiYmJ0mUOmzdvhq6uLnx8fLB8+XJYWlrCyckJDx8+xJIlS4p1MAHsOWnk3LlzsLS0xBdffIEXL15g4cKFiI6ORp06dWBtbY0nT54gISFBOp6UfedlU1NTANzDfF85j+VFR0fDzMwMISEheP78OWJjY6WLmKtUqYLWrVvD1NQUNjY2Wq66cMjMzMTkyZPh5eUFc3NzXL16VbqexszMDJUrV5aGoHN+DoGBgfD398fIkSNRpkwZLS+FvJ07dw4hISFo2rQp9uzZg+nTp0MIgYSEBJiYmCApKQmzZs3C1KlTOfScA3tO72nnzp2YN28ejIyMkJ6ejilTpqBq1aqYMGECypcvDz8/P9jY2ODRo0c4cOAAgKxQMjU1lfb+GUzvJ/vmuN7e3li4cCECAwNhb2+PkiVLIiUlRWp7hUKBmjVrSsHE/a78hYeHQ6VSYfbs2VCr1VAqlVAqlWjSpAnq16+PypUrIzQ0FEeOHAEAKZiWLFmC/fv3Y/To0QymNwgMDJR6nUlJSUhMTESDBg0QFhaG/fv3Q6FQwMTEBC9fvsT06dNhaGgoneFLWXgq+XsqV64cWrZsiaNHj6JSpUpITExE3759AQAlSpTAjRs3UKZMGbi6uqJatWq5puVJD5pRKpX47bff8MUXX2Dw4MHYu3cv7t27h4oVK2LChAkICgpC7dq1MXny5FzTcSfgzZRKJe7fv4/g4GDY2Nhg2bJlMDY2xqhRo3DkyBE4OTmhRo0aOHDggDR0d+vWLSxYsABOTk4YMWKElpdAvtLT0xEcHIxbt25BCAEjIyPp8oXZs2dj2bJlOHbsGOrXrw9/f380a9YMgwYN0nLV8sNhvfd09uxZeHl5wcbGBuvWrcOIESNgaWmJUaNG4ezZs1ixYgWWLl0KExMTbZdapIwYMQJmZmaIi4uDhYUFdu/ejc2bNyM8PByGhobS79ZwyDR/OU96OHjwICZMmAAXFxdMmjQJvXr1wvjx4xEZGYng4GBERUXBwsICY8eOha6uLjZt2gQnJyfeGPcdpaam4sKFC9izZw+uXLmCGjVqwNHRESVKlIChoSGUSiXKlSuHevXqabtUWWI4vafIyEgcOnQIiYmJaNasGSpWrAhPT0/Y2dkhLCwMo0aNQvPmzbVdZpGRvTFNTU1FWFgYMjIyYG9vj6FDh2LYsGG57rjBYMpfzmBKSEhAWloa1q9fDyMjI3h4eCAiIgITJkzApk2bYGpqips3b0rtyzs9aG7jxo24ffs2OnXqhFOnTuHIkSOYOXNmrjuU0OsYThpav349EhMT4eHhAUNDQ0RERKBy5cowNjbWdmmFUn7X1mSHTmhoKLZt24a7d++iR48e6NWr1yeusvDKbsOkpCR4e3tDpVKhVq1aGDJkCBYuXIhy5crBzc0NkydPhpmZGX755ZfXpiXNbNmyBQYGBtI9CKOjo4vF7Yc+FI855SHnhvLVL2b2c+3atcOGDRvw77//okePHrz9kIay20tHR+eN19Zkt7+trS0UCgXKly/PL/c7unr1KurVqweFQoGIiAh4e3vDxcUFX3/9NXr06AF7e3u4uLjgzz//xHfffYf58+e/dqyUwfRhHj9+jM8//1z6m79m/W7Yc8ohZxClp6fnus1/zuey/3/9+nVUqlSJZyxp4NWfTQ8ICMCqVatgb28PBweHXLdqyT7LUUdHBxkZGdDX1//k9RY22evoli1bEBQUBHd3d9SpUwdHjx6FpaUlAgMDERMTgytXrmDr1q1QqVSIj4+XDtxzJ6vgLFy4EE2bNkXjxo21XUqhwrUvh+zwOXbsGEaOHIk1a9bA398/13M5L0K0srLivcQ0kJmZiUmTJiE6OhpA1t79ypUrMWHCBLi5uaF27dp49uwZgKwQy+5ZBQYGYubMmYiPj9dm+bKnUqmk9fXbb7/FtWvXsHnzZpQpUwY1a9bE33//DVdXV7i7uyMhIQGnTp2CqakpqlevLq3bDKaCM2LECAaTBrgGIvf1MCEhIdi6dSsmTZqE+Ph47N+/H2FhYQD+70uvo6OD06dP45dffpE2sPRueG3Nx5fdZmvWrMGKFSvQv39/3Lp1C1FRUbC0tISRkRFWrVqF33//HQsXLoSTk5M0LYfwCl7OEQJ6d8X6mFP20IdCocB///2H9PR0PHz4EC1btsSVK1dw+/ZttGvXTvqtoJwbyoiICPj4+OQaS6b88dqaTyM1NRXjx49HhQoV4OTkhGrVqiEmJgazZs3CpEmT0LhxYzx48AATJkxA6dKlAfCkB5KfYtlzevHiBYDce4m7du3Cnj17YGdnh3nz5uHixYtYv349nj9/jsDAQABZG8r+/fvD0tISc+fOZTC9o+xjRgYGBkhOTsaMGTOwbds2LFiwAHfu3MHjx48xaNAgrFu3Dp6enlAqlejduzdSU1Nx6tQpTJs2LdfePeVPrVZDCIH//e9/2LVrF5YtWwaVSoVbt25h8ODBqFKlCoYNG4bSpUvzriUkW8XuhIi4uDgsX74cY8eORUpKCv7991/07t0bz58/x5w5czBp0iSsXbsWT548gY6ODvT19TFlyhQYGxvjn3/+ga2tLSpWrKjtxSg0eG3Np6dSqXD27Fk8ePAAlSpVgqWlJbZs2YL+/ftDX19f+ml6IjkrduEEZH15r169ipIlS8Lb2xsdOnRA1apVERoaiqFDh8LAwAD3799HfHw8vv76awAc9tAEr63RroCAADx+/BjHjx9H06ZNMXDgQAA8E48Kh2IRTq9+GYUQGDFiBL766iv88MMP2L17N65evYqgoCBMnjw5188sZL+eG8p3l31tDYA8r63Jvr3Tn3/+ibCwsDyvraEPFxUVhSNHjqBu3bqoX7++tsshei/FIpyyHT9+HKGhoWjbti3KlCmDyZMn46effoK9vT0uXbqEtWvXomfPnmjVqpW2Sy2UeG2NfHEHiwqbIhtOr34Z58yZg4iICFhZWeH58+dwdnZGUlKSdLqtiYkJEhMTeZKDhl79zaVu3brBxsYGq1atwpUrV7BlyxYMGTIEycnJGDRoEMaNGyed5MANJxG9qkjupqrVamljl5SUhEePHsHc3BxLly5FxYoVER4ejhMnTqBatWqwtbXFiRMnAIDB9AF4bQ0RFaQi23MCgGXLluHq1aswMDCAg4MDbty4AUtLS3z++efYvHkz7OzsMGDAgFy3KSLN5Ly25ttvv0W1atWwfv16hIeHY9KkSbh48SIePHgADw8PXltDRG9VJHtOiYmJWLBgAV68eIFp06ZBR0cHR48eRWZmJtq2bYujR4/Czs4O/fr1k4KpCGf0J8Fra4ioIBXJntPFixexcuVKtG3bFt999x3i4+Px008/4cqVK+jUqRPq1asHT09PANx7Lyi8toaIClKRvH1R/fr14ejoiLCwMNy5cwfW1tbo168f9u3bh+nTp0u/ucRgKji6urpo1qwZlEolHj9+jI0bN6Jp06YoX748AJ6JR0Tvp0j2nICsoT1fX18kJiZi+PDhb/z5CypYvLaGiApCkQ0nALh27RrOnj2L7t27w9TUFAD34D8l7gQQkaaKdDgREVHhVCy6EMxfIqLCpViEE4eWiIgKl2IRTkREVLgwnIiISHYYTkREJDsMJyIikp3/B7ABQXG597eHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show values in bar graph \n",
    "plt.figure(figsize=(7, 4))\n",
    "keys = model_results_dict.keys()\n",
    "values = model_results_dict.values()\n",
    "plt.bar(keys, values)\n",
    "plt.xticks(rotation='35')\n",
    "plt.title(\"Time Series Forecasting Model MAPE (%) Comparison Over 4W Sample\");\n",
    "# note: MAPE = lower is better "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PACKAGES TO IMPORT\n",
    "* Note: the XGBoost and other model sections require some additional package imports at the tops of their respective sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import packages ## \n",
    "import warnings\n",
    "import sys\n",
    "if not sys.warnoptions:\n",
    "    warnings.filterwarnings(\"ignore\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# core packages\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import datetime\n",
    "from datetime import date, timedelta, datetime\n",
    "\n",
    "## graphing packages ## \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import zivot_andrews\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
    "from arch.unitroot import DFGLS\n",
    "\n",
    "# import a common error for the sarimax models \n",
    "from numpy.linalg import LinAlgError\n",
    "#from statsmodels.tools.sm_exceptions import ValueWarning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## optional packages / display preferences ##\n",
    "pd.options.display.float_format = \"{:,.2f}\".format\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "from IPython.display import display\n",
    "pd.options.display.max_columns = 60  \n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='data_load_and_clean'></a>\n",
    "\n",
    "## LOAD THE DATA FROM EXCEL (XLSX) FILE\n",
    "* Data includes at a store-level, on a daily basis: \n",
    "    * Sales / GP / Promo Sales \n",
    "    * Baskets (# of transactions) and average basket \n",
    "    * Recorded Waste / Markdowns / Cost adjustments (internal stock adjustments E.G. theft, IT errors etc.)\n",
    "    * number of items sold / number of items sold at normal price / number sold on a promo price\n",
    "    * Loyalty Sales (purchases made by someone using a loyalty card)\n",
    "    * GST "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import xlsx file\n",
    "daily_data_df = pd.read_excel(r'daily_aggsales_baskets_promoqty_gst.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# === NOTE === # \n",
    "# to protect the anonymity of the data, the actual store_name has been replaced with a code word\n",
    "# this store name has been hidden below, replaced by the string \"####\"\n",
    "daily_data_df['Store'] = daily_data_df['Store'].replace(\"####\", \"MW\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Sales $</th>\n",
       "      <th>GP $</th>\n",
       "      <th>Promo Sales $</th>\n",
       "      <th>Promo Sales %</th>\n",
       "      <th>Baskets</th>\n",
       "      <th>Avg Basket $</th>\n",
       "      <th>Waste$</th>\n",
       "      <th>Markdown$</th>\n",
       "      <th>Items</th>\n",
       "      <th>Normal Qty</th>\n",
       "      <th>Promo Qty</th>\n",
       "      <th>Loyalty Sales $</th>\n",
       "      <th>Cost Adjustment$</th>\n",
       "      <th>GST $</th>\n",
       "      <th>DATEALTERNATEKEY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MW</td>\n",
       "      <td>23,935.00</td>\n",
       "      <td>5,185.96</td>\n",
       "      <td>10,006.91</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1251</td>\n",
       "      <td>19.13</td>\n",
       "      <td>78.27</td>\n",
       "      <td>138.66</td>\n",
       "      <td>4822</td>\n",
       "      <td>2,912.33</td>\n",
       "      <td>1,703.68</td>\n",
       "      <td>563.81</td>\n",
       "      <td>688.07</td>\n",
       "      <td>1,204.91</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MW</td>\n",
       "      <td>23,143.62</td>\n",
       "      <td>5,040.71</td>\n",
       "      <td>8,577.01</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1249</td>\n",
       "      <td>18.53</td>\n",
       "      <td>631.34</td>\n",
       "      <td>52.22</td>\n",
       "      <td>4593</td>\n",
       "      <td>2,900.68</td>\n",
       "      <td>1,361.61</td>\n",
       "      <td>455.57</td>\n",
       "      <td>-1,957.71</td>\n",
       "      <td>1,178.69</td>\n",
       "      <td>2019-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MW</td>\n",
       "      <td>25,507.01</td>\n",
       "      <td>4,930.69</td>\n",
       "      <td>8,092.75</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1319</td>\n",
       "      <td>19.34</td>\n",
       "      <td>136.37</td>\n",
       "      <td>92.87</td>\n",
       "      <td>4416</td>\n",
       "      <td>2,822.38</td>\n",
       "      <td>1,325.85</td>\n",
       "      <td>405.81</td>\n",
       "      <td>57.36</td>\n",
       "      <td>1,145.91</td>\n",
       "      <td>2019-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MW</td>\n",
       "      <td>25,124.87</td>\n",
       "      <td>5,199.56</td>\n",
       "      <td>11,219.92</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1276</td>\n",
       "      <td>19.69</td>\n",
       "      <td>54.79</td>\n",
       "      <td>101.97</td>\n",
       "      <td>4721</td>\n",
       "      <td>2,806.18</td>\n",
       "      <td>1,704.90</td>\n",
       "      <td>732.95</td>\n",
       "      <td>1,636.20</td>\n",
       "      <td>1,352.07</td>\n",
       "      <td>2019-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MW</td>\n",
       "      <td>25,241.23</td>\n",
       "      <td>5,437.24</td>\n",
       "      <td>10,073.29</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1328</td>\n",
       "      <td>19.01</td>\n",
       "      <td>66.59</td>\n",
       "      <td>58.32</td>\n",
       "      <td>4844</td>\n",
       "      <td>2,866.64</td>\n",
       "      <td>1,698.61</td>\n",
       "      <td>284.96</td>\n",
       "      <td>-155.23</td>\n",
       "      <td>1,295.07</td>\n",
       "      <td>2019-01-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Store   Sales $     GP $  Promo Sales $  Promo Sales %  Baskets  \\\n",
       "0    MW 23,935.00 5,185.96      10,006.91           0.42     1251   \n",
       "1    MW 23,143.62 5,040.71       8,577.01           0.37     1249   \n",
       "2    MW 25,507.01 4,930.69       8,092.75           0.32     1319   \n",
       "3    MW 25,124.87 5,199.56      11,219.92           0.45     1276   \n",
       "4    MW 25,241.23 5,437.24      10,073.29           0.40     1328   \n",
       "\n",
       "   Avg Basket $  Waste$  Markdown$  Items  Normal Qty  Promo Qty  \\\n",
       "0         19.13   78.27     138.66   4822    2,912.33   1,703.68   \n",
       "1         18.53  631.34      52.22   4593    2,900.68   1,361.61   \n",
       "2         19.34  136.37      92.87   4416    2,822.38   1,325.85   \n",
       "3         19.69   54.79     101.97   4721    2,806.18   1,704.90   \n",
       "4         19.01   66.59      58.32   4844    2,866.64   1,698.61   \n",
       "\n",
       "   Loyalty Sales $  Cost Adjustment$    GST $ DATEALTERNATEKEY  \n",
       "0           563.81            688.07 1,204.91       2019-01-01  \n",
       "1           455.57         -1,957.71 1,178.69       2019-01-02  \n",
       "2           405.81             57.36 1,145.91       2019-01-03  \n",
       "3           732.95          1,636.20 1,352.07       2019-01-04  \n",
       "4           284.96           -155.23 1,295.07       2019-01-05  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## the final day of data must be incompletely loaded from the data-source as it's only 1/3 of a usual day; remove it\n",
    "daily_data_df = daily_data_df[:-1]\n",
    "\n",
    "# display head\n",
    "daily_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA COLUMN NAMES CLEANING & REINDEXING DF\n",
    "* clean up the messy column names: remove the symbols / punctuation marks / whitespace and make it all lower case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import regex for this step; then it is no longer needed \n",
    "import regex as re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loop over the DF's column names and replace\n",
    "for i, val in enumerate(daily_data_df.columns.values):\n",
    "    daily_data_df.columns.values[i] = re.sub('[^A-Za-z0-9]+', '', val.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [optional] make a copy of it, to specify a separate named DF that indicates the date as index \n",
    "daily_data_date_df = daily_data_df.copy()\n",
    "\n",
    "# set date column as index\n",
    "daily_data_date_df = daily_data_date_df.set_index('datealternatekey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>sales</th>\n",
       "      <th>gp</th>\n",
       "      <th>promosales</th>\n",
       "      <th>promosales</th>\n",
       "      <th>baskets</th>\n",
       "      <th>avgbasket</th>\n",
       "      <th>waste</th>\n",
       "      <th>markdown</th>\n",
       "      <th>items</th>\n",
       "      <th>normalqty</th>\n",
       "      <th>promoqty</th>\n",
       "      <th>loyaltysales</th>\n",
       "      <th>costadjustment</th>\n",
       "      <th>gst</th>\n",
       "      <th>publichol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datealternatekey</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>MW</td>\n",
       "      <td>23,935.00</td>\n",
       "      <td>5,185.96</td>\n",
       "      <td>10,006.91</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1251</td>\n",
       "      <td>19.13</td>\n",
       "      <td>78.27</td>\n",
       "      <td>138.66</td>\n",
       "      <td>4822</td>\n",
       "      <td>2,912.33</td>\n",
       "      <td>1,703.68</td>\n",
       "      <td>563.81</td>\n",
       "      <td>688.07</td>\n",
       "      <td>1,204.91</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>MW</td>\n",
       "      <td>23,143.62</td>\n",
       "      <td>5,040.71</td>\n",
       "      <td>8,577.01</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1249</td>\n",
       "      <td>18.53</td>\n",
       "      <td>631.34</td>\n",
       "      <td>52.22</td>\n",
       "      <td>4593</td>\n",
       "      <td>2,900.68</td>\n",
       "      <td>1,361.61</td>\n",
       "      <td>455.57</td>\n",
       "      <td>-1,957.71</td>\n",
       "      <td>1,178.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 store     sales       gp  promosales  promosales  baskets  \\\n",
       "datealternatekey                                                             \n",
       "2019-01-01          MW 23,935.00 5,185.96   10,006.91        0.42     1251   \n",
       "2019-01-02          MW 23,143.62 5,040.71    8,577.01        0.37     1249   \n",
       "\n",
       "                  avgbasket  waste  markdown  items  normalqty  promoqty  \\\n",
       "datealternatekey                                                           \n",
       "2019-01-01            19.13  78.27    138.66   4822   2,912.33  1,703.68   \n",
       "2019-01-02            18.53 631.34     52.22   4593   2,900.68  1,361.61   \n",
       "\n",
       "                  loyaltysales  costadjustment      gst  publichol  \n",
       "datealternatekey                                                    \n",
       "2019-01-01              563.81          688.07 1,204.91          1  \n",
       "2019-01-02              455.57       -1,957.71 1,178.69          0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# E.G. cleaned column names & datetime index\n",
    "daily_data_date_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='exog_var'></a>\n",
    "\n",
    "## DETECT PUBLIC-HOLIDAYS & CREATE A DUMMY VARIABLE INDICATOR\n",
    "* For example, mark particularly busy days such as Labour Day, so feed into the time series models as an exogenous variable later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import special library for holidays: / may require pip install \n",
    "import holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initalise an instance of Australia-Queensland specific regional holidays\n",
    "aus_qld_holidays = holidays.CountryHoliday('AUS', prov='QLD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## append some custom holidays or holiday phenomena unique this retail business\n",
    "# E.G. The day before mothers day is busy, mothers day itself is not so much > etc. \n",
    "aus_qld_holidays.append({\"2019-05-11\": \"Mothers Day Eve\",\n",
    "                         \"2019-12-23\": \"Pre Xmas Eve\",\n",
    "                         \"2029-12-24\": \"Christmas Eve\",\n",
    "                        \"2020-12-24\" : \"Christmas Eve\",\n",
    "                        \"2020-12-23\" : \"Pre Xmas Eve\",\n",
    "                        \"2020-05-09\" : \"Mothers Day Eve\",\n",
    "                        \"2021-05-08\" : \"Mothers Day Eve\",\n",
    "                        \"2021-12-23\" : \"Pre Xmas Eve\",\n",
    "                        \"2021-12-24\" : \"Christmas Eve\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print out a visualisation of each holiday per year in the sample period \n",
    "unique_holidays = []\n",
    "\n",
    "for i in daily_data_date_df.index: #daily_data_date_df['datealternatekey']:\n",
    "    if aus_qld_holidays.get(i) is not None:\n",
    "        unique_hol = str(aus_qld_holidays.get(i)) + \", \" + str(i.year)\n",
    "        unique_holidays.append(unique_hol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"New Year's Day, 2019\",\n",
       " 'Australia Day, 2019',\n",
       " 'Australia Day (Observed), 2019',\n",
       " 'Good Friday, 2019',\n",
       " 'Easter Saturday, 2019',\n",
       " 'Easter Sunday, 2019',\n",
       " 'Easter Monday, 2019',\n",
       " 'Anzac Day, 2019',\n",
       " 'Labour Day, 2019',\n",
       " 'Mothers Day Eve, 2019',\n",
       " 'The Royal Queensland Show, 2019',\n",
       " \"Queen's Birthday, 2019\",\n",
       " 'Pre Xmas Eve, 2019',\n",
       " 'Christmas Day, 2019',\n",
       " 'Boxing Day, 2019',\n",
       " \"New Year's Day, 2020\",\n",
       " 'Australia Day, 2020',\n",
       " 'Australia Day (Observed), 2020',\n",
       " 'Good Friday, 2020',\n",
       " 'Easter Saturday, 2020',\n",
       " 'Easter Sunday, 2020',\n",
       " 'Easter Monday, 2020',\n",
       " 'Anzac Day, 2020',\n",
       " 'Labour Day, 2020',\n",
       " 'Mothers Day Eve, 2020',\n",
       " 'The Royal Queensland Show, 2020',\n",
       " \"Queen's Birthday, 2020\",\n",
       " 'Pre Xmas Eve, 2020',\n",
       " 'Christmas Eve, 2020',\n",
       " 'Christmas Day, 2020',\n",
       " 'Boxing Day, 2020',\n",
       " 'Boxing Day (Observed), 2020',\n",
       " \"New Year's Day, 2021\",\n",
       " 'Australia Day, 2021']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# E.G. of holidays present in sample\n",
    "unique_holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## INITIALISE A DUMMY-VARIABLE TO MARK PUBLIC HOLIDAYS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set a row's \"public_hol\" value to (1) if that day was a holiday, otherwise set it (0)\n",
    "daily_data_date_df['publichol'] = [int(1) if aus_qld_holidays.get(i) is not None else int(0) for i in daily_data_date_df.index] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# optional: March 17th 2020; COVID lockdown announcement - could manually insert an additional holiday marker for this event\n",
    "#daily_postbp_df.loc['2020-03-17', 'publichol'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CREATE OUT-OF-SAMPLE (OOS) DATE + HOLIDAY DUMMY-VARIABLE\n",
    "* Extend the most recent in-sample date by 365 days (which is longer than the forecast will actually be, but this just gives flexibility to adjust forecasts lengths at will in subsequent steps)\n",
    "\n",
    "#### OOS - NOT USED IN THIS NOTEBOOK \n",
    "* Will be forked from this for a future notebook experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset the index to make the datetime index offsets easier \n",
    "daily_data_date_df_reset = daily_data_date_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## define an extended series for inputting future dummy variables in the forecasts\n",
    "\n",
    "#start_date = pd.DatetimeIndex(daily_data_df['datealternatekey'][:1])\n",
    "\n",
    "start_date = pd.DatetimeIndex(daily_data_date_df_reset['datealternatekey']).max() + pd.DateOffset(1)\n",
    "end_date = pd.DatetimeIndex(daily_data_date_df_reset['datealternatekey'][-1:] + pd.DateOffset(365))\n",
    "\n",
    "extended_date = pd.date_range(start_date, end_date.max())\n",
    "## now start/end of series is 2021-03-24 to 2022-03-23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert series to DF to make it easier to pass into the subsequent time series models as an exog with matching indexes\n",
    "extended_date_df = pd.DataFrame(data=(extended_date), columns=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the same dummy-variable principle as above\n",
    "extended_date_df['publichol_x'] = [int(1) if aus_qld_holidays.get(i) is not None else int(0) for i in extended_date] \n",
    "\n",
    "# set date as the index\n",
    "extended_date_df = extended_date_df.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle DF object (optional)\n",
    "extended_date_df.to_pickle(\"./extended_date_df.pkl\")\n",
    "##extended_date_df = pd.read_pickle(\"./extended_date_df.pkl\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FINAL DF INFO CHECK\n",
    "* Check there are no NaN / Missing Values etc. before loading the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 813 entries, 2019-01-01 to 2021-03-23\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   store           813 non-null    object \n",
      " 1   sales           813 non-null    float64\n",
      " 2   gp              813 non-null    float64\n",
      " 3   promosales      813 non-null    float64\n",
      " 4   promosales      813 non-null    float64\n",
      " 5   baskets         813 non-null    int64  \n",
      " 6   avgbasket       813 non-null    float64\n",
      " 7   waste           807 non-null    float64\n",
      " 8   markdown        813 non-null    float64\n",
      " 9   items           813 non-null    int64  \n",
      " 10  normalqty       813 non-null    float64\n",
      " 11  promoqty        813 non-null    float64\n",
      " 12  loyaltysales    813 non-null    float64\n",
      " 13  costadjustment  811 non-null    float64\n",
      " 14  gst             813 non-null    float64\n",
      " 15  publichol       813 non-null    int64  \n",
      "dtypes: float64(12), int64(3), object(1)\n",
      "memory usage: 108.0+ KB\n"
     ]
    }
   ],
   "source": [
    "daily_data_date_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No NaNs; data looks good thus far. Else, most reasonable course is to drop Nan(s) depending on volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sub_sampling'></a>\n",
    "\n",
    "## SELECTING THE RIGHT TRAINING / TEST SPLIT SUB-SAMPLE\n",
    "* Issues arise over a regime shift which occured around the time of covid, let's see if we can analytically pin point this time\n",
    "* We know QLD state formerly announced a lockdown around March 20, 2020. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [optional] import pickle if needed\n",
    "daily_data_date_df = pd.read_pickle(\"./daily_data_date_df.pkl\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the breakpoint \n",
    "zivot_andrews_bp_sales = zivot_andrews(daily_data_date_df.sales,\n",
    "                                            trim=0.2, maxlag=7, regression='c', autolag='t-stat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated breakpoint date is: 2020-03-13 with corresponding test statistic: -6.89 vs. 5% CV of: -4.81067\n"
     ]
    }
   ],
   "source": [
    "# create a string version of the index location of the breakpoint (indexed by the 4th object in the ZA return dict)\n",
    "date_location_za = str(daily_data_date_df.index[zivot_andrews_bp_sales[4]])\n",
    "\n",
    "# for visual appeal, strip back the timestamp elements (00:00:00) as the smallest frequency of the data is daily\n",
    "date_location_za_strp = datetime.strptime(date_location_za, '%Y-%m-%d %H:%M:%S')\n",
    "            \n",
    "date_location_za_clean = date_location_za_strp.strftime('%Y-%m-%d')\n",
    "\n",
    "# if test_stat is < CV (more negative) reject null hypothesis = there is statistically significant breakpoint \n",
    "print(\"calculated breakpoint date is: {} with corresponding test statistic: {} vs. 5% CV of: {}\".format(\n",
    "date_location_za_clean, np.round(zivot_andrews_bp_sales[0], 2), zivot_andrews_bp_sales[2]['5%']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "## split the DF and use the data post-BP\n",
    "# variable: date_location_za_clean   is from the ZivotAndrews BP calculation above \n",
    "daily_postbp_df = daily_data_date_df[daily_data_date_df.index > date_location_za_clean]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grab min and max of time index:\n",
    "date_min_str = str(daily_data_date_df.index.min())\n",
    "date_min_strptime = datetime.strptime(date_min_str, '%Y-%m-%d %H:%M:%S')\n",
    "date_min = date_min_strptime.strftime('%Y-%m-%d')\n",
    "\n",
    "date_max_str = str(daily_data_date_df.index.max())\n",
    "date_max_strptime = datetime.strptime(date_max_str, '%Y-%m-%d %H:%M:%S')\n",
    "date_max = date_max_strptime.strftime('%Y-%m-%d')\n",
    "\n",
    "# construct plots\n",
    "fig, ax1 = plt.subplots(1,1, figsize=(12,6))\n",
    "fig.suptitle(\"daily sales from {} to {} for store_code: {}\".format(date_min, \n",
    "                                                                  date_max,\n",
    "                                                                  daily_data_date_df.store.iloc[0]))\n",
    "\n",
    "sns.lineplot(x=daily_data_date_df.index, \n",
    "             y='sales', \n",
    "             data=daily_data_date_df,  \n",
    "             palette='winter',\n",
    "             markers=True,\n",
    "             dashes=False,\n",
    "             legend='full',\n",
    "             ax=ax1)\n",
    "\n",
    "# apply vertical red line for the Zivot Andrews calculated breakpoint\n",
    "ax1.set_ylabel(\"DAILY SALES\", fontsize=12)\n",
    "ax1.axvline(date_location_za_strp, 0, color='r', linestyle='--', label='ZA BREAKPOINT')\n",
    "ax1_labels=['ZA BREAKPOINT']\n",
    "handles_1, _ = ax1.get_legend_handles_labels()\n",
    "ax1.legend(handles=handles_1, labels=ax1_labels)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Fairly easy to observe that the dynamics of the series after the breakdate are rather different from those from before. A visible upwards trend is now in the data and the period: 2021-01 (as well as the mid 2020-05 section) now exhibit the 3 highest peak values in the data set post breakpoint. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='initial_data_exploration'></a>\n",
    "\n",
    "## INITIAL DATA EXPLORATION & AUTO-CORRELATION INTRODUCTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another important concept to consider is partial correlation function (PACF), which is a conditional correlation. It is the correlation between two variables under the assumption that we consider the values of some other set of variables. In regression, this partial correlation could be found by correlating the residuals from two different regressions.\n",
    "\n",
    "In a time series data set, the autocorrelation for a value at a time stamp and another value at a prior time stamp consists of both the direct correlation between these two values and indirect correlations. These indirect correlations are a linear function of the correlation of the value under observation, with values at intervening time stamps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# two additional imports for the statsmodels plots for ACF and PACF\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def acf_pacf_subplots(series, sample_length='full', number_lags=None, **kwargs):\n",
    "    \"show side-by-side ACF and PACF plots for selected series with a default lag length of 30\"\n",
    "    \n",
    "    # set default lags to 30 if not passed\n",
    "    if number_lags is None:\n",
    "        number_lags = 30\n",
    "       \n",
    "    # check to see if the passed-in series is valid\n",
    "    if series not in ['sales', 'gp','promosales','baskets','avgbasket','waste','loyaltysales']:\n",
    "        raise ValueError(\"The series passed is either illogical or invalid in an ACF/PACF plot: pass a correct timeseries\")\n",
    "    \n",
    "    # check if passed in sample_length argument is valid, else raise an error and suggestion\n",
    "    if sample_length not in ['full','post_bp','pre_bp','both_bp']:\n",
    "        raise ValueError(\"sample_length must be one of: whole sample [full], post-za breakpoint [post_bp], pre [pre_bp], or both bp [both_bp]\")\n",
    "    \n",
    "    # return the respective graph based off user defined input for sample length\n",
    "    if sample_length=='full':\n",
    "        fig, ax = plt.subplots(1,2, figsize=(13, 6))\n",
    "        fig.suptitle(\"full-sample length ACF and PACF plots for series: {}\".format(series))\n",
    "        sm.graphics.tsa.plot_acf(daily_data_date_df[series], lags=number_lags, ax=ax[0])\n",
    "        sm.graphics.tsa.plot_pacf(daily_data_date_df[series], lags=number_lags, ax=ax[1])\n",
    "        return plt.show()\n",
    "    \n",
    "    if sample_length=='post_bp':\n",
    "        \n",
    "        # raise error if a breakpoint sample length was passed without a date\n",
    "        if kwargs.get(\"breakpoint_date\") is None:\n",
    "            raise ValueError(\"must pass in variable ['breakpoint_date'] to use the breakpoint function\")        \n",
    "    \n",
    "        breakpoint_date = kwargs.get(\"breakpoint_date\")\n",
    "        \n",
    "        # construct plot\n",
    "        fig, ax = plt.subplots(1,2, figsize=(13, 6))\n",
    "        fig.suptitle(\"post ZA-breakpoint length ACF and PACF plots for series: {}\".format(series))\n",
    "        \n",
    "        sm.graphics.tsa.plot_acf(daily_data_date_df[daily_data_date_df.index > breakpoint_date][series], \n",
    "                                 lags=number_lags, ax=ax[0])\n",
    "        \n",
    "        sm.graphics.tsa.plot_pacf(daily_data_date_df[daily_data_date_df.index > breakpoint_date][series], \n",
    "                                  lags=number_lags, ax=ax[1])\n",
    "        return plt.show()        \n",
    "    \n",
    "\n",
    "    if sample_length=='pre_bp':\n",
    "        \n",
    "        # raise error if a breakpoint sample length was passed without a date\n",
    "        if kwargs.get(\"breakpoint_date\") is None:\n",
    "            raise ValueError(\"must pass in variable ['breakpoint_date'] to use the breakpoint function\")\n",
    "            \n",
    "        breakpoint_date = kwargs.get(\"breakpoint_date\")    \n",
    "        \n",
    "        # construct plot \n",
    "        fig, ax = plt.subplots(1,2, figsize=(13, 6))\n",
    "        fig.suptitle(\"post ZA-breakpoint length ACF and PACF plots for series: {}\".format(series))\n",
    "        \n",
    "        sm.graphics.tsa.plot_acf(daily_data_date_df[daily_data_date_df.index < breakpoint_date][series], \n",
    "                                 lags=number_lags, ax=ax[0])\n",
    "        \n",
    "        sm.graphics.tsa.plot_pacf(daily_data_date_df[daily_data_date_df.index < breakpoint_date][series], \n",
    "                                  lags=number_lags, ax=ax[1])\n",
    "        return plt.show()  \n",
    "    \n",
    "    \n",
    "    if sample_length=='both_bp':\n",
    "        \n",
    "        # raise error if a breakpoint sample length was passed without a date\n",
    "        if kwargs.get(\"breakpoint_date\") is None:\n",
    "            raise ValueError(\"must pass in variable ['breakpoint_date'] to use the breakpoint function\")\n",
    "        \n",
    "        breakpoint_date = kwargs.get(\"breakpoint_date\")\n",
    "        \n",
    "        # reconstruct fig size for 4 plots: \n",
    "        fig, ax = plt.subplots(2,2, figsize=(16, 9))\n",
    "        \n",
    "        fig.suptitle(\"both [post & pre] ZA-breakpoints ACF and PACF plot comparisons for series: {}\".format(series))\n",
    "        \n",
    "        sm.graphics.tsa.plot_acf(daily_data_date_df[daily_data_date_df.index > breakpoint_date][series], \n",
    "                                 lags=number_lags, ax=ax[0, 0])\n",
    "        \n",
    "        ax[0,0].set_title(\"post BP ACF\")\n",
    "        \n",
    "        sm.graphics.tsa.plot_pacf(daily_data_date_df[daily_data_date_df.index > breakpoint_date][series], \n",
    "                                  lags=number_lags, ax=ax[0, 1])\n",
    "        \n",
    "        ax[0,1].set_title(\"post BP PACF\")\n",
    "        \n",
    "        # pre breakpoint plots \n",
    "        sm.graphics.tsa.plot_acf(daily_data_date_df[daily_data_date_df.index < breakpoint_date][series], \n",
    "                                 lags=number_lags, ax=ax[1,0])\n",
    "        \n",
    "        ax[1,0].set_title(\"pre BP ACF\")\n",
    "        \n",
    "        sm.graphics.tsa.plot_pacf(daily_data_date_df[daily_data_date_df.index < breakpoint_date][series], \n",
    "                                  lags=number_lags, ax=ax[1,1])\n",
    "        \n",
    "        ax[1,1].set_title(\"pre BP PACF\")\n",
    "        \n",
    "        return plt.show()      \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show the ACF and PACF on the entire sample (2019-2021)\n",
    "acf_pacf_subplots('sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show pre-break point and post-breakpoint ACF and PACF side-by-side for comparison\n",
    "acf_pacf_subplots('sales', sample_length='both_bp',breakpoint_date=date_location_za_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACF / PACF OBSERVATIONS \n",
    "\n",
    "#### FULL-SAMPLE\n",
    "* comparing the first ACF/PACF plot, there is extreme persistence in the autocorrelation from day-to-day, which would be a strong initial indication of a stationary dataset (although a proper statistical test is still necessary later). \n",
    "    * The PACF indicates strong weekly correlation, which makes sense given that particular days of the week are likely to be strong correlated with the same day in the previous week E.G. Sunday to Sunday\n",
    "    * However, the full-sample PACF also has strong (well above the confidence interval) partial autocorrelation for various, seemingly random lag lengths-- this is possibly attributable to days of the week (E.G. monday / tuesday / wednesday ) affecting each one of the same days in the following week, as week-days tend to exhibit the same kinds of consumer behaviour. \n",
    "    \n",
    "#### ZA-BREAKPOINT SPLIT \n",
    "* We notice a lot of the persistent autocorrelation in the pre-breakpoint ACF disappears; the partial ACF phenomena in lags of (7) remains however. This might suggest daily sales from week to week tend to flucuate / be more volatile than what is found in the post-BP sub-sample\n",
    "* The post-BP has significantly stronger AC, with lags (1-2) becoming significant again; moreover, the PACF 7 day consistency is also present although some of that \"seemingly\" random PACF phenomena reemerges. This might possibly suggest the same phenomena described above about saturday > sunday or weekdays being fairly similar regarding consumer purchasing behaviour between Monday to Wednesdays, for instance. \n",
    "    * The tendency for the PACF to fade out quicker than "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOTES ON TRAIN/TEST SPLIT SIZE\n",
    "* Provided the general business problem was establishing a forward-looking 1m forecast for purchase budgeting, the sales forecasts had to be locked in 1m in advance\n",
    "    * Accordingly, an initial point for these budgets was to utilise a train/test split that gives a test size of roughly 1m as the option to do walk-forward validation is not available in practice\n",
    "    * This results in a 92% training size to a 8% test size, which is roughly (345 to 30 observations respectively)\n",
    "    \n",
    "\n",
    "* There is also the added issue of December (Christmas) + Christmas in Covid, having a skewed effect on the test size if the training size is too small and being dominated by the above average December period\n",
    "\n",
    "\n",
    "* NOTE: the \"daily_postbf_df\" is defined from the ZA-breakpoint (defined above) or follow this link: [to_za_breakpoint](#sub_sampling) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " training size: 345 \n",
      " test size: 30\n"
     ]
    }
   ],
   "source": [
    "# set a training/test split to 92% / 8% -- that way the test size is roughly 1m \n",
    "training_size_percent = 0.92\n",
    "\n",
    "# use floor to ensure values are integers \n",
    "training_size = np.floor(len(daily_postbp_df) * training_size_percent)\n",
    "\n",
    "print(\" training size: {} \\n test size: {}\".format(int(training_size), \n",
    "                                                     int(len(daily_postbp_df) - training_size)))\n",
    "\n",
    "split_date = daily_postbp_df.index[0] + pd.DateOffset(training_size)\n",
    "\n",
    "daily_postpb_train = daily_postbp_df.loc[daily_postbp_df.index < split_date]\n",
    "daily_postb_test = daily_postbp_df.loc[daily_postbp_df.index >= split_date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>store</th>\n",
       "      <th>sales</th>\n",
       "      <th>gp</th>\n",
       "      <th>promosales</th>\n",
       "      <th>promosales</th>\n",
       "      <th>baskets</th>\n",
       "      <th>avgbasket</th>\n",
       "      <th>waste</th>\n",
       "      <th>markdown</th>\n",
       "      <th>items</th>\n",
       "      <th>normalqty</th>\n",
       "      <th>promoqty</th>\n",
       "      <th>loyaltysales</th>\n",
       "      <th>costadjustment</th>\n",
       "      <th>gst</th>\n",
       "      <th>publichol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datealternatekey</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-19</th>\n",
       "      <td>MW</td>\n",
       "      <td>26,024.62</td>\n",
       "      <td>5,786.80</td>\n",
       "      <td>12,273.13</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1354</td>\n",
       "      <td>19.22</td>\n",
       "      <td>32.45</td>\n",
       "      <td>258.96</td>\n",
       "      <td>5242</td>\n",
       "      <td>2,784.35</td>\n",
       "      <td>2,216.89</td>\n",
       "      <td>861.98</td>\n",
       "      <td>91.32</td>\n",
       "      <td>1,478.49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-20</th>\n",
       "      <td>MW</td>\n",
       "      <td>28,570.45</td>\n",
       "      <td>6,063.46</td>\n",
       "      <td>13,303.86</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1444</td>\n",
       "      <td>19.79</td>\n",
       "      <td>245.51</td>\n",
       "      <td>490.99</td>\n",
       "      <td>5583</td>\n",
       "      <td>2,922.33</td>\n",
       "      <td>2,317.02</td>\n",
       "      <td>821.73</td>\n",
       "      <td>-343.87</td>\n",
       "      <td>1,513.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-21</th>\n",
       "      <td>MW</td>\n",
       "      <td>27,667.87</td>\n",
       "      <td>6,588.10</td>\n",
       "      <td>13,206.88</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1339</td>\n",
       "      <td>20.66</td>\n",
       "      <td>489.45</td>\n",
       "      <td>322.03</td>\n",
       "      <td>5812</td>\n",
       "      <td>3,071.46</td>\n",
       "      <td>2,389.17</td>\n",
       "      <td>732.69</td>\n",
       "      <td>276.26</td>\n",
       "      <td>1,476.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 store     sales       gp  promosales  promosales  baskets  \\\n",
       "datealternatekey                                                             \n",
       "2021-02-19          MW 26,024.62 5,786.80   12,273.13        0.47     1354   \n",
       "2021-02-20          MW 28,570.45 6,063.46   13,303.86        0.47     1444   \n",
       "2021-02-21          MW 27,667.87 6,588.10   13,206.88        0.48     1339   \n",
       "\n",
       "                  avgbasket  waste  markdown  items  normalqty  promoqty  \\\n",
       "datealternatekey                                                           \n",
       "2021-02-19            19.22  32.45    258.96   5242   2,784.35  2,216.89   \n",
       "2021-02-20            19.79 245.51    490.99   5583   2,922.33  2,317.02   \n",
       "2021-02-21            20.66 489.45    322.03   5812   3,071.46  2,389.17   \n",
       "\n",
       "                  loyaltysales  costadjustment      gst  publichol  \n",
       "datealternatekey                                                    \n",
       "2021-02-19              861.98           91.32 1,478.49          0  \n",
       "2021-02-20              821.73         -343.87 1,513.97          0  \n",
       "2021-02-21              732.69          276.26 1,476.60          0  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_postpb_train.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>sales</th>\n",
       "      <th>gp</th>\n",
       "      <th>promosales</th>\n",
       "      <th>promosales</th>\n",
       "      <th>baskets</th>\n",
       "      <th>avgbasket</th>\n",
       "      <th>waste</th>\n",
       "      <th>markdown</th>\n",
       "      <th>items</th>\n",
       "      <th>normalqty</th>\n",
       "      <th>promoqty</th>\n",
       "      <th>loyaltysales</th>\n",
       "      <th>costadjustment</th>\n",
       "      <th>gst</th>\n",
       "      <th>publichol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datealternatekey</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-22</th>\n",
       "      <td>MW</td>\n",
       "      <td>27,605.25</td>\n",
       "      <td>6,767.90</td>\n",
       "      <td>12,910.30</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1324</td>\n",
       "      <td>20.85</td>\n",
       "      <td>27.58</td>\n",
       "      <td>170.80</td>\n",
       "      <td>5635</td>\n",
       "      <td>3,057.31</td>\n",
       "      <td>2,183.59</td>\n",
       "      <td>1,181.88</td>\n",
       "      <td>-148.03</td>\n",
       "      <td>1,495.54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-23</th>\n",
       "      <td>MW</td>\n",
       "      <td>25,594.30</td>\n",
       "      <td>5,839.91</td>\n",
       "      <td>11,310.23</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1332</td>\n",
       "      <td>19.21</td>\n",
       "      <td>13.31</td>\n",
       "      <td>146.63</td>\n",
       "      <td>4815</td>\n",
       "      <td>8,937.30</td>\n",
       "      <td>1,918.94</td>\n",
       "      <td>989.24</td>\n",
       "      <td>-114.26</td>\n",
       "      <td>1,340.16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-24</th>\n",
       "      <td>MW</td>\n",
       "      <td>25,040.84</td>\n",
       "      <td>5,836.96</td>\n",
       "      <td>10,900.06</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1321</td>\n",
       "      <td>18.96</td>\n",
       "      <td>9.44</td>\n",
       "      <td>289.38</td>\n",
       "      <td>5015</td>\n",
       "      <td>2,836.36</td>\n",
       "      <td>1,867.37</td>\n",
       "      <td>848.74</td>\n",
       "      <td>-109.51</td>\n",
       "      <td>1,413.53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 store     sales       gp  promosales  promosales  baskets  \\\n",
       "datealternatekey                                                             \n",
       "2021-02-22          MW 27,605.25 6,767.90   12,910.30        0.47     1324   \n",
       "2021-02-23          MW 25,594.30 5,839.91   11,310.23        0.44     1332   \n",
       "2021-02-24          MW 25,040.84 5,836.96   10,900.06        0.44     1321   \n",
       "\n",
       "                  avgbasket  waste  markdown  items  normalqty  promoqty  \\\n",
       "datealternatekey                                                           \n",
       "2021-02-22            20.85  27.58    170.80   5635   3,057.31  2,183.59   \n",
       "2021-02-23            19.21  13.31    146.63   4815   8,937.30  1,918.94   \n",
       "2021-02-24            18.96   9.44    289.38   5015   2,836.36  1,867.37   \n",
       "\n",
       "                  loyaltysales  costadjustment      gst  publichol  \n",
       "datealternatekey                                                    \n",
       "2021-02-22            1,181.88         -148.03 1,495.54          0  \n",
       "2021-02-23              989.24         -114.26 1,340.16          0  \n",
       "2021-02-24              848.74         -109.51 1,413.53          0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_postb_test.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PICKLE THE TRAIN/TEST SETS [OPTIONAL]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#daily_postpb_train.to_pickle(\"./daily_postpb_train.pkl\")\n",
    "#daily_postb_test.to_pickle(\"./daily_postb_test.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='forecasting_intro'></a>\n",
    "\n",
    "# FORECAST INTRODUCTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialise dict to save results \n",
    "model_results_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to tabulate! \n",
    "\n",
    "def evaulate_model_forecast(yhat_values, tester_df_values):\n",
    "    \"\"\"\n",
    "    A function to quickly report the key accuracy statistics for each model's forecast\n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"Root Mean Squared Error (RMSE): {}\".format(np.round(\n",
    "        mean_squared_error(yhat_values, tester_df_values, squared=False)),4),'\\n', \"--\"*25, '\\n')\n",
    "    \n",
    "    #print(\"Mean Absolute Percentage Error (MAPE): {:.2}%\".format(\n",
    "    #    mean_absolute_percentage_error(yhat_values, tester_df_values) *100), '\\n', \"--\"*25, '\\n')   \n",
    "    \n",
    "    mape = mean_absolute_percentage_error(yhat_values, tester_df_values) * 100\n",
    "    print(\"Mean Absolute Percentage Error (MAPE): {}%\".format(np.round(mape,2)), '\\n', \"--\"*25, '\\n')    \n",
    "    \n",
    "    \n",
    "    print(\"Mean Absolute Erorr (MAE): {}\".format(np.round(\n",
    "        mean_absolute_error(yhat_values, tester_df_values)),4),'\\n', \"--\"*25, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "def store_model_forecast(yhat_values, tester_df_values, just_mape=False):\n",
    "    \"\"\"\n",
    "    return the model evaulation metrics to be stored in a dictionary for model comparison\n",
    "    \"\"\"\n",
    "    \n",
    "    # round each metric to 4 decimals\n",
    "    mape = np.round(mean_absolute_percentage_error(yhat_values, tester_df_values) * 100, 2)\n",
    "    mse = np.round(mean_squared_error(yhat_values, tester_df_values, squared=False),4)\n",
    "    mae = np.round(mean_absolute_error(yhat_values, tester_df_values),4)\n",
    "    \n",
    "    # if iteration is just_mape, return just MAPE, else return all 3 metrics\n",
    "    if just_mape==False:\n",
    "        return mape, mse, mae\n",
    "    else: \n",
    "        return mape\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='naive_forecast'></a>\n",
    "\n",
    "# NAIVE FORECAST\n",
    "* Important to include a naive forecast to measure the \"gain\" of more complicated models\n",
    "* Accordingly, it makes little sense to implement a more complicated models if it doesn't involve any significant gain in accuracy Vs. the most simple method (simply predicting the next value to be the mean of the series to date)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define series mean (sales)\n",
    "sales_mean_postb = daily_postbp_df.sales.mean()\n",
    "\n",
    "# convert the entire series mean intro an array of equal length with the mean value as each daily element\n",
    "sales_mean_postb = np.array([sales_mean_postb for u in range(len(daily_postbp_df))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE): 4187.0 \n",
      " -------------------------------------------------- \n",
      "\n",
      "Mean Absolute Percentage Error (MAPE): 9.68% \n",
      " -------------------------------------------------- \n",
      "\n",
      "Mean Absolute Erorr (MAE): 2666.0 \n",
      " -------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaulate_model_forecast(sales_mean_postb, daily_postbp_df.sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Unsurprisingly, the Naive Forecast performs quite poorly. \n",
    "* This is likely because the naive mean can't account for the complexities in the nature of the data; namely, weekends are higher than weekdays, but Mondays might be lower than your average weekday. Furthermore, there are holidays and other special events that aren't captured well by the Naive forecast. Additionally, the naive mean does a poor job capturing the rising trend of the data over the months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you wanted to save all metrics to the dictionary; uncomment\n",
    "\n",
    "#model_results_dict['naive_forecast'] = {'MSE': store_model_forecast(sales_mean_postb, daily_postbp_df.sales)[0],\n",
    "#                             'MAPE': store_model_forecast(sales_mean_postb, daily_postbp_df.sales)[1],\n",
    "#                             'MAE': store_model_forecast(sales_mean_postb, daily_postbp_df.sales)[2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store the results in a dictionary using the above defined evaulation function\n",
    "model_results_dict['naive_forecast'] = store_model_forecast(sales_mean_postb, daily_postbp_df.sales,\n",
    "                                                           just_mape=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sarimax'></a>\n",
    "\n",
    "## SARIMAX: Seasonal Autoregressive Integrated Moving Average (Exogenous Variable: X)\n",
    "* the base notation for the model is: SARIMAX(p,d,q)(P,D,Q,m)(X)\n",
    "    * denoting the: Trend Autoregression order, Trend Difference Order, Trend Moving Average Order, (repeated for the seasonal comonent in that order), and the specified exogenous variable (Holidays/Special Events) in this case.\n",
    "\n",
    "\n",
    "## SARIMAX AUTO \n",
    "* The model is initially trained on the training DF, then used to make predictions on the test DF\n",
    "* pm.auto_arima = A module for iteratively calculating the optimal ARIMA & Seasonal Terms in the SARIMAX environment, subject to user specified restraints and optimisation preferences\n",
    "\n",
    "#### PARAMETERS EXPLANATION:\n",
    "* m=7 (seasonality, for daily data (7) = weekly)\n",
    "* method = 'lbfgs' (optimisation method) is: limited-memory BFGS with optional box constraints\n",
    "* Stepwise = the stepwise algorithm outlined in Hyndman and Khandakar (2008) \n",
    "    * This method of identifying optimal model parameters is significantly faster than fitting all (or a random subset) of hyper-parameter combinations, and is less likely to over-fit the model as a result\n",
    "* return_valid_fits = will return all valid ARIMA models (these will be later filtered on)\n",
    "* Information Criterion = how to score each mode, optons are (AIC, BIC, HQIC, OOB)\n",
    "    * The bic tends to favour less complicated models relative to the AIC, which favours model parsimony\n",
    "* the max and start (p, P, Q, q, D, d) are the max and starting values for the:\n",
    "    * p = AR component of the arima model\n",
    "    * P = AR component of the seasonal component \n",
    "    * d = the order of first-differencing for the ARIMA\n",
    "    * D = the order of first-differencing for the seasonal component \n",
    "    * q = MA component of the ARIMA\n",
    "    * Q = MA component of the seasonal component \n",
    "* max_order = maximum integer which is a sum of any of the above mentioned individual model components, set to None meaning no maximum restriction imposed (although individual restraints specified by max_p, max_q etc. do indeed enforce an eventual max_order)\n",
    "* X = here is an array of (1, 0) which represent public holidays; reshaped(-1, 1) to fit the column-length wise dimensions needed for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the auto-(s)arima packages\n",
    "import pmdarima as pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import pickles if necessary \n",
    "#daily_postpb_train = pd.read_pickle(\"./daily_postpb_train.pkl\") \n",
    "#daily_postb_test = pd.read_pickle(\"./daily_postb_test.pkl\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize bic\n",
      " ARIMA(2,1,2)(3,0,2)[7] intercept   : BIC=6661.527, Time=2.49 sec\n",
      " ARIMA(0,1,0)(0,0,0)[7] intercept   : BIC=6817.811, Time=0.01 sec\n",
      " ARIMA(1,1,0)(1,0,0)[7] intercept   : BIC=6722.540, Time=0.10 sec\n",
      " ARIMA(0,1,1)(0,0,1)[7] intercept   : BIC=6635.740, Time=0.50 sec\n",
      " ARIMA(0,1,0)(0,0,0)[7]             : BIC=6812.089, Time=0.13 sec\n",
      " ARIMA(0,1,1)(0,0,0)[7] intercept   : BIC=6646.148, Time=0.22 sec\n",
      " ARIMA(0,1,1)(1,0,1)[7] intercept   : BIC=inf, Time=0.68 sec\n",
      " ARIMA(0,1,1)(0,0,2)[7] intercept   : BIC=6633.880, Time=0.98 sec\n",
      " ARIMA(0,1,1)(1,0,2)[7] intercept   : BIC=inf, Time=1.15 sec\n",
      " ARIMA(0,1,1)(0,0,3)[7] intercept   : BIC=6640.928, Time=1.37 sec\n",
      " ARIMA(0,1,1)(1,0,3)[7] intercept   : BIC=6652.761, Time=1.52 sec\n",
      " ARIMA(0,1,0)(0,0,2)[7] intercept   : BIC=6802.359, Time=0.10 sec\n",
      " ARIMA(1,1,1)(0,0,2)[7] intercept   : BIC=6636.109, Time=1.24 sec\n",
      " ARIMA(0,1,2)(0,0,2)[7] intercept   : BIC=6640.225, Time=0.66 sec\n",
      " ARIMA(1,1,0)(0,0,2)[7] intercept   : BIC=6724.619, Time=0.44 sec\n",
      " ARIMA(1,1,2)(0,0,2)[7] intercept   : BIC=inf, Time=0.99 sec\n",
      " ARIMA(0,1,1)(0,0,2)[7]             : BIC=6637.794, Time=0.23 sec\n",
      "\n",
      "Best model:  ARIMA(0,1,1)(0,0,2)[7] intercept\n",
      "Total fit time: 12.860 seconds\n"
     ]
    }
   ],
   "source": [
    "## == DF TRAINING == ## \n",
    "sarima_auto = pm.auto_arima(daily_postpb_train.sales,\n",
    "             trace=True,\n",
    "             error_action='warn',\n",
    "             seasonal=True,\n",
    "             m=7,\n",
    "             stepwise=True,\n",
    "             method='lbfgs',\n",
    "             return_valid_fits=True,\n",
    "             information_criterion='bic',\n",
    "                           max_p = 5,\n",
    "                           max_q = 4,\n",
    "                           max_Q = 4,\n",
    "                           max_P = 5,\n",
    "                           max_D = 1,\n",
    "                           max_d = 1,\n",
    "                           start_P = 3,\n",
    "                           start_q = 2,\n",
    "                           start_Q = 2,\n",
    "                           start_p = 2,\n",
    "                           max_order=None,\n",
    "                           X = np.array(daily_postpb_train.publichol).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ARIMA(0,1,1)(0,0,2)[7] intercept 6633.880169059311\n",
      " ARIMA(0,1,1)(0,0,1)[7] intercept 6635.7401772824915\n",
      " ARIMA(1,1,1)(0,0,2)[7] intercept 6636.1087149197365\n",
      " ARIMA(0,1,1)(0,0,2)[7]           6637.793511931245\n",
      " ARIMA(0,1,2)(0,0,2)[7] intercept 6640.22472525722\n",
      " ARIMA(0,1,1)(0,0,3)[7] intercept 6640.928010429546\n",
      " ARIMA(0,1,1)(0,0,0)[7] intercept 6646.1480073896455\n",
      " ARIMA(0,1,1)(1,0,3)[7] intercept 6652.760958871001\n",
      " ARIMA(2,1,2)(3,0,2)[7] intercept 6661.527209530503\n",
      " ARIMA(1,1,0)(1,0,0)[7] intercept 6722.5401474956325\n"
     ]
    }
   ],
   "source": [
    "## print out the top 10 model iterations orders sorted by BIC\n",
    "top_arima_models_by_bic = []\n",
    "top_number_models = 10\n",
    "\n",
    "for i, val in enumerate(sarima_auto[:top_number_models]):\n",
    "    print(val, val.bic())\n",
    "    top_arima_models_by_bic.append(val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOTES FOR CHOSING AN INITIAL SARIMAX FORECASTING MODEL\n",
    "* Be careful to identify that all comparable models are of the same differencing order, otherwise the BIC cannot be directly compared\n",
    "\n",
    "* We noted earlier in the ACF and PACF that the autoregressive component stays significant for quite a long period, thus, out of the top models, we will likely favour models which have an AR component initially (provided the BIC is not substantially different). Ultimately, the chosen models should be compared in terms of their predictive strength in multiple sub-samples in terms of RMSE / MAE etc.\n",
    "\n",
    "* We also note that the PACF which showed strong 7x multiple lags (same days affecting the same days from week to week, E.g. Sunday to Sunday) is reflected by the range (1-3) of MA terms in the seasonal part of the model. \n",
    "\n",
    "* The ACF plots also show very strong AC, so we will favour a model with a higher AR order to begin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARIMA(order=(2, 1, 2), scoring_args={}, seasonal_order=(3, 0, 2, 7),\n",
       "      suppress_warnings=True)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the final model based of the specified SARIMA terms from the top_models_list \n",
    "\n",
    "# specify index of the chosen model\n",
    "model_interation_index = 8\n",
    "\n",
    "# fit the model iteration to the training data \n",
    "sarimax_auto_fitted = top_arima_models_by_bic[model_interation_index].fit(daily_postpb_train.sales)\n",
    "sarimax_auto_fitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WALK-FORWARD PREDICTION\n",
    "* Allow the prediction to be iteratively updated at each time step\n",
    "* This is primarily for research and testing purposes, as in reality, submitted forecasts should be finalised for 1M periods in advance and are not changable (in the absence of extreme events) \n",
    "    * The subsequent \"static\" forecast will be the focus / comparable forecast to be used in the final evaulation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ARIMA TERMS: (2, 1, 2) \n",
      " SEASONAL TERMS: (3, 0, 2, 7)\n",
      "Wall time: 1min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "warnings.filterwarnings(\"ignore\") \n",
    "\n",
    "# set the orders to the optimised orders from the auto-sarimax & print for convenience \n",
    "ar_order = sarimax_auto_fitted.order\n",
    "s_order = sarimax_auto_fitted.seasonal_order\n",
    "print(\" ARIMA TERMS: {} \\n SEASONAL TERMS: {}\".format(ar_order, s_order))\n",
    "\n",
    "# store the model predictions in a list\n",
    "yhat = []\n",
    "\n",
    "for i in range(len(daily_postb_test.sales)):\n",
    "    sales_train = daily_postbp_df[:len(daily_postpb_train) +i]\n",
    "    \n",
    "    model_sarimax = sm.tsa.statespace.SARIMAX(sales_train.sales, order=ar_order, seasonal_order=s_order,\n",
    "                                             exog=sales_train.publichol)\n",
    "    \n",
    "    model_sarimax_fitted = model_sarimax.fit()\n",
    "    \n",
    "    # note: prediction passes in test DF exog values\n",
    "    model_sarimax_pred = model_sarimax_fitted.predict(start=len(sales_train), end=len(sales_train), dynamic=True,\n",
    "                                                     exog=daily_postb_test.publichol[i])\n",
    "    \n",
    "    yhat = yhat + [model_sarimax_pred]\n",
    "\n",
    "# concat the series of 1D walk-forward validation forecasts to get one continuous series of predictions    \n",
    "yhat = pd.concat(yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the original data Vs. the walk-forard prediction in SARIMAX\n",
    "fig, ax = plt.subplots(1,1, figsize=(10,6))\n",
    "fig.suptitle(\"SARIMAX MODEL WALK-FORWARD PREDICTIONS - MODEL AR TERMS: {}, SEASONAL TERMS: {}\".format(\n",
    "sarimax_auto_fitted.order, sarimax_auto_fitted.seasonal_order))\n",
    "plt.plot(daily_postb_test.index, daily_postb_test.sales.values, label='Original Series')\n",
    "plt.plot(daily_postb_test.index, yhat.values, color='red', label='SARIMAX PREDICTION', linestyle=\"--\")\n",
    "plt.xticks(rotation='45')[::2]\n",
    "plt.legend(bbox_to_anchor=(1.30, 1), loc='upper right')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SARIMAX WALK-FORWARD PREDICTION INSIGHTS\n",
    "\n",
    "#### RECALL: this model could not be used in reality, but it simply a broad oversight of how daily sales could be predicted if the values were allowed to be updated daily\n",
    "* The model seems to capture the weekend (seasonality 7D to 7D phenomena) reasonably well\n",
    "* However, it seems to consistenly overpredict the mid-week drops\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE): 1205.0 \n",
      " -------------------------------------------------- \n",
      "\n",
      "Mean Absolute Percentage Error (MAPE): 3.48% \n",
      " -------------------------------------------------- \n",
      "\n",
      "Mean Absolute Erorr (MAE): 952.0 \n",
      " -------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "## quick print model evaluation\n",
    "evaulate_model_forecast(daily_postb_test.sales.values, yhat.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='static_sarimax'></a>\n",
    "\n",
    "## STATIC SARIMAX FORECAST\n",
    "\n",
    "* As previously mentioned, the ability to perform a walk-forward validation is not feasible in reality, as 1M forecasts must be submitted ahead of time to be passed to individual stores\n",
    "* Hence, we also should test how the model performs with 1M forward, static predictions that don't update with each day's previous values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ARIMA TERMS: (2, 1, 2) \n",
      " SEASONAL TERMS: (3, 0, 2, 7)\n"
     ]
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\") \n",
    "# static 1m forecast\n",
    "\n",
    "ar_order = sarimax_auto_fitted.order\n",
    "s_order = sarimax_auto_fitted.seasonal_order\n",
    "print(\" ARIMA TERMS: {} \\n SEASONAL TERMS: {}\".format(ar_order, s_order))\n",
    "   \n",
    "model_sarimax = sm.tsa.statespace.SARIMAX(daily_postpb_train.sales, \n",
    "                                          order=ar_order, \n",
    "                                          seasonal_order=s_order,\n",
    "                                          exog=daily_postpb_train.publichol)\n",
    "    \n",
    "model_sarimax_fitted = model_sarimax.fit()\n",
    "\n",
    "\n",
    "model_sarimax_fcast = model_sarimax_fitted.forecast(steps=30, exog=daily_postb_test.publichol)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graph static prediction vs real series\n",
    "fig, ax = plt.subplots(1,1, figsize=(10,6))\n",
    "fig.suptitle(\"SARIMAX MODEL STATIC PREDICTIONS - MODEL (ARIMA) TERMS: {}, SEASONAL TERMS: {}\".format(\n",
    "sarimax_auto_fitted.order, sarimax_auto_fitted.seasonal_order))\n",
    "plt.plot(daily_postb_test.index, daily_postb_test.sales.values, label='Original Series')\n",
    "plt.plot(daily_postb_test.index, model_sarimax_fcast[-30:], color='red', label='SARIMAX STATIC PREDICTION', linestyle=\"--\")\n",
    "plt.xticks(rotation='45')[::2]\n",
    "plt.legend(bbox_to_anchor=(1.35, 1), loc='upper right')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE): 1358.0 \n",
      " -------------------------------------------------- \n",
      "\n",
      "Mean Absolute Percentage Error (MAPE): 3.56% \n",
      " -------------------------------------------------- \n",
      "\n",
      "Mean Absolute Erorr (MAE): 1017.0 \n",
      " -------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "## quick print model evaluation\n",
    "evaulate_model_forecast(model_sarimax_fcast, daily_postb_test['sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store the results in a dictionary using the above defined evaulation function\n",
    "model_results_dict['static_sarimax'] = store_model_forecast(model_sarimax_fcast, \n",
    "                                                            daily_postb_test['sales'],\n",
    "                                                           just_mape=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BRIEF TAKEAWAYS ON THE SARIMAX: STATIC VS WALK FORWARD\n",
    "* Most notably, the walkforward procedure only provides a very marginal increase in accuracy ~0.08% MAPE\n",
    "* One common weakness is that both models tend to over-estimate the mid-week lows\n",
    "* The static forecast generally does a good job in capturing the weekend seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='tbats'></a>\n",
    "\n",
    "## TBATS \n",
    "* A strong model for capturing multiple seasonalities, however, it cannot handle special exogenous variables that don't fit a usual pattern very well. \n",
    "* There is room for a hybrid model to use TBATS for non-holiday days, and use an ARIMAX model to capture the holiday/special event sales days accordingly\n",
    "\n",
    "\n",
    "#### We could experiment with solely using TBATS in months wherewe are forecasting a period that is without a public holiday (which represents roughly 5-6 months of each year) \n",
    "\n",
    "### PARAMETER EXPLANATION\n",
    "* T = trigonometric regressions to model multiple seasonalities \n",
    "* B = Box-Cox Transformations\n",
    "* A = ARMA errors\n",
    "* T = Trend\n",
    "* S = Seasonality \n",
    "\n",
    "\n",
    "* The TBATS is a time series model for modeling series which exhibit multiple complex seasonalities. \n",
    "* SOURCE:\n",
    "    * https://github.com/intive-DataScience/tbats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tbats import TBATS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "## based off PACF; load 2 seasonality periods \n",
    "tbats_estimator = TBATS(seasonal_periods=[7, 14])\n",
    "\n",
    "fitted_model_tbats = tbats_estimator.fit(daily_postpb_train.sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graph static prediction vs real series\n",
    "fig, ax = plt.subplots(1,1, figsize=(10,6))\n",
    "fig.suptitle(\"TBATS MODEL STATIC PREDICTIONS - 30D\")\n",
    "\n",
    "plt.plot(daily_postb_test.index, daily_postb_test.sales.values, label='Original Series')\n",
    "plt.plot(daily_postb_test.index, fitted_model_tbats.y_hat[-30:], color='red', label='TBATS STATIC PREDICTION', linestyle=\"--\")\n",
    "plt.xticks(rotation='45')[::2]\n",
    "plt.legend(bbox_to_anchor=(1.35, 1), loc='upper right')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE): 2601.0 \n",
      " -------------------------------------------------- \n",
      "\n",
      "Mean Absolute Percentage Error (MAPE): 7.14% \n",
      " -------------------------------------------------- \n",
      "\n",
      "Mean Absolute Erorr (MAE): 2131.0 \n",
      " -------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "## quick print model evaluation\n",
    "evaulate_model_forecast(fitted_model_tbats.y_hat[-30:], daily_postb_test['sales'][-30:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store the results in a dictionary using the above defined evaulation function\n",
    "model_results_dict['static_tbats'] = store_model_forecast(fitted_model_tbats.y_hat[-30:], \n",
    "                                                            daily_postb_test['sales'][-30:],\n",
    "                                                         just_mape=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to store all metrics for TBATS\n",
    "\n",
    "#model_results_dict['static_tbats'] = {'MSE': store_model_forecast(fitted_model_tbats.y_hat[-30:],  daily_postb_test['sales'][-30:])[0],\n",
    "#                             'MAPE': store_model_forecast(fitted_model_tbats.y_hat[-30:],  daily_postb_test['sales'][-30:])[1],\n",
    "#                             'MAE': store_model_forecast(fitted_model_tbats.y_hat[-30:],  daily_postb_test['sales'][-30:])[2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='xgboost'></a>\n",
    "\n",
    "# XGBOOST (eXtreme Gradient Boosting)\n",
    "\n",
    "* In a nutshell, the XGBoost package will implement the gradient boosting decision tree algorithm. boosting is an ensemble technique where new models are added to correct the errors made by existing (previous) models, these models are added sequentially until no further improvements can be made. The gradient boosting refers to the approach in that new models aim to predict the residuals (errors) of prior models, and then are added together to make the final prediction; the gradient boosting gets its name from the fact it uses a gradient descent algorithm to minimise the loss when adding the newer models\n",
    "\n",
    "### Three types of parameters before running the XGBoost model:\n",
    "* General Parameters\n",
    "    * E.G. which booster to use: tree or linear model etc.\n",
    "* Boosting Parameters\n",
    "    * Parameter Explanations are available in the packages docs\n",
    "    * https://xgboost.readthedocs.io/en/latest/tutorials/model.html\n",
    "* Learning Task Parameters:\n",
    "    * which depend on the learning scenario, E.G. regression taks may use different criteria to evaulate their accuracy vs classification problems etc. \n",
    "\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# may need to pip install xgboost \n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from matplotlib import pyplot\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from xgboost import plot_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read pickle files [optional if not already executed above]\n",
    "daily_postpb_train = pd.read_pickle(\"./daily_postpb_train.pkl\") \n",
    "daily_postb_test = pd.read_pickle(\"./daily_postb_test.pkl\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='feature_engineering'></a>\n",
    "\n",
    "## FEATURE ENGINEERING \n",
    "\n",
    "\n",
    "### Create Time-Series Features & Filter DF to simplify for un-needed model inputs in the subsequent models \n",
    "* It might be tempting to include \"baskets\" which would give an incredibly accurate forecast, however, this would overlook the fact that an out-of-sample forecast will not have an accurate value for \"baskets\" (transactions) and thus, this model would not perform well out of sample. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "## restrict to just necessary univariate sales + exog variable publichol\n",
    "train_df_x = daily_postpb_train[['sales','publichol']]\n",
    "test_df_x = daily_postb_test[['sales','publichol']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ADD TIME SERIES FEATURES\n",
    "* Dayofweek\n",
    "* Weekofyear\n",
    "* \"days_since_hol\" (days since last public holiday) - [NOT USED AFTER ITERATION]\n",
    "* \"days_until_hol\" (days until the next public holiday) [NOT USED AFTER ITERATION]\n",
    "* \"weekend_bool\" - deemed obsolete next to dayofweek [NOT USED AFTER ITERATION]\n",
    "\n",
    "\n",
    "#### OTHER OPTIONS NOT USED IN THE 30D FORECAST DUE TO TIME HORIZON BEING TOO SMALL\n",
    "* Month, Quarter, Year Etc.\n",
    "* hourly Data is not available past 7 days, thus no sine/cosine transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to quickly construct some time series features\n",
    "\n",
    "def construct_time_features(df,target=None):\n",
    "    \"\"\" Creates a host of time series features from a datetime index, \n",
    "    moves the \"target\" variable into a seperate series when called \"\"\"\n",
    "    \n",
    "    # specify a date variable (which is the index of this DF; else, can call reset_index() first)\n",
    "    df['date'] = df.index\n",
    "    \n",
    "    # using datetime package, extract various time features relating to the (daily) base-level data\n",
    "    df['dayofweek'] = df['date'].dt.dayofweek\n",
    "    \n",
    "    #df['dayofmonth'] = df['date'].dt.day\n",
    "    df['weekofyear'] = df['date'].dt.weekofyear\n",
    "    \n",
    "    ## ==  other potential options - not relevant if the sample period is too short == ##\n",
    "    #df['month'] = df['date'].dt.month\n",
    "    #df['dayofyear'] = df['date'].dt.dayofyear\n",
    "    #df['quarter'] = df['date'].dt.quarter\n",
    "    #df['year'] = df['date'].dt.year\n",
    "    \n",
    "    # == useful if hourly data == # \n",
    "    ## note: the (sine, cosine) transformation of the time of day variable ensure that E.G. 0 and 23H are close to each other\n",
    "    #df['hour'] = df['date'].dt.hour\n",
    "    \n",
    "    #df['sin_day'] = np.sin(df['dayofyear'])\n",
    "    #df['cos_day'] = np.cos(df['dayofyear'])\n",
    "    \n",
    "    # drop the transient date column now the features are done\n",
    "    X = df.drop(['date'],axis=1)\n",
    "    \n",
    "    # E.G. if target=sales, move this target variable from the original DF, and specify it as 2nd parameter (y)\n",
    "    # this will effectively move it so it's own single-variable series when the function is called with target!= none\n",
    "    if target:\n",
    "        y = df[target]\n",
    "        X = X.drop([target],axis=1)\n",
    "        return X, y\n",
    "    \n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data / time series features to feed into models:\n",
    "scaled_train_df_x, y_train = construct_time_features(train_df_x, target='sales')\n",
    "scaled_test_df_x, y_test = construct_time_features(test_df_x, target='sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publichol</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekofyear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datealternatekey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-14</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-15</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-17</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-18</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-19</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-20</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-21</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>345 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  publichol  dayofweek  weekofyear\n",
       "datealternatekey                                  \n",
       "2020-03-14                0          5          11\n",
       "2020-03-15                0          6          11\n",
       "2020-03-16                0          0          12\n",
       "2020-03-17                0          1          12\n",
       "2020-03-18                0          2          12\n",
       "...                     ...        ...         ...\n",
       "2021-02-17                0          2           7\n",
       "2021-02-18                0          3           7\n",
       "2021-02-19                0          4           7\n",
       "2021-02-20                0          5           7\n",
       "2021-02-21                0          6           7\n",
       "\n",
       "[345 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#E.G. of training DF \n",
    "scaled_train_df_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  NOTE ON SCALER TRANSFORMATION\n",
    "* Tried adding a scarse matrix representation for the \"publichol\" but this iteration was worse and hence reverted to \"mean=True\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalisation \n",
    "scaler = StandardScaler(with_mean=True)\n",
    "scaler.fit(scaled_train_df_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.transform(scaled_train_df_x)\n",
    "X_test = scaler.transform(scaled_test_df_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_df = pd.DataFrame(X_train, columns=scaled_train_df_x.columns)\n",
    "X_test_df = pd.DataFrame(X_test, columns=scaled_test_df_x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publichol</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekofyear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>1.49</td>\n",
       "      <td>-1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>-1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>-1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-1.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   publichol  dayofweek  weekofyear\n",
       "0      -0.22       0.99       -1.15\n",
       "1      -0.22       1.49       -1.15\n",
       "2      -0.22      -1.50       -1.09\n",
       "3      -0.22      -1.01       -1.09\n",
       "4      -0.22      -0.51       -1.09"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DAYS SINCE / UNTIL NEXT PUBLIC HOLIDAY (NOT USED)\n",
    "* Public holidays were found to be too scarsely distributed for the variable to keep significance, also clustered holidays E.G. Easter were misrepresented by this feature; hence it was dropped after experimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy to test\n",
    "scaled_train_df_x_reset = scaled_train_df_x.copy()\n",
    "\n",
    "# move date to separate column for the days since/until holiday calculation\n",
    "scaled_train_df_x_reset = scaled_train_df_x_reset.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a mask which replaces the initial (0) values with NaN\n",
    "mask = scaled_train_df_x_reset.publichol.cumsum().replace(0,False).astype(bool) \n",
    "\n",
    "# assign a new column labelled \"days_since_hol\" to be the cumcount() of days occured\n",
    "scaled_train_df_x_2 = scaled_train_df_x_reset.assign(days_since_hol=scaled_train_df_x_reset.groupby(\n",
    "    scaled_train_df_x_reset.publichol.astype(bool).cumsum()).cumcount().where(mask))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# restore the date as index \n",
    "scaled_train_df_x_2 = scaled_train_df_x_2.set_index('datealternatekey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WEEKEND BOOLEAN VARIABLE (NOT USED)\n",
    "* Found to be obsolete vs. the dayoftheweek variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "## == [NOT INCLUDED IN FINAL MODEL] - ITERATION WAS UNSUCCESSFUL  == ## \n",
    "\n",
    "# add a weekend indicator to the dayofweek to attempt to correct the underfitting of weekends \n",
    "#scaled_train_df_x['weekend'] = scaled_train_df_x.dayofweek.apply(lambda x: 1 if x ==(5) or x ==(6) else 0)\n",
    "\n",
    "# repeat for test DF \n",
    "#scaled_test_df_x['weekend'] = scaled_test_df_x.dayofweek.apply(lambda x: 1 if x ==(5) or x ==(6) else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='xgboost_param_tuning'></a>\n",
    "\n",
    "# XGBOOST\n",
    "\n",
    "## GENERAL APPROACH TO PARAMETER TUNING IN XGBOOST\n",
    "\n",
    "* objective = \"regression\" and \"squared error\" thus denoted: \"reg:squarederror\"\n",
    "\n",
    "* 1) Tune the learning rate (starting from a relatively higher point ~ 0.5); can use the XGBoost package's function called \"cv\" which can return the optimum number of required trees by performing cross validation at each iteration\n",
    "    * Refers to the step size shrinkage used in update to prevent overfitting; the learning rate shrinks the feature weights (after each boosting step, directly get the weights of the new features) to make the boosting process more conservative \n",
    "\n",
    "\n",
    "* 2) Tune tree-specific parameters for the decided learning rate and number of trees:\n",
    "    * min_child_weight\n",
    "        * minimum sum of instance weight needed in a child; if a partitioning step in a tree results in a leaf node with the sum of the instance weight being less than the min_child_weight specified value, the building process will cease further partitioning. The larger the value, the more conservative the algorithm. \n",
    "            * the smaller the value, the leaf nodes can have smaller sized groups \n",
    "    * max_depth\n",
    "        * Increasing this value makes a model more complex and consequently, more likely to overfit\n",
    "            * values range from (3-10); (4-6) is a good starting point\n",
    "    * gamma\n",
    "        * Minimum loss reduction required to make a futher partition on a leaf node of the tree; a larger gamma is a more conservative algorithm\n",
    "            * Starting value (0.1 - 0.2) \n",
    "    * subsample\n",
    "        * The subsample ratio of the training instances; subsampling occurs once in every boosting iteration, setting a value to 0.5 would randomly sample half of the training data prior to growing the trees- which would assist in preventing overfitting \n",
    "    * colsample_by_tree\n",
    "        * The subsample ratio of columns when constructing each tree\n",
    "            * Values range from (0.5 - 0.9)\n",
    "    \n",
    "    \n",
    "* 3) Tune regularisation parameters: performance improvements / reduction in model complexiy: Both are regularisation terms on weights, increasing either value will make the model more conservative \n",
    "    * alpha (L1) (labelled: 'reg_alpha') \n",
    "    * lambda (L2) (labelled: 'reg_lambda')\n",
    "    \n",
    "    \n",
    "* 4) Lower the learning rate and decide upon the optimal parameters for the model to be used in forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tune \"max_deph\" and \"min_child_weight\" \n",
    "* NOTE: due to the large amount of potential models / tuning, the n_estimators will be temporarily lowered to 140 to reduce the computational time; depending on your system / available time, this parameter could be adjusted according "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:40:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Wall time: 2min 18s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.8, gamma=0, gpu_id=None,\n",
       "                                     importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_delta_step=None,\n",
       "                                     max_depth=5, min_child_weight=1,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None,\n",
       "                                     num_parallel_tree=None,\n",
       "                                     objective='reg:squarederror',\n",
       "                                     random_state=None, reg_alpha=0,\n",
       "                                     reg_lambda=1, scale_pos_weight=None,\n",
       "                                     subsample=0.8, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'max_depth': range(2, 10, 2),\n",
       "                         'min_child_weight': range(1, 6, 2)},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "#Grid seach on subsample and min_child_weight (param_test_1)\n",
    "# first test will have a wider range of (2); e.g. min_child_weight = (1,3,5)\n",
    "\n",
    "param_test_1 = {\n",
    "    'max_depth':range(2,10,2),\n",
    "    'min_child_weight':range(1,6,2)}\n",
    "\n",
    "gsearch_1 = GridSearchCV(estimator = xgb.XGBClassifier(objective = 'reg:squarederror',\n",
    "                                n_estimators=140,\n",
    "                                learning_rate=0.1,\n",
    "                                min_child_weight=1,\n",
    "                                gamma=0,\n",
    "                                reg_lambda=1,\n",
    "                                reg_alpha=0,\n",
    "                                max_depth=5,\n",
    "                                subsample=0.8,\n",
    "                                colsample_bytree=0.8),\n",
    "                         param_grid = param_test_1, \n",
    "                         scoring='roc_auc', \n",
    "                         n_jobs=-1, \n",
    "                         cv=5)\n",
    "\n",
    "# note, seed default = 0\n",
    "gsearch_1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'max_depth': 2, 'min_child_weight': 1}, nan)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print grid-search (#1) optimisation best values \n",
    "gsearch_1.best_params_, gsearch_1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further refine the step size to be more narrow at the lower end of the scale for each metric\n",
    "* NOTE: this could be combined with the first param_test_1 depending on your system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:43:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Wall time: 1min 10s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.8, gamma=0, gpu_id=None,\n",
       "                                     importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_delta_step=None,\n",
       "                                     max_depth=5, min_child_weight=1,\n",
       "                                     min_split_loss=1, missing=nan,\n",
       "                                     monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None,\n",
       "                                     num_parallel_tree=None,\n",
       "                                     objective='reg:squarederror',\n",
       "                                     random_state=None, reg_alpha=0,\n",
       "                                     reg_lambda=1, scale_pos_weight=None,\n",
       "                                     subsample=0.8, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'max_depth': range(2, 5),\n",
       "                         'min_child_weight': range(1, 3)},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "param_test_2 = {\n",
    "    'max_depth':range(2,5,1),\n",
    "    'min_child_weight':range(1,3,1)}\n",
    "\n",
    "gsearch_2 = GridSearchCV(estimator = xgb.XGBClassifier(objective = 'reg:squarederror',\n",
    "                                n_estimators=140,\n",
    "                                learning_rate=0.1,\n",
    "                                min_child_weight=1,\n",
    "                                gamma=0,\n",
    "                                reg_lambda=1,\n",
    "                                reg_alpha=0,\n",
    "                                max_depth=5,\n",
    "                                subsample=0.8,\n",
    "                                colsample_bytree=0.8),\n",
    "                         param_grid = param_test_2, \n",
    "                         scoring='roc_auc', \n",
    "                         n_jobs=-1, \n",
    "                         cv=5)\n",
    "\n",
    "gsearch_2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'max_depth': 2, 'min_child_weight': 1}, nan)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print grid-search (#2) optimisation best values \n",
    "gsearch_2.best_params_, gsearch_2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define these variables from the outputed dictionary\n",
    "optimal_max_depth = list(gsearch_2.best_params_.values())[0]\n",
    "optimal_min_cw = list(gsearch_2.best_params_.values())[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further tune \"min_child_weight\" \n",
    "* Check for the upper-range of values for this parameter, leaving max_depth fixed to its optimal value from above\n",
    "* A larger min_child_weight results in a more conservative algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:47:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Wall time: 1min 7s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.8, gamma=0, gpu_id=None,\n",
       "                                     importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_delta_step=None,\n",
       "                                     max_depth=2, min_child_weight=1,\n",
       "                                     min_split_loss=1, missing=nan,\n",
       "                                     monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None,\n",
       "                                     num_parallel_tree=None,\n",
       "                                     objective='reg:squarederror',\n",
       "                                     random_state=None, reg_alpha=0,\n",
       "                                     reg_lambda=1, scale_pos_weight=None,\n",
       "                                     subsample=0.8, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             n_jobs=4, param_grid={'min_child_weight': range(6, 12)},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "param_test_2b = {\n",
    "    'min_child_weight':range(6,12,1)}\n",
    "\n",
    "gsearch_2b = GridSearchCV(estimator = xgb.XGBClassifier(objective = 'reg:squarederror',\n",
    "                                n_estimators=140,\n",
    "                                learning_rate=0.1,\n",
    "                                min_child_weight=optimal_min_cw,\n",
    "                                gamma=0,\n",
    "                                reg_lambda=1,\n",
    "                                reg_alpha=0,\n",
    "                                max_depth=optimal_max_depth,\n",
    "                                subsample=0.8,\n",
    "                                colsample_bytree=0.8),\n",
    "                         param_grid = param_test_2b, \n",
    "                         scoring='roc_auc', \n",
    "                         n_jobs=4, \n",
    "                         cv=5)\n",
    "\n",
    "gsearch_2b.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'min_child_weight': 6}, nan)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch_2b.best_params_, gsearch_2b.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the new optimal min_child_weight\n",
    "optimal_min_cw = list(gsearch_2b.best_params_.values())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tune optimal gamma \n",
    "* [OPTIONAL] - recalibrate the number of boosting rounds (n_estimators) from 140 to (250-1000), but this depends on your computing power available, once again\n",
    "* NOTE: if optimal gamma is != 0; refine the scale by making a smaller fraction (i/20) for instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:52:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Wall time: 2min 2s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.8, gamma=0, gpu_id=None,\n",
       "                                     importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_delta_step=None,\n",
       "                                     max_depth=2, min_child_weight=6,\n",
       "                                     min_split_loss=1, missing=nan,\n",
       "                                     monotone_constraints=None,\n",
       "                                     n_estimators=250, n_jobs=None,\n",
       "                                     num_parallel_tree=None,\n",
       "                                     objective='reg:squarederror',\n",
       "                                     random_state=None, reg_alpha=0,\n",
       "                                     reg_lambda=1, scale_pos_weight=None,\n",
       "                                     subsample=0.8, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             n_jobs=-1, param_grid={'gamma': [0.0, 0.1, 0.2, 0.3, 0.4]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "param_test_3 = {\n",
    "    'gamma':[i/10 for i in range(0, 5, 1)]}\n",
    "\n",
    "gsearch_3 = GridSearchCV(estimator = xgb.XGBClassifier(objective = 'reg:squarederror',\n",
    "                                n_estimators=250,\n",
    "                                learning_rate=0.1,\n",
    "                                min_child_weight=optimal_min_cw,\n",
    "                                gamma=0,\n",
    "                                reg_lambda=1,\n",
    "                                reg_alpha=0,\n",
    "                                max_depth=optimal_max_depth,\n",
    "                                subsample=0.8,\n",
    "                                colsample_bytree=0.8),\n",
    "                         param_grid = param_test_3, \n",
    "                         scoring='roc_auc', \n",
    "                         n_jobs=-1, \n",
    "                         cv=5)\n",
    "\n",
    "gsearch_3.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'gamma': 0.0}, nan)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch_3.best_params_, gsearch_3.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the new optimal gamma\n",
    "optimal_gamma = list(gsearch_3.best_params_.values())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tune subsample and colsample_bytree in (2) stages\n",
    "* depending on your computational power, this step could be combined with the subsequent step for param_test_4b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:58:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Wall time: 4min 43s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.8, gamma=0.0,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_delta_step=None,\n",
       "                                     max_depth=2, min_child_weight=6,\n",
       "                                     min_split_loss=1, missing=nan,\n",
       "                                     monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None,\n",
       "                                     num_parallel_tree=None,\n",
       "                                     objective='reg:squarederror',\n",
       "                                     random_state=None, reg_alpha=0,\n",
       "                                     reg_lambda=1, scale_pos_weight=None,\n",
       "                                     subsample=0.8, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'colsample_bytree': [0.5, 0.6, 0.7, 0.8, 0.9],\n",
       "                         'subsample': [0.5, 0.6, 0.7, 0.8, 0.9]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "param_test_4 = {\n",
    " 'subsample':[i/10.0 for i in range(5,10)],\n",
    " 'colsample_bytree':[i/10.0 for i in range(5,10)]}\n",
    "\n",
    "gsearch_4 = GridSearchCV(estimator = xgb.XGBClassifier(objective = 'reg:squarederror',\n",
    "                                n_estimators=140,\n",
    "                                learning_rate=0.1,\n",
    "                                min_child_weight=optimal_min_cw,\n",
    "                                gamma=optimal_gamma,\n",
    "                                reg_lambda=1,\n",
    "                                reg_alpha=0,\n",
    "                                max_depth=optimal_max_depth,\n",
    "                                subsample=0.8,\n",
    "                                colsample_bytree=0.8),\n",
    "                         param_grid = param_test_4, \n",
    "                         scoring='roc_auc', \n",
    "                         n_jobs=-1, \n",
    "                         cv=5)\n",
    "\n",
    "gsearch_4.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'colsample_bytree': 0.5, 'subsample': 0.5}, nan)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch_4.best_params_, gsearch_4.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Refine a more narrow param test around the subsample and colsample_bytree variables\n",
    "* note: can either pass in the above dict and automatically specify a range; or do it manually depending on how refined you want to be when optimising the parameters\n",
    "    * Here, I opt to simply hard-code it in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:14:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Wall time: 8min 44s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.6, gamma=0.0,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_delta_step=None,\n",
       "                                     max_depth=2, min_child_weight=6,\n",
       "                                     min_split_loss=1, missing=nan,\n",
       "                                     monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None,\n",
       "                                     num_parallel_tree=None,\n",
       "                                     objective='reg:squarederror',\n",
       "                                     random_state=None, reg_alpha=0,\n",
       "                                     reg_lambda=1, scale_pos_weight=None,\n",
       "                                     subsample=0.6, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'colsample_bytree': [0.2, 0.25, 0.3, 0.35, 0.4, 0.45,\n",
       "                                              0.5],\n",
       "                         'subsample': [0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# do a more narrow param test (0.05) intervals around the previously optimal value\n",
    "param_test_4b = {\n",
    " 'subsample':[i/100.0 for i in range(20,55,5)],\n",
    " 'colsample_bytree':[i/100.0 for i in range(20,55,5)]}\n",
    "\n",
    "gsearch_4b = GridSearchCV(estimator = xgb.XGBClassifier(objective = 'reg:squarederror',\n",
    "                                n_estimators=140,\n",
    "                                learning_rate=0.1,\n",
    "                                min_child_weight=optimal_min_cw,\n",
    "                                gamma=optimal_gamma,\n",
    "                                reg_lambda=1,\n",
    "                                reg_alpha=0,\n",
    "                                max_depth=optimal_max_depth,\n",
    "                                subsample=0.6,\n",
    "                                colsample_bytree=0.6),\n",
    "                         param_grid = param_test_4b, \n",
    "                         scoring='roc_auc', \n",
    "                         n_jobs=-1, \n",
    "                         cv=5)\n",
    "\n",
    "gsearch_4b.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'colsample_bytree': 0.2, 'subsample': 0.2}, nan)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch_4b.best_params_, gsearch_4b.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define these variables from the outputed dictionary\n",
    "optimal_colsample_bytree = list(gsearch_4b.best_params_.values())[0]\n",
    "optimal_subsample = list(gsearch_4b.best_params_.values())[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune for the alpha param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:05:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Wall time: 1min 3s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.4, gamma=0.0,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_delta_step=None,\n",
       "                                     max_depth=2, min_child_weight=6,\n",
       "                                     min_split_loss=1, missing=nan,\n",
       "                                     monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None,\n",
       "                                     num_parallel_tree=None,\n",
       "                                     objective='reg:squarederror',\n",
       "                                     random_state=None, reg_alpha=0,\n",
       "                                     reg_lambda=1, scale_pos_weight=None,\n",
       "                                     subsample=0.4, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             n_jobs=-1, param_grid={'reg_alpha': [1e-05, 0.01, 0.1, 1, 10]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "param_test_5 = {\n",
    " 'reg_alpha':[1e-5, 1e-2, 0.1, 1, 10]}\n",
    "\n",
    "\n",
    "gsearch_5 = GridSearchCV(estimator = xgb.XGBClassifier(objective = 'reg:squarederror',\n",
    "                                n_estimators=140,\n",
    "                                learning_rate=0.1,\n",
    "                                min_child_weight=optimal_min_cw,\n",
    "                                gamma=optimal_gamma,\n",
    "                                reg_lambda=1,\n",
    "                                reg_alpha=0,\n",
    "                                max_depth=optimal_max_depth,\n",
    "                                subsample=optimal_subsample,\n",
    "                                colsample_bytree=optimal_colsample_bytree),\n",
    "                         param_grid = param_test_5, \n",
    "                         scoring='roc_auc', \n",
    "                         n_jobs=-1, \n",
    "                         cv=5)\n",
    "\n",
    "gsearch_5.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'reg_alpha': 1e-05}, nan)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch_5.best_params_, gsearch_5.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further refine the alpha param with a more narrowed range around the previously optimal value \n",
    "* Note: hard-coded variable here given the variability of the output and user-preferences for the width of the range in further optimimsation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:15:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Wall time: 59.2 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.2, gamma=0.0,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_delta_step=None,\n",
       "                                     max_depth=2, min_child_weight=6,\n",
       "                                     min_split_loss=1, missing=nan,\n",
       "                                     monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None,\n",
       "                                     num_parallel_tree=None,\n",
       "                                     objective='reg:squarederror',\n",
       "                                     random_state=None, reg_alpha=0,\n",
       "                                     reg_lambda=1, scale_pos_weight=None,\n",
       "                                     subsample=0.2, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             n_jobs=-1, param_grid={'reg_alpha': [0, 0.001, 0.005, 0.01, 0.05]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "param_test_5b = {\n",
    " 'reg_alpha':[0, 0.001, 0.005, 0.01, 0.05]}\n",
    "\n",
    "gsearch_5b = GridSearchCV(estimator = xgb.XGBClassifier(objective = 'reg:squarederror',\n",
    "                                n_estimators=140,\n",
    "                                learning_rate=0.1,\n",
    "                                min_child_weight=optimal_min_cw,\n",
    "                                gamma=optimal_gamma,\n",
    "                                reg_lambda=1,\n",
    "                                reg_alpha=0,\n",
    "                                max_depth=optimal_max_depth,\n",
    "                                subsample=optimal_subsample,\n",
    "                                colsample_bytree=optimal_colsample_bytree),\n",
    "                         param_grid = param_test_5b, \n",
    "                         scoring='roc_auc', \n",
    "                         n_jobs=-1, \n",
    "                         cv=5)\n",
    "\n",
    "gsearch_5b.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'reg_alpha': 0}, nan)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch_5b.best_params_, gsearch_5b.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the optimal alpha \n",
    "optimal_alpha = list(gsearch_5b.best_params_.values())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tune the learning rate parameter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:17:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Wall time: 1min 18s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.2, gamma=0.0,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_delta_step=None,\n",
       "                                     max_depth=2, min_child_weight=6,\n",
       "                                     min_split_loss=1, missing=nan,\n",
       "                                     monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None,\n",
       "                                     num_parallel_tree=None,\n",
       "                                     objective='reg:squarederror',\n",
       "                                     random_state=None, reg_alpha=0,\n",
       "                                     reg_lambda=1, scale_pos_weight=None,\n",
       "                                     subsample=0.2, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "param_test_6 = {\n",
    " 'learning_rate':[0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]}\n",
    "\n",
    "gsearch_6 = GridSearchCV(estimator = xgb.XGBClassifier(objective = 'reg:squarederror',\n",
    "                                n_estimators=140,\n",
    "                                learning_rate=0.1,\n",
    "                                min_child_weight=optimal_min_cw,\n",
    "                                gamma=optimal_gamma,\n",
    "                                reg_lambda=1,\n",
    "                                reg_alpha=optimal_alpha,\n",
    "                                max_depth=optimal_max_depth,\n",
    "                                subsample=optimal_subsample,\n",
    "                                colsample_bytree=optimal_colsample_bytree),\n",
    "                         param_grid = param_test_6, \n",
    "                         scoring='roc_auc', \n",
    "                         n_jobs=-1, \n",
    "                         cv=5)\n",
    "\n",
    "gsearch_6.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'learning_rate': 0.05}, nan)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch_6.best_params_, gsearch_6.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the optimal learning rate \n",
    "optimal_lr = list(gsearch_6.best_params_.values())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TUNE REG LAMBDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:24:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Wall time: 1min 4s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.2, gamma=0.0,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.05, max_delta_step=None,\n",
       "                                     max_depth=2, min_child_weight=6,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None,\n",
       "                                     num_parallel_tree=None,\n",
       "                                     objective='reg:squarederror',\n",
       "                                     random_state=None, reg_alpha=0,\n",
       "                                     reg_lambda=1, scale_pos_weight=None,\n",
       "                                     subsample=0.2, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             n_jobs=-1, param_grid={'reg_lambda': [0.1, 0.5, 0.7, 1, 1.5]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "param_test_7 = {\n",
    " 'reg_lambda':[0.1, 0.5, 0.7, 1, 1.5]}\n",
    "\n",
    "gsearch_7 = GridSearchCV(estimator = xgb.XGBClassifier(objective = 'reg:squarederror',\n",
    "                                n_estimators=140,\n",
    "                                learning_rate=optimal_lr,\n",
    "                                min_child_weight=optimal_min_cw,\n",
    "                                gamma=optimal_gamma,\n",
    "                                reg_lambda=1,\n",
    "                                reg_alpha=optimal_alpha,\n",
    "                                max_depth=optimal_max_depth,\n",
    "                                subsample=optimal_subsample,\n",
    "                                colsample_bytree=optimal_colsample_bytree),\n",
    "                         param_grid = param_test_7, \n",
    "                         scoring='roc_auc', \n",
    "                         n_jobs=-1, \n",
    "                         cv=5)\n",
    "\n",
    "gsearch_7.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'reg_lambda': 0.1}, nan)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch_7.best_params_, gsearch_7.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the optimal learning rate \n",
    "optimal_reglam = list(gsearch_7.best_params_.values())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FIT THE OPTIMISED PARAMETER MODEL \n",
    "* Then, use the model to predict future values on the test_df and plot / evaulate \n",
    "* Update the n_estimators now that the computational burden is less\n",
    "\n",
    "\n",
    "### in this instance for the 30D train-test split, the optimal parameters were:\n",
    "                                learning_rate=0.05,\n",
    "                                min_child_weight=6,\n",
    "                                gamma=0,\n",
    "                                reg_lambda=0.1,\n",
    "                                reg_alpha=0,\n",
    "                                max_depth=2,\n",
    "                                subsample=0.2,\n",
    "                                colsample_bytree=0.2,\n",
    "                                n_estimators=1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.2, gamma=0.0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.05, max_delta_step=0, max_depth=2,\n",
       "             min_child_weight=6, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=1000, n_jobs=8, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=0.1, scale_pos_weight=1, subsample=0.2,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# try fitting optimal params\n",
    "xgboost_model = xgb.XGBRegressor(objective = 'reg:squarederror',\n",
    "                                n_estimators=1000,\n",
    "                                learning_rate=optimal_lr,\n",
    "                                min_child_weight=optimal_min_cw,\n",
    "                                gamma=optimal_gamma,\n",
    "                                reg_lambda=optimal_reglam,\n",
    "                                reg_alpha=optimal_alpha,\n",
    "                                max_depth=optimal_max_depth,\n",
    "                                subsample=optimal_subsample,\n",
    "                                colsample_bytree=optimal_colsample_bytree)\n",
    "\n",
    "xgboost_model.fit(X_train, y_train, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the prediction series from the model, now predicted on the test set\n",
    "yhat_xgboost = xgboost_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the forecast\n",
    "fig, ax = plt.subplots(1,1, figsize=(10,6))\n",
    "fig.suptitle(\"XGBoost {}D Forecast\".format(len(daily_postb_test)))\n",
    "plt.plot(test_df_x.index, test_df_x.sales.values, label='Original Series')\n",
    "plt.plot(test_df_x.index, yhat_xgboost, color='red', label='XGBoost Prediction', linestyle=\"--\")\n",
    "plt.xticks(rotation='35')[::2]\n",
    "plt.legend(bbox_to_anchor=(1.30, 1), loc='upper right')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE): 1051.0 \n",
      " -------------------------------------------------- \n",
      "\n",
      "Mean Absolute Percentage Error (MAPE): 2.99% \n",
      " -------------------------------------------------- \n",
      "\n",
      "Mean Absolute Erorr (MAE): 838.0 \n",
      " -------------------------------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# evaulate 4W model\n",
    "evaulate_model_forecast(yhat_xgboost[:28], test_df_x.sales.values[:28])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "# update model predictions params to dict:\n",
    "model_results_dict['XGboost'] = store_model_forecast(yhat_xgboost[:28], test_df_x.sales.values[:28],\n",
    "                                                    just_mape=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to save all metrics for XGboost to dictionary\n",
    "\n",
    "#model_results_dict['xgboost'] = {'MSE': store_model_forecast(yhat_xgboost[:28],  test_df_x.sales.values[:28])[0],\n",
    "#                             'MAPE': store_model_forecast(yhat_xgboost[:28],  test_df_x.sales.values[:28])[1],\n",
    "#                             'MAE': store_model_forecast(yhat_xgboost[:28],  test_df_x.sales.values[:28])[2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBOOST BRIEF TAKEAWAYS\n",
    "* Model performs reasonably well, although it tends to either slightly underfit or overfit the mid-week drops\n",
    "* Model captures weekends quite well\n",
    "* Unsurprisingly, the model doesn't capture the final drop of the test set, this might be some form of exogenous shock and it is not feasible to assume the training data set is rich enough to account for these surprise exogenous shocks. \n",
    "* Even with the final low data point that isn't captured at all by the model, it still achieves a ~ 3% MAPE for a static 30D forecast, which is quite satisfactory considering the volatile retail climate of March 2021\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='xgboost_shap'></a>\n",
    "\n",
    "# FINAL MODEL RESULTS INSIGHTS & XGBOOST - FEATURE IMPORTANCE BREAKDOWN \n",
    "\n",
    "### BRIEF SUMMARY OF THE TABLES BELOW: \n",
    "* The 1st Summary Plot shows that \"weekofyear\" tends to - on average- exhibit the greatest mean impact on the model output; this is likely reflective of the upwards trend noted by the graphs at the start of this notebook. As each subsequent week progresses, the significance of the growing week number is indicative of the upwards growth trend visible in the 'sales' series.\n",
    "\n",
    "\n",
    "\n",
    "* The 2nd summary plot tells a more detailed story\n",
    "    * The 'weekofyear' has the highest degree of clustering above (0) (that is a positive impact on 'sales') which reflects the aforementioned upwards trend over time in the data. This adds strength to the above hypothesis, as on average, more weeks are on average above 0 then below, and the feature value of these above 0 observations is high.\n",
    "    * The 'dayofweek' is somewhat in-line with what we expect, there are two disinct (high feature value) positive observations which most likely reflect weekends; the two purple and red dots slightly below 0 is somewhat guess work but potentially reflects the late week (Thursday - Friday) phenomena. \n",
    "        * Additionally, the purple dot at the bottom of the spectrum, is likely the significant observation that Mondays are consistently the weakest day of sales week-after-week\n",
    "\n",
    "\n",
    "* The 'publichol' variable is interesting, in the first table, it is observed that the variable has an average positive impact, although when a day is not a public-holiday, the model reads this as having a slightly negative impact on sales; however, the net impact of the variable is positive because of the high feature-value red dot which represents when public holidays do arise: the sales of these days are often extreme and push the variable mean into positive territory.\n",
    "    * important to note that this is trained on the \"X_train_df' as not public holidays actuall fall in the \"test_df\" for this particular 30D train/test split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import shap package \n",
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initiate a shap explainer instance from the above defined exgoobst model\n",
    "shap_values_2 = shap.TreeExplainer(xgboost_model).shap_values(X_train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x194.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the breakdown of each variables effect; using the explainer instance + training DF\n",
    "shap.summary_plot(shap_values_2, X_train_df, plot_type='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x194.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# breakdown futher with positive and negative relationships of each predictor with the target variable\n",
    "# also gives an insight into the feature value (strength of relationship) with the colour grade\n",
    "plt.figure()\n",
    "shap.summary_plot(shap_values_2, X_train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VISUALISE THE IMPACT OF EACH FEATURE ON A GIVEN (SINGLE) DAY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise a particular day\n",
    "explainer = shap.Explainer(xgboost_model)\n",
    "shap_values_3 = explainer(X_train_df)\n",
    "shap.plots.waterfall(shap_values_3[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* for any given day of the model too, we can visualise that individual days predictors contribution\n",
    "* thus, we  can see that the 'dayofweek' is likely mid-week and it is definitely not a public holiday\n",
    "* the weekofyear significance represents the upwards trend in the data nicely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FINAL MODEL COMPARISONS\n",
    "* All models show a considerable improvement over the naive forecast\n",
    "* Given the current iteration test sample didn't contain a public holiday, TBATS was expected to perform better\n",
    "* The static SARIMAX performed impressively well, almost as good as the XGboost but with a fraction of the time to implement the model \n",
    "    * Although, a human intervention was needed to select from the top models (AR / MA / SAR / SMA) terms based off the ACF/PACF plots\n",
    "* The XGBoost slightly outperforms the SARIMAX, although as just noted, it is significantly more intensive to establish\n",
    "    * It is worth noting that the XGBoost would be expected to significantly outperform at longer time horizons \n",
    "    * The next experimentive step is to forecast 1Q (3M) and compare again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show values in bar graph \n",
    "plt.figure(figsize=(10, 7))\n",
    "keys = model_results_dict.keys()\n",
    "values = model_results_dict.values()\n",
    "plt.bar(keys, values)\n",
    "plt.xticks(rotation='45')\n",
    "plt.title(\"Time Series Forecasting Model MAPE Comparison for 4W Accuracy\");\n",
    "# note: MAPE = lower is better "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}