III__Model_RandomForest.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"III__Model_XGBoost.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"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":"3.6.7"}},"cells":[{"cell_type":"markdown","metadata":{"colab_type":"text","id":"TenfhbyBcz0o"},"source":["# XGBoost Modeling\n","\n","In this Notebook XGBoost can be used to make some magic!\n","Because each of the model needs to be safed properly. Please specify a name for the model, which describes briefly its specialities:"]},{"cell_type":"code","metadata":{"id":"5fMsSmIRk386","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1592489206730,"user_tz":-120,"elapsed":1167,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["model_name: str= \"Naive_XGBoost_Model\""],"execution_count":8,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"P94sbukY70Ka"},"source":["# 1. Setting up Colab\n","\n","Here we set up colab. U knooow it :D \n","\n","In case u don't use colab, please specify the variable ```use_colab``` to false. In case u don't use it its unavoidable to make sure that noone changed something online while u change it offline. The results of this would be worse than a division by zero.  "]},{"cell_type":"code","metadata":{"colab_type":"code","id":"9OwEnQ9q90xk","colab":{},"executionInfo":{"status":"ok","timestamp":1592489207084,"user_tz":-120,"elapsed":382,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["use_colab: bool = True"],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"-aKdIiCP-DAh"},"source":["In case u accidentally run the following code twice u will get following weird and confusing error:\n","\n","```\n","OSError: [Errno 107] Transport endpoint is not connected\n","```\n","\n","In this case just restart the runtime above. \n"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"NW-V0LwRFBrz","colab":{"base_uri":"https://localhost:8080/","height":139},"executionInfo":{"status":"ok","timestamp":1592489229485,"user_tz":-120,"elapsed":21819,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}},"outputId":"e00a5060-e37e-4a27-96b0-61b01bd30fc7"},"source":["if use_colab:\n","  import os\n","  from google.colab import drive \n","  drive.mount(\"/content/gdrive\", force_remount=True)\n","  # Change directory for nicer imports\n","  %cd \"/content/gdrive/My Drive/Data_Mining_Cup/05 Code/\"\n"],"execution_count":10,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/gdrive\n","/content/gdrive/My Drive/Data_Mining_Cup/05 Code\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"o0G7khZfI-7B"},"source":["# 2. Notes and Ideas"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"FhkpJPxiJBhP"},"source":["## Ideas for new Features\n","Please specify assignment in bold and brackets behind. If nobody is assigned put the \"To Be Assigned (TBA)\" Flague.\n","*"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"xuI6qXPWJehT"},"source":["## Notes\n","\n","Currently no notes :( "]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"qg-T8Iok8z_X"},"source":["# 3. Setting up Notebook\n","\n","Here we setup some parameters for the notebook to work in the expected ways."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"8LRU5vmI9eCt"},"source":["If we want to **use the final test phase** for the later submission, we need to specify the following parameter **```make_final_submission``` to true.** Otherwise the selfmade test set will be used."]},{"cell_type":"code","metadata":{"colab_type":"code","id":"LDWM8LmV_KkE","colab":{},"executionInfo":{"status":"ok","timestamp":1592489237831,"user_tz":-120,"elapsed":1273,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["make_final_submission: bool = False"],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"3JVVjiq__R7I"},"source":["In case we don't want to make a final submission, we now have to specify the test_set. In case you change it, please leave a comment on why and what the new one is. If the end is defined as None we use all the data from start_period (inclusive) till the end of dataset. \n","\n","In case make_final_submission is True we do not use those parameters."]},{"cell_type":"code","metadata":{"colab_type":"code","id":"KvKp58Ug_QwD","colab":{},"executionInfo":{"status":"ok","timestamp":1592489238117,"user_tz":-120,"elapsed":869,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["# This will set the start of test period to 2 weeks before end of data\n","test_period_start: str=\"2018-06-02\"\n","# Use till the end\n","test_period_end: str=\"2018-06-15\""],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"ThuLP_PksLxg","colab":{},"executionInfo":{"status":"ok","timestamp":1592489238613,"user_tz":-120,"elapsed":1188,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["# These datapoints are for the final submision\n","final_submission_start: str=\"2018-06-30\"\n","final_submission_end: str=\"2018-07-14\""],"execution_count":13,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"P01Jd3yw9GXT"},"source":["#### Imports\n","\n","Here you can add imports u need. Please use alphabetical order to not import new stuff twice."]},{"cell_type":"code","metadata":{"colab_type":"code","id":"vWcDzESD8HZ6","colab":{},"executionInfo":{"status":"ok","timestamp":1592489240981,"user_tz":-120,"elapsed":3118,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["\n","from datetime import date, datetime, timedelta\n","import hyperopt\n","from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n","from itertools import product\n","import numpy as np\n","from math import sqrt \n","import matplotlib.pyplot as plt\n","import pandas as pd\n","import pickle\n","from sklearn import metrics\n","from sklearn.model_selection import train_test_split as tts\n","from sklearn.preprocessing import StandardScaler\n","from typing import List, Union, Tuple\n","\n","import xgboost as xgb \n","from xgboost import plot_importance \n","\n","%matplotlib inline"],"execution_count":14,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"cbSkLqn3eAmr"},"source":["In case of a \"Model not found\" Error concerning the Hyperopt package. Please uncomment and run the following line"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"4M6kXwVftN0o","colab":{},"executionInfo":{"status":"ok","timestamp":1592489240981,"user_tz":-120,"elapsed":2752,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["# !pip install hyperopt"],"execution_count":15,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"eaMSZ6KdG9v4"},"source":["## Loading data\n","\n","Here we now load the dataframes by the preceeding notebooks.\n","Please do not import single datasets and merge them here cause it will end in fatal inconsistencies.\n","\n","For comfort the following row will define a ```small_data``` attribute. is this True, only a part will be used for training, if False all the data points will be used in the following code.\n","\n","It will leave the testing and validation data in the same size, only will decrease the data for training.\n"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"IUqwkmndHxFl","colab":{},"executionInfo":{"status":"ok","timestamp":1592489240982,"user_tz":-120,"elapsed":2414,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["small_data: bool = True"],"execution_count":16,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"4AK5qsidKg3J"},"source":["In case small data should be used, the following line defines the start of this. "]},{"cell_type":"code","metadata":{"colab_type":"code","id":"tP16QRO8Kfud","colab":{},"executionInfo":{"status":"ok","timestamp":1592489240983,"user_tz":-120,"elapsed":2082,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["small_data_start_date: str = '20180418'"],"execution_count":17,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"cYJyv2lMIXEk"},"source":["For the training of XGBoost, we need to specify a validation set. Herefore we no can set the traning_end. Between this and the start of testing will be used as validation set. \n"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"SLEapT_nIbqz","colab":{},"executionInfo":{"status":"ok","timestamp":1592489240984,"user_tz":-120,"elapsed":1757,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["training_end: str = '20180501'"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"1q8DC7gOOeob"},"source":["### Label\n","\n","The last thing which need to be specified is, which label should be used for the training, so what do u want to be predicted. Sadly because of xg boos we only can use exactly one label :( "]},{"cell_type":"code","metadata":{"colab_type":"code","id":"sLjArlOzOeKv","colab":{},"executionInfo":{"status":"ok","timestamp":1592489240985,"user_tz":-120,"elapsed":1292,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["labels: List[str] = [\"order\"]"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"05O1lQn9OuLN"},"source":["Therefore this assert. Remove it if we change to another model"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"J9pzeNYlOyOw","colab":{},"executionInfo":{"status":"ok","timestamp":1592489240985,"user_tz":-120,"elapsed":993,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["assert len(labels) == 1"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"O4YTmnAvPTK3"},"source":["## Features to delete for training\n","\n","Turns out another parameter needs to be set. Which feature do we have in the data and should be deleted for training. The date will be deleted seperately"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"WT38nzS6Pglf","colab":{},"executionInfo":{"status":"ok","timestamp":1592489240986,"user_tz":-120,"elapsed":703,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["columns_to_delete: List[str] = []"],"execution_count":21,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"58iRvhQpH5N-"},"source":["# 4. Preparation\n","Here the data is finally fetched"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"sSC4rEXDSoqr","colab":{},"executionInfo":{"status":"ok","timestamp":1592489247090,"user_tz":-120,"elapsed":6516,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["# OPEN PANDAS DATAFRAMES FROM PREPROCESSING 2\n","try:\n","  if make_final_submission:\n","    dynamic_features = pd.read_pickle('./data/dynamic_features_final_nb2.pk')\n","    static_features = pd.read_pickle('./data/static_features_final_nb2.pk')\n","  else:\n","    dynamic_features = pd.read_pickle('./data/dynamic_features_nb2.pk')\n","    static_features = pd.read_pickle('./data/static_features_nb2.pk')\n","    labels_for_test = pd.read_pickle('./data/orders_for_test.pkl')\n","except FileNotFoundError as e:\n","  print(f\"One File could not be found. Please rerun the first Notebook, {e}\")"],"execution_count":22,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"u7sFhrLZgaim"},"source":["Combine the two datasets together"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"x02Z19f2gaEr","colab":{},"executionInfo":{"status":"ok","timestamp":1592489251197,"user_tz":-120,"elapsed":10300,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["data = dynamic_features.join(static_features.drop('avg_salesPrice_nonunique', axis=1), on=\"itemID\")\n","data = data.reset_index()"],"execution_count":23,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"iXfGKad5KtI5"},"source":["Here we make the dataframe a bit smaller:"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"u30_iB0yKT58","colab":{},"executionInfo":{"status":"ok","timestamp":1592489252415,"user_tz":-120,"elapsed":11162,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["if small_data: \n","  data = data[data['date'] > small_data_start_date]"],"execution_count":24,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"jVrBGgpIkrMy"},"source":["#### Deletion of unused feature\n"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"Fc79L0Zskqxw","colab":{},"executionInfo":{"status":"ok","timestamp":1592489252416,"user_tz":-120,"elapsed":10752,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["try:\n","  # do the deletion\n","  data = data.drop(columns_to_delete, axis=1)\n","except KeyError as e:\n","  print(f\"I could not delete the columns {e} cause they were not there. \\n But hey, they are gone, like intented :D\")"],"execution_count":25,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"BkGtmL7RnaIc"},"source":["#### Adding the label\n","\n","After this step, data does not exist, only X, y left"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"kRPnmUpVnd7Z","colab":{},"executionInfo":{"status":"ok","timestamp":1592489252417,"user_tz":-120,"elapsed":10284,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["# We need to put date also into y to make the split\n","y = data[['date', *labels]].copy()\n","X = data.drop(labels, axis=1).copy()"],"execution_count":26,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"04RyklpNHf1P"},"source":["#### Split into train/val/test\n","\n","Here we create tuples for the respective datapoints.\n","(X,y) \n","In the end we will have train, val and test according to the variables defined above"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"jLTI2BfmFcHR","colab":{"base_uri":"https://localhost:8080/","height":68},"executionInfo":{"status":"ok","timestamp":1592489254669,"user_tz":-120,"elapsed":12048,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}},"outputId":"1c1b1945-896b-44a4-9ae2-c1ec4486f17b"},"source":["def split(X, y, training_end, testing_start, split_feature='date', labels_for_test=None, delete_split_feature=True):\n","  \n","  # Training\n","  print(f\"CREATING TRAINING from [begin, {training_end}]\")\n","  train_X = X[X[split_feature] <= training_end]\n","  train_y = y[y[split_feature] <= training_end]\n","\n","  # Validation\n","  print(f\"CREATING VALIDATION from ]{training_end}, {testing_start}[\")\n","  val_X = X[(X[split_feature] > training_end)]\n","  val_X = val_X[(val_X[split_feature] < testing_start)]\n","  val_y = y[(y[split_feature]> training_end)]\n","  val_y = val_y[(val_y[split_feature] < testing_start)]\n","\n","  assert len(val_X)>0, \"val_X must be greater 0\"\n","  assert len(val_y)>0, \"val_y must be greater 0\"\n","\n","   # Testing\n","  print(f\"CREATING TESTING from ({testing_start}, end]\")\n","  test_X = X[X[split_feature] >= testing_start]\n","  test_y = y[y[split_feature] >= testing_start]\n","    \n","  assert len(test_X)>0, \"test_X must be greater 0\"\n","  assert len(test_y)>0, \"test_y must be greater 0\"\n","    \n","  if delete_split_feature:\n","    train_X = train_X.drop([split_feature,'itemID'], axis=1)\n","    train_y = train_y.drop(split_feature, axis=1)\n","    val_X = val_X.drop([split_feature,'itemID'], axis=1)\n","    val_y = val_y.drop(split_feature, axis=1)\n","    test_X = test_X.drop(split_feature, axis=1)\n","    test_y = test_y.drop(split_feature, axis=1)\n","    \n","  if labels_for_test is not None:\n","    test_y['order'] = labels_for_test.reset_index()['order'].values\n","  return (train_X, train_y), (val_X, val_y), (test_X, test_y)\n","\n","test_start = final_submission_start if make_final_submission else test_period_start\n","train, val, test = split(X,y, training_end, test_start, labels_for_test=labels_for_test)\n","del X\n","del y\n","#del data"],"execution_count":27,"outputs":[{"output_type":"stream","text":["CREATING TRAINING from [begin, 20180501]\n","CREATING VALIDATION from ]20180501, 2018-06-02[\n","CREATING TESTING from (2018-06-02, end]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"edijbaQml9Yw","colab_type":"text"},"source":["### Save Test, train and val data\n","\n","Here we safe the preprocessing data.\n","\n","First the Test data:"]},{"cell_type":"code","metadata":{"id":"atVIbQIwl_5O","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1592489257893,"user_tz":-120,"elapsed":14773,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["with open(f\"./data/{model_name}_test.pk\",\"wb\") as f:\n","    pickle.dump(test, f)\n","    f.close()"],"execution_count":28,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Aw5H_f5Ll-v5","colab_type":"text"},"source":["Then the training data"]},{"cell_type":"code","metadata":{"id":"NqwqF1_PmEke","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1592489261340,"user_tz":-120,"elapsed":17702,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["with open(f\"./data/{model_name}_train.pk\",\"wb\") as f:\n","    pickle.dump(train, f)\n","    f.close()"],"execution_count":29,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-g-u9eM6mJtF","colab_type":"text"},"source":["At last the validation data"]},{"cell_type":"code","metadata":{"id":"hLRHgQXImHUp","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1592489263900,"user_tz":-120,"elapsed":19750,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["with open(f\"./data/{model_name}_val.pk\",\"wb\") as f:\n","    pickle.dump(val, f)\n","    f.close()"],"execution_count":30,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JHb2fdTZmjQ-","colab_type":"text"},"source":["# The Model\n","\n","### Load the data\n","Here we load the data from above step. __Why?__ This is to make sure that also one of the three main parts of the notebook can be run by themself :) In case all of the notebook is run, this part can be ignored. "]},{"cell_type":"code","metadata":{"id":"CIXjBUA-mi59","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1592489264912,"user_tz":-120,"elapsed":20266,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["with open(f\"./data/{model_name}_train.pk\",\"rb\") as f:\n","    train = pickle.load(f)\n","    f.close()\n","\n","with open(f\"./data/{model_name}_test.pk\",\"rb\") as f:\n","    test = pickle.load(f)\n","    f.close()\n","    \n","with open(f\"./data/{model_name}_val.pk\",\"rb\") as f:\n","    val = pickle.load(f)\n","    f.close()   "],"execution_count":31,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"BNF7KlOMnQ07"},"source":["## The Custom Evaluation and Objective Function\n","\n","Here is space to create several custom evaluation and objective functions to later use in XGBoost."]},{"cell_type":"code","metadata":{"colab_type":"code","id":"JFIbtar6s8uR","colab":{},"executionInfo":{"status":"ok","timestamp":1592489264913,"user_tz":-120,"elapsed":19686,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["def monetary_value_objective(predt: np.ndarray, dtrain: Union[xgb.DMatrix, np.ndarray]) -> Tuple[np.ndarray, np.ndarray]:\n","  \"\"\"\n","  Loss is the difference to the optimal value (how much more would we get, if we \n","  predicted correctly). Minimal loss 0 is reached when the prediction matches \n","  the true value exactly.\n","\n","  Squared version (for nicer gradients).\n","\n","  predt = model prediction\n","  dtrain = labels and sample weights\n","  Currently, dtrain is a numpy array.\n","  \"\"\"\n","  item_price = 1\n","\n","  # Use the following if dtrain is in xgb.DMatrix format.\n","  # y = dtrain.get_label()\n","  y = dtrain\n","\n","  mask1 = predt <= y  # Predict too few\n","  mask2 = predt > y  # Predict too much\n","\n","  grad = item_price**2 * (predt - y)  \n","  # Gradient is negative if prediction is too low, and positive if it is too high\n","  # Here scale it (0.72 = 0.6**2 * 2)\n","  grad[mask1] = 2 * grad[mask1]\n","  grad[mask2] = 0.72 * grad[mask2]\n","\n","  hess = np.empty_like(grad)\n","  hess[mask1] = 2 * item_price**2\n","  hess[mask2] = 0.72 * item_price**2\n","\n","  # For some reason, gradient must be negated, but hessian not\n","  grad = -grad\n","\n","  return grad, hess"],"execution_count":32,"outputs":[]},{"cell_type":"code","metadata":{"id":"PnJJjYRmo7-V","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1592489264915,"user_tz":-120,"elapsed":19347,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["def monetary_value_eval(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:\n","  item_price = 1\n","  y = dtrain.get_label()\n","\n","  mask1 = predt <= y  # Predict too few\n","  mask2 = predt > y  # Predict too much\n","\n","  revenue = np.empty_like(predt)\n","  revenue[mask1] = item_price * predt[mask1]\n","  revenue[mask2] = item_price * (1.6 * y[mask2] - 0.6 * predt[mask2])\n","\n","  total_revenue = revenue.sum()\n","\n","  return \"monetary-value\", total_revenue\n","\n","monetary_value = metrics.make_scorer(monetary_value_eval)"],"execution_count":33,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"yyTupCIAUJ2D"},"source":["## XGBoost Regressor Model\n","\n","In case we just need to fast train the model to see if it works we can set this following amount variable. This will set the training data to this amount. Set it to ```None``` if all the data should be used."]},{"cell_type":"code","metadata":{"colab_type":"code","id":"8Cenu-lTQBpn","colab":{},"executionInfo":{"status":"ok","timestamp":1592489301771,"user_tz":-120,"elapsed":926,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["amount: Union[int, None] = 1  # 1000"],"execution_count":34,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"Puz8I-hTV2ns"},"source":["### Hyper parameters tuning "]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"rfIdORwJQi-M"},"source":["more parameters can be added here in the parameter space: \n","range can also be changed for more fine tuning "]},{"cell_type":"code","metadata":{"colab_type":"code","id":"0HjG9wmstR0l","colab":{},"executionInfo":{"status":"ok","timestamp":1592489302810,"user_tz":-120,"elapsed":928,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["para_space = {\n","            'n_estimators': hp.quniform('n_estimators', 10, 20, 1),\n","            'max_depth': hp.quniform(\"max_depth\", 3, 10, 1),\n","            'min_child_weight': hp.quniform('min_child_weight', 1, 5, 1),\n","            'learning_rate':hp.quniform('learning_rate', 0.05, 0.2, 0.05),  \n","            'gamma': hp.quniform('gamma', 0.8, 1, 0.1),\n","            'subsample':hp.quniform('subsample', 0.7, 1, 0.1),\n","            'eval_metric': monetary_value,            #'rmse',\n","            'tree_method':'auto', # Sadly I need to put this to auto\n","            'objective': monetary_value_objective,          #'reg:squarederror',\n","            'seed': 7\n","        }"],"execution_count":35,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"L3ohw7LCQr21"},"source":["The following parameter explains how many rounds for hyperparameter tuning should be used:"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"W7FKcr79QzvV","colab":{},"executionInfo":{"status":"ok","timestamp":1592489303672,"user_tz":-120,"elapsed":1077,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["evaluation_round: int = 1"],"execution_count":36,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"jHS9JHePRVHE"},"source":["### Actual Hyperparameter training\n","This method returns a evaluation function so it creates a function which creates a model"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"3K_mWKpstUV3","colab":{},"executionInfo":{"status":"ok","timestamp":1592489303673,"user_tz":-120,"elapsed":487,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["def eval_scoring(amount=None):\n","    amount = amount or len(train[0])\n","        \n","    def eval_fc(para_space):\n","        xgb_model = xgb.XGBRegressor(\n","                              n_estimators = int(para_space['n_estimators']), \n","                              max_depth = int(para_space['max_depth']),\n","                              min_child_weight=int(para_space['min_child_weight']),\n","                              learning_rate = para_space['learning_rate'],\n","                              gamma = para_space['gamma'],\n","                              subsample = para_space['subsample'],\n","                              feval = para_space['eval_metric'],\n","                              tree_method = para_space['tree_method'],\n","                              objective = para_space['objective'],\n","                              seed = para_space['seed']\n","                            )\n","        \n","        \n","        eval_set = [(train[0][:amount],train[1][:amount]), (val[0][:amount], val[1][:amount])]\n","        \n","        xgb_model.fit(train[0][:amount], train[1][:amount], eval_set = eval_set, early_stopping_rounds = 10, verbose = False)\n","\n","        train_pred = xgb_model.predict(train[0][:amount])\n","        train_rmse = sqrt(metrics.mean_squared_error(train[1][:amount], train_pred))\n","\n","        val_pred = xgb_model.predict(val[0][:amount])\n","        val_rmse = sqrt(metrics.mean_squared_error(val[1][:amount], val_pred))\n","        \n","        print(\"\\n-----------------------------------------------\")\n","        print(f\"Train RMSE: {train_rmse} | Val RMSE: {val_rmse}\")\n","        print(f\"Trained with parameters: \\n  {para_space}\")\n","        print(\"--------------------------------------------------\\n\")\n","        \n","        return {'loss':val_rmse, 'status': STATUS_OK, 'model': xgb_model }\n","    return eval_fc"],"execution_count":37,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"6v9_jcp4RpKi"},"source":["The actual hyperparameter tuning will be done now:"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"zythyo63tW2g","colab":{},"executionInfo":{"status":"ok","timestamp":1592489305233,"user_tz":-120,"elapsed":1027,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["def hyperpara_tuning(para_space):\n","    \"\"\"\n","    Returns the best set of hyperparamters, \n","    given a parameter space and a evaluation function \n","    \"\"\"\n","    trials = Trials()\n","    best_model = fmin(fn=eval_scoring(amount=amount),\n","                      space=para_space,\n","                      algo=tpe.suggest,\n","                      max_evals=evaluation_round,\n","                      trials=trials\n","                     )\n","    \n","    return trials, best_model"],"execution_count":38,"outputs":[]},{"cell_type":"code","metadata":{"id":"F_pte4AvRnVD","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":309},"executionInfo":{"status":"ok","timestamp":1592489306899,"user_tz":-120,"elapsed":2163,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}},"outputId":"f90749a0-5d63-4303-cfcd-1e4b1fa01634"},"source":["# set runtime environment to GPU at: Runtime -> Change runtime type \n","trials, best_hyperparams = hyperpara_tuning(para_space)\n","final_xgb_model = trials.best_trial['result']['model']\n","assert final_xgb_model is not None, \"Oooops there is no model created :O \"\n","\n","print(\"\\n\\n\\n############################################\\n\")\n","print(\"THE BEST PARAMETER WHERE:\")\n","print(best_hyperparams)"],"execution_count":39,"outputs":[{"output_type":"stream","text":["[14:08:25] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","\n","-----------------------------------------------\n","Train RMSE: 0.06743720173835754 | Val RMSE: 0.06743720173835754\n","Trained with parameters: \n","  {'eval_metric': make_scorer(monetary_value_eval), 'gamma': 0.9, 'learning_rate': 0.2, 'max_depth': 6.0, 'min_child_weight': 4.0, 'n_estimators': 14.0, 'objective': <function monetary_value_objective at 0x7fa864260f28>, 'seed': 7, 'subsample': 1.0, 'tree_method': 'auto'}\n","--------------------------------------------------\n","\n","100%|██████████| 1/1 [00:00<00:00,  1.16it/s, best loss: 0.06743720173835754]\n","\n","\n","\n","############################################\n","\n","THE BEST PARAMETER WHERE:\n","{'gamma': 0.9, 'learning_rate': 0.2, 'max_depth': 6.0, 'min_child_weight': 4.0, 'n_estimators': 14.0, 'subsample': 1.0}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"pMhuAPxQWF2L"},"source":["### final xgb model based on Grid Search\n","\n","\n","Here we can train the model again based on the best hyperparameters. But this is only in some times useful and therefore commented out."]},{"cell_type":"code","metadata":{"colab_type":"code","id":"XxekGTg3FTgK","colab":{},"executionInfo":{"status":"ok","timestamp":1592489306900,"user_tz":-120,"elapsed":856,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}}},"source":["def best_xgb_model(x_train, y_train, x_val, y_val, best_hyperparams):\n","  xgb_model = xgb.XGBRegressor(n_estimators = int(best_hyperparams['n_estimators']), \n","                               learning_rate = best_hyperparams['learning_rate'], \n","                               gamma = best_hyperparams['gamma'],\n","                               max_depth = int(best_hyperparams['max_depth']), \n","                               min_child_weight = int(best_hyperparams['min_child_weight']),\n","                               subsample = best_hyperparams['subsample'],\n","                               tree_metric = 'gpu_hist', \n","                               objective = monetary_value_objective,\n","                               feval = monetary_value,\n","                               seed = 7)\n","\n","  xgb_model.fit(x_train, y_train, verbose = True, eval_metric = monetary_value_eval, #'rmse',\n","                  eval_set = [(x_train, y_train), (x_val, y_val)])\n","    \n","  return xgb_model"],"execution_count":40,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"-BX8s7Y1FTgP","colab":{"base_uri":"https://localhost:8080/","height":292},"executionInfo":{"status":"ok","timestamp":1592489387166,"user_tz":-120,"elapsed":79959,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}},"outputId":"4ec28400-69ad-44e5-e8e8-c75bc4d560b9"},"source":["final_xgb_model = best_xgb_model(train[0], train[1], val[0], val[1], best_hyperparams)"],"execution_count":41,"outputs":[{"output_type":"stream","text":["[14:08:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[0]\tvalidation_0-rmse:24.4976\tvalidation_1-rmse:23.9182\tvalidation_0-monetary-value:11083.5\tvalidation_1-monetary-value:30167.9\n","[1]\tvalidation_0-rmse:21.9313\tvalidation_1-rmse:22.1514\tvalidation_0-monetary-value:41339.9\tvalidation_1-monetary-value:93273.9\n","[2]\tvalidation_0-rmse:19.6868\tvalidation_1-rmse:20.8665\tvalidation_0-monetary-value:65609.4\tvalidation_1-monetary-value:139721\n","[3]\tvalidation_0-rmse:18.1124\tvalidation_1-rmse:20.2738\tvalidation_0-monetary-value:83146\tvalidation_1-monetary-value:172474\n","[4]\tvalidation_0-rmse:16.7944\tvalidation_1-rmse:19.6257\tvalidation_0-monetary-value:97639.6\tvalidation_1-monetary-value:198028\n","[5]\tvalidation_0-rmse:15.9581\tvalidation_1-rmse:19.2225\tvalidation_0-monetary-value:107854\tvalidation_1-monetary-value:216000\n","[6]\tvalidation_0-rmse:15.2561\tvalidation_1-rmse:18.9904\tvalidation_0-monetary-value:115642\tvalidation_1-monetary-value:228829\n","[7]\tvalidation_0-rmse:14.4303\tvalidation_1-rmse:19.0596\tvalidation_0-monetary-value:122510\tvalidation_1-monetary-value:236497\n","[8]\tvalidation_0-rmse:13.6922\tvalidation_1-rmse:19.1633\tvalidation_0-monetary-value:127967\tvalidation_1-monetary-value:242355\n","[9]\tvalidation_0-rmse:13.3355\tvalidation_1-rmse:19.0999\tvalidation_0-monetary-value:131909\tvalidation_1-monetary-value:246869\n","[10]\tvalidation_0-rmse:12.9817\tvalidation_1-rmse:19.0277\tvalidation_0-monetary-value:135034\tvalidation_1-monetary-value:251447\n","[11]\tvalidation_0-rmse:12.7503\tvalidation_1-rmse:18.9887\tvalidation_0-monetary-value:136968\tvalidation_1-monetary-value:254282\n","[12]\tvalidation_0-rmse:12.4597\tvalidation_1-rmse:18.9956\tvalidation_0-monetary-value:139293\tvalidation_1-monetary-value:256599\n","[13]\tvalidation_0-rmse:12.2936\tvalidation_1-rmse:19.0024\tvalidation_0-monetary-value:140657\tvalidation_1-monetary-value:258212\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"fPpgzPheWOIJ"},"source":["### Model Results \n","Here we can have a analytic look on the trained model. Was it good ? Did it make sense? Hungry for answers? Then take a look at following crazy lines of code"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"J1JNwriXFTgV","colab":{"base_uri":"https://localhost:8080/","height":51},"executionInfo":{"status":"ok","timestamp":1592489428202,"user_tz":-120,"elapsed":21731,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}},"outputId":"e76b7bc1-a6fe-4f0a-bbb0-1f2da62bb3b2"},"source":["train_predictions = final_xgb_model.predict(train[0])\n","train_predictions = [round(i) for i in train_predictions]\n","print(\"Train RMSE : %.8g\" % sqrt(metrics.mean_squared_error(train[1], train_predictions)))\n","\n","val_predictions = final_xgb_model.predict(val[0])\n","val_predictions = [round(i) for i in val_predictions]\n","print(\"Val RMSE : %.8g\" % sqrt(metrics.mean_squared_error(val[1], val_predictions)))"],"execution_count":42,"outputs":[{"output_type":"stream","text":["Train RMSE : 12.294651\n","Val RMSE : 19.002793\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab_type":"code","id":"wEtx2KLyTrEm","colab":{"base_uri":"https://localhost:8080/","height":281},"executionInfo":{"status":"ok","timestamp":1592489428203,"user_tz":-120,"elapsed":20766,"user":{"displayName":"Nicolas Bauer","photoUrl":"","userId":"12446973663910439645"}},"outputId":"5531488a-5c96-44b4-979f-8b01b9acaaab"},"source":["results = final_xgb_model.evals_result()\n","epochs = len(results['validation_0']['rmse'])\n","x_axis = range(0, epochs)\n","fig, ax = plt.subplots()\n","ax.plot(x_axis, results['validation_0']['rmse'], label='Train')\n","ax.plot(x_axis, results['validation_1']['rmse'], label='Val')\n","ax.legend()\n","plt.ylabel('rmse')\n","plt.title('XGBoost Regression Error')\n","plt.show()"],"execution_count":43,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"9cpTdj7wppQ4","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":281},"executionInfo":{"status":"ok","timestamp":1592325029468,"user_tz":-120,"elapsed":18967,"user":{"displayName":"Karin Frlic","photoUrl":"","userId":"13720692297605852861"}},"outputId":"b0a29672-9703-49dd-aa85-5f00a7b02865"},"source":["fig, ax = plt.subplots()\n","ax.plot(x_axis, results['validation_0']['monetary-value'], label='Train')\n","ax.plot(x_axis, results['validation_1']['monetary-value'], label='Val')\n","ax.legend()\n","plt.ylabel('monetary value')\n","plt.title('Monetary value')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab_type":"code","id":"fP-v-V2yFTge","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1592325031225,"user_tz":-120,"elapsed":20704,"user":{"displayName":"Karin Frlic","photoUrl":"","userId":"13720692297605852861"}},"outputId":"a77636bd-1ee6-4560-ea70-c2bb2edb7566"},"source":["ax = plot_importance(final_xgb_model)\n","fig = ax.figure\n","fig.set_size_inches(54, 42)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 3888x3024 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"YiZwmgXfm5Oz"},"source":["### Save the model\n","\n","Here we now save the model. To distinguish between used models, we can use suffixes. A suffix will then be used to find the right model. If the following variable equals None, simply the current date will be taken as suffix. The final file then is called {modelname}_{suffix}."]},{"cell_type":"code","metadata":{"colab_type":"code","id":"XxyEdzfgnv4p","colab":{}},"source":["suffix: str= None"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-bDcPWednnxC","colab_type":"text"},"source":["---"]},{"cell_type":"code","metadata":{"id":"Mevn2BE1nkJf","colab_type":"code","colab":{}},"source":["suffix = suffix if suffix else datetime.now().strftime('%m%d%Y')\n","with open(f\"./models/{model_name}_{suffix}.pk\",\"wb\") as f:\n","    pickle.dump(final_xgb_model, f)\n","    f.close()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"yucX_Bn5nwzy","colab_type":"text"},"source":["# Prediction\n","\n","This part is used to save the model, and create the predictions already. In case we run the whole notebook, no changes need to be made. \n","In case **only this part** should be executed, we need to input the name of the model.  Because we know already the name of the model (see upthere somewhere) we only need the suffix (Most probably the date of training).\n"]},{"cell_type":"code","metadata":{"id":"ZNsrVDsBnq43","colab_type":"code","colab":{}},"source":["suffix_for_import = None"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"K5urqdJknzza","colab_type":"text"},"source":["### Load Model"]},{"cell_type":"code","metadata":{"id":"qJlN_EhUn1Mn","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":241},"executionInfo":{"status":"error","timestamp":1591564267660,"user_tz":-120,"elapsed":1791,"user":{"displayName":"Markus Böbel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXYFM9JYHyVFq4xIi6XWGViOZEOn3v9Yyn-QfT=s64","userId":"02095279838384385228"}},"outputId":"0f152a94-ca1e-4f99-ac90-98a162e03fa0"},"source":["if final_xgb_model is None:\n","    suffix_for_import = suffix_for_import if suffix_for_import else datetime.now().strftime('%m%d%Y')\n","    with open(f\"./models/{model_name}_{suffix_for_import}.pk\",\"rb\") as f:\n","        final_xgb_model = pickle.load(f)\n","        f.close()\n","    print(\"Loaded model\")\n","else:\n","    print(\"Use the already created model\")"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-10-55b96eb6681d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mfinal_xgb_model\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0msuffix_for_import\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuffix_for_import\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msuffix_for_import\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%m%d%Y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"./models/{model_name}_{suffix_for_import}.pk\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mfinal_xgb_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'final_xgb_model' is not defined"]}]},{"cell_type":"code","metadata":{"id":"0DQTm72Un2lM","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"error","timestamp":1591564273983,"user_tz":-120,"elapsed":1818,"user":{"displayName":"Markus Böbel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXYFM9JYHyVFq4xIi6XWGViOZEOn3v9Yyn-QfT=s64","userId":"02095279838384385228"}},"outputId":"b3078101-2ece-4496-a349-c494cb810524"},"source":["with open(f\"./data/{model_name}_test.pk\",\"rb\") as f:\n","    test = pickle.load(f)\n","    f.close()"],"execution_count":null,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-11-3848aa02b3db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"./data/{model_name}_test.pk\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './data/Naive_XGBoost_Model_test.pk'"]}]},{"cell_type":"markdown","metadata":{"id":"SlavyOmen6lE","colab_type":"text"},"source":["Here we also load the data again"]},{"cell_type":"code","metadata":{"id":"C10lzCMHn4Hh","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":241},"executionInfo":{"status":"error","timestamp":1591564289092,"user_tz":-120,"elapsed":1410,"user":{"displayName":"Markus Böbel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXYFM9JYHyVFq4xIi6XWGViOZEOn3v9Yyn-QfT=s64","userId":"02095279838384385228"}},"outputId":"4395b2fe-7161-4a30-f2f7-2ff4b0fd4559"},"source":["with open(f\"./data/{model_name}_train.pk\",\"rb\") as f:\n","    train = pickle.load(f)\n","    f.close()\n","\n","with open(f\"./data/{model_name}_test.pk\",\"rb\") as f:\n","    test = pickle.load(f)\n","    f.close()\n","    \n","with open(f\"./data/{model_name}_val.pk\",\"rb\") as f:\n","    val = pickle.load(f)\n","    f.close()   \n","\n","labels_for_test = pd.read_pickle('./data/orders_for_test.pkl')"],"execution_count":null,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-12-07c75dcf84d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"./data/{model_name}_train.pk\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"./data/{model_name}_test.pk\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './data/Naive_XGBoost_Model_train.pk'"]}]},{"cell_type":"markdown","metadata":{"id":"Ww6f9kY6n944","colab_type":"text"},"source":["## Recursive Prediction\n","\n","When doing recursive predictions, we predict the next value and feed them into the data for the next prediction. The following code line enable a recursive prediction.\n","The following line specifies the feature which should be inserted in each iteration, in 99% of times this will be orders."]},{"cell_type":"code","metadata":{"id":"I4O-AWdhn755","colab_type":"code","colab":{}},"source":["sliding_feature_name:str = \"order\""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OXAVnEosoCPn","colab_type":"text"},"source":["In the next step we need to get the derived lack features. (They were created in notebook no. 2 therefore will have a ** _t-x** suffix.)"]},{"cell_type":"code","metadata":{"id":"uzlMrfBXoAT-","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":241},"executionInfo":{"status":"error","timestamp":1591564334354,"user_tz":-120,"elapsed":1497,"user":{"displayName":"Markus Böbel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXYFM9JYHyVFq4xIi6XWGViOZEOn3v9Yyn-QfT=s64","userId":"02095279838384385228"}},"outputId":"989aeee0-aa1b-4d5a-f244-74c11c61f216"},"source":["# Maximum lack size\n","_max_wind_size = 100\n","# All possible columns\n","_possible_col = set([f\"{sliding_feature_name}_t-{i}\" for i in range(_max_wind_size)])\n","sliding_features = _possible_col.intersection(set(train[0].columns))\n","_idx = list(map(lambda x: int(x.split('order_t-')[1]), sliding_features))\n","_idx.sort()"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-14-2e731058ecd9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# All possible columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0m_possible_col\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"{sliding_feature_name}_t-{i}\"\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_max_wind_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0msliding_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_col\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0m_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'order_t-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msliding_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0m_idx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name '_col' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"xe_b542woR-A","colab_type":"text"},"source":["In the next step we need to get the derived lack features. (They were created in notebook no. 2 therefore will have a **_t-x** suffix.)"]},{"cell_type":"code","metadata":{"id":"p6VLClcooG77","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":207},"executionInfo":{"status":"error","timestamp":1592429353542,"user_tz":-120,"elapsed":978,"user":{"displayName":"Markus Böbel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXYFM9JYHyVFq4xIi6XWGViOZEOn3v9Yyn-QfT=s64","userId":"02095279838384385228"}},"outputId":"5dfe4b5c-9382-4ba3-a528-67076068489c"},"source":["ids = list(test[0]['itemID'].unique())\n","\n","time_range = list(map(lambda x: str(x), pd.date_range( end=test_period_end, periods=14)))\n","sample_submission = pd.DataFrame(index = ids, columns = ['demandPrediction', *time_range])"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-55a69ddef651>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'itemID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtime_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_period_end\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperiods\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0msample_submission\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'demandPrediction'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mtime_range\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'test' is not defined"]}]},{"cell_type":"code","metadata":{"id":"xATc90FvIPDm","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":223},"executionInfo":{"status":"error","timestamp":1592429367341,"user_tz":-120,"elapsed":748,"user":{"displayName":"Markus Böbel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXYFM9JYHyVFq4xIi6XWGViOZEOn3v9Yyn-QfT=s64","userId":"02095279838384385228"}},"outputId":"9b0d455e-0f23-464c-839c-256a5650b495"},"source":["# Copy the test dataframe, to make the indexing easier\n","test_test= test[0].copy()\n","# We need a multilevel index so that we can go over each day and can predict\n","# the next value using vectors instead naive iterating. (like the depricated old version)\n","test_test = test_test.set_index(['day_of_year','itemID'])\n","# Now we sort the index, otherwise it does not behave as we want it to. \n","test_test = test_test.sort_index(level=0)"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-2d487ab9bdff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Copy the test dataframe, to make the indexing easier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtest_test\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# We need a multilevel index so that we can go over each day and can predict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# the next value using vectors instead naive iterating. (like the depricated old version)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtest_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'day_of_year'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'itemID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'test' is not defined"]}]},{"cell_type":"code","metadata":{"id":"hTPUhVfaIviN","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":223},"executionInfo":{"status":"error","timestamp":1592429370508,"user_tz":-120,"elapsed":772,"user":{"displayName":"Markus Böbel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhXYFM9JYHyVFq4xIi6XWGViOZEOn3v9Yyn-QfT=s64","userId":"02095279838384385228"}},"outputId":"f206e735-1161-4039-83c4-b78d092839e5"},"source":["# Now we are iterating over each of the values of the first layer of our index.\n","# -> In other words, over the days\n","for date_index in range(0, len(test_test.index.get_level_values(0).unique())):\n","  print(f\"DOING DAY {date_index} OUT OF{len(test_test.index.get_level_values(0).unique())}\")   \n","  ############\n","  # 1. MAKE PREDICTION\n","  ############\n","  # We make a prediction. Therefore we are doing the following:\n","  ## 1.1: We get the an dataframe with all the values of the respective day\n","  step1 = test_test.loc[test_test.index.get_level_values(0).unique()[date_index]]\n","  ## 1.2: We are now resetting the index to get simple rows and furthermore delete the ItemID, \n","  #     cause we didnt used it for training\n","  step2 = step1.reset_index().drop('itemID', axis=1)\n","  ## 1.3: Reindex the columns, so that we have the same order as in the training.\n","  #     This is neccessary, because we made this indexing stuff\n","  step3 = step2.reindex(train[0].columns, axis=1)\n","  ## 1.4: Fiiinaaalllyyy we can predict.\n","  prediction = final_xgb_model.predict(step3)\n","  \n","  ############\n","  # SAVE PREDICTION TO THE SUBMISSION\n","  ############\n","  # Here, we save the vector of prediction into the predefined dataframe.\n","  sample_submission.iloc[:, date_index] = prediction\n","\n","  ############\n","  # RECURSIVE ADDING OF PREDICTIONS TO DATA\n","  ############\n","  # We now use the information of the windowed feature and iterate over the lags.\n","  # These lags were calulated above        \n","  for i in range(1,max(_idx)):\n","      print(f\"--> add prediction to order {i}\")\n","      # We define the name of the feature to which the prediction will be added.\n","      feature_name = f'{sliding_feature_name}_t-{i}'\n","      # We also define the day to whose features we want to add the prediction.\n","      frame_selector = test_test.index.get_level_values(0).unique()[date_index+i]\n","      # We finally add the predictions in there\n","      test_test.loc[[frame_selector]][feature_name] = prediction\n","\n","# At last we round the number to have ints \n","sample_submission = sample_submission.round().astype(np.int)"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-3-40ad34bd8dc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Now we are iterating over each of the values of the first layer of our index.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# -> In other words, over the days\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdate_index\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"DOING DAY {date_index} OUT OF{len(test_test.index.get_level_values(0).unique())}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0;31m############\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'test_test' is not defined"]}]},{"cell_type":"code","metadata":{"id":"Bx4eWRpUohN1","colab_type":"code","colab":{}},"source":["# OLD AND DEPRECATED CAUSE ITS LAME !\n","# for x, item in enumerate(ids):\n","    \n","#     data = test[0].query(f'itemID=={ids[0]}').copy()\n","#     for i in range(0, max(_idx)):\n","#         pred = final_xgb_model.predict(data.iloc[[i]].drop('itemID', axis=1))[0]\n","#         for j in range(1, max(_idx)-i):\n","#             data.iloc[j+i, data.columns.get_loc(f\"{sliding_feature_name}_t-{j}\")] = pred\n","        \n","#         date = pd.datetime(day=data.iloc[i].day_of_month, month=data.iloc[i].month, year=2018)\n","#         sample_submission.loc[item, f\"{date}\"] = pred\n","        \n","#     print(f\"{x} / {len(ids)} | {round((x/len(ids))*10000)/100} % | currently it:{item} | orders={sample_submission.loc[item, :].sum()}\")\n","            "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RmTiMhG79szu","colab_type":"code","colab":{}},"source":["sample_submission"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pBHeSdDV9t5D","colab_type":"text"},"source":["### Safe the sample submission"]},{"cell_type":"code","metadata":{"id":"qhQl8ZPt9trF","colab_type":"code","colab":{}},"source":["sample_submission.to_csv(f'submission_{model_name}_{suffix_for_import}.csv', sep='|')"],"execution_count":null,"outputs":[]}]}